ICT and mean efficiency

If firms in Africa are to be competitive in the global market it is imperative for these firms to increase their level of efficiency as this leads to high growth and productivity which from the view point of the structural approach tends to increase competitiveness of firms in the long-run. The potential of increasing technical efficiency levels in the various countries can be ascertained by analysing the technical efficiency scores presented in Table 3.13. The efficiency scores suggest that firms across the continent operate at varying technical efficiency levels with firms in most of the countries operating at very low technical efficiency levels. This confirms our earlier results which show that technical inefficiency is high among SMEs in Africa relative to their potential, given their respective technologies. The results paint a gloomy picture of SMEs across the sampled countries.

Table 3.13 presents estimates of mean technical efficiency of firms across the countries. The results show that with the exception of SMEs in South Africa, SMEs in the remaining countries operate below the 50 percent level of technical efficiency. The low mean technical efficiencies across the countries indicate that firms have the potential to increase turnover by improving technical efficiency levels using their existing resources and technologies in the short run. The low mean technical efficiencies across the countries may be attributed to the low level of ICT capital as a share of total capital.

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Table 3. 12: Mean technical efficiency of firms

Country Log of ICT capital Computer access Internet access

Botswana 0.185 0.169 0.172 Cameroon 0.448 0.461 0.446 Ethiopia 0.316 0.328 0.356 Ghana 0.186 0.218 0.218 Kenya 0.127 0.188 0.186 Mozambique 0.207 0.320 0.240 Namibia 0.391 0.375 0.382 Nigeria 0.218 0.186 0.196 Rwanda 0.219 0.173 0.180 South Africa 0.561 0.839 0.835 Tanzania 0.143 0.194 0.156 Uganda 0.205 0.167 0.190 Zambia 0.126 0.196 0.183 Zimbabwe 0.391 0.331 0.450

Source: Own estimates, obtained from estimation of Translog production for various countries.

Figure 3. 7: Mean technical efficiency computed from Translog production specification

The findings suggest firms located in South Africa are more technically efficient relative to firms in other parts of sub-Sahara Africa. Technical efficiency among South African firms ranges between 56 percent and 83 percent. The results indicate that South African firms experience a short fall in turnover ranging between 0.44 percent and 0.17 percent, implying that these firms are more likely to increase turnover up to 44 percent on average if technical efficiency improves. The high technical efficiency level of firms in South Africa maybe attributed to the high return to ICT capital. Botswana, Kenya Tanzania, Uganda and Zambia in most cases register a mean technical efficiency of less than 25 percent. The results of the mean efficiency from the Translog specification are similar to that of the Cobb-Douglas functional form. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

122 5 Conclusion

This chapter investigates the effect of ICT usage on the turnover of SMEs based on a cross-section dataset cutting across fourteen countries in 2006. The information collected by the survey is comprehensive with regards to a firm’s turnover, raw material usage, the disaggregation of capital into ICT and non-ICT capital, the characteristics of SMEs, the employees used by the SMEs and also the disaggregation of technology. The chapter contributes the empirical literature on ICT adoption among firms by providing cross-country evidence on the effect of ICT on turnover. Studies that examine the effect of ICT on firm are mainly based on a single country, but this chapter provides evidence across 14 countries. The chapter also contribute to the existing literature by accessing the effect on ICT adoption on technical efficiency in developing countries.

The chapter employs both Cobb-Douglas and Translog production functional specifications to examine the effect of ICT capital on turnover of firms in Africa. However, unlike other studies that imposes the production functional form on the data this chapter uses a likelihood ratio test to determine which production functional specification best fits the data. To account for heterogeneity that may arise, the chapter uses quantile regression technique and estimates the distributional effect of ICT capital. This is because ICT capital is likely to have varying effects at different points of turnover distribution. The chapter also uses a stochastic frontier approach to investigate the effect of ICT capital, computers and the Internet access on technical efficiency of SMEs in the selected countries.

