




































American Research Journal of Economics, Finance and Management 

Volume 10 Issue 1, January-March 2022 

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BRIDE PAYMENTS AND FERTILITY CHOICES: A MENA 
PERSPECTIVE 

 
 

Dr. Fatima A. Al-Mansoori and Dr. Ahmed M. Al-Saud 
Islamic Economics Institute, King Abdul-Aziz University, Jeddah, Saudi Arabia 

 
Abstract: This study examines the impact of bride payments, a significant cultural and economic 
element within the MENA (Middle East and North Africa) region, on the economic independence of 
women within households and its potential influence on fertility decisions. Previous research has 
highlighted the pivotal role of economic independence in shaping fertility choices among women. 
Greater economic autonomy may expand women's decision-making power within households, 
potentially affecting fertility outcomes. 
In the context of the MENA region, where women's roles in economic and public life have evolved 
significantly, the study investigates the empirical relationship between bride payments and women's 
empowerment. These payments, intrinsic to marriage contracts and often associated with the Mahr 
in Islamic law, play a crucial role in the financial dynamics of marriages in the region. Despite their 
historical and cultural significance, bride payments remain relatively unexplored by economists in 
the context of the MENA region. 
Utilizing data from Egypt, Tunisia, and Algeria, this research seeks to shed light on the economic 
implications of bride payments and their potential influence on women's economic autonomy within 
households. The study's findings aim to contribute to a deeper understanding of the complex interplay 
between cultural practices, economic dynamics, and women's empowerment in the MENA region. 
Keywords: Bride Payments, Economic Independence, Fertility Decisions, Women's Empowerment, 
MENA Region 
 
 
1. Introduction 
Several studies have documented the effect of economic independence of female position within the 
household on fertility decisions, both in developed and developing countries and found different 
outcomes. Greater economic independence of the wife may increase her options within a household, 
thereby increasing her options for fertility (Jennings & Pierotti, 2016). Reducing the male-female wage 
gap increases women's choice of several children by improving the woman's intra-household bargaining 
power (Siegel, 2017). Several methods can be used to increase an individual's independence. While the 
participation of women in the labor force is the most extensively investigated, the investigation of the 
effects of alternative indicators that could boost the status of women within the household has been 
comparatively limited. Several extant literatures on intra-household decision-making have 
demonstrated that women's empowerment significantly impacts further economic development (Pierre 
et al., 2018).  

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The MENA region has seen a significant change in the role of women in economic life and politics. 
There are about 22 countries located in the MENA region that have improved women's status, especially 
in the gulf countries, where women have become more active in business, education, and public 
activities. The bride payment, which is a fundamental element of the marriage contract and coincides 
with women's right to have it at the time of marriage from their husbands, has not been examined 
empirically by economists in the case of the MENA region. The ancient civilizations of Egyptians, 
Mesopotamians, Hebrews, Aztecs, and Incas all used bride prices (Quale, 1988). The Muslim marriage 
contract differs from a standard western civil marriage license in terms of the bride price or what is 
called Mahr, which can be a sum of money or any other valuables such as gold that the husband gives 
or undertakes to give to the bride upon Marriage (Ambrus et al., 2010). More than 70% of the costs of 
marriage in MENA countries are paid by the groom and his family  (Goodarzi, 2018). This study uses 
data from three countries in the MENA region, namely Egypt, Tunisia, and Algeria, and most of their 
population follows the Islamic religion. More importantly, Islamic law requires a form of a bride price 
to make any marriage valid (Rapoport, 2000; Quale, 1988). It is widespread in societies of the MENA 
region for the bride and her family to use money from the Mahr to help cover the bride-side contribution 
of furniture and other household items (Elbadawy, 2009).   
In this paper, I use the number of children to indicate a female's bargaining power to see how it varies 
with Mahr payments. Two recent articles find ambiguous results in studying the impact of bride prices 
on women's fertility.   
Mbaye and Wagner (2017) found a significant effect with a negative sign on examining the relationship 
between bride price payments and fertility for women in the case of rural Senegal. In contrast, Lowes 
and Nunn (2018) found no evidence that a high bride price payment is linked with earlier marriage or 
higher fertility in the Democratic Republic of the Congo.  
It is, therefore, important from a policy point of view to understand the effect of bride prices on women's 
bargaining power in each country through the lens of the traditions and institutions specific to that 
country's culture. I employed the instrumental Variable estimation technique to address the 
endogeneity of bride price payment. I use the average price of gold at the time of marriage as a source 
of exogenous variation to proxy for bride price payment since the price of gold provides information on 
how much gold the bride can receive from the groom at the time of marriage. I assume that the groom's 
side will react to a high gold price at the time of marriage and estimate its effect on fertility decisions 
inside the marriage. Because the price of gold is established worldwide outside of the MENA region, it 
allows for reasonable exogenous fluctuation in the groom's and bride's first payments.  
Since the groom or groom's family is responsible for paying the amount of gold at a given price of gold, 
the amount of gold does provide if the bride price is low or high and would affect the psychology of the 
couple. This paper contributes to a better understanding of the impact of bride price payment by 
examining the link between the bride price value and fertility rate in the MENA region. Moreover, I 
conduct a detailed heterogeneity analysis by exploring various variables through which the (Mahr) 
influences fertility decisions, such as education, age, and urbanization. Due to the absence of empirical 
studies of bride price payments for the MENA countries, this paper is the first empirical study to analyze 
such a relationship by using a unique dataset of married couples consisting of 54,800 observations. 
This paper's main result confirms that a high bride price paid by the groom is associated with less 

