Climatic shocks associate with innovation in science and technology
Climatic shocks associate with innovation in science and technology
Carsten K. W. De Dreu 0 1
Mathijs A. van Dijk 1
0 Institute of Psychology, Leiden University , Leiden , The Netherlands , 2 Center for Experimental Economics and Political Decision Making, University of Amsterdam , Amsterdam , The Netherlands , 3 Rotterdam School of Management, Erasmus University Rotterdam , Rotterdam , The Netherlands
1 Editor: David A Lightfoot, College of Agricultural Sciences , UNITED STATES
Human history is shaped by landmark discoveries in science and technology. However, across both time and space the rate of innovation is erratic: Periods of relative inertia alternate with bursts of creative science and rapid cascades of technological innovations. While the origins of the rise and fall in rates of discovery and innovation remain poorly understood, they may reflect adaptive responses to exogenously emerging threats and pressures. Here we examined this possibility by fitting annual rates of scientific discovery and technological innovation to climatic variability and its associated economic pressures and resource scarcity. In time-series data from Europe (1500?1900CE), we indeed found that rates of innovation are higher during prolonged periods of cold (versus warm) surface temperature and during the presence (versus absence) of volcanic dust veils. This negative temperature? innovation link was confirmed in annual time-series for France, Germany, and the United Kingdom (1901?1965CE). Combined, across almost 500 years and over 5,000 documented innovations and discoveries, a 0.5?C increase in temperature associates with a sizable 0.30?0.60 standard deviation decrease in innovation. Results were robust to controlling for fluctuations in population size. Furthermore, and consistent with economic theory and micro-level data on group innovation, path analyses revealed that the relation between harsher climatic conditions between 1500?1900CE and more innovation is mediated by climate-induced economic pressures and resource scarcity.
Data Availability Statement: All relevant data and
computing scripts are within the manuscript and
Supporting Information Files.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Throughout history, humans have displayed strong capacity for creativity and innovation that
have returned substantial benefits. Scientific discovery and technological innovations provided
effective cure and prevention of epidemic disease, enabled increasingly efficient production of
food and care for expanding populations with increasing life expectancies, and may enable
societies to combat the effects of climate change on societal functioning. Given these benefits,
it is not surprising that, both within and across societies, the industry as well governments
place a high premium on scientific discovery and technological innovation [
]. It is therefore
unfortunate that scientific discovery and technological innovation seem hard to predict and
difficult to regulate. In fact, the rate of scientific discovery and technological innovations is
neither linear nor progressive: It varies across cultures and fluctuates over time [
]. As much as
eminent scientists are creative some but not all of the time [
], countries, cities and citizens
go through periods of creative bursts and rapid cascades of technological innovations that
alternate with sometimes prolonged periods of relative stability and inertia [
Time-dependent fluctuations in scientific discovery and technological innovations may fit
the intuition that creative insights ?come out of the blue? and that innovations are ?stumbled
upon.? However, an alternative and arguably more tractable perspective is that scientific
discovery and technological innovations are adaptive responses to recurrent problems and
imminent threats that confront individuals and their societies [
]. If true, the rise and fall of
scientific discovery and technological innovation will be conditioned by exogenous pressures
and threats societies and their peoples face, and which they seek to manage and avert.
The possibility that temporal fluctuations in the rate of innovations track temporal
fluctuations in exogenous pressures and societal threat was examined here with annual time-series
data on scientific discovery and technological innovations in Europe. We link these time-series
data to the often unanticipated and sometimes rather abrupt changes in climatic conditions,
and surface temperature in particular. Surface temperature can vary substantially across years
due to, for example, volcanic eruptions that eject dust into the high atmosphere and reduce the
amount of light reaching the Earth's surface. It can have climatic effects that last for years, with
food shortages and famine as possible consequences [
]. Surface temperature can also
vary as a function of North Atlantic Oscillations in Europe and the build-up of El Nino in
Latin America [
]. These indices and associated fluctuations in temperature also associate
with impaired crop yields and food security [
], as well as with migration [
] and group
conflict and interstate warfare [
Consistent with our main thesis, historical case studies and archaeological excavations
show that, besides migration and warfare, societies can also respond to climatic shocks with
ingenuity and innovation [
]. For example, in 1953 (CE; Common Era) a North Sea storm
tide caused significant flooding of Northwest European coasts, leading to the loss of over 2,000
lives and extensive material damage. Affected countries responded with technological studies
on the strengthening of coastal defenses and built innovative systems of dams and storm surge
barriers . Such a response echoes that of the Peruvian Chimu? society (1200?1470CE),
which adapted to recurrent flooding by constructing hundreds of crescent-shaped sand breaks
that inhibited the intrusion of saltating sands into their irrigation canals [
]. Even the advent
of, and subsequent innovations in agriculture in the early Holocene have been linked to rather
profound changes in climatic conditions [
Although archaeological evidence and historical cases are in line with sophisticated model
], a systematic analysis of whether and how climate shocks affect innovation is
lacking. Furthermore, the mechanisms that account for such impact remain undocumented
and poorly understood. One possibility is that climatic shocks engender the social and
economic pressures that, in turn, trigger scientific inquiry and technological innovation. Indeed,
social and economic pressures condition creative problem solving and innovation: Studies in
organizations and with R&D teams show more innovation under mild rather than no time
], or when organizational slack tightens [
]. And although extreme
competition among firms can erode the economic rents that render innovation worthwhile, some
competition incentivizes innovation [
Taken together, the sometimes erratic and seemingly unpredictable rise and fall of scientific
discovery and technological innovation may be due to the social and economic pressures that
are triggered by sharp climatic changes. We tested this possibility in one discovery study with
annual time-series data for Western Europe between 1500?1900CE, and then with three
2 / 16
confirmation studies with annual time-series data for France, Germany, and the United
Kingdom between 1901?1965CE.
