# Prenatal Sugar Consumption and Late-Life
Human Capital and Health: Analyses Based
on Postwar Rationing and Polygenic Scores

Gerard J. van den Berg  University of Groningen, University Medical Center
Groningen, IFAU Uppsala, IZA, ZEW, J-PAL and CEPR. Stephanie von Hinke
Corresponding author: School of Economics, University of Bristol; Institute
for Fiscal Studies. E-mail<EMAIL_ADDRESS>R. Adele H. Wang School
of Economics, University of Bristol.

We thank Andreas Peichl, Pietro Biroli, Fleur Meddens, Hans van Kippersluis,
Niels Rietveld and participants at a seminar at the Erasmus School of
Economics and a CESifo/NORFACE conference for useful comments and discussions.
Samuel Baker, Sean Harrison, and Emil Nørmark Sørensen provided valuable help
with the data. We gratefully acknowledge financial support from NORFACE DIAL
(Grant Reference 462-16-100) and the European Research Council (DONNI 851725).

(This version of the paper first circulated as a working paper in
September 2022)

###### Abstract

Maternal sugar consumption in utero may have a variety of effects on
offspring. We exploit the abolishment of the rationing of sweet confectionery
in the UK on April 24, 1949, and its subsequent reintroduction some months
later, in an era of otherwise uninterrupted rationing of confectionery
(1942-1953), sugar (1940-1953) and many other foods, and we consider effects
on late-life cardiovascular disease, BMI, height, type-2 diabetes and the
intake of sugar, fat and carbohydrates, as well as cognitive outcomes and
birth weight. We use individual-level data from the UK Biobank for cohorts
born between April 1947–May 1952. We also explore whether one’s genetic
predisposition to the outcome can moderate the effects of prenatal sugar
exposure. We find that prenatal exposure to derationing increases education
and reduces BMI and sugar consumption at higher ages, in line with the
“developmental origins” explanatory framework, and that the sugar effects are
stronger for those who are genetically predisposed to sugar consumption.

Keywords: Nutrition; food consumption; gene-environment interplay; education;
developmental origins.
JEL Classifications: I12, I18, I15, D45

##  1 Introduction

Sugar has been part of the human diet for centuries, providing energy and
making food more palatable. More recently, sugar consumption has been related
to a range of diet-related health problems. WHO (2015) recommends that the
individual intake of sugars is restricted to less than 10% of the individual’s
total energy intake, with the following motivations: (i) to prevent obesity,
(ii) to prevent that sugar-laden foods substitute more healthy dietary items,
and (iii) to prevent caries. Both (i) and (ii) are thought to mitigate the
risks of cardiovascular disease, type-2 diabetes and other diet-related
diseases.

These health concerns are backed up by countless studies of adults and
children, documenting effects that materialize in the short to medium run.
Much less is known about effects of exposure to sugars in utero and in
particular, about long-run hazards of such exposure on health at advanced ages
or on human capital outcomes. Following the literature on the developmental
origins of late-life health, one may hypothesize the existence of in utero
exposure effects. At first sight, this literature seems to suggest that a high
intake helps to prevent undernutrition and provides an in utero environment
that is aligned with later-life nutritional conditions, improving late-life
health.111See e.g., Almond and Currie (2011), Lindeboom et al. (2010),
Roseboom et al. (2006), Scholte et al. (2015), and Bengtsson and Lindstrom
(2000, 2003) for overviews and studies of the causal evidence of effects of in
utero nutrition on late-life health and economic outcomes. However, this
literature does not analyze effects of sugar in isolation from other nutrients
and hence it ignores the specific nature of sugar as a nutrient with
problematic aspects. Sugar exposure may adversely affect the build-up of the
body, leading to worse late-life health. This second pathway is supported by
the fact that the set of late-life health outcomes that are known to be
responsive to adverse in utero conditions in general (such as cardiovascular
diseases, type-2 diabetes and cognitive decline) closely resembles the set of
health outcomes responsive to adult abundant consumption of sugars. In either
case, knowing whether causal long-run effects exist is vastly important for
public policy.

A number of descriptive studies have examined the association between maternal
gestational sugar consumption and offspring early-life health outcomes such as
birth weight, childhood cognition and obesity. A higher intake of sugars (e.g.
as sweets, snacks and soft drinks) is often found to be associated with less
healthy outcomes although the evidence is not unanimous.222See e.g. Walsh et
al. (2011) for obesity, Phelan et al. (2011) for birth weight and Cohen et al.
(2018) for childhood cognition. Bédard et al. (2017) find an association with
atopy and asthma in later childhood. See Goran et al. (2018) and Casas et al.
(2020) for reviews of existing studies. These studies, however, do not
consider late-life outcomes, and, crucially, they do not control for the
endogeneity of sugar consumption.333Controlled laboratory experiments with
rodents have demonstrated adverse effects of in utero sugar exposure on
offspring’s adult obesity; see e.g. Bocarsly et al. (2012).

Clearly, in general, parents choose their sugar consumption levels, and as a
result, these levels will depend on a wide range of characteristics of the
parents and their circumstances. Many of those characteristics also affect
other choices and behaviors, thus influencing the outcomes of their offspring.
Any association between sugar intake and offspring health may therefore
reflect shared unobserved determinants. To determine whether the associations
also reflect causal effects calls for causal inference.

The present paper uses a causal inference approach to study effects of in
utero exposure to sugar on late-life health and human capital outcomes. We
exploit features of the food rationing system in the UK in the years
immediately following World War II. Specifically, we exploit the abolition of
the rationing of confectionery (including chocolate and sweets) in the UK on
April 24, 1949 and its subsequent reintroduction on August 13, 1949. This
“derationing period” occurred in an era of otherwise uninterrupted rationing
of confectionery (1942-1953) and of raw sugar (1940-1953) as well as many
other foods. In a nutshell, we compare individuals who were in utero during
the short derationing period to those who were in utero just before or after
this period.

For our purposes, it is an advantage that the derationing in April 1949 and
its reversal in August 1949 took place in a period without other rationing
abolitions or introductions, neither of sugar nor of other consumption goods
(see Zweiniger-Bargielowska, 2000; see also Appendix A of our paper). The vast
majority of rationing measures was introduced before mid-1942, and most ended
in the 1950s. The derationing interval in 1949 did not witness other major
societal events either. The April 1949 derationing itself was imposed through
an administrative act taken by civil servants in response to pressure by the
confectionery industry. Importantly, therefore, the decision was not taken by
the cabinet which could have used it to curry favor with the electorate and
which often discussed minor changes in ration levels. The subsequent
(unplanned) reintroduction in August 1949 was due to the inability of the
confectionery industry to meet the demand for its products, which in turn was
due to the unwillingness of the government to allocate or transfer larger
amounts of sugar to that industry, coupled with a lack of willingness to allow
for market price mechanisms (see Zweiniger-Bargielowska, 2000, for background
information on the derationing and its reversal).444Other changes in rationing
took place in time periods that witnessed additional upheavals, making it more
challenging to identify their separate contributions to health and human
capital outcomes. Notably, the onset of rationing coincided with preparations
for the war, while the ultimate derationing of confectionery and sugar in 1953
took place in a period in which the rationing of many other products was
abolished as well (see Appendix A) and in which the economy moved from a
socialist design to a free-market design following a right-wing election
victory. A notable event close to our derationing interval is the formal
establishment of the NHS universal health service in England in July 1948.
However, the actual health care system and supply changed rather gradually in
this period (see Rivett, 2014, for a comprehensive overview, and Thomson,
2018). Many individuals were insured before July 1948 and aspects of universal
care were already available regionally, while after July 1948 certain
provisions were in short supply and led to queueing, and other provisions
remained costly. The year 1948 also witnessed a gradual tightening of
educational requirements for nurses. These overall changes in health care can
therefore be accounted for by a flexible trend specification in calendar time
in our empirical analysis.

The reversal of the derationing facilitates the identification of gestational
ages with high exposure. By analogy to the Dutch Hunger Winter studies, the
identification exploits that the exposure period is substantially shorter than
the length of gestation (see Lumey et al., 2011, and Scholte et al., 2015).
Study designs based on a single discontinuity do not have this advantage.

Notice also that the maternal dietary changes induced by the brief derationing
are relatively mild compared to the major shocks (such as famines) that are
commonly exploited in the causal inference literature on conditions in utero.
This makes our analysis less sensitive to selective fertility issues that have
plagued the latter literature. In addition, sugar and confectionery
consumption is amenable to specific intervenable policy targets, whereas
nutritional policy motivated by famine studies requires extrapolations from
extreme events to modest interventions.

