Sunday, August 7, 2022

Parishes affected by the Dissolution of the English monasteries, 1535, experienced a rise of the gentry & had more innovation, higher yield in agriculture, more population working outside of agriculture, & ultimately higher levels of industrialization

The Long-Run Impact of the Dissolution of the English Monasteries. Leander Heldring, James A Robinson, Sebastian Vollmer. The Quarterly Journal of Economics, Volume 136, Issue 4, November 2021, Pages 2093–2145,

Abstract: We use the effect of the Dissolution of the English Monasteries after 1535 to test the commercialization hypothesis about the roots of long-run English economic development. Before the Dissolution, monastic lands were relatively unencumbered by inefficient feudal land tenure but could not be sold. The Dissolution created a market for formerly monastic lands, which could now be more effectively commercialized relative to nonmonastic lands, where feudal tenure persisted until the twentieth century. We show that parishes affected by the Dissolution subsequently experienced a rise of the gentry and had more innovation and higher yield in agriculture, a greater share of the population working outside of agriculture, and ultimately higher levels of industrialization. Our results are consistent with explanations of the Agricultural and Industrial Revolutions which emphasize the commercialization of society as a key precondition for taking advantage of technological change and new economic opportunities.

JEL N43 - Europe: Pre-1913N63 - Europe: Pre-1913N93 - Europe: Pre-1913O14 - Industrialization; Manufacturing and Service Industries; Choice of TechnologyQ15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment

6 Conclusions

In this paper we conducted what to our knowledge is the first empirical investigation of one aspect of the salient commercialization thesis about the causes of industrialization and the industrial revolution in England. Though we cannot test the idea that it was commercialization that caused the industrial revolution, we used the impact of the Dissolution of the monasteries in England between 1536 and 1540 as a source of variation in the extent of commercialization within England. Tawney (1941a,b) first proposed that the Dissolution and subsequent sell off of church land, representing around 1/3 of agricultural land in England, created a huge shock to the land market with profound consequences. We argue that this can be viewed as a natural experiment in the modernization of economic institutions and we hypothesized that the subsequent thickening of the land market would have had a major positive impact on resource allocation and incentives. This was particularly because monastic lands were relatively free of customary perpetual copyhold tenancies which were a direct legacy of feudalism. To investigate this we digitized the 1535 Valor Ecclesiasticus, the census that Henry VIII commissioned on monastic incomes.

Using the presence of monastically owned land at the parish level as our main explanatory variable we showed that the Dissolution had significant positive effects on industrialization which we measured using data from the 1838 Mill Census, the first time the British government collected systematic data on this driving sector of the Industrial Revolution.

We also showed the Dissolution was associated with structural change, specifically the movement of labor out of agriculture and into more industrialized sectors of the economy.

We then examined several channels which might link the Dissolution to these longrun outcomes. We showed that the Dissolution was associated, as Tawney hypothesized, with social change and the rise of a new class of commercially minded farmer. It was also associated with faster conversion from Catholicism, another factor plausibly linked to better economic performance.

We further found the Dissolution to be associated with greater agricultural investment, measured by parenting and land enclosures, and higher wheat yields. All in all, our findings support a quite traditional theory of the industrial, and perhaps the agricultural, revolution; that it was at least partially caused by the increasing commercialization of the economy which had a series of institutional, social and economics effect.

Probability of males to outlive females: an international comparison from 1751 to 2020

Probability of males to outlive females: an international comparison from 1751 to 2020. Marie-Pier Bergeron-Boucher et al. BMJ Open, Volume 12, Issue 8, Aug 2022.


Objective To measure sex differences in lifespan based on the probability of males to outlive females.

Design International comparison of national and regional sex-specific life tables from the Human Mortality Database and the World Population Prospects.

Setting 199 populations spanning all continents, between 1751 and 2020.

Primary outcome measure We used the outsurvival statistic ( φ ) to measure inequality in lifespan between sexes, which is interpreted here as the probability of males to outlive females.

