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

### Citations

4053 |
Some tests of Specification for panel data: Monte Carlo evidence and an application to employment equations
- Arellano, Bond
- 1991
(Show Context)
Citation Context ...n uncertain and delayed outcome in terms of subsequent innovation, the possible reverse effect is between future successful innovation and future sales (Piva and Vivarelli, 2007: 697). The above model testing the relationship between demand and innovation is defined as a dynamic equation by including the lagged value of the R&D expenditure in order to account for the adjustment and persistence in R&D activities. The inclusion of lagged dependent variable leads to another endogeneity problem that can be solved by firstorder dynamic panel data models that use General Method of Moments (GMM; see Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). However, a weakness of GMM estimators is that their properties hold when N is very large, so they can be severely biased and imprecise in panel data with a small number of cross-sectional units (Bruno, 2005b). Hence, this method is not suitable for small samples like the ones used in our analysis since we select a sub-sample of firms from the original panel to test the demand-pull effect under different firm characteristics. Therefore, we used an alternative method based upon the bias correction of least-squares dummy variable (LSDV) estima... |

2391 | Initial conditions and moment restrictions in dynamic panel data models
- Blundell, Bond
- 1998
(Show Context)
Citation Context ...ent innovation, the possible reverse effect is between future successful innovation and future sales (Piva and Vivarelli, 2007: 697). The above model testing the relationship between demand and innovation is defined as a dynamic equation by including the lagged value of the R&D expenditure in order to account for the adjustment and persistence in R&D activities. The inclusion of lagged dependent variable leads to another endogeneity problem that can be solved by firstorder dynamic panel data models that use General Method of Moments (GMM; see Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). However, a weakness of GMM estimators is that their properties hold when N is very large, so they can be severely biased and imprecise in panel data with a small number of cross-sectional units (Bruno, 2005b). Hence, this method is not suitable for small samples like the ones used in our analysis since we select a sub-sample of firms from the original panel to test the demand-pull effect under different firm characteristics. Therefore, we used an alternative method based upon the bias correction of least-squares dummy variable (LSDV) estimator in dynamic panel data models proposed by Kiviet ... |

2292 | [1942]) Capitalism, Socialism and Democracy,
- Schumpeter
- 1992
(Show Context)
Citation Context ...nnovative activity known as ‘demand-pull’ versus ‘technology-push’ forces of technical change. ‘Demand-pull’ influences in innovative activity include effects driven by changes in consumer demand, the competitive structure of markets and factors affecting the valuation and appropriability of innovations. In the Schumpeterian tradition, increasing sales induce an increase in the effort to innovate by allowing the funding of expensive and uncertain research and development (R&D) activities, while at the same time the appropriability of the returns from innovative activity rise with market size (Schumpeter, 1942). From an empirical point of view, Schmookler (1966) was the first author to test the demand-pull hypothesis at the sectoral level. In his debated work, Schmookler argued that the main stimulus to innovation came from the changing pattern of demand as measured by the investment in new capital goods in various industries. Schmookler found that firms in “science-based industries” produced much more innovation for a given amount of sales than firms in other sectors and underlined the importance of different technological regimes that characterize the different industrial sectors. Later on, Schere... |

1530 |
Another Look at the Instrumental Variable Estimation of Error– Components Models
- Arellano, Bover
- 1995
(Show Context)
Citation Context ...utcome in terms of subsequent innovation, the possible reverse effect is between future successful innovation and future sales (Piva and Vivarelli, 2007: 697). The above model testing the relationship between demand and innovation is defined as a dynamic equation by including the lagged value of the R&D expenditure in order to account for the adjustment and persistence in R&D activities. The inclusion of lagged dependent variable leads to another endogeneity problem that can be solved by firstorder dynamic panel data models that use General Method of Moments (GMM; see Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). However, a weakness of GMM estimators is that their properties hold when N is very large, so they can be severely biased and imprecise in panel data with a small number of cross-sectional units (Bruno, 2005b). Hence, this method is not suitable for small samples like the ones used in our analysis since we select a sub-sample of firms from the original panel to test the demand-pull effect under different firm characteristics. Therefore, we used an alternative method based upon the bias correction of least-squares dummy variable (LSDV) estimator in dynamic panel data ... |