An obvious challenge to the usage of rationing as a proxy for sugar and
confectionery consumption is that individuals may adjust their consumption of
other foods in response to rationing. First, confectionery may be consumed in
combination with certain other nutrients, such as coffee. However, the
widespread rationing of other products means that the derationing of
confectionery is unlikely to have led to a change in the consumption of those
nutrients, simply because the latter faced an ongoing binding constraint.
Secondly, during rationing, sugar may be substituted by replacement products.
In the rationing era this included saccharin as well as barley extract and
liquorice (see Royle, 2020). This is relevant for our study design insofar
that such substitutes may affect health at higher ages. In their overviews,
Goran et al. (2018) and Casas et al. (2020) report evidence of a positive
association between the gestational intake of artificial sweeteners and infant
and child obesity (as well as an increased preference for sweet tastes) and
they conjecture that this is explained by effects of sweeteners on the gut
microbiome of the child. It is not clear if this extends to late-life health,
but Palatnik et al. (2020) report long-run effects along these lines in animal
studies.

Another challenge concerns effects of exposure to sugar and confectionery
after birth. In our setting, children born before the derationing period in
mid-1949 are exposed during some months in 1949. Moreover, every child born
before the ultimate permanent derationing in 1953 is exposed from 1953
onwards. The literature on long-run effects of early-life conditions cited
above generally finds that effects of in utero exposure exceed those of
exposure after birth. In our paper we estimate a variety of model
specifications and we use various subsamples of individuals born before,
during and after the derationing period in mid-1949. We also point out that
exposures after 1953 are fully in line with our aim to study effects of
prenatal exposure for those who, in later childhood and in adulthood up to
high ages, live in a society with abundant nutrition. We return to this below.

The data set we use is the UK Biobank which follows the health and well-being
of approximately 500,000 individuals in the UK aged 40-69 between 2006-2010
(see Sudlow et al., 2015). All participants are born between 1934 and 1971,
and the vast majority belongs to the 1940s-1960s birth cohorts. UK Biobank
participants have provided survey information on their health and well-being
and have given blood, urine and saliva samples, and all individuals have been
genotyped. The individuals exposed in utero to derationing were on average
approximately 60 years old upon entering the Biobank. As motivated by the
literature, our set of late-life outcome variables includes educational
attainment, cardiovascular disease, BMI, height, type-2 diabetes, and the
intake of sugar, fat and carbohydrates. We also examine effects on birth
weight.555Birth weight has been used as a biomarker of conditions in utero
although recently doubt has been cast on the extent to which it reflects
conditions that are relevant for late-life health (Schulz, 2010; Van den Berg
and Modin, 2013; Maruyama and Heinesen, 2020). An obvious advantage of birth
weight as an outcome measure is that it does not depend on exposure after
birth.

Our paper provides two sets of major and novel findings. First, these concern
the direct effects of prenatal exposure to sugar derationing. We find that the
latter improves education and reduces BMI and sugar intake later in life, with
some evidence that it also led to an increase in birth weight. Second, we
explore whether one’s genetic predisposition can moderate the effects of
prenatal exposure on later life outcomes. Historically, the study of gene-by-
environment ($G\times E$) interactions is hampered by the possible endogeneity
of the environment, as an individual’s genetic predisposition may drive them
to self-select, seek out or create certain environments (so-called gene-
environment correlation, or $rGE$; Plomin and Bergeman, 1991). Natural
experiments such as the exogenous exposure to temporary derationing can
provide a solution to this issue. This makes our paper one of the first of
only a handful of recent studies to analyze $G\times E$ interactions within a
natural-experiment setting, enabling us to make causal inferences on the
nature-nurture interplay.666Previous examples include Fletcher (2012, 2019),
Schmitz and Conley (2016), Barcellos, Carvalho and Turley (2018), Biroli and
Zünd (2020), Pereira et al. (2020) and Muslimova et al. (2020). A concurrent
example is von Hinke and Sørensen (2022).

We use individuals’ so-called polygenic scores (also known as polygenic
indices) for the respective health and education outcomes as measures of the
genetic predisposition for these traits. Classical twin studies indicate that
the heritability777Heritability is the proportion of the variance in a trait
that can be attributed to genetic differences between people in a population.
of the health outcomes ranges between 30% and 72% (see e.g., Avery and Duncan,
2019; Maes, Neale and Eaves, 1997; Marenberg et al., 1994) while the
heritability of educational attainment ranges from 25% to 40% (see e.g.,
Branigan, McCallum and Freese, 2013; Lucchini, Della Bella and Pisati, 2013).
This suggests that variation in these traits is under at least a moderate
degree of genetic influence. The recent development of large-scale genome-wide
association studies (GWAS) has allowed for the identification of specific
genetic variation, namely single nucleotide polymorphisms (SNPs), associated
with traits of interest.888See Appendix B for an introduction to the genetic
concepts used here. The results of these studies allow us to construct
polygenic scores for each individual, quantifying one’s genetic
“predisposition” to each of the outcomes. For our purposes, these can be
straightforwardly interacted with exogenous changes in the prenatal sugar
environment.

We find some evidence of genetic heterogeneity of effects of exposure. More
specifically, we find that high polygenic scores for cardiovascular disease
and for sugar and carbohydrate intake affect the impact of a high-sugar
prenatal environment. Interestingly, those who are exposed to a high-sugar
prenatal environment reduce their late-life sugar intake more strongly if they
happen to be genetically predisposed to the consumption of sugar in adulthood.
Relating this to the “developmental origins of late-life health” framework,
this suggests that the protective effect of fetal programming is stronger for
those who are genetically predisposed to sugar consumption.

The rest of the paper is structured as follows. Section 2 describes wartime
and post-wartime rationing in the UK in general, and for sugar and
confectionery in particular. Section 3 discusses the data. Section 4 describes
the empirical specification and Section 5 presents the results. Section 6
explores the robustness of our findings by providing a range of sensitivity
analyses. This includes alternatives for the length of the exposure interval
in 1949, the birth cohorts used, the specification for the trend effect of
calendar time at birth on the outcomes, analyses by gender, and specificities
of the exposure by gestational trimester. Section 7 concludes.

##  2 Rationing in the UK during and after World War II

###  2.1 The rationing system

On January 8, 1940, four months after the start of World War II, the UK
wartime government introduced food rationing.999This section draws heavily on
Zweiniger-Bargielowska, 2000, and references therein. At the time, for many
food categories, the UK imported almost everything it consumed. With an
envisaged significant drop in food imports, the government expected severe
food shortages, rising prices, and a growing inability of low SES households
to afford food. In addition, food availability was expected to be volatile,
leading to hoarding. By analogy to the experiences in Germany towards the end
of World War I, food shortages were expected to affect the morale and lead to
food riots and civil unrest. The main aim of food rationing was therefore to
ensure that everyone had access to necessary foods. Incidentally, the UK
rationing system did not only cover food but also clothing, fuel, soap, paper,
furniture and other products. Furthermore, the rationing system was not only
targeted at individual consumers but, depending on the product involved, also
restricted inputs of industries, sectors and public services.

To purchase rationed foods, individuals were issued ration books, with
different coupons for different foods, e.g., separate coupons for butter,
cheese, and sugar. Ration coupons were the government guarantee that you would
be able to get your share of the food, though it could only be used at the
shop where the individual had registered, with shop keepers cutting out the
relevant coupons when individuals purchased the product.101010In December
1941, so-called “point rationing” was introduced, allowing some freedom to
choose both the product (mainly canned food) and shops. There was a very small
black market in luxury goods; a black market in necessities hardly existed
(Ministry of Health, 1946). Appendix A provides a table with rationed food
types and the time intervals in which each of these was rationed.

Throughout the rationing era, pregnant women, nursing mothers and children
under five were entitled to special rations for milk, eggs, meat, orange
juice, cod liver oil, and vitamin A and D (Davies, 2014). By the end of the
war, most schools served school dinners, which were either free or at cost-
price. Furthermore, it was compulsory for any factory employing over 200
workers to open a canteen to provide meals to its workers. These did not
require usage of coupons.

As a secondary aim, the rationing system was used to improve the quality of
individuals’ nutritional intake and reduce nutritional inequalities between
SES groups by steering the accessible food bundle towards a nutritionally
healthy diet available to all. The evidence shows that this was successful
(see Ministry of Health, 1946; Burnett, 1987; Department of Health, 2002;
Goldring et al., 2011).111111Intakes of protein, calcium, iron, vitamin B,
riboflavin, vitamin C, and nicotinic acid all increased considerably during
wartime rationing. This meant that the average diet of all classes was better
balanced than ever before (Ministry of Health, 1946). Children were taller and
heavier than before the war, infant mortality rates reduced and maternal
mortality rates dropped (Ministry of Health, 1946; Burnett, 1987).

The years immediately following the end of World War II witnessed a global
food crisis, while the UK continued to be heavily dependent on food imports.
The war had taken a heavy toll on the UK national debt and the UK insisted on
maintaining a large military force abroad after the war. As a result, it faced
budgetary and balance of payments difficulties. The postwar socialist
government saw food rationing as a policy to limit consumption of imported
goods without putting poor households in jeopardy. Hence, it decided to
continue the food rationing system and to even further extend the types of
food products subject to rationing. This policy continued into the early 1950s
when the socialist government lost the national election. Note that the
postwar rationing exploited in our study design took place in a setting that
was less dramatic than rationing in war-related famines (see e.g. van den Berg
et al., 2016).

