Abstract

In 1924, the Morton Salt Company began nationwide distribution of iodine-fortified salt. Access to iodine, a key determinant of cognitive ability, rose sharply. We compare outcomes for cohorts exposed in utero with those of slightly older, unexposed cohorts, across states with high versus low baseline iodine deficiency. Income increased by 11%, labor force participation rose 0.68 percentage points, and full-time work went up 0.9 percentage points due to increased iodine availability. These impacts were largely driven by changes in the economic outcomes of young women. In later adulthood, both men and women had higher family incomes due to iodization.

I. Introduction

INADEQUATE access to essential micronutrients such as iron, vitamin A, iodine, and zinc has staggering costs in terms of mortality, poor health, and lost productivity in low-income countries (Black et al., 2013). The benefits of improving micronutrient availability in the short term, especially for young children, are clear (Bhutta et al., 2013), but less is known about long-run impacts, particularly of large-scale supplementation policies. How long do the effects of improved access to vital micronutrients last? Do health effects spill over onto socioeconomic outcomes? Which individuals are most affected by blanket campaigns? With notable recent exceptions (Clay, Schmick, & Troesken, 2019; Feyrer, Politi, & Weil, 2017; Niemesh, 2015; Politi, 2014, 2010), these questions have received scant attention, and are the focus of our study.

We draw lessons from the historical experience of the United States, where until the mid-1920s, natural access to iodine was limited in some areas of the country compared to others. An essential micronutrient, iodine regulates thyroid hormone availability, which determines the density of fetal neural networks (Lamberg, 1991). Physiological studies suggest that iodine deficiency has a negative effect on cognitive function at all ages, but is particularly detrimental during gestation, when even mild deficiency can greatly hamper cognitive development (Cao et al., 1994). Moreover, the effects of fetal iodine deficiency disorder (IDD) are irreversible: an inadequate supply of iodine in the first trimester of gestation permanently reduces IQ, regardless of subsequent supplementation (Pharoah & Connolly, 1987; Hetzel & Mano, 1989; Zimmermann, 2009).1

We study the economic impacts of rapid, large-scale salt iodization in the twentieth-century United States. The Morton Salt Company, the largest salt producer in the United States, initiated nationwide iodized salt distribution shortly after the invention of iodine-fortified salt in the mid-1920s. In less than half a decade, the United States went from zero to nearly universal availability of iodized salt (Markel, 1987). Iodine deficiency rates plummeted in the following decade, most markedly in areas that were highly iodine deficient prior to the introduction of iodized salt (Brush & Atland, 1952; Hamwi et al., 1952; Schiel & Wepfer, 1976).

Using a difference-in-differences strategy similar to the one used in this paper, Feyrer et al. (2017) show that among men who enlisted in the Army during World War II, exposure to iodized salt increased the likelihood of being assigned to the Air Force (an indication of a high score on the Army General Classification Test). They estimate that the introduction of iodized salt increased IQ by approximately 15 points for those most deficient in iodine prior to this intervention. Building on this important work, which provides both a first-stage and motivation for our study, we tackle several important questions about the economic consequences of this natural experiment. What happened to the labor market outcomes of those whose in utero access to iodine improved? Did increased IQ affect incomes, as was true in Switzerland (Politi, 2014), and labor supply? Importantly, because the sample in Feyrer et al. (2017) was exclusively male, we seek to estimate labor market effects for women. Did men and women benefit differentially? How did these impacts evolve over the life cycle? Did related outcomes like educational attainment and marriage respond differentially?

To answer these questions, we use a strategy similar to that of Feyrer et al. (2017) but look to the U.S. Census for our comprehensive set of economic outcomes. We compare outcomes for cohorts born just before iodization (1920–1923) to those born during (1924–1927) and after (1928–1931), across areas with varying pre-iodization deficiency rates. For the latter source of variation, we use pre-iodization rates of goiter, the main physical manifestation of IDD (Love & Davenport, 1920; Olesen, 1929). We follow these cohorts through their productive lives, using data from the 1950–1980 Censuses, during which these individuals were 19 to 60 years old. We present estimates of early career outcomes, pooling the 1950 and 1960 Censuses, and later career outcomes, using the 1970 and 1980 Censuses.

Individuals affected by salt iodization saw improved economic outcomes. For a state at the 75th percentile of the goiter distribution compared to a state at the 25th percentile, labor force participation rose by about 0.68 percentage points and total income increased by 11% in the pooled sample of individuals aged 19 to 60 in the 1950–1980 Censuses. Women experienced the largest changes in employment (greater than 1 percentage point) and income (15%), though men also saw small increases in their income conditional on working (1%). The much larger labor supply effects for women likely reflect the fact that women exhibited much lower employment rates than men during this time period.2

These large increases in female labor supply were concentrated among women early in their careers (under age 40); women affected by salt iodization were not significantly more likely to be in the labor force in the 1970 and 1980 Censuses. Impacts on female incomes do persist at later ages but are smaller and less precisely estimated. This pattern is consistent with affected women transitioning out of the labor force at later ages due to a negative income effect generated by their higher accumulated lifetime income.3 Supporting this idea, we find that affected women married at later ages (nearly a quarter of a year), married more educated and higher-income men, and had higher family income in their 40s and 50s.

Like Politi (2010), which focuses on Switzerland's historical experience with salt iodization, we find a small but precisely estimated positive effect on educational attainment. Theoretical effects of increased cognition on schooling are ambiguous, as the direct wage returns to ability in entry-level labor opportunities may counteract any impacts of ability on schooling returns at early ages. Because the magnitude of our estimated effect is very small (equivalent to about two weeks of school), we interpret this result as evidence that both of these opposing effects are in play.4

Our study aims to contribute to three literatures. First, we add to the literature on the long-term effects of early life conditions.5 Much of this “fetal origins” work has focused on demonstrating the impacts of traumatic experiences (e.g., disease, natural disasters, environmental factors) in early life. Fewer studies have estimated the gains to exposure to the purposeful large-scale distribution of resources. The distinction between the two types of studies is important because the latter “shock” can yield actionable information: policies with demonstrated positive impacts can be advocated for and reproduced. A growing set of studies—including Hoynes, Schanzenbach, & Almond (2016), Bleakley (2007, 2010), Field et al. (2009), Almond et al. (2010), Politi (2010), Bhalotra and Venkataramani (2015), and Feyrer et al. (2017)—have recently made strides in this direction. We build on this evidence base: the results of these studies and ours offer lessons from historical policy experiments from which present-day policymakers, particularly in developing countries, might profitably draw.

Second, we contribute to the understanding of women's decisions regarding the labor force in the historical United States. We find that labor market effects of iodine are particularly pronounced for women, consistent with evidence from recent studies on the effects of other early life interventions (Hoynes et al., 2016; Field et al., 2009; Bleakley, 2007; Maccini & Yang, 2009). Moreover, this pattern relates to important previous work on the drivers of the marked rise in labor force participation of women over the twentieth century. Goldin (1991) and Goldin and Olivetti (2013) estimate that World War II led to a roughly 20% rise in female participation for higher-educated women in cohorts born between 1915 and 1924. Bailey (2006) and Goldin and Katz (2002) show that increased access to oral contraceptives led to later marriage, higher likelihood of professional and graduate training in high-skill occupations, and increased rate and duration of labor force participation among cohorts born after 1940. We present complementary evidence documenting that salt iodization also contributed to the rise in female labor force participation, generating a 3% increase among females born in between the two sets of cohorts studied in these papers.

