NBER WORKING PAPER SERIES

HEALTH CAPITAL AND THE PRENATAL ENVIRONMENT:THE EFFECT OF MATERNAL FASTING DURING PREGNANCY

Douglas AlmondBhashkar Mazumder

Working Paper 14428http://www.nber.org/papers/w14428

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138October 2008

We gratefully acknowledge comments from seminar participants at the NBER Cohorts meeting Spring2007, UC Davis, Chicago Harris, the National Poverty Center Life Events Conference, Brown University,Stockholm University (IIES), the NBER Childrens Program, the University of Illinois (Chicago), theFederal Reserve Bank of San Francisco, Notre Dame, the Tinbergen Institute, Bristol University, UVA,Cornell, and the University of Chicago (Booth School of Business). We also thank Lena Almond,Hoyt Bleakley, Janet Currie, Carlos Dobkin, Phoebe Ellsworth, Andrew Gelman, Jon Guryan, JimHeckman, Hilary Hoynes, Darren Lubotsky, Doug Miller, Kevin Milligan, Diane Whitmore-Schanzenbach,and Kosali Simon for helpful comments. We thank Ana Rocca, Kenley Barrett, Sarena Goodman,and Shyue-Ming Loh for excellent research assistance and Christine Pal for careful copy editing. Theviews expressed here do not reflect the views of the Federal Reserve system, nor those of the NationalBureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.

© 2008 by Douglas Almond and Bhashkar Mazumder. All rights reserved. Short sections of text, notto exceed two paragraphs, may be quoted without explicit permission provided that full credit, including© notice, is given to the source.

Health Capital and the Prenatal Environment: The Effect of Maternal Fasting During PregnancyDouglas Almond and Bhashkar MazumderNBER Working Paper No. 14428October 2008, Revised May 2010JEL No. I1,I12,J1,J14

ABSTRACT

We use the Islamic holy month of Ramadan as a natural experiment in fasting and fetal health. In Michiganbirths 1989-2006, we find prenatal exposure to Ramadan among Arab mothers results in lower birthweightand reduced gestation length. Exposure to Ramadan in the first month of gestation is also associatedwith a sizable reduction in the number of male births. In Census data for Uganda, Iraq, and the USwe find strong associations between in utero exposure to Ramadan and the likelihood of being disabledas an adult. Effects are particularly large for mental (or learning) disabilities. We also find significanteffects on proxies for wealth, earnings, the sex composition of the adult population, and more suggestiveevidence of effects on schooling. We find no evidence that negative selection in conceptions duringRamadan accounts for our findings, suggesting that avoiding Ramadan exposure during pregnancyis costly or the long-term effects of fasting unknown.

Douglas AlmondDepartment of EconomicsColumbia UniversityInternational Affairs Building, MC 3308420 West 118th StreetNew York, NY 10027and NBERda2152@columbia.edu

Bhashkar MazumderFederal Reserve Bank of Chicago230 S. LaSalle StreetChicago, IL 60604bmazumder@frbchi.org

1 Introduction

Restricted maternal nutrition during critical windows of fetal development can lead to

adaptive physiologic responses that are irreversible and later lead to poor adult outcomes

[Gluckman and Hanson, 2005]. Recent studies by economists have utilized exogenous

shocks “caused by conditions outside the control of the mother” [Currie, 2009] to provide

compelling observational evidence on the importance of prenatal development, which can

impact both subsequent health capital and skill formation [Cunha and Heckman, 2007].

These studies have typically leveraged uncommon and severe historical events, such as

exposure to famine or infectious disease, for identification. It has yet to be established,

however, whether more commonly encountered circumstances also exert significant long-

term effects. Such exposures are not only more directly relevant to the physiologic path-

ways described in the biomedical literature, but also may be more amenable to policy

intervention.

In this study, we consider a common early-childhood exposure that is ongoing today:

disruptions to the timing of prenatal nutrition from meal skipping or dieting during preg-

nancy.1 Specifically we consider the effects of maternal fasting. Muslims generally fast

each day during the lunar month of Ramadan. Fasting includes abstaining from eating

and drinking during daylight hours. Certain persons are automatically exempted from

fasting: “children, those who are ill or too elderly, those who are traveling, and women

who are menstruating, have just given birth, or are breast feeding” [Esposito, 2003]. Preg-

nant women are not explicitly exempted. Three in four Muslim pregnancies overlap with

Ramadan and surveys indicate the majority of pregnant Muslims observe the fast.

Previous studies have shown that fasts associated with Ramadan during pregnancy

can lead to sharp declines in maternal glucose levels along with other biochemical changes

[Prentice et al., 1983, Malhotra et al., 1989]. The supply of glucose and oxygen are

considered to be the key signals of the maternal environment during early embryonic

development and disruptions in their availability can lead to permanent physiological

adaptations that may later lead to disease [Gluckman and Hanson, 2005]. Therefore,

repeated maternal fasting during pregnancy may present a more direct way to assess

1Nearly 1 in 4 women report skipping meals during pregnancy in the US [Siega-Riz et al., 2001].

1

the long term effects of alterations in the fetal environment than other more indirect

treatments such as exposure to disease. We provide new evidence on fasting’s effects on

birth outcomes and the first evidence of effects later in life using large-sample microdata

on Muslims in the United States, Iraq, and Uganda.

Our methodological approach addresses a key flaw in previous studies of Ramadan fast-

ing and birth outcomes. These epidemiological studies have compared pregnant women

who fasted to those who did not at a point in time, under the basic assumption that the de-

cision to fast is exogenous.2 Instead, we compare births over many years where Ramadan

overlaps with pregnancy to those where Ramadan does not and estimate the reduced

form effect of Ramadan’s timing.3 That is, we estimate an “intent to treat” (ITT) effect

without relying on the decision whether to fast for identification.4 This approach yields

distinct ITT estimates for specific months of gestation; Muslim births where Ramadans

falls in the early postnatal period serve as the control group. Because Ramadan follows

a lunar calendar, its occurrence moves forward by roughly 11 days each year. Thus, we

can disentangle the effect of prenatal overlap with Ramadan from season of birth, which

is also related to health in adulthood [Doblhammer and Vaupel, 2001, Costa and Lahey,

2005, Costa et al., 2007, Buckles and Hungerman, 2008].

Using natality data from Michigan, we find that prenatal exposure to Ramadan low-

ers birth weight and reduces gestation length. Further, the likelihood of a male birth

is about 10% lower when Ramadan falls very early in pregnancy and occurs during the

peak period of daylight fasting hours. Using Census data for the United States, Iraq, and

Uganda we find long-term effects on adult health and economic outcomes. We generally

find the largest effects on adults when Ramadan falls early in pregnancy. Rates of adult

disability are roughly 20% higher, with specific mental disabilities showing substantially

larger effects. Our estimates are conservative to the extent that Ramadan is not univer-

sally observed. Importantly, we detect no corresponding outcome differences when the

2Pre-pregnancy BMI, along with other characteristics, has been found to predict fasting observance[Kavehmanesh and Abolghasemi, 2004].

3We do not observe whether mothers fasted in our data. See Section 6.4We draw an analogy with research designs where there is random assignment to treatment and control

groups but where compliance may be endogenous. In our case we assume that the timing of Ramadanrelative to pregnancy is exogenous, but that the decision to fast is endogenous and is not universallyobserved.

2

same design is applied to non-Muslims as a falsification test.

As we discuss in Section 2, our findings are plausible in the context of the biomedical

literature where studies have documented that even relatively short fasts result in dra-

matic changes in the metabolic biochemical profiles of pregnant mothers, a phenomenon

known as “accelerated starvation”. For this reason, medical authorities generally discour-

age meal skipping during pregnancy. Accelerated starvation has specifically been found

in the context of Ramadan fasting in multiple countries suggesting that Ramadan is of

direct relevance to more general concerns about meal skipping or dieting during preg-

nancy. The altered metabolic profiles that occur with fasting have been associated with

diminished cognitive function during childhood and experimental animal studies suggest

that these alterations may hamper neurological development. One recent study has doc-

umented heightened levels of the hormone cortisol among pregnant women fasting during

Ramadan [Dikensoy et al., 2009], which has been associated with adverse long-term health

effects through fetal programming of the neuro-endocrine system. Our results are also

consistent with studies of the Dutch famine and 1918 Influenza Pandemic which found par-

ticularly large long-term health effects associated with early-pregnancy exposure. Several

studies have also documented that maternal nutrition during pregnancy varies positively

with male births (in the cross-section). One specific hypothesis suggests that a drop in

maternal glucose levels provides a signal of a poor future environment leading to fewer

completed pregnancies of male offspring.

Our identifying assumption is that pregnancies are not timed relative to Ramadan

along unobserved determinants of health. We present evidence that pre-determined ma-

ternal and paternal characteristics are not systematically related to the timing of concep-

tion relative to Ramadan. In our Michigan data, we observe maternal education, whether

previous birth was low birth weight, maternal diabetes, whether mother smoked, and

whether Medicaid paid for the delivery: each is unrelated to the timing of pregnancy

relative to Ramadan. Not surprisingly, controlling for these factors has a negligible effect

on our ITT estimates.

The remainder of the paper is organized as follows. Section 2 describes previous

epidemiological work on Ramadan and health, referencing additional supporting material

in the Appendix. Section 3 describes our natality and Census data, ITT measures, and

3

econometric model. Section 4 presents our results for birth outcomes in Michigan and

Section 5 describes our findings for adult outcomes in Uganda, Iraq, and the US. Section

6 synthesizes and interprets our results and discusses future research.

2 Previous Literature

We briefly discuss previous studies relating maternal fasting to health or human capital

outcomes. In the interest of space, we refer the reader to additional background material

in Appendix, Section A.5 In discussing previous work on fasting and health it is instruc-

tive to separate studies that have evaluated: 1) measures of maternal and fetal health

during pregnancy, and; 2) health at birth. In contrast to prenatal health, measurement

of newborn health is relatively standardized (e.g., by birth weight or infant mortality).

However, studies of maternal and fetal health allow for comparisons over time for the

same pregnancy – in and out of the fasting state – addressing the potential endogeneity

of the fasting decision.

Our review of the previous literature is distilled into several hypotheses that are laid

out in Appendix Table A1 which we use to inform our analysis. The table highlights which

outcomes may be affected and which specific months of pregnancy are most vulnerable to

exposure to fasting for each outcome. Fasting early in pregnancy is most likely to matter

for adult outcomes whereas birth outcomes (e.g. birthweight) could potentially be affected

throughout gestation.

2.1 Ramadan and Health During Pregnancy

Writing in The Lancet, Metzger et al. [1982] documented a set of divergent biochemical

measures among pregnant women who skipped breakfast in the second half of pregnancy.

Relative to twenty-seven non-pregnant women with similar characteristics, “circulating

fuels and glucoregulatory hormones” changed profoundly in twenty-one pregnant women

when the “overnight fast” was extended to noon on the following day (relative to post-

5Appendix Section A summarizes the effects of fasting on caloric intake and weight gain; the ratesof observance of Ramadan fasting by pregnant women; the potential health effects of maternal biochem-ical changes on offspring; fasting and fetal programming; studies of Ramadan fasting’s effect on birthoutcomes; and our hypotheses relating specific periods of exposure to particular outcomes.

4

prandial baseline). Further, plasma glucose and alanine was lower in the pregnant women

than in the non-pregnant women after 12 hours of fasting while levels of free fatty acids

and beta-hydroxybutyrate, a ketone, were significantly higher. This set of biochemi-

cal changes, also known as “accelerated starvation”, occurred after only “minor dietary

deprivation” for both lean and obese women. Metzger et al. [1982] concluded that meal-

skipping “should be avoided during normal pregnancy.” Meis and Swain [1984] found

that daytime fasts during pregnancy caused significantly lower glucose concentrations

than nighttime fasts. Accelerated starvation has been associated with diminished cog-

nitive function [Rizzo et al., 1991] and animal studies have linked ketone exposure very

early in pregnancy to neurological impairments[Hunter and Sadler, 1987, Moore et al.,

1989, Sheehan et al., 1985]. Gluckman and Hanson [2005] emphasize the importance of

glucose supply during early embryonic development noting that “the developing embryo

will change the relative assignment of cells to the inner cell and outer cell mass according

to whether it perceives a problem in glucose supply” and show that among rats “poor

maternal nutrition at this stage produces offspring with higher blood pressure”.

According to survey data, most pregnant Muslims observe the Ramadan fast (See

Appendix Section A.1.2 for details). Following the study of breakfast skipping by Metzger

et al. [1982], Ramadan fasting was likewise found to cause accelerated starvation among

pregnant women in Gambia [Prentice et al., 1983] and in England [Malhotra et al., 1989].

Mirghani et al. [2004] found that maternal glucose levels were lower in the fasting state

compared to the postprandial baseline, a difference accentuated by the number days

fasted: “the effect on maternal glucose levels during Ramadan fasting is cumulative.”

Several studies of maternal fasting during Ramadan have found adverse effects carried over

to measures of fetal health: fetal breathing movements and fetal heart rate accelerations

[Mirghani et al., 2004, 2005].

Recently, Dikensoy et al. [2009] reported that Ramadan fasting is associated with

increases in cortisol levels during pregnancy, but not for non-fasting pregnant women

(both relative to pre-pregnancy levels). This finding is of interest because cortisol is

a stress hormone frequently invoked as a potential mechanism through which prenatal

experiences may “program” adult health [Kapoor et al., 2006] (See Appendix Section A.3

for more details).

5

To summarize, there is fairly consistent evidence that fasting during pregnancy has a

“first-stage” effect on maternal and fetal health measures. We summarize the literature

on potential fasting sequelae in Appendix Section A. Despite uncertainty whether these

first stage effects carry over to birth outcomes and longer-term effects (See Section 2.2

below), the Institute of Medicine nevertheless recommends pregnant women should “eat

small to moderate sized meals at regular intervals, and eat nutritious snacks” [Institute of

Medicine, 1992:45]. Similarly, the American College of Obstetricians and Gynecologists

recommends that pregnant women avoid skipping meals.6

2.2 Ramadan and Perinatal Health

Whether there is an effect of fasting on birth outcomes has not been established. However,

it is important to note that measures of birth size are highly imperfect proxies for capturing

nutritional disruptions during embryonic or fetal development [Gluckman and Hanson,

2005]. Therefore, the absence of a finding of effects of fasting on birth weight, for example,

does not preclude the possibility of adverse effects on long-term outcomes. Nevertheless

it is useful to review the previous literature on fasting and birth outcomes. Most previous

studies have drawn comparisons over only a single Ramadan season. Since the panel-data

dimension is generally absent for analyses of birth outcomes, studies have resorted to

strong assumptions on the comparability of fasters and non-fasters. These two groups

are likely different in ways that would generate differences in birth outcomes absent any

causal effect of fasting. Pre-pregnancy BMI, along with other characteristics, has been

found to predict fasting observance [Kavehmanesh and Abolghasemi, 2004]. This basic

weakness in design has been exacerbated by: 1) small sample sizes that in general would

only be able to distinguish quite large effects from zero; 2) consideration of Ramadan fasts

observed exclusively in mid or late gestation. We refer the reader to the more detailed

discussion of these studies in Appendix A.4.1.

No previous study has exploited idiosyncratic variation across birth cohorts in the tim-

ing of Ramadan relative to birth. As Ramadan’s forward movement through the western

calendar is slow, the separation of Ramadan from seasonal effects on birth outcomes (e.g.,

6http://www.acog.org/publications/patient education/bp087.cfm?printerFriendly=yes

6

Doblhammer and Vaupel [2001], Costa and Lahey [2005]) requires data across many birth

years. Data availability, therefore, may have precluded implementation of an ITT analysis

like ours. Similarly, no previous study has exploited the number of daylight hours during

the Ramadan fast for identification (which is clearly not feasible for populations living

near the equator, e.g., in Uganda or Indonesia).

Finally, ours is the first study to analyze the relationship between outcomes in adult-

hood and in utero Ramadan exposure. The study closest to ours in this respect is by

Azizi et al. [2004] who found no significant difference in the IQ’s of school-age children by

maternal fasting behavior during the third trimester (please see Appendix Section A.4.2

for details). Subsequent to our study, Ewijk [2009] analyzed IFLS data from Indonesia,

finding evidence of long-term effects of fasting.7

3 Data and Methodology

Our identification strategy requires microdata with information on:

1. a substantial number of Muslims;

2. precise information on birth date (i.e., more detailed than age in years);

3. coverage of many birth cohorts (i.e., birth years);

4. health outcomes.

In datasets with a large number of both Muslims and non-Muslims, we would also

like to distinguish between these groups. We summarize the handful of suitable datasets

below (see Appendix B for details), followed by our econometric approach.

3.1 Michigan Natality Files

From Michigan’s Division for Vital Records and Health Statistics, we obtained birth cer-

tificate microdata for 1989 to 2006 in Michigan – approximately 2.5 million birth records.8

7Ewijk [2009] graciously notes that we are “the first to systematically examine [Ramadan’s] long-termeffects.”

8We thank Michael Beebe and Glenn Copeland in Michigan’s Vital Statistics Office for their assistancewith these data.

7

Although, there is no information on religion, ancestry of the mother is reported (ances-

try information is not recorded in the national vital statistics data produced by NCHS).

This feature of Michigan’s natality data allows us to construct a proxy for whether the

mother is Muslim based on reported “Arab” ancestry (or reported ancestries from pre-

dominantly Muslim countries).9 Compared to other US states, Michigan has a relatively

large Arab population.10 There are a total of about 50,000 births to mothers of Arab an-

cestry (about 2.2 percent of MI births) over this period. While there is a large population

of Arabs around Detroit, the Arabs are reasonably dispersed throughout the State (see

Appendix Figure A2, Panel A).

Since a large fraction of Arabs in Michigan are actually Chaldeans – a sect of Chris-

tianity – our proxy may misclassify many mothers and thereby attenuate our estimated

effects.11 We use the 2000 US Census SF3 (1 in 6 sample) data to identify Michigan

zipcodes with heavy concentrations of Chaldeans – who presumably do not observe the

fast – relative to Arabs (see Appendix Figure A2, Panel B). In some specifications, we

will drop observations from these zipcodes to compare ITT estimates. Appendix Table

A2 provides summary statistics for Michigan’s natality data.

3.2 Data from National Censuses

To consider whether health in adulthood is affected by prenatal Ramadan exposure, we

analyze Census microdata for the three countries where our identification strategy can

be implemented in publicly-available data. Data from the Uganda 2002 Census are best

suited for our analysis because religion is reported, there are large numbers of both Mus-

lims and non-Muslims in Uganda, month of birth is reported, and a host of disability

measures are queried. While less well-suited to our analysis, 1997 Iraq Census data and

US Census data allow us to assess whether the basic Uganda results are replicated in

other settings.

9See Section B.1 of the Appendix for more detail.10We thank Carlos Dobkin (UCSC) for suggesting we focus on Michigan’s Muslim population.11According to the 2000 Census, about a quarter of those of an Arabic speaking ancestry in Michigan

are Chaldean Christians. Our estimates based on the Detroit Arab American Study (DAAS) suggestthat about 47% of those who self-identify as “Arab American” in the Detroit area are Chaldean.

8

3.2.1 Uganda Census 2002

Our sample of Muslim adults includes approximately 80,000 men and women between

the ages of 20 and 80 in 2002. Muslims constitute about 11% of Uganda’s population

and have more schooling and lower rates of disability than non-Muslims (Appendix Table

A3). Both Muslims and non-Muslims share a strong seasonality in the number of births.

Muslims tend to live in the southeastern portion of the country.