###  2.2 Rationing of sugar and confectionery

Before the war, about 70% of the sugar used in the UK was imported. Sugar was
one of the first batches of products to be rationed in early 1940 and among
the last to be derationed in late 1953. Importantly for our purposes, sugar
rationing was regarded to be more binding and restrictive than the rationing
of most other food products, in the sense that its consumption was reduced
more dramatically (see Zweiniger-Bargielowska, 2000, and references therein).

By June 1942, UK food consumption had become heavily dependent on imports from
the US and other allies, and food trade was coordinated at the international
level. In the first half of 1942, the US lost the Philippines (its prime
supplier of sugar) to Japan (see e.g. Keeton, 2011), and import from other
areas was hampered by sea warfare. This led to the introduction of sugar
rationing in the US, which was followed by the rationing of confectionery
products in the UK on July 26, 1942. The latter adopted the “point rationing”
system with individual (rather than household) allowances of $\approx$3 oz
(85g) per person per week.121212The exact amount varied slightly throughout
the rationing years depending on the supply of raw ingredients and special
events, see Figure C.1. For example, those under 18 were given an additional 2
oz “Christmas bonus” in December 1944 and December 1946 (MAF 156/262, 1947).
Sugar and confectionery are not identical but confectionery mostly consists of
sugar. Additional ingredients may include small amounts of fat, flour and
proteins. Chocolate and sugar confectionery were items that could be purchased
with the relevant ration coupons. These two items were consumed in
approximately equal proportions in the pre-war years, but the rationing era
saw a preference for chocolate with occasional minor surpluses of sugar
confectionery, which was subsequently used by the confectionery industry as an
argument for the abolishment of rationing in 1949 (Financial Times,
15/02/1949).

As we alluded to in Section 1, pressure from the industry and an improvement
in the supplies of sugar and other ingredients made the relevant ministry
decide to completely deration confectionery starting on April 24, 1949. The
announcement was made on February 21, 1949 (Financial Times, 22/02/1949). The
industry estimated that the supply of raw materials at the time would allow
weekly consumption per capita to rise to 4.75 oz (135g). The trade bodies
estimated that demand and supply would find an equilibrium at this level
without price adjustments which would have been politically infeasible
(Financial Times, 22/02/1949). To allow traders to build up stock and meet the
impact of derationing, civil servants allocated an immediate 50% additional
credits to sweets wholesalers and retailers between February and April 1949,
plus a further one-off grant of 50% of the coupons collected in March 1949
(MAF 156/270, 1952).

As it happened, the actual demand of 8-10 oz (227-283 g) far outstripped
supply, with sales in late April 1949 exceeding the levels in April 1948 by
84% (Financial Times, 06/11/1949), leading to long queues at confectionery
shops, and a rapid running down of any stocks (see Zweiniger-Bargielowska,
2000; Royle, 2020). By late May 1949, many confectionery shops had run out of
stock and closed for business (Fitzgerald, 1995). In a nutshell, the
confectionery industry was caught in-between the lack of inputs and the fact
that consumer product prices were fixed.131313Other industries also faced
restrictions due to sugar rationing; for example, beer breweries had to modify
the composition of their products. In addition, export of confectionery
production had priority over domestic consumption (Royle, 2020). With the
Chancellor of the Exchequer announcing a cut in sugar imports for July, the
government decided on July 14 to re-introduce confectionery rationing on
August 13, 1949; less than four months after the initial derationing. Final
derationing of confectionery and sugar only took place in February and
September of 1953, respectively, along with the abolishment of rationing of
most other products. Subsequent years witnessed steeply increasing
consumption.

The above implies that the derationing on April 24, 1949 was exogenous,
precisely delineated in time, and difficult to anticipate at the individual
level. The reinstallment of rationing was also exogenous at the individual
level. However, the point in time at which individuals started to experience
confectionery purchase constraints (due to shops running out of stock) most
likely took place before the official re-instalment of rationing on August 13,
1949. Therefore we perform a sensitivity analysis, stipulating derationing to
end on May 31, 1949.

##  3 Data

The UK Biobank (UKB) is a prospective, population-based cohort study that
contains data on the health and wellbeing of over 500,000 individuals across
the United Kingdom. Recruitment and collection of baseline data took place
between 2006 and 2010, when participants were aged between 40-69. Individuals
provided information on demographics, health status, lifestyle measures,
cognitive testing, personality self-report, and physical and mental health
measures via questionnaires and interviews. Furthermore, anthropometric
measures were taken by nurses, along with blood pressure readings and samples
of blood, urine and saliva. All individuals have been genotyped.

We restrict our sample in three ways. First, we only consider those born
between April 24, 1947 and May 13, 1952 (i.e., two years prior to the start of
derationing and two years after the last individuals exposed to derationing in
utero were born). This restricts the sample to 106,608 individuals. Second, as
individuals’ genetic architecture is known to differ across ancestry groups,
we follow the literature and exclude non-whites. Third, we drop individuals
for whom no geographical birth coordinates are available or who were born in
Scotland or Northern Ireland as region of birth is not available for those
parts of the UK. This leads to dropping an additional 15,221 individuals. Our
final sample includes a maximum of 84,539 individuals, depending on the
outcome of interest.

We select a set of nine outcomes as motivated by the literature on long-term
effects of poor prenatal environments (see Section 1) and their availability
in the UK Biobank. We create a measure of years of education based on the
International Standard Classification of Education (ISCED) scale, along the
lines of the literature (see e.g. Rietveld et al., 2013; Okbay et al., 2016,
2022; Lee et al., 2018). Birth weight is self-reported in the UK Biobank, but
it is considered to be reliable in that it correlates with variables that are
known to correlate with true birth weight, such as gender, ethnicity and
maternal smoking (Horikoshi et al., 2016; Tyrrell et al., 2013). BMI is based
on in-clinic measurements of height and weight. A binary indicator for
cardiovascular disease is obtained from primary and secondary diagnoses codes
(ICD-10) in participants’ hospital inpatient records that are linked to the UK
Biobank, or via the cause of death that is available in the National Death
Registries. Type-2 diabetes is self-reported by the participant. Finally,
macro-nutrient intakes represent nutrient densities; i.e., the proportion of
the total energy intake that is due to sugar, carbohydrates and fats (see
e.g., Willett et al., 1997).

In addition to the extensive socio-demographic and health information observed
in the UK Biobank, all participants have been genotyped. We use this genetic
information to create “polygenic scores”: index variables measuring
individuals’ genetic predisposition towards a specific trait. In short, a
polygenic score is a weighted sum of the individual SNP effects (see Appendix
B for a more detailed discussion), defined as:

$G_{i}=\sum_{j=1}^{J}\widehat{\beta}_{j}X_{ij}$ (1)

where $G_{i}$ is the polygenic score of a specific trait for individual $i$,
$X_{ij}\in{0,1,2}$ is a count of the number of “risk” alleles for individual
$i$ at SNP $j$, and $\widehat{\beta}_{j}$ is a weight associated with SNP $j$.
These weights are obtained from independent genome-wide association studies
(GWAS). Using a GWAS sample that is more similar to the analysis sample
increases predictive power, since the polygenic score captures the genetic
predisposition within the environmental context of individuals from the
discovery sample (Domingue et al., 2020). To construct the polygenic scores
used here, we therefore run our own tailor-made GWAS on UKB participants,
excluding our analysis sample (i.e., those born between April 24, 1947 and May
13, 1952) to avoid over-fitting, and we drop any siblings of UKB participants
to avoid clustering. We then use the summary statistics from this GWAS to
create the relevant polygenic scores for our analysis sample, using LDpred
(Vilhjálmsson et al., 2015; see Appendix B for more information on how the
polygenic scores are constructed). All polygenic scores are standardized to
have mean zero and standard deviation one.

Table 1 presents the descriptive statistics of the outcomes of interest,
including the maximum sample sizes for the analysis sample of each outcome.
This shows that, on average, UKB participants born between April 1947 and May
1952 have almost 14 years of education, a birth weight of 3332g and a BMI of
27.6. Approximately 33% of the sample has been diagnosed with cardiovascular
disease and 5% have been diagnosed with type-2 diabetes. Approximately 23% of
individuals’ total energy intake is derived from sugar, 48% from carbohydrates
and 33% from fats. Just under half of the sample is male.

[Table 1 about here]

Figure 1 shows the trends in each outcome variable for our period of
observation by individuals’ year-month of birth, where the two vertical lines
indicate the first and last cohort exposed prenatally to the temporary
confectionery derationing period.141414Henceforth, “year-month” is used to
denote the combination of calendar year and calendar month, e.g. April 1949.
To create Figure 1, we separate each month into “early” (born prior to the
12th of the month), “mid” (between the 13th and 23rd of the month) and “late”
(born on the 24th of the month or after). Hence, we plot three observations
for each month. The exposed cohorts are born between April 24, 1949 and May
12, 1950 (i.e., 9 months (minus one day) after the reintroduction of rationing
on August 13, 1949). The figures suggest a slight reduction in BMI and sugar
intakes for those prenatally exposed to the derationing. However, there is
substantial variation in each of the outcome variables. We explore the data
more rigorously below.

