metadata: authors: 0: author: Lederman,Daniel
count: World
docna: 0: docna: Product innovation by incumbent firms in
            developing economies : the roles of research and development
            expenditures, trade policy, and the investment climate
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Trade Policy,International Trade and Trade Rules,Rules of Origin,Business Cycles and Stabilization Policies,Trade and Multilateral Issues
teratopic: Science and Technology Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Poverty Reduction,Private Sector Development
abstracts: cdata!: A model of firm innovation illustrates
            the effects of the threat of imitation and product varieties
            on a representative firm's decision to invest in
            research and development to produce new product varieties.
            The model motivates two empirical questions: (1) Is research
            and development partially correlated with firms'
            propensity to introduce new products or product innovation
            in developing countries? (2) Do trade policies and the
            national investment climate affect firms' propensity
            for product innovation? The econometric evidence suggests
            that the answers are yes and yes, but the investment climate
            affects product innovation in a manner that is consistent
            with the presence of market failures and state capture.
            National trade-policy distortions appear to reduce the
            probability of product innovation, and the density of
            exporting firms at the national level also seems to
            positively affect the propensity to introduce new products
            by individual firms. The paper discusses some policy implications.
repnme: repnme: Product innovation by incumbent firms in
            developing economies : the roles of research and development
            expenditures, trade policy, and the investment climate
display_title: Product innovation by incumbent
            firms in developing economies : the roles of research and
            development expenditures, trade policy, and the investment climate
listing_relative_url: /research/2008/08/10064236/product-innovation-incumbent-firms-developing-economies-roles-research-development-expenditures-trade-policy-investment-climate
topicv3: Investment Climate m1326293 405,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Science and Technology Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Poverty Reduction,Private Sector Development
no_of_pages: 31
author_keys: 0: author_key: 125180
model_extractions: WPS4319 Policy ReseaRch WoRking PaPeR 4319 Product Innovation by Incumbent Firms in Developing Economies: The Roles of Research and Development Expenditures, Trade Policy, and the Investment Climate _Daniel Lederman_ The World Bank Development Research Group Trade Team August 2007
Policy ReseaRch WoRking PaPeR 4319

# **Abstract**

A model of firm innovation illustrates the effects of the threat of imitation and product varieties on a representative firm’s decision to invest in research and development to produce new product varieties. The model motivates two empirical questions: (1) Is research and development partially correlated with firms’ propensity to introduce new products or product innovation in developing countries? (2) Do trade policies and the national investment climate affect firms’ propensity for product innovation? The econometric evidence suggests that the answers are yes and yes, but the investment climate affects product innovation in a manner that is consistent with the presence of market failures and state capture. National tradepolicy distortions appear to reduce the probability of product innovation, and the density of exporting firms at the national level also seems to positively affect the propensity to introduce new products by individual firms. The paper discusses some policy implications.

This paper—a product of the Office of the Chief Economist for Latin America/Caribbean—is part of a larger effort in the department to understand the microeconomic determinants of economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dlederman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Product Innovation by Incumbent Firms in Developing Economies: The Roles of Research and Development Expenditures, Trade Policy, and the Investment Climate**

Daniel Lederman<sup>∗</sup> The World Bank Keywords: Product innovation, R&D, investment climate, developing countries JEL Classification: O14, L25

> ∗ Lederman is Senior Economist with the Development Economics Research Group of the World Bank. Address correspondence to: <u>dlederman@worldbank.org, 1818 H Street NW, Washington, DC 20433; (t) 202-473-9015, (f) 202-522-1159. The author</u> gratefully acknowledges the impeccable research assistance provided by Daniel Chodos and Javier Cravino. Roberto Alvarez, Pablo Fajnzylber, Rodrigo Fuentes, Alvaro González, José L. Guasch, J. Humberto López, and Marcelo Olarreaga provided insightful comments on earlier drafts. Financial support from the World Bank’s Latin America and Caribbean Regional Studies Program is gratefully acknowledged. The views expressed in this paper do not represent the views of the World Bank or of its member countries. All remaining errors are the responsibility of the author.
# **1.  Introduction**

It is so widely recognized that innovation is a key driver of economic growth that it is almost cliché to say so.<sup>1</sup> In spite of the extensive literature on the importance of expenditures in research and development (R&D) and science and technology policy to innovation, the distinction between adoption and invention in developing countries should lead us to explore numerous other areas that may pose barriers to the emergence of innovative firms. There is an emerging literature on what can be called “product” innovation, which focuses on the introduction of new products by firms. Hausmann and Rodrik (2003), for example, present a theoretical framework where market failures affecting the introduction of new export products in developing countries might be more severe than those affecting innovation in the developed countries, because in the latter most innovations can be patented, thus providing at least a partial institutional solution to the appropriability problem that inhibits private sector innovation. In developing countries, where most innovations are probably not patentable, other policy instruments would need to be devised to stimulate private-sector investments in product innovation. A related theoretical literature has emphasized the role of entrepreneurship that is responsible for commercializing research outputs, which are then reflected in the introduction of new products (Michelacci 2003).