Finally, we add evidence on the long-run effects of micronutrient fortification campaigns and, in particular, mass salt iodization as a means of eradicating iodine deficiency. Nearly 2 billion people worldwide—a third of the world's population—do not have adequate access to iodine (De Benoist et al., 2004). Recent estimates from the economics literature suggest that the incidence of iodine deficiency, and thus the returns to reducing IDD, may be very large (Field et al., 2009; Politi, 2010; Feyrer et al., 2017). Policymakers in IDD-endemic countries, as well as the WHO, UNICEF, and other international organizations, have made increasing access to iodine a high priority. Mass salt iodization to prevent IDD is far and away the preferred policy: iodizing salt is much cheaper than continuous supplementation in populations with iodine-deficient diets, and, taken with other micronutrients such as iron, is highly cost-effective in terms of fetal and infant deaths averted (Horton, Alderman, & Rivera, 2008).

The rest of the paper is organized as follows. Section II discusses iodine deficiency and its prevalence in the early twentieth century, as well as the history of salt iodization in the United States. Section III discusses our data sources, section IV our empirical strategy, and section V the results. Section VI concludes with a discussion of the size of the economic benefits of iodization.1

II. Background

A. Iodine Deficiency and Its Consequences

Iodine is crucial to the functioning of every body cell. The thyroid gland in the lower part of the neck uses iodine from foods to produce thyroid hormones, which are released into the bloodstream to control metabolism (the conversion of oxygen and calories to energy). Foods with high iodine content include some milks, leafy vegetables, and seafood, but individuals in areas without natural access to iodine—far from the ocean or in mountain regions susceptible to erosion (Hetzel, 1989)—are at risk of not meeting recommended levels of iodine intake.

At any point from the fetal stage to adulthood, insufficient iodine intake can cause a number of functional and developmental abnormalities, often referred to as iodine deficiency disorders (IDD). The main physical manifestation of IDD is goiter (the enlargement of the thyroid gland),6 but other IDDs include hypothyroidism (which results in fatigue, lethargy, slow speech, and thought), impaired mental function, retarded physical development, and increased susceptibility of the thyroid gland to nuclear radiation (De Benoist et al., 2004). This paper focuses on the availability of iodine in utero because fetal iodine levels are particularly crucial for brain development: insufficient iodine intake during gestation can cause irreversible cognitive damage (De Escobar et al., 2004; Zimmermann, 2009).

B. Introduction of Iodized Salt in the US

The map in figure 1 illustrates the geographic distribution of goiter incidence across the United States before the introduction of iodized salt, based on data from the 1917 World War I draft examinations. To our knowledge, this is the first nationwide goiter survey in the United States. As we show in the online appendix, evidence from other sources suggests that the draft statistics offer a good representation of the geographic pattern of iodine availability for the general population.
Figure 1.

State-Level Goiter Rates from World War I Draft Examinations

State-level goiter rates, calculated from Love and Davenport (1920), are summarized in table A1 in the online appendix. Each shade represents a different quintile of the goiter distribution, with the darkest gray representing the highest quintile. Thick lines denote census division boundaries.

Figure 1.

State-Level Goiter Rates from World War I Draft Examinations

State-level goiter rates, calculated from Love and Davenport (1920), are summarized in table A1 in the online appendix. Each shade represents a different quintile of the goiter distribution, with the darkest gray representing the highest quintile. Thick lines denote census division boundaries.

The introduction of iodized salt to the United States was due in large part to Dr. David Cowie, of the University of Michigan, who aimed to eliminate the widespread goiter in his home state. Inspired by recent experiments showing that iodine supplementation could control and prevent goiter and a recent salt iodization campaign in Switzerland, Cowie worked with several small salt manufacturers based in Michigan, who were eager to supply a product that they perceived would have a large demand with little extra cost to produce. The Executive Council of the Michigan State Medical Society officially endorsed iodized salt on March 12, 1924, and the product appeared in Michigan groceries on May 1, 1924. The product proved so popular that Morton Salt Company, which initially resisted because of concerns about the high cost of separating a different version of salt for Michigan retailers, began nationwide distribution a few months later. With the educational efforts of the Michigan State Medical Society, which gave lectures on the medical benefits of iodized salt, and zealous advertisements by salt producers, iodized salt rapidly grew popular. By 1930, iodized salt sales were eight times that of plain salt sales (Markel, 1987). (See Feyrer et al., 2017 and section A1 in the online appendix for more detail about iodine deficiency and the introduction of iodized salt to the United States.)

III. Data

A. Goiter Data

Our information on the geographic distribution of goiter before 1924 comes from the data used to create figure 1: medical examinations of over 2.5 million drafted men aged 18 to 30 before World War I. Conducted on a large sample of men from all over the United States within a short period of time between 1917 and 1918, these examinations offer a snapshot of the geographic distribution of various mental and physical defects prior to the iodization of salt.7 Love and Davenport (1920) document prevalence rates in this sample for over 200 medical conditions, including goiter. In this paper, we use state-level prevalence rates, although rates for smaller regions (collections of counties known as sections) were recorded as well. Unfortunately, we cannot use the section-level goiter data because we have only state of birth, not county of birth, for the individuals in our sample. Table A1 in the online appendix reports for each state the goiter rate recorded in Love and Davenport (1920). The median state-level goiter rate is 0.214%, and the maximum is 2.69%.

B. Census Data

We also use data from the U.S. Decennial Census (Ruggles et al., 2015), restricting to individuals born in the twelve-year period spanning 1920 to 1931, which includes the years before, during, and after the nationwide spread of iodized salt. We are interested in labor, education, and marriage outcomes for this cohort in the 1950 to 1980 Censuses, during which they were working-aged adults aged 19 to 60. We use the 1% samples for 1950 to 1970 and the 5% sample for 1980.8

Individuals are assigned to the goiter rate in their state of birth, which proxies for their risk of being born to an iodine-deficient mother. Individuals are also grouped according to their birth year. Those born in the years 1920 to 1923 are marked as “pre-iodization,” those born in 1924 to 1927 are classified as born “during iodization,” and those born in 1928 to 1931 are considered “post-iodization.” Iodized salt first appeared in grocery stores in 1924 and was reported to have generated eight times more sales than regular salt by 1930 (Markel, 1987). In creating the “during” category, we allow four years of leeway following the initial introduction of iodized salt to ensure that the after cohort was exposed to an environment with sufficiently widespread iodized salt availability.

Outcome and control variables.

We are primarily interested in labor market outcomes. To represent employment, we use two variables: a dummy for labor force participation (which includes job seekers as participants) and a dummy for employment (which is set to 0 for job seekers). Individuals who report working in the previous year also report the number of weeks they worked (in intervals). We create an indicator variable to represent individuals who worked at least 40 weeks (conditional on having worked in the past year) to study the intensive margin of labor supply. We also look at total income, which includes income from all sources, including wages and self-employment income. We transform total income using the inverse hyperbolic sine function.9 For all of these labor market variables, we pool individuals from the 1950 to 1980 Censuses, during which our sample was aged 19 to 60.

In addition to these labor market outcomes, we also study years of educational attainment, whether an individual has ever been married, and age at first marriage. Because these are all stock variables that are unlikely to be determined in the teenage years or 20s, we only look at individuals in the 1970 and 1980 Censuses (when our sample is aged between 39 and 60). To further investigate marriage quality, we also study spousal income and education for individuals currently married (to a spouse currently living in the same the household), along with total family income.

Additional variables taken from the Census include gender and race. In addition to including female and black indicator variables as controls, we also control for pre-iodization demographic conditions in the individual's state of birth. This is done by calculating the black and female proportions from the 1920 Census in the individual's state of birth. For more details on the construction of variables, see the data appendix (section A3 in the online supplement).

Summary statistics.

Table 1 reports summary statistics for our sample population (individuals born between 1920 and 1931). The first column summarizes our outcome variables for the entire sample, the second restricts to females, and the third focuses on males. Women have much lower labor force participation, labor supply, and income than men. Marriage rates, age at first marriage, and years of schooling are more similar across both genders, although women are more likely to be married and get married slightly earlier.

