Has the Internet Fostered Inclusive Innovation in the Developing World?

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World Development Vol. 78, pp. 587–609, 2016 0305-750X/Ó 2015 Elsevier Ltd. All rights reserved. www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2015.10.029

Has the Internet Fostered Inclusive Innovation in the Developing World? CAROLINE PAUNOV a and VALENTINA ROLLO b,* a OECD, France b International Trade Centre, Switzerland Summary. — The adoption of the Internet has been widespread across countries, making much more information available and thus facilitating knowledge diﬀusion among businesses to boost their innovation performance. However, diﬀerences in ﬁrms’ capabilities to use this newly available knowledge could create a new ‘‘digital divide” instead. Using 50,013 ﬁrm observations covering 117 developing and emerging countries over the 2006–11 period, this paper tests for knowledge spillover eﬀects from industries’ adoption of the Internet on ﬁrms’ productivity and innovation performance. We test for heterogeneous spillover impacts on groups of ﬁrms that are commonly less engaged in innovation and on ﬁrms with diﬀerent productivity levels. Our speciﬁcation regresses ﬁrm productivity and innovation performance – i.e., their investment in equipment and ownership of quality certiﬁcates and patents – on industries’ use of the Internet. Spillover eﬀects are identiﬁed by controlling for ﬁrms’ own investment in Internet technology, industry and country-year ﬁxed eﬀects as well as extensive ﬁrm-level controls. Our results show that industries’ use of the Internet positively aﬀects the average ﬁrm’s productivity and its investment in equipment. We also identify modest impacts of industries’ use of the Internet on the likelihood that ﬁrms obtain quality certiﬁcates and patents. On average, we ﬁnd that the returns to productivity are larger for ﬁrms that commonly engage less in innovation, including single-plant establishments, non-exporters, and ﬁrms located in small agglomerations. However, results from quantile regressions show that only the most productive ﬁrms reap productivity gains from Internet-enabled knowledge access. Firms with productivity levels below the 50th percentile do not beneﬁt much. The spillover eﬀects from industries’ adoption of the Internet identiﬁed in our work justify public policies aimed at fostering industries’ use of the Internet. However, since we show that only ﬁrms with adequate absorptive capabilities beneﬁt from the widespread Internet adoption, policy support should also focus on facilitating ﬁrms’ access to networks and strengthening their capacities to use them. Ó 2015 Elsevier Ltd. All rights reserved. Key words — Information and communication technology (ICT), knowledge spillovers, Internet, innovation, productivity, ﬁrm heterogeneities

1. INTRODUCTION

50,013 ﬁrm observations for 117 developing and emerging countries for the 2006–11 period. Our empirical methodology exploits information on industries’ adoption of the Internet as a tool for communicating with suppliers and clients to identify Internet-enabled knowledge spillover eﬀects. Our speciﬁcation regresses ﬁrm productivity and innovation performance – i.e., their investment in equipment and ownership of quality certiﬁcates and patents – on industries’ use of the Internet, a comprehensive set of ﬁrm controls as well as industry and country-year ﬁxed eﬀects. We control for ﬁrms’ own use of the Internet in all speciﬁcations. Our identiﬁcation exploits within country-year diﬀerences in the adoption of the Internet across industries. An industry’s adoption of the Internet is unlikely to be aﬀected by an individual ﬁrm’s productivity and innovation performance and, hence, the risk of reverse causality is low. Notwithstanding, we cannot exclude a small risk of endogeneity which would arise if the most productive and innovative ﬁrms’ use of the Internet caused other ﬁrms in the industry

The adoption of the Internet has been widespread across countries (ITU, 2014). With the Internet, increasingly large sets of knowledge and information (‘‘big data”) can be more easily diﬀused to large groups of people. By allowing for wider access to ideas, the Internet may boost innovation as innovation arises from new combinations of existing pieces of knowledge (Arthur, 2007). With the Internet, opportunities to beneﬁt from knowledge created by others are possibly higher. Such knowledge spillovers are critical for economic growth as they generate increasing returns (Grossman & Helpman, 1991; Krugman, 1991; Romer, 1986). In addition, the Internet can support more inclusive innovation, i.e., the widening of the often very small group of innovating ﬁrms in emerging and developing economies (OECD, 2015; Paunov, 2013). The Internet may help groups of ﬁrms that engage less in innovation, by improving their access to knowledge. By contrast, leading innovators often have access to quality ‘‘oﬄine” knowledge and consequently may have less to gain from Internet-enabled knowledge transfers. However, less innovative ﬁrms may lack capabilities to use newly available knowledge for their business purposes (Cohen & Levinthal, 1989). This paper provides evidence of knowledge spillover eﬀects from industries’ adoption of the Internet on ﬁrms’ productivity and innovation performance. It analyses whether the Internet as a conduit of knowledge spillovers has heterogeneous impacts on groups of ﬁrms that diﬀer with regard to their innovation performance and productivity. This study provides comprehensive evidence on these questions for a sample of

* The authors would like to thank Richard Baldwin, Nicolas Berman, Ana Margarida Fernandes, Dominique Guellec, Eric J. Bartelsman and participants of the 2014 ABCDE Conference, the Inter-American Development Bank’s internal seminar series and the 7th Annual Conference of the Academy of Innovation and Entrepreneurship (CAED) for valuable comments. Valentina Rollo gratefully acknowledges support from the SNF, Project Number PDFMP1_135148. The ﬁndings expressed in this paper are those of the authors and do not necessarily represent the views of the OECD, the International Trade Center or their member countries. Final revision accepted: October 3, 2015. 587

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to adopt the Internet. Consequently, higher industry adoption rates could be positively correlated with strong performance. We address these endogeneity concerns as part of our robustness tests. We use ordinary least squares regressions to study aggregate eﬀects on ﬁrm productivity and equipment investment rates as well as logistic and probit regressions to analyze impacts on ﬁrms’ ownership of quality certiﬁcates and patents. In addition, we apply quantile regressions to test whether beneﬁts from industries’ adoption of the Internet diﬀer across ﬁrms of diﬀerent productivity levels, which proxy for their capacities to absorb new knowledge. We also use quantile regression to ensure our results are not driven by non-normal errors and outliers as might be the case for ordinary least squares regressions. We ﬁnd that industries’ use of the Internet has positive impacts on ﬁrms’ labor productivity and on their investments in equipment. We also identify modest impacts on the likelihood that ﬁrms obtain quality certiﬁcates and patents. The evidence is robust to various tests such as removing potential outliers and using alternative sources of Internet-enabled knowledge spillovers, including the Internet uptake by ﬁrms within geographic locations and industries as well as at the country rather than at the country-industry level. Moreover, we show that, on average, the Internet adoption of their industries beneﬁts more groups of ﬁrms that commonly engage less in innovation: ﬁrms that do not export, ﬁrms that are not part of multi-plant establishments and ﬁrms that operate in small agglomerations. Quantile regression results show that the more productive ﬁrms gain more from their industries’ intensive use of the Internet. Firms with productivity levels below the 50th percentile do not beneﬁt much. Moreover, only the most productive non-exporting ﬁrms and single-plant establishments beneﬁt from knowledge spillovers. Interestingly, while we do not ﬁnd that ﬁrm size aﬀects productivity gains of the average ﬁrm, quantile regression results reveal larger payoﬀs for the most productive small ﬁrms compared to larger ﬁrms. Several policy implications arise from our analysis. First, the existence of spillover eﬀects from industries’ adoption of the Internet, which do not depend on ﬁrms’ own investments, justify public policies aimed at fostering industries’ use of the Internet. The potential returns from policy support of industries’ Internet adoption are high because, diﬀerently from other private sector development policies, beneﬁts arise even for ﬁrms that commonly engage less in innovation and for ﬁrms that face cumbersome business environment conditions (Paunov & Rollo, 2015). However, diﬀerences in capabilities to use the knowledge made available on the Internet could create a new ‘‘digital divide”. Only ﬁrms with absorptive capabilities can beneﬁt from business intelligence platforms, which give access to knowledge relevant to their scientiﬁc and technological needs. Therefore, facilitating ﬁrms’ access to such networks and strengthening their capacities to use them deserve policy support. Human capital investments are core complementary policies (Indjikian & Siegel, 2005). This paper relates to the research on the contributions of ICT to development. An ongoing debate focuses on adequate infrastructure conditions in developing and emerging economies such as bandwidth capacity and aﬀordable access prices. Forbiddingly high prices to access the Internet, which may be caused by technical or market imperfections, can reduce uptake (Hilbert, 2010; Howard & Mazaheri, 2009). These are pre-conditions for ﬁrms to use ICT for their business operations to support their productivity (Ref. Section 2(a)).

