The unique identifier included in the ENIA allows us to follow plants over time and identify exit of plant A in year _t+1_ if plant A is part of the ENIA in year _t_ but is not part of the ENIA in year _t+1_ and after. Another advantage of our data is that we can identify multi-plant firms, which may exhibit important differences relative to single-plant in terms of survival (Disney et al., 2003).<sup>10</sup> Table 1 shows that the average yearly exit rate in the Chilean manufacturing sector is about 9%. Exit rates differ across industries ranging from 6% in the basic metals industry to 11% in textile, wearing apparel and leather, wood and wood products.

The crucial feature of our dataset is that it provides for each plant and year information on the entire set of products manufactured and sold classified at the 7-digit ISIC level (revision 2).<sup>11</sup> This information allows us to construct two novel measures of innovation. Our first main measure of product innovation is a dummy variable that equals one for a plant in year _t_ if the plant sells one or more new 7-digit products while our second main measure explores the quantitative aspect of innovation and is a continuous measure capturing the number of new 7-digit products sold by a plant in year _t_ . For both measures a new 7-digit product is one that the plant has never sold prior to year _t-1_ , but that product may not be new to the market or the world. Table 1 shows that the average percentage of plants introducing new products is 14%. The innovation rate is lowest for the food, beverage, and tobacco industries - where only about 8% of plants innovate - and well-above average for a diverse set of industries from textiles, wearing apparel and leather, wood and wood products to chemicals, basic metals and fabricated metal products. The largest numbers of new products are introduced in the basic metals and fabricated metal products and machinery and equipment industries. Most product innovators are multi-product plants. For innovating plants overall, their new products generally account for less than 50% of revenues and they tend to add to the plants‟ existing product scope in most industries.

To provide a richer characterization of product innovation and assess the role that risk plays for the innovation-survival link we construct a variety of measures which interact both the dummy and

> plant-year observations have less than 10 employees. Of those 939 plant-year observations, in most cases plants remain in the ENIA survey reporting less than 10 employees for multiple years. Hence, we are confident that plant exit from the ENIA sample does indicate real failure. Nevertheless, we conduct an econometric exercise focusing on plants with more than 15 employees in Section 4.2.

> 10 The INE collects information on which plants in the ENIA survey are part of a multi-plant firm, i.e., a firm with at least two plants responding to the survey. The information was kindly provided to the authors for the purposes of this research project. During the 1997-2003 sample period on average 8.3% of firms are multi-plant firms. In the rest of the paper we will be particularly careful to denote single-unit establishments which are the object of ENIA‟s survey as „plants‟ and refer to „firms‟ only when this corresponds to units with multiple plants.

> 11 See Navarro (2012) and Fernandes and Paunov (2009) for details on the products data. Due to a change in product classification from ISIC Rev. 2 to ISIC Rev. 3 classification in 2001, we omit that year in the econometric analysis.

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