[Figure 1 about here]

To gauge evidence for gene-environment correlations ($rGE$), subsection
Appendix C in Appendix C plots the densities of each of the (standardized)
polygenic scores for those exposed to derationing in utero (solid line) and
those unexposed (dashed line). From the very similar polygenic score
distributions we conclude that there is little evidence of polygenic scores
being correlated with the exposure indicator. The scatter plots overlaying the
densities are the averages of each outcome for 200 equal sized bins of the
corresponding polygenic score. The fitted curve through the dots is obtained
from a kernel-weighted local polynomial regression of the outcome on the
corresponding polygenic score, where we again distinguish between the exposed
and unexposed groups. This shows a strong correlation between the polygenic
score and each of the corresponding outcomes. The statistical strength of this
relationship is investigated below. Finally, the figures suggest that we can
approximate the relationship between the polygenic score and the respective
outcome as linear.

##  4 Empirical approach

The baseline specification for the empirical analysis is:

$y_{i}=\alpha_{0}+\alpha_{1}E_{i}+\delta X_{i}+f(X_{i},E_{i})+\varepsilon_{i}$
(2)

where $y_{i}$ is the outcome of interest for individual $i$ and $E_{i}$ is a
binary indicator equal to one if the individual was exposed to at least one
day of confectionery derationing whilst in utero (i.e., was born between April
24, 1949 and May 12, 1950).151515In the absence of data on gestational age, we
here assume that all pregnancies last 9 months. Hence, our coefficient of
interest is $\alpha_{1}$, capturing the effect of prenatal exposure to
confectionery derationing on the outcome of interest. $X_{i}$ includes gender
and calendar time by way of a (in the baseline specification) linear function
of the year-month of birth. It also includes 11 binary indicators for the
month of birth to remove seasonality in outcomes. The function
$f(X_{i},E_{i})$ includes interactions between the covariates $X_{i}$ and our
“exposure indicator” $E_{i}$.161616This only includes the interaction of
$E_{i}$ with calendar time and with gender. We add the $f(X_{i},E_{i})$
interactions to allow for comparisons of the estimates with those from
Equation 3 below, though our estimates are robust to not including any such
interactions. We do not include interactions with the month fixed effects
since its coefficient would partially capture the effect of interest. For
example, the interaction between the month of birth February and $E_{i}$
captures those who were in utero at some point between April 24, 1949 and May
12, 1950 and were born in February. By construction, this only includes all
individuals born in February 1950. Since this is part of our exposure effect,
the inclusion of that interaction would inadvertently capture that. Finally,
$\varepsilon_{i}$ captures omitted determinants of the outcome as well as
measurement error in the outcome. We report heteroskedasticity-robust standard
errors clustered by year-month of birth.

Note that the above approach constitutes an “Intention To Treat” (ITT) design
for the evaluation of actual intake, as we do not observe the individual
consumption of confectionery.171717Historical documents and newspaper coverage
document that the demand for confectionery rose dramatically during the
derationing period compared to the period before. Due to a lack of data on
actual confectionery consumption, however, we cannot estimate (or instrument)
the prenatal consumption effect on offspring outcomes. The National Food
Survey, the main UK survey of household food consumption that started in the
1940s, does not record intake of chocolates and sugar confectionery for this
period since it is considered a personal rather than household good (Ministry
of Food, 1951). Given that the derationing was announced only two months
before its implementation, we assume that the timing of derationing relative
to pregnancies is exogenous. We examine this empirically in more detail in a
robustness analysis, where we also estimate model varieties with a more narrow
exposure interval.

There is geographical variation in the nutritional environment during the
period of rationing. For example, it is plausible that eggs, butter and meat
were more available in rural areas where individuals had more access to home-
grown foods. Similarly, the demand for confectionery was higher in cities and
there is evidence that confectionery shops in city centres were among the
first to run out of stocks (Fitzgerald, 1995). To account for differential
local supply and demand of confectionery, we re-estimate equation (2)
including region-of-birth fixed effects. During the period of observation,
England and Wales were divided into over 60 counties, 230 administrative
counties, and 1400 local government districts (henceforth: districts). Using
information on individuals’ location of birth, we assign to each individual a
county, administrative county and district ID. Including such fixed effects
enables us to compare individuals in close proximity but at different points
in time, some of whom would have been exposed to prenatal derationing and
others not. In these specifications, we cluster our standard errors by region
of birth.

The estimates of Equation 2 capture the average effect of prenatal exposure to
confectionery on the outcome of interest. We next explore whether those who
are genetically predisposed to the outcomes are differentially affected. For
example, those who are at higher genetic risk of type-2 diabetes may be more
affected by prenatal sugar exposure compared to those who are genetically at
lower risk. Alternatively, those at higher genetic risk may be more protected
by the high-sugar environment as these fetuses may be “programmed” to cope
better with later-life abundant nutrition. We explore this by modelling the
gene-environment interplay in our empirical specification as:

$y_{i}=\beta_{0}+\beta_{1}G_{i}+\beta_{2}E_{i}+\beta_{3}G_{i}\times
E_{i}+\gamma
X_{i}+f_{1}(X_{i},E_{i})+f_{2}(X_{i},G_{i})+\sum_{p=1}^{10}\delta_{p}PC_{i}^{p}+\varepsilon_{i}$
(3)

where $G_{i}$ denotes the polygenic score that is specific to outcome $y_{i}$.
The right-hand side also includes the first 10 principal components of the
genetic data to account for ancestry (Price et al., 2006).181818Recall that
Appendix B includes a brief discussion of some of the genetic concepts and
jargon and explains in more detail how the polygenic scores are created. The
results in the paper are strongly robust to the exclusion of the principal
components. Furthermore, next to the interactions $f_{1}(X_{i},E_{i})$ between
covariates $X_{i}$ and the exposure indicator $E_{i}$, we add interactions
$f_{2}(X_{i},G_{i})$ between the covariates $X_{i}$ and the polygenic score
$G_{i}$. This prevents that effects of such interactions are inadvertently
picked up by the estimated effects of the $G_{i}\times E_{i}$ interaction (see
Keller, 2014).191919For reasons discussed above, we do not add the interaction
between the month effects and $E_{i}$, but we do include the interactions
between the month effects and the polygenic score $G_{i}$.

Note that the above specification resembles a health production function with
gene-environment interactions. The coefficients of prime interest are
$\beta_{2}$ and $\beta_{3}$. The parameter $\beta_{2}$ replaces the parameter
$\alpha_{1}$ in the baseline specification. The parameter $\beta_{3}$ captures
whether those who are genetically at a higher risk of the outcome are more (or
less) likely to be affected by the exposure, as compared to those at a lower
genetic risk.202020As we apply an ITT design and we do not observe actual
individual consumption levels early in life, one needs to be careful with the
interpretation of interaction effects. Specifically, for the interaction
effect of $G_{i}$ and actual consumption to have the same sign as the
interaction effect between $G_{i}$ and the exposure instrument $E_{i}$, the
genetic background should not strongly moderate the effect of exposure on
consumption. This is likely to be satisfied e.g. if actual consumption after
derationing remains restricted by a limited availability of affordable
confectionery, as is the case in our setting. Either way, one might argue that
what matters from a policy evaluation perspective are variables that can be
directly manipulated, which in our case are the environment indicators (i.e.,
the rationing of confectionery) rather than individuals’ actual confectionery
choices. The parameter $\beta_{1}$ captures the change in $y_{i}$ for a one
standard deviation change in $G_{i}$.212121Note that $G_{i}$ may also capture
parental genetic predispositions that can shape the child’s environment (so-
called “genetic nurture”; see e.g. Bates et al., 2018, and Kong et al., 2018;
see also Biroli et al., 2022, for a detailed discussion and interpretation of
$G\times E$ studies). In that case, the estimate of $\beta_{1}$ may
potentially reflect effects of certain environmental characteristics that are
correlated with genetic endowments. Since genetic variants are randomly
distributed across siblings, one way of isolating the direct (causal) genetic
effect is by way of a within-family analysis. Although in some study settings
this has been possible in the UK Biobank – there are approximately 40,000
siblings – it is too restrictive in our setting, since it relies on observing
families where one sibling is exposed to the derationing and the other is not.

##  5 Results

Tables and Figures presents the baseline results for the outcome variables.
Panel A presents the estimates from regressions that exclude area fixed
effects, whilst panels B, C and D report the results from regressions that
include county (of which there are 65), administrative county (232), and
district of birth (1434) fixed effects, respectively. The results in Panel A
suggest that in general the derationing (i.e., more confectionery consumption)
leads to better outcomes along some dimensions while it does not affect the
other outcomes. Importantly, being exposed to derationing has a positive
effect on years of education. More specifically, being in utero during the
derationing led to an increase in years of education of 0.17, so about 2
months on an annual base. Note that this result controls for the season of
birth. We also find that BMI is 0.09 units lower and sugar intake is 1% or 0.2
percentage points less for those exposed to the derationing compared to those
not exposed.222222A 0.09 drop in BMI is similar to a 0.25kg drop in weight for
an individual of average (1.68m) height in the data.