Methods and results for the 1500-1900CE Time-series
The 1500?1900CE sample provides the longest consistent and uninterrupted time-series with
cross-validated and psychometrically robust annual indices of innovation [
], obtained by
combining six historical sources on over 5,000 landmark innovations such as the development
of production facilities, modes of transportation, communication technologies, and
discoveries in biochemical and medical sciences (Fig 1A) [Materials and Methods]. Importantly, at
least for this time period, reverse causality (i.e., innovations affecting climate, [
]) is unlikely
to obscure inference. Furthermore, this sample provides a reasonable model of agrarian
societies that are less technologically advanced than Western Europe nowadays and perhaps as
vulnerable to climatic shocks as Western Europe between 1500?1900CE [
In the 1500?1900CE sample, innovation was related to two types of climatic shocks based
on (i) the reconstructed paleo-climatic annual surface temperature for Europe [
], and (ii)
the weighted Dust Veil Index [
], which quantifies the impact of various volcanic
eruptions' release of dust and aerosols over the years on the European continent [Materials and
Methods] (Fig 1B). Because innovation was expected to respond to prolonged climatic shocks,
we analyzed five-year moving averages in innovation as well as climatic shocks (based on both
temperature and dust veils). To preclude spurious results as a consequence of analyzing
nonstationary data, we detrended the annual time-series for innovation [
] [Materials and
Fig 1. Annual time-series of innovation, temperature, and volcanic dust veils for Europe (1500?1900CE). (A)
Innovation in science and technology expressed in factor-loading weighted average across six indicators (observed
range -1.153, +3.037; M = 0.0, SD = 1.0). (B) Reconstructed paleo-climatic data of annual surface temperature (dotted
lines) and five-year moving averages (solid lines) expressed in deviation from the period mean (8.1533?C), and Volcanic
Dust Veils (observed range 0, +650; M = 64.214, SD = 99.952).
3 / 16
Regressions of (detrended 5-year moving averages in) innovation on temperature and absence/presence of dust veils (Lamb's Dust Veil Index or DVI), controlling for
changes in population size, for 1500?1900CE (Top Panel) and 1500?1800CE (Bottom Panel). Coefficients are standardized. Intercepts in the regressions on
temperature are suppressed to conserve space. The final three rows report the p-value of a Wald test on the equality of the coefficients on DVI absent and DVI present,
the number of observations, and the R2 of the regressions.
Table 1 presents the estimation results of regressions of innovation on surface temperature
and the absence or presence of volcanic dust veils. Innovation regressed on surface
temperature in a negative and linear manner (standardized coefficient b = -0.120, t = -2.407, p = 0.017,
R2 = 0.014; Table 1, Top Panel): thus, colder temperatures are associated with higher
innovation (Fig 2A). An analogous effect was observed in a regression of innovation on a
dichotomized index of volcanic dust veils (5-year moving averages; 0 = DVI absent; 1 = DVI present).
The absence of dust veils significantly associated with lower innovation (b = -0.236, t = -2.309,
p = 0.021; Table 1, Top Panel), while the presence of dust veils associated with higher
innovation, albeit not significantly so at conventional significance levels (b = 0.084, t = 1.480, p =
0.140, total R2 = 0.018). A Wald test on the equality of the coefficients on DVI absent and DVI
present rejected the null hypothesis that coefficients are equal with a p-value of 0.007. Thus,
the presence of dust veils tends to be associated with significantly higher rates of innovation
relative to their absence (Fig 2B). These baseline results indicate that a 0.5?C decrease in
temperature (respectively, the presence of dust veils) is associated with a sizable 0.30 (0.32)
standard deviation increase in innovation.
4 / 16
Fig 2. Innovation as a function of climatic shocks (Europe, 1500?1900CE). (A) Scatter and linear regression
showing negative association between innovation and deviation from average temperature (shown are five-year
moving averages, detrended series). (B) More innovation when volcanic dust veils are present rather than absent
(fiveyear moving averages; shown are Mean ?1SEM).
Innovative capacity may be related to population size and changes therein, and population
size may be affected by climatic shocks [
]. To verify that the presently observed climate?
innovation linkages were robust to possible covariation in population size, we re-analyzed data
with five-year moving averages in changes in population size as covariate [Materials and
Methods]. Innovation did not relate to changes in population size (b = -0.033, t = -0.650, p = 0.516;
Table 1, Top Panel), and the earlier observed effect of surface temperature was maintained
(b = -0.120, t = -2.398, p = 0.017, total R2 = 0.016). Likewise, the effect of volcanic dust veils on
innovation remained after controlling for changes in population size (b = -0.241, t = -2.349,
p = 0.019 for DVI absent and b = 0.088, t = 1.550, p = 0.409 for DVI present; total R2 = 0.021;
Wald test rejected equality of coefficients with p = 0.005). We conclude that the patterns
shown in Fig 2A and 2B are robust to controlling for (fluctuations in) population size.
One possible concern about the analyses thus far is that, perhaps, result are obscured by the
fact that (i) from 1800CE onwards relatively high levels of volcanic dust were present and (ii)
innovation steeply increased (i.e., the onset of the Industrial Revolution in Western Europe)
(see also Fig 1A and 1B). Put differently, even though trends are removed from all variables, it
cannot be ruled out that the above results are driven by the coincidental covariation in dust
veils on the one hand and the onset of the Industrial Revolution on the other. To examine this
possibility, we estimated our models for the series between 1500?1800CE, thus omitting the
data most strongly reflecting the Industrial Revolution.
