€¦ · web viewcultural attitudes may impact economic decisions, especially those concerning...

Mahmoud Salari

PhD CandidateGraduate Part-time Instructor

Department of Economics, Texas Tech University, PO Box 41014, Lubbock, TX 79409-1014, United States

Tel. +1 806 474 9549

E-mail address: [email protected]

Culture and Economic Decision Making

Cultural attitudes may impact economic decisions, especially those concerning female labor force

participation. To distinguish the economic/institutional effects from the cultural effects on female

labor force participation, an epidemiological approach is used studying second-generation immigrant

women. Cultural proxies are found to be statistically significant for women who kept their heritage

languages when controlling for characteristics of the women, their husbands and their families.

Additionally, the results show that the cultural proxies are positively significant explanatory

variables for those women who kept their heritage languages when controlling origin characteristics.

Economic outcomes have experienced large variation across nations since World War II. Female

Labor Force Participation (FLFP) as one of the main economic outcomes varies remarkably across

time and countries [1,2]. Many theories have been proposed as an explanation for this deviation. The

majority of these theories highlight the role of market prices, technological factors, policies and

institutions. Also, few current studies suggest that cultural factors can explain this deviation [1,3–6].

Culture is defined as those traditional beliefs, values and ethics of social groups that transmit from

one generation to the next generation without substantial changes [5,7,8]. Most economists believe

that culture plays an important role in economic decisions; however, designing testable hypotheses

for measuring the cultural effect is quite challenging. The key argument of any cultural analysis asks

how to distinguish between the cultural effects and the impact of other economic factors. One of the

best ways to measure the cultural effect is assessing second-generation immigrants’ behaviors in a

host country. Each immigrant has a specific culture that he/she brings to the host country and may

transmit to his/her own second generation [9,10]. Immigrant families may present their origin culture

to own children through speaking their Heritage Language (HL) [11,12]. HL is a primary instrument

that transmits parents’ origin cultural beliefs to the second-generation. When parents speak HL to

their children, they can easily convey their own values, beliefs and experiences [11,13].

There are two main reasons why immigrant parents desire to transmit their heritage culture through

speaking HL to their own children. First, immigrants who have a strong ethnic orientation like to be

involved in activities that present their culture to their family; therefore, their children need to speak

HL if they want to participate in these cultural events. Second, speaking HL is an important factor

for second-generation children to make sense of themselves as a part of the heritage group, and HL

links them to their parents’ heritage country [11,14]. The purpose of this study is twofold. 1) To

show that cultural proxy is a kind of dynamic interaction that moves slowly over time and is not the

same for all second-generation immigrant women. 2) To show that speaking HL in the host country

allows parents to fully transmit their heritage cultures’ beliefs about FLFP to their own children.

This study examines the labor supply of second-generation American women who grow up in the

U.S. and have the same economic, institutional environment, and English language competency as

other native women in the U.S. labor market. The main difference for second-generation immigrant

women compared to other native women is being exposed to their ancestors’ different cultures. This

study results indicate that there is not a same cultural impact on the married women decisions to

work. Additionally, there is strong evidence of the cultural impact for the second-generation

immigrant women who kept their HLs. The main finding shows female second-generation

immigrants who kept their HLs and whose parents immigrated from higher/lower FLFP countries

work more/less hours in the U.S., respectively.

This section is followed by literature review, theoretical frameworks, and motivations, which is then

succeeded by dataset descriptions and sample selection. Epidemiological approach and results are

presented in subsequent sections. Finally, the last section presents the conclusion.

1. Literature Review, Theoretical Framework and Motivation

FLFP varies notably across countries due to existing different beliefs about women working outside

the home. In some cultures, women who work are appreciated and actively participate in the job

market outside the home. However, in other cultures, women who stay at home and pay attention to

family duties are preferred to those who have jobs outside the home. These different views about a

woman’s role in society is observed significantly across countries [15]. Figure 1 presents the FLFP

for OECD countries in 2013 [16]. This variety of FLFP among countries implies that some countries

strongly depend on the women working, while others do not rely on women’s contributions to their

economics.

**Figure 1**

Female labor supply is a broad topic, and economists explain it using various models from the

sophisticated structural econometric models to more qualitative ones [17–19]. Most of these models

focused on investigating how structural changes in the economy resulted in increased FLFP.

Few studies in the field of Economics have shown that culture impacts women’s decisions to work

outside the home. Reimers (1985) shows ethnic groups have different views about men and women’s

roles in the family and working outside the home. These differences are based on various cultures

and therefore may result in dissimilar utility functions for the various groups [20]. Bisin and Verdier

(1998) find that children born without specific beliefs and attitudes and they achieve preferences

through their observations and experiences they received from their parents and their own

environments [21]. Similarly, Antecol (2000) suggests that the cultural influence is not an

observable variable that impacts FLFP. He finds that the country of ancestry plays a significant role

in the existing inter-ethnic gap in Labor Force Participation (LFP) [22].

Fernández et al. (2004) find a man’s mother working significantly increased the probability of his

wife working in the U.S. [23]. Moreover, Morrill and Morrill (2013) provide evidence that show a

married woman’s LFP is related to both her mother and her mother-in-law’s working experiences

[24]. Additionally, Fernández and Fogli (2009) and Blau et al. (2013) show that the married FLFP is

a kind of economic behavior that transmits from parents to their children; they indicate that second-

generation FLFP depends on FLFP in their parents’ heritage countries [25].

This study examines cultural impacts on women’s decisions to work in the U.S. The results suggest

that the cultural attitudes about women working exist and pass from one generation to the next while

they kept their HLs. Therefore, the role of HL in intergenerational transmission of culture regarding

the women’s decisions to work and the number of hours women worked are examined in several

models to reach accurate and precise results.

2. Datasets and sample selection

The main data source is the 1% 1970 Form 2 Metro sample from the Integrated Public Use

Microdata Series (IPUMS) [26]. The year 1970 is the last year when individuals specifically

answered the question about their parents’ birthplaces and also reported the language spoken in their

home as a child (mother tongue)1. However, IPUMS does not fully specify the country of origin of

the individual’s birth mother when both parents were born outside of the U.S. Therefore, this study

uses father’s birthplace as a cultural proxy for the second-generation immigrant women due to the

data limitation. Consequently, the women include those fathers who were born outside the U.S.,

while their mothers were born in the U.S. Furthermore, this study refines the sample data to the U.S.

women between 25-54 years old. This age range which is chosen due to three main reasons: women

are old enough to seek a full-time job; their education generally has been completed 2; and they have

their own families (as in Staubli 2011; Blau and Kahn 2013).

In this study, heritage country is defined as the father’s birthplace, and HL is identified as the main

languages that are spoken in the father’s birthplace3. Since second-generation immigrant women

were born and raised in the U.S., they certainly are able to speak the English language the same as

other native women4.

This study limits the countries in the main sample. The Soviet Union was a multinational federal

1 Questions about child mother tongue and parents’ birthplaces directly connect to the goals of this study.2 This paper excludes women who are attending to the school.3 This paper focuses on the father’s characteristics due to the lack of data ability for second-generation immigrant mothers.4 Therefore, these women do not have any difficulty in finding a job because of linguistic barriers.

union that was included 16 countries that were governed as a single-party state that existed from

1922 to 1991. Thus, this study eliminates those countries that were parts of the Soviet Union (as in

Fernández and Fogli 2009; Fernández 2007). During the post war years, some countries economic

systems developed in the way of other centrally planned economies; therefore, this study excludes

this group of countries1.

