Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Impacts of capital intensity on family formationand gender equality in Vietnam
TIEN MANH VU、山田浩之
2024 年 1 月 29 日
DP2024-001
https://ies.keio.ac.jp/publications/23317/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
29 January, 2024

Impacts of capital intensity on family formation and gender equality in Vietnam
TIEN MANH VU、山田浩之
IES Keio DP2024-001
2024 年 1 月 29 日

JEL Classification: J16, O15, J12, I24, R23
キーワード: 資本集約度, 男女不平等, 家族形成, サイクロン, ベトナム【要旨】

We examine whether changes in capital intensity from Vietnamese firms during 19992019 influence family formation and gender inequality, using panel data of communes.
We use the recorded trajectories of cyclones to create a damage index as an
instrumental variable. We find that higher capital intensity is associated with a higher
share of single people and a lower share of families with multiple generations living
together. Also, women prepared for high capital intensity industries by increasing theireducational attainment. However, the results also indicate the sex ratio at birth ismore skewed in communes with high capital intensity.
TIEN MANH VU
中央大学
東京都八王子市東中野742-1

mvu264@g.chuo-u.ac.jp
山田浩之
慶應義塾大学東京都港区三田2-15-45

hyamada@econ.keio.ac.jp

Impacts of capital intensity on family formation and gender equality in
Vietnam

TIEN MANH VU
Corresponding author. Faculty of Global Management, Chuo University742-1 Higashinakano, Hachioji, Tokyo 192-0393, Japan
Email: mvu264@g.chuo-u.ac.jp
https://orcid.org/0000-0002-2251-0795

HIROYUKI YAMADA
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
E-mail: hyamada@econ.keio.ac.jp
https://orcid.org/0000-0003-3709-1914

Abstract: We examine whether changes in capital intensity from Vietnamese firms during
1999-2019 influence family formation and gender inequality, using panel data of communes.
We use the recorded trajectories of cyclones to create a damage index as an instrumental
variable. We find that higher capital intensity is associated with a higher share of single people
and a lower share of families with multiple generations living together. Also, women preparedfor high capital intensity industries by increasing their educational attainment. However, theresults also indicate the sex ratio at birth is more skewed in communes with high capitalintensity.
Keywords: Capital intensity, Gender inequality, Family formation, Cyclones, Vietnam
JEL classification: J16, O15, J12, I24, R23
Acknowledgements: This work was supported by JSPS (Japan Society for the Promotion of
Science) KAKENHI Grant Numbers 21K01455, 22K18534 and Chuo University.

11. Introduction
In the US, it took many decades before women achieved the levels of labor force participation
seen today. This “quiet revolution” began when women born in the 1940s realized that theirlives would be substantially different from those of previous generations (Goldin, 2006).
Goldin (2006) indicates that later-born cohorts of women prepared for their future by investing
more in their own human capital, for example, courses that helped them to close the gap with
their male counterparts in terms of math and reading scores. However, it is not clear what
specific economic factors led to the quiet revolution. Also, to our knowledge, no previous
studies have investigated the potential for women in developing countries to follow a similar
path and the factors that may trigger a similar revolution today.
Against this backdrop, we hypothesized that capital intensity would be an importanteconomic factor leading to such a revolution. Capital intensity is often used in neo-classicalgrowth models such as those of Solow and Ramsey to explain economic growth and differences
in income per capita across countries. Derived from the Cobb–Douglas production function,
capital intensity is the ratio between two inputs, namely, capital and labor. High capitalintensity is often associated with technological advancement and innovation. High capitalintensity also requires higher levels of human capital as well as the skills to handle moreadvanced technology.
Our hypothesis is derived from the literature on the impacts of production technologies
on gender inequality. Some technologies are associated with specific gender-related absoluteadvantages such as physical strength; for example, the plow (Alesina, Giuliano, and Nunn,2013) favors men. Meanwhile, studies in China reported comparative advantages in tea andcotton production for women (Qian, 2008; Xue, 2021). We argue that as capital intensity
increases, the demand for advanced skills with fewer gender-specific requirements would leadto a revolution that reduces gender inequality.

2

We seek empirical evidence for possible influences of capital intensity on genderequality and family formation (i.e., marriage and number of generations in the family 1) in adeveloping country, namely, Vietnam. We choose Vietnam because of some appealing facts.
Vietnam has shown a high rate of growth in gross domestic product over the last three decades,
averaging 6.5% per year, while the fertility rate has declined substantially, from 3.6 in 1990 to
1.95 in 2019. 2 The World Economic Forum’s Global Gender Gap Reports ranked Vietnam at
41st, 33rd, and 31st in the world in terms of economic participation and opportunity for womenin 2015, 2017, and 2020, respectively. 3 In addition, the 2001 US–Vietnam bilateral trade
agreement brought many workers from the informal sector into the formal sector (McCaig andPavcnik, 2018), particularly in (unskilled) labor-intensive industries with high wage growthduring the period 2000-2007 (Fukase, 2013) and lifted many households out of poverty(McCaig, 2011). Vu and Yamada (2018) show that the gender wage gap diminished between
2002 and 2014. However, the World Economic Forum ranked Vietnam at the bottom in termsof survival health for females (139th, 138th, and 151st out of 145, 144, and 153 countries in
2015, 2017, and 2020, respectively) because of its highly skewed sex ratio at birth. These mixed
facts on gender equality suggest a gap in the research on the casual impacts of economic
development on family formation and gender equality.
In this paper, we examine whether the changes in capital intensity of firms aggregatedat the Vietnamese commune level 4 are associated with changes in family formation schemes
and gender equality within the commune. We examine the outcomes during the period from1999 to 2019. We combine the Population and Housing Census of Vietnam in 1999, 2009, and2019 with censuses of firms (information from 2000, 2006, and 2016) to obtain panel data on
Vietnamese communes. We use the instrumental variable (IV) approach to identify the causal
effect of capital intensity on the outcomes. The IV is the cyclone damage index for each

The connection between the “revolution” and family formation would be strong. Giuliano (2010) notes thatstrong family ties are associated with larger family size and a reliance on unequal division of household work
between male and female family members (i.e., the “male-breadwinner hypothesis”).

1

2

See Appendix 15.

3

Source: https://www.weforum.org/publications/series/global-gender-gap-report/

Commune is the official third-level administrative division in Vietnam, after province and district. In 2009,
Vietnam had approximately 63 provinces, 689 districts, and 11,065 communes. The average commune has an areaof 29.97 km2 and a population of 13,131 people. Estimations using commune as a unit have a higher resolution
than using a 0.25° gridded degree area because 0.25° gridded degree near the equator is about 27.75 km.
43

commune. We take advantage of the exogenous traces of cyclones and their wind strength inorder to construct the damage index for each 5-year period prior to the censuses of firms.
Our estimation results indicate that high capital intensity is associated with a higher
share of people remaining single relative to those who marry. We find that the share of families
with multiple generations living together—as is traditional in Vietnam—is negatively
associated with high capital intensity. We also find that high capital intensity leads to mixed
results regarding gender equality. High capital intensity is associated with a higher probabilityof skewed sex ratio among children under 5 years of age, which leads to a decrease in genderequality. The opportunity costs of having additional children in order to obtain a boy under theson preference would be higher in communes where the capital intensity is high. Therefore,
parents are more likely engage in sex selection to obtain the desired gender, thereby increasing
the sex ratio. In contrast, high capital intensity is associated with lower probability of school
dropouts among girls aged 15-17 years compared with their male peers. Also, girls are more
likely than boys to complete junior high school. The results are consistent among people living
in communes for more than 5 years. High capital intensity also attracts incoming migrants ofprime working age (23-55 years) 5 with higher educational attainment, particularly women.
Our study contributes to the literature in several aspects. To our knowledge, this study
is among the few to provide empirical evidence regarding the impacts of economic
development, specifically, the effects of capital intensity on family formation and genderequality. Our study provides further explanation for the revolution in gender equality as
described by Goldin (2006) as well as empirical evidence regarding the cause of some stylized
facts related to structural changes and the family (e.g., decline in marriage and household size,
increase in educational attainment) as modeled in Greenwood, Guner, and Marto (2023). Our
study extends the literature on the impacts of production technologies on gender equality (e.g.,
Qian, 2008; Alesina, Giuliano, and Nunn, 2013) and family formation in contemporary
Vietnamese society. Our study also suggests the application of a new IV—the cyclone damage
index—for economic variables.

As of the end of 2020, under Vietnamese law, the retirement age for men is 60 years while that for women is 55
years.

5

4

The remainder of this paper is organized as follows. Section 2 provides the backgroundof our study. Section 3 describes the data as well as the construction of the cyclone damage
index. Section 4 explains the methodology and the identification strategy. Section 5 reports anddiscusses the results, and Section 6 concludes the paper.

2. Background
The literature on family economics and gender equality is closely related to our study. In thissection, we review the most important research from which our study is derived and developed.
Technological advancements and economic factors
Production technologies can create and change gender inequality. In particular, someproduction technologies may favor the labor force participation of one gender over the other.
Alesina, Giuliano, and Nunn (2013) find that early agricultural technology had a strong
influence on gender norms. For example, the invention of the plow resulted in a culture thatfavored men over women because men had an absolute advantage in terms of physical strength,
which was needed to use the plow. Qian (2008) finds the survival rate of Chinese women was
higher when they could earn higher incomes from female-enabling industries such as tea
production, assuming that the income of men was constant. Similarly, Xue (2021) shows therising value of cotton (1300-1840 CE) in China led to less sex selection, lower dowries, andfemale empowerment in a continuous manner. This was because women played an importantrole in cotton production and were major income earners in the family as a result of absoluteand comparative advantages such as adroitness and persistence instead of physical strength(Xue, 2021).
However, with applications increased adoption of automation and information
technologies, new emerging industries in contemporary society (i.e., services) and specificprofessionals (i.e., managers) might not require physical strength or gender-specific skills (interms of both absolute and comparative advantages) and therefore depart from (local) natural
conditions. Black and Juhn (2000) report that the number of women in professional andmanagerial positions in the US in the 1980s and 1990s was a response to the increased demand
for skilled workers and the accompanying higher wages. However, it remains unclear what
factors have the potential to ignite a revolution that causes girls and women to alter their life
course in contemporary (future) society.

5Advancements in technology gave women effective contraceptive methods that enabled
them to plan their future. The advent of oral contraceptives contributed to the change in career
paths for American women in the 1960s and 1970s, leading to increased wages for women in
their 30s and 40s (Goldin and Katz, 2002; Bailey, 2006; Bailey, Hershbein, and Miller, 2012).
However, “the pill” had no direct influence on cohorts younger than 18-20 years in the US.
Rather, it was a tool with which women could achieve their objectives, not a motivation for
revolution. Furthermore, whether the pill or other effective and inexpensive contraceptive
methods might lead to greater investment in education for girls under the age of 18 years needs
to be investigated with caution, perhaps as a new research agenda.
Economic incentives for family formation and division of household labor
From the perspective of economists, families form when the potential utility (fromproduction and consumption) gained from formation outweighs remaining single, an idea thatmay date back to Becker (1991). Potential economic incentives for marriage include thedivision of household labor to exploit the comparative advantages of family members while
sharing collective goods such as housing (Weiss, 1997). According to Grossbard-Shechtman(1984), the decisions of husbands and wives regarding labor supply should be jointly made. In
addition, the incentive can also be considered risk pooling to secure consumption, for example,
in the event that one or the other is unemployed (Weiss, 1997).
However, if marriage made people worse off, they would be reluctant to marry in thefirst place. For example, Bertrand, Kamenica, and Pan (2015) suggest that the marriage ratewould decrease if women’s incomes were higher than those of men. This is because when thewife outperforms the husband in the labor market, it is associated with low marital satisfaction
and a higher probability of divorce. Still, to date, little empirical evidence has been reported
regarding the linkage between capital intensity and family formation (gender equality).
Other factors
Beyond the scope of economic factors, culture (norms), institutions, and policies canalso play a role in fomenting or suppressing revolution. Regulations (e.g., informal institutions
co-existing within culture and norms) on family formation, lineage, and breakup, including
marriage transfers, number of spouses, location of marriage, kinship, inheritance, and how
assets are divided after divorce, can shape and help to maintain gender inequality (Anderson

6and Bidner, 2023). 6 Culture, which includes beliefs and social norms as well as racial, religious,and social groups, also plays an important role. Culture remains relatively constant over time, 7
and is transferred from generation to generation but can vary greatly depending on thegeographic boundaries (Bau and Fernandez, 2023).
However, such factors are often endogenous. For example, Bau and Fernandez (2023)
note the following about the modern variation in developed countries with first and second
demographic transitions: the first transition is related to decreasing fertility rate, mortality, andchanges in the age of marriage, while the second transition is related to the low fertility rate
(less than 2.1), delaying marriage, and unstable marital status or new family structure. 8 The
origins of these transitions are difficult to disentangle because the changes are related to eachother, as indicated by Guinnane (2011) in the case of fertility transition. This suggests theimportance of using causal inference methods to identify any possible effects and overcome
issues of endogeneity.

3. Data
We use three main data sources to construct a balanced panel of Vietnamese commune data forour analyses. The first dataset is the Population and Housing Census conducted in April of
1999, 2009, and 2019. For each commune, we calculate indicators proxied for family formationand gender inequality as the main outcomes. The main indicators per commune are ratios ofhouseholds having one, two, or three or more generations 9 living together among all
households; the ratio of single people to married people; the sex ratio among children aged
under 5 years of age; the school dropout ratio of the population aged 15-17 years; and the ratioof the population that completed junior high school, high school, or university. We further

6

See Anderson and Bidner (2023) for a detailed review on each issue.

Bau and Fernandez (2023) also report cases in which culture has been influenced by sudden changes intechnologies, institutions, and knowledge, causing incentives to behave in conventional ways to disappear.

7

8

See Bau and Fernandez (2023) for a detailed review.

