HIAS-E-102

DEMOGRAPHIC SHOCKS AND WOMEN'S LABOR MARKET
PARTICIPATION: EVIDENCE FROM THE 1918 INFLUENZAPANDEMIC IN INDIAJAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

University of Warwick

December, 2020

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET
PARTICIPATION: EVIDENCE FROM THE 1918 INFLUENZAPANDEMIC IN INDIAJAMES FENSKE† , BISHNUPRIYA GUPTA‡ , AND SONG YUAN?

Abstract. How did the 1918 influenza pandemic affect female labor force participationin India over the short run and the medium run? We use an event-study approach at the
district level and four waves of decadal census data in order to answer this question. We
find that districts most adversely affected by influenza mortality saw a temporary increasein female labor force participation in 1921, an increase that was concentrated in the service
sector. By 1931, this increase had been reversed. We find suggestive evidence that distresslabor supply by widows and rising wages help account for these results.

1. Introduction
Women’s labor force participation is an important driver of economic development andgender equality (Duflo, 2012; Jayachandran, 2015). In this paper, we study the short andmedium run effects of a major demographic shock on women’s employment: the 1918 influenza pandemic in India. Demographic shocks, even when mortality is roughly equal bygender, may affect female labor supply. The Black Death, for example, is seen to have led
to the growth of a labor market for women in North-Western Europe (Broadberry, 2013;
De Moor and Van Zanden, 2010). Empirical evidence for this pattern is scarce, however,particularly for regions outside the Western world. Where mortality is more unbalanced bygender, effects may be more pronounced; the World Wars were another shock to the labormarket that had consequences for women’s labor market participation (Boehnke and Gay,
2019; Fernández et al., 2004). We focus on a different economic, social, and cultural context– that of India – in order to gain a different perspective on the drivers of women’s economic
participation. Both historically and in the present, India has had low levels of participationof women in economic activity outside the home. A recent literature (e.g. Fletcher et al.
†

University of Warwick
University of Warwick†
University of Warwick
E-mail addresses: J.Fenske@warwick.ac.uk, B.Gupta@warwick.ac.uk, S.Yuan.2@warwick.ac.uk.
Date: July 30, 2020.
We are grateful to the Economic History Society Carnevali Small Research Grants Scheme and the Centre forCompetitive Advantage in the Global Economy for their funding. We also thank Debraj Ray and audiencesat the Delhi School of Economics, the Economic History Society, the European Historical Economics Society,
NYU Abu-Dhabi, and the University of Warwick for their comments.
‡

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JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

(2017)) on female labor force participation (FLFP) in India has highlighted a declining trend,
despite rising incomes and greater education of women. Early marriage, social conservatism,and limited participation of women in certain types of economic activities are among the
explanations that have been cited. In recent times, FLFP has declined as economic growth
has increased.
From 1918 to 1919, a deadly influenza pandemic hit India. It first appeared in Bombay
and then spread to the North and the West. Regions that received less than normal rainfall
were more affected (Chandra and Kassens-Noor, 2014; Hill, 2011). Mortality estimates for
India are wide-ranging, from 12 to 20 million (Chandra et al., 2012; Davis, 1951; Patterson
and Pyle, 1991). Mortality was as high as 15% in some districts. This exogenous demographic shock might have affected female labor force participation in both the short run
and the long run. The pandemic led to a shortage of labor and created a situation where
women could potentially participate in the labor force and substitute for men in activities
previously performed by men. This would generate a short-run increase in female labor force
participation during the 1920s and 1930s, similar to what Goldin (1991) identifies after theSecond World War. Although the population eventually reverted back to previous levels, theimpact of this demographic shock on female labor supply could have persisted if women’s
working behaviour was transmitted to subsequent generations, for example by updating theirbeliefs toward the role of women (Fernández, 2013; Fernández et al., 2004). By contrast, if
norms against women’s work are strong and difficult to change, low levels of FLFP could
be durable despite changes in the economic environment (Fernandez, 2007). The influenza
epidemic provides a natural experiment with which to look at the effect of a demographic
shock on FLFP in India. Was there a response, and if so, was it transitory or permanent?
We use district-level influenza mortality data for India from the Sanitary Reports of 1918
and 1919 and historical data on FLFP from the decadal censuses of 1901 through 1931 tocreate district-level panel data on women’s labor market participation. We consider bothaggregate participation and participation in the specific sectors of agriculture, industry, andservices. We estimate the short and medium-run effects by adopting the event study method,which compares the change in women’s employment in districts that were exposed to different
levels of influenza mortality, before and after the influenza pandemic. We consider the 1931
census data to see if the changes in women’s labor market participation lasted beyond theimmediate short run. We find that FLFP increased only in services and only in the 1921
census; there is no evidence of a sustained response. Our results suggest that the mortality
shock experienced by a typical district led FLFP to be 2.3 percentage points higher in 1921
than it would have been otherwise.
We test two potential mechanisms that could potentially drive the short run results. On
the one hand, women might have needed to enter the labor market in order to mitigate

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

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the economic shock due to the death of their husbands or other male relatives during the
pandemic. On the other hand, the general shortage of labor could have driven up wages andinduced more women to join the labor force. Reduced population may, however, have also
reduced demand, lessening the availability of jobs. Over the medium term, the same cultural
norms that explain sectoral and regional variations in FLFP prior to the epidemic could help
explain why the response to the epidemic was transitory. By contrast, if FLFP were initially
low due to frictions in the labor market, the increase could have been permanent. We showthat the share of women who were widowed rose in districts more greatly affected by the pandemic, though there is no similar increase in the share of never-married women. Population
density fell in more affected districts and wages rose, including in services. Districts withgreater shares of widows saw greater FLFP, both generally and in services. Similarly, more
densely populated districts saw lower FLFP in services. However, while both these channels can help explain our results, they do not fully mediate them. We find little evidenceof a response of male labor force participation to the influenza pandemic, suggesting thatwomen’s reactions were not part of a more generalized increase in labor force participationin response to higher wages.
We subject our results to several robustness exercises. The availability of labor force participation data in 1901, two census waves prior to the epidemic, allows us to show positive
evidence that the parallel trends assumption of the difference-in-differences estimator holds
conditional on controls. We show additional robustness to controlling for alternative sourcesof mortality, differential time trends due to agricultural potential and other geographic characteristics, and time-varying urbanization and land use. We show that our results hold using
the male influenza death rate as an alternative measure of treatment.
1.1. Contribution. This research project is related to several strands of literature. The
first considers the role of women in South Asian economies. Recent work has emphasized
the response of Indian FLFP to economic incentives, including agro-ecological zones (Chen,
1989), cropping patterns (Gulati, 1975; Reddy, 1975), deep tillage (Carranza, 2014), andcaste (Eswaran et al., 2013; Luke and Munshi, 2011). This literature has emphasized therole of culture and the high relative returns to home production in explaining why female
labor force participation is both low and declining. In addition to evaluating the response ofIndian female labor force participation to the influenza pandemic, our paper makes a firstorder contribution in terms of data digitization and description. Ours is the first paper ofwhich we are aware to document district-level variations in FLFP over the first three decadesof the 20th century for the Indian subcontinent.
The second strand of literature to which we contribute studies the impact of demographic
shocks on women’s labor supply. Existing research focuses mainly on the Second World War,with a particular emphasis on the United States. Acemoglu et al. (2004) and Goldin and4

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Olivetti (2013) use exogenous variation in mobilization rates across states and find that the
impact of the Second World War on female labor force participation was still present in the
1960s. Fernández et al. (2004) find an effect on female labor supply that persists through the
1980s. To our knowledge, ours is the first paper to put together a data set on women’s labor
force participation at the district level in colonial India and study the role of a historicalepidemic. The historical literature has discussed the consequences of the Black Death on
women’s work in Europe, but little is known about the consequences of a major epidemic in
economies outside Europe, especially in the context of different family structures and social
norms. Both gender-biased and gender-neutral demographic shocks might increase FLFP,
though these responses may be constrained by social norms and other factors.
The third strand of literature to which we contribute examines the relationship between
FLFP and economic development. One branch of recent work focuses on how FLFP evolves
with economic development (Boserup, 1970; Goldin, 1995; Mammen and Paxson, 2000) andunder the constraints of culture (Alesina et al., 2013; Giuliano, 2014; Grosjean and Khattar,2019). Another has shown that FLFP increases in response to a shortage of male labor in
the context of other shocks such as wars, immigration, and the slave trade (Boehnke andGay, 2019, 2020; Fogli and Veldkamp, 2011; Luo, 2017; Teso, 2019). We connect these two
strands in the literature, providing novel evidence from a developing country and consideringa more gender-balanced demographic shock.
The paper that is most closely related to ours is Donaldson and Keniston (2016). Theyshow that the influenza pandemic in India had effects consistent with a Malthusian model:
the greater land-labor ratios induced by mortality led to increased agricultural output per
worker. This was then used to support both greater child quantity and greater child quality.
Fertility rose in affected districts, and children born in these districts achieved both greater
adult heights and literacy. We differ in focus, as our paper is concerned more narrowly withthe labor force outcomes of women. However, their results complement ours.
The rest of this paper proceeds as follows. Section 2 outlines the historical context, identification strategy, and data sources. Section 3 presents our main results. Section 4 evaluates
the empirical evidence for the mechanisms explaining our results. Section 5 examines therobustness of our results. In section 6, we conclude.
2. Context, Identification, and Data
2.1. Context.
2.1.1. Influenza in India. The Influenza pandemic that began in 1918 killed perhaps 50
million people globally. Over 13 million of those who died were in India – close to 5% of thetotal population (Arnold, 2019; Chandra et al., 2012; Hill, 2011). The pandemic began in
the Bombay region and spread to the north and east. Regions that experienced less humidity