The findings of the chapter indicate positive and significant correlations between ICT

capital and SMEs’ turnover across most of the selected countries, with the exception

of Ghana, Nigeria and Zambia. The chapter also finds that in eight of the countries the return to ICT capital is greater than return to non-ICT capital, with South Africa having the highest return to ICT capital. This finding gives an indication of the potential of ICT capital in SSA. The effect of ICT capital on turnover differs across countries and there is a lack of consistency across SSA region. To obtain a summary measure of the effect of ICT on turnover we apply meta-analysis techniques. The finding of the meta-analysis shows that ICT capital has a positive and significant

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Dealing with the possible problem of heterogeneity the chapter estimates a quantile regression and finds that the effect of ICT capital on turnover varies along the distribution. Contrary to the findings of the OLS we find that ICT has a positive and significant relationship with turnover along different points of the distribution in Zambia and Nigeria. Although ICT capital has no effect on turnover at the mean of the distribution, ICT capital impacts positively on SMEs with low turnover in Nigeria. In Zambia, ICT has a significant and positive effect on SMEs with low turnover and those with relatively high turnover. This finding requires the development of ICT policies and strategies that will take into account varying effects of ICT capital on SMEs with different levels of turnover.

The chapter also finds the presence of a substitution effect between ICT and non-ICT capital in Cameroon, Kenya and Zimbabwe. On the contrary, Tanzania shows a complementary effect between ICT capital and non-ICT capital. Overall findings of the positive and significant effect of ICT on firm output are confirmed in most of the sampled countries. As such, there does not appear to be an ICT productivity paradox among SMEs in Africa.

Using two different functional production specifications, we examine effect of ICT adoption on technical efficiency of SMEs in Africa. However, we base the discussion mainly on the Translog functional framework as it is more flexible than the Cobb- Douglas and second, the likelihood ratio test indicates that Translog functional specification best fits the data. The results show that ICT capital has significant and positive effect on technical efficiency in 8 of the countries. We further find that firms with access to computer and Internet are more likely to improve technical efficiency relative to firms with no access to the technologies in Mozambique. However, our findings show that SMEs in all the countries, except South Africa, operate below a technical efficiency level of 50 percent. While this situation looks gloomy, it may also be an indication of potential gains if the right policies are put in place to support SMEs in these countries to be more efficient.

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CHAPTER 4 – VARIATION IN RETURNS TO INFORMATION AND

COMMUNICATION TECHNOLOGY: A DECOMPOSITION ANALYSIS

1 Introduction

In recent times several studies have resorted to the use of firm-level data to examine output and productivity growth among SMEs (hereafter we refer to SMEs as firms). This is largely due to the belief that firm growth translates into industrial growth, which arguably drives economic growth (Trimmer and Van Ark, 2005). The adoption and usage of new technology has been an important contributing factor to increasing output and productivity and by extension higher economic growth rates. The World Bank (2006) indicates that firms with access to ICT grow faster, invest more, and tend to be more productive and profitable than those with no access to ICT. There is a consensus among many economists on the positive impact of ICT adoption and usage on firms’ output and productivity in recent times, at least in developed countries. The previous chapter of this thesis also finds a significant and positive effect of ICT capital on a firm’s turnover in selected SSA countries. However, significant differences exist regarding the contribution of ICT to output and productivity growth across key firm level characteristics, such as firm size, access to computers and Internet, and the managerial control type employed by the firm. For instance, evidence suggests that large firms have higher turnover and also are more likely to adopt new technologies as they have the financial capabilities to install and use these new technologies.

There are also significant differences in turnover of firms in rich and poorer countries and thus difference in their usage and return to ICT adoption. The OECD (2007) asserts that the impact of the Internet on firm output and productivity has a far wider reach than just information and technology industries. The efficiency level of firms with access to computer and internet tend to increase relative to firms with no access to these technologies, as it allows for connectivity and interaction among market participants Avgerou (2003). Furthermore, the use of these technologies creates an easy flow of information, leading to faster and better matching processing resulting in high returns in output and productivity (Grimes et al., 2012; OECD, 2010; Forman and van Zeebroeck, 2010; Bertschek et al., 2011). With higher levels of efficiency,

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firms with computer and internet access are likely to have higher turnover compared to their counterparts with no access to ICT.

The literature (see, for example Caselli, 2005; Hall and Jones, 1999) shows significant differences in output and productivity of firms in relatively high income countries compared to those in LIC. Tybout (2000) and World Bank (2004) examine why firms in LIC tend to have low output and productivity. These studies show that the lack of adequate infrastructure, dominance of the informal sector, poor regulations and slow judicial system, poor trade policies, and a lack of highly skilled human capital together work to inhibit the productivity of firms. This in turn contributes to the low output levels in poorer countries. Bloom et al. (2010) also find evidence to suggest that financial constraints in LIC, especially among smaller firms, hinder productivity and output growth. In this regard, firms in high income countries are expected to have relatively high turnover compared to firms in lower income countries.