525 |
Invention and Economic Growth
- Schmookler
- 1966
(Show Context)
Citation Context ...chnology-push’ forces of technical change. ‘Demand-pull’ influences in innovative activity include effects driven by changes in consumer demand, the competitive structure of markets and factors affecting the valuation and appropriability of innovations. In the Schumpeterian tradition, increasing sales induce an increase in the effort to innovate by allowing the funding of expensive and uncertain research and development (R&D) activities, while at the same time the appropriability of the returns from innovative activity rise with market size (Schumpeter, 1942). From an empirical point of view, Schmookler (1966) was the first author to test the demand-pull hypothesis at the sectoral level. In his debated work, Schmookler argued that the main stimulus to innovation came from the changing pattern of demand as measured by the investment in new capital goods in various industries. Schmookler found that firms in “science-based industries” produced much more innovation for a given amount of sales than firms in other sectors and underlined the importance of different technological regimes that characterize the different industrial sectors. Later on, Scherer (1982) tested the demand-pull hypothesis together ... |

342 |
On bias, inconsistency, and efficiency of various estimators in dynamic panel data models.
- Kiviet
- 1995
(Show Context)
Citation Context ..., 1998). However, a weakness of GMM estimators is that their properties hold when N is very large, so they can be severely biased and imprecise in panel data with a small number of cross-sectional units (Bruno, 2005b). Hence, this method is not suitable for small samples like the ones used in our analysis since we select a sub-sample of firms from the original panel to test the demand-pull effect under different firm characteristics. Therefore, we used an alternative method based upon the bias correction of least-squares dummy variable (LSDV) estimator in dynamic panel data models proposed by Kiviet (1995), Bun and Kiviet (2003) and extended by Bruno (2005a). Moving from a standard autoregressive panel data model, based on the possibility of collecting observations over time and across individuals; our problem can then be described as follows: (2) where y is the vector of observations for the dependent variable, D is the matrix of individual dummies, E is the vector of individual effects, W is the matrix of explanatory variables including lagged dependent variable, J is the vector of coefficients and ∑ the usual error term. The Least Squares Dummy Variable (LSDV) estimator is given by: (3) wher... |

294 | Research, Innovation and Productivity: An Econometric Analysis at the Firm Level”, - Crepon, Guguet, et al. - 1998 |

280 | Empirical Studies of Innovation and the Market Structure - Cohen, Levin - 1989 |

236 | Is Public R&D a Complement or Substitute for Private R&D? A Review of the Econometric Evidence.” Research Policy 29:497–529. - David, Hall, et al. - 2000 |

69 | The Non-trivial Choice Between Innovation Indicators - Kleinknecht, Montfort, et al. - 2002 |

68 | Does cash flow cause investment and R&D? An exploration using panel data for French, Japanese, and United States scientific firms”, - Hall, Mairesse, et al. - 1999 |

43 | Innovation and economic performance in services: a firm-level analysis”, - Cainelli, Evangelista, et al. - 2006 |

31 |
Innovative Activity Over the Business Cycle
- Geroski, Walters
- 1995
(Show Context)
Citation Context ...al regimes that characterize the different industrial sectors. Later on, Scherer (1982) tested the demand-pull hypothesis together with the sectoral peculiarities in innovation by using technology-class dummies. His results confirmed the correlation between patenting and investment and suggested that differences in technological opportunities must be taken into account for demand-pull stimuli. As far as econometric studies are concerned, empirical literature has provided further evidence supporting demandpulled innovation both at the aggregate level (entire economy and industrial sectors, see Geroski and Walters, 1995; Kleinknecht and Verspagen, 1990) and at the microeconomic (firm) level (see, Brouwer and Kleinknecht, 1999; Cainelli et. al., 2006; Crepon et. al., 1998; Piva and Vivarelli, 2007; 2009; Scherer, 1982) by using different innovation indicators such as patent statistics and R&D expenditures. Previous studies seem to be affected by some limitations that are surveyed in Piva and Vivarelli (2007). First, most of these studies use cross-section analyses that focus mainly on the between differentials. Availability of panel data wipes out possible firm specific fixed effects and deal with endogeneity... |

29 |
On the diminishing returns of higher order terms in asymptotic expansions of bias”,
- Bun, Kiviet
- 2003
(Show Context)
Citation Context ...r, a weakness of GMM estimators is that their properties hold when N is very large, so they can be severely biased and imprecise in panel data with a small number of cross-sectional units (Bruno, 2005b). Hence, this method is not suitable for small samples like the ones used in our analysis since we select a sub-sample of firms from the original panel to test the demand-pull effect under different firm characteristics. Therefore, we used an alternative method based upon the bias correction of least-squares dummy variable (LSDV) estimator in dynamic panel data models proposed by Kiviet (1995), Bun and Kiviet (2003) and extended by Bruno (2005a). Moving from a standard autoregressive panel data model, based on the possibility of collecting observations over time and across individuals; our problem can then be described as follows: (2) where y is the vector of observations for the dependent variable, D is the matrix of individual dummies, E is the vector of individual effects, W is the matrix of explanatory variables including lagged dependent variable, J is the vector of coefficients and ∑ the usual error term. The Least Squares Dummy Variable (LSDV) estimator is given by: (3) where A is the within trans... |

