# Compilation of a City-Level & Four-Digit Industry Code MRIO Table Based on Firm-Level Data
## 5 Conclusions  
This study proposes a new construction method for compiling a high-resolution MRIO table. Our approach takes data from China as an example and relies on two large-scale enterprise-level datasets from 2000 to 2013, namely the China Annual Survey of Industrial Firms (CASIF) survey and the China Customs Data (CCD) database. By expanding the direct decomposition method, together with some auxiliary mathematical models necessary for estimations and consistency adjustment, we construct an MRIO table covering 284 prefecture-level administrative cities and 334 four-digit sectors, which is by far the most comprehensive MRIO table covering the largest number of cities and the most segmented industries in China. A comparative analysis shows that the MRIO by our method is stable and reliable. The proposed method can be implemented not only in China but also in any country with available sectoral output, value-added, and enterprise-level customs data. The presented MRIO table reflects the heterogeneity between cities and sectors. Benefiting from its coverage of the detailed nested among various cities and industries in China, its application can support the investigation of upstream and downstream linkages between sectors and regions. Moreover, when used in conjunction with other datasets, it enables the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \([0,1.0)\) & 14.24\% & 16.28\% & [0, 1.0) & 30.20\% & 34.92\% \\ \([1.0,+\infty)\) & 59.60\% & 9.89\% & [1.0,+\infty) & 20.29\% & 5.58\% \\ \([\)P0, P50\(]\) & (P50, P100\(]\) & & [P0, P50] & (P50, P100\(]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of statistics from two tables (links: Table 2; right: Table 3). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(cv_{ij}\)} & \multicolumn{8}{c}{**Coefficient \(a_{ij}\)**} \\ \cline{2-10}  & **P10** & **P20** & **P30** & **P50** & **P70** & **P80** & **P90** & **Total** \\ \hline \([0,0.5)\) & 0.00\% & 0.11\% & 0.05\% & 0.11\% & 0.21\% & 0.63\% & 1.59\% & 2.70\% \\ \([0.5,0.8)\) & 0.16\% & 2.06\% & 1.43\% & 2.33\% & 2.96\% & 3.96\% & 2.38\% & 15.29\% \\ \([0.8,1.0)\) & 0.37\% & 2.96\% & 1.85\% & 2.80\% & 1.69\% & 2.06\% & 0.79\% & 12.53\% \\ \([1.0,2.0)\) & 3.65\% & 8.30\% & 5.44\% & 6.55\% & 3.86\% & 3.65\% & 0.79\% & 32.26\% \\ \([2.0,+\infty)\) & 11.48\% & 4.86\% & 17.93\% & 1.37\% & 1.16\% & 0.42\% & 0.00\% & 37.22\% \\ Total & 15.66\% & 18.30\% & 26.71\% & 13.17\% & 9.89\% & 10.73\% & 5.55\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coefficients of variation (NBS method). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(cv_{ij}\)} & \multicolumn{8}{c}{**Coefficient \(a_{ij}\)**} \\ \cline{2-10}  & **P10** & **P20** & **P30** & **P50** & **P70** & **P80** & **P90** & **Total** \\ \hline \([0,0.5)\) & 0.15\% & 0.27\% & 0.14\% & 0.26\% & 0.64\% & 1.54\% & 3.99\% & 6.99\% \\ \([0.5,0.8)\)  
identification and analysis of key drivers of social and environmental changes. Meanwhile, some packages or systems can be developed to facilitate the retrieval and interpretation of input-output data (e.g., [40; 41]). As stated above, researchers have applied MRIO tables to study environmental impact, social ecology and social influences [26]. Furthermore, scholars have proposed an input-output-based analysis to develop a set of indicators for assessing economic performance. A study by [PERSON] et al. [42] applies the 2007 and 2010 Chinese MRIO tables to identify the importance of inter-provincial spillover effects in regional CO\({}_{2}\) emissions growth. More advanced, the MRIO table presented in this paper facilitates the construction of inter-city and intra-city spillover effects in China. Therefore, our approach and the developed MRIO table have significant policy implications for various fields of economics, including international trade and economic growth. Our method can improve the utility of MRIO analysis for regional scientists and decision-makers alike. This study has several limitations.