Over the past two decades, China has emerged as the world’s leading producer of scientific publications (Tollefson 2018, Xie and Freeman 2019, Brainard and Normile 2022). This rise has sparked concerns – particularly in the US and Europe – that Western countries are losing their long-held technological edge (The Economist 2024). These worries have been exacerbated by recent geopolitical tensions, as innovation plays a critical role not only in driving economic growth but also in military capabilities, pandemic response, and addressing climate change. In reaction, Western nations have revived industrial policies, employing such strategies as trade sanctions and industrial subsidies (Campos et al. 2023).
At the same time, China’s growing prominence in scientific research has sparked an ongoing debate about the quality and impact of its output (Huang 2018). Many rankings of researchers, universities, and countries rely heavily on citation counts to assess the influence and quality of scientific work. However, citations can be a biased measure of quality if they are driven by strategic considerations. In particular, citations often exhibit distinct geographic patterns, with researchers citing work from within their own country. In these instances, citations provide a biased measure of research quality or impact.
This issue is particularly relevant in the case of China. In a recent study analysing citation data from the top 10% of journals across 20 broad fields between 2000 and 2021, we show that China receives a striking 57.2% of its citations from other researchers based in Chinese institutions (Qiu et al. 2024). This is by far the largest share across all countries, as shown in Figure 1.
Figure 1 Share of home citations
It might be tempting to interpret Figure 1 as evidence that Chinese citations are artificially inflated. After all, there is some evidence that interpersonal relationship norms (or guanxi) in China may encourage researchers to cite one another (Tang et. al 2015). However, the US also has a high ‘home citation share’, at 37.1%, and this trend is evident across all large countries in Figure 1. When researchers from the same country cite each other frequently, it may simply reflect the larger pool of potential citers within that country rather than bias. This effect, driven by country size, is particularly pronounced in China due to the country’s significant investment in its scientific sector over the past two decades. For instance, between 2000 and 2017, the number of universities and research institutions grew by 140%, while the number of scientists increased by 69% (National Bureau of Statistics of China 2017).
In Qiu et al. (2024), we propose a new method to measure home bias across countries, drawing from the literature on home bias in international trade (Santamaría et al. 2023). Specifically, our method to measure home bias uses a dartboard approach and asks: How many citations would a country receive if citations were distributed randomly – based solely on the size of both citing and cited countries – without any bias or preference for specific countries? We define ‘home bias’ as the difference between a country’s actual citations and this benchmark of expected citations.
Figure 2 Home bias in citations
In Figure 2, we present home bias in citations for the other countries in our sample. Every country demonstrates some level of home bias, but China stands out as a clear outlier, exhibiting a home bias nearly twice as large as that of other nations. Its citation counts are 42.3 percentage points larger than in the unbiased benchmark model. In comparison, the other countries with significant home bias, such as Iran and India, show a much smaller bias of 23.2 percentage points each.
China’s home bias is not a recent phenomenon. While it has been steadily increasing over the past two decades, Chinese citations already displayed a strong home bias as early as 2000, the beginning of our observation period. Neither is China’s home bias driven by any particular research field. Rather, as Figure 3 shows, China exhibits the strongest home bias in 18 out of 20 broad scientific fields.
Figure 3 Home bias in citations by field
Home bias has the potential to distort rankings that rely on citation counts. To address this issue, we apply a country-specific debiasing factor, defined as the number of benchmark home citations divided by the number of actual home citations. This adjustment enables us to recalibrate the rankings of countries based on citations, as demonstrated in Figure 4. While China ranks second behind the US in raw citations, it drops to fourth place when we apply our debiased metric, falling behind the US, the UK, and Germany. Although this is still an impressive position, our findings indicate that home bias has inflated China’s rise in the scientific league tables and distorted the perceptions of innovation policymakers.
Figure 4 Rankings of countries, correcting for home-bias
We believe this analysis could help temper the debate surrounding China’s rise in science and inform the economic consequences of the technological decoupling between the US and China (Jinji and Ozawa 2024, Cao et al. 2024, Goes and Bekkers 2022). For China’s leadership, our findings offer a chance to prioritise quality over quantity in scientific output. For Western nations, demonstrating that the perceived threat from China may be smaller than believed could reduce the impetus for escalating trade wars or policies designed to restrict scientific collaborations with Chinese teams.
References
Brainard, J and D Normile (2022), “China rises to first place in most cited papers”, Science Insider, 17 August.
Campos, R, J Estefania Flores, D Furceri and J Timini (2023), “Geopolitical fragmentation and trade”, VoxEU.org, 31 July.
Cao, Y, F de Nicola, A Mattoo and J Timmis (2024), “US entity list restrictions slow the innovation of Chinese firms and their US collaborators”, VoxEU.org, 6 November.
Goes, C and E Bekkers (2022), “The impact of geopolitical conflicts on trade, growth, and innovation: An illustrative simulation study”, VoxEU.org, 29 March.
Huang, F (2018), “Quality Deficit Belies the Hype”, Nature 564: S70–S71.
Jinji, N and S Ozawa (2024), “Economic consequences of US-China technological decoupling: An illustrative quantitative analysis”, VoxEU.org, 28 August.
National Bureau of Statistics of China (2017), China Statistical Yearbook, China Statistics Press.
Qiu, S, C Steinwender and P Azoulay (2024), “Paper Tiger? Chinese Science and Home Bias in Citations”, NBER Working Paper 32468.
Santamaría, M, J Ventura and U Yeşilbayraktar (2023), “Exploring European Regional Trade”, Journal of International Economics 146: 103747.
Tang, Li, P Shapira and J Youtie (2015), “Is There a Clubbing Effect Underlying Chinese Research Citation Increases?”, Journal of the Association for Information Science and Technology 66: 1923–32.
The Economist (2024), “How worrying is the rapid rise of Chinese science?”, 15 June.
Tollefson, J (2018), “China Declared World’s Largest Producer of Scientific Articles”, Nature 553: 390.
Xie, Q and R B Freeman (2019), “Bigger Than You Thought: China’s Contribution to Scientific Publications and Its Impact on the Global Economy”, China & World Economy 27: 1–27.
