0

Innovation and Its Impact on Lower- and Middle-Income Groups:

An Overview of the Literature on Innovation for Inclusive Development

Martijn A. Boermans

August 14, 2012

Prepared for the OECD, Directorate for Science, Technology and Industry

Executive summary:

Low- and middle-income countries are currently overthinking the implementation of and strategies for innovation-based growth that can address inequalities. There exist substantial inequalities in the technological capacities and productivity across firms within sectors and accordingly there are high levels of income differences. This project responds to this demand by providing background material for the pilot report on innovation and inclusive development by taking stock of the existing knowledge on this specific topic. It provides a review of 200 studies that focus on (i) the growth-inequality nexus at the macro level (ii) micro-level studies that explore islands of excellence and the role of technology driving within and across industry disparities within countries, and (iii) a conceptual overview of policies that make economic growth and innovation policies more inclusive.

This paper benefited from supervision of Caroline Paunov who provided invaluable inputs and directions. All errors are mine.

1

Introduction

Developing countries are trying to modernize their economies by investments in science, technology and innovation to spur economic growth. Several middle-income and some low-income countries have developed high value-added activities and innovative sectors (see Box 1 and 2). For example, knowledge-intensive production of higher-end sophisticated products takes place in cities like Shanghai (China) which is rapidly upgrading its electronics and computer hardware industries, Bangalore (India) which harbours a high-tech software sector and is gaining prominence in pharmaceutical and biomedical research, or Karachi (Pakistan) which is competing with German manufacturers in surgical equipment production (see Box 3). Technologically advanced firms like Embraer (Brazil) and Tata Motors (India) have acquired prominent positions as innovators in the global market.

However, this process of creating of world-class innovative capacity at the regional or firm level is not necessarily an inclusive process at the country level and can produce rising inequality. Although technological change spurs economic growth locally, the development of an innovative sector will bring greater benefits to some compared to others so that regional income and wealth inequality rises, and across and within sector wage and productivity disparities increase. So the question arises if innovation and inequality are related and if so, what is can be done to make innovation-based growth more a more inclusive process.

Part I “Innovation and its impact on lower- and middle-income groups at the macro level” of this literature reviewi summarizes core studies at the macro level to show that the transition to a modern economy with great innovative capacity does not always need to be associated with higher inequality. This part builds on a wide set of papers on growth-inequality nexus, highlights key mechanisms and providing examples of development paths of different countries. In particular, the following questions are covered:

• What is the relationship between innovation-based growth and inequality? What do we know

from past growth experiences?

• Are inequalities in production processes transitory and necessary phenomenon in the

development process?

Part II “Innovation and its impact on lower- and middle-income groups at the micro level” will focus on emerging evidence that differences in productivity at the firm-level to a large extent are explained by differences in technological capabilities. Such firm heterogeneity also translates into a larger wage gaps between high-skilled workers and low-skilled workers. Different innovation-related growth strategies are discussed that revolve around the question how to foster innovation at the firm-level while paying attention to the widespread dissemination of innovations and knowledge versus breeding “islands of excellence”. The key questions that arise are:

• Is there a bigger payoff from focusing on developing “islands of excellence” for growth and

improving well-being? • What explains the existence of substantial inequalities in innovation activities and the

resulting differences in productivities within countries and sectors of activities? Does the

existence of such differences at the micro level create bottlenecks for innovation? • Should anything be done to address inequalities in production structures (and notably the

informal sector)? Is competition and its impact on industry dynamics enough to address these

questions? Part III “Conceptual framework for innovation and inclusive development” focuses on practical

policies related to innovation that could address this potential rise in inequality in production structures in developing countries. A synthetic conceptual framework is established to identify the main dimensions related to the debate on innovation and inclusive development that sets the course for the innovation policy platform (IIP). It will set out some further unaddressed questions on inclusive innovation programs that aim to reduce income inequality and firm inefficiencies to help inform policy makers in this area.

• What could be done to address inequalities in production structures?

• Main dimensions and policy recommendations for innovation and inclusive development

2

Part 1: Innovation and its impact on lower- and middle-income groups at the macro level

At the macroeconomic level there has been much research on the relationship between growth and inequality.ii After decades of mean-income divergence, income levels across the world are converging since 2001 due to growth in Africa, Latin America and transition economies (Milanovic, 2012; Ortiz & Cummins, 2011). Within countries large income differences exist. These within country inequalities are often larger in low- and middle-income countries than in rich countries. In spite of growing involvement with the world economy though exports, FDI and foreign aid, many low-income countries have difficulties in upgrading their production structures and consequently have stagnant income growth and increasing inequality (Guiliani et al., 2004). The economic literature on this growth-inequality nexus is divided and presents mixed results about this relationship within countries, mainly because of methodological issues and possible non-linear and transitory paths. In this part we try to disentangle the effects of growth-oriented policies on inequality from the effects of initial conditions such as low inequality on economic performance. This task in search of a mechanic relationship ignores the joint determination, especially as both growth and inequality are amenable to analogous policies (e.g. Lundberg & Squire, 2003).iii 1.1. Growth and impacts on inequality at the macro-level

Following the classic work by Kuznets (1955, 1963), with early economic development rising inequality seems inevitable to push growth.iv Low-income countries have to make the transition from agriculture and labor-intensive production towards a modern sector that is more technologically intensive and requires skilled-labor inputs. The evolution towards a capital-intensive technology-based poses a dilemma: in the development process economic growth tends to exaggerate within-country inequality in terms of income, wages and wealth and tends to create regional income disparities as knowledge production is completed in clusters (Sutz & Arocena, 2006). This expansion of an innovative sector can inflate income inequality as development in science, technology and innovation may not directly benefit the poor, or can even be harmful to low-tech enterprises (Maier & Trippl, 2012).The reason is that technology adoption has high costs which most cash-strapped entrepreneurs in developing countries cannot bear, so that innovations diffuse slowly as only high-skilled workers and entrepreneurs with resources are able to implement innovations. In turn, the most competitive innovative firms will outcompete inefficient enterprises so that market shares are reallocated to technologically advanced firms. In the short run that creates adjustment costs for low-tech enterprises.

Early innovation-based growth will mostly benefit the higher segments of society and widen the income gap (Rogers, 1995), although poverty levels fall and welfare increases due to overall productivity gains within industries (Melitz, 2003). Kuijs and Wang (2005) argue that huge investments in capital and technology enhanced labour productivity and growth to which agricultural productivity cannot keep up with which results in a larger productivity gap between the agricultural and manufacturing sectors, so as to raise inequality between urban and rural areas. The rise in income inequality in rural areas is further magnified by the fact that some households have migrant workers in the family with non-farm incomes while others have no access to non-agricultural opportunities (Benjamin et al., 2005).v

There are many theoretical and empirical studies that find a Kuznets’ path where with development inequality first rises and then steadily falls (e.g. Anand & Kanbur, 1993; Aghion et al., 1999; Barro, 2000; Galor & Moav, 2004). During economic growth periods, to explain falling income inequalities within countries Acemoglu and Robinson (2002) argue that with growth high income inequality puts pressure on the rich to improve the economic situation of the poor; otherwise destructive tensions could arise between the modern sector and the low-tech sector. As the economy continues to grow the rich at some point cannot afford tension but can give more democratic rights to the poor and install redistribution policies to reduce inequality. However, the model does not predict that voluntary transfers remove income inequality so relatively large post-redistribution wage difference may endure as long as the poor do not revolt (Barro, 2000, see also Kanbur, 2010). A recent study by Oxfam (2012) reports that there is no relationship between the development stage and income inequality changes. This pattern of persistent inequality after a high growth period is inconsistent with the Kuznets’ curve, but several rich countries seem to have taken this route, most notably the United

3

States and the United Kingdom (Autor & Dorn, 2009; Gordon & Dew-Becker, 2007; Chusseau et al., 2008; Haskel et al., 2012; Heathcote et al. 2010; Snower, 1999).vi

Other scholars argue that growth-oriented policies over time simply will lift all boats, so that economic development and inequality are not related, as growth typically reduces poverty (e.g. Dollar & Kraay, 2002; Milanovic, 2012). Still, there are important differences between countries in how much the poor gain from economic growth and there can be diverse impacts among the poor in a given country (Ravallion, 2001). Also, different groups may benefit disproportionally. For example, Morrison et al. (2010) argue that in general growth reduces gender inequality. Box 1 presents a recent overview of regions (see further Appendix 1 for individual country analysis) that increased innovation efforts and economic growth which in turn had different effects on income inequality.

1.2. Inequality and impacts on growth at the macro-level

Another strand of literature on the growth-inequality nexus looks at how inequality affects growth (Alesina & Perotti, 1996; Barro, 2000; Perotti, 1996). On strand explains that inequality can foster growth. Following Acemoglu and Robinson (2002), more unequal societies tend to favour more redistribution as the mean income exceeds the median income so that with majority voting resources flow from the rich to the poor. Typically, these transfers and accompanying taxes distort economic incentives and decisions which reduce investment so that during the transition process with redistribution economic growth declines (Forbes, 2000; see also Bénabou, 1997). Another mechanism follows from the assumption that individual savings rates rise with income. In that case, redistribution tends to lower aggregate saving rates so that in reverse, rising inequality fuels investment and growth (Barro, 2000). In the early stages of development inequality facilitates economic growth because as physical capital accumulation fuels growth, higher inequality will enhance development by channelling resources towards individuals whose marginal propensity to save is higher (Galor, 2011). Basically, growth-enhancing investments need savings. The rich have a higher marginal propensity to save than the poor so that a relative transfer of income from the poor to the rich can foster capital accumulation and bring about a higher steady-state level of capital and output per workers. Galor and Tsiddon (1997) further suggest that higher income inequality allows from greater mobility and a concentration of skilled workers in the innovative sector which result in faster technological change and growth. Using a different setup they further show that if there are strong externalities which influence the individual’s level of human capital, then higher inequality may be necessary to kick-start economic growth in low-income countries.

There is some evidence for the fact that higher inequality spurs growth (Forbes, 2000; Li & Zou, 1998). In particular, Forbes (2000) finds that increased inequality in the short and medium term is related to higher growth within a given country. She estimates that a 10 percent rise in a country Gini coefficient is associated with a 1.3 percent increase in annual growth over the next five years (for a critique, see Atkinson & Brandoli, 2009; Roodman, 2009). Heathcote and coauthors (2010) demonstrate the greater inequality in the United States may have increased firm productivity. In contrast, another strand of literature suggests that inequality hampers economic growth (Aghion et al., 1999); see for a contrasting experience of low inequality and high growth, Box 2 on South Korea. By decreasing inequality with redistributions, the rate of investment and economic growth can increase through several mechanisms. First, if there are production externalities such as learning-by-doing effects and knowledge spillovers, and, production is characterized by decreasing returns to individual capital investment, then lower inequality fosters growth (Bénabou, 1997). Second, in later stages of development human capital becomes more important for growth than physical capital. At this stage, in the presence of financial constraints income inequality impedes human capital formation and growth (Banerjee & Duflo, 2005; Galor, 2011). Third, a complementary view to Acemoglu and Robinson (2002) is that for developing countries, inequality lowers long run growth because the rich may invest in unproductive projects instead of building up a modern sector (Arocena & Sutz, 2012).vii The rich may try to obstruct redistribution through lobbying and other rent-seeking behavior which lowers growth. Inequality may further reduce growth because of possible destructive revolt by the poor against the rich. As Barro (2000, p. 7) explains, inequality “motivates the poor to engage in crime, riots and other disruptive activities [which] represents a direct waste of resources because the time and energy of the criminals are not devoted to productive efforts”. In addition, the rich are less focused on inclusive growth as the rich consume mostly imported goods

4

while the demand of the poor is biased towards local products that can induce growth (Albuquerque, 2007).viii The presence of a strong middle class can therefore foster growth and innovation through skewed demand for mass produced manufacturing goods by the middle class in contrast to the poor who cannot afford such goods (Birdsall, 2010; Kharas, 2010). Also, inequality produces greater scope for discrimination across gender, ethnicity or other criteria that can be disruptive to the local economy if resources are not allocated in line with talent (Arocena & Sutz, 2012; Banerjee & Duflo, 2005). Finally, inequality may create political instability and distortions from increased government activity (Persson & Tabellini, 1994; Perotti, 1996) and the quality of legal institutions could weaken (Keefer & Knack, 2002).

Over time, one may expect that redistribution of wealth towards the poor increases productivity and growth, because the poor have a relatively high marginal productivity of investment and face financial constraints. In the presence of credit market imperfections the lack of access to capital can reduce investments in human capital. Talented individuals who would benefit from further education are excluded and only those with financial resources have access to it. If talented but low-income entrepreneurs have limited access to financing, potentially successful projects cannot be realised. Since capital market imperfections are greater in emerging and developing countries, the downsides to inequality described here are likely much larger than in developed countries (Aghion et al., 1999).ix The presence of financial constraints implies that rates of return on investment are not equalized so that the exploitation of investment projects depends on income and wealth (e.g. Barro, 2000; Birdsall, 2010; Galor, 2011). Therefore, the poor are likely to forego human capital investment even though there are potentially high returns so that higher inequality is detrimental to economic development.

Several empirical studies show that there is a negative effect of inequality on growth (e.g. Bénabou, 1997). In cross-country regressions Perotti (1996) finds that inequality reduces growth. Barro (2000) shows that inequality decreases growth only in low-income countries. More recent papers strengthen the case that inequality is bad for growth. Bourguignon (2004) and Birdsall (2010) find that developing countries with high inequality tend to grow more slowly. Similarly, Ortiz and Cummins (2011) show there is a strong, negative correlation between high inequality and high growth for 94 developing countries since 1990. Easterly (2007) reports that inequality is bad for growth because without a broad middle class growth-enhancing institutions cannot be developed (see Kharas, 2010). Weinhold and Nair-Reichert (2009) further link the size of the middle class to patenting activities suggesting that inequality hampers innovation. For OECD countries, De Mello and Tiongson (2006) argue that due to imperfect capital markets, inequality is perpetuated over time as the poor cannot get access to finance (De Mel et al., 2011). They show that in unequal societies redistributive spending is less, creating persistent inequality that may hamper growth opportunities for poor entrepreneurs. Such lost potential for opportunity creation effects from meagre redistribution implies that poor entrepreneurs should have high marginal returns on capital.x Bjørnskov (2008) shows that the impact of inequality on growth depends on political institutions. His estimates based on 73 countries suggest that for leftwing governments income inequality is negatively related to growth while the association is positive under rightwing governments. Freeman (2011) further attributes rising inequality to capitalist institutions.

1.3. Development and transitory inequality

Economic development depends on technological change. In order to grow, economies require certain institutions, absorptive capacity and learning capabilities to “assimilate, adapt and improve known technologies, and (ultimately) [to] create new technologies in-house” to foster innovation (Caniëls & Romijn, 2004). In early development stages, large parts of the population do not have access to education and financial markets that are necessary to generate human and physical capital for the modern sector. A related reason for inequality to increase with development is that economic growth produces higher initial returns for the relatively well-off in terms of capital and skills. So, adoption of new technologies requires time and is costly such that the dissemination goes slowly and is not all inclusive at the initial stages of development. Aghion et al. (1999, p. 1635) state that “the acceleration in the diffusion of new technologies can result in episodes of increasing wage inequalities across skill groups”. However, this rising inequality is often temporarily as the implementation spreads. In the initial stages, the traditional sector relies on old technologies while the innovative sector utilizes the

5

latest technologies (Galor & Tsiddon, 1997; Barro, 2000; see also Box 2 on South Korea which invested in education and job training). The build-up of specialized industries that compete at world-markets presents a greater need for skilled labor, such that the supply in the labor force must adjust accordingly though investment in education in a process of familiarization with new production structures so that like with many technological innovations like “the factory system, electrical power, computers, and the internet [innovations] tend initially to raise inequality” (Barro, 2000, p. 9). As more workers leave the traditional sector and adopt most recent technologies income inequality declines over time as relatively few workers are left behind and newcomers can catch up more quickly. Another reason to reduce inequality is because that may facilitate the innovation-based growth process. With development, the rich are more likely to redistribute.xi

With growth, more the use of general purpose technology flourishes. However, initially this may increase inequality because of frictions in the workforce that do not match new skill requirements (Galor & Tsiddon, 1997). Along the economic development process, the distribution of firm-level productivity becomes less skewed and average productivity increases as general purpose technology use increases (Chang & Van Marrewijk, 2011; Hsieh & Klenow, 2009). This suggests that over time the most inefficient producers are competed away together with low quality employment (Melitz, 2003; Bloom & Van Reenen, 2010). The piecemeal expansion of a middle-class spurs the supply of better paid employment further and on the producer side allows for the creation of more productive firms as complementarities across skill within sectors and firm build up. Atolia (2006) shows that wage inequality will decline in the long run but may rise temporarily because different sectors show heterogeneity in adjustment rates and vary in the degree of complementarities so that the reallocations towards more skill-intensive production is transitory as later jobs for unskilled workers can expand.

