Journal of Economic Structures

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

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How to evaluate the reliability of regional input–output data? A case for China

Journal of Economic StructuresThe Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)20176:35

https://doi.org/10.1186/s40008-017-0096-5

Received: 3 September 2017

Accepted: 10 November 2017

Published: 22 November 2017

Abstract

Accurate statistical data are essential to a credible and cogent empirical analysis. However, there currently is no mature and specialized methodology to evaluate the accuracy of input–output (IO) data. This research constructs a comprehensive yet relatively concise framework for evaluating the accuracy of regional IO data by including several indicators that measure all three quadrants. The framework examines regional IO data from the perspectives of time consistency and variation, coefficient correlation and its homogeneity with national-level data. A score indicating the overall accuracy and detailed information that presents concrete shortcomings of regional IO data could be offered after analysis using this framework. As an example, the provincial-level IO data for 30 provinces for 3 years (2002, 2007 and 2012) are analyzed by this framework, and possible explanations of the results are offered. The main contribution and innovation of this research is the construction of an applicable and exhaustive quality evaluation framework for regional IO data. This framework enables researchers to realize flaws in IO data before utilizing them. It also allows government agencies to improve the quality of their data by avoiding issues that emerged in previous data quality evaluations.

Keywords

Data qualityQuality evaluationChinaRegional IO data

1 Background

Economic research has been increasingly concerned with structural issues and value-added in multi-sector international trade. Therefore, input–output (IO) data have become a prevalent data source and been more frequently used in empirical analyses. As a result, the accuracy and reliability of IO data raise serious concerns since the credible data ensure credible empirical results. In fact, there has been ample research dedicated to the quality of government statistics (Zhao et al. 2011). It turns out that no socioeconomic statistical data are completely precise due to statistical regime defects (Xu 1994; Jin and Tao 2010; Holz Holz 2013a, b), investigation and aggregation errors (Park and Wang 2001; Agafiţei et al. 2015) and lack of independence in statistical agencies (Outrata 2015). It is reasonable to presume that IO statistics, as a type of government statistical data, also suffer from some similar quality issues. Thus, the core problem is how to assess the accuracy and quality of current IO data.

There has been a prolonged history of using IO data in government statistics. As a part of national accounting, input–output first appeared in A System of National Accounts (SNA1968; United Nations 1968). Then, in System of National Accounts 1993 (SNA1993; United Nations et al. 1993), the non-investigative supply-use table was introduced, and IO data were considered a major component of national accounting. In System of National Accounts 2008 (SNA2008; European Commission et al. 2009), IO tables were considered an extension of production accounts. Furthermore, SNA2008 recommends a supply-use table framework instead of survey-based IO tables since supply-use tables are much easier to acquire, which could enable statistical agencies to release IO table more efficiently and frequently. Two specific IO manuals, UN (1999) and Eurostat (2008), guide the compilation of IO tables. In terms of regional IO data, Miller and Blair (2009) present how to apply several non-survey methods, such as RAS, to compile regional IO tables, and offer theoretical regional models as well.

Before implementing evaluations, standards respecting statistical quality need to be specified. International organizations have long possessed major concerns over the standards for statistical data. The first organization that paid attention to the quality of statistical data was the UN in 1980. Different from other organizations, the UN (2003) primarily focused on optimizing the structure of statistical agencies, arguing that these agencies should obtain independence, relevance, credibility and respondent policies as their foundations. The IMF (2013a, b) required all subscribers of the Special Data Dissemination Standard (SDSS) to follow four statistics criteria. First, the data must have ample coverage, periodicity and timeliness. Second, the data must be publicly accessible. Third, the data and the process must possess integrity. Fourth, the data must have proper quality, meaning the methodology and data must be reasonable and pass cross-checking. The General Data Dissemination Standard (GDDS) was also released by the IMF (2013a, b). It is designed for relatively less-developed government statistical systems but shares the same general requirements. OECD and Eurostat provide more detailed standards than those previously discussed in this paragraph. The OECD (2011) measures statistical data from eight dimensions, including accuracy, coherence, timeliness and accessibility, among others. Eurostat (2011) presents a method that is constituted by 15 principles that cover the institutional environment, the statistical production processes and the output of statistics. This process is aimed at ensuring accurate, coherent and comparable data. Outside of organizations, individual researchers have also established several data quality standards that address data accuracy, timeliness and availability (Brackstone 1999).

All data quality evaluation methods are classified into two branches: data-driven and theory-driven. Data-driven methods are based on the data itself, only use statistics, mainly focus on finding outliers in a group of data points and determine the data quality by the number of outliers. For instance, Zhang (2003) introduces a statistical test to find outliers by assuming that the data distribution is exponential. Another example comes from machine learning, which offers various algorithms (such as support vector machines) that can be used to separate outliers from the remaining points (James et al. 2014).