Results In random pairs of one male and one female at age 0, the probability of the male outliving the female varies between 25% and 50% for life tables in almost all years since 1751 and across almost all populations. We show that φ is negatively correlated with sex differences in life expectancy and positively correlated with the level of lifespan variation. The important reduction of lifespan inequality observed in recent years has made it less likely for a male to outlive a female.

Conclusions Although male life expectancy is generally lower than female life expectancy, and male death rates are usually higher at all ages, males have a substantial chance of outliving females. These findings challenge the general impression that ‘men do not live as long as women’ and reveal a more nuanced inequality in lifespans between females and males.


Our study reveals a nuanced inequality in lifespan between females and males, with between one and two men out of four outliving a randomly paired woman in almost all points in time across 199 populations. These results complement the picture given by the comparisons based on life expectancy, which is a summary measure with no information on variation. A blind interpretation of life expectancy differences can sometimes lead to a distorted perception of the actual inequalities. Not all females outlive males, even if a majority do. But the minority that do not is not small. For example, a sex difference in life expectancy at birth of 10 years can be associated with a probability of males outliving females as high as 40%, indicating that 40% of males have a longer lifespan than that of a randomly paired female. Not all males have a disadvantage of 10 years, which is overlooked by solely making comparisons of life expectancy. However, a small number of males will live very short lives to result in that difference. For example, more baby boys die than baby girls in most countries.

The length of the lifespan of an individual results from a complex combination of biological, environmental and behavioural factors. Being male or female does impact lifespan, but it is not the only determinant contributing to inequalities. Lifespan has been shown to be influenced by marital status, income, education, race/ethnicity, urban/rural residence, etc.33 As we only disaggregated the population by sex and because of this complex interaction, lifespan distributions of females and males overlap. This nuance is captured by the φ metric. Males with a lower education level or who are unmarried have a particularly low chance of outliving a female. But males with a university degree or who are married have a higher chance of outliving females, in particular females with a lower education level and who are single.

As previously discussed, the φ metric expresses the probability of males to outlive females among randomly paired individuals, assuming independence between populations. However, males and females in a population are generally not random pairs but often couples, whose health and mortality have been found to be positively correlated due to a strong effect of social ties on health and longevity.34 Coupled individuals also influence each other’s health,35 and this is particularly true for males, who benefit more than females from being in a stable relationship.36 The datasets used for the analysis do not permit the estimation of the probability of males outliving females for non-randomly paired individuals. However, the outsurvival statistic relates to the probability of the husbands to outlive their wives, and even though such a measure accounts for the difference in age between husband and wife, it has been shown generally to be between 30% and 40%,37–39 values that are quite close to φ .

Other measures of overlap and distance between distributions could have been used. In the online supplemental materials, we compare the outsurvival statistic with a stratification index used by Shi and colleagues20 and the KL divergence. We found that all three indicators are strongly correlated and using any one of these would not have changed the general conclusions from this article. However, unlike the other indicators, φ directly indicates when males live longer than females, which we found in a few instances.

Trends over time in φ are consistent with the reversed trends in sex differences in life expectancy40: in developed countries, the probability of males outliving females decreased until the 1970s, after which it gradually increased in all populations. Studies showed that the increase in sex differences in mortality emerged in cohorts born after 1880,10 41 which is consistent with our analysis of φ (see online supplemental materials). The increase and decrease in sex differences in life expectancy were mainly attributed to the smoking epidemic and other behavioural differences between sexes.7 13 42

The φ values are generally higher in low/middle-income countries. However, this should not be interpreted as a sign of greater gender equality in survival. Southern Asian countries had very high φ values, above 50% in the 1950s and 1960s. Studies for India showed that mortality below age 5 was higher for females than males and remained higher for females in recent years.43 44 However, females had a growing mortality advantage above age 15 years since the 1980s, ‘balancing out’ the disadvantage at younger ages. The reasons for the higher φ and decreasing trends in developing regions vary across countries. It is outside the scope of this study to provide a detailed explanation for the trends in each country.

