soft skills research paper

MINI REVIEW article

Expanded yet restricted: