We provide some of the first empirical evidence of maternal and fetal health effects of working during pregnancy by using a unique dataset from the New Jersey Department of Health that includes information not only on pregnancy and birth outcomes but also on maternal employment. We match the mother’s occupation with the Metabolic Equivalent of Task, provided by the Census Occupational Classification System and used as a measure for the strenuousness of the work activities performed. Focusing on an empirical setting where laws regarding reasonable accommodations for pregnant women are already in place, we still find consistent and robust evidence that working in a relatively more strenuous job during pregnancy raises the likelihood of an adverse birth outcome, specifically fetal macrosomia, by about 1.5 percentage points. While there are no statistically or economically significant effects on other birth outcomes, our finding of a significant increase in fetal macrosomia nevertheless highlights a possible deficiency of existing accommodation laws intended to protect pregnant workers. In addition, our study indicates an under-studied link between gestational diabetes (a known risk factor for fetal macrosomia) and intensive physical activities at work during pregnancy, potentially mediated by disrupted sleep due to greater work intensity.
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Our study uses the New Jersey birth certificate data obtained from the New Jersey Department of Health (NJDOH). We are not allowed to share the data with others under the Data Use Agreement established with the NJDOH. Nonetheless, the New Jersey birth certificate data used by our study are available to researchers through a standardized application process, which is described at this website: https://sites.rowan.edu/officeofresearch/compliance/irb/njdoh/index.html.
We can provide the programs used for our estimations to permit replication.
Authors’ calculations were based on data from the March Current Population Surveys (CPS). Pregnant women in the CPS are identified as women who have children under the age of one at the time of the survey (Dave et al. 2015). Using data from the U.S. Census Bureau’s Survey of Income and Program Participation (SIPP), the U.S. Census Bureau reports a similar increase in work during pregnancy among first-time mothers, from 44.4% during the early 1960s to 69.2% during 2001–2005, declining somewhat to 65.6% over 2006–2008 as a result of the recession (Laughlin 2011).
See Jackson et al. (2018). Also see: https://www.cdc.gov/niosh/topics/repro/specificexposures.html (accessed in August 2019) and http://www.who.int/ceh/capacity/occupational.pdf (accessed in August 2019).
The high demand for blood flow to the uterus and placenta can limit cardiac output and reserve capacity for vigorous activity levels (Bonzini, Coggon, and Palmer 2007).
Alternatively, a pregnant woman may be able to obtain an accommodation at work, under the Americans with Disabilities Act (ADA), if and only if they have a pregnancy-related medical condition (for instance, anemia, sciatica, gestational diabetes, depression, and others) that meets the ADA definition of “disability.” See: https://www.eeoc.gov/eeoc/publications/pregnant_workers.cfm (accessed in November 2018). At the federal level, the Pregnant Workers Fairness Act, which among other protections ensures that employers provide reasonable accommodations to pregnant women who want to continue working, has been opposed every time it has been introduced (Pisko 2016). Part of the opposition stems from the stance that such requirements would impose an undue burden on businesses and raise business costs.
For details, see https://www.nytimes.com/interactive/2018/10/21/business/pregnancy-discrimination-miscarriages.html (accessed in August 2020).
For details, see http://www.nationalpartnership.org/our-work/resources/workplace/pregnancy-discrimination/reasonable-accommodations-for-pregnant-workers-state-laws.pdf (accessed in September 2020).
Protections offered by these laws can also vary by the eligible population. For instance, in some of those 30 states the accommodation laws apply only to public employers.
Casas et al. (2015) do not specifically look at strenuousness or physical activity, but consider the effects of working in one of 14 occupational groups on birth outcomes, based on mother-child pairs across 13 European cohorts. Working in certain occupations was also associated with pregnancy outcomes. Specifically, they find that working as a nurse was associated with favorable birth outcomes, whereas working in the food industry reduced gestation and raised preterm births.