Table 1.
Summary Statistics
(1)(2)(3)
Whole SampleFemalesMales
1950–1980 Censuses    
1(Employed) 0.644 0.429 0.872 
 (0.479) (0.495) (0.334) 
1(Participated in Labor Force) 0.670 0.448 0.905 
 (0.470) (0.497) (0.293) 
1(Worked at least 40 weeks) 0.790 0.659 0.867 
conditional on working (0.408) (0.474) (0.339) 
Total income 19,551.4 8,690.0 31,193.1 
 (20,266.0) (12,478.3) (20,544.1) 
Number of observations 23,83,143 12,36,420 11,46,723 
1970–1980 Censuses    
Years of schooling 11.38 11.31 11.45 
 (3.137) (2.841) (3.426) 
1(Ever Married) 0.945 0.951 0.937 
 (0.229) (0.216) (0.242) 
Age at first marriage 22.74 21.43 24.16 
 (5.345) (5.013) (5.331) 
Spouse's schooling 11.45 11.31 11.59 
 (3.007) (3.391) (2.568) 
Spouse's total income 25,498.3 41,443.6 9,934.9 
 (23,175.8) (20,015.6) (13,414.9) 
Total family income 48,666.0 46,772.0 50,716.1 
 (20,703.5) (21,436.8) (19,674.9) 
Number of observations 17,89,907 930,333 859,574 
(1)(2)(3)
Whole SampleFemalesMales
1950–1980 Censuses    
1(Employed) 0.644 0.429 0.872 
 (0.479) (0.495) (0.334) 
1(Participated in Labor Force) 0.670 0.448 0.905 
 (0.470) (0.497) (0.293) 
1(Worked at least 40 weeks) 0.790 0.659 0.867 
conditional on working (0.408) (0.474) (0.339) 
Total income 19,551.4 8,690.0 31,193.1 
 (20,266.0) (12,478.3) (20,544.1) 
Number of observations 23,83,143 12,36,420 11,46,723 
1970–1980 Censuses    
Years of schooling 11.38 11.31 11.45 
 (3.137) (2.841) (3.426) 
1(Ever Married) 0.945 0.951 0.937 
 (0.229) (0.216) (0.242) 
Age at first marriage 22.74 21.43 24.16 
 (5.345) (5.013) (5.331) 
Spouse's schooling 11.45 11.31 11.59 
 (3.007) (3.391) (2.568) 
Spouse's total income 25,498.3 41,443.6 9,934.9 
 (23,175.8) (20,015.6) (13,414.9) 
Total family income 48,666.0 46,772.0 50,716.1 
 (20,703.5) (21,436.8) (19,674.9) 
Number of observations 17,89,907 930,333 859,574 

Sample includes all individuals born 1920 to 1931. Statistics are calculated using person-level weights provided by the census. Total income in 1999 dollars.

C. Other Controls

Because of the clear regional patterns in the distribution of goiter, an important part of our strategy involves allowing for differential birth cohort trends by region. We use the nine Census Bureau divisions, listed in table A1, to categorize states into regions. Another way we control for regional patterns is by using the average latitude of each individual's state of birth.10

In our robustness checks, we also utilize controls for pre-iodization rates of malaria and hookworm, state-level unemployment rates from the 1930 Census, state-level migration between 1920 and 1940, compulsory schooling laws, and World War II state mobilization rates. We describe the data sources for these controls in the online appendix.

IV. Empirical Strategy

A. Overview of Strategy

Once Morton Salt Co.'s decision to iodize its supply was made, of iodized salt spread widely and rapidly. Since iodization happened nationwide, incidentally there was no true exclusion from exposure. In the spirit of Bleakley (2010), Hornbeck (2012), and others, our basic strategy is to compare trends in economic outcomes among individuals born in states with different levels of pre-iodization iodine deficiency rates. Feyrer et al. (2017) uses a similar strategy to identify the impacts of iodization on recruits' placement into the Army versus the Air Force.

We use the spatial distribution of goiter in 1924 in the continental United States to identify differences in pre-iodization deficiency rates. As described in section III, we use data from the Love and Davenport (1920) survey of military recruits. We link each individual in the census to a goiter rate using their state of birth. We use state of birth to draw focus to the effects of in utero exposure to iodine rather than exposure through one's life.

We interpret the goiter value as a proxy for the extent of iodine deficiency in one's state of birth during early life. This proxy will not fully reflect actual iodine exposure; nevertheless, as shown in the online appendix, as well as in Feyrer et al. (2017), the spatial distribution of goiter generally mirrors the distribution of iodine content in water sources. While admittedly an imperfect proxy, the distribution allows a rough ordering of individuals according to their exposure to iodine in utero.

In our main results, we consider the outcomes of three cohorts: those born before (1920–1923), during (1924–1927), and after (1928–1931) salt iodization. We use a fairly small window of birth cohorts to ensure that we compare cohorts of relatively similar ages in each census wave and to avoid other important historical events around the same time (e.g., the Spanish influenza outbreak in 1919). In our main specification, we do not go back further than 1920, but in alternative specifications, we explore longer “before” periods. We consider the middle (“during”) group because, while the proliferation of iodized salt across the United States was rapid, we do not have data on the geographic pattern of this nationwide spread. During the proliferation period, it would be possible to find muted effects simply because iodized salt had not yet reached some markets. To allow for this, we separate the “during” and “after” iodization periods. We also show that our results are robust to the use of a more flexible specification (an event-study analysis) that does not rely on this somewhat arbitrary assignment of cohort dummies (see figure 2).
Figure 2.

Year-by-Year Effects of Salt Iodization on Labor and Income Outcomes

Each point represents the coefficient estimate (and 95% confidence interval) for goiter rate interacted with the birth year indicator listed on the x-axis. These regressions use the 1950 to 1980 Censuses, restricting to individuals born from 1914 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during, after, and pre-1920 dummies interacted with average state latitude and 1920 state-level female and black proportions.

Figure 2.

Year-by-Year Effects of Salt Iodization on Labor and Income Outcomes

Each point represents the coefficient estimate (and 95% confidence interval) for goiter rate interacted with the birth year indicator listed on the x-axis. These regressions use the 1950 to 1980 Censuses, restricting to individuals born from 1914 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during, after, and pre-1920 dummies interacted with average state latitude and 1920 state-level female and black proportions.

We interpret differences in trends in economic outcomes coincident with the proliferation of iodized salt across individuals born in states with varying pre-iodization levels of goiter as causally related to salt iodization. Because all three cohorts were eventually exposed to iodized salt by late childhood and for the remainder of their lives, we are identifying the impact of differential exposure to iodine specifically in utero, our primary interest because of the irreversible nature of the cognitive damage that can be caused by lack of iodine during the fetal period. Our estimates are therefore somewhat conservative because they do not consider the potential benefits of increased iodine availability later in life, which all of our cohorts (including our control cohort) may have experienced.

B. Specification

The basic difference-in-differences strategy, then, is to compare the outcomes of cohorts born before to those born during and after iodization, across individuals born in states of varying levels of iodine deficiency. We estimate the following specification for individual i born in year t in state s (census division d), for outcome y recorded in census year c, where Gs is the continuous goiter rate, Dt is a dummy for belonging to the “during” cohort, and At is a dummy for belonging to the “after” cohort:
yistc=β1GsAt+β2GsDt+μs+ζdt+λct+ηXistc+ɛistc.
(1)

Here, β1 and β2 are the main coefficients of interest, measuring the difference in birth cohort trends in outcome y across individuals living in states with different levels of iodine deficiency. The specification includes state of birth fixed effects (μs) and year of birth fixed effects (which are interacted with census waves, in λct, as well as census divisions, in ζdt) that absorb the main effects of Gs, Dt, and At. The census division of birth by birth year interactions (ζdt) is crucial to control for any regional trends over time that may coincide with the national goiter distribution.11 By including division by birth year fixed effects, we ensure that we are comparing outcome variable trends (by birth cohort) across high and low goiter states of birth in their deviations from each census division's average nonlinear trend.12 Census wave by birth year interactions (λct) are included to account for differential cohort trends in the outcome variables as the cohorts age (from one census wave to the next). Included in Xist are individual controls for race and gender, as well as controls for the proportion of the population that is female and that is black (measured in 1920) in the individual's state of birth, interacted with the during and after dummies. Finally, we also include average latitude (of the state of birth) interacted with during and after dummies in order to alleviate concerns about differential trends for northern and southern states confounding our estimates. Standard errors are clustered at the state of birth level to allow for arbitrary correlation of the errors for individuals born in the same state.