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bargaining power for women by giving more birth. These results are robust to conditioning on a variety 
of socio-demographic, temporal, and spatial parameters.   
2. Related Literature   
Based on the theoretical literature, several factors are essential in changing fertility preferences, such 
as access to information, control of resources, and participation in decision-making (Hindin, 2000). 
Also, education and social media access can help empower women and positively impact the ideal 
family size. Other essential factors critical in household decisions about reproduction and overall 
fertility levels are culture, religious beliefs, and gender relations (Atake & Gnakou, 2019). According to 
the literature that studies the association between women's empowerment and fertility preferences in 
the MENA (the Middle East and North Africa) and SSA (SubSaharan African) countries, there are three 
dimensions of a woman's empowerment. One dimension is the sociocultural dimension which contains 
education achievement and access to information. The second dimension focuses on economic 
participation as the ownership status and sustainable income. The third dimension focuses on the 
familial dimension and contains factors such as age and participation in household decisions.  
Horne et al. (2013) and Kaye et al. (2005) find evidence that bride price payment strengthens normative 
constraints on women's reproductive autonomy and limits their fertility preferences to their partners 
in Ghana. Zhang and Chan (1999) find that the bride price does not affect the bargaining position within 
Marriage in Taiwan. A related contribution is the case study of Mbaye and Wagner (2017), who found 
that the higher bride price payments decrease the fertility rate for women in rural Senegal. Recent 
literature by Lowes and Nunn (2018) found evidence that women who receive more wealth at the time 
of marriage are less likely to accept domestic violence and are happier. These contradictory empirical 
findings may be attributed to the fact that bride price has a different influence over variety in the norms 
and social institutions of countries and cultures. The study contributes to the existing literature by 
employing a large sample of three essential countries in the MENA region, namely Egypt, Algeria, and 
Tunisia, to better understand the impact of bride price payment on the fertility rate.  
On the other hand, Suran et al. (2004) find the opposite outcome in explaining the bequest theory. That 
is, married women who paid dowry at marriage have a higher probability of reporting domestic violence 
than those who did not. This paper concentrates on studying the bride price variable's mechanism 
rather than the dowery variable since it is the fundamental tool of marital payments and understanding 
marriage practices in the MENA region. Several studies, such as (Mincer, 1963; Becker, 1981; Willis, 
1973), find a strong relationship between women's salary and time allocation for raising children. That 
is, an improvement in females' salaries increases the opportunity cost of time allocation for raising 
children, encouraging women to engage in the labor market. In other words, female employment has a 
negative impact on the fertility rate.   
Similarly, Phan (2016) examined the link between women's empowerment and fertility preferences of 
women in four Southeast Asian countries and found that women's empowerment factors are one of the 
keys to women's fertility preferences, including the ideal number of children and their preference for 
sons.   
Two types of instruments for dowry and bride price payments are used by Zhang (1999), which are 
regional grain yield shocks to have an important influence on household wealth accumulation and 
sibling sex composition to likely affects the savings available for marital payments. However, these 

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instruments are not appropriate for this study since their sample of surveyed households is in rural 
areas where livelihoods have long depended on farming. In contrast, this study used a random sample 
between rural and urban areas.   
The second instrument used by them is the sibling sex composition of the bride and groom, which is 
not an essential determinant of a household's wealth, and it has been approved by (Rajan et al., 2018) 
that wealth or sibling sex composition and education are not associated with the fertility. Average gold 
price is used as an instrument for bride price values when estimating the effect of pre-marital 
endowments on the decision of fertility rate in this study. There is more information about this type of 
instrument in the identification section.   
 3. Data & Descriptive Statistics   
3.1 Data   
The study is based on micro-level data on Egypt and Tunisia from the 1998 integrated labor market 
panel survey (ILMPS). Based on a nationally representative household sample, the survey provides data 
for 54,832 households. The ILMPS is a data set that integrates and harmonizes data and variables from 
five rounds of the Egypt labor market panel survey (in years 1988, 1998, 2006, 2012, and 2018), two 
rounds of the Jordan labor market panel survey (2010 and 2016), and the 2014 Tunisia labor market 
panel survey. It contains created, compatible variables that are harmonized (to the extent possible) 
across all rounds. The questionnaire was carefully designed to understand marriage practices in MENA 
countries comprehensively. In the data collection, three separate questionnaires were used to collect 
information from the selected sample: the household survey, the women's survey, and the men's survey. 
This paper uses data from the women's questionnaire, which was used to collect information from all 
cases, and I restrict the sample to married individuals at the time of the survey. These women were 
asked questions about themselves and their children born on topics including but not limited to 
education, bride price, wealth, health, marriage, occupation and husband's background characteristics, 
childhood mortality, and domestic violence. The survey collects data on bride price payments to capture 
women's empowerment effect. Women have been asked about the value of the bride price (Mahr) given.   
3.2 Dependent variable   
I measure the fertility rate by using the number of children at the time of the survey. It is a continuous 
variable of all births reported in a woman's history. In the sample, women were asked about the number 
of births they gave, and most had only 4. The global fertility rate declined from 3.2 births per woman in 
1990 to 2.5 in 2019 (United Nations, 2020). Also, the fertility rate declined in Northern Africa and 
Western Asia over the same period (from 4.4 to 2.9). In addition, the highest proportion of women who 
reported having more children than their ideal number was found in Egypt (42%), followed by Jordan 
(31%) and Tunisia (13%). This dependent variable type has been used widely in the literature and linked 
with women's empowerment (see Jejeebhoy & Sathar, 2001; Seetha, 2020). The robustness of the result 
is conducted by employing different types of dependent variables, including a survey question asking 
women whether the husband is justified to beat his wife when she burns food and whether women are 
afraid of disagreeing with the husband or other males in the household.  
3.3 Descriptive Statistics  
The summary statistics for all the variables used in the empirical models are shown in Table 1. Table 1 
shows that the average fertility rate is four children. The average age of the sample mothers and fathers 

