18 Hsieh and Klenow’s (2009) method in using the average book values to construct the capital stock. The limitations of using this approach are discussed in appendix A.

While constructing the capital stock from investment flows is viewed to be more representative of current values, there are three main complications arising from this approach. First, the censuses are five years apart; thus, we do not observe investments outside the census periods. We observe that many firms drop out from the estimation because of zero investment during the three census periods. Second, the prices of capital stock for each industry are not available to adjust the values to the base year 2010. Third, the depreciation rate of 5% may not be representative of all industries. For example, technologically advanced industries may depreciate their capital at a faster rate. In addition, the adjustment of capital stock for depreciation accounts for normal wear and tear of the capital stock and has limited implications for productive capacity. The implication for production is different if a firm decides to retire, depreciate, or dispose of capital with the intention of adopting new or advanced technologies.

(ii) Trimming of Data The data cleaning process is similar to that used by Nguyen, Taskin, and Yilmaz (2016). First, firms with zero capital stock, investment, and compensation are removed from the census because we assume that firms cannot operate without any capital or labor. Second, industries that are represented by fewer or equal to 20 firms are also removed. Thirdly, to “normalize” the distribution of firms, only firms above the 10-employee threshold are included in the TFPRsi Asi estimation framework. Finally, 1% tails of , , and capital-to-value added log( TFPR̅̅̅̅̅̅̅̅s ) log( A̅s ~~)~~ ratios across industries were trimmed when calculating the productivity gains derived from the hypothetical elimination of distortions. After the data cleaning and trimming process, the original dataset, which consists of 78,845 observations, was trimmed to 24,621 observations. The main trimming occurred at the normalization of the distribution of firms stage, where more than half of the observations (43,292) dropped out from the estimation framework.

# **5.0 Findings**

# **5.1 Measuring Allocative Distortions**

Table 2 shows the manufacturing sector’s hypothetical TFP gains derived from a reduction of misallocations either through a complete elimination of distortions or a partial elimination to that of levels experienced by the United States, the benchmark economy in this study.

The larger the hypothetical gain (table 2), the further the manufacturing sector is from the allocative efficiency frontier, which implies the more severe the resource misallocation is across firms within the sector of a country. Gain 1, which is the hypothetical efficiency gain derived from a complete elimination of distortions, is estimated to be between 60% and 95%. Had resources been allocated _“_ perfectly” across firms, the hypothetical productivity gain is calculated to be an average of 79% for the three census periods. The estimated productivity gains across the three periods show that the productivity gaps not only persisted but allocative efficiency across firms in the manufacturing sector appears to have worsened over time.