There’s a few studies on this already. This is a rare event/person situation, so sampling error is large and problematic (cf. discussion in esports paper). Ignoring this, we can draw some general conclusions by a quick literature survey.

Based on data from RetractionWatch database. This is somewhat wrong approach because a lot of fraud committed in Western countries is done by foreign researchers. One needs to do the analyses by name and inferring the ancestry of the persons by building a machine learning model off data from e.g. behindthename.com (see my prior study on first names). As I noted on Twitter, in Denmark, most famous faker is Milena Penkowa. That doesn’t sound very Danish, she is actually half Bulgarian.

If we look at the top list of fraudsters in RW database:

Name Retractions Ancestry European East Asian South Asian Other Yoshitaka Fujii 183 Japanese 1 Joachim Boldt 97 German 1 Yoshihiro Sato 84 Japanese 1 Jun Iwamoto 64 Japanese 1 Diederik Stapel 58 Dutch 1 Yuhji Saitoh 53 Japanese 1 Adrian Maxim 48 European 1 Chen-Yuan Peter Chen 43 Chinese 1 Fazlul Sarkar 41 Bengali 1 Hua Zhong 41 Chinese 1 Shigeaki Kato 40 Japanese 1 James Hunton 37 European 1 Hyung-In Moon 35 Korean 1 Naoki Mori 32 Japanese 1 Jan Hendrik Schön 32 German 1 Soon-Gi Shin 30 Korean 1 Tao Liu 29 Chinese 1 Bharat Aggarwal 28 Indian 1 Cheng-Wu Chen 28 Chinese 1 A Salar Elahi 27 Persian 1 Ali Nazari 27 Persian 1 Richard L E Barnett 26 European 1 Antonio Orlandi 26 Italian 1 Prashant K Sharma 26 Indian 1 Rashmi Madhuri 24 Indian 1 Scott Reuben 24 European 1 Shahaboddin Shamshirband 24 Malaysian 1 Thomas M Rosica 23 European 1 Alfredo Fusco 22 Italian 1 M Ghoranneviss 22 Persian 1 Anil K Jaiswal 22 Indian 1 Gilson Khang 22 Korean 1 Sum 10 13 5 4

I manually coded these by googling them, and if not helpful, then relying on names. I don’t know any of ancestry/name based count of scientific productivity, but if we use the Nature index that Anatoly Karlin wrote about recently, and do a crude count (meaning I allocate Euro-dominant countries entirely to European, including Brazil and Israel):

Country FC12 FC13 FC14 FC15 FC16 FC17 FC18 Mean USA 37.20 36.50 34.90 35.00 34.60 34.10 32.80 35.01 China 8.90 10.20 12.00 12.90 14.00 15.80 18.40 13.17 Germany 8.00 8.00 7.90 7.80 7.80 7.60 7.40 7.79 UK 6.40 6.40 6.30 6.50 6.60 6.30 6.10 6.37 Japan 6.80 6.60 6.20 5.70 5.50 5.30 5.00 5.87 France 4.60 4.40 4.30 4.10 4.00 3.80 3.60 4.11 Canada 3.00 2.90 2.90 3.00 2.70 2.70 2.60 2.83 Switzerland 2.30 2.30 2.50 2.30 2.30 2.30 2.30 2.33 Korea 2.30 2.30 2.30 2.40 2.30 2.20 2.20 2.29 Spain 2.40 2.30 2.10 2.00 2.10 1.90 1.90 2.10 Australia 1.70 1.80 1.90 2.00 2.00 1.80 2.00 1.89 Italy 2.10 2.10 2.00 2.00 1.80 1.80 1.70 1.93 India 1.50 1.70 1.80 1.60 1.60 1.70 1.60 1.64 Netherlands 1.50 1.50 1.50 1.50 1.60 1.60 1.50 1.53 Singapore 0.90 0.90 1.00 1.00 1.00 1.00 1.00 0.97 Sweden 0.90 1.00 1.00 1.10 1.00 1.00 1.00 1.00 Israel 1.00 0.90 1.00 1.00 1.00 1.00 1.00 0.99 Taiwan 1.20 1.10 0.90 0.80 0.80 0.70 0.60 0.87 Russia 0.60 0.70 0.70 0.70 0.70 0.70 0.70 0.69 Belgium 0.70 0.60 0.70 0.70 0.80 0.70 0.70 0.70 Austria 0.50 0.50 0.60 0.50 0.60 0.60 0.60 0.56 Denmark 0.60 0.60 0.60 0.60 0.70 0.60 0.70 0.63 Brazil 0.40 0.50 0.50 0.40 0.40 0.40 0.50 0.44 Poland 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 Czechia 0.20 0.20 0.20 0.30 0.30 0.30 0.30 0.26 Sum 96.10 96.40 96.20 96.30 96.60 96.30 96.60 96.36 European East Asian South Asian 71.54 23.17 1.64

Europeans produce ~72% of ‘good science’ in 2012-2018, East Asians ~23% and South Asians ~2%, and there is a remainder category of ~4%. Relative to the top list, Europeans produce more science than top science cheaters, and vice versa for the other groups.

A comparative world map of scientific misconduct reveals that countries with the most rapid growth in scientific publications also have the highest retraction rate. To avoid polluting the scientific record further, these nations must urgently commit to enforcing research integrity among their academic communities.

