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Quality of pro-market national institutions and firms’ decision to invest in R&D: evidence from developing and transition economies

Quality of pro-market national institutions and firms’ decision to invest in R&D: evidence from... Does institutional quality affect firms’ decision to invest in R&D? This paper inves- tigates the idea that well-functioning pro-market institutions spur firms’ R&D propensity by reducing transaction costs and uncertainties related to research and innovation activities. Evidence from the World Bank Enterprise Surveys on a large sample of firms in 57 developing and transition economies between 2002 and 2017 corroborates this idea. Controlling for countries’ level of economic development, industry fixed effects and a number of firm-level characteristics, the results show that firms’ propensity to invest in R&D is higher in countries in which national insti- tutions ensure socio-political stability and enforcement of law and contracts. Keywords R&D · Innovation · Institutional quality · Economic development · WBES data 1 Introduction The efforts of firms to upgrade technological capabilities and invest in innovation are crucial in processes of catching up and economic development (Lee et al., 2021; Malerba & Nelson, 2011). When combined with changes in knowledge, technology and demand, these efforts may lead to structural changes, economic transformations, catching up and changes in industrial leadership (Lee & Malerba, 2017). R&D and innovation are however costly and demanding activities, subject to high uncertainties, and that often generate profitability and economic benefits several years after companies begin to invest in technological upgrading. The possibilities * Fulvio Castellacci fulvio.castellacci@tik.uio.no TIK Centre, University of Oslo, Oslo, Norway Grenoble Ecole de Management, Grenoble, France Vol.:(0123456789) 1 3 36 Eurasian Business Review (2022) 12:35–57 for firms’ to invest in R&D and innovation are constrained by resource availability and idiosyncratic firm-level characteristics, as well as institutional characteristics of each national economy. Specifically, national pro-market institutions are important to ensure socio-political stability that spurs investments and reduce uncertainties related to R&D, well-functioning legal institutions and contract enforcing systems that reduce transaction costs in R&D collaborations, and an efficient IPRs system that enable firms to reap the benefits of innovative activities (Castellacci, 2015; Kaf- ouros & Aliyev, 2016). Although the general idea that the quality of national institutions affects R&D activities is well-known and central in the literature on national systems of innova- tion, prior literature on this topic has until now investigated this relationship focus- ing on country- and industry-level factors only. Various country-level studies show that R&D investments in a given country is correlated with its institutional quality, tertiary education system, and patent rights protection (e.g. Castellacci & Natera, 2012; Wang, 2010). Further, industry-level studies show that sector-specific R&D investments are associated with the quality of legal systems of OECD countries, especially in industries in which contract enforcement are crucial (Seitz & Watz- inger, 2017). Extant research on this topic, though, has not yet investigated the specific rela- tionship between country-level institutional quality, on the one hand, and firm-level decision to invest in R&D, on the other. Given the set of domestic institutions, firms in different countries may face different opportunities and constraints to their inno- vative activities, different possibilities to mobilize resources for R&D, and differ - ent opportunities to reap the related benefits (Alam et  al., 2019; Choi et  al., 2014; Munari et  al., 2010). From a comparative institutional perspective, it is therefore paramount to investigate how firms’ R&D activities are shaped by the quality of national pro-market institutions. This is important because, as observed by Chang et al. (2006), innovation studies have not yet achieved a clear understanding of how firm-level strategies and innovative activities interact with the macro environment in which business companies operate. The present paper tackles this gap in extant research by empirically studying the relationship between the quality of pro-market institutions and firms’ propensity to invest in R&D in a large sample of transition and developing countries. Our empiri- cal analysis relies on two secondary datasets: (1) the World Bank Enterprise Sur- veys (WBES), providing information on more than 10,000 firms in 57 transition and developing countries between 2002 and 2017; (2) the World Governance Indicators (WGI), measuring different aspects of the national institutional setting. The focus of this analysis on firms in transition and developing economies is important, because previous literature on this topic has mostly focused on advanced economies, whose national institutions are typically better able to ensure the socio-political and legal conditions that facilitate economic activity and market transactions (Bothello et al., 2019). Little is known, though, about firms’ decisions to invest in R&D in develop- ing countries, as well as whether and how national institutional differences influence firms’ decision to invest in R&D. The results of our econometric analysis show that in developing countries that have higher quality of domestic pro-market institutions firms have on average a 1 3 Eurasian Business Review (2022) 12:35–57 37 substantially higher propensity to invest in R&D. The paper contributes to the catch- ing up literature by showing that the extent to which national institutions facilitate pro-market interactions influence the possibilities for learning and building compe- tences in developing and transition economies. Thus, while industrial policy targeted to support the development of a specific industrial sector may create incentives and resources for catching-up (Bodas Freitas & Iizuka, 2012), ensuring a well-function- ing institutional context that reduces uncertainties and transaction costs related to R&D is a necessary condition for that to occur. The paper is organized as follows. Section 2 briefly reviews the relevant literature on and the role of institutional quality for R&D investments. Section 3 presents the data and empirical methods. Section 4 presents the results. Section 5 concludes the paper and discusses the implications of our findings. 2 Theory R&D is crucial for advancing capabilities for the development, acquisition and mod- ification of technologies (Kim & Kogut, 1996), and it increases firms’ profitability, growth and economic performance (Capasso et  al., 2015). Investment in R&D is then key in processes of industrial catching up (Kim, 1997, 1998). Institutional economics theory points out that firms’ decisions are shaped by the national institutional context. This is because national institutions affect the trans- action costs and learning opportunities related to different strategic alternatives (Jacobides & Winter, 2012; North, 1990). Institutions may then benefit more busi- ness strategies than others (Castellacci, 2015; Chittoor et  al., 2015), leading firms to develop different characteristics and capabilities in different institutional contexts (Freeman, 1995, 2004). The literature provides extensive evidence of how institu- tions influence firms’ strategic decisions, especially their effects on market entry modes, and on the forms of governance of international alliances (Ang et al., 2015; Hagedoorn et al., 2005). Extant research, though, has not explicitly investigated how national institutions shape firms’ decision to invest in R&D. Prior research studied the relationships between institutional quality and R&D investments at the country-level and at the industry-level (Castellacci & Natera, 2012; Seitz & Watzinger, 2017; Wang, 2010). These studies show that R&D invest- ment is associated with the efficiency of the national legal systems in enforcing con- tracts, indicating for instance that in institutional contexts characterized by weak law enforcement, the expected positive effect of R&D may be weaker (Qian et al., 2017). Indeed, evidence on OECD countries suggests that in industries in which contracts are more relevant for the functioning of the technological and production activities, the industry levels of R&D investment are more responsive to the quality of national institutions (Seitz & Watzinger, 2017). Alam et al. (2019) present a relevant empiri- cal analysis of the relationships between national institutions and firms’ R&D inten- sity based on a panel of 664 firms from 20 transition and developing economies. The present paper intends to contribute to this literature by investigating the rela- tionship between the quality of pro-market institutions, on the one hand, and firm- level decisions to invest in R&D, on the other. Focusing explicitly on the firm-level 1 3 38 Eurasian Business Review (2022) 12:35–57 of analysis, we posit four main channels through which institutional quality may affect agents’ R&D decision. The first channel refers to uncertainties related to innovation. Since R&D invest- ments are subject to uncertainty and they typically lead to innovation output and profit several years after innovation investments are undertaken, national institutions may play an important role to reduce such uncertainties and thus strengthen firms’ R&D incentives. In countries where the possibility to enforce contracts is low, the costs of engaging in opportunistic behavior and leakage crucial proprietary knowl- edge to competitors are low, thus raising the risks associated with investment in R&D (Seitz & Watzinger, 2017). In economies characterized by political and social instability, and where the legal systems do not efficiently enforce law and contracts, firms may perceive as likely to lose their investment as a result of crime and cor - ruption or as a result of sudden changes in regulations that change unexpectedly the possibilities to bring new products to the market (Kafouros & Aliyev, 2016). A second channel linking national institutions to firms-level R&D decision relates to companies’ possibilities to reap the benefits of innovation. A well-func- tioning system of intellectual property rights (IPRs), combined with an efficient enforcement system, contributes to strengthen appropriability conditions related to innovation activities, and thus firms’ incentives to carry out R&D. Third, national institutions may also be important to facilitate firms’ access to finance and venture capital that can spur R&D activities. When policy programs to support firms’ R&D are in place (e.g. subsidies, tax incentives), effectiveness and transparency of these programs foster firms’ participation in public R&D funding, thus increasing R&D propensity in the national system. Finally, a fourth channel relating the quality of pro-market institutions to firms’ R&D decision refers to agents’ interactions within the innovation system. Innova- tion typically involves interactions with a number of partners within and beyond the value-chain, between innovative companies, suppliers, users and public research institutions. Being innovation a social and interactive process (Lundvall, 2016), firms’ efforts to innovate are likely to be influenced by the perceived costs of inter - acting and contracting with different partners, as well as by the possibilities to learn and appropriate value from the R&D activities undertaken (Jacobides & Winter, 2012). In countries in which national institutions have lower quality (e.g. in terms of legal institutions that regulate and enforce contracts in case of legal disputes among partners), firms perceive higher risk of interacting with external partners, as their innovation partners or technology suppliers may be also limited to comply timely and according to stipulated quality and standards. Specifically, in a well-known study of courts and relational contracts in for - mer Communist countries, Johnson et  al. (2002) showed that well-functioning legal courts foster the development of new commercial relationships and contracts between producers and suppliers, since they help to settle legal disputes between partners when these arise. Further, when legal institutions are weak, there may arise informational asymmetries and hold up problems between suppliers and produc- ers, so that the supplier will not invest an optimal amount of resources in relation- specific investments. This will affect firms’ innovation incentives and performance, since hold up problems raise production costs and weaken comparative advantage. It 1 3 Eurasian Business Review (2022) 12:35–57 39 has also been shown that such hold-up problems are more relevant in contract-inten- sive industries with more complex and differentiated products (Essaji & Fujiwara, 2012; Ma et al., 2010; Nunn, 2007). In summary, based on the four points noted above, our empirical analysis will investigate the hypothesis that firms’ propensity to invest in R&D is on average higher in developing and transition economies that have higher quality of and better functioning national pro-market institutions. 