# **1 Introduction**

Total factor productivity (TFP)—the ability to generate greater value or output with fewer inputs—is one of the key elements of economic growth. There is now a broad consensus that TFP differences account for the bulk of observed cross-country income differences (Klenow & Rodriguez-Clare 1997, Hall & Jones 1999). As Krugman (1994) succinctly put it “productivity isn’t everything, but, in the long run, it is almost everything”.

Considerable scholarly analysis has been devoted to measuring productivity. The recent, increased availability of detailed firm-level datasets has further intensified the interest in the subject, including investigations of how productivity varies by characteristics of firms (see, for instance, Syverson (2011)). To the extent that data can be disaggregated, researchers can delve into within-economy differences; if data are comparable across economies and across time, cross-economy differences can be examined. For example, a large and growing body of work explores the link between within-industry productivity dispersion across firms and cross-country differences in aggregate productivity (Banerjee & Duflo 2005, Restuccia & Rogerson 2008, Hsieh & Klenow 2009, Bartelsman et al. 2013).

In the absence of comparable census data, researchers have attempted to estimate productivity using survey-based data, which are often the only available data in less developed economies. The Enterprise Surveys (ES), detailed firm-level data collected by the World Bank’s (WB) Enterprise Analysis unit, are well suited for such inquiry. To facilitate the study of productivity by researchers and policymakers, the data published with this paper provide estimates of both TFP and factor ratios. The latter, more straightforward estimates are provided as TFP estimation may be troublesome for multiple reasons, e.g. the endogeneity of input choice (Olley & Pakes 1996, Levinsohn & Petrin 2003, Ackerberg et al. 2015). Unlike TFP estimates, some of these ratios are also available for most non-manufacturing firms.

The rest of this paper is organized as follows. Section 2 briefly summarizes the ES data, including steps taken for comparability and regarding outlier observations. Section 3 discusses the estimation of revenue-based TFP, so-called TFPR. Estimates of output elasticities and their derived characteristics are presented in section 4.

# **2 Data**

The World Bank’s Enterprise Analysis unit has been conducting surveys using a methodology that allows cross-economy analysis since 2006. To date, over 168,000 face-to-face interviews with top managers and business owners in 144 economies have taken place under this “Global Methodology”. This note uses these data to estimate TFP. Surveys not using the Global Methodology are excluded, as are the surveys conducted earlier than 2006. The data from Zimbabwe 2011 are excluded from analysis due to the complications due to hyperinflation just prior to the data collection. An additional 25 surveys are dropped because at least one of the key variables used in the analysis was not collected in these surveys.<sup>1</sup> This leaves 267 surveys in 134 economies and more than 161,000 interviews with top managers and business owners of firms spanning more than 40 different industries (specified by two-digit ISIC

> 1These are: Bangladesh 2007, Benin 2009, Bhutan 2009, Cambodia 2013, Cabo Verde 2009, Central African Republic 2011, Chad 2009, Congo 2009, Eritrea 2009, Fiji 2009, Gabon 2009, Lesotho 2009, Liberia 2009, Malawi 2009, Micronesia 2009, Niger 2009, Pakistan 2007, Rwanda 2011, Samoa 2009, Sierra Leone 2009, Timor-Leste 2009, Togo 2009, Tonga 2009, Vanuatu 2009, and the Rep ́ublica Bolivariana de Venezuela 2006.

2