Reflecting on the relationship between economics and history from the perspective of empirical research has become an urgent task. Photo: TUCHONG

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Both economics and history seek to explain and reflect on the empirical world we inhabit, especially human society. As such, both disciplines have an empirical research component. Economics was the first to construct a mathematical logical system grounded in probability theory, which enables the “leap” from empirical observation to theoretical inference, giving rise to the prominent subfield of econometrics. Today, econometrics has become a general method of empirical research, widely used across social sciences such as sociology and political science, and has even entered the domain of history. Both history and economics aim to offer causal explanations for observed phenomena. When empirical observations meet fundamental statistical assumptions, such as large sample sizes and randomness, econometrics provides a universal method. Not only does it facilitate more rigorous inferences, but it also offers a reliable basis for assessing the confidence of those inferences. In the past two decades, the application of econometric methods in the interdisciplinary study of economics and history has increasingly overshadowed economic theory, giving rise to more debates. Consequently, reflecting on the relationship between economics and history from the perspective of empirical research has become an urgent task. 

Divergent paths in explanation  

It is important to acknowledge the subtle differences in the aims of causal explanation between history and economics. History tends to focus on the dynamic causal relationships between different subjects or forces within specific processes, while economics generally aims to trace explanations back to human behavior, extracting more general principles. This distinction means that the inferences made by each discipline often target hypotheses of different types, and the choice of methodology must therefore be adapted accordingly. When various subjects interact in a chronological sequence, textual analysis is the most effective method for explaining the causes of events. For example, the Ming Dynasty’s (1368-1644) “One Lash Method,” a major fiscal reform, has been the subject of numerous studies. By examining the actions and documents of key decision-makers, one can broadly explain the background of this decision and the practices it referenced, thereby inferring who played a decisive role in the reform and what problems their actions aimed to address. However, when we consider how the “One Lash Method” impacted local finances or social governance, textual research alone is insufficient. This is because policies need to manifest through complex behaviors among various actors in the socio-economic realm, and the temporal relationship between these actions does not constitute a causal link. Moreover, the social economy is influenced by numerous factors and cannot speak for itself. Even when historical documents record causal judgments made by individuals at the time, these are based on their limited observations and cannot serve as definitive proof. Therefore, to test causal relationships between policies and the social economy, it is more meaningful to gather a sufficient number of samples and perform statistical inferences using quantitative methods. 

However, the issue does not end here. Trygve Haavelmo, the founder of modern econometrics, introduced the concept of “self-sufficiency” in models, stressing that meaningful tests should focus on fundamental theoretical relationships, as they represent the stable core behind the complex array of empirical observations. Clearly, the relationship between policies and the social economy does not meet the criterion of “self-sufficiency,” as it results from the interplay of many factors. The “One Lash Method” had different effects in the northern and southern regions, and the outcomes were also influenced by how the policy was implemented. Even the implementation of the policy itself could alter the economic structure, thus changing the underlying behavioral patterns that influence the relationship between the policy and the social economy. In this context, even with sufficient data, careful sample selection, and the control of various factors to statistically test the effects of the “One Lash Method,” the results would only reveal the effects of this historical event in the specific sample context. These results cannot easily be used to probe the underlying mechanisms driving government actions or individual behaviors. To explain these mechanisms, it is often necessary to return to the historical process itself and engage in the “storytelling” work. This is especially true when powerful structural or institutional factors are influencing entities’ behaviors. Historical research on these areas can help delineate a strategic interaction framework for analyzing behavior. 

Thus, a paradox arises: quantitative methods ultimately remain a tool subordinate to historical narratives. While they can assist in verifying the impacts of historical events, their distance from economic theories might even exceed that of certain profound historical narratives. This is not a unique feature of quantitative history; empirical research in economics also faces similar challenges, and the economics community is becoming increasingly aware of these issues. However, when engaging with a discipline such as history, which has a strong narrative tradition, it is crucial to have a clear understanding of the role that quantitative methods play in the research process. This understanding is necessary to maximize their effectiveness in the study. 