3 Data and methods 3.1 WBES survey methodology Information on firms is obtained from the Enterprise Surveys administered by the World Bank (WBES) in 57 transition and developing countries between 2002 and 2017 in Southeast and Central Europe; Eastern Europe, Caucasus and Central Asia; Latin America, Sub-Saharan Africa; South and Southeast Asia; Middle East and North Africa; and Western Asia. The WBES surveys focus on emerging economies (as defined by World Bank) and address establishments that are formally registered with the relevant authorities (excluding firms in the informal economy). The pur - pose of the surveys is to collect detailed information on the nature of the business activities, the ways in which firms operate, innovate and relate to their environment, as well as on their performance. Interviewees are typically top managers or business owners. The surveys use a stratified random sampling. The strata for WBES are firm size, business sector, and geographic region within a country. Firm size levels are 1–5, 5–19, 20–99, and 100 + employees (large-sized firms). Since in most economies a large majority of firms are small and medium-sized, WBES oversample large firms based on the understanding that larger firms tend to be important engines of job cre- ation and economic growth. Sector breakdown by WBES is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata based on employment, value-added, and total num- ber of establishments. In the WBES surveys, geographic regions within a country are selected based on which cities/regions collectively represent most of the eco- nomic activity. Each firm is thus associated with a single geographic region of resi- dence, even though such firms may operate in markets outside of their home region. The 15-year period of the study was between 2002 and 2017. This period was considered due to the similarities of the survey instruments used. Furthermore, we have restricted our sample in this study to manufacturing firms only, excluding firms in service and extractive industries. Given the cross-sectional nature of our study, we have excluded firms that were part of repeated longitudinal panel surveys (less than 10% of the population). After dropping missing values, unrealistic figures (such as negative values of firm size) and years with less than 100 observations, the sample consisted of 10,393 firms in 57 countries. 1 3 40 Eurasian Business Review (2022) 12:35–57 Table 1 Sample firms by industrial sector Industry No R&D Performed Performed Total frequency Total percent (fre- R&D (fre- R&D (per- quency) quency) cent) Electronics and machinery 585 546 48.28 1131 10.88 Food, Beverage and tobacco 1519 1257 45.28 2776 26.71 Metal products 821 477 36.75 1298 12.49 Petroleum, chemical, and 942 967 50.65 1909 18.37 non-metallic mineral products Textile, garment and leather 1782 756 29.79 2538 24.42 Wood, paper, publishing 532 209 28.21 741 7.13 and furniture Total 6181 4212 40.53 10,393 100 Tables 1, 2 and 3 provide further information on the sample composition. Table 1 reports the industrial sectors represented in our sample, the number of firms in each manufacturing sector, and the number of firms that are engaged and not engaged in R&D. Table 2 provides a list of the countries represented in these surveys, reporting again the number of respondent firms in each country and the corresponding number of R&D and non-R&D companies. Table  3 reports the distribution of firms (total, R&D active and non-R&D active) by survey year during the period 2002–2017. In addition to WBES firm-level data, our study also uses the World Governance Indi- cators (WGI) to measure different aspects of the national institutional setting (see further below). 3.2 Econometric model We seek to estimate firms’ probability to invest in R&D, as indicated in Eq.  (1) below: Pr (R&D )=  + INSTIT + GDPPC + FIRMS ijt j;t−1 j; t−1 ijt (1) +INDUSTRY + COUNTRY + YEAR + ijt where the sub-index i indicates the firm, j the country, and t the year of the survey. The dependent variable R&D is a dichotomous variable that takes the value 1 if the firm indicated in the survey that it invested in R&D during the preceding 3 years, and 0 otherwise. The main explanatory variable INSTIT accounts for the (lagged) quality of pro-market institutions 2 years before the corresponding survey year. Institutional quality is measured by using different variables available in the World Governance Indicators (WGI) database: – Control of corruption captures perceptions of the extent to which public power is not exercised for private gain, including both petty and grand forms of corrup- tion, as well as "capture" of the state by elites and private interests. 1 3 Eurasian Business Review (2022) 12:35–57 41 Table 2 Sample firms by country Country No R&D Performed Performed Total frequency Percent (fre- R&D (fre- R&D (per- quency) quency) cent) Southeast and Central Europe 1389 1029 42.56 2418 23.27 Albania 14 19 24.68 77 0.74 Bosnia and Herzegovina 55 79 58.96 134 1.29 Bulgaria 47 41 46.59 88 0.85 Croatia 37 71 65.74 108 1.04 Czech Republic 58 93 61.59 151 1.45 Estonia 59 61 50.83 120 1.15 Hungary 198 83 29.54 281 2.7 Kosovo 41 15 26.79 56 0.54 Latvia 65 26 28.57 91 0.88 Lithuania 61 70 53.44 131 1.26 Montenegro 13 3 18.75 16 0.15 North Macedonia 72 41 36.28 113 1.09 Poland 286 153 34.85 439 4.22 Romania 218 96 30.57 314 3.02 Serbia 20 42 67.74 62 0.6 Slovakia 52 47 47.47 99 0.95 Slovenia 49 89 64.49 138 1.33 Eastern Europe, Caucasus and 1468 878 37.43 2346 22.57 Central Asia Armenia 140 38 21.35 178 1.71 Azerbaijan 51 4 7.27 55 0.53 Belarus 45 43 48.86 88 0.85 Georgia 79 38 32.48 117 1.13 Kazakhstan 257 62 19.44 319 3.07 Kyrgyz Republic 62 33 34.74 95 0.91 Moldova 69 61 46.92 130 1.25 Russia 383 377 49.61 760 7.31 Tajikistan 97 44 31.21 141 1.36 Ukraine 236 145 38.06 381 3.67 Uzbekistan 49 33 40.24 82 0.79 Latin America 2005 1666 45.38 3671 35.32 Argentina 323 348 51.86 671 6.46 Bolivia 50 48 48.98 98 0.94 Chile 116 110 48.67 226 2.17 Colombia 409 466 53.26 875 8.42 Dominican Republic 31 15 32.61 46 0.44 El Salvador 200 41 17.01 241 2.32 Guatemala 103 67 39.41 170 1.64 Mexico 47 54 53.47 101 0.97 Nicaragua 54 22 28.95 76 0.73 1 3 42 Eurasian Business Review (2022) 12:35–57 Table 2 (continued) Country No R&D Performed Performed Total frequency Percent (fre- R&D (fre- R&D (per- quency) quency) cent) Paraguay 64 43 40.19 107 1.03 Peru 362 304 45.65 666 6.41 Uruguay 229 139 37.77 368 3.54 Venezuela 17 9 34.62 26 0.25 Sub-Saharan Africa 574 330 36.50 904 8.7 Benin 25 3 10.71 28 0.27 Cameroon 24 4 14.29 28 0.27 Kenya 88 73 45.34 161 1.55 Niger 17 2 10.53 19 0.18 Nigeria 230 93 28.79 323 3.11 Tanzania 24 36 60.00 60 0.58 Uganda 33 12 26.67 45 0.43 Zambia 25 16 39.02 41 0.39 Zimbabwe 108 91 45.73 199 1.91 South and Southeast. Asia 179 31 14.76 210 2.02 Afghanistan 14 1 6.67 15 0.14 Bangladesh 39 23 37.10 62 0.6 Myanmar 126 7 5.26 133 1.28 Middle East, North Africa and 566 278 32.94 844 8.12 West. Asia Jordan 106 16 13.11 122 1.17 Lebanon 53 9 14.52 62 0.6 Morocco 60 14 18.92 74 0.71 Turkey 347 239 40.78 586 5.64 Total 6181 4212 40.53 10,393 100 – Rule of law measures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. – Regulatory quality is an indicator of the perceptions of the ability of the govern- ment to formulate and implement sound policies and regulations that allow and promote private sector development. – Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the cred- ibility of the government’s commitment to such policies. – Political stability and absence of violence/terrorism measures perceptions of the likelihood of avoiding political instability and/or politically motivated violence, including terrorism. 1 3 Eurasian Business Review (2022) 12:35–57 43 Table 3 Sample firms by year Survey year No R&D Performed R&D Performed R&D Total frequency Percent (frequency) (frequency) (percent) 2002 44 407 90.24 451 4.34 2005 1002 288 22.33 1290 12.41 2006 431 380 46.86 811 7.8 2008 573 286 33.29 859 8.27 2009 1545 962 38.37 2507 24.12 2010 798 982 55.17 1780 17.13 2011 83 97 53.89 180 1.73 2013 430 379 46.85 809 7.78 2014 304 98 24.38 402 3.87 2016 401 67 14.32 468 4.5 2017 570 266 31.82 836 8.04 Total 6181 4212 40.53 10,393 100 – Voice and accountability accounts for perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. These indicators are defined by WGI based on aggregations of large num- bers of local expert surveys and indexes using a statistical procedure based on an unobserved components model. Each of the six indicators was then rescaled in the WGI database in units of a standard normal distribution, with mean zero, standard deviation of one, and spanning between approximately − 2.5 to 2.5, with higher values corresponding to better institutional quality for all variables. Since it is well-known from previous studies that several indicators of institutional quality are highly correlated with each other (representing closely related and complementary aspects of countries’ institutional quality), we have used these six indicators in a principal component analysis in order to reduce the multidi- mensionality of our explanatory factor. The principal component analysis yielded one single factor with Eigenvalue greater than 1, which accounts for 98% of the variance across all six variables. Cronbach’s alpha is 93.9%, suggesting that the six variables have high internal consistency and are closely related. In our econo- metric analysis, we will therefore use this composite factor as an overall measure of countries’ institutional quality in baseline regressions, and we will also use each of the six indicators in additional separate regressions to test the effect of the various institutional indicators. Our regression model includes a number of control variables. Evidence suggests that countries where the level of economic development is higher tend to have insti- tutions that facilitate market transactions (Seitz & Watzinger, 2017). Thus, we con- trol for the (lagged) level of economic development of the country 2 years before the corresponding survey years. GDP per capita indicates the ratio of the gross domes- tic product of a country in purchasing power parity terms, measured in international 1 3 44 Eurasian Business Review (2022) 12:35–57 dollars, to the population of the country. This variable is obtained from the World Bank based on reporting from national governments. Regarding firm level controls, we include a number of standard variables. Firms that trade (import or export) have been shown to be more likely to invest in R&D (Alvarez & Robertson, 2004; MacGarvie, 2006). We thus include two dichoto- mous variables. Importer indicates firms that import raw materials directly (i.e. not through third-party intermediaries). Exporter indicates that a firm exports its prod- ucts directly. Larger firms have more resources, and thus they may be more likely to invest in technology (Lee & Sung, 2005; Shefer & Frenkel, 2005; Veugelers, 1997). We then include the ordinal variable Firm size. It takes four different values between 0 and 3 (value 0 if the number of workers employed by a firm in the last fiscal year was less than five; whereas value 3 indicates that the firm had more than 100 work - ers). This ordinal variable is included as a continuous indicator in the regressions. We also account for the age of the firm since older firms may be more embedded in national institutions. Business group affiliation may affect a firms access to techno- logical and financial resources for R&D (Castellacci, 2015; Chang et al., 2006), and it is therefore important to include such control variable in the regressions. In addi- tion, foreign direct investment may be a means of technology transfer that affects R&D investment decisions (Arora & Fosfuri, 2000; Saggi, 2002). We thus include two dichotomous variables in our model. The variable domestic subsidiary indicates subsidiaries that are less than 50% foreign owned. Foreign subsidiary takes value 1 if the firm has 50% or more foreign-owned capital. To account for the availability of financial resources we also include a variable called financial constraint, which is an ordinal variable with values ranging from 0 to 4, where 0 indicates that the firm does not consider access to finance as an obstacle to its operations and higher values indicate that firms perceive access to finance as an important obstacle. This ordinal variable is included as a continuous indicator in the regressions. Srholec (2011) has previously shown that access to finance can affect investment in innovation in devel- oping countries. Finally, industry, country and year account for the related set of fixed effects. Since our dependent variable is a dichotomous variable, we estimate our regression model using a probit regression with country, year and industry fixed effects and robust standard errors () . All variables and sources are reported in a table in the appendix of the paper. Before presenting the results, it is worth noticing some of the possible limitations of the econometric approach that we have used in our analysis, which are mostly related to the nature of the data that we have used. First, since R&D activities unfold over time and are characterized by sunk costs and path-dependency, it would in principle be better to analyze them by using a dynamic panel framework, although this is not possible in the context of the pooled cross-sectional survey data that we are using. Second, some of the firm-level control variables in our regressions may be endogenous, such as e.g. firm size, import, export and financial constraints. Our main explanatory variable, though, is less subject to such possible issues as insti- tutional quality is measured at the country level, and lagged 2 years vis-à-vis the dependent variable. Finally, it is also possible that our results are subject to some sample bias related to the way in which the WBES survey was collected (e.g. large firms are 1 3 Eurasian Business Review (2022) 12:35–57 45 over-represented as noted above). Nevertheless, our sample represents a large num- ber of firms in all manufacturing sectors and a large number of transition and devel- oping economies, such that the results are on the whole representative and robust. 4 Results Table  4 reports descriptive statistics and the correlation coefficients among the variables. The correlation coefficients between the various firm level variables, and those among firm level variables and national variables are on the whole quite low, not suggesting possible issues of multicollinearity. On the other hand, the correlation coefficients among the various country-level governance indicators are as expected rather high, reason why we make use of the composite variable obtained through the principal component analysis in our baseline regressions. Table  5 reports the results of the probit regression. Model 00 is the baseline model including only the control variables. Models 01–07 add the national institu- tional characteristics. The firm level controls behave as expected. The coefficients of importer, exporter, age, firm size, domestic subsidiary and financial constraints are positively correlated with firms’ R&D propensity. They indicate that R&D propen- sity is on average higher for larger and older firms, for companies that import from and export to foreign markets, as well as for firms that are part of domestic business groups. The coefficient of the foreign subsidiary variable is non-significant. Shifting the focus to the main explanatory variable of our main interest, the esti- mated coefficient of the composite variable accounting for countries’ quality of pro-market institutions is positive and significant, indicating a positive relationship between firms’ propensity to invest in R&D and the extent to which national institu- tions are able to provide socio-political stability and efficient legal institutions that reduce uncertainties and transaction costs related to R&D investments and innova- tive activities. The estimated coefficient of GDP per capita is also positive and sig- nificant. Our result on the role of institutional quality does therefore hold after con- trolling for countries’ overall level of economic development. As the coefficients in our non-linear probit regression model do not provide an estimate of the marginal effects, we plot the related marginal effects. Figure  1 reports the marginal effect of the quality of pro-market institutions on the firms’ probability The variable measuring financial constraints is positively correlated with the dependent variable. In line with previous empirical analysis based on firm-level survey data (e.g. WBES surveys; CIS surveys), the interpretation of this positive correlation is that firms tend to be more aware of the importance of financial constraints when they engage in R&D investments, since these require the availability of finan- cial resources to be invested in uncertain and longer-term projects. In more general terms, survey-based obstacle indicators (such as the financial constraints variable used here) measure firms’ perceptions and awareness of the importance of a given factor for the firm’ strategy: the more important the factor, the more likely is that companies will perceive and report it as a critical aspect or obstacle to their operations and performance (Castellacci, 2015). 2 2 The Wald test comparing the two models (X = 15.61; p < 0.001) indicates that the model including the pro-market institutions variable (model 01) is a significant improvement vis-à-vis the baseline model without this variable (model 00). 1 3 46 Eurasian Business Review (2022) 12:35–57 1 3 Table 4 Descriptives and correlations Variables Descriptives Correlation matrix Mean Std. dev Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (1) R&D 0.405 0.491 0 1 1.000 (2) Pro- 0.046 0.978 − 2.138 2.403 0.041 1.000 market Institu- tions (3) Con- − 0.33 0.657 − 1.42 1.377 0.060 0.946 1.000 trol of corrup- tion (4) Rule of − 0.39 0.694 − 1.852 1.313 0.025 0.989 0.925 1.000 law (5) Regu- − 0.02 0.71 − 2.118 1.519 0.028 0.895 0.783 0.869 1.000 latory quality (6) Gov- − 0.18 0.59 − 1.546 1.168 0.060 0.957 0.892 0.922 0.867 1.000 ernment effec- tiveness (7) − 0.4 0.859 − 2.419 1.186 − 0.04 0.641 0.560 0.645 0.460 0.575 1.000 Political stabil- ity and absence of vio- lence/ terror- ism (8) Voice − 0.04 0.725 − 2.118 1.167 0.071 0.869 0.819 0.820 0.749 0.820 0.612 1.000 and account- ability Eurasian Business Review (2022) 12:35–57 47 1 3 Table 4 (continued) Variables Descriptives Correlation matrix Mean Std. dev Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (9) GDP 11,140 5679 1048.4 29,001.4 0.077 0.596 0.516 0.561 0.515 0.691 0.496 0.500 1.000 per capita (10) 0.501 0.5 0 1 0.147 0.094 0.106 0.098 0.056 0.072 0.074 0.095 0.062 1.000 Importer (11) 0.403 0.491 0 1 0.196 0.184 0.163 0.185 0.154 0.179 0.094 0.181 0.141 0.342 1.000 Exporter (12) Age 22 20.0 1 180 0.143 0.033 0.071 0.015 − 0.02 0.048 − 0.01 0.079 0.051 0.159 0.145 1.000 (13) Firm 1.934 0.833 0 3 0.218 − 0.01 − 0.02 − 0.01 − 0.02 − 0.02 0.020 − 0.00 0.036 0.303 0.354 0.264 1.000 size (14) 0.27 0.444 0 1 − 0.01 0.049 − 0.00 0.070 0.014 0.044 0.165 0.015 − 0.13 − 0.04 − 0.04 − 0.01 − 0.03 1.000 Domes- tic subsidi- ary (15) 0.051 0.22 0 1 0.060 0.057 0.039 0.061 0.036 0.054 0.091 0.048 0.017 0.071 0.158 − 0.01 0.134 − 0.14 1.000 Foreign subsidi- ary (16) 1.602 1.286 0 4 − 0.01 − 0.11 − 0.11 − 0.12 − 0.12 − 0.09 − 0.08 − 0.08 − 0.03 − 0.10 − 0.10 − 0.02 − 0.11 − 0.03 − 0.10 1.000 Finan- cial con- straint 48 Eurasian Business Review (2022) 12:35–57 Table 5 Probit estimation of the probability to invest in R&D (00—controls only) (01—full model) GDP per capita 0.0000441** (0.0000177) 0.0000407** (0.0000178) Importer 0.200*** (0.0318) 0.200*** (0.0319) Exporter 0.280*** (0.0329) 0.275*** (0.0330) Age 0.00267*** (0.000740) 0.00260*** (0.000741) Firm size 0.250*** (0.0203) 0.250*** (0.0203) Domestic subsidiary 0.114** (0.0442) 0.108** (0.0443) Foreign subsidiary 0.0498 (0.0725) 0.0465 (0.0727) Financial constraint 0.0187* (0.0113) 0.0180 (0.0114) Pro-market institutions 0.642*** (0.162) Constant − 5.585*** (0.754) − 4.125*** (0.824) Pseudo R 0.1961 0.1965 Log Pseudolikelihood − 5640.6629 − 5609.4582 Wald X 2666.46*** 2667.24*** Observations 10,165 10,129 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 Predictive Margins -2 -1 0 1 2 Pro-market institutions Fig. 1 Probability of a firm investing in R&D based on national pro-market institutions to invest in R&D. The figure shows clearly that the probability to invest in R&D increases steady and linearly with the quality of national pro-market institutions. We undertook some robustness tests to further analyze our results. First, we examine the effect of each of the six WGI indicators in separate regressions to see if the results we obtained using the composite variable measuring the quality of pro- market institutions are driven by any specific institutional characteristics. The results 1 3 R& D probab ility 0 .2 .4 .6 .8 Eurasian Business Review (2022) 12:35–57 49 of these additional regressions (Table 6) show that the estimated coefficients of the variables measuring control of corruption, rule of law, government effectiveness and political stability and absence of violence/terrorism (p < 0.01) and regulatory qual- ity (p < 0.05) are as expected positive and statistically significant. These relation- ships are illustrated further by means of the plots of the marginal effects for each of the five variables in Figs.  2, 3, 4, 5 and 6. The marginal effects show the same over - all patterns noted for Fig.  1. On the other hand, the estimated coefficient of voice and accountability is lower than the others and less statistically precise (although the variable is closely correlated with the other institutional indicators, as reported in Table 4 above). Second, we split our sample in two groups based on the countries’ level of GDP per capita (above or below the median sample value of GDP per capita). Regres- sion results reported in Table  7 show that in countries with GDP per capita below the sample median, the coefficient of quality of the pro-market institutions is posi- tive and significant, whereas the same variable is not significant for the sub-sample of companies in countries with higher than average GDP per capita. This pattern suggests that less developed economies present on average a broader variation in institutional quality, such that R&D propensity of firms is higher for those countries that have better functioning institutional systems. On the other hand, economies that have higher GDP per capita levels have presumably lower cross-country variation in institutional quality, which may explain why the estimated coefficient is not signifi- cant in this sub-sample. In short, the result presented in this paper about the relation- ship between country-level institutional quality and firm-level R&D propensity is mainly driven by the patterns of firms in less developed economies in our sample. 