Table 1 (Bottom Panel) gives the linear regression results (with and without controlling for
population size changes). When comparing the results from the top Panel (1500?1900CE) to
the bottom Panel (1500?1800CE), we can see that main results are similar in both samples:
lower temperatures and the presence of dust veils are associated with higher innovation. Both
the statistical significance and the magnitude of the effect of temperature on innovation are
stronger in the pre-Industrial Revolution era, but the statistical significance and magnitude of
5 / 16
Fig 3. Annual time-series of innovation and temperature for France, Germany, and the United Kingdom (1901?
1965CE). (A) Innovation in science and technology for each country expressed in factor-loading weighted averages across
three indicators. (B) Scatter and linear regression of five-year moving averages in innovation and deviation from average
temperature for Germany. (C) Scatter and linear regression of five-year moving averages in innovation and deviation from
average temperature for France. (D) Scatter and linear regression of five-year moving averages in innovation and deviation
from average temperature for United Kingdom.
the effect of dust veils are somewhat diminished relative to the longer period. All in all, we
conclude that the relation between innovation and climatic shocks that this study uncovers is not
driven by the Industrial Revolution.
Methods and results for the 1901?1965CE Time-series
In the period 1500?1900CE, harsher climatic conditions (prolonged cold temperatures,
presence of volcanic dust veils) are thus associated with higher innovation than more benign
climatic conditions. To examine the generality of this finding, we created three new annual
timeseries for innovation and surface temperature in France, Germany, and the U.K. between
1901?1965CE (Fig 3A) [Materials and Methods]. This time period begins where the discovery
study ended and runs until the beginning of the Anthropocene [
]. While reverse causality
may thus still be limited, this time period approximates contemporary conditions in
technologically advanced, industrialized countries.
For the 1901?1965CE period, we were able to compute an index of innovation very similar
to the one used in the discovery study, and obtained country-specific temperature data and
estimates of population size [Materials and Methods]. As before, we computed five-year
moving averages for innovation and for surface temperature, detrended the time-series, and
6 / 16
Regressions of (detrended 5-year moving averages in) innovation on temperature, controlling for changes in population size, for France, Germany, and the U.K. for
1901?1965CE. Coefficients are standardized. Intercepts in the regressions on temperature are suppressed to conserve space. The final two rows report the number of
observations and the R2 of the regressions. For Germany, no continuous time-series of population size is available for 1901?1965CE.
regressed country-specific innovation on the deviation from average temperature within that
country [Materials and Methods].
Table 2 presents the estimation results of regressions of country-level innovation in France,
Germany, and the U.K. on local surface temperature. To account for multiple testing with
potentially correlated independent variables and dependent variables, we first established that
the multivariate effect for the linear term across all three samples was indeed significant
(Hotellings F(9,158) = 8.778, p<0.001). For Germany, the linear effect was negative but not significant
(Fig 3B: b = -0.156, t = -1.212, p = 0.230; R2 = 0.024), possibly because of the economic sanctions
imposed on, and the exodus of eminent scientists and engineers from, Germany following both
WW-I and WW-II [
]. Indeed, regressions returned significant negative linear effects of
temperature on innovation for both France (Fig 3C: b = -0.290, t = -2.324, p = 0.024, R2 = 0.084;
after controlling for changes in population size: b = -0.326, t = -2.693, p = 0.009, somewhat
stronger) and the U.K. (Fig 3D: b = -0.266, t = -2.117, p = 0.039, R2 = 0.071; after controlling for
changes in population size: b = -0.246, t = -1.891, p = 0.064, somewhat weaker). Again, the
magnitudes of the observed effects are large: averaged across the three countries, a 0.5?C decrease in
temperature is associated with a 0.65 standard deviation increase in innovation.
Economic pressures as mediating mechanism
The finding that harsher climatic conditions consistently associate with more innovation fits
studies showing that individual and group innovation benefit from some exogenous pressure
], and that some rather than no inter-firm competition and resource scarcity can
incentivize innovation [
]. We hypothesized that this climate?innovation link may be
partly due to climate-induced economic pressures and resource scarcity. For the 1500?1900CE
series, we were able to examine this possibility by including annual time-series data on wheat
prices [Materials and Methods]. Wheat crops are sensitive to climatic conditions [
because wheat formed a major part of the diet in large parts of Europe over this period ,
wheat prices provide a reasonable basic proxy for economic scarcity. As before, we used
fiveyear moving averages and detrended the time-series.
Table 3 presents the estimation results of regressions of wheat prices on surface temperature
and the absence or presence of volcanic dust veils. We find that wheat was indeed more
expensive during colder periods (b = -0.238, t = -4.860, p<0.001, R2 = 0.056), and when volcanic
dust veils were present (b = -0.149, t = -1.453, p = 0.147 for DVI absent and b = 0.057, t =
0.993, p = 0.321 for DVI present; total R2 = 0.008; Wald test rejected equality of coefficients
with p = 0.081, marginal) (Fig 4A). Both effects were robust to controlling for population size:
7 / 16
Regressions of (detrended 5-year moving averages in) wheat prices on temperature and absence/presence of dust veils (Lamb's Dust Veil Index or DVI), controlling for
changes in population size, for 1500?1900CE. Coefficients are standardized. Intercepts in the regressions on temperature are suppressed to conserve space. The final
three rows report the p-value of a Wald test on the equality of the coefficients on DVI absent and DVI present, the number of observations, and the R2 of the regressions.
wheat prices did relate negatively to changes in population size (b = -0.459, t = -10.620,
p<0.001; we note the possibility of reverse causality here), but, importantly, the earlier
observed effect of surface temperature was maintained when population size was controlled
for (b = -0.233, t = -5.388, p<0.001). The effect of volcanic dust veils on wheat prices became
slightly stronger after controlling for changes in population size (Wald test now rejected the
equality of coefficients on DVI absent and DVI present with p = 0.011).