We conduct this analysis on all individuals born between 1920 and 1931,13 using the 1950 to 1980 Censuses. We then look at men and women separately. In order to trace out the effects of salt iodization on labor market outcomes as our cohorts age, we also run these by-gender regressions separately for the 1950–1960 Censuses (when our sample was aged 19 to 40) and the 1970–1980 Censuses (when they were aged 39 to 60). We test the robustness of our results to the inclusion of controls for contemporaneous disease eradication programs (related to hookworm and malaria), unemployment rates in 1930, demographic changes from 1920 to 1940, compulsory schooling laws, and World War II mobilization rates in an individual's state of residence.

V. Results

A. Labor Supply and Income

In all of the regressions discussed in this section, our coefficients of interest are the after-by-goiter rate interaction and the during-by-goiter rate interaction: these represent the effect of salt iodization on our outcomes of interest. Although the following tables report only these two coefficients, all specifications also include state of birth fixed effects, year of birth by census wave interactions, census division of birth by birth year interactions, a female dummy, a black dummy, and after and during dummies interacted with average state latitude and 1920 state-level female and black proportions. We multiply each relevant coefficient by the interquartile range of the goiter distribution (0.709) to obtain a value that can be interpreted as the effect of moving from a relatively low goiter state (at the 25th percentile) to a high goiter state (at the 75th percentile) when discussing the results.

Table 2 reports the full-sample regression results for our labor outcomes of interest. The effects of salt iodization on the probability of being employed (column 1) and labor force participation (column 2) are both positive and significant, with effect sizes around 0.7 percentage points for the after-by-goiter interactions. The during-by-goiter interactions in these regressions are also positive but smaller and statistically insignificant. These smaller during coefficients might be an indication that it took time for the take-up of iodized salt to spread nationwide, but we discuss evidence later (in figure 2 and table 5) that the effects of salt iodization do show up relatively quickly—just not immediately.

Table 2.
Effects of Salt Iodization on Labor and Income Outcomes
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
After × Goiter Rate 0.00707*** 0.00680** 0.00877** 0.105*** 
 (0.00248) (0.00284) (0.00371) (0.0290) 
During × Goiter Rate 0.00355 0.00323 0.00848*** 0.0267 
 (0.00231) (0.00233) (0.00268) (0.0259) 
Observations 2,383,143 2,383,143 1,537,003 2,135,396 
Mean of dependent variable 0.644 0.670 0.790 7.902 
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
After × Goiter Rate 0.00707*** 0.00680** 0.00877** 0.105*** 
 (0.00248) (0.00284) (0.00371) (0.0290) 
During × Goiter Rate 0.00355 0.00323 0.00848*** 0.0267 
 (0.00231) (0.00233) (0.00268) (0.0259) 
Observations 2,383,143 2,383,143 1,537,003 2,135,396 
Mean of dependent variable 0.644 0.670 0.790 7.902 

Standard errors, clustered by state of birth, in parentheses ***p < 0.01, **p < 0.05, and *p < 0.1. “Goiter rate” is the goiter rate in the individual's state of birth from Love and Davenport (1920), scaled by the difference between the 75th and 25th percentile of the goiter distribution (0.71). “After” is a dummy equal to 1 for those born 1928–1931. “During” is a dummy equal to 1 for those born 1924 to 1927. These regressions use the 1950–1980 Censuses, restricting to individuals born from 1920 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during and after dummies interacted with average state latitude and 1920 state-level female and black proportions. “1(Worked at least 40 weeks)” is conditional on having worked in the past year. “sinh-1(Income)” takes the inverse hyperbolic sine of total income, including 0 s for those not working.

In table 2, we also find that salt iodization increased the likelihood of working at least 40 weeks in the year, conditional on having worked in the previous year, for both the during and after cohorts. In addition, we find an 11% increase in total income (for the after cohort).

Our interpretation of these coefficients relies on attributing the change in trends after 1924 to the introduction of iodized salt. If, however, high and low goiter states were trending differently before 1924, this would suggest that the difference in trends after 1924 may not be due to salt iodization. In order to test for the existence of differential pretrends, we add cohorts born in an even earlier period (1916–1919) and include a “pre-1920” indicator for cohorts born in this period, interacted with the goiter variable. Results are reported in table 3, where the pre-1920-by-goiter coefficient estimates the difference across the goiter distribution in cohort trends prior to the introduction of iodized salt. Across all specifications, these pre-1920 coefficients are not significantly different from 0. This alleviates concerns that states were experiencing different cohort trends—before 1924—systematically correlated with the goiter distribution. Moreover, the during-by-goiter and after-by-goiter coefficient estimates are almost identical to those in the previous table.

Table 3.
Pretrends in Labor and Income Outcomes
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
After × Goiter Rate 0.00707*** 0.00680** 0.00880** 0.107*** 
 (0.00248) (0.00284) (0.00370) (0.0288) 
During × Goiter Rate 0.00356 0.00323 0.00854*** 0.0274 
 (0.00231) (0.00233) (0.00269) (0.0259) 
Pre-1920 × Goiter Rate −0.00317 −0.00264 −0.00422 0.0107 
 (0.00210) (0.00183) (0.00370) (0.0253) 
Observations 3,114,884 3,114,884 1,940,335 2,784,412 
Mean of dependent variable 0.635 0.660 0.794 7.930 
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
After × Goiter Rate 0.00707*** 0.00680** 0.00880** 0.107*** 
 (0.00248) (0.00284) (0.00370) (0.0288) 
During × Goiter Rate 0.00356 0.00323 0.00854*** 0.0274 
 (0.00231) (0.00233) (0.00269) (0.0259) 
Pre-1920 × Goiter Rate −0.00317 −0.00264 −0.00422 0.0107 
 (0.00210) (0.00183) (0.00370) (0.0253) 
Observations 3,114,884 3,114,884 1,940,335 2,784,412 
Mean of dependent variable 0.635 0.660 0.794 7.930 

Standard errors, clustered by state of birth, in parentheses: ***p < 0.01, **p < 0.05, and *p < 0.1. “Goiter Rate” is the goiter rate in the individual's state of birth from Love and Davenport (1920), scaled by the difference between the 75th and 25th percentile of the goiter distribution (0.71). “Pre-1920” is a dummy equal to 1 for those born before 1920. “After” is a dummy equal to 1 for those born 1928 to 1931. “During” is a dummy equal to 1 for those born 1924 to 1927. These regressions use the 1950 to 1980 Censuses, restricting to individuals born from 1916 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and pre-1920, during, and after dummies interacted with average state latitude and 1920 state-level female and black proportions. “1(Worked at least 40 weeks)” is conditional on having worked in the past year. “sinh-1(Income)” takes the inverse hyperbolic sine of total income, including zeros for those not working.

In figure 2, we present further graphical evidence using an event study analysis. Here, we employ a more flexible specification, modifying the placebo specification reported in table 3 by replacing the before, during, and after interactions with birth year dummies interacted with goiter rate. We extend our study period to include the 1914 birth cohort. We let 1923 serve as the omitted category because it is the last cohort with no exposure to iodized salt during the in utero period.14 Figure 2 plots the coefficients and 95% confidence intervals for the birth year by goiter interactions, for each of our labor market outcomes of interest.

