| ZHU Chao,XU Longqiang,易祯.A New GVC Positioning Framework Based on the New Gaokao Scoring Mechanism[J].The Journal of quantitative and technical economics,2025,(12):151-173 |
| 基于赋分制的全球价值链位置测算新方法 |
| A New GVC Positioning Framework Based on the New Gaokao Scoring Mechanism |
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| DOI： |
| 中文关键词: 全球价值链 等级赋分制 微笑曲线 |
| 英文关键词: Global Value Chain Graded System Smile Curve Theory |
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| 中文摘要: |
| 本文引入了中国高考科目选考改革后的赋分制改革，发展了一种新的全球价值链位置度量方法。具体地，我们在计算全球价值链上游度和下游度后，将其原始数值由高到低划分为若干等级，等级内部按照等比例转换法则将原始数值转换为等级数值，获得新的全球价值链位置。该方法的主要优势是，在兼顾原有度量指标经济含义和理论出发点的基础上，解决了经典度量方法的数值信息与实际位置背离、低估行业门类内部差异和误导行业门类间差异三方面问题，并且具有更高的稳定性。本文的主要贡献是提供了一种更准确的、在时间维度和行业维度之间可比的全球价值链位置测度方式，并在新方法的数据体系下重新契合了“微笑曲线”理论。本文的政策意义在于，为实现更高水平对外开放提供判断尺度，也为构建更加精准有效的全球价值链发展战略提供基础数据支持。 |
| 英文摘要: |
| Two main methods are used to measure the position of the global value chain (GVC)—the proxy variable method and the input-output table-based method. However, both approaches have three significant shortcomings that can distort the understanding of GVC. First, when the refinement of division of labor leads to a lengthening of the production chain, the current measures reveal an increase in value, which means a greater distance from the initial factor input sector or the final consumption sector. However, the relative position in the chain may not change, which leads to the problem of deviation between numerical information and actual position. Second, due to the similar characteristics of the same industry sector, the absolute numerical differences in GVC under the current measurement methodology are small. However, the actual position of the sub-sectors is still very different, so the calculation methodology based on the absolute numerical values underestimates the intra-industry differences in the industry sectors. Third, the measurement approaches can misrepresent differences between industry sectors. For example, the service industry is closer to the two ends of the chain, which overestimates the value chain advantages of the service industry to the manufacturing industry.Addressing the previously identified limitations of existing GVC measurement techniques, this study introduces the grading system following the reform of subject selection in China’s college entrance examination and develops a new measure of GVC position. The grading system uses the relative position of the candidates to calculate grades, thus addressing the problem of incomparable absolute scores across subjects. Similarly, after calculating the upstream and downstream degrees of GVC, we divide their raw values from high to low into several grades and convert these values into standardized grade scores using an equal-proportional conversion method, yielding a refined GVC position metric. By standardizing the production chain, this new metric overcomes the three limitations outlined above. Meanwhile, the new method proposed in this study has the same theoretical basis compared with the current metric method, which is also based on the production stage splitting of vertically specialized division of labor production. The difference is that this study provides a measure that is comparable between the time and industry dimensions.The new method proposed in this study can more accurately describe inter-industry variation and provides new evidence for the smile curve theory. The smile curve theory is fundamental in the field of GVCs. However, its empirical validation across industries remains inconsistent, partly due to limitations in the measurement of GVC position. There are significant differences in the ability of firms in different industries to verticalize their division of labor when engaging in production in the GVC. For example, the division of production in some industries can be refined (e.g., food, beverages, and tobacco; fabricated metal products; computers; electronic; and optical equipment), while in others the ability to segment is relatively weak, and intra-industry competition is more intense (e.g., accommodation and food services and education). Both factors can cause the smile curve theory to be nonuniversal at the industry level. The approach proposed in this study directly addresses the issue of small absolute numerical differences that potentially mask significant positional variations. After this treatment, the empirical evidence in this study validates the smile curve theory.A major contribution of this study is that it proposes a new, more accurate measure for GVC position. This new measure solves three problems found in traditional methods. These problems are values not matching real positions, underestimating differences within sectors, and misleading comparisons between sectors. This new method has four main advantages. First, it keeps the economic meaning of the GVC position. Our method starts by splitting production stages. It calculates upstreamness and downstreamness. Then, it ranks these to create the new measure. This meaning aligns with common measures used in the existing literature. Second, it solves the mismatch problem. Sometimes the absolute value does not match the relative position. Our method captures a production sector’s place in global trade more accurately. Third, our measure accurately reveals the GVC position. It also fully reveals differences in a sector. This highlights how gains are distributed along the GVC. Fourth, the new measure offers more stable data. It is also less sensitive to parameters used in the grading process. The results of the grading-based method would help future research. They also aid policymakers. They allow for a more precise understanding of GVC patterns. This provides fundamental data support for this advanced field of international trade.The second contribution of this study is the re-evaluation and validation of the smile curve theory within the GVC framework. Empirical evidence on the smile curve reveals that industries with pronounced vertical specialization, such as electronics and optical equipment, can support this theory, while industries with limited verticalization, such as textiles, are difficult to support this conclusion. This study argues that a key factor underlying this inconsistency is the inadequacy of prevailing metrics in discerning inter-industry variations. The study provides new evidence for this foundational theory in GVCs.We also suggest other uses of our grading system method. It can improve other measures of GVC position. Prevailing methodologies employed in the extant literature for measuring position in GVC, including alternative approaches grounded in the production stage decomposition, exhibit the three aforementioned limitations. When we split production into finer stages, a problem appears. Measures based on counting these stages reveal a mismatch. The absolute value does not reflect the true position. These measures also blur differences within and between sectors. Our grading system method can fix these three types of problems. It improves our understanding and grasp of GVC structures. |
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