Baumard proposes that the high levels of innovation observed in the United Kingdom during the Industrial Revolution stem from a process whereby increasing living standards promoted a shift to slower life history strategies – including decreases in fertility and increases in long-term oriented psychological characteristics such as optimism – which, in turn, prompted greater innovation. We find the proposal interesting and consistent with previous theory regarding effects of ecology on human life history behaviors. We applaud the author for proposing an explanation for sociocultural change rooted in evolutionary biology (see Varnum & Grossmann Reference Varnum and Grossmann2017; Reference Varnum and Grossmann2019). However, in the target article these proposed linkages are not assessed using data which allow rigorous empirical tests.
Baumard's theory seeks to explain not only the increase in innovation that produced the first Industrial Revolution in Britain, but also purports to provide a broader explanation for how rising resource levels may lead to increases in innovation by promoting a slow life history strategy. High-quality year-by-year time series data on living standards, life history-relevant behaviors, and innovation are sparse or unavailable before the latter part of the first Industrial Revolution; thus we opted to test Baumard's theory (1) using time series data on these markers for the United Kingdom (Gross Domestic Product [GDP] per capita, optimistic language, patents, and unique book titles) during a time span that included the late period of the first Industrial Revolution to the end of the second Industrial Revolution (1790–1913), and (2) using time series data on a broader set of these markers (GDP per capita, optimistic language, birth rates, patents, trademark applications, and unique book titles) for a broader time span in the United States, where the first Industrial Revolution started somewhat later than in Great Britain, covering the end of the first Industrial Revolution up to the putative third Industrial Revolution underway in recent years (1790–2015). To assess the direction of these relationships we used cross-correlation function analyses with first-order differencing, because relevant time series showed strong levels of first-order autocorrelation, zeroing in on lagged relationships up to ± 25 years. When high degrees of autocorrelation are present in time series, detrending the data before performing analysis is highly recommended (Jebb et al. Reference Jebb, Tay, Wang and Huang2015; McCleary et al. Reference McCleary, Hay, Meidinger and McDowall1980; Tiokhin & Hruschka Reference Tiokhin and Hruschka2017), although alternative methods which explicitly model the linear trends may be used (Jebb et al. Reference Jebb, Tay, Wang and Huang2015; Varnum & Grossmann Reference Varnum and Grossmann2017; Reference Varnum and Grossmann2019; Varnum et al. Reference Varnum, Krems, Morris and Grossmann2019). All results including zero-order correlations, CCF analyses with and without first differences detrending, and all data can be accessed at the Open Science framework (OSF, available at: osf.io/ws6by).
Our findings either provide limited support for Baumard's theory or explicitly contradict it. In the United Kingdom, we find evidence of links between resource levels and life history in the direction opposite Baumard's predictions, such that increases in GDP per capita preceded decreases in optimistic language in books (we view optimism as a psychological characteristic associated with slow life history) at lags of 1 and 9 years (based on the Google Books United Kingdom corpus). When examining the relationship between optimistic language in books and the number of patents (a typical marker of innovation), the relationship was negligible, whereas the relationship between changes in optimistic language and the number of unique book titles per million inhabitants (another marker of innovation) was bidirectional and in the direction opposite that predicted by Baumard's theory – increases in unique book titles preceded a decrease in optimism at lags of 16 and 21 years, and decreases in optimism preceded increases in unique book titles at a lag of 4 years. Analyzing the data without detrending did not lend stronger support for Baumard's hypotheses, either (results available at https://osf.io/ws6by).
In the United States, we observe evidence largely inconsistent with Baumard's theory. Changes in optimism-related language (from the Google Books United States corpus) preceded increases in per capita GDP at a lag of 24 and 25 years. Further, there was no significant relationship between GDP per capita and birth rate (a marker of fast life history). We also find evidence in the direction opposite Baumard's theory regarding life history and innovation, such that increases in birth rates (a marker of fast life history) preceded increases in patents per million at a lag of 10 years, and increases in birth rates preceded increases in the number of unique book titles published at a lag of 19 years. Further, changes in birth rates were only negligibly related to changes in trademark applications per million (another marker of innovation). Finally, changes in optimistic language in books were largely unrelated to changes in patents, number of unique book titles, or trademark applications per million. Again, analyzing the data without detrending did not lend stronger support for Baumard's hypotheses (results available at https://osf.io/ws6by).