See, for instance, Dave and Kelly (2012), Hanna et al. (1994), Hauge et al. (2012), Lobel et al. (2008), and Saffer and Dave (2005). The link from stress to unhealthy eating and nutrition is not necessarily unidirectional. There is some evidence of complex interactions, wherein maternal psychosocial stress, dietary behavior, and nutrition likely regulate and counter-regulate one another during pregnancy (Lindsay et al. 2017). Much of this literature, specifically for pregnant women, captures associations and is hard-pressed to draw firmer conclusions regarding causality.
For details about the Census 2002 Occupation Codes, please see https://www.bls.gov/tus/census02iocodes.pdf (pp. 7–17, accessed in April 2018).
For details, see https://epi.grants.cancer.gov/physical/MET under the section titled “MET Values for Activities in the 2002 Census Occupational Classification System (OCS)” (accessed in April 2018). We gave two examples here: (1) For descriptions entered in the data such as “housing keeping”, “house cleaning”, “housekeeping”, “cleaning person”, “cleaning lady”, “cleaning houses”, and “merry maids”, we converted them to the occupation code 4230 (“Maids and housekeeping cleaners”) and the occupation code 4230 is linked to a MET value of 4.5; (2) For descriptions entered in the data such as “administrative assistant”, “admin assistant”, “admin asst”, and “secretary”, we converted them to the occupation code 5700 (“Secretaries and administrative assistants”) and the occupation code 5700 is linked to a MET value of 1.5.
The MET values are attached to the 2002 Census occupation codes, which were the most up-to-date MET values at the time of our study. See Tudor-Locke et al. (2009). Since the birth outcomes in our study were measured in 2014 and 2015, our use of these MET values may incur measurement error if occupations have differentially shifted towards less physical demands or shifted differentially relative to each other, which may lead to an attenuation bias. However, with our main indicator being ordinal, comparing moderate intensity vs. light intensity jobs, any changes in actual MET values within these categories (moderate and light) would not bias our estimates.
Because of the use of a person’s resting metabolic rate as a common denominator in the ratio calculation, two MET values are cardinal in nature. So, the difference between the two values is interpretable. For the average adult, one MET represents about one calorie per every 2.2 pounds of bodyweight per hour, which can be used as the interpretation for an increase in MET of one unit. For more details, see https://www.hsph.harvard.edu/nutritionsource/mets-activity-table (accessed in July 2020).
For more details, see https://www.hsph.harvard.edu/nutritionsource/mets-activity-table (accessed in July 2020).
The ACS has information on whether the woman gave birth in the past 12 months, and a separate question on the ages of biological children in the household. These can be used to identify women who were pregnant and gave birth in the past year, along with labor market outcomes over this reference period to impute women who were working during their pregnancy year.
Ideally, we would want to use the 2014–2015 ACS data to merge with the 2014–2015 NJDOH data. However, doing so makes the socio-demographic-by-occupation group cells small since we are limiting to pregnant women in New Jersey. To ensure an adequate sample size after merging the ACS data with the NJDOH data, we use the ACS data from 2010 to 2017.
In Appendix Table 10 we provided a detailed list of variables used in our study, regarding the variable descriptions, observation levels and information sources.
The U.S. rates of LBW, preterm birth, and fetal macrosomia among singleton births averaged over 2014 and 2015 were all obtained from the CDC WONDER Online Database (https://wonder.cdc.gov/natality.html, accessed on August 13, 2017).
In our estimation sample, the average annual household income measured at the zip code level is approximately $75,300 (shown in Table 1).
This information was obtained from the CDC WONDER Online Database. See https://wonder.cdc.gov/controller/saved/D66/D89F185 for more details.
This information was obtained from the CDC WONDER Online Database. See https://wonder.cdc.gov/controller/saved/D66/D91F688 for more details.
An identifier for each mother is unobserved in our birth data. As a result, we are unable to add mother fixed effects to our regression model.
The 22 groups are: management; business and financial operations; computer and mathematical; architecture and engineering; life, physical, and social science; community and social services; legal; education, training, and library; arts, design, entertainment, sports, media; healthcare practitioner and technical; healthcare support; protective service; food preparation and serving related; building and grounds cleaning and maintenance; personal care and service; sales and related; office and administrative support; farming, fishing, and forestry; construction and extraction; installation, maintenance, and repair; production; and transportation and material moving. For details, see “MET values for activities in 2002 Census Occupational Classification System (OCS)” at https://epi.grants.cancer.gov/physical/MET/#ocs, (accessed in April 2018).