Table 4 presents the estimation results of regressions of innovation on wheat prices.
Consistent with the hypothesized effect of economic scarcity on innovation, we find that higher
wheat prices were related to more innovation (b = 0.160, t = 3.224, p = 0.001, R2 = 0.026). This
linear effect of wheat prices becomes stronger when we include a quadratic wheat price term
as additional independent variable (b = 0.273, t = 4.743, p<0.001). Moreover, the significantly
negative quadratic term (b = -0.214, t = -3.714, p<0.001, total R2 = 0.059) suggests that
extremely high or low wheat prices undermine innovation relative to moderate price levels
(Fig 4B). These effects remained strong and significant after controlling for changes in
population size. We note that this inverted U-shape fits the idea that whereas some exogenous
pressure benefits innovation, intense competition and economic scarcity can undermine the
economic, social, and psychological resources needed to invent and innovate [
We concluded analyses with computing indirect path estimates for the linear impact of
climatic shocks on innovation through the curvilinear impact of economic scarcity on
innovation [Materials and Methods] [
]. Controlling for wheat prices reduced the direct
temperature-innovation linkage to non-significance (b = -0.061, t = -1.209, p = 0.227), and the indirect
climate?pressure?innovation path (instantaneous indirect effect) [
] was significant (Fig 4C).
Although the direct dust veil?innovation linkage remained significant after controlling for
wheat prices (b = 0.011, t = 6.916, p = 0.001), here also the indirect effect was significant (Fig
4C). It follows that climatic conditioning of innovation is partly predicted by climate-induced
Conclusions and discussion
Creative discovery and technological innovation fluctuate across time and space. In this study,
across four annual time-series that cover almost 500 years and 5,000 documented instances of
8 / 16
Fig 4. Economic pressure mediates climatic conditioning of innovation (Europe, 1500?1900CE). (A) Wheat price is higher when volcanic dust veils
are present rather than absent (five-year moving averages; shown are Mean ?1SEM). (B) Scatter and linear (solid) and quadratic (dotted) regression lines
for the association between innovation and wheat price (shown are five-year moving averages, detrended series). (C) Indirect path model showing
fiveyear moving averages in climatic shocks (prolonged deviation from average temperature; presence of volcanic dust veils) impact on innovation through
wheat prices. Shown estimates based on MEDCURVE using 5,000 bootstraps and 95% Confidence Intervals. For the temperature?wheat?innovation (dust
veil?wheat?innovation) path, instantaneous indirect effects ? are shown at Mean and ? 1SD of surface temperature (dust veil) to the right (left) side of
PLOS ONE | https://doi.org/10.1371/journal.pone.0190122
9 / 16
scientific discovery and technological innovation in Europe, we observed that colder periods
are associated with higher innovation. Furthermore, we saw that this relation between colder
temperatures and higher innovation is also related to higher wheat prices, providing more direct
evidence of a link with economic pressures. To some extent, at the least, fluctuations in scientific
discovery and technological innovation track climatic shocks?volcanic eruptions that can
suppress surface temperatures for several years, or alterations in North Atlantic Oscillations that
lead to periods of relatively elevated surface temperatures and reduced economic hardship.
Innovation in the present study was broadly defined in inclusive terms. Others before us
linked climatic conditions to specific innovations in, for example, agriculture [
] or tool
]. For example, one study found that prolonged changes in surface temperature in the
U.S.A. not only threatened wheat production but also triggered agricultural and technological
innovations that enabled wheat production in ways and areas traditionally considered
unfeasible . Such problem-driven innovation fits the adagio that ?necessity is the mother of
invention? and that human creativity and innovation benefit from exogenous threats and pressures
]. The broad and inclusive nature of the innovation indices studied here allows for
the possibility that, in addition to innovative activity aimed at solving a specific and local
climate-induced problem, human ingenuity and innovation along with tendencies for
experimentation and exploration, require exogenous pressure. In fact, at the individual, group, and
societal level, innovation is inherently costly and with unknown returns on investment . It
stands to reason that groups and societies engage in such costly and risky endeavors only, or
especially, when there is a need to, and such felt need may be created by deteriorating climatic
conditions and concomitant economic hardship.
Although data pertain to the relatively temperate climate in Western Europe and are
correlational in nature, results support the general idea that climatic shocks affect innovation when
and because they create socio-economic pressures to which human societies adapt. Over our
period of study, we find that rising temperatures in Europe associate with lower innovation,
and we attributed this inverse relation between surface temperature and innovation to
climate-induced economic pressures. A possible caveat here is that our indices of innovation,
both in the discovery and in the confirmation study, were derived from historical source
books and events such as Nobel laureates. In both studies we used multiple sources that
according to our psychometric analyses (see Materials and Methods) were sufficiently
correlated to provide a single index. This generates some confidence in the validity of our data and
results. In our analyses we further controlled for bias in coding (e.g., that year endings at 0, 5,
10, and 50 had relatively high numbers of innovation [
] [Materials and Methods] and we
detrended time-series to reduce the possibility of statistical artefacts due to non-stationary
time-series. Nevertheless, time-series of the type used here are noisy and we may have missed
out important relations between climatic shocks and innovation in science and technology.
