The Nobel Prize Time Gap

The 2019 Nobel Prize in Economics was awarded to Abhijit Banerjee, Esther Duflo, and Michael Kremer for their work in transforming the research on global poverty alleviation. The research performed by these notable scientists had already had a clear impact on many nations' economic policies by the time their work was recognized by the Nobel Prize Committee. However, their original results had had an effect as far back as the mid-1990s (Economic Sciences Prize Committee, 2019). Therefore, about a quarter of a century elapsed before their pioneering contributions were rewarded with a Nobel Prize.

Extended time lags between the publication of a pioneering work and the complete evaluation of its impact are not uncommon in the field of economics, where the "waiting period" for a Nobel Prize has been longer than in any other discipline where Nobel Prizes are awarded. The current study utilizes data for Nobel laureates in economics, chemistry, medicine and physiology, and physics, in order to investigate the time lag between the publication of substantial scientific accomplishments and their recognition with the conferment of a Nobel medal. The study also examines the expected reasons for the growing gap between publication and recognition of work across disciplines, and discusses probable factors why this time lag is more pronounced in the field of economics.

The issue of this Nobel Prize delay has been mostly overlooked by the existing literature, in spite of how important dissemination of academic research and the pace at which this occurs is considered to be. Receiving a major award signals that the qualities of the scholars awarded have come to the attention of their peers, it provides validation, and promotes the related research fields (Merton, 1973). With each new Nobel Prize, attention will be devoted to the subject matter of the research, which received the award, and with that, other researchers may be influenced to pursue similar topics and build on the progress that was made. In addition, the Nobel Prize may serve as both a direct and an indirect incentive for innovation, and bring prestige to the affiliated institutions (Frey and Neckermann, 2009; Frey and Gallus, 2014). As such, a scientific explanation of any factors that may explain the length of time between the publication of a significant scientific work and the bestowal of a Nobel Prize is essential in developing a more complete understanding of the reward system for the sciences.

Another good reason to investigate the delay between prominent work and the conferment of a Nobel Prize is that it indicates how the landscape of science changes over time. In other words, how long Nobel laureates have to wait for their award underscores the evolving complexity of scientific work. During Alfred Nobel's lifetime, a scientist could discover something amazing on their own, and Mr. Nobel's last will and testament reflected that. Nowadays, though, breakthrough science is usually a multi-year effort by teams, which can number in the thousands. On top of that, the theoretical discovery is often done by one team and the empirical confirmation by another, maybe decades later, and the delay in Nobel Prize recognition may reflect that.

The issue of the Nobel Prize time gap is also related to the problem of public understanding of science. The work of Nobel laureates is so distant from the general public's grasp of scientific matters, that it takes the specialized knowledge of the different Nobel Prize Committees to even understand it. This increasing complexity of scientific knowledge, as well as the democratic deficit in science resulting from it, may be reflected in the time it takes for a breakthrough scientific discovery to be awarded the Nobel medal. Therefore, the time lag itself is not as important as the underlying reasons behind it. Studying those reasons, therefore, may identify whether this growing "deficit of time" is totally or partially caused by the current curriculum in science education, and, also, what can be done to improve it.

The rest of this paper is organized as follows: the following section provides the literature review, while the subsequent sections describe the data used and the modeling framework. The empirical findings section presents and discusses the estimation results, while the final section provides a summary of the concluding remarks.

Determining the time of scientists' most impactful publications falls into the vibrant research field of the science of science, a discipline, which has emerged to investigate discoveries in research fields that have achieved a certain degree of maturity (Clauset et al., 2017). Researchers who have sought to determine when Nobel laureates published their most prominent work (more specifically, the work, which led to their recognition with a Nobel Prize) have been forced to rely on a wide variety of approaches. A major obstacle in their efforts is the fact that the Nobel Prize Committees do not systematically indicate the publications for which Nobel Prizes are awarded.

The data set of Jones (2010) identifies great achievements in science, which led to Nobel Prizes in economics, physics, chemistry, and medicine or physiology (referred to hereafter simply as "medicine"). He determines the year(s) in which the pioneering research is performed by obtaining information from the official website of the Nobel Foundation and, in cases where the time period of the key research is not identified, by consulting printed materials. In a similar manner, Jones and Weinberg (2011) determine the years at which Nobel laureates produced their prize-winning work using the Nobel Foundation's website combined with other sources, such as biographies and citation indices. Schlagberger et al. (2016) identify the paper(s) in which the researchers publish their Nobel prize-winning work in the fields of physics, chemistry and medicine (all three referred to hereafter as "the natural sciences") from 1994 to 2014. In their identification, they cite the Nobel Foundation's website and a variety of secondary resources, such as literature databases and Encyclopedia Britannica. Similarly, Li et al. (2019) specify the publication period of scientific work honored with a Nobel Prize in the natural sciences from 1900 to 2016. They perform this task by extracting information from the website of the Nobel Foundation, laureates' official biographies, Google Scholar and other resources.