Even in the context of high-income countries, the determinants of product innovation across firms might be different from those of patentable innovation. Criscuolo, Haskel, and Slaughter (2005) find in a panel of firms from the United Kingdom that the correlates of patents and product innovation are different, particularly with respect to the role played by linkages between firms and universities, the latter being more important for patentable innovations. Another example is the study by Aghion et al. (2006) that found that the response of U.K. firms (measured by productivity changes and patenting) to increased competition (due to the regulatory

> 1 Some studies reveal that much of the widening gap between rich and poor countries is due, not to differences in capital investment, but in technological progress. For example, according to Hall and Jones (1999) and Dollar and Wolf (1997), roughly half of cross-country differences in per capita income and growth are driven by differences in total factor productivity, generally associated with technological progress. Easterly and Levine (2003) also argue that productivity differences explain the lion’s share of global income differentials. To the extent that productivity is driven by innovation, both patentable and non-patentable, then we can infer that innovation has become an important ingredient in the new growth agenda. Furthermore, there is empirical evidence that the rates of return to investments in R&D can be high (Jones and Williams 1998). At the level of the firm, Klette and Kortum (2004) provide an analytical framework for understanding widely recognized stylized facts linking productivity, firm size, R&D, and patenting across firms. The empirical literature on firm-level R&D, patenting, and productivity is enormous.

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reforms of the Thatcher government) was different across firms, depending on their distance to the technological frontier (proxied by the productivity gap with respect to the most productive firms in each industry). Yet we still have much to learn about the empirical correlates of product innovation in developing countries.

This paper examines the empirical determinants of firm-level innovation in a large sample of manufacturing firms, covering at least 36 and up to 60 developing economies, 8 manufacturing industries, and totaling thousands of firms, depending on the empirical model. More specifically, we address two questions: First, is there evidence of market failures that would justify government involvement to raise private-sector investments in product innovation? In the presence of market failures, aspects of the investment climate associated with the extent of market competition can have unexpected effects on the private firms’ propensity to innovate, especially among firms that are farthest from the global technological frontier. For instance, regulatory reforms, as in the U.K. during the Thatcher era, might reduce private sector innovation as the enhanced entry of firms raises the prospects of imitation, thus leading entrepreneurs to reduce their innovation expenditures.

Second, is R&D investment correlated with product innovation in developing-country firms? If so, then the “D” in R&D – investments in product development -- might be an important correlate of the propensity to innovate by firms in developing countries even when such innovations are not patentable.

The evidence discussed herein, which is motivated by a simple model of firm behavior with respect to product innovation, suggests that market failures are empirically noticeable. Thus regulatory reforms can be beneficial for enhancing the diffusion of ideas and technology across firms and thus for productivity growth, but are not enough to stimulate product innovation.<sup>2</sup> Also, we find that R&D expenditures, which are mis-measured in the firm data, are highly correlated with the propensity for product innovation by firms in developing countries, although our estimates do not prove that there is a causal effect. Nevertheless, in our view, the main policy implication from these findings is that in the context of reforms that improve the investment

> 2 On the effects of entry regulations on new firm entry and incumbent-firm growth see, for example, Klapper, Laeven, and Rajan (2006).

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climate, the public sector has an important role to play to stimulate private R&D expenditures associated with product innovation, even in developing economies where R&D is often viewed as a non-critical aspect of private-sector development and R&D is viewed as an important factor mostly for firms in developed economies that are close to the global technological frontier (e.g., Aghion and Howitt 2005).

The rest of the paper is organized as follows. Section 2 briefly presents a model of a representative firm’s decision to spend resources for product innovation. Section 3 presents the data. Section 4 focuses on the partial correlation between firm-level R&D expenditures and product innovation. Section 5 discusses the econometric strategy and presents the results concerning the role of the investment climate in determining firms’ propensity for product innovation. Section 6 summarizes the main findings.