Despite measurement noise, the linear relation between climatic shocks and innovation
replicated across two studies and was observed in four of the five tests. Accordingly, we suggest
that new research focuses on more detailed understanding of this climate-pressure-innovation
relation. In our confirmatory studies, we localized the climate?innovation relation at the
country level. More fine-grained (e.g., region or city level) analyses were not feasible due to the
low number of innovations we obtained from the historical sources used here. In more recent
years, however, alternative indices of scientific and technological innovations are increasingly
well-documented, as are climate indicators. Our current results suggests such a panel data
approach  may be possible and would allow a deeper and more fine-grained understanding
of the social and psychological micro-foundations of innovation triggered by climatic shocks.
Our findings reveal an association between climatic shocks and innovation but because of
the nature of our data cannot establish a causal effect [
]. At the same time, our time-series
10 / 16
started at the end of the middle-ages and continued into the last century, a period in history
where the impact of human activity on climate has been very small [
] so that climatic shocks
can be reasonably considered as exogenous. Whereas we cannot exclude the possibility that
economic activity, innovation in science and technology included, can significantly affect
climatic conditions, current findings may best be understood in directional terms. Human
activity in our past did not create volcanic dust veils, nor did it significantly alter surface
temperature. It follows that, across the past 500 years in Europe, climate shocks may have
created economic pressures and concomitant innovations in science and technology.
Materials and methods
Annual time-series for Europe, 1500?1900CE
The 1500?1900CE series for innovation is based on six different historical sources [
entries from Williams [51, 52] include the solution for cubic equation and the invention of the
watch (both around 1500CE), a manual on the irrigation of grasslands, the discovery of barium
sulphite, and the binocular telescope (all around 1600CE), the discovery that hydrogen
detonates when exposed to air, development of steam engines, and the publication of Newton's
optics (all around 1700CE), the discovery of infra-red solar rays and palladium, Volta's
electricity from a cell, the publication of Bell's ?system of comparative surgery? (around 1800CE),
and the publication of Max Planck's Quantum Theory, the first wireless transmission of
speech, and the discovery of hormones (around 1900CE). The six indices formed a reliable
composite (Cronbach's ? = 0.931), and all indices loaded on one single factor in a Principal
Component Analysis (Eigenvalue ? = 4.12, R2 = 0.6738). In further analyses, we combined the
six indices weighted by their factor-loadings (with M = 0.00, and SD = 1.0).
Annual surface temperature was retrieved from the European Seasonal Temperature
]. From the data, we computed mean annual temperature (averaged over
the four seasonal temperatures). Annual temperature associated with lower variance in
temperature across the four seasons within a given year, r = -0.509, p 0.001, suggesting that
hotter and colder years also had more extreme summer highs and/or winter lows than temperate
years. We tracked whether variations in surface temperature were related to the North Atlantic
Oscillation (NOA) index, which is the standardized (1901-1980CE) difference between the SLP
average of four grid-points on a 5x5 longitude-latitude grid over the Azores and over Iceland
(WDCA Paleo; [
]). It contains monthly (1659-2001CE) and seasonal (1500-1658CE) NOA
indices, estimated using instrumental and documentary proxy independent variables from
Eurasia. We computed the annual NOA mean and found in a simple regression that the five-year
moving average in NAO correlated strongly and positively with the five-year moving average in
surface temperature, b = 0.573, t = 13.892, p<0.001, R2 = 0.331.
In addition to annual surface temperature, we retrieved Lamb?s Dust Veil Index (DVI), a
numerical index that quantifies the impact of a particular volcanic eruptions release of dust
and aerosols over the years following the event in north-western Europe, from (World Data
Center for Paleoclimatology Data Contribution Series #2000?075) [
For the years 1500?1868CE, we used wheat prices expressed in Shillings and Pence/Bushel,
with one Bushel being the equivalent of 35.238 liters [
]. For the years 1869?1900CE, we
computed annual averages for Europe from the monthly prices (in 1960 USD/Kilogram) from
Jacks , and converted these into the metric used in Allen [
]. The overlap in series (1800?
1868CE) provided a ?test-retest? correlation of r(68) = 0.85, p<0.001, and we used the average
of two indices when available.
We validated the time-series for wheat prices against the annual time-series for craftsmen's
consumer price index (CPI) [
]; the CPI is a statistical estimate constructed from the prices of
11 / 16
a sample of representative items whose prices are collected periodically. An increase in CPI is a
generally accepted measure of inflation and thus a marker of (increasing) economic pressure.
Wheat price and CPI were strongly correlated indeed (r = 0.449, p<0.001), and curve-fit
regressions with the five-year moving average in innovation as the dependent variable and the
five-year moving average in CPI as independent variable (instead of wheat price as indicator of
economic scarcity) revealed a positive (non-significant) linear term (b = 0.058, t = 1.293,
p = 0.197) and a significant negative quadratic term (b = -0.244, t = -3.532, p<0.001). We take
this as convergent evidence for the observation that economic pressure associates with
innovation in an inverted U-shaped manner.
Both wheat price and innovative capacity may be related to population size and changes
therein, and population size may be affected by climatic shocks [
]. To verify that the
presently observed climate?innovation linkages were robust to possible covariation in population
size, we derived population size estimates from the Maddison Project (2013 version; [
downloaded on March 16, 2016 from www.ggdc.net.maddison/maddison-project). The
database gives a yearly estimate of population size in Western Europe from 1820?2000, and
estimates at 1500CE, 1600CE, 1700CE and 1750CE. To obtain an annual estimate, missing values
for the years 1501?1599CE, 1601?1699CE, 1701?1749CE, and 1751?1819CE were estimated
by interpolation. This approximation ignores within century fluctuations in population size
due to pandemics, famine, warfare, and other exogenous pressures , yet fits the general
notion that European population size between 1500?1800CE was fairly stable [
]. In the main
analyses, we used the five-year moving average of (detrended) changes in population size
estimate as control variable.
