Cobb-Douglas production function

In order to estimate the effect of ICT on the firm’s output we follow Hempell (β005), and make a distinction between ICT capital stock and non-ICT stock. It is hypothesized that ICT capital stock is positively related to the output of the firm. The chapter augments the Cobb-Douglas production function by following Commander et al. (2011) and Hempell (2005), and includes the value of raw material inputs (intermediate goods) used by the firm. Raw material input is defined as the amount of intermediary inputs or other goods that the firm purchases/uses to undertake the production process. The use of raw material input is important in the production process in virtually every sector of an economy. In spite of its importance in the production process, the common total factor productivity measures and estimates

82

Intermediate good is introduced in the output function because the extent to which

capital and labour contribute to firms’ output levels is dependent on the quantity and

price of intermediate goods employed by a firm.

Our output is a function of physical capital stock (decomposed into ICT capital stock and non-ICT capital stock), labour employed, value of raw materials and a set of variables to capture observable firm heterogeneity, and is given as,

(3.1)

Where Yi is output of firm i, Li represents labour input, ICTi and Ki are the corresponding amounts of ICT and conventional (non-ICT) capital25 respectively, while Ai captures the multifactor productivity26 and Mi measures the value of raw materials used by the firm. The subscripts i and c represents firm i and country c, respectively. Taking logs on both sides, equation (3.1) can be rewritten,

(3.2)

Where lowercase letters denote the corresponding logarithmic values and the multifactor productivity is given as,

(3.3)

Here log (Ai) is decomposed into firm specific characteristics denoted by z27 and error term. The firm–effect captures fixed or quasi–fixed factors affecting productivity, such as management style, education attainment of the firm owner, industrial sector of the firm, and age of the firm, also the formality of the firm (formal, informal or semi-formal sector firm). The residual i comprises measurement errors and firm–specific productivity shocks as well as firm

heterogeneity in terms of unobserved firms’ endowments. The parameters and

are the elasticities of output with respect to labour, ICT capital, non-ICT capital and raw material respectively. Dobbelaere and Mairesse (2010), point out that under

25

Non-ICT capital is calculated by the perpetual inventory method from replacement investments

(Black and Lynch, 2001; Hempell, 2002; Zwick, 2003).

26 _{Marschak and Andrews (1944) note that the firm is aware of A}

i when input choices are made, but

this is not observed by the econometrician.

27 _{To avoid the problem of omitted variable bias, firm-specific and employee characteristics are added}

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the assumption of perfect competition these parameters indicate the share of the input in total production. The sum of the parameters is indicative of the return to scale.

Most empirical studies have used the ordinary least squares (OLS) approach to

analyse the relationship between ICT capital and a firm’s output. This approach

expresses the expected value of production output as a function of a set of explanatory variables. Two major challenges are anticipated in our attempt to

estimate the effect of ICT on firms’ output. We anticipate the presence of unobserved

heterogeneity and endogeneity problems. The productivity of firms may be enhanced by the adoption and usage of technology, which in turn increase the profit margins of firms. On the other hand, firms with higher profit margins or higher output levels, and hence higher incomes will find it easier and less expensive to adopt and use technology. Thus, there is the possibility of a reverse causality of adoption of ICT, and productivity of firms. Also it is likely that unobserved firm and employee characteristics, which are captured by the idiosyncratic term, are correlated with some of our explanatory variables. There is also the possibility of measurement errors in non-ICT and ICT capital, which has the potential downward bias effect of these variables on a firm’s output. Furthermore, differences in productivity levels of adopted firms and non-adopted firms could be as a result of unobserved heterogeneity among the firms. The presence of a significant level of firm heterogeneity may restrict the average output effect of ICT adoption, when OLS estimation is used, to efficiently explain the effect of ICT capital stock on firm’s output.

In this regard, OLS estimation of the productivity effect of ICT adoption is bound to lead to inconsistent and biased estimates, which could have adverse implications on policy if we fail to account for the causal effect of technology adoption and unobserved heterogeneity. Most studies examining the impact of ICT and firms productivity have used an instrumental variables (IV) approach to solve the problem of endogeneity, while other studies have resorted to lagging of both ICT and non- ICT capital before employing OLS techniques to ascertain the impact productivity of ICT capital. It is also possible to apply generalized method of moments (GMM) to deal with the endogeneity problems. These methodologies may solve the problem of endogeneity.

84 3.1.1 Instrumental variable estimation

As stated under this section there is the likelihood of a reverse causality of ICT adoption and firm output, also there is a high probability that ICT capital may be correlated with some omitted variables such as managerial and employee skills, as well as other specific firm and industry characteristics. Non-ICT capital is also potentially endogenous in the baseline model, as presented in Table 3.1, as it is possible that it may be correlated with other unobserved firm characteristics, furthermore, there is the possibility of the existence of a reverse causality between non-ICT capital and output. The potential endogeneity of non-ICT capital may also stem from measurement errors. This may cause ICT capital and non-ICT capital to be correlated with the error term, which can result in inconsistent OLS estimates. To deal with the issue of endogeneity, the chapter could employ the instrumental variables approach and estimates the production function using a two-stage least- squares (2SLS) method.

However, it is quite difficult to find suitable instruments in which the structural variables are correlated with the error terms and this case is made even more complicated as we need to find suitable instruments in all the 14 countries’ estimations. The criteria for a good instrument are a high correlation with the endogenous independent variable but not correlated with the error term. Due to the lack of suitable instrument(s), this chapter does not proceed to with the estimation of the two-stage least-squares (2SLS) method technique.

3.2 Meta-regression analysis

Given that the impact of ICT on firm’s turnover may vary considerably across

countries the chapter uses Meta-analysis to obtain an overall estimate for the effect of ICT on firm turnover. A Meta-analysis is usually used to combine results of various studies while controlling for heterogeneity to obtain an average weighted effect size. A detailed discussion on meta-analysis is provided in chapter two of this thesis.