These results are consistent with our previous findings. Across all outcomes, we see the coefficients shift upward starting in either 1924 or 1925, and coefficients remain higher than 0 throughout the post-iodization period (with only two exceptions—1927 in the first two panels). Though these coefficients are not precisely estimated, the patterns display an upward shift by 1925 (that continues to increase in panels A, B, and D), which is consistent with the results of Feyrer et al. (2017).

Importantly, prior to 1924, the trend in coefficients is fairly flat across all outcomes, though there is a considerable amount of variation for the worked 40 weeks variable prior to 1924 and a slight downward trend in the income variable prior to 1924. Overall, the patterns are noisier for these two outcomes compared to those for employment and labor force participation (panels A and B). We confirm (see figure A4 in the online appendix) that the positive 1914 coefficient in the income regression appears to be a random fluctuation and the pre-1924 trend flattens out when we extend the period back to 1912.

These results are indicative of a rapid, though not instantaneous, take-up of iodized salt by the U.S. population and validate our definitions of the during and after cohorts. Though we lack statistical precision in our estimates of this rigorous specification, these results are consistent with our baseline specification, which we use for the remainder of the paper.

Gender heterogeneity.

We next ask whether this cognitive shock impacted labor market outcomes differently for men and women. Table 4 reports the results of two separate regressions: one for women (panel A) and one for men (panel B), along with the difference in our main coefficients across the two specifications (panel C). Stark gender differences are apparent.5 All of the positive effects on labor supply and income, reported in the previous tables, are driven by women. In fact, there are no significant coefficients in the male regressions, and for labor force participation and income, the after interaction coefficients are significantly larger for women than for men.

Table 4.
Effects of Salt Iodization on Labor and Income Outcomes, By Gender
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
A. Females     
After × Goiter Rate 0.0108*** 0.0121*** 0.0144*** 0.149*** 
 (0.00360) (0.00395) (0.00525) (0.0505) 
During × Goiter Rate 0.00519 0.00579 0.0206*** 0.0192 
 (0.00372) (0.00405) (0.00466) (0.0398) 
Observations 1,236,420 1,236,420 606,704 1,108,650 
Mean of dependent variable 0.429 0.448 0.659 5.586 
B. Males     
After × Goiter Rate 0.00197 0.000148 0.00562 0.0288 
 (0.00392) (0.00364) (0.00443) (0.0173) 
During × Goiter Rate 0.000934 −0.000487 0.000888 0.0116 
 (0.00237) (0.00247) (0.00384) (0.0240) 
Observations 1,146,723 1,146,723 930,299 1,026,746 
Mean of dependent variable 0.872 0.905 0.867 10.38 
C. Female-male difference     
After × Goiter Rate 0.00884 0.0119** 0.00874 0.120** 
 (0.00576) (0.00521) (0.00592) (0.0504) 
During × Goiter Rate 0.00425 0.00628 0.0197*** 0.00759 
 (0.00464) (0.00516) (0.00658) (0.0424) 
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
A. Females     
After × Goiter Rate 0.0108*** 0.0121*** 0.0144*** 0.149*** 
 (0.00360) (0.00395) (0.00525) (0.0505) 
During × Goiter Rate 0.00519 0.00579 0.0206*** 0.0192 
 (0.00372) (0.00405) (0.00466) (0.0398) 
Observations 1,236,420 1,236,420 606,704 1,108,650 
Mean of dependent variable 0.429 0.448 0.659 5.586 
B. Males     
After × Goiter Rate 0.00197 0.000148 0.00562 0.0288 
 (0.00392) (0.00364) (0.00443) (0.0173) 
During × Goiter Rate 0.000934 −0.000487 0.000888 0.0116 
 (0.00237) (0.00247) (0.00384) (0.0240) 
Observations 1,146,723 1,146,723 930,299 1,026,746 
Mean of dependent variable 0.872 0.905 0.867 10.38 
C. Female-male difference     
After × Goiter Rate 0.00884 0.0119** 0.00874 0.120** 
 (0.00576) (0.00521) (0.00592) (0.0504) 
During × Goiter Rate 0.00425 0.00628 0.0197*** 0.00759 
 (0.00464) (0.00516) (0.00658) (0.0424) 

Standard errors, clustered by state of birth, in parentheses ***p < 0.01, **p < 0.05, and *p < 0.1. “Goiter Rate” is the goiter rate in the individual's state of birth from Love and Davenport (1920), scaled by the difference between the 75th and 25th percentile of the goiter distribution (0.71). “After” is a dummy equal to 1 for those born 1928 to 1931. “During” is a dummy equal to 1 for those born 1924 to 1927. These regressions use the 1950–1980 censuses, restricting to individuals born from 1920 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during and after dummies interacted with average state latitude and 1920 state-level female and black proportions. “1(Worked at least 40 weeks)” is conditional on having worked in the past year. “sinh-1(Income)” takes the inverse hyperbolic sine of total income, including 0 s for those not working.

Comparing the dependent variable means for men and women, it is clear that women have a much lower labor supply than men during this period. This implies a much larger scope for growth in female employment than male employment, which could explain this drastic heterogeneity. These results are also consistent with the hypothesis that female fetuses are more sensitive to maternal thyroid deficiency than male fetuses (Field et al., 2009; Friedhoff et al., 2000), but we suspect this biological explanation is a secondary one.

In table A4 in the online appendix, we repeat our analysis (separately for each gender) for three variants of our total income variable: income levels in dollars (including nonearners with zero income), income levels conditional on working, and log income (conditional on working). Interestingly, we do find positive effects on male income that are small in magnitude but significantly different from 0. In fact, both men and women show a 1% increase in income (significant for men, insignificant for women, but not significantly different from each other) conditional on working.

Taken together, our results suggest that the most meaningful labor market effect of salt iodization was a large increase in female labor supply. In addition, however, this shock generated small increases in income (conditional on working) that were similar in percentage terms for both men and women. Given that Feyrer et al. (2017) document that iodized salt had large effects on male cognitive ability, it appears that this cognitive improvement led to only small changes in male economic outcomes (a statistically significant increase in conditional income). Women, for whom the magnitude of the cognitive improvement generated by iodized salt is not known,15 seemed to have been much more dramatically affected by iodization. However, it is important to note that the substantially larger effects on female labor force participation do not necessarily imply that the cognitive effects of iodine were larger for women. Indeed, the fact that men showed such large cognitive effects suggests that the reason for the gender difference we find is not biological but instead market related. As Molina (forthcoming) shows, an early-life health shock can have vastly different effects on men and women because of the different labor market conditions that men and women face. In our context, almost all men in our sample were in the labor force, while most women were not. Put differently, the marginal man affected by salt iodization was already in the labor force, while the marginal woman was likely not, which could be an important explanation why a large cognitive shock affected only women along this dimension.

Table 5 breaks our sample down even further in order to study how the effects of salt iodization may have differed over the course of these individuals' careers. In particular, we are interested in comparing effects in young adulthood and prime ages to effects in later adulthood. Focusing on women, the only ones significantly affected by salt iodization, we run our labor market outcome regressions using only the 1950 and 1960 Censuses (during which our sample individuals were aged 19 to 40) and then using only the 1970 and 1980 Censuses (when they were 39 to 60 years old). Sample sizes are substantially larger in panel B because we are using a 1% sample for all census waves except 1980 (which shows up in panel B), for which we use the 5% sample. Table 5 reports these regressions in panels A and B, respectively, and reveals a clear pattern. The effects of salt iodization on labor supply seem to be entirely driven by the large impact salt iodization had on women early in their careers. For all outcomes, the early census coefficients are larger in magnitude than the late census coefficients. With the exception of income, for which we see a 6% increase even in later census waves, none of the late census coefficients are significantly different from 0. It is worth noting that for three of the four outcomes of interest in panel A, there appear to be significant effects on the during cohort, emphasizing that the effects of iodization do appear to show up quite rapidly (as was the case in Feyrer et al., 2017).

