likelihood function with respect to θ means identifying a specific value, which is denotedθ<sup>~~ˆ~~</sup> . This estimate maximizes the probability that a sample value has actually been observed.

The log-likelihood function for probit is The widespread use of maximum likelihood estimator is largely due to a range of desirable properties. The major properties of the maximum likelihood estimator (MLE) are large sample or asymptotic ones. They hold under fairly general conditions. The properties are: consistency (true parameter value generated from large sample data), asymptotic normality (unknown parameters are asymptotically distributed as the original parameter), asymptotic efficiency (minimum variance in the class of consistent, asymptotically estimators), the score has zero mean and variance of the original parameter.<sup>12</sup> The explanatory variables _xj_ are divided into 3 main groups: absorptive capacity, human capital and collaboration with knowledge centers. Additional variables on firms’ characteristics and on innovation obstacles are added where available.

The surveys used for the analysis are the third National Survey of Technology Innovation in Firms for Chile and the second Survey of Development and Technological Innovation for Colombia. The surveys are mainly composed of qualitative data (answer yes/no to questions) and some concerns regarding the quality of the data have arisen during the preparation of this work, in particular regarding the Chilean survey (please refer to Annex 5 for concerns on data quality). However, these surveys represent the best-available source of statistical information on innovation in these countries at a firm-level and they are still instrumental in drawing some interesting conclusions on the main factors affecting firms’ capacity to innovate.

The third National Survey of Technological Innovation in Firms was conducted in 2001 by Chile’s Executive Secretary of Technological Innovation Program and the National Institute of Statistics. It contains data for the years 2001 and predictions for 2002. The survey had 896 respondents representing a weighted total population of 4,932 establishments. The present work focuses on the manufacturing sector, with a total of 706 respondents and 687 valid observations weighting to a total of 4,570 observations.<sup>13</sup> The main concern regarding this survey is that there is no information regarding the response rate. In this way, weighting the data might risk strengthening the underlying bias

> 12 For more information on the properties of MLE, cfr, among others, Johnston and DiNardo (1997).

> 13 The nature of the data is mainly qualitative, since the firms are often asked questions with a yes/no answer and only a few variables are of a strictly quantitative nature (these include the data on innovation and R&D expenditure). To overcome the problem of the qualitative nature of the data, a metric scale has been introduced, with 0 denoting a negative answer (meaning either “no”, “none”, or “0”) and where a scale of 1 to 4 denotes a positive answer, with 1 representing the least positive answer and 4 the most positive. The data were collected at the firm-level. This means that if a firm has more than one establishment the questionnaire was sent to the central management, in order to capture all the innovation activities within the firm. This methodology is in line with the procedures adopted by Eurostat for collecting the Community Innovation Surveys.

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