Several studies find firm level characteristics have an impact on the performance of firms in both developed and developing countries. Harvie et al. (2010) for instance find that firm characteristics are an important determinant of firm’s participation in production networks and by extension high performance. Shiels et al. (2003) also points out that firm and industry level characteristics affect the adoption and usage of ICT among firms. Most firms in Africa are poorly managed (Rogerson, 2008; Abor and Quartey, 2010; Smit and Watkins, 2012) and in many of these firms the owners tend to make all major decisions even when they lack the expertise, thus there is a lack of delegation of decision making to experts. This practice is largely due to apprehension on the part of owners of expropriation by managers employed to run the daily activities of these firms. However, many of these SME owners lack the time and capacity to make expert decisions hence resulting in low productivity as well as low output growth. Several studies have suggested that firms have increased their productivity levels largely as a result of better management practices. Chandler et al (2009) for instance posits that in the early 1990s the United States and Germany experienced high productivity growth partly due to superior management practices. Thus managerial control type among firms is important in assessing differences in

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This chapter is an extension of the preceding chapter, and aims at analysing variations in turnover across various groups of firms. The chapter focuses on the contribution of ICT capital stock to turnover differentials among the different groups of firms. It examines the possible sources of this turnover gap within countries and across various income groupings (low, low-middle and upper-middle income countries), paying particular attention to ICT capital stock. It also assesses the contribution of the traditional factors of production in each country and across the income groups. In relation to the above discussion, there is a possibility of differences in turnover based on the characteristics of firms in the various countries. Turnover differentials are estimated over the following variables:

 Firm’s computer accessibility (firms with computer access as against

those with no access)

 Internet accessibility of the firm (firms with Internet access as against

those with no access)

 Firm’s managerial control type (firms with a full time manager against

other forms of managerial control types)

 The size of the firm (micro sized firms against small to medium sized

firms)

The contribution of covariates (the factors of production and firm-level characteristics) to differentials in an outcome (turnover) can be investigated by using decomposition analysis. The technique decomposes the overall differential between the outcome of two groups into two broad components – endowment and returns to endowment. It also enables the determination of contribution of each covariate to the two components and the overall differential. Originally used by Oaxaca (1973) and Blinder (1973) to decompose the gender wage gap and the racial wage gap, the technique has been extended to analysis of differences in several outcomes, such wealth, gender cognitive performance, inequality in living standards and more. In recent times the technique has been extended to decompose total factor productivity. Mean decomposition is simple to perform using Oaxaca-Blinder approach. However, differences in turnover at both ends of the distribution make decomposition at the mean an inappropriate representation of the entire distribution.

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The chapter’s contribution to the existing literature on ICT and firm level performance is twofold. To the best of our knowledge this is the first study that attempts to investigate and determine the contributing factors to turnover differentials emphasising the contribution of ICT capital across sub-Saharan Africa. Second, the chapter employs a recent decomposition technique, based on the creation of a counterfactual argument, to determine the contribution of the factors of production at both the mean and at various points along the distribution, focusing on the contribution of ICT capital as an additional input besides the traditional factors of production. Using the quantile decomposition technique we also determine the contribution of ICT capital to the turnover differentials at different points along the distribution. The remainder of the chapter is set out as follows. Section 2 provides a detailed discussion of Fortin et al. (2010) decomposition techniques, and Section 3 presents the results of the decomposition analysis with a discussion of the results. The conclusion and policy implications are provided in Section 4.

2 Empirical methodology

This section outlines the mean and quantile decomposition techniques to be employed in the chapter The Oaxaca Blinder decomposition has been used extensively in the labour market and discrimination literature to study wage differentials and discrimination between different groups of workers and race (Oaxaca, 1973; Blinder, 1973). The method has seen numerous extensions since its introduction which go beyond conducting decompositions at the mean, and one such extension has been the more recent quantile-based decomposition methods. The chapter relies heavily on the approach adopted in Fortin, Lemieux, and Firpo (2011), especially for the quantile decompositions and empirical approach.