However, the theory-driven methods design indicators based on economic theories. These methods also fall into two categories depending on how many indicator(s) they use. Some methods only use one or two statistics that are closely linked with data (Huenemann 2001). Other methods construct a multi-indicator system (Klein and Özmucur 2011; Mehrotra and Pääkkönen 2011; Reis et al. 2015; Ye 2011) or use the multivariable regression method (Liu and Huang 2009). In addition, variations and trends are common indicators for analyzing the consistency of data and can be assessed using time-series analysis or simple comparisons (Sinton 2001). Different from these methods that are based on the calculations of real data, Wang and Jin (2010) created a questionnaire that included measurements of respondents’ subjective impressions of the quality of statistics.

Several conclusions can be drawn from the research discussed above. An apparent issue is that none of this research is IO specific. The majority concentrate on GDP, and the remaining study transportation, energy and other particular areas other than input–output data. A derivative problem is that, although standards or principles remain the same, these methods are only compatible with simple statistical indicators that reflect an economic scale. However, IO data consist of hundreds of interrelated statistics that concurrently demonstrate economic scale and structure. The delicate correlations between data indicate that a systematic method or framework needs to be established to evaluate the quality of IO data.

Therefore, the main contribution of this paper is to construct a plausible framework to evaluate regional IO data. The reason that we assess regional instead of national data is that a benchmark is necessary during evaluation. National data are usually of better quality and more consistent, making it more appropriate as the benchmark.

To be precise, not all the principles of statistical data mentioned above will be implemented in the following IO data analysis. Since the objective of this paper is to evaluate the quality of data, standards such as data availability that measure the quality of statistical agency services instead of the data itself are omitted. Additionally, standards that are not applicable to IO data, such as coverage, are also omitted. The standards measured in this paper are data accuracy, coherence between regional and national data, and time-series consistency.

The remainder of this paper is arranged as follows: In Sect. 2, a framework evaluating regional IO data as a whole and individually is constructed. Section 3 is the empirical analysis that uses the framework constructed in Sect. 2 and applies the framework to China. Section 4 offers possible explanations of the results. Section 5 concludes the work.

2 Constructing the evaluation framework

An IO table consists of hundreds or thousands of interrelated numbers. This large quantity of data could be highly favored by scholars and policy makers. Nevertheless, it may lead to more difficulties when compared to the evaluation of the quality of single-number data, like CPI, since it is not possible to find reference indicators outside the IO table for every single number. Therefore, when constructing the evaluation framework, two premises have been set as follows.
  1. (1)

    The source of data used in an evaluation is the regional and national IO data only.

     
  2. (2)

    Only limited but representative data will be involved in the analysis of IO data quality. Specifically, relatively important (and large enough) direct input coefficients or key coefficients (KC) will be representative numbers.

     

Before we introduce the specific indicators for quality evaluation, it is essential to delineate how we plan to construct the evaluation framework. Since regional input–output data is a data system constituted by multiple input–output matrices, and every matrix is composed by the same bundle of sectors, it is natural to measure the data quality using the regional and industrial angles. The regional angle examines whether there are significant differences in data quality between individual regions. The industry angle examines whether the data quality of some industry is questionable, regardless of the region. As we previously stated, for direct input coefficients, it is more desirable to examine only key coefficients, which achieves a balance between the evaluation accuracy and time costs. In terms of the final demand and value-added, due to the relatively small number of cells, all data could be assessed. Then, several indicators are constructed according to the IO and economic theories, which includes concerns about the consistency and coherency of the individual data table and the entire data system. The rationales for indicators are explained below. The final step is to summarize all those indicators to evaluate the quality of different regions and sectors.

Now we can begin the construction of the evaluation framework. It is reasonable to construct an indicator using the ratio of the number of aberrant KC(s) to the number of all KCs. A higher ratio indicates a lower-quality IO table. Likewise, it can also be used to examine the IO data quality in a certain sector or even data of all regions as a whole (national IO data system).