However, the relationship between wage inequality and development depends strongly on political institutions (Bjørnskov, 2008). For example, in India and China the state have played different roles in obstructing competition, where in India (small) incumbent manufacturing firms were protected by red tape and in China inefficient (large) state-owned enterprises received favors (Felipe et al., 2010; Hsieh & Ossa, 2012; Kochar et al, 2006; Song et al., 2011). Moreover, determinants of firm productivity at the macro-level such as trade policy, taxation, labor standards, unions, minimum wages and access to education vary by country but strongly shape the income distribution also across workers. Therefore, institutions can make the growth effects on inequality persistently negative (e.g. as happened in Latin America until recently, see Box 1) or inclusive and positive (e.g. South Korea until recently, see Box 2).

6

Box 1. Examples of countries’ growth experience and inequality

Appendix 1 provides a list of selected (mostly) developing countries to gain more insights on the growth experience and subsequent changes in income inequality for individual countries. If applicable remarks about their specific innovation strategies to foster growth are given so as to link particular policies to within-country income differences. Across countries, clear regional patterns emerge. First, Latin America remains the most unequal region in the world (Cassiolato et al., 2008; Kattel & Primi, 2010), but witnessed a decline in income inequality during 2006-2011, partly because of growing popularity of socialist and welfare-focused governments (Sutz & Arocena, 2006; Arocena & Sutz, 2012). Between 1980 and the 1990s the continent made a shift to market-based economy which dramatically worsened income inequality between skilled and unskilled workers (Behrman et al., 2003) as the local economy was not ready to upgrade production structures to incorporate new technologies (Guiliani et al., 2004; Kattel & Primi, 2010; Palma, 2005). The specialization in labor-intensive production even hampered the generation of endogenous technological capabilities as there was weak demand for skilled workers in local firms as innovation policy relied on market incentives and overcome perceived market failures especially in information availability (Cimoli et al., 2005). Recently, with the integration of innovation policies with social policies, initial steps are taken towards societal transformation to make innovation-based growth a more inclusive process (e.g. Argentina, Bolivia, Brazil, Chile, Peru, Uruguay). Second, the Middle East and North Africa suffered from low investments in R&D, ICT and innovative capacity relative to their level of development (e.g. Egypt, Oman, Tunisia) and in these regions income inequality is persuasively high and persistent, even though most country are in the middle- and high-income group. Third, Sub-Saharan Africa experienced good growth performance, but according to the African Progress Panel (2012) most economies saw income inequality increase in recent years (e.g. Angola, Botswana, Ghana, Kenya) while, in contrast, Ortiz and Cummins (2011) report that this region achieved the largest gains towards reducing income inequality as the Gini coefficient declined on average with five points between 1990 and 2008 (e.g. Ethiopia, Nigeria, Malawi, Senegal) even though there are stark contrast with these societies, e.g. Ethiopia and Nigeria (APP, 2012). Fourth, after GDP tripled in emerging Asia over the period 1990-2010, mostly because of China and India, at a $1.25 a day 714 million people were lifted out of poverty, however, in most countries income inequality increased (e.g. in China, India and Indonesia) as the rich gained more than the poor (Asian Development Bank, 2012), in recent years exceptions are Malaysia and Thailand. These changes in Asia’s inequality are worse than in Africa and Latin America but income inequality remains comparatively low even though Asia’s inequality levels are much higher than in most OECD countries. By looking at across sector labor reallocations, Ungor (2012) shows that the different growth paths of Asia and Latin America stem from labor reallocations towards productive manufacturing sector away from agriculture in only Asia, which potentially further also explains the difference in inequality adjustments. Finally, transition economies scored very low on innovation and manufacturing productivity. Between 1990 and 2008 economies in Eastern Europe and Central Asia were the worst performance in terms of inequality. These countries saw income inequality rise enormously during the transition towards a market-based economy (e.g. Poland, Ukraine) as the Gini-coefficients rose on average with nearly 10 points (Kattel & Primi, 2010; Ortiz & Cummins, 2011), although in recent years some of the most unequal former Soviet countries are experiencing falling income inequality together with high growth (Azerbaijan, Estonia, Moldova). In sum, there is much variation across different regions and within countries overall effects of growth and inequality.

7

Box 2. The case of South Korea’s growth and inequality

From the 1960s to 1990s South Korea used industrial policy targeted at the manufacturing sector to steer export-oriented industrialization (Kim, 1998; Lin & Chang, 2009; Rodrik, 1995). During this period, the economy was booming and at the same time enrolment to secondary education sparked from 42 percent to 96 percent and wage inequality was reduced, with a Gini coefficient of only 33 in 1988 (Bénabou, 1997; Fields & Yoo, 2000). Labor-intensive sectors expanded rapidly and a transition towards heavy and chemical industries was initiated which improved wages for low-income groups (Choi, 2003). High saving rates encouraged capital investment which gradually shifted from textiles, shipbuilding and steel (Posco) sectors to more advanced manufacturing industries like automobiles (Hyundai, Kia) and electronic machinery and consumer products (e.g. LG, Samsung) and semiconductors. South Korea’s innovation-based growth process aimed at further vertical integration of intermediaries and final products to improve firm productivity (Sutz & Arocena, 2006) under guidance of relatively qualified managers that wanted to increase innovativeness (Hobday, 2005).

During this transformation, South Korea’s middle class heavily relied on reverse engineering and industrial upgrading by sourcing foreign technologies (for high royalty payments) from Japan and the United States and stimulated R&D in steel production which allowed it to quickly jump on the technology ladder during the 1980s (Bernard & Ravenhill, 1995; Smith, 1997). Firms invested heavily in on-the-job training programs to retain skilled-workers and develop innovative capacity. The government also favoured local firms - especially large enterprise networks organized in chaebol business conglomerates, and avoided dependence on FDI (Hobday, 2005; Smith, 1997; Lin & Chang, 2009). From the 1960s to 1990s, the firm size differentials widened and these enterprises pay higher wages (Fields & Yoo, 2000).

To facilitate such rapid industrialization, the government promoted innovation sectors by stimulating education, training and R&D (Freeman, 2002). In late 1980s, the authoritarian regime gave way to a more democratic regime which took up social welfare policies. During this period, general education expenditures were very high for South Korea’s development stage. Consequently, a large share of the population could participate in the growth process so that low income inequality combined with the absence of a land-owning elite contributes to economic development with low income inequality (Albuquerque 2007; Rodrik, 1995). Other institutional arrangements such as strong labor unions that emerged had little effect on wage inequality (Fields & Yoo, 2000; Kan & Yun, 2008). In fact, in the mid-1990s, income inequality increased in South Korea. This occurred together with a rising importance of the ICT sector which may have increased the relative demand for skilled workers. Also, a deindustrialization took off in which manufacturing firms relocated low-skill production to China or other "cheap labor" countries and South Korea increased imports of consumer goods for these countries which thus decreased demand for unskilled workers and raised inequality. However, recent estimates show that between 2000 to 2008 South Korea managed to lower income inequality (Ortiz & Cummins, 2011).xii

Further readings

Amsden, A. (1989), “Asia’s Next Giant: South Korea and Late Industrialization”, New York: Oxford University Press.

8

Part 2: Innovation and its impact on lower- and middle-income groups at the micro level

2.1. Islands of excellence

The jobs in the innovative sector are often of better quality which is a first-order reason to try to breed islands of excellence, such as Silicon Valley (Caniëls & Romijn, 2004; Engel & Del-Palacio, 2011). The second-order effect is that potentially, the innovative sector creates spillovers that foster productivity in other industries as well. Especially, high innovative sectors can act as backbone of the economy at large through spillovers to other industries and backward and forward supply chains. Although identification of such innovative sectors is difficult, in theory nurturing these activities makes sense because of underinvestment in research in development in developing areas in general, and in specific innovation will pose positive externalities and raise the benefits from investment in schooling and exports. These complementariness are vital for long run growth and to create knowledge sharing and technology diffusion from the innovative sector to the rest of the economy.

There are large productivity gaps across firms in the innovative sector and the rest of the economy. These differences among firms mainly arise from differences in R&D investment, foreign technology adoption and other innovation activities which translate into productivity gains. As such, within a country there may exist a dual economy where some highly productive firms operate at the technology frontier while others rely on outdated technology and have highly inefficient production structures, even within industries. For instance, there is a large foreign-owned wood processing company in a region where many informal woodcutters operate without any capital equipment. Such discrepancy tends to result in income inequality among workers across high and low productive firms. Typically, these differences are most pronounced between firms and workers in urban and rural areas (Lee, 2010). The lack of inclusiveness associated with the huge dispersion of firm productivity and workers’ income is also a fundamental reason for the lack of convergence between developing and developed countries. In this regard, Banerjee and Duflo (2005) suggest within developing countries some firms thrive and use the latest technologies while others use more obsolete modes of production. That is, most low-income countries do not suffer from overall technological backwardness but rather from a lack of inclusive development (see e.g. on India, Hsieh & Klenow, 2009; McKinsey Global Institute, 2001).xiii

For this reason policy makers and government officials want to encourage the innovative sector to become more wide spread. Still, the relatively productive firms from low- and middle-income countries are typically recipients of advanced technologies which make them dependent of collaboration with foreign firms for technology transfer (Smith, 1997). Other firms are simply less efficient as they rely on less sophisticated production techniques (Bloom & Van Reenen, 2010). To improve firm productivity, governments can try to stimulate investment in ear-marked sectors to initiate islands of excellence in which the country can compete on world markets. The idea behind such strategy is that productivity improvements in the innovative sector will trickle down to the rest of the economy through knowledge spillovers and technology transfers across firms and industries (Trippl & Maier, 2010).

Much of the production of the innovative sector and more generally, national innovation policies do not address the needs of the poor in the informal sector (Cassiolato, et al., 2008; IDRC, 2011; Kattel & Primi, 2010). There is a strong need to develop innovation systems that focus on learning capabilities and strengthening of complementarities between sectors using an economy-wide approach (Freeman, 2002). Regional innovation policy in low- and middle-income countries tends to center around the creation of specialized hubs that are part of a global production networks (Chaminade & Vang, 2008). In practice the dissemination of innovation from these hubs is often problematic and can give rise to a dual economy with huge productivity differences between firms across sectors (Albuquerque, 2007; Baláž, 2007; Lach et al., 2008; Radosevic, 2009; Trajtenberg, 2009; Woodward et al., 2011; Zajac & Baláž, 2007). There are several examples from middle income countries to illustrate that the build-up of a concentrated innovative sector has its limitations. Examples of islands of excellence in e.g. Israel and Slovakia clearly show that the spillovers from the innovative sector are very localized within clusters and geographic agglomerations in that particular sector (Ellison & Glaeser, 1997), thus excluding the rest of the economy to benefit (for Israel, Lach et al., 2008; for Slovakia, Zajac & Baláž, 2007)

9

First, Lach et al. (2008) describe how Israel’s booming ICT sector failed to reach other sectors and remained an “island of excellence” (Trajtenberg, 2009; Engel & Del-Palacio, 2011). Although the government encouraged the development of the innovative sector through R&D grants with the idea that this sector could serve as the locomotive that pulls the rest of the economy along, there were no spillovers to the rest of the economy which remained stagnant while productivity level in the innovative sector increased rapidly. In the case of Israel there are two reasons for this lack of diffusion to the rest of the economy. First, the ICT sector was mainly export-oriented and connections to other sectors were unnecessary. Second, a large fraction of the industrial R&D centres are local labs of multinationals (e.g. IBM, Intel and Motorola) so that the newly-generated knowledge serves the parent company as may be of little relevance to the local economy. Third, the innovative sector was biased towards product innovation rather than process innovation such that major economic sectors could not improve productivity as they dependent upon process innovations instead.

Second, Zajac and Baláž (2007) look at the pharmaceutical industry in Slovakia. They show that after the transition to a market-based economy in the 1990s, domestic firms adopted low-key technologies and were often very inefficient. In contrast, newcomers for abroad had high productivity levels and took over R&D facilities which in turn created “islands of excellence”. In this small network with foreign firms, only a selected number of domestic firms such as Zentiva could benefit from participation with more advanced partners. Like in Israel, as the pharmaceutical industry gained world-class standards, other parts of the Slovak economy remained underdeveloped. The Slovak government did not have an inclusive innovation system so this process of increased competition created huge regional disparities in employment rates, education and income levels (Baláž, 2007). Majcen et al. (2009) confirm the important role of foreign firms in for manufacturing firms from Estonia, Hungary, Poland, Slovakia and Slovenia. They show that industrial integration through FDI increases the productivity, technology usage and product quality but such improvements in are very much localized within the production structure of the firm itself. Another example from a former Soviet state is given by Kattel and Kalvet (2006) who describe the rise of an advanced ICT sector in Estonia which had no linkages with the rest of the local economy. Likewise, Radosevic (2009) analyzes the development of science parks in Kazakhstan and finds that firms located in these clusters are not more innovative than other firms as they operate in traditional sectors with output supplies for the local markets. Still, in these transition economies, the expansion of innovative R&D centres can further widen the gap with the local business sector.

Third, in Brazil after several firms gained access to foreign technologies, the dissemination of technology to other parts of the economy did not occur. Albuquerque (2007) argues that the use of foreign labor-saving technologies displaced unskilled workers and increased unemployment as low-tech local firms could not absorb these workers nor compete with the capital-intensive large firms that operated close to the frontier. This dual economy, once created, is hard to combat as it first existed between agriculture and manufacturing and now between the formal and informal sector. As the technological change takes place elsewhere, the gap between rich and poor countries remains unchanged (Albuquerque, 2007) and such misallocation of resources across firms lowers productivity of the overall economy (Restuccia & Rogerson, 2008; Hsieh & Klenow, 2009). A similar process occurred after Mexico opened up its economy and expanded a labor-intensive manufacturing sector based on export. Mexico gained a high-tech sector while this export sector de-linked from the rest of the economy (Palma, 2005). The rise of the so-called enclave economy and maquila industries resulted in a dual economy where high-tech innovative outsource platforms in global networks had hardly any connection with local low-tech firms (Gallagher & Zarsky, 2007). The domestic firms tend to specialize in relatively low-tech production as multinationals take the lead of the international supply chains and control regional firms in the periphery thus creating a divide between the traditional and modern sector and increasing the wage gap (Cimoli et al., 2005).

Fourth, a somewhat related story to the enclave IT sector in Mexico comes from India. India’s district Bangalore is one of the most important ICT clusters located in a developing country. Basically, the combination of a skilled workforce and presence of multinationals with strong linkages to local firms boosted the development of local dynamic capabilities that bring about an innovation system that outcompetes ICT hubs in Brazil, China and Israel (Arora & Gambardella, 2004; Augier & Teece, 2009). Chaminade and Vang (2008) explain that the build-up of this innovative sector was accomplished without explicit national innovation policies, government procurement or other demand

10

pulls for local users in the initial stages of development but the market forces were important to stimulate specialization and positioning against global market competition. However, it is unclear to what extent the IT sector contributed to productivity growth in other sectors as the manufacturing sector is haunted by low productivity (see Filipe et al., 2010; Hsieh & Klenow, 2009)

Fifth, there are several cases from developed countries where ICT in the innovative sector could not materialize wider spillovers to other industries. Green and co-authors (2004) argue that in the 1990s in Ireland, ICT computer hardware cluster was depended on FDI and was therefore depended on foreign technology adoption from the United States (Collins & Pontikakis, 2006). Ireland created skilled employment for a relatively small group while there were few signs of more rapid technology diffusion for other sectors, in potential because of the little self-generated innovations and R&D investment. In Australia, ICT development relied on imports in a wide range of sectors without the existence of a strong local ICT cluster (Green et al., 2004). In Greece, the development of a science park did not affect the innovative capacity of the wider region because there were very few linkages across firms and research facilities (Antonopoulos et al., 2009; Collins & Pontikakis, 2006).