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is 30 and 37, respectively. The proportion of literate men (.56 %) exceeds the proportion of women who 
are literate (.37 %) by 19 percentage points. Around six household members live in a typical household. 
On average, the age of the wife and husband at first marriage is 20.8 and 26.4, respectively. I 
differentiate between households who do live in urban and rural areas to control for influential 
geography. Fifty percent of the household live in urban areas. To capture the household characteristics, 
I use ownership status at the time of the marriage, showing that only 22 percent own their house. 
Women's well-being and empowerment would improve if the husband were related to the wife by blood 
(Institute for Women's Policy Research, 2015). In the sample, 6% of wives are related to their husbands.  
4. Identification & Empirical Specification  
4.1 Identification   
Different methodological approaches have been used to analyze bridal payments. Most literature 
presents descriptive statistics based on data collected from household surveys and specifically from 
women's questionnaires on topics including but not limited to education, bride price, wealth, health, 
marriage, occupation and husband's background characteristics, childhood mortality, and domestic 
violence (Zhang & Chan, 1999; Horne et al., 2013; Kaye et al., 2005; Mbaye & Wagner, 2017; Bishai & 
Grossbard, 2010; Gaspart & Platteau, 2010; Ashraf et al.,  
2016). Instrumental variables for bride price to control for both simultaneity and omitted variables in 
the case of China, namely the deviation from the trend in provincial per capita grain yield in the year 
immediately preceding marriage, the sibling sex composition of the bridegroom, and parental 
education to reflect savings available at the time of marriage (Zhang & Chan, 1999; Brown, 2009).    
The methodology in this study differs from (Lowes & Nunn, 2018) in dealing with bridal payments as 
an exogenous variable ignoring several issues that may arise, such as omitted variables. Hence, their 
identification strategy is thus subject to endogeneity problems. (Zhang & Chan 1999) used two types of 
instruments for dowry and bride price payments in the case of China, which are regional grain yield 
shocks to have an important influence on household wealth accumulation and sibling sex composition 
to likely affects the savings available for marital payments. However, these instruments are not 
appropriate to be used in the case of our sample since their sample of surveyed households is only in 
rural areas where livelihoods have long depended on farming. Therefore, I implement a two-stage 
estimation strategy in which the deflated average gold price at the time of marriage is used as an 
instrument for bridal payments when estimating the effect of pre-marital endowments on the decision 
of fertility rate in our sample.  
4.2 Empirical Specification  
Following (Becker, 1981; McElroy and Horney, 1981), I use the number of births given by the wife as 
the dependent variable Υ and BP as the independent variable reflecting the average bridal payment 
during the first marriage; to see each couple in the dataset of observations was above or below the 
average bride price for the year they were married. Specifically, I apply the bridal payments ln(BP+1) 
logarithm to account for any payment equal to 0 and incorporate the control variables to construct the 
following fixed effect model: 
Υ whtc = α + β1 Ln (BP)whtc + Z1 β2 whtc + δt + γc + Ɛ whtr   (1) 
The subscript w denotes wife, h household, t year, and c country. Z is a vector of demographic and 
explanatory variables, including differences in the husband and wife's age and differences in education 