Baumard's theory suggests that living standards indirectly promote innovation. Notably, an indirect relationship can mean a path mediated through another cultural psychological process, as well as a lagged effect, with time as a mediator itself. Therefore, we sought to test whether there might be an indirect relationship between these variables in the latter sense, as well. In the United Kingdom, the strongest relationship between increases in GDP per capita and the number of patents occurred at a lag of 21 years, and we also find that increases in patents preceded increases in GDP per capita at a lag of 10 years, suggesting a bidirectional relationship between these variables. In the United States, increases in GDP per capita preceded increases in patents at lags of 14, 22, and 25 years (although there was also a negative lagged correlation such that increases in patents preceded decreases in GDP per capita at a lag of 25 years). Notably, increases in innovation may also lead to increases in living standards. In the United Kingdom, increases in unique book titles preceded increases in GDP per capita at a lag of 15 years. In the United States, increases in trademark applications preceded increases in GDP per capita at a lag of 8 years, and there was also a significant contemporaneous relationship. Links between GDP per capita and unique book titles were negligible in the United States. In sum, these results suggest that overall there may be a fairly consistent lagged, and possibly bidirectional, relationship between living standards and innovation.
Taken together, analyses of time series data from the United Kingdom and the United States provide inconsistent evidence for Baumard's first claim – that rising resource levels led to slower life history behaviors – and either fail to support or contradict his second claim – that a shift to slower life history led to greater innovation, at least during the (late) first and second Industrial Revolutions and into the putative third Industrial Revolution underway in recent years. However, our analyses do provide support for lagged, often bidirectional, associations between living standards and innovation. Based on our initial tests, it appears that more sophisticated analytical models, or additional factors, are necessary to explain the rapid increases in innovation that occurred during the late first Industrial Revolution, as well as during the second and third Industrial Revolutions.
Baumard proposes that the high levels of innovation observed in the United Kingdom during the Industrial Revolution stem from a process whereby increasing living standards promoted a shift to slower life history strategies – including decreases in fertility and increases in long-term oriented psychological characteristics such as optimism – which, in turn, prompted greater innovation. We find the proposal interesting and consistent with previous theory regarding effects of ecology on human life history behaviors. We applaud the author for proposing an explanation for sociocultural change rooted in evolutionary biology (see Varnum & Grossmann Reference Varnum and Grossmann2017; Reference Varnum and Grossmann2019). However, in the target article these proposed linkages are not assessed using data which allow rigorous empirical tests.
Baumard's theory seeks to explain not only the increase in innovation that produced the first Industrial Revolution in Britain, but also purports to provide a broader explanation for how rising resource levels may lead to increases in innovation by promoting a slow life history strategy. High-quality year-by-year time series data on living standards, life history-relevant behaviors, and innovation are sparse or unavailable before the latter part of the first Industrial Revolution; thus we opted to test Baumard's theory (1) using time series data on these markers for the United Kingdom (Gross Domestic Product [GDP] per capita, optimistic language, patents, and unique book titles) during a time span that included the late period of the first Industrial Revolution to the end of the second Industrial Revolution (1790–1913), and (2) using time series data on a broader set of these markers (GDP per capita, optimistic language, birth rates, patents, trademark applications, and unique book titles) for a broader time span in the United States, where the first Industrial Revolution started somewhat later than in Great Britain, covering the end of the first Industrial Revolution up to the putative third Industrial Revolution underway in recent years (1790–2015). To assess the direction of these relationships we used cross-correlation function analyses with first-order differencing, because relevant time series showed strong levels of first-order autocorrelation, zeroing in on lagged relationships up to ± 25 years. When high degrees of autocorrelation are present in time series, detrending the data before performing analysis is highly recommended (Jebb et al. Reference Jebb, Tay, Wang and Huang2015; McCleary et al. Reference McCleary, Hay, Meidinger and McDowall1980; Tiokhin & Hruschka Reference Tiokhin and Hruschka2017), although alternative methods which explicitly model the linear trends may be used (Jebb et al. Reference Jebb, Tay, Wang and Huang2015; Varnum & Grossmann Reference Varnum and Grossmann2017; Reference Varnum and Grossmann2019; Varnum et al. Reference Varnum, Krems, Morris and Grossmann2019). All results including zero-order correlations, CCF analyses with and without first differences detrending, and all data can be accessed at the Open Science framework (OSF, available at: osf.io/ws6by).