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

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compared to the seasonal average were more affected (Chandra and Kassens-Noor, 2014),
and mortality was greater where population density and long-distance railroad travel weregreater and rainfall was lower (Reyes et al., 2018). The pandemic disproportionately killedindividuals of working age (20-45) and was concentrated in districts that experienced lowerthan-normal levels of humidity. The most-affected districts of India experienced mortality
rates of 15% during the pandemic (Donaldson and Keniston, 2016; Schultz, 1967).
The economic impacts of the pandemic varied across countries. In the United States, areasthat were hardest-hit experienced rapid catch-up growth (Brainerd and Siegler, 2003). The
pandemic reduced manufacturing employment in the US through both demand and supply
side channels. (Barro et al., 2020; Correia et al., 2020). In Sweden, by contrast, the areas
most affected saw increased poverty and reduced returns to capital (Karlsson et al., 2014).
In addition, there has been considerable work in both economics and economic history on
the long-term effects of exposure to the pandemic; cohorts exposed in early life achieved
worse adult outcomes in terms of health and mortality (Almond, 2006; Lin and Liu, 2014).
2.1.2. Women and work in India. The literature on FLFP finds an inverted-U relationshipbetween women’s labor market participation and development (Boserup, 1970; Goldin, 1995).
The conventional explanation is that this is driven by structural change. In early stages of
economic development, agriculture is the dominant sector, income is low, and women workin agriculture within a household-based production process. With industrialization, risingincome, and marketization of work, fewer women work in factories outside the home associal norms discourage participation in the labor market. Women’s labor force participation
falls with industrialization. Fertility and social conventions regarding women’s work reduce
married women’s participation in particular. The rise of the male breadwinner family during
the industrial revolution in England is a commonly-cited example of falling FLFP with risingincomes (Horrell and Humphries, 1995). A similar argument in Goldin (1995) suggests thatsocial stigma against men whose wives work in blue collar jobs reduces women’s labor market
participation. On the demand side, blue collar jobs are mainly in male-dominated activities.
In this explanation, women’s labor force participation increases with further increases inincome, expansion of education, and a structural shift towards service sector white collar
jobs (Mammen and Paxson, 2000).
In present-day India, the trend has been quite different. For its level of economic development, India is an outlier in terms of FLFP. In 1970, Roy (2005, p. 135-6) claims thatthe participation rate was 12%, while peer countries in East Asia and Latin America had a
FLFP rate of 30% . Despite economic growth, rising literacy, and a structural shift to services, FLFP has declined in recent decades. Unpacking this trend shows that the decline has
mainly been in rural India among women with secondary education (Fletcher et al., 2017).
In the present, cross-sectional comparisons suggest higher levels of FLFP for Indian women6

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

aged 15-24, but that they withdraw from the labor force after marriage, only to return laterin life (Klasen and Pieters, 2015). Women without education are typically in families with
low income and work to supplement family income. Women with tertiary education increase
their labor market participation. It is the women in the middle who drop out of the labor
force.
Changes in the sectoral structure of employment have contributed to the recent reduction in FLFP rates. Most employment growth has occurred in construction and low-skilledservices, while expansion of employment in white-collar services has not been sufficient toabsorb the growing female population of working age (Klasen and Pieters, 2015). Preference
for men in clerical jobs lowers the participation of women with secondary education in thelabor market (Chatterjee et al., 2018).
Various socio-cultural factors also reduce women’s participation. Staying out of the labormarket signals social status (Chen and Drèze, 1992). Education of high-caste women increases their marriage market returns, but reduces participation in the labor market due tosocial barriers (Eswaran et al., 2013). Low-caste women have higher rates of labor market
participation (Klasen and Pieters, 2015). Women with more education marry more educatedmen and have higher family income, which reduces the incentives for female labor market
participation; Afridi et al. (2016) find that, from 1987 to 2009, rising education among rural
men and women increased the returns to women’s work in home production relative to workin the labor market. Women with secondary education increase investment in the education of their children and withdraw from the labor market (Behrman et al., 1999). Whileparticipation of married women has declined, participation of widows in the labor market
has increased and participation of never married women has stayed the same (Afridi et al.,2016).
Women’s labor market participation in colonial India was close to 30% (Thorner andThorner, 1962). This fits in with the general pattern of higher participation rates in agricultural societies and within the household economy. In 1911 just under one-third of the workers
were women in agriculture and industry. Within the service sector, the share of women was
as high as 38-40% in a sector referred to as “insufficiently described occupations” in official
sources. Agriculture was the main activity for most women. In 1911, 73% of women workers
were in agriculture and related activities, 11% in industry, mining and construction, 12% intrade, transport and other services, and 4% in insufficiently defined activities. (Thorner andThorner, 1962).
In colonial India, Roy (2005) identifies gender segmentation in the labor market. The
work done by women was concentrated in specific industries and services, including bidi
rolling, hand spinning, basket weaving, grain processing by hand, and stone cutting. Some
industries in colonial India rarely hired women. These included metal working, chemicals,DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

7

and printing. Some industries employed both men and women, though specific tasks werelargely segregated by gender, as in mining, cotton textiles, and quarries. In table 1, we show
the distribution of workers in selected sectors reported by Thorner and Thorner (1962).
There were strong regional differences in FLFP as well. Our data show regional variation in1911 – FLFP was relatively high in the Central Provinces, Madras, Bihar and Orissa, andin the United Provinces. It is relatively low in Punjab and Bengal. This pattern fits withthe regional variation found after independence. In 1961, for example, women’s labor force
participation was 18% in Uttar Pradesh, 6%, in Punjab, and 9% in West Bengal, while it
was closer to 30% in Assam, 38% in Maharashtra and 43% in Himachal Pradesh (Gulati,1975). From 1961, Roy’s estimates show a sharp decline in FLFP.
2.2. Identification. In order to test for short-run and medium-run effects of the 1918 Influenza epidemic on female labor supply in India, we compare the change in women’s employment in districts that were exposed to different levels of influenza mortality, before andafter the influenza epidemic. This allows us to control for unobservable characteristics of
districts that do not change over time, and for unobserved variables in specific time periods
that affect all of India equally. In particular, we use an event-study approach. We estimate:(1)

F LF Pdt = βt Inf luenzad + (x0d × ηt )γ + δd + ηt + ϕp × t + dt .

Here, F LF Pdt is female labor force participation in district d in year t. We compute
this as the ratio of women working to the population of women in the district.1 In various
specifications we either use total FLFP or FLFP for the separate sectors of agriculture,industry, or services. Inf luenzad is the influenza mortality rate in district d in 1918 and
1919. βt is a separate coefficient for each year, equivalent to interacting Inf luenzad withyear fixed effects. The omitted category is 1911, the last pre-treatment year. An insignificant
coefficient for 1901 validates the parallel trends assumption. The coefficients for 1921 and1931 are our coefficients of interest.
x0d × ηt includes the interactions of three key district-specific variables with our year fixed
effects. These are humidity, latitude and longitude. These are selected because they are
predictive of influenza mortality and ensure that there are parallel trends in FLFP between
high-exposure and low-exposure districts prior to the 1918 pandemic. These controls also
help avoid the possibility that our results are driven by correlates of influenza mortality,
rather than influenza mortality itself. In particular, previous writers have stressed that
humidity helps explain the variation in the severity of the epidemic across space in India(Chandra and Kassens-Noor, 2014; Hill, 2011). In addition, in some specifications we control
1Because of the existence of labor by both children and the elderly, as well as the sometimes incomplete

enumeration of the population by age, we use the total female population as the denominator.