We divide families into dummies based on the number of relationships to the household head: spouse, child,grandchild, parent, grandparent, and other. If a family has zero or “spouse” for the relationships to the household
head, we classify it as a one-generation family. If a family has two different types of relationship such as “spouse”
and “child,” we classify it as a two-generation family. The three-generation-or-more family dummy is constructedin a similar way.
9

7

examine the outcomes according to gender, time (i.e., whether people live in the commune for≥5 years), and the relative ratio of females to males for the abovementioned indicators whereappropriate.
The second dataset is the economic census conducted by the General Statistics Officeof Vietnam in 2001 10 , 2007, and 2017. The census collects information on active firms,
including their performance in the year before the year of the census. We use and aggregate
information on the capital (workers) of all firms located in the commune and take the logarithmof the results. 11 We finalize the calculation by using the logarithm of the ratio between capitaland the number of workers to form our main explanatory variable—the capital intensity of each
commune.
The third dataset is the International Best Track Archive from the National Centers forEnvironment Information of the National Oceanic and Atmospheric Administration in the
US. 12 This dataset contains data on cyclone traces between 1994 and 2015. We use the cyclone
routes and detailed information on the strength of the cyclone at its eye every 6 hours in orderto estimate a 5-year index of the potential damage to building structures in commune j (𝐻𝐻𝑗𝑗𝑗𝑗 )caused by strong winds from cyclones prior to the economic censuses. Previous studies haveused this measure as a proxy for the cyclone damage index.
Specifically, following Naguib et al. (2022), the index 𝐻𝐻𝑗𝑗𝑗𝑗 for the 5-year period 𝑇𝑇 andfrom the total of 𝑛𝑛 cyclones can be written as𝑚𝑚

𝑠𝑠
𝐻𝐻𝑗𝑗𝑗𝑗 = ∑𝑛𝑛𝑠𝑠=1 𝐻𝐻𝑗𝑗𝑗𝑗 = ∑𝑛𝑛𝑠𝑠=1 ∑𝑡𝑡=1
Ω𝑠𝑠𝑠𝑠𝑠𝑠 ,(1)

where Ω𝑠𝑠𝑠𝑠𝑠𝑠 is a measure of the extent to which the residents of commune 𝑗𝑗 are negatively
impacted by cyclone 𝑠𝑠 in an observed record at time 𝑡𝑡. 13 As suggested by Yang (2008), Pelli

and Tschopp (2017), and Naguib et al. (2022), the measure Ω𝑠𝑠𝑠𝑠𝑠𝑠 is the square of windspeed

10
The GSO conducted the first economic census (often referred to as the 2001 census of enterprises) in 2001 tocollect information on firms in 2000.

We use the adjusted value 𝑋𝑋 as in 𝑙𝑙𝑙𝑙𝑙𝑙 = 𝑙𝑙𝑙𝑙(𝑥𝑥 + (𝑥𝑥^2 + 1)^(1/2)) when we aggregate capital (labor) as a raw
value of 𝑥𝑥 at the commune level in order to deal with the problem that some communes have no firms.
11

12

The data are available from https://www.ncei.noaa.gov/products/international-best-track-archive.

13

In general, a cyclone trace is recorded every 6-hour period (for example, for 𝑚𝑚𝑠𝑠 times) until it subsides.

8

(estimated at the centroid of the commune) above 33 knots 14 (or ≥17 m/s) normalized by the
maximum value of this variable:
�𝑊𝑊

−33�

2

𝑠𝑠𝑠𝑠𝑠𝑠Ω𝑠𝑠𝑠𝑠𝑠𝑠 = (𝑊𝑊 𝑚𝑚𝑚𝑚𝑚𝑚
if 𝑊𝑊𝑠𝑠𝑠𝑠𝑠𝑠 ≥ 33.
−33)2
𝑠𝑠

(2)

The windspeed at the centroid of each commune, 𝑊𝑊𝑠𝑠𝑠𝑠𝑠𝑠 , is estimated by using the distance fromeach commune centroid to the cyclone eye, 𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠 (measured in nautical miles), and thewindspeed 𝑤𝑤𝑠𝑠𝑠𝑠𝑠𝑠 observed at the cyclone eye 𝑠𝑠 at the measured time 𝑡𝑡 as follows:
26.99780.5

𝑤𝑤𝑠𝑠𝑠𝑠𝑠𝑠 × � 𝐷𝐷 � , 𝑖𝑖𝑖𝑖 𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠 > 26.9978
𝑠𝑠𝑠𝑠𝑠𝑠.
𝑊𝑊𝑠𝑠𝑠𝑠𝑠𝑠 = �
𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠
𝑤𝑤𝑠𝑠𝑠𝑠𝑠𝑠 × 26.9978 , 𝑖𝑖𝑖𝑖 𝐷𝐷𝑠𝑠𝑠𝑠𝑠𝑠 ≤ 26.9978(3)Equation (3) 15 and the value 26.9978 follow Naguib et al. (2022), which is based on
Deppermann (1947), Simpson & Riehl (1981), and Hsu & Zhongde (1998).
Finally, we combine the three types of data by commune and for the three periods (1999, 2009,
and 2019). A detailed statistical description is presented in Appendix 1.

4. Methods, identification strategy, and econometric specification
We aim to estimate the impact of capital intensity (𝛽𝛽 ) on outcomes in panel data ofcommune 𝑖𝑖 at three different times (𝑡𝑡). The general reduced-form equation of interest is asfollows.

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 = 𝛽𝛽 × 𝑙𝑙𝑙𝑙(𝐾𝐾/𝐿𝐿)𝑖𝑖𝑖𝑖 + 𝑖𝑖 + 𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖(4)Equation (4) uses a commune fixed effect (𝑖𝑖) and a time fixed effect (𝑡𝑡) but this faces two
potential issues. The first is reverse causality; that is, the outcome causes the observed
differences in capital intensity. However, the outcomes are from data collected in 1999, 2009,

According to the literature, 33 knots (17 m/s) is often considered the minimum wind speed at which damage
can occur. One knot equals 1 nautical mile (1.852 km) per hour.
14Wind speed accelerates to maximum speed and falls sharply. Naguib et al. (2022) note that 26.9978 is the
Simpson and Riehl radius of maximum wind speed (measured in nautical miles). Ideally, the Simpson and Riehl
radius of maximum wind speed should be estimated from differences in barometric pressure between the centerand outer part of the cyclones. However, because the best track data have many missing values for barometricpressure, Naguib et al. (2022) use the average radius of maximum windspeed and apply it to every cyclone. We
assume that 26.9978 nautical miles (or 50 km) also applies to our selected data.
15