Next, it is critical we define aberrant KC(s). From IO theory, direct input coefficients (as symbols of production technology) remain stable at least in short terms. Hence, once a mutation occurs, a zero KC turns into a significant nonzero one in the next year, or, vice versa, this KC is categorized as aberrant. The term “mutation” implies that these kinds of changes usually indicate a technological revolution in production, and new sectors emerge, or old sectors die. Any of these changes can be regarded as so tremendous that it is highly unlikely they occur in a short time period. Nevertheless, KCs do not retain absolute stability, and minor changes are inevitable between two accounting years. However, those changes are neither random nor without constraints. An assumption is that these changes follow similar features or trends for KCs within a sector, since the same national macroeconomic and industry policies and similar market and technology conditions are shared by all KCs in a certain sector regardless of the region. Accordingly, if some change(s) of KC(s) become outliers of all changes, these KC(s) are also considered as aberrant.

In summary, we need to stress that not all changes are viewed as quality flaws. Instead, only drastic, irregular changes are treated as errors and mistakes. These changes are far from those caused by normal disturbances and are highly unlikely to be explained by minor issues such as price differences or random errors.

Therefore, we must address what standards are satisfied by low, aberrant KC indicators. It is obvious to see that time consistency is satisfied, as the change is so minor that data from 1 year do not contradict that from another year. Another standard is accuracy, although accuracy is not directly examined. Assume an alternative scenario in which quite a few numbers are erratic. In this case, data in at least 1 year are not accurate since they are challenged by data in another year. Conclusively, an ideal indicator of an aberrant KC ratio does not necessarily mean accurate data, whereas a poor indicator certainly shows flaws in data. In other words, this indicator is the prerequisite but not a sufficient condition of accuracy.

A simplified example is shown below to more clearly illustrate aberrant KC indicator. Assume we have a nation with two regions and three sectors. By some method, eight direct input coefficients have been confirmed as KCs, including all direct input coefficients except \(a_{12}\). The locations of aberrant KCs are given in Table 1, and the number of aberrant KCs is displayed in Table 2.
Table 1

An example of locations of aberrant KCs in an imaginary nation

 

Region 1

Region 2

 

Sector 1

Sector 2

Sector 3

Sector 1

Sector 2

Sector 3

Sector 1

  

aKCa

aKC

  

Sector 2

aKC

   

aKC

aKC

Sector 3

 

aKC

   

aKC

aAberrant KCs are marked by notation aKC

Table 2

An example of the number of aberrant KCs in an imaginary nation

 

Region 1

Region 2

The number of aberrant KCs

3

4

In sector 1

2

2

In sector 2

2

3

In sector 3

2

3

Note that when counting the number of sectors, if \(a_{mn}\) is a KC, it is treated as a KC in both sectors m and n. If \(m = n\), \(a_{mn}\) is treated as 2 KCs in sector m (n).

Therefore, the KC indicators are calculated as follows.
$${\text{Ratio}}\;{\text{of}}\;{\text{KC}}\;\left( {{\text{Region}}\; 1} \right) = 3/8 = 0.375$$
$${\text{Ratio }}\;{\text{of }}\;{\text{KC }}\;\left( {{\text{Region}}\; 2} \right) = 4/8 = 0.500$$
$${\text{Ratio}}\;{\text{of}}\;{\text{KC }}\;\left( {{\text{Sector}}\; 1} \right) = \left( {2 + 2} \right)/\left( {5 + 5} \right) = 0.400$$
$${\text{Ratio}}\;{\text{of}}\;{\text{KC }}\;\left( {{\text{Sector }}\; 2} \right) = \left( {2 + 3} \right)/\left( {5 + 5} \right) = 0.500$$
$${\text{Ratio}}\;{\text{of}}\;{\text{KC}}\;\left( {{\text{Sector }}\;3} \right) = \left( {2 + 3} \right)/\left( {6 + 6} \right) = 0.417$$
$${\text{Ratio}}\;{\text{of}}\;{\text{KC}}\; \left( {\text{Nation}} \right) = \left( {3 + 4} \right)/\left( {8 + 8} \right) = 0.438$$

Coherence in each sector and all regions also need to be taken into consideration. From the analyses above, evaluations for individual regions compare data from different years to draw conclusions. Therefore, evaluations must use a data package that includes 2-year datasets and concurrently displays the results of 2 years together. However, as for each sector and all regions together, national data serve as a benchmark, and results of single years are available.

To utilize national-level statistics as benchmarks, a new indicator is introduced. A simple character of a coherent data system is the aggregation of regional-level data approximately equal to the national data. Accordingly, a ratio of the aggregation to national data is a reasonable measurement, which accounts for total output, consumption, capital formation, labor compensation and other indicators. If the ratio of a sector significantly deviates from 1, the data of the sector is incoherent since the data contradict other data. Thus, the ratio of incoherent sectors (IS) to all data is a proper indicator aimed at the data quality of each sector and all regions together. Similarly, the aggregation of total inward flows and outward flows (goods and services imported from/exported to other region but not from/to foreign countries) should be approximately equal. If the ratio of the aggregation of inward flows to one of outward flows is significantly larger or smaller than 1 certain sector, this sector needs to also be treated as IS.