# **2. A Model of Product Innovation**

We follow Klette and Kortum (2004) by modeling firms’ innovation behavior in terms of an innovation production function with product varieties. A discussion of the model follows its presentation.

# 2.a. _The model_

Let the innovation production function I(.) depend on R&D expenditures, R, and knowledge capital embodied in the number of product varieties, n, produced by a representative firm: If both research expenditures and product varieties (current and in the past) were observed, one could estimate an empirical counterpart of this equation directly. These variables are usually not observed in firm data.

The corresponding research cost function, C(.), which is homogenous of degree 1 in _I_ and _n_ can be written as: 3
This equation can be interpreted as the reverse function of (1). Again, it could be estimated only if product varieties were observed, and additional assumptions are thus required to find an empirical counterpart. Standard assumptions about constant returns to scale in g(.) and c(.) can are used. Equation (2) simply tells us that the total cost of research for product innovation is the product of the number of varieties times the research-cost intensity function, c(.), of each product variety.<sup>3</sup> But we do need to introduce mechanisms through which a firm interacts with the market and competitors.

Under the assumptions that the price of each variety is exogenous, and that each firm faces an exogenous probability that one of its product varieties will become obsolete or be replaced in the market by a competitor’s newer or superior substitute product, the firm’s _expected_ profit at any point in time, _E_ (π) , can be written as: where 0 ≤ρ ≤ 1 represents the probability of losing one variety. _P_ is the exogenously marketdetermined average price for the firm’s product varieties. The model can be re-written for the general case where the firm faces the threat of losing multiple varieties. The research-cost intensity is the same under both scenarios, with or without losing a product variety, because the firm has already incurred all costs before a competitor’s entrance. In order for the firm to incur further research costs, the expected profits would have to be larger than zero, and the corresponding research-cost intensity of the firm in the positive-profits state is: 3 Klette and Kortum (2004) use the term “innovation intensity” for I(n)/n, where I(.) is the innovation production function.

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The relationship between research-cost intensity and the number of varieties is indeterminate, as _n_ appears in both the numerator and the denominator. Market prices are expected to be positively correlated with research-cost intensity. Moreover, unlike Hausmann and Rodrik (2003), the appropriability problem does not affect prices, but can be thought as affecting the probability of losing a product variety.

This model also predicts that the research-cost intensity of the firm will depend positively on the average price and negatively (and linearly) on the probability of losing one variety to competitors. The function is also inversely proportional to the probability of losing a variety. Since prices are given, innovation expenditures of firms as a share of sales would also depend on the probability of future imitation and on the number of varieties currently produced by the firm. We do observe this variable in firm data. Proxies for market conditions that are likely to be correlated with the average (relative) price of the firm’s varieties, such as the growth of manufacturing value added can also be used to capture the effect of market prices. The probability of entry can be thought to be affected by policies, especially the regulatory environment, as suggested by Aghion et al. (2006).

# 2.b. _Discussion_

The model is quite tractable and intuitive, but it does open the door to many questions of relevance for the empirical work. Although the empirics discussed in the following sections are done with cross-sections of firms, most of the interesting questions are related to dynamics.

First, would the predictions of the model change with the introduction of financing costs or real interest rates? The answer is no as long as all firms face the same opportunity costs or real interest rates.

Second, how would the firm respond when market demand for and the relative price of its product varieties fall? One response would be to reduce research-cost intensity, but another would be to increase research so as to enhance the chances of raising the number of varieties. The latter would be a “retooling” strategy, whereas the former could be called the “costreduction” strategy. Neither strategy is analyzed here, but the point is that either option could be viable, depending on an unspecified production function. One need only assume that firms’ 5
overall production function (as opposed to the innovation function analyzed here) is positive with respect to I(.).

Third, would the predictions of the model change if the probability of imitation is endogenous with respect to firms’ number of varieties or research costs? Probably not, because any plausible strategic game among firms that would need to be modeled would not change the signs of the predictions. What would come out from such a modeling approach is an optimal dynamic path for the firm in terms of different combinations of research-cost intensity changes and number of varieties, rather than the impact of the probability of imitation imposed by other firms’ strategic behavior.