Table 5.
Effects of Salt Iodization on Female Labor and Income Outcomes in Early and Late Censuses
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
A. 1950–1960 Censuses (ages 19 to 40)     
After × Goiter Rate 0.0185*** 0.0221*** 0.0288*** 0.241** 
 (0.00593) (0.00657) (0.0106) (0.100) 
During × Goiter Rate 0.0133** 0.0135* 0.0426*** 0.0309 
 (0.00631) (0.00695) (0.0111) (0.0761) 
Observations 306,087 306,087 80,777 180,375 
Mean of dependent variable 0.357 0.374 0.568 4.534 
B. 1970–1980 Censuses (ages 39 to 60)     
After × Goiter Rate 0.00266 0.00152 0.00286 0.0604* 
 (0.00330) (0.00329) (0.00405) (0.0327) 
During × Goiter Rate −0.00346 −0.00254 0.00218 0.00762 
 (0.00265) (0.00278) (0.00457) (0.0337) 
Observations 930,333 930,333 525,927 928,275 
Mean of dependent variable 0.505 0.526 0.736 6.701 
(1)(2)(3)(4)
1(Employed)1(Participated in the Labor Force)1(Worked at least 40 weeks)sinh-1(Income)
A. 1950–1960 Censuses (ages 19 to 40)     
After × Goiter Rate 0.0185*** 0.0221*** 0.0288*** 0.241** 
 (0.00593) (0.00657) (0.0106) (0.100) 
During × Goiter Rate 0.0133** 0.0135* 0.0426*** 0.0309 
 (0.00631) (0.00695) (0.0111) (0.0761) 
Observations 306,087 306,087 80,777 180,375 
Mean of dependent variable 0.357 0.374 0.568 4.534 
B. 1970–1980 Censuses (ages 39 to 60)     
After × Goiter Rate 0.00266 0.00152 0.00286 0.0604* 
 (0.00330) (0.00329) (0.00405) (0.0327) 
During × Goiter Rate −0.00346 −0.00254 0.00218 0.00762 
 (0.00265) (0.00278) (0.00457) (0.0337) 
Observations 930,333 930,333 525,927 928,275 
Mean of dependent variable 0.505 0.526 0.736 6.701 

Standard errors, clustered by state of birth, in parentheses ***p < 0.01, **p < 0.05, and *p < 0.1. “Goiter Rate” is the goiter rate in the individual's state of birth from Love and Davenport (1920), scaled by the difference between the 75th and 25th percentile of the goiter distribution (0.71). “After” is a dummy equal to 1 for those born 1928 to 1931. “During” is a dummy equal to 1 for those born 1924 to 1927. These regressions restrict to women born from 1920 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during and after dummies interacted with average state latitude and 1920 state-level female and black proportions. “1(Worked at least 40 weeks)” is conditional on having worked in the past year. “sinh-1(Income)” takes the inverse hyperbolic sine of total income, including zeros for those not working.

Salt iodization, as a positive shock to cognitive ability, made women more employable and increased their earning potential early in their careers. Later in life, the affected women appear to have dropped out of the labor force (as a result of higher accumulated lifetime income or higher-earning husbands, as we discuss in section VC), leaving them no more likely to be employed than their unaffected counterparts. In the online appendix, table A5 reveals no effects for men in either census wave pair, with the exception of a small income increase of 2% in later census waves (significant at the 10% level), much smaller than the female income effects.

B. Robustness

There are a number of reasons why trends in labor market outcomes across birth cohorts in the 1920s might differ across states. In order to interpret the coefficients discussed above as causal estimates of the effect of iodized salt specifically, we must assume that any other drivers of these differential birth cohort trends across states are uncorrelated with the distribution of goiter. To rule out alternative explanations for the effects that we find, we control for a number of important events or policies that could have potentially affected the state-specific trends in outcomes across our before, during, and after cohorts: contemporaneous health improvements, compulsory schooling laws, World War II mobilization rates, the Great Depression, and the Great Migration. In the online appendix, we describe in more detail the data sources for these additional controls (section A3.1) and report the results of regressions that control for these potential confounders (table A6). Across all outcomes, we find very similar results to those reported above: positive and significant effects on female labor supply and income but no effects on male outcomes.

First, we consider contemporaneous health improvements that occurred roughly contemporaneously to the rollout of iodized salt. In particular, malaria and hookworm eradication programs, both concentrated in the South, took place in the decades immediately before and during the spread of iodized salt. Malaria eradication programs started in the 1920s, while the hookworm eradication campaign began around 1910. The correlation between early 1900s goiter prevalence and malaria and hookworm rates are weak and negative (−0.33 and −0.35, respectively) and thus unlikely to be driving our results. We validate this, however, by including controls for pre-iodization prevalence rates of malaria and hookworm (taken from Bleakley, 2010, which collected these from various sources), interacted with the during and after dummies.

We also address the possibility that changes in compulsory schooling laws, implemented at different times across states, resulted in differential trends in labor outcomes, which we attribute to the introduction of iodized salt. We use data collected by Lleras-Muney (2002), which record the minimum years of schooling required by law in each state from 1915 to 1939. Like Lleras-Muney (2002), we match each individual to the compulsory schooling laws in place in their state of birth at age 14 (the lowest minimum leaving age across all states). For the analysis discussed here, we use the number of years of school required according to compulsory attendance laws, although the results are similar when we use the number of years required according to child labor laws.

It is also well documented that state mobilization rates for World War II affected labor force participation, particularly for women during this period (Acemoglu, Autor, & Lyle, 2004). To control for this, we use the state-level mobilization rate in an individual's state of residence from Acemoglu et al. (2004).

Finally, all of our sample individuals either lived through the Great Depression or its immediate aftermath, but the before, during, and after cohorts were exposed at different points in their life, which could have had important implications for the severity of the long-term impact on each cohort. If, in addition, the Great Depression hit some states harder than others, it becomes another potential reason for differential birth cohort trends across states. In order to proxy for a state's economic conditions during the Great Depression, we calculate state-level unemployment rates from the 1930 Census (unemployment rates are not available in the 1920 Census). We match this to individuals using their state of birth and control for the interaction with during and after dummies.

We use a similar strategy to address concerns that the Great Migration could have also resulted in the differential cohort trends that we are attributing to iodized salt. To rule out the possibility of this demographic shift driving our results, we allow for differential trends across states that experienced different racial composition changes and different levels of population growth between 1920 and 1940, two decades of substantial migration that coincided with the childhood years of our cohorts. Specifically, we include during and after interactions with the following two variables: the 1920–1940 change in the black population share in an individual's state of birth and the 1920–1940 change in the share of the total U.S. population living in an individual's state of birth.

In addition to showing robustness to the inclusion of the above controls, the online appendix contains other robustness checks, where we (a) account for mean reversion (table A7), (b) show that the Dust Bowl was not an important confounder (table A8), (c) show that our results are robust to dropping states below the Mason-Dixon line (table A9), (d) show that it is indeed goiter in the state of birth (rather than the state of residence) that is driving our results (table A10), and (e) show that our results are robust to a specification that compares individuals at more similar ages (table A11). We also show in the online appendix that increased access to iodine did not affect the mortality rates of our cohorts, which alleviates concerns about our results being driven by a changing sample composition induced by differential mortality. Table A12 shows no differential trends across the goiter distribution in terms of cohort size or cohort gender composition.

C. Additional Outcomes

Having established that improved access to iodine substantially improved labor market outcomes, particularly for females, we next study the effects of this cognitive shock on other dimensions of life. Table 6 reports the results of our main regressions on educational attainment and marriage outcomes, restricting to the 1970–1980 census waves in order to focus on a sample of individuals old enough to have completed their schooling and made their first marriage decisions.