5 Conclusions The catching up literature stresses the importance of firms’ learning efforts and investments in technological upgrading, along with industrial policy (Lee et  al., 2021; Malerba & Nelson, 2011). Less attention has however been given to the char- acteristics of the national institutions in influencing firm’s learning and innovating decisions. We address this gap by building on the institutional economics literature that argues that the extent to which national institutions facilitate market interaction defines firms’ perceived transaction costs of alternative strategies. We propose that in countries where national institutions are less able to enforce law and contracts, and to assure political and social stability, firms will perceive higher transaction costs, and will therefore be less likely to invest in R&D activities, which increas- ingly involve interaction and contracting with external partners. Data on 57 developing and transition countries corroborate our expectations that the better the quality of the pro-market national institutions, the more likely that firms in that country will perceive low transaction costs in innovation activities, and thus the more likely that they will decide to invest in R&D. This result is in line with the one obtained by the Seitz and Watzinger’s (2017). In their industry-level analysis in OECD countries, they show that the observed industry levels of R&D investment 1 3 50 Eurasian Business Review (2022) 12:35–57 1 3 Table 6 Probit regressions of full model including all sample firms (02) (03) (04) (05) (06) (07) GDP per capita, PPP 0.0000398** 0.0000374** 0.0000546*** 0.0000394** 0.0000462** 0.0000489*** (0.0000179) (0.0000179) (0.0000178) (0.0000179) (0.0000182) (0.0000182) Importer 0.202*** (0.0319) 0.198*** (0.0318) 0.199*** (0.0318) 0.198*** (0.0318) 0.201*** (0.0319) 0.201*** (0.0318) Exporter 0.282*** (0.0330) 0.279*** (0.0330) 0.279*** (0.0329) 0.281*** (0.0329) 0.276*** (0.0330) 0.280*** (0.0329) Age 0.00274*** (0.000740) 0.00264*** (0.000740) 0.00265*** (0.000740) 0.00267*** (0.000740) 0.00257*** (0.000741) 0.00267*** (0.000740) Firm size 0.248*** (0.0203) 0.251*** (0.0203) 0.251*** (0.0203) 0.252*** (0.0203) 0.252*** (0.0203) 0.250*** (0.0203) Domestic subsidiary 0.114*** (0.0443) 0.111** (0.0443) 0.109** (0.0443) 0.112** (0.0443) 0.106** (0.0444) 0.111** (0.0443) Foreign subsidiary 0.0496 (0.0726) 0.0476 (0.0726) 0.0464 (0.0727) 0.0481 (0.0727) 0.0464 (0.0729) 0.0507 (0.0725) Financial constraint 0.0198* (0.0114) 0.0183 (0.0114) 0.0188* (0.0114) 0.0184 (0.0114) 0.0169 (0.0114) 0.0190* (0.0113) Control of corruption 0.588*** (0.167) Rule of law 0.455** (0.184) Regulatory quality 0.360** (0.160) Government effective- 0.778*** (0.207) ness Political stability and 0.365*** (0.118) absence of violence/ terrorism Voice and account- 0.239 (0.185) ability Constant − 4.805*** (0.779) − 4.662*** (0.829) − 5.044*** (0.781) − 4.290*** (0.810) − 4.441*** (0.832) − 5.250*** (0.800) Pseudo R 0.1969 0.1965 0.1963 0.1969 0.1962 0.1961 Log Pseudolikelihood − 5634.8808 − 5637.4861 − 5638.8325 − 5635.0693 − 5611.7816 − 5640.2856 Wald X 2677.86*** 2671.92*** 2670.88*** 2677.94*** 2662.84*** 2667.81*** Observations 10,165 10,165 10,165 10,165 10,129 10,165 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 Eurasian Business Review (2022) 12:35–57 51 Predictive Margins -2 -1 0 1 2 Control of Corruption Fig. 2 Probability of a firm investing in R&D based on national control of corruption Predictive Margins -2 -1 0 1 2 Rule of Law Fig. 3 Probability of a firm investing in R&D based on national rule of law in which contract enforcement is crucial are particular sensitive to the quality of national legal systems. Our results hint at a necessary condition for catching up and economic growth, which is that national institutions can assure socio-political stability and enforce- ment of law and contracts. Only by perceiving a pro-market institutional environ- ment firms may have more favorable perceptions of different catching-up strategies, and may perceive lower uncertainty and costs in investing in learning and innova- tion activities. When firms are reassured that the business markets will not suddenly change as result of socio-political instability, they will perceive investment in R&D as less risky. The more firms are reassured about the possibility to enforce contracts, 1 3 R& D probabi lity R& D probab ility .2 .4 .6 .8 .2 .4 .6 .8 52 Eurasian Business Review (2022) 12:35–57 Predictive Margins -2 -1 0 1 2 Regulatory Quality Fig. 4 Probability of a firm investing in R&D based on national regulatory quality Predictive Margins -2 -1 0 1 2 Government Effectiveness Fig. 5 Probability of a firm investing in R&D based on national government effectiveness the higher benefit they may perceive from contracting and collaborating with other firms for innovation, market entry and/or technology development. Results from the sample split exercise in particular corroborate this argument. The estimated coef- ficient of pro-market institutions is positive and significant (non-significant) for the sub-sample of firms in countries with GDP per capital lower (higher) than the sam- ple median value. This paper contributes to the catching up literature by proposing and showing that even small differences in the extent to which national institutions facilitate pro- market interactions among developing and transition countries may significantly influence firms’ engagement in learning and technological building activities. In 1 3 R& D probabi lity R& D probab ility .2 .3 .4 .5 .6 0 .2 .4 .6 .8 Eurasian Business Review (2022) 12:35–57 53 Predictive Margins -2 -1 0 1 Political Stability and Absence of Violence/Terrorism Fig. 6 Probability of a firm investing in R&D based on national political stability and absence of vio- lence/terrorism Table 7 Probit regressions of full model, split sample for countries with GDP per capita levels above and below the median (01A—full model, below median GDP (01B—full model, above per capita) median GDP per capita) GDP per capita − 0.0000204 (0.0000624) 0.0000765 (0.0000802) Importer 0.251*** (0.0454) 0.156*** (0.0456) Exporter 0.228*** (0.0474) 0.326*** (0.0468) Age 0.00228** (0.00113) 0.00264*** (0.000986) Firm size 0.239*** (0.0285) 0.267*** (0.0296) Domestic subsidiary 0.186*** (0.0663) 0.0471 (0.0614) Foreign subsidiary 0.344*** (0.107) − 0.181* (0.100) Financial constraint 0.00920 (0.0162) 0.0255 (0.0163) Pro-market institutions 0.533** (0.238) − 0.177 (0.920) Constant − 4.398*** − 2.716 (0.895) (1.837) Pseudo R 0.2189 0.1503 Log pseudolikelihood − 2719.7658 − 2757.7945 Wald X 1685.82*** 7931.51*** Observations 5226 4775 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 addition, we contribute by showing that the direct influence of pro-market institu- tions on firms’ investment decision in capability building and innovation is particu- lar relevant in countries with low levels of development. This is in line with the 1 3 R& D probab ility .2 .3 .4 .5 .6 .7 54 Eurasian Business Review (2022) 12:35–57 literature studying the middle income country trap in processes of catching up and economic development (Lee et al., 2021). Appendix 1: Variables: definition and sources Variable Definition Source R&D Dummy variable that takes the value 1 if the Enterprise surveys firm has invested in R&D during the preced- ing 3 years, and 0 otherwise Pro-market Institutions Principal component of control of corruption, Enterprise surveys rule of law, regulatory quality, govern- ment effectiveness, political stability and absence of violence/terrorism, and voice and accountability Control of corruption The extent to which public power is not World governance indicators exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests Rule of law The extent to which agents have confidence in World governance indicators and abide by the rules of society, and in par- ticular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence Regulatory quality The ability of the government to formulate World governance indicators and implement sound policies and regula- tions that permit and promote private sector development Government effectiveness The quality of public services, the quality World governance indicators of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the govern- ment’s commitment to such policies Political stability and The likelihood of avoiding political instabil- World governance indicators absence of violence/ ity and/or politically motivated violence, terrorism including terrorism Voice and accountability The extent to which a country’s citizens are World governance indicators able to participate in selecting their govern- ment, as well as freedom of expression, freedom of association, and a free media GDP per capita The ratio of the gross domestic product of a National governments, com- country in purchasing power parity terms, piled by World Bank measured in international dollars, to the population of the country Importer The firm imports raw materials directly (i.e. Enterprise surveys not through third-party intermediaries) Exporter The firm exports its products directly Enterprise surveys 1 3 Eurasian Business Review (2022) 12:35–57 55 Variable Definition Source Age Number of years that the firm has been in Enterprise surveys operation Firm size Number of workers at the firm (0: less than Enterprise surveys 5 employees; 1: 5–19 employees; 2: 20–99 employees; 3: 100 or more employees) Domestic subsidiary Firm belongs to a majority domestic-owned Enterprise surveys business group Foreign subsidiary Firm belongs to a majority foreign-owned Enterprise surveys business group Financial constraint 0 indicates that the firm does not consider Enterprise surveys access to finance as an obstacle to its operations, and values from 1 to 4 indicate increasing prominence of access to finance as an obstacle Funding Open access funding provided by University of Oslo (incl Oslo University Hospital). 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Quality of pro-market national institutions and firms’ decision to invest in R&D: evidence from developing and transition economies

