characteristics that proxy for different types of innovation: the share of firms that introduce new products, use new methods of production and contribute to R&D spending. Third, we use three characteristics indicative of “learning-by-doing”: the share of large firms (indicating potential for scale economies), the share of firms using a licensed technology from a foreign-owned firm, and the share of firms with formal training programs (indicating on-the-job learning). Last, we analyze three characteristics indicative of the nature of factor use: capital expenditure per employee (measuring capital intensity), years of schooling (measuring skill intensity), and the (full-time) employment elasticity of output. They reveal the importance of physical and human capital, relative to labor, in the organization of production and thereby reflect the ease of securing employment for a predominantly unskilled labor force.

The choice of the level of industry disaggregation in the typology is guided by categories that facilitate meaningful economic analysis subject to data constraints. The statistical analysis is based on the latest available World Bank Enterprise Survey for Brazil, China, India, Russia, Egypt and Nigeria. This sample covers low- and middle-income countries from different regions of the world and provides representative firm-level information at the two-digit ISIC sector level. Seventeen sub-sectors/industries<sup>6</sup> are part of the stratification of each enterprise survey and therefore considered in the analysis to follow.

# <u>Methodology</u>

As an illustrative example, consider the distinction between small, medium and large firms. The numbers presented in Table 5 indicate the average share of large firms across different industries, where ‘large’ firms are defined as those with more than 100 employees. This ‘average’ represents a simple average for the 6 countries in the sample where the number for each country, in turn, is a simple average of the sampled firms. For each country in the sample, data from the most recent enterprise survey are used. The corresponding Z-scores for the numbers in Table 5 indicate the number of standard deviations away from the mean a particular value is. Hence, a Z-score of −0.40 implies that for hotels and restaurants, the share of “large” firms is 0.40 standard deviations below the mean. In contrast, a Z-score of 1.10 implies that for electronics and communications equipment, the share of “large” firms is 1.10 standard deviations above the mean.

The data appear to be normally distributed, because 95% of the observations lie within two standard deviations of the mean. Given that the data are normally distributed, around 50 percent of all observations fall within 0.67 standard deviations above or below the mean. This establishes unique threshold points – observations that are more than 0.67 standard deviations above the mean are classified as ‘high’, while those that are more than 0.67 standard deviations below the mean are classified into the ‘low’ category. The remaining are classified into the ‘medium’ category. Using this formal statistical technique as a means of classification, following Nayyar (2013), we classify the different sectors into the following categories:

> 6 Basic metals & metal products; fabricated metal products; non-metallic mineral products; food; wood products; plastics and rubber products; leather products; garments and textiles; chemicals and chemical products; electronics and communications equipment; machinery and equipment; motor vehicles; construction; hotels and restaurants; IT services; wholesale and retail; transport and communications.

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