There are also studies that determine the time of scientists' Nobel prize-winning publications with a focus on a single discipline. These include Zhou et al. (2014), who examine landmark papers written by Nobel laureates in physics from 1901 to 2012, via the application of bibliometric methods. Liang et al. (2018) selected the research that led to the bestowal of Nobel Prizes in medicine by utilizing the website of the Nobel Foundation, as well as laureates' biographical texts.

Studies examining the timing of economics Nobel laureates' prize-winning contributions include Van Dalen (1999), who identifies the year of publication of economists' prize-winning work from 1969 to 1998. Van Dalen uses the Nobel Prize Committee's reports, biographies, and citation indices. Weinberg and Galenson (2019), on the other hand, determine the years when economics Nobel laureates published their work(s) with the highest number of citations, instead. They argue that laureates' most cited works frequently coincide with the works for which they received their Nobel Prizes. Even in the cases they do not, Weinberg and Galenson point out that "the opinion of Nobel Prize Committee is not necessarily preferable to that of economics discipline as a whole".

Possible determinants of Nobel Prize conferment are a vital focus of this strand of research (see, e.g., Gingras and Wallace 2010; Chong et al., 2012). Seminal work in this area, published by Inhaber and Przednowek (1976) and Ashton and Oppenheim (1978), emphasizes the ability of bibliometric data (e.g., the number of citations) to forecast Nobel Prize winners in the fields of chemistry, medicine, and physics. Other studies focus on understanding the link between early career distinctions and winning the Nobel Prize. There also papers demonstrating the impact of laureates' age, nationality, and affiliated institutions (see, e.g., Chong et al., 2012).

Studies that examine Nobel Prizes from a science-of-science perspective include Chan and Torgler (2013), who estimate that the time delay between prize-winning discoveries and the scientists' recognition with a Nobel Prize is twice as long in medicine than in physics. Additionally, according to the findings of Becattini et al. (2014), the time lag between work and recognition has continuously been increasing ever since the commencement of the Nobel Prizes. As a result, Nobel Prize winners in the natural sciences are being bestowed their awards at an increasingly older age, which poses a threat to the prestige and authority of the Nobel Prize by increasing the risk that important work cannot be recognized by it due to the death of its authors.

However, none of these studies uses a quantitative method to examine the factors, which may impact the Nobel Prize time gap. Baffes and Vamvakidis (2011) make a step in that direction by examining the existence of statistically significant correlations between the age of each scientist when the Nobel Prize is awarded with factors such as the age of the laureates when the Nobel-worthy research is published, the age at which they earned their last degree, their gender, the existence of a backlog of high-achieving individuals in their field, and whether they had conducted their ground-breaking research in a developed country. Following the spirit of Baffes and Vamvakidis (2011), Polemis and Stengos (2022) use panel data models to determine the underlying reasons behind the Nobel Prize time lag in the natural sciences. Their findings suggest that the Nobel Committees favor older nominees, since the delay gap decreases as the age of the laureates when they publish their scientific achievements increases. Other parameters they find to have an effect on the time delay include the age at which the Nobel laureates receive their last education degree, the number of recipients who share the award for the same research, and certain demographic factors, such as geographic origin and the gender of the laureates.

The present paper extends the results of the latter study from the natural sciences to economics. The factors examined include a measure of the impact that the contribution of the laureates had at the time of their award, and whether the laureates had previously received an accolade widely perceived as a Nobel Prize precursor (i.e., the Lasker Award and the John Bates Clark Medal). The current study also re-examines the impact of some of the factors mentioned above, such as gender of the laureates, age at which they were awarded their doctoral degree, whether their prize was shared, and whether there is a backlog of potential laureates in the specific discipline.