Moreover, our work relates to studies on the opportunities for ICT to support very small ﬁrms and entrepreneurs from disadvantaged socio-economic backgrounds. ICT have helped smallholder farmers and ﬁsheries obtain market information (including on price trends) as well as knowledge about production techniques (Jensen, 2007; Muto & Yamano, 2009; Ogutu, Okello, & Otieno, 2014). Several studies have identiﬁed gains from ICT-based services for disadvantaged producers (e.g., Aker & Mbiti, 2010; Donner, 2004, 2006; Donner & Escobari, 2010; Duncombe & Heeks, 2002; Esselaar, Stork, Ndiwalana, & Deen-Swarra, 2007; Kaushik & Singh, 2004). However, improved access to knowledge has not always beneﬁtted these groups as they often lack capabilities to exploit new knowledge (e.g., Tadesse & Bahiigwa, 2015). 1 In addition, our paper relates to the literature on knowledge spillovers. Several studies have shown that gross social returns to knowledge investments exceed private returns (Bloom, Schankerman, & Van Reenen, 2013). Audretsch and Feldman (2004) and Keller (2004) discuss research ﬁndings on the international and geographic dimensions of knowledge spillovers. There is also evidence on barriers to knowledge spillovers, including from foreign direct investment (see Go¨rg & Greenaway, 2004). Firms’ lack of ‘‘absorptive” capacity to make use of newly available knowledge explains some of the limitations (Girma, 2005; Kokko, 1994; Kokko, Tansini, & Zejan, 1996). Geographical proximity also matters for spillovers (Ref. Section 2(c)). Our paper makes several contributions to the literature. To the best of our knowledge, this is the ﬁrst study to provide comprehensive cross-country evidence of knowledge spillovers of industries’ Internet adoption on ﬁrm productivity and innovation performance. Using data for the 2006–11 period allows estimating global impacts at a point in time when the Internet adoption gained some maturity. Data for earlier years may underestimate impacts. Moreover, our study expands on the previous analyses by testing whether groups of ﬁrms that commonly innovate less beneﬁt more from their industries’ Internet adoption. In addition, we apply quantile regression techniques to explore whether average eﬀects – obtained from conventional estimation techniques – hide diﬀerences in impacts across ﬁrms of diﬀerent productivity levels. The remainder of the paper is organized as follows. Section 2 discusses the conceptual framework while Section 3 presents the data we use for our analysis. Section 4 introduces the empirical framework. Section 5 provides descriptive statistics while Section 6 describes the results of the analysis. Section 7 concludes. 2. CONCEPTUAL FRAMEWORK (a) ICT investments as driver of eﬃciency improvements Firms, industries, and countries that invested in ICT have improved the eﬃciency in which they transform inputs into outputs. Research, conducted at industry and ﬁrm levels, has consistently found that ICT investments positively relate to higher productivity (e.g., Bartel, Ichniowski, & Shaw, 2007; Bloom, Sadun, & Van Reenen, 2012; Jorgenson, 2001; Jorgenson & Vu, 2005; Oliner & Sichel, 2000; Stiroh, 2002). 2 There is also some evidence for ﬁrms in developing and emerging economies (e.g., Commander, Harrison, & Menezes-Filho, 2011; ECLAC, 2011; Motohashi, 2008; Pohjola, 2001; UNCTAD, 2008; World Bank, 2006). In addition, ﬁrms’ ICT investments also relate positively to their innovation performance (see, for example, Spezia, 2011, for an analysis of eight OECD countries).

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

However, ICT do not automatically boost productivity. Evidence prior to the mid-1990s did not identify such positive impacts (Brynjolfsson & Yang, 1996). Hence, Solow’s famous quote (1987) ‘‘you can see the computer age everywhere but in the productivity statistics”. Limited productivity gains also reﬂected the need for production process adjustments before ICT had positive productivity impacts, including the need for organizational changes and adequate human capital. The complementarity between on the one hand ICT investment and on the other hand organizational change and management capacities is critical for ICT investments to boost productivity (Black & Lynch, 2001, 2004; Bloom et al., 2012; Bresnahan, Brynjolfsson, & Hitt, 2002; Bresnahan, Greenstein, Brownstone, & Flamm, 1996). Diﬀerences in organizational and managerial capabilities are among the reasons why ICT contributed more to US than to Europe’s productivity (Cardona et al., 2013). Bloom et al. (2012) ﬁnd that for ﬁrms operating in the United Kingdom, the productivity of ICT capital has been signiﬁcantly higher in US-owned establishments compared to other ﬁrms. (b) Knowledge spillovers and ﬁrms’ innovation performance In addition to ﬁrms’ productivity improvements from their own ICT investments, further productivity and innovation performance gains may arise from industries’ adoption of the Internet as a means of communication to improve the diffusion of knowledge. The Internet’s contribution is well illustrated by an analogy: drawing balls from an urn that holds relevant knowledge. The Internet helps access a larger number of balls from that urn. Improved communication among members of an industry facilitates learning about new technologies and consequently accelerates the rate of innovation (e.g., Conley & Udry, 2010 and references therein). Information from clients, suppliers, and competitors can strengthen ﬁrms’ innovation performance in the following ways: First, information on customer preferences helps identify market opportunities for new products and services. Uncertainty about future market demands for new products is a major obstacle for ﬁrms to invest in innovation (Collard-Wexler, Asker, & De Loecker, 2011). User feedback can also help ﬁrms develop new product and services; it is used systematically to identify bugs in software codes and to create improved software programs. 3 Second, developments of technology relevant to ﬁrms’ production processes determine the technical feasibility of new products and processes. Hence, better communication with suppliers to learn about new technological possibilities and discuss ﬁrms’ needs can support innovation. Third, knowledge about competitors’ practices allows ﬁrms to learn about alternative production techniques and innovations. However, knowledge from competitors might be less accessible because they have an interest in keeping information secret from each other (see e.g., Fernandes & Paunov, 2012; Javorcik, 2004). Aside from knowledge spillovers, there are other potential sources of beneﬁt from industries’ adoption of the Internet on ﬁrms’ productivity and innovation performance. Widespread adoption of the Internet allows ﬁrms to order online from suppliers and deliver products more eﬃciently to customers. The use of ICT can also improve the evidence-base in ﬁrms’ decision-making (e.g., Brynjolfsson, Hitt, & Kim, 2011). Taken together these impacts are closely related to ﬁrms’ own adoption of the Internet rather than to their industries’ adoption. Knowledge spillovers are likely to be more important sources of productivity and innovation performance gains from industries’ Internet adoption.

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(c) Knowledge spillovers and the Internet Knowledge lends itself to spillovers since, once created, it can be replicated and disseminated at virtually no cost, and consequently beneﬁt more ﬁrms (Arrow, 1962). The Internet has contributed to reducing dissemination costs further. However, there are barriers to Internet-enabled knowledge diﬀusion: only codiﬁed knowledge can be transmitted while tacit knowledge cannot (Leamer & Storper, 2001). This is why geographic proximity matters for knowledge diﬀusion (Audretsch & Feldman, 1996; Krugman, 1991). However, ICT have reduced barriers for transmitting knowledge; for instance, videoconference opportunities mimic face-to-face interactions better than other ways of communicating across geographic distances. Potential beneﬁts from Internet-facilitated knowledge spillovers do not depend on the individual ﬁrm’s use of the Internet but on adoption by a critical mass of an industry’s ﬁrms. Even ﬁrms that do not use the Internet can beneﬁt from Internet-enabled knowledge diﬀusion within their industry by other means, such as participation in business associations and recruitment of staﬀ from other ﬁrms. The question whether the Internet facilitates knowledge spillovers is, therefore, distinct from asking how ﬁrms’ own adoption of ICTs has beneﬁted their performance. (d) The Internet as a potential facilitator of inclusive innovation More inclusive innovation i.e., expanding the group of innovators to groups that are traditionally not engaged in innovation, is an important issue for developing and emerging economies. Small and young ﬁrms can be drivers of innovation as they often have greater agility to introduce novel ideas (Acs & Audretsch, 1990). Yet, fewer opportunities for these ﬁrms to access credit stiﬂe their contributions to innovation, particularly in developing and emerging economies. Larger incumbent businesses often have access to more funding opportunities. Other obstacles are also less of a challenge for large incumbent ﬁrms compared to small and young ﬁrms. Consequently, a few incumbent ﬁrms are ‘‘islands of excellence” in a sea of smaller businesses of low productivity (OECD, 2015). Such industrial structures are ineﬃcient as aggregate productivity would be much higher if all ﬁrms were as productive as the best performing ones (Hsieh & Klenow, 2009). Wage inequality is also higher as less productive ﬁrms pay lower wages than the more productive ﬁrms (Mueller, Ouimet, & Simintzi, 2015). Potential Internet-enabled knowledge diﬀusion to improve the performance of less productive ﬁrms is consequently critical. The beneﬁts from the Internet adoption of their industries may be larger for some ﬁrms than for others. Coming back to the analogy of the urn introduced above, better knowledge networks give ﬁrms access to all of the relevant knowledge since the full number of available balls is ﬁxed. All else equal, ﬁrms that are connected to rich (poor) oﬄine knowledge networks may have fewer (stronger) productivity and innovation performance gains from new online networks. Some groups of ﬁrms diﬀer in the quality of oﬄine knowledge networks; those with bad quality network connections commonly also engage less in innovation. Diﬀerences may arise for exporters and foreign-owned ﬁrms, ﬁrms located in large (small) agglomerations, ﬁrms of diﬀerent sizes as well as multi- and single plant ﬁrms and informal businesses. First, exporters and foreign-owned ﬁrms may have less to gain from Internet-enabled knowledge spillovers because they access foreign expertise, which is a critical source of advanced