Thus far the discussion has been interpreting research-cost intensity as referring strictly to R&D expenditures. Since there is a substantial literature on international technology diffusion (e.g. Keller 2004), it is worth asking whether this model would apply to other forms of innovation expenditures. The answer is yes, as the model does not have sufficient structure to distinguish between innovation expenditures to import capital goods or licensing payments to use foreign technologies. Some of the empirical exercises presented later in this paper use data on R&D as well licensing payments as proxies for investments in product innovation.

The simplicity of this setup, however, is attractive for empirical analysis, in spite of the open questions discussed above or many others.<sup>4</sup> An important advantage is that it explicitly models the direct (equation 1) and the reverse (equation 4) models of innovation expenditures. The following section presents the data that are used to explore the partial correlation between firms’ R&D as a share of sales – a proxy for research-cost intensity when product prices are exogenous – and firms’ propensity to introduce a new product in developing countries, as well as to assess the role of the investment climate. The reverse model turns out to be an important tool to estimate the “true” partial correlation between product innovation and research-cost intensity.

# **3. Data**

> 4 One such additional question is whether the average price index in the model should be a relative price, for example, with respect to the price of alternative varieties not produced by the firm. Working with relative prices that are also exogenously given does not change the analysis.

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We first discuss the data sources and definitions. We then present some descriptive statistics of relevance for the empirical analyses that follow.

# 3.a. _Data definitions and sources_

The present study characterizes the role of the investment climate within which firms operate, and how it affects product innovation. This is done with data from the World Bank’s numerous Investment Climate Surveys (ICS) and Business Environment and Enterprise Performance Surveys (BEEPS). There is substantial overlap between the ICS and BEEPS questionnaires, but there are, however, some differences in their sampling approaches. The ICS tend to focus on manufacturing firms; the BEEPS are drawn from a broad range of economic activities including services (actually the BEEPS database is slightly skew towards services firms). We restricted the coverage of the BEEPS data to firms in the manufacturing sectors.

Three sets of variables are used in our regression analyses discussed below, namely firm-, sector- and country-level variables. The first set includes our product innovation proxy that is also de dependent variable: the introduction of a new product. The surveys asked managers whether the firm had introduced a new product during the past two years. Hence our dependent variable is dichotomous.

Regarding explanatory variables, firm characteristics that may affect a firm’s proclivity to innovate include firm size, measured by the natural logarithm of the average number of permanent and temporary workers and its squared term (to test for a nonlinear relationship); a firm’s exporter status, measured by a dummy variable equal to 1 when a firm exports at least 10 percent of its sales; firm ownership, measured by a dummy variable equal to 1 for foreign ownership (when more than 10 percent of assets of the firm are owned by foreigners); and capacity utilization, measured as the average utilization of its productive capacity over the year preceding the survey. The surveys also provide information about the value of R&D expenditures and firm sales. From those data we calculated the share of R&D in sales. Since the literature on innovation has paid much attention to the adoption of foreign technologies, we also use data derived from a question in the surveys that asked managers whether the firm had paid licensing fees during the past two years. This variable is also dichotomous.

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Sector-level variables include information on trade policies, namely a composite index of the average applied tariffs and its standard deviation. The index was estimated as the first principal components derived from factor analysis. The other trade policy indicator measure at the industry level is the share of tariff lines within each industry that faces one of the so-called “core” non-tariff barriers. These data were taken from Nicita and Olarreaga (2006).

Country-level explanatory variables capture various aspects of the investment climate, besides trade policies. They include an index of infrastructure coverage (from the _World Development Indicators_ (WDI) database), institutional quality (from Kaufmann et al. 2005), and real manufacturing GDP growth (also from WDI). In some models, we also include the level of development (GDP per capita from the Penn World Tables). An important explanatory variable for our analysis is a regulatory index capturing the ease of entry, which was calculated from data from the World Bank’s _Doing Business_ database. We use principal components analysis to calculate a composite index on infrastructure coverage (including paved roads per square kilometer and telephone lines per capita), the institutional index (including corruption, political stability, and rule of law), and the regulatory index (including Difficulty of Firing Index, Difficulty of Hiring Index and Days for Starting a Business). We also use patent-counts data from Lederman and Saenz (2005), namely the sum (stock) of utility patents granted to researchers in each country during 1963-2000 by the United States Patent and Trademark Office (USPTO) per person. The latter provides a measure of the density of innovative ideas available to firms operating in each country.