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JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

for the time-varying urbanization rate. We do not include it in our baseline set of controls, as
it might both respond to influenza mortality and proxy for a critical channel through which
the pandemic may have influenced women’s work outcomes, e.g. in capturing the thickness ofthe potential labor market or the abundance of labor relative to other factors of production,
most notably land. In robustness specifications, we will include additional time-invariant
controls interacted with year fixed effects and additional time-varying controls. δd and ηt are
district and year fixed effects, respectively. ϕp × t is a province-specific time trend, i.e. aninteraction of province fixed effects with linear year variables. Standard errors are clusteredby district.
2.3. Data. Historical data on our dependent variable, female labor force participation, exists
in the colonial censuses collected every ten years between 1901 and 1931. To our knowledge,
we are the first to digitize these data at the district level and disaggregated into the major
categories of industry, agriculture, and services. For each district in each census year, thecensus reports counts of men and women working in several occupations. We have categorized these ourselves as agricultural, industrial, or service-based. We begin with the broadclassifications already employed by the colonial census. These are:• Sub-Class I. Exploitation of Animals and Vegetation• Sub-Class II. Exploitation of Minerals• Sub-Class III. Industry• Sub-Class IV. Transport• Sub-Class V. Trade• Sub-Class VI. Public Force• Sub-Class VII. Public Administration• Sub-Class VIII. Professions and Liberal Arts
• Sub-Class IX. Persons Living Principally on Their Income• Sub-Class X. Domestic Service• Sub-Class XI. Insufficiently Described Occupations• Sub-Class XII. Unproductive Labour
We classify as “agriculture” all occupations in Sub-Class I. We classify as “industry” alloccupations in Sub-class III. We classify as “services” all others apart from Sub-Class II,
which we have not included in our analysis. Mining was only more than 3% of employment
in four districts. In the census, Sub-Class XI is sometimes referred to as “General Labour,”while Sub-Class XII includes the category of beggars, vagrants, and prostitutes. It also
includes inmates of jails, asylums and almshouses. In our primary results, we also include
these in “Services”, we remove them in a robustness exercise. To convert these to labor-force
participation rates, we divide these counts by the total population of the district of a given
gender. We exclude two districts from the analysis whose data on FLFP contain obvious

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

9errors. For Garhwal, recorded FLFP is between 61% and 69% in 1901, 1921, and 1931, but
is only 8% in 1911. For Narsinghpur, the number of female workers recorded in 1931 is morethan three times the female population. We show maps of FLFP in 1911 and its change
from 1911 to 1921 at the district level in Figures 1 and 2, respectively.
Figure 1. FLFP in 1911

We have collected and entered the 1918 influenza mortality data for India from the Sanitary
Reports for the years 1918 and 1919. These provide deaths by cause and gender for each
district in the sample. Because these Sanitary Reports are available only for the Britishruled districts of India and exclude the princely states, and because not every district reports
occupational data in every year, our baseline sample includes 196 districts over four periods
in time, giving us a maximum of 784 observations in each regression.
While the inclusion of district and year fixed effects in our regressions implies that we neednot, and cannot, control for time-invariant controls, we do interact three variables with year
fixed effects in order to discipline the pre-pandemic district trends in our data. In particular,
these are latitude, longitude, and humidity. To compute each of these, we begin by creatinga shapefile map of these districts based on the paper map in the 1931 census. Following
the procedure in Fenske and Kala (2017), we correspond each colonial district mapped in
the 1931 census with all current sub-district units (e.g. tehsils) that intersect the erstwhile
historical districts, and treat the union of these polygons as the polygon corresponding to10

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Figure 2. Change in FLFP from 1911 to 1921

that colonial district. We use the centroid of this polygon to compute the latitude andlongitude of the district. For variables originally available in raster format, such as humidity
or crop suitabilities, we average over raster points in a district. Our humidity data are takenfrom the Climatic Research Unit at the University of East Anglia.
In some specifications, we control for population density and urbanization rates. These
are computed using the areas, populations, and populations of cities from each census wave.
In robustness checks, we employ data on crop suitabilities. These are taken from the Foodand Agriculture Organization of the United Nations Global Agro-Ecological Zones’s project(FAO-GAEZ), and are reported as expected yields in kilograms per hectare under low levels
of inputs. We focus on crops that are important in Indian agriculture, in particular banana,chickpea, cocoa, coffee, cotton, groundnut, wet and dry rice, oil palm, onion, soybean, sugar,tea, potato and wheat. We will also control, for robustness, for the areas planted to major
crops during each census year. We have digitized these from the Agricultural Statistics ofBritish India, and focus on rice, wheat, sugarcane, jute, opium, tea, tobacco, and cotton.
In our analysis of possible mechanisms, we use two additional variables that we computeusing the population counts by district, gender and marital status in the colonial census.
These are the fraction of the female adult population that is unmarried and the fractionthat is widowed. We also test whether greater FLFP is explained by sector-specific increases

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

11

in wages due to labor scarcity arising from the epidemic. To do this, we assemble an unbalanced panel of more than 6,500 wage observations from a number of sources, the mostimportant of which are Wages and Prices in India and the wage censuses of each province.
We classify these by sector (agriculture, industry, and services) and by skill intensity (skilled
or unskilled).2
In Figures 3, 4 and 5, we show the timing and geographical distribution of the influenza
pandemic. The first two figures depict the number of deaths and mortality rates (deaths asa share of the total population), respectively. The final figure is a map of the death rate by
district. Two patterns are clear immediately. The first is the sharp concentration of mortality
in 1918; influenza deaths spiked in that year but had largely returned to their pre-pandemic
levels by 1919 and were back at their 1917 levels by 1920. Second, while mortality rates
were lower in southern India and in Assam than elsewhere, there was geographic dispersion
in mortality rates both within and across states.
Figure 3. Influenza Mortality: Deaths

We present summary statistics for our results in Table 2. Looking at mean FLFP by year,
the rate of women working fell from roughly 31% in 1901 to 29% in 1911, where it remained
2Our classifications by sector are as follows.

In agriculture: agricultural, harvester, sower, weeder, reaper,ploughman, farm servant. In industry: blacksmith, carpenter, earth worker, fitter, mason, cotton weaver,hardware, metal worker, mill hand. In services: syce, thatcher, boatmen, domestic servant, coolie, carter,
fireman, general labor. Our classifications by skill are as follows. Skilled: blacksmith, carpenter, mason,cotton weaver, hardware, metal worker, fitter, fireman. Unskilled non-agricultural: syce, earth worker,
thatcher, boatmen, domestic servant, coolie, carter, general labor, mill hand. Our sources of wage data arelisted in the appendix.

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JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Figure 4. Influenza Mortality: Rates

Figure 5. Influenza Mortality: Distribution

in 1921. By 1931, it had fallen below its 1901 value. The decline to 1931 has many causes.
Partly, this is about how the census counted secondary occupations, which we expect to havea uniform level effect across the districts in our data and that we expect to be uncorrelated

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

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Figure 6. Scatterplots: Influenza mortality and changes in FLFP acrossdistricts, by sector

with the extent of influenza mortality. That is, we expect it would be accounted for by a year
fixed effect. To the extent that is was due to the Great Depression, we account for this in
our robustness exercises by allowing for flexible time trends by crop suitabilities (accounting
flexibly for possible shocks to global commodity markets).

3. Results
3.1. Graphical analysis. Before presenting our main event study results, we documentthat the same trends we will uncover in these specifications are also apparent in the raw
data. In Figure 6, we show four scatterplots. In each scatterplot, each dot represents a
district in our data. The x axis of each plot is the same: the death rate due to influenza
during the pandemic. The y axis in each figure is a different measure of the change in FLFP
between 1911 and 1921, i.e. through the pandemic. It is clear from the figure that there
is a positive correlation between influenza mortality and the change in overall FLFP from
1911 to 1921. Modest upward-sloping relationships are also apparent for the agriculturaland services sectors, and there appears to be no correlation for industrial FLFP. Our event
study results will verify the relationships for overall FLFP and services below.

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JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

3.2. Main results. We present our main results in Table 3. In column (1), we report asparser specification with only district fixed effects, year fixed effects, and influenza mortality
interacted with the year fixed effects. We find a statistically significant increase in female
labor force participation in more influenza-affected districts in 1921, followed by a decline in
1931. In column (2), we add province-specific linear time trends. In column (3), we control
as well for variables that help discipline the differences in pre-trends across districts: latitude,
longitude, and humidity, each interacted with year fixed effects. Finally, in column (4), we
control for time-varying urbanization. Across columns, we continue to find a statisticallysignificant increase in female labor force participation in 1921 in districts that experienced
greater mortality during the pandemic, and that this is reversed in 1931.
To put the magnitude of these results in context, the influenza death rate has a mean of
0.0796 across districts and a standard deviation across districts of 0.032.3 The coefficientof approximately 0.294 implies that a typical mortality shock increased FLFP by 2.3 percentage points in 1921, with a comparable reverse effect almost three times as large in 1931.
Alternatively, a one standard deviation increase in mortality led to a 0.94 percentage point
increase in FLFP in 1921, and a reduction almost three times as large in 1931.
There are several ways to put the magnitude of these results in perspective. One is tocompare them with other effects of influenza in India. In their study of the pandemic,
Donaldson and Keniston (2016) find that a 10% death rate raises birth rates by 18% in thedecade afterwards while reducing marriage rates by 10%. Another approach is to contrast
them with other studies of demographic shocks and female labor force participation. For
France, moving from the 25th to the 75th percentile of the male death rate during the FirstWorld War increased FLFP by 3.5 percentage points during the interwar period (Boehnke
and Gay, 2019), an effect that has risen to 5 percentage points since the 1960s (Boehnke
and Gay, 2020). Finally, we can compare them to modern correlates of FLFP in India.
For example, the rural-urban gap in labor force participation among women with low levelsof education is 8 percentage points in the present. Today, 22% of women with a secondary
education are in the labor force in India. This is lower than for less educated women (Fletcher
et al., 2017). Our results are modest in comparison, though of a similar order of magnitude.
3.3. Results by Sector. We show results by sector in Table 4. That is, we now compute
FLFP for agriculture, industry, and services. In 1921, FLFP in services increases in the mostadversely affected districts. In 1931, it falls relative both to its baseline mean and relative
to other districts where the mortality rate was lower. We find no significant patterns ofresponse in agriculture or industry.
3The smaller standard deviation in Table 2 reflects the variation across both districts and census years, whichis less because the variable is not time-varying.