Unlike other national censuses, the Uganda Census asks a battery of questions about

specific disabilities, including: blindness or vision impairments, deafness or hearing im-

pairments, being mute, disabilities affecting lower extremities, disabilities affecting upper

extremities, mental/learning disabilities, and psychological disabilities (lasting six months

or longer). As only about 5% of adults report a disability compared to over 10% in the US

Census, disabilities recorded in the Uganda Census may be more severe. Further, Uganda

reports information on the origin of disabilities: congenital, disease, accident, aging, war

injury, other or multiple causes. In the absence of direct measures of economic status

we use home ownership. We also consider several other socioeconomic outcomes such as

literacy, schooling, and employment.

3.2.2 Iraq Census 1997

Although religion is not reported in the Iraq Census, roughly 97% of the population

is Muslim, minimizing concerns about misclassification of religion. Our main sample

includes over 250,000 individuals born from 1958 to 1977 who were between the ages of

20 and 39 in 1997 and for whom we have reliable information on birth month.12 Because

we only cover 20 birth cohorts compared to 60 in Uganda, we may be more concerned

about confounding from seasonality. In addition, although our sample size is large this is

offset to some degree by surprisingly low rates of reported disabilities. At 1.5%, Iraqis are

substantially less likely than Americans (around 12%) or Ugandans (around 5%) to report

a disability. Part of this is of course, due to the fact that we have a younger sample. Along

with a general disability question, there are specific questions about disabilities involving

sight, hearing, lower extremities, upper extremities, and psychological disabilities. In

12Only 20 percent of those born prior to 1958 provide reliable data on birth month. We discuss thesedata limitations in greater detail in Appendix section B.3

9

contrast to Uganda, there is no variable to assess mental/learning disabilities.

In addition to home ownership, we consider a second proxy for wealth/status, polyg-

yny. Under Iraqi law, courts may only allow polygyny if husbands are able to financially

support multiple wives and if they are able to maintain separate households for each

wife.13 (Iraq Legal Development Project, 2005). More generally, polygyny reflects high

male status [Edlund, 1999]. Since polygyny is relatively infrequent for a young sample,

we expand our sample to include up to 45 year olds. Sample means for our outcomes are

shown with the regression results in Table 7.

3.2.3 US Census 1980, ACS 2005-2007

Our third Census sample is composed of immigrants to the US who were born in pre-

dominantly Muslim countries.14 We use a 6% sample from the 1980 Census along with

a pooled sample of the American Community Surveys (ACS) for the years 2005 through

2007 (3% sample). The ACS is modelled on the long form of the decennial Census. We

use these years because they provide the quarter of birth.15 Not observing birth month

dulls the empirical comparisons that we can make. On the positive side, we obtain a large,

national sample of US immigrants from Muslim countries in which to implement our ITT

analysis. Data quality is high, and includes additional outcomes beyond disability, such

as earnings.16 Selected sample means are shown at the bottom of Table 8.

13Under Iraqi Personal Status Code Number 188, Article 3(4) it is written that: Marriage of morethan one wife is prohibited in the absence of judicial permission on two conditions: (a) The husband hasfinancial sufficiency to marry more than one wife. (b) He should have a legal interest.

Iraqi Personal Status Code Number 188, Article 26 states that: The husband should not house hissecond wife in the same house with the first one without her approval, and should not house any otherrelative with her without her approval, except his minor child.

Roughly 2% of Iraqi men report polygynous unions.14Specifically, we use countries with at least an 80 percent Muslim population. To asses the magnitude

of misclassification attributable to this birth-county proxy, the same proxy variable was created in the2001 Canada Census, which includes self-reported religion. 67% of Canadian immigrants from these 80%Muslim countries reported being Muslim. We thank Kevin Milligan (UBC) for this information. SeeAppendix Section B.5 for additional details.

15In addition to not knowing birth month, we do not know the exact birth year in the ACS since it isnot asked and age is not reported as of a specific enumeration date as it is in the decennial Census. Giventhat Ramadan exposure shifts by only 11 days from year to year, using survey year - age provides agood approximation of birth year for the purposes of constructing Ramadan exposure measures at thequarterly level. The correlation between Ramadan exposure using survey year minus age, and surveyyear minus age minus 1, is about 0.93.

16Specifically we take the log of annual earnings and use the CPI for inflation adjustment. For consis-tency, we restrict the sample to ages between 20 and 80. Earnings are restricted to a sample between the

10

3.3 Ramadan Measures

We record start and end dates for the 104 Ramadans in the 20th century and construct

the following measures of prenatal Ramadan exposure:17

exppct calculates the fraction of days over the subsequent 30 days that overlap with

Ramadan.

exphrspct utilizes the number of daylight hours in each day to proxy for the length of

the Ramadan fast.18 The numerator is the number of daylight hours over the next

30 days that overlap with Ramadan; the denominator is the maximum number of

daylight hours over any 30 day period over the entire sample period (which depends

only on latitude). Daylight hours in Michigan vary from a low of around 9 to a high of

over 15 at the summer solstice when the effects of accelerated starvation may be most

evident. Please see Appendix Figure A3 for a an illustrative example of exphrspct

versus exppct coding from 1989 (and the associated description in Appendix Section

B.1).

rampct is the fraction of days in each month that overlap with Ramadan.19 rampct is

used when we observe just the birth month, as in our Uganda and Iraq samples and

is calculated for each month (not each day as for measures above). As Uganda is at

the equator, the number of daylight hours is fairly constant over the year at 12.

Each of the above measures is calculated at up to nine different points in time corre-

sponding to the gestation months (ten in some specifications where we include the month

prior to conception). exppct and exphrspct are calculated for the day beginning each preg-

nancy month. This feature of our research design is useful as specific health outcomes

ages of 20 and 60.17Many websites translate dates from the Islamic (Hijri) calendar. We used the following website

http://www.oriold.unizh.ch/static/hegira.html, but verified the dates from a second source.18The beginning of the Ramadan fast actually precedes sunrise and begins at the time of the morning

prayer (fajr). The precise timing of the morning prayer may vary across mosques and typically dependson a rule regarding the angle of the sun relative to the horizon. For this reason we actually understatethe number of fasting hours in our data. Daylight hours are measured for the city of Dearborn whichcontains a large share of the state’s Arab population.

19We opted to use this measure, rather than a simple dummy variable since it provides a continuousmeasure of treatment (more power).

11

may be more or less susceptible to nutrition depending on the stage of pregnancy (see

Appendix Table A1 and accompanying text in Appendix Section A). Finally, we construct

a fourth exposure measure by taking the average of rampct for each quarter of the year.

This quarterly measure is used for our US sample, where we observe just the quarter of

birth.

In general, we assign the Ramadan ITT by counting backwards from the birth date.

We consider alternatives to this approach where we have estimates of gestation length

(Michigan). In the rest of our data, we do not observe gestation length. Furthermore,

measurement error in gestation length, commonly based on date of last menstrual period,

may be substantial [Pearl et al., 2007]. For Michigan, we consider the following three

assignment rules:

1. The first approach uses the exact date of birth and simply assumes that all births

have a normal gestation length of 40 weeks since last menstrual period. The Ra-

madan exposure measures are assigned by going backwards from the birth date in

30 day increments and using daily exposure measures (exphrspct and exppct) from

30 days prior to birth to 270 days prior to birth. Using this approach the measure

of Ramadan exposure 9 months prior to birth is a proxy for the actual exposure

during the “first month” of gestation.

2. Our second approach incorporates the measures of gestation from the natality data

to match each individual to an estimated date of conception.20 We then assign

Ramadan exposure for the first month based on the daily exposure measures for the

date that is 4 days prior to the estimated date of conception.21 We then proceed

to assign Ramadan exposure measures forward in 30-day increments. Using this

approach a child born premature after say, 34-35 weeks of gestation would only have

been in utero for about 8 months and therefore only the first 8 exposure variables

are actually relevant for birth outcomes.

20Gestation based on last menstrual period (LMP) is used except if it is missing or if it differs withthe physician estimated gestation by more than 14 days, in which case the physician estimated measureis substituted. The conception date is estimated as occurring 14 days after LMP.

21This ensures that we have lined up the gestation month exposure in a parallel way to that used inthe first approach. In other words, if a birth has exactly 40 weeks or 280 days of gestation, using eitherapproach we will start measuring gestation exactly 270 days prior to birth.

12

3. Our third approach ignores information on exact birth date and actual gestation

and mimics what we can do with our Census samples where we only know month of

birth. Here we match individuals to the rampct measure for each of the 9 months

prior to birth.

3.4 Econometric Model

We regress each outcome, yi, on a set of nine Ramadan exposure measures (either ex-

phrspct, exppct or rampct). Separate exposure measures for each gestation month k are

included simultaneously in each regression, even though an individual will only be exposed

to Ramadan in at most two (adjacent) months of gestation. The effects of Ramadan ex-

posure in a given month of gestation, therefore, are measured relative to no prenatal

exposure to Ramadan – i.e., when Ramadan falls in the two to three months after birth.

yi = α +9∑

k=1

θk · exphrspctk + yeary + monthm + geogg + εi (1)

Additional controls include birth year dummies, a set of calendar birth month dummies

(or conception month dummies if gestation information is used), and a set of dummies

that measure geographic location at the time of birth.22 In the Michigan analysis we

also include a number of largely predetermined variables as additional controls: mother’s

age, mother’s age squared, mother’s years of education, father’s age, father’s age squared,

father’s education, a dummy for missing father’s education, parity, tobacco use during

pregnancy, alcohol use during pregnancy, the number of previous pregnancies where the

child was born dead, and whether the birth was paid for by Medicaid (an income proxy).23

In our pooled samples of adult men and women in Uganda, Iraq, and the US we also

include a female dummy.

In a typical specification where we include the nine exposure measures simultaneously,

we also run an F -test on the joint significance of all nine coefficients. This tests the

overall effect of Ramadan exposure during any point in gestation. In addition, since

22In Michigan we use 84 county dummies. In Uganda we use 56 district of birth dummies. In Iraq weuse 18 governates of birth and in the US we use 38 countries of birth.

23Parity is defined as the number of previous live births. Alcohol and tobacco use are arguably endoge-nous since their use may be reduced during the month of gestation that overlaps with Ramadan.

13

our hypotheses for some outcomes (Table A1) suggest an effect only in specific gestation

months, we also run tests of equality of all coefficients.

When we use a non-Muslim sample as a falsification check (in a separate regression),

the birth timing and birth location effects are allowed to vary across groups (along with

the other parameters). For estimates on population counts by month we use aggregate

measures at the cell level where cells are defined by each of the distinct conception or

birth months over the sample period. For Michigan, this yields 216 cells (18 years × 12

calendar months).

For the adult US sample where we only observe quarter of birth, there is an added

difficulty in identifying a control period with no in utero Ramadan exposure. If we assume

that all births were of normal gestation length, then individuals were in utero for each of

the two quarters prior to the birth quarter, along with some portion of their birth quarter.

This would suggest that we use the quarter three quarters prior to the birth quarter as the

omitted category. A more conservative approach would treat only those with Ramadan

exposure in the two quarters prior to birth as treated and omit both of the other two

birth quarters. We will show results using each of the two approaches.

4 Michigan Results

4.1 Birthweight Weight and Gestation

Table 1 presents the results for birthweight. In the first four columns we assign births

their Ramadan exposures by counting backwards from the date of birth (i.e., ignoring

the reported information on gestation and assuming pregnancies all had normal gestation

length). We designate the coefficients on Ramadan exposure according to the number of

months prior to birth.

Columns (1) and (2) use measures of Ramadan exposure that incorporate hours of

daylight (exphrspct). The first entry of column (1) indicates that birthweight was about

40 grams lower if an Arab mother was in the first month of pregnancy (i.e. nine months

before birth) during the peak period of daylight hours and Ramadan coincided with the

first month of gestation. This result is significant at the 5 percent level. In addition,

14

gestation months 2, 4, and 6 also show significant negative effects of around 40 grams.

We also find that the F -test on the joint importance of all the prenatal Ramadan exposure

measures is significant at the 8 percent level. The test of the equality of coefficients is

not rejected at conventional significance levels.

In column (2), we also include Ramadan exposure 10 months prior to birth as a

falsification and robustness check. As expected there is no detectable effect on birth weight

and its inclusion does not substantially affect the point estimates for other months. In

column (3) we measure Ramadan exposure by the fraction of days in the gestation month

that overlap with Ramadan. This treats a Ramadan falling in winter the same as one

during summer. It also changes the interpretation of the coefficient to reflect the average

effect of Ramadan exposure across all seasons. As expected, this reduces the magnitude

of the estimated effects to around 25-30 grams, which remain statistically different from

zero. In column (4) we again include exposure in the month prior to the beginning of

pregnancy and find no effect. Column (5) ignores information about the exact date of

birth and only utilizes the birth month to assign exposure measures of the fraction of

days in each month that overlap with Ramadan. With this approach we now find only

the second month of gestation to be significant and that the coefficients are far from

jointly significant or significantly different from one another. Nevertheless, the effect size

remains in the 30 gram range. Using only birth month re-allocates some Ramadan ITT

assignments to adjacent months.24

In columns (6)-(9) of Table 1, we assign Ramadan exposure using the time elapsed

since conception, and exclude the 7% of births that are pre-term (< 37 weeks gestation).

This ensures that at least the first 8 exposure measures are reasonably interpretable since

all of these births would have been in utero for 8 months.25 Columns (6) and (7) utilize

the daylight hours index of exposure. We find effects that are statistically significant at

the 5 percent level for exposure in month 1. As in columns (1) and (2), the size of these

effects are between 35 and 40 grams. In column (7) we again show no effect in the month

prior to conception. Thus, the pattern of birth weight effects we find are not an artifact

24For example, the effect of Ramadan falling nine months prior to birth in column (4) appears smoothedinto the adjacent month (8 months before birth) in column (5).

25In our tables we use italics to signify estimates that may include post natal exposure for some samplemembers.

15

of assigning the Ramadan ITT using date of birth. Columns (8) and (9) show smaller

effects from first month exposure when seasonal differences in the length of the diurnal

fast is ignored. The smaller sample used in columns (6) through (9) that ignores gestation

length have considerably higher significance levels on our joint test of an effect across all

gestation months (i.e., when we drop premature births and estimate the specifications

in columns (1)-(5)). This suggests that part of the difference between the two panels of

Table 1 may be due to selection on nearly full-term births. For example, the magnitude

of the effects for Ramadans that fall 4 months prior to birth are attenuated in the full

term sample.

We explore the role of conditioning on different gestation lengths and using a more

restricted sample of zipcodes that excludes zipcodes with a large fraction of Christian

Arabs (Chaldeans) in Appendix Table A4. We note that when we condition on full-

term births (39-42 weeks) and restrict the sample to those with a likely higher degree of

adherence to the fast we find larger ITT effects that are also significant in mid-gestation

months 5 and 7. The associated text in the Appendix discusses impacts on gestation

length itself, which are modest compared to those for intrauterine growth retardation

(IUGR).

4.2 Refutability Check Using Non-Arab Births

Identification of our ITT estimates comes from an idiosyncratic, non-linear function of

date of birth. If this function somehow introduced a mechanical relationship between

birth date and health outcomes (e.g. picking up a time trend), it should also be observed

among non-Muslims. As a check on the validity of the results above, we apply the same

ITT to our non-Arab sample in Table 2. The first six columns assign exposure assuming

normal gestation for the whole sample, while the seventh through ninth columns utilize

the gestation data and present the results for the sample of full-term births. Within each

of these sets of results, we use either all non-Arab mothers or a “super control” group

of non-Arab mothers living in zipcodes without any Arabs (according to the 2000 census

data).26 We find no birthweight effects among non-Arabs comparable to the results in

26The results are unaffected if we use the natality data to identify non-Arab zipcodes. However, giventhat we have more confidence in the reporting of ancestry detail in the Census we opted to use those

16

Table 1.27

4.3 Summary and Discussion of Birth Weight Results

Overall, in utero exposure to Ramadan is associated with lower birth weight among

Michigan’s Arab mothers. A full month of exposure to Ramadan during the peak period

of daylight hours could lead to a reduction in birth weight of about 40 grams especially

when Ramadan falls in the first month of gestation. Nevertheless, the size of this effect is

relatively small: 40 grams is only about 1.2 percent of the mean birth weight for Arabs.

These effects are population averages and do not account for the fact that some fraction

of these women are not actually fasting and more importantly, perhaps, that we may be

including a sizable fraction of Non-Muslim women among the Arabs. Table A4 provides

suggestive evidence that the effects may be larger and also occur during mid-gestation

when we account for these factors. In terms of the birth weight distribution, it appears

that most of the estimated effect for early pregnancy exposure is in the middle of the

distribution (see Appendix Figure A4), rather than a disproportionate increase in the

likelihood of low birth weights. Gluckman and Hanson [2005] emphasize that adaptive

responses to nutritional restrictions may occur throughout the birth weight distribution

(p.99). On the other hand, increases in low birth weight may be more closely tied

to other measures of newborn health than reductions at higher birth weights [Almond

et al., 2005]. In any event, since birth weight may be a poor proxy for the underlying

effects of nutritional shocks on fetal development (e.g. Franko et al. [2009]), we interpret

our findings on birth outcomes conservatively, using them primarily as confirmation that

prenatal fasting is indeed having a “first stage” effect on health measured at birth.

Finally, if Ramadan observance during pregnancy varied by socioeconomic or health

status, treatments effects would presumably also show a corresponding gradient, other

things equal. Interestingly, we observe no systematic gradient in the size of the birth

weight effects by maternal education, Medicaid use, or month prenatal care was initiated

(results available from authors). If treatment effects are relatively homogeneous, this

figures.27In column (3) of Table 2 we obtain two estimates of about -4 grams that are only significant at the

10 percent level, which may be chance occurrences among the 81 reported point estimates.

17

suggests that fasting observance is high or fairly uniform across socioeconomic groups by

month of gestation.

4.4 Fetal Death and the Sex Ratio at Birth

Mathews et al. [2008] found that poor maternal nutrition (possibly due to breakfast

skipping), around the time of conception skews the sex ratio in favor of girls, most likely

through the selective attrition of male conceptuses. Similarly, Almond et al. [2009] found

that severe morning sickness in early pregnancy is associated with female births, but also

a 50% fetal death rate due to severe nausea and vomiting.28 More generally, maternal

nutrition among mammals close to conception is positively associated with the likelihood

of male offspring Cameron [2004].

We consider Ramadan’s effect on sex at birth and the number of lives births in Table

3. Using our full sample of Arab mothers (column 1) we find a large effect of -3.7 percent-

age points (p-value = 0.06) on the likelihood of a male birth from exposure to Ramadan

during the longest diurnal fast in month 1 of pregnancy. In column (2) when we restrict

the zipcodes to those with fewer Chaldeans relative to Arabs, this point estimate rises

substantially to -6.6 percentage points and is significant at the 1 percent level. Inter-

estingly, we find some suggestive evidence of an effect in the month prior to conception

possibly reflecting the persistent effect of fasting just prior to conception. Columns (3)

and (4) show no analogous effects for our non-Arab samples.

To shed light on the cause of the sex ratio change, we aggregate counts of total births,

male births, and female births based on the month of conception. This results in 216

observations for conceptions between April 1988 and March 2006. The mean fraction of

births (weighted) that are male is 51.8 percent for Arabs over this period and the mean

sex ratio (male to female births) is 1.086. For non-Arabs, the respective means are 51.3

and 1.054. We regressed the log of the number of births on the exphrspct measures of

Ramadan exposure including dummies for month and year of conception and our full set

of controls.

Results for the more restricted sample of Arabs are shown in columns (5) through (7).

28By fetal death, we mean any attrition between conception and live birth. This could include attritionduring embryonic development before a fetus is fully formed.

18

Peak exposure to the Ramadan fast in the month after conception is associated with a 16

percent decline in total births. If male vulnerability [Kraemer, 2000] is the culprit, this

drop should be concentrated among male births. When we examine this by sex, we indeed

find this is driven by a 26 percent drop in male births (p-value = 0.005), while female

births fall by a statistically insignificant 4 percent.29 This decline in births associated with

fasting around the time of conception is probably not due to other behavioral changes

associated with Ramadan since it is difficult to imagine an alternative mechanism which

impacts sex-specific fertility.