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levels, as well as household characteristics, including ownership status at the time of the marriage and 
whether the husband is related to the spouse. The reason behind adding these vectors of variables is 
that the groom's and bride's characteristics would play a vital role if the bride's wealth were paid to 
attract a better bride or if the bird's family demanded it. Finally, I control for the fixed effect to account 
for survey FE, δ, and location FE, γ, to reflect women's bridal payment differences at the country level. 
Since many of the younger women are not completed their fertility, I include age-fixed effects in the 
regression models to compare women of the same age.    
Marriage payments are unlikely to be exogenous in equation (1) since any unobserved characteristic of 
the female that affects these payments may also affect her decision to have a certain number of children. 
In addition, females with very likable personalities may receive higher wealth from their spouses and 
have a better household position than those with unsavory characters. In either case, using ordinary 
least squares (OLS) to estimate equation (1) would produce biased and inconsistent estimators. The 
bridal payments may thus be estimated by:  
BP whtc = α2 + Z1 δ1 + Z2 δ2 + + δt + γc + Ɛ 2  (2) 
The instrumental variable is represented by Z2, which explains BP and should be independent of Y. 
Several channels could potentially affect intra-household bargaining, and bridal payments at the time 
of marriage are one of these channels. I use the average deflated price of gold at the time of marriage as 
a source of exogenous variation to reflect the bridal payments. Since the gold price is set internationally, 
this exogenous shock affects household wealth accumulation and households' ability to make transfers 
linked with marriage. Plus, it provides plausibly exogenous variation in the initial endowment of the 
bride and groom. Figure.1 presents the average deflated trend of the price of gold at the time of 
marriage. Since gold is an integral part of the Mahr basket, the unusually high price of gold at the time 
of marriage and an economic slowdown has negatively affected the wedding season demand.  
Table 2 presents first-stage estimates for the determinants of bride price. Column 1 shows the 
determinants of bride price, including exogenous shocks to gold prices, and controlling for the 
characteristics of the wife CW and husband CH respectively by using age, age of the spouse at marriage, 
and a binary variable equal to 1 if the spouse is literate. Also, I consider household characteristics, CHH, 
by controlling the type of ownership status at marriage to proxy for the wealth and whether a husband 
is related to his wife or not, column 2 including the squared for some variables to see a nonlinear 
relationship. The results of the first stage estimation in Table 2 are consistent with the second scenario 
with a fixed Mahr basket. That is, a high average price of gold at the time of marriage increases the value 
of the bridal payments if the amount of these payments. The coefficient of the gold price is highly 
significant. It indicates that, on average, a 1% increase in average gold price at the time of marriage is 
associated with a 0.6 % improvement in the bridal payments.  
5. Empirical Results   
5.1 Main result  
Table 3 presents the ordinary least squares and the IV regression results investigating the relationship 
between the average price of bridal payments at the time of marriage and fertility decision proxied by 
the number of children. The fertility decision is measured by the number of children as a continuous 
variable. Column 1 presents the bivariate estimates conditions on year fixed effects, survey fixed effects, 

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and characteristics of both couples. Column 2 additionally conditions contemporaneous age and its 
square, the woman's age at her current marriage, and its square.   
I find that a higher average of price payments at the time of marriage increases the predicted probability 
of giving more children by the married women within the household with an estimated coefficient of 
0.15 in column 2. That is, at the mean bride price of 4084, a woman gives birth to about .015 children 
more if the average bride price increase by 10%. The result is consistent with previous studies; such 
payment is considered one of the critical factors associated with domestic violence and reduces 
women's bargaining power options within the household (Kaye et al., 2005; Bishai & Grossbard, 2010; 
Gaspart & Platteau, 2010; Ashraf et al., 2016). From the IV estimation, the coefficient linked with bride 
price payment remains significant and positive in all specifications and highly significant at 1% in Table 
3, columns 3-4. The coefficient is 0.09 in column 4 and indicates that increases in the average bride's 
wealth by %10 are associated with about 0.009 more birth given by married women in the MENA 
region.  
The pattern of predictors on other controls is informative and presented in full in Columns 1-4 of Table 
3 Although the education variable for both males and females are insignificant in explaining the 
variation of the outcome, they appear with the correct sign. The result shows that the younger wife at 
the time of marriage has given less birth at the survey date. It seems that related spouses are more likely 
to have more children compared to non-related families. The binary variable of ownership status at 
marriage appears with a negative sign, and it is statically significant to explain the variation of our 
outcome. Also, both co-efficient of the IV estimation are higher than the co-efficient of the OLS 
estimation.     
5.2 Robustness Check  
Instead of using the primary outcome variable of fertility decision to see the impact of bride price 
payments increase, I alternatively tried to employ different types of dependent variables. Women can 
shop without permission, whether the husband is justified to beat his wife when she burns food, and 
women are afraid to disagree with the husband or other males in the household. I present the results in 
Tables 4-6. The statistical significance of the estimates of the correlation between the different outcome 
coefficients implies that there is evidence that women in the MENA countries lose their autonomy in 
the case of receiving the full Mahr basket. Table 4 shows the estimated result of the bride price and 
shopping without getting permission. The coefficient associated with bride price payments remains 
significant and negative, suggesting that married women classified in shopping without getting 
approval are more likely to influence by increasing the bride price payments relative to those classified 
in shopping with getting a permit. Having a related household (within blood relations) increases the 
rate of doing shopping without getting permission by 0.8 ppts. Table 5 provides the estimated result of 
bride price payments and whether women fear disagreeing with their husbands or other males. The 
coefficient associated with bride price payments remains significant and positive in both specifications.   
It indicates that married women who receive a high rate of bride price payments are more likely to be 
afraid of disagreeing with their husbands or other males in the household. Tale 6 shows that married 
women who justify their husband beating them when they burn food rises by.008 ppts as pride price 
payments increase.  