We find no statistically significant effects on height, cardiovascular
disease, type-2 diabetes or on carbohydrate or fat intake. Indeed, most of
these coefficients are relatively precisely estimated at values close to zero.
Regarding birth weight we find a 14g increase for those exposed to
confectionery derationing, though this is not statistically significant in
Panel A. Controlling for county (Panel B), administrative county (Panel C) or
district of birth (Panel D) fixed effects. does not affect any of these
findings in a notable way.

[Tables and Figures about here]

The results on BMI and sugar intake, and to some extent those on education and
birth weight, are in line with the “fetal programming” hypothesis or the more
general “developmental origins of health and disease” framework to explain
later-life health outcomes. This stipulates that late-life health may benefit
from a pre- and neonatal environment that is well aligned to the later-life
environment. In our setting, a high-sugar environment in utero is better
aligned with abundant nutrition later in life. Such a high-sugar environment
in utero then “programs” the fetus to better cope with abundant-nutrition
environments later in life, thereby improving its later-life outcomes.

Essentially the same argument can be made when considering confectionery
intake during pregnancy as a means to reduce stress. In the absence of
confectionery food, cravings during pregnancy may not be met, which may
increase stress levels of the mother and thereby prepare the fetus for a life
full of stress. This could decrease birth weight and increase BMI and sugar
intake later in life.232323Recent research on mice has shown that sugar intake
of adults increases the activation of tissue-resident memory T cells in the
intestinal wall in order to fight infections. This leads to faster clearance
of an infection (Konjar et al., 2022). Among pregnant women this mechanism may
explain cravings for sweet food. Either way, according to the developmental
origins framework, a lower disease occurrence during gestation protects the
offspring against adverse health outcomes later in life. Such a mechanism may
amplify the stress-related pathway mentioned in the main text.

A shortcoming of explanations based on fetal programming is that height,
cardiovascular disease and type-2 diabetes are found to not respond to
derationing. These three outcomes are the outcomes that have been found to be
most responsive to early-life conditions in the literature. Of course, the
exposure we consider differs somewhat from the in utero exposures in the
literature, such as famines, stressful bereavements, recessions, epidemics and
seasonal variation in food availability. The fact that we do find effects on
later-life sugar intake suggests that adult food preferences depend on the in-
utero environment. This may in turn be a mediator of effects on other
outcomes, in a way that differs from the mechanisms behind effects of the more
typical early-life exposures.

A slightly different explanation for our findings is that access to
confectionery boosted pregnant women’s happiness, which in turn could have led
to an improvement in bonding with the newborn and hence more favorable
educational and late-life health outcomes of the offspring.242424Indeed, the
consumption of sugar has been associated with reduced stress-induced cortisol
(Tryon et al., 2015), and prenatal stress has been linked to an increase in
adverse child outcomes (Gitau et al., 1998; Aizer, Stroud and Buka, 2016;
Persson and Rossin-Slater, 2018). Therefore, a higher consumption of sweets
could have provided the mother with psychological benefits with subsequent
effects on their children. In this case the nutritional value of confectionery
is not of prime relevance.

The coefficient estimates in Tables and Figures reflect average effects for
our analysis sample. To proceed, we explore whether those who are genetically
predisposed to a trait are differentially affected by the environmental
prenatal exposure, by incorporating the gene-environment interplay in the
empirical model. Panels A and B of 3 $G\times E$ effect of prenatal exposure
to confectionery derationing present the estimates without and with
administrative county fixed effects, respectively. The additive effects of
prenatal exposure to derationing are very similar to those shown in Tables and
Figures above. Also, not surprisingly, the additive effects of the respective
polygenic scores are strongly positive on all outcomes of interest, and the
results are robust to including area fixed effects. A one standard deviation
increase in the polygenic score for educational attainment, for example, is
associated with an increase of 1.2-1.3 years of schooling; consistent with the
existing literature (see e.g. Muslimova et al., 2020). Similarly, a one
standard deviation increase in the polygenic score for birth weight increases
birth weight by $\approx$100 grams, and a one standard deviation increase in
the polygenic score for BMI leads to an increase of 1.5 units. For height, the
estimates show an effect of $\approx$3.4 cm for each standard deviation
increase in the polygenic score, and for cardiovascular disease and type-2
diabetes, we find increases of five and two percentage points respectively.
Finally, we find that a one standard deviation increase in the polygenic score
for the macronutrients increases the consumption of sugar, carbohydrates and
fats (as a proportion of the total energy intake) by 0.4, 0.5, and 0.3
percentage points, respectively.

More importantly, we find evidence of $G\times E$ effects. Consider the sugar
density in older age for individuals with an average polygenic score (i.e., of
0). This density is 0.2 percentage points lower among those prenatally exposed
to derationing compared to those not exposed. 3 $G\times E$ effect of prenatal
exposure to confectionery derationing shows that this decrease is larger than
0.2 for those with a higher polygenic score for sugar. In other words, while a
one standard deviation increase in the polygenic score for sugar consumption
increases individuals’ intake by 0.4 percentage points, we find that this
effect is approximately 50% (i.e., 0.2 percentage points) smaller among those
exposed to prenatal derationing. Thus, a high sugar intake in utero causes
people with a genetic preference for sugar to dampen their later-life
consumption of sugar. In a fetal-programming perspective, it seems that a
favorable in-utero environment is particularly protective for individuals who
have a high genetic propensity to consume unhealthy food later in life. This
makes sense from an evolutionary point of view. Put bluntly, individuals who
by their genetic constitution do not consume sugar as adults do not need to be
prepared by certain early-life exposures that are aimed at influencing sugar
consumption. Note that we find a significant negative $G\times E$ interaction
effect for other outcomes as well, including carbohydrate intake and
cardiovascular disease (the latter only in Panel B). This again suggests that
although a high polygenic score increases one’s carbohydrate intake and risk
of disease, this risk is reduced for those programmed to better survive in
high-sugar environments.

For the other outcomes, we find no heterogeneous response of exposure to
derationing with respect to one’s genetic predisposition for the trait. In
other words, there is no evidence that one’s genetic endowment for education,
birth weight, BMI, height, type-2 diabetes and fat intake aggravates or
alleviates individuals against a high sugar prenatal environment. Indeed, the
interaction effects for these specifications are close to zero and relatively
precisely estimated.252525Polygenic scores are measured with error. One way of
dealing with this measurement error is by using an instrumental variables (IV)
specification, using polygenic scores obtained from GWAS summary statistics
based on external (i.e., non-UKB) data (DiPrete et al., 2018; van Kippersluis
et al., 2021). External GWAS summary statistics are available for all our
outcomes other than the macronutrient intakes (Okbay et al., 2016; Locke et
al. 2015; Morris et al. 2012; Wood et al., 2014; Schunkert et al., 2014). We
therefore create two sets of polygenic scores for each outcome (other than
macronutrients) and use Obviously-Related Instrumental Variables (ORIV; Gillen
et al., 2019), specifying each polygenic score as an instrument for the other.
As expected, this increases the predictive power of the polygenic score and
typically increases the coefficient on the interaction term, but also its
standard error. Hence, it does not lead to substantially different conclusions
with respect to the $E$ or $G\times E$ effects (results not shown here but
available upon request).

[3 $G\times E$ effect of prenatal exposure to confectionery derationing about
here]

##  6 Robustness analysis

The following subsections discuss a series of additional analyses and
sensitivity checks to ensure that our analysis is robust to different
assumptions. This includes an analysis of trimester-specific effects,
sensitivity to the timing of the derationing period, to accounting for
potential selection into pregnancy, and to alternative specifications of the
calender-time trend covariate. We also explore possible gender differences in
the treatment effect. For each of these, we focus on the main effect of
exposure to the derationing, rather than the $G\times E$ effects (the latter
are available upon request). All analyses control for administrative county
fixed effects, unless otherwise stated.

###  6.1 Trimester-specific exposure

Our baseline analysis above estimates effects of being exposed to
confectionery derationing averaged over all those in utero during this period.
However, the literature on the long-term effects of intrauterine exposures
highlights potentially heterogeneous effects due to exposure at different
stages of the pregnancy, generally distinguishing between three trimesters
(see e.g. Lumey, Stein and Susser, 2011; Almond and Mazumder, 2011). For
example, most organs are formed during the first trimester, so adverse
circumstances in this period may primarily affect organ development.