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Abstract

Does institutional quality affect firms’ decision to invest in R&D? This paper inves- tigates the idea that well-functioning pro-market institutions spur firms’ R&D propensity by reducing transaction costs and uncertainties related to research and innovation activities. Evidence from the World Bank Enterprise Surveys on a large sample of firms in 57 developing and transition economies between 2002 and 2017 corroborates this idea. Controlling for countries’ level of economic development, industry fixed effects and a number of firm-level characteristics, the results show that firms’ propensity to invest in R&D is higher in countries in which national insti- tutions ensure socio-political stability and enforcement of law and contracts. Keywords R&D · Innovation · Institutional quality · Economic development · WBES data 1 Introduction The efforts of firms to upgrade technological capabilities and invest in innovation are crucial in processes of catching up and economic development (Lee et al., 2021; Malerba & Nelson, 2011). When combined with changes in knowledge, technology and demand, these efforts may lead to structural changes, economic transformations, catching up and changes in industrial leadership (Lee & Malerba, 2017). R&D and innovation are however costly and demanding activities, subject to high uncertainties, and that often generate profitability and economic benefits several years after companies begin to invest in technological upgrading. The possibilities * Fulvio Castellacci fulvio.castellacci@tik.uio.no TIK Centre, University of Oslo, Oslo, Norway Grenoble Ecole de Management, Grenoble, France Vol.:(0123456789) 1 3 36 Eurasian Business Review (2022) 12:35–57 for firms’ to invest in R&D and innovation are constrained by resource availability and idiosyncratic firm-level characteristics, as well as institutional characteristics of each national economy. Specifically, national pro-market institutions are important to ensure socio-political stability that spurs investments and reduce uncertainties related to R&D, well-functioning legal institutions and contract enforcing systems that reduce transaction costs in R&D collaborations, and an efficient IPRs system that enable firms to reap the benefits of innovative activities (Castellacci, 2015; Kaf- ouros & Aliyev, 2016). Although the general idea that the quality of national institutions affects R&D activities is well-known and central in the literature on national systems of innova- tion, prior literature on this topic has until now investigated this relationship focus- ing on country- and industry-level factors only. Various country-level studies show that R&D investments in a given country is correlated with its institutional quality, tertiary education system, and patent rights protection (e.g. Castellacci & Natera, 2012; Wang, 2010). Further, industry-level studies show that sector-specific R&D investments are associated with the quality of legal systems of OECD countries, especially in industries in which contract enforcement are crucial (Seitz & Watz- inger, 2017). Extant research on this topic, though, has not yet investigated the specific rela- tionship between country-level institutional quality, on the one hand, and firm-level decision to invest in R&D, on the other. Given the set of domestic institutions, firms in different countries may face different opportunities and constraints to their inno- vative activities, different possibilities to mobilize resources for R&D, and differ - ent opportunities to reap the related benefits (Alam et  al., 2019; Choi et  al., 2014; Munari et  al., 2010). From a comparative institutional perspective, it is therefore paramount to investigate how firms’ R&D activities are shaped by the quality of national pro-market institutions. This is important because, as observed by Chang et al. (2006), innovation studies have not yet achieved a clear understanding of how firm-level strategies and innovative activities interact with the macro environment in which business companies operate. The present paper tackles this gap in extant research by empirically studying the relationship between the quality of pro-market institutions and firms’ propensity to invest in R&D in a large sample of transition and developing countries. Our empiri- cal analysis relies on two secondary datasets: (1) the World Bank Enterprise Sur- veys (WBES), providing information on more than 10,000 firms in 57 transition and developing countries between 2002 and 2017; (2) the World Governance Indicators (WGI), measuring different aspects of the national institutional setting. The focus of this analysis on firms in transition and developing economies is important, because previous literature on this topic has mostly focused on advanced economies, whose national institutions are typically better able to ensure the socio-political and legal conditions that facilitate economic activity and market transactions (Bothello et al., 2019). Little is known, though, about firms’ decisions to invest in R&D in develop- ing countries, as well as whether and how national institutional differences influence firms’ decision to invest in R&D. The results of our econometric analysis show that in developing countries that have higher quality of domestic pro-market institutions firms have on average a 1 3 Eurasian Business Review (2022) 12:35–57 37 substantially higher propensity to invest in R&D. The paper contributes to the catch- ing up literature by showing that the extent to which national institutions facilitate pro-market interactions influence the possibilities for learning and building compe- tences in developing and transition economies. Thus, while industrial policy targeted to support the development of a specific industrial sector may create incentives and resources for catching-up (Bodas Freitas & Iizuka, 2012), ensuring a well-function- ing institutional context that reduces uncertainties and transaction costs related to R&D is a necessary condition for that to occur. The paper is organized as follows. Section 2 briefly reviews the relevant literature on and the role of institutional quality for R&D investments. Section 3 presents the data and empirical methods. Section 4 presents the results. Section 5 concludes the paper and discusses the implications of our findings. 2 Theory R&D is crucial for advancing capabilities for the development, acquisition and mod- ification of technologies (Kim & Kogut, 1996), and it increases firms’ profitability, growth and economic performance (Capasso et  al., 2015). Investment in R&D is then key in processes of industrial catching up (Kim, 1997, 1998). Institutional economics theory points out that firms’ decisions are shaped by the national institutional context. This is because national institutions affect the trans- action costs and learning opportunities related to different strategic alternatives (Jacobides & Winter, 2012; North, 1990). Institutions may then benefit more busi- ness strategies than others (Castellacci, 2015; Chittoor et  al., 2015), leading firms to develop different characteristics and capabilities in different institutional contexts (Freeman, 1995, 2004). The literature provides extensive evidence of how institu- tions influence firms’ strategic decisions, especially their effects on market entry modes, and on the forms of governance of international alliances (Ang et al., 2015; Hagedoorn et al., 2005). Extant research, though, has not explicitly investigated how national institutions shape firms’ decision to invest in R&D. Prior research studied the relationships between institutional quality and R&D investments at the country-level and at the industry-level (Castellacci & Natera, 2012; Seitz & Watzinger, 2017; Wang, 2010). These studies show that R&D invest- ment is associated with the efficiency of the national legal systems in enforcing con- tracts, indicating for instance that in institutional contexts characterized by weak law enforcement, the expected positive effect of R&D may be weaker (Qian et al., 2017). Indeed, evidence on OECD countries suggests that in industries in which contracts are more relevant for the functioning of the technological and production activities, the industry levels of R&D investment are more responsive to the quality of national institutions (Seitz & Watzinger, 2017). Alam et al. (2019) present a relevant empiri- cal analysis of the relationships between national institutions and firms’ R&D inten- sity based on a panel of 664 firms from 20 transition and developing economies. The present paper intends to contribute to this literature by investigating the rela- tionship between the quality of pro-market institutions, on the one hand, and firm- level decisions to invest in R&D, on the other. Focusing explicitly on the firm-level 1 3 38 Eurasian Business Review (2022) 12:35–57 of analysis, we posit four main channels through which institutional quality may affect agents’ R&D decision. The first channel refers to uncertainties related to innovation. Since R&D invest- ments are subject to uncertainty and they typically lead to innovation output and profit several years after innovation investments are undertaken, national institutions may play an important role to reduce such uncertainties and thus strengthen firms’ R&D incentives. In countries where the possibility to enforce contracts is low, the costs of engaging in opportunistic behavior and leakage crucial proprietary knowl- edge to competitors are low, thus raising the risks associated with investment in R&D (Seitz & Watzinger, 2017). In economies characterized by political and social instability, and where the legal systems do not efficiently enforce law and contracts, firms may perceive as likely to lose their investment as a result of crime and cor - ruption or as a result of sudden changes in regulations that change unexpectedly the possibilities to bring new products to the market (Kafouros & Aliyev, 2016). A second channel linking national institutions to firms-level R&D decision relates to companies’ possibilities to reap the benefits of innovation. A well-func- tioning system of intellectual property rights (IPRs), combined with an efficient enforcement system, contributes to strengthen appropriability conditions related to innovation activities, and thus firms’ incentives to carry out R&D. Third, national institutions may also be important to facilitate firms’ access to finance and venture capital that can spur R&D activities. When policy programs to support firms’ R&D are in place (e.g. subsidies, tax incentives), effectiveness and transparency of these programs foster firms’ participation in public R&D funding, thus increasing R&D propensity in the national system. Finally, a fourth channel relating the quality of pro-market institutions to firms’ R&D decision refers to agents’ interactions within the innovation system. Innova- tion typically involves interactions with a number of partners within and beyond the value-chain, between innovative companies, suppliers, users and public research institutions. Being innovation a social and interactive process (Lundvall, 2016), firms’ efforts to innovate are likely to be influenced by the perceived costs of inter - acting and contracting with different partners, as well as by the possibilities to learn and appropriate value from the R&D activities undertaken (Jacobides & Winter, 2012). In countries in which national institutions have lower quality (e.g. in terms of legal institutions that regulate and enforce contracts in case of legal disputes among partners), firms perceive higher risk of interacting with external partners, as their innovation partners or technology suppliers may be also limited to comply timely and according to stipulated quality and standards. Specifically, in a well-known study of courts and relational contracts in for - mer Communist countries, Johnson et  al. (2002) showed that well-functioning legal courts foster the development of new commercial relationships and contracts between producers and suppliers, since they help to settle legal disputes between partners when these arise. Further, when legal institutions are weak, there may arise informational asymmetries and hold up problems between suppliers and produc- ers, so that the supplier will not invest an optimal amount of resources in relation- specific investments. This will affect firms’ innovation incentives and performance, since hold up problems raise production costs and weaken comparative advantage. It 1 3 Eurasian Business Review (2022) 12:35–57 39 has also been shown that such hold-up problems are more relevant in contract-inten- sive industries with more complex and differentiated products (Essaji & Fujiwara, 2012; Ma et al., 2010; Nunn, 2007). In summary, based on the four points noted above, our empirical analysis will investigate the hypothesis that firms’ propensity to invest in R&D is on average higher in developing and transition economies that have higher quality of and better functioning national pro-market institutions. 