9and 2019, which were after the time when capital intensity measures had been collected inmany cases. Therefore, this problem is not so likely in our analyses. The second issue is theendogeneity of capital intensity. There might be an association of capital intensity with someomitted variables in 𝜀𝜀𝑖𝑖𝑖𝑖 , which jointly decides the outcomes (i.e., demographic structure andsocial norms). Therefore, we need a valid time-varying IV to claim the causal effect from thecapital intensity to the outcomes. Specifically, we need an IV that can explain the capitalintensity and satisfy both the exclusion and exogeneity conditions.
The index for damage by cyclones per commune, 𝐻𝐻𝑗𝑗𝑗𝑗 (or 𝐻𝐻𝑖𝑖𝑖𝑖 per equation 5), satisfies

all the above conditions for several reasons. First, cyclone tracks are exogenous to local people
and businesses. Specifically, the changes (strength) in cyclone tracks over time are exogenous.
In addition, firms with high capital intensity in Vietnam alone are least likely to be able toinfluence the cyclones’ tracks, although the firms may contribute to global climate change,
which could lead to irregular cyclones.
Second, the changes in the index for damage by cyclones per commune would not
directly affect family formation (marital status and number of generations living in the family)
and gender equality. If they did, we would not be able to observe, perhaps, stable norms overcenturies prior to our period of interest (1999-2019). As suggested by Vu and Yamada(forthcoming), our construction of outcomes for equality considers the relative gender ratio in
outcomes; therefore, a direct impact of cyclones on the outcomes is highly unlikely.
There are several additional supporting arguments. Although child marriage varies withweather shocks in Sub-Saharan Africa and India (Corno, Hildebrandt, and Voena, 2020)
because of marriage transfers, this is less likely to occur in Vietnam because of strict family
registration regulations and laws prohibiting girls under the age of 18 years from marrying.
Also, excessive marriage transfers (bride price) are not required. In the selected sample, thenumber of households with girls under the age of 18 years who are the household head with
their spouse or the household head’s spouse is less than 5 per 10,000 households (as shown inAppendix 1). We further examine the relationship between the cyclone damage index and theratio of households with child marriage (where the wife is younger than 18 years of age) among
all households in the commune. As shown in column 9 of Appendix 3, the negative relationshipis counterintuitive with the possible mechanism and findings of Corno, Hildebrandt, and Voena(2020). We repeat similar exercises with sex ratio as the outcome (column 8 of Appendix 3)

10and find another negative 16 and counterintuitive statistical result, which further strengthens ourarguments on the exclusion condition of the IV.
In addition, tropical cyclones have limited impacts on labor outcomes. Karbownnik andWray (2018) show that cyclones occurring during the important periods of fetal development
and infancy affect future income via the health channel (due to disruption of the food supply
and nutrition) but not labor supply. Other than these important periods, Karbownnik and Wray
(2018) indicate that tropical cyclones have no significant effects in the long term.
We also examine the possibility that cyclones might directly drive people away throughpermanent migration and population displacement. Berlemann and Tran (2021) suggest thattropical storms induce more people to temporarily leave their homes, based on household datafrom the Vietnamese Household Living Standard Survey in 2012, 2014, and 2016. We perform
a series of estimations, directly regressing different population cohorts—all, the elderly (age>64 years), children (age >10 years), prime working age adults (age 23-55 years), and longterm residents (living in the commune for more than 5 years)—on the cyclone damage index.
As shown in columns 1-7 of Appendix 3, the cyclone damage index has either a statisticallyinsignificant correlation or a positive correlation with all population sizes for each cohort. We
repeat similar estimations with population density (per square kilometer) as the outcomes as inAppendix 4. The cyclone damage index does not show any statistically significant impacts on
population density or the density of the prime working age population. Therefore, such(counterintuitive) evidence disproves the hypothesis of direct effects on (out-going) permanent
migration and population displacement.
However, the index for cyclone damage per commune might intuitively be associatedwith capital intensity regarding mid-term and long-term perspectives. 17 For example, firms

One may hypothesize that because of hard-hitting cyclones, parents may neglect their daughters or engage insex selection due to a shortage of income, thereby potentially skewing the sex ratio.

16

The accumulated damage index of cyclones over a period of 5 years is estimated to explain the capital intensity
in the 6th year. In addition, we consider only the changes over every 10-year period with the commune fixed
effects. Therefore, the coefficient of the damage index is less likely to capture the positive relationship between
damage by cyclones and infrastructure repairs due to the damage (i.e., the high investment in reconstruction
shortly after the cyclones leads to modified infrastructure designed to mitigate the effects of cyclones). This isbecause the headquarters of firms that repair infrastructure are not necessarily located in the commune and are theleast likely to remain in the commune permanently. Government aid provided to compensate for cyclone damage(if any) is not very likely to change existing firms’ capital intensity, nor is there a higher probability of it to capture
the temporary economic slowdown due to direct hit of the cyclone.
17

11

with higher capital intensity are less likely to risk locating their assets (e.g., plants, factories,
stores) in areas prone to cyclones. If they did, they would reconsider and reallocate elsewhere
after first experiencing the impacts of cyclone damage. In the extreme case, the firms would
exit. As shown in Appendix 13, changes in the number of firms located in the commune havea negative nexus with cyclone damage, which is evidence of reallocation (exit) or a low
probability of new firms locating in the affected communes.
In addition, Vietnam has a long coastline and is prone to tropical cyclones (as shown in
Figure 1). Of the 7,763 communes in our sample, 6,903 are subject to at least some potential
cyclone damage from strong wind in the periods 1994-1998, 2001-2005, and 2011-2015.
Therefore, in the first stage, we estimate
𝑙𝑙𝑙𝑙(𝐾𝐾/𝐿𝐿)𝑖𝑖𝑖𝑖 = 𝛼𝛼 × 𝐻𝐻𝑖𝑖𝑖𝑖 + 𝑖𝑖 + 𝑡𝑡 + 𝜖𝜖𝑖𝑖𝑖𝑖 .

(5)

Specifically, we use 𝐻𝐻𝑖𝑖𝑖𝑖 in the periods of 1995-1999, 2001-2005, and 2011-2015 to explain thecapital intensity of each commune 𝑖𝑖 measured in the 2000, 2006, and 2016 economic censuses,

respectively. We note that equation (5) uses commune fixed effects (𝑖𝑖). The climate and theconstant risk to cyclones are captured in the commune fixed effects. The time fixed effect (t)combined with the commune fixed effects (i) help to identify idiosyncratic local shocks (Dell,
Jones, and Olken, 2014). The equation considers only changes in 𝐻𝐻𝑖𝑖𝑖𝑖 that impact changes incapital intensity. We use the predicted value of capital intensity from equation (5) to estimate

𝛽𝛽 in equation (4), using district clustered standard errors. The simple correlation checks, which

are similar to the first stage (equation 5) in Appendix 2, shows a statistically significant
negative association between 𝐻𝐻𝑖𝑖𝑖𝑖 and 𝑙𝑙𝑙𝑙(𝐾𝐾/𝐿𝐿)𝑖𝑖𝑖𝑖 (−0.023).