To explore whether there are differential effects of exposure by trimester, we
extend the baseline specification by allowing for three separate “trimester
effects”. This requires a mapping from the actual pregnancy interval into the
gestational trimester $j$ of exposure. In this we follow the literature on the
effects of the Dutch Hunger Winter famine which also lasted about 4 months
(Lumey, Stein and Susser, 2011). In equation (2), we replace $\alpha_{1}E_{i}$
by $\sum_{j=1}^{3}\alpha_{j}E_{ij}$, where each $E_{ij}$ is binary and
$\sum_{j=1}^{3}E_{ij}\leq 1$.262626Exposure in the first trimester is defined
as one for those born between December 24, 1949 and May 12, 1950, and zero
otherwise. Those born between September 24, 1949 and December 23, 1949 are
defined as being exposed in the second trimester, and those born between April
24, 1949 and September 23, 1949 are defined as being exposed in the third
trimester. The results are presented in Panel A of Tables and Figures. This
shows that the positive effect on years of education is largely driven by
exposure in the third trimester, with this leading to an increase of 0.2 years
of schooling, with a negative (insignificant) effect for first trimester
exposure. The (marginally significant) positive effect on birth weight is
driven by exposure in the first and second trimester. Finally, the negative
effect on BMI is mainly driven by exposure during the second and third
trimester, and the negative effect on sugar consumption is similar across the
three trimesters.

During the third trimester, the fetus gains most weight, so malnutrition in
this trimester may cause the caloric needs to support intrauterine growth not
to be fulfilled. Conditions in the UK in 1949 cannot be accurately described
as malnutrition. However, appetite may depend on the type of food. Put simply,
in the third trimester it may be more convenient to consume confectionery than
to eat a huge plate of cooked potatoes. In settings where caloric supply is
sufficient, such considerations may be relevant and may have long-run
implications, which may explain some of the trimester-specific results.

###  6.2 Timing of confectionery derationing

Although confectionery derationing started on April 24, 1949 and was abolished
on August 13, 1949, its effects on actual consumption may have been restricted
to a narrower time interval. As discussed earlier in the paper, the demand for
confectionery was so high that there were long queues outside shops, with
evidence that, by May 1949, some confectionery shops had run out of stock.
This would imply that some individuals who are currently defined as treated
are in fact not exposed to confectionery prenatally due to a non-availability,
potentially attenuating our estimate of interest. To explore this in more
detail, we re-run the analysis, setting $E_{i}$ equal to one for individuals
who were in utero between April 24, 1949 and the end of May 1949 (instead of
August 13, 1949). The estimates are presented in Panel B of Tables and
Figures. These confirm the positive effect of derationing exposure on years of
education and birth weight. Furthermore, we again find that prenatal exposure
to derationing led to a drop in BMI of 0.08 units (equivalent to a reduction
in body weight of 0.23 kg for a person of average height) and a 0.2 percentage
point drop in the sugar density of the diet. All these results are consistent
with those found for the baseline specification.

In Panel C, we additionally report the trimester-specific effects when
exposure is defined as having been in utero between April 24, 1949 and the end
of May 1949.272727First trimester exposure is now defined as those born
between November 13, 1949 and February 28, 1950. Those born between August 13,
1949 and November 12, 1949 are defined as being exposed in the second
trimester, and those born between April 24, 1949 and August 12, 1949 are
defined as being exposed in the third trimester. The findings are broadly
consistent with those above, with second trimester exposure for birth weight
being most important, third trimester exposure for BMI, and first trimester
exposure for height. We also find a significant effect for years of education
of second trimester exposure, which is also driving the effect on sugar
intake.

[Tables and Figures about here]

###  6.3 Timing of pregnancy

We next explore the assumption that pregnancies are not timed relative to the
derationing period. For this we exclude individuals who (we infer) were not
yet conceived at the time of the government announcement (as opposed to
implementation) of confectionery derationing on 21 February 1949 but who were
conceived before the end of derationing. In effect, we compare pregnancies
which were unexposed to derationing (including all those born before April 24,
1949) to those who were already pregnant when the government announced the
policy, removing any potential selection into pregnancy due to the announced
derationing. The latter group now includes those born between April 24, 1949
and November 24, 1949 (i.e., 9 months after the announcement on 21 February
1949). 5 Only including those already in utero at the time of the derationing
announcement presents the estimates, showing a positive effect on education
and a negative effect on BMI and sugar intake. The magnitude of the effects is
very similar to those shown above, suggesting there is no selection into
pregnancy after the government announcement to deration confectionery.

[5 Only including those already in utero at the time of the derationing
announcement about here]

###  6.4 Alternative functional forms for calendar-time trend effects

With a relatively small observation window (those born between April 24, 1947
and May 24, 1952), most outcomes do not display strong non-linear trends in
birth date among the non-exposed, suggesting that linear specifications in the
year-month of birth are sufficient. We test the robustness of this in 6
Robustness analysis - allowing for different specifications of the calendar
time trend effect. Here, Panel A only accounts for a linear function in the
year-month of birth (i.e., not allowing this to differ by exposure status). In
Panel B a quadratic function in the year-month of birth is assumed whereas in
Panel C we allow for differential quadratic functions by exposure status. The
results are generally consistent to those above: being exposed to the
derationing in utero increases individuals’ years of schooling, with some
evidence that it reduces BMI and sugar consumption later in life. Although the
magnitude of the point estimates are similar to those above, they are not
always statistically significantly different from zero. The exception is birth
weight, for which the estimates are less robust to the different functional
form specifications.

[6 Robustness analysis - allowing for different specifications of the calendar
time trend effect about here]

###  6.5 Heterogeneous effects

Finally, we explore heterogeneity of the treatment effects with respect to
gender. Male fetuses are known to be more vulnerable than female fetuses, with
the latter being more likely to survive adverse intrauterine conditions (see
e.g., Kraemer, 2000). As the derationing is likely to have led to a relatively
mild change in the prenatal environment, it is unlikely to cause differential
survival between the genders. 7 Gender differences reports estimates
separately for women (Panel A) and men (Panel B). This shows that although
both genders show a positive effect on years of education, it is larger and
only significant for men, whilst the positive effect on birth weight is driven
by women. We find a reduction in BMI for both genders, though with the reduced
sample sizes and inflated standard errors they are not significantly different
from zero at conventional levels. Finally, although the negative effect on the
share of energy from sugar is found for both genders, it is larger and
significantly different from zero only for men.

[7 Gender differences about here]

##  7 Conclusions

Due to high rates of obesity and diet-related health problems in the developed
world, the potential consequences of excessive sugar intake have received much
attention in academic research, with governments across the world trying to
encourage individuals to reduce their sugar consumption. Indeed, the WHO
recommends individuals to reduce their daily sugar intake to under 10% of
their total energy intake and it claims that a further reduction to a level
below 5% would provide additional health benefits (WHO, 2015). These
guidelines mainly refer to the short term health benefits of reduced sugar
intake. The longer term effects on health and well-being are much less known.
In particular, there is no evidence on the long term causal effects of sugar
exposure in utero.

Our analysis has directly targeted this omission in the literature. We exploit
a temporary increase in the availability of confectionery in a period that is
characterized by a mostly time-invariant availability of food items, where the
nutritional composition of diets remained fixed due to the majority of foods
being rationed. We find that the prenatal exposure to confectionery
derationing increased education by $\approx$0.15 years (1.8 months), lowered
later-life BMI by 0.08 units and reduced later-life sugar consumption by 0.2
percentage points (0.9%), with some evidence that it also led to an increase
in birth weights of approximately 10-15 grams.

The results on BMI and sugar intake, and to some extent those on education and
birth weight, are in line with the “fetal programming” or “developmental
origins of health and disease” framework to explain later-life health
outcomes. Along these lines, a high-sugar environment in utero “programs” the
fetus to better cope with abundant-nutrition environments later in life,
thereby improving its later-life outcomes. Essentially the same argument can
be made when considering confectionery intake during pregnancy as a means to
reduce stress. In the absence of confectionery food, cravings during pregnancy
may not be met, which may increase stress levels of the mother and thereby
prepare the fetus for a more stressful life.

The programming explanation is more difficult to reconcile with the absence of
effects on other outcome variables. Here it should be kept in mind that, in
contrast to the literature on long-run effects of early-life conditions, we
are not analyzing omnibus measures of adversity (such as famines or recessions
or the availability of common staple foods) but we focus on access to a highly
specific food type. The fact that we find effects on later-life sugar intake
suggests that adult food preferences depend on the in-utero environment. This
may in turn be a mediator of effects on other outcomes. We view it as an
important topic for future research to examine long-run effects of a wide
range of specific food types in order to enhance our understanding of their
roles in effects on later-life outcomes. An advantage of our setting is that
individuals were unlikely to substitute or complement the increase in
confectionery with changes in other food intake, since most other foods were
rationed. This implies that we identify the effects of increased prenatal
exposure to confectionery per se, without the potential simultaneous
adjustments to other food items (though it is possible that our estimates
partially capture a reduced exposure to artificial sweeteners such as
saccharin).

