Empirical analysis of evaluation of the development of China’s information esource industry

To verify the effectiveness of IRIDI system, the research team has used China’s information resource industry data in 2013 for an empirical analysis and compared it with that.

Ranking by indicators

By measuring information resource industry data of 31 provinces, municipalities and autonomous regions in China in 2013 and 2014, we obtained the IRIDI ranks in 2014 and the changes from the 2013 ranking, as shown in Table 7.4.

Table 7.5 shows the industrial value ranking for IRIDI in 2014, and the changes compared with 2013.

Table 7.6 shows the industrial environment ranking for IRIDI in 2014 and the changes compared with 2013.

Explanation of ranking by indicators

Industrial value and industrial environment, two Level 1 indicators of IRIDI, respectively reflect the economic value created by information resource industry and the environmental factors that affect information resource industry in each region. The evaluation and analysis of IRIDI reveal the following.

In 2014, China’s information resource industry' maintained good momentum, and there is little change in the rankings by each region when compared with 2013. The overall scale was on a steady increase, and there are high scores for infrastructure, industrial structure, and public policy. Comparison between IRIDI and the economic development index between regions shows a clear correlation between the two: the better the economy in a region, the better the information resource industry' development in that region. However, there are still certain problems concerning information resource industry’s development. Unbalanced development across China is a major problem, with “East China Outperforming West China.”

Information resource industry infrastructure is being improved, and the utilization level of information resources needs to be improved

Level 2 indicators include industrial scale, industrial contribution, industrial development, industrial structure, public policy, infrastructur e, and decision-making intensity.

Table 7.4 China's information resource industry development index rankings 2014 (provincial-level administrative regions)

Table 7.5 Industrial value ranking for China’s information resource industry

(Continued)

Table 7.5 (Continued)

Table 7.6 Industrial environment ranking for China’s information resource industry

The industrial scale indicator reflects the relative scale of information resource industry in a region, which is measured by four indicators: operating revenue, employed population, number of legal entities, and the size of public companies. Beijing (94.9), Guangdong (94.1), and Jiangsu (89.6) ranked in the top three, while the national average score was only 78.5, and the standard deviation was

6.1. There were 21 provincial-level administrative regions scoring lower than the national average. All top six places were held by East China, and all bottom seven places by West China. In tenus of industrial scale indicators, information resource industry is still quite small in China, and there are significant differences between regions, with most provincial administrative regions lagging behind in information resource industry’s development.

The industrial contribution indicator reflects how much information resource industry contributes to a region’s economy, which is measured by two indicators: contribution to employment and contribution to economy. The highest score belongs to Beijing (90.5), the lowest Henan (74.7). The national average score is

79.1, and the standard deviation is 4.0. To sum up, information resource industry has not contributed significantly to China’s economy. Compared with other industries, information resource industry still plays a secondary role in the different province in China economy and is still weak.

The industrial development indicator shows the dynamic development of information resource industry in a region since 2004. It is measured by the development of industrial scale, the development of the number of legal entities, and the development of employed population. The highest score goes to Guangdong (96.6), the lowest score is Qinghai (72.9). The national average score is 79.3, and the standard deviation is 6.2. A total of 19 provincial-level administrative regions score lower than the national average. In conclusion, China’s information resource industry development is growing at varying paces between regions.

The industrial structure indicator depicts the optimization of information resource industry structure in a region, and it is measured by two indicators: industrial resource structure and industrial factor intensity. The highest score belongs to Shanxi (84.0), the lowest Guizhou (75.8). The national average score is 82.2, and the standard deviation is 1.5. Information resource industry structure is relatively reasonable across China, with little difference between regions, which means that all regions share similar paths to develop information resource industry.

The policy environment indicator represents the optimization of information resource industry policy environment in a region, and it is measured by two indicators: supply of industrial policies and opening and interaction of government affairs. Guangdong has the highest score (96.1) and Tibet the lowest score (72.7). The national average score is 82.8, and the standard deviation is 6.3. There is a huge gap between the supply of information resource industry policies and the opening of government affairs in various regions of China. Many regions fail to pay enough attention to developing information resource industry. The government needs to step up efforts in constructing information resource industry, make reasonable planning, establish uniform standards, and strengthen the construction of the legal system and security system.

The infrastructure indicator shows how well supporting infrastructure is provided for information resource industry, and it is measured by two indicators: development of industrial parks development and utilization of information technologies. The highest score belongs to Beijing (96.2), the lowest one is Tibet (85.0). The national average score is 89.9, and the standard deviation is 3.1. It is worth noting that the lowest score in 2013 was with Gansu (79.2), with an average score of 86.1 and a standard deviation of 4.4. This indicates that in 2014, all provincial-level administrative regions have made great progress in constructing information resource industry infrastructure, and the infrastructure gap between these regions is being closed.

The decision-making strength indicator represents the amount of attention paid by the government to develop information resource industry and the intensity of relevant government work. It is measured by two indicators: attention from decision makers and government work intensity. The highest score goes to Tibet (86.7), and the bottom scores are seen in ten provinces and cities (72.0), including Inner Mongolia. The national average score is 75.8 and the standard deviation is 4.1. It should be noted that the lowest score for 2013 belongs to Jilin (77.5), with an average score of 80.2 and a standard deviation of 3.3. In other words, all regions across China still have not paid enough attention to and worked hard enough in developing information resource industry in 2014, and the performance in 2013 is even worse.

In all indicators listed in Figure 7.1, the lowest score (75.8) of 2014 is with decision-making intensity, the highest (89.9) with infrastructure. Correspondingly, Level 3 indicators - attention from decision makers and government work intensity score low points, while the development of industrial parks and

Figure 7.1 Level 2 indicators for tire information resource industry’s development in China (2013-2014)

utilization of information technologies score high points. The evaluation of the development index of information resources industry means China has come a long way in constructing industrial parks and advancing informatization, along with smooth progress in economic restructuring. However, information resource industry’s development has not drawn enough attention, and the decision-makers have not guided it in the right way, which becomes a top constraint on information resource industry’s development. The scores of industrial structure and public policy indicators are 82.2 and 82.8, relatively high. The scores of industrial scale, industrial contribution, and industrial development are 78.5, 79.1, and 79.3, respectively, still large gaps from the three indicators mentioned earlier. It is evidenced that the construction of information resource industry is far from being perfect, and the exploitation and utilization of information resources are still at a primitive stage, and the industrial scale is small, so there is much room for future growth.