Among those conceived shortly after Ramadan, (i.e., exposure in month zero, the

month prior to pregnancy) there is a statistically insignificant increase of 6.3 percent in

total live births associated with full exposure with some suggestive evidence of a female

skew.30 The fact that total births rise somewhat after Ramadan is not so surprising since

the end of Ramadan (Eid ul-Fitr) is a major event for Muslims and is celebrated with

a three day period of festivities.31 Further, the end of Ramadan marks the end of a

restriction on sex during daytime hours and the end of a period of piety. Nevertheless, the

increase in fertility is small, statistically insignificant and perhaps skewed toward female

births. We address potential selection issues that might arise from fertility changes in

section 4.6.

4.5 Other Birth Outcomes

We briefly summarize results for nine other birth outcomes: low birth weight, infant death,

APGAR scores, maternal weight gain, NICU unit, C-section, abnormal conditions, and

congenital anomalies. We used three different specifications and two different samples

but found that many of the results were sensitive to these choices.32 Appendix Table

A5 shows results with our more restricted sample of zipcodes and the broadest sample

29Several other gestation months show larger drops for female births associated with Ramadan exposure.30The increase in male births (one third of one percent) lags female births (9 percent). When we use

our full sample of Arab births (not shown) the increase in total births is 4 percent with male births risingby 0.5 percent and female births rising by 6.5 percent.

31In Hong Kong natality microdata, the lunar new year celebration is associated with a comparableincrease in births approximately nine months later (results available from authors).

32We varied gestation length (25-42 weeks, 37-42 weeks, and 39-42 weeks) and whether we used thefull sample of Arabs or the more restricted sample of zipcodes.

19

in terms of gestation (25-42 weeks) where only the first 5 gestation month coefficients

are readily interpretable (due to to compositional effects). The most consistent finding

concerned lower maternal weight gain due to exposure in the 7th month of gestation that is

apparent in all of our samples, though not always statistically significant. While maternal

weight gain might appear to be a desirable “first stage” measure, it has at least one

(unobserved) self-reported component (pre-pregnancy weight) and thereby suffers from

substantially more measurement error than newborn’s birth weight [Schieve et al., 1999].

4.6 Selective Timing of Conceptions Around Ramadan

Our identifying assumption is that the composition of Muslim parents does not change

systematically by their children’s in utero exposure to Ramadan. A general concern could

be that mothers of higher socioeconomic status (SES) seek to avoid having pregnancies

overlap with Ramadan by concentrating conceptions during the two to three months just

after Ramadan.33 Another concern could be if less healthy or less educated women are

more likely to conceive in the month prior to Ramadan which might account for our con-

sistent findings on first month exposure. Finally, although we did not detect a statistically

significant increase in conceptions following Ramadan, one might be concerned that there

is selection associated with which parents conceive just after Ramadan due to general

behavioral changes in society during this festive period.

We assess whether Ramadan exposure during pregnancy and the month prior to con-

ception is associated with a set of pre-determined characteristics of the pregnancy that

may be correlated with birth outcomes.34 Table 4 estimates equation (1) with twelve

“outcome” variables: mothers’ education, whether the pregnancy was paid for by Med-

icaid (income proxy), mother’s age, father’s age, father’s education, tobacco use during

pregnancy, alcohol use during pregnancy, parity, whether a previous child was born dead,

an indicator for missing father’s education, whether the mother had previously delivered

a small baby and whether diabetes was considered a risk factor for the mother. Out of the

33This might not alter our conclusions concerning differences due to exposure within the gestationperiod.

34Because we only observe those conceptions which result in a live birth, effects of post-conception Ra-madan exposure may be manifested in pre-determined characteristics if Ramadan-induced fetal mortalityhas a gradient in these same characteristics.

20

120 estimates, only 1 coefficient was significant at the 5 percent level. We found exposure

during the last month of pregnancy was associated with lower alcohol use. We found only

4 coefficients that were significant at the 10 percent level and three of them suggested Ra-

madan exposure was positively selected. For example mothers who had high exposure in

the fourth month of gestation had higher education than mothers whose pregnancies did

not overlap with Ramadan. Most importantly we found no evidence indicating positive

selection in mothers who conceive in the month after Ramadan and no evidence suggesting

that mothers who conceive in the month before Ramadan are negatively selected. In an

additional check, we have run all of our birthweight and gestation length results dropping

mothers who conceived in the month after Ramadan so that our effects are estimated

only relative to mother’s who conceived two to three months after Ramadan but whose

pregnancies did not overlap with Ramadan, and found very similar results.35

5 Census Results

5.1 Results from Uganda Census

Our presentation of potential long-term effects begins with Uganda, where self-reported

religion, birth month, and various health outcomes are available for a sizable number of

adult Muslims and non-Muslims. As in data from other countries (e.g., the US Census),

disability is the primary measure of health in the Uganda Census.

5.1.1 Disability Outcomes

Table 5 shows disability outcomes for Muslims and non-Muslims. Because these outcomes

have a low incidence rate we have multiplied the coefficients and standard errors by 100

to make them easier to read. The effects are therefore measured in percentage points. In

the first column we show the effects of Ramadan exposure over each of the nine months

preceding birth. In column (1) we find a statistically significant increase in the likelihood

35If anything, point estimates tend to increase in absolute value. For example the Table 1 column(6) estimate for the first month of gestation rises to -53.3 from -37.9 with a p-value of 0.02. The TableA4 column (6) estimate of the 2nd month effect on weeks of gestation is -0.16 rather than -0.11 with ap-value of 0.02.

21

of a disability (of any kind) for Muslims born nine months after Ramadan (point estimate

of 0.819 and p-value of 0.02). Relative to the mean disability rate of 3.8 percent, the effect

is substantial at 22 percent. We find that no other month prior to birth is statistically

significant and the p-value on the joint test of all nine coefficients does not approach

statistical significance. We cannot reject that all of the coefficients are equal.

Turning to specific disabilities (columns (2) to (5)), the most striking finding is the

increased incidence of a mental or learning disability (column (4)) when Ramadan occurs

during the first month pregnancy. The point estimate is 0.250 with a p-value of 0.001.

Given the mean rate of 0.14 percent this implies that the occurrence of Ramadan early

in pregnancy nearly doubles the likelihood of a disability related to diminished cognitive

function. Thus, the increase in mental/learning disabilities from month-one Ramadan

exposure would account for about 15% of all mental/learning disabilities among Muslims.

Furthermore, those with exposure in month 8 have a 100% increase (significant at the

5% level) and those with Ramadan exposure in months 5 or 6 show smaller increases

(significant at the 10% level). The joint test on all gestation months of no effect is

rejected at the 4 percent significance level.

We also find that the incidence of sight/blindness and hearing/deafness are higher for

those born 9 months after Ramadan. Specifically, the magnitude of the effects relative to

those not in utero are 33 percent for blindness (p-value = 0.07) and 64 percent for deafness

(p-value = 0.04). For hearing/deafness we also find a marginally significant effect for those

exposed to Ramadan in the fifth month of gestation.

We run the same specifications on our sample of non-Muslims in columns (6) - (10).

We find no cases of a corresponding significant result for Muslims also occurring for

Non-Muslims for these outcomes. We tested the sensitivity of the results for Muslims to

also including exposure during the 10th month prior to birth and found that the results

were unaffected and that in no case was the coefficient on the 10th month statistically

significant (see Appendix Table A6). We also ran our specifications separately for men

and women (not shown) and found that the results were qualitatively similar though the

estimates were much less precise.

22

5.1.2 Causes of Disability

Previous falsification tests have considered Ramadan exposure outside of pregnancy and

Ramadan exposure during pregnancy for non-Muslims. Information on the causes of dis-

abilities provides a third falsification test. We group these reported causes – accident,

occupational injury, war injury, aging, disease, or congenital – by whether they can rea-

sonably be linked to fasting via the mechanisms discussed earlier. Clearly, disabilities that

arise from accidents, occupational injuries, or war injuries are postnatal and should not

be related to maternal fasting during Ramadan. On the other hand, the fetal origins hy-

pothesis suggests that extended periods of nutritional restriction may be associated with

a reprogramming of the body’s systems that result in poor health outcomes later in life

(see Appendix for additional discussion). This would be consistent with those who report

“aging” as the source of a disability. Since it is conceivable that fasting might contribute

to a weakened immune system, respondents who report disabilities due to “disease” could

plausibly be related to the timing of Ramadan. Finally, whether maternal nutrition affects

congenital disabilities (those present at birth) is not clear-cut.36

We find no significant effects from accidents, occupational injury or war injuries for

Muslims or non-Muslims in any gestation month (Appendix Table A7). In contrast,

Muslims born nine months after Ramadan have an increased incidence of disabilities

due to aging of 0.37 percentage points (p-value = 0.006). We find no evidence linking

Ramadan exposure to disease-related or congenital disabilities (consistent with Michigan

results for congenital anomalies). We found no comparable effect of first month exposure

to Ramadan on disabilities caused by aging for non-Muslims.37

In order to address possible concerns about selective timing of pregnancy in Uganda,

we used a sample of children aged 17 or under and living with their parents and regressed

parent characteristics (education, illiteracy, and disability) on the child’s Ramadan expo-

sure using equation (1). As with Michigan, we found no statistically significant effects of

negative selection on parent characteristics. Finally, we also found that the results were

insensitive to excluding outlier cohorts that had extremely large or small disability rates.

36If the disability is epigenetic then it may be associated with maternal fasting.37Among non-Muslims the only significant effect is that those exposed to Ramadan one month before

birth are 0.12 percentage points (p-value = 0.017) more likely to have a congenital disability. This is a20 percent effect relative to the mean.

23

If anything, excluding outliers slightly increased the point estimates and their precision.

5.1.3 Sex Composition of Adult Population

With the Uganda data we explore the possibility that maternal fasting may influence the

sex composition of the adult population. This could arise either from alterations to the

sex composition at birth or because of selective mortality by sex after birth as implied by

some of the fetal origins literature (see Appendix Section 1). To assess this, we conduct

an analysis parallel to our Michigan analysis. First we simply regress male as an outcome

in equation (1). Second, we aggregate the population by cells constructed by birth month

both for the pooled sample as well as separately by sex and take the log of the population

counts as an outcome.

Results are shown in the left most panel of Table 6. In column (1) we find that every

implied gestational month has a negative coefficient and that the 1st, 4th and 7th months

of gestation are statistically significant at the 5 percent level The joint test of all the

exposure months is significant at the 10 percent level. In column (2) we find only slight

evidence cohort size is related to Ramadan exposure when we pool men and women.

When we look at the log of population counts of males in column (3), seven of the nine

months have negative coefficients and the 7th month of gestation has a particularly large

and statistically significant effect (15%). The effects on the sex in column (1) appear to

be driven by reductions in the number of males. In column (4) we show the analogous

results for women where the effects are all positive but only significant in one month.

In other results (not shown) we find no comparable effects on the sex composition for

non-Muslims.

5.1.4 Other Outcomes in Uganda

The remaining columns of Table 6 show results for non-health outcomes. Unfortunately

preferred economic outcomes, such as wages, income, and wealth, are not available. In

column (5) we examine whether home ownership, a proxy for wealth, is affected. We

restrict the sample to men since they are the vast majority of property owners in Uganda.38

38Uganda is a patriarchal society where land is passed down through sons. Although women arenot prevented from owning land, by one estimate, 93 percent of Ugandan land is owned by men.

24

We find that men exposed to Ramadan in the first month of gestation are 2.6 percentage

points less likely to own their home (p-value=0.027) and that men exposed in the 2nd

month of gestation are 2.1 percentage points less likely to own their home (p-value=0.051).

Given the high rate of male home ownership (73.4 percent), these effects are not especially

large. We can reject that there is no effect of Ramadan exposure over all gestation months

on home ownership at the 5 percent level. In contrast, we find no statistically significant

effects of Ramadan exposure on home ownership for non-Muslims.

In columns (6) through (9) we examine illiteracy, completed years of schooling, a

dummy for no schooling, and employment status at the time of the Census. We find

no statistically significant effects that associate greater Ramadan exposure with higher

illiteracy or lower schooling. In fact those born 8 months after Ramadan appear to have

higher human capital levels by both of these measures. The magnitude of these effects,

however, is very small. For example, the increase in years of schooling for these individuals

is only about a tenth of a year, or 1.6 percent of the sample mean.

In developing countries a reduction in health capital could be manifested in less pro-

ductive childhood labor and possibly lead to increased schooling. We also speculate that

these small but positive results might reflect a selective effect on surviving males, who

seem to bear the brunt of Ramadan-related attrition (either prenatally or postnatally). In

developing countries where average mortality rates are high, the selective effect of health

disruptions operating through early-life mortality may overwhelm “fetal origins” effects

on adult survivors [Deaton et al., 2009]. Scotland [1956] provides supportive evidence

that death rates among the frail were very high in Uganda during roughly the middle

of our sample period in the 1950s. When we split the sample by gender, we only found

these positive education effects for men and found negative (though insignificant) effects

on women. When we split the sample by those above age 50 versus those aged 50 or

younger, the effects are much larger for the older groups. These facts are consistent with

the possibility of sex-specific selective mortality.

(http://www.womensenews.org/article.cfm/dyn/aid/1456/context/archive).

25

5.2 Results from Iraq Census

We replicate the basic Uganda results using 1997 Iraq Census data. Columns (1) to (4)

of Table 7 show the effects on disability. Full exposure to Ramadan nine months before

birth is associated with a 0.33 percentage point increase in the probability of having a

disability (p-value = 0.016). While in Uganda the overall disability rate was 3.8 percent,

in Iraq it is just 1.5 percent. However, the effect size relative to the mean in Iraq is 23

percent, nearly identical to the 22 percent effect size that we estimated in Uganda. In Iraq

the rates of disabilities involving sight and hearing, however, are a much smaller fraction

of the reported rates for Uganda and this may explain why we detect no effect on these

measures for first month exposure in columns (2) and (3).39 We do find that exposure in

month 5 of pregnancy has an effect on vision related disabilities.

“Insane” is the sole mental disability queried, which IPUMS relabeled as “psycholog-

ical” disability. Interestingly, at 0.36 percent, Iraq’s psychological disability rate is actu-

ally higher than the combined rate of 0.28 percent for mental/learning plus psychological

disabilities in Uganda (despite Iraq’s lower overall disability rate). This suggests that

mental/learning disabilities that are related to cognitive impairments may be subsumed

in the psychological disability measure for Iraq. In column (4) we find strong effects on

psychological disabilities just as we did for mental/learning disabilities in Uganda. First

month exposure to Ramadan is associated with 0.23 percentage point increase in the like-

lihood of a psychological disability or a 63 percent effect relative to the mean (p-value

= 0.001). We also estimate positive but insignificant effects in 6 of the other 8 gestation

months. As was the case in Uganda with mental/learning disabilities, the joint test of

zero effect across all gestation months is easily rejected at the 5 percent level, as is the

test of equality of coefficients. The fact that both overall disability as well as disabilities

that likely capture cognitive impairments appear to be impacted in precisely the same

period of fetal development in two different societies is remarkable and reinforces that our

findings are not due to chance.

In columns (5) through (8) of Table 7 we turn to socioeconomic outcomes.40 The

39For vision/blindness only 0.14 percent report this disability which is only about one-tenth of the sharereporting a comparable disability in Uganda. For deaf/hearing only 0.02 percent report this disabilitywhich is only one-sixteenth of the rate found in Uganda.

40We experimented with measures of human capital such as years of schooling and illiteracy but found

26

1997 Iraqi Census asks about instances of men having multiple wives which we use to

proxy for wealth (as described earlier). For this measure, shown in column (5) we find

that men with first month exposure are more than half a percentage point less likely to

have multiple wives and negative point estimates are found throughout pregnancy. A

large and significant effect is also found during month 6 of gestation. Similarly, for home

ownership (column 6), we see highly significant effects of exposure throughout the in utero

period and the joint test of all gestation month coefficients is significant at the 8 percent

level. In column (7) we see no effects on the sex composition of the adult population

which may not be so surprising given that Iraq’s sex ratio is already skewed toward

women. Finally, in column (8) we find both small positive and small negative effects of

Ramadan exposure on employment that are statistically significant. We note that among

males, home owners are less likely to be employed (73%) than non-home owners (82%)

suggesting that employment may be a poor proxy for economic status in Iraq and may

actually signal lower status.41 As with our Uganda results, we have also run all of these

estimates including exposure 10 months prior to birth and in no case did it meaningfully

alter the results.

5.3 Results from US Census

The US data provide potentially higher quality measures of disability as well as direct

measures of labor market outcomes (unavailable for Iraq and Uganda). As mentioned in

Section 3.2.3, the data’s chief drawback is absence of month of birth information, which

dulls assignment of the Ramadan ITT. On the other hand, findings of long-term effects

in the US may address concerns that the Uganda and Iraq results do not generalize to

Muslims living in a high-income Western society.

Panel A of Table 8 presents results for four outcomes across three samples. In each

regression we estimated effects of Ramadan exposure in one’s birth quarter and in the

that there were extremely strong month of birth trends in these variables that could not be adequatelycontrolled for without having a full set of birth cohorts for whom Ramadan occurred throughout theentire calendar year. The seasonality in birth month are likely related to institutional issues concerningeducation (e.g. cutoff ages for starting or ending school tied to specific dates).

41If we control for home ownership and multiple wives the instances of positive effects of Ramadanexposure on male employment are eliminated.

27

prior two birth quarters, omitting Ramadan exposure three quarters prior to the birth

quarter and thereby treating that cohort as the control group. Since we have averaged our

monthly exposure measures within each birth quarter, our quarterly exposure measure

never exceeds one third (0.33) in any quarter. Therefore, our coefficients which represent

the effect of a unit change in exposure should be scaled down by one-third to be inter-

pretable as the effect of fasting for 30 days within the birth quarter. For this table we

have also multiplied all the dichotomous outcomes by 100.

The first four columns pool samples drawn from the 1980 Census and the 2005-2007

ACS. We find a significant effect of exposure in the quarter prior to birth on the incidence

of disability (column (1)). The coefficient of 3.50 suggests that Ramadan’s occurrence in

a particular month within that quarter would raise the likelihood of a disability by about

1.17 percentage points. Since the mean rate of disability in the sample is 6.07 percent, this

implies about a 19 percent effect at the mean, again quite similar to the corresponding

magnitudes in Uganda and Iran. The p-value on the joint test of the three quarters is

0.01. However, the timing of exposure appears to be concentrated more in the second

trimester rather than early in gestation.42 Of course it may be that effects are large in

the first month of gestation (as in Uganda and Iraq) in addition to the second trimester

but we are obviously unable to check this.

In column (4), we find that exposure both in the birth quarter and two quarters prior

to birth is associated with a reduction in log annual earnings of about 5.5 to 6 percent

(re-scaled). Both estimates are significant at the 5 percent level and the effects are jointly

significant at the 0.11 level. To measure human capital we began by examining completed

years of schooling as an outcome (not shown) and found a statistically significant effect of

about 0.15 years from one month’s exposure that occurs two quarters prior to birth. Since

this was a relatively small sized effect, we then looked for whether this was concentrated

at the low end of the education distribution by looking at failure to complete 12 years

of schooling as an outcome (“less than HS”). Column (2) shows a coefficient of 2.513 for

Ramadans falling two quarters prior to the birth quarter, significant at the 11 percent

42Depending on when in the quarter an individual is born, the prior quarter could reflect effectsanywhere from the 4th gestation month to the 9th. If births were uniformly distributed within a quarterthen the mean exposure month would be the fifth month of gestation.

28

level. The point estimate implies that one month of exposure to Ramadan fasting is

associated with about a 0.84 percentage point increase in the high school dropout rate

which implies a 6.4 percent effect size evaluated at the mean.