Our findings either provide limited support for Baumard's theory or explicitly contradict it. In the United Kingdom, we find evidence of links between resource levels and life history in the direction opposite Baumard's predictions, such that increases in GDP per capita preceded decreases in optimistic language in books (we view optimism as a psychological characteristic associated with slow life history) at lags of 1 and 9 years (based on the Google Books United Kingdom corpus). When examining the relationship between optimistic language in books and the number of patents (a typical marker of innovation), the relationship was negligible, whereas the relationship between changes in optimistic language and the number of unique book titles per million inhabitants (another marker of innovation) was bidirectional and in the direction opposite that predicted by Baumard's theory – increases in unique book titles preceded a decrease in optimism at lags of 16 and 21 years, and decreases in optimism preceded increases in unique book titles at a lag of 4 years. Analyzing the data without detrending did not lend stronger support for Baumard's hypotheses, either (results available at https://osf.io/ws6by).
In the United States, we observe evidence largely inconsistent with Baumard's theory. Changes in optimism-related language (from the Google Books United States corpus) preceded increases in per capita GDP at a lag of 24 and 25 years. Further, there was no significant relationship between GDP per capita and birth rate (a marker of fast life history). We also find evidence in the direction opposite Baumard's theory regarding life history and innovation, such that increases in birth rates (a marker of fast life history) preceded increases in patents per million at a lag of 10 years, and increases in birth rates preceded increases in the number of unique book titles published at a lag of 19 years. Further, changes in birth rates were only negligibly related to changes in trademark applications per million (another marker of innovation). Finally, changes in optimistic language in books were largely unrelated to changes in patents, number of unique book titles, or trademark applications per million. Again, analyzing the data without detrending did not lend stronger support for Baumard's hypotheses (results available at https://osf.io/ws6by).
Baumard's theory suggests that living standards indirectly promote innovation. Notably, an indirect relationship can mean a path mediated through another cultural psychological process, as well as a lagged effect, with time as a mediator itself. Therefore, we sought to test whether there might be an indirect relationship between these variables in the latter sense, as well. In the United Kingdom, the strongest relationship between increases in GDP per capita and the number of patents occurred at a lag of 21 years, and we also find that increases in patents preceded increases in GDP per capita at a lag of 10 years, suggesting a bidirectional relationship between these variables. In the United States, increases in GDP per capita preceded increases in patents at lags of 14, 22, and 25 years (although there was also a negative lagged correlation such that increases in patents preceded decreases in GDP per capita at a lag of 25 years). Notably, increases in innovation may also lead to increases in living standards. In the United Kingdom, increases in unique book titles preceded increases in GDP per capita at a lag of 15 years. In the United States, increases in trademark applications preceded increases in GDP per capita at a lag of 8 years, and there was also a significant contemporaneous relationship. Links between GDP per capita and unique book titles were negligible in the United States. In sum, these results suggest that overall there may be a fairly consistent lagged, and possibly bidirectional, relationship between living standards and innovation.
Taken together, analyses of time series data from the United Kingdom and the United States provide inconsistent evidence for Baumard's first claim – that rising resource levels led to slower life history behaviors – and either fail to support or contradict his second claim – that a shift to slower life history led to greater innovation, at least during the (late) first and second Industrial Revolutions and into the putative third Industrial Revolution underway in recent years. However, our analyses do provide support for lagged, often bidirectional, associations between living standards and innovation. Based on our initial tests, it appears that more sophisticated analytical models, or additional factors, are necessary to explain the rapid increases in innovation that occurred during the late first Industrial Revolution, as well as during the second and third Industrial Revolutions.