5. Results
5.1 Family formation
We find that capital intensity affects family formation within the commune. Specifically,
capital intensity reduces the proportion of households with two, three, or more generations
living together, as shown in columns (2) and (3) of Table 1. Meanwhile, the proportion of
single-generation households increases with higher capital intensity (column 1 of Table 1).
This is because people tend to remain single (either temporarily or permanently) in communes
where capital intensity is high. As shown in column 4 of Table 1, higher capital intensity is
associated with a higher ratio of single people to married people. This is true for both the male12and female populations in our two separate estimations in columns 5 and 6 in Table 1. The
results suggest that family ties become weaker, a phenomenon that is likely to accelerate in the
future, thereby helping to increase gender equality. This is because Alesina and Giuliano (2010)
show that strong family ties are associated with larger family size, higher home production,
lower labor force participation of women and young people, and lower geographical mobility.
[Insert Table 1 here]
A potential mechanism may lie in the tendency for high capital intensity to be
associated with industries having formal employment and perhaps higher wages, particularly
among young people. Vietnamese have strong family ties (Alesina and Giuliano, 2014). The
traditional family formation in which multiple generations live together may function as a kindof insurance and buffer against risk. However, workers with formal employment and perhaps
higher wages may no longer need to adhere to such traditions. When wages are higher, people
might want to delay marriage. This is because people need to accumulate more human capitalsuch as education and training to get a job at a firm with higher capital intensity. In addition,
the findings of Bertrand, Kamenica, and Pan (2015) suggest that the marriage rate would
decline if women earned more than men, assuming that both are randomly selected.
5.2 Gender equality
We find mixed results regarding gender equality. On the one hand, higher capitalintensity is associated with a higher ratio of boys to girls under the age of 5 years (Table 2),that is more “missing women.” Many factors may explain the phenomenon. Son preference ispersistent in Vietnam (Vu, 2014). The non-economic value of boys also outweighs that of girls(Schultz, 1997). As Schultz (1997) suggests, only male children can carry on the family lineage
and perform certain rituals after the parents pass away. Because there are no goods that can
substitute the value of boys, parents would desperately pursue having a son. Meanwhile, capitalintensity probably steers people away from marriage (as shown in columns 4, 5, and 6 of Table1). Therefore, it is more likely that both men and women would delay having children. Also,
high capital intensity may be associated with high income and increased opportunity costs of
having more children in order to obtain a son. We note that the fertility rate of Vietnamese
women has been well under 2.1 since 1999 (see Appendix 15). Thus, such parents would face13

a higher time constraint compared with others. 18 They would engage in sex selection tominimize the opportunity costs of obtaining a son, skewing the sex ratio in the short run.
However, in the long run, a skewed sex ratio might offset gender inequality because of thehigher marriage market price for women in cohorts with a skewed sex ratio (Grossbard, 2015),
particularly the bride price, 19 and may even shift the culture in favor of empowering women(Teso, 2019).
[Insert Table 2 here]
On the other hand, we find that high capital intensity is associated with a higher
probability of having higher educational attainment, particularly for women. When capitalintensity is high, more girls complete junior high school (as shown in column 3 of Table 3) andsubsequently complete high school at higher rates compared with boys (as shown in column 6
of Table 3). This is because girls are motivated to learn at the ages of 15-17 years in association
with higher capital intensity. Given that the school dropout rates for both boys and girls arenegatively correlated with higher capital intensity (as shown in columns 1, 2, and 3 of Appendix
6), girls are less likely than boys to drop out of the school. It is likely that when capital intensity
is high, girls prepare for jobs that require at least a junior high school education. We note that
it is the girl who likely decides her own future. This is because of the parents’ son preference.
Because son preference is not merely sex selection, the parents who prefer sons may not invest
adequately in unwanted daughters, as suggested by Jayachandran and Pande (2017) and
Rastogi and Sharma (2022).
[Insert Table 3 here]
One explanation for women’s efforts toward higher educational attainment might be that they
have higher marginal returns to education compared with men. Vu and Yamada (2018) suggestthat the wage gap between men and women narrowed during the period 2002-2014. In addition,
women living in places where capital intensity is high might have more job opportunities
compared with women living elsewhere (as shown in column 1 of Appendix 14).

18
For example, such parents would have invested more time in their own education (we will present evidence forthis below) and married later in life. In addition, these parents may want to increase the quality of their children(e.g., by investing more in their children’s education) as their income rises.
19
Wei and Zhang (2011) show that household saving rates are higher when the sex ratio is high. This is becauseof competition in the marriage market, reflecting the shortage of brides.

14

The results are consistent among people who live in the commune for 5 or more years,
as shown in Table 4. 20 The results contribute to the literature on the revolution of female
participation in the workforce, particularly in developing countries. In the early 2000s, laborintensive industries attracted many Vietnamese workers away from informal sectors (Fukase,2013). However, looking at a longer window of time, from 1999 to 2019, our estimation results
suggest that women ultimately find high capital intensity industries attractive. And more
importantly, they pursue higher education to be prepared for the labor market’s requirements.
[Insert Table 4 here]
We further consider internal migration given that high capital intensity might also be
attractive for such migration. 21 As shown in columns 1 and 2 of Table 5, higher capital intensity
attracts more incoming migrants, especially women. A higher ratio of women to men iscommon among incoming migrants aged 23-55 years (column 3 of Table 5). Such evidence
further supports our argument of an association between more job opportunities and higher
capital intensity. The evidence suggests that women have already migrated in response to highcapital intensity, although the rate of internal migration remains limited in general.
[Insert Table 5 here]
We also examine what educational attainment pattern is typical among incoming
migrants in association with high capital intensity (Table 6). High capital intensity tends toattract incoming female migrants with at least a junior high school education. The results
suggest a positive correlation between high capital intensity and the number of women withcollege degrees among incoming migrants (column 7 of Table 6). Women with a high school
education are more likely than men to migrate to a commune with high capital intensity.
[Insert Table 6 here]
5.3 Which industries contribute to the revolution?

20
The 2009 and 2019 data contain information on the duration of residence in its 15% and 10%populationquestionnaires, respectively, while the 1999 data contain information in its 100% population questionnaires. One
question asks whether the person has been living in the commune for more than 5 years.
21
We note that the proportion of incoming migrants aged 23-55 years among the commune population is rather
small, 1.3% for women and 1% for men, as shown in Appendix 1.

15

We further examine what specific industries might contribute to the revolution, using a
back-of-the-envelope calculation. We select some “female-enabling” industries such as foodand beverages, textiles and garments, shoes and leather, trading, restaurants and hotels,
information and communication technology, and health and aggregated the employment
numbers and capital intensity over time and then compare them against those of the agriculture,
forestry, and aquaculture industries (Figure 2). Industries with high capital intensity combinedwith high employment are very likely to be the main driving force. As shown in Figure 2, thedriving industries might be trading, food and beverages (manufacturing), and restaurants andhotels. This calculation is in line with the relative level of female educational attainment
estimated in Table 3. However, despite having high employment, shoes and leather(manufacturing), and even textiles and garments do not show an outstanding increase in capitalintensity; therefore, these industries are not likely to provide the main leverage.
[Insert Figure 2 here]
5.4 Discussion
We note that many factors can have causal effects on the quiet revolution of Vietnamese
women, and capital intensity is just one such factor. Other factors, including changes in public
policy aimed at promoting gender equality and women’s empowerment such as maternal healthcare, family planning (safe sex), land reform—women’s right to jointly possess land-use rights
with their husbands (Menon, Rodgers, and Nguyen, 2014; Nguyen and Le, 2023)—occurringin the same period might also contribute to the revolution. The legacy of communist ideology,
which promotes women’s inclusion in economic activities (Campa and Serafinelli, 2019),
might also serve as a basis for the revolution. Nevertheless, we identify the causal impacts ofcapital intensity, based on temporal and regional variations in capital intensity and the IV
approach (based on exogenous traces of cyclones and regional fixed effects).
Policy changes that aim to benefit both genders would not be a direct cause. For
example, building more schools would facilitate the revolution by extending the supply of
educational services. According to the Statistics Yearbooks of the General Statistics Office ofVietnam, the number of schools increased from 21,769 primary and junior high schools and1,645 high schools in 1998 to 23,521 primary and junior high schools and 2,386 high schools
in 2019. However, the increase in educational supply cannot be a direct cause for the revolution
because it benefits both genders and it does not explain why boys pursue higher education at a
slower rate compared with girls. Similarly, compulsory primary education might also facilitate16

