Innovation versus technology imitation development strategy: what influences government decision?

ABSTRACT

This paper investigates the effect of state-business relations (SBR) and institutional settings on government decisions to foster innovation in developing countries. It differentiates between informal SBR-based cronyism and formal SBR-based lobbying and how they could influence a government’s decision to implement policies fostering innovation. After a theoretical discussion on the available literature, a theoretical model building on and complementing the Aghion and Howitt (2009) growth model with institutions is introduced. The model provides predictions on which institutional settings induce the government to support innovation, rather than technology imitation/transfer strategies. Using the random-effects regression model, the empirical results support some of the model’s predictions. This includes the negative effect of cronyism and the positive effect of public frustration from cronyism on choosing the innovation strategy. A positive effect also results from a situation where natural resources-caused economic growth is matched by institutional reform that curbs cronyism and mitigates the resource curse. A short discussion on some case studies follows before the paper ends with a conclusion.

KEYWORDS:

• Innovation
• technology imitation
• state-business relations
• cronyism
• institutions
• governance

Acknowledgments

The author would like to sincerely thank the two anonymous reviewers who have significantly contributed to this work by providing valuable suggestions and feedback during the review process.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 According to Evans (2012, 12) and (1989, 563), predation are actions in which state officials appropriate for themselves a large share of resources, leaving less for their society. Accordingly, their individual gains are being maximized at the expense of societal collective goals.

2 It is to be noted that {U_G(σ = 1)} has a generally flatter curve, while {U_G(σ = 0)} has a steeper, curve. {U_G(σ = 1)} has generally a higher share of cronyism (c) than {U_G(σ = 0)}, while {U_G(σ = 0)} has a higher government budget share (y) than {U_G(σ = 1)}.

3 For estimating the effect of change in cronyism caused by an institutional change (∂C/∂I), the following equation should have been used for calculating the variable’s overall effect (accounting for its interaction term) on R&D: (13) $\mathrm{O}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{f}\left(\mathrm{\partial }C/\mathrm{\partial }I\right)\mathrm{o}\mathrm{n}\mathrm{R}\mathrm{\&}\mathrm{D}={\text{β}}_3+{\text{β}}_9{}^{\ast }\left(\mathrm{M}\mathrm{e}\mathrm{a}\mathrm{n}\mathrm{o}\mathrm{f}\mathrm{\partial }g/\mathrm{\partial }R\right)$(13) β₃ is the coefficient of the effect of ∂C/∂I (without interaction), while β₉ is the coefficient of the interaction term [(∂g/∂R)*(∂C/∂I)]. However, ∂C/∂I is insignificant in both regressions.

4 In order to obtain the results, TSLS regressions were done using R&D Expenditure (% of GDP) as the dependent variable and the listed variables as independent variables, one regression with one variable at a time. The following instruments were used and obtained from Sabry (2013): British Legal Origins, French Legal Origins, Protestant, Catholic, Orthodox, Buddhism, Islam, Transition Economies, and Ethnic Fractionalization. Another used instrument is Judicial Independence obtained from the GCI. Inconsistency of the OLS estimators gives evidence on endogeneity.