There are other reasons why there are little spillovers from innovations at the local regional level. Duflo et al. (2003) show that there is little information sharing in agricultural production techniques in Kenya because certain social norms may have prevented technology adoption as this requires people to significantly alter their behavior (Trajtenberg, 2009). De Mel et al. (2011) show that improving access to information on microfinance did result in larger loan uptakes, however, this increase in credit supply largely can about among relatively rich households that could least benefit from the funds compared to more cash-strapped entrepreneurs with high returns. Also, history and belief matters, because regions and firms may obtain collectively a bad reputation for inferior quality (Banerjee & Duflo, 2005). A somewhat related mechanism that hampers broader diffusion of technology is that in developing countries local industries rely on technology adoption that is driven by consumer demands in advanced markets (Klevorick et al., 1995). If firms are located nearby their customers it is much easier to innovate and the reorganize production structures, which is also explained by scholars who argue that it is important to have a middle class which can stimulate innovative activities.

These studies that touch upon the concept on islands of excellence point out that for developing countries in particular there exist huge productivity and income dispersion within countries. These differences within sectors and across formal and informal firms are even larger in terms of R&D investments, innovative capacity and patent licensing. In these economies, a limited number of firms typically accounts for the majority of national R&D investment yet fail to produce spillovers to improve productivity of the broader economy. Therefore, these low-income countries are often characterized by the presence of few productive innovative firms which are surrounded by many unproductive small informal firms.

These differences are often exemplified in cities (see Lee, 2010) as detailed below in Box 3. Inequality is higher in cities with high-tech innovative sectors in the United States (Echeverri-Carrol & Ayala, 2009) and to a lesser extent also in Europe (Lee, 2010). To explain this pattern, several mechanisms are at work when stimulating local innovative capacity. First, jobs in the innovative sector require skilled workers who tend to receive higher wages. Workers in the region may also benefit from new products, knowledge spillovers and technology transfers that alter the type of employment in the region as innovations substitute for particular kinds of jobs (Acemoglu, 2002; Berman et al., 1998; Lemieux, 2008). Regional innovation therefore has a positive effect on productivity so that firms and certain groups of (skilled) workers benefit in the region (Faggio et al., 2010). A region with a critical mass of innovators will shape the demand for certain service industries to develop, which in turn affects the composition of the local labor market (see Autor & Dorn, 2009; Georgiadis & Manning, 2007; Glaeser et al., 2008; Wheeler, 2005). In this regard, Manning (2004) argues that unskilled workers increasingly rely on co-location of skilled workers, suggesting that the increased demand from the innovators of certain jobs can raise the income of unskilled workers in areas of childcare, cleaning, food, health and security services, gardening etc. (Author & Dorn, 2009; Suedekum, 2006) thus the effect on inequality may be ambiguous as it substitutes jobs away from manufacturing and can drive up the costs of living for the poor. Also, if innovations disseminate locally, skill-biased technological change may complement or substitute for certain skilled work. Second, Faggio et al. (2010) explain that in the innovative sector the dispersion of income is higher than in other industries

11

because this sector may use decentralized wage structures as there are highly variable rates of return to particular innovations. The inherent uncertainty in the innovative process and the newness of the industry result in less formal workplace regulations that affect income inequality further. New innovative sectors tend to hire more part-time and specialized workers who have lower union power which creates higher income dispersion (Autor & Dorn, 2009; Card et al., 2004; Guadalupe, 2005). Third, there may be a selection mechanism where the innovative sector simply attracts skilled workers because of localized skill-specific complementarities so that skilled-workers can demand relatively high incomes (Echeverri-Carrol & Ayala, 2009; Glaeser et al., 2008). 2.2. ICTs as a cause of inequalities

ICTs are seen as the driving force behind much of the productivity growth (e.g. Jorgenson & Stiroh, 1999; Oliner & Sichel, 2000; Papaioannou & Dimelis, 2007). The ICT revolution has led to inter alia (i) increased availability of information, (ii) decentralized production structures and integrated networks of firms across countries, (iii) importance of human capital, (iv) greater diversity and flexibility, (v) prominence of niche markets and segmentation, (vi) clustering of specialized activities, (vii) versatile work tasks, (viii) reorganization of work and monitoring, and (ix) local de-industrialization due to outsourcing and offshoring. Gordon and Dew-Becker (2007) show that the introduction of ICT technologies in the United States improved firm productivity levels in the late 1990s, but that these gains translated into rising incomes for the highest wage earners only, thus increasing income inequality. In this regard, Chusseau et al. (2008) offer an overview of studies that look at the role of technological change on the wage gap. Early papers using data from the United States suggest that the spread of technology has increased the demand for skilled workers (Berman et al., 1994; Doms et al., 1997; Dunne and Schmitz, 1995). A fundamental reason for ICT to raise inequality may be that the adoption of ICT advances in other sectors requires changes in work methods and production processes that are associated with large adjustment costs and organizational restructuring (Gordon, 2000; Bresnahan et al., 2002). Hence, to reap broader benefits from ICTs, complementary investments are necessary. As Lach et al. (2008, pp. 6-7) explain: “if the rest of the economy fails to adopt the ICT, or, fails to make complementary innovations in the adopting sectors, economy-wide growth will not materialize”. Gordon (2000) shows that the ICT revolution benefits high-tech electronics and ICT-producing sectors but not others. He argues that if the increase in demand for skill comes mostly from the innovative sector, the correlation between skill demand and R&D and ICT demand is trivial, but, the issue remains why R&D and ICT expenditures have increases more rapidly in most sectors.

ICTs are therefore often seen as a cause of rising inequality. Strategic complementarities may exist between various sectors that can lead to lock-ins (arising from network externalities), that is, firms may choose not to adopt and implement new technologies as long as others do not work with the GPT (Aghion et al., 1999; Oliner & Sichel, 2000). Galor and Tsiddon (1997) show that the arrival of a new technology can erode specific human capital so as to induce across sector labour mobility. This results in a higher concentration of high-skilled workers in the innovative sector so that inequality across sectors rises. Also, the diffusion of a GPT may be associated with social learning, which in potential is very low between the modern sector and the low-technology sector (Lach et al., 2008). Therefore, the diffusion of GPTs will first raise inequality, but later reduce it because the other sector can catch up and with the GPT improve productivity. Aghion et al., (1999, p. 1654) explain that technological progress will give “rise to a “temporary” Kuznets’ curve during the transition from the old to the new GPT”.

Indeed, recent innovations have tended to raise the relative demand for skilled workers (see on a related role of trade, Box 4). The literature on the so-called skill-biased technological change (SBTC) strongly suggests that the ICT revolution is a major cause of rising income inequalities (Acemoglu, 1998; Dolton & Makepeace, 2004).xiv For this to be so, the introduction of ICT technologies must precede or concur with a higher demand for skilled workers. Autor et al. (2003) and Chusseau et al. (2008, p.437) argue that most of the widening of the wage gap in rich countries occurred over the 1980s while the adoption of ICT happened mostly in the 1990s, thus refuting the link between technological change and rising wage inequality.xv Still, this pattern may fit with Acemoglu (1998) model where an increase in the supply of skilled labor at first decreases the wage gap between skilled and unskilled worker. Verhoogen (2008) finds that Mexican firms that innovative

12

by upgrading quality experience relatively higher wage increases for skilled workers than for unskilled workers, suggesting that productivity improvements within firms may be associated with rising income inequality. In reverse, Faggio et al. (2010) study within country increases in inequality and argue that subsequent to inflated differences across gender, age and skill groups, firm level dispersion of productivity has also widened. This suggests that the increased firm productivity dispersion is associated with new technologies (see Caselli, 1999). However, in the long term, greater availability of human capital and knowledge-intensive production work directs the innovative sector towards more skill-complementary technologies (Beaudry et al., 2006) which unevenly raise the demand for skilled workers, thus creating more income inequality. Acemoglu et al. (2001) present another channel through which skill-biased technical change raises inequality. SBTC will erode the coalition between skilled and unskilled workers in unions as skilled workers will gain more outside option for employment opportunities, thus creating a process of deunionization.

Others argue that ICT does not have to (temporarily) raise income inequality but actually will reduce inequality. Jensen (2007) shows that ICT can assist low-tech firms by the provision of timing market information. He analyses the effect a private initiative to introduce of mobile phone services in Kerala, India on the fishing industry. With the advent of ICT, the improved functioning of the market reduced price dispersion, increased producers revenue and profits as fewer resources were wasted and consumer benefited from lower prices. Such initiative is self-sustaining as the mobile phone companies provide service for profits and fishermen are willing to pay for it because they increase profits. However, the gains from mobile phones were largest for vendors and boat that used the new technology, even though non-users also profited to a lesser extent, suggesting there is further room for welfare improvements as adoption rates increase.xvi A related example of an ICT success story comes from Kenya. Safaricom offers a mobile money transfer service, M-Pesa which has dramatically improved financial services in the region (APP, 2012). In 2006 only 19 percent of the people in Kenya had access to bank accounts, while after M-Pesa’s launch in 2007 this increased to 40 percent in 2010. Access to financial services through new ICT technologies increased rural household incomes by 5 to 30 percent after the adoption of mobile banking. Another case presented by the APP (2012) that highlights the importance of information dissemination and ICT is the introduction of Ghana’s ESOKO commodity index that lists market prices at wholesale and retail levels for key agricultural products that consumers can obtain real time though SMS mobile texting services (see Aker & Mbiti, 2010 for a critical review on the benefits of mobile services). 2.3. Large inequalities in innovation activities and productivity within countries and sectors Several already touched upon mechanisms explain the existence of large inequalities in innovation activities and firm productivity levels within the country as well as within sectors. The dispersion of productivity appears much larger in developing economies than in rich countries. For example, in Latin American there is wide dispersion of productivity across firm within sectors, and these differences are larger in low-income countries (Chang & Van Marrewijk, 2011). In similar vein, Bloom and Van Reenen (2010) find that the differences in managerial capabilities vary more in developing countries. These empirical findings suggest large inequality in productivity across firms within countries and sectors. As Banerjee and Duflo (2005, p. 478) explain: “many firms in poor countries do use the latest technologies, while others in the same country use obsolete modes of production” which leads to the question why some firms do not adopt available technologies. These intra-sector but also across regions inequalities within a country are vital to explain the effects of innovation-based growth on inequality and arise for various reasons.

First, because of financial market imperfections, many enterprises are financially constrained and cannot obtain optimal levels of capital and other resources for production. Hsieh and Klenow (2009) perform an exercise in which they estimate the potential gains from reallocation of capital among manufacturers in China and India. They find huge gaps in the marginal product of labor and capital within narrowly defined sectors. If these differences are removed by improving financial market imperfections to the level of the United States, then productivity levels in China and India could increase by 30 to 60 percent.xvii

Second, institutions related to competition matter. Firms with high marginal returns may underinvest because of weak institutions that protect their investments (Banerjee & Duflo, 2005; Djankov et al., 2002). First, there may be cronies in that state-owned firms that are overprotected

13

overinvest and have low marginal returns (e.g. Aghion et al., 2008). Second, excessive intervention of the state may discourage entry and innovation so that regulation results in little competition and too few firms, so there are only inefficient firms (Caselli & Gennaioli, 2006). Third, incumbent firms in developing countries may block newcomers to adopt new technologies because of vested interests. In this regard, the results of Hsieh and Klenow (2009) show that if more efficient firms in China (1998-2005) and India (1987-1994) got more room for maneuver, then many state-owned enterprises and state-backed behemoths would be downsized while other large private firms are likely to expand production. Banerjee and Duflo (2005) suggest that in India this is because particular government policies and licensing restrictions. As such, the Indian government obstructs large productive private firms from obtaining their optimal scale while supporting inefficient producers to prevent their market exit (see Aghion et al., 2008; Felipe et al., 2010; Kochar et al, 2006).xviii Combined, institutions that foster more market competition can alter the productivity distribution as more productive firms can expand and use new resources will inefficient firms loose market shares and exit the market (Melitz, 2003).

Third, with the advent of new technologies, firms are more able than others to transform the organization to make best use of innovations. First, weak absorptive and innovative capacity of the local economy in terms of lower human capital levels has held back technology adoption and productivity growth in the skill-intensive innovative sector (Caselli, 1999; Comin & Hobijn, 2004). The dissemination of world technologies is thus an important determinant of productivity and this process is mediated by capabilities.xix Second, Caselli and Gennaioli (2006) explain the dispersion of productivity across firms by the limited availability, heterogeneity and allocation of capital of managerial capabilities (see Buera, 2008). Third, Eapen (2012) provides a detailed account of how social structure of the firm affects technological spillovers across foreign and domestic firms, highlighting the role of network ties. Finally, managerial capabilities, organizational practices and decision-making processes within the firm are important for innovation as they shape the success of firm innovations (Augier & Teece, 2009; Bloom & Van Reenen, 2010; Caselli & Gennaioli, 2006; Faggio et al., 2010). For instance, Hobday (2005) provides evidence for the role of firm-level innovation management in newly industrialized countries (Korea, Taiwan).

Fourth, the supply of and demand for skilled labor are context-specific in the sense that workers need to meet particular task requirements that differ across firms and sectors. The organization of firms and completing various task often rely more on versatility based on new combinations of skills. The increasing usage of ICT has made organizations flatter, more flexible, more involved with multiple products and more informed. As Lindbeck and Snower (1996) highlight in a simple model in which production faces a trade-off between returns to specialization and ‘informational task complementarity’ the movement towards more holistic organization drives a wedge between specialized and versatile workers. This organizational change does not just influence the firms need for particular skills, but it redefines the required skills. This process implies that organizations will restructure and shed jobs so that versatile workers’ wages rise while non-versatile workers’ wages are stagnant or end up unemployed. Snower (1999) argues that this Organizational Revolution (see also Caroli & Van Reenen, 2001), where worker use ICT for combining multiple skills to perform various tasks, indeed exerts a major influence on the pattern of income inequality within groups (e.g. industry, age, education, experience, gender). As workers perform different sets of complementary tasks, their output becomes more idiosyncratic and therefore productivity differences widen further as people differ substantial in their versatility across tasks. Moreover, organizational transformations to increase innovative capabilities may call for more skilled workers in general (Antonioli et al., 2011; Bartel et al., 2007). In line with the above skill-biased technological change argument, firms in the innovative sector pay relatively high wages to skilled-workers and relatively low wages to unskilled workers (e.g. Autor et al., 1998; Snower, 1999). Haskel and Slaughter (2001) find that the wage gap between skilled and unskilled workers rises with innovation-based growth as technological changes occur mostly in the knowledge-intensive sector.xx Also across firms the introduction of new technologies can widen the productivity gaps as the implementation of ICT and related general purpose technologies often require significant adjustment costs. Likewise, skilled worker quickly reshape the organization to adopt the new technology while unskilled worker will not obtain such premium (Gordon & Dew-Becker, 2007; Helpman & Trajtenberg, 1998; Snower, 1999),

14

thus widening the wage gap temporarily as unskilled workers require longer adjustment time (Caselli, 1999; Galor & Moav, 2000; Jacobs & Nahuis, 2002; Chusseau et al., 2008).

