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The effects of teenage childbearing on adult soft skills development


Research examining impacts of teenage childbearing on economic and social outcomes have focused on completed schooling and labor force outcomes. In this paper, we examine outcomes that have remained largely unexplored, soft skills and personality. We use Add Health data to construct relevant controls for teenage mothers and explore a set of measures that proxy for what is usually deemed in economics as “non-cognitive” or “soft skill” traits. We find that teenage childbearing increases impulsivity, a trait that has been found to have negative effects on a large set of outcomes and has a negative effect on other personality traits perceived as positive, such as openness to experiences. Our results remain consistent through a set of robustness checks, and we interpret our findings to suggest that adolescence may be a sensitive period for the development of soft skills and that childbearing may interrupt this process.

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  1. In particular, the cerebral neocortex—an area of the brain governing perception, behavior, and cognition—undergoes two waves of development: First during pre-natal and early childhood periods, and a second one during late childhood and adolescence (Pletikos et al. 2013). In this sense, adolescence can be considered a sensitive period of human development where personality traits and other non-cognitive abilities and soft skills are shaped, and which can be affected by life events such as teenage childbearing.

  2. An alternative interpretation is that having a miscarriage as a teenage may affect non-cognitive skill development. We know of no evidence of the magnitude of these potential effects and view them as an unlikely explanation of our findings.

  3. While nearly all research in this area is focused estimating the effects of teenage motherhood, some research has begun to examine teenage fatherhood (Fletcher 2012). A separate literature has explored peers as an important determinant of teenage pregnancy (Yakusheva and Fletcher 2015, Fletcher and Yakusheva 2016).

  4. Ashcraft et al. (2013) develop a consistent estimator assuming that miscarriage is random conditional on some controls. We proceed in this spirit and use the same type of control variables (age at conception and smoking status during pregnancy) in our preferred specification.

  5. Results in Bond and Lang (2014) suggest that results using variables measured using Likert scales can be difficult to interpret. We present results for both the overall factor scores for these combined variables as well as the variables separately.


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The authors would like to thank the anonymous referees for helpful comments and suggestions.

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Correspondence to Jason Fletcher.

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Responsible editor: Erdal Tekin

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( No direct support was received from grant P01-HD31921 for this analysis. The authors thank the anonymous referees of this Journal for helpful comments.


Appendix 1



Although there is strong evidence that personality has effects on an individual’s socio-economic trajectory, there is little evidence on the extent to which personality traits are developed, changed or remain stable over the life cycle (Almlund et al. 2011). Our goal is to explore whether the ‘treatment’ of becoming a teenage mother results in observable differences in personality later in life.

The Add Health survey fielded a 20-item short-form version of the 50-item International Personality Item Pool-Five-Factor Model known as the Mini-IPIP. Previous studies have validated this instrument’s consistency (Donnellan et al. 2006). The Mini-IPIP scale has four items per Big Five trait: Extraversion, neuroticism, agreeableness, conscientiousness, and openness. Responses to each item were coded in a five point likert scale ranging from 1 (strongly agree) to 5 (strongly disagree); with a neutral point 3 (neither agree nor disagree). As discussed in (Almlund et al. 2011) the “Big Five” posits a hierarchical organization of personality traits. In this context, the five components of the Big Five are at the highest level and summarize a larger set of more specific personality facets. In appendix 1, we present a table with brief descriptions of the components of the Big Five.


Modern literature in the field of psychology defines impulsivity as “a predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to the impulsive individual or to others” ((Moeller et al. 2001) (Grant and Potenza 2011)). Studies on addiction, delinquency and crime have investigated the association of impulsivity and these behaviors as well as its effects on the treatment outcomes for addiction and other conditions (Krishnan-Sarin et al. 2007; Nagin and Pogarsky 2003; Mitchell 1999). In general, high levels of impulsivity are associated with preferences for immediate gratification, risky activities, novel sensations, and easier routes to self-gratification, as well as an inability to persist at a task and shorter reaction times (Mitchell 1999).

Although some measures of impulsivity are widely used (such as the Barratt’s Impulsivity Scale (BIS)), we are constrained by the survey questions available in the Add Health survey. For our measure of self-control we follow Nagin and Pogarsky (2003) and Fletcher et al. (2003) ((Fletcher et al. 2009; Nagin and Pogarsky 2003)) and use the survey question “When making decisions, you usually go with your gut feeling without thinking too much about the consequences of each alternative”. This question has been used previously as a proxy for impulsivity, and is in line with Barratt’s measure of Motor impulsivity. The answers include five categories from “strongly agree” through “strongly disagree” (the neutral response is the omitted category). In addition, we use two other survey questions: “I like to take risks” and “I live my life without much thought for the future”, and a factor variable of the three measures available in the survey.