3 Data and methods 3.1 WBES survey methodology Information on firms is obtained from the Enterprise Surveys administered by the World Bank (WBES) in 57 transition and developing countries between 2002 and 2017 in Southeast and Central Europe; Eastern Europe, Caucasus and Central Asia; Latin America, Sub-Saharan Africa; South and Southeast Asia; Middle East and North Africa; and Western Asia. The WBES surveys focus on emerging economies (as defined by World Bank) and address establishments that are formally registered with the relevant authorities (excluding firms in the informal economy). The pur - pose of the surveys is to collect detailed information on the nature of the business activities, the ways in which firms operate, innovate and relate to their environment, as well as on their performance. Interviewees are typically top managers or business owners. The surveys use a stratified random sampling. The strata for WBES are firm size, business sector, and geographic region within a country. Firm size levels are 1–5, 5–19, 20–99, and 100 + employees (large-sized firms). Since in most economies a large majority of firms are small and medium-sized, WBES oversample large firms based on the understanding that larger firms tend to be important engines of job cre- ation and economic growth. Sector breakdown by WBES is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as additional strata based on employment, value-added, and total num- ber of establishments. In the WBES surveys, geographic regions within a country are selected based on which cities/regions collectively represent most of the eco- nomic activity. Each firm is thus associated with a single geographic region of resi- dence, even though such firms may operate in markets outside of their home region. The 15-year period of the study was between 2002 and 2017. This period was considered due to the similarities of the survey instruments used. Furthermore, we have restricted our sample in this study to manufacturing firms only, excluding firms in service and extractive industries. Given the cross-sectional nature of our study, we have excluded firms that were part of repeated longitudinal panel surveys (less than 10% of the population). After dropping missing values, unrealistic figures (such as negative values of firm size) and years with less than 100 observations, the sample consisted of 10,393 firms in 57 countries. 1 3 40 Eurasian Business Review (2022) 12:35–57 Table 1 Sample firms by industrial sector Industry No R&D Performed Performed Total frequency Total percent (fre- R&D (fre- R&D (per- quency) quency) cent) Electronics and machinery 585 546 48.28 1131 10.88 Food, Beverage and tobacco 1519 1257 45.28 2776 26.71 Metal products 821 477 36.75 1298 12.49 Petroleum, chemical, and 942 967 50.65 1909 18.37 non-metallic mineral products Textile, garment and leather 1782 756 29.79 2538 24.42 Wood, paper, publishing 532 209 28.21 741 7.13 and furniture Total 6181 4212 40.53 10,393 100 Tables 1, 2 and 3 provide further information on the sample composition. Table 1 reports the industrial sectors represented in our sample, the number of firms in each manufacturing sector, and the number of firms that are engaged and not engaged in R&D. Table 2 provides a list of the countries represented in these surveys, reporting again the number of respondent firms in each country and the corresponding number of R&D and non-R&D companies. Table  3 reports the distribution of firms (total, R&D active and non-R&D active) by survey year during the period 2002–2017. In addition to WBES firm-level data, our study also uses the World Governance Indi- cators (WGI) to measure different aspects of the national institutional setting (see further below). 3.2 Econometric model We seek to estimate firms’ probability to invest in R&D, as indicated in Eq.  (1) below: Pr (R&D )=  + INSTIT + GDPPC + FIRMS ijt j;t−1 j; t−1 ijt (1) +INDUSTRY + COUNTRY + YEAR + ijt where the sub-index i indicates the firm, j the country, and t the year of the survey. The dependent variable R&D is a dichotomous variable that takes the value 1 if the firm indicated in the survey that it invested in R&D during the preceding 3 years, and 0 otherwise. The main explanatory variable INSTIT accounts for the (lagged) quality of pro-market institutions 2 years before the corresponding survey year. Institutional quality is measured by using different variables available in the World Governance Indicators (WGI) database: – Control of corruption captures perceptions of the extent to which public power is not exercised for private gain, including both petty and grand forms of corrup- tion, as well as "capture" of the state by elites and private interests. 1 3 Eurasian Business Review (2022) 12:35–57 41 Table 2 Sample firms by country Country No R&D Performed Performed Total frequency Percent (fre- R&D (fre- R&D (per- quency) quency) cent) Southeast and Central Europe 1389 1029 42.56 2418 23.27 Albania 14 19 24.68 77 0.74 Bosnia and Herzegovina 55 79 58.96 134 1.29 Bulgaria 47 41 46.59 88 0.85 Croatia 37 71 65.74 108 1.04 Czech Republic 58 93 61.59 151 1.45 Estonia 59 61 50.83 120 1.15 Hungary 198 83 29.54 281 2.7 Kosovo 41 15 26.79 56 0.54 Latvia 65 26 28.57 91 0.88 Lithuania 61 70 53.44 131 1.26 Montenegro 13 3 18.75 16 0.15 North Macedonia 72 41 36.28 113 1.09 Poland 286 153 34.85 439 4.22 Romania 218 96 30.57 314 3.02 Serbia 20 42 67.74 62 0.6 Slovakia 52 47 47.47 99 0.95 Slovenia 49 89 64.49 138 1.33 Eastern Europe, Caucasus and 1468 878 37.43 2346 22.57 Central Asia Armenia 140 38 21.35 178 1.71 Azerbaijan 51 4 7.27 55 0.53 Belarus 45 43 48.86 88 0.85 Georgia 79 38 32.48 117 1.13 Kazakhstan 257 62 19.44 319 3.07 Kyrgyz Republic 62 33 34.74 95 0.91 Moldova 69 61 46.92 130 1.25 Russia 383 377 49.61 760 7.31 Tajikistan 97 44 31.21 141 1.36 Ukraine 236 145 38.06 381 3.67 Uzbekistan 49 33 40.24 82 0.79 Latin America 2005 1666 45.38 3671 35.32 Argentina 323 348 51.86 671 6.46 Bolivia 50 48 48.98 98 0.94 Chile 116 110 48.67 226 2.17 Colombia 409 466 53.26 875 8.42 Dominican Republic 31 15 32.61 46 0.44 El Salvador 200 41 17.01 241 2.32 Guatemala 103 67 39.41 170 1.64 Mexico 47 54 53.47 101 0.97 Nicaragua 54 22 28.95 76 0.73 1 3 42 Eurasian Business Review (2022) 12:35–57 Table 2 (continued) Country No R&D Performed Performed Total frequency Percent (fre- R&D (fre- R&D (per- quency) quency) cent) Paraguay 64 43 40.19 107 1.03 Peru 362 304 45.65 666 6.41 Uruguay 229 139 37.77 368 3.54 Venezuela 17 9 34.62 26 0.25 Sub-Saharan Africa 574 330 36.50 904 8.7 Benin 25 3 10.71 28 0.27 Cameroon 24 4 14.29 28 0.27 Kenya 88 73 45.34 161 1.55 Niger 17 2 10.53 19 0.18 Nigeria 230 93 28.79 323 3.11 Tanzania 24 36 60.00 60 0.58 Uganda 33 12 26.67 45 0.43 Zambia 25 16 39.02 41 0.39 Zimbabwe 108 91 45.73 199 1.91 South and Southeast. Asia 179 31 14.76 210 2.02 Afghanistan 14 1 6.67 15 0.14 Bangladesh 39 23 37.10 62 0.6 Myanmar 126 7 5.26 133 1.28 Middle East, North Africa and 566 278 32.94 844 8.12 West. Asia Jordan 106 16 13.11 122 1.17 Lebanon 53 9 14.52 62 0.6 Morocco 60 14 18.92 74 0.71 Turkey 347 239 40.78 586 5.64 Total 6181 4212 40.53 10,393 100 – Rule of law measures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. – Regulatory quality is an indicator of the perceptions of the ability of the govern- ment to formulate and implement sound policies and regulations that allow and promote private sector development. – Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the cred- ibility of the government’s commitment to such policies. – Political stability and absence of violence/terrorism measures perceptions of the likelihood of avoiding political instability and/or politically motivated violence, including terrorism. 1 3 Eurasian Business Review (2022) 12:35–57 43 Table 3 Sample firms by year Survey year No R&D Performed R&D Performed R&D Total frequency Percent (frequency) (frequency) (percent) 2002 44 407 90.24 451 4.34 2005 1002 288 22.33 1290 12.41 2006 431 380 46.86 811 7.8 2008 573 286 33.29 859 8.27 2009 1545 962 38.37 2507 24.12 2010 798 982 55.17 1780 17.13 2011 83 97 53.89 180 1.73 2013 430 379 46.85 809 7.78 2014 304 98 24.38 402 3.87 2016 401 67 14.32 468 4.5 2017 570 266 31.82 836 8.04 Total 6181 4212 40.53 10,393 100 – Voice and accountability accounts for perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. These indicators are defined by WGI based on aggregations of large num- bers of local expert surveys and indexes using a statistical procedure based on an unobserved components model. Each of the six indicators was then rescaled in the WGI database in units of a standard normal distribution, with mean zero, standard deviation of one, and spanning between approximately − 2.5 to 2.5, with higher values corresponding to better institutional quality for all variables. Since it is well-known from previous studies that several indicators of institutional quality are highly correlated with each other (representing closely related and complementary aspects of countries’ institutional quality), we have used these six indicators in a principal component analysis in order to reduce the multidi- mensionality of our explanatory factor. The principal component analysis yielded one single factor with Eigenvalue greater than 1, which accounts for 98% of the variance across all six variables. Cronbach’s alpha is 93.9%, suggesting that the six variables have high internal consistency and are closely related. In our econo- metric analysis, we will therefore use this composite factor as an overall measure of countries’ institutional quality in baseline regressions, and we will also use each of the six indicators in additional separate regressions to test the effect of the various institutional indicators. Our regression model includes a number of control variables. Evidence suggests that countries where the level of economic development is higher tend to have insti- tutions that facilitate market transactions (Seitz & Watzinger, 2017). Thus, we con- trol for the (lagged) level of economic development of the country 2 years before the corresponding survey years. GDP per capita indicates the ratio of the gross domes- tic