Fifth, firms may have strong incentives to keep technologies inhouse, thus preventing transfers of technologies to other firms. As stipulated in the section on islands of excellence, especially foreign firms which use the latest technologies are often characterized by weak linkages with the rest of the economy. Harrison and Rodríguez-Clare (2009) list a set of 47 papers that investigate the extent to which knowledge and technology spillovers arise from the presence of foreign firms. Recent papers tend to show insignificant or negative horizontal externalities, e.g. in China, Czech Republic, India, Lithuania, and Mexico. Foreign firms have no reason to share knowledge with domestic firms in the same industry as they must compete for the same markets. In contrast, foreign firms require better suppliers of inputs and intermediaries. Recent studies tend to find positive backward FDI spillovers, e.g. for transition economies, China, Indonesia and Mexico (Harrison & Rodriguez-Clare, 2009) where China (and India) is a special example as the country uses domestic content requirements that may facilitate linkages between foreign and domestic firms.

There are other reasons why spillovers within and across sectors may not arise. First, people may find it difficult to work with new technologies because of certain beliefs and learned behavior. Also, information dissemination in rural areas may be slow as social norms prevent the implementation process of innovations (Duflo et al., 2003; Trajtenberg, 2009). Moreover, the production in innovative sectors may not target the needs of the local community and, as we have seen in the above section on islands of excellence, focus on customers foreign markets (Klevorick et al., 1995). Another mechanism is the trade-off between the ease of imitation of foreign technologies and protection of domestic innovation through a countries intellectual property rights choice. Using a panel of 64 developing countries, Chen and Puttitanun (2005) find a U-shaped relationship between the level of economic development and protective institutions, although in general protection has a positive effect on innovation. Branstetter et al. (2011) analyse the effects of intellectual property right protection reforms and for the US find that protective institutions spur innovative activities, especially at multinational firms, and this increase more than offsets the decrease in imitations, so as to increase industrial development and exports of new goods. Acemoglu et al. (2003) distinguish between strategies aimed at the creation of technology leaders or pure innovators versus policies that enhance innovative capacity through imitation and technology adoption by complementary investment in productivity enhancing inputs. Governments need to consider the country’s difference to the technology frontier from sector to sector, and from technology to technology when choosing between these two growth-oriented policies (Cimoli et al., 2005).

Hence, at the firm-level the presence of financial constraints, entry barriers and lack of competition, limited availability of managerial capabilities, low levels of human capital, foreign interests, institutions and existence of traditional social norms create bottlenecks for inclusive innovation. These processes have led to the rise of informal sector activities in much of the developing world, most notably in Latin America and Africa (see Box 5). To address such differences, more competition is not enough as other factors also play an important role for inclusive development. For instance, Anwar and Sun (2012) find that competition increases firm productivity and reduces the wage gap between skilled and unskilled workers, however, the effects of globalization trump the positive role of competition. Furthermore, fiercer competition in the innovative and formal sector may modernize this part of the economy, however, linkages to the informal sector are not guaranteed. As innovation and technology may contribute to economic growth and productivity improvements, these gains may not materialize for those at the bottom of the income distribution within the country, often informal entrepreneurs and workers. Moreover, competition enhancement does not necessarily increase the vitality of the informal economy where many resources in the form of talents lay idle.

15

Box 3: City elegance and inequality in low- and middle-income countries

World most recognized cities are located in developing countries. These broad metropolitan areas often constitute the main driver of the country’s economy (Weiss, 2001, 2007). Dhaka (Bangladesh) provides advanced global banking services that funds an export-oriented modern textile industry, Bangalore (India) harbours a competitive knowledge-intensive software sector and is gradually moving into pharmaceutical and biomedical research, and Karachi (Pakistan) is competing with German manufacturers in surgical equipment production. Bangkok contributes nearly 40 percent of Thailand’s GDP, Budapest generates over a third of Hungary’s GDP, Lima makes up for 45 percent of Peru’s economy, Manila generates 25 percent of the Philippines’ GDP, San Paulo is good for around 37 percent of the Brazil’s GDP. Also in Africa, there is an uprising of large city areas. The APP (2012) notes that in African large cities, retail sectors and upper-market housing are booming even though only 4 percent of Africans earn an income above $10 a day. The province of Gauteng in South-Africa is one of the most visible place in the developing world. It is more or less a super-metropolitan areas centered by the capital Johannesburg and Pretoria and contributes to over a third of South Africa’s GDP. The metropolis is actively trying to fight the post-apartheid legacy of high inequality (Weiss, 2001). Similarly, Brazil is actively trying to reduce major inequality in its key cities San Paulo and Rio de Janiero by encouraging entrepreneurship through better financing facilities and installing better protected property rights (De Soto, 2000). Weiss (2001) explains that the urbanization has taken off in developing countries, thus where average incomes are lowest. One implication is that the issue of inequality is no longer a rural phenomenon but increasingly an urban issue, equally so in rich economies. Cities are the engines of the economy meaning they are also in a good position to eliminate inequality. Hence, there is a duality of productive cities and inclusive cities as, by themselves, many of cities in low- and middle-income countries can be viewed as islands of excellence because linkages to rural areas are often weak. For example, China’s urban-led growth took place in coastal areas and special economic zones where the government invested heavily in hard infrastructure and welcomed foreign investors to rapidly upgrade its skill-intensive production of modern harbours and subsequently allowed the development of banking and electronics and IT hardware industries in these major cities (Weiss, 2001). Now, Shanghai with only 6 percent of the China’s population earns around 15 percent of its GDP. This process not only magnified the urban-rural divide, but also increased the wage gap within cities as large manufacturing activities that employ unskilled workers are around the high-tech, knowledge-intensive sectors in the city centre (Benjamin et al., 2005)

16

Box 4: The role of trade-induced growth for inequality

Any review on inclusive development cannot exclude the importance of trade-related mechanisms that shape productivity and wage distributions within countries. Globalization is one of the most widely-cited reasons for increasing inequality in much of the world (IMF, 2007). Offshoring and outsourcing are in particular visible examples which displace unskilled workers in rich countries will creating low-skilled, low-paid jobs in the developing world. Within country evidence is more nuanced. For instance, Chusseau et al. (2008) give a review of studies that look at the effect of North-South trade on inequality. Early papers using data from the United States find little, if any, influence of trade on the wage gap between skilled and unskilled workers (Berman et al., 1994; Borjas & Ramey, 1994; Katz & Murphy, 1992; Krugman & Lawrence, 1993; Revenga, 1992; Sachs & Shatz, 1994). Feensta and Hanson (2001) explain that the role of trade is very similar to the skill-bias technological change process for differences in income between skilled and unskilled workers. A stylized fact pointing in this direction is that low- and middle-income countries which increased export shares of skill-intensive sectors experienced the largest increases in income inequality. Zhu and Trefler (2005) explain that technological catch-up in the developing countries through offshoring and outsourcing industries cause relatively unskilled work to disappear in rich economies, while such services are relatively skill-intensive for low income countries. Hence, income inequality in both rich and developing economies rises. Similarly, Meschi and Vivarelli (2009) argue that trade has increased inequality based on a sample of 65 developing countries over the period 1980 to 1999. For middle income countries they show technological upgrading and its skill-biased nature is important for shaping the distributional effects of trade on inequality. Still trade is important for growth and for firms to improve technology and productivity (Coe et al., 2009). Harrison and Rodríguez-Clare (2009) list a set of 176 papers on the relationship between openness and growth which tends to favour the idea that trade, trade composition and export partners’ location are important for upgrading productivity levels within countries. Bustos (2011) provides a detail analysis of the effects of large trade reforms on the productivity of Argentinean manufacturers. She shows that trade induces firms to invest in R&D faster to upgrade technologies and that this effect is most visible for firms in the upper-middle productivity quintiles. This could suggest that after trade liberalization the low productivity firms are competed away while more productive firms have greater incentives to compete with more innovative products and services. Still, developing countries exporters tend to be hyper-specialized. Helpman et al. (2008) explain that difference in firm productivity within industries may be bounded from above and confirm there are many “zero” in bilateral trade flows across countries, just as only few firms export. This implies that in each industry there is a maximum productivity level which firm can attain so that for “pairs of countries in which the importer has high trade barriers or a small market, no firm in the exporting country will be productive enough to justify the fixed cost” related to exporting (Hanson, 2012, p. 59). Another explanation for hyper-specialization comes from Easterly and Reshef (2009) who argue that exporters face production externalities. That is, if one firm enters a foreign market, then it lowers the entry costs for other local firms through knowledge spillovers and pecuniary externalities. Hence, for innovation policy nursing regional innovative clusters may kick start the development process, however, most sectors still concentrate on low-technology outputs and exports are dominated by primary commodities, industries that are likely to face little such externalities. Notice that because globalization and technological change move together, as innovations lie behind the surge in fragmentation of production and offshoring technologies, it is difficult to disentangle the skill-biased technological change component from the globalization patterns (Freeman, 2011).

17

Box 5: Inclusive innovation in the informal sector

Stimulating innovation in the informal sector is important to achieve inequality reductions. The IDRC (2011) argues that formal science, technology and innovation policies in developing countries insufficiently address the informal sector, or worse, completely ignore it. More research is needed to develop better links with the formal sector and create innovative capacity to improve firm productivity in the informal sector. The IDRC (2011) gives three examples of failed innovation policies. In Ghana, Suame Magazine is an enormous industrial district that provides space for more than 10,000 informal vehicle repair shops. Several business obstacles including poor electricity, ICT and transport infrastructure hamper the development of advanced computer-based business solutions to create modern machine shop practices that can compete with expectations of customers from the formal sector. In China, attempts to diversify rural incomes through industrial decentralization failed to improve income inequality in mountainous coastal areas as there existed no formal innovation system linkages. Formal innovation policies did not reach the poor in this region, and, in fact, led to more inequality. Similarly, in three rural Indian clusters, because of the absence of intermediaries, informal rural enterprises in textiles, footwear, and terracotta pottery were unable to benefit from government support for technology development. The IDRC (2011) also reports some more optimistic stories on how innovation for the bottom of the pyramid can help the informal services sector, emphasizing the need for interactions between poor communities and intermediaries. In Cambodia, a local NGO acted as technology intermediary for floating poor communities to develop the world’s first community-based human waste treatment barge. In the Philippines, a social microenterprise acted as intermediary to enable network of “sari-sari” stores to adopt a new business model to legally include affordable generic drugs in their product range. In Vietnam, a more formal actor, the Center for Marine Conservation and Development, assisted coastal communities to improve their informal eco-tourism services. These examples hint at the importance of the recognition of talents of entrepreneurs in the informal sector to make better usage of productive assets. As De Soto (2000) forcefully argues, the establishment of property rights and registered citizenship for low-income settlements are vital to raise productivity along with complementary investments in infrastructure, general education and improved access to financial services.

18

Part 3: Conceptual framework for innovation and inclusive development

3.1. What could be done to address inequalities in production structures?

The interest in innovation policy among scholars has been expanding rapidly since the 1990s, mostly outside the core economics journal outlets (Radosevic, 2008). Innovation for inclusive development constitutes a much broader notion than technological transfers but focuses on designing a national innovation system which provides incentives for entrepreneurs to engage in productivity-enhancing investment while addressing inequality. Such innovation policies for low- and middle-income countries focus on interdependencies to induce complementary innovations and technology

dissemination across sectors (see Morrison et al., 2008; Trajtenberg, 2009). Typically, the level of investment in innovation is too low due to market failures (Banerjee & Duflo, 2005; Trajtenberg, 2009). First, firms that innovate reap only part of the benefits from their innovation as knowledge and technology has strong public good attributes, or, they tend to create positive externalities. After the innovation is on the market is does not cost much to imitate and reproduce such that without strong property rights, innovators have to little incentives to invest as they know they with only partially receive compensation for their efforts due to spillovers (Branstetter et al., 2011). One way developing countries benefit from this dissemination process is getting access to more advanced technologies such as knowledge-intensive goods and capital equipment to improve productivity via imports (Coe et al., 2009) and FDI.

As we have seen, breeding islands of excellences in particular sectors that have few linkages to the rest of the economy will exaggerate inequality. The previous overview of studies touches upon a wide areas of topic, not all of which agreement is reached upon. Hence, it is hard to distill normative policy guidelines from strict empirical analyses as innovation, growth and inequality dynamics are a complex process (Hobday, 2005; Radosevic, 2008). The sketched relationships between growth and technological change on income inequality and productivity differences across industries and within-industries are highly context-specific and differ between countries which need to be address when innovation policies for developing countries are designed and implemented (Chusseau et al., 2008; Freeman, 2002; Todling & Trippl, 2005).

First, policies that increase knowledge spillovers across sectors are important as they tend not to destroy the incentives of firms to innovate. That is, general policies to encourage technology dissemination mean that within industries firms will disinvest in R&D because the gains from potential innovations are not protected. Also, government must tunnel limited resources to spur innovation towards sectors with the greatest spillover potential such eg. biotechnology, agribusiness, health care and arguably the ICT sector. Also, to encourage domestic linkages, encourages industries that serve local demand means that the local economy can benefit from innovations (Klevorick et al., 1995; Birdsall, 2010).

Second, firms in the informal sector are often excluded from financial markets. These enterprises are in this way excluded from the usage of the latest technologies. Recent microfinance projects have proven successful to let these entrepreneurs upgrade their businesses (Banerjee et al., 2009; De Mel et al., 2008). Access to finance allows firms to invest in project with high returns, including R&D efforts. This could reduce the problem of misallocation of capital (De Mel et al., 2008; Hsieh and Klenow, 2009). Also, with imperfect capital markets, innovative entrepreneurs may not get access to credits. This problem is more eminent in the innovative sector because of information asymmetries: for investors and banks it is hard to predict the potential returns to investment in innovations, thus raising the barriers to finance. Trajtenberg (2009) notes this funding gap is especially stringent for the very early stage of the technology development. Therefore firms often invest less in R&D and rely more on imitation, even though the local market may require specific innovations which cannot easily be derived from technologies from advanced markets.

Third, to stimulate investment in innovation in the traditional sector, institutions play an important role. For innovation-based growth to be more an inclusive process the role of the welfare states has to expand. This can be achieved by incorporating depolarization measures into a national innovation system which in the long run strengthens the absorptive capacity of the country (Albuquerque, 2007; Bjørnskov, 2008). Also, labor market institutions influence the allocation of capital towards risky projects (Bartelsman et al., 2011) that include firm R&D investment. Another important area discussed is to stimulate competition that also explore the linkages with the informal

19

sector by making firm entry easier to cutting red tape (Djankov et al., 2002). Further, intellectual property right protection may reduce spillovers but can induce inhouse innovation (Branstetter et al., 2011; Chen & Puttitanun, 2005) while lowering skill-intensive imitation of technology (Acemoglu et al., 2003).

Fourth, inequalities can be reduced by creating firms with dynamic capabilities (Augier & Teece, 2009). There are various studies that indicate that managerial capabilities and within organizational linkages shape technology adoption and business strategies across firms (Bloom & Van Reenen, 2010; Caselli & Gennaioli, 2006; Eapen, 2012; Hobday, 2005; Faggio et al., 2010) which may explain persistent productivity differences across firms within industries. That is, the adoption of technology and implementation of new business processes across firms take time and requires management. Especially because of weak absorptive capacities, e.g. low human capital levels and managerial capabilities, some firms are unable to use the latest technologies (Caselli, 1999; Comin & Hobijn, 2004). Indirect evidence for this comes from Chun et al. (2011) who argue that the introduction of new innovations gives early adopters with better management quasi-rents so they outcompete less productive firms (Melitz, 2003).