We find negative point estimates for the likelihood of being male (column (3)) that

are not statistically significant, but nevertheless potentially meaningful quantitatively.

Complete exposure in the quarter two quarters prior to the birth quarter is associated with

a roughly 1 percentage point decline in the male share of the adult Muslim population.

The General Accounting Office noted the “impact of measurement error appears to be

greater for the ACS” than for the long-form of the decennial US Census [GAO, 2002]. In

columns (5) through (8) we restrict the sample to those observed in the 1980 Census,43

about a third of the full sample, and observe broadly similar effects. With this sample

we also observe that the other two quarters of Ramadan appear to have strong effects

on adult disability. So it could be that in 1980, at least, there were in fact long-term

repercussions among those whose mothers fasted early in pregnancy. In columns (9)

through (12) we restrict the sample to the pooled ACS sample from more recent years.

We again see disability effects of a similar magnitude and timing. For both subsamples

the effects on earnings are insignificant but if anything appear to be stronger earlier in

pregnancy (two quarters prior to the birth quarter).

Panel B takes a more conservative approach in designating the control group. Although

we know that the vast majority of pregnancies that overlap with Ramadan’s occurrence

in the two quarters prior to birth will actually be exposed to Ramadan for the entirety

of the three months of the quarter, this is much less likely for the quarter of birth itself

and the quarter three quarters prior to the birth quarter. Therefore, here both of these

quarters are omitted and serve as the reference group. As can be seen in columns (1)

through (4) of Panel B, this does not alter our basic results. The effect size on disability

is a bit smaller but still highly significant. The effect on having fewer than 12 years of

schooling actually rises, becomes significant at the 5 percent level, and the joint test of

both exposure coefficients is now significant at the 8 percent level. On the other hand the

effect on earnings drops considerably and is no longer statistically significant.

We have also run all of these specifications on a sample of US born whites that are

43The last decennial Census to report quarter of birth.

29

presumably non-Muslims based on reported ancestry and found no effects of similar mag-

nitudes.

To summarize, in the US Census we find: 1) strong evidence of elevated rates of disabil-

ity from in utero exposure to Ramadan generally toward the middle of pregnancy; 2) evi-

dence of lower earnings with Ramadan exposure early in pregnancy (e.g., first trimester);

and 3) suggestive evidence linking Ramadan exposure early in pregnancy to a lower like-

lihood of completing high school.

6 Discussion and Future Research

6.1 How does fasting observance affect our estimates?

As rates of fasting by pregnant women during Ramadan approach unity, our ITT estimate

approaches the treatment effect of fasting (which cannot be said of previous comparisons

between fasters and non-fasters). Fasting observance may be highest in early pregnancy,

both because mothers may be unaware they are pregnant and the burden of pregnancy

is lower.44 Thus, the estimated health damage attributable to Ramadan falling in the

first month of pregnancy may approximate the treatment effect of fasting during this

period. Correspondence between our ITT estimate and fasting’s effect is likely higher

in Iraq and Uganda where we have little classification error in Muslim status. In our

Michigan data, our proxy for Muslims will include a higher fraction of non-Muslims due

to the likely presence of Chaldeans who report Arab ancestry. This is also likely to be

the case, albeit to a lesser extent, with our US Census data where we may include some

non-Muslim immigrants from predominantly Muslim countries. As compliance (fasting

during Ramadan) is presumably zero for non-Muslims, our US ITT estimates are likely

attenuated.

Ideally, we would observe fasting behavior by month of pregnancy and subsequent

health or human capital outcomes for a large sample of Muslims. With this information

44The only study that we are aware of that documented differences in fasting behavior across pregnancywas by Arab and Nasrollahi [2001] who found that of the 4,343 women delivering in hospitals in Hamadan,Iran in 1999, fasting was only slightly more common when Ramadan fell in the first trimester (77% )than in the second trimester (72%) or third trimester (65%).

30

and a sufficiently long span of birth years, we could construct Wald estimates of the

effect of fasting on health during each pregnancy month. Ramadan’s coincidence with

pregnancy month would be the binary instrumental variable for fasting observance. As

long as Muslims are not fasting for other reasons during the month of Ramadan (as seems

reasonable), this Wald estimate could be interpreted as the effect of fasting on fasters

(i.e., the treatment on the treated rather than simply a LATE estimate, see Angrist and

Pischke [2009]). Failing this, data on fasting behavior and pregnancy month could be used

to estimate the first stage effects of Ramadan timing (preferably for the US, Uganda, or

Iraq), and combined with our ITT estimates in a two-sample IV procedure. This approach

would also integrate potential heterogeneity in fasting rates by pregnancy month.

The most compelling previous studies of the developmental origins of health and dis-

ease have relied on exogenous shocks “caused by conditions outside the control of the

mother” [Currie, 2009]. These shocks have also typically involved relatively uncommon

and severe historical events and so the relevance to policy may be somewhat tenuous. Our

study departs from these in considering a treatment that to a greater degree is within the

control of the mother (but still identified by exogenous timing) and may potentially be

amenable to interventions. We also study a phenomenon that conforms more closely to

the established theories relating a decline in circulating levels of maternal glucose during

critical windows of embryonic and fetal development. That obtaining a dispensation to

postpone fasting until after pregnancy is apparently the exception rather than the norm

(see Appendix A.1.2) suggests two possibilities. First, the cost of requesting the dispen-

sation may be high – in part because mothers usually become aware of their pregnancies

after the first month [Floyd et al., 1999]. Alternatively, it may be that the full health

consequences of Ramadan fasting during pregnancy are unknown. This explanation also

seems plausible as ours is the first study to find long-term effects.

An alternative approach families could adopt is to time pregnancies to commence

shortly after Ramadan, and thereby avoid the overlap. That we do not observe this

behavior could suggest that timing pregnancies is costly or unreliable,45 or again that

fasting during pregnancy is not considered teratogenic.

45Dickert-Conlin and Chandra [1999] found a responsiveness to tax incentives in the timing of deliveries,not conceptions.

31

6.2 Synthesizing the Results

In table A1 we present hypotheses concerning which outcomes are likely to be affected by

fasting, and in which months of pregnancy. In accordance with our hypotheses, we find

evidence that fasting affects birth weight, gestation length and the sex composition at

birth using natality data from Michigan. For birth weight it appears that fasting during

the first month or two is consistently associated with lower birthweight. However, when

we limit our sample full-term births in non-Chaldean zipcodes, we find negative effects in

other stages of pregnancy. Our results on the sex composition of births are also consistent

with the hypothesis that nutrition shortly after conception matters.

We take these findings primarily as confirmation that there is a detectable effect of

fasting that is evident at birth. The absence of such evidence would make the case for

long-term effects superficially more suspect but still plausible from the point of view of

biological theory. Although some may interpret evidence of negative effects on birth

weight as an important finding in and of itself, we take the more conservative view that

it merely demonstrates the potential importance of nutritional disruptions during fetal

development on long-term outcomes.

Our literature review further suggests that irrespective of when in pregnancy fasting

may affect birth outcomes, adult outcomes are generally likely to be affected by prenatal

nutritional disruptions early in pregnancy.46 Accordingly, we find large effects on disability

from early exposure in Uganda and Iraq. In the US, where we have much more blunt data

on birth timing, we find that rates of disability are elevated by a very similar magnitude as

in Iraq and Uganda but that the timing of effects is now concentrated during the second

trimester, though we cannot rule out the possibility of large effects early in pregnancy

and find evidence of this when we use only the 1980 US Census (the last decennial Census

to report birth quarter).

With respect to education, although we find no evidence of a negative effect in Uganda

we do estimate a significantly lower likelihood of completing high school that is associated

with early exposure in the US. We note that theory is not clear on whether reductions

in health capital in the context of a developing country should reduce schooling since it

46Evidence from the 1918 and 1957 influenza pandemics suggests that the first half of pregnancy isparticularly important to subsequent health and human capital [Almond, 2006, Kelly, 2009].

32

may also reduce productivity among child laborers. For Uganda, we also find evidence

that is consistent with the possibility that sex-specific selective mortality may have led

to a positive association. It would be useful for future research to use administrative

educational data (e.g. test scores) to more directly ascertain whether there are effects on

human capital during adolescence.

Finally, with respect to economic outcomes, we find that fasting early in pregnancy

is associated with lower wealth using home ownership in Uganda and home ownership

and polygyny in Iraq. In the US we similarly find that early exposure is associated with

reduced earnings, although effects are also apparent in other periods of pregnancy.

6.3 Generalizability and Future Research

An important caveat of our analysis is that we only measure the reduced form effect of

exposure to all aspects of Ramadan’s occurrence, not just fasting. The fact that Ramadan

may alter other behaviors (e.g. sleeping patterns) may lead one to question whether the

effects of fasting during Ramadan generalizes to other contexts such as dieting during

pregnancy. We would first emphasize that there is a strong physiologic and empirical basis

in the medical literature for expecting that maternal fasting can lead to metabolic changes

in the intra-uterine environment (i.e. reductions in glucose and increases in ketones) that

could potentially result in adverse birth outcomes. Further, no other behavioral aspect of

Ramadan observance that we are aware of has been linked to adverse pregnancy or birth

outcomes. Therefore, the fact that accelerated starvation has in fact been documented

in both developed and developing countries during Ramadan provides a priori evidence

that Ramadan is of direct relevance for understanding the implications of nutritional

deprivation during pregnancy more generally. The presence of elevated levels of cortisol

provides further evidence of a likely effect. At a minimum, the results of this paper are

a clarion call for further research. It would be fruitful for future studies to analyze the

extent to which other behavioral aspects of Ramadan may interact with fasting behavior

and whether these other factors may serve to amplify or dampen the effects of restricted

prenatal nutrition. Finally, setting aside the issue of generalizability, the fact that millions

of pregnant Muslim women will fast each year implies that understanding the long-term

33

impacts of Ramadan is an important question per se.

Future research should also confirm whether other commonly-experienced disruptions

to prenatal nutrition exert similar effects as Ramadan fasting. Most US pregnancies are

not recognized until after the first month of gestation [Floyd et al., 1999]. Given the results

of this study, maternal behavior particularly during the first month of pregnancy, can have

permanent impacts on offspring health. Roughly 40% of US women of childbearing age

are attempting to lose weight [Cohen and Kim, 2009] and 24% of women reported meal-

skipping during pregnancy [Siega-Riz et al., 2001].47 Even in relatively well-nourished

populations, prenatal nutrition (and at a minimum its timing) may be sub-optimal for

fetal development. Future research should employ new identification strategies to evaluate

both short and long-term health effects of nutrition in early pregnancy on health and other

end points, e.g., test scores.

47Furthermore, approximately 5% of pregnant women manifest eating disorders [Turton et al., 1999].

34

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38

Tab

le1:

Effec

tsof

Ram

adan

Expos

ure

onB

irth

Wei

ght

by

Ges

tati

onM

onth

Mic

hig

anA

rabs

Tab

le 1

: E

ffec

ts o

f R

amad

an E

xpos

ure

on B

irth

Wei

ght

by G

esta

tion

Mon

th, M

ichig

an A

rabs

Dd

Vi

bl

iB

ih

ih

Cffi

iR

dE

(%fd

lih

h%

fd

)D

epen

den

t V

ari

able

is

Bir

thw

eigh

t, C

oeffic

ients

are

on R

amad

an E

xpos

ure

mea

sure

s (%

of day

light

hou

rs, %

of day

s)

Dep

enden

t V

aria

ble

is

Bir

thw

eigh

t, C

oeffic

ients

are

on R

amad

an E

xpos

ure

mea

sure

s (%

of day

ligh

t hou

rs, %

of day

s)

Ram

adan

Exp

osure

Usi

ng

Ges

tati

onD

ata

(37

42w

ks)

Ram

adan

Exp

osure

Ass

um

ing

Nor

mal

Ges

tati

onR

amad

an E

xpos

ure

Usi

ng

Ges

tati

on D

ata

(37-

42 w

ks)

Ram

adan

Exp

osure

Ass

um

ing

Nor

mal

Ges

tati

on

Mon

ths

(1)

(2)

(3)

(4)

(5)

Ges

tati

on(6

)(7

)(8

)(9

)

pg

()

pg

Mon

ths

(1)

(2)

(3)

(4)

(5)

Ges

tati

on(6

)(7

)(8

)(9

)(

)(

)(

)(

)(

)(

)(

)(

)(

)pri

orto

Bir

thD

ayB

irth

Day

Bir

thD

ayB

irth

Day

Bir

thM

oM

onth

Bir

thD

ayB

irth

Day

Bir

thD

ayB

irth

Day

pri

or t

oB

irth

Day

Bir

th D

ay

Bir

th D

ay

Bir

th D

ay

Bir

th M

o.

Mon

thB

irth

Day

Bir

th D

ay

Bir

th D

ay

Bir

th D

ay

Bir

thH

ours

Hou

rsD

ays

Day

sD

ays

expos

ure

Hou

rsH

ours

Day

sD

ays

Bir

thH

ours

Hou

rsD

ays

Day

sD

ays

expos

ure

Hou

rsH

ours

Day

sD

ays

10--

6.4

--4.

3--

0--

6.0

--5.

510

--6.

4--

4.3

--0

--6.

0--

5.5

(19

7)(1

38)

(18

1)(1

24)

(19.

7)(1

3.8)

(18.

1)(1

2.4)

(19.7

)(1

3.8

)(1

8.1

)(1

2.4)

939

8**

389

**26

7**

259

**10

81

379

**36

9**

223

*21

3*

9-3

9.8

**-3

8.9

**-2

6.7

**-2

5.9

**-1

0.8

1-3

7.9

**-3

6.9

**-2

2.3

*-2

1.3

*(1

85)

(18

7)(1

27)

(13

0)(1

27)

(16

9)(1

71)

(11

5)(1

17)

(18.5

)(1

8.7

)(1

2.7)

(13.0

)(1

2.7

)(1

6.9

)(1

7.1

)(1

1.5

)(1

1.7)

838

0**

357

*24

5**

228

*33

2**

*2

272

*25

016

914

78

-38.

0**

-35.

7*

-24.

5**

-22.

8*

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2**

*2

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9-1

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838

.035

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27.2

25.0

16.9

14.7

(17

7)(1

91)

(12

4)(1

36)

(12

4)(1

61)

(17

5)(1

11)

(12

2)(1

7.7

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9.1

)(1

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(13.6

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7.5

)(1

1.1

)(1

2.2)

()

()

()

()

()

()

()

()

()

77

74

77

77.

69.

67.

59.

0-8

.83

-4.7

-2.8

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1(1

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(19

2)(1

28)

(13

6)(1

28)

(16

6)(1

76)

(11

5)(1

23)

(18.

3)

(19.

2)(1

2.8)

(13.

6)

(12.

8)

(16.

6)(1

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(11.

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(12.

3)(1

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2.8)

(13.6

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6-3

76

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96

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6**

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(17

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(13.9

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2.8

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51

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018

55

160

141

98

79

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2.5

4.0

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55

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-14.1

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-7.9

(18

8)(1

97)

(13

2)(1

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(13

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(17

8)(1

19)

(12

6)(1

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3.2)

(14.0

)(1

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()

()

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()

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433

8*

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6*

222

82

621

920

214

813

04

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-32.

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-23.

6**

-22.

2-8

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9-2

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-14.

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433

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26

21.9

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14.8

13.0

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7)(1

95)

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2)(1

39)

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()

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213

511

97

66

312

28

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812

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()

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N44

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232

101

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641

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Not

es:

Col

um

ns

1-4

use

exac

t bir

th d

ate

but

do

not

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-10 u

se e

ither

sel

f re

port

ed o

r N

otes

:C

olu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

dh

’d

bl

hl

fh

’d

df

fh

’d

fh

’f

h’

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

dh

’d

bl

hl

fh

’d

df

fh

’d

fh

’f

h’

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ednum

ber

ofpre

ious

pre

gnan

cies

that

resu

lted

indea

that

bir

thco

nce

pti

on(o

rbir

th)

mon

thdum

mie

sco

unt

dum

mie

sand

bir

thea

rdum

mie

s

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ednum

ber

ofpre

ious

pre

gnan

cies

that

resu

lted

indea

that

bir

thco

nce

pti

on(o

rbir

th)

mon

thdum

mie

sco

unt

dum

mie

sand

bir

thea

rdum

mie

s

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed

esti

mate

sm

ay

repre

sent

post

nata

lex

posu

refo

rso

me

sam

ple

mem

ber

s

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed

esti

mate

sm

ay

repre

sent

post

nata

lex

posu

refo

rso

me

sam

ple

mem

ber

s

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

Colu

mns

1-4

use

exact

bir

th d

ate

but

do n

ot

uti

lize

ges

tati

on d

ata

. C

olu

mn 5

only

use

s bir

th m

onth

.

Colu

mns

6-1

0 u

se e

ither

sel

f re

port

ed o

r physi

cian e

stim

ate

d g

esta

tion (

see

text)

. E

ntr

ies

show

the

coef

fice

nt

on t

he

rele

vant

Ram

adan e

xposu

re m

easu

re. C

ontr

ols

incl

ude

moth

er’s

age,

moth

er’

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion, dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

squar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

dbir

th y

ear

dum

mie

s.It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Tab

le2:

Effec

tsof

Ram

adan

Expos

ure

onB

irth

Wei

ght

by

Ges

tati

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onth

Mic

hig

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Tab

le 2

: E

ffec

ts o

f R

amad

an E

xpos

ure

on B

irth

Wei

ght

by G

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tion

Mon

th, M

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Exp

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Ass

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ata

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Zip

All

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All N

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Mon

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(1)

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Day

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93

32

21

414

111

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81

32

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81

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19)

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80

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38

44

69

72

25

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53

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10

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(33)

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join

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N15

6580

015

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012

2844

1228

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1070617

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84675

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015

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015

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012

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Not

es:

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ntr

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are

coef

fici

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on R

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day

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ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

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olu

mns

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nd

Not

es:

All e

ntr

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are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

rs o

ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

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olu

mns

1 a

nd

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es:

All e

ntr

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are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

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ver

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quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

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olu

mns

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sum

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on. C

olum

ns

3 an

d 4

use

eit

her

sel

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por

ted o

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it

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es:

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ntr

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coef

fici

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on R

amad

an e

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day

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day

s as

frac

tion

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k d

aylight

hours

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olu

mns

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nd

2 as

sum

e nor

mal

ges

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olum

ns

3 an

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use

eit

her

sel

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por

ted o

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ed g

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tion

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wee

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2 w

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fi

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it

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es:

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ntr

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are

coef

fici

ents

on R

amad

an e

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day

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rs o

ver

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quen

t 30

day

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frac

tion

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olu

mns

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nd

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mal

ges

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olum

ns

3 an

d 4

use

eit

her

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por

ted o

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cian

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imat

ed g

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se b

irth

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wee

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2 w

eeks

gest

atio

n. C

olum

ns

1 an

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road

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ple

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pute

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rab m

other

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ed o

n r

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race

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olu

mns

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nd 4

furt

her

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tric

t th

issa

mple

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oth

ers

livin

gin

zipco

des

wit

hno

Ara

bs

acc

ord

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toth

e2000

Cen

sus

Contr

ols

incl

ude

moth

er’s

age

moth

er’s

age

square

dm

oth

er’s

Not

es:

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ntr

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are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

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ver

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quen

t 30

day

s as

frac

tion

of pea

k d

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hours

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olu

mns

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nd

2 as

sum

e nor

mal

ges

tati

on. C

olum

ns

3 an

d 4

use

eit

her

sel

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por

ted o

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cian

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imat

ed g

esta

tion

and u

se b

irth

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wee

n 3

9 a

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2 w

eeks

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atio

n. C

olum

ns

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d 3

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ple

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pute

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ed o

n r

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olu

mns

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furt

her

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t th

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mple

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oth

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gin

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des

wit

hno

Ara

bs

acc

ord

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toth

e2000

Cen

sus

Contr

ols

incl

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moth

er’s

age

moth

er’s

age

square

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oth

er’s

Not

es:

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ntr

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are

coef

fici

ents

on R

amad

an e

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ure

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day

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rs o

ver

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quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

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olu

mns

1 a

nd

2 as

sum

e nor

mal

ges

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on. C

olum

ns

3 an

d 4

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eit

her

sel

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por

ted o

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imat

ed g

esta

tion

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se b

irth

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wee

n 3

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atio

n. C

olum

ns

1 an

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ple

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or

race

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olu

mns

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her

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t th

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ample

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g in

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codes

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h n

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sus.