The outsurvival statistic can be informative for public health interventions.21 Governments develop public health programmes to reduce lifespan inequalities at different levels (eg, socioeconomic status, race, sex, etc). It would be misleading to say that half of the population is disadvantaged by sex differences in lifespan. The inequalities are more nuanced. If 40% of males live longer than females, it could be argued that if a policy aiming at reducing inequalities between sexes targeted the full male population, some of the efforts and investments would be misallocated. Such a policy could be more efficient if φ approaches 0, indicating that sex would explain a large part of the lifespan inequalities within the population, whereas a φ closer to 0.5 indicates that other characteristics (eg, socioeconomic and marital statuses) are involved in creating inequalities. We showed that some subpopulations of males have a high probability (above 50%) of outliving some subpopulations of females. Males who are married or have a university degree tend to outlive females who are unmarried or do not have a high school diploma. Inequalities in lifespan between sexes are attributable to some individuals within each population and not to the whole population. Indeed, Luy and Gast12 found that male excess mortality is mainly caused by some specific subpopulations of males with particularly high mortality. Being able to better identify the characteristics of the short-lived men could more efficiently help tackle male–female inequality.

An important result of our analysis is that the smaller the SD in the age at death, the smaller the φ . The reduction of lifespan inequality observed over time has then made it less likely for males to outlive females. This is partly explained by the fact that lifespan variation reduction has been driven by mortality declines at younger ages.45 When looking at the lifespan distribution (as in figure 1, scenario D), survival improvements at younger ages narrowed the left tails of the distribution for both sexes. By reducing the left tail of female distribution, without increasing the right tail of the male distribution, the overlapping area is reduced. In other words, the number of females with shorter lifespan, easier to outlive, decreased over time. Indeed, it has been shown that mortality declined at a faster pace for females than males below age 50, especially in the first half of the 20th century.46 47 This finding implies that more efforts are required today than in the past to reduce these inequalities, for a same difference in life expectancy. While inequalities were mainly attributable to infant and child mortality, they are today increasingly attributable to older and broader age groups. Men maintained their disadvantage at younger ages, but also faced an increasing disadvantage at older ages. Men are more prone to accidents and homicides in their 20s and 30s than females, and they tend to smoke and drink more leading to higher cancer prevalence and death in their 60s. At the same time, women benefited from reduced maternal mortality and recorded faster mortality decline at older ages. Efforts in reducing lifespan inequalities must thus target diverse factors, causes and ages.13 46 48

A decrease of φ might indicate a discrepancy in the causes of death that affect males and females. External mortality due to accidents and suicide has become more relevant in shaping sex differences in survival in recent years in high-income populations.12 Another example is observed in Latin American populations, where homicides and violent deaths have had an increased burden among males in comparison with females since the 1990s.49 50 In Mexico, for example, the increase in homicide mortality, especially among men between 20 and 40 years, contributed to increasing the gap in mortality between females and males.51 This phenomenon is reflected in the decrease over time in the overlapping of lifespan distributions, directly informing healthcare systems of emerging inequalities.

However, one might ask if a wider overlapping is necessarily better for healthcare systems. On the one hand, a larger overlapping means less inequality between sexes, but on its own it does not ensure that there is more ‘health justice’. For example, if the overlapping areas are large, this still shows a situation of great uncertainty in lifespan for both groups. One health evaluator actor could even prefer a situation where there is a small gap between groups but less inequality within the groups. In the case of sex differences, there might always be between-group differences due to biological factors,2 52 but more health equity could be reached by reducing within-group inequalities. We argue that the outsurvival statistic is a new tool to evaluate health inequalities between groups within a population by uncovering underlying dynamics that are otherwise hidden when looking only at conventional indicators. Therefore, it can inform healthcare systems of the subsequent directions to reach the preferred goal.