Finally, once an innovative sector is kick-started, export promotion can be an important way to make the industry more competitive. In this regards, government often think infant industry protection for developing countries may help (see Luzio & Greenstein, 2005), although empirically the relationship between protection and growth of favoured sectors appears weak. Harrison and Rodríguez-Clare (2009) list a range of studies from Brazil, Cote d Ivoire, India, Japan, Mexico, South Korea and Yugoslavia that show that the removal of protections tend to generate within and across sector gains in productivity. Like in countries as South Korea and Taiwan, or innovative sectors in Israel and India, to breed a competitive sector, integration with the rest of world is crucial instead of shielding these industries off from foreign firms. However, these examples also point that agglomeration effects and spillovers to other sectors depend on the way in which production is carried out and organized and are not intrinsic to innovative sectors. Still, having a competitive innovative export sector will mostly benefit those employed there. This means that the direct effect of the industrial policies that promote exports are to raise the incomes for those who are already well-off, typically relatively high skilled workers. In addition, innovative sectors often create relatively few jobs, such that the employment formation effects may be limited, especially given the lack of human capital. Therefore, in order to facilitate the future growth of innovative sectors, government must make corresponding investment in education. Having a better skilled workforce increases the incentives to innovate, complements access to finance as well as managerial capabilities so as to induce further economic growth. 3.2. Main dimensions and policy recommendations for innovation and inclusive development

Several key dimensions for innovation and inclusive development have emerged, which include inter alia (i) a system approach to innovation, (ii) financial development targeted to the poor, (iii) increasing competition and institutional reform, (iv) education and managerial capability building, and (v) redistribution.

First, much of the recent literature stresses the importance of dealing with innovation networks (e.g. Asheim et al., 2011; Chaminade & Vang 2008; Lundvall et al., 2002; Metcalfe & Ramlogan, 2008). Such national innovation systems must focus on bottom-up incentives to create linkages within and across sector for various actors and stakeholder. Innovation policies in developing countries must emphasize the role of linkages for the creation of national innovation systems. By linking firms and other organizations, knowledge and technology spillovers are more likely to arise. Government agencies can play a vital role in the matching process, because for individual firms such costs have a public good aspect that they are unlikely to bear. Innovation systems can aim to detect local demand and subsequent potential suppliers, provide downstream linkages and evaluate how the modern and informal sector can co-create such production. The system approach covers various other aspects such as R&D infrastructure, access to finance, grants, technology centres and platforms, clusters, incubators, university-industry linkages, entrepreneurship training, and formalization programs to jump-start sustainable entrepreneurship in the informal sector. Currently, in developing countries such connections are typically weak because there are shortcomings related to the capabilities of firms in the region of the innovative sector and the needs for innovative services in the traditional sector may

20

differ from the activities in the high-tech cluster. There are often no intermediaries that can identify these implicit demands for technical assistance and local R&D services to the informal sector (Antonopoulos et al., 2009). Poor financial markets further hamper domestic firms from technology upgrading and to seek innovations. In many developing countries, there was weak cooperation between the public and private (informal) sector, similarly, norms to cooperate across firms appear to be weak as economies of scale from network production are often not materialized in absence of managers and entrepreneurs seeking such linkages. Likewise, foreign firms have little connection to the local economy. Government from developing countries can use recently popularized marketing strategies from multinationals their corporate social responsibility and sustainability programs to link these foreign firms to social purpose technologies in the local market. For example, Unilever has set-up large factories in poor regions of India to improve nutrition intakes of low-income households by hiring locals and giving on-the-job trainings. Capturing international expertise will at first have limited outreach, however, it may enable firms to climb up the technology ladder and make workers more aware of civil society. Therefore, policy makers need to identify how an innovation policy platform (IPP) may look while accounting for the dynamic network relationships across actors in various sectors. For instance, Sutz and Arocena (2006) provide a framework for innovation policies for development which highlight (i) the role of building dynamic capabilities and enlargement of learning and absorptive capacity so as to transform the latest technologies for use in the local development context (ii) enhancement of knowledge demand e.g. my public procurement and social programs, and (iii) promotion of linkages and co-production. As the IDRC (2011) stipulates: the innovation systems

framework is important because it illustrates that innovation is not a linear process whereby research

and development (R&D) leads to commercialization, industrialization, and growth. Instead, it

illustrates that what is most important are the dynamic linkages and interactions that take place

among actors such as firms, government departments, universities, and science granting councils, that

result in systemic learning, the distribution of knowledge throughout the system and lead to

strengthening of capabilities. These policies for the development of regional R&D and innovation capacities need to be aligned and fine-tuned in accordance with the local economy, culture, and the political system.

Second, access the finance is an important bridge for inclusive development. Through microfinance, the poor are enabled to expand their microenterprises and reap investment opportunities that currently lay idle, especially so for talents in the informal economy. These microfinance programs currently are being extended to include health insurance scheme, saving methods and business training to make better use of the social capital is accumulated in the group lending process. Also, the movement to larger loan instalments signals that larger capital investments may be necessary to grow a competitive firm to enjoy economies of scale and avoid problems associated with the missing middle. At least, the development of financial markets can partially overcome the misallocation of capital so that resources can flow to more productive use to prepare for innovation and growth. Ultimately, if such access reaches the lower segments of society, consequently, inequalities will be reduced.

Third, as argued, more competition can also foster the gales of creative destruction where innovators stimulate competitive responses that bring about a feedback process with spiralling innovations. This requires local governments to break concentrated innovative sectors and to encourage new entrants in these static markets while preventing incumbent firms from denying first-time innovators a foothold. That is, the government needs to dismantle excessive red tape and other regulatory barriers that favour existing firms (Djankov et al., 2002; Aghion et al., 2008).Other institutional reforms that focus on labor markets, property right protection, red tape, redistribution policies are important at the macro-level to create a stable business environment that is conductive to innovation.

Fourth, building capacities through linkages and finance are not sufficient. There have to be absorptive capacities to make the network connections work. Especially upgrading managerial talents by not only using expariates but also through circular migration of former emigrants can be effective to install better management of large firms. In addition to this, school systems and general education programs can strengthen future capacities. To participate closer to the technology frontier and developing an innovative sector, FDI and partnerships with multinationals can upgrade the absorptive capacities by spurring local technological skills, management practices, ICT standards and so on to

21

allow for greater spillovers through a demonstration effect (Comin & Hobijn, 2004; Jorgenson & Vu, 2005). As such, in the labor markets this requires policies which encourage on-the-job training and high mobility across firms, sectors and geographical areas. Also, basic knowledge in literacy and ICT usage are essential. Other complementary investments to education include combating malnutrition, health and sanitation, pollution and water supplies, ICT infrastructure and training. To enhance absorptive capacities to adopt foreign technologies, governments in low-income countries need to mobilize resources for ICT infrastructure that reach the informal sector so that new jobs are created that can contribute to sustainable development.

Finally, redistribution might be an alternative way to reduce inequalities if these are instrumental for growth, e.g. targeted at general human capital development or allow poor entrepreneurs to graduate to the modern sector. However, careful attention must be given to future reallocation mechanisms not to give rise to destructive political instability. Thus the debate continues how to make economic growth more inclusive and if rising inequality poses a threat to the development process.

The national innovation policy combined with access to finance for microenterprises has to rely on a bottom-up process where the government can further facilitate the flow of information through information and communication technologies that are of practical nature. What can governments do to induce innovation and inclusive growth? Government that want to stimulate innovative firms and industries can make support conditional. The public private partnerships are here important such that if the government invests in a broadband connection or other local public goods, it can tax firms or industries in the future. Cooperation across sectors increases the potential of such programs. For the literature the view emerges that there are potential dangers to focus on cluster formation with a strong regional aspect, e.g. the creation of islands of excellence tend to increase inequality and benefit only those active in the innovative sector. Exclusive focus on high-tech industries is a risky and potentially costly strategy especially if the required expertise for such as activities is relatively uncompetitive, prospective knowledge spillovers to other sectors are limited, and intellectual property that arise from innovations there may transfer to foreign firms. This is especially the case in large cities (see Box 4). For example regional innovation systems and policies initiated in the Bangalore software industry in India show that developing countries’ SMEs can undertake relatively high value added activities (Chaminade & Vang, 2008). The impact of specialized hubs that provide a single or a few narrowly defined functions in innovation and production network that are part of a larger global value chain on the rest of the economy is, however, debatable. Moreover, to build such islands of excellence, a high concentration of knowledge providers and educational institutions needs to be in operation to produce a critical mass of available skilled workers. Many local SMEs provided body-shopping services that required sending programmers to foreign multinationals for maintenance services. As the cluster matured, inter-cultural competences were built to manage outsourcing and offshoring to create their own local networks (Arora & Gambardella, 2004). For middle-income countries the creation of regions or special economic zones where the government invest in research and development activities, strong enforcement of job contracts and standard (e.g. working hours, minimum wage etcetera) should be in place to signal to firms the commitment to the “good innovators”. Combining innovation stimuli with export promotion programs may enforce monitoring. That is, if government’s assistance to specific targeted sectors is followed by increased exporting activities, then that may be a sign that it is working. Moreover, exporting may induce further innovation and importing of technologies, hence setting a self-enforcing cycle to progress. For low-income countries, the APP (2012) identifies several key sectors that are potential targets for further innovation, including: i) biotech based on indigenous knowledge for biodiversity, ii) water energy against desertification, iii) laser and post-harvest technologies, iv) ICT for satellites and weather systems, and v) apps for cloud computing and smart phones to address local needs. The main challenge is that in these industries local firms do not and cannot simply imitate and adopt foreign technologies so as to end up as “dumping ground of redundant technologies”. In this regard, Hall (2005) suggests a focus on capacity development in biotechnology sectors in developing countries using an innovation system approach, suggesting there is a large role for science and technology stimulation in these particular fields.

Formal policy instruments that are popular in rich countries such as science parks may not work because of lack of absorptive capacity in developing countries. For example, R&D tax credit

22

may not work given large informal sector (Box 5) and weak public institutions. Also, investments in basic science are too costly as the returns may only materialize in the long run and the benefits from such inventions have a strong public good aspect that current property right systems in developing countries cannot defend. The incorporate foreign technologies at the country level there is a need to develop absorptive capacities by investing in education and R&D. Policies will have to pay close attention to (i) the provision of incentives for spillovers from local R&D efforts (Acemoglu et al., 2003; Chen & Puttianun, 2005), (ii) the allocation of R&D to incorporate existing innovations and (iii) the financial constraints to innovation efforts (Trajtenberg, 2009). Further, in the past, redistribution policies by the government have been successfully implemented, but careful attention needs to be paid to the weak institutional environment, defect tax system and the dominance of agricultural and commodity based sectors in developing countries before addressing such programs. 3.3. Unaddressed issues for the future This overview on innovation and inclusive development highlights that innovation-based growth-oriented policies in low- and middle-income countries can affect inequality in various manners. In the past there has been little attention to inequality when addressing innovation policies. Likewise, empirical evidence on innovation-based growth and inclusive development is scares (Ortiz & Cummins, 2011). However, at the macro-level there is a large literature that investigates the growth-inequality nexus and provides theoretical mechanisms and empirical support for each development path so there is no broad consensus. Still, what most of these studies fail to explain this why a country follows a particular trajectory and how particular government policies can break that path. For instance, Latin America is characterized by high and persistent income inequality even during periods of economic growth (Box 1). Asia has relatively low income inequality and high growth, yet recently the wage gap seems to be increasing, even in exemplary cases such as South Korea (Box 2). Most work uses data from advanced economies to study the relationship between growth and inequality. Similarly, much of the focus is on the country-level, while industry-level studies based on firm data could be useful. What stands out in the literature on innovation and inequality it that only recent studies look at within industry differences at the country level. However, a good overview of the differences across firms at intra-sector level is missing as little is known about the heterogeneity in wages and human capital inputs that explain firm productivity within firms. For low- and middle-income countries there is no systematic analysis that investigates how technological change at a designated industry shapes the wage distributions within firms for different groups (e.g. in terms of skills, experience, gender or across plants in different regions). An emerging literature on national systems of innovation emphasizes the role of local linkages across different stakeholder in the innovation process. Although these approaches offer many useful insights, there is still a risk of falling back to old ideas that do not address specific needs of the country at hand. Moreover, there are no systematic evaluations of national innovation systems so that it is difficult to judge the effectiveness of such policies. In the future, careful attention needs to be paid to make an evaluations system that is not prone to confounding factors. For instance, randomized controlled trails can be set-up in different regions within selected countries to compare the effects of various policy measures so that successful policies can be extended and unsuccessful tools abandon. Still, special attention to the local context and culture which reflect the countries comparative advantage need to be accounted for. Some questions that arise from this overview include why knowledge spillovers from islands of excellence often do not arise. Similarly, many developing countries have highly productive firms, often with foreign ownership, yet again these powerhouses typically operate as export-platforms that forego the local economy. So, how can the links between the modern and innovative sector that relies on skilled workers be strengthened with the informal sector that mainly consists of low-tech enterprises with huge potential that now remains idle. A related question is what governments can ask of productive firms to focus more on links with the rest of the economy. That is, for example, a large biochemical company can offer services to rural farmers at special, subsidized rates. In the long run this type of cooperation may make local innovative companies more aware of the potential at the bottom of the pyramid. This brings us to the point of what type of innovations are inclusive. Kuijs and Wang (2005) suggest that policies must give less support for investment in manufacturing, more

23

support for services and increase labor mobility across sectors so as to increase linkages that are vital part of national innovation systems. In this regard, Trajtenberg (2009) also stipulates that developing countries focus more on a kind of prudent innovation which are less flashy or overtly innovative but rely on complementary actions to adopt ICT and steer local innovation in the traditional sectors. What

needs to happen is that ever expanding segments of the economy adopt ICT in ways that increase their

own productivity…What developing countries typically need is not more features in already highly

complex and cluttered software packages, but rather simplicity of operation, sturdiness, and backward

compatibility, so that barely literate workers could use software in a reliable fashion. Investments in science are not always required for development (Sutz & Arocena, 2006). This relates to the question posed by Mohnen and Röller (2005) who argue that government must rethink whether they want more firms to innovate, or, firms to innovate more, which calls for different fine-tuning of policies.

References

Acemoglu, D. (1998), “Why Do New Technologies Complement Skills? Directed Technological Change and Wage Inequality”, Quarterly Journal of Economics, Vol. 113, pp. 1055-1189.

Acemoglu, D. (2002), “Technological Change, Inequality and the Labour Market”, Journal of

Economic Literature, Vol. 40 (1), pp. 7-72. Acemoglu, D. (2003), “Cross-Country Inequality Trends”, Economic Journal, Vol. 113, pp.121-149. Acemoglu, D., P. Aghion and G.L. Violante (2001), “Deunionization, Technical Change and

Inequality”, Carnegie-Rochester Conference Series on Public Policy, Vol. 55 (1), pp. 229-264.

Acemoglu, D.,P. Aghion and F. Zilibotti (2003), “Vertical Integration and Distance to Frontier”, Journal of the European Economic Association, Vol. 1(2-3), pp. 630-638.

Acemoglu, D. and R.A. Robinson (2002), “The Political Economy of the Kuznets Curve”, Review of

Development Economics, Vol. 6 (20), pp. 183-203. Africa Progress Panel (2012), “Jobs Justice and Equity: Seizing Opportunities in Times of Global

Change”, Geneva. Aghion, P., E. Caroli and C. García-Penalosa (1999), “Inequality and Economic Growth: The

Perspective of the New Growth Theories”, Journal of Economic Literature, Vol. 37 (4), pp. 1615-1660.

Aghion, P., R. Burgess, S. Redding and F. Zilibotti (2008), “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India”, American Economic Review, Vol. 94 (4), pp. 1397-1412.

Ahlin, C., & Jiang, N. (2008), “Can Micro-Credit Bring Development? ”, Journal of Development

Economics, Vol. 86 (1), pp. 1-21. Ahn, H. (1997), “Trends in and Determinants of Income Distribution in Korea”, Journal of Economic

Development, Vol. 2 (2), pp. 27-56. Aker, J. and I. Mbiti (2010), “Mobile Phones and Economic Development in Africa”, Journal of

Economic Perspectives, Vol. 24 (3), pp. 207-232. Albuquerque (2007), “Inadequacy of Technology and Innovation Systems at the Periphery”,

Cambridge Journal of Economics, Vol. 31, pp. 669-690. Alesina, A. and R. Perotti (1996), “Income Distribution, Political Instability and Investment”,

European Economic Review, Vol. 40 (6), pp. 1203-1228. Anand, S. and S.M.R. Kanbur (1993), “The Kuznets Process and the Inequality-Development

Relationship”, Journal of Development Economics, Vol. 40 (1), pp. 25-52. Antonioli, D., R. Manzalini and P. Pini (2011), “Innovation, Workers Skills and Industrial Relations:

Emprical Evidence from Firm-Level Italian Data”, Mimeo.