In the four groups listed above, the coefficients of variations are all above 0.2; that is, standard deviations are more than 20% of the means. In Appendix Table 11, we also see sufficient variations in MET values in all 22 groups (indicated by the standard deviation and the coefficient of variation) except the following three groups: (1) legal; (2) education, training, and library; (3) farming, fishing, and forestry. Thus, there is no identifying variation in the MET variable from these three groups in the regression model that uses maternal occupation fixed effects.
The estimates remain very similar whether we control for mother’s pre-pregnancy BMI or mother’s body weight prior to pregnancy.
Casas et al. (2015) find that employed pregnant women had a significantly lower risk of having a preterm birth relative to those who were non-employed. In our birth data we also find that the birth weight and gestational length of babies born to mothers who worked during pregnancy are both slightly greater than those of babies born to mothers who did not work during pregnancy.
Regarding fetal macrosomia, it is notable that the diagnosis can only be made after childbirth, by measuring the birth weight (see https://emedicine.medscape.com/article/262679-overview, accessed in August 2020). As a result, it is unlikely for the mother to knowingly change her behavior because of fetal macrosomia during pregnancy, therefore precluding an effect of fetal macrosomia on maternal physical activity during pregnancy.
In the New Jersey birth data the variable on the mother’s smoking status is binary (1/0), which is equal to one (or zero) for a yes (or no) answer to this question: “Did mother smoke cigarettes before or during pregnancy?” As a result, this variable on the mother’s smoking status cannot capture the mother’s smoking behavior that is exclusively during pregnancy.
In the regression model used by column (4) of Table 4, the coefficient of this regressor—whether the mother had a previous preterm birth—is highly statistically significant (p-value = 2.159 × 10−6).
In the regression model used by column (4) of Table 4, the coefficients of these regressors—whether the mother had private insurance during pregnancy, whether the mother had Medicaid during pregnancy, and whether the mother smoked cigarettes before or during pregnancy—are all statistically insignificant.
In our estimation sample all mothers live in New Jersey, and their employers are located in New Jersey (86.96%), New York (8.51%) and Pennsylvania (4.53%).
The sample size in Table 5 is smaller than that in Table 3 because of missing values for mothers’ employers’ counties or zip codes (columns 1 through 4) and also because of the merge with the ACS data (columns 5 through 8); standard errors in Table 5 are larger, compared with the standard errors in column 4 (Panel A) of Table 3.
Using information on job attributes and labor supply behaviors from the ACS data (previously discussed), we further investigate the relationship between job strenuousness and the typical mode of commute, which can reflect commute time. The results are reported in Appendix Table 12. We do not find any statistically significant association between the mode of commute (defined as the prevalence of pregnant mothers in New Jersey within each age/education/marital status/race/occupation group) and job strenuousness. In other words, there does not appear to be any systematic relationship between the physical demands of the job that a pregnant mother engages in and the average mode of commuting to that job; such an association might materialize for instance due to socioeconomic status and due to differences in clusters of where certain jobs are located and where women reside. Greater time spent commuting may disrupt or crowd-out sleep, a potential link between job strenuousness and gestational diabetes. We are unable to directly investigate the role of sleep as a potential mechanism driving the health effect of job strenuousness, due to lack of information in the ACS data. However, these results do provide some validation that differences in time spent commuting (proxied by the mode of commute) across jobs based on their physical demands are not driving our main results.
The results are reported in Appendix Table 13. In this table we also find that job strenuousness appears to be negatively associated with wage income. This finding emphasizes the importance of controlling for individual-level demographic variables that are strongly correlated with income, such as education (which we did control for in our regression model), to mitigate the potential under-estimation of the fetal health effect of job strenuousness, given that higher income is usually correlated with higher birth weight. Moreover, controlling for earnings derived from the ACS, by occupation and socio-demographic groups, does not materially change our main results.