If children with low socio-economic level parents were to grow up in counties with economic connectedness comparable to that of the average child with high level parents, their incomes in adulthood would increase by 20% on average

Social Capital I: Measurement and Associations with Economic Mobility. Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong, Federico González. NBER Working Paper 30313. July 2022. DOI 10.3386/w30313

Abstract: In this paper—the first in a series of two papers that use data on 21 billion friendships from Facebook to study social capital—we measure and analyze three types of social capital by ZIP code in the United States: (i) connectedness between different types of people, such as those with low vs. high socioeconomic status (SES); (ii) social cohesion, such as the extent of cliques in friendship networks; and (iii) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analyzing their associations with economic mobility across areas. The fraction of high-SES friends among low-SES individuals—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date, whereas other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at

Social Capital II: Determinants of Economic Connectedness. Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong, Federico Gonzalez. NBER Working Paper 30314. July 2022, revised August 2022. DOI 10.3386/w30314

Abstract: Low levels of social interaction across class lines have generated widespread concern and are associated with worse outcomes, such as lower rates of upward income mobility. Here, we analyze the determinants of cross-class interaction using data from Facebook, building upon the analysis in the first paper in this series. We show that about half of the social disconnection across socioeconomic lines—measured as the difference in the share of high-socioeconomic status (SES) friends between low- and high-SES people—is explained by differences in exposure to high- SES people in groups such as schools and religious organizations. The other half is explained by friending bias—the tendency for low-SES people to befriend high-SES people at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of high-SES students across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Hence, socioeconomic integration can increase economic connectedness in communities where friending bias is low. In contrast, when friending bias is high, increasing cross-SES interaction among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure, and friending bias for each ZIP code, high school, and college in the U.S. at

How does early menopause and menopause symptoms affect women’s careers? The conservative estimate of the cost of early menopause for a woman is £20k, while the cost of suffering an average level of menopause symptoms is £10k

The consequences of early menopause and menopause symptoms for labour market participation. Alex Bryson et al. Social Science & Medicine, Volume 293, January 2022, 114676.


• Early natural menopause substantially reduces employment rates among women.

• The number of menopause symptoms women face is also associated with lower employment rates.

• These effects are larger for symptoms which women say “bother me a lot”.

• Psychological problems due to menopause are associated with the biggest employment effects.

Popular version: How does early menopause and menopause symptoms affect women’s careers? Mar 8 2022.

Abstract: Using a difference-in-difference estimator we identify the causal impact of early menopause and menopause symptoms on the time women spend in employment through to their mid-50s. We find the onset of early natural menopause (before age 45) reduces months spent in employment by 9 percentage points once women enter their 50s compared with women who do not experience early menopause. Early menopause is not associated with a difference in full-time employment rates. The number of menopause symptoms women face at age 50 is associated with lower employment rates: each additional symptom lowers employment rates and full-time employment rates by around half a percentage point. But not all symptoms have the same effects. Vasomotor symptoms tend not to be associated with lower employment rates, whereas the employment of women who suffer psychological problems due to menopause is adversely affected. Every additional psychological problem associated with menopause reduces employment and full-time employment rates by 1–2 percentage points, rising to 2–4 percentage points when those symptoms are reported as particularly bothersome.

Keywords: MenopauseEarly menopauseMenopausal symptomsVasomotor symptomsEmploymentFull-time employmentBirth cohort

5. Conclusions

Our paper is the first to estimate the effects of early menopause and menopausal symptoms on employment and full-time employment rates among women. We exploit prospective birth cohort data for all women born in a particular week in 1958 to estimate the causal effects of menopause on employment rates using a difference-in-difference strategy. This technique compares the gap in employment rates during their 20s and early 30s with the employment gap in their 50s for women who went onto experience early menopause versus those who did not. We make similar comparisons between women according to the intensity with which they experienced menopausal symptoms when aged 50. In doing so we control for a rich array of variables collected at birth, in childhood, and in early adulthood which can affect employment prospects and experiences of menopause. We show employment and full-time employment trends during their 20s and early 30s did not differ significantly between the ‘treated’ group – those who went on to experience early menopause or more menopausal symptoms – and their ‘control’ groups who did not experience early menopause or did not suffer many menopausal symptoms. This provides some assurance that their employment rates may have trended in similar fashions later in their lives if they had not experienced menopause differently.