Some studies, such as Criscuolo et al. (2005), also treat explanatory variables measured at a higher level of aggregation than the firm level as exogenous factors, but these are measured with data from the firm surveys themselves. Our approach is different in this regard, as we use objective data from other sources. As discussed elsewhere in this volume, the use of aggregate variables derived from the same dataset as the firm data can be assumed to be exogenous only under certain conditions, namely firms’ deviations from the average must be orthogonal to the average and normally distributed with expected value of zero. We do not have to make any assumptions in this regard, because our data are objectively measured at the country level from data from other sources. The disadvantage of our approach is that we have fewer degrees of 8
freedom to estimate the relevant coefficients of the variables measure at the national level, which is limited by the number of countries.

Missing data inevitably introduce ambiguity into the inferences that can be drawn from a study, so another caveat is in order. This section, as well as the regression analyses, relies on variables that were taken from firm-survey questions that are straightforward and with no ambiguity. That is, the question on whether a firm introduced a new product in the previous two years is straightforward. Therefore, it is safe to assume that firms that did not answer this question had not in fact introduced a new product. Although this change in the data is marginal, we do obtain more realistic estimates of the share of firms by country that introduced new products. For example, the percentage of firms reporting a New Product in China changes from 25 percent to 15 percent for all firms, which is a more reasonable share. In other countries, such as Turkey, there are no missing values. The data from the countries in Latin America and the Caribbean (LAC) are a mixed bag, but the same procedure was applied to all. R&D/Sales and licensing payments are likely to be measured with error, and this issue is discussed in the context of the econometric methodology in section 4.

# 3.b. _Descriptive statistics_

Table 1 presents some descriptive statistics for a sample of 36 countries, which cover the sample used in the econometric analysis discussed in section 5 below. The sample used for the analyses of section 4 is larger, because that estimations of the partial correlation between R&D and product innovation uses country dummies to control for any country-level characteristic rather than specific aspects of the business environment. The number of firms by country and survey year in the various samples appears in the Data Appendix. Hence the restricted sample was due to the availability of data on the other country-level determinants of product innovation discussed above.<sup>5</sup> The following paragraphs focus on the restricted sample, because it poses some issues about the representativeness of the sample of firms among developing countries.

> 5 Please note, however, that the regressions reported in Table 2 include the average number of years of education of workers employed by each firm. China and Indonesia do not have these data and thus are not in the samples of 59 and 60 countries that are included in the regressions reported in Table 2. But they do appear in the sample of countries used for the estimation of the models reported in Table 3, and thus appear in Table 1.

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The sample includes 6 countries that are high-income countries, namely Germany (Eastern after re-unification), Greece, Ireland, Portugal, Spain, and Korea. It includes 13 countries from LAC, 9 from Europe and Central Asia (ECA), 4 from East Asia and the Pacific (EAP) including China, 3 African countries, plus Egypt. Clearly, this sample, which is used for econometric analysis, is not representative across all regions of the world. However, the sample of firms might be representative of manufacturing firms from around the world, especially from developing countries. Since this might not be true, some of the relevant regressions use weights, based on each country’s labor force (i.e., population aged 15-64 years). This is reasonable if the number of firms from each country is proportional to the labor force in each.

Regarding the incidence of firms innovating via the introduction of a new product, the data show a wide range of country experiences, ranging from 15 percent of Chinese and Egyptian firms to 75 percent in Argentina. It is noteworthy that the percentages for the richer countries in the sample are not above the overall sample average of 43 percent. But LAC’s average is above the sample average.

In most countries, a large share of firms that reported new products also report R&D expenditures. In the total sample, 60 percent of firms with product innovation also report R&D, whereas only 15 percent of non-innovative firms report some R&D expenditures. This pattern holds for most countries individually for R&D, licensing, export status, and foreign ownership. China is the only exception. In this country, the percentage of non-innovative firms that report R&D expenditures, licensing payments, exporting, and foreign ownership is higher than among the innovative firms.<sup>6</sup> Although the high correlation between innovation and the other firm characteristics is expected, it is clear that identifying the partial correlations between the propensity to introduce a new product and the other firm characteristics is important since high correlations among all the firm-level characteristics are also expected.

There is no clear relationship between trade policies and the share of innovative firms across countries, however. For example, Argentina appears with 75 percent of firms being innovative, but it also utilizes numerous non-tariff barriers (NTBs) covering, on average across

> 6 The data for China is consistent with product-level export data that suggests that mainland China introduced comparatively few new export products during 1994-2003 (Klinger and Lederman 2006).

