# **I. INTRODUCTION**

The process of industrial development, resource reallocation and productivity dynamics can be decomposed into three complementary types of “structural transformation”, namely: (1) between-industry structural change, as inputs are reallocated across industrial activities; (2) within-industry structural change, as inputs are reallocated across enterprises within industries; and (3) within-firm structural change, as input reallocation and innovation within enterprises alters the structure and efficiency of the firm. Eichengreen, Park and Shin (2013) speculate that countries often face “middle-income traps” when productivity gains from between-industry structural change are largely exhausted and remaining gains are available primarily via more difficult-to-achieve within-firm structural change. In related work, Bollard, Klenow and Sharma (2013) decompose productivity dynamics in India into this three-way typology. They report that the large speed-up in the productivity of formal Indian manufacturing plants in the 1990s and early 2000s was mainly driven by within-plant productivity growth. Based on employment and productivity growth over the life cycle of Indian and Mexican manufacturing plants relative to the US, Hsieh and Klenow (2014) point to differences in within-plant productivity being more important than gains from resource reallocation across firms, plausibly accounting for about 25 percent of the gap in aggregate manufacturing total factor productivity between rich and poor countries.

This paper builds on a recent literature on “new sources of growth.” It directly measures key knowledgerelated elements of within-firm productivity in a developing country based on public and private investments in intangible assets, also referred to as knowledge capital (OECD 2013, Hulten 2013, Dutz 2013a). Measurement of business spending on knowledge capital is relatively recent, with seminal work applying direct expenditure methods initially done for the U.S. economy (OECD 1998, Nakamura 2001, Corrado, Hulten and Sichel (“CHS”) 2005, 2009). In contrast to the conventional approach of treating spending on knowledge assets as intermediate expenditures and thereby assuming that all their benefits are reflected in the current year’s output of tangible goods and services, the knowledge capital approach correctly capitalizes intangible outlays that contribute to production and value beyond the taxable year and treats them as longer-lived investments – using the same cost-based accounting that is used for physical capital. The knowledge capital approach expands the conventional proximate sources of growth beyond human and physical capital to include knowledge investments in the innovation and risk management capabilities of the enterprise. Importantly, these investments go significantly beyond traditionally-measured R&D expenditures to include a range of co-investments required for enterprises to decide what to produce and how, to develop new ideas, to execute and translate these ideas into products, processes, organizational changes and markets, and to manage associated risks. The CHS classification divides knowledge capital into three categories:<sup>2</sup> (1) **_digital assets_** _( “computerized information”) -_ software and databases (2) **_intellectual assets_** _(“innovative property”) -_ R&D, entertainment, literary and artistic originals (creative capital), architectural, engineering and other designs, new products and systems in the financial industry, and mineral exploration and evaluation (3 _)_ **_human-organizational assets_** _(“economic competencies”)_ - market research, advertising and brand equity (marketing capital), firm-specific human capital (worker skills and managerial capital),

> 2 Investments in risk management are here not conceptualized as a separate category of knowledge capital, but a consequence of appropriate investments in these three existing categories of knowledge assets. Risk management is a by-product of investments in these assets.

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