Here is an example illustrating this indicator. Suppose again that a nation has three sectors and IS data are identified and noted in Table 3.
Table 3

An example of IS indicator in an imaginary nation

 

Sector 1

Sector 2

Sector 3

Total output

   

Consumption

ISa

IS

 

Capital formation

IS

IS

 

Labor compensation

  

IS

Inward/outward flow

 

IS

 

aIncoherent sectors are marked by notation IS

Therefore, the IS indicators are calculated as follows.
$${\text{Ratio}}\; {\text{of}}\; {\text{IS}}\; \left( {{\text{Sector}}\; 1} \right) = 2/5 = 0.400$$
$${\text{Ratio}}\;{\text{of}}\;{\text{IS}}\;\left( {{\text{Sector}}\; 2} \right) = 3/5 = 0.600$$
$${\text{Ratio}}\;{\text{of}}\;{\text{IS}}\;\left( {{\text{Sector}}\; 3} \right) = 1/5 = 0.200$$
$${\text{Ratio }}\;{\text{of}}\;{\text{IS}}\;\left( {\text{Nation}} \right) = \left( {2 + 3 + 1} \right)/15 = 0.400$$
To sum up, Table 4 demonstrates the quality evaluation framework for the regional IO data established above.
Table 4

Quality evaluation framework for regional IO data

Aberrant KC indicators

Mutation

Trend outlier

IS indicators

Total output

Consumption

Capital formation

Labor compensation

Inward/outward flow

The data quality of each region is given by aberrant KC indicators, while the quality of each sector and the whole nation is the average of the individual aberrant KC indicator and IS indicator.

For example, the data quality of the imaginary nation in the example above is 0.419.
$$\left( {0.438 + 0.400} \right)/2 = 0.419$$

3 Evaluation of province-level IO data in china

In this section, in order to apply the established framework, China’s provincial-level IO data are analyzed.

3.1 Data

The IO accounting years in China end with 2 or 7 (based on real IO survey) and 0 or 5 (updated using general national accounting data). IO data in the most recent three consecutive accounting years based on the real survey (2002, 2007 and 2012) constitute the data source. The evaluation includes all provinces in mainland China except Tibet. In short, 30 provincial-level tables in 3 years (90 total tables) are included. In addition, the national IO table in these years is used for benchmarks. All data are available from the National Bureau of Statistics of China (NBS).

All tables used here contain 42 sectors. However, there are some minor changes in sector classifications between any 2 years. Therefore, sector adjustments have been implemented to keep the sector classification consistent over time, and the adjustment procedures are listed in appendix (Tables 9, 10 for the result of adjustments). Therefore, all tables are modified into a 39-sector version. Specific changes on sectors and sector classifications after adjustments are listed in appendix.

3.2 Aberrant KC indicators

3.2.1 Choose key coefficients (KCs)

It is true that there are several methods to choose KCs. However, to simplify the calculation, a single rule has been adopted for choosing KCs such that if \(a_{mn}\) is larger than 0.05 in two of the 3 years in national tables, \(a_{mn}\) is identified as a KC. After calculations, 87 coefficients (5.72% of all coefficients) satisfy the rule. The sum of these coefficients account for 47.39% (2002), 52.48% (2007) and 53.15% (2012) of the sum of total direct input coefficients in the 3 years. Figure 1 shows the sector distributions of KCs.
Fig. 1

Sector distributions of key coefficients, viewed by row (left) and column (right)

3.2.2 Mutation

As mentioned above, mutation means a sudden change in number from zero to nonzero or vice versa. After examining 2610 key coefficients, there are 66 (2002–2007) and 52 (2007–2012) mutations arise. Viewed by province, Qinghai possesses the most mutations (13) in 2002–2007, and no other province own a mutation number over ten, no matter what year. Most mutations happened in provinces in middle and western China. As for sectors, most mutations happen in sector coal mining products, and other manufacturing, both 20 mutations in 2002–2007, and sector other manufacturing also have 20 mutations in 2007–2012, which ranks at first of the period, followed by 15 mutations in sector coal mining products and 14 mutations in gas production and supply. Detailed results of mutation, along with results of following evaluations, are all found in appendix.