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technologies (Coe & Helpman, 1995; Fagerberg, 1994; Freeman & Soete, 1997). National ﬁrms and non-exporters may consequently beneﬁt more from Internet-enabled knowledge transfer. Second, ﬁrms located in large agglomerations often have access to dense local networks, including frequent business meetings with other companies located in the same cluster. This is diﬀerent for ﬁrms located in small agglomerations. With the Internet’s ability to cross geographical distance better than previous communication technology (e.g., Cairncross, 1997; Forman & Van Zeebroeck, 2012; Friedman, 2005), ﬁrms located in smaller agglomerations consequently stand to beneﬁt more. 4 Third, smaller-sized ﬁrms have fewer employees, lower revenues, and often smaller R&D investments in absolute terms. Therefore, agglomeration beneﬁts from their own R&D investments are lower as is the availability of internal sources of knowledge (Cohen, 2010; Klepper & Simons, 2005). Smaller ﬁrms may consequently beneﬁt more from Internet-enabled knowledge spillovers than larger ﬁrms (cf. Acs, Audretsch, & Feldman, 1994). Single-plant establishment may also have more to gain from their industry’s adoption of the Internet than multi-plant establishments. Fourth, informal businesses may also have larger beneﬁts from Internet-enabled knowledge spillovers. These ﬁrms have fewer resources to build knowledge networks and are often excluded from formal businesses networks. There is evidence to show ICT beneﬁt informal businesses (e.g., Jensen, 2007; Muto & Yamano, 2009 and references provided in the introduction). (e) Knowledge spillovers and ‘‘absorptive” capacities Firms need the capacity to apply the knowledge they gain access to. If absorptive capacities are weak, knowledge spillovers eﬀects are much lower or absent (cf. Go¨rg & Greenaway, 2004). Indigenous capacities are needed because innovations developed elsewhere are often inappropriate in speciﬁc ﬁrm contexts, unless incremental innovations to adjust them are undertaken (Atkinson & Stiglitz, 1969). The empirical evidence conﬁrms ﬁrms own capacities complements access to knowledge (Hu, Jeﬀerson, & Jinchang, 2005; Kokko, 1994; Kokko, Tansini & Zejan, 1996). Thus, industries’ adoption of the Internet may have heterogeneous eﬀects on ﬁrms at diﬀerent productivity levels: the most productive ﬁrms with stronger absorptive capacities may beneﬁt more from knowledge spillovers than less productive ﬁrms that lack such capacities. (f) Testable hypotheses Based on the discussion, the following testable hypotheses for our empirical analysis arise: 1. We expect the use of the Internet by industries as a tool for communication to have positive impacts on ﬁrms’ productivity and innovation performance. 2. We hypothesize that the Internet has heterogeneous impacts on groups of ﬁrms that engage to diﬀerent extents in innovation: (i) exporting and non-exporting ﬁrms, (ii) ﬁrms located in larger and smaller agglomerations, (iii) single- and multi-product ﬁrms and (iv) diﬀerently sized ﬁrms. We also analyze (v) whether informal businesses beneﬁts from their industries’ adoption of the Internet. 3. We hypothesize that the impact of industries’ adoption of the Internet on ﬁrms’ productivity and innovation performance diﬀers for ﬁrms of diﬀerent productivity levels to proxy for ﬁrms’ diﬀerent levels of absorptive capacities.

3. DATA We use data from the second wave of the World Bank Enterprise Surveys (WBES) for our empirical analysis. The WBES collect information on a representative sample of formal ﬁrms in the countries’ non-agricultural sector; the selection of ﬁrms is done by stratiﬁed random sampling (Dethier, Hirn, & Straub, 2011). The WBES have been widely used, including in Almeida and Fernandes (2008), Beck, Demirgu¨c¸-Kunt, and Maksimovic (2008), Fisman and Svensson (2007) and Paunov (2016). 5 Our analysis uses a cross-section of ﬁrms, information for 50,013 ﬁrm observations across 117 countries for the 2006– 11 period. This sample is drawn from 65,285 ﬁrm observations available, excluding observations with missing information on labor productivity and industries’ use of the Internet. 6 Table 1 summarizes data coverage across world regions, industries, ﬁrm size categories, years, and country income levels. Table 1. Descriptive statistics Number of observations

Share in total (%)

Region Africa Eastern Europe and Central Asia Latin America and the Caribbean Middle East East Asia Paciﬁc South Asia

13,741 9,968 19,772 1,007 3,677 1,848

27.5 19.9 39.5 2.0 7.4 3.7

Industry Food Garments Textiles and leather Wood and furniture Non-metallic and plastic materials Metals, machinery and electronics Chemicals and pharmaceuticals Other manufacturing activities Total manufacturing

6,326 3,987 2,567 689 2,337 3,738 2,387 6,921 28,952

12.7 8.0 5.1 1.4 4.7 7.5 4.8 13.8 57.9

Services (incl. construction) Hotels and restaurants Retail and wholesale trade Construction and transportation Other services Total services

1,816 11,641 2,629 4,975 21,061

3.6 23.3 5.3 10.0 42.1

Size Micro (1–10 employees) Small (11–50 employees) Medium (51–150 employees) Large (more than 150 employees)

16,549 20,022 7,772 5,670

33.1 40.0 15.5 11.3

Year 2006 2007 2008 2009 2010 2011

12,280 8,261 2,382 14,057 11,182 1,851

24.6 16.5 4.8 28.1 22.4 3.7

Income level High income Upper-middle income Lower-middle income Low income

2,627 21,126 17,925 8,335

5.3 42.2 35.8 16.7

Full sample

50,013

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

Interestingly for our purposes, the WBES has information on ﬁrms’ use of the Internet – whether ﬁrms used email to communicate with suppliers and customers – rather than of their investment in ICT, which says little about actual use. The indicator informs about whether the Internet is used for communication, which is critical for our study since it relates to ﬁrms’ access to knowledge. What is more, ﬁrms’ email use is positively correlated with the intensity of their use of ICT in business operations. Table 2 shows results using linear probability, logistic and probit regression models for the sample of ﬁrms for which we have information on whether they use the Internet to conduct R&D, purchase inputs from suppliers, deliver services to clients, and market the ﬁrm on a website. 7 Results reported in columns (1), (2), and (3) of Table 2 indicate that ﬁrm email use is positively correlated with advanced uses of the Internet i.e., those using the Internet in all these ways. By contrast, as shown in columns (7), (8), and (9) of Table 2, ﬁrm email use is negatively correlated with weak uses of the Internet i.e., those using the Internet for none of the purposes described above. For median users the correlation is positive but less strong than for advanced users (columns (4), (5), and (6) of Table 2). The WBES also has information on ﬁrms’ labor productivity and on innovation activities including their investments in equipment as well as their ownership of quality certiﬁcates and patents. (Information on patenting is unfortunately only available for a smaller number of observations because the variable is not collected across all WBES surveys we combine in our analysis.) The WBES also has ample information on ﬁrm characteristics, including on sales, employment, ownership type, and export performance. We also use the informal ﬁrm dataset, provided by the WBES, which covers 1,557 ﬁrms for seven countries 8 in 2010. The informal business survey collects information on ﬁrms’ uptake of mobile phones as well as on their sales and their equipment investments. 4. EMPIRICAL FRAMEWORK To study the impact of industries’ adoption of the Internet on ﬁrms’ innovation and productivity performance, we use the following estimation model:

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Y ijct ¼ a þ b1 ICT jct þ b2 ICT ijct þ C X ijct þ kj þ kct þ eijct ð1Þ where Yijct is a measure of ﬁrm i’s labor productivity or its innovation performance, i.e., whether the ﬁrm owns a quality certiﬁcate or patent and its level of equipment investment. Coeﬃcient b1 is our variable of interest to identify knowledge-spillover eﬀects from the uptake of the Internet: ICTjct is an indicator of industry j’s use of email to communicate with clients and suppliers in country c in year t. This measure is built for industries (by countryyear) with at least 10 observations. ICTijct is a measure of ﬁrm i’s use of the Internet to control for direct impacts of ﬁrms’ Internet adoption while kj and kct are respectively a set of industry and country-year dummies. In consequence, our identiﬁcation strategy exploits diﬀerences in industry’s adoption of the Internet across countries while controlling for characteristics speciﬁc to industries and countries in any year. We obtain a measure of country-year industry adoption, identiﬁed across 15 diﬀerent industries. Xijct is a vector of ﬁrm-level control variables as described in Section 6. Acs et al. (1994) and Haskel, Pereira, and Slaughter (2007) use similar empirical methodologies to study respectively the impacts of industries’ R&D and FDI intensities. 9 Three possible challenges may be raised with regard to the proposed empirical model: (i) endogeneity, that is, while ICT might support innovation performance, more innovative ﬁrms also rely more on ICT, (ii) omitted variable bias, i.e., there might be other factors that aﬀect the productivity-ICT adoption relationship and (iii) measurement error of the explanatory variable. First, endogeneity is less of a challenge for our analysis compared to an analysis of ﬁrms’ own investment in ICT on their performance. It is unlikely that ﬁrms’ innovation and productivity performance has a direct impact on their industry’s adoption of the Internet. To avoid potential endogeneity concerns, we exclude ﬁrm i’s own use of the Internet from the industry average we compute. Notwithstanding, we cannot exclude a small risk of endogeneity, which could arise if the most productive and innovative ﬁrms’ adoption of the Internet led other ﬁrms to use the Internet. Higher industry adoption rates could then be correlated with strong performance and explain possible

Table 2. Correlations between diﬀerent intensities of ﬁrms’ ICT use and their email use Dependent variables: Advanced use of ICT

Median use of ICT

Low use of ICT

LPM (1)

Logit (2)

Probit (3)

LPM (4)

Logit (5)

Probit (6)

LPM (7)

Logit (8)

Probit (9)

Email use

0.223***

0.690*** [0.166] (0.104) Yes Yes Yes

0.426*** [0.165] (0.064) Yes Yes Yes

0.388***

(0.013) Yes Yes Yes

1.049*** [0.364] (0.098) Yes Yes Yes

0.165***

Firm-level controls Industry ﬁxed eﬀects Country-year ﬁxed eﬀects

1.920*** [0.405] (0.199) Yes Yes Yes

1.918*** [0.195] (0.108) Yes Yes Yes

1.147*** [0.216] (0.064) Yes Yes Yes

13,655

13,655

13,678 0.04

13,677

13,677

0.08

0.08

0.12

0.12

Observations R2 Pseudo-R2

13,678 0.10

(0.024) Yes Yes Yes

13,677 0.08

(0.023) Yes Yes Yes 13,678 0.11

0.08

Note: Advanced, median, and low uses of ICT are deﬁned depending on whether ﬁrms used the Internet to: (i) conduct R&D, (ii) purchase inputs from suppliers, (iii) deliver services to clients and (iv) market the ﬁrm on a website. The table reports results from linear probability model regressions (LPM) in columns (1), (4), and (7) and for logistic and probit regressions in columns (2), (5), and (8) and (3), (6), and (9), respectively. Robust standard errors are shown in parentheses. For logistic and probit regressions, marginal eﬀects are reported in brackets. Firm-level controls are the same as those of column (5) of Panel A of Table 4. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