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ontr

ols

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her

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age

square

d, m

oth

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ed

uca

tion

, to

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co u

se, al

cohol

use

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ity, fa

ther

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duca

tion

, dum

my for

mis

sing

fath

er’s

educa

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, fa

ther

’s a

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fat

her

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square

d, num

ber

of

Not

es:

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ntr

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are

coef

fici

ents

on R

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day

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frac

tion

of pea

k d

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hours

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olu

mns

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nd

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sum

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ns

3 an

d 4

use

eit

her

sel

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por

ted o

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ed g

esta

tion

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se b

irth

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wee

n 3

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n. C

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olu

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t th

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ample

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h n

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2000

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sus.

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tion

, to

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co u

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cohol

use

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ity, fa

ther

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tion

, dum

my for

mis

sing

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ther

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of

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es:

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frac

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k d

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mns

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3 an

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use

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her

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se b

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n r

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t th

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ample

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s livin

g in

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codes

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h n

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g to

the

2000

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sus.

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her

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er’s

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square

d, m

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er’s

ed

uca

tion

, to

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co u

se, al

cohol

use

, par

ity, fa

ther

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tion

, dum

my for

mis

sing

fath

er’s

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tion

, fa

ther

’s a

ge,

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er’s

age

square

d, num

ber

of

pre

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gnan

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dum

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s, c

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es:

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ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

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day

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ver

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quen

t 30

day

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frac

tion

of pea

k d

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olu

mns

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sum

e nor

mal

ges

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on. C

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ns

3 an

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use

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her

sel

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por

ted o

r physi

cian

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imat

ed g

esta

tion

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se b

irth

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n 3

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n. C

olum

ns

1 an

d 3

use

a b

road

sam

ple

of im

pute

d n

on-A

rab m

other

s bas

ed o

n r

epor

ted a

nce

stry

or

race

. C

olu

mns

2 a

nd 4

furt

her

res

tric

t th

is s

ample

to

mot

her

s livin

g in

zip

codes

wit

h n

o A

rabs

acco

rdin

g to

the

2000

Cen

sus.

C

ontr

ols

incl

ude

mot

her

’s a

ge,

moth

er’s

age

square

d, m

oth

er’s

ed

uca

tion

, to

bac

co u

se, al

cohol

use

, par

ity, fa

ther

’s e

duca

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of

pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s.*

%**

%**

*%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

rs o

ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

. C

olu

mns

1 a

nd

2 as

sum

e nor

mal

ges

tati

on. C

olum

ns

3 an

d 4

use

eit

her

sel

f re

por

ted o

r physi

cian

est

imat

ed g

esta

tion

and u

se b

irth

s bet

wee

n 3

9 a

nd 4

2 w

eeks

gest

atio

n. C

olum

ns

1 an

d 3

use

a b

road

sam

ple

of im

pute

d n

on-A

rab m

other

s bas

ed o

n r

epor

ted a

nce

stry

or

race

. C

olu

mns

2 a

nd 4

furt

her

res

tric

t th

is s

ample

to

mot

her

s livin

g in

zip

codes

wit

h n

o A

rabs

acco

rdin

g to

the

2000

Cen

sus.

C

ontr

ols

incl

ude

mot

her

’s a

ge,

moth

er’s

age

square

d, m

oth

er’s

ed

uca

tion

, to

bac

co u

se, al

cohol

use

, par

ity, fa

ther

’s e

duca

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of

pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%

; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

rs o

ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

. C

olu

mns

1 a

nd

2 as

sum

e nor

mal

ges

tati

on. C

olum

ns

3 an

d 4

use

eit

her

sel

f re

por

ted o

r physi

cian

est

imat

ed g

esta

tion

and u

se b

irth

s bet

wee

n 3

9 a

nd 4

2 w

eeks

gest

atio

n. C

olum

ns

1 an

d 3

use

a b

road

sam

ple

of im

pute

d n

on-A

rab m

other

s bas

ed o

n r

epor

ted a

nce

stry

or

race

. C

olu

mns

2 a

nd 4

furt

her

res

tric

t th

is s

ample

to

mot

her

s livin

g in

zip

codes

wit

h n

o A

rabs

acco

rdin

g to

the

2000

Cen

sus.

C

ontr

ols

incl

ude

mot

her

’s a

ge,

moth

er’s

age

square

d, m

oth

er’s

ed

uca

tion

, to

bac

co u

se, al

cohol

use

, par

ity, fa

ther

’s e

duca

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of

pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%

; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

rs o

ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

. C

olu

mns

1 a

nd

2 as

sum

e nor

mal

ges

tati

on. C

olum

ns

3 an

d 4

use

eit

her

sel

f re

por

ted o

r physi

cian

est

imat

ed g

esta

tion

and u

se b

irth

s bet

wee

n 3

9 a

nd 4

2 w

eeks

gest

atio

n. C

olum

ns

1 an

d 3

use

a b

road

sam

ple

of im

pute

d n

on-A

rab m

other

s bas

ed o

n r

epor

ted a

nce

stry

or

race

. C

olu

mns

2 a

nd 4

furt

her

res

tric

t th

is s

ample

to

mot

her

s livin

g in

zip

codes

wit

h n

o A

rabs

acco

rdin

g to

the

2000

Cen

sus.

C

ontr

ols

incl

ude

mot

her

’s a

ge,

moth

er’s

age

square

d, m

oth

er’s

ed

uca

tion

, to

bac

co u

se, al

cohol

use

, par

ity, fa

ther

’s e

duca

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of

pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%

; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

to

day

ligh

t hou

rs o

ver

subse

quen

t 30

day

s as

frac

tion

of pea

k d

aylight

hours

. C

olu

mns

1 a

nd

2 as

sum

e nor

mal

ges

tati

on. C

olum

ns

3 an

d 4

use

eit

her

sel

f re

por

ted o

r physi

cian

est

imat

ed g

esta

tion

and u

se b

irth

s bet

wee

n 3

9 a

nd 4

2 w

eeks

gest

atio

n. C

olum

ns

1 an

d 3

use

a b

road

sam

ple

of im

pute

d n

on-A

rab m

other

s bas

ed o

n r

epor

ted a

nce

stry

or

race

. C

olu

mns

2 a

nd 4

furt

her

res

tric

t th

is s

ample

to

mot

her

s livin

g in

zip

codes

wit

h n

o A

rabs

acco

rdin

g to

the

2000

Cen

sus.

C

ontr

ols

incl

ude

mot

her

’s a

ge,

moth

er’s

age

square

d, m

oth

er’s

ed

uca

tion

, to

bac

co u

se, al

cohol

use

, par

ity, fa

ther

’s e

duca

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of

pre

vio

us

pre

gnan

cies

that

res

ult

ed in d

eath

at

bir

th, co

nce

pti

on (

or b

irth

) m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%

; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Tab

le 3

: E

ffec

ts o

f R

amad

an E

xposu

re o

n S

ex a

t B

irth

and L

ive

Bir

ths,

Mic

hig

an A

rabs

and N

on A

rabs

Ges

tati

on(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)M

onth

All

Ara

bs

Exc.

All

Non

-Ara

bex

pos

ure

Ara

bs

Chal

dea

n Z

ipN

on-A

rabs

Zip

codes

Tot

alM

ale

Fem

ale

Tot

alM

ale

Fem

ale

0-0

.032

-0.0

330.

003

0.00

70.

063

0.00

30.

090

0.03

00.

050

0.04

4(0

.021

)(0

.025)

(0.0

03)

(0.0

12)

(0.0

68)

(0.0

87)

(0.0

97)

(0.0

40)

(0.0

49)

(0.0

47)

1-0

.037

*-0

.066

***

0.00

20.

011

-0.1

64**

-0.2

58**

*-0

.043

0.04

20.

051

0.03

1(0

.020

)(0

.023)

(0.0

03)

(0.0

12)

(0.0

63)

(0.0

82)

(0.0

87)

(0.0

35)

(0.0

44)

(0.0

42)

2-0

.008

-0.0

080.

001

-0.0

19-0

.007

0.01

70.

021

-0.0

16-0

.005

0.02

2(0

.020

)(0

.024)

(0.0

03)

(0.0

12)

(0.0

66)

(0.0

86)

(0.0

93)

(0.0

41)

(0.0

49)

(0.0

48)

3-0

.002

-0.0

070.

002

-0.0

09-0

.088

-0.1

43*

-0.0

600.

029

-0.0

100.

076

(0.0

20)

(0.0

24)

(0.0

03)

(0.0

12)

(0.0

65)

(0.0

83)

(0.0

93)

(0.0

39)

(0.0

48)

(0.0

46)

4-0

.022

-0.0

300.

002

-0.0

070.

047

-0.0

370.

084

0.04

50.

054

0.06

6(0

.020

)(0

.024)

(0.0

03)

(0.0

12)

(0.0

70)

(0.0

90)

(0.0

96)

(0.0

42)

(0.0

49)

(0.0

47)

50.

005

-0.0

15

0.0

01

0.01

4-0

.035

-0.0

550.

004

0.02

30.

032

0.02

1(0

.020

)(0

.024)

(0.0

03)

(0.0

12)

(0.0

69)

(0.0

84)

(0.0

97)

(0.0

38)

(0.0

48)

(0.0

46)

6-0

.013

-0.0

220.

001

-0.0

110.

037

-0.0

210.

078

-0.0

12-0

.018

0.02

4(0

.020

)(0

.024)

(0.0

03)

(0.0

12)

(0.0

66)

(0.0

85)

(0.0

90)

(0.0

38)

(0.0

48)

(0.0

45)

70

012

000

90

002

001

80

113

008

30

148

001

20

039

001

4

Dep

enden

t V

aria

ble

is

Mal

e

Ara

b S

ample

Non

-Ara

b S

ample

Dep

enden

t V

aria

ble

is

Log

Liv

e B

irth

s (T

otal

, M

ale

Fem

ale)

7-0

.012

-0.0

090.

002

0.01

8-0

.113

-0.0

83-0

.148

0.0

120.0

39-0

.014

(0.0

20)

(0.0

23)

(0.0

03)

(0.0

12)

(0.0

69)

(0.0

84)

(0.0

94)

(0.0

39)

(0.0

48)

(0.0

47)

8-0

.026

-0.0

310.

001

-0.0

050.

008

-0.0

630.

088

0.05

60.

038

0.07

7*(0

.019

)(0

.023)

(0.0

03)

(0.0

11)

(0.0

63)

(0.0

78)

(0.0

87)

(0.0

35)

(0.0

45)

(0.0

42)

9-0

.030

-0.0

300.

003

0.00

10.

003

-0.0

630.

060

0.00

70.

020

0.00

6(0

.020

)(0

.024

)(0

.003

)(0

.012

)(0

.068

)(0

.083

)(0

.097

)(0

.039

)(0

.049

)(0

.046

)

join

t te

st, co

effici

ents

on m

onth

s 1

to 9

equal

to

0p

-valu

e0.

520.3

41.

000.

210.

190.

340.

630.

690.

800.

58

N41

153

2995

514

3401

411

2516

216

216

216

216

216

216

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks

and incl

ude

contr

ols

for

moth

er’s

age,

moth

er’s

age

square

d, m

oth

er’s

educa

tion, to

bacc

o u

se, alc

ohol use

, pari

ty, fa

ther

’s e

duca

tion,

dum

my for

mis

sing fath

er’s

educa

tion, fa

ther

’s a

ge,

fath

er’s

age

square

d, num

ber

of pre

vio

us

pre

gnanci

es t

hat

resu

lted

in d

eath

at

bir

th,

conce

pti

on m

onth

dum

mie

s, c

ounty

dum

mie

s and b

irth

yea

r dum

mie

s.

The

Ara

b s

am

ple

in c

olu

mns

5 t

hro

ugh 7

dro

ps

Ara

b m

oth

ers

livin

g in z

ipco

des

with a

larg

e sh

are

of C

hald

eans.

T

he

non-A

rab s

am

ple

in c

olu

mns

8 t

hro

ugh 1

0 d

rops

pre

sum

ed N

on A

rab m

oth

ers

livin

g in z

ipco

des

wit

h a

n A

rab p

rese

nce

. Sta

ndard

err

ors

in p

are

nth

eses

. It

alici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s.

*si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Tab

le4:

Effec

tsof

Ram

adan

Expos

ure

onC

har

acte

rist

ics

ofP

regn

anci

esR

esult

ing

inLiv

eB

irth

sM

ichig

anA

rabs

Tab

le 4

: E

ffec

ts o

f R

amad

an E

xpos

ure

on C

har

acte

rist

ics

of P

regn

anci

es R

esult

ing

in L

ive

Bir

ths,

Mic

hig

an A

rabs

gg

g

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Ges

tati

onP

revio

us

Fat

her

’sP

revio

us

Dia

bet

esG

esta

tion

Pre

vio

us

Fat

her

sP

revio

us

Dia

bet

esM

hM

h’

Mh

’F

h’

Fh

’C

hild

Ed

Sll

Ri

kM

onth

Mot

her

’sM

other

’sFat

her

’s

Fat

her

’sC

hild

Educ.

Sm

all

Ris

kM

onth

Mot

her

sM

other

sFat

her

s Fat

her

sC

hild

Educ.

Sm

all

Ris

kE

Ed

tiM

di

idA

AE

dti

Tb

Al

hl

Pit

BD

dM

iB

bF

tE

xpos

ure

Educa

tion

Med

icai

dA

geA

geE

duca

tion

Tob

acco

Alc

ohol

Par

ity

Born

Dea

dM

iss.

Bab

yFac

tor

Expos

ure

Educa

tion

Med

icai

dA

geA

geE

duca

tion

Tob

acco

Alc

ohol

Par

ity

Born

Dea

dM

iss.

Bab

yFac

tor

00

030

000

10

111

025

60

007

000

10

002

004

00

029

000

70

001

000

80

0.0

300.

001

-0.1

11-0

.256

-0.0

070.

001

-0.0

020.

040

-0.0

29-0

.007

0.00

1-0

.008

00.

030

0.00

10.

111

0.25

60.

007

0.00

10.

002

0.04

00.

029

0.00

70.

001

0.00

8(0

138

)(0

019)

(022

6)(0

261)

(013

4)(0

008)

(000

2)(0

060)

(002

6)(0

010)

(000

3)(0

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19)

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54)

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30)

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07)

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join

tte

stco

effici

ents

onm

onth

s1

to9

equal

to0

join

t te

st, co

effici

ents

on m

onth

s 1 t

o 9

equal

to

0j

,9

q0

pvalu

e0

480

720

810

880

560

520

320

840

560

210

790

24p

-val

ue

0.48

0.72

0.81

0.88

0.56

0.52

0.32

0.84

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0.79

0.2

4p jo

int

test

coef

fici

ents

onm

onth

s1

to9

are

equal

join

t te

st, co

effici

ents

on m

onth

s 1 t

o 9

are

equal

p-v

alue

039

064

09

083

049

047

026

078

068

02

08

018

p-v

alue

0.39

0.64

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0.47

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0.68

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0.1

8

N42

441

4311

643

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000

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971

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643

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4095

143

000

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142

971

Not

es:

All

entr

ies

are

coef

fici

ents

onR

amad

anex

pos

ure

today

ligh

thou

rsov

ersu

bse

quen

t30

day

sas

fract

ion

ofpea

kdaylight

hours

Not

es:

All

entr

ies

are

coef

fici

ents

onR

amad

anex

pos

ure

today

ligh

thou

rsov

ersu

bse

quen

t30

day

sas

fract

ion

ofpea

kdaylight

hours

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

ample

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian

est

imat

ed. A

ll s

ample

s use

bir

ths

wit

h g

esta

tion

bet

wee

n 3

7 an

d 4

2 N

otes

:A

ll e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

ample

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian

est

imat

ed. A

ll s

ample

s use

bir

ths

wit

h g

esta

tion

bet

wee

n 3

7 an

d 4

2 N

otes

:A

ll e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks.

R

egre

ssio

ns

incl

ude

dum

mie

s fo

r co

nce

pti

on m

onth

, co

unty

and b

irth

yea

r. I

talici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l f

lb

St

dd

ith

*i

ifi

tt

10%

**

iifi

tt

5%

***

iifi

tt

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks.

R

egre

ssio

ns

incl

ude

dum

mie

s fo

r co

nce

pti

on m

onth

, co

unty

and b

irth

yea

r. I

talici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l f

lb

St

dd

ith

*i

ifi

tt

10%

**

iifi

tt

5%

***

iifi

tt

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks.

R

egre

ssio

ns

incl

ude

dum

mie

s fo

r co

nce

pti

on m

onth

, co

unty

and b

irth

yea

r. I

talici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s. S

tandar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks.

R

egre

ssio

ns

incl

ude

dum

mie

s fo

r co

nce

pti

on m

onth

, co

unty

and b

irth

yea

r. I

talici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s. S

tandar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re t

o d

aylight

hours

over

subse

quen

t 30 d

ays

as

fract

ion o

f pea

k d

aylight

hours

duri

ng s

am

ple

per

iod. G

esta

tion is

eith

er s

elf re

port

ed o

r physi

cian e

stim

ate

d. A

ll s

am

ple

s use

bir

ths

wit

h g

esta

tion b

etw

een 3

7 a

nd 4

2

wee

ks.

R

egre

ssio

ns

incl

ude

dum

mie

s fo

r co

nce

pti

on m

onth

, co

unty

and b

irth

yea

r. I

talici

zed e

stim

ate

s m

ay r

epre

sent

post

nata

l ex

posu

re for

som

e sa

mple

mem

ber

s. S

tandar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Tab

le5:

Effec

tsof

Ram

adan

Expos

ure

inM

onth

sP

rior

toB

irth

onD

isab

ilit

yO

utc

omes

inU

ganda

Tab

le 5

: E

ffec

ts o

f R

amad

an E

xpos

ure

in M

onth

s P

rior

to

Bir

th o

n D

isab

ilit

y O

utc

omes

in U

ganda

Mli

NM

liM

usl

ims

Non

-Musl

ims

Mth

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Musl

ims

Non

Musl

ims

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pi

tSi

ht/

Hi

/M

tl/

Si

ht/

Hi

/M

tl/

Pri

or t

oSig

ht/

Hea

ring/

Men

tal/

Sig

ht/

Hea

ring/

Men

tal/

Pri

or t

oSig

ht/

Hea

ring/

Men

tal/

Sig

ht/

Hea

ring/

Men

tal/

Bi

thD

ibilit

Bli

dD

fL

iP

hD

ibilit

Bli

dD

fL

iP

hB

irth

Dis

abilit

yB

lind

Dea

fLea

rnin

gP

sych

.D

isab

ilit

yB

lind

Dea

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join

tte

stco

effici

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onm

onth

s1

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to0

join

t te

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80%

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1.49

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N80

924

8092

280

923

809

2180

921

6408

2564

0789

640781

640777

640776

N80

924

8092

280

923

8092

180

921

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N80

924

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280

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809

2180

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640777

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Not

es:

All

entr

ies

are

coef

fici

ents

on

Ram

adan

exposu

rem

easu

red

as

the

per

cent

ofdays

over

lappin

gw

ith

Ram

adan

duri

ng

the

nin

eN

otes

:A

ll e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

spre

cedin

gbir

th(r

am

pct

).E

ach

outc

om

eis

mult

iplied

by

100,so

that

coef

fici

ents

are

inunit

sofper

centa

ge

poin

ts.