This paper restricts the main sample to the heritage countries2 that have at least 20 observations. The

observations for women whose fathers were born in China are only 36, and this might not represent

China’s population properly; therefore, this paper eliminates China from the main sample. Finally,

the women who do not specify their own mother tongues are excluded from the main sample. The

final sample contains 7526 American women whose fathers were born outside the U.S. from 23

different heritage countries.

This study identifies a woman’s decision about working status based on whether the woman

participated in the labor force. Additionally, to measure the number of hours a woman worked, this

study uses the number of hours a woman was at work during the previous week of her interview 3. To

determine cultural proxies for the number of hours a woman worked, this study employs ancestors’

FLFPs data that are available on the International Labour Organization Statistics Database

(ILOSTAT) [29]. This study suggests that the cultural effect is a kind of dynamic phenomenon that

shifts slowly. Therefore, FLFP in heritage countries in 1945 to 1995 is used to examine culture

transmission through generations during this period. Therefore, this study uses the average annual

FLFP of heritage countries for two quarter-centuries (25 year periods) that starts from 1945. ILO

provides detailed information about all kinds of LFP, and this study uses the FLFP of heritage

countries where the women are between the ages of 25 to 544.

Women also report their mother tongues in IPUMS, and this study links the second-generation

immigrant women to their own HLs. The cross-country data for HL is based on Pearson Education

[30]. There are some countries where people speak more than one specific language; therefore, this

study defines HL as the language spoken by at least 1%5 of a country’s population. Also, there are

some countries where people speak English, and it is not easy to find whether or not second-

generation immigrant women from those countries whose mother tongues are English keep their

1 Albania, Bulgaria, Czechoslovakia, Estonia, Hungary, Poland, Romania, and Yugoslavia.2 Luxembourg, Estonia, Korea, India, Iran, Israel, Palestine, Jordan, Lebanon, Africa, Australia, Turkey, and New Zealand.3 The woman reports the number of hours worked in intervals instead of a certain number of hours, and this study uses the midpoint of each interval as hours they worked in the previous week.4 Total (5-year age bands).5 There are countries where some populations speak English. This paper considers the English language as a HL when more than 5% of population speaks English.

HLs. Therefore, this study assumes that women from United Kingdom, Canada, Ireland, and

Philippines (UCIP) whose mother tongues are English kept their HLs.

Table 1 provides descriptive statistics for the main variables of the second-generation immigrant

women in the sample. The observations contain women whose ancestors were from European,

American, Asian, and Middle Eastern countries. The majority of the observations consist of the

women whose fathers’ heritage countries were Italy, Canada, Germany, and the United Kingdom1.

FLFP for the first quarter (1945-1970) is 22.30% on average and this varies from heritage countries.

The highest value for FLFP of first quarter belongs to the Finland by 36.15%, and Syria has the

lowest value of FLFP by 5.45%. The average of FLFP in the second quarter (1970-1995) is almost

25% more compared to the first quarter.

**Table 1**

3. The Epidemiological Approach and Results

The most important part of cultural analysis in economic decisions is to find a way to examine an

individual’s culture’s impact on economic attitudes. Designing a suitable model to capture the effect

of culture regarding economic outcomes is the most important part of economic analysis. The

epidemiological approach relies on observing second-generation immigrant women’s attitudes in a

host country to isolate the effect of culture from economic conditions and institutions. This approach

suggests that women who were born and live in the same country and face the same law, markets,

and institutions may have different economic decisions regarding their cultures. The main idea of

this approach implies that native women from various cultures that have the same opportunity might

be treated differently regarding the job market. Therefore, examining the work outcome of women

from an identical country, while their fathers were born in different countries has motivated recent

studies to pick out dissimilarities in heritage cultures [4,6,31].

3.1. Empirical Model for FLFP

This study evaluates the cultural effects on a woman’s decision to work outside the home. This study

uses a probit model to investigate on women’s decisions to participate in the labor supply. This

paper uses the cultural proxy as one of the main control variables for the women labor supply, while

considering other key variables in the model as follows:

P (Work ihs=1∨Zihs ,W ihs , S ihs ,F ihs) (1)

1 It contains England, Scotland and Wales- Ireland

Where the variable work shows female participation, which takes the value 1 if the female

participates in the labor supply and 0 otherwise. Z defines the variable that measures the culture

effect by a woman’s heritage country (father’s birthplace country) FLFP, W shows a woman’s

characteristics including her age and educational level, S presents a woman’s spouse’s

characteristics such as his educational level and income, and F represents a woman’s family’s

characteristics such as her family size and the number of children younger than 5 years old.

Additionally, the following estimation model is used to measure precisely the impact of heritage

culture on the number of hours second-generation immigrant women work in the U.S.

Y ihs=α+ β1~ZhT+ β2(

~ZhT∗HL)+∑ a' W ihs+∑ b ' Sihs+∑ c ' F ihs+ƒs+CS+εihs(2)

Where Y ihs is the total number of hours a woman i worked during the previous week whose heritage

country is h and lives in region s. The variable that measures cultural impact is defined as~ZhT . The

value of ~ZhTis determined by a woman’s heritage country (father’s birthplace country) that is FLFP

during time T. Time T defines the average of FLFP for a woman’s ancestor country in the first

quarter (1945-1970) and the second quarter (1970-1995). W ihs shows a woman’s characteristics, Sihs

represents a spousal characteristics, and F ihs represents her family’s characteristics. Since the

working status varies by different regions, this study includes full fixed effects (full set of MSA

effects), ƒs, and full set of heritage country fixed effects, C s, in the model to capture unobservable

factors of heritage countries.

3.2. Women’s decisions to work and cultural proxies

Culture is a kind of dynamic interaction that moves slowly over time. Individuals’ cultures are

created mostly in their early ages, while they often communicate with the surrounding people who

mostly have similar heritage cultures. Therefore, individuals who might not be able to visit their

heritage countries are often able to keep their beliefs updated by direct or indirect contact with

people from their heritage countries.

This study supposes that FLFP of ancestors’ countries may fluctuate over time, while ranking of

countries regarding their FLFPs normally wouldn’t change. There is a positive correlation between

the first quarter of FLFPs and the second quarter of FLFPs that are statistically significant1. This

correlation implies that the heritage cultures for the women’s decisions to work outside the home

may shift over time, while these changes move simultaneously among countries. This study repeats

the cultural analysis with the second quarter data, and the findings about cultural effect are similar to

1 The correlation is 0.72 and it is statistically significant at 1% level.

the first quarter data. There is no evidence for transmitting cultural values regarding participation in

the job market for all women, but the results show that there are significantly positive cultural effects

for the women who kept their HLs during the focused 50 years of the time frame. Since it is difficult

to control all variables that may affect a woman’s decision to work, this study attempts to use main

control variables including a woman’s age and her educational level (High school 1, some college2,

College+3) as well as her husband’s educational level and income. Finally, this study controls family

characteristics including her family size and the number of children under age 5. These variables

helped to measure more precisely the effect of culture on women’s decisions to work.

Table 2 shows the effect of dependent variables on the second-generation immigrant women labor

force participation from 1945 to 1995.

**Table 2**

Column 1 to 3 show the probit results that use first quarter FLFP’s heritage country and other main

explanatory variables for women’s decisions to work in the U.S. The coefficient for the FLFP is

insignificant in column 1 and ancestors’ FLFP cannot explain women’s decisions to work, while

column 2 indicates that the cultural proxy for the women who kept their HLs is economically and

significantly positive. The findings show that a woman who kept her HL and whose father was born

in a heritage country with a higher level of FLFP, most likely worked more than another woman in

the identical country.