Table 6.
Effects of Salt Iodization on Education and Marital Outcomes
(1)(2)(3)(4)(5)(6)(7)
Years of Schooling1(Ever Married)Age at First MarriageSpouse's Years of Schoolingsinh-1(Spouse's Income)sinh-1(Family Income)1(Ever Married)
A. Females        
After × Goiter Rate 0.0712* 0.000541 0.232*** 0.0888** 0.00755 0.0177* −0.00488** 
 (0.0379) (0.00182) (0.0478) (0.0402) (0.0123) (0.00936) (0.00237) 
During × Goiter Rate 0.0359 0.000702 0.0394 0.0727*** 0.0277** 0.0113 0.000885 
 (0.0274) (0.00191) (0.0543) (0.0264) (0.0112) (0.00812) (0.00236) 
Age       0.0276*** 
       (0.000414) 
Observations 930,333 930,333 761,885 695,464 692,713 922,124 25,581,376 
Mean of dependent variable 11.31 0.951 21.43 11.31 11.05 11.18 0.731 
B. Males        
After × Goiter Rate 0.0313 0.00288 −0.0405 0.0189 0.0178 0.0168** 0.00388** 
 (0.0464) (0.00235) (0.0454) (0.0288) (0.0448) (0.00793) (0.00168) 
During × Goiter Rate 0.0990** 0.00220 −0.0355 0.0249 −0.00546 0.0202*** 0.00321 
 (0.0396) (0.00187) (0.0594) (0.0259) (0.0511) (0.00641) (0.00250) 
Age       0.0334*** 
       (0.000207) 
Observations 859,574 859,574 692,623 716,526 714,625 846,971 23,566,464 
Mean of dependent variable 11.45 0.937 24.16 11.59 5.833 11.35 0.641 
C. Female-male difference        
After × Goiter Rate 0.0399 −0.00234 0.273*** 0.0699** −0.0102 0.000901 −0.00875*** 
 (0.0369) (0.00355) (0.0575) (0.0262) (0.0499) (0.0103) (0.00294) 
During × Goiter Rate −0.0631** −0.00150 0.0749 0.0478* 0.0331 −0.00885 −0.00232 
 (0.0302) (0.00267) (0.0716) (0.0251) (0.0571) (0.00834) (0.00282) 
(1)(2)(3)(4)(5)(6)(7)
Years of Schooling1(Ever Married)Age at First MarriageSpouse's Years of Schoolingsinh-1(Spouse's Income)sinh-1(Family Income)1(Ever Married)
A. Females        
After × Goiter Rate 0.0712* 0.000541 0.232*** 0.0888** 0.00755 0.0177* −0.00488** 
 (0.0379) (0.00182) (0.0478) (0.0402) (0.0123) (0.00936) (0.00237) 
During × Goiter Rate 0.0359 0.000702 0.0394 0.0727*** 0.0277** 0.0113 0.000885 
 (0.0274) (0.00191) (0.0543) (0.0264) (0.0112) (0.00812) (0.00236) 
Age       0.0276*** 
       (0.000414) 
Observations 930,333 930,333 761,885 695,464 692,713 922,124 25,581,376 
Mean of dependent variable 11.31 0.951 21.43 11.31 11.05 11.18 0.731 
B. Males        
After × Goiter Rate 0.0313 0.00288 −0.0405 0.0189 0.0178 0.0168** 0.00388** 
 (0.0464) (0.00235) (0.0454) (0.0288) (0.0448) (0.00793) (0.00168) 
During × Goiter Rate 0.0990** 0.00220 −0.0355 0.0249 −0.00546 0.0202*** 0.00321 
 (0.0396) (0.00187) (0.0594) (0.0259) (0.0511) (0.00641) (0.00250) 
Age       0.0334*** 
       (0.000207) 
Observations 859,574 859,574 692,623 716,526 714,625 846,971 23,566,464 
Mean of dependent variable 11.45 0.937 24.16 11.59 5.833 11.35 0.641 
C. Female-male difference        
After × Goiter Rate 0.0399 −0.00234 0.273*** 0.0699** −0.0102 0.000901 −0.00875*** 
 (0.0369) (0.00355) (0.0575) (0.0262) (0.0499) (0.0103) (0.00294) 
During × Goiter Rate −0.0631** −0.00150 0.0749 0.0478* 0.0331 −0.00885 −0.00232 
 (0.0302) (0.00267) (0.0716) (0.0251) (0.0571) (0.00834) (0.00282) 

Standard errors, clustered by state of birth, in parentheses ***p < 0.01, **p < 0.05, and *p < 0.1. “Goiter Rate” is the goiter rate in the individual's state of birth from Love and Davenport (1920), scaled by the difference between the 75th and 25th percentile of the goiter distribution (0.71). “After” is a dummy equal to 1 for those born 1928 to 1931. “During” is a dummy equal to 1 for those born 1924 to 1927. These regressions use the 1970–1980 Censuses, restricting to individuals born from 1920 to 1931. All regressions include state of birth fixed effects; year of birth × census year dummies; census division of birth × birth year dummies; gender; race; and during and after dummies interacted with average state latitude and 1920 state-level female and black proportions. “1(Worked at least 40 weeks)” is conditional on having worked in the past year. “sinh-1(income variable)” takes the inverse hyperbolic sine of the relevant income variable (spouse's total income or total family income), including 0 s for those not working. Column 7 uses a panel data set, where each observation represents an individual age for each age from 14 to 45 (the 1st and 99th percentiles of age at first marriage).

First, we ask whether educational attainment was an important mechanism behind the positive labor market effects of improved cognitive ability. Column 1 of table 6 suggests that it was not. Although the after-by-goiter coefficient is positive and statistically significant for women (and the during-by-goiter coefficient is positive and significant for men), the magnitudes of these coefficients are small, translating to about two weeks of school—much too small to be generating the large effects on income reported in table 5. It should be noted that in a standard model of educational attainment (Card, 2001), a positive shock to the ability endowment can lead to either an increase or a decrease in educational attainment because it can raise the returns to education, as well as the initial wage earned (without any education).16 In this case, these two opposing effects appear to almost cancel each other out, leading to a small but significant increase in average educational attainment.

Next, we ask whether changes in labor market outcomes were accompanied by changes in marital decisions. Increased iodine availability does not appear to have affected overall ever-married rates, which is unsurprising given that over 90% of individuals have been married at least once. However, this cognitive shock does appear to have resulted in delays in marriage for women: we estimate a small but statistically significant increase in the age at first marriage (conditional on having ever married) of approximately a quarter of a year. Because age of marriage is a censored variable, we verify that our results hold when we conduct this analysis at the individual-age level, where each observation represents an individual at a particular age (from 14 to 45, the 1st and 99th percentiles of the age at first marriage variable). In column 7, we regress an indicator for whether the individual has ever been married by that age on our usual specification, controlling additionally for age. Consistent with column 3, column 7 reveals that salt iodization reduces the likelihood of a woman being ever married at any given age. We see the opposite effect on men, who are more likely to be ever married at any given age as a result of iodization.

Interestingly, the increased availability of iodine also appears to have affected spousal quality for women, where spousal characteristics are measured for individuals living in the same household as their spouse. In columns 4 and 5, we see that women affected by salt iodization marry more-educated and higher-income spouses. We do not see the same effects on spousal quality for men. Consistent with the findings that exposure to iodine resulted in higher-income spouses as well as higher individual income for women (column 4 of panel B of Table 5), increased access to iodine led to significantly higher family income for women. This result is also true for men (and the effect sizes are the same across genders).