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By using alternative methods of coding, the measure of women's bargaining power. Firstly, I created an 
ordinal measure of women's bargaining power equal to the above three outcome variables of intra-
household bargaining to the respondent is exposed to. This is an index (0,3) which is 0 if women's 
bargaining power does not change in the household and gradually adds 1 for a non-zero response to 
each of the 3 questions mentioned above. Table 7 provides the result of the IV estimation, and the 
coefficient linked with bride price payment remains significant and positive in column 4. It indicates 
that married women are more likely to face domestic violence by lowering their bargaining power with 
an estimated coefficient of -0.12.  
Another robustness check is conducted in this study by using the oil price as an alternative to the gold 
price. Historically, fertility tends to decline during the fluctuation of oil prices (Sobotka, 2011). Since 
gold and oil prices have correlated positively in the previous 50 years (Shahbaz et al.,2017), I used the 
average price of crude oil adjusted for inflation at the time of marriage to examine if the effect is an 
artifact of unobservable market fluctuations. Table 8 provides evidence that a higher average of price 
payments at the time of marriage increases the predicted probability of giving more children by the 
married women within the household, with an estimated coefficient of 0.53 in column 2. The result is 
similar to the main result in Table 6, with a slight difference in the coefficient magnitude. It suggests 
that increases in the average bride's wealth by %10 are associated with about 0.053 more birth given by 
married women in the MENA region.  
  
 5.3 Heterogeneity Analysis   
I conduct a detailed heterogeneity analysis by exploring different variables over which the (Mahr) 
influences the fertility decisions, such as religion, education level, year of marriage, and the location of 
the household. Table 9 provides the heterogeneity estimation of the average bride price and fertility 
rate to compare different groups. The result in Table 9 shows that, under the high average value of the 
bride's wealth, married women characterized as rural, less educated, poorest, unemployed, and aged at 
first marriage between 21-35 years are more likely to give more birth compared to the contradictory 
groups.  
The result indicates that non-educated married women are more likely to give birth than educated 
women as the payments of (Mahr) increased. It has been proven (Lundberg & Pollak, 1993) that 
education is vital in improving women's bargaining power within their households since it gives them 
knowledge, skills, and resources to make life choices that enhance their welfare. Due to access to 
services and infrastructure, more opportunities are available to engage in paid employment and enjoy 
a relaxation of sociocultural restrictions; urban women generally are better off compared to women 
living in rural areas (Institute for Women's Policy Research, 2015). Table 9 distinguishes between urban 
and rural women to see how they respond to a change in the bride price wealth (Mahr). The coefficient 
associated with average bride price payments indicates that rural married women would be less 
bargaining power by giving more birth as the value of the average bride price increases. Mahr dynamics 
can be further differed by analyzing the nature of the marriage, namely age at first marriage. The 
coefficient associated with married women aged at first marriage between 21-35 years average would 
be less bargaining power by giving more birth as the value of the average bride price increases.   
5. Conclusion   

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In many Arab countries, the bride's wealth system has a long history and is still ingrained in the culture 
there. This marriage payment system dramatically impacts women's position and bargaining power. 
Such payments are essential in the marriage market institution even as countries and regions develop. 
This paper has examined the mechanism of average bride price payments and its impact on the intra-
household bargaining of panel survey data on Egypt, Jordan, and Tunisia. The dataset used in this study 
covered five rounds of the Egypt Labor Market Survey (1988, 1998, 2006,2012, and 2018), two rounds 
of the Jordan labor market survey (2010 and 2016), and the 2014 Tunisia Labor Market survey. I 
contribute to the existing literature by providing a better understanding of the impact of average bride 
price payment on the fertility decision by using two crucial instruments: the average price of gold and 
oil. Due to the absence of empirical studies of bride price payments for the MENA countries, this paper 
is the first empirical study, based on reviewing the existing literature, that concentrates on analyzing 
such a relationship in this area.  
This study shows that the price of gold at the time of marriage is a source of exogenous variation in the 
initial endowment of the bride at the time of marriage. The persistent precision of the estimates adds 
to the existing literature by showing that Mahr practices continue to be widespread in the MENA 
region, and on average, a Mahr basket is likely to contain a high amount of gold along with cash. This 
paper's main result confirms that an increase in the average price payments paid by the groom is 
associated with many births by married women. These findings provide significant empirical support 
for the theoretical literature that links resource control to marital outcomes. Moreover, based on the 
robustness of these findings, it is plausible that the bride price will lead women to lose their autonomy, 
and these results are compatible with anthropological literature.   
References 
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Appendix  
Table 1. Descriptive Statistics  
  

  Mean  Std.Dev.  Min  Max  

Number of Children  3.90  1.97  0  17  

Gold Price at time of Marriage  1075.80  451.27  294.12  1668.86  

Bride Price (Mahr)  2152.39  5527.41  0  90000  

Average Bride Price grouped by the 
first year of marriage (Mahr)  