The results show that a woman’s educational level is associated with the women’s working statuses.

Adding the educational levels indicates that a woman with a higher educational level has more

opportunity to get a proper job outside the home. Women with higher educational levels prefer to

work more than women with lower educational levels since the opportunity cost for educated women

is higher than uneducated women. Furthermore, husbands’ characteristics may have an effect on the

decision of a woman to work outside the home. A woman who tends to stay at home is more likely

to marry a man with a higher income [32,33]. The results indicate that a woman with an higher

educated, richer husband potentially prefers to work less than other women. Thus, the women’s

decisions to work are negatively affected by their husbands’ educational levels and incomes. Finally,

family variables including the family size and the number of children less than five years old

negatively affected the women’s decisions to work. The findings indicate that cultural effect is

statistically significant for the women who kept their HLs when controlling all other explanatory

1 Below high school and above some college (more than 1 year) omitted.2 Below 1 year of college and above Bachelor’s degree omitted.3 Includes 5+ years of college and above.

variables. Column 4 to column 6 show results for second quarter FLFP’s heritage country for

women’s decisions to work regarding their cultural attitudes over the next 25 years. The results

indicate that culture would slowly change over time. Remarkably, the results show that the cultural

proxy is statistically significant for women who kept their HLs throughout this period.

3.3. Age of women and culture proxies

Data restrictions limit this study to find a year when a woman’s father left his heritage country and

immigrated to the U.S. Therefore, this study uses two main age cohorts of women to link them in the

first quarter of their ancestors’ countries. Accordingly, married women are studied by two age

cohorts: women in the age cohort 25-401 (to be denoted as “young” married women) and women in

the age cohort 40-54 (to be denoted as “middle-aged” women). The value of FLFP’s heritage

countries has been reported since 1945. Morover, fathers of women in the young cohort were

expected to be in the U.S. by 1945, while fathers of women in the middle-aged cohort were likely to

be in the U.S. before 1930. The young women may tend to keep the most recent culture more than

the middle-aged women. In particular, if the female labor supply is related to the period when their

parents left their heritage countries, then one would expect that the recent heritage culture mostly

influences the younger married women compared to the older. Accordingly, the cultural proxies for

the young cohort of married women should be more significant than the middle-aged cohort of

married women when the cultural proxies are not dynamic experiences. Most likely, if women kept

up to date with their heritage cultures, then cultural proxies would be economically and statistically

significant for both cohorts. Results for different age cohorts are reported in Table 3.

**Table 3**

Column 1 and column 3 show cultural proxies are not statistically significant for young and middle-

aged women without considering their HLs. On the other hand, column 2 and column 4 indicate that

cultural proxies can explain women’s decisions to work for both cohorts when they kept their HLs.

The findings indicate that culture is a kind of interaction that is dynamic, and women kept

themselves up to date regarding their heritage cultures.

Previous studies (such as Fernández 2007; Fernández and Fogli 2009; Tabellini 2010; Fernández

2011; Marcén 2014) assume that the effect of heritage culture is the same for the women from the

identical heritage country. However, this study shows that the effect of the heritage culture exists

among those women who kept their HLs. This study reports the women’s decision to work are

positively associated with FLFP’s in their heritage countries for both cohorts when the women kept

1 This study uses an age cutoff of 40 since it is the median age of the sample.

their HLs. Therefore, women’s behaviors regarding the job market are similar for younger and

middle-aged married women during a quarter before 1970 while the results are consistent for a

quarter after 1970. This result strengthens the dynamic effect of heritage culture on the women’s

decisions to work.

3.4. Regression and cultural proxies

This study uses cultural proxies to estimate the number of hours women worked regarding different

specifications. Table 4 reports the main results of cultural proxies for the women with different

specifications regression models.

**Table 4**

Column 1 and column 4 demonstrate regression results when using the cultural proxy for estimation

the number of hours women worked, while column 2 and column 5 adds women’s characteristics to

the primary model. Column 3 and column 6 includes their spousal characteristics for estimating the

number of hours women worked. Finally, column 4 and column 8 show the main model for

estimating the number of hours women worked. Column 1 to column 4 demonstrate the effect of

cultural proxies for the women without considering their HLs, while column 4 to column 8 show the

impact of heritage culture for the women who kept their HLs.

The coefficients for the cultural effect in column 1 to column 4 are not statistically significant

regarding different regression specifications. Previous studies (such as Fernández 2007; Fernández

and Fogli 2009; Tabellini 2010; Fernández 2011) find significant results for cultural proxies for all

women. There are two main possibilities that explain the findings of previous studies. Firstly, they

have not included all main variables for women’s characteristics in their models that may affect the

women’s decisions to work1. Secondly, they have not fixed the effect of unobservable variables for

the countries of origin in their studies. Therefore, countries of origin’ variables may correlate to

cultural proxies. These two biases most likely resulted in overestimating the coefficient for cultural

proxies.

The findings indicate that there is no economically and statistically significant cultural effect for all

women. Meanwhile, there is an economically and statistically significant cultural impact on the

number of hours women worked when they kept their HLs in different specification regression

models. In order to test if the results are driven by the cultural proxies are robust, this study

performed several tests. This study performs robustness checks on alternative new data sets.

Therefore, this study re-estimates the number of hours women worked while excluding some

1 This study uses their models and find similar results, while using all main variables and the fixed effect for heritage countries would change the results and cultural proxies are not statistically significant for all the women.

countries from the main sample2. The robustness analysis indicate that the cultural proxies are

economically and statistically significant to present the number of hours women worked under

different sub-set samples.

3.5. HL’s scenarios

There are some countries that have English language, as their HLs. Women from these countries

may tend to participate U.S. culture due to their potential willingness to join the American society.

The goal of this section is to approximate HL’s role on the transmission of heritage culture under

different scenarios. In this regard, this section exercises five different scenarios that are derived

based on a woman’s culture regarding her mother tongue. Table 5 shows the impact of HL on the

number of hours women worked in a range of different scenarios.

**Table 5**

Each row of Table 5 represents a different scenario that has a main assumption about the women’s

HLs. These scenarios are using the full set of the control variables in their models. Scenario I is

developed based on the following assumption: (1) English language can be a HL for the women

whose fathers were born in UCIP. In this scenario, UCIP’s second-generation immigrant women

whose mother tongues were English are assigned as women who kept their HLs. The scenario I is

the main model that are presented in Table 5. The cultural proxy for the women who kept their HLs

is economically and statistically significant in all levels.

Scenario II assumes that women whose mother tongues are English and whose fathers were born in

UCIP counties did not keep their HLs. These women learned the English language in the U.S. from

their parents and their society. The results demonstrate that cultural proxies about the number of

hours women worked are economically and statistically significant for the women who kept their

HLs. Scenario III suggests that assigning HL for the women originally from UCIP countries is a

controversial issue. Thus, this scenario excludes women who are originally from UCIP countries

from the main sample. Dropping all four English language countries leads to more accurate

estimation across remaining countries regarding transmitting heritage culture through speaking HL.

This scenario shows that the cultural proxy is economically and statistically significant in all levels.

