identify the amount of misallocation derived from specific sources but provides an allencompassing measure.

HK find sizeable misallocation of resources, which decreases aggregate TFP. If the distortions that cause the misallocation were to be eliminated, TFP in the manufacturing sector in China would increase by 86%-115%; in India by 100%-128% and in the United States by 30%-43%. Interestingly, their analysis allows to take a step towards the direct method. While sources of misallocation are not identified, the extent of misallocation is correlated with various observables to shed more light on the underlying mechanisms. Through this analysis, HK show that highproductivity firms in all three countries are smaller than efficiency would dictate, while some lowproductivity firms (such as state-owned enterprises in China) are too large. The low estimation requirements coupled with the capacity to inform about potential sources have contributed to the popularity of the HK approach. It has been widely applied –see, for example, Busso, Madrigal, and Pagés (2013) for Latin American countries; Nguyen, Taskin, and Yilmaz (2016) for Turkey; Cirera, Fattal Jaef, and Maemir (2020) for African countries; and Chuah, Loayza, and Nguyen (2020) for Malaysia.

While being a valuable first step toward measuring resource misallocation, the HK approach depends on rather strong assumptions.<sup>4</sup> One important assumption is that firms’ inputs and revenue are free of measurement error. Since the HK approach uses revenue and inputs to estimate marginal productivity, this assumption could bias the estimated resource misallocation. Mismeasurement is likely present in most firm-level data sets, especially in developing countries, because of weak capacity at firms’ accounting systems and at national statistical agencies.

<mark>Bils, Klenow, and Ruane</mark> (2020) (henceforth BKR) propose a methodology that exploits the panel structure of the data to correct for potential measurement error. The authors build on the HK approach and allow for additive<sup>5</sup> measurement error in revenues and intermediate inputs. When measurement error is additive but _i.i.d_ . over time, the levels and first differences of revenue relative to inputs provide independent signals of the true dispersion in marginal revenue products. Hence, by taking the covariance between first differences and levels of average revenue products, one could estimate the variance of the true marginal products, which captures resource misallocation within a narrowly defined industry.

We apply both HK and BKR frameworks, highlighting the differences in estimation driven by accounting for measurement error. We rely on firm-level data available from three developing Southeast Asian countries: Indonesia, Malaysia, and Vietnam. These three large and diverse economies are considered to have good panel firm-level data. Yet, results indicate that measurement error may play an important role, and accounting for it can substantially affect the extent of estimated misallocation in these economies.

> 4As acknowledged by HK, their approach relies on restrictive assumptions such as CES aggregation of differentiated products within a narrowly defined sector (allowing to derive TFPQ from revenue data in the absence of information on input and output quantities), and hence constant mark-ups within the same sectors. Under these conditions, any variation in TFPR is attributable to resource misallocation.

> 5 Assuming additive rather than multiplicative measurement error yields more conservative estimates.

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