4083.98  831.17  106.14  5581.44  

Characteristics of the wife          

Age of the wife  30.88  8.38  15  75  

Age of the wife at Marriage  20.84  4.17  7  52  

Literacy of the wife  
Characteristics of the husband  

0.37  0.48  0  1  

Age of the husband  37.14  16.39  16  82  

Age of the husband at Marriage  26.40  4.86  14  73  

Literacy of the husband  
Characteristics of the Household  

0.56  0.49  0  1  

Ownership status at marriage 
Husband related to wife  

0.22 0.06  0.41 0.24  0  
0  

1  
1  

          
  

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https://www.ndi.org/
https://doi.org/10.1016/j.jdeveco.2019.102389
https://doi.org/10.1016/j.worlddev.2006.08.005
https://doi.org/10.31899/pgy2.1017
http://hdr.undp.org/en/reports/global/
https://doi.org/10.1086/260152
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Table 2. Results of First Stage IV Regressions Dependent Variable: Average Bride Price Payment  

Variable  
FSLS  
(1)  

FSLS  
(2)  

Log of Gold Price  
            .214 (.002) 
***  

             .626 
(.002) ***  

Age of the wife  
-.0140  
(.0043) ***  

-0.0107 (.0225)  

The age of the wife squared    
0.0003  
(0.0003)  

Age of the wife at Marriage  
-0.0105 (0.007)  -0.0101 (0.041)  

Age of the wife at marriage squared    
.0014  
(0.0009)  

Wife's education  
-1.366  
(5.566)  

-0.344  
(5.566)  

Age of the husband  
-.1032  
(0.1169)  

-0.0824  
(0.1175)  

The age of the husband squared    
0.0048  
(.0244)  

Age of the husband at Marriage  
-0.0119  
(0.0021)  

-.0780  
(.0451) *  

Literacy of the husband  
.7438  
(5.565)  

.7329  
(5.566)  

Ownership status at Marriage  
-0.204  
(.0414) ***  

-.2051  
(.0413) ***  

Husband related to wife  
0.5531  
(0.0850) ***  

0.2032  
(0.0609) ***  

Country dummies  Yes  Yes  

Year dummies  Yes  Yes  

Constant  
421.059  
(19.888) ***  

420.807  
(19.904) ***  

Observations  54,832  54,832  

F- test  471.22  392.96  

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R^2  0.224  0.224  
Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance 
of the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance levels, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
Table 3. OLS and Instrumental Regression of Bride Price and Fertility Rate  

Variable  
Dependent Variable: Number of Children  

OLS(1)  OLS(2)  IV(3)  IV(4)  

Log of Average Bride Price         .110  
   (.016)***     

        .111    
(.014)***   

0.110  
(.020) ***  

0.090  
 (.020) ***  

Age of the wife  
0.162  
(0.001) ***  

0.398  
(0.015) ***  

0.187  
(0.001) ***  

0.299  
(0.008) ***  

The age of the wife squared    
-.0041  
(.0002) 
***  

  
-0.0015  
(0.0001) ***  

Age of the wife at Marriage  
-0.162  
(0.002) ***  

-0.251  
(0.016) ***  

-0.1990  
(0.0026) 
***  

-0.410  
(0.003) ***  

Age of the wife at marriage 
squared  

  
0.010  
(0.005) *  

  
0.021  
(0.004) ***  

Wife's education  
-1.200  
(1.195)  

-1.148  
(1.180)  

-1.110  
(2.123)  

-1.006  
(2.226)  

Age of the husband  
0.016  
(0.025)  

.032  
(.025)  

0.0025  
(0.0446) *  

0.013  
(0.046)  

The age of the husband 
squared  

  
- 0.001  
(0.001)  

  
- 0.001  
(0.001)  

Age of the husband at 
Marriage  

-0.013  
(0.005) **  

-0.026  
(0.002) 
***  

-0.012  
(0.003) ***  

-0.010  
(0.001) ***  

Literacy of the husband  
-1.112  
(1.194)  

-1.064  
(1.180)  

-1.018 
(2.123  

-1.423  
(2.537)  

Ownership status at marriage  
-.002  
(0.008)  

-.001  
(0.008)  

-0.179  
(0.017) ***  

-0.141  
(0.016) ***  

Husband related to wife  
.085  
(0.016) ***  

0.086  
(0.016) ***  

0.0746  
(0.0234) ***  

0.142  
(0.028) ***  

Country dummies  Yes  Yes  Yes  Yes  

Year dummies  Yes  Yes  Yes  Yes  

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Constant  
.942  
(0.102) ***  

-1.311  
(0.218)  

-.336  
(0.119) ***  

-2.195  
(.271) ***  

Observations  54,832  54,832  54,832  54,832  

Wald chi2(2)  .  .  23646.42  21690.42  

F- test  1058.11  924.32  .  .  

R^2  0.512  0.524  0.530  0.484  
Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance of 
the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance levels, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
4- Instrument Variable: The Gold Price at the time of marriage (log). 
 