3.2.3 Trend outlier

Normally, the changes between two consecutive years share some similarities or trends, as mentioned in Sect. 2. Therefore, trend breaker(s) are signs of flaws in the data. We must examine how to identify these trend breakers or outliers. Imagine a scatter plot that shows the coefficients of a KC in all regions. The two axes represent the values of coefficients in different years. The existence of a certain trend means normal data points should be somewhat concentrated. However, outliers are not concentrated with normal data points. Figure 2 shows the general idea of this scatter plot. From this plot, it is apparent that points A and B are outliers.
Fig. 2

A general idea of scatter plot and trend outliers

However, not all outliers could be identified so clearly (such as point C in Fig. 2). Based on this scatter plot, an algorithm is developed to help find outliers.
  1. (1)

    Calculate the center of points in the plot using the leave-one-out method. The coordinate of the center is given by the arithmetic average of the coordinate of each point except the left-out one.

     
  2. (2)

    Calculate the Euclidean distance between the center and each point except the left-out one, and sum all the distances.

     
  3. (3)

    Repeat steps (1) and (2) while changing the left-out point. Stop repeating this step when all points have been left out once.

     
  4. (4)

    List all sums and use the “2 times standard deviation rule” to identify the outliers.

     
  5. (5)

    If a sum is identified as an outlier, the corresponding point that was left out is the trend outlier.

     

Compared with the number of mutations, there are more outliers. Individually, 209 and 194 outliers are identified in each respective period. In 2002–2007, Hainan had the most outliers (12), followed closely by Qinghai (11) and Beijing (11). This is similar in 2007–2012, although Qinghai (17) ranked first followed by Hainan (14) and Beijing (11). In terms of sectors, the two sectors that own the most outliers in both periods are chemical products (27, 27) and metal smelting and rolling processing (18, 20).

3.2.4 Summary

Figure 3 shows the aggregation of mutations and outliers (all aberrant KCs in each province), while Fig. 4 shows those in each sector.
Fig. 3

Aberrant key coefficients in each province

Fig. 4

Aberrant key coefficients in each sector

From the figures above, it is apparent that there is only a slight improvement in data quality in 2007–2012 compared with 2002–2007 when measured with aberrant KCs. However, this conclusion does not hold over all sectors and regions. Another transparent conclusion is the correlation between two periods. To be precise, the Pearson correlation coefficients and significance tests are calculated and listed in Table 5. It turns out that all KC indicators are positively correlated when the significance level \(\alpha\) is 0.05. In fact, expect for mutations, the correlations of all indicators are statistically significant, even when \(\alpha\) equals 0.01.
Table 5

Correlation between two periods regarding aberrant key coefficients

 

Mutation

Trend outlier

Sum

By provincea

0.437

0.787

0.798

(0.016)

(0.000)

(0.000)

By sector

0.933

0.915

0.938

(0.000)

(0.000)

(0.000)

aNumbers in parenthesis are significance level

Since no IS indicator is designed for regional evaluation, data quality in each province is given by aberrant KC indicators. Table 6 shows the five best and worst quality provinces. Some of the results, such as the poor quality of Beijing and Shanghai, may be counterintuitive, and possible explanations are offered in Sect. 4.
Table 6

Best and worst IO data quality regarding province in each period

Best data quality, 1–5

Worst data quality, 1–5

Rank

Province

Aberrant KCsa

Rank

Province

aberrant KCs

Period 2002–2007

Period 2002–2007

1

Sichuan

0.000

1

Qinghai

0.253

2

Hebei

0.023

2

Hainan

0.184

2

Heilongjiang

0.023

3

Shanghai

0.149

2

Hubei

0.023

4

Beijing

0.138

2

Hunan

0.023

5

Guangdong

0.126

Period 2007–2012

Period 2007–2012

1

Hunan

0.011

1

Qinghai

0.230

1

Liaoning

0.011

2

Hainan

0.207

2

Sichuan

0.023

3

Beijing

0.149

2

Guangxi

0.023

4

Shanxi

0.126

2

Jiangsu

0.023

4

Ningxia

0.126

aAberrant KCs mean the ratio of aberrant KCs to all KCs, lower is better

3.3 IS indicators

The data quality in each sector has been evaluated and presented above. However, IS indicators still need to be calculated to assess the data quality of sectors and the whole nation’s IO data system.