Women whose parents immigrated to the U.S. may have attitudes more similar to the U.S. culture

compared to their heritage cultures. This paper argues that HL can transmit the culture from one

2 The baseline model that includes all the main variables for estimating the number of hours women worked. The second model estimated the number of hours women worked without those second-generation immigrant women whose fathers were born in Italy because Italy is the country with the highest frequently observations. The third model is estimating the model without women whose fathers are from Turkey because Turkey is the country with the highest level of FLFP. The fourth model excludes women whose fathers were born in the Syria because Syria is the country with the lowest level of FLFP. Finally, the fifth model eliminates women whose fathers born in Canada because Canada is the English language country that has highest frequency of observations.

generation to the next generation. If a woman did not keep her HL, she may assimilate to the host

country; thus, women whose mother tongues are English, may tend to the U.S. culture more than

their heritage cultures. Scenario IV assumes that women whose mother tongues are English tend to

the U.S. culture regarding the labor market. This scenario assigns FLFP of the U.S. to women whose

mother tongues are English and whose fathers were born in UCIP countries. The cultural proxy for

the women who kept their HLs is economically and statistically significant.

**Table 6**

Finally, Scenario V assumes that all women whose mother tongues are English more likely tend to

the U.S. culture. Thus, this paper uses FLFP of the U.S. for the women whose mother tongues are

English. This scenario supposes that these women’s culture may tend to the U.S. culture. The

cultural proxy coefficient is presented in the last row of Table 6.

3.6. Social Networks

The existing empirical studies indicate that people’s decisions are associated with ethnic groups that

those people are involved with. People who are exposed to geographic locations connected more

closely with their heritage cultures are more likely to maintain connections with their heritage

cultures. This section provides evidence for second-generation immigrant women who are contacted

by more people from their countries of origin. Figure 2 presents recent data on immigration densities

in various states.

**Figure 2**

Social networks strengthen cultural transmission in the female labor supply. This study uses the

“spoken at home” variable to show the existing social networks among people in the same region.

Second-generation immigrant women who are living in areas where they are exposed to more people

from their countries of origin and who kept their HLs will most likely keep their own heritage

cultures more than women who did not keep their HLs. Similar to the strategy used by Bertrand et al.

(2000) and Furtado et al. (2013), this study examines the cultural effect on the number of hours

women work while considering their social networks. Therefore, this study uses the following model

to measure the effect of social networks on the number of hours women worked.

Y ihs=α+ β1 NEhs+ β2~ZhT∗HL+β3 (~ZhT∗HL∗NEhs )+∑ a' W ihs+∑ b' S ihs+∑ c ' F ihs++CS+εihs

(3)

Where, NEhms shows the social networks that exist among people from the same country of origin (h)

that belong to language group (m) and live in the region (s). The other variables are defined same as

before in the model. The social networks are calculated as follows:

NEhms=ln ¿¿) (4)

Where Chms is the number of people whose heritage country is (h), whose mother tongue is (m), and

who live in region (s), As is the total number of people who live in region (s), Lhm is the total number

of people in the U.S. and their mother tongue is m, and T is the total number of people in the U.S.

This study uses the same datasets with more observation to prevent underestimating the social

networks. Thus, this study redefines the main sample (IPUMS) for social networks. The new sample

consists of people who are between the ages of 25 to 54 and have defined birthplace regions. The

new sample is restricted to the people whose heritage countries are known and defined. Also, the

states in which people were living are defined1. The new sample provides an index for social

networks among people for different geographical areas. This social network index is assigned to the

second-generation immigrant women from the main sample2.

Interaction of cultural effect and density of people from the same country of origin examines the

power of cultural transmission among women who are exposed differently to the people from the

same country of origin. Table 7 presents the results for social network and cultural transmission.

**Table 7**

The findings indicate that existing social network among people with the same culture is resulted to

have more chance to keep given heritage culture.

4. Country of Origin and Unobserved Heterogeneity

This study controls all main observable variables that may affect women’s decisions to work but

unobservable variables may also affect their decisions. The main results may also be accounted by

the characteristics of the country of origin. This section examines the impact of the characteristics of

the country of origin on the number of hours women worked. This study controls a variety of those

characteristics that might impact the second-generation immigrant women’s behaviors in the U.S.

labor market. This section includes characteristics of the country of origin: GDP per capita, human

capital, and women’s attitudes to work3. Table 8 reports the summary of these variables for each

country.1 This paper excludes the U.S. territories: Puerto Rico, State groupings, Military/Mil, Reservations, and District of Columbia.2

This study excludes UCIP countries in the final sample because the main language of these countries is English.3 The results also are robust under the physical distance between the country of origin and the U.S.

**Table 8**

4.1. GDP

The level of GDP among countries of origin may be biased against the findings rather than the

cultural effect. Since women tend to stay at home more to take care of their families, they might

become less involved in production for their own country; however, the second-generation

immigrant women that originally belong to richer countries (higher GDP) may tend to participate on

the production more than other women. Therefore, the results may be concluded that in difference

level of GDP at the country of origin, rather than cultural effect.

This study includes the level of GDP in the country of origin in the model to examine the possibility

that the results are mainly driven by an important aggregate variable of the country of origin. This

study uses three kinds of GDP in the main model: per capita GDP in 1945 and the average per capita

GDP from 1945 to 1970, and per capita GDP in 1970 for the countries of origin. Table 9 presents

results when including GDP per capita in the model.

**Table 9**

The results show that the per capita GDP does not impact the hours that women worked, while the

cultural proxies are statistically significant for those women who kept their HLs. The results are

robust to this specification.

4.2. Human Capital

Human capital is one of the important component in many of the immigrants’ economic outcomes.

The results might arise from omitting a human capital equality variable that could explain the FLFP

in the country of origin. This study uses the human capital index, which is from Barro and Lee

dataset [34] and provides information for educational achievement across countries1. This dataset

was created by Barro and Lee and used human capital as an estimate of the educational attainment

for the population over 15 and 25 years old2. Table 10 shows the results of including human capital

in the regression model.

**Table 10**

The results show that cultural proxies for women who kept their HLs are not changed in magnitude

1 This study uses “Education Attainment by 5-year Age Group” that includes average years of schooling. 2 This study uses average schooling years of the total population as the human capital for 25 years old.

and still are statistically significant, while the coefficients for human capital are not statistically

significant. Thus, cultural proxies for women who kept their HLs remain statistically significant

even after including this variable to the model.

4.3. Women’s attitudes

The World Value Survey (WVS) includes a number of questions that evaluate people’s beliefs and

values across almost 100 countries. The WVS is administered to a representative sample of the

world population from 1981 to 2014 and has been carried out six times (1981-84, 1990-1993, 1995-

97, 1999-2004, 2005-2009 and 2010-2014). The survey contains questions about demographics and

economic characteristics, and specific questions about people’s attitudes, including women’s

attitudes toward work. The main advantage of using WVS is that the same questions on attitudes

and beliefs are asked among countries.

This study uses the fifth wave (2005-2009) of the survey, which covered 58 countries. This wave

includes the main questions about women’s attitudes regarding the job market. This paper focuses on

two key questions that can particularly measure women’s attitudes to work. The first question asked

about prioritizing men in the job market compared to the women: “When jobs are scarce, men

should have more right to a job than women.”1 The second question asked about willingness to work

at home or outside the home: “Being a housewife is just as fulfilling as working for pay.” 2 This

study uses these two questions to measure the women’s attitudes regarding the job market in each

heritage country and provides the “women’s attitude index.”3 Table 11 presents the results of

including women’s attitudes in the model. The results indicate that cultural proxies for women who

kept their HLs are statistically significant, while the coefficients of WVS’s questions (women’s

attitude index) are not statistically significant.