We also explored whether there is evidence that one’s genetic predisposition
can exacerbate or alleviate the effects. To do so, we use the molecular
genetic data available in the UK Biobank, constructing polygenic scores for
each outcome of interest and interacting these with the treatment effect. We
find some evidence of genetic heterogeneity of treatment effects. More
specifically, with high polygenic scores for cardiovascular disease, sugar and
carbohydrate intake in a high-sugar prenatal environment, the beneficial
effects of derationing are higher. In a fetal-programming perspective, it
seems that a favorable in-utero environment is particularly protective for
individuals who have a high genetic propensity for adverse late-life outcomes.
This may make sense from an evolutionary point of view: individuals who by
their genetic constitution face favorable late-life outcomes may not need to
be do programmed through certain early-life exposures.

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## Tables and Figures

Table 1: Descriptives | N | mean | sd
---|---|---|---
Years of education | 83,647 | 13.94 | (5.140)
Birth weight (in g) | 47,476 | 3331.8 | (672.2)
BMI | 83,647 | 27.57 | (4.790)
Height (in cm) | 83,647 | 168.52 | (9.216)
Cardiovascular disease | 83,647 | 0.33 | (0.471)
Type-2 diabetes | 83,647 | 0.05 | (0.222)
Sugar intake | 36,131 | 0.23 | (0.069)
Carbohydrate intake | 36,131 | 0.48 | (0.082)
Fat intake | 36,131 | 0.33 | (0.067)
Male | 84,669 | 0.45 | (0.498)
Sample sizes, means and standard deviations of the estimation sample,
including individuals born between April 1947 (i.e., two years prior to the
temporary sugar derationing) and May 1952 (i.e., two years and nine months
post the end of the temporary sugar derationing).  Figure 1: Trends in the
outcomes of interest

Notes: The figures plot the trends of the relevant outcome variable by year-
month of birth. Table 2: Effects of prenatal exposure to confectionery
derationing

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---
|  Years of Education |  Birth weight (in g) | BMI |  Height (in cm) |  Cardiovascular Disease |  Type II Diabetes |  Sugar intake |  Carbohydrate intake |  Fats intake
Panel A: No area fixed effects |  |  |  |  |  |  |  |  |
E: In utero during derationing |  0.167*** | 13.872 |  -0.096* | 0.018 | -0.004 | -0.003 |  -0.002* | -0.001 | -0.001
| (0.056) | (9.594) | (0.050) | (0.065) | (0.005) | (0.002) | (0.001) | (0.001) | (0.001)
Panel B: County FE (n=65) |  |  |  |  |  |  |  |  |
E: In utero during derationing |  0.154*** |  14.582* |  -0.090** | 0.006 | -0.004 | -0.003 | -0.002 | -0.001 | -0.001
| (0.050) | (8.330) | (0.034) | (0.049) | (0.005) | (0.002) | (0.001) | (0.001) | (0.001)
Panel C: Administrative county FE (n=232) |  |  |  |  |  |  |  |  |
E: In utero during derationing |  0.147*** | 14.399 |  -0.084** | 0.002 | -0.004 | -0.003 |  -0.002* | -0.002 | -0.001
| (0.052) | (10.347) | (0.042) | (0.066) | (0.006) | (0.002) | (0.001) | (0.001) | (0.001)
Panel D: District FE (n=1434) |  |  |  |  |  |  |  |  |
E: In utero during derationing |  0.142*** | 13.531 |  -0.087** | 0.005 | -0.003 | -0.003 |  -0.002** | -0.002 | -0.001
| (0.051) | (9.645) | (0.043) | (0.064) | (0.006) | (0.002) | (0.001) | (0.001) | (0.001)
No. of observations | 84184 | 47476 | 84554 | 84641 | 84672 | 84637 | 36131 | 36131 | 36131

Estimates from Equation (2), with the treatment dummy ($E$) specified as those
born between 24 April 1949 and 12 May 1950. Robust standard errors (clustered
by year-month in Panel A and by geographic area in the Panels B–D) in
parentheses. * $p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table 3: $G\times E$ effect of prenatal exposure to confectionery derationing

Estimates from Equation (3), with the treatment dummy ($E$) specified as those
born between 24 April 1949 and 12 May 1950 and the polygenic score $G$
obtained from using the summary statistics from our tailor-made GWAS on the
excluded UKB sample and any siblings. Panel A reports the analysis without
controlling for area of birth fixed effects; Panel B reports the analysis that
controls for administrative county of birth fixed effects. Robust standard
errors (clustered by year-month in Panel A and by administrative county in
Panel B) in parentheses. * $p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table 4: Timing of derationing and trimester-specific results

Estimates from Equation (2). Panel A reports the estimates of exposure to
derationing on the different outcomes of interest, distinguishing by the
trimester of exposure. Exposure in the first trimester is defined as those
born between December 24, 1949 and May 12, 1950. Those born between September
24, 1949 and December 23, 1949 are exposed in the second trimester, and those
born between April 24, 1949 and September 23, 1949 are exposed in the third
trimester. Panel B defines the derationing period to be between 24 April 1949
and 31 May 1949, following the literature that suggests that many shops had
run out of confectionery by then. Panel C distinguishes between trimesters of
exposure within this shorter period of derationing. First trimester exposure
is here defined as those born between November 13, 1949 and February 28, 1950.
Those born between August 13, 1949 and November 12, 1949 are exposed in the
second trimester, and those born between April 24, 1949 and August 12, 1949
are exposed in the third trimester. All regressions include administrative
county of birth fixed effects. Robust standard errors, clustered by
administrative county of birth, in parentheses. * $p<0.10$, ** $p<0.05$, ***
$p<0.01$.

Table 5: Only including those already in utero at the time of the derationing
announcement

Sample includes those born between April 1947 and 24 November 1949 (i.e., 9
months post the announcement of derationing), and between 13 May 1950 (i.e., 9
months after re-rationing) and 12 May 1952, with those born between 24 April
1949 and 24 November 1949 defined as treated. Panel A does not control for
region of birth fixed effects; Panel B controls for administrative county
fixed effects. Robust standard errors, clustered by year-month of birth in
Panel A and by administrative county of birth in Panel B, in parentheses. *
$p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table 6: Robustness analysis - allowing for different specifications of the
calendar time trend effect

Estimates from Equation (2), with the treatment dummy ($E$) specified as those
born between 24 April 1949 and 12 May 1950. Panel A accounts for a linear
function in the year-month of birth; Panel B allows for a quadratic function
in the year-month of birth; Panel C allows for different quadratic functions
for exposed and non-exposed. All regressions control for administrative-
county-of-birth fixed effects. Robust standard errors, clustered by
administrative county, in parentheses. * $p<0.10$, ** $p<0.05$, *** $p<0.01$.

Table 7: Gender differences

Estimates from Equation (2), with the treatment dummy ($E$) specified as those
born between 24 April 1949 and 12 May 1950. Panel A shows the results for
women; Panel B shows the results for men. All regressions include
administrative county of birth fixed effects. Robust standard errors,
clustered by adminsitrative county of birth, in parentheses. * $p<0.10$, **
$p<0.05$, *** $p<0.01$.

For Online Publication

###  Appendix A Additional detail on the UK rationing system

Before the Second World War, Britain imported 55m tons of food per year,
including 50% of meats, 70-80% of its cheese and sugar, nearly 80% of fruits,
about 70% of cereals and fats, and 90% of butter, cereal and flour (Ministry
of Food, 1951). A month after the war had started, this had dropped to 12m on
an annual base.

Five days after the war was declared, in September 1939, individuals received
their ration books. Households had to register with a local supplier; the
details of which were stamped in the book, as one could only purchase the
ration from the registered supplier. The ration books were not used until four
months later (on January 8, 1940). The ration coupon was necessary (and a
government guarantee) for the individual to get their share of the item. The
coupons could not be used on other produce, nor could they be carried forward.
As such, rationing was not applied to e.g. seasonal items, as the government
was unable to guarantee their year-round supply. Other items that the
government did not want to ration include fresh fruit and vegetables, bread,
potatoes, cigarettes and beer. One reason was that supplies were limited
anyway (e.g., fresh fruit) so the government could not guarantee its
availability, or the government wanted to provide sufficient quantities of
cheap energy (i.e., bread, potatoes). Another was that rationing these foods
would impact morale, which the government wanted to avoid, and – for tobacco –
it would have conflicted with its revenue-raising function (Zweiniger-
Bargielowska, 2000).282828The “Dig for Victory” campaign meant that the
consumption of homegrown fruit and vegetables from individuals’ allotments
also increased during the war. Furthermore, daylight savings were put ahead by
two hours every March, ensuring more daylight hours available for farming and
gardening. In summary, rationing was based on the principle that all essential
nutrients, even those in short supply, should be equally available to
everyone, to the extent necessary to maintain health, and at controlled prices
(Ministry of Health, 1946).

There were different ration books for different population groups. For
example, most people received the standard blue ration book, but pregnant
women, nursing mothers and children under five received green ones, which
entitled them to additional milk, eggs and meat, orange juice, cod liver oil,
and vitamin A and D (Davies, 2014). “Points rationing” was introduced in
December 1941 via a separate pink rationing book. As explained in Section 2,
it ran in conjunction with regular coupon rationing, but was more flexible,
giving shoppers greater choice. It could also be used for purchases at grocers
other than the one the household had registered with. However, points
rationing was mainly used for tinned goods. The Ministry of Food could adjust
the price (i.e. the number of points that an item would cost) in order to try
to achieve a balance between supply and demand. In general, prices of
unrationed goods were strictly controlled as well by the government. As a
result, unrationed goods were not always available.