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

spre

cedin

gbir

th(r

am

pct

).E

ach

outc

om

eis

mult

iplied

by

100,so

that

coef

fici

ents

are

inunit

sofper

centa

ge

poin

ts.

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge

poin

ts. A

ll

regre

ssio

ns

incl

ude

an indic

ato

r fo

r fe

male

, bir

th m

onth

dum

mie

s, d

istr

ict

of bir

th d

um

mie

s and b

irth

yea

r dum

mie

s.

Sta

ndard

err

ors

in p

are

nth

eses

, *si

gnific

ant

at

10%

; ** s

ignific

ant

at

5%

; *** s

ignific

ant

at

1%

Tab

le6:

Effec

tsof

Ram

adan

Expos

ure

inM

onth

sP

rior

toB

irth

onO

ther

Outc

omes

Uga

ndan

Musl

ims

Tab

le 6

: E

ffec

ts o

f R

amad

an E

xpos

ure

in M

onth

s P

rior

to

Bir

th o

n O

ther

Outc

omes

, U

gandan

Musl

ims

p,

g

SC

ii

fA

dl

Pl

iS

ii

OSex

Com

pos

itio

n o

f A

dult

Popula

tion

Soc

ioec

onom

ic O

utc

om

es

h(

)(

)(

)(

)(

)(

)(

)(

)(

)

Sex

Com

pos

itio

n o

f A

dult

Pop

ula

tion

Soc

ioec

onom

ic O

utc

om

es

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Pi

LL

LH

Yf

NP

rior

to

Log

Log

Log

Hom

e Y

ears

of

No

Pri

or t

oLog

Log

Log

Hom

e Y

ears

of

No

Bi

thM

lP

lti

Ml

Fl

Ohi

Illit

tS

hli

Sh

liE

ld

Bir

thM

ale

Popula

tion

Mal

esFem

ales

Ow

ner

ship

Illite

rate

Sch

ooling

Sch

ooling

Em

plo

yed

Bir

thM

ale

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ula

tion

Mal

esFem

ales

Ow

ner

ship

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rate

Sch

ooling

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ooling

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plo

yed

90

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09)

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47)

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59)

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65)

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Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng

the

nin

e N

otes

:A

ll e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng

the

nin

e N

otes

:A

ll e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

tt

dt

thl

lfbi

thd

bi

thth

Cl

1d

l5

9l

il

ddi

ti

tfbi

thd

i

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

tt

dt

thl

lfbi

thd

bi

thth

Cl

1d

l5

9l

il

ddi

ti

tfbi

thd

i

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n5

isre

stri

cted

tom

enC

olum

ns

69

incl

ude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n5

isre

stri

cted

tom

enC

olum

ns

69

incl

ude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n 5

is

rest

rict

ed t

o m

en. C

olum

ns

6-9

incl

ude

a dum

my for

fem

ales

.

Sta

ndard

erro

rsin

pare

nth

eses

*si

gnific

ant

at

10%

;**

signific

ant

at

5%

;***

signific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n 5

is

rest

rict

ed t

o m

en. C

olum

ns

6-9

incl

ude

a dum

my for

fem

ales

.

Sta

ndard

erro

rsin

pare

nth

eses

*si

gnific

ant

at

10%

;**

signific

ant

at

5%

;***

signific

ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n 5

is

rest

rict

ed t

o m

en. C

olum

ns

6-9

incl

ude

a dum

my for

fem

ales

.

Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n 5

is

rest

rict

ed t

o m

en. C

olum

ns

6-9

incl

ude

a dum

my for

fem

ales

.

Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amadan d

uri

ng t

he

nin

e m

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. C

olu

mns

2-4

use

data

on p

opula

tion

counts

aggre

gate

d t

o t

he

level

of bir

th y

ear

and b

irth

month

. C

olu

mn 1

and c

olu

mns

5-9

als

o incl

ude

dis

tric

t of bir

th d

um

mie

s.

Col

um

n 5

is

rest

rict

ed t

o m

en. C

olum

ns

6-9

incl

ude

a dum

my for

fem

ales

.

Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Tab

le7:

Effec

tsof

Ram

adan

Expos

ure

inM

onth

sP

rior

toB

irth

onV

ario

us

Outc

omes

Iraq

Tab

le 7

: E

ffec

ts o

f R

amad

an E

xpos

ure

in M

onth

s P

rior

to

Bir

th o

n V

ario

us

Outc

omes

, Ir

aqp

,q

Di

bili

OS

ii

OD

isab

ilit

y O

utc

omes

Soc

ioec

onom

ic O

utc

omes

h(

)(

)(

)(

)(

)(

)(

)(

)

Dis

abilit

y O

utc

omes

Soc

ioec

onom

ic O

utc

omes

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Mon

ths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pi

Bli

d/

Df/

Ml

il

HP

rior

to

Blind/

Dea

f/M

ult

iple

Hom

eP

rior

to

Blind/

Dea

f/M

ult

iple

Hom

eB

ith

Di

bilit

Vi

iH

iP

hW

iO

hi

Ml

El

dB

irth

Dis

abilit

yV

isio

nH

eari

ng

Psy

ch.

Wiv

esO

wner

ship

Mal

eE

mplo

yed

Bir

thD

isab

ilit

yV

isio

nH

eari

ng

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ch.

Wiv

esO

wner

ship

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eE

mplo

yed

90

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e per

cent

of days

over

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g w

ith R

am

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uri

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ntr

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fici

ents

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am

adan e

xposu

re m

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s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

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cedin

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irth

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mpct

). A

ll r

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ssio

ns

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ude

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th m

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irth

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r dum

mie

s. E

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om

e is

mult

iplie

tht

ffi

it

iit

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it

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5d

6t

it

dt

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il

d

Not

es:

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ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

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ssio

ns

incl

ude

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th m

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irth

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r dum

mie

s. E

ach

outc

om

e is

mult

iplie

tht

ffi

it

iit

ft

it

Cl

5d

6t

it

dt

All

il

d

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

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g w

ith R

am

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uri

nm

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cedin

g b

irth

(ra

mpct

). A

ll r

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ssio

ns

incl

ude

bir

th m

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irth

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r dum

mie

s. E

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outc

om

e is

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iplie

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ffic

ients

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in u

nit

s of

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centa

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oints

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ns

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d 6

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tric

ted t

o m

en. A

ll r

egre

ssio

ns

on p

oole

d s

am

pan

dw

omen

incl

ude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

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over

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g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

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r dum

mie

s. E

ach

outc

om

e is

mult

iplie

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. C

olum

ns

5 an

d 6

are

res

tric

ted t

o m

en. A

ll r

egre

ssio

ns

on p

oole

d s

am

pan

dw

omen

incl

ude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. E

ach

outc

om

e is

mult

iplie

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. C

olum

ns

5 an

d 6

are

res

tric

ted t

o m

en. A

ll r

egre

ssio

ns

on p

oole

d s

am

pan

d w

omen

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ude

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my for

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ales

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ndard

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nth

eses

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gnific

ant

at

10%

;**

signific

ant

at

5%

;***

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ant

at

1%

Not

es:

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ntr

ies

are

coef

fici

ents

on R

am

adan e

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re m

easu

red a

s th

e per

cent

of days

over

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g w

ith R

am

adan d

uri

nm

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cedin

g b

irth

(ra

mpct

). A

ll r

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ssio

ns

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ude

bir

th m

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irth

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r dum

mie

s. E

ach

outc

om

e is

mult

iplie

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coe

ffic

ients

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in u

nit

s of

per

centa

ge p

oints

. C

olum

ns

5 an

d 6

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res

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ted t

o m

en. A

ll r

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ssio

ns

on p

oole

d s

am

pan

d w

omen

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ude

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my for

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ales

.Sta

ndard

erro

rsin

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nth

eses

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gnific

ant

at

10%

;**

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ant

at

5%

;***

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ant

at

1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. E

ach

outc

om

e is

mult

iplie

that

coe

ffic

ients

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in u

nit

s of

per

centa

ge p

oints

. C

olum

ns

5 an

d 6

are

res

tric

ted t

o m

en. A

ll r

egre

ssio

ns

on p

oole

d s

am

pan

d w

omen

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ude

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my for

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ales

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rror

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eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th m

onth

and b

irth

yea

r dum

mie

s. E

ach

outc

om

e is

mult

iplie

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. C

olum

ns

5 an

d 6

are

res

tric

ted t

o m

en. A

ll r

egre

ssio

ns

on p

oole

d s

am

pan

d w

omen

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ude

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my for

fem

ales

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ndar

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rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

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es:

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ntr

ies

are

coef

fici

ents

on R

am

adan e

xposu

re m

easu

red a

s th

e per

cent

of days

over

lappin

g w

ith R

am

adan d

uri

nm

onth

s pre

cedin

g b

irth

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mpct

). A

ll r

egre

ssio

ns

incl

ude

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th m

onth

and b

irth

yea

r dum

mie

s. E

ach

outc

om

e is

mult

iplie

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ffic

ients

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in u

nit

s of

per

centa

ge p

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. C

olum

ns

5 an

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res

tric

ted t

o m

en. A

ll r

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ssio

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pan

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ude

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my for

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ales

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nific

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Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Di

ht

t(d

ib

ilit

lH

Sd

l)

lti

lid

b10

0th

tffi

it

iit

ft

it

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Di

ht

t(d

ib

ilit

lH

Sd

l)

lti

lid

b10

0th

tffi

it

iit

ft

it

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

onpoo

led

sam

ple

sof

men

and

wom

enin

clude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

onpoo

led

sam

ple

sof

men

and

wom

enin

clude

adum

my

for

fem

ales

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

der

rors

inpar

enth

eses

*sig

nific

ant

at10

%;**

sign

ific

ant

at5%

;**

*si

gnific

ant

at1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

der

rors

inpar

enth

eses

*sig

nific

ant

at10

%;**

sign

ific

ant

at5%

;**

*si

gnific

ant

at1%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

quar

ters

pre

cedin

g (o

r in

cludin

g) b

irth

(ra

mpct

). A

ll r

egre

ssio

ns

incl

ude

bir

th q

uar

ter,

bir

th y

ear

and c

ountr

y o

f bir

th d

um

mie

s.

Dic

hot

omou

s ou

tcom

es (

dis

bai

lity

, le

ss H

S a

nd m

ale)

are

mult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

are

on p

oole

d s

ample

s of

men

and w

omen

incl

ude

a dum

my for

fem

ales

.Sta

ndar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Ramadan Appendix Material

Contents

A Biomedical Studies of Fasting ii

A.1 First Stage Effects of Ramadan . . . . . . . . . . . . . . . . . . . . . . . . ii

A.1.1 Caloric Intake and Weight Among Fasting Adults . . . . . . . . . . ii

A.1.2 Is Ramadan Observed by Pregnant Muslims? . . . . . . . . . . . . iii

A.2 Ramadan and Fetal Health . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

A.2.1 Pathways from Maternal to Fetal Health . . . . . . . . . . . . . . . iv

A.2.2 Empirical Studies of Fetal Health . . . . . . . . . . . . . . . . . . . v

A.3 Mechanisms of Fetal Programming . . . . . . . . . . . . . . . . . . . . . . v

A.4 Ramadan and Perinatal Health . . . . . . . . . . . . . . . . . . . . . . . . vii

A.4.1 Birth Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

A.4.2 Longer-term Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

A.5 Nutrition and the Sex Ratio at Birth . . . . . . . . . . . . . . . . . . . . . viii

A.6 Hypotheses: Outcomes and Timing . . . . . . . . . . . . . . . . . . . . . . x

B Data x

B.1 Michigan Natality Microdata . . . . . . . . . . . . . . . . . . . . . . . . . . x

B.2 Uganda Census 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

B.3 Iraq Census 1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

B.4 US Census 1980, ACS, 2005-2007 . . . . . . . . . . . . . . . . . . . . . . . xiii

B.5 Other Suitable Datasets? . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

C Birth Outcomes by Gestation Length xiv

D Distributional Effects on Birth Weight xvi

E Appendix Tables & Figures

Tables A1-A7, Figures A1-A4

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A Biomedical Studies of Fasting

We begin by summarizing evidence on the “first stage” effect of fasting during Ramadan.

That is, what is the existing evidence that Ramadan fasting can have a detectable effect

on health? In Section A.1, we consider studies of caloric intake and weight change during

intermittent fasting and summarize survey data on the prevalence of Ramadan fasting

among pregnant women. Second, we discuss the potential impacts of maternal biochemical

changes caused by fasting (accelerated starvation) on the fetus in Section A.2. Third,

we examine potential pathways by which intermittent fasting could have lasting effects

through “fetal programming” in Section A.3. Fourth, we review the empirical studies that

have explicitly examined the effects of Ramadan on birth and early childhood outcomes

in Section A.4. Fifth, we briefly summarize a separate literature on nutrition and the sex

ratio at birth – which to date has not used Ramadan fasting for identification – in Section

A.5 . Finally, we distill the preceding into research hypotheses which we will apply to our

data in Section A.6.

A.1 First Stage Effects of Ramadan

A.1.1 Caloric Intake and Weight Among Fasting Adults

Ramadan fasting in the adult population (i.e. not conditioning on pregnancy) has been

associated with modest but statistically significant declines in the weight of fasters of

around 1 to 3 kg (Husain et al. [1987]; Ramadan et al. [1999]; Adlouni et al. [1998]; Mansi

[2007]; Takruri [1989]) Reductions in weight are sometimes (but not always) accompanied

by declines in caloric intake and likely depend on dietary customs in specific countries.1

Two studies are of particular relevance. First, in a study of 185 pregnant women,

Arab [2003] found that over a 24 hour period encompassing the Ramadan fast, over 90

percent of the women had a deficiency of over 500 calories relative to the required energy

intake and 68 percent had a deficiency of over 1000 calories. Second, in the only large

scale population-based study we are aware of, Cole [1993] found striking evidence of sharp

weight changes during Ramadan for women in Gambia. The study was notable because it

used fixed effects with 11 years of panel data and controlled for calendar month, calendar

year, and stage of pregnancy (or lactation). Appendnix Figure A1, taken from the study,

shows that relative to the rest of the year, there is an increase in weight during the four

1For example, Husain et al. [1987] found reductions in caloric intake of between 6 percent and 25percent relative to nonfasting conditions among Malaysians. In contrast, Adlouni et al. [1998] found a 20percent increase in calories per day among Moroccans.

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weeks prior to Ramadan and a sharp increase in weight at the very beginning of Ramadan.

This is followed by an abrupt fall in weight of over 1kg (2.2 pounds) during the subsequent

3 weeks of fasting. The figure provides striking visual evidence that daytime fasting during

Ramadan is affecting weight gain.

In any case, as we discuss in section 2.1 of the paper, fasting may induce maternal

biochemical changes and reprogramming of the neuro-endocrine system due to alterations

in the the timing of nutritional intake even if overall caloric intake or weight change is

unaffected.

A.1.2 Is Ramadan Observed by Pregnant Muslims?

Although pregnant women may request an exemption from fasting, they are expected to

“make up” the fasting days missed during pregnancy after delivery and this requirement

may discourage pregnant women from seeking the exemption since they may be the only

member of the household fasting [Hoskins, 1992, Mirghani et al., 2004].2 Mirghani et al.

[2004] noted: “Most opt to fast with their families rather than doing this later”:636.

In addition, some Muslims interpret Islamic Law as requiring pregnant women to fast.

For example, the religious leader of Singapore’s Muslims held that: “a pregnant woman

who is in good health, capable of fasting and does not feel any worry about herself or

to her foetus, is required and expected to fast like any ordinary woman” [Joosoph and

Yu, 2004].3 Furthermore, since fasting during Ramadan is one of the five pillars of Islam

and is a central part of the culture of the Muslim community, many women fear a loss

of connection with the community or would feel guilty about not observing Ramadan

[Robinson and Raisler, 2005].

As far as we are aware, comprehensive data on Ramadan fasting during pregnancy

do not exist. Various surveys of Muslim women suggest that fasting is the norm. For

example, of the 4,343 women delivering in hospitals in Hamadan, Iran in 1999, 71%

reported fasting at least 1 day, “highlighting the great desire of Muslim women to keep

fasting in Ramadan, the holy month”[Arab and Nasrollahi, 2001]. In a study in Singapore,

87% of the 181 muslim women surveyed fasted at least 1 day during pregnancy, and

74% reported completing at least 20 days of fasting [Joosoph and Yu, 2004]. In a study

2There are some differences in interpretation of the Koran among Imams regarding whether pregnantwomen must make up the fasting days later or simply pay alms for the poor, or both. See, for example,http://islam1.org/iar/imam/archives/2006/09/09/fasting the month of ramadaan.php

3Similarly, Arab and Nasrollahi [2001] noted that “According the Islamic teaching pregnant womenare allowed to fast if it is not harmful to them”; faculty at the Kurdistan Medical Science Universityin Iran noted that pregnant and breastfeeding women “who fear for the their well being or that of thefoetus/child” may be exempted from fasting [Shahgheibi et al., 2005].

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conducted in Sana’a City, Yemen, more than 90 percent fasted over 20 days [Makki, 2002].

At the Sorrento Maternity Hospital in Birmingham, England, three quarters of mothers

fasted during Ramadan [Eaton and Wharton, 1982]. In a study conducted in Gambia, 90

percent of pregnant women fasted throughout Ramadan [Prentice et al., 1983]. In the

US, a study of 32 Muslim women in Michigan found that 28 had fasted in at least one

pregnancy and reported that 60-90 percent of women from their communities fast during

pregnancy [Robinson and Raisler, 2005].

In summary, survey data indicate that most but not all women observe the Ramadan

fast during pregnancy. To the extent that pregnant Muslim women do not fast, ITT

estimates are conservative estimates of fasting’s effect. As discussed in Section 6 of the

main paper, fasting observance is likely highest in early pregnancy.

A.2 Ramadan and Fetal Health

A.2.1 Pathways from Maternal to Fetal Health

Does exposure to ketones during “accelerated starvation” (Section 2.1 of the main text)

impair the neural development of the fetus? Controlled studies of mice and rats have

shown that prenatal exposure to ketones result in impaired neurological development.

[Hunter and Sadler, 1987, Moore et al., 1989, Sheehan et al., 1985]. Hunter and Sadler

[1987] reference studies showing ketones “rapidly diffuse from the maternal circulation

across extraembryonic membranes”:263. They also point out that in addition to the

period of neurulation (3rd to 4th week of gestation in humans), the earliest stages of

embryogenesis when the “primitive streak” is observed (the 13th day post-conception),

may be especially susceptible to ketones. Moore et al. [1989] noted that “even a relatively

brief episode of ketosis might perturb the development of the early embryo”:248. They also

emphasize that the effects of ketones were to slow neurological development rather than

to produce a malformation. This may explain why similar studies in human populations

have not (for the most part) found evidence of congenital malformations [ter Braak et al.,

2002]

A related literature has examined the effects of poor metabolic regulation during

pregnancy in mothers with Type 1 diabetes. In this case although the primary concern

is avoiding hyperglycemia (abnormally high blood glucose), this sometimes results in

severe cases of hypocglycemia (abnormally low blood glucose). The latter case may be

instructive for understanding the potential effects of accelerated starvation since blood

glucose drops after a prolonged fast. Some studies of in utero exposure to hypocglycemia

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among diabetic mothers have shown that fetal growth is reduced and that the key period

is between the fourth to sixth weeks of gestation [ter Braak et al., 2002]). It has also

been shown that hypoglycemia among non-diabetic mothers is also associated with lower

birth weight [Scholl et al., 2001]. Studies of diabetic mothers have shown long-term effects

of accelerated starvation on cognitive functioning during childhood (Rizzo et al. [1991],

Langan et al. [1991]).

