Programa de Rehabilitación Urbana de la Intendencia de Paysandú

Following Furman, et al. (2002), Hu & Mathews (2005) and Li (2009), a panel data model was employed. There are several advantages of panel data compared to pure cross-section and time-series analysis (Baltagi, 2008; Hsiao, et al., 2002). Panel data considers both time variances and cross-section variances and is able to control the time and entity invariant variables (Baltagi, 2008). On the other hand pure cross- section data covers the variances between sections but no time-variant information (Wooldridge, 2002) and the primary purpose of time-series analysis is understanding dynamics (Hamilton, 1994). Since this study was comparing many regions over a long time period, panel data is needed. Panel data also have the following advantages (Baltagi, 2008; Hsiao, et al., 2002). First, using panel data can better uncover the dynamics of change. It is suitable to study economic phenomenon and can reveal the speed of adjustment to economic policy change with panels that are long enough. Second, panel data is able to control the effects of missing or unobserved variables, and consequently control the heterogeneity among individuals, regions, or countries and reduce the bias of the results. Third, panel data allows construction and testing of more complicated behavioral models. Finally, panel data can sometimes generate more accurate predictions for individual outcomes and provides more informative results, less collinearity between variables, and increased efficiency.

Every method has pros and cons and panel data are no exception. Selectivity and heterogeneity biases are the two main issues that need to be considered (Baltagi, 2008; Hsiao, et al., 2002). As the study has a population of relevant regions, selectivity is not a problem. However, heterogeneity biases exist due to the influence

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of factors not included in the model. Since it is impossible to include all the factors affecting the outcome of all the regions, the heterogeneity biases are unavoidable. Overall, the advantages of panel data outweigh the limitations, and using panel data will serve the purpose of this study well.

According to Wooldridge (2002) and Baltagi (2008), the basic econometric model which considers both time-variant and time-invariant variables with panel data is interpreted as follows:

(1)

Where represents the cross-sectional unit and represents time; is the dependent

variable; is the coefficient for the independent variable; represents one

independent variable; and is the error term. The two basic approaches are the

random effects model and fixed effect model.

Following Allison, Baltagi and Wooldridge (2009; 2008; 2002), the study employed a fixed effect model, which can be specified as:

(2)

Where represents the cross-sectional unit and represents time; is the dependent

variable; is the coefficient for the independent variable; represents one

independent variable; is the intercept; is the unobserved unit effect; and is

the error term.

A fixed effect model has several advantages over a random effect model in the context of this study. In the case of this research, the most important consideration is

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the random effect model is appropriate if the sample is randomly drawn from a large population, and the fixed effect model is more suitable for a specific set of units (Baltagi, 2008). The units used here are not randomly sampled, they are the specific 30 regions in China (it is almost the population of China, except Tibet). This rules out the appropriateness of random effect model at the first instance. Second, in a random effect model the unobserved variables are assumed to be independent of all the observed variables, while in a fixed effect model the unobserved variables are allowed to have associations with the observed variables (Allison, 2009; Baltagi, 2008; Wooldridge, 2002). Allowing associations is the way to control the effects of unobserved variables, as the unobserved variables are treated as fixed parameters (Allison, 2009). In this study only some factors that may have direct influence on the innovation capacity are included, so it is a big risk to assume the unobserved variables are not correlated to the observed ones. For example, the number of graduates from HEI may be correlated to full time employed scientists and engineers.

Substituting the variables of this study into the generic fixed effect model (2), the following model for patent applications results:

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For granted patents, the model is:

(4)

In this study fixed effect models are estimated in STATA, which is one of many packages, such as SAS, LIMDEP, which can perform panel data analysis,. As we applied the fixed effect model with the command xtreg and option fe in STATA, the model was estimated by fixed-effect estimator in other words, within estimator (Baltagi, 2008; StataCorp, 2009). Different from GLS random estimator, which considers both within and between variation, the within estimator subtracts the between variation and only the within variation is left (Allison, 2009; Baltagi, 2008).

4.5 Summary

Drawing on the literature of NIS/RIS and NIC/RIC this chapter developed the conceptual framework of the thesis, consisting of innovation actors, innovation inputs, and interactions as the explanatory variables. Data for the measures were collected from various statistic yearbooks, detailing 30 administrative regions in China from 1991 to 2005. According to the research questions, the data will be analysed in three steps. Step one is for overall regions covering the whole period. Step two is to compare the differences in drivers of RIC between two phases and step three is to compare the variations in drivers of RIC among regions. Fixed effect

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models with panel data were employed as the main research method to explore to what extent different factors matter for RIC in different situations. Cluster analysis was also employed to classify regions according to their innovative capacity.

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