592

WORLD DEVELOPMENT

positive coeﬃcients on industry adoption rates. We address endogeneity concerns as part of our robustness tests. Second, we address omitted variable biases by introducing industry and country-year ﬁxed eﬀects in addition to ﬁrmlevel controls, described in Appendix Table 11A. Countryyear ﬁxed eﬀects allow isolating potential diﬀerences across countries in speciﬁc years. This includes government policies with possible impacts on ﬁrms’ productivity and innovation performance. Controlling for industry eﬀects is also important because certain industries are more technology-intensive than others. Third, measurement error is less of a concern than for analyses that focus on interpreting ﬁrm-level evidence. Our variable of interest is aggregated at the industry level and consequently possible misreporting at ﬁrm-level is better controlled for. In order to test for possible heterogeneous eﬀects across ﬁrms we estimate the following modiﬁed model:

includes all explanatory variables as in (1), (2), and (3) (Koenker & Bassett, 1978). Estimating h from 0 to 1 gives the entire conditional distribution of Prodict, conditional on zijct (Buchinsky, 1998). In other words, using quantile regressions shows the impact of industries’ Internet adoption at diﬀerent levels of the conditional productivity distribution, rather than at the conditional mean of the dependent variable. Empirical applications of quantile regression techniques include, for example, Yasar and Morrison Paul (2007), Fattouh, Scaramozzino, and Harris (2005) and Coad and Rao (2008). Quantile regressions also allow for a robustness test of our main results as they are less sensitive to outliers than standard regression models (Koenker & Bassett, 1978). 10

5. DESCRIPTIVE STATISTICS Many ﬁrms in developing and emerging countries have adopted the Internet to support their operations over the 2006–11 period, but uptake rates diﬀered between countries and ﬁrms. While 47% and 57% of the ﬁrms in low-income and lower-middle-income economies communicated with clients and supplier by email, 84% and 93% of the ﬁrms in upper-middle-income and high-income economies did so. Moreover, small ﬁrms were less active users than larger businesses: their uptake was of 44.5% compared to 96.9% for businesses with more than 150 employees. Informal businesses were also active users of mobile phones. Table 3 shows that 76.2% of the African businesses in our sample used mobile phones in 2009–10 even though more than two thirds of these ﬁrms experienced power outages and more than one in four ﬁrms did not have electricity. The use of the Internet varied across diﬀerent countries’ industries. In the textiles industry, for instance, the share of ﬁrms using the Internet for communication was of only 21% in Nigeria, 25% in Indonesia, and 33% in Pakistan while the share was of 100% in Argentina, Costa Rica, and Peru. In the retail and wholesale trade sector, the same shares range from 20% for Uzbekistan and 30% for Angola to almost complete adoption in Hungary (96%) and Estonia (99%). Figure 1 shows substantial dispersion existed across countries in industries’ adoption rates. 11 In the food, garment, and service industries – i.e., retail and wholesale trade and also hotels and restaurants – several industries had weakly adopted the Internet. By contrast, chemicals and

Y ijct ¼ a þ bType1 ½ICT jct Typeijct þ b1 ICT jct þ b2 ICT ijct þ C X ijct þ kj þ kct þ eijct

ð2Þ

Y ijct ¼ a þ bADV 1 ½ICT jct TypeADV ijct þ bDIS1 ½ICT jct TypeDIS ijct þ b2 ICT ijct þ C X ijct þ kj þ kct þ eijct

ð3Þ

where Typeijct indicates ﬁrm characteristics (such as ﬁrm i’s size) and TypeADVijct and TypeDISijct are dichotomous variables of ﬁrm characteristics (for instance, whether the ﬁrm is an exporter, TypeADVijct, or not, TypeDISijct). We use various estimation models in our analysis. To estimate impacts on the average ﬁrm’s labor productivity and equipment investment we apply ordinary least squares regressions. We use logistic and probit estimation models to assess the impacts of industry Internet adoption on the two binary outcome variables: quality certiﬁcates and patents. Robust standard errors clustered at country-industry-year level are applied systematically to account for the fact that industry adoption of the Internet is an aggregate variable (Moulton, 1990). Moreover, we apply quantile regressions to assess whether impacts diﬀer based on ﬁrms’ level of labor productivity. Quantile regressions can be expressed in the general form Prodict = x0ict b + eict with Qh (Prodict/zijct) = z0ijct bh, where zijct

Table 3. Statistics on technology use of the informal sector Overall

AFR

LAC

Firm nbr.

%

Firm nbr.

%

Firm nbr.

%

Use of cell-phone No Yes

1,026 1,495

40.7 59.3

295 943

23.8 76.2

674 489

58.0 42.1

Use of electricity No Yes

553 1,668

24.9 75.1

369 873

29.7 70.3

178 681

20.7 79.3

46.1 53.9

275 591

31.8 68.2

489 190

72.0 28.0

Experienced power outages No 765 Yes 894

Note: The statistics are based on ﬁrm observations for 14 countries: Angola, Argentina, Botswana, Burkina Faso, Cameroon, Cape Verde, Democratic Republic of Congo, Ivory Coast, Guatemala, Madagascar, Mali, Mauritius, Nepal, and Peru.

Percentage of firms using the Internet

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

Max 75th percentile

100 80

Median

60

25th percentile

40 20

Min

0

Figure 1. Diﬀerences in industries’ adoption of the Internet across countries. Note: The deciles for diﬀerent industries are computed based on the following number of country observations on the share of ﬁrms using the Internet to communicate by email with clients and users: 110 for food, 50 for textiles, 71 for garments, 48 for chemicals and pharmaceuticals, 68 for metals and machinery, 64 for non-metallic and plastic materials, 123 for retail and wholesale trade, 74 for hotels and restaurants and 81 for construction and transportation. Statistics provided are obtained for the 50,013 ﬁrms included in our baseline sample.

pharmaceuticals industries had high adoption rates in most countries. 6. RESULTS (a) Baseline results: ICT-enabled spillovers on ﬁrm productivity and innovation performance First, we test whether the wider diﬀusion of ICT leads to knowledge spillovers that result in higher ﬁrm productivity and improved innovation performance. Panel A of Table 4 shows regression results of Eqn. (1) for labor productivity: column (1) reports results for industries’ use of the Internet with industry and country-year ﬁxed eﬀects. We identify a positive signiﬁcant eﬀect. We progressively add ﬁrm-level controls: ﬁrms’ employment and age (column 2), indicators of public ownership and whether the establishments are part of multiplant establishments (column 3) and controls for whether the ﬁrm has connections abroad (i.e., foreign-ownership and exporter status) (column 4). We also add proxies for managerial quality and access to ﬁnance (column 5). Consistently with the literature, we ﬁnd that these factors are positively correlated with ﬁrms’ productivity. Only public ownership is negatively correlated with ﬁrms’ productivity. We also control for ﬁrms’ own use of the Internet to exclude direct eﬀects on ﬁrms (column 6). Our variable of interest, the industry-wide adoption of the Internet as a means of communication, remains positive signiﬁcant but decreases as other factors, notably ﬁrms’ own use of the Internet, are added to the speciﬁcation. Appendix Table 11B provides a correlation matrix of the independent variables; levels of correlation are modest and justify the use of all dependent variables in our empirical estimations. 12 Panel B of Table 4 shows positive signiﬁcant eﬀects on average ﬁrms’ investment in equipment (column 1). We also identify positive eﬀects on ﬁrms’ ownership of quality certiﬁcates (columns 2 and 3) and patents (columns 4 and 5). Both results from probit and logistic estimation models are included. For brevity we only report results from logistic estimation models in subsequent tables. 13 Overall, our results provide evidence that industries’ adoption of the Internet facilitates positive spillover eﬀects on ﬁrms’ productivity and innovation performance. Results

593

conﬁrm our ﬁrst empirical hypothesis. As for the magnitude of estimated eﬀects, all else equal, an increase by one standard deviation in the intensity of a industries’ use of the Internet improves ﬁrm labor productivity by an amount equivalent to productivity increasing from the 50th to the 54th percentile of the distribution. For equipment investment the increase is from the 50th to the 55th percentile of the corresponding distribution. Impacts on ﬁrms’ ownership of quality certiﬁcates and patents are modest. An increase by one standard deviation would, all else equal, lead to an increase in formal intellectual property rights’ ownership of 3% and 5% respectively. (b) Robustness tests This section presents robustness tests of our results. Findings for labor productivity are reported in Table 5. First, in order to ensure that our variable of interest does not pick up the eﬀects of other industry characteristics, we include measures of national industries’ average employment, their age, foreign ownership status, the volume of exporter activities, an indicator of public ownership, the share of multi-plant establishments, the average of years of managers’ experience, and an indicator of credit access. Results, reported in column (1) of Table 5, conﬁrm our evidence is robust to including these control variables. Unreported tests show our results also hold if we include location ﬁxed eﬀects and ﬁrms’ past productivity performance to account for a variety of possible omitted factors. Second, we check whether our results are robust to potential outliers. We exclude the 5% most and least productive ﬁrms for each country-year group. 14 Results, reported in column (2) of Table 5, show outliers do not drive results. Results from quantile regressions, reported in Section 6(d), provide additional evidence that outliers do not aﬀect our ﬁndings. A related concern arises in those cases where few observations are used to obtain averages of industry Internet adoption. We, therefore, raise the threshold for averages to 30 observations. Results shown in column (3) of Table 5 conﬁrm our results are robust. The downside to raising the threshold is that fewer observations can be included as part of the analysis. This introduces a potential sample selection bias. This is why we allow for a lower threshold for our main results. Third, our main results focus on spillover eﬀects within the ﬁrm’s industry, as product markets are sources of relevant knowledge for ﬁrms’ productivity and innovation performance. The most adequate sources of knowledge for ﬁrms may, however, be not be ﬁrms’ own industries. In order to account for possible alternative sources for knowledge spillovers we compute alternative measures, including separate measures of industries’ adoption of the Internet for smaller and large ﬁrms. Smaller ﬁrms may have more to gain from other ﬁrms of similar size as processes adopted by large ﬁrms may be out of reach for them. By contrast, large ﬁrms may have little to gain from knowing about the practices adopted by smaller entities. Results, reported in column (4) of Table 5, are positive significant and conﬁrm our main ﬁndings. Moreover, we obtain separate measures of industries’ adoption of the Internet for diﬀerent types of locations as in Fisman and Svensson (2007). Firms in rural areas with very few inhabitants may have more to gain from the practices of other ﬁrms located in similar types of locations. 15 We ﬁnd a positive signiﬁcant impact (column (5) of Table 5). In addition, unreported results using a measure of Internet adoption for country-location-year level (as in Arnold, Mattoo, & Narciso, 2008, and Dollar et al., 2006), are also positive significant.

