Empirical analysis of innovation and productivity in services firms: The case of South Africa

Abstract

The purpose of this paper is to investigate the impact of innovation on productivity in the services sector firms in South Africa. Using a three-stage econometric model we find that service sector firms are innovative. We show that firm size, public financial support, patent protection and market sources of information have a positive impact on the decision to innovate. The level of investment in innovation significantly influences the success of both technological and non-technological innovations in service sector firms. The results find that both technological and non-technological innovations have a positive impact on labour productivity, with a greater impact from non-technological innovation.

Keywords:

• productivity
• innovation
• services
• innovation survey

JEL Classification:

• O12
• O14
• O31
• O33
• O40

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The services sector aggregate is comprised of the following sub-sectors or industries: trade, catering and accommodation services; wholesale and retail trade; transport and storage; communication, finance and insurance; business services; general government services; and other community, social, and personal services (CSP).

2 The First stage is the input stage which constitutes the innovation inputs such as R&D expenditure, people involved in innovation; the second stage of innovation is the throughput stage in which innovation facilitates the operations of the firm through partner co-operation; third stage is the output stage which encompasses the output of the firm as a result of innovation inputs, i.e., productivity.

3 The selection bias arises because in each time period only of handful of firms report positive investment in innovation activities. Deleting firms with zero activity will bias the sample. It is important to note that the elimination of this bias becomes even more important when applying the model to the services sector, for services are characterized by the informal and ad hoc nature of their innovation activities.

4 Innovation indicators from innovation surveys are noisy and hence contain measurement errors. Thus, factors that are not observed and that affect the probability of innovation may lead companies to invest more in innovation activities. Furthermore, other unobservable factors which explain productivity may also affect the choice of inputs implying correlation between the error in the productivity equation and explanatory variables.

5 Since the CIS data used in this paper does not have data on capital stocks, production costs or sales prices, we use sales per worker as our measure of productivity. Refer to literature review for an in-depth discussion.