 
 
 
 
 
 
Table 4. Estimation Result of Average Bride Price and if a married women Shopping Without Getting 
Permission 

Variable  
Dependent Variable: Shopping Without Getting 
Permission (0,1)  
OLS(1)  OLS(2)  IV(3)  IV(4)  

Log of Average Bride 
Price  

-0.0051  
(0.0006)***  

-0.0061  
(0.0006)***  

-0.001  
(0.0060)***  

-0.0012  
(0.0007)*  

Wife's Characteristics   Controlled   Controlled  Controlled   Controlled  
Husband's 
Characteristics  

Controlled  Controlled  Controlled  Controlled  

Household's 
Characteristics  

Controlled  Controlled  Controlled  Controlled  

Country dummies  Yes  Yes  Yes  Yes  
Year dummies  Yes  Yes  Yes  Yes  

Constant  
0.255  
(0.006) ***  

0.285  
(0.006) ***  

.097  
(0.058) *  

.180  
(0.057) ***  

Observations  54,832  54,832  54,721  54,721  
F – test  55.22  58.56  62.67  182.46  
R^2  0.002  0.008  0.017  0.086  

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Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance of 
the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance levels, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors. 4- Instrument 
Variable: The Gold Price at time of marriage (log)  
Table 5. Estimation Result of Average Bride Price and Whether Women are Afraid of Disagree. With 
Husband  
or Other Males in HH  
Dependent variable: whether women are afraid of disagreeing with the husband or 
other males in HH  
(0,1)  
Variable  OLS(1)  OLS(2)  IV(3)  IV(4)  
Log of Average Bride Price  .0047  

(.0009) ***  
.0041  
(.0009) ****  

.0440  
(.008) ***  

.0356  
(.008) ***  

Wife's Characteristics   Controlled   Controlled  Controlled   Controlled  

Husband's Characteristics  Controlled  Controlled  Controlled  Controlled  

Household's Characteristics  Controlled  Controlled  Controlled  Controlled  

Country dummies  Yes  Yes  Yes  Yes  
Year dummies  Yes  Yes  Yes  Yes  

Constant  
0.447  
(0.009) ***  

0.412  
(0.012) ***  

.0422  
(0.034) *  

.101  
(0.105)  

Observations  54,832  54,832  54,721  54,721  
F – test  8.16  10.34  7.13  6.03  

R^2  0.000  0.001  0.015  0.016  

Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance 
of the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance levels, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
4- Instrument Variable: The Gold Price at the time of marriage (log).  
  
Table 6. Estimation Result of Average Bride Price and Husband Justify Beat His Wife When She Burns 
the Food  
Dependent Variable: Spouse justifies beating his wife when she burns food  
Variable  OLS(1)  OLS(2)  IV(3)  IV(4)  

Log of Average Bride Price  
.0001  
(.0005)  

.0009  
(.0005) *  

.0081  
(.0009) ***  

.0074  
(.0009) ***   

Wife's Characteristics   Controlled   Controlled  Controlled   Controlled  
Husband's Characteristics  Controlled  Controlled  Controlled  Controlled  
Household's Characteristics  Controlled  Controlled  Controlled  Controlled  

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Country dummies  Yes  Yes  Yes  Yes  
Year dummies  Yes  Yes  Yes  Yes  

Constant  
0.068  
(0.004) ***  

0.041  
(0.006) ***  

.052  
(0.030) *  

.115  
(0.109)  

Observations  54,832  54,832  54,721  54,721  
F – test  0.05  21.20  7.98  6.06  
R^2  0.000  0.002  0.015  0.016  
Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance 
of the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance levels, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
4- Instrument Variable: The Gold Price at the time of marriage (log).  
Table 7. Estimation Result of Alternative Ways of coding women’s bargaining power.  
  
Dependent Variable:  women’s bargaining power (0-3)  
Variable  
  

OLS  
  (1)  

OLS  
  (2)  

  IV   (3)    IV   (4)  

Log of Average Bride 
Price  

-.274  
(.008) ***  

-.301  
(.008) ***  

-.059  
(.024) **  

-.123  
(.034) ***  

Age of the wife  -.004  
(.0007) ***   

-.004  
(.002)***  

-.028  
(.001)***  

-.017  
(.001)***  

The age of the wife 
squared  

  -.0007  
(.0002)  

  -.0001  
(.0001)  

Age of the wife at 
marriage  

-.0001  
(.0004)  

-.0001  
(.0005)  

-.0007  
(.0014)  

-.0141  
(.0110)  

Age of the wife at 
marriage squared  

  .0003  
(.0028)  

  .0003  
(.0022)  

Wife’s education  -.0541  
(.0025) ***  

-.0592  
(.0020) ***  

-.0540  
(.0085) ***  

-.0532  
(.0082) ***  

Age of the husband  .001  
(.0015)  

-.002  
(.002)  

-.0012  
(.0061)  

-.0004  
(.0060)  

The age of the husband 
squared  

  .0011  
(.0160)  

  .0014  
(.0141)  

Age of the husband at 
marriage  

-.0132  
(.0241)  

-.0140  
(.0254)  

-.0164  
(.0401)  

-.0180  
(.0351)  

Literacy of the husband  -.0017  
(.0168)  

-.0042  
(.0110)  

-.0015  
(.015)  

-.0080  
(.0151)  

Ownership status at 
marriage  

-.0085  
(.005)  

-.0042  
(.0025)  

-.0019  
(.0024)  

-.0030  
(.0024)  

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Husband related to wife  -.0065  
(.002)  

-.0038  
(.0070)  