594

WORLD DEVELOPMENT Table 4. Baseline results

Panel A: Labor productivity Dependent variable: Labor productivity (1) Industry Internet use

(2)

***

***

0.010 (0.001)

Firm-level controls Employment Age

(3)

(4) 0.007 (0.001)

0.007 (0.001)

0.006*** (0.001)

0.151*** (0.010) 0.083*** (0.011)

0.132*** (0.010) 0.078*** (0.011) 0.133* (0.073) 0.333*** (0.022)

0.082*** (0.010) 0.087*** (0.011) 0.149** (0.072) 0.280*** (0.022) 0.443*** (0.025) 0.258*** (0.022)

0.058*** (0.010) 0.078*** (0.011) 0.121* (0.073) 0.285*** (0.022) 0.476*** (0.026) 0.241*** (0.022) 0.302*** (0.016) 0.026** (0.011)

0.023** (0.010) 0.075*** (0.011) 0.136* (0.071) 0.254*** (0.022) 0.453*** (0.025) 0.191*** (0.021) 0.279*** (0.016) 0.027** (0.011)

Foreign ownership Exporter status Credit access Managerial expertise

***

Firm-level Internet use Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects Observations R2 Panel B: Innovation performance

(6)

0.008 (0.001)

Multi-plant ﬁrm

***

(5)

0.008 (0.001)

Public ownership

***

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

0.386*** (0.017) Yes Yes

56,169 0.79

55,121 0.80

52,839 0.80

52,146 0.81

50,107 0.81

50,013 0.81

Equipment investment

Quality certiﬁcates

Patents

OLS (1)

Probit (2)

Logistic (3)

Probit (4)

Logistic (5)

Industry Internet use

0.009*** (0.003) Yes Yes Yes Yes

0.005** [0.002] (0.002) Yes Yes Yes Yes

0.007** [0.002] (0.003) Yes Yes Yes Yes

0.012**

Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects

0.003** [0.001] (0.001) Yes Yes Yes Yes 54,586

54,586

9,061

9,061

0.25

0.25

0.19

0.19

Observations R2 Pseudo R2

33,080 0.45

(0.005) Yes Yes Yes Yes

Note: Panel A reports results from ordinary least squares regressions. Robust standard errors clustered at country-industry-year level are shown in parentheses. For logistic and probit regressions, marginal eﬀects are reported in brackets. Firm-level controls in Panel B are the same as those of column (6) of Panel A. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

Finally, we compute a measure of Internet adoption at the country level. This speciﬁcation does not allow including country ﬁxed eﬀects. In order to ensure that other diﬀerences in countries’ business environment do not aﬀect results, we add a comprehensive set of country controls to our speciﬁcations: GDP per capita, gross capital formation, net foreign direct and portfolio investment, domestic credit to the private sector (as % of GDP), school enrollment in primary and secondary education, literacy rates as well as world region dummies. Our results reported in column (6) of Table 5 are positive signiﬁcant. Fourth, we test whether exposure to better technology improves impacts on ﬁrm productivity and innovation

performance. We interact our variable of interest with a measure of industries’ use of imported technologies. As shown in column (7) of Table 5, we ﬁnd that spillover returns from the Internet are larger where the exposure to technology is more important. Finally, we check whether our results are diﬀerent for ﬁrms in the manufacturing and services sectors. As shown in column (8) of Table 5, we ﬁnd positive signiﬁcant eﬀects for both types of ﬁrms but larger returns for services ﬁrms. This may be because knowledge is more important for services than for manufacturing ﬁrms. Alternatively, services ﬁrms may beneﬁt more because better knowledge about customer preferences may be more critical for them than for manufacturing ﬁrms.

Table 5. Robustness tests

Adding context controls (1) Industry Internet use

***

0.005 (0.001)

Controlling for outliers Removing the top and bottom 5% (2) ***

0.005 (0.001)

Alternative aggregation

Setting the threshold at N P 30 (3)

Extensions

Firm size

Location type

Country level

(4)

(5)

(6)

Exposure to technology (7)

0.006 (0.002)

0.007*** (0.001) 0.009*** (0.001)

Industry Internet use (location type)

0.244*** (0.018)

Country Internet use

0.006*** (0.001) 0.005*** (0.001)

Industry Internet use * high exposure to technology Industry Internet use * low exposure to technology

Yes Yes Yes Yes No

Yes Yes Yes Yes No

Yes Yes Yes Yes No

Yes Yes Yes Yes No

Yes Yes Yes Yes No

Yes Yes Yes No Yes

0.00 Yes Yes Yes Yes No

0.005*** (0.001) 0.008*** (0.001) 0.01 Yes Yes Yes Yes No

49,790 0.81

44,959 0.87

44,115 0.81

44,476 0.82

41,442 0.82

9,777 0.83

50,013 0.81

50,013 0.81

Industry Internet Use * manufacturing Industry Internet Use * services

Observations R2

(8)

***

Industry Internet use (ﬁrm size)

P-value for the diﬀerence in coeﬃcients Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects Country-level controls

Manufacturing vs. services

Note: The table reports results from ordinary least squares regressions. Firm-level controls are the same as those of column (6) of Panel A of Table 4. Country controls included in regressions reported in column (6) are country GDP per capita, the level of gross capital formation, national foreign direct and portfolio investment, domestic credit to private sector (as % of GDP), country school enrollment in primary and secondary education as well as country literacy rates. Robust standard errors clustered at level of the Internet use variable are shown in parentheses. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

Dependent variable: Labor productivity Controls

595

596

WORLD DEVELOPMENT

Robustness tests for our measures of innovation performance are reported in Appendix Table 12. As shown in Panel A, robustness tests conﬁrm ﬁndings regarding ﬁrms’ investments in equipment. In this case, we do not ﬁnd impacts to be diﬀerent with regard to the exposure to technology (column 7), or across manufacturing and services ﬁrms (column 8). With regard to quality certiﬁcates and patents, Panels B and C of Appendix Table 12 show the evidence is less robust than that on productivity and equipment investments. In the case of quality certiﬁcates, manufacturing ﬁrms beneﬁt more than services ﬁrms. 16 (c) Testing for heterogeneous impacts of the Internet across ﬁrms We test our second hypothesis on diﬀerential gains from industry Internet adoption across diﬀerent groups of ﬁrms. We test for diﬀerences in impacts across (i) exporters and non-exporters, (ii) ﬁrms located in larger and smaller agglomerations, (iii) single- and multi-product ﬁrms, and (iv) smaller and larger ﬁrms. We also analyze whether (v) informal businesses beneﬁt from their industries’ adoption of the Internet. Table 6A reports results for impacts on labor productivity. We ﬁnd that there is more to be gained for non-exporters (column 1 of Table 6A). Unreported results indicate that national ﬁrms also beneﬁt more than foreign-owned ﬁrms. Column (2) of Table 6A shows results where we distinguish those ﬁrms located in countries’ capitals or in cities of more than 1 million inhabitants from those located in smaller agglomerations. We ﬁnd statistically signiﬁcant stronger

impacts on labor productivity for ﬁrms in small agglomerations, even if location eﬀects are controlled for. Column (3) of Table 6A shows that single-plant ﬁrms beneﬁt more compared to multi-plant ﬁrms, but the diﬀerence is not statistically signiﬁcant. Finally, with regard to diﬀerently sized ﬁrms, reported in column (4) of Table 6A, we do not ﬁnd evidence of heterogeneous eﬀects when it comes to labor productivity. The evidence for innovation indicators also points to differential eﬀects, but is more mixed. We identify no eﬀects on exporters, as shown in columns (1), (5), and (9) of Table 6B. The diﬀerence in coeﬃcients is statistically significant for equipment investment and patenting. As for location, while we ﬁnd that ﬁrms in small agglomerations have larger returns to innovation performance, the diﬀerence in coeﬃcients is statistically signiﬁcant for ﬁrms’ ownership of quality certiﬁcates. Results are shown in columns (2), (6), and (10) of Table 6B. As reported in columns (3), (7), and (11) of Table 6B we also ﬁnd single-plant ﬁrms are more likely to obtain quality certiﬁcates and patents as a result of their industries’ adoption of the Internet. We do not identify diﬀerential impacts on equipment investment rates. As for ﬁrm size diﬀerences, results reported in columns (4), (8), and (12) show no signiﬁcant diﬀerences in impacts except for results on ﬁrm quality certiﬁcate adoption. Finally, column (1) of Table 7 shows that informal businesses also beneﬁt from ICT-enabled knowledge spillovers in terms of sales gains. We use the industry’s adoption of mobile phones as a proxy for the stronger use of ICT by informal businesses. The evidence is maintained if we control for

Table 6A. Firm characteristics and beneﬁts from the Internet’s adoption: Labor productivity Dependent variable: Labor productivity Exporters (1) Industry Internet use * exporters Industry Internet use * non-exporters

Firm location (2)

Multi-plant ﬁrms (3)

0.003* (0.002) 0.006*** (0.001) 0.005*** (0.001) 0.008*** (0.001)

Industry Internet use * big agglomeration Industry Internet use * small agglomeration

0.004*** (0.002) 0.006*** (0.001)

Industry Internet use * multi-plant ﬁrms Industry Internet use * single-plant ﬁrms

