Persistence Studies
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Persistence studies is scholarship in the
social science Social science (often rendered in the plural as the social sciences) is one of the branches of science, devoted to the study of societies and the relationships among members within those societies. The term was formerly used to refer to the ...
s that links, usually through
quantitative Quantitative may refer to: * Quantitative research, scientific investigation of quantitative properties * Quantitative analysis (disambiguation) * Quantitative verse, a metrical system in poetry * Statistics, also known as quantitative analysis ...
causal inference Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference an ...
, historical events with later political, economic and social outcomes. It is particularly prevalent in economics, economic history, political science, and sociology. The scholarship emerged in the early 2000s. Early landmark studies include two studies by economists
Daron Acemoglu Kamer Daron Acemoğlu (;, ; born September 3, 1967) is a Turkish Americans, Turkish-American economist of Armenians in Turkey, Armenian descent who has taught at the Massachusetts Institute of Technology since 1993, where he is currently the Ja ...
, Simon Johnson, and James Robinson in 2001 and 2002 that linked colonial institutions to variations in contemporary economic outcomes. According to Alexandra Cirone and
Thomas Pepinsky Thomas B. Pepinsky (born 1979) is an American political scientist. He specializes in comparative politics and international political economy, with a regional focus on Maritime Southeast Asia. He is the Walter F. LaFeber Professor of Government ...
, there are typically five steps in persistence scholarship: # "Define and measure a causal variable of interest." # "Characterize its assignment mechanism." # "Define and measure a relevant outcome variable." # "Estimate the effect of the causal variable on the outcome variable using a research design that is appropriate given the proposed assignment mechanism." # "Characterize the causal mechanisms that link the causal variable and the outcome." According to Nathan Nunn, persistence studies usually take the following form,
cholarsnbsp;begin by collecting new data, often from archival sources, that measure aspects of the historical episode of interest. These data are then connected to contemporary outcomes of interest, matched through populations, societies, or locations, to test whether the historical factor has a causal effect on the contemporary factors being examined. Statistical analysis is undertaken, studying variation across individuals, ethnicities, or countries and using empirical techniques (such as instrumental variables, regression discontinuity, difference-in-difference, or natural experiments) that are aimed at distinguishing causal relationships from mere correlation. Having established the importance of a historical factor or episode for outcomes today, an attempt is then made to understand the exact causal mechanisms that account for the observed relationship. This generally requires the collection of additional data and additional statistical analysis, as well as an integration of the historical literature and descriptive evidence.
What distinguishes persistence research from broader comparative research on historical legacies is the use of precise causal inference methods. Critics of persistence studies argue the pitfalls of the approach lie in a failure to recognize institutional change ("anti-persistence"), vague mechanisms, the insufficient use (or misuse) of historical sources and narratives, the compression of history, and a failure to account for the effects of geography." A 2024 review of 30 prominent persistence studies articles in leading journals found that after correcting the standard errors, few of the results approach
statistical significance In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by \alpha, is the ...
at conventional levels.


References

{{Social sciences Political science terminology Subfields of political science Institutionalism Economic history studies