# **1. Introduction**

Productivity improvement is the foundation of economic growth and, consequently, development and well-being (Hall and Jones, 1999). Understanding the sources of economic growth is crucial to devise policies that efficiently and effectively promote economic development (Kim and Loayza, 2019). The topic has attracted substantial attention from policy makers and researchers alike. While physical and human capital accumulation are important drivers of economic growth, the evidence indicates that productivity growth accounts for most of the cross-country differences in income per capita growth (Easterly and Levine, 2001, and Caselli, 2005).

Measuring productivity is challenging. First, multiple definitions of productivity exist. Labor productivity (output or value added per worker) is commonly used. The measure does not impose extensive data requirement, and its simplicity allows for easy comparability over time and place (sectors and/or countries). However, it depends on more than efficiency: firms can raise labor productivity by investing in capital and not only by improving the use of their resources. Total factor productivity (TFP) is a more precise but harder to estimate measure. It provides an estimate of economic efficiency, accounting for the contributions from labor, capital, and possibly intermediate products. In practice, this implies that TFP is measured as a residual, carrying, therefore, the measurement errors of outputs, inputs, and production functions. Difficulties arise in measuring the contribution of the factors of production, starting with, for instance, estimating the value of capital and skill-adjusted labor. In the words of Hicks (1981), “the measurement of capital is one of the nastiest jobs that economists have set to statisticians,” and this applies to human capital as well. Data problems also extend to the measure of revenue and output. Measuring gross output relies on sales and inventory data, both prone to statistical errors. Moreover, price data are usually not available for different products at the firm, so that inferring quantities from revenues may be inaccurate as products and their quality change.

Roughly speaking, there are two main sources of productivity growth. The first source is withinfirm productivity growth; that is, firms becoming more productive thanks to better technology or management practices. In this case, policy prescriptions to increase productivity are geared towards increasing technological diffusion and innovation, learning and skill upgrading, and infrastructure provision. This conventional understanding of productivity growth has been the subject of a long-standing literature and dominated much of policy discussions until the end of the twentieth century. The second source consists of improving allocative efficiency, that is, the reallocation of resources towards more productive uses. This can imply the transfer of capital and labor across firms and across sectors, as well as the entry and exit of firms. This second source of productivity growth has been emphasized in the literature since the turn of the century (see, for example, Banerjee and Duflo, 2005 and Restuccia and Rogerson, 2008). Policies associated with allocative efficiency emphasize correcting or reducing market distortions. In this paper, we focus on the second source of productivity improvement, concentrating our analysis on resource allocation across firms within a narrowly defined industry.

What is resource misallocation in a narrowly defined industry? It is misallocation of production inputs across firms that produce similar products and compete with each other, while retaining some degree of control over their prices. In an economy with low levels of distortion, more 2