Yes Yes Yes Yes 0.00

Yes Yes Yes Yes 0.01

Yes Yes Yes Yes 0.08

0.006*** (0.001) 0.006*** (0.001) Yes Yes Yes Yes 0.45

50,013 0.81

44,706 0.82

50,013 0.81

50,013 0.81

Industry Internet use * bigger ﬁrms Industry Internet use * smaller ﬁrms Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects P-value of the diﬀerence in coeﬃcients Observations R2

Firm size (4)

Note: The tables reports results from ordinary least squares regressions. Firm-level controls are the same as those of column (6) of Panel A of Table 4. Robust standard errors clustered at country-industry-year level are shown in parentheses.***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

Table 6B. Firm characteristics and beneﬁts from the Internet’s adoption: Innovation performance Dependent variables Equipment investment Exporters

Industry Internet use * exporters

Industry Internet use * non-exporters

OLS regressions Logistic regressions Firm Multi-plant ﬁrms Firm size Exporters Firm Multi-plant ﬁrms Firm size Exporters Firm Multi-plant ﬁrms Firm size location location location (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

0.001

0.003

0.002

(0.005) 0.010***

[0.000] (0.003) 0.006***

[0.000] (0.006) 0.015***

(0.003)

[0.001] (0.002)

[0.003] (0.005)

Industry Internet use * big agglomeration

Industry Internet use * small agglomeration

0.008**

0.002

0.010**

(0.004) 0.011**

[0.000] (0.002) 0.006**

[0.002] (0.005) 0.014**

(0.004)

[0.001] (0.003)

[0.003] (0.006)

Industry Internet use * multi-plant ﬁrms

Industry Internet use * single-plant ﬁrms

0.009***

0.002

0.005

(0.004) 0.009***

[0.000] (0.002) 0.007***

[0.001] (0.006) 0.013**

(0.003)

[0.001] (0.002)

[0.002] (0.005)

Industry Internet use * bigger ﬁrms

Industry Internet use * smaller ﬁrms

Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects P-value of the diﬀerence in coeﬃcients Observations R2 Pseudo R2

Patents

0.009***

0.006***

0.011**

(0.003) 0.009***

[0.001] (0.002) 0.004**

[0.002] (0.005) 0.013**

Yes Yes Yes Yes 0.00

Yes Yes Yes Yes 0.31

Yes Yes Yes Yes 0.05

[0.002] (0.005) Yes Yes Yes Yes 0.31

Yes Yes Yes Yes 0.01

Yes Yes Yes Yes 0.44

Yes Yes Yes Yes 0.89

(0.003) Yes Yes Yes Yes 0.93

31,281 0.45

27,612 0.44

31,281 0.45

31,281 0.45

Yes Yes Yes Yes 0.18

Yes Yes Yes Yes 0.08

Yes Yes Yes Yes 0.00

[0.001] (0.002) Yes Yes Yes Yes 0.01

54,586

49,048

54,586

54,586

9,061

9,061

9,061

9,061

0.25

0.25

0.25

0.25

0.19

0.19

0.19

0.19

597

Note: Firm-level controls are the same as those of column (6) of Panel A of Table 4. Robust standard errors clustered at country-industry-year level are shown in parentheses. For logistic regressions, marginal eﬀects are reported in brackets. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

(1)

Quality certiﬁcates

598

WORLD DEVELOPMENT Table 7. The impact of industry cell phone use on the performance of informal businesses Dependent variables Sales

Industry cell phone use Firm-level controls Firm-level cell phone use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects

Machinery investment

(1)

(2)

(3)

(4)

(5)

(6)

0.010*** (0.003) No No Yes Yes

0.011*** (0.003) Yes No Yes Yes

0.010*** (0.003) Yes Yes Yes Yes

0.017** (0.009) No No Yes Yes

0.016* (0.009) Yes No Yes Yes

0.013 (0.009) Yes Yes Yes Yes

1,406 0.80

1,207 0.83

1,207 0.84

1,430 0.09

1,219 0.14

1,219 0.15

Observations R2

Note: The table reports results from ordinary least squares regressions. Firm-level controls include employment size, their age, their ownership of bank accounts and whether they had a loan. Robust standard errors are shown in parentheses. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

ﬁrm-level control variables – employment size, age, ownership of bank accounts, and whether ﬁrms had a loan (column (2)). Results are also robust to controlling for ﬁrms’ use of cell phones (column (3). 17 However, the evidence of positive impacts on informal ﬁrms’ machinery investments is weak (columns (4)–(6) of Table 7). Our estimated eﬀects become insigniﬁcant once we control for ﬁrms’ own use of cell phones (column (6) of Table 7). In conclusion, with regard to our second hypothesis, we ﬁnd that the Internet provides larger opportunities for productivity improvements of groups of ﬁrms that have limited access to oﬄine knowledge networks. This, however, does not hold for diﬀerently sized ﬁrms. (d) Testing for the eﬀects of ‘‘absorptive capacities” We test our third hypothesis on the role of ﬁrms’ ‘‘absorptive” capacities for positive impacts from industry Internet use on ﬁrm productivity and innovation performance. In order to test whether the average ﬁrm eﬀects identiﬁed hide diﬀerences in beneﬁts across ﬁrms of diﬀerent productivity levels, we conduct quantile regressions of Eqn. (1). Results, which are reported in Figure 2, show that there are diﬀerences

in the productivity gains from industries’ adoption of the Internet. Gains are small for ﬁrms with productivity levels below the 35th percentile but increase for more productive ﬁrms, before leveling oﬀ at the 70th percentile. This evidence supports our hypothesis that ﬁrms’ ‘‘absorptive” capacities matter; the least productive ﬁrms have limited returns from their industries’ Internet adoption. In addition, we test whether average impacts across the ﬁrm characteristics reported in Section 6(c) diﬀer across the productivity distribution. With regard to exporter status, Figure 3 (a) shows highest diﬀerential returns for non-exporting ﬁrms at productivity levels above the median. While the gains for non-exporters rise across the productivity distribution, beneﬁts for exporters are low for all producers, including the most productive ﬁrms. In other words, the larger average impacts on non-exporters’ performance identiﬁed in our previous analysis are driven by larger productivity gains for the most productive non-exporters. There is little diﬀerence in (low) beneﬁts among the least productive exporters and nonexporters. Figure 3(b) plots the coeﬃcients of our variable of interest for ﬁrms located in diﬀerent agglomerations. The least productive ﬁrms reap minor productivity gains from industries’ Inter-

0.01 0.009

Coefficient

0.008 0.007 0.006 0.005 0.004 0.003 0.002 10

15

20

25

30

35

40

45

50 55 Quantiles

60

65

70

75

80

85

90

Figure 2. Impacts of the Internet’s adoption on productivity across the ﬁrm productivity distribution. Note: The ﬁgure plots coeﬃcients from quantile regressions of the impact of the share of ﬁrms using email on labor productivity from the 10th to the 90th quantile of the productivity distribution.

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

599

(a) By exporter status 0.010

Non-exporters

0.009

Exporters

0.008 Coefficient

0.007 0.006 0.005 0.004 0.003 0.002 0.001 10

15

20

25

30

35

40

45

50 55 Quantiles

60

65

70

75

80

85

90

(b) By agglomeration type 0.012 Firms in small agglomerations 0.010

Coefficient

Firms in big agglomerations 0.008 0.006 0.004 0.002 0.000 10

15

20

25

30

35

40

45

50 55 Quantiles

60

65

70

75

80

85

90

40

45

50 55 Quantiles

60

65

70

75

80

85

90

(c) By single- and multi-plant firms 0.009 Single-plant firms

0.008

Multi-plant firms

Coefficient

0.007 0.006 0.005 0.004 0.003 0.002 0.001 10

15

20

25

30

35

Figure 3. Diﬀerent types of ﬁrms and the impact of the Internet’s adoption on productivity across the ﬁrm productivity distribution. Note: The ﬁgures plots coeﬃcients from quantile regressions of the impact of industry in big and small agglomerations as in column (3) of Panel A of Table 5 on labor productivity from the 10th to the 90th quantile of the distribution.

600

WORLD DEVELOPMENT

net adoption. By contrast, returns are larger for ﬁrms with productivity levels above the median with larger gains for ﬁrms located in smaller agglomerations. The gap between ﬁrms located in bigger and smaller locations is largest for ﬁrms in the median productivity range. Firms at higher levels of

productivity do not beneﬁt more from knowledge spillovers than those at median productivity. Figure 3(c) reports results for multi- and single-plant ﬁrms. The evidence shows that the Internet provides limited returns to the least productive single- and multi-plant ﬁrms. However,

Table 8. The eﬀect of size on the impact of the Internet’s adoption across the productivity distribution Dependent variable: Labor productivity Quantile regression Q1 (1)

Q2 (2)

Q3 (3)

Q4 (4)

Q5 (5)

Q6 (6)

Q7 (7)

Q8 (8)

Q9 (9)

Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects

0.000 (0.001) 0.003 (0.002) Yes Yes Yes Yes

0.001** (0.000) 0.007*** (0.002) Yes Yes Yes Yes

0.002*** (0.000) 0.009*** (0.002) Yes Yes Yes Yes

0.002*** (0.000) 0.011*** (0.002) Yes Yes Yes Yes

0.002*** (0.000) 0.012*** (0.002) Yes Yes Yes Yes

0.003*** (0.000) 0.014*** (0.002) Yes Yes Yes Yes

0.003*** (0.000) 0.015*** (0.002) Yes Yes Yes Yes

0.003*** (0.001) 0.015*** (0.002) Yes Yes Yes Yes

0.003*** (0.001) 0.016*** (0.003) Yes Yes Yes Yes

Observations R2

50,107 0.78

50,107 0.80

50,107 0.81

50,107 0.81

50,107 0.81

50,107 0.81

50,107 0.81

50,107 0.80

50,107 0.78

Industry Internet use * size Industry Internet use

Note: Firm-level controls are the same as those of column (6) of Panel A of Table 4. Robust standard errors clustered at country-sector-year level are shown in parentheses. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