A.2.2 Empirical Studies of Fetal Health

Fetal health measures have the advantage of permitting panel data techniques to address

selection in to maternal fasting but the disadvantage of not being standardized health

metrics. Several studies of maternal fasting during Ramadan have found adverse effects

on at least two of these fetal health indicators. Mirghani et al. [2004] found evidence of

reduced fetal breathing movements where measures of fetal breathing were taken both

before and after fasting on the same day. The same study, however, found no change

in overall body movements, fetal tone or maternal appreciation.4 Mirghani et al. [2005]

found a significantly fewer heart rate accelerations among pregnant women who were

fasting during Ramadan late in pregnancy compared to controls. This was observed

despite relatively short diurnal fasts (less than 10 hours duration) and the absence of

significant changes in glucose levels. DiPietro et al. [2007] found a strong association

between variation in fetal heart rate in utero and mental and psychomotor development

and language ability during early childhood. Finally, Mirghani et al. [2007] found no effect

of Ramadan fasting on uterine arterial blood flow.

In contrast, studies of hypoglycemia in animals and humans have examined the fetal

heart rate, fetal breathing movements, and limb and body movements in order to identify

impairments to fetal development. A review of these studies in ter Braak et al. [2002] do

not show much affect of moderate hypoglycemia on fetal conditions.

A.3 Mechanisms of Fetal Programming

We now discuss how disruptions to fetal health can have permanent effects. In a review

of epidemiological studies on the fetal origins of adult diseases, Jaddoe and Witteman

[2006] describe two hypotheses related to our study. The first is described as “fetal under-

nutrition.” According to this view, inadequate prenatal nutrition leads to developmental

adaptations that are beneficial for short-term survival but lead to lower birth weight.

4A significant reduction in upper limb movements was noted but there was a concern that this mightbe due to observer bias.

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However, by permanently reprogramming the physiology and metabolism of the fetus,

this ultimately makes the body susceptible to heart disease and diabetes during adult-

hood.5 Although most studies of fetal origins have relied on blunt measures such as birth

weight to proxy for nutritional restriction during pregnancy, a recurring theme in many

studies is that fetal programming may occur even in the absence of birth weight effects.

For example, studies of the Dutch famine have showed that those exposed to the famine

early in gestation had dramatically higher rates of heart disease but did not have lower

birth weight [Painter et al., 2005]. Similarly animal studies have often found evidence

of fetal programming without detecting significant changes in fetal weight. e.g. Nishina

et al. [2004]

A second prominent hypothesis is that nutritional restrictions inhibit the development

of a placental enzyme that is required to convert cortisol into inactive cortisone, thereby

exposing the fetus to excessive amounts of cortisol. It is suggested that exposure to

glucocorticoids such as cortisol in utero leads to a reprogramming of the hypothalamic–

pituitary adrenal axis (HPA) which in turn, could lead to impaired fetal development and

worse health during adulthood.

In controlled animal studies, researchers have linked nutritional restrictions very early

in gestation to an altered neuro-endocrine system, e.g., Nishina et al. [2004]. With respect

to humans, Herrmann et al. [2001] have shown an association between fasts of 13 hours or

longer and higher levels of plasma corticotrophin-releasing hormone (CRH) which could

reflect a reprogramming of the HPA axis. As noted in the main text, Dikensoy et al. [2009]

show that Ramadan fasting is associated with elevated cortisol levels during pregnancy

(relative to pre-pregnancy levels), but not for non-fasting mothers. Kapoor et al. [2006]

describe how the effects of fetal programming of HPA in humans may result in cognitive

impairment; due to the complex feedback mechanisms involved, these effects may not be

evident “until adulthood or early old age”. The authors also emphasize that many of the

long-term effects may be sex-specific.

The existing literature on fetal origins however, has made little use of quasi-experimental

research designs to address potential confounding factors or to identify the underlying

mechanisms. Jaddoe and Witteman [2006] recently concluded: “Thus far, it is still not

known which mechanisms underlie the associations between low birth weight and diseases

in adult life. The causal pathways linking low birth weight to diseases in later life seem

to be complex and may include combined environmental and genetic mechanisms in var-

5Jaddoe and Witteman [2006] note that this view has evolved into a more “general developmen-tal plasticity model in which various fetal and post-natal environmental factors lead to programmingresponses”:93.

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ious periods of life. Well-designed epidemiological studies are necessary to estimate the

population effect size and to identify the underlying mechanisms” Jaddoe and Witteman

[2006, 91].

A.4 Ramadan and Perinatal Health

A.4.1 Birth Outcomes

Existing studies of birth outcomes have relied on comparisons between mothers who re-

ported fasting to those who did not. Kavehmanesh and Abolghasemi [2004] compared 284

births to mothers in Tehran with a “history of fasting during pregnancy” to 255 mothers

who did not fast. Although there were no statistically significant differences with respect

to maternal education or height, pre-pregnancy BMI’s were substantially higher in the

fasting group. For such comparisons, the conditional independence assumption required

for causal inference [Angrist and Pischke, 2009] is tenuous. Shahgheibi et al. [2005] studied

179 newborns for whom Ramadan fell in the third trimester of pregnancy. Among fasters,

birth weight was lower by 33 grams, birth length was lower by about 0.2 centimeters

while head circumference was larger by 0.08 centimeters. Since these differences were not

statistically significant with the small sample used, the authors concluded that fasting

during the third trimester had “no effect” on growth indices. Arab and Nasrollahi [2001]

studied 4,343 pregnancies in the Hamdan province of Iran and concluded that fasting

did not impact birth weight. They did note however, that the incidence of low birth

weight (< 2500 grams) was higher among fasters in the second trimester but that this

was significant only at the 9 percent level.

The largest and perhaps most commonly cited study on the effects of Ramadan on

birth weight conducted a retrospective analysis of 13,351 babies born at full term from

1964-84 in Birmingham, England Cross et al. [1990]. Babies were categorized as Muslim

on the basis of the first three letters of the mother’s surname and were matched to control

groups by age. However, this study did not compare the birthweights of Muslims in

utero during Ramadan to Muslims who were not in utero during Ramadan but instead

compared across groups of Muslims and Non-Muslims. In addition, by design the study

did not look at the potential effects of Ramadan on gestation length. Although Cross et al.

[1990] found no significant effects on mean birth weight, like Arab and Nasrollahi [2001],

they also found a higher incidence of low birth weight among fasters during the second

trimester. Opaneye et al. [1990] found that in Al-Kharj, Saudi Arabia, the incidence of

low birth weight increased during Islamic festivals, Ramadan in particular. 9.9% of the

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415 births were below 2,500 grams during Ramadan, versus 6.3% for the 4,865 births in

non-Ramadan months. Finally, Malhotra et al. [1989] and Mirghani and Hamud [2006]

found no effects on birthweight and APGAR scores, even though they detected substantial

biochemical changes.

A separate literature has found that skipping meals (not associated with Ramadan)

has been associated with preterm delivery. Siega-Riz et al. [2001] studied diets during

the second trimester of pregnancy for over 2000 women in North Carolina and found that

women who did not follow the optimal guidelines of three meals and two snacks a day

were 30 percent more likely to deliver preterm. They suggest that this is consistent with

experimental evidence from animal studies. Herrmann et al. [2001] also reported that

women who fasted for 13 hours or more were three times more likely to deliver preterm.

While most studies have focussed on birth weight, Mirghani and Hamud [2006] consid-

ered a broader range of birth outcomes. Specifically, they compared 168 pregnant fasters

to a control group of 156 non-fasting mothers and found significantly higher rates of ges-

tational diabetes, induced labor, cesarian sections, and admission to the special baby care

unit.

A.4.2 Longer-term Effects

We are aware of just one previous study of on long-term effects of Ramadan. Azizi et al.

[2004] surveyed outcomes among 191 children enrolled in 15 Islamic primary schools in

Iran and their mothers about Ramadan fasting during pregnancy. Approximately half of

the mothers selected for the analysis sample reported fasting. More than 1,600 mothers

returned questionnaires regarding their fasting behaviour during pregnancy. However,

the fraction of this initial sample who fasted during pregnancy is not reported by Az-

izi et al. [2004]. Among fasting mothers, those fasting during the third trimester were

over-sampled. No significant difference in the IQ’s of the children were found by mater-

nal fasting behaviour. As mentioned in the main text, Ewijk [2009] analyzes long-term

Ramadan effects using the Indonesian Family Life Study data. This work was inspired

by ours and generally finds corroborative results.

A.5 Nutrition and the Sex Ratio at Birth

Widely studied in evolutionary biology, the Trivers-Willard hypothesis posits that the

reproductive success of sons is more sensitive to maternal condition than that of daughters

[Trivers and Willard, 1973]. Therefore, parents experiencing better conditions may favor

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male offspring. More generally, the sex ratio at birth and early childhood may proxy for

unobserved health conditions given disproportionate male susceptibility to fetal and infant

mortality [Kraemer, 2000, Mathews and Hamilton, 2005]. One proposed mechanism by

which adjustment to the sex ratio may take place is through the nutritional status of

the mother while pregnant [Cameron, 2004]. Roseboom et al. [2001] found that prenatal

exposure to the Dutch famine of 1944-45 reduced the sex ratio of live births. Similarly,

Almond et al. [2007] found the sex ratio in China was skewed toward females for cohorts

born during the Great Leap Forward Famine. Askling et al. [1999] showed that women

who experience severe morning sickness were much more likely to have girls.

A widely-publicized study by Mathews et al. [2008] has for the first time drawn a link

between maternal nutrition prior to conception and the sex ratio at birth. The authors

collected detailed information on food intake prior to pregnancy, early in pregnancy (14

weeks gestation) and late in pregnancy (28 weeks gestation) in Britain. They found no

differences in the rates of male births arising from differences in nutritional intake either

early or late in pregnancy but found a highly statistically significant positive relationship

between high nutritional scores prior to conception and the birth of male offspring. They

further examined the detailed data on sources of nutrition and found that among 133

food items consumed prior to pregnancy, only breakfast cereals was strongly associated

with infant sex. The authors speculated that the mechanism underlying this connection

is that the skipping of breakfast

“extends the normal period of nocturnal fasting, depresses circulating glucose

levels and may be interpreted by the body as indicative of poor environmental

conditions.”

Mathews et al. [2008] also referenced work by Larson et al. [2001] on in vitro fertiliza-

tion of bovine embryos showing that glucose “enhances the growth and development of

male conceptuses while inhibiting that of females.”

The study by Mathews et al. [2008] was observational and did not explore the source

of dietary differences across mothers, nor did it control for some other factors known to

influence the sex ratio (e.g., partnership status at the time of conception [Norberg, 2004]).

Short of a controlled experiment, the research design utilized here has the advantage of

leveraging plausibly exogenous differences in maternal fasting.

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A.6 Hypotheses: Outcomes and Timing

In this section, we distill findings from the biomedical literature most relevant to our

Ramadan analysis. Appendix Table A1 summarizes the set of health outcomes we might

expect to be affected by fasting (column 1), notes the mechanism (column 2), and lists

the months of prenatal exposure that have been found or suggested to be particularly

important (column 3). These hypotheses are based on either a clearly defined pathway

linking fasting to a particular outcome, or an empirical result that has been established,

irrespective of whether there is an explicit mechanism described in the study. In many of

the studies, the period of in utero exposure was selected by design and therefore does not

exclude effects in other periods.

In the case of birthweight, we describe four mechanisms through which fasting might

operate and one empirical finding based on the Dutch famine. Two of the birthweight

mechanisms are tightly linked to exposures occurring in early pregnancy. For several

outcomes there are no clear hypotheses concerning timing that we could discern; a rea-

sonable hypothesis would be to jointly test the effects of Ramadan exposure during all

gestation months.

With respect to longer-term effects, in virtually all cases exposure to fasting during

early pregnancy is the predominant hypothesis. For cognitive function, there are several

arguably distinct channels through which prenatal fasting might be detrimental.

B Data

B.1 Michigan Natality Microdata

Our ancestry-based proxy for Muslim status is coded as follows. For births from 1989

to 1992, we include mothers who report their ancestry as “Arab/Middle Eastern” in

the ITT (whose pregnancies also overlap with a Ramadan). Starting in 1993, several

specific country codes for ancestry are reported. From 1993 to 2006 our ITT group

includes mothers who report ancestry as: Arab/Middle Eastern, Arab/North African,

Iran, Afghanistan, Mauritania, Somalia, Turkey or Western Sahara. Overall, 96% of our

treatment group report their ancestry as Arab/Middle Eastern, hence we refer to the

group as Arabs.

We also implement several other sample selection rules to minimize measurement

error and misclassification of Muslims into the control group. We dropped births with no

reported ancestry or where the ancestry might possibly include parents who are practicing

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Muslims (e.g. Southeastern Asians). We also dropped non-Arab Blacks to avoid the

possibility that there might be “Black Muslims” in our sample. We also dropped twin

births and restricted the sample to births among mothers between the ages of 14 and 45.

The summary statistics are shown in Appendix Table A2. Arab mothers reported a

year less education than non-Arab mothers on average, and are substantially more likely

to receive Medicaid (46% versus 27%). Arab families are also larger (average parity is 18%

higher for Arabs). Despite these differences in socioeconomic measures, birth outcomes

are more similar. Rates of low birth weight and prematurity are actually slightly lower for

Arabs than for non-Arabs. The geographic distribution of Arabs by zipcode in Michigan

is shown in Appendix Figure A2. Somewhat surprisingly perhaps, Arabs do not appear

noticeably more concentrated around the Dearborn and Detroit area than non-Arabs

(Panel A).

The key variables for assigning in utero Ramadan exposure are birth date and gestation

length. Michigan natality data include exact date of birth and a self-reported date of last

menstrual period (LMP) for about 70 percent of the sample. The problem of selective

reporting of LMP based on socioeconomic status is well known [Hediger et al., 1999].

There is also a field containing the physician’s estimate of gestation length, but we do not

know how it is calculated or when during gestation.6 We follow related epidemiological

studies that utilize a simple algorithm for coding gestation (e.g., Siega-Riz et al. [2001],

Herrmann et al. [2001]): gestation based on LMP is used except if it is missing or if

it differs with physician estimated gestation by more than 14 days, in which case the

physician estimated measure is substituted.

Appendix Figure A3 provides a hypothetical example to illustrate how our daily mea-

sures of Ramadan exposure are calculated. In 1989, Ramadan began on April 7th and

ended on May 6th. For someone who was conceived on April 6th, his or her entire first

month of gestation would overlap with Ramadan, i.e. exppct=1. Since during this Ra-

madan, daylight hours averaged about 13.7 hours per day, compared to 15.2 during the

summer solstice, the hours exposure measure (exphrspct) peaks at about 0.9.

6A key concern is that this could be endogenous to Ramadan exposure. For example, if Ramadanaffects fetal size and if physician estimates of LMP are based on measures of fetal size, this could lead tomis-measurement of the timing of Ramadan exposure. In addition, this measure might not be calculateduniformly and may depend on the timing of the first doctor visit and could therefore, be correlated withmother’s socioeconomic status.

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B.2 Uganda Census 2002

The Uganda Census contains roughly 2.5 million records (10% sample). Our main anal-

ysis sample includes men and women ages 20 to 80. Individuals whose birth month or

birth year were imputed are dropped.7 For each outcome measure, we recoded those with

imputed data to missing. The disability question in the Uganda survey instrument asks:

“Does (name) have any difficulty in moving, seeing, hearing, speaking difficulty, mental

or learning difficulty, which has lasted or is expected to last 6 months or more?” The

following specific disabilities are recorded in the dataset: blind or vision impaired, deaf

or hearing impaired, mute, disability affecting lower extremities, disability affecting up-

per extremities, mental/learning disabilities and psychological disabilities. The original

unharmonized variables label the last two variables “mental retardation” and “mental

illness” while IPUMS-I relabelled them as “mental” and “psychological”. Our own read-

ing of the instructions to the Uganda Census enumerators suggests that this relabelling

was indeed appropriate. The former measure appears to identify those with “mental or

learning disabilities” while the latter identifies those exhibiting “strange behaviors”. A

subsequent question asks about the origin of the reported disability. The responses are

coded into the following categories: congenital, disease, accident, aging, war injury, other

or multiple causes.

The summary statistics are reported in Appendix Table A3. In contrast to Michigan,

Uganda Muslims tend to have higher average SES. Muslims are less likley to be illiterate

than non-Muslims (30% versus 36%) and completed more schooling. Disability rates for

Muslims are also lower – 3.8% versus 5.2% for non-Muslims. Both Muslims and non-

Muslims share a strong seasonality in the frequency of births by month. For both groups,

birth in June was more than 50% more likely than birth in December. The frequency

distribution across Ramadan ITT gestation months is much more uniform, and similar

between Muslims and non-Muslims.

ITT assignment is determined by the reported birth month. We found age heaping:

spikes in the number of respondents reporting of ages ending in zeroes (e.g. 20, 30,

40), suggesting measurement error. We therefore excluded records reporting these round-

number ages.

7The IPUMS-I “unharmonized” variables contain imputation flags. We allowed records with “logicalimputations” but dropped records imputed by “hot-deck”.

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B.3 Iraq Census 1997

The Iraq Census is also a 10 percent sample. We dropped individuals who reported ages

ending in seven because of heaping at those ages. We also drop those reporting birth

months of January and July because of heaping at those months. We also drop those

born before 1958 due to extremely high levels of missing values for month of birth. This

leaves us with a sample of over 250,000 individuals between the ages of 20 and 39 in 1997.

The reduced number of birth cohorts can potentially affect our ability to separate the

effects of Ramadan exposure from season of birth trends for outcomes that are highly

seasonal. We found school related outcomes to be highly seasonal in Iraq. We suspect

that this is due to institutional factors that determine school starting or leaving ages at

particular dates of the calendar year. We find, for example, that mean schooling levels were

about 12 percent higher for those born between September and December than for those

born between February and April. Because of the timing of Ramadan among the 1958 to

1977 cohorts, those born between February through April had no exposure to Ramadan

in the first month of pregnancy, while those born between September and December had

mean exposure of about 0.11 thereby inducing a highly positve correlation between early

Ramadan exposure and schooling. In contrast, we find no evidence of strong season of

birth patterns in our main outcomes of interest. For example, mean disability rates are

only about 1.2 percent lower for those born in September through December compared

to those born between February and April with no discernible monthly pattern.

B.4 US Census 1980, ACS, 2005-2007

We assigned our Muslim proxy to respondents born in a country which currently has at

least 80 percent Muslims.8 We used the following 38 countries: Saudi Arabia, Soma-

lia, Afghanistan, Maldives, Western Sahara, Turkey, Iran, Algeria, Mauritania, Yemen,

Tunisia, Oman, Comoros, Djibouti, Morocco, Pakistan, Libya, Iraq, Iraq/Saudi Arabia,

Tajikistan, Jordan, Qatar, Senegal, Azerbaijan, Egypt, Mali, Niger, Gambia, Uzbekistan,

Turkmenistan, Indonesia, Bangladesh, Syria, Guinea, Kuwait, Bahrain, West Bank and

Gaza. We excluded individuals who reported languages that would suggest they were

non-Muslims.9 We also dropped Iranian immigrants living in California who are predom-

inantly Jewish.

8We took estimates derived from US State Departments’s International Religious Freedom Report,the CIA World Factbook and Adherents.com as reported on the following Wikipedia URL:http://en.wikipedia.org/wiki/Islam by country9For example, some individuals born in Indonesia may report “Dutch” as their language.

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In order to roughly gauge the extent of classification error, we can consider data

from the 2001 Canada Census, which includes measures both of religion and country of

birth. 67% of Canadian immigrants born in our predominantly-Muslim countries reported

Muslim as their religion.10 If the immigration patterns were assumed to be identical, then

this would imply that roughly one in three of our sample of Census sample will not observe

the Ramadan fast because they are not Muslim. Our control sample was restricted to

whites born in the United States who did not report ancestries outside of the US or

Western Europe. We chose not to use reported ancestry for assigning the Muslim proxy

because of high rates of underreporting and the fact that it would combine first and second

generation immigrants. We might suspect that that Ramadan observance is higher for

first generation immigrants.