We find women's employment rates, and their full-time employment rates fall as the number of menopausal symptoms they report rises. Effects are larger for symptoms that are reported as ‘bothersome’. The effects are quantitatively large. For instance, a woman who experiences the mean number of menopausal symptoms at age 50 can expect to have an employment rate in her 50s that is 4 percentage points lower than a woman who has no menopausal symptoms.

Different types of menopause symptom have different employment effects. For instance, vasomotor symptoms do not affect full-time employment rates, and they only affect employment rates where they are considered ‘bothersome’. In contrast, psychological health problems associated with menopause significantly lower employment and full-time employment rates, and effects are much larger when those symptoms are ‘bothersome’.

Early menopause is associated with a very large (9 percentage point) reduction in employment rates once women reach their 50s, yet it has no statistically significant effect on women's full-time employment rates. It is unclear why early menopause should affect employment rates, but not full-time employment rates. This issue is worthy of further investigation.

It is striking that the inclusion and exclusion of potential confounders makes very little difference to the impact of early menopause and menopause symptoms on employment and full-time employment rates. Even though their inclusion increases the variance in employment rates explained by our models (as indicated by the adjusted R-squared) the coefficient and statistical significance of the interaction capturing the impact of menopause are nearly identical in all cases. Following Oster (2019) we take coefficient stability in the face of adjustments to conditioning covariates as an indication that results are unlikely to be biased by omitted variables.

There are some limitations to this study. First, although women are asked specifically to identify health-related symptoms due to the menopause, in some cases those symptoms may be due to other changes women are going through at the same time which are not directly linked to the menopause. Second, our data only collect information on symptoms related to menopause in the year leading up to the survey interview at age 50. Some women may have experienced symptoms earlier which did not persist to age 50, leading to some error in our ability to accurately capture symptoms related to menopause. Some women who experienced symptoms, but not at age 50, will be misclassified as having no symptoms. However, assuming symptoms experienced earlier than age 50 also have a detrimental impact on employment, this will mean our estimates of symptoms’ effects on employment are downwardly biased. Third, it is worth recalling that the Great Recession hit when the women in the study reached age 50. This was a very severe recession creating what were, at the time, unprecedented labour market problems for many. It would be valuable to see whether our results are replicated in more benign labour market conditions.

These negative employment effects of early menopause and menopausal symptoms are cause for some concern, not only because the size of the effects is large, but also because so many women suffer these problems. As we have shown, the mean number of menopausal symptoms experienced by women in this birth cohort when aged 50 was 8, including 2 particularly ‘bothersome’ symptoms. Five percent of women in the estimation sample had experienced early menopause.

These employment effects of early menopause and menopause symptoms add to the personal costs they have for women suffering from them in terms of their physical and mental health, and potentially their effects on women's private lives, although we do not quantify them here. They also have costs for society, in terms of the health care costs of treating women's symptoms, potential productivity losses from women's lost hours of work and ability to work productively. It is conceivable that they will also affect women's retirement decisions and thus pension entitlements.

Having identified the size and extent of the problem government and employers should consider steps that could be taken to ameliorate the problems women face in their working lives due to the menopause. That said, this is the first study of its kind, so there is value in seeking to replicate and extend research investigating the impact of early menopause and menopausal symptoms on labour market outcomes. First, it would be valuable to know whether the effects we identify might vary for other cohorts of women including more recent entrants to the labour market. Second, there would be value exploring the heterogeneity of menopausal effects and whether there are aspects of women's experiences that may ameliorate the effects of menopause. For instance, it may be that women are better able to manage menopause symptoms where they have greater opportunities to manage working patterns or working hours, as might be the case among self-employed women or employees in workplaces with policies and practices expressly intended to assist women affected by menopause. Third, we know very little about the effects of early menopause and menopausal symptoms on other aspects of women's labour market experiences. We would have a better picture if studies were undertaken to investigate the impacts of menopause on women's wellbeing at work, their job satisfaction and their earnings. Finally, we know of no studies piloting policies or practices in the workplace that might assist women in raising health-related problems they may have during menopause, nor in coping with those problems. These evaluations are needed to provide the evidence base employers and government need so they know what actions to take to improve women's working lives.