Table 9. Impacts of productivity diﬀerences on the Internet adoption’s impact on ﬁrm innovation performance Equipment investment

Quality certiﬁcates

OLS regressions (1) Industry Internet use * below median

0.005

Industry Internet use * above median

(0.003) 0.007*

(2)

0.006

Industry Internet use * Q2

(0.003) 0.004

Industry Internet use * Q3

(0.003) 0.006*

Industry Internet use * Q4

(0.003) 0.008**

Observations R2 Pseudo-R2

Yes Yes Yes 0.02

(4)

(0.004) Yes Yes Yes

Yes Yes Yes 0.02

0.12 26,642 0.46

26,642 0.46

(5)

(6) *

0.004 [0.000] (0.002) 0.005** [0.001] (0.002)

(0.003)

Firm-level controls Industry ﬁxed eﬀects Country-year ﬁxed eﬀects P-value of diﬀerence in coeﬃcients (below and above median) P-value of diﬀerence in coeﬃcients (between Q1 and Q4)

Logistic regressions (3) *

Industry Internet use * Q1

Patents

0.009 [0.002] (0.005) 0.010** [0.002] (0.005) 0.004* [0.001] (0.002) 0.004 [0.000] (0.002) 0.005* [0.001] (0.002) 0.006*** [0.001] (0.002) Yes Yes Yes

Yes Yes Yes 0.47

0.02

0.011** [0.002] (0.005) 0.008 [0.001] (0.005) 0.010* [0.002] (0.005) 0.009* [0.002] (0.005) Yes Yes Yes 0.40

41,720

41,720

7,087

7,087

0.26

0.26

0.18

0.18

Note: Firm-level controls are the same as those of column (6) of Panel A of Table 4. Robust standard errors clustered at country-industry-year level are shown in parentheses. For logistic regressions, marginal eﬀects are reported in brackets. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

for the group of single-plant ﬁrms the beneﬁts rise for ﬁrms with higher than median productivity. By contrast, for multi-plant ﬁrms the rise is modest. As is the case of results for exporters and non-exporters, we ﬁnd that the higher average gains reported in column (3) of Table 6A is driven by the large returns for the most productive single-plant ﬁrms. Table 8 shows quantile regression estimates, which include a variable that interacts industry Internet adoption with ﬁrm size. Diﬀerently from average impacts reported in column (4) of Table 6A, quantile regression results indicate that there are diﬀerences across smaller and larger ﬁrms: more productive smaller ﬁrms beneﬁt more than larger ﬁrms. Finally, with respect to impacts on innovation, we ﬁnd, as shown in Table 9, that the returns from industries’ use of the Internet on equipment investment and ownership of quality certiﬁcates are larger for ﬁrms with abovemedian productivity than for those with below-median productivity. We ﬁnd no diﬀerence with regard to ﬁrm patenting. 7. CONCLUDING REMARKS Using 50,013 ﬁrm observations for 117 countries over the 2006–11 period, we provide evidence of a positive

601

impact of industries’ Internet use on ﬁrm performance. These gains, which do not depend on ﬁrms’ own ICT investments, justify public policies aimed at fostering industries’ use of the Internet. We also ﬁnd that the Internet provide larger beneﬁts to ﬁrms located in smaller agglomerations, to single-plant establishments and to nonexporters. These ﬁrms commonly engage less in innovation and consequently Internet-enabled knowledge spillovers help increase the group of innovating ﬁrms. Positive eﬀects are the more so notable as they also arise where ﬁrms face ﬁnancial constraints, frequent power outages, skills shortages, corruption, and cumbersome labor regulations (Paunov & Rollo, 2015). However, complementary policies aimed at building ﬁrms’ absorptive capacities matter if the Internet is to support ﬁrms’ productivity and innovation performance. Several questions for future research arise, including how increasingly sophisticated uses of the Internet inﬂuence potential spillovers and returns to ﬁrm performance. Ensuring ﬁrm surveys capture these uses is critical for research to better assess impacts. Informal ﬁrm surveys are also important to explore the potential of the Internet to help these businesses. Questions related to innovation should be added as these ﬁrms also engage in diverse non-technological innovation activities (OECD, 2015).

NOTES 1. Ding, Levin, Stephan, and Winkler (2010) ﬁnd the Internet facilitated the inclusion of women scientists and those working at non-elite institutions in collaborative research. Agrawal and Goldfarb (2008) show that the adoption of Bitnet, an early version of the Internet, disproportionately beneﬁted middle-tier universities’ collaboration with leading universities. 2. Cardona, Kretschmer, and Strobel (2013) provide a comprehensive review of the literature.

10. We analyze diﬀerential impacts on other innovation variables by interacting our variable of interest, industries’ adoption of the Internet, with above or below median ﬁrm productivity at t 3 or, respectively, the quartile of the distribution of productivity at t 3 the ﬁrm was part of (Table 9). 11. As described in the notes of Figure 1 the number of country observations diﬀers across industries.

3. User involvement can also stimulate innovation if it leads to coinnovation with users (Bresnahan, Brownstone, & Flamm, 1996; Von Hippel, 2005).

12. Unreported robustness tests show that results also hold if only the variable of interest i.e., industry’s use of the Internet and ﬁrm’s own adoption of the Internet are included as part of the analysis.

4. Forman, Goldfarb, and Greenstein (2014) conclude from their analysis of Internet investment and patenting across US counties, that ‘‘the Internet has the potential to weaken the longstanding importance of the geographic localization of innovative activity” (p. 5).

13. All unreported results are available from the authors upon request.

5. Dethier et al. (2011) provide a review of the WBES.

14. Our results are also robust to excluding outliers within industrycountry-year categories.

6. The routines used by the authors to clean the original dataset are available upon request. 7. Only information on whether ﬁrms owned websites is also available for the large sample of ﬁrms.

15. Unreported results for a measure of Internet adoption by locationtype, ﬁrm size, sector, country, and year are also positive signiﬁcant. Aterido, Hallward-Driemeier, and Page´s (2007) apply this approach in their analysis.

8. These countries are Angola, Argentina, Botswana, the Democratic Republic of Congo, Guatemala, Mali, and Peru.

16. We cannot report similar tests for patents, for which we have mainly information on manufacturing ﬁrms.

9. S ß eker (2012) and Dollar, Hallward-Driemeier, and Mengistae (2006) apply this approach to analyze the impacts of business conditions on ﬁrm performance.

17. We select a diﬀerent set of control variables due to the diﬀerent nature of ﬁrms analyzed and the diﬀerent variables contained in the informal ﬁrm survey.

602

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APPENDIX

604

Table 10. Observations by country Country

Percentage share in total

Country

Observations

Percentage share in total

Country

Observations

Percentage share in total

199 659 116 1,790 262 291 114 120 193 146 90 215 681 252 502 1,077 1,171 310 265 320 96 135 120 1,702 1,774 517 91 408 462 561 165 134 289 836 884 91 232 47 108

0.40 1.32 0.23 3.58 0.52 0.58 0.23 0.24 0.39 0.29 0.18 0.43 1.36 0.50 1.00 2.15 2.34 0.62 0.53 0.64 0.19 0.27 0.24 3.40 3.55 1.03 0.18 0.82 0.92 1.12 0.33 0.27 0.58 1.67 1.77 0.18 0.46 0.09 0.22

The Gambia Georgia Ghana Grenada Guatemala Guinea Guinea-Bissau Guyana Honduras Hungary Indonesia Iraq Jamaica Kazakhstan Kenya Kosovo Kyrgyz Republic Laos Latvia Lesotho Liberia Lithuania Macedonia Madagascar Malawi Mali Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montenegro Mozambique Namibia Nepal Nicaragua Niger Nigeria

153 243 475 129 858 192 133 136 595 248 1,122 707 225 400 636 200 154 271 211 88 111 209 292 336 83 654 214 275 2,454 35 327 336 60 440 307 328 633 85 1,865

0.31 0.49 0.95 0.26 1.72 0.38 0.27 0.27 1.19 0.50 2.24 1.41 0.45 0.80 1.27 0.40 0.31 0.54 0.42 0.18 0.22 0.42 0.58 0.67 0.17 1.31 0.43 0.55 4.91 0.07 0.65 0.67 0.12 0.88 0.61 0.66 1.27 0.17 3.73

Pakistan Panama Paraguay Peru Philippines Poland Romania Russian Federation Rwanda Samoa Senegal Serbia Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Swaziland Tajikistan Tanzania Timor-Leste Togo Tonga Trinidad and Tobago Turkey Uganda Ukraine Uruguay Uzbekistan Vanuatu Venezuela Vietnam Yemen Zambia Zimbabwe

843 587 719 1,464 944 260 304 717 183 35 479 327 126 165 243 895 462 117 130 129 152 259 247 388 82 102 107 308 835 515 544 907 320 81 158 953 300 434 547

1.69 1.17 1.44 2.93 1.89 0.52 0.61 1.43 0.37 0.07 0.96 0.65 0.25 0.33 0.49 1.79 0.92 0.23 0.26 0.26 0.30 0.52 0.49 0.78 0.16 0.20 0.21 0.62 1.67 1.03 1.09 1.81 0.64 0.16 0.32 1.91 0.60 0.87 1.09

WORLD DEVELOPMENT

Albania Angola Antigua and Barbuda Argentina Armenia Azerbaijan The Bahamas Barbados Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Chile Colombia Democratic Republic of the Congo Republic of the Congo Costa Rica Ivory Coast Croatia Czech Republic Dominica Dominican Republic Ecuador El Salvador Eritrea Estonia Fiji Gabon

Observations

Table 11A. Description of variables used Name Dependent variables Labor productivity Equipment investment

Patents Industry Internet use Industry Internet use

Firm-level controls Employment Age Public ownership Multi-plant ﬁrm Foreign ownership Exporter status Credit access Managerial expertise Internet use Industry

Variables used for robustness tests High (low) exposure to technology Interaction variables Size Big (small) agglomeration Bigger (small) ﬁrms Above (below) median of past productivity Q1, Q2, Q3 and Q4 of past productivity

Mean

Std. dev.