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

15

What are the most quantitatively important sectors in which women worked, and thathave the potential to drive our results? In Table 5, we consider the five sub-sectors thateach accounted for at least 1% of the female population in 1911: cultivators, farm servants,
textiles, trade, and domestic service. Here, our results can only be taken as suggestive. Thereis a significant, though small, differential increase in FLFP in textiles in more pandemicaffected districts in 1921. There is a large differential increase in female participation indomestic service as well, but here the significant coefficient on the interaction of influenza
mortality and the 1901 dummy implies that we cannot rule out violations of the parallel
trends assumption.
In Table 5, we also consider a category of FLFP that can be easily interpreted as “distress’
participation: that of beggars and prostitutes. We find no evidence that FLFP in thiscategory rose differentially in 1921 in districts that experienced greater influenza mortality.

4. Mechanisms
To evaluate the mechanisms that could potentially explain our results, we begin by considering three possible mediating variables: the share of widows in the female population, theshare of never-married women in the female population, and population density. The shareof widows is likely to capture distress labor by women who have lost husbands and otherrelatives during the pandemic. The share of what the census refers to as “single” women
may capture a similar effect, working through the unavailability of potential husbands. Population density is interpreted here as a measure of the overall availability of labor. In a morecompetitive labour market, employers may give priority to male workers, leading women towithdraw from the labor force. The increase in FLFP may have been driven by an aggregate
shortage of labor in the economy and a corresponding rise in wages.
We begin by testing whether these variables respond to the influenza pandemic in Table
6. In columns (1) and (2), we show there is indeed a significant increase in the prevalence ofwidows in the female population in 1921 in districts that experienced higher mortality rates
during the pandemic. Though the coefficient for 1931 suggests that there were still more
widows in these districts more than a decade later, this is not statistically significant. In
columns (3) and (4), by contrast, we see no change in the share of never-married women.
In columns (5) and (6), we do show that there is a statistically significant decline in 1921
in population density in districts with more mortality. In addition to suggesting that labor
became more scarce in these districts, it also validates the mortality data, which are takenfrom the Sanitary Reports, and not from the census.
In Table 7, we then ask the degree to which these variables mediate our main results,
including them as additional controls. Columns (1) and (2) are analogous to columns (3)and (4) of Table 3. Districts with a higher share of widows in the population have greater

16

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

female labor force participation, though the magnitudes of the coefficients on the interactionsof the influenza death rate and the year fixed effects have only fallen slightly. The same istrue in columns (7) and (8), which are analogous to columns (5) and (6) in Table 4. So,
while distress labor by widows may help explain the temporary increase in FLFP in more
influenza-affected districts, it is not a complete explanation.
In Table 8, we perform a similar exercise, but for population density. In columns (1) and(2), we find no evidence that the overall supply of potential labor drove aggregate FLFP.
In columns (7) and (8), we do find a negative coefficient on population density, suggestingthat female participation in services grew when labor was more scarce generally. Again,
however, this does little to diminish the magnitudes of the coefficients on the interactionsof the influenza death rate and the year fixed effects. The labor scarcity induced by the
pandemic is, then, only a partial explanation, and a sector-specific one.
In a related exercise, we use Table 9 to demonstrate that greater mortality did increase
wages. Following a procedure similar to that in Donaldson and Keniston (2016), we estimate
the following event study specification using our unbalanced panel of wage data:ln(wodpt ) =β1 Inf luenzad × Y ear1901 + β2 Inf luenzad × Y ear1902−11+ β3 Inf luenzad × Y ear1919−21 + β4 Inf luenzad × Y ear1922−28
(2)

0
+ zot
ρ + (x0d × ηt )γ + δd + ηt + ϕp × t + odpt .

Here, wodpt is the log wage for occupation o in district d in province p in year t. As in (1),Inf luenzad is the mortality rate from influenza in district d during the pandemic. Because
the wage data come as an unbalanced panel, we collapse our time periods together into
broader groups: 1901 and earlier, 1902 to 1911, 1919 to 1921, and 1922 to 1928. Insignificant
estimates of the coefficients β1 and β2 validate the parallel trends assumption. β3 is an
estimate of the immediate impact of influenza mortality on wages, while β4 estimates whether
this impact persisted throughout the 1920s.
zot includes occupational characteristics – whether the wage is urban or rural, whether it
is paid in cash or kind, and fixed effects for occupation. Other terms in (2) are defined as in(1); x0d × ηt is the interaction of district-specific controls with year fixed effects, δd is district
fixed effects, ηt is year fixed effects, and ϕp × t is province-specific time trends. As before,
we cluster standard errors by district.
Pooling all wage observations together in Column (1) of Table 9, we confirm the resultsfrom Donaldson and Keniston (2016) – greater influenza mortality created a scarcity oflabor, increasing wages throughout the 1920s. Our results allow us to show results by sector
that Donaldson and Keniston (2016) do not report. We find no wage effects in agriculture.
Rather, it is in industry, services, general labor, and the labor of skilled workers that wage

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

17

increases are apparent. For industry and skilled work, these increases persist throughout the
1920s. Because our wage data form an unbalanced panel, we cannot control for wages in amediation analysis similar to Table 8.
If aggregate labor scarcity were to explain women’s responses, male labor force participation (MLFP) would increase in 1921 in a manner similar to FLFP. In Table A1, in the
appendix, however, we show that we find no evidence of such an effect. One possible explanation of this result is that MLFP was largely universal and remained so after the pandemic.
Finally, to evaluate whether the impact of the influenza pandemic was greatest in districts
where there was a greater pent up supply of underutilized female labor, Table 10 separates
the sample by initial FLFP. That is: we divide the sample into districts with above andbelow median levels of FLFP in 1901, the first year in our data. We find that the FLFP
response was, rather, driven by districts with initially greater levels of FLFP. Rather than
the influenza pandemic working to break the constraints imposed by social norms on FLFP,
these results suggest that the response itself was constrained by the presence of these norms
or by the prevalence of activities in which women did not work.

5. Robustness
In this section, we outline robustness exercises that we report in the appendix. We begin
by showing that our results survive controlling for cholera mortality in 1918, interacted withyear fixed effects, in Table A2. We do this to demonstrate that our results are not driven bydifferential trends in districts that have high levels of mortality for reasons unrelated to thepandemic.
Because of the possibility that our results for 1931 are confounded with India’s exposure
to the Great Depression, we add as controls the interactions of sutiabilities for major crops inIndia with year fixed effects. These are computed using data from the Food and Agriculture
Organization of the United Nations’ Global Agro-Ecological Zones (FAO-GAEZ) project,
and represent expected yields under rainfed agriculture with low input use based on climate
and soil productivity. These are widely used in the literature as exogenous predictors ofcrop yields (Donaldson and Storeygard, 2016). Note that this is equivalent to controlling
flexibly for how the world prices of these crops affect these districts based on an exogenous
determinant of their propensity to produce them. These results are reported in Table A3.
The general result remains: districts that were most affected by the influenza pandemic saw
an increase in female labor force participation in services that was reversed by 1931.
For similar reasons, we control in Table A4 for the time-varying areas planted to major
crops recorded in the Agricultural Statistics of British India in our census years. In particular,
we consider rice, wheat, sugarcane, jute, opium, tea, tobacco, and cotton. Results remainsimilar to our baseline analysis.

18

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Because it is possible that influenza mortality during the pandemic was correlated withgeographical characteristics that might predict differential trends in FLFP even in the absence of the pandemic, we use Table A5 to show that our results survive controlling for anumber of additional geographic controls interacted with year fixed effects: temperature,
precipitation, and altitude, all computed using data from the FAO-GAEZ data, the Nunn
and Puga (2012) ruggedness index, and the Kiszewski et al. (2004) index of the stability ofmalaria transmission.
So far, we have considered the overall rate of influenza mortality, combining genders.
Gender-specific mortality rates were strongly correlated across districts during the pandemic:
the male and female death rates have a correlation coefficient of 0.97, and each has a correlation coefficient of at least 0.99 with the overall death rate. Although this gives us limited
scope to evaluate whether FLFP responded differently to male mortality, we replace theoverall death rate with male mortality in Tables A6, A7, and A8. The table analogous toour main results, Table A6, shows patterns similar to our baseline. In Table A7, which isanalogous to Table 6, we show that the share of widows in the female population rises in
response to male influenza deaths, as it had to overall mortality. In Table A8, which isanalogous to Table 7, we again show evidence that the increase in widowhood due to thepandemic helps explain the FLFP response, but does not completely mediate it. In Table
A9, we show that using the female death rate gives results that are similar in size to ourbaseline, but only significant at conventional levels for FLFP in services.
Finally, we demonstrate that the smaller categories of beggars and prostitutes and inmatesof jails, asylums and almshouses do not drive our results. In Table A10, we show that
excluding these categories from the numerators of both total FLFP and FLFP in services
does not change our main results. In Table A11, we do the same for our potential mediating
variables.