a higher attendance rate among girls in the 15-17-year of age group. However, the policy wasapplied nationwide at the same time, whereas our estimation relies on regional variations.
Therefore, our estimated impacts of capital intensity are distinct from the possible influencesof compulsory primary education.
Is there a potential ceiling for the revolution? As shown in column 9 of Table 3, capitalintensity is associated with a lower share of women having college degrees compared with men
living in the same commune. Similarly, as shown in column 3 of Appendix 14, capital intensity
is negatively associated with the employment of women in firms compared with that of men.
The reasons may lie on the labor supply side rather than the demand side. This is because we
do not find any evidence for women with college degrees being disadvantaged compared with
their male counterparts when migrating to other communes where capital intensity is high(columns 7 and 9 of Table 6). Although women prepare themselves by pursuing higher
education, their preparation is perhaps slower than the speed of change in capital intensity. Thisis because the revolution needs to start from low educational attainment and proceed to higher
educational attainment. Moreover, male-dominated industries do not die out when capitalintensity rises. If such industries expand because of higher capital intensity and require moreworkers with tertiary education, the results described above might be the consequences.
However, future research should be conducted when new data covering a longer period of time
become available.
Although the gender wage gap due to differences in productive characteristics can beeliminated (Goldin, 2014), our findings should not be interpreted to suggest that genderequality worldwide will converge as technology progresses. As described above, many factors
can decrease or increase gender equality within a given country. For example, the revolution
might close the gap between men and women in terms of educational outcomes, as is now
occurring in Japan. Japan is ranked high (47th) by the World Economic Forum in terms of thegender gap in educational attainment (a minimal gap 22). However, it is ranked very low (123rd)in terms of economic participation and opportunity for women. 23

The World Bank reports that 62% of Japanese women and 63% of Japanese men were enrolled in tertiary
educationin2020(https://data.worldbank.org/indicator/SE.TER.ENRR.FE?locations=JP,
https://data.worldbank.org/indicator/SE.TER.ENRR.MA?locations=JP).
22

23

Source: https://www3.weforum.org/docs/WEF_GGGR_2023.pdf

17

6. Conclusions
We examined the impacts of capital intensity aggregated from censuses of formal firms
located within communes on family formation and gender equality, using an IV approach. We
found that the share of single people in the commune population increased with higher capitalintensity, while the share of households with multiple generations living together decreased.
Educational attainment among women increased relative to that among men in communes
where capital intensity was high. However, we also found that the sex ratio among children
under 5 years of age increases with high capital intensity. Perhaps the higher opportunity costsof having a son when income potentially rises would induce people to engage in sex selectionin order to get obtain a son. Our results suggest that capital intensity is an important economic
factor that contributed to the revolution of female participation in the labor force over the last
30 years in Vietnam.
However, our study also has some limitations. First, the earliest economic census was
conducted after 2000; therefore, the direction between capital intensity and the main outcomesis the opposite of that for the first wave of the panel data examined. Second, our study lacks
external validity because the IV relies on traces of cyclones. Third, where cyclones left severe
damage, the government might have provided subsidies to compensate for the cyclones’ impact.
We lack data at the commune level to control for such remedies. Fourth, similarly, firms maylearn from their experience with cyclones and relocate or build new assets in areas lessvulnerable to cyclones. Therefore, the impacts of cyclones on capital intensity should not bestrictly (and implicitly) assumed to be linear over time, as in our estimations. However, theselimitations suggest that future studies should be conducted when detailed microdata on theenvironment become available thanks to more newly installed sensors.

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Figure 1. Cyclone eyes’ trajectories in in-land territory of Vietnam

1994-1998

2001-20052011-2015

Note: The arrows indicate cyclones’ eyes recorded in the International Best Track Archive.

24

Figure 2. Employment and capital intensity by female-enabled industry

Source: Authors’ calculation from 7763 communes in the selected sample.

25

Table 1. Impacts of capital intensity on family formation
Variablesln(K/L)Year fixed effects
Commune fixed effects
F-statistics in the first stage
Observations
Number of communes

Ratio HHs having
1 generation among
all HHs(1)

Ratio HHs having
2 generations among
all HHs(2)

Ratio HHs having
3 generations ormore among all HHs(3)

AllSingle/Married

Among males
Single/Married

Among females
Single/Married

(4)

(5)

(6)

0.0115***(0.0018)
Yes
Yes
105
23,2897,763−0.0085***(0.0013)
Yes
Yes
105
23,2897,763−0.0023*(0.0012)
Yes
Yes
105
23,2897,763

0.0085***(0.0015)
Yes
Yes
105
23,2897,763

0.0090***(0.0016)
Yes
Yes
105
23,2897,763

0.0079***(0.0015)
Yes
Yes
49.79
23,2897,763

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

26

Table 2. Capital intensity and sex ratio among population aged 0-4
VARIABLES

Sex ratio(1)ln(K/L)0.0065***(0.0014)
Yes
Yes
105
23,289
7,763

Year fixed effects
Commune fixed effects
F-statistics in the first stage
Observations
Number of communes

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

27

Table 3. Capital intensity and educational attainment among population aged 23-55
VARIABLES

Ratio of population completed junior highschool (9-year) as the highest degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)0.0115***(0.0012)
Yes
Yes
105
23,2897,763

0.0019***(0.0004)
Yes
Yes
105
23,2897,763

0.0008***(0.0002)
Yes
Yes
105
23,289
7,763

Year fixed effects
Commune fixed effects
F-statistics in the first stage
Observations
Number of communes

0.0089***(0.0009)
Yes
Yes
105
23,2897,763

0.0192***(0.0049)
Yes
Yes
104
23,2777,763

0.0001(0.0004)
Yes
Yes
105
23,2897,763

0.0194***(0.0047)
Yes
Yes
104
23,2697,763

0.0010***(0.0002)
Yes
Yes
105
23,2897,763−0.0929***(0.0122)
Yes
Yes
90
22,927
7,763

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

28

Table 4. Capital intensity and educational attainment among population aged 23-55 who stayed in the commune for more than 5 years
VARIABLES

Ratio of population completed junior highschool (9-year) as highest the degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)0.0115***(0.0012)
Yes
Yes
105
23,2897,763

0.0019***(0.0004)
Yes
Yes
105
23,2897,763

0.0007***(0.0002)
Yes
Yes
105
23,289
7,763

Year fixed effects
Commune fixed effects
F-statistics in the first stage
Observations
Number of communes

0.0082***(0.0009)
Yes
Yes
105
23,2897,763

0.0289***(0.0067)
Yes
Yes
101
23,1857,756−0.0005(0.0004)
Yes
Yes
105
23,2897,763

0.0336***(0.0072)
Yes
Yes
100
23,1167,754

0.0008***(0.0002)
Yes
Yes
105
23,2897,763−0.0929***(0.0122)
Yes
Yes
90
22,927
7,763