Logarithm of the ratio of total annual sales over full time employment windsorized at the top and bottom 1% for any country-year, reported in thousand USD Logarithm of the sum of 1 and the ratio of total annual expenditure for purchases of equipment over full time employment, reported in thousand USD A dummy equal to one if the establishment has an internationally-recognized quality certiﬁcation, such as ISO 9000 or 14000 certiﬁcations A dummy equal to one if the establishment has a registered patent and zero otherwise

18

2.17

2.1

1.08

0.21 0.39

Percentage share of plants using email to communicate with clients and suppliers in industry j of country c in year t. Robustness tests include alternative measures for Internet (i) by industry, country-year and ﬁrm size, (ii) by industry, country-year and location type, (iii) by industry, country-year and geographic location and (iv) by country-year

68.7

27.1

Logarithm of the plant’s full-time employment Logarithm of the diﬀerence between the year the survey was conducted and the year the plant was created A dummy equal to one if the government or state own a share of 10% or more of the plant and zero otherwise A dummy equal to one if the plant belonged to a ﬁrm that had at least one other plant and zero otherwise A dummy equal to one if the share of foreign ownership is bigger or equal to 10% and zero otherwise An indicator that is equal to one if the plant exports (direct or indirect) Dummy variable is equal to one if the plant has a line of credit or loan from a ﬁnancial institution and zero otherwise Logarithm of years of the managers’ experience Dummy variable where the plant has its own website and uses email and zero otherwise A variable indicating in which sector the plant is operating: (i) food, (ii) wood and furniture, (iii) textiles, (iv) garments, (v) leather, (vi) non-metallic and plastic materials, (vii) chemicals and pharmaceuticals, (viii) electronics, (ix) metals and machinery, x) auto and auto components, xi) other manufacturing, (xii) retail and wholesale trade, (xiii) hotels and restaurants, (xiv) construction and transportation, (xv) other services

3.2 [25] 2.7 [15] 0.01 0.15 0.12 0.23 0.42 2.70 [15] 0.41

1.4 0.7

0.68

Indicator of whether the number of establishments that use foreign technology in the industry is above (below) the average number of ﬁrms that use foreign technology, across industries Establishment’s full time employment Indicator of whether the plant is (not) located in the capital or a city of more (less) than 1 million inhabitants Indicator of whether the plant’s full-time employment is above or below the median distribution Indicator of whether the plant’s productivity at t 3 (windsorized at the top and bottom 1% for any country-year) was above or below the median productivity distribution Dummy variable indicating if the plant’s productivity at t 3 (windsorized at the top and bottom 1%) was in the ﬁrst (Q1), second (Q2), third (Q3) or fourth (Q4) quartile of the productivity distribution

Variables used for the analysis of informal businesses Sales Logarithm of total sales of the establishment, windsorized at the top and bottom 1% for any country-year, reported in thousand USD, value in parenthesis Machinery investments A dummy equal to one if the establishment invested in machinery and zero otherwise Industry cell phone use Share of establishments in a country sector who used cell phones for their operations Employment Logarithm of total employment of the business Age Logarithm of the diﬀerence between the year the survey was conducted and the year the ﬁrm was created

0.5

1.41

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

Certiﬁcates

Description

0.23 51.18 21.52 0.46 [1.6] 0.68 1.98 [7.2] 0.90 (continued on next page) 605

606

Table 11A (continued) Name Bank account Cell phone use Loan Sectors

Description

Mean

A dummy equal to one if the ﬁrm owned a bank account A dummy equal to one if the ﬁrm used a cell phone for its operations and zero otherwise Indicator of whether the ﬁrm had a bank loan or not A variable indicating in which sector the ﬁrm is operating in (i) food, (ii) furniture, (iii) handicrafts, (iv) clothes and shoes, (v) other manufacturing, (vi) construction, (vii) sales, and (viii) other services

0.15 0.59 0.12

Std. dev.

Note: Labor Productivity, Equipment Investment, and Sales are used in LCU, when included in regressions.

Industry Internet use Employment Age Public ownership Multi-plant ﬁrm Foreign ownership Exporter status Credit access Managerial expertise Firm Internet use

Industry Internet use

Employment

Age

1.00 0.28 0.24 0.01 0.06 0.05 0.24 0.35 0.27 0.41

1.00 0.32 0.11 0.24 0.23 0.37 0.30 0.14 0.42

1.00 0.06 0.08 0.01 0.17 0.16 0.43 0.20

Public ownership

Multi-plant ﬁrm

Foreign ownership

Exporter status

Credit access

Managerial expertise

1.00 0.06 0.05 0.03 0.01 0.01 0.04

1.00 0.20 0.08 0.04 0.00 0.17

1.00 0.19 0.00 0.03 0.14

1.00 0.20 0.12 0.30

1.00 0.15 0.26

1.00 0.16

Note: Correlations reported are computed for the estimating sample described in Table 1.

Firm Internet use

1.00

WORLD DEVELOPMENT

Table 11B. Correlation matrix

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

607

Table 12. Robustness tests for innovation performance results Dependent variable: Equipment investment Controls Adding context controls

Panel A: Equipment investment Industry Internet use

Controlling for outliers

(1)

Removing the top and bottom 5% (2)

Setting the threshold at N P 30 (3)

0.012*** (0.004)

0.008** (0.003)

0.010** (0.004)

Alternative aggregation

Extensions

Firms size

Location type

Country level

Exposure to technology

Manufacturing and services

(4)

(5)

(6)

(7)

(8)

0.015*** (0.002)

Industry Internet use (ﬁrm size)

0.006*

Industry Internet use (location type)

(0.003) 0.247*** (0.028)

Country Internet use

0.009***

Industry Internet use * high exposure to technology

(0.003) 0.009***

Industry Internet use * low exposure to technology

(0.003) 0.009***

Industry Internet use * manufacturing

0.89

(0.003) 0.009** (0.004) 0.96

Industry Internet use * services P-value for the diﬀerence in coeﬃcients Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects Observations R2

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes No

Yes Yes Yes Yes

Yes Yes Yes Yes

32,954 0.45

28,231 0.45

29,282 0.45

29,408 0.45

27,236 0.44

6,466 0.49

33,080 0.45

33,080 0.45

(continued on next page)

608

WORLD DEVELOPMENT Table 12 (continued)

Panel B: Quality certificates Dependent variable: Quality certiﬁcates Controls Adding context controls (1) Industry Internet use

0.003 [0.000] (0.002)

Controlling for outliers Removing the top and bottom 5% (2) ***

0.007 [0.001] (0.002)

Setting the threshold at N P 30 (3)

Alternative aggregation

Extensions

Firms size

Location type

Country level

Exposure to technology

Manufacturing and services

(4)

(5)

(6)

(7)

(8)

***

0.007 [0.001] (0.003)

0.007*** [0.001] (0.001)

Industry Internet use (ﬁrm size)

Industry Internet use (location type)

0.002 [0.000] (0.002)

Country Internet use

0.010 [0.001] (0.010) 0.005**

Industry Internet use * high exposure to technology

[0.001] (0.002) 0.003

Industry Internet use * low exposure to technology

[0.000] (0.002) 0.008***

Industry Internet use * manufacturing

0.04

[0.001] (0.002) 0.002 [0.000] (0.002) 0.00

Industry Internet use * services

P-value for the diﬀerence in coeﬃcients Firm-level controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects Observations Pseudo R2

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes No

Yes Yes Yes Yes

Yes Yes Yes Yes

54,344 0.25

44,280 0.23

47,945 0.25

48,527 0.26

45,417 0.25

10,711 0.25

54,586 0.25

54,586 0.25

(continued on next page)

HAS THE INTERNET FOSTERED INCLUSIVE INNOVATION IN THE DEVELOPING WORLD?

609

Table 12 (continued) Panel C: Patents Dependent variable: Patents Controls

Controlling for outliers

Adding context controls (1) Industry Internet use

*

0.009 [0.002] (0.006)

Removing the top and bottom 5% (2) **

0.014 [0.003] (0.006)

Setting the threshold at N P 30 (3)

Alternative aggregation

Extensions

Firms size

Location type

Country level

Exposure to technology

Manufacturing and services

(4)

(5)

(6)

(7)

(8)

0.007 [0.001] (0.006) 0.013*** [0.002] (0.003)

Industry Internet use (ﬁrm size)

Industry Internet use (location type)

0.006 [0.001] (0.005) 0.080*** [0.015] (0.013)

Country Internet use

0.010**

Industry Internet use * high exposure to technology

[0.002] (0.005) 0.008

Industry Internet use * low exposure to technology

[0.002] (0.005) 0.012***

Industry Internet use * manufacturing

0.11

[0.002] (0.005) 0.004 [0.001] (0.009) 0.05

Industry Internet use * services

P-value for the diﬀerence in coeﬃcients Firm controls Firm-level Internet use Industry ﬁxed eﬀects Country-year ﬁxed eﬀects Observations Pseudo R2

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes No

Yes Yes Yes Yes

Yes Yes Yes Yes

9,019 0.19

7,406 0.18

8,002 0.18

8,019 0.19

8,335 0.19

3,614 0.17

9,061 0.19

9,061 0.19

Note: Panel A reports results from ordinary least squares regressions while Panels B and C report results from logistic regressions. Firm-level controls are the same as those of column (6) of Panel A of Table 4. Robust standard errors clustered at the level of the Internet use variable are reported in parentheses. For logistic regressions, marginal eﬀects are reported in brackets. ***, **, and * indicate signiﬁcance at 1%, 5%, and 10% conﬁdence levels, respectively.

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Has the Internet Fostered Inclusive Innovation in the Developing World?

Has the Internet Fostered Inclusive Innovation in the Developing World?

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