DOI: 10.5061/dryad.ttdz08m4h
title: Data from: epidemic, factor misallocation and efficiency of digital enterprises: heterogeneity study of labor and capital

description: In order to improve the development quality of the digital economy, the
 factor misallocation shall be reduced effectively in the post-epidemic
 era. This study discussed the impact mechanism of how the labor and
 capital misallocation affect heterogeneously the epidemic’s
 effect on the efficiency of digital enterprises. The epidemic outbreak was
 considered a quasi-natural experiment to set a PSM-DID model and
 an intermediary effect model. Empirical research was carried out using the
 data of 1752 digital and non-digital high-tech enterprises from
 the fourth quarter of 2017 to the first quarter of 2022. The results show
 that: (1) The epidemic improved digital
 enterprises' efficiency, mainly from the positive effect on
 technological efficiency and scale
 efficiency. (2) The epidemic worsened the
 misallocation of labor but improved capital
 allocation. (3) Through the
 factor misallocation, the epidemic had a
 positive impact on the efficiency of digital
 enterprises, contributing to the
 worsening technical progress. Through
 the capital misallocation, the epidemic had a
 positive effect on the TFP of
 the digital enterprises but there
 is no significant evidence of its
 source.
 While through the labor misallocation had a positive effect on the TFP of the digital enterprises because of its positive effect on technological efficiency and pure efficiency. 
description: The panel data of
 1034 listed DENs and
 718 listed non-digital high-tech enterprises from the fourth quarter of
 2017 to the first quarter of 2022 were used, which were from the
 Guotai'an database and Shenzhen and Shanghai Stock Exchanges.
 Incomplete data, ST, *ST, suspension of listing and delisted enterprises
 data samples were excluded. The sample interval entertained the PSM-DID
 method's requirement. The min-max normalization method was used to
 standardize the data. The Stata 16.0 software was used for
 calculation.
description: # Data from: epidemic, factor misallocation and efficiency of digital
 enterprises: heterogeneity study of labor and capital
 [https://doi.org/10.5061/dryad.ttdz08m4h](https://doi.org/10.5061/dryad.ttdz08m4h) ### **Files and variables** ### l File Covid 20240820.xlsx containing the data for our analysis. ### l File: PSM-DID 20240820 containing the code for our analysis. ### **Description of the data and file structure** ### These data are data for the analysis of the epidemic’s effect on efficiency of digital enterprises and factor misallocation’s role in it. The data presented here are processed data and are presented for the purposes of study repeatability. The raw data used can be accessed via contacting the Shenzhen and Shanghai Stock Exchanges and CSMAR database.  **Description:** Covid 20240820.xlsx contains the data for our analysis. ### Variables:  ### l firm: Number of enterprises, including 1034 listed digital enterprises (DENs) and 718 listed non-digital high-tech enterprises. ### l time: Seasons, from the fourth quarter of 2017 to the first quarter of 2022. ### l post:Take the first quarter of 2020 as the boundary. The value of the post was 0 before this time and 1 after. ### l com: com represented whether the enterprise was a DEN. When it was a DEN, the value was 1; otherwise, the value was 0. ### l lntfp::TFP. TFP represents total factor productivity. ### l lnse: Logarithmic SE. SE represents scale efficiency. ### l lnpe: Logarithmic PE. PE represents pure technical efficiency. ### l lnte: Logarithmic TE. TE represents technical efficiency.TFP, SE, PE, and TE is measured by using y and pt as output factors and rnd and totwage as input factors. ### l ynew: Logarithmic of total revenue. ### l gknew: Logarithmic Operating cost ### l rndnew: Logarithmic R\&D funds ### l ptnew: Logarithmic Knowledge output ### l totwagenew: Logarithmic Total payroll ### l distnew: Logarithmic factor misallocation.   ### l dist2new: Logarithmic factor misallocation \* 100. ### l distknew: Logarithmic capital. ### l distlnew: Logarithmic labor. The original value of distnew, dist2new, distknew, and distlnew is measured by referring to the approaches of Hsieh and Klenow ([2009](#c17)). ### l lntotass: Logarithmic total asset. ### l lnir: Logarithmic growth rate of enterprise net profit. ### l lnfb: Logarithmic ratio of current assets to income. ### l lny: Logarithmic total operating income. ### l lnemp: Logarithmic total employment. ### l lnage: Logarithmic enterprises’ age.   **Description of Data**     Variable |        Obs        Mean    Std. Dev.       Min        Max \-------------+---------------------------------------------------------         firm |     29,784       876.5    505.7672          1       1752         time |     29,784           9    4.899062          1         17         post |     29,784    .5294118    .4991426          0          1          com |     29,784    .5901826    .4918081          0          1         ynew |     29,784    .0159473    .0407685          0          1 \-------------+---------------------------------------------------------        gknew |     29,784    .0162498    .0421873          0          1       rndnew |     29,784     .011705    .0315493          0          1        ptnew |     29,784    .0059534    .0314484   -.000344          1   totwagenew |     29,784    .0169219    .0473249          0          1        lntfp |     29,784   -.0378258    .3866882  -2.424532   4.710519 \-------------+---------------------------------------------------------         lnse |     29,784   -.0104223    .3848167  -3.352407   4.278735         lnpe |     29,784   -.0287261    .3939992  -4.645091   2.907509         lnte |     29,784    .0012193    .3725406  -3.103317   2.447767     dist2new |     29,784   -20.30226    17.25596  -47.37596          0      distnew |     29,784   -2030.226    1725.596  -4737.596          0 \-------------+---------------------------------------------------------     distknew |     29,784   -22.74765    19.14687  -49.87755          0     distlnew |     29,784    2.381998    2.425883   -4.14221    11.0222     lntotass |     29,784    2232.895    118.9374   1795.865   2762.097         lnir |     29,784    1437.867    245.3416   -460.517   2501.373         lnfb |     29,784    76.31049    96.82135  -261.7296   1289.995 \-------------+---------------------------------------------------------          lny |     29,784    20.96417    1.526594   12.23758   26.32169        lnemp |     29,784    7.749478    1.273747          0   12.86432        lnage |     29,784    298.1576     28.2606   179.1759    378.419 Notes: Missing data is replaced with 0. The missing data is mainly caused by taking the logarithm of the original data. ### **Sharing/Access information** ### Data was derived from the following sources: ### l Shenzhen Stock Market: [http://www.szse.cn/](http://www.szse.cn/) ### l Shanghai Stock Market: [http://www.sse.com.cn/](http://www.sse.com.cn/) ### l CSMAR database: [https://www.gtarsc.com/](https://www.gtarsc.com/) ### **Code/Software** ### Covid 20240820.doc contains the code for our analysis. ### Data was run by Stata 16.0.software.