product of a country in purchasing power parity terms, measured in international 1 3 44 Eurasian Business Review (2022) 12:35–57 dollars, to the population of the country. This variable is obtained from the World Bank based on reporting from national governments. Regarding firm level controls, we include a number of standard variables. Firms that trade (import or export) have been shown to be more likely to invest in R&D (Alvarez & Robertson, 2004; MacGarvie, 2006). We thus include two dichoto- mous variables. Importer indicates firms that import raw materials directly (i.e. not through third-party intermediaries). Exporter indicates that a firm exports its prod- ucts directly. Larger firms have more resources, and thus they may be more likely to invest in technology (Lee & Sung, 2005; Shefer & Frenkel, 2005; Veugelers, 1997). We then include the ordinal variable Firm size. It takes four different values between 0 and 3 (value 0 if the number of workers employed by a firm in the last fiscal year was less than five; whereas value 3 indicates that the firm had more than 100 work - ers). This ordinal variable is included as a continuous indicator in the regressions. We also account for the age of the firm since older firms may be more embedded in national institutions. Business group affiliation may affect a firms access to techno- logical and financial resources for R&D (Castellacci, 2015; Chang et al., 2006), and it is therefore important to include such control variable in the regressions. In addi- tion, foreign direct investment may be a means of technology transfer that affects R&D investment decisions (Arora & Fosfuri, 2000; Saggi, 2002). We thus include two dichotomous variables in our model. The variable domestic subsidiary indicates subsidiaries that are less than 50% foreign owned. Foreign subsidiary takes value 1 if the firm has 50% or more foreign-owned capital. To account for the availability of financial resources we also include a variable called financial constraint, which is an ordinal variable with values ranging from 0 to 4, where 0 indicates that the firm does not consider access to finance as an obstacle to its operations and higher values indicate that firms perceive access to finance as an important obstacle. This ordinal variable is included as a continuous indicator in the regressions. Srholec (2011) has previously shown that access to finance can affect investment in innovation in devel- oping countries. Finally, industry, country and year account for the related set of fixed effects. Since our dependent variable is a dichotomous variable, we estimate our regression model using a probit regression with country, year and industry fixed effects and robust standard errors () . All variables and sources are reported in a table in the appendix of the paper. Before presenting the results, it is worth noticing some of the possible limitations of the econometric approach that we have used in our analysis, which are mostly related to the nature of the data that we have used. First, since R&D activities unfold over time and are characterized by sunk costs and path-dependency, it would in principle be better to analyze them by using a dynamic panel framework, although this is not possible in the context of the pooled cross-sectional survey data that we are using. Second, some of the firm-level control variables in our regressions may be endogenous, such as e.g. firm size, import, export and financial constraints. Our main explanatory variable, though, is less subject to such possible issues as insti- tutional quality is measured at the country level, and lagged 2 years vis-à-vis the dependent variable. Finally, it is also possible that our results are subject to some sample bias related to the way in which the WBES survey was collected (e.g. large firms are 1 3 Eurasian Business Review (2022) 12:35–57 45 over-represented as noted above). Nevertheless, our sample represents a large num- ber of firms in all manufacturing sectors and a large number of transition and devel- oping economies, such that the results are on the whole representative and robust. 4 Results Table  4 reports descriptive statistics and the correlation coefficients among the variables. The correlation coefficients between the various firm level variables, and those among firm level variables and national variables are on the whole quite low, not suggesting possible issues of multicollinearity. On the other hand, the correlation coefficients among the various country-level governance indicators are as expected rather high, reason why we make use of the composite variable obtained through the principal component analysis in our baseline regressions. Table  5 reports the results of the probit regression. Model 00 is the baseline model including only the control variables. Models 01–07 add the national institu- tional characteristics. The firm level controls behave as expected. The coefficients of importer, exporter, age, firm size, domestic subsidiary and financial constraints are positively correlated with firms’ R&D propensity. They indicate that R&D propen- sity is on average higher for larger and older firms, for companies that import from and export to foreign markets, as well as for firms that are part of domestic business groups. The coefficient of the foreign subsidiary variable is non-significant. Shifting the focus to the main explanatory variable of our main interest, the esti- mated coefficient of the composite variable accounting for countries’ quality of pro-market institutions is positive and significant, indicating a positive relationship between firms’ propensity to invest in R&D and the extent to which national institu- tions are able to provide socio-political stability and efficient legal institutions that reduce uncertainties and transaction costs related to R&D investments and innova- tive activities. The estimated coefficient of GDP per capita is also positive and sig- nificant. Our result on the role of institutional quality does therefore hold after con- trolling for countries’ overall level of economic development. As the coefficients in our non-linear probit regression model do not provide an estimate of the marginal effects, we plot the related marginal effects. Figure  1 reports the marginal effect of the quality of pro-market institutions on the firms’ probability The variable measuring financial constraints is positively correlated with the dependent variable. In line with previous empirical analysis based on firm-level survey data (e.g. WBES surveys; CIS surveys), the interpretation of this positive correlation is that firms tend to be more aware of the importance of financial constraints when they engage in R&D investments, since these require the availability of finan- cial resources to be invested in uncertain and longer-term projects. In more general terms, survey-based obstacle indicators (such as the financial constraints variable used here) measure firms’ perceptions and awareness of the importance of a given factor for the firm’ strategy: the more important the factor, the more likely is that companies will perceive and report it as a critical aspect or obstacle to their operations and performance (Castellacci, 2015). 2 2 The Wald test comparing the two models (X = 15.61; p < 0.001) indicates that the model including the pro-market institutions variable (model 01) is a significant improvement vis-à-vis the baseline model without this variable (model 00). 1 3 46 Eurasian Business Review (2022) 12:35–57 1 3 Table 4 Descriptives and correlations Variables Descriptives Correlation matrix Mean Std. dev Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (1) R&D 0.405 0.491 0 1 1.000 (2) Pro- 0.046 0.978 − 2.138 2.403 0.041 1.000 market Institu- tions (3) Con- − 0.33 0.657 − 1.42 1.377 0.060 0.946 1.000 trol of corrup- tion (4) Rule of − 0.39 0.694 − 1.852 1.313 0.025 0.989 0.925 1.000 law (5) Regu- − 0.02 0.71 − 2.118 1.519 0.028 0.895 0.783 0.869 1.000 latory quality (6) Gov- − 0.18 0.59 − 1.546 1.168 0.060 0.957 0.892 0.922 0.867 1.000 ernment effec- tiveness (7) − 0.4 0.859 − 2.419 1.186 − 0.04 0.641 0.560 0.645 0.460 0.575 1.000 Political stabil- ity and absence of vio- lence/ terror- ism (8) Voice − 0.04 0.725 − 2.118 1.167 0.071 0.869 0.819 0.820 0.749 0.820 0.612 1.000 and account- ability Eurasian Business Review (2022) 12:35–57 47 1 3 Table 4 (continued) Variables Descriptives Correlation matrix Mean Std. dev Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (9) GDP 11,140 5679 1048.4 29,001.4 0.077 0.596 0.516 0.561 0.515 0.691 0.496 0.500 1.000 per capita (10) 0.501 0.5 0 1 0.147 0.094 0.106 0.098 0.056 0.072 0.074 0.095 0.062 1.000 Importer (11) 0.403 0.491 0 1 0.196 0.184 0.163 0.185 0.154 0.179 0.094 0.181 0.141 0.342 1.000 Exporter (12) Age 22 20.0 1 180 0.143 0.033 0.071 0.015 − 0.02 0.048 − 0.01 0.079 0.051 0.159 0.145 1.000 (13) Firm 1.934 0.833 0 3 0.218 − 0.01 − 0.02 − 0.01 − 0.02 − 0.02 0.020 − 0.00 0.036 0.303 0.354 0.264 1.000 size (14) 0.27 0.444 0 1 − 0.01 0.049 − 0.00 0.070 0.014 0.044 0.165 0.015 − 0.13 − 0.04 − 0.04 − 0.01 − 0.03 1.000 Domes- tic subsidi- ary (15) 0.051 0.22 0 1 0.060 0.057 0.039 0.061 0.036 0.054 0.091 0.048 0.017 0.071 0.158 − 0.01 0.134 − 0.14 1.000 Foreign subsidi- ary (16) 1.602 1.286 0 4 − 0.01 − 0.11 − 0.11 − 0.12 − 0.12 − 0.09 − 0.08 − 0.08 − 0.03 − 0.10 − 0.10 − 0.02 − 0.11 − 0.03 − 0.10 1.000 Finan- cial con- straint 48 Eurasian Business Review (2022) 12:35–57 Table 5 Probit estimation of the probability to invest in R&D (00—controls only) (01—full model) GDP per capita 0.0000441** (0.0000177) 0.0000407** (0.0000178) Importer 0.200*** (0.0318) 0.200*** (0.0319) Exporter 0.280*** (0.0329) 0.275*** (0.0330) Age 0.00267*** (0.000740) 0.00260*** (0.000741) Firm size 0.250*** (0.0203) 0.250*** (0.0203) Domestic subsidiary 0.114** (0.0442) 0.108** (0.0443) Foreign subsidiary 0.0498 (0.0725) 0.0465 (0.0727) Financial constraint 0.0187* (0.0113) 0.0180 (0.0114) Pro-market institutions 0.642*** (0.162) Constant − 5.585*** (0.754) − 4.125*** (0.824) Pseudo R 0.1961 0.1965 Log Pseudolikelihood − 5640.6629 − 5609.4582 Wald X 2666.46*** 2667.24*** Observations 10,165 10,129 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 Predictive Margins -2 -1 0 1 2 Pro-market institutions Fig. 1 Probability of a firm investing in R&D based on national pro-market institutions to invest in R&D. The figure shows clearly that the probability to invest in R&D increases steady and linearly with the quality of national pro-market institutions. We undertook some robustness tests to further analyze our results. First, we examine the effect of each of the six WGI indicators in separate regressions to see if the results we obtained using the composite variable measuring the quality of pro- market institutions are driven by any specific institutional characteristics. The results 1 3 R& D probab ility 0 .2 .4 .6 .8 Eurasian Business Review (2022) 12:35–57 49 of these additional regressions (Table 6) show that the estimated coefficients of the variables measuring control of corruption, rule of law, government effectiveness and political stability and absence of violence/terrorism (p < 0.01) and regulatory qual- ity (p < 0.05) are as expected positive and statistically significant. These relation- ships are illustrated further by means of the plots of the marginal effects for each of the five variables in Figs.  2, 3, 4, 5 and 6. The marginal effects show the same over - all patterns noted for Fig.  1. On the other hand, the estimated coefficient of voice and accountability is lower than the others and less statistically precise (although the variable is closely correlated with the other institutional indicators, as reported in Table 4 above). Second, we split our sample in two groups based on the countries’ level of GDP per capita (above or below the median sample value of GDP per capita). Regres- sion results reported in Table  7 show that in countries with GDP per capita below the sample median, the coefficient of quality of the pro-market institutions is posi- tive and significant, whereas the same variable is not significant for the sub-sample of companies in countries with higher than average GDP per capita. This pattern suggests that less developed economies present on average a broader variation in institutional quality, such that R&D propensity of firms is higher for those countries that have better functioning institutional systems. On the other hand, economies that have higher GDP per capita levels have presumably lower cross-country variation in institutional quality, which may explain why the estimated coefficient is not signifi- cant in this sub-sample. In short, the result presented in this paper about the relation- ship between country-level institutional quality and firm-level R&D propensity is mainly driven by the patterns of firms in less developed economies in our sample. 