27 |
Demand-Pull and Technological Invention: Schmookler Revisited
- Scherer
- 1982
(Show Context)
Citation Context ... 1942). From an empirical point of view, Schmookler (1966) was the first author to test the demand-pull hypothesis at the sectoral level. In his debated work, Schmookler argued that the main stimulus to innovation came from the changing pattern of demand as measured by the investment in new capital goods in various industries. Schmookler found that firms in “science-based industries” produced much more innovation for a given amount of sales than firms in other sectors and underlined the importance of different technological regimes that characterize the different industrial sectors. Later on, Scherer (1982) tested the demand-pull hypothesis together with the sectoral peculiarities in innovation by using technology-class dummies. His results confirmed the correlation between patenting and investment and suggested that differences in technological opportunities must be taken into account for demand-pull stimuli. As far as econometric studies are concerned, empirical literature has provided further evidence supporting demandpulled innovation both at the aggregate level (entire economy and industrial sectors, see Geroski and Walters, 1995; Kleinknecht and Verspagen, 1990) and at the microeconomic (f... |

23 |
Keynes-Plus? Effective Demand and Changes in firm-level R&D: An Empirical Note
- Brouwer, Kleinknecht
- 1999
(Show Context)
Citation Context ...d-pull hypothesis together with the sectoral peculiarities in innovation by using technology-class dummies. His results confirmed the correlation between patenting and investment and suggested that differences in technological opportunities must be taken into account for demand-pull stimuli. As far as econometric studies are concerned, empirical literature has provided further evidence supporting demandpulled innovation both at the aggregate level (entire economy and industrial sectors, see Geroski and Walters, 1995; Kleinknecht and Verspagen, 1990) and at the microeconomic (firm) level (see, Brouwer and Kleinknecht, 1999; Cainelli et. al., 2006; Crepon et. al., 1998; Piva and Vivarelli, 2007; 2009; Scherer, 1982) by using different innovation indicators such as patent statistics and R&D expenditures. Previous studies seem to be affected by some limitations that are surveyed in Piva and Vivarelli (2007). First, most of these studies use cross-section analyses that focus mainly on the between differentials. Availability of panel data wipes out possible firm specific fixed effects and deal with endogeneity problems associated with simultaneous occurrence of innovation and increasing sales within the firms. Exami... |

16 | Is demand-pulled innovation equally important in different groups of firms?”,
- Piva, Vivarelli
- 2007
(Show Context)
Citation Context ...sing technology-class dummies. His results confirmed the correlation between patenting and investment and suggested that differences in technological opportunities must be taken into account for demand-pull stimuli. As far as econometric studies are concerned, empirical literature has provided further evidence supporting demandpulled innovation both at the aggregate level (entire economy and industrial sectors, see Geroski and Walters, 1995; Kleinknecht and Verspagen, 1990) and at the microeconomic (firm) level (see, Brouwer and Kleinknecht, 1999; Cainelli et. al., 2006; Crepon et. al., 1998; Piva and Vivarelli, 2007; 2009; Scherer, 1982) by using different innovation indicators such as patent statistics and R&D expenditures. Previous studies seem to be affected by some limitations that are surveyed in Piva and Vivarelli (2007). First, most of these studies use cross-section analyses that focus mainly on the between differentials. Availability of panel data wipes out possible firm specific fixed effects and deal with endogeneity problems associated with simultaneous occurrence of innovation and increasing sales within the firms. Examining the relationship between demand and innovation for Dutch manufactur... |

4 |
Demand-pulled innovation under liquidity constraints”,
- Piva, Vivarelli
- 2009
(Show Context)
Citation Context ...features that are important in determining the scope of the demand-pull effect. The finding of Hall et. al. (1999) that R&D in US firms appears to be more sensitive to past sales and cash flow than their French and Japanese counterparts, suggests another improvement in the analysis of demand-pull hypothesis. This is an important suggestion of the possible role of liquidity constraints that can make the demand-pull impact more of less effective since sales and cash flow might be more crucial in inducing and funding R&D for firms having difficulties in raising credit. There is only one study by Piva and Vivarelli (2009) that examines whether demand-pulled innovation is important for liquidity constrained firms. They found a significant confirmation of the demand-pull innovation for the liquidity constrained Italian manufacturing firms for 1995-2001. This paper studies a dynamic specification of the demandpull hypothesis at the firm level, which takes into account both within and between effects across Turkish non-financial firms listed at Istanbul Stock Exchange (ISE) over a period of ten years (1998–2007). First, the demand-pull hypothesis is tested using all of the firms that constitute the dataset. Taking... |