Data and identification of prize-winning work: The study analyzes Nobel laureates in economics, chemistry, medicine/physiology, and physics. For natural sciences, the timing of prize-winning work is primarily from Li et al. (2019), who compiled career histories and identified prize-winning contributions using Nobel Foundation data, CVs, Google Scholar, and supplementary sources. For economics, the author replicates this identification using Nobel Foundation materials, biographies, citation indices, and, when necessary, correspondence with laureates or expert scholars. For economics laureates (1969–2019), landmark contributions are identified by combining the "breakthrough" and "motherlode" publications approach of Van Dalen (1999); the time of prize-winning work is taken as the midpoint between these landmark outputs when work spans multiple years.

Measurement and variables: The dependent variable is the Nobel Prize delay, defined as the (logged) number of years between the publication of prize-winning work and the award year. Explanatory variables include:

• Time trend (t) to capture secular changes in delay.
• Citations: log citations of the prize-winning publication(s) measured at the time of Nobel announcement (sourced from Google Scholar).
• Precursor award: dummy for whether the laureate had received a recognized precursor (John Bates Clark Medal for economics, Lasker Award for natural sciences) before the Nobel.
• Age at Ph.D/MD: log age at which the laureate obtained their terminal degree.
• Shared Nobel Prize: dummy equal to 1 if the prize in that discipline-year was shared among 2–3 laureates.
• Memberships: growth rate of memberships in the field’s professional association as a proxy for backlog of potential laureates (ACS for chemistry, APS for physics, AEA for economics; AMA unavailable for medicine, so omitted where necessary). This enters as log first differences due to non-stationarity in levels.
• Gender: dummy for female laureate.
• Scandinavia: dummy if the Nobel-related work was conducted in Denmark, Norway, or Sweden.
• Discipline controls: dummy variables for chemistry, medicine, and physics (economics as reference) in pooled models across all sciences.

Econometric framework: A panel-data approach compares laureates across disciplines and time. The core semi-log specification is: NobelDelay = α + α1·t + α2·Citations + α3·Precursor + α4·PhDAge + α5·Shared + α6·Memberships + α7·Gender + α8·Scandinavia + Σ discipline dummies + u. Expected signs: positive for trend and Memberships; negative for Citations, Precursor, Shared, and possibly Scandinavia; ambiguous for PhDAge; no a priori for Gender.

Diagnostics and estimation: Panel unit root tests (Fisher-type ADF) confirm stationarity of variables used (with Memberships stationary in first differences). Presence of random and fixed effects assessed via Breusch-Pagan LM and F-tests; Hausman tests decide between fixed/random when relevant. Additional diagnostics include Wooldridge tests for serial correlation, Modified Wald tests for heteroscedasticity, and Ramsey RESET for specification. For discipline-specific regressions, pooled OLS is appropriate (no fixed/random effects detected). For the combined all-sciences model, fixed (group) effects with robust (White) standard errors are used; robustness checks re-estimate the model under alternative error structures: IID, White-robust, clustered, FGLS, and PCSE. Summary statistics (1969–2016) and distributions by time gap and decade (1901–2019) contextualize trends across fields.

Descriptive patterns (1901–2019):

• Mean waiting time from prize-winning work to Nobel: Economics 31.4 years; Chemistry 18.2; Medicine 16.8; Physics 18.5; All sciences 19.5 years.
• Distribution of delays (all sciences): 25% within 1–10 years; 32% within 11–20; 25% within 21–30; 14% within 31–40; 3% within 41–50; 1% within 51–60 years. In economics, 0% within 1–10 years; largest share (45%) in 21–30 years; 36% in 31–40; 12% in 41–50; 2% in 51–60.
• Decadal averages indicate increasing delay in natural sciences since the 1950s (e.g., Chemistry rising from 16 years in 1951–1960 to 30 years in 2011–2019; Physics from 12 to 28; Medicine from 13 to 29). Economics remains relatively stable around 30–34 years across decades.

Econometric estimates (discipline-specific regressions):

• Time trend: Positive and significant in natural sciences, indicating annual increases in delay of approximately Chemistry 2.2%, Medicine 2.3%, Physics 3.2%; not significant in Economics.
• Citations: Higher citations at award time associated with shorter delays in natural sciences. A 1% increase in citations corresponds to ~0.09% (Chemistry), 0.12% (Medicine), and 0.16% (Physics) reductions in delay; no significant effect in Economics.
• Precursor awards: Associated with shorter delays in Economics (−19.5%) and Chemistry (−55.9%); not significant or positive in Medicine/Physics.
• Shared prize: Sharing correlates with shorter delays in Economics (−11%) and Chemistry (−31%); weaker or insignificant effects in Medicine/Physics.
• Membership growth (backlog proxy): Positive and significant in Chemistry and Physics, indicating larger candidate pools lengthen delays.
• Age at PhD/MD: Small negative effects (earlier PhD linked to slightly shorter delays) significant only in Chemistry and Physics.
• Gender: Significant only in Economics (interpret cautiously given very few female laureates); generally no robust effect elsewhere.
• Scandinavia affiliation: Positive coefficients (unexpected), often not significant.