Time-series for Germany, France, and the United Kingdom (1901?1965CE)
For each country, we created annual time-series for innovation in ways similar to the one
designed by Simonton [
] and used in the discovery study. Each series combined entries
from three distinct sources. Country-based entries were derived as follows. Two historical
source books [
] provided yearly entries of scientific discoveries and technological
innovation, and we retained those that could be attributed to one of the three countries under study.
Examples include the discovery of chlorophyll and the publication of Einstein's theory of
special relativity in Germany, Binet's formulation of a measure of intelligence in France (both in
1905CE), the introduction in Germany of the Leica (a 35 millimeter camera with adjustable
shutter), the discovery of the element rhenium in Germany, of the Auger electron in France,
and the packing fraction in the U.K. (all in 1925CE), the invention of the radio interferometer
that improves the resolution of radio telescopes in the U.K., the discovery of the Schwarzschild
radius in Germany, and the development of homological algebra in France (all in 1955CE). In
addition, we added yearly entries for the mid-career age of Nobel Laureates in chemistry,
physics, and medicine obtained from [
]. Laureates were placed in the country where the
prizewinning work was conducted.
The three series fully overlap between 1901?1965CE and for each country, and Principal
Component Analysis on the three indices yielded one-factor solutions for each country
(Germany: ? = 1.340, R2 = 0.457; France: ? = 1.370, R2 = 0.446; U.K.: ? = 1.268, R2 = 0.423). As in
the discovery study, analyses were conducted on the factor-loading regression index of
innovation (Germany: observed range -1.11, +3.917, M = 0.00, SD = 1.0; France: observed range
-1.849, +3.133, M = 0.00, SD = 1.0; U.K.: observed range -1.407, +4.079, M = 0.00, SD = 1.0).
Annual surface temperature for each country was obtained from (IGBP PAGES/World
Data Center for Paleoclimatology/ Data Contribution Series # 2010?047) [
]. The dataset
provides Gridded April-September multiproxy European temperature reconstructions,
12 / 16
expressed in ?C anomalies relative to the 1961?1990CE average. We took the surface
temperature 5?x5? grids corresponding to the hemispheric coordinates for Berlin (German-series),
Paris (France series), and London (U.K. series). Annual estimates of country's population size
were obtained from the Cross-country Historical Adoption of Technology (CHAT) dataset
]. For the German series, not enough entries were available (due to missing observations
for the WWI and WWII periods) to perform meaningful analyses. For the U.K. and France
series, we used five-year moving averages of the (detrended) change in population size in the
Data preparation and analytic strategy
As we expect innovation to be related to prolonged climatic shocks, we analyzed five-year
moving averages in innovation as well as climatic shocks (based on both temperature and dust veils)
and did the same for population size and wheat prices. An important concern is the use of
nonstationary time-series in the regressions, since they can lead to spurious results [
detrended the annual time-series for innovation, population size and wheat prices the annual
series and for the series based on five-years moving averages. Furthermore, Simonton 
noted that innovations in science and technology more often appeared in years ending at 0, 5,
50, or 00, and attributed this to a dating bias among historians documenting innovations. To
avoid inaccurate conclusions regarding cyclical tendencies in innovation, we thus controlled for
four dummy variables (one for each of these year-endings, coded as 0 = absent; 1 = present).
Second, and in addition to the four dating-bias dummies, we partialled out the linear and
quadratic trends found in the data, and used the Augmented Dickey?Fuller test (ADF) to test the
null hypothesis of whether a unit root was present in the detrended time-series. In all analyses,
involving annual and/or five-year averages, the null hypothesis was rejected [
We used standard multiple regression models to estimate innovation and wheat price as a
function of surface temperature (or the absence/presence of volcanic dust veils), and with and
without controlling for changes in population size. All models included an intercept (except
when dummy variables for both the absence and the presence of volcanic dust veils were
included), and were interpreted in terms of the explained variance R2 and the significance of
standardized regression coefficients (see Tables 1 through 4). Indirect paths (shown in Fig 4D)
were estimated with MEDCURVE (IBM SPSS v23) [
]. We specified the X ! M and X ! Y
paths as linear and the X ! Y path as quadratic, and used 5000 bootstraps to estimate the 95%
Confidence Intervals for the test parameter ? (Theta). The instantaneous indirect effects ? are
provided at the sample mean of X and at ?1SD above/below the sample mean. Significant ? (i.e.,
the 95%CI does not include zero) indicates the presence of an indirect path (X ! M ! Y) [
Our use of five-year moving averages may result in tighter confidence for the residual
variance than is present in the actual data, and thus may lead to an overestimation of the effects.
We verified, first, whether results replicate when using the (detrended) annual series rather
than five-year moving averages. This was the case for all relations reported in the Main Text,
except for the direct association between annual temperature and innovation. Second, we
performed our analyses on sequential five-year periods, taking the average within each period and
thus having 1/5th of the number of observations but also no built-in autocorrelation. Again, we
replicated our results and were able to draw the same conclusions (the first author can be
contacted for further detail).
13 / 16
We thank DK Simonton for making available the time-series data on innovation used in the
discovery study. CKWDD and MAVD constructed annual time-series, analyzed the data, and
wrote the paper. No conflict of interest declared.
Conceptualization: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Data curation: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Formal analysis: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Methodology: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Project administration: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Resources: Carsten K. W. De Dreu.
Writing ? original draft: Carsten K. W. De Dreu, Mathijs A. van Dijk.