Antonopoulos, C.N., V.G. Papadakis, C.D. Stylios, M.P. Efstathiou and P.P. Groumpos (2009), “Mainstreaming Innovation Policy in Less Favoured Regions: The Case of Patras Science Park, Greece”, Science and Public Policy, Vol. 36 (7), pp. 511-521.

Anwar, S. and S. Sun (2012), “Trade Liberalization, Market Competition and Wage Inequality in China’s Manufacturing Sector”, Economic Modelling, Vol. 29, pp. 1268-1277.

24

Arocena, R. and J. Sutz (2012), “Research and Innovation Policies for Social Inclusion: An Opportunity for Developing Countries”, Innovation and Development, Vol. 2 (1), 147-158.

Arora, A. and A. Gambardella (2004), “The Globalization of the Software Industry: Perspectives and Opportunities for Developed and Developing Countries”, NBER Working Paper No. 10538.

Asheim, B.T., R. Boschma and P. Cooke (2011), “Constructing Regional Advantage: Platform Policies Based on Related Variety and Differentiated Knowledge Bases”, Regional Studies, Vol. 45 (7), pp. 893-904.

Atkinson, A.B. (2001), “A Critique on the Transatlantic Consensus on Rising Income Inequality”, World Economy, Vol. 24 (4), pp. 433-452.

Atkinson, A.B. and A. Brandolini (2009), “On Data: A Case Study of the Evolution of Income Inequality Across Time and Across Countries”, Cambridge Journal of Economics, Vol. 33, pp. 381-404.

Atolia, M. (2006), “Trade Liberalization and Rising Wage Inequality in Latin America: Reconciliation with HOS Theory”, Mimeo, Florida State University.

Augier, M., and D.J. Teece (2009), “Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance”, Organization Science, Vol. 20 (2), pp. 410-421.

Autor, D.H., L.F. Katz and A.B. Krueger (1998), “Computing Inequality: Have Computers Changed the Labor Market?” Quarterly Journal of Economics, Vol. 113 (4), pp. 1169-1213.

Autor, D.H. and D. Dorn (2009), “Inequality and Specialization: The Growth of Low-Skill Service Jobs in the United States”, IZA Discussion Paper No. 4290.

Aw, B.Y., M.J. Roberts and Winston, T. (2007), “Export Market Participation, Investments in R&D and Worker Training, and the Evolution of Firm Productivity”, World Development, Vol. 30 (1), pp. 83-104.

Baláž, V. (2007), “Regional Polarization under Transition: The Case of Slovakia”, European

Planning Studies, Vol. 15 (5), pp. 587-602. Banerjee, A.V. and E. Duflo (2003), “Inequality and Growth: What Can the Data Say?”, Journal of

Economic Growth, Vol. 8 (3), pp. 267-299. Banerjee, A.V. and E. Duflo (2005), “Growth Theory Through the Lens of Development Economics”,

in P. Aghion and S. Durlauf (eds.), Handbook of Growth, Volume 1A, Elsevier, Amsterdam. Banerjee, A.V., E. Duflo, R. Glennerster and C. Kinnan (2009). The Miracle of Microfinance?

Evidence from a Randomized Evaluation”, Mimeo, MIT. Bartel A., C. Ichniowsi and K. Shaw (2007), “How Does Information Technology Affect

Productivity? Plant-Level Comparison of Product Innovation, Process Improvement and Worker Skills”, Quarterly Journal of Economics, Vol. 122 (4), pp.1721-1758.

Barro, R.J. (2000), “Inequality and Growth in a Panel of Countries”, Journal of Economic Growth, Vol. 5 (1), pp. 5-32.

Bartelsman, E., P. Gautier and J. de Wind (2011), “Employment Protection, Technology Choice and Worker Allocation”, DNB Working Paper No. 295, May.

Behrman, J.R., N. Birdsall and M. Székely (2003), “Economic Policy and Wage Differentials in Latin America”, Center for Global Development Working Paper No. 29.

Bénabou, R. (1997), “Inequality and Growth”, NBER Working Paper No. 5658. Benavente, J.M. (2006), “The Role of Research and Innovation in Promoting Productivity in Chile”,

Economics of Innovation and New Technology, Vol. 15 (4-5), 301-315. Benjamin, D., L. Brandt and J. Giles (2005), “The Evolution of Income Inequality in Rural China”,

William Davidson Institute Working Paper No. 654.

Berman, E., J. Bound and Z. Griliches (1994), “Changes in the Demand for Skilled Labor within U.S. Manufacturing: Evidence from the Annual Survey of Manufactures”, Quarterly Journal of

Economics, Vol. 109, pp. 367–397. Berman, E., J. Bound and S. Machin (1998), “Implications of Skill-Baised Technoligcal Change:

International Evidence”, Quarterly Journal of Economics, Vol. 133 (4), pp. 1245-1279. Bernard, M. and J. Ravenhill (1995), Beyond Product Cycles and Flying Geese: Regionalization,

Hierachy, and the Industrialization of East Asia, World Politics, Vol. 47, pp. 171-209. Birdsall, N. (2010), “The (Indispensible) Middle Class in Developing Countries”, in R. Kanbur and M.

Spence (eds.), Equity and Growth in a Globalizing World, Commission on Growth and Development, The World Bank.

25

Bjørnskov, C. (2008), “The Growth-Inequality Association: Government Ideology Matters”, Journal

of Development Economics, Vol. 87 (xxx), pp. 300-308. Bloom, N., and J. Van Reenen (2010), “Why Do Management Practices Differ across Firms and

Countries?”, Journal of Economic Perspectives, Vol. 24(1), pp. 203-224. Borjas, G.J. and V.A. Ramey (1994), “Rising Wage Inequality in the United States: Sources and

Consequences”, American Economic Review, Vol. 84 (2), pp. 10-16. Borjas, G.J. (2006), “Native Internal Migration and the Labor Market Impact of Immigration.” Journal

of Human Resources, Vol. 41 (2), pp. 221-258. Bourguignon, F. and C. Morrisson (1998), “Inequality and Development: The Role of Dualism”,

Journal of Development Economics, Vol. 57 (2), pp. 233-257. Bourguignon, F. (2004), “The Poverty-Growth-Inequality Triangle,” Mimeo. Branstetter, L., R. Fisman and C.F. Foley (2006), “Do Stronger Intellectual Property Rights Increase

International Technology Transfer? Empirical Evidence from U.S. Firm-Level Data”, Quarterly Journal of Economics, Vol. 121 , pp. 321-349.

Branstetter, L., R. Fisman, C.F. Foley and K. Saggi (2011), Does Intellectual Property Rights Reform Spur Industrial Development?”, Journal of International Economics, Vol. 83 (1), pp. 27-26.

Bresnahan, T., E. Brynjolfsson and L. Hitt (2002), “Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-level Evidence”, Quarterly Journal of Economics, Vol. 117 (1), 339-376.

Buera, F. (2008),“Persistency of Poverty, Financial Frictions, and Entrepreneurship”, Mimeo. Bustos, P. (2011), “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact

of MERCOSUR on Argentinean Firms”, American Economic Review, Vol. 101 (1), pp. 304-340.

Caniëls, M.C.J. and H. Romijn (2004), “Technological Learning in Small-Enterprise Clusters: Conceptual Framework and Policu Implications”, In S. Mani and H. Romijn, Innovation,

Learning, and Technologival Dynamism of Developing Countries, United Nations University Press.

Card, D. and J.E. DiNardo (2002), “Skill-Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles”, Journal of Labor Economics, Vol. 20 (4), pp. 733-783.

Card, D., T. Limieux and W.C. Riddell (2004), “Unions and Wage Inequality”, Journal of Labor

Research, Vol. 25 (4), pp. 519-559. Caroli, E. and J. Van Reenen (2001), “Skill Biased Organizational Change? Evidence from a Panel of

British and French Establishments”, Quarterly Journal of Economics, Vol. 116 (4), pp. 1449-1492.

Caselli, F. (1999), “Technological Revolutions”, American Economic Review, Vol. 89 (1), pp. 78-102. Caselli, F. and N. Gennaioli, (2006), “Dynastic Management”, CEP Discussion Paper No. 741. Cassiolato, J., M.C. Couto Soares and H. Lastres (2008), “Innovation in Unequal Societies: How Can

it Contribute to Improve Equality?”, Mimeo. Chaminade, C. and J. Vang (2008), “Globalisation of Knowledge Production and Regional Innovation

Policy: Supporting Specialized Hubs in the Bangalore Software Industry”, Research Policy, Vol. 37 (10), pp. 1684-1696.

Chang, H.H. and C. Van Marrewijk (2011), “Firm heterogeneity and development: Evidence from Latin American countries”, Tjalling C. Koopmans Research Institute Discussion Paper Series,

11-14, Utrecht University, School of Economics. Chen, Y. and T. Puttitanun (2005), “Intellectual Property Rights and Innovation in Developing

Countries”, Journal of Development Economics, Vol. 78, 474-493. Choi, K. (2003), “Measuring and Explaining Income Inequality in Korea”, Mimeo. Chudnovsky, D., A. López and G. Pupato (2006), “Innovation and Productivity in Developing

Countries: A Study of Argentine Manufacturing Firms’ Behavior (1992–2001)”, Research

Policy, Vol. 35 (2), pp. 266-288. Chun, H., J-W. Kim and R. Morck (2011), “Varying Heterogeneity among U.S. Firms: Facts and

Implications”, Review of Economics and Statistics, Vol. 93(3), pp. 1034-1052. Chusseau, N., M. Dumont and J. Hellier (2008), “Explaining Rising Inequality: Skill-Biased

Technoligcal Change and North-South Trade”, Journal of Economic Surveys, Vol. 22 (3), pp. 409-457.

26

Cimoli, M., J.C. Ferraz and A. Primi (2005), “Science and Technology Policies in Open Economies: The Case of Latin America and the Caribbean”, Mimeo.

Coe, D.T., E. Helpman and A.W. Hoffmaister (2009), “International R&D Spillovers and Institutions”, European Economic Review, Vol. 53 (7), pp. 723-741.

Collins, P. and D. Pontikakis (2006), “Innovation Systems in the European Periphery: The policy Approaches of Ireland and Greece”, Science and Public Policy, Vol. 33 (10), 757-769.

Comin, D. and B. Hobijn (2004), “Cross-Country Technological Adoption: making the Theories Face the Facts”, Journalf of Monetary Economics, Vol. 51 (1), pp. 39-83.

Conway, G. and Waage, J. (2010), “Science and Innovation for Development”, Mimeo. Cozzens, S.E. (2010), “Innovation and Inequality”, in R Smits, S. Kuhlman and P. Shapira (eds.), The

Theory and Practice of Innovation Policy: An International Research Handbook, Edward Elgar.

Datt, G and M. Ravallion (2011), “Has India’s Economic Growth Become More Pro-Poor in the Wake of Economic Reforms? ”, World Bank Policy Research Working Paper No. 5103.

De Mel, S., D.J. McKenzie and C. Woodruff (2008), “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, Quarterly Journal of Economics, Vol. 123 (4), pp. 1329-1372.

De Mel, S., D.J. McKenzie and C. Woodruff (2011), “Getting Credit to High Return Microentrepreneurs: The Results of an Information Intervention”, World Bank Economic

Review, Vol. 25 (3), pp. 456-485. De Mello, L. and E.R. Tiongson (2006), “Income Inequality and Redistributive Government

Spending”, Public Finance Review, Vol. 34 (3), pp. 282-305. De Soto, H. (2000), The Mystery of Capital: Why Capitalism Triumphs in the West and Fails

Everywhere Else, Basic Books. Djankov, S., R. La Porta, F. Lopez-de-Silanes and A. Shleifer (2002), “The Regulation of Entry”,

Quarterly Journal of Economics, Vol. CXVII, pp. 1-37. Dollar, D. and A. Kraay (2002), “Growth is Good for the Poor”, Journal of Economic Growth, Vol. 7

(3), pp. 195-225. Dolton, P. and G. Makepeace (2004), “Computer Use and Earnings in Britain”, Economic Journal,

Vol. 114, pp. 117-129. Doms, M., T. Dunne and K. Troske (1997), “Workers, Wages, and Technology”, Quarterly Journal of

Economics, Vol. 112 (1), pp. 253-290. Duflo, E., M. Kremer and J. Robinson (2003), “Understanding Technology Adoption: Fertilizer in

Western Kenya”, Mimeo, MIT. Dunne, T. and J.A. Schmitz (1995), “Wages, Employment Structure and Employer Size-Wage Premia:

Their Relationship to Advanced-Technology Usage at US Manufacturing Establishments”, Economica, Vol. 62 (1), pp. 89-107.

Eapen, A. (2012), “Social Structure and Technology Spillovers from Foreign to Domestic Firms”, Journal of International Business Studies, Vol. 43, pp. 244-263.

Easterly, W. (2007), “Inequality Does Cause Underdevelopment: Insights from a New Instrument”, Journal of Development Economics, Vol. 84 (2), pp. 755-776.

Easterly, W. and A. Reshef (2009), “The Power of Exports”, Mimeo. Echeverri-Carroll, E. and S.G. Ayala (2009), “Wage Differentials and the Spatial Concentration of

High-Technology Industries”, Papers in Region Science, Vol. 88 (3), pp. 623-641. Ellison, G. and E. Glaeser (1997), “Geographic Concentration in US Manufacturing Industries: A

Dartboard Approach”, Journal of Political Economy, Vol. 105 (5), pp. 889-927. Engel, J.S. and I. Del-Palacio (2011), “Global Clusters of Innovation: The Case of Israel and Silicon

Valley”, California Management Review, Vol. 53 (2), pp 27-49. Faggio, G., Salvanes, K.G., & Van Reenen, J. (2010), “The Evolution of Inequality in Productivity

and Wages: Panel Data Evidence”, Industrial and Corporate Change, Vol. 19 (6), pp. 1919-1951.

Feenstra, R.C. and G.H. Hanson (1995), “Foreign Investment, Outsourcing and Relative Wages”, NBER Working Paper No. 5121.

Feenstra, R. and G. Hansen (2001), “Global Production Sharing and Rising Inequality: A Survey of Trade and Wages”, NBER Working Paper No. 8372.

27

Felipe, J., U. Kumar and A. Abdon (2010), “Exports, Capabilities, and Industrial Policy in India”, Levy Economics Institute of Bard College Working Paper No. 638.

Fields, G.S. and G. Yoo (2000), “Falling Labor Income Inequality in Korea’ Economic Growth: Patterns and Underlying Causes”, Review of Income and Wealth, Vol. 46 (2), pp. 139-159.

Forbes, K.J. (2000), “A Reassessment of the Relationship Between Inequality and Growth”, American Economic Review, Vol. 90 (4), 869-887.

Freeman, C. (2002), “Continental, National and Sub-National Innovation Systems – Complementarity and Economic Growth”, Research Policy, Vol. 31 (2), pp. 191-211.

Freeman, C. (2011), “Technology, Inequality and Economic Growth”, Innovation and Development, Vol. 1 (1), pp. 11-24.

Gallagher, K.P. and L. Zarsky (2007), The Enclave Economy, The MIT Press. Galor, O. (2011), “Inequality, Human Capital Formation and the Process of Development”, NBER

Working Paper No. 17058.

Galor, O. and O. Moav (2000), “Ability-Biased Technological Transition, Wage Inequality and Economic Growth”, Quarterly Journal of Economics, Vol. 115, pp. 469-496.

Galor, O. and O. Moav (2004), “From Physical to Human Capital Accumulation: Inequality and the Process of Development”, Review of Economic Studies, Vol. 71.

Galor, O. and D. Tsiddon (1997), “Technological Progress, Mobility, and Economic Growth”, American Economic Review, Vol. 87, No. 3, pp. 363-382.

Georgiadis, A. and A. Manning (2007), “Spend It Like Beckham? Inequality and Redistribution in the UK, 1983-2004”, CEP Discussion Paper No. 16.

