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    <responseDate>2026-10-12T13:21:39Z</responseDate>
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    <identifier>10.57760/sciencedb.34386</identifier>
    <datestamp>2026-04-22T09:44:52Z</datestamp>
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  <dc:date>2026-04-22</dc:date>
  <dc:title>Business dates of SRDI Enterprise from 2016 to2024</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.34386</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The dataset used in this study is obtained from the CSMAR (China Stock Market &amp;amp; Accounting Research) database after screening and downloading, followed by data cleaning in Stata. The sample period spans from 2016 to 2024, resulting in a panel dataset of &amp;ldquo;specialized, refined, distinctive, and innovative&amp;rdquo; enterprises over this period, including &amp;ldquo;Little Giant&amp;rdquo; firms, small and medium-sized enterprises, and innovative firms. The dataset contains firm-level financial information such as stock codes, reporting dates, total assets, total liabilities, and other related financial indicators.Due to occasional missing data for certain years in the CSMAR database, as well as discrepancies in variable definitions during the merging of different datasets, a small portion of observations is lost. However, given the large overall sample size, this issue is negligible and does not affect the reliability of the analysis.Based on the firm-level financial data, proxy variables for the explanatory variables, dependent variables, moderating variables, and control variables are constructed. Finally, the specified econometric models are implemented in Stata to obtain the regression results.</dc:description>
  <dc:subject>SRDI; Total factor productivity; DID</dc:subject>
  <dc:creator>binyu zeng</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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