All-sciences pooled model (economics as reference):

• Time trend: Delay increases at ~2.2% per year overall.
• Citations: 1% more citations associated with ~0.13% shorter delay.
• Precursor award: ~14.4% shorter delay on average.
• Shared prize: ~22.7% shorter delay on average.
• No significant overall effects for Gender or Age at PhD.
• Discipline differences: Relative to Economics, delays are shorter by ~37.1% (Chemistry), ~30.0% (Medicine), and ~36.2% (Physics).

Robustness: Results are consistent across alternative error structures (IID, White, clustered, FGLS, PCSE) in sign and significance patterns.

Findings indicate that Nobel delays have increased over time in the natural sciences but not in economics, implying that scientific recognition increasingly lags behind discovery, potentially raising the average age at award and the risk of posthumous ineligibility. Greater impact (citations) and early-career recognition (precursor awards) are associated with shorter delays, consistent with committees responding to visible influence and reputational signals. Shared prizes correlate with reduced delays in economics and chemistry, possibly reflecting committees' responses to growing candidate backlogs by recognizing multiple contributors in related areas.

Disciplinary differences are pronounced, with economics exhibiting substantially longer delays than the natural sciences. Explanations include: a larger backlog due to the later establishment of the economics prize; committee practices that avoid awarding the same subfield in consecutive years; and longer empirical validation cycles for economic theories compared to many natural science discoveries. Prior research suggests codification differences also matter—discoveries in physics and chemistry may be more clearly defined and thus recognized more swiftly than those in medicine or economics.

The lack of an overall relationship between age at PhD and delay, and the generally null gender effect, suggest these factors are not primary determinants of recognition timing in the combined sample (though limited female representation constrains inference). The unexpected positive sign for Scandinavian affiliation is hard to interpret with available data.

Overall, the results support a model in which visibility (citations), early recognition (precursors), and award-splitting practices can moderate, but do not eliminate, secular increases in recognition lags, with economics remaining an outlier due to structural and institutional factors not fully captured by the measured variables.

The study shows that the gap between discovery and Nobel recognition varies by field and over time, rising in the natural sciences while remaining persistently long in economics. Visibility (citations) and precursor awards are associated with shorter delays, and sharing the prize appears to reduce waiting times in economics and chemistry—consistent with committees managing growing candidate pools. However, substantial discipline-specific differences persist, with economics exhibiting the longest delays.

The most plausible drivers of these differences include factors not fully modeled here, such as laureates’ age (complicated by endogeneity), the larger backlog in economics due to its later start, committee practices regarding subfield rotation, and longer empirical validation periods for economic research. Future research could incorporate broader precursor award sets, address endogeneity (e.g., via instruments or natural experiments), expand membership/backlog measures (including medicine), and consider near-miss candidates to better characterize the distribution of recognition times and potential policy levers to shorten them.

• Sample restriction to laureates only: The analysis excludes near-miss candidates and deceased scholars whose work could not be recognized posthumously, limiting generalizability and complicating measurement of delays for non-winners.
• Missing data: AMA membership data were unavailable, preventing inclusion of a backlog proxy for medicine. Citation records are incomplete for early laureates whose publications predated widespread citation practices.
• Endogeneity/simultaneity: Age at award and delay are jointly determined; similarly, academic age and time from PhD to breakthrough co-determine delays, limiting causal interpretation. Laureate age was excluded to avoid endogeneity.
• Model specification: Despite diagnostics, Ramsey RESET indicated misspecification in the pooled model; robustness checks mitigated concerns but cannot ensure full specification correctness.
• Limited female representation: Small numbers of female laureates limit statistical power to detect gender effects and constrain generalization.
• Measurement choices: Identification of prize-winning work, especially in economics, relies on curated sources and expert judgment (e.g., "breakthrough" and "motherlode"), which may introduce classification uncertainty. Citations as impact proxies may conflate productivity and influence.