3.3.1 Ratios

To identify incoherent sectors (ISs), the ratio of all provincial-level data to the real national data first has to be calculated. Take total output as an example.
$${\text{Ratio}}\;{\text{of}}\;{\text{sector}}\;{\text{A}} = \frac{{\sum {\text{Total }}\;{\text{outputs}}\;{\text{of}}\;{\text{sector}}\;{\text{A }}\;{\text{in }}\;{\text{all}}\; {\text{province}}}}{{{\text{National}}\;{\text{total}}\;{\text{output}}\;{\text{in}}\;{\text{sector}}\;{\text{A}}}}$$
This formula is compatible with all IS indicators in Table 4, except for inward/outward flow. It should apply the following formula.
$${\text{Ratio}}\;{\text{of}}\;{\text{sector}}\;{\text{A}} = \frac{{\sum {\text{total}}\;{\text{inward}}\;{\text{flow}}\;{\text{of}}\;{\text{sector}}\;{\text{A }}\;{\text{in}}\;{\text{all}}\;{\text{province}}}}{{\sum {\text{total}}\;{\text{outward}}\;{\text{flow }}\;{\text{of}}\;{\text{sector}}\;{\text{A }}\;{\text{in}}\;{\text{all}}\;{\text{province}}}}$$
In this analysis, the data of inward/outward flows are only available in 2012. Figure 5 shows the ratios of total output calculated using the formula above.
Fig. 5

Ratios of the aggregation of total outputs in all regions to the real national one

Theoretically, all ratios should be equal or at least approximately equal to 1. However, the norm is that these ratios may be a greater than or less than 1 for the following reasons.
  1. (1)

    The price standard. Local producer prices are used in the regional table instead of national prices as used in the national table.

     
  2. (2)

    Lack of data. In 2002 and 2007, Tibet did not conduct IO investigations and thus has no IO table.

     
  3. (3)

    Statistical errors.

     

Despite these reasons, the differences between the aggregated data and real national data still should be slight. First, the economic scale of Tibet is small, even when compared to other middle and western China provinces that are less developed. Additionally, issues of price levels and errors are usually minor. The price levels within a country should converge according to the free market theory, and a national price level could be considered as an average. For errors, a large statistical error itself is a sign of low data quality.

3.3.2 IS indicators

In the following analysis, sectors with ratios greater than 1.2 or less than 0.8 are considered incoherent sectors (ISs). In Fig. 5, the majority of total output ratios are located in this range, while there are a few ratios too large or small. However, in terms of the ratios of final demand (consumption, capital formation) and labor compensation, two features need to be stressed. First, more peculiar ratios emerge. For instance, only 19.3% of capital formation ratios lie in the range 0.8–1.2. Second, more extreme ratios emerge. Still, with respect to the capital formation ratios, some ratios are negative, and some are larger than 30. These extreme ratios indicate that the national IO data system could not maintain coherence within it, and the reliability of data should be questioned.

3.4 Data quality of sectors and national IO system

With all indicators calculated, the data quality of sectors and the whole system can be evaluated. First, the quality of each sector is the arithmetic average of aberrant KC indicators and IS indicators.1 The problem is that aberrant KC indicators are calculated in a 2-year package. To solve this, aberrant KC indicators for 2002–2007 are treated as the indicators for 2002, indicators for 2007–2012 are treated as the indicators for 2012, and the indicators for 2007 are the average of those two. Figure 6 shows the quality of individual sectors in the 3 years.
Fig. 6

IO data quality in each sector (lower is better)

Generally, for years \(t_{0} , t_{1} , \ldots , t_{n} , t_{n + 1}\) where the aberrant KCs for any next 2 years \(aKC_{ij}\) are given, aberrant KC for a single year \(aKC^{t}\) is defined as follows:
$$aKC^{0} = aKC_{01}$$
$$aKC^{i} = \frac{1}{2}\left( {aKC_{i - 1,i} + aKC_{i,i + 1} } \right)$$
$$aKC^{n + 1} = aKC_{n,n + 1}$$
where \(i = 1, \ldots , n\).
In Fig. 6, data qualities of scarp processing sector, gas production and supply, and R&D and technical services are the worst among all sectors in all 3 years. However, sectors such as agriculture and agricultural services, communication, computer and other electronic equipment, and education possess a relatively good data quality. The correlations between the data quality of sectors in different years are also calculated. The results (listed in Table 7) indicate that the data qualities in different years are strongly positively correlated.
Table 7

Correlations between data quality of individual sectors in different years

 

2002 and 2007

2002 and 2012

2007 and 2012

Correlation coefficients

0.695

0.544

0.637

Test p value

(0.000)

(0.000)

(0.000)

At the end of this section is the calculation of IO data quality of national IO data system, or the country as a whole. The method is the same with the calculations of sectors, and Table 8 shows the results. For overall data quality, the data quality in 2007 is only slightly better than in the other 2 years. While the fewest aberrant KC indicators occur in 2012, the IS indicators in 2007 are better to a relatively large extent. The data quality in 2002 is the worst with respect to aberrant KC indicators and IS indicators.
Table 8

IO data quality of national IO data system

 