In a meta-analysis Mozurkewich et al. (2000) find no statistically significant association between long working hours and adverse birth outcomes (e.g., preterm births), implying that omitting work hours may not lead to bias in models of birth outcomes.
Weight gain is measured as the difference between body weight at delivery and pre-pregnancy body weight. The average weight gain in our estimation sample is about 30 pounds and the standard deviation is about 15 pounds. To avoid the influence of outliers in our estimation, we dropped from our estimation sample weight gain that is below zero or above 60 pounds, that is, two standard deviations away from the mean.
The definition of mother gaining too much weight during pregnancy (1/0) is given by the Institute of Medicine Weight Gain Recommendations for Pregnancy (available at https://www.acog.org/Clinical-Guidance-and-Publications/Committee-Opinions/Committee-on-Obstetric-Practice/Weight-Gain-During-Pregnancy, accessed in April 2018), which gives a recommended range of weight gain specific to the mother’s pre-pregnancy body weight category (underweight, normal weight, overweight, and obese).
Comparing the results of columns (2)–(3) between Panels A and B, we find that it is the MET exceeding a certain threshold (Panel A), not the MET itself (Panel B), that is significantly associated with a greater risk of fetal macrosomia, even after controlling for weight gain and gestational diabetes. This points to possible nonlinearities in the effect of maternal job strenuousness during pregnancy on fetal health, for instance, when the strenuousness exceeds a threshold, such as a MET of three, which is the lower bound used in the definition of moderate-intensity activity.
Stress and sleep disturbances can go hand in hand, both being affected by job strenuousness. Once controlling for physical exertion entailed in a job, however, Homer, James and Siegel (1990) do not find occupational psychological stress to be independently associated with adverse birth outcomes.
We conducted analyses with the American Time Use Surveys (2003–2018) to assess the link between job strenuousness, as measured in this study, and sleep. Consistent with the findings of these other papers, we also find that less sleep (measured by reduced sleep time) is significantly associated with working in more intense jobs (measured by higher MET values) among females aged 18–40, based on a regression model that controls for total time spent working on jobs, demographic characteristics (e.g., age, education, race and ethnicity), and county of residence and time effects (e.g., day, month, year and holiday).
Note that this dummy variable “mother had previous preterm birth” is not controlled for when the estimation sample includes only first-time mothers since it is always equal to zero. As a result, this zero value does not indicate the absence of health problems that potentially could complicate a pregnancy (as indicated by prior preterm births) for first-time mothers. Therefore, to gauge the effectiveness of using “mother had previous preterm birth” as a control variable for mothers’ health problems, it is necessary to do a separate regression analysis only for pregnant women who are not first-time mothers, which we did in column (4) of Table 9. The results for this separate regression analysis (column 4) are similar to the main results we reported in Panel A column (4) of Table 3.
In Oster (2019), the coefficient of proportionality is referred to as “delta,” which is the ratio of selection on unobservables divided by selection on observables. In Oster (2019), selection on observables (or unobservables) is characterized by the covariance between the (binary) treatment dummy variable and the observables (or unobservables).
Authors’ calculation was based on the CPS Annual Social and Economic Supplement.
For details, see: http://www.nationalpartnership.org/our-work/resources/workplace/pregnancy-discrimination/reasonable-accommodations-for-pregnant-workers-state-laws.pdf (accessed in September 2020).
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We are thankful to two anonymous reviewers and the editor. We thank Wei Fu and Chia-Lun Liu for excellent research assistance. We also thank Sebastian Tello-Trillo, Thanh Tam Nguyen, participants at the 2018 Annual Conference of the Southern Economic Association, and participants at the 2019 Annual Conference of the Eastern Economic Association for their helpful comments and suggestions. All errors are our own.
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The authors declare that they have no conflict of interest.
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Dave, D.M., Yang, M. Maternal and fetal health effects of working during pregnancy. Rev Econ Household (2020). https://doi.org/10.1007/s11150-020-09513-y
- Physical activity
- Job strenuousness
- Pregnancy and birth outcomes
- Fetal macrosomia
- Gestational diabetes