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

29

Table 5. Impacts of capital intensity on incoming migrants aged 23-55
VARIABLES

Females (F)Males (M)

F/M

(1)

(2)

(3)

0.0008***0.0005**0.2303***(0.0002)(0.0002)(0.0309)

Year fixed effects

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

F-statistics in the first stage

105

105

92

Observations

23,28923,28920,120

Number of communes

7,763

7,763

7,325

ln(K/L)

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

30

Table 6. Capital intensity and educational attainment among population aged 23-55 who moved in the commune for less than 5 years
VARIABLES

Ratio of population completed junior highschool (9-year) as highest the degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)

0.0003***(0.0001)
Yes
Yes
105
23,2897,763

0.0001**(0.0000)
Yes
Yes
105
23,2897,763

0.0002**(0.0001)
Yes
Yes
105
23,289
7,763

Year fixed effects
Commune fixed effects
F-statistics in the first stage
Observations
Number of communes

0.0001*(0.0001)
Yes
Yes
105
23,2897,763

0.1443***(0.0373)
Yes
Yes
62
13,773
5,695−0.0000(0.0001)
Yes
Yes
105
23,2897,763

0.0830***(0.0239)
Yes
Yes
66
12,951
5,314−0.0000(0.0000)
Yes
Yes
105
23,2897,763

0.0036(0.0211)Yes
Yes
45
10,3664,255

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

31

Appendix 1. Descriptive statistics

Variables

N

Mean

SD

Min

Max

ln(K/L)

23,28912.314

9.152

0.000

25.242

Cyclone damage

23,28933.31955.5440.000

290.403

Ratio of 1-generation HHs among all HHs

23,289

0.143

0.091

0.000

0.868

Ratio of 2-generation HHs among all HHs

23,289

0.604

0.082

0.090

0.866

Ratio of 3-generation or more HHs among all
HHs

23,289

0.245

0.092

0.015

0.772

Married people

23,289

0.389

0.147

0.038

2.214

Married people (Males)

23,289

0.442

0.151

0.047

3.320

Married people (Females)

23,289

0.336

0.154

0.019

2.426

Females (F)23,285

0.329

0.226

0.000

1.000

Males (M)23,289

0.329

0.185

0.000

1.000

Relative ratio (F/M)

23,248

0.994

0.505

0.000

16.667

Sex ratio (M/F) among age 0-4

23,289

1.083

0.115

0.471

1.973

Ratio of HHs with child marriage among all HHs

23,2890.00049

0.001

0.000

0.029

Females (F)23,289

0.053

0.051

0.000

0.261

Males (M)23,289

0.056

0.043

0.000

0.264

F/M

23,277

0.830

0.343

0.000

25.000Females (F)23,289

0.043

0.036

0.000

0.199

Males (M)23,289

0.049

0.032

0.000

0.189

F/M

23,269

0.783

0.334

0.000

9.000

Females (F)23,289

0.017

0.023

0.000

0.196

Males (M)23,289

0.017

0.021

0.000

0.182

F/M

22,927

0.922

0.502

0.000

12.000

Ratio of (male or female) singles among

School dropout rate among 15-17
years of age

A. Aged 23-55 (All)Completed junior high school over commune
population

Completed high school over commune population

Having college degree over commune population

32

Appendix 1. Descriptive statistics (Cont.)

Variables

N

Mean

SD

Min

Max

Females (F)23,289

0.050

0.051

0.000

0.244

Males (M)23,289

0.051

0.043

0.000

0.220

F/M

23,192

0.887

0.458

0.000

19.000Females (F)23,289

0.038

0.034

0.000

0.211

Males (M)23,289

0.041

0.029

0.000

0.214

F/M

23,125

0.837

0.451

0.000

8.000

Females (F)23,289

0.015

0.020

0.000

0.181

Males (M)23,289

0.014

0.017

0.000

0.157

F/M

22,019

1.079

0.853

0.000

16.000Females (F)23,289

0.013

0.015

0.000

0.254

Males (M)23,289

0.010

0.015

0.000

0.239

F/M

20,555

1.943

2.235

0.000

37.000Females (F)23,289

0.003

0.005

0.000

0.140

Males (M)23,289

0.002

0.004

0.000

0.117

F/M

15,556

1.583

1.942

0.000

40.000Females (F)23,289

0.003

0.004

0.000

0.086

Males (M)23,289

0.002

0.004

0.000

0.082

F/M

15,267

1.220

1.673

0.000

97.000Females (F)23,289

0.003

0.006

0.000

0.120

Males (M)23,289

0.002

0.005

0.000

0.122

F/M

12,975

1.105

1.315

0.000

22.000B. Aged 23-55 and living more than 5 years
Completed junior high school over commune
population

Completed high school over commune population

Having college degree over commune population

C. Aged 23-55 and living less than 5 years
Over commune population

Completed junior high school over commune
population

Completed high school over commune population

Having college degree over commune population

33

Appendix 2. Correlation between cyclone damage index and the capital intensity(1)
VARIABLES

ln(K/L)

Cyclone damage−0.0232***(0.0023)

Year fixed effectYes

Commune fixed effectYes

R-squared

0.393

Number of communes

7,763

Observations

23,28934

Appendix 3. Correlation between cyclone damage index and some outcomes

Population by age
VARIABLES

Population

< 10

> 64

23-55

23-55 (Male)

23-55

Being 5years ormore

(Female)in thecommune(< 5 years ofage)

Ratio ofhouseholdwith childmarriage

Sex ratio

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.4628***0.2599−0.0560

2.0522***1.0011***

1.0511***0.0001***−0.0002***−0.0000***(0.7981)(0.1879)(0.0583)(0.4674)(0.2358)(0.2336)(0.0000)(0.0000)(0.0000)

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

R-squared

0.036

0.138

0.452

0.131

0.152

0.108

0.328

0.055

0.074

Observations

23,28923,28923,28923,28923,28923,28923,28923,28923,289Number of commune

7,763

7,763

7,763

7,763

7,763

7,763

7,763

7,763

7,763

Cyclone damage

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

35Appendix 4. Correlation between cyclone damage index and the population density per squared kilometer

Age
VARIABLES

All

< 10

> 64

23-55

23-55 (Male)

23-55 (Female)

(1)

(2)

(3)

(4)

(5)

(6)

0.13840.1940***−0.1212***−0.0169−0.02360.0067(0.2437)(0.0743)(0.0453)(0.1295)(0.0682)(0.0627)

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

R-squared

0.000

0.014

0.054

0.005

0.005

0.005

Observations

23,28923,28923,28923,28923,28923,289Number of commune

7,763

7,763

7,763

7,763

7,763

7,763

Cyclone damage

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

36

Appendix 5. Capital intensity and the population density (per squared kilometer)

Age
VARIABLES

All

< 10

> 64

23-55

23-55 (Male)

23-55 (Female)

(1)

(2)

(3)

(4)

(5)

(6)