Glaeser, E.L., M.G. Resseger and K. Tobio (2008), “Urban Inequality ”, NBER Working Paper No.

14419.

Goedhuys, M. (2007), “Learning, Product Innovation, and Firm Heterogeneity in Developing Countries: Evidence from Tanzania”, Industrial and Corporate Change, Vol. 16 (2), pp. 269-292.

Goedhuys, M., N. Janz and P. Mohnen (2008), “Knowledge-based Productivity in “low-tech” Industries: Evidence from Firms in Developing Countries”, Mimeo.

Goldberg, P.K. and N. Pavcnik (2005) “Trade, Inequality and Poverty”, Mimeo. Gordon, R.J. (2000), “Does the ‘New Economy’ Measure up to the Great Inventions of the Past?”,

Journal of Economic Perspectives, Vol. 14 (4), pp. 49-74. Gordon, R.J. and I. Dew-Becker (2007), “Selected Issues in the Rise of Income Inequality”, Brookings

Papers on Economic Activity, Vol. 2007 (2), pp. 169-190. Green, R., J. Burgess and G. Turner (2004), “The ICT Sector, Growth and Productivity: Ireland and

Australia Compared”, The Economics and Labour Relations Review, Vol. 15 (1), pp. 99-127. Griffith, R., S. Redding and J. Van Reenen (2004), “Mapping the Two Faces of R&D: Productivity

Growth in a Panel of OECD Industries”, Review of Economics and Statistics, Vol. 86 (4), pp. 883-895.

Guadalupe, M. (2005), “Product Market Competition, Returns to Skill and Wage Inequality”, Journal

of Labour Economics, Vol. 25, pp. 439-474. Guiliani, E., C. Pietrobelli and R. Rabellotti (2004), “Upgrading in Global Value Chains: Lessons

from Latin American Clusters”, World Development, Vol. 33 (4), pp. 549-573. Hanson, 2012 Hall, A. (2005), “Capacity Development for Agricultural Biotechnology in Developing Countries: An

Innovation Systems View of What It Is and How to Develop It”, Journal of International

Development, Vol. 17 (5), pp. 611-630. Harding, T. and J. Rattsø (2007), “Looking Abroad to Understand Productivity Growth: The World

Technology Frontier and Industrial Sector Productivity in South Africa”, Mimeo. Harris, R. and J. Moffat (2011), “R&D, innovation and exporting”, SERC Discussion Papers,

SERCDP0073, Spatial Economics Research Centre (SERC), London School of Economics and Political Sciences, London, UK.

Harrison, A. and A. Rodríguez-Clare (2009), “Trade, Foreign Investment and Industrial Policy for Developing Countries”, Mimeo.

28

Haskel, J., R.Z. Lawrence, E.E. Leamer and M.J. Slaughter (2012), “Globailization and U.S. Wages: Modifying Classic Theory to Explain Recent Facts”, Journal of Economic Perspectives, Vol. 26 (2), pp. 119-140.

Haskel, J. and M.J. Slaughter (2001), “Trade, Technology and UK Wage Inequality ”, Economic

Journal, Vol. 111 (468), pp. 163-187. Heathcote, J., K. Storesletten and G.L. Violante (2010), “The Macroeconomic Implications of Rising

Wage Inequality in the United States”, Journal of Political Economy, Vol. 118 (1), pp. 681-722.

Helpman, E. and M. Trajtenberg (1998), “A Time to Sow and a Time to Reap: Growth Based on General Purpose Technologies”, In E. Helpman (ed.), General Purpose Technologies and

Economic Growth, Cambridge, MA: MIT Press. Helpman et al. (2008) Hobday, M. (2005), “Firm-level Innovation Models: Perspectives on Research in Developed and

Developing Countries”, Technology Analysis & Strategic Management, Vol 17 (2), pp. 121-146.

Hsieh, C-T. and P.J. Klenow (2009), “Misallocation and Manufacturing TFP in China and India”, Quarterly Journal of Economics, Vol. 124 (4), pp. 1403-1448.

Hsieh, C-T. and R. Ossa (2012), “A Global View of Productivity Growth in China”, Mimeo. IDRC (2011), “Innovation for Inclusive Development”, Mimeo, International Development Research

Centre. IMF (2007), “Globalization and Inequality”, IMF World Economic Outlook 2007, Washington, D.C. Jacobs, B. and R. Nahuis (2002), “A General Purpose Technology Explains the Solow Paradox and

Wage Inequality”, Economics Letters, Vol. 74, pp. 243-250. Jensen, R. (2007), “The Digital Provide: Information (Technology), Market Performance, and Welfare

in the South Indian Fisheries Sector”, Quarterly Journal of Economics, Vol. CXXII (3), pp. 879-924.

Jorgenson, D.W. and K.S. Stiroh (1999), “Information Technology and Growth”, American Economic

Review, Vol. 89 (2), pp. 109-115. Jorgenson, D.W. and K. Vu (2005), “Information Technology and the World Economy”, Scandinavian

Journal of Economics, Vol. 107 (4), pp. 631-650. Kan, B. and M. Yun (2008), “Changes in Korean Wage Inequality, 1980−2005”, IZA Discussion

Paper No. 3780.

Kanbur, R. (2010), “Globalization, Growth, and Distribution: Framing the Questions”, in R. Kanbur and M. Spence (eds.), Equity and Growth in a Globalizing World, Commission on Growth and Development, The World Bank.

Kattel, R. and Kalvet (2006), “Knowledge-Based Economy and ICT-related Education”, Mimeo. Kattel, R. and A. Primi (2010), “Innovation and Innovation Policy in the Periphery: Latin America and

Central and Eastern Europe Compared”, Mimeo.

Katz, L.F. and K.M. Murphy (1992), “Changes in Relative Wages, 1963-1987: Supply and Demand Factors”, Quarterly Journal of Economics, Vol. 107 (1), pp. 35-78.

Keefer, P. and S. Knack (2002), “Polarization, Politics and Property Rights: Links Between Inequality and Growth”, Public Choice, Vol. 111, pp. 127-154.

Keller, W. (2004), “International Technology Diffusion”, Journal of Economic Literature, (xxx), pp. 752-782.

Kharas, H. (2010), “The Emerging Middle Class in Developing Countries”, OECD Working Paper

No. 285. Kim, L. (1998), “Technological Policies and Strategies for Developing Countries: Lessons from the

Korean Experience”, Technology Analysis & Strategic Management, Vol. 10 (3), pp. 311-324. Klevorick, A.K., R.C. Levin, R.R. Nelson and S.G. Winter (1995), “On the Sources and Significance

of Interindustry Differences in Technological Opportunities”, Research Policy, Vol. 24, pp. 185-205.

Kochar, K., U. Kumar, R. Rajan, A. Subramanian and I. Tokatlidis (2006), “India’s Pattern of Development: What Happened, What Follows”, NBER Working Paper No. 12023.

Kuijs, L. and T. Wang (2005), “China’s Pattern of Growth: Moving to Sustainability and Reducing Inequality”, World Bank Policy Research Working Paper 3767.

29

Kuznets, S. (1955). “Economic Growth and Income Inequality”, American Economic Review, Vol. 45(1), pp. 1-28.

Kuznets, S. (1963). “Quantitative Aspects of the Economic Growth of Nations”, Economic

Development and Cultural Change, Vol. 11(2), pp. 1-80. Lach, S., G. Shiff and M. Trajtenberg (2008), “Together but Apart: ICT and Productivity Growth in

Israel”, The Foerder Institute for Economic Research and The Sackler Institute of Economic Studies, Mimeo.

Leach, M. and L. Waldman (2009), “Centres of Excellence? Questions of Capacity for Innovation, Sustainability, Development”, STEPS Working Paper 23, Brighton: STEPS Centre.

Lee, N. (2010), “Are Innovative Regions More Unequal? Innovation and Regional Inequality in European Regions”, Mimeo.

Li, H. and H. Zou (1998), “Income inequality is Not Harmful to Growth: Theory and Evidence”, Review of Development Economics, Vol. 2 (3), pp. 318-334.

Lemieux, T. (2008), “The Changing Nature of Wage Inequality”, Journal of Population Economics, Vol. 21 (1), pp. 1432-1475.

Levy, F. and P. Temin, (2007), “Inequality and Institutions in the 20th Century America”, Mimeo, MIT.

Lin, J. and H.J. Chang (2009), “Should Industrial Policy in Developing Countries Conform or Comparative Advantage or Defy it? A Debate Between Justin Lin and Ha-Joon Chang”, Development Policy Review, Vol. 27(5), pp. 483-502.

Lindbeck, A. and D.J. Snower (1996), “Reorganization of Firms and Labor Market Inequalities”, American Economic Review, Vol. 86 (2), 315-321.

Lundberg, M. and L. Squire (2003), “The Simultaneous Evolution of Growth and Inequality”, Economic Journal, Vol. 113 (487), pp. 326-344.

Lundvall, B-A., B. Johnson, E.S. Andersen and B. Dalum (2002), “National Systems of Production, Innovation and Competence Building”, Research Policy, 31(2), 213-231.

Luzio, E, and S Greenstein (1995), “Measuring the Performance of a Protected Infant Industry: The Case of Brazilian Microcomputers”, Review of Economics and Statistics, Vol. 77 (4), pp. 622-633.

Maier, G. and M. Trippl (2012), “The Pitfalls and Booby Traps of Cluster Policy”, SRE - Discussion

Papers, 2012/01. WU Vienna University of Economics and Business, Vienna. Majcen, B., S. Radosevic and M. Rojec (2009), “Nature and Determinants of Productivity Growth of

Foreign Subsidiaries in Central and East European Countries”, Economic Systems, Vol. 33, pp. 168-184.

Manning, A. (2004), “We Can Work It Out: The Impact of Technological Change on the Demand for Low-Skilled Workers”, Scottish Journal of Political Economy, Vol. 51 (5), pp. 581-608.

Matsuyama, K. (2007), “Credit Traps and Credit Cycles”, American Economic Review, Vol. 97 (1), 503-516.

Melitz, M.J. (2003), “The Impact of Trade on Aggregate Industry Productivity and Intra-Industry Reallocations”, Econometrica, Vol. 71 (6), 1695-1725.

McKinsey Global Institute (2001), “India: The Growth Imperative”, McKinsey Global Institute. Meschi, E. and M. Vivarelli (2009), “Trade and Income Inequality in Developing Countries”, World

Development, Vol. 37(2), pp. 287-302. Metcalfe, S. and R. Ramlogan (2008), “Innovation Systems and the Competitive Process in

Developing Economies”, Quarterly Review of Economics and Finance, Vol. 48, 433-466. Milanovic, B. (2012), “Global Inequality Recalculated and Updated: The Effect of New PPP Estimates

on Global Inequality and 2005 Estimates”, Journal of Economic Inequality, Vol. 10 (1), pp. 1-18.

Mohnen, P. and L-H. Röller (2005), “Complementarities in Innovation Policy”, European Economic

Review, Vol. 49, pp. 1431-1450. Morrison, A., C. Pietrobelli and R. Rabellotti (2008), “Global Value Chains and Technological

Capabilities: A Framework to Study Learning and Innovation in Developing Countries”, Oxford Development Studies, Vol. 36 (1), 39-58.

30

Morrison, A., D. Raju and N. Sinha (2010), “Gender Equality, Poverty Reduction, and Growth: A Copernican Quest”, in R. Kanbur and M. Spence (eds.), Equity and Growth in a Globalizing

World, Commission on Growth and Development, The World Bank. Oliner, S.D. and D.E. Sichel (2000), “The Resurgence of Growth in the late 1990s: Is Information

Technology the Story? ”, Journal of Economic Perspectives, Vol. 14 (4), pp. 3-22. Ortiz, I and M. Cummins (2011), “Global Inequality: Beyond the Bottom Billion - A Rapid Review of

Income Distribution in 141 Countries ”, UN Working papers 1102, UNICEF, Division of Policy and Practice.

Oxfam (2012), “Left Behind by the G20? How Inequality and Environmental Degradation Threaten to Exclude Poor People from the Benefits of Economic Growth”, Oxfam Briefing Paper No. 157.

Palma, J.G. (2005), “The Seven Main Stylized Facts of the Mexican Economy Since Trade Liberalization and NAFTA”, Industrial and Corporate Change, Vol. 14 (6), pp. 941-991.

Papaioannou, S.K. and S.P. Dimelis (2007), “Information Technology as a Factor of Economic Development: Evidence from Developed and Developing Countries”, Economics of

Innovation and New Technology, Vol. 16 (3), 179-194. Persson, T. and G. Tabellini (1994), “Is Inequality Harmful for Growth? ”, American Economic

Review , Vol. 84, pp. 600-621. Perotti, R. (1996), “Growth, Income Distribution and Democracy”, Journal of Economic Growth, Vol.

1 (2), pp. 149-187. Radosevic, S. (2008), “A Review of a Literature on Innovation, with a Focus on Policy and

Institutional Implications”, Mimeo, University College London. Radosevic, S. (2009), “Between Vision and Reality: Promoting Innovation Through Technoparks in

an Emerging Economy”, Technovation, Vol. 29 (10), pp. 645-656. Ravallion, M. (2001), “Growth, Inequality and Poverty: Looking Beyond Averages”, World

Development, Vol. 29 (11), pp. 1803-1815. Ravallion, M. (2010), “Do Poorer Countries Have Less Capacity for Redistribution?”, Journal of

Globalization and Development, Vol. 1 (2), pp. 1-20. Rodrik, D. (1995), “Getting Interventions Right: How South Korea and Taiwan Grew Rich, Economic

Policy”, Vol. 20 (1), pp. 55-107. Rogers, E.M. (1995), “Diffusion of Innovations”, Free Press. Restuccia, D. and R. Rogerson (2008), “Policy Distortions and Aggregate Productivity with

Heterogeneous Plants,” Review of Economic Dynamics, Vol. 11, pp. 707-720. Roodman, D. (2009), “A Note on the Theme of Too Many Instruments”, Oxford Bulletin of

Economics and Statistics, Vol. 71 (1), pp. 135-158. Roud, V. (2007), “Firm-level Research on Innovation and Productivity: Russian Experience”, Mimeo. Smith, D.A. (1997), “Technology, Commodity Chains and Global Inequality: South Korea in the

1990s”, Review of International Political Economy, Vol. 4 (4), pp. 734-762. Snower, D.J. (1999), “Causes of Changing Earnings Inequality”, Mimeo.

Song, Z., K. Storesletten and F. Zilibotti (2011), “Growing Like China”, American Economics

Review, Vol. 101 (1), 196-233. Srholec, M. (2011), “A Multilevel Analysis of Innovation in Developing Countries”, Industrial and

Corporate Change, Vol. 20 (6), pp. 1539-1569. Srinivas, S. and J. Sutz (2008), “Developing Countries and Innovation: Searching for a New

Analytical Approach”, Technology in Society, Vol. 30 (1), pp. 129-140. Suedekum, J. (2006), “Human Capital Externalities and the Growth of High and Low Skilled Jobs”,

IZA Discussion Paper No. 1969. Sutz, J. and R. Arocena (2006), “Integrating Innovation Policies with Social Policies: A Strategy to

Embed Science and Technology into Development Processes”, International Development

Research Centre, Canada. Tödtling, F. and M. Trippl (2005), “One Size Fits All? Towards a Differentiated Regional Innovation

Policy Approach”, Research Policy, Vol. 34 (8), pp 1203-1219. Townsend, R. and K. Ueda (2006), “Financial Deepening, Inequality, and Growth: A Model-Based

Quantitative Evaluation”, Review of Economic Studies, Vol. 73 (1), pp. 251-280. Trajtenberg, M. (2009), “Innovation Policy for Development: An Overview”, in Foray (ed.), The New

Economics of Technology Policy, Edward Elgar.

31

Trippl, M. and G. Maier (2010), “Knowledge Spillover Agents and Regional Development”, Papers in

Regional Science, Vol. 89 (2), pp. 229-233. Ungor, M. (2012), “Productivity Growth and Labor Reallocation: Latin America vs. East Asia”,

Mimeo. Verhoogen, E. (2008), “Trade, Quality Upgrading, and Wage Inequality in the Mexican

Manufacturing Sector”, Quarterly Journal of Economics, Vol. 123 (2), 489-530. Weiss, M. (2001), “Productive Cities and Metropolitan Economic Strategy”, Mimeo.

