|Sindh|54.75%|13.51%|18.99%|16.67%|31.58%|18.87%|
|---|---|---|---|---|---|---|
|Khyber-Pakhtunkhwa|4.70%|20.26%|3.80%|1.85%|0.00%|15.09%|
|Islamabad|3.22%|10.92%|3.80%|1.85%|0.00%|1.89%|
|_Herfindahl concentration_|0.26|0.17|0.44|0.55|0.42|0.29|

Source: Author’s elaboration from Enterprise Survey (2014)

# 6. Exploring the Causal Links Between Innovation and Productivity

As Paul Krugman famously claimed, "Productivity isn't everything, but in the long run it is almost everything. A country's ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.” (Krugman, 1994). Although several studies have been conducted to explore the links between innovation and productivity, most of the evidence available relies on the findings for developed countries, with little evidence for developing economies. It is therefore the objective of this section to fill this gap by focusing the analysis on the South Asia region.

## 6. 1 Methodology

In order to examine the empirical relationship between innovation and productivity, we follow a conceptual framework based on Figure 1, which forms the basis of the CDM model. The framework holds that firms invest in knowledge inputs that can be transformed into innovation outputs according to an innovation function. At a later stage, these innovation results impact on firm-level productivity, contingent on the capacity of firms to transform innovation outputs into improvements in product quality and efficiency.

As a result, the CDM model requires the estimation of three main components: (i) the knowledge function, which involves estimating the R&D and ICT functions, (ii) the innovation equation, and (iii) the productivity equation. When estimating these components, there are two critical choices to be made. The first is to define the scope of the knowledge inputs and innovation outputs to be included in the analysis. This is often related to the availability of data in the innovation surveys. In most of the CDM applications, data are restricted to the use of R&D for knowledge activities; for innovation outputs, either product and process innovation dummies, or the number of patents and the revenue associated to new products are used. Regarding innovation outputs, we focus on technological innovations -either product or process innovations – and look at revenue originated from product innovations, since patenting activity in the South Asian countries is minimal.

The second choice when examining these three innovation components relates to how the model is solved to produce economic estimates that can be interpreted in a causal way. A parameter of a model is causal, or structural, sometimes referred to as a deep parameter _,_ when it untangles cause from effect, and is derived from a theoretical model where the directions governing the laws of motion are straight forward (Heckman, 2000). In contrast, in a reduced form setting, cause and effect are confounded in the presence of unobserved factors. Decisions regarding knowledge inputs and innovation outcomes can be made simultaneously and there can be feedback effects, especially between innovation outcomes and productivity in the sense that more productive firms may have an advantage to develop new products and/or processes (such feedbacks would be accounted for by including lagged terms in the other respective equation).

The endogeneity of R&D and innovation arises with the innovation and productivity equations, respectively. Dealing with endogeneity is crucial, as the significance of the effects of innovation on productivity tends to fall and lose statistical significance. To a large degree, the endogeneity in our context is likely driven by an error in variable measurement, more than a simultaneity problem, given the inherent 26