Writing ? review & editing: Carsten K. W. De Dreu, Mathijs A. van Dijk.
14 / 16
15 / 16
1. European Commission ( 2013 ). Innovation Union: A pocket guide on a Europe 2020 Initiative . http://ec. europa.eu/research/innovation-union/index_en.cfm.
2. OECD policy of innovations review ( 2016 ). Downloaded from http://www.oecd.org/innovation/inno/ oecdreviewsofinnovationpolicy.htm
3. Harrington JR & Gelfand MJ ( 2014 ). Tightness?Looseness across the 50 United States . Proc Nat Acad Sciences USA , 111 , 7990? 7995 .
4. Kolodny O , Creanza N & Feldman MW ( 2015 ). Evolution in leaps: The punctuated accumulation and loss of cultural evolutions . Proc Nat Acad Sciences USA , E6762? E6769 .
5. Simonton DK ( 1975 ). Sociocultural context of individual creativity?trans-historical time-series analysis . J Pers Soc Psych , 32 , 1119? 1133 .
6. Jones BF & Weisbuch BA ( 2011 ). Age dynamics in scientific creativity . Proc Nat Acad Sciences USA , 108 , 18910? 18914 .
7. Bettencourt LMA , Lobo J , Helbing D , Kuhnert C & West GB ( 2007 ). Growth, innovation, scaling, and the pace of life in cities . Proc Nat Acad Sciences USA , 104 , 7301? 7306 .
8. Porter ME ( 2000 ). Location, competition, and economic development: Local clusters in a global economy . Econ Devel Quart , 14 , 15? 34 .
9. Richerson PJ , Boyd R & Bettinger RL ( 2001 ). Was agriculture impossible during the Pleistocene but mandatory during the Holocene? A Climate Change Hypothesis . Am Antiq , 66 , 387? 411 .
10. Kreindler GE & Young HP ( 2014 ). Rapid innovation diffusion in social networks . Proc Nat Acad Sciences USA , 111 , 10881? 10888 .
11. Reader SM , Morand-Ferron J , & Flynn E. ( 2016 ). Animal and human innovation: Novel problems and novel solutions . Ph T Roy Soc B , 371 , 20150182
12. Roskes M , De Dreu CKW , & Nijstad BA ( 2012 ). Necessity is the mother of invention: Avoidance motivation stimulates creativity through cognitive effort . J Pers Soc Psych , 103 , 242? 256 .
13. Bolton MK ( 1993 ). Organizational innovation and substandard performance?when is necessity the mother of innovation . Org Science , 4 , 57? 75 .
14. Gilligan I ( 2010 ). The prehistoric development of clothing: Archaeological implications of a thermal model . J Arch Meth Theory , 17 , 15? 80 .
15. Newell A & Simon HA ( 1972 ). Human Problem Solving . New York: Prentice Hall.
16. Lamb HH ( 1970 ). Volcanic dust in the atmosphere; with a chronology and assessment of its meteorological significance . Phil T Roy Soc London A , 266 , 425? 533 .
17. Guiot JC , Corona and ESCARSEL members ( 2010 ). Growing season temperatures in Europe and climate forcings over the past 1400 years . PLoS ONE 5 ( 4 ):e9972. https://doi.org/10.1371/journal.pone. 0009972 PMID: 20376366
18. Rampino MR , Self S & Stothers RB ( 1988 ). Volcanic winters . Annu Rev Earth Planet Sci , 16 , 73? 99 .
19. Luterbacher J , Xoplaki E , Dietrich D , Jones PD , Davies TD . . . Wanner H ( 2002 ). Extending North Atlantic Oscillation Reconstructions Back to 1500. Atmosph Science L: https://doi.org/10.1006/asle. 2001 . 0044
20. Wheeler T & von Braun J ( 2013 ). Climate change impacts on global food security . Science , 341 , 508? 513 . https://doi.org/10.1126/science.1239402 PMID: 23908229
21. Stewart JR & Stringer CB ( 2012 ). Human evolution out of Africa: the role of refugia and climate change . Science , 335 , 1317? 1321 . https://doi.org/10.1126/science.1215627 PMID: 22422974
22. Burke M , Hsiang SM & Miguel E ( 2015 ). Global non-linear effect of temperature on economic production . Nature , 527 , 235? 239 . https://doi.org/10.1038/nature15725 PMID: 26503051
23. Hsiang SM , Burke M & Miguel E ( 2013 ). Quantifying the influence of climate on human conflict . Science , 341 , 12353? 12367 .
24. Olmstead AL & Rhode PW ( 2011 ). Adapting North American wheat production to climatic challenges, 1839?2009. Pr Nat Acad Sciences USA , 108 , 480? 485 .
25. Banks WE , d'Errico F , & Zilhao J ( 2013 ). Human-climate interaction during the Early Upper Paleolithic: Testing the hypothesis of an adaptive shift between Proto-Aurignacian and the Early Aurignacian . J Hum Evol , 64 , 39? 55 . https://doi.org/10.1016/j.jhevol. 2012 . 10 .001 PMID: 23245623
26. McRobie A , Spencer T & Gerritsen H ( 2005 ). The big flood: North Sea storm surge . Phil Trans Roy Soc London A , 363 , 1263? 1270 .
27. Dillehay TD & Kolata AL ( 2004 ). Long-term human response to uncertain environmental conditions in the Andes . Proc Nat Acad Sciences USA , 101 , 4325? 4330 .
