The construction of the PCI modules requires a standardization of the answers. The answers can range from numerical answers (for example, the number of days to register a business) to textual answers (for example, yes/no where yes can be a positive or a negative response depending on the question). Following the methodology of the PCI survey, the scores for each question are standardized to a 10-point scale.<sup>8</sup> The unweighted average standardized score of the questions is taken to create a score for the PCI module, which gives a measure of the quality of that aspect of local governance. The scores of each PCI module are then added up to create the overall PCI, which gives a measure of the overall business environment. The scores are also normalized so that a higher value indicates better quality of governance for that module in the province.

It is important to note that our construction of the overall PCI and individual PCI modules differs significantly from other studies in the literature. In general, the other studies use the PCI to examine how the changes in provincial level changes affect firm level or province level outcomes but takes the PCI data as is (Dell, et al., 2015; Bai et al., 2013; Schmitz et al., 2012). To the best of our knowledge, these papers rely on the composite index calculated by the PCI team and did not recalculate, disaggregate the PCI into modules or account for the changes in survey questions over time. McCulloch et al. (2013) do acknowledge that there are inconsistencies in the questions for the individual PCIs. While they still use the composite index as provided in the website in their main estimation, they also use an alternative PCI measures that only contained a consistent set of questions over time. The changes in the questionnaire may not be an issue in these studies as they do not examine a particular aspect of governance or business climate. As such, these studies use the composite PCI as a general indication of the business environment across Vietnamese provinces similar to the World Bank’s Doing Business index for cross-country comparisons. This paper focuses on local governance. As a result, we only include questions that capture aspects of the business environment that can be controlled by local bureaucrats to measure these aspects consistently over time and to examine which aspects of the business environment matter more to firms.

# 3 Empirical strategy

This paper follows a two-step procedure to analyze how changes in local governance affect two main outcomes: firm-level productivity and resource allocation. First, we estimate a sector-level production function to estimate firm-level productivity and indicators of market allocative efficiency. We use both revenue labor productivity and TFP (TFPR) estimated semi-parametrically following Levinsohn and Petrin (2003).<sup>9</sup> Next, following Bartelsman et al. (2009, 2013) and Asker et al. (2014), we use the estimated TFPR and capital and labor coefficients to calculate at the industry-province level: (i) dispersion in firm TFPR, and (ii) the covariance between firm size and TFPR. These two measures are our proxies for market allocation efficiency. Previous theoretical and empirical studies have shown that that the size of market distortions will be positively correlated with dispersions in TFPR and negatively correlated with the sizeproductivity correlation.<sup>10</sup>

> 8 For questions where a higher score indicates better outcomes like local governance and business environment, the formula used was: 9*(Score of Province _n_ – Min Score)/(Max Score - Score of Province _n_ ) +1. For questions where a lower score indicates better outcomes, the formula used was: 11-[9*(Score of Province _n_ - Min Score)/(Max Score - Min Score)+1].

> 9 Since our data are a mixed panel including a census of large firms and sample of small firms, we cannot use exits from the data as measures of firm exits. As a result, we do not use the Olley and Pakes (1996).

> 10 Hsieh and Klenow (2009) and Bartelsman et al. (2013).

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