5 Conclusions The catching up literature stresses the importance of firms’ learning efforts and investments in technological upgrading, along with industrial policy (Lee et  al., 2021; Malerba & Nelson, 2011). Less attention has however been given to the char- acteristics of the national institutions in influencing firm’s learning and innovating decisions. We address this gap by building on the institutional economics literature that argues that the extent to which national institutions facilitate market interaction defines firms’ perceived transaction costs of alternative strategies. We propose that in countries where national institutions are less able to enforce law and contracts, and to assure political and social stability, firms will perceive higher transaction costs, and will therefore be less likely to invest in R&D activities, which increas- ingly involve interaction and contracting with external partners. Data on 57 developing and transition countries corroborate our expectations that the better the quality of the pro-market national institutions, the more likely that firms in that country will perceive low transaction costs in innovation activities, and thus the more likely that they will decide to invest in R&D. This result is in line with the one obtained by the Seitz and Watzinger’s (2017). In their industry-level analysis in OECD countries, they show that the observed industry levels of R&D investment 1 3 50 Eurasian Business Review (2022) 12:35–57 1 3 Table 6 Probit regressions of full model including all sample firms (02) (03) (04) (05) (06) (07) GDP per capita, PPP 0.0000398** 0.0000374** 0.0000546*** 0.0000394** 0.0000462** 0.0000489*** (0.0000179) (0.0000179) (0.0000178) (0.0000179) (0.0000182) (0.0000182) Importer 0.202*** (0.0319) 0.198*** (0.0318) 0.199*** (0.0318) 0.198*** (0.0318) 0.201*** (0.0319) 0.201*** (0.0318) Exporter 0.282*** (0.0330) 0.279*** (0.0330) 0.279*** (0.0329) 0.281*** (0.0329) 0.276*** (0.0330) 0.280*** (0.0329) Age 0.00274*** (0.000740) 0.00264*** (0.000740) 0.00265*** (0.000740) 0.00267*** (0.000740) 0.00257*** (0.000741) 0.00267*** (0.000740) Firm size 0.248*** (0.0203) 0.251*** (0.0203) 0.251*** (0.0203) 0.252*** (0.0203) 0.252*** (0.0203) 0.250*** (0.0203) Domestic subsidiary 0.114*** (0.0443) 0.111** (0.0443) 0.109** (0.0443) 0.112** (0.0443) 0.106** (0.0444) 0.111** (0.0443) Foreign subsidiary 0.0496 (0.0726) 0.0476 (0.0726) 0.0464 (0.0727) 0.0481 (0.0727) 0.0464 (0.0729) 0.0507 (0.0725) Financial constraint 0.0198* (0.0114) 0.0183 (0.0114) 0.0188* (0.0114) 0.0184 (0.0114) 0.0169 (0.0114) 0.0190* (0.0113) Control of corruption 0.588*** (0.167) Rule of law 0.455** (0.184) Regulatory quality 0.360** (0.160) Government effective- 0.778*** (0.207) ness Political stability and 0.365*** (0.118) absence of violence/ terrorism Voice and account- 0.239 (0.185) ability Constant − 4.805*** (0.779) − 4.662*** (0.829) − 5.044*** (0.781) − 4.290*** (0.810) − 4.441*** (0.832) − 5.250*** (0.800) Pseudo R 0.1969 0.1965 0.1963 0.1969 0.1962 0.1961 Log Pseudolikelihood − 5634.8808 − 5637.4861 − 5638.8325 − 5635.0693 − 5611.7816 − 5640.2856 Wald X 2677.86*** 2671.92*** 2670.88*** 2677.94*** 2662.84*** 2667.81*** Observations 10,165 10,165 10,165 10,165 10,129 10,165 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 Eurasian Business Review (2022) 12:35–57 51 Predictive Margins -2 -1 0 1 2 Control of Corruption Fig. 2 Probability of a firm investing in R&D based on national control of corruption Predictive Margins -2 -1 0 1 2 Rule of Law Fig. 3 Probability of a firm investing in R&D based on national rule of law in which contract enforcement is crucial are particular sensitive to the quality of national legal systems. Our results hint at a necessary condition for catching up and economic growth, which is that national institutions can assure socio-political stability and enforce- ment of law and contracts. Only by perceiving a pro-market institutional environ- ment firms may have more favorable perceptions of different catching-up strategies, and may perceive lower uncertainty and costs in investing in learning and innova- tion activities. When firms are reassured that the business markets will not suddenly change as result of socio-political instability, they will perceive investment in R&D as less risky. The more firms are reassured about the possibility to enforce contracts, 1 3 R& D probabi lity R& D probab ility .2 .4 .6 .8 .2 .4 .6 .8 52 Eurasian Business Review (2022) 12:35–57 Predictive Margins -2 -1 0 1 2 Regulatory Quality Fig. 4 Probability of a firm investing in R&D based on national regulatory quality Predictive Margins -2 -1 0 1 2 Government Effectiveness Fig. 5 Probability of a firm investing in R&D based on national government effectiveness the higher benefit they may perceive from contracting and collaborating with other firms for innovation, market entry and/or technology development. Results from the sample split exercise in particular corroborate this argument. The estimated coef- ficient of pro-market institutions is positive and significant (non-significant) for the sub-sample of firms in countries with GDP per capital lower (higher) than the sam- ple median value. This paper contributes to the catching up literature by proposing and showing that even small differences in the extent to which national institutions facilitate pro- market interactions among developing and transition countries may significantly influence firms’ engagement in learning and technological building activities. In 1 3 R& D probabi lity R& D probab ility .2 .3 .4 .5 .6 0 .2 .4 .6 .8 Eurasian Business Review (2022) 12:35–57 53 Predictive Margins -2 -1 0 1 Political Stability and Absence of Violence/Terrorism Fig. 6 Probability of a firm investing in R&D based on national political stability and absence of vio- lence/terrorism Table 7 Probit regressions of full model, split sample for countries with GDP per capita levels above and below the median (01A—full model, below median GDP (01B—full model, above per capita) median GDP per capita) GDP per capita − 0.0000204 (0.0000624) 0.0000765 (0.0000802) Importer 0.251*** (0.0454) 0.156*** (0.0456) Exporter 0.228*** (0.0474) 0.326*** (0.0468) Age 0.00228** (0.00113) 0.00264*** (0.000986) Firm size 0.239*** (0.0285) 0.267*** (0.0296) Domestic subsidiary 0.186*** (0.0663) 0.0471 (0.0614) Foreign subsidiary 0.344*** (0.107) − 0.181* (0.100) Financial constraint 0.00920 (0.0162) 0.0255 (0.0163) Pro-market institutions 0.533** (0.238) − 0.177 (0.920) Constant − 4.398*** − 2.716 (0.895) (1.837) Pseudo R 0.2189 0.1503 Log pseudolikelihood − 2719.7658 − 2757.7945 Wald X 1685.82*** 7931.51*** Observations 5226 4775 Heteroskedasticity robust standard errors in parentheses. Country, industry, and survey year fixed effects are included but not shown *p < 0.1, **p < 0.05, ***p < 0.01 addition, we contribute by showing that the direct influence of pro-market institu- tions on firms’ investment decision in capability building and innovation is particu- lar relevant in countries with low levels of development. This is in line with the 1 3 R& D probab ility .2 .3 .4 .5 .6 .7 54 Eurasian Business Review (2022) 12:35–57 literature studying the middle income country trap in processes of catching up and economic development (Lee et al., 2021). Appendix 1: Variables: definition and sources Variable Definition Source R&D Dummy variable that takes the value 1 if the Enterprise surveys firm has invested in R&D during the preced- ing 3 years, and 0 otherwise Pro-market Institutions Principal component of control of corruption, Enterprise surveys rule of law, regulatory quality, govern- ment effectiveness, political stability and absence of violence/terrorism, and voice and accountability Control of corruption The extent to which public power is not World governance indicators exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests Rule of law The extent to which agents have confidence in World governance indicators and abide by the rules of society, and in par- ticular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence Regulatory quality The ability of the government to formulate World governance indicators and implement sound policies and regula- tions that permit and promote private sector development Government effectiveness The quality of public services, the quality World governance indicators of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the govern- ment’s commitment to such policies Political stability and The likelihood of avoiding political instabil- World governance indicators absence of violence/ ity and/or politically motivated violence, terrorism including terrorism Voice and accountability The extent to which a country’s citizens are World governance indicators able to participate in selecting their govern- ment, as well as freedom of expression, freedom of association, and a free media GDP per capita The ratio of the gross domestic product of a National governments, com- country in purchasing power parity terms, piled by World Bank measured in international dollars, to the population of the country Importer The firm imports raw materials directly (i.e. Enterprise surveys not through third-party intermediaries) Exporter The firm exports its products directly Enterprise surveys 1 3 Eurasian Business Review (2022) 12:35–57 55 Variable Definition Source Age Number of years that the firm has been in Enterprise surveys operation Firm size Number of workers at the firm (0: less than Enterprise surveys 5 employees; 1: 5–19 employees; 2: 20–99 employees; 3: 100 or more employees) Domestic subsidiary Firm belongs to a majority domestic-owned Enterprise surveys business group Foreign subsidiary Firm belongs to a majority foreign-owned Enterprise surveys business group Financial constraint 0 indicates that the firm does not consider Enterprise surveys access to finance as an obstacle to its operations, and values from 1 to 4 indicate increasing prominence of access to finance as an obstacle Funding Open access funding provided by University of Oslo (incl Oslo University Hospital). 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Journal

Eurasian Business Review – Springer Journals

Published: Mar 1, 2022

Keywords: R&D; Innovation; Institutional quality; Economic development; WBES data

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