**Table 11**

5. Conclusion

Female labor supply in the economy has been the most meaningful change in labor markets over the

last several decades, especially for married women. A few recent studies propose that culture plays a

significant role in women’s decisions regarding the job market. This study redefines the cultural

proxy and shows that its impact exists through economic decisions, particularly on women’s

1 The individuals have three choices to answer this question: (i) agree, (ii) neither (iii) disagree. This study dropped the individuals who responded with “neither”. 2 The Individuals have four choices to answer this statement (i) strongly disagree, (ii) disagree, (iii) agree, and (iv) strongly agree3 This study provides the women’s attitudes index calculated from the ratio of Individuals who “disagreed” or “strongly disagreed” for those statements.

decisions to work and the number of hours women worked.

This study shows the cultural proxies are economically and statistically significant in explaining the

women’s decisions to work whenever they kept their HLs. Even as the heritage culture changes its

values over time, women, who kept their HLs, clearly choose their participation according to their

culture’s trend. This study shows cultural effect is a dynamic interaction. The empirical results are

consistent with the expectations regarding HL’s role in cultural transmission. There is considerable

evidence of intergenerational transmission in the female labor supply regarding women’s attitudes

toward keeping their HLs. Different estimation models are employed to show that an ancestor’s

FLFP is more likely to have an impact on the number of hours a woman works when she is able to

make the connection to her own heritage culture. This study shows that cultural transmission is

statistically significant for women whose social networks are stronger than other women’s. Finally,

the results are consistent when controlling country of origin and unobserved heterogeneity among

those women. This study proposes the new model to quantitative impact of culture, which is the

main factor for economic decisions. This paper shows culture plays an important role in explaining

the large variation of FLFP across time and countries. There are other possible interpretations that

might exist for these empirical results, while they would not explain why the impact of the cultural

proxy is statistically significant in all models.

Moreover, policy makers and planners make different strategies regarding female labor supply and

immigration policies in the labor market. They can use the results of this study to better estimate

future LFP of women from different cultures with various approaches to keep women’s heritage

culture. Finally, this paper suggests that the second-generation immigrant who kept their HLs can

represent their heritage cultures.

References[1] Fernández R, Fogli A. Culture: An Empirical Investigation of Beliefs, Work, and Fertility. American

Economic Journal: Macroeconomics 2009;1:146–77.[2] Fogli A, Veldkamp L. Nature or Nurture ? Learning and the Geography of Female Labor Force

Participation. Econometrica 2011;79:1103–38.[3] Marcén M. The role of culture on self-employment. Economic Modelling 2014.[4] Fernández R. Does Culture Matter ? In: Benhabib J, Bisin A, Jackson MO, editors. Handbook of

Social Economics, 2011, p. 481–510.[5] Guiso L, Sapienza P, Zingales L. Does Culture Affect Economic Outcomes ? The Journal of

Economic Perspectives 2006;20:23–48.[6] Fernández R. Alfred Marshall lecture women, work, and culture. Journal of the European Economic

Association 2007;5:305–32.[7] Guiso L, Sapienza P, Zingales L. Cultural Biases in Economic Exchange? The Quarterly Journal of

Economics 2009;124:1095–131.[8] Luttmer EFP, Singhal M, Alesina A, Glaeser E. Culture, Context, and the Taste for Redistribution.

American Economic Journal: Economic Policy 2011;3:157–79.[9] Alesina A, Giuliano P. The power of the family. Journal of Economic Growth 2010;15:93–125.[10] Tabellini G. Culture and institutions: economic development in the regions of Europe. Journal of the

European Economic Association 2010;8:677–716.[11] Tsai KM, Park H, Liu LL, Lau AS. Distinct pathways from parental cultural orientation to young

children’s bilingual development. Journal of Applied Developmental Psychology 2012;33:219–26.[12] Hughes D, Rodriguez J, Smith EP, Johnson DJ, Stevenson HC, Spicer P. Parents’ ethnic-racial

socialization practices: a review of research and directions for future study. Developmental Psychology 2006;42:747–70.

[13] Fillmore LW. When learning a second language means losing the first. Early Childhood Research Quarterly 1991;6:323–46.

[14] Kim SY, Chao RK. Heritage language fluency, ethnic identity, and school effort of immigrant Chinese and Mexico adolescents. Cultural Diversity & Ethnic Minority Psychology 2009;15:27–37.

[15] Alesina AF, Giuliano P, Nunn N. On the Origins of Gender Roles: Women and the Plough. The Quarterly Journal of Economics 2013;128:469–530.

[16] OECD. OECD Stat, (database) 2014.[17] Fernández R. Cultural Change as Learning: The Evolution of Female Labor Force Participation over a

Century. The American Economic Review 2013;103:472–500.[18] Euwals R, Knoef M, Vuuren D van. The Trend in Female Labour Force Participation: What Can Be

Expected for the Future? Empirical Economics 2011;40:729–53.[19] Goldin C. The Quiet Revolution That Transformed Women ’ s Employment , Education , and Family.

The American Economic Review 2006;96:1–21.[20] Reimers CW. Cultural Differences in Labor Force Participation Among Married Women. The

American Economic Review 1985;75:251–5.[21] Bisin A, Verdier T. On the cultural transmission of preferences for social status. Journal of Public

Economics 1998;70:75–97.[22] Antecol H. An examination of cross-country differences in the gender gap in labor force participation

rates. Labour Economics 2000;7:409–26.[23] Fernández R, Fogli A, Olivetti C. Mothers and Sons : Preference Formation and Female Labor Force

Dynamics. The Quarterly Journal of Economics 2004;4:1249–99.[24] Morrill MS, Morrill T. Intergenerational links in female labor force participation. Labour Economics

2013;20:38–47.[25] Blau FD, Kahn LM, Liu AY-H, Papps KL. The transmission of women’s fertility, human capital, and

work orientation across immigrant generations. Journal of Population Economics 2013;26:405–35.[26] Ruggles S, Alexander JT, Genadek K, Goeken R, Schroeder MB, Sobek. M. No Title. Integrated

Public Use Microdata Series: Version 50 [Machine-Readable Database] Minneapolis: University of Minnesota 2010.

[27] Blau FD, Kahn LM. Female labor supply: Why is the US falling behind? The American Economic Review 2013;103:251–6.

[28] Staubli S. The impact of stricter criteria for disability insurance on labor force participation. Journal of

Public Economics 2011;95:1223–35.[29] ILOSTAT Database (Geneva). International Labour Office (ILO) 2013.[30] Languages Spoken in Each Country of the World. Pearson Education, Publishing as Infoplease 2007.[31] Furtado D, Marcén M, Sevilla A. Does culture affect divorce? evidence from European immigrants in

the United States. Demography 2013;50:1013–38.[32] Lundberg S. Labor Supply of Husbands and Wives: A Simultaneous Equations Approach. The

Review of Economics and Statistics 1988;70:224–35.[33] Blau FD, Kahn LM. Changes in the Labor Supply Behavior of Married Women : 1980 – 2000. Journal

of Labor Economics 2007;25:393–438.[34] Barro R, Lee J-W. A New Data Set of Educational Attainment in the World, 1950-2010. Journal of

Development Economics 2010;104:184–98.