6. Conclusion
In this paper, we have asked whether the influenza pandemic in India led to an increasein FLFP. Our answer is yes, but that the increase is temporary. We observe a short-lived
increase, concentrated in services, which is reversed within a decade. Population density (theaggregate land-labor ratio) is not a sufficient statistic for these results. Influenza increased
the share of widows in the population. This explains part of the short-run effect, but not
all of it. Mortality also raised wages, including in services. The FLFP response to a majordemographic shock in India was confined to services and was not durable. This contrasts
with other historical cases. One possible explanation of this contrast is cultural norms. It
may be that the ability of women to increase their participation in the labor market is limitedin this context to periods of economic distress.

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

19

References
Acemoglu, D., Autor, D. H., and Lyle, D. (2004). Women, war, and wages: The effect offemale labor supply on the wage structure at midcentury. Journal of Political Economy,
112(3):497–551.
Afridi, F., Dinkelman, T., and Mahajan, K. (2016). Why are fewer married women joining
the work force in India? A decomposition analysis over two decades. IZA Discussion
Paper No. 9722.
Alesina, A., Giuliano, P., and Nunn, N. (2013). On the Origins of Gender Roles: Womenand the Plough. The Quarterly Journal of Economics, 128(2):469–530.
Almond, D. (2006). Is the 1918 influenza pandemic over? Long-term effects of in utero
influenza exposure in the post-1940 US population. Journal of Political Economy,
114(4):672–712.
Arnold, D. (2019). Death and the Modern Empire: The 1918-19 Influenza Epidemic in India.
Transactions of the Royal Historical Society, 29:181–200.
Barro, R. J., Ursúa, J. F., and Weng, J. (2020). The coronavirus and the great influenza pandemic: Lessons from the “Spanish flu” for the coronavirus’s potential effects on mortality
and economic activity.
Behrman, J. R., Foster, A. D., Rosenweig, M. R., and Vashishtha, P. (1999). Women’s
schooling, home teaching, and economic growth. Journal of Political Economy, 107(4):682–
714.
Boehnke, J. and Gay, V. (2019). The Missing Men: World War I and Female Labor Force
Participation. Toulouse School of Economics Working Paper.
Boehnke, J. and Gay, V. (2020). The missing men world war i and female labor force
participation. Forthcoming, Journal of Human Resources.
Boserup, E. (1970). Woman’s role in economic development. Routledge.
Brainerd, E. and Siegler, M. V. (2003). The economic effects of the 1918 influenza epidemic.
CEPR Discussion Paper No. 3791.
Broadberry, S. (2013). Accounting for the great divergence. Working Paper: London Schoolof Economics and Political Science.
Carranza, E. (2014). Soil endowments, female labor force participation, and the demographic
deficit of women in India. American Economic Journal: Applied Economics, 6(4):197–225.
Chandra, S. and Kassens-Noor, E. (2014). The evolution of pandemic influenza: evidence
from India, 1918–19. BMC infectious diseases, 14(1):1–10.
Chandra, S., Kuljanin, G., and Wray, J. (2012). Mortality from the influenza pandemic of
1918–1919: the case of India. Demography, 49(3):857–865.
Chatterjee, E., Desai, S., and Vanneman, R. (2018). Indian paradox: Rising education,
declining women’s employment. Demographic Research, 38:855–881.

20

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Chen, M. and Drèze, J. (1992). Widows and health in rural north India. Economic andPolitical Weekly, 27(43/44):WS81–WS92.
Chen, M. A. (1989). Women’s work in Indian agriculture by agro-ecologic zones: Meetingneeds of landless and land-poor women. Economic and Political Weekly, 24(43):WS79–
WS89.
Correia, S., Luck, S., and Verner, E. (2020). Pandemics depress the economy, public health interventions do not: Evidence from the 1918 flu. Available at SSRN:
https://ssrn.com/abstract=3561560 or http://dx.doi.org/10.2139/ssrn.3561560.
Davis, K. (1951). The Population of India and Pakistan. Princeton University Press.
De Moor, T. and Van Zanden, J. L. (2010). Girl power: The European marriage pattern
and labour markets in the North Sea region in the late medieval and early modern period.
The Economic History Review, 63(1):1–33.
Donaldson, D. and Keniston, D. (2016). Dynamics of a Malthusian Economy: India in the
Aftermath of the 1918 Influenza. Working Paper.
Donaldson, D. and Storeygard, A. (2016). The view from above: Applications of satellite
data in economics. Journal of Economic Perspectives, 30(4):171–98.
Duflo, E. (2012). Women empowerment and economic development. Journal of Economic
Literature, 50(4):1051–79.
Eswaran, M., Ramaswami, B., and Wadhwa, W. (2013). Status, caste, and the time allocation of women in rural India. Economic Development and Cultural Change, 61(2):311–333.
Fenske, J. and Kala, N. (2017). Linguistic distance and market integration in India. Centre
for Competitive Advantage in the Global Economy Working Paper 331.
Fernandez, R. (2007). Women, work, and culture. Journal of the European Economic Association, 5(2-3):305–332.
Fernández, R. (2013). Cultural change as learning: The evolution of female labor force
participation over a century. American Economic Review, 103(1):472–500.
Fernández, R., Fogli, A., and Olivetti, C. (2004). Mothers and sons: Preference formationand female labor force dynamics. The Quarterly Journal of Economics, 119(4):1249–1299.
Fletcher, E., Pande, R., and Moore, C. M. T. (2017). Women and work in India: Descriptive
evidence and a review of potential policies. HKS Working Paper No. RWP18-004.
Fogli, A. and Veldkamp, L. (2011). Nature or nurture? Learning and the geography of female
labor force participation. Econometrica, 79(4):1103–1138.
Giuliano, P. (2014). Female labor force participation: Persistence and evolution. The New
Palgrave Dictionary of Economics, Edited by Steven N. Durlauf and Lawrence E. Blume.
Goldin, C. (1995). The U-shaped female labor force function. “Economic Development andEconomic History” in Investment in Women’s Human Capital, TP Schultz (ed.) Chicago:
The University of Chicago Press.

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

21

Goldin, C. and Olivetti, C. (2013). Shocking labor supply: A reassessment of the role ofWorld War II on women’s labor supply. American Economic Review, 103(3):257–62.
Goldin, C. D. (1991). The role of World War II in the rise of women’s employment. The
American Economic Review, 81(4):741–756.
Grosjean, P. and Khattar, R. (2019). It’s raining men! Hallelujah? The long-run consequences of male-biased sex ratios. The Review of Economic Studies, 86(2):723–754.
Gulati, L. (1975). Female work participation: a study of inter-state differences. Economic
and Political Weekly, 10(1/2):35–42.
Hill, K. (2011). Influenza in India 1918: excess mortality reassessed. Genus, 67(2):9–29.
Horrell, S. and Humphries, J. (1995). Women’s labour force participation and the transition
to the male-breadwinner family, 1790-1865. Economic History Review, 48(1):89–117.
Jayachandran, S. (2015). The roots of gender inequality in developing countries. Annual
Review of Economics, 7(1):63–88.
Karlsson, M., Nilsson, T., and Pichler, S. (2014). The impact of the 1918 Spanish flu
epidemic on economic performance in Sweden: An investigation into the consequences ofan extraordinary mortality shock. Journal of Health Economics, 36:1–19.
Kiszewski, A., Mellinger, A., Spielman, A., Malaney, P., Sachs, S. E., and Sachs, J. (2004).
A global index representing the stability of malaria transmission. The American journalof tropical medicine and hygiene, 70(5):486–498.
Klasen, S. and Pieters, J. (2015). What explains the stagnation of female labor force participation in urban India? The World Bank.
Lin, M.-J. and Liu, E. M. (2014). Does in utero exposure to illness matter? The 1918
influenza epidemic in Taiwan as a natural experiment. Journal of Health Economics,
37:152–163.
Luke, N. and Munshi, K. (2011). Women as agents of change: Female income and mobility
in India. Journal of Development Economics, 94(1):1–17.
Luo, P. (2017). The Other Gender Gap: Female Entrepreneurship after World War II.
Working Paper, Harvard Business School.
Mammen, K. and Paxson, C. (2000). Women’s work and economic development. Journal of
Economic Perspectives, 14(4):141–164.
Nunn, N. and Puga, D. (2012). Ruggedness: The blessing of bad geography in Africa. Review
of Economics and Statistics, 94(1):20–36.
Patterson, K. D. and Pyle, G. F. (1991). The geography and mortality of the 1918 influenza
pandemic. Bulletin of the History of Medicine, 65(1):4–21.
Reddy, D. N. (1975). Female Work Participation: A Study of Inter-State Differences: A
Comment. Economic and Political Weekly, 10(23):902–905.

22

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Reyes, O., Lee, E. C., Sah, P., Viboud, C., Chandra, S., and Bansal, S. (2018). Spatiotemporal patterns and diffusion of the 1918 influenza pandemic in British India. American
journal of epidemiology, 187(12):2550–2560.
Roy, T. (2005). Rethinking economic change in India: labour and livelihood. Routledge.
Schultz, T. W. (1967). Significance of India’s 1918-19 losses of agricultural labour-A reply.
The Economic Journal, 77(305):161–163.
Teso, E. (2019). The long-term effect of demographic shocks on the evolution of genderroles: Evidence from the transatlantic slave trade. Journal of the European Economic
Association, 17(2):497–534.
Thorner, D. and Thorner, A. (1962). Land and labour in India. Bombay: Asia Publishing
House.