They give us a nice table, and I did the same kind of crude calculations (grouping Latin Americans as Europeans, under assumptions people who do science there are European ancestry mostly):

Country Publications Misconducts Ratio Ratio of European China 2741274 4353 0.001588 67.66 Malaysia 157198 50 0.000318 13.55 Mexico 121193 31 0.000256 10.90 Taiwan 252497 46 0.000182 7.76 Pakistan 71350 10 0.000140 5.97 Iran 271403 38 0.000140 5.96 Saudi Arabia 97886 8 0.000082 3.48 Hong Kong 100036 8 0.000080 3.41 South Korea 465211 32 0.000069 2.93 Egypt 92328 6 0.000065 2.77 India 747844 39 0.000052 2.22 Singapore 117089 6 0.000051 2.18 Thailand 78124 4 0.000051 2.18 Australia 529779 19 0.000036 1.53 Netherlands 343352 12 0.000035 1.49 Romania 87280 3 0.000034 1.46 Japan 787157 27 0.000034 1.46 Canada 606562 20 0.000033 1.40 Italy 624340 18 0.000029 1.23 Greece 114300 3 0.000026 1.12 United Kingdom 1145434 30 0.000026 1.12 Ireland 79950 2 0.000025 1.07 Germany 1010967 25 0.000025 1.05 Czech Republic 130262 3 0.000023 0.98 United States 3876791 88 0.000023 0.97 Portugal 134433 3 0.000022 0.95 Austria 142689 3 0.000021 0.90 Poland 238095 5 0.000021 0.89 Belgium 192437 4 0.000021 0.89 Turkey 246018 5 0.000020 0.87 Sweden 227239 4 0.000018 0.75 France 712371 10 0.000014 0.60 Argentina 77402 1 0.000013 0.55 Russian Federation 340791 4 0.000012 0.50 New Zealand 87919 1 0.000011 0.48 Spain 526613 5 0.000009 0.40 South Africa 110908 1 0.000009 0.38 Norway 119574 1 0.000008 0.36 Brazil 394107 3 0.000008 0.32 Switzerland 258541 0 0.000000 0.00 Israel 119452 0 0.000000 0.00 Denmark 147828 0 0.000000 0.00 Finland 115287 0 0.000000 0.00 Hungary 63662 0 0.000000 0.00 Ukraine 59555 0 0.000000 0.00 Chile 62837 0 0.000000 0.00 Region Publications Misconducts Ratio Ratio of European European 12739113 299 0.000023 1.00 East Asian 4463264 4472 0.001002 42.69 Southeast Asian 235322 54 0.000229 9.78 South Asian 747844 39 0.000052 2.22 MENAP 778985 67 0.000086 3.66

A staggering ratio for East Asians.

Fanelli, D., Costas, R., Fang, F. C., Casadevall, A., & Bik, E. M. (2019). Testing hypotheses on risk factors for scientific misconduct via matched-control analysis of papers containing problematic image duplications. Science and engineering ethics, 25(3), 771-789.

It is commonly hypothesized that scientists are more likely to engage in data falsification and fabrication when they are subject to pressures to publish, when they are not restrained by forms of social control, when they work in countries lacking policies to tackle scientific misconduct, and when they are male. Evidence to test these hypotheses, however, is inconclusive due to the difficulties of obtaining unbiased data. Here we report a pre-registered test of these four hypotheses, conducted on papers that were identified in a previous study as containing problematic image duplications through a systematic screening of the journal PLoS ONE. Image duplications were classified into three categories based on their complexity, with category 1 being most likely to reflect unintentional error and category 3 being most likely to reflect intentional fabrication. We tested multiple parameters connected to the hypotheses above with a matched-control paradigm, by collecting two controls for each paper containing duplications. Category 1 duplications were mostly not associated with any of the parameters tested, as was predicted based on the assumption that these duplications were mostly not due to misconduct. Categories 2 and 3, however, exhibited numerous statistically significant associations. Results of univariable and multivariable analyses support the hypotheses that academic culture, peer control, cash-based publication incentives and national misconduct policies might affect scientific integrity. No clear support was found for the “pressures to publish” hypothesis. Female authors were found to be equally likely to publish duplicated images compared to males. Country-level parameters generally exhibited stronger effects than individual-level parameters, because developing countries were significantly more likely to produce problematic image duplications. This suggests that promoting good research practices in all countries should be a priority for the international research integrity agenda.

Which produced this (low quality) figure:

Data are pretty noisy, but of the ones with p<.05, we see Germany and Japan below USA, Argentina, Belgium, India, China and other above. Don’t know what is up with Belgium here, but otherwise, the results aren’t so surprising.

All in all, Hajnal pattern applies. Everybody cheats, but people outside Hajnal cheat a lot more. There are more data out there, a lot of it private. A friend of mine works with foreign applications for people who want to study in the UK (scholarships). People send in essays and the like English test scores (TOEFL etc.). The agencies screen the essays for plagiarism, so one gets a per capita rate of plagiarism. If screened OK, they are given an interview in English. Often, people with perfect essays can’t seem to talk English very well, which is generally because they hired someone to write their essays for them, which is not detectable by plagiarism testing but results in massive discrepancies between English written and spoken ability. One can also look up various cheating scandals, as summarized by Those Who Can See.