Table 1. Data descriptive by heritage countries.County Obs Hours

workFLFP

1945-1970

FLFP

1970-1995

Age Family size

Children HL High School

Some

CollegeCollege +

Canada 1,084 12.70 19.70 31.10 39.73 4.30 2.20 0.95 0.49 0.19 0.03

Mexico 460 10.40 10.90 15.90 35.95 5.49 3.25 0.93 0.27 0.09 0.00

Cuba 25 12.08 11.95 23.00 38.08 3.92 1.80 0.76 0.60 0.20 0.00

Denmark 164 14.04 31.50 46.42 40.78 4.22 2.13 0.24 0.54 0.20 0.04

Finland 82 15.77 36.15 43.22 42.13 4.15 2.00 0.62 0.63 0.20 0.00

Norway 270 14.05 19.90 42.85 42.63 3.96 1.88 0.39 0.47 0.26 0.02

Sweden 341 14.10 29.77 44.92 42.01 3.96 1.86 0.34 0.54 0.26 0.03

United Kingdom 814 13.14 30.95 34.35 41.02 3.98 1.88 0.95 0.51 0.25 0.04

Ireland 332 10.69 21.00 21.27 41.48 4.51 2.38 0.98 0.61 0.18 0.04

Belgium 42 10.44 20.27 28.50 39.31 4.36 2.26 0.21 0.57 0.10 0.05

France 108 11.68 27.95 24.37 39.81 4.33 2.22 0.37 0.56 0.23 0.02

Netherlands 155 11.19 17.80 19.00 40.88 4.36 2.31 0.28 0.48 0.24 0.02

Switzerland 81 13.40 28.57 37.85 41.64 4.12 2.00 0.37 0.48 0.20 0.05

Greece 145 10.27 20.45 21.07 38.92 4.28 2.17 0.46 0.54 0.19 0.03

Italy 1,814 11.30 20.60 23.15 39.45 4.31 2.17 0.59 0.54 0.14 0.02

Portugal 109 12.51 16.30 32.25 39.26 4.31 2.27 0.54 0.43 0.14 0.01

Spain 48 10.51 12.90 16.50 37.56 4.54 2.42 0.58 0.50 0.23 0.04

Austria 346 11.61 35.50 34.23 41.49 4.22 2.12 0.18 0.53 0.18 0.04

Germany 940 14.02 31.57 35.10 42.39 4.01 1.91 0.40 0.53 0.18 0.02

Japan 78 19.33 35.38 37.35 35.74 4.22 1.90 0.74 0.54 0.29 0.08

Philippines 47 13.04 18.80 14.10 33.94 4.66 2.53 0.85 0.53 0.17 0.02

Lebanon 32 12.08 9.50 9.50 37.50 4.75 2.59 0.63 0.56 0.28 0.03

Syria 25 10.82 5.45 4.10 39.48 3.84 1.80 0.36 0.52 0.16 0.08

Average 327.91 12.57 22.30 27.83 39.62 4.30 2.18 0.55 0.52 0.20 0.03

Std. Dev 441.74 2.10 8.98 11.76 2.31 0.35 0.33 0.26 0.07 0.05 0.02Source: IPUMS data U.S. Census, 1% 1970 from 2 Metro sample and ILO.

Table 2. The effects of the women’s cultures on the probability of female labor participation.(1) (2) (3) (4) (5) (6)

FLFP (1945-1970)0.016

(0.186)-0.011(0.019)

HL*FLFP (1945-1970)0.005

(0.001)***0.005

(0.001)***

FLFP (1970-1995)0.009

(0.010)0.004

(0.010)

HL*FLFP (1970-1995))0.004

(0.001)***

0.004(0.001)***

Age0.009

(0.022)0.008

(0.022)0.008

(0.022)0.009

(0.022)0.008

(0.022)0.008

(0.022)

Age square / 100-0.014

(-0.027)-0.014(0.027)

-0.014(0.027)

-0.014(0.027)

-0.013(0.027)

-0.013(0.027)

High school0.217

(0.039)***0.219

(0.039)***0.219

(0.039)***0.217

(0.039)***0.219

(0.039)***

0.219(0.039)***

Some college0.399

(0.055)***0.402

(0.055)***0.402

(0.055)***0.399

(0.054)***0.402

(0.054)***

0.402(0.054)***

College+1.092

(0.111)***1.094

(0.111)***1.094

(0.111)***1.092

(0.111)***1.093

(0.111)***

1.093(0.111)***

Husband high school-0.095

(0.039)**-0.095

(0.039)**-0.095

(0.039)**-0.095

(0.039)**-0.095

(0.039)**-0.095

(0.039)**

Husband some college-0.21

(0.048)***-0.212

(0.048)***-0.212

(0.048)***-0.21

(0.049)***-0.213

(0.048)***

-0.213(0.048)***

Husband college+-0.414

(0.073)***-0.416

(0.073)***-0.025

(0.003)***-0.414

(0.073)***-0.417

(0.073)***

-0.417(0.073)***

Husband’s income-0.025

(0.003)***-0.025

(0.003)***-0.025

(0.003)***-0.025

(0.003)***-0.025

(0.003)***

-0.025(0.003)***

Family size-0.064

(0.011)***-0.065

(0.011)***-0.065

(0.011)***-0.064

(0.011)***-0.065

(0.011)***

-0.065(0.011)***

Child < 5-0.553

(0.034)***-0.553

(0.034)***-0.553

(0.034)***-0.553

(0.034)***-0.553

(0.034)***

-0.553(0.034)***

Observations 7526 7526 7526 7526 7526 7526Pseudo R2 0.0937 0.0949 0.0949 0.0937 0.0948 0.0948

***Statistically significant at the 1% level**Statistically significant at the 5% level*Statistically significant at the 10% level

Table 3. Cultural proxies and the probability of women decision to work by different age cohorts.Women 25 to 40 years old women 40 to 54 years old

(1) (2) (3) (4) (5) (6)

FLFP 0.003(0.027)

0.009(0.028)

-0.024(0.028)

-0.019(0.028)

HL*FLFP 0.005(0.002)**

0.005(0.002)**

0.005(0.002)***

0.005(0.002)***

Age -0.303(0.095)***

-0.304(0.095)***

-0.304(0.095)***

0.147(0.119)

0.148(0.119)

0.148(0.119)

Age square / 100 0.466(0.146)***

0.467(0.146)***

0.467(0.146)***

-0.168(0.126)

-0.169(0.126)

-0.169(0.126)

High school 0.111(0.065)*

0.113(0.065)*

0.113(0.065)*

0.276(0.051)***

0.278(0.051)***

0.278(0.051)***

Some college 0.282(0.087)***

0.286(0.087)***

0.286(0.087)***

0.480(0.073)***

0.482(0.073)***

0.482(0.073)***

College+ 0.928(0.160)***

0.929(0.160)***

0.929(0.160)***

1.251(0.164)***

1.257(0.164)***

1.257(0.164)***

Husband high school -0.098(0.062)

-0.098(0.062)

-0.098(0.062)

-0.112(0.051)**

-0.112(0.051)**

-0.112(0.051)**

Husband some college -0.217(0.075)***

-0.218(0.075)***

-0.218(0.075)***

-0.187(0.066)***

-0.189(0.066)***

-0.189(0.066)***

Husband college+ -0.356(0.105)***

-0.357(0.105)***

-0.357(0.105)***

-0.426(0.106)***

-0.428(0.106)***

-0.428(0.106)***

Husband’s income -0.033(0.005)***

-0.033(0.005)***

-0.033(0.005)***

-0.024(0.003)***

-0.024(0.003)***

-0.024(0.003)***

Family size -0.084(0.018)***

-0.083(0.018)***

-0.083(0.018)***

-0.053(0.014)***

-0.054(0.014)***

-0.054(0.014)***

Child < 5 -0.525(0.038)***

-0.526(0.038)***

-0.526(0.038)***

-0.683(0.096)***

-0.677(0.096)***

-0.677(0.096)***



Table 4. Culture and women hours worked. (1) (2) (3) (4) (5) (6) (7) (8)