Table 1. Changes in the share of female workers in the total workforce
1881
1901
1911
1921
1931
Total
Agriculture, fishing,forestry
Manufacturing,
Mining, Construction
TradeTransport and Other
Services
General labour

30

31

32

31

29

27

31

32

32

28

39

34

35

32

29

18

30

33

30

27

13

20

22

21

24

42

42

43

44

45

Source: Calculated from Thorner and Thorner (1962), Chapter VI Table 1, pp.78-81

1901
Female labor force participation rate
Female LFP in agriculture
Female LFP in industry
Female LFP in services
1911
Female labor force participation rate
Female LFP in agriculture
Female LFP in industry
Female LFP in services
1921
Female labor force participation rate
Female LFP in agriculture
Female LFP in industry
Female LFP in services
1931
Female labor force participation rate
Female LFP in agriculture
Female LFP in industry
Female LFP in services
1918 death rate
LatitudeLongitude
Humidity
Population density
Urbanization

Table 2. Summary Statistics
(1)
(2)
mean
min

(3)
max

(4)
sd

(5)
N

0.30790.21140.03680.05940.02860.00460.00120.00870.67540.60730.09980.19370.19320.17740.02070.0316

196
196
196
196

0.2946
0.21390.04010.04000.02580.00550.00280.00840.6416
0.61530.11160.18560.18050.17300.02180.0266

196
196
196
196

0.28610.21430.03340.03770.01010.00200.00160.0026

0.67930.65820.12080.15170.17640.16670.02040.0236

196
196
196
196

0.26160.16740.02680.06690.01960.00270.00230.00600.66770.65380.21980.43050.17250.13630.02160.1004

196
196
196
196

0.079624.343380.455162.6667
0.63280.10930.01388.824268.014245.18170.02670.00520.179234.4261
94.9060
79.518136.2647
1.00000.03145.41456.00408.42382.47140.1239

784
784
784
784
784
784

Table 3. Main Results
(1)
(2)
(3)
(4)
Female labor force participation rate
Influenza X 1901
Influenza X 1921
Influenza X 1931

-0.098-0.215(0.181)(0.182)0.294** 0.411***(0.124)(0.134)-1.080*** -0.846***(0.264)(0.262)-0.027(0.261)0.386**(0.189)-0.763**(0.341)-0.031(0.262)0.388**(0.188)-0.764**(0.342)0.047(0.170)

Yes
Yes
Yes
Yes
Yes
784
0.933

Yes
Yes
Yes
Yes
Yes
784
0.933

Urbanization

District and year fixed effects
Province-specific time trends
Latitude x year fixed effects
Longitude x year fixed effects
Humidity x year fixed effects
Observations
R-squared

Yes

Yes
Yes

784
0.901

784
0.926

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 4. Results by Sector(1)

Influenza X 1901
Influenza X 1921
Influenza X 1931

(2)

(3)

(4)

(5)

(6)

FLFP in agriculture

FLFP in industry

FLFP in services

0.153

0.157

-0.060-0.062-0.120-0.126(0.217)(0.217)(0.052)(0.053)(0.074)(0.077)

0.093

0.091

0.028

0.029

0.266***

0.269***(0.146)(0.148)(0.036)(0.035)(0.094)(0.093)-0.411-0.409-0.062-0.063-0.292**-0.294**(0.290)(0.289)(0.054)(0.054)(0.125)(0.125)

Urbanization-0.050

0.028

0.062

(0.146)(0.023)(0.064)

District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Observations

784

784

784

784

784

784

R-squared

0.939

0.939

0.788

0.788

0.846

0.847

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 5. Results by Sub-Sector
(1)
(2)
(3)

Influenza X 1901
Influenza X 1921
Influenza X 1931

FLFP for cultivators

FLFP for farm servants

0.059(0.249)0.087(0.125)-0.685*(0.353)-0.170(0.178)-0.059(0.112)0.155(0.190)Urbanization

0.080(0.242)0.075(0.130)-0.679*(0.348)-0.267**(0.111)FLFP in trade
Influenza X 1901
Influenza X 1921
Influenza X 1931

-0.170(0.183)-0.059(0.113)0.155(0.190)0.007(0.119)

FLFP in domestic
service(5)(6)

FLFP in textiles
0.022(0.022)0.034*(0.018)0.040(0.028)0.021(0.022)0.035*(0.018)0.040(0.028)0.017(0.012)

FLFP in beggars andprostitutes

0.018(0.029)0.046(0.028)-0.021(0.023)0.017(0.029)0.046(0.028)-0.021(0.023)0.009(0.008)-0.158**(0.061)0.181***(0.056)-0.267**(0.115)-0.163**(0.063)0.184***(0.055)-0.269**(0.116)0.062(0.060)-0.009(0.010)0.010(0.007)0.021(0.019)-0.010(0.010)0.011(0.007)0.021(0.019)0.021*(0.011)

Yes
Yes
Yes
Yes
Yes
784

Yes
Yes
Yes
Yes
Yes
784

Yes
Yes
Yes
Yes
Yes
784

Yes
Yes
Yes
Yes
Yes
784

Yes
Yes
Yes
Yes
Yes
784

Yes
Yes
Yes
Yes
Yes
784

Urbanization

District and year fixed effects
Latitude x year fixed effects
Longitude x year fixed effects
Humidity x year fixed effects
Province-specific time trends
Observations(4)*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 6. Influenza and Possible Mediators
(1)

(2)

Female widowed share

(3)

(4)

(5)

(6)

Female single share

Population Density

Influenza X 1901

-0.089-0.112

0.031

0.026

1.218

1.305

Influenza X 1921

(0.135)0.211*(0.136)0.224*(0.045)-0.005(0.045)-0.002(1.073)-0.953**(1.107)-0.999**(0.123)(0.116)(0.052)(0.052)(0.465)(0.476)0.137(0.116)0.130(0.116)0.010(0.044)0.009(0.042)-7.512(5.761)-7.487(5.748)

Influenza X 1931
Urbanization

0.288*

0.062**-1.074*(0.164)(0.025)(0.586)

District and year fixed effects
Latitude x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Longitude x year fixed effects
Humidity x year fixed effects
Province-specific time trends

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations
R-squared

784
0.821

784
0.826

784
0.925

784
0.926

784
0.984

784
0.984

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 7. Widow Share as Mediator(1)(2)
Female LFP
Influenza X 1901
Influenza X 1921

(3)
(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services-0.002

0.001

0.166

0.175

-0.055-0.057-0.112-0.117(0.256)(0.256)(0.216)(0.216)(0.051)(0.051)(0.073)(0.075)0.326*

0.324*

0.063

0.057

0.017

0.018

0.248***

0.251***(0.187)(0.188)(0.144)(0.147)(0.038)(0.038)(0.093)(0.092)

Influenza X 1931

-0.802**-0.801**-0.430-0.429-0.069-0.069-0.304**-0.304**(0.331)(0.330)(0.287)(0.285)(0.052)(0.052)(0.122)(0.122)

Female widowed share

0.284***

0.287***

0.144***

0.153***

0.051**

0.050**

0.088***

0.084***(0.065)(0.069)(0.045)(0.047)(0.021)(0.022)(0.024)(0.026)

Urbanization-0.036-0.099

0.014

0.047

(0.158)(0.141)(0.025)(0.063)District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

784

784

784

784

784

784

784

784

R-squared

0.935

0.935

0.939

0.939

0.791

0.791

0.848

0.848

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 8. Population Density as Mediator(1)(2)
Female LFP
Influenza X 1901
Influenza X 1921
Influenza X 1931
Population density(3)(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services-0.021-0.025

0.147

0.151

-0.060-0.062-0.109-0.114(0.260)(0.261)(0.217)(0.217)(0.052)(0.053)(0.076)(0.078)0.382**

0.384**

0.098

0.096

0.027

0.029

0.258***

0.260***(0.189)(0.189)(0.146)(0.148)(0.036)(0.036)(0.094)(0.093)-0.798**-0.799**-0.375-0.375-0.065-0.065-0.360*** -0.360***(0.344)(0.345)(0.298)(0.298)(0.055)(0.055)(0.104)(0.104)-0.005-0.005

0.005

0.005

-0.000-0.000-0.009**-0.009**(0.009)(0.009)(0.006)(0.006)(0.000)(0.000)(0.004)(0.004)

Urbanization

0.044-0.048

0.027

0.063

(0.173)(0.150)(0.023)(0.063)District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

784

784

784

784

784

784

784

784

R-squared

0.933

0.933

0.939

0.939

0.788

0.788

0.849

0.849

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 9. Influenza and Wages
(1)

(2)

(3)

(4)

(5)

(6)

(7)

GeneralLabour in
Services

SkilledUnskilled
nonagriculturalln Wage
All
Agriculture
occupations

Influenza X Year before 1901
Influenza X Year 1902 - 1911

4.041

4.157

(3.778)(3.572)

Industry

Services-0.0880.578-0.164-0.3851.591-0.3790.329(0.523)(0.654)(0.738)(0.944)(1.036)(0.709)(0.895)1.095**-0.1171.368*