−5.9550−8.3476**

5.2135**0.7282

1.0150−0.2867(10.5438)(3.4426)(2.1912)(5.5581)(2.9210)(2.6984)

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

F-statistics first stage

105

105

105

105

105

105

Number of commune

7,763

7,763

7,763

7,763

7,763

7,763

Observations

23,28923,28923,28923,28923,28923,289ln(K/L)

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

37

Appendix 6. Capital intensity and school dropouts among population aged 15-17

VARIABLES

ln(K/L)Year fixed effects
Commune fixed effects
F-statistics first stage
R-squared
Observations
Number of communes

School dropouts
Females (F)(1)

Males (M)
(2)

F/M
(3)

−0.0023***(0.0003)
Yes
Yes
105
0.620
23,285
7,763−0.0009***(0.0002)
Yes
Yes
105
0.326
23,2897,763−0.0047***(0.0007)
Yes
Yes
105
0.391
23,248
7,763

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

38

Appendix 7. Correlation between capital intensity and family formation

Variablesln(K/L)Year fixed effects
Commune fixed effects
R-squared
Observations
Number of communes

Ratio HHs having
1 generation among
all HHs(1)

Ratio HHs having
2 generations among
all HHs(2)

Ratio HHs having
3 generations ormore among all HHs(3)

AllSingle/Married

Among males
Single/Married

Among females
Single/Married

(4)

(5)

(6)

0.0002**(0.0001)
Yes
Yes
0.719
23,2897,763−0.0002**(0.0001)
Yes
Yes
0.606
23,2897,763

0.0001(0.0001)
Yes
Yes
0.181
23,2897,763

0.0006***(0.0001)
Yes
Yes
0.746
23,2897,763

0.0008***(0.0002)
Yes
Yes
0.654
23,2897,763

0.0004***(0.0001)
Yes
Yes
0.756
23,2897,763

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

39

Appendix 8. Correlation between capital intensity and sex ratio among population aged 0-4
VARIABLES

Sex ratio(1)ln(K/L)

0.0001(0.0002)
Yes
Yes
0.052
23,289
7,763

Year fixed effects
Commune fixed effects
R-squared
Observations
Number of communes

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

40Appendix 9. Correlation between capital intensity and educational attainment among population aged 23-55
VARIABLES

Ratio of population completed junior highschool (9-year) as highest the degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)

0.0005***(0.0001)
Yes
Yes
0.727
7,763
23,2890.0006***(0.0001)
Yes
Yes
0.606
7,763
23,289−0.0001***(0.0000)
Yes
Yes
0.626
7,763
23,289Year fixed effects
Commune fixed effects
R-squared
Number of communes
Observations

0.0005***(0.0001)
Yes
Yes
0.740
7,763
23,2890.0006(0.0005)
Yes
Yes
0.262
7,763
23,2770.0004***(0.0001)
Yes
Yes
0.614
7,763
23,2890.0029***(0.0004)
Yes
Yes
0.265
7,763
23,269−0.0001***(0.0000)
Yes
Yes
0.655
7,763
23,289−0.0043***(0.0008)
Yes
Yes
0.091
7,763
22,927

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

41

Appendix 10. Correlation between capital intensity and educational attainment among population aged 23-55 who stayed in the commune for morethan 5 years
VARIABLES

Ratio of population completed junior highschool (9-year) as highest the degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)

0.0005***(0.0001)
Yes
Yes
0.689
7,763
23,2890.0006***(0.0001)
Yes
Yes
0.557
7,763
23,289−0.0001***(0.0000)
Yes
Yes
0.545
7,763
23,289Year fixed effects
Commune fixed effects
R-squared
Number of communes
Observations

0.0005***(0.0001)
Yes
Yes
0.699
7,763
23,289−0.0002(0.0006)
Yes
Yes
0.206
7,763
23,1920.0004***(0.0001)
Yes
Yes
0.523
7,763
23,2890.0012**(0.0005)
Yes
Yes
0.238
7,763
23,125−0.0000**(0.0000)
Yes
Yes
0.568
7,763
23,289−0.0043***(0.0008)
Yes
Yes
0.091
7,763
22,927

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

42Appendix 11. Correlation between capital intensity and incoming migrants aged 23-55

VARIABLES

Females (F)Males (M)

F/M

(1)

(2)

(3)

0.0000**0.00000.0184***(0.0000)(0.0000)(0.0039)

Year fixed effects

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

R-squared

0.061

0.011

0.177

Number of communes

7,763

7,763

7,760

Observations

23,28923,28920,555ln(K/L)

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

43Appendix 12. Correlation between capital intensity and educational attainment among population aged 23-55 who moved in the commune for lessthan 5 years
VARIABLES

Ratio of population completed junior highschool (9-year) as highest the degree
Females (F) Males (M)
F/M
(1)
(2)
(3)

Ratio of population completed high school(12-year) as highest the degree
Females (F) Males (M)
F/M
(4)
(5)
(6)

Ratio of population having college degrees(4-5 years)
Females (F) Males (M)
F/M
(7)
(8)
(9)

ln(K/L)

0.0000***(0.0000)
Yes
Yes
0.186
7,763
23,2890.0000***(0.0000)
Yes
Yes
0.026
7,763
23,289−0.0000***(0.0000)
Yes
Yes
0.180
7,763
23,289Year fixed effects
Commune fixed effects
R-squared
Number of communes
Observations

0.0000**(0.0000)
Yes
Yes
0.029
7,763
23,2890.0070*(0.0039)
Yes
Yes
0.188
7,478
15,5560.0000***(0.0000)
Yes
Yes
0.040
7,763
23,289−0.0027(0.0034)
Yes
Yes
0.073
7,630
15,267−0.0000***(0.0000)
Yes
Yes
0.025
7,763
23,2890.0028(0.0032)
Yes
Yes
0.181
6,864
12,975

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

44Appendix 13. Correlation between cyclone damage index and number of firms (by ownershiptypes) located in the commune

VARIABLES

All

POE

FOE

SOE

(1)

(2)

(3)

(4)

−0.0031***−0.0030***0.0000−0.0009***(0.0003)(0.0004)(0.0001)(0.0002)

Year fixed effects

Yes

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

Yes

R-squared

0.397

0.397

0.397

0.397

Observations

23,28923,28923,28923,289Number of commune

7,763

7,763

7,763

7,763

Cyclone damage

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1). SOE: state-owned
enterprises; POE: private affiliated enterprises; FOE: foreign affiliated enterprises.

45

Appendix 14. Capital intensity and number of employees from formal sectors in the commune

VARIABLES

Ln(F)

Ln(M)

Ln(F/M)

(1)

(2)

(3)

0.2109***0.2553***−0.0444***(0.0214)(0.0182)(0.0095)

Year fixed effects

Yes

Yes

Yes

Commune fixed effects

Yes

Yes

Yes

F-statistics in the first stage

105

105

105

Observations

23,28923,28923,289Number of commune

7,763

7,763

7,763

Ln(K/L)

Note: District-clustered robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

46

Appendix 15. GDP growth rate and fertility rate in Vietnam

Source: The World Bank (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=VN andhttps://api.worldbank.org/v2/en/indicator/SP.DYN.TFRT.IN?downloadformat=excel).

47