28. Ashraf Q & Michalopoulos S ( 2015 ). Climatic fluctuations and the diffusion of agriculture . Rev Econ Stat , 97 , 589? 609 . https://doi.org/10.1162/REST_a_00461 PMID: 27019534
29. Rosen AM & Rivera-Collazo I ( 2012 ). Climate change, adaptive cycles, and the persistence of foraging economies during the late Pleistocen/Holocene transition in the Levant . Proc Nat Acad Sciences USA , 109 , 3640? 3645 .
30. Baer M & Oldham GR ( 2006 ). The curvilinear relation between experienced creative time pressure and creativity: Moderating effects of openness to experience and support for creativity . J Appl Psych , 91 , 963? 970 .
31. Voss GB , Sirdeshmukh D & Voss ZG ( 2008 ). The effects of slack resources and environmental threat on product exploration and exploitation . Acad Mgmt J , 51 , 147? 164 .
32. Lavie D , Stettner U & Tushman ML ( 2010 ). Exploration and exploitation within and across organizations . Acad Mngmt Annals , 4 , 109? 155 .
33. Ph Aghion , Bloom R , Griffith R & Howitt P ( 2005 ). Competition and innovation: An inverted-U relation . Quart J Econ , 120 , 701? 728 .
34. Nohria N & Gulati R ( 1996 ). Is slack good or bad for innovation? Acad Mngmt J , 39 , 1245? 1264 .
35. Arrow K ( 1962 ). Economic welfare and the allocation of resources for invention , In RR Nelson , Princeton University Press, 609 ? 625 .
36. Simonton DK ( 1980 ). Techno-scientific activity and war. A yearly time-series analysis 1500?1903 AD . Scientometrics, 2 , 251? 255 .
37. Lewis SL & Maslin MA ( 2015 ). Defining the Anthropocene . Nature , 519 171 ?180. https://doi.org/10. 1038/nature14258 PMID: 25762280
38. Kennett DJ & Marwan N ( 2015 ). Climatic volatility, agricultural uncertainty, and the formation, consolidation and breakdown of preindustrial agrarian states . Phil Trans R Soc A , 373 , 20140458. https://doi.org/ 10.1098/rsta. 2014 .0458 PMID: 26460110
39. Luterbacher J , Dietrich D , Xoplaki E , Grosjean M & Wanner H ( 2004 ). European seasonal and annual temperature variability , trends and extremes since 1500. Science , 303 , 1499? 1503 . https://doi.org/10. 1126/science.1093877 PMID: 15001774
40. Granger CWJ & Newbold P ( 1974 ). Spurious regressions in econometrics . J Econom , 2 , 111? 120 .
41. Zhang DD , Brecke P , Lee HF , He YQ & Zhang J ( 2007 ). Global climate change, war, and population decline in recent human history . Proc Nat Acad Sciences , 104 , 19214? 19219 .
42. Moser P , Voena A & Wladinger F ( 2014 ). German Jewish emigres and US invention . Am Econ Rev , 104 , 3222? 3255 .
43. De Dreu CKW ( 2006 ). When too much and too little hurts: Evidence for a curvilinear relation between task conflict and innovation in teams . J Mngmt , 32 , 83? 107 .
44. Allen RC ( 2001 ). The great divergence in European prices and wages from the Middle Ages to the First World War . Explorations in Economic History, 38 .
45. Hayes AF & Preacher KJ ( 2010 ). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear . Multivariate Behavioral Research , 45 , 627? 660 . https://doi. org/10.1080/00273171. 2010 .498290 PMID: 26735713
46. Sitkin SB ( 1992 ). Learning through failure: The strategy of small losses . Res Org Behav , 14 , 231? 266 .
Baltagi B. , 2008 . Econometric analysis of panel data . John Wiley & Sons.
Hsiang S. , 2016 . Climate econometrics . Annual Review of Resource Economics , 8 , 43? 75 .
49. Dell M. , Jones B.F. and Olken B.A. , 2014 . What do we learn from the weather? The new climate?economy literature . Journal of Economic Literature , 52 ( 3 ), pp. 740 ? 798 .
50. Crutzen P ( 2002 ). Geology of mankind . Nature , 415 , 23. https://doi.org/10.1038/415023a PMID: 11780095
Williams N ( 1965 ). Chronology of the Expanding World: 1492 to 1762 . New York: David McKay.
Williams N ( 1969 ). Chronology of the Modern World: 1763 to the present time . New York: David McKay.
53. Luterbacher J ( 2006 ). European seasonal temperature reconstructions . IGBP PAGES/World Data Center for Paleoclimatology. Data Contribution Series # 2006 ? 060 . NOAA/NCDC Paleoclimatology Program, Boulder CO , USA.
54. Mann ME , Gille EP , Bradley RS , Hughes MK , Overpeck JT , . . . Gross WS ( 2000 ). Global temperature patterns in past centuries: An interactive presentation , Earth Interactions , 4 , Paper 4.
55. Bolt J & Van Zanden JL ( 2014 ). The Maddison Project: Collaborative research on historical national accounts . Economic History Review , 67 , 627? 651 .
56. Jacks DS ( 2006 ). What drove nineteenth century commodity market integration? Explor Econ Hist , 43 , 383? 412 .
57. Ochoa G & Corey M ( 1995 ). Timeline book of science . New York: Ballantine.
58. Comin DA & Hobijn B ( 2009 ). The CHAT dataset . Cambridge MA: National Bureau Econ Res Working Paper 15319 .
59. Montgomery DC , Peck EA & Vining GG ( 2001 ). Introduction to Linear Regression Analysis (3rd Ed) . New York: Wiley.
60. Elliott G , Rothenberg TJ , Stock JH ( 1996 ). Efficient tests for an autoregressive unit root . Econometrica , 64 , 813? 836 .