2.609***

3.048***

1.398*

1.487(0.498)(0.911)(0.710)(0.996)(1.060)(0.764)(1.041)1.288**

0.507

1.972***-0.599-0.1601.997***

0.374(0.514)(0.667)(0.581)(1.491)(1.627)(0.608)(1.116)0.203-1.371*

1.251

2.557***

2.557***

1.285**

1.421(0.544)(0.711)(0.769)(0.855)(0.903)(0.618)(1.667)

Occupation characteristics

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

6,806

2,557

3,498

751

343

3,622

627

R-squared

0.833

0.771

0.787

0.785

0.824

0.788

0.851

Influenza X Year 1919 - 1921
Influenza X Year 1922 - 1928
Urbanization*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table 10. Heterogeneity by Initial FLFP
(1)

(2)

Female LFP

(3)

(4)

FLFP in agriculture

(5)

(6)

FLFP in industry

(7)

(8)

FLFP in services

Panel A. Above-median FLFP in 1901
Influenza X 1901

0.451

0.430

0.386

0.362

0.086

0.092

-0.019-0.022(0.376)(0.377)(0.332)(0.335)(0.070)(0.071)(0.134)(0.135)0.736***

0.741***

0.015

0.021

0.094**

0.093**

0.631***

0.632***(0.242)(0.242)(0.226)(0.223)(0.044)(0.044)(0.154)(0.155)-1.397***-1.390***-0.853**-0.845*

0.147

0.145

-0.689***-0.688***(0.524)(0.528)(0.423)(0.428)(0.090)(0.089)(0.199)(0.200)Observations

392

392

392

392

392

392

392

392

R-squared

0.850

0.850

0.881

0.882

0.769

0.770

0.865

0.865

Influenza X 1921
Influenza X 1931

Panel B. Below-median FLFP in 1901
Influenza X 1901
Influenza X 1921
Influenza X 1931

-0.034-0.087

0.118

0.097

-0.050-0.055-0.102-0.128(0.303)(0.300)(0.250)(0.244)(0.061)(0.062)(0.085)(0.086)-0.046-0.028-0.037-0.030-0.050-0.049

0.039

0.048

(0.272)(0.267)(0.217)(0.217)(0.055)(0.053)(0.077)(0.077)-0.607-0.604-0.437-0.436-0.207***-0.207***

0.034

0.035

(0.502)(0.507)(0.401)(0.404)(0.078)(0.078)(0.161)(0.161)

Observations

392

392

392

392

392

392

392

392

R-squared

0.854

0.854

0.857

0.857

0.861

0.861

0.837

0.839

Urbanization

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Appendix: Not for publication

34

JAMES FENSKE, BISHNUPRIYA GUPTA, AND SONG YUAN

Appendix A. Sources of Wage Data
Our sources of wage data are as follows:• Wage Census of Bengal taken in 1911, 1916, 1925
• Statement of Rural and Urban wages prevailing in the Central Provinces and Berarfor 1910-1923• Wage Census of Bombay Presidency taken in 1911, 1916
• Report on an enquiry into agricultural wages in the Bombay presidency• Report on the Wage Census of Eastern Bengal and Assam 1911
• Madras Wage Census report 1908, 1911
• Wage Census of the North-west Frontier Province taken in 1912, 1917, 1923, 1928
• Report on the Regular Wage Survey of the Punjab 1912, 1917, 1922, 1927
• The wage census of the United Provinces taken in 1906, 1911, 1916, 1928
• Prices and Wages in India. 1919 (1911, 1916)
These provide wage observations for the following provinces and years:• Assam: 1911, 1916
• Bengal: 1911, 1916, 1925
• Bihar and Orissa: 1911, 1916
• Bombay: 1900-1922• Central Provinces and Berar: 1910-1923• Madras: 1908, 1911, 1916
• Punjab: 1909, 1911, 1912, 1916, 1917, 1922, 1927
• United Provinces: 1906, 1911, 1916, 1928
• North-west Frontier Province: 1911, 1912, 1916, 1917, 1923, 1928

DEMOGRAPHIC SHOCKS AND WOMEN’S LABOR MARKET PARTICIPATION

Appendix B. Additional Tables

35

Table A1. Male Labor Force Participation
(1)

(2)

(3)
(4)
MLFP in agriculture(5)(6)
MLFP in industry(7)(8)
MLFP in services

Male LFP
Influenza X 1901

0.164

0.158

0.106

0.123

0.083

0.078

-0.027-0.044

Influenza X 1921

(0.156)0.084(0.158)0.087(0.136)0.186(0.137)0.177(0.052)0.010(0.051)
0.013(0.125)-0.114(0.133)-0.105(0.125)-0.017(0.159)(0.125)-0.019(0.160)(0.130)0.052(0.165)(0.127)0.057(0.161)(0.043)0.020(0.053)(0.044)0.018(0.051)(0.123)-0.088(0.123)(0.116)-0.093(0.124)

Influenza X 1931
Urbanization

0.073(0.073)-0.209**(0.089)0.070*(0.036)0.209***(0.053)District and year fixed effects
Latitude x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Longitude x year fixed effects
Humidity x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Province-specific time trends
Observations
R-squared

Yes
784
0.802

Yes
784
0.803

Yes
784
0.911

Yes
784
0.912

Yes
784
0.940

Yes
784
0.941

Yes
784
0.925

Yes
784
0.927

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A2. Control for Cholera X Year Fixed Effects
(1)
(2)
(3)
(4)
(5)
(6)
Female LFP
FLFP in agriculture
FLFP in industry
Influenza X 1901
Influenza X 1921
Influenza X 1931

-0.047(0.259)0.396**(0.189)-0.785**(0.337)-0.051(0.261)
0.398**(0.188)-0.785**(0.339)0.048(0.163)0.120(0.215)0.091(0.146)-0.447(0.282)0.125(0.215)0.088(0.148)-0.446(0.281)-0.062(0.139)-0.059(0.052)0.024(0.036)-0.060(0.054)-0.062(0.053)0.026(0.035)-0.060(0.055)0.029(0.023)-0.108(0.076)0.282***(0.091)-0.279**(0.128)-0.115(0.079)0.286***(0.089)-0.280**(0.128)0.079(0.066)

Yes
Yes
Yes
Yes
Yes
Yes
784
0.934

Yes
Yes
Yes
Yes
Yes
Yes
784
0.934

Yes
Yes
Yes
Yes
Yes
Yes
784
0.940

Yes
Yes
Yes
Yes
Yes
Yes
784
0.940

Yes
Yes
Yes
Yes
Yes
Yes
784
0.789

Yes
Yes
Yes
Yes
Yes
Yes
784
0.789

Yes
Yes
Yes
Yes
Yes
Yes
784
0.848

Yes
Yes
Yes
Yes
Yes
Yes
784
0.848

Urbanization

Cholera X year fixed effects
District and year fixed effects
Latitude x year fixed effects
Longitude x year fixed effects
Humidity x year fixed effects
Province-specific time trends
Observations
R-squared(7)(8)
FLFP in services*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A3. Control for Crop Suitabilities X Year Fixed Effects(1)(2)
Female LFP(3)(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services

Influenza X 1901

0.284

0.290

0.387

0.385

-0.044-0.041-0.054-0.049Influenza X 1921

(0.286)
0.368**(0.162)(0.287)0.369**(0.163)(0.254)0.147(0.161)(0.255)0.146(0.161)(0.057)
0.066*(0.038)(0.057)0.067*(0.038)(0.083)0.153*(0.082)(0.084)0.154*(0.082)-0.281(0.307)-0.278(0.308)0.011(0.254)0.010(0.254)-0.099*(0.057)-0.097*(0.058)-0.190(0.138)-0.188(0.138)

Influenza X 1931
Urbanization

0.067(0.185)-0.019(0.153)0.037(0.025)0.048(0.069)Crop suitabilities X year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District and year fixed effects
Latitude x year fixed effects
Longitude x year fixed effects
Humidity x year fixed effects
Province-specific time trends
Observations
R-squared

Yes
Yes
Yes
Yes
Yes
784
0.945

Yes
Yes
Yes
Yes
Yes
784
0.945

Yes
Yes
Yes
Yes
Yes
784
0.950

Yes
Yes
Yes
Yes
Yes
784
0.950

Yes
Yes
Yes
Yes
Yes
784
0.827

Yes
Yes
Yes
Yes
Yes
784
0.828

Yes
Yes
Yes
Yes
Yes
784
0.890

Yes
Yes
Yes
Yes
Yes
784
0.890

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A4. Control for Agricultural Commodity Areas(1)(2)
Female LFP(3)(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services

Influenza X 1901

-0.033-0.035

0.173

0.180

-0.077-0.079-0.130*-0.135*

Influenza X 1921

(0.264)0.330*(0.264)0.332*(0.222)0.059(0.222)0.053(0.054)0.001(0.055)0.003(0.074)0.272***(0.077)0.277***(0.195)-0.808**(0.343)(0.195)-0.807**(0.344)(0.150)-0.437(0.294)(0.153)-0.439(0.292)(0.039)-0.070(0.054)(0.039)-0.070(0.055)(0.097)-0.301**(0.128)(0.096)-0.300**(0.128)Agricultural commodity areas

Yes

0.021
(0.172)
Yes

Yes-0.078(0.146)
Yes

Yes

0.026
(0.024)
Yes

Yes

0.071
(0.066)
Yes

District and year fixed effects
Latitude x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Longitude x year fixed effects
Humidity x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Province-specific time trends
Observations
R-squared

Yes
784
0.934

Yes
784
0.934

Yes
784
0.940

Yes
784
0.940

Yes
784
0.796

Yes
784
0.796

Yes
784
0.848

Yes
784
0.848

Influenza X 1931
Urbanization*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses. Agricultural commodities include rice, wheat, sugarcane, jute, opium,tea, tobacco, and cotton.