FLFP0.087

(0.249)0.096

(0.247)0.105

(0.244)0.192

(0.239)0.028

(0.250)0.031

(0.248)0.040

(0.245)0.127

(0.240)

FLFP*HL0.050

(0.020)**0.054

(0.017)***0.054

(0.019)***0.054

(0.019)***

Age0.032

(0.250)0.363

(0.249)**0.079

(0.270)0.020

(0.250)0.351

(0.249)0.071

(0.270)

Age square / 1000.286

(0.313)-0.107(0.311)

-0.075(0.335)

0.303(0.313)

-0.091(0.313)

-0.063(0.335)

High school1.898

(0.489)***3.169

(0.508)***3.097

(0.497)***1.919

(0.489)***3.192

(0.508)***3.120

(0.682)***

Some college1.941

(0.627)***5.410

(0.698)***5.293

(0.683)***1.959

(0.627)***5.433

(0.698)***5.315

(0.683)***

College+8.160

(1.304)***12.897

(1.369)***12.583

(1.340)***8.169

(1.304)***12.909

(1.369)***12.593

(1.340)***


(0.5106)*-1.048

(0.494)**-0.911

(0.506)*-1.050

(0.494)**


(0.622)***-2.374

(0.608)***-2.427

(0.621)***-2.389

(0.608)***


(0.901)***-5.372

(0.881)***-5.666

(0.90)***-5.376

(0.881)***


(0.031)***-0.285

(0.031)***-0.327

(0.031)***-0.285

(0.031)***

Family size-0.927

(0.131)***-0.931

(0.131)***

Child < 5-5.345

(0.372)***-5.338

(0.371)***

Observations 7542 7542 7542 7542 7542 7542 7542 7542Adjusted R2 0.0097 0.0270 0.0527 0.0963 0.0104 0.0278 0.0536 0.0973


Table 5. Scenario's description and type.Scenarios Implementation Description

Scenario I Baseline Assign English Language as the HL to women whose fathers were born in UCIP countries.

Scenario II English language mainly from the U.S Define HL is not English for women whose fathers were born in UCIP countries.

Scenario III Exclusion of UCIP countries Exclude the UCIP countries from the sample.

Scenario IV UCIP women’s who speak English consider as U.S. women

Assign FLFP of the U.S. to women whose mother tongues are English and whose fathers were born in UCIP countries.

Scenario V Women who speak English consider as the U.S. women

Replace FLFP of the U.S. for women whose mother tongues are English.

Table 6. Languages and cultures in alternative scenarios

Scenario I Scenario II Scenario III Scenario IV Scenario V

FLFP 0.127(0.240)

0.183(0.238)

0.475(0.645)

0.127(0.240)

0.181(0.239)

FLFP*HL0.055

(0.019)***0.054

(0.019)***0.055

(0.019)***0.055

(0.020)***0.026

(0.030)

Observations 7542 7558 5265 7542 7542Adjusted R2 0.0973 0.0972 0.0948 0.0973 0.0963***Statistically significant at the 1% level**Statistically significant at the 5% level*Statistically significant at the 10% level

Table 7. Social networks and culture(1) (2) (3) (4)

Density HL in States0.087

(0.035)**-0.053(0.104)

-0.001(0.142)

FLFP*HL0.083

(0.058)0.139

(0.119)

FLFP*HL* Density HL in States-0.006(0.011)

0.004(0.001)***

Age0.658

(0.327)**0.655

(0.327)**0.654

(0.327)**0.657

(0.327)**

Age square / 100-0.790

(0.407)*-0.787

(0.407)*-0.786

(0.407)*-0.788

(0.407)*

High school3.259

(0.586)***3.255

(0.586)***3.257

(0.586)***3.257

(0.586)***

Some college4.989

(0.823)***4.986

(0.823)***4.988

(0.823)***4.988

(0.823)***

College+10.839

(1.720)***10.887

(1.720)***10.873

(1.720)***10.867

(1.720)***


(0.585)**-1.181

(0.585)**-1.178

(0.585)**-1.182

(0.585)**


(0.732)***-2.285

(0.732)***-2.298

(0.733)***-2.268

(0.732)***


(1.099)***-6.616

(1.099)***-6.632

(1.10)***-6.576

(1.099)***


(0.037)***-0.256

(0.037)***-0.256

(0.037)***-0.257

(0.037)***

Family size-0.964

(0.155)***-0.965

(0.155)***-0.969

(0.155)***-0.963

(0.155)***

Child < 5-4.981

(0.453)***-4.979

(0.453)***-4.973

(0.453)***-4.984

(0.453)***

Observations 5265 5265 5265 5265Adjusted R2 0.0945 0.0947 0.0946 0.0947


Table 8. Summary of variables for origin countries.

County GDP 1945

GDP 1945-1970

GDP 1970

H.Capital 1950

H.Capital 1950-1970

H.Capital 1970 WVS-Q1 WVS-Q2 WVS-Q1&2

Canada 7133 8872 12050 7.39 8.04 8.66 77.90 2.40 40.15

Mexico 2134 3023 4320 2.17 2.42 2.89 67.60 2.90 35.25

Finland 3450 5896 9577 3.82 4.38 5.4 81.50 0.80 41.15

Norway 3980 6968 10027 7.54 7.72 8.27 88.60 18.0 53.30

Sweden 5454 8660 13011 6.44 7.18 7.96 94.10 8.80 51.45

United Kingdom 7056 8336 10767 6.11 6.68 7.54 76.10 5.20 40.65

France 2573 7019 11410 4.31 4.46 4.96 73.80 17.60 45.70

Netherlands 2686 7714 11967 6.03 6.7 8.03 81.40 11.60 46.50

Switzerland 7752 11842 16904 8.66 9.08 9.76 62.90 7.20 35.05

Italy 1922 5598 9719 4.04 4.61 5.17 59.20 5.06 32.40

Spain 2102 3471 6319 3.47 3.81 4.34 76.00 22.10 49.05

Japan 1346 4044 9714 5.91 6.68 7.08 17.90 0.40 9.15

Cuba 1776 2052 1917 3.44 4 4.83

Denmark 5066 8588 12686 5.39 4.99 5.79

Ireland 3019 4246 6199 6.09 6.56 7.01

Belgium 4333 6898 10611 6.52 6.91 7.19

Greece 938 3154 6211 3.80 5.56 6.04

Portugal 1804 3030 5473 1.62 2.01 2.49

Austria 1725 5656 9747 6.42 6.62 7.63

Germany 4514 6564 10839 6.71 7.42 7.71

Philippines 646 1384 1764 1.88 3.03 4.03

Syria 2409 3246 3540 0.61 0.94 1.36

Lebanon 2429 2571 2917

Average 3315.09 5601.39 8595.17 4.93 5.45 6.1 16.1 66 41

Std. Dev 2023.85 2693.42 3993.52 2.15 2.14 2.18 7.56 14.66 9.47

Table 9. GDP of origin countries and women’s work(1) (2) (3) (4) (5) (6)

GDP 19452.53

(3.133)1.672

(3.146)