For earnings we use the CPI to convert values into 1997 dollars and restrict the sample

to those aged 20 to 60 years.

B.5 Other Suitable Datasets?

The Uganda and Iraq Census microdata were obtained from the Integrated Public Use

Microdata Series - International (IPUMS-I). Other potentially relevant IPUMS-I samples

are those for Egypt, Jordan, and Malaysia. Each has a large population of Muslims with

Census data that purportedly include birth month.11 Religion is not reported for Egypt

and Jordan, but like Iraq, are overwhelmingly Muslim. However, in Egypt 85% of the

sample is missing birth month. 40% are missing birth month in Malaysia, and only .5%

of adults report a work disability. In Jordan’s data, birth year and place of birth are

missing.

In the US, month of birth is not reported in the decennial Census. While the National

Health Interview Survey (NHIS) reports birth month, it does not disclose religion, detailed

ethnicity, or country of birth.

C Birth Outcomes by Gestation Length

As noted in the main text, variation in gestation length can generate compositional effects

for pregnancies exposed to Ramadan late in gestation. For example, pregnancies with

exposure to Ramadan in month 9 of pregnancy are exclusively full-term, but ITT estimates

10We thank Kevin Milligan (UBC) for providing these estimates.11Birth month and religion are available in the census of South Africa (unharmonized variables in

IPUMS-I), but South Africa’s Muslim population is relatively small (roughly 1.5% of the population).

xiv

Ramadan Appendix Material

of the month 9 effect include premature births where Ramadan fell after delivery. Even

for earlier months of pregnancy, the relationship to the ITT may differ depending on

gestation length. Finally, gestation itself may respond to the ITT.

In Appendix Table A4, we vary the samples used to estimate birthweight effects based

on the length of gestation. In column (1) we start with a sample of all births with gestation

length 25 weeks to 42 weeks allowing us to include preterm births. The coefficients for

months 1 to 5 are interpretable for all sample members.12 We then progressively tighten

the sample restriction by increasing the lower bound on gestation age producing samples

of length 31-42 weeks in column (2), 35-42 weeks in column (3) and 39-42 weeks in

column (4). This gradually increases the number of exposure month coefficients that can

be interpreted. Interestingly, this does not appear to have any pronounced effects on the

results. With the larger samples that include some preterm births, exposure during the

sixth months is significant at the 10 percent level in two of the three specifications. The

gestation samples that include pre-term births now provide additional evidence (on top

of the “no gestation” sample results from Table 1 of the main text), that the coefficients

are jointly different from zero with significance levels ranging from 6.1 percent to 12.4

percent.

In column (5) we attempt to address the fact that out ITT estimates may be attenuated

by the inclusion of Chaldeans, who are not Muslim but might self identify as Arab.

Specifically we use the sample of full-term births and drop mothers whose zipcode of

residence has a ratio of Chaldeans to Arabs of greater than 0.5 according to the 2000

Census. This drops our sample by more than 25 percent and raises the standard errors

considerably. However, there is some suggestive evidence of a stronger ITT effect. The

most dramatic change is for exposure in the 7th month of gestation where we now see

an effect: nearly a 60 gram drop in birthweight that is significant at the 1 percent level.

The p-value on the F -test of joint significance also falls to 0.071 from 0.277 in column

(4). In other specifications we have not always found much of a difference between the

overall sample and the sample that drops heavily Chaldean zipcodes. This may be due to

the large geographic overlap in the concentrations of Arabs and Chaldeans zipcodes and

the relatively coarse approach dictated by the available data. In this sub-sample, we are

arguably, estimating the effects of Ramadan exposure among Arabs in less concentrated

areas where observance rates may be lower.

In columns (6) through (9) we estimate the effects of in utero Ramadan exposure on

12For example, exposure in months 6 to 9 will include the effects of Ramadan exposure in the postnatalperiod for the small subsample of those with only 25 weeks gestation.

xv

Ramadan Appendix Material

gestation weeks using the same approach on the full sample of Arabs. Among those with

25-42 weeks gestation, we estimate that Ramadan exposure during the second and fifth

months of pregnancy reduces gestational age by a little more than 0.1 weeks or by roughly

a day (p < 0.10). However, these are the only months where exposure is statistically

significant and we cannot reject that the first five gestation months jointly, have no effect.

The effect from month 5 exposure weakens a bit as the sample is gradually restricted.

Interestingly, column (9) shows that when the sample is confined to only full term births,

four gestation months (2, 3, 5 and 8) are significant at at least the 10 percent level.

Finally, in column (10) with the more restricted sample of non-Chaldean zipcodes we find

a highly significant effect from second month exposure. In order to assess what portion

of the birthweight effect might be attributed to decreased gestation, we ran a separate

regression of birthweight on gestation for the sample of non-Arab mothers and found that

each additional week of gestation adds about 165 grams. This suggests that a reduction

in gestation of 0.1 weeks is associated with a 16 gram reduction in birthweight. From this

rough exercise, it appears that less than half of the overall reduction in birthweight of

around 40 grams that we typically estimate in Tables 1 and Appendix Table A4 is due to

reduced gestation as opposed intrauterine growth restriction (IUGR). For those exposed

in the first month of pregnancy, it appears that virtually all of the birthweight reduction

is due to IUGR.

D Distributional Effects on Birth Weight

In Figure A4 we show which parts of the birthweight distribution are affected by early

exposure to Ramadan fasting. We plot kernal densities comparing the birthweight distri-

bution of those Arabs with no in utero exposure to those who had a significant exposure

to Ramadan in the first month of gestation (exphrspct1 > 0.5).13 Most of the effect is

in the middle part of the distribution. Specifically, those with first month exposure to

Ramadan are more likely to have birthweight between 2800 and 3200 grams and less likely

to have birthweight between 3250 and 3900 grams.14 This suggests that little of the effect

is at the low end of the distribution among those classified as “low birthweight” (<2500

grams).

13We also ran linear probability regressions using indicator variables of being in specific intervals ofthe birthweight distribution (e.g. 300-600 grams, 600-900 grams etc.) as an outcome including our othercontrols and obtained very similar results.

14There are also small differences between the samples in the interval from 2100 grams to 2700 grams,and for birthweight greater than 4500 grams.

xvi

Ramadan Appendix Material

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xx

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Not

es:

This

tab

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ed o

n a

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man

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t w

as fou

nd in t

he

chos

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iod d

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not

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out

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sible

Not

es:

This

tab

le is

bas

ed o

n a

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iew

of s

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ted s

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d doe

s n

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s in

clude

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fect

s in

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es:

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ed o

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ed o

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hat

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out

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sible

ef

fect

s in

oth

er p

erio

ds.

Table A2: Summary Statistics for Michigan Natality Data, 1989-2006

mean s.d. N mean s.d. N

Mother’s Age 27.54 5.72 46979 27.41 5.73 1638059

Mother’s Education 12.03 3.55 45584 13.18 2.37 1625226

Father’s Age 33.81 6.48 45588 30.21 6.13 1462349

Father’s Education 12.92 3.33 43931 13.40 2.39 1428050

Male Child 0.52 0.50 46983 0.51 0.50 1638213

Tobacco 0.04 0.19 46203 0.19 0.39 1611440

Alcohol 0.00 0.04 46170 0.02 0.12 1608527

Maternal Weight Gain 29.73 12.70 42216 31.04 13.03 1520595

No Prenatal Care 0.01 0.10 45068 0.01 0.08 1607940

Prenat. Care Begins 1st Trim. 0.86 0.34 45068 0.87 0.34 1607940

Prenat. Care Begins 2nd Trim. 0.10 0.29 45068 0.11 0.31 1607940

Prenat. Care Begins 3rd Trim. 0.03 0.17 45068 0.02 0.13 1607940

Medicaid 0.46 0.50 46315 0.27 0.45 1616231

Fraction Arab, Zipcode 0.21 0.25 46369 0.01 0.03 1612481

Birthweight 3325.08 513.65 46896 3427.71 565.23 1635183

Low Birthweight 0.04 0.21 46988 0.05 0.21 1638244

Infant Death 0.01 0.07 46988 0.01 0.08 1638244

Parity 1.64 1.74 46592 1.39 1.49 1628783

Preterm 0.06 0.23 46868 0.07 0.25 1633654

Gestation (author’s calc.) 39.27 1.72 46868 39.29 1.85 1633654

Apgar 5 minute 8.94 0.56 46902 8.94 0.67 1632994

NICU 0.03 0.17 46915 0.04 0.19 1634113

Complication 0.25 0.43 46188 0.28 0.45 1618589

Abnormal Condition 0.06 0.24 46012 0.07 0.25 1611065

Medical Risk 0.20 0.40 46169 0.23 0.42 1618107Medical Risk Diabetes 0.03 0.16 46169 0.03 0.17 1618107

Born January 0.077 0.27 46988 0.078 0.27 1638244

Born February 0.074 0.26 46988 0.077 0.27 1638244

Born March 0.083 0.28 46988 0.087 0.28 1638244

Born April 0.079 0.27 46988 0.084 0.28 1638244

Born May 0.084 0.28 46988 0.088 0.28 1638244

Born June 0.087 0.28 46988 0.086 0.28 1638244

Born July 0.089 0.29 46988 0.089 0.28 1638244

Born August 0.091 0.29 46988 0.088 0.28 1638244

Born September 0.087 0.28 46988 0.085 0.28 1638244

Born October 0.084 0.28 46988 0.083 0.28 1638244

Born November 0.081 0.27 46988 0.076 0.27 1638244

Born December 0.083 0.28 46988 0.078 0.27 1638244

Mo. 1, Ram Hrs Exposure 0.056 0.15 46868 0.056 0.15 1633654

Mo. 2, Ram Hrs Exposure 0.059 0.15 46868 0.058 0.16 1633654

Mo. 3, Ram Hrs Exposure 0.058 0.15 46868 0.059 0.16 1633654

Mo. 4, Ram Hrs Exposure 0.059 0.15 46868 0.060 0.16 1633654

Mo. 5, Ram Hrs Exposure 0.057 0.15 46868 0.060 0.16 1633654

Mo. 6, Ram Hrs Exposure 0.056 0.15 46868 0.060 0.16 1633654

Mo. 7, Ram Hrs Exposure 0.056 0.15 46868 0.061 0.16 1633648

Mo. 8, Ram Hrs Exposure 0.057 0.15 46865 0.061 0.16 1633617

Mo. 9, Ram Hrs Exposure 0.059 0.16 46861 0.060 0.16 1633475

Arab Non-Arab

Table A3: Summary Statistics for Uganda Census Sample

mean s.d. N mean s.d. Nfemale 0.494 0.500 81197 0.498 0.500 643300age 34.546 12.675 81197 36.697 13.907 643300illiterate 0.304 0.460 78990 0.356 0.479 626473years of schooling 6.944 3.269 60117 6.797 3.599 449968no schooling 0.247 0.431 80142 0.290 0.454 635282employed 0.660 0.474 74348 0.631 0.483 581842elementary occupation 0.042 0.200 46284 0.042 0.200 347248home ownership (males)# of wives (males)

disability 0.0380 0.191 80924 0.0521 0.222 640825blind/vision impaired 0.0106 0.102 80922 0.0149 0.121 640789deaf/hearing impaired 0.0038 0.062 80923 0.0061 0.078 640781mute/speech impaired 0.0009 0.030 80921 0.0015 0.038 640780lower extremities 0.0125 0.111 80921 0.0161 0.126 640794upper extremities 0.0039 0.062 80921 0.0056 0.075 640779mental/learning 0.0014 0.037 80921 0.0017 0.041 640777psychological 0.0014 0.038 80921 0.0020 0.045 640776epilepsy 0.0005 0.023 80921 0.0009 0.031 640777rheumatism 0.0009 0.030 80921 0.0016 0.039 640776

congen 0.0050 0.070 80921 0.0058 0.076 640778disease 0.0203 0.141 80924 0.0283 0.166 640803accident 0.0056 0.074 80921 0.0079 0.088 640782occupational injury 0.0053 0.072 80921 0.0074 0.086 640786war�injury 0.0007 0.027 80921 0.0013 0.036 640777aging 0.0053 0.072 80921 0.0074 0.086 640786

Born January 0.105 0.306 81197 0.096 0.294 643300Born February 0.076 0.265 81197 0.075 0.263 643300Born March 0.072 0.258 81197 0.072 0.259 643300Born April 0.110 0.313 81197 0.106 0.308 643300Born May 0.070 0.256 81197 0.070 0.256 643300Born June 0.102 0.302 81197 0.105 0.307 643300Born July 0.094 0.292 81197 0.098 0.298 643300Born August 0.079 0.269 81197 0.083 0.275 643300Born September 0.079 0.269 81197 0.081 0.272 643300Born October 0.078 0.268 81197 0.077 0.267 643300Born November 0.069 0.253 81197 0.069 0.253 643300Born December 0.067 0.250 81197 0.068 0.251 643300

rampct1 0.081 0.215 81197 0.081 0.216 643300rampct2 0.079 0.214 81197 0.079 0.215 643300rampct3 0.077 0.211 81197 0.078 0.212 643300rampct4 0.084 0.219 81197 0.083 0.218 643300rampct5 0.086 0.223 81197 0.085 0.221 643300rampct6 0.084 0.217 81197 0.083 0.217 643300rampct7 0.087 0.222 81197 0.085 0.221 643300rampct8 0.090 0.226 81197 0.089 0.226 643300rampct9 0.087 0.221 81197 0.087 0.221 643300

Muslim Non-Muslim

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Not

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ntr

ies

are

coef

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ents

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am

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re t

o d

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hours

over

subse

quen

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ays

as

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duri

ng

Not

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ntr

ies

are

coef

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ents

on R

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xposu

re t

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quen

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ays

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Not

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ntr

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ple

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iod. G

esta

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elf re

port

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r physi

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ate

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see

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as

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ple

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esta

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elf re

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see

text)

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as

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ple

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elf re

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see

text)

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as

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see

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see

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fici

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that

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es:

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ntr

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ple

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iod. G

esta

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ate

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see

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as

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fici

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that

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rel

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ple

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see

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fici

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rel

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ndard

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see

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k d

aylight

hours

duri

ng

sam

ple

per

iod. G

esta

tion

is

eith

er s

elf re

por

ted o

r physi

cian

est

imat

ed. A

ll s

ample

s use

bir

ths

wit

h g

esta

tion

bet

wee

n 2

5 a

nd 4

2

wee

ks.

A

ll r

egre

ssio

ns

incl

ude

contr

ols

for

mot

her

s ag

e, m

other

’s a

ge s

quar

ed, m

other

’s e

duca

tion

, t

obac

co u

se, al

cohol use

, pari

ty,

fath

er’s

educa

tion

, dum

my for

mis

sing

fath

er’s

educa

tion

, fa

ther

’s a

ge, fa

ther

’s a

ge s

quar

ed, num

ber

of pre

vio

us

pre

gnan

cies

that

re

sult

ed in d

eath

at

bir

th, co

nce

pti

on m

onth

dum

mie

s, c

ounty

dum

mie

s an

d b

irth

yea

r dum

mie

s. T

he

sam

ple

dro

ps

Ara

b m

oth

ers

livin

g in

zip

codes

wit

h a

lar

ge s

har

e of

Chal

dea

ns.

Ita

lici

zed e

stim

ates

indic

ate

that

the

esti

mat

e m

ay r

epre

sent

pos

t nat

al e

xposu

re for

som

e sa

mple

mem

ber

s. S

tandar

d e

rror

s in

par

enth

eses

, *s

ignific

ant

at 1

0%; **

sig

nific

ant

at 5

%; **

* si

gnific

ant

at 1

%

Tab

leA

6:E

ffec

tsof

Ram

adan

Expos

ure

onD

isab

ilit

yO

utc

omes

Uga

ndan

Musl

ims

by

Mon

ths

Pri

orto

Bir

thT

able

A6:

Effec

ts o

f R

amad

an E

xpos

ure

on D

isab

ilit

y O

utc

omes

, U

gandan

Musl

ims

by M

onth

s P

rior

to B

irth

py

,g

y

Mon

ths

Mon

ths

Pri

orto

Men

tal/

Low

erU

pper

Pri

or t

oM

enta

l/Low

erU

pper

Bir

thD

isab

ilit

ySig

ht/

Blind

Hea

ring/

Dea

fLea

rnin

gP

sych

olog

ical

Extr

emit

ies

Extr

emit

ies

Bir

thD

isab

ilit

ySig

ht/

Blind

Hea

ring/

Dea

fLea

rnin

gP

sych

olog

ical

Extr

emit

ies

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emit

ies

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16)

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32)

(0.0

79)

(0.0

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(0.2

36)

(0.1

33)

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03)

(0.2

16)

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32)

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79)

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80)

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N80

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li

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hi

Not

es:

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ntr

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are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

nin

e m

onth

spre

cedin

gbir

th(r

ampct

)E

ach

outc

ome

ism

ult

iplied

by

100

soth

atco

effici

ents

are

inunit

sof

per

centa

gepoi

nts

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

nin

e m

onth

spre

cedin

gbir

th(r

ampct

)E

ach

outc

ome

ism

ult

iplied

by

100

soth

atco

effici

ents

are

inunit

sof

per

centa

gepoi

nts

All

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

incl

ude

anin

dic

ator

for

fem

ale

bir

thm

onth

dum

mie

sdis

tric

tof

bir

thdum

mie

san

dbir

thyea

rdum

mie

s

Not

es:

All e

ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

of d

ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

is m

ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

per

centa

ge p

oints

. A

ll

regr

essi

ons

incl

ude

anin

dic

ator

for

fem

ale

bir

thm

onth

dum

mie

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tric

tof

bir

thdum

mie

san

dbir

thyea

rdum

mie

s

Not

es:

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ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

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ays

over

lappin

g w

ith R

amad

an d

uri

ng

the

nin

e m

onth

s pre

cedin

g bir

th (

ram

pct

). E

ach o

utc

ome

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ult

iplied

by 1

00, so

that

coe

ffic

ients

are

in u

nit

s of

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centa

ge p

oints

. A

ll

regr

essi

ons

incl

ude

an indic

ator

for

fem

ale,

bir

th m

onth

dum

mie

s, d

istr

ict

of b

irth

dum

mie

s an

d b

irth

yea

r dum

mie

s.

Not

es:

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ntr

ies

are

coef

fici

ents

on R

amad

an e

xpos

ure

mea

sure

d a

s th

e per

cent

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ays

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ith R

amad

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ng

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e m

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s pre

cedin

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th (

ram

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utc

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ult

iplied

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that

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nit

s of

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centa

ge p

oints

. A

ll

regr

essi

ons

incl

ude

an indic

ator

for

fem

ale,

bir

th m

onth

dum

mie

s, d

istr

ict

of b

irth

dum

mie

s an

d b

irth

yea

r dum

mie

s.

Tab

leA

7:E

ffec

tsof

Ram

adan

Expos

ure

onC

ause

sofD

isab

ilit

ies

Uga

ndan

Musl

ims

by

Mon

ths

Pri

orto

Bir

thT

able

A7:

Effec

ts o

f R

amad

an E

xpos

ure

on C

ause

s of D

isab

ilit

ies,

Uga

ndan

Musl

ims,

by M

onth

s P

rior

to

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thg

y

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tt

lt

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nat

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Figure A1: Women’s Weight Change Around Ramadan in Gambia

Source: Cole (1993)

Figure A2: Michigan Arab Population by Zipcode

Panel A: Quartiles of the Arab Population

Panel B: Ratio of the Chaldean to Arab Population

Source: Author's calculations using the 2000 Census SF3 file

Panel A: Quartiles of the Arab Population

Panel B: Ratio of the Chaldean to Arab Population

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Kernal density

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Kernal density

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Kernal density

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Kernal density

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