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The Role of Liquidity Constraints in Fuelling The Demand-Pulled Innovation

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Abstract (2. Language): 
The motivation to classify industries in their effort to innovate with the structure of demand, lead to a theoretical controversy in innovative activity known as ‘demand-pull’ versus ‘technology-push’ forces of technical change. Previous empirical literature has provided evidence supporting demandpulled innovation both at the aggregate level and at the firm level. This paper studies a dynamic specification of the demand-pull hypothesis at the firm level, which takes into account both the within and the between effects across Turkish non-financial firms listed at Istanbul Stock Exchange (ISE) over a period of ten years (1998–2007). Moreover, the study also investigates the demand-innovation relationship in liquidity constrained firms since inducing an increase in the effort to innovate mostly depends on the funding of expensive and uncertain R&D activities. Our findings confirm the demand-pull hypothesis, yet the role of sales in inducing R&D expenditures is 99% significant in the overall sample. More specifically, liquidity constrained firms and firms not receiving public subsidies seem to be particularly sensitive to sales when deciding how much to spend on R&D.
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REFERENCES

References: 

Arellano, M. and Bond, S. (1991): “Some tests of specification for
panel data: Monte Carlo evidence and an application to employment
equations”, Review of Economic Studies, 58: 277–297.
Arellano, M. and Bover, O. (1995): “Another look at the instrumental
variables estimation of error components models”, Journal of
Econometrics, 68: 29–51.
Blundell, R. and Bond, S. (1998): “Initial conditions and moment
restrictions in dynamic panel data models”, Journal of Econometrics,
78(1): 115–143.
Bruno, G. (2005a): “Approximating the bias of the LSDV estimator
for dynamic unbalanced panel data models”, Economics Letters, 87:
361–366.
Bruno, G. (2005b): “Estimation and inference in dynamic unbalanced
panel data models with a small number of individuals”, The Stata
Journal, 5(4): 473–500.
Brouwer, E. and Kleinknecht, A. (1999): “Keynes-plus? Effective demand
and changes in firm-level R&D: an empirical note”, Cambridge Journal
of Economics, 23: 385–391.
Bun, G. and Kiviet, J. F. (2003): “On the diminishing returns of higher
order terms in asymptotic expansions of bias”, Economics Letters, 79:
145–152.
Cainelli, G., Evangelista, R. and Savona, M. (2006): “Innovation and
economic performance in services: a firm-level analysis”, Cambridge
Journal of Economics, 30: 435–458.
Cohen, W. M. and Levin, R. C. (1989): “Empirical studies on innovation
and market structure”, In Schmalensee, R. and Willig, R.D. (eds.),
Handbook of Industrial Organization (Vol.2, pp. 1060–1107).
Amsterdam: North-Holland.
Crepon, B., Duguet, E. and Mairesse, J. (1998): “Research, Innovation,
and Productivity: an Econometric Analysis at the Firm Level”, NBER
Working Paper Series, No. 6696.
David, P. A., Hall, B. H. and Toole, A. A. (2000): “Is public R&D a
complement or substitute for private R&D? A review of econometric
evidence”, Research Policy, 29: 497–529.
Geroski, P. and Walters, C. F. (1995): “Innovative activity over the
business cycle”, Economic Journal, 105: 916–928.
Hall, B., Mairesse, J., Branstetter, L. and Crepon, B. (1999): “Does cash
flow cause investment and R&D? An exploration using panel data for
French, Japanese, and United States scientific firms”, in Audretsch, D.
and Thurik, R. (eds.) Innovation, Industry Evolution and Employment,
Cambridge University Press, Cambridge, pp. 129–156.
Kiviet, J. F. (1995): “On bias, inconsistency, and efficiency of various
estimators in dynamic panel data models”, Journal of Econometrics, 68:
53–78.
REFERENCES
1. Establishments employing fewer than 250 people are classified as
‘‘small and medium-sized enterprise’’ (SME). Large-scale enterprises
(LSE) employ 250 or more people.
2. Sector is defined at the ISIC (revision 2) 2-digit level.
3. For a further discussion on LSDVC estimator and the finite-sample
performance of three bias corrections with different estimators
(Anderson–Hsiao, Arellano–Bond and Blundell–Bond), see Bruno
(2005b).
4. The ‘long-run’ elasticity takes into account the impact of both current
and lagged sales according to the formula
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