GDP1945-19702.727

(3.383)1.805

(3.396)

GDP 19702.238

(2.777)1.481

(2.788)

FLFP*HL0.055

(0.019)***0.055

(0.019)***0.055

(0.019)***

Age0.079

(0.270)0.079

(0.270)0.079

(0.270)0.071

(0.270)0.071

(0.270)0.071

(0.270)

Age square / 100-0.075(0.335)

-0.075(0.335)

-0.075(0.335)

-0.063(0.335)

-0.063(0.335)

-0.063(0.335)

High school3.097

(0.497)***3.097

(0.497)***3.097

(0.497)***3.120

(0.497)***3.120

(0.497)***3.120

(0.497)***

Some college5.293

(0.683)***5.293

(0.683)***5.293

(0.683)***5.315

(0.683)***5.315

(0.683)***5.315

(0.683)***

College+12.583

(1.341)***12.583

(1.341)***12.583

(1.341)***12.592

(1.340)***12.592

(1.340)***12.593

(1.340)***Husband high school

-1.048(0.494)**

-1.048(0.494)**

-1.048(0.494)**

-1.050(0.494)**

-1.050(0.494)**

-1.050(0.494)**

Husband some college

-2.374(0.608)***

-2.374(0.608)***

-2.374(0.608)***

-2.389(0.608)***

-2.389(0.608)***

-2.389(0.608)***


(0.881)***-5.372

(0.881)***-5.372

(0.881)***-5.376

(0.881)***-5.376

(0.881)***-5.376

(0.881)***


(0.031)***-0.285

(0.031)***-0.285

(0.031)***-0.285

(0.031)***-0.285

(0.031)***-0.285

(0.031)***

Family size-0.927

(0.131)***-0.927

(0.131)***-0.927

(0.131)***-0.932

(0.131)***-0.932

(0.131)***-0.932

(0.131)***

Child < 5-5.345

(0.372)***-5.345

(0.372)***-5.345

(0.372)***-5.338

(0.371)***-5.338

(0.371)***-5.338

(0.371)***

Observations 7542 7542 7542 7542 7542 7542

Adjusted R2 0.0963 0.0963 0.0963 0.0973 0.0973 0.0973


Table 10. Human capital for countries of origin and women’s work.(1) (2) (3) (1) (2) (3)

H.Capital 19500.071

(0.116)0.041

(0.128)

H.Capital 1950-19700.085

(0.122)0.014

(0.117)

H.Capital 19700.071

(0.116)0.028

(0.123)

HL*FLFP0.046

(0.016)***0.046

(0.016)***0.046

(0.016)***

Age0.041

(0.270)0.044

(0.268)0.041

(0.268)0.049

(0.268)0.046

(0.268)0.048

(0.268)

Age square / 100-0.029(0.333)

-0.033(0.333)

-0.029(0.333)

-0.038(0.333)

-0.032(0.332)

-0.035(0.333)

High school3.067

(0.491)***3.064

(0.491)***3.067

(0.491)***3.062

(0.491)***3.069

(0.491)***3.065

(0.491)***

Some college5.220

(0.679)***5.214

(0.679)***5.218

(0.679)***5.206

(0.678)***5.217

(0.678)***5.212

(0.678)***

College+12.525

(1.336)***12.518

(1.336)***12.525

(1.336)***12.487

(1.336)***12.503

(1.336)***12.495

(1.336)***


(0.493)**-1.084

(0.493)**-1.083

(0.493)**-1.093

(0.492)**-1.088

(0.492)**-1.091

(0.492)**


(0.607)***-2.397

(0.607)***-2.394

(0.607)***-2.438

(0.606)***-2.429

(0.607)***-2.433

(0.606)***


(0.880)***-5.391

(0.880)***-5.390

(0.880)***-5.444

(0.880)***-5.438

(0.880)***-5.441

(0.880)***


(0.031)***-0.287

(0.030)***-0.287

(0.030)***-0.286

(0.031)***-0.286

(0.031)***-0.286

(0.031)***

Family size-0.912

(0.130)***-0.911

(0.130)***-0.912

(0.130)***-0.915

(0.130)***-0.916

(0.130)***-0.915

(0.130)***

Child < 5-5.435

(0.372)***-5.434

(0.372)***-5.435

(0.372)***-5.422

(0.372)***-5.424

(0.372)***-5.422

(0.372)***

Observations 7510 7510 7510 7510 7510 7510

Adjusted R2 0.0989 0.0989 0.0989 0.0998 0.0997 0.0998


Table 11. Attitudes and women’s decisions to work.(1) (2) (3) (4) (5) (6)

WVS-Q1-0.004(0.003)

-0.003(0.003)

WVS-Q2-0.113(0.104)

-0.103(0.104)

WVS- Q1&2-0.007(0.007)

-0.007(0.007)

FLFP*HL0.004

(0.002)*0.004

(0.002)*0.004

(0.002)*

Age0.008

(0.026)0.008

(0.026)0.008

(0.026)0.008

(0.026)0.008

(0.026)0.008

(0.026)

Age square / 100-0.014(0.032)

-0.014(0.032)

-0.014(0.032)

-0.013(0.032)

-0.013(0.032)

-0.013(0.032)

High school0.179

(0.047)***0.179

(0.047)***0.179

(0.047)***0.180

(0.047)***0.180

(0.047)***0.180

(0.047)***

Some college0.401

(0.066)***0.401

(0.066)***0.401

(0.066)***0.401

(0.066)***0.401

(0.066)***0.401

(0.066)***

College+1.063

(0.135)***1.063

(0.135)***1.063

(0.135)***1.057

(0.135)***1.057

(0.135)***1.057

(0.135)***


(0.047)**-0.117

(0.047)**-0.117

(0.047)**-0.116

(0.047)**-0.116

(0.047)**-0.116

(0.047)**


(0.058)***-0.235

(0.058)***-0.235

(0.058)***-0.235

(0.058)***-0.235

(0.058)***-0.235

(0.058)***


(0.088)***-0.490

(0.088)***-0.490

(0.088)***-0.489

(0.088)***-0.489

(0.088)***-0.489

(0.088)***


(0.003)***-0.027

(0.003)***-0.027

(0.003)***-0.027

(0.003)***-0.027

(0.003)***-0.027

(0.003)***

Family size-0.074

(0.013)***-0.074

(0.013)***-0.074

(0.013)***-0.074

(0.012)***-0.074

(0.012)***-0.074

(0.012)***

Child < 5-0.546

(0.040)***-0.546

(0.040)***-0.546

(0.040)***-0.545

(0.039)***-0.545

(0.039)***-0.545

(0.039)***



Turkey

Mexico Ita

lyChile

KoreaGree

ce

Hungary

Polan

d

Belgium

Slovak

Repub

lic

Irelan

d

Luxe

mbourg

Japan

Czech R

epub

lic

Sloven

iaFra

nce

United St

atesIsr

aelSp

ain

Portu

gal

Australi

a

Austria

Eston

ia

United Kingd

om

German

y

New Ze

aland

Finlan

d

Canad

a

Netherl

ands

Denmark

Norway

Switz

erlan

d

Swed

en

Icelan

d20

30

40

50

60

70

80

90

Country

Fem

ale

labo

r fo

rce

part

icip

atio

n

Figure 1. Female labor force participation for OECD countries in 2013.

Figure 2. Immigration densities in the U.S.

€¦ · web viewcultural attitudes may impact economic decisions, especially those concerning...

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