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the 8 manufacturing sectors, slightly over 29 percent of its tariff lines. In contrast, Bolivia has a low NTB coverage rate of about 3 percent, but only 43 percent of firms reported a product innovation. Hence it is possible that trade policy has little to do with product innovation, but econometric estimations might help to clarify this potential link between innovation and trade policy by controlling for other factors that might be correlated with both sets of variables.

# **4. Is R&D Related to Product Innovation?**

To answer this question we estimated reduced-form models of product innovation, but also considered the possibility that R&D expenditures and perhaps the sales variables that were recorded in the firm surveys are measured with error. If they are, the standard direct regression model with product innovation as the dependent variable and the R&D/Sales variable (our proxy for research-cost intensity derived from the theoretical model) might be biased, possibly suffering from attenuation bias if the measurement error is random.

# 4.a. _Econometric strategy_

Due to the dichotomous nature of our variable of interest, the direct empirical model of product innovation can be written as: where P is the probability of observing a value of one for product innovation, y. Subscript i represents firms, the s’s are manufacturing sectors, and c’s are countries. The Xs are matrices of the relevant explanatory variables, measure at the three levels of aggregation (firms, sectors, and countries). The betas, alphas, and deltas are the parameters to be estimated with a Probit estimator, which assumes a standard normal distribution of the relevant parameters with respect to the latent threshold variable. ε _isc_ is, therefore, the standard white noise error. Below we report results that are robust to heteroskedasticity of regression errors clustered around the observations of each country, ε _c_ . This correction becomes particularly important for the estimation of the delta parameters associated with industry and country variables when the dependent variable is a micro unit (see Moulton 1990). Allowing for error clusters around industries or industry11
countries provides identical coefficient estimates but with lower standard errors than those reported later in the paper.

In this case, the variable of interest is R&D/Sales measured at the level of the firm. Since for this exercise we are not interested in uncovering industry- and country-level characteristics that might affect a firm’s propensity to innovate, we can safely control for both by including industry/country dummy variables. Since the enterprise surveys for all countries were not implemented in the same year and global economic conditions might affect firm behavior, we also control for survey-year dummies.

To assess the influence of measurement errors, we follow Leamer (1978, chapter 8) by estimating the reverse regression model. In this approach the dependent variable becomes R&D/Sales and the dummy variable for product innovation becomes an explanatory variable. If the innovation variable is measured accurately, whereas the R&D/Sales is measured with error, then the inverse of the estimated coefficient from the reverse regression is the “true” partial correlation between product innovation and R&D/Sales.

The same strategy can be followed for assessing the partial correlation between product innovations and licensing payments. In this case, however, the licensing variable is also dichotomous, but that does not mean that all firms accurately report whether they made some licensing payments.

# 4.b. _Results_

The results from the estimation of equation (5) with the appropriate set of dummy variables are presented under the first column of Table 2. The table reports the marginal coefficients, or the elasticities calculated at the sample mean. The R&D/Sales variable is not statistically significant and the point estimate of the elasticity is negligible. Is this unsatisfactory result due to measurement errors?

The results from the reverse regression model are presented under the second column. In this case, the estimated Tobit coefficient is highly significant. Furthermore, its inverse implies a rather large partial correlation between R&D/Sales and product innovation. The elasticity of the 12
probability of introducing a new product with respect to R&D/Sales would be around 5, which is the inverse of the reverse-regression elasticity shown in Column 3. And the inclusion of country dummies (in Column 4) does not affect the Tobit coefficient.

Columns 5-7 report the corresponding estimates for the licensing variable. In this case, the direct regression results suggest that licensing is positively correlated with product innovation, with and without country dummies. Nevertheless, the implied marginal effect estimated with the reverse regression model (Column 7) is significantly larger, thus also suggesting the there might be measurement errors in the licensing variable as well. We now turn to the analysis of the role of the investment climate. Since the evidence suggests that R&D/Sales and the dichotomous licensing variable are both measured with error, these variables are not included in the analyses. Furthermore, the results reported in Table 2 suggest that the coefficients of R&D and licensing are unaffected by the inclusion of country-dummy variables, thus also indicating that excluding these variables from the following estimations will not affect the coefficients on the investment-climate variables. The underlying assumption is that the introduction of a new product by firms reflects past research expenditures, as in Klette and Kortum (2004).

# **5. The Role of the Investment Climate**

The estimation strategy is similar to the one pursued in the previous section. But there are additional complications.