-.0031  
(.0080)  

-.0038  
(.007)  

Country dummies  Yes  Yes  Yes  Yes  
Year dummies  Yes  Yes  Yes  Yes  
Constant   3.03  

(0.092) ***  
3.29  
(0.090) ***  

.825  
(0.260)***  

1.57  
(0.391)   

Observations   54,832  54,832  54,832  54,832  
F – test / Wald  816.30  850.86  733.42  728.38  
R^2  0.05  0.07  0.14  0.13  

Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance of 
the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
4- Instrument Variable: The Gold Price at the time of Marriage (log).  
5- The dependent variable is a count from 0 to 3. 
Table 8. Effect of price of bride’s wealth on number of children when introduces oil price as IV  
  

Variable  
  

  IV   (1)    IV   (2)  

Log of Average Bride Price  .867  
(.063)***  

.531  
(.041)***  

Age of the wife  -.166  
(.004)***  

-.147  
(.002)***  

The age of the wife squared      
Age of the wife at marriage  -.0003  

(.0010)  
-.0003  
(.0011)  

Age of the wife at marriage 
squared  

    

Wife’s education  -.0254  
(.0063) ***  

-.0224  
(.0068) ***  

Age of the husband  -.0025  
(.0085)  

-.0021  
(.0080)  

The  age  of  the 
 husband  
squared  

    

Age of the husband at 
marriage  

-.0120  
(.0352)  

-.0119  
(.0358)  

Literacy of the husband  -.0020  
(.005)  

-.0020  
(.005)  

Ownership status at 
marriage  

-.0021  
(.0022)  

-.0017  
(.0020)  

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63 | P a g e  

Husband related to wife  -.0029  
(.0012)  

-.0024  
(.0012)  

Country dummies  Yes  Yes  
Year dummies  Yes  Yes  
Constant   8.930  

(0.657)***  
5.808  
(0.440)***  

Observations   54,832  54,832  
 Wald  1827.52  1124.52  
R^2  0.23  0.326  

Note: 1- The F test has normal distribution N (0,1) and tests the null hypothesis of the insignificance of 
the estimated parameters against the alternative hypothesis of the significance of the estimated 
parameters.  
2- *** and *denote significance at 1 and 10 % significance, respectively.  
3- The figure in parenthesis below the coefficient estimates are standard errors.  
4- Instrument Variable: The average price of oil at the time of Marriage (adjusted for inflation).      
Table 9. The heterogeneity impact of average bride price on the number of children  
  

Dependent Variable :  Number of Children  

  
Group  

  
Categories  

OLS  
(1)  

IV (2)  

Religion  Muslim  
  

.067  
(.020)***  

.535  
(.050) 
***  

  Christian  .204  
(.080) **  

.040  
(.180) **  
  

Country  Jordan  .181  
(.042) 
***  

.664  
(.073) **  

  Tunisia  .100  
(.077)  

.752  
(.132) 
***  

  Egypt  .300  
(.017) ***  

.249  
(.028) 
***  
  

Work Status  Employed  -.294  
(.02) ***  

-.136  
(.045) 
***  

  Unemployed  .160  
(.093) **  

.610  
(.041) 
***  

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Volume 10 Issue 1, January-March 2022 

ISSN: 2836-9416 

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64 | P a g e  

  Out of work  .247  
(021) ***  

.529  
(.036) 
***  
  

Location of Household  Rural  .145  
(.021) ***  

.569  
(.054) 
***  

  Urban  .049  
(.023) **  

.032  
(.001) **  
  

Wealth quintile  Poorest  
  

.266  
(.043) 
***  

.799  
(.101) **  

  Poorer  
  

.316  
(.039) 
***  

.443  
(.090) 
**  

  Middle  
  

.266  
(.039) 
***  

.163  
(.006) 
***  

  Richer  
  

.324  
(.036) 
***  

.134  
(.005) 
***  

  Richest  
  

.200  
(.039) 
***  

.451  
(.077) 
***  
  

Years of Schooling  No Schooling  
  

.075  
(.040) *  
  

.540  
(.100) 
***  

  < 5 years  
  

.073  
(.034) **  

.524  
(.084) 
***  

  6-10 years  
  

.009 (.01) 
*  

.152  
(.044) **  

  >10  
  

.029  
(.01)  
  

.132  
(.002) 
***  

Age At First Marriage   < 20 years  
  

.278  
(.029) 
***  
  

.552  
(.062) 
***  

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American Research Journal of Economics, Finance and Management 

Volume 10 Issue 1, January-March 2022 

ISSN: 2836-9416 

Impact Factor: 4.85 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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65 | P a g e  

  21-35 years  
  

.168  
(.016) ***  

.694  
(.243) **  

  >36 years  .192  
(.077) **  

.021  
(.010) **  

Characteristics of the 
wife  

  Yes  Yes  

Characteristics of the 
groom  

  Yes  Yes  

Characteristics of the 
household  

  Yes  Yes  

Country dummies    Yes  Yes  

Year dummies    Yes  Yes  

Observations     54,832  54,832  

  
  

  
Figure 1. Average Price of Gold Series Internationally from Dec 1960 to Dec 2018.  
  

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