2002

2007

2012

Aberrant KC indicators

0.080

0.077

0.074

IS indicators

0.537

0.469

0.503

Data qualitya

0.309

0.273

0.289

aLower is better

4 Interpretations and explanations of the result

In the results listed in Sect. 3, we easily find that the data qualities of relatively underdeveloped provinces (like Qinghai and Hainan) are lower than in more developed regions. The development extent could be the most obvious explanation that may occur. Nevertheless, there are some interesting issues in conclusions, such as Beijing suffering the worst data quality, which requires interpretations and explanations. However, before the analysis, we must note there are no formal explanations that could be easily tested by empirical measures. This is due to the large quantity of data in the IO table (as we previously mentioned) and the lack of statistical data, which are necessities when testing some of the theories offered below.

First, we address the characteristics of the evaluation method itself. Limited by the features of IO data, we exploit a time-series self-referenced method that evaluates data quality by comparing the data from the same source (province, industry, or even the same cell in IO table) over different years. While this method is time and data efficient and is also easy to deploy in real-world applications, it does have weaknesses. One of them is that when structural changes occur they may not be recognized and would be considered as a sign of poor data quality, since time consistency is treated as an assumption and could not be violated. Thus, it is possible that the evaluation method instead of the IO data is faulty under the following circumstances.
  1. (1)

    The change in the national accounting methodology may influence the results of Guangdong in 2002–2007, since the accounting modifications concern the processing trade. Previously, the processing trade was considered two transaction processes. One process was the importation of raw materials and intermediate products, and the other was the exportation of finished, processed products. Therefore, it largely affected both imports and exports. However, under the current regime, only the added value of the processing trade would be measured. Similar situations hold for the construction, public management, social security and social organizations sectors, whose accounting methods have been changed or at least modified.

     
  2. (2)

    Economic activity changes. Since regions such as Qinghai and Hainan have relatively small regional activities compared to provinces in eastern China, the data are more likely to fluctuate. In addition, structural changes are more likely to occur due to the small quantity.

     
  3. (3)

    Visible data improvement. Suppose the data quality in account year \(t\) is poor and has been significantly improved in year \(t + 1\). However, since the evaluation results depend on consistency, the improvement could not be recognized. For instance, the accurate value of \(a_{ij}\) in some region is 0.010 in year \(t\) and 0.011 in year \(t + 1\). However, because of some mistakes, there was a huge bias, and an inaccurate value in year \(t\) was recorded as 0.020. In the next year, they corrected the statistical methods and the value they offered was 0.012, which is a significant improvement. However, as the data quality under the framework of this research is measured in a 2-year window, the data quality could be recognized as poor in those 2 years (instead of year \(t\) only), since there is a huge jump in the coefficient that should be stable. According to NBS, the data quality in Beijing in 2012 may follow this trend.

     
  4. (4)

    Major city effects. There are some specialties of major cities that should be taken into consideration. One of them is distortions due to the concentration of corporate headquarters. While corporations may operate throughout the country, most choose to establish national headquarters in the three major cities of Beijing, Shanghai and Guangzhou. Although statistical codes require that branches in local regions should be accounted for at the regional level, it is difficult for government statistical officers to separate the activities of branches of different levels within a corporation. Therefore, at least some of the economic activities that occur in other regions are calculated in those three major cities, which could cause significant fluctuations in data consistency. For instance, even if the local branches keep their own production techniques unchanged (heterogeneous), as long as the proportions of their production to the total production change, the aggregated coefficients in major cities still may significantly fluctuate. This problem should still be considered as a quality flaw. Another scenario is that developed regions are inclined to be more dynamic and less consistent because of quickly evolved technologies. Mature markets and fierce competition force businesses in developed areas to constantly upgrade their technologies and create more innovative business models. While it may seem that inconsistencies of this kind are not quality flaws, we still argue that these inconsistencies could be avoided if government statistical agencies released IO table more frequently, such as annually.

     
  5. (5)

    False data. We regret to report that, while the data quality in Liaoning seems acceptable, the officials of the local statistical bureau admitted that they used false data to fabricate nonexistent economic booms. The most likely scenario is that they employ the same time-consistency assumption to fabricate the false data. If this is the case, it may even be useless to include more non-IO data, since the statistical agency would possibly change other data to make sure that the IO and non-IO data would not contradict each other.