Table A.1 shows a rough timeline of when some of the main foods came on and
off rationing. The amount of food that one could get for one coupon tended to
vary somewhat over time in line with the quantity of food imports and home-
grown produce; see Zweiniger-Bargielowska (2000) for detailed information.

There were other food-related schemes in operation before, during and after
the War. For example, the National Milk Scheme provided milk to vulnerable
individuals (e.g., children, expectant and nursing mothers). The Milk in
Schools Scheme had already been in place since October 1934, providing milk to
many (state) school children. The milk provided through these schemes
increased somewhat during the war years but there were no large changes to the
eligibility requirements.

Table A.1: Start and end dates of rationing in the UK for selected main foods | Start | End | Notes
---|---|---|---
National Registration Day |  |  | Sep 29 1939: Provide details of all household members
Gammon, ham | Jan 8, 1940 | Oct, 3 1952 |
Bacon | Jan 8, 1940 | Jul, 3 1954 |
Butter | Jan 8, 1940 | May, 8 1954 |
Sugar | Jan 8, 1940 | Sep, 27 1953 |
Meat (incl beef, veal, mutton, pork) | Mar, 11 1940 | Jul, 4 1954 |
Margarine | Jul 1940 | May 8, 1954 |
Tea | Jul 1940 | Oct 5, 1952 |
Cooking fat | Jul 1940 | May 8, 1954 |
Cheese | May 5, 1941 | May 1954 | Vegetarians were allowed an extra 85g on top
|  |  | of the standard ration
Eggs | Jun 1941 | Mar 26, 1953 |
Milk | Nov 1941 | 1950 |
Tinned veg, breakfast cereals | Feb 9, 1942 | May 19, 1950 | Points rationing
Dried and canned fruit, rice | Jan 1942 | May 19, 1950 | Points rationing
Biscuits | Aug 1942 | May 19, 1950 | Points rationing
Oats (flaked and rolled) | Dec 1942 | May 19, 1950 | Points rationing
Syrup, treacle | Jun 1942 | May 19, 1950 | Transferred from standard rationing to points scheme
Confectionery | Jul 26, 1942 | Feb 5, 1953 | Temporary derationing between April 24 - August 13 1949
Notes: The precise date of introduction and removal of rationing is not always
known.

## References

Davies, N. (2014), Europe: A history, Random House.

Ministry of Food (1951), WWII National Food Survey, 1951. Rationing
statistics: Straight rationing (Part II), 1948-1951. MAF 156/263. The National
Archives.

Ministry of Health (1946), On the state of the public health during six years
of war. London, His Majesty’s Stationery Office.

Zweiniger-Bargielowska, I. (2000), Austerity in Britain: Rationing, Controls,
and Consumption, 1939-1955, Oxford University Press.

###  Appendix B Genetic Analysis

####  B.1 An introduction to genetics

The human genome consists of over 3 billion base pairs (6 billion bases) in
each cell nucleus, with four possible bases: adenine (A), thymine (T), guanine
(G) and cytosine (C).292929A base pair is set of two bases, with A always
pairing with T, and C always pairing with G. Over 99% of the human genome is
identical between any two unrelated human beings. The remaining $<$1% differs
between individuals, with a Single Nucleotide Polymorphism (or SNP, pronounced
‘snip’) being the most common form of genetic variation. A SNP is a one base-
pair substitution at a particular location (locus) on the human genome.

To identify genetic variants that are associated with a particular trait of
interest, such as years of education or BMI, so-called Genome-Wide Association
Studies (GWAS) relate each SNP to the trait in a hypothesis free-approach.
This is done by a series of OLS regressions of the trait on the genotype of a
single SNP and a set of covariates (normally sex, age, genotype array, and the
first set of principal components of the genetic data to account for
ancestry). GWAS have shown that most human traits are highly polygenic,
meaning that they are affected by many SNPs, each with very small effect
sizes. To increase their predictive power, they can then be aggregated into
so-called polygenic scores (also known as polygenic indices), as shown in
Equation 1. Here, it is important that the $\beta_{j}$’s, the effect sizes of
each SNP $j$, are obtained from an independent GWAS, as not doing so leads to
overfitting.

GWAS sample sizes have grown substantially in recent years, meaning that (a)
SNPs with very small effect sizes are more likely to be identified, and (b)
that the effect sizes are estimated with increased precision. Indeed, we have
seen large improvements in genetic prediction, with initial GWASs being able
to explain less than 1% of the variation in the trait of interest (e.g., years
of education), to more recent ones explaining over 10% of its variation (see
e.g., Okbay et al., 2022).

####  B.2 Our tailor-made GWAS

The full UK Biobank data release contains all successfully genotyped samples
(n=488,377). Pre-imputation QC, phasing and imputation are described in detail
in Bycroft et al. (2018). In brief, prior to phasing, we removed multiallelic
SNPs or those with a minor allele frequency (MAF) $\leq$1%. Phasing of
genotype data was performed using a modified version of the SHAPEIT2 algorithm
(O’Connell et al., 2016). Genotype imputation to a reference set combining the
UK10K haplotype and HRC reference panels (Huang et al., 2015) was performed
using IMPUTE2 algorithms (Howie et al., 2011). Our analysis is restricted to
autosomal variants within the HRC site list using a graded filtering with
varying imputation quality for different allele frequency ranges.

We follow the literature and exclude individuals with sex-mismatch (derived by
comparing genetic sex and reported sex) or individuals with sex-chromosome
aneuploidy from the analysis (n=814). Furthermore, we restrict the sample to
individuals of white British ancestry who self-report as ‘White British’ and
who have very similar ancestral backgrounds according to the PCA (n=409,703),
as described by Bycroft et al. (2018). Finally, our GWAS discovery sample only
includes unrelated individuals; we exclude genetically related individuals as
well as our analysis sample (i.e., those born between 24 April 1947 and 13 May
1952) before running our GWAS.

Depending on the distribution of the outcome, we use a linear or logistic
regression in the genome-wide association study, using the software
PLINKv2.00. We specify the genotype array, gender and the first 10 principal
components as covariates.

####  B.3 Calculating Polygenic Scores

We use LDpred (Vilhjálmsson et al., 2015) to construct all polygenic scores.
This Python-based software package adjusts GWAS summary statistics for the
effects of linkage disequilibrium (LD). It uses a Bayesian method, assuming a
prior for the genetic architecture of the trait of interest in combination
with information on the linkage disequilibrium from an external reference
panel (30,000 unrelated individuals in UK Biobank) to estimate posterior mean
effect sizes from summary statistics. We construct the polygenic scores using
an LD window of M/3000 where M is the total number of SNPs. We assume the
fraction of SNPs with non-zero effects to be 0.3. Our results are robust to
taking a prior of 1 instead of 0.3. We restrict the analysis to 1.2M HapMap 3
SNPs (International HapMap3 Consortium, 2010) to ensure a set of common, well-
imputed variants.

## References

Bycroft, C., Freeman, C., Petkova, D., …, Marchini, J. (2018), The UK Biobank
resource with deep phenotyping and genomic data, Nature 562, 203–209.

Howie, B., Marchini, J. and Stephens, M. (2011), Genotype Imputation with
Thousands of Genomes, Genes|Genomes|Genetics 1, 457–470.

Huang, J., Howie, B., McCarthy, S., …, Soranzo, N. (2015), Improved imputation
of low-frequency and rare variants using the UK10K haplotype reference panel,
Nature Communications 6, 8111.

International HapMap 3 Consortium (2010), Integrating common and rare genetic
variation in diverse human populations, Nature 467, 52–58.

O’Connell, J., Sharp, K., Shrine, N., …, Marchini, J. (2016), Haplotype
estimation for biobank-scale data sets, Nature Genetics 48, 817–820.

Okbay, A., Wu, Y., …, Young, A. (2022), Polygenic prediction of educational
attainment within and between families from genome-wide association analyses
in 3 million individuals. Nature Genetics 54, 437–449

Vilhjálmsson, B. J., Yang, J., Finucane, H. K., …, Hayeck, T. (2015), Modeling
linkage disequilibrium increases accuracy of polygenic risk scores, The
American Journal of Human Genetics 97.4, 576–592.

###  Appendix C Additional Figures

Figure C.1: Variation in the sweets ration; 1942 – 1948 The figure shows the
variation in the sweets ration between its introduction on July 26, 1942 and
the end of 1948. Source: MAF 156/263, 1951. Figure C.2: Gene-environment
correlations

Notes: The figures plot the distribution of the different polygenic scores by
treatment status and show the predictive power of the polygenic score (created
using our tailor-made GWAS on the non-sibling sample of the UK Biobank,
excluding the estimation sample) for their respective outcomes.