Weiss, M. (2007), “Blueprint for National Prosperity Project, Brooking Institution Metropolitan Policy Program”, Mimeo.

Wheeler, C. (2005), “Cities, Skills, and Inequality”, Growth and Change, Vol. 36 (3), pp. 329-353. Weinhold, D. and U. Nair-Reichert (2009), “Innovation, Inequality and Intellectual Property Rights”,

World Development, Vol. 37 (5), pp. 889-901. Woodward, R., S. Radosevic and D.E. Yoruk (2011), “Knowledge Based Firms from Central and

Eastern European Countries: A Comparative Overview of Case Studies”, CASE Network

Studies and Analysis No. 428.

Zajac, Š. and Baláž, V. (2007), “Dual Economy and Impacts of Foreign Investment on Private R&D in Slovakia”, Ekonomický Casopis, Vol. 55 (9), pp. 851-872.

Zhu, S.C. and D. Trefler (2005), “Trade and Inequality in Developing Countries: A General Equilibrium Analysis”, Journal of International Economics, Vol. 65 (1), pp. 21-48.

32

Appendix 1. List of countries growth experience and changes in inequality (incomplete)

Country Growth-inequality nexus Innovation based-growth policy

Argentina This middle-income country experienced high growth since 2000 followed by a reduction in income inequalities.

Much of the growth was due to a shift of production structures from raw materials to resource processing activities (Kattel & Primi, 2010). Innovation policy was based on restructuring the science and technology instutions by increasing linkages across different players (Cimoli et al., 2005).

Brazil

This now middle-income country grew at an average annual rate of 5.6 percent between 1965 and 1980 while inequality was very high and stagnant. After reaching the status of middle-income country in the 1980s, inequality has steadily declined. Inequality remains relatively high as most social services and public education efforts are insufficient to break the divide (Since 2000, the Gini coefficient has fallen from above 0.60 to below 0.55).

From the 1970s and 1990s, a rudiment welfare state promoted local and sectoral innovation systems to integrate research institutions and universities with the private sector. Albuquerque (2007) and Mazzoleni (2003) gives several examples including the aerospace (Embraer), agribusiness (Embrapa), petrochemicals (Petrobra) and the steel sector (Escola de Minas). However, the barely non-existent middle-class depressed consumption rates to spur a wider transformation of an innovation and knowledge-based economy (Khasar, 2010) as the economy continues to rely on exports of commodities. The country promoted linkages and gained access to key actors in biotech to benefits from its local biodiversity resources (Sutz & Arocena, 2006) which fostered export growth. See also Luzio and Greenstein (1995) on the role of infant industry protection in science capacity.

Chile Income inequality increased. Currently income inequality is very high with an estimated Gini coefficient of 0.50.

Much of the growth was due to a shift of production structures from raw materials to resource processing activities (Kattel & Primi, 2010).

China This low- to middle income country has grown over the past 30 years with average annual growth above 8 percent. Since the 1980s the Gini coefficient has increased sharply from under 0.30 to above 0.45 (Khasar, 2010) as the wage gap between skilled and unskilled worker has widened as the export sector expanded (Birdsall, 2010). See also Hsieh and Ossa (2012) and Song et al. (2011) for excellent overview of China’s growth experience

Due to massive reallocations of labor from rural, low productivity jobs to higher end manufacturing employment (Hsieh & Klenow, 2009), many farmers have sought non-farm employment in manufacturing where massive innovation efforts have taken place to upgrade productivity and improve hard infrastructure in less developed provinces (Benjamin et al., 2005). The differences in schooling and educational attainment are the most important explanation for income inequality. Huge investments in manufacturing have resulted in a fall of the wage share to GDP from two-thirds in 1980 to just about half today. This is surprising given that China also expanded education efforts and raised human capital. To increase the power of the middle-class the country needs to raise the share of consumption in GDP (Khasar, 2010, see e.g. South Korea vs. Brazil experience) or introduce financial sector reforms to increase employment and subsequently raise the share of household income. Between 2000 and 2006 wage differentials between skilled and unskilled workers increased mostly due trade liberalization while increased market competition dampened the rise of the wage gap (Anwar & Sun, 2012). Since 1995, income inequality has sharply risen within-villages as absolute income at the bottom end of the income distribution has even fallen in absolute terms (Benjamin et al., 2005) but recently regional income dispersion is declining. China also set local content requirements to promote linkages with foreign firms (Harrison & Rodríguez-Clare, 2009)

33

India Despite high growth, income inequality between skilled and unskilled worker increased (Birdsall, 2010). Also, massive amounts of red tape magnified the firm productivity difference within sectors as unproductive firms were protected (e.g. Aghion et al., 2008; Datt & Ravallion, 2011; Kochar et al., 2006).

In the 1960s huge experimental innovative schemes in grain and other agricultural production (crop differentiations, introduction of new high-yield seeds and fertilizers and adoption of new irrigation techniques) were initiated under the header of the Green Revolution, which massively raised agricultural productivity, which by far employs most people in India. Exports are lagging relative to China which may explain why most manufacturing firms have relatively low productivity (Felipe et al., 2010). Felipe et al. (2010) argue that labor-intensive manufacturing is lagging behind.

Mexico For this (high) middle-income, with relative good growth performance over the past decades, income inequality is still rampant. The Gini coefficient is 0.48 but declining, suggesting a recent reduction in inequality.

Trade liberalization increased income inequality as the local economy had insufficient absorptive capacity while foreign-owned firms created a labor market for the inclusive few skilled workers (Palma, 2005). Much of the economy was reoriented in so-called maquila types of industries with labor-intensive production (Kattel & Primi, 2010). Innovation policy relied on decentralization that had to follow the different technological and specialization patterns in different regions where the focus since the 1990s shifted from basic research and knowledge generation towards private sector demands for commercialization of innovations and technological service provision (Kattel & Primi, 2010). Thus emphasis lay in relative high-tech islands of excellence to supply foreign markets (mainly the United States).

Russia Between 2006 and 2011 inequality rose sharply (Gini coefficient went up from 0.41 to 0.47)

The business environment is not conductive to innovative activities and most firms rely on acquisition of foreign technologies instead of R&D and innovation development at home (Roud, 2007)

South Korea* While reaching the status as middle-income country after a period of high growth (average annual growth rate of 6.5 percent between 1965 and 1986) income inequality was low and unchanged. In the late 1980s with political regime change, growth continued with average annual growth of 5.3 percent while inequality was kept low, but recently rising a bit (see Box 2)

South Korea’s evenly- distributed growth produced a sizeable middle-class. A focus on general education to allow for technology sourcing and a gradual expansion of a high-tech export sector. The country capitalized on the demand from this large middle class to grow its services industries and create the building blocks for a knowledge economy (see Box 2).

Turkey From the mid-1980s to the late 2000s income inequality decreased (Gini coefficient is now 0.41)

According to the IRDC (2011), R&D spending increased 600 pecent over the past 10 years. See Ferreira & Gignoux (2010) who analyze school enrollment patterns in Turkey and highlight the importance of continued school attendance to increase job opportunities than can lower inequality across various groups in society in terms of gender, family background and regions.

34

United States This high-income country grew at relatively high rates since the 1980s while differences in income inequality rapidly expanded. Between 1960 and 2000, real wage income in the United States remained basically unchanged, and only the top 10 percent of the income distribution experienced sharp wage increases.

The bad balance between growth and inequality can be largely attributed to increased reliance on imported goods (Gordon & Dew-Becker, 2007; Zhu & Trefler, 2005), deunionization (Acemoglu et al., 2001; Card & DiNardo, 2002) and subsequently falling real minimum wages, the rise of immigration (Borjas, 2006) and potentially, the erosion of progressive taxation (Levy & Temin, 2007). For a recent overview, see Haskel et al. (2012) who argue that the role of trade structure explaining rising inequality is becoming more important.

Uruguay Local innovation efforts to speed up economic development were stimulated in the 1990s through “R&D projects to take care of social emergencies” (Sutz & Arocena, 2006, p. 37). Several positive outcomes were the result of long-term investments in education and public policies that channel innovation circuits through downstream linkages and fostering interrelationships among different actors (Lundvall et al., 2002). Some examples of relatively small scale social innovation projects include, (i) increased joint efforts of veterinarian laboratories and universities to combat local cattle diseases, (ii) cooperation between universities and technology firms to build special-purpose machines to automate the washing process of wool – a key export commodity, (iii) cheap anti-frost tools development by local researchers to protect fruit harvests from sudden frosts that frequently occur in the region, (iv) in a joint project women from rural areas worked with engineers in fluid mechanics to achieve better energy efficiency for cooking herb based on local demands, and (v) support for a local firm producing cheap electronic peacemakers by granting “free” pacemakers to its citizen in need though a special purpose fund to procure technology

Note: Information on income inequality in terms of Gini coefficients are taken from Ortiz and Cummins (2011).

35

Table 1: Pros and cons of innovation-based growth-oriented policies for inequality (incomplete)

Pros (growth reduces inequality) Cons (growth increases inequality)

Economic development increases the pie and lifts all boats, thus increasing income, mostly leaving inequality unaffected (Barro, 2000)

Innovation-based growth raises the demand for skilled labor, thus increasing the wage gap between skilled and unskilled labor as the supply of workers for the innovative sector falls short (Snower, 1999)

Growth raises the incentives for the poor to revolt, thus increasing the likelihood of democratization and more redistributions which lower income inequality (Acemoglu & Robinson, 2002)

Innovation-based growth increases income inequality as investments in capital and labor saving technologies lift productivity to which agricultural productivity cannot keep up with. The demand for more capital and knowledge intensive work increases, and so does the wage gaps between traditional and innovative sectors, between urban and rural areas, and between skilled and unskilled workers (Albuquerque, 2007).

Banerjee & Duflo Galor (2011) Innovation-based growth increases income inequality

as development in science, technology and innovation may not directly benefit the poor and unskilled workers, or can even be harmful to low-tech enterprises as these groups lack absorptive capacities in the early growth stage to introductce new technologies (Rogers, 1995; Sutz & Arocena, 2006).

36

Endnotes

i A disclaimer applies here as the project was initiated in July 2012 only. Therefore, the title of the paper may be misleading and overtly optimistic. It is a daunting task to compile a thorough literature review within a few weeks. So, this paper is meant as a first attempt to bring together selected work on innovation and inequality which are relevant for developing countries. Admittedly, not all cited material has been completely read due time constraints so there may be mistakes in interpretation. ii For excellent overviews, see Aghion et al., 1999; Banerjee & Duflo, 2005; Barro, 2000. iii See for various methodological issues, Atkinson & Brandolini, 2009; Banerjee & Duflo, 2003, 2005; Lundberg & Squire, 2003; Ravallion, 2001; Townsend & Ueda, 2006; Weinhold & Nair-Reichert, 2009. iv What is interesting that in the 1955 paper, Kuznets derives this famous inverse-U shaped relation between growth and inequality on six observations of regions in Germany, five observations for the United States and five for the United Kingdom on which he commented: I am acutely conscious of the meagerness of reliable information presented. The paper is

perhaps 5 percent empirical information and 95 percent speculation (1955, p. 26). v For a critique on this development view based on technology upgrading and manufacturing, see Smith (1997). vi The so-called Transatlantic Consensus explains the concomitance of rising income inequalities in the United States (and United Kingdom) and increasing unemployment in Continental Europe by assuming that the demand for skilled-workers rose more quickly than its supply. To this phenomenon, each region responded with its own institutions to tackle this shift. The more market-based Anglo-Saxon approach has let the wage gap grow much further than in the social welfare states of Europe, where of the generated unemployment among unskilled workers was cushioned by safety nets. In similar vein, Acemoglu (2003) argues that innovation is less skill-biased in Europe as labour market institutions suppress the wage gap which induces relatively more investment in technologies that foster productivity growth of unskilled workers (for a critique, see Atkinson, 2001; Snower, 1999). vii Another aspect of the growth-inequality nexus is land ownership. Under an unequal land distribution, landowners hamper human capital formation to reduce labor mobility, thus reducing growth (Galor, 2011). viii However, redistribution policies to reduce inequality and accelerate human capital accumulation are only feasible after a certain level of development is reached. Taxes on the supposedly rich in developing countries often are a burden to the still relatively poor (Ravallion, 2010). ix Ahlin and Jiang (2008) show that lifting financial constraints (either through redistribution policies or other mechanisms like growth and microfinance) can have a mix effect on long run growth. Poor entrepreneurs may not be able graduate to running a full-scale firm (with a modern technology), simply because they have not enough wealth and saving is insufficient. Matsuyama (2007) demonstrates how improved access to finance may in fact increase the adoption of non-frontier technologies by poor entrepreneurs which lowers the productivity of the economy. Thus, redistribution may reduce inequality but also hamper growth. x Recently extensive evidence is emerging which suggests the coexistence of very high and very low rates of return within the same industry (Banerjee & Duflo, 2005; De Mel et al., 2009, 2010; McKenzie & Woodruff xxx) xi Note that the growth trajectories witnessed in the past may be fundamentally different from those of the future, especially because countries that start to industrialize now often specialize in specific activities that are part of global supply chains where the role of services relative to manufacturing has grown (e.g. Chusseau et al., 2008; Ravallion, 2010). xii For a critique on the view that South Korea combined growth-oriented policies with non-rising income inequality, see Ahn (1997) who argues that the income distribution actually worsened over the 1980s due to high inflation, mostly in the housing market xiii A McKinsey Global Institute (2001) report on India examined the main sources of inefficiency in a range of industries in India. In some of these industries (dairy processing, steel, software) better firms were using more or less global best-practice technologies wherever they were economically viable. The latest (or if not the latest, relatively recent) technologies were thus available in India. xiv As Chusseau et al. (2008) explain, sector based SBTC follows from changes in the economies across sector production structure, that is, by between-industry shifts. Further, international outsourcing affects the within-industry factor utilization which can explain skill biases (see Feenstra & Hanson, 1995, 2001; Katz & Murphy, 1992). xv A similar problem of timing for the impact of globalization and trade on inequality is put forward by Snower (1999, p.16) where trade expanded rapidly during the 1970s and leveled off somewhat after that while rising inequality occurred a decade later. xvi Concerning the inequality effects of the ICT introduction Jensen himself (2007, p. 920) argues: “While it was primarily the largest fishermen who adopted mobile phones in the present case, there were significant spillover gains for the smaller fishermen who did not use phones, due to the improved functioning of markets. Thus, rather than simply excluding the poor or less educated, the “digital provide” appears to be shared more widely throughout society”. xvii Bartelsman et al. (2011) show that high employment protection schemes discourage innovative sectors to flourish which could explain inequalities across industries but not inequalities within the same industry. xviii Note that India lifted many of its firm size restrictions after 1997 which were not analyzed by Hsieh and Klenow (2009). xix For the importance of technology transfers for firms from developing countries, Harding and Rattsø (2007) analyze the role of the technology frontier in driving productivity in South-Africa. They argue that most of the growth of productivity depends on international technological spillovers and show that there are wide productivity dispersions across firms. Goedhuys et al. (2008) use firm-level data to analyse the importance of knowledge to explain productivity levels in low-tech sectors in developing countries. They find there exists large across sector heterogeneity concerning the importance of the knowledge-productivity link. xx According to the skill-biased technological change thesis, most gains in income fall on those who work in sector reliant on new technologies, however, Snower (1999) argues that this pattern does not fit the data for the United States. First, younger

37

workers are more likely to quickly adopt new technologies as they will easier adapt to new organizational practices and received the latest educational training, but they earn much lower wages than experienced workers. Second, as Snower (1999, p. 20) puts it: “Some of the biggest gains have gone to investment bankers, corporate executives, and lawyers, who surely make far less use of computers and the new, flexible, programmable machine tools than engineers, programmers, and computer operatives” which seems at odds with the SBTC thesis.