     

However, we cannot deny the reasonability of the evaluation framework. In most instances, time-consistency assumptions still deserve their own merits. One of the phenomena is data division. Although the NBS and its provincial-level branches conduct specialized input–output statistical surveys, there are still large amounts of statistics borrowed from other sources that are not concrete enough for an IO table. This indicates that it is inevitable to divide data into different sectors or other categories. There are still many situations that could not be forecasted or require flexibility because of distinct issues in different regions. Therefore, the judgment of the statistical staff is crucial to retain the accuracy and comparability of data. The best-quality province, Sichuan, retains the same official to manage its IO data.

The last explanation we offer in this section concerns the balance of the IO table. It is natural that several data are treated as balanced items in the system of the national account. In quadrant II, consumption data are considered relatively accurate since the investigation data are more detailed and frequent. In China, export data are directly recorded by customs and are recognized as even more accurate. Thus, the inward/outward flow in 2012 and capital formation in earlier years are treated as balanced items whose accuracy could not be assured. In other words, the data quality of these two items is not a concern of the statistical system. It is not difficult to explain why their coherencies are questioned by IS indicators.

5 Conclusion

In this paper, a novel framework for evaluating regional IO data has been constructed. It contains two types of indicators. Aberrant key coefficients measure accuracy and consistency in the time series, and incoherent sectors examine coherence. Therefore, the framework accounts for the most important standards of data quality.

The framework possesses several features. Its structure is relatively simple compared to existing data quality measuring systems. However, it covers various issues, including the data quality of regions, sectors and the national IO data system, and utilizes information from all three quadrants. Additionally, evaluations under this framework do not need additional data from other sources, which, along with its simple structure, makes the framework easy to apply.

As a trail example, China’s regional IO data are evaluated under this framework. When examined by province, both less-developed provinces (like Qinghai) and highly developed areas (like Beijing) suffer from low IO data quality. For sectors, extreme and unstable IS indicators expose a coherency problem that cannot be ignored. In terms of the national IO data system, the overall data quality is not ideal enough, and there is no significant data quality improvement between 2002 and 2012. The extent of economic development, characteristics of the evaluation framework, accounting measures and local situations are offered as explanations of the results.

The framework presented in this paper is not perfect. A better, more accurate evaluation that reveals more information could be achieved by replenishing more data from additional data sources or exploiting more interrelationships between numbers in the IO table. Nevertheless, the simple constructed framework still holds its own merits for government statistical officers, policy makers and academic researchers. Since it contains only nine indicators, and all are easy to calculate, this evaluation framework enables statistical officers to efficiently examine the regional data quality and spot the possible flaws and errors in the IO data. Combined with the additional information inside the Statistical Bureau System, officers should be able to judge the whether the inconsistent evaluation results are triggered by ameliorations, staff changes or simply mistakes. For policy makers and researchers, it is also crucial to check the data quality before making policy measures or conducting their own research. If it turns out that the data are not reliable enough, they can decide whether they need to enlarge the database or exclude some questionable data to acquire more accurate analysis results.

Footnotes
1

Some sectors do not have key coefficients. In that case, the IS indicator singly determines the data quality of the sector.

 

Abbreviations

IO: 

input–output

SDDS: 

Special Data Dissemination Standard

GDDS: 

General Data Dissemination Standard

KC: 

key coefficient

aKC: 

aberrant key coefficient

IS: 

incoherent sectors

NBS: 

National Bureau of Statistics of China

BEA: 

US Bureau of Economic Analysis

Declarations

Authors’ contributions

HZ is involved in co-construction of the evaluation framework (Sect. 2) with JX; majority of calculation of evaluation and R programming (Sect. 3); result discussion and interpretation (Sect. 4). JX raised the original idea of the paper; introduction and literature review (Sect. 1); co-construction of the evaluation framework (Sect. 2) with HZ. XL is involved in data assembly and primitive calculation (Sect. 3); conclusion (Sect. 5); modification of language expression of the paper. All authors read and approved the final manuscript.

Acknowledgements

Thanks for anonymous reviewers’ generous suggestions and comments. Thanks for Huaju LI and her colleagues (National Bureau of Statistics of China) and Jianqin YUAN (State Information Center of China). They provided useful information and precious suggestions to aid the analysis in Sect. 4. The preliminary version of the paper was presented in 25th International Input–Output Association (IIOA) Annual Conference (June 2017, Atlantic City, USA) as a working paper. Thanks for conference participants’ generous suggestions. If there are some mistakes and flaws, however, authors would take full responsibilities, of course.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All the IO data employed in this paper are available from National Bureau of Statistics of China. For the sake of convenience, data can be found in Github with the web address given below. The R code for analyzing key coefficients in Sect. 3 can also be found on author’s GitHub: https://github.com/zhaohaoyangruc/IO-data-quality.

Funding

Not applicable.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Economics and Management, University of Chinese Academy of Sciences

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