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We expect that the management training offered in survival clusters will decrease σ<sup>2</sup> . It is not clear _a priori_ whether innovative firms have higher or lower σ<sup>2</sup> in dynamic clusters. But as appropriate management practices are established, we expect to observe that large firms have high μ and low σ<sup>2</sup> .

# **Case Studies**

In this section, we confront the above hypotheses with enterprise data collected in two industrial clusters in China and one each in Vietnam, Ghana, Ethiopia, and Bangladesh. This empirical exercise is not intended to perform rigorous hypothesis testing, but rather to provide suggestive evidence. The six clusters that we take up here as examples have different degrees of success in multifaceted innovation, as will be explained shortly. Table 1 shows each cluster’s location, main product, population of final goods producing firms, the mean and median of employment size and labor productivity (which is defined as value added divided by the number of workers) in the year in which we gathered information.

Figure 2 consists of 12 panels of scatterplots showing the distributions of labor productivity and employment size in these six clusters for two years. The summary statistics of these distributions are reported in columns (5) and (6) of Table 1. The vertical axis of the scatterplots measures the monthly value added per worker in US dollars as labor productivity.<sup>5</sup> The horizontal axis measures the logarithm of the number of workers.<sup>6</sup> The scatterplots share the same scale on each axis to allow comparison among the clusters and are useful for seeing whether the data are consistent with our hypotheses. Later in this section, we will turn to Table 2, which presents the results of some randomized controlled experiments in which basic management training was provided for entrepreneurs.

Cluster 1 produces sweater and other knitwear items for an export market in Eastern Europe and for a small domestic market in the northern provinces in Vietnam (Nam, Sonobe, and Otsuka, 2009). Geographically, the cluster is small and coincides with a village. Our sample is nearly equal to the population of knitwear firms in this cluster. The quality of the cluster’s products was improved when exporting began. Several firms adopt the factory production system employing more than 100 workers, but all other firms rely heavily on a large number of household subcontractors in neighboring villages for a substantial part of the production process. Thus, a small part of this cluster is dynamic. It is little wonder that small firms’ labor productivity has a very large variance as shown in Figure 2-1a, if they are poorly managed. Note, however, that while we are talking about the random variation in labor productivity at each firm, the figure shows the variation among firms in a cross section. Can we take the range of the distribution of dots in the vertical direction at each employment size as indicating wide fluctuations in labor productivity? This is a legitimate question because in theory, if some firms always have a higher productivity than other firms, they will become larger. In reality, however, labor productivity fluctuates widely in this cluster. As column (7) of Table 1 shows, the

> 5 This monthly value added per worker is not the value added per worker in a particular month, but it is the value added in a year divided by the number of months worked and the number of workers. 6 The numbers, 3, 30, and 300 on the horizontal axis stand for 3 persons, 30 persons, and 300 persons.