Better performance at a country level will provide benefits to the whole population. This issue has been studied from various perspectives using empirical methods. However, little effort has as yet been made to address the issue of endogeneity in the interrelationships between productive performance and its determinants. We address this issue by proposing a Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach. The joint use of stochastic and fuzzy approaches – within the ambit of 2DFMCA – offers methodological tools to mitigate epistemic uncertainty while increasing research validity and reproducibility: (i) preliminary performance assessment by fuzzy ideal solutions; and (ii) robust stochastic regression of the performance scores into the epistemic sources of uncertainty related to the levels of physical and human capitals measured in distinct countries at different epochs. By applying the proposed method to a sample of 23 countries for 1890–2018, our results show that the best and worst-performing countries were Norway and Portugal, respectively. We further found that the intensity of human capital and the age of equipment (capital stock) have different impacts on productive performance – it has been established that capital intensity and total factor productivity are influenced by productivity performance, which, in turn, has a negative impact on labor productivity and GDP per capita. Our analysis provides insights to enable government policies to coordinate productive performance and other macroeconomic indicators.

国家层面更好的表现将为全体人民带来好处。人们使用实证方法从多个角度研究了这个问题。然而，迄今为止，人们还没有做出多少努力来解决生产绩效与其决定因素之间相互关系的内生性问题。我们通过提出二维模糊蒙特卡罗分析 (2DFMC) 方法来解决这个问题。在 2DFMCA 范围内，随机和模糊方法的联合使用提供了方法论工具，以减轻认知不确定性，同时提高研究的有效性和可重复性：（i）通过模糊理想解决方案进行初步绩效评估； (ii) 将绩效得分稳健地随机回归到与不同国家在不同时期测量的物质和人力资本水平相关的不确定性的认知来源。通过将所提出的方法应用于 1890 年至 2018 年 23 个国家的样本，我们的结果表明，表现最好和最差的国家分别是挪威和葡萄牙。我们进一步发现，人力资本密集度和设备（资本存量）年限对生产绩效有不同的影响——已经确定资本密集度和全要素生产率受到生产率绩效的影响，而生产率绩效又对生产绩效产生负向影响。对劳动生产率和人均GDP的影响。我们的分析提供了见解，使政府政策能够协调生产绩效和其他宏观经济指标。