Lanka than the original sample, which was heavily skewed toward microenterprises.<sup>18</sup> While the SLLSE booster is still over-representative of smaller firm employment relative to the universe of establishments as approximated by the 2004 Census (only 12% of workers in the SLLSE sample are employed in large firms with +100 employees versus 64% of workers in the Census), there is representation of all size classes in the sample. Summary statistics are provided in Table 9. The types of innovation examined are whether the firm: (1) introduced new products or services; (2) significantly improved an existing product (this question was only available from the SLLSE); (3) introduced new or improved business processes; (4) introduced new organizational innovations; and (5) introduced new marketing innovations. The comparable 2011 WBES innovation measures and business environment indicators are systematically higher than the 2009 SLLSE variables, typically at least double in value: introduction of new goods or services (31 versus 9% of firms), introduction of a new or improved business process (44 vs 9%), use of internet (45 vs 21%), and share of sales made on credit (31 vs 18%). This is no doubt linked to the different composition of firms by size and industry: while 26% of firms in the 2011 WBES are medium and large, only 6% are of these larger sizes in the SLLSE; and the relative share of manufacturing relative to services firms is 60:40 for the WBES versus 20:80 for the SLLSE, with a significantly larger number of SLLSE firms being in the typically less innovation-intensive or less skills-intensive retail and wholesale trade, hotels and restaurants, and construction service sectors.

# **Methodology and empirical model**

Motivated by the model of innovation proposed by de Mel et al. (2009), we test the correlation of both firm/owner-specific and business environment indicators with various measures of firmlevel innovation. Our basic empirical specification is of the form: where [Innovate]i is a measure of innovation undertaken at firm _i_ , _A_ is a vector of industry controls, Z is a vector of business environment indicators which vary at the firm level, and _K_ is a vector of objective firm- or owner-specific characteristics which may influence innovation. εi is a randomly distributed disturbance term. We estimate this equation using a probit estimator, controlling for industry and regional (provincial) fixed effects.

# **What explains innovation within firms?**

Estimates from equations exploring the relationship between available business environment variables and a core set of innovation measures are reported in Tables 10 (from the 2011 WBES) and 11 (from the 2009 SLLSE).<sup>19</sup> Across both datasets, measures of innovation are most

> 18 The sample frame for this dataset came from two sources. 400 enterprises were selected from a census of firms carried out by AC Nielsen, Lanka. The Nielsen census covered only part of the geographic area of the survey, so the sample was supplemented by asking wage workers in the representative listing for the name and size of the firms where they are employed. An additional 210 firms with 5 to 250 workers were thereby added to the sample. 19 We also have estimated the business environment correlates to a wider range of innovation measures, available from the authors upon request.

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