There is ample evidence on the association of impulsivity and addictive behaviors, delinquency and treatment outcomes. In addition, laboratory task investigations have indicated that individuals with high impulsivity tend to perform poorly on fine perceptual-motor performance tasks (Barratt et al. 1981). This indicates that this individual trait may have large effects on an individual’s returns to education and ultimately on his ability to perform in the labor market and as a parent.

Locus of Control

Locus of control has been increasingly studied in the economics literature as part of the wave of studies that for the last decade have focused on the economic returns to non-cognitive or ‘soft’ skills (Heckman et al. 2006; Coleman and DeLeire 2003). Measures of locus of control have also been explored in the public health context, and evidence indicates that a stronger internal locus of control is associated with better outcomes and adherence to treatment, as well as other positive behavioral outcomes (AbuSabha and Achterberg 1997; Currie 2009).

Locus of control measures the extent to which individuals believe they have control over their lives and outcomes. There is a substantial discussion in the literature on the development of locus of control and personality during childhood (Bradley and Corwyn 2002; Cobb-Clark and Schurer 2011). However, whether events later in life have or not an effect on an individual’s locus of control has been somewhat less studied.

Cobb-Clarke and Schurer (2011) find that at least in a 4 year period, the measure they use is stable within individuals. Relevant to our study, they investigate whether negative/positive life events resulted in a change to an individual’s baseline locus of control. However none of their ‘life event’ measures included a teenage childbirth.

We use the following available variables independently, and present also results of the factor: “There is little I can do to change the important things in my life”; “Other people determine most of what I can and cannot do”; “There are many things that interfere with what I want to do”; “I have little control over the things that happen to me”; “There is really no way I can solve the problems I have”.


The literature on dispositional optimism defines optimism as “generalized positive expectations about future events” (Puri and Robinson 2007). Different measures and instruments of optimism have consistently found that optimism has large and far reaching effect over a wide set of outcomes. For instance, optimism is positively associated with coping habits and behavior (Carver et al. 2010), as well as faster recovery from surgery (Kiecolt-Glaser et al. 1998). In the economics realm, optimism has also been found to be related to many work and life choices. In particular, optimistic people are found to work harder, expect to retire later, invest more in individual stocks, and save more (Puri and Robinson 2007; Laajaj 2013).

The question whether life events may change an individual’s optimism has not yet been answered conclusively. Although some evidence indicates that adverse health circumstances (such as the risk of death from coronary disease) decreases the level of an individual’s optimism (Giltay et al. 2006; Mols et al. 2010). However, whether teenage childbearing in particular has an effect on an individual’s optimism and how persistent this effect is has not been studied before.

We use two separate measures and also present results of the factor of the two: “I'm always optimistic about my future” and “Overall, I expect more good things to happen to me than bad.” In addition, we explore a separate variable as a proxy for pessimism: “I rarely count on good things happening to me”.


We explore two separate measures for depression. One is a measured using the Center for Epidemiological Studies Depression Scale (CES-D 10), a widely used instrument in depression research, and for which a short form is available in the Add Health. This measure is designed to capture current levels of depressive symptoms.

A separate measure is the answer to the question: “Has a doctor, nurse or other health care provider ever told you that you have or had: depression?”

Control Variables

As discussed in Fletcher and Wolfe (2009) and Ashcraft et al. (2013), including variables that are correlated with both the outcomes of interest and the birth outcomes could worsen results or change the sign of the bias in our estimating equations; So we follow Fletcher and Wolfe (2009) and Aschcraft et al. (2013), and only control for factors that have been cited in the literature as being risk factors for miscarriage such as whether pregnancy occurred before age 15 and whether teenage smoke, drank alcohol, or used drugs during pregnancy. We include race (Black, Hispanic and reference group, White), age at wave four, maternal education and a dummy for parent in the household.

Appendix 2

Table 7

Table 7 The big five personality traits

Appendix 3

Robustness checks

Table 8

Table 8 Robustness results

Table 9

Table 9 Robustness results

Table 10

Table 10 Robustness results

Table 11

Table 11 Robustness results

Table 12

Table 12 Robustness results

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Fletcher, J., Padrón, N. The effects of teenage childbearing on adult soft skills development. J Popul Econ 29, 883–910 (2016).

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  • Soft skills
  • Teenage childbearing
  • Personality
  • Non-cognitive skills

JEL Classification

  • J13
  • J24