# 5.a. _Estimation strategy_

As mentioned, we estimate partial correlations to help us characterize the relationship between firm-level probabilities of introducing a new product (i.e., a non-patentable innovation) and firm, sector, and country characteristics. While the estimated partial correlations among the firm-level variables could be due to endogeneity, the results concerning the sector- and countrylevel variables are less likely to be contaminated by this problem. That is, if each individual firm is too small to determine the level of a country’s trade protection or its aggregate level of patents 13
accumulated since 1963, then the corresponding empirical relationships are likely to be due to causal effects.<sup>7</sup> Within this framework, we estimate the stylized model of the probability of introducing a new product by firms in equation (5). To deal with the issue of whether our sample of firms is a representative sample of firms of operating in developing economies, we present both unweighted and weighted regressions. In the latter, each country’s observations are weighted by the size of its labor force, and thus large countries such as China, Indonesia, and Brazil influence the coefficient estimates to a larger extent than smaller economies.

To deal with one potential source of joint endogeneity of the firm- and sector-level variables and the probability of observing a product innovation, we can control for correlated country effects. Woolridge (2005) proposed modeling fixed effects in panel data by including the over-time averages of the unit of analysis as additional explanatory variables.<sup>8</sup> In our case, we do not have a time dimension, but we do have the country dimension. Hence we can control for correlated country-specific effects by including the country averages of the variables that are measured at the firm and sector levels. We called these estimations “Quasi Fixed Effects.” Finally, it is worth noting that identifying the effects on firms’ product innovation of the country-level variables that capture different aspects of the national investment climate might be difficult due to the expected correlation among the relevant variables. For example, countries with good infrastructure coverage can also be expected to have higher incomes per capita, higher innovation densities, etc. If the point estimates seem to be stable across various specifications and the country-level variables of interest are jointly statistically significant, we can find some comfort in our estimates. Hence we also report F-tests for the joint significance of the firm-, sector- and country-level variables.

# 5.3. _Results_

> 7 Omitted explanatory variables that are correlated with firm-level product innovation and the aggregate level of trade protection might bias the results. However, we showed in Table 2 that the coefficients of most firm-level variables are unaffected by the inclusion of country-dummy variables, thus suggesting that omitted variables measured at the level of the firm, which are correlated with the observed firm-level explanatory variables, might not be systematically correlated with country characteristics. 8 Besides being concerned about unobserved heterogeneity, Woolridge (2005) is also concerned about dynamics; the article is about dynamic Probit models with fixed effects. Our case is simpler, since we do not have dynamics. If we had dynamics, to deal with the endogeneity of the lagged dependent variable, we would need to control for the initial value of the dependent variable.

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Table 3 presents the regression results. The first two columns contain the results from our baseline models. The first shows the results from the unweighted estimation, the second has the weighted-regression results. The last four columns show the results with Quasi Country Fixed Effects, which, as mentioned above, are captured by the country-level averages of the firm- and sector-level variables. This allows us to distinguish between variables that are associated with product innovation at the various levels of aggregation. The p-values of specification tests for the joint endogeneity of our three sets of variables (measured at the level of the firm, sector and country) appear in the bottom three rows.

Among the firm-level variables, the most robust results are associated with the size of the firm as captured by the number of employees (but not its squared term). It is highly significant and positive across all specifications. The magnitude of the coefficient is slightly lower in the weighted regressions, thus suggesting that scale is less important among the countries with the largest populations (e.g., China, Indonesia, and Brazil). Interestingly, foreign ownership is always negative and significant in the weighted regressions, but it also negative in all nonweighted estimations. This indicates that foreign owned firms might not settle in developing countries to undertake product innovations, although it is likely that multinational corporations can produce goods that local firms do not produce. Export status is always statistically significant and positive in the non-weighted regressions, but not in the weighted estimates with Quasi Country Fixed Effects. In those estimations, however, the national share of exporting firms does appear as positive and significant, thus suggesting the firms that operate in countries with a high share of exporters tend to have a higher propensity to undertake product innovations. These results are broadly consistent with emerging evidence concerning the effect of exporting on firm performance, as in the sample of Turkish firms in Yasar and Rejesus (2005).