Table A5. Control for Geographic Controls X Year Fixed Effects
(1)

(2)

Female LFP
Influenza X 1901

(3)

(4)

FLFP in agriculture

(5)

(6)

FLFP in industry

(7)

(8)

FLFP in services

0.039

0.040

0.130

0.130

-0.069-0.068-0.024-0.023(0.257)(0.258)(0.224)(0.224)(0.052)(0.053)(0.070)(0.071)

Influenza X 1921

0.319*

0.324*

0.111

0.109

0.050

0.052

0.158**

0.162**(0.169)(0.169)(0.146)(0.147)(0.036)(0.036)(0.078)(0.077)

Influenza X 1931

-0.673**-0.671**-0.455*-0.456*-0.055-0.054-0.165-0.163(0.323)(0.324)(0.275)(0.274)(0.050)(0.050)(0.135)(0.135)

Urbanization

0.075-0.036

0.034

0.075

(0.176)(0.153)(0.024)(0.059)Geographic controls X year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

784

784

784

784

784

784

784

784

R-squared

0.935

0.935

0.942

0.942

0.796

0.796

0.876

0.876

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses. Geographic controls include temperature, precipitation, altitude, ruggedness,and malaria.

Table A6. Robustness to Using the Male Influenza Death Rate
(1)
(2)
Female LFP(3)(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services

Male Influenza X 1901

-0.078-0.082

0.063

0.068

-0.051-0.053-0.088-0.094

Male Influenza X 1921

(0.260)0.483**(0.262)0.486**(0.215)0.146(0.215)0.143(0.053)0.028(0.054)0.029(0.081)0.309***(0.084)0.313***(0.189)-0.873**(0.356)(0.188)-0.873**(0.357)(0.147)-0.533*(0.301)(0.149)-0.532*(0.300)(0.038)-0.060(0.056)(0.037)-0.060(0.056)(0.100)-0.280**(0.137)(0.098)-0.281**(0.136)

Male Influenza X 1931
Urbanization

0.051(0.168)-0.051(0.145)0.028(0.023)0.072(0.064)

District and year fixed effects
Latitude x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Longitude x year fixed effects
Humidity x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Province-specific time trends
Observations
R-squared

Yes
784
0.934

Yes
784
0.934

Yes
784
0.939

Yes
784
0.939

Yes
784
0.787

Yes
784
0.788

Yes
784
0.847

Yes
784
0.847

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A7. Male Death and Marital Outcomes
(1)

(2)

Female widowed share
Male Influenza X 1901
Male Influenza X 1921
Male Influenza X 1931

-0.087(0.145)0.232*(0.124)0.168(0.124)

Urbanization
District and year fixed effects
Province-specific time trends
Latitude x Year dummyLongitude x Year dummy
Humidity x Year dummy
Observations
R-squared-0.113(0.146)
0.248**(0.117)0.166(0.123)(3)

Female single share
0.049(0.047)0.014(0.053)-0.003(0.046)0.290*(0.165)
Yes
Yes
Yes
Yes
Yes
784
0.821

Yes
Yes
Yes
Yes
Yes
784
0.826

(4)

0.044
(0.047)0.017(0.053)-0.004(0.044)0.062**(0.025)

Yes
Yes
Yes
Yes
Yes
784
0.925

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Yes
Yes
Yes
Yes
Yes
784
0.926

Table A8. Male Death and Widow Share as Mediator(1)(2)
Female LFP
Male Influenza X 1901

(3)
(4)
FLFP in agriculture(5)(6)
FLFP in industry(7)(8)
FLFP in services-0.053-0.050

0.076

0.085

-0.046-0.047-0.080-0.085(0.255)(0.254)(0.214)(0.214)(0.051)(0.052)(0.079)(0.081)0.417**

0.415**

0.113

0.105

0.016

0.017

0.289***

0.293***(0.188)(0.190)(0.145)(0.148)(0.040)(0.039)(0.099)(0.098)

Male Influenza X 1931

-0.920*** -0.920***-0.557*-0.557*-0.068-0.068-0.295**-0.295**(0.343)(0.343)(0.297)(0.295)(0.054)(0.054)(0.133)(0.133)

Female widowed share

0.283***

0.286***

0.142***

0.151***

0.051**

0.050**

0.088***

0.084***(0.065)(0.069)(0.045)(0.047)(0.022)(0.022)(0.024)(0.026)

Male Influenza X 1921

Urbanization-0.032-0.095

0.014

0.047

(0.157)(0.141)(0.025)(0.063)District and year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province-specific time trends

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Latitude x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Longitude x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Humidity x Year dummy

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

784

784

784

784

784

784

784

784

R-squared

0.935

0.935

0.940

0.940

0.791

0.791

0.848

0.848

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A9. Robustness to Using the Female Influenza Death Rate
(1)
(2)
Female LFP(3)(4)
FLFP in agriculture(5)(6)
FLFP in industry

Female Influenza X 1901

0.027

0.025

0.232

0.236

-0.066-0.068-0.141**-0.145**

Female Influenza X 1921

(0.258)0.275(0.258)0.277(0.219)0.041(0.219)0.039(0.050)0.026(0.050)0.027(0.066)0.209**(0.068)0.211**(0.185)-0.617*(0.318)(0.185)-0.618*(0.319)(0.145)-0.269(0.272)(0.146)-0.267(0.271)(0.033)-0.062(0.050)(0.033)-0.063(0.051)(0.086)(0.085)-0.288*** -0.290***(0.109)(0.109)0.029(0.023)0.070(0.067)

Female Influenza X 1931
Urbanization

0.043(0.172)-0.058(0.146)(7)(8)
FLFP in services

District and year fixed effects
Latitude x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Longitude x year fixed effects
Humidity x year fixed effects

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Province-specific time trends
Observations
R-squared

Yes
784
0.932

Yes
784
0.932

Yes
784
0.939

Yes
784
0.939

Yes
784
0.788

Yes
784
0.788

Yes
784
0.846

Yes
784
0.846

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

Table A10. Results excluding beggars and prostitutes
(1)
(2)
(3)
(4)
Female LFP
FLFP in service
Influenza X 1901
Influenza X 1921
Influenza X 1931
Urbanization-0.019(0.258)0.376**(0.187)-0.785**(0.337)-0.021(0.259)0.377**(0.187)-0.785**(0.338)0.027(0.165)-0.112(0.074)0.256***(0.092)-0.314***(0.114)

District and year fixed effects
Yes
Yes
Yes
Province-specific time trends
Yes
Yes
Yes
Latitude x year fixed effects
Yes
Yes
Yes
Longitude x year fixed effects
Yes
Yes
Yes
Humidity x year fixed effects
Yes
Yes
Yes
Observations
784
784
784
R-squared
0.934
0.934
0.851
*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in-0.116(0.076)
0.258***(0.091)-0.315***(0.114)0.051(0.061)
Yes
Yes
Yes
Yes
Yes
784
0.851

Table A11. Mediation analysis excluding beggars and prostitutes
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Female LFP
FLFP in service
Influenza X 1901
Influenza X 1921
Influenza X 1931
Female widowed share

0.007(0.254)0.315*(0.185)-0.824**(0.327)0.287***(0.063)0.012(0.253)0.312*(0.187)-0.823**(0.326)
0.292***(0.066)

Population density
Urbanization-0.057(0.153)-0.016(0.258)0.374**(0.187)-0.801**(0.343)-0.018(0.259)0.375**(0.187)-0.801**(0.344)-0.002(0.009)-0.002(0.009)0.024(0.165)District and year fixed effects
Yes
Yes
Yes
Yes
Latitude x Year dummy
Yes
Yes
Yes
Yes
Longitude x Year dummy
Yes
Yes
Yes
Yes
Humidity x Year dummy
Yes
Yes
Yes
Yes
Province-specific time trends
Yes
Yes
Yes
Yes
Observations
784
784
784
784
R-squared
0.936
0.936
0.934
0.934
*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by district in parentheses.

-0.104(0.072)
0.237***(0.091)-0.326***(0.112)
0.091***(0.022)-0.106(0.074)0.238***(0.090)-0.326***(0.112)0.088***(0.024)-0.104(0.075)0.249***(0.092)-0.363***(0.102)-0.107(0.077)0.251***(0.091)-0.363***(0.103)-0.007*(0.003)-0.006*(0.003)0.044(0.060)

Yes
Yes
Yes
Yes
Yes
784
0.852

Yes
Yes
Yes
Yes
Yes
784
0.852

0.025
(0.059)
Yes
Yes
Yes
Yes
Yes
784
0.852

Yes
Yes
Yes
Yes
Yes
784
0.852

(8)