Evaluation of measurement methods 

Even when quantitative methods are more applicable in principle, how should their relative advantages be assessed? This question arises because measurement methods entail relatively high costs in data collection, model diagnostics, and other aspects. In particular, historical research often lacks systematic data, requiring researchers to piece together various historical sources to construct data that can serve as proxies for certain variables. The inherent fuzziness in constructing proxy variables and the rigor of statistical inference are often mismatched, making it difficult to assess the robustness of the entire study. At this point, a careful balance of costs and benefits is essential. 

For example, many regions throughout history have built water conservancy facilities. How can we determine whether they have positively impacted the local economy? Logically, these facilities have withstood various changes and random shocks, maintained by people under different circumstances. This maintenance cannot be attributed to specific interests but rather must be because they hold universal positive value. 

By conducting case studies to show how different stakeholders maintain these facilities, we can verify this logic. If we can find a few cases where certain differences are long-lasting, while other changes are sporadic or independent of these differences, we could even explore the key mechanisms behind the maintenance of water conservancy facilities. 

These analyses do not provide statistical inference, but a solid understanding of econometrics concepts such as randomness, exogeneity, and ceteris paribus can certainly help in designing the research more rigorously. However, direct econometric analysis is much more challenging due to the limited number of well-documented water conservancy samples and the fragmented nature of the data. A simple remedy is to interpolate various data before and after the construction of water facilities at regular intervals, which could expand the sample size into a much larger panel data set. However, this only superficially improves the statistical inference’s credibility, while the greater uncertainty shifts to the data construction process. Compared to the aforementioned case studies, it becomes difficult to judge whether the overall reliability has improved, and further trimming of the research object is required, which might make the results feel less valuable. 

Vitality of empirical research 

If we must occasionally accept quantitative analyses or even empirical studies that fail to meet the strict standard of falsification, this raises evaluation issues within a given disciplinary paradigm. Clearly, no matter how intriguing the historical story may be, economists have no inherent reason to care about historical narratives—but this is not entirely so. A classic example is Max Weber’s research on the Protestant ethic and the rise of capitalism. Weber examined the revolutionary role that the “worldly asceticism” of the Protestant ethic played, “accidentally” challenging the dominance of traditional ethics. Even if this explanation is causal, it is at the level of historical narratives. However, this transformation was so profound and impactful that it still attracts economists to test the so-called “Weber thesis.” This shows that certain historical turning points that shape human society are perennial topics in the social sciences. We care about them not as adornments for general theories but for history itself—even when it contains only contingencies. 

Similarly, the “fiscal-military state” theory that has gained attention in economic research over the last decade is an ideal model and explanatory framework derived from the fiscal history of early modern Europe. This framework significantly differs from behavioral economic theory. The “fiscal-military state” theory describes the dynamic process of fiscal transformation and the formation of modern states triggered by war, with each link connected to various factors, lacking “self-sufficiency.” It is also difficult to test it through statistical inference. However, this theory provides a logically powerful explanation for the fundamental issue of modern state formation, while also connecting the seemingly opposing concepts of war and modern development, offering theoretical insights for the “return of the state” in economics. 

In fact, this insightful research is grounded in a thorough review of various facts and connects these aspects, which are not easily integrated, with creative ideas. Basic theoretical research in economics often starts with paradoxes found in typical facts and explains them through a theory as simple as possible, in a similar manner. Thus, accepting such research does not imply lowering academic standards; rather, it demonstrates that empirical research still retains its vitality. 

It should not be forgotten that economics and history each have their own essence beyond empirical research. History seeks to understand the meaning and resonance of human experience amidst the changes of the world, while economics is concerned with the axiomatic system of human behavior. Interdisciplinary research that is primarily empirical should not disregard these two aspects. 

Quite the opposite, the opportunity to engage with these two extremes is a privilege unique to interdisciplinary researchers. If one is fortunate enough to capture the light of both poles in a rare and inspired work, it would be the greatest reward for those dedicated to such pursuits. 

Peng Kaixiang is a professor from the Economics and Management School at Wuhan University.

Edited by ZHAO YUAN