Regarding the trade policy variables measured at the sector level, the import tariff index is negative and significant in the baseline regressions, but it becomes non-significant and changes sign when controlling for the Quasi Country Fixed Effects. But the country-average import-tariff index is negative and significant. These results suggest that it is not the cross-sector variation in tariff policies that affects product innovation, but rather it is the cross-country variance that matters. The NTB coverage rate always appears with a negative sign, but it is never statistically different from zero. The test for joint significance of the trade policy variables does 15
suggest that they are highly significant determinants of product innovation. Thus we can conclude that trade-policy distortions matter for product innovation in general, but the most relevant aspect is probably the use of tariffs, and countries (not sectors) with high import tariffs and tariff dispersion tend to have firms with lower propensities to undertake product innovations.

The country-level variables supposedly capture each country’s investment climate. Unfortunately, there are very few variables that appear to be statistically significant. The most robust result concerns the regulatory index. It appears with a positive sign and it is statistically significant in all specifications except in the two weighted regressions with Quasi Country Fixed Effects. This result is consistent with the view that market failures affect the propensity of firms to invest in product innovation: As entry becomes more restricted by regulatory policies, the propensity of incumbent firms to introduce new products tends to rise. In terms of the theoretical model, the regulatory environment seems to affect the probability of imitation, thus reducing incentives for product innovation. The lack of significance of this variable in the weighted estimations with Quasi Country Fixed Effects is less worrisome when we look at the test for the joint significance of the country-level variables. They are always jointly highly significant, as reflected in the low p-values of the test of the null of lack of significance at the bottom of Table 3. Furthermore, as mentioned, when we allow for error clustering around industries or industriescountries, the regulatory index is statistically significant across all specifications.

Regarding the other national variables, manufacturing GDP growth, the density of patenting activity, and the institutional index always appear with the same signs. The infrastructure variable changes sign in one specification. The positive effect of patent density could be interpreted as indicating the presence of knowledge spillovers, whereby firms that have access to a higher density of commercial ideas tend to have higher propensities to innovate than firms in countries with lower innovation densities. The negative coefficient of manufacturing GDP growth might suggest that product innovation is counter-cyclical, thus supporting the view that firms tend to choose the retooling strategy during downturns, which is consistent with the Schumpeterian view of creative destruction. Finally, the negative coefficients on the institutional quality variable can be interpreted as an indication that firms that reside in countries where governance is dysfunctional can find mechanisms to capture the state in order to impose barriers to competition that might not be reflected in the trade and regulatory policy variables. Again, the 16
discussion of the results pertaining to the national variables is worthwhile because they do seem to be jointly significant, even after controlling for Quasi Country Fixed Effects and the level of development. It is actually surprising that the investment climate variables appeared with consistently estimated signs, even when they do not appear to be individually statistically significant when the regression errors are assumed to be clustered around countries.

# **6. Concluding Remarks**

The theoretical model presented above motivated empirical analyses of the determinants of product innovation by firms. Both market conditions and the threat of entry by competitors were shown to be theoretical predictors of the research-cost intensity of firms, even when prices are exogenously determined by market conditions.

The empirical analyses of Section 4 suggested that in fact data from 60 countries, covering thousands of firms, support the main prediction of the model: Research-cost intensity tends to be significantly associated with product innovation. The analysis also highlighted a potential pitfall in the firm data, as both R&D/Sales and licensing seem to be measured with error, thus shedding some doubt on the usefulness of direct regression estimates of the innovation function.

The investment climate also seems to play an important role for product innovation. But the evidence also highlights market failures that hamper innovation. Of particular relevance in this regard were the results concerning the regulatory barriers to firm entry. Whereas deregulation is desirable to increase competition and knowledge diffusion, the results suggest that other policy instruments are needed to stimulate product innovation, especially after deregulation and trade liberalization. Also, the results concerning the density of patent counts suggest that knowledge spillovers might also be important. From a policy perspective, it is also worth noting that product innovation seems to be counter-cyclical, and thus the budgets of programs to stimulate product innovation need to be protected during downturns, so as to prevent the demise of firms that could have survived through retooling in terms of product innovation. The latter might have social benefits that greatly exceed the private returns, because private agents can benefit from the knowledge embodied in the product innovations of their competitors.

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Finally, trade-policy distortions seem to hamper product innovation, although it seems that it is cross-national variation in tariff policies rather than inter-sector variation in tariffs that affects product innovation by individual firms. Moreover, the density of exporting firms (that is, the percentage of exporting firms at the national level) also affects positively the propensity to introduce new products by individual firms. Hence an open trade environment with a dynamic and dense export sector seems to be an important ingredient for maintaining an innovative private sector.

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