These results—in particular, the spousal quality effects for women—help shed light on our findings in table 5, which revealed that the positive labor force participation effects for women were largest in the early census waves and faded out later in their careers. Greater access to iodine increased female income early in their careers and also resulted in marriages to more-educated and higher-income men. Both of these factors likely led to higher accumulated wealth for these women later in life (which is consistent with, though not fully captured by, the positive effects on total family income in table 6). The fade-out of the female labor force participation effects can therefore be explained by a simple income effect (wealthier women demanding more leisure).

Of course, this is not the only possibility. An alternative explanation for the fade-out of female labor force participation effects is that women who were not affected by iodization eventually caught up to their affected counterparts, joining the labor force later in life. We are unable to provide any evidence that this was the case, though it is certainly possible that both of these explanations played a role.

In sum, increased availability of iodine in utero affected female marriage outcomes in addition to their labor market outcomes, which sheds some light on why the female labor force participation effects did not persist into the late census waves. Importantly, these results reveal several other outcomes (spousal quality for women and total family income for both genders) that, unlike female labor force participation, appear to have been persistently affected by the increased access to iodine.

VI. Conclusion

In this study, we document the effects of the rapid nationwide iodization of salt in the United States. We estimate substantial impacts on employment and labor force participation for women early in their careers. There is evidence of smaller income effects for both genders that persist into their 40s and 50s. Additional results show that women marry more-educated and higher-income spouses at later ages, consistent with treated new labor force entrants transitioning out of the workforce at later ages due to a negative income effect.

Our results contribute to several strands of literature and current policy debates. First, this study contributes to the growing literature on the long-term effects of early-life conditions, particularly the smaller set of recent work, estimating gains to purposeful and beneficial large-scale policy interventions like fortification schemes. These results differ from earlier studies of early-life shocks in that they validate the impacts of actionable policies that can then be reproduced elsewhere. In this way, the study of historic successes, and failures for that matter, in the United States and other developed settings can potentially provide important predictions for academic researchers and policymakers faced with similar issues in developing countries today.

Second, while previous studies have estimated the roles of historical events, such as World War II and the staged rise in access to contraception, in explaining increases in labor force participation among women (Goldin, 1991; Goldin & Olivetti, 2013; Goldin & Katz, 2002; Bailey, 2006), we contribute complementary evidence that salt iodization explains a rise of 2.21 percentage points in early censuses (roughly 6% of the total rise from 1950 to 1990). Our evidence pertains to cohorts born after those most affected by the war, but before those most affected by increased access to oral contraceptives.17 Unlike for these previously studied events, impacts on participation of salt iodization are not focused on higher-educated women but prevail despite negligible impacts on schooling completion.

Additionally, our study provides evidence of the magnitude of benefits from eradication of deficiencies in essential micronutrients such as iodine. Many developing country populations face myriad nutritional constraints, which have long-lasting impacts on health, economic livelihoods, and general welfare. Our estimates show that salt iodization led to a roughly 1.21 percentage point rise in female labor force participation. From a base labor force of 13 million women in 1940 (Durand & Goldfield, 1944), this amounts to almost $2 billion in additional income using the mean income for the female sample inflated to 2016 dollars ($13 million × $0.0121 × $12,514 = $1.97 billion).18

Finally, it should be noted that the “intervention” cost the taxpayer nothing, in that the rollout of iodized salt was completely undertaken by the private sector. That is, the cost of salt iodization was fully borne by the salt producer,19 while the cognitive benefit was realized by the general population. We conjecture that the rapid rise in both supply and demand might be attributable to the efficiency and underlying profit motive of the private firm that undertook the intervention.

Notes

1

It bears mention that studies from the medical literature are either correlational or based on animal studies; causal evidence on the effects of iodine exposure in humans is limited. The study by Feyrer et al. (2017), which we discuss, is important in this sense because it provides the most rigorous evidence to date of the impact of fetal iodine access on adult cognitive performance.

2

This result is also consistent with medical evidence that female fetuses are more sensitive to maternal thyroid deficiency than male fetuses (Field, Robles, & Torero, 2009; Friedhoff et al., 2000), though we argue in section VA that this biological explanation is likely secondary.

3

This pattern has been documented before, specifically in the “career, then family” cohorts discussed in Goldin, Ehrenberg, & Blau (1997) and Goldin and Katz (2002).

4

This is consistent with Bleakley (2010), who finds mixed results on the effect of malaria eradication on schooling in four different countries.

5

See Heckman (2006), Almond and Currie (2011), and Currie and Vogl (2013) for useful syntheses.

6

Goiter may not be visible if iodine deficiency is minimal, but iodine deficiency is the primary, but not exclusive, cause of goiter. Goiter, when sufficiently large, may cause complications such as respiratory difficulty.

7

Because this paper focuses on in utero exposure to iodine, the ideal data set would consist of goiter rates from a representative sample of women of childbearing age instead of men. In goiter surveys of schoolchildren collected by Olesen (1929), there is a high correlation (0.87) between goiter rates among school-aged girls and boys, which suggests that the distribution of female goiter rates across states should be similar to what is captured by the goiter rates in Love and Davenport (1920), calculated from men of the relevant age range. We do not use the Olesen (1929) goiter rates as our measure of iodine availability because the samples are not representative of each state, and numerical rates are available for only 37 states.

8

Summary statistics by census year are reported in table A2 in the online appendix.

9

sinh-1(Income^)=ln(Income+(Income2+1)1/2). The income variable is therefore not conditional on working and includes 0 s for those who do not work.

10

These numbers were obtained from the online database MaxMind: http://dev.maxmind.com/geoip/legacy/codes/state_latlon/.

11

Divisions with different average levels of goiter were trending differently prior to salt iodization for some outcome variables, and it is important that we control for these division by cohort interactions to avoid picking up division-specific trends in our goiter coefficient of interest.

12

There are many types of differential cohort trends that we could in theory control for, but given the geographic distribution of goiter that we observe, our major concern is in broad regional trends that may be nonlinear (rather than linear trends at the state level, for example). Once we have controlled for these division-by-birth-year interactions, we argue that controlling additionally for state-specific linear trends is less important, given that our analysis is now within division (and divisions are relatively small).

13

As we explain in section IVA, we use a relatively short window of birth years to ensure that the cohorts we are comparing are relatively similar in age and to avoid picking up the effects of other important historical events, but we show robustness to extending this period back to 1914.

14

Although 1924 is only a partially treated year, our specification allows us to observe whether we begin to see any effects in this year.

15

The sample in Feyrer et al. (2017) was exclusively male.

16

If the ability endowment shock increases the initial wage regardless of education and the return to education is relatively low, then lifetime income can increase with lower schooling. If there is disutility associated with schooling, then there is further downward pressure on educational attainment.

17

Indeed, we estimate our rise in labor force participation due to salt iodization relative to the war-affected cohort.

18

This calculation does not take into account the value of home production, which could have decreased with more women entering the labor force.

19

“The producers and the wholesale grocers each bore one-half of the added expense so that the iodized salt would not cost the consumer one cent more” (Kimball, 1937, 32).

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External Supplements

Author notes

Thanks to Martha Bailey, Prashant Bharadwaj, Mark Duggan, Jeanne Lafortune, Claudia Olivetti, Dimitra Politi, Paul Rhode, John Shea, Atheen Venkataramani, and David Weil for helpful conversations and seminar audiences at the NBER (CS; CH), Stanford SITE, Maryland, Michigan, Michigan State, Appalachian State, Indian School of Business, NEUDC, and SOLE for useful comments. A.A. gratefully acknowledges funding from the NIH/NICHD (5K01HD071949). T.M. gratefully acknowledges funding from the USC Provost's PhD Fellowship, the USC Dornsife INET graduate student fellowship, and the Oakley Endowed Fellowship. Thanks to David Carel for excellent research assistance. All errors are our own.

A supplemental appendix is available online at http://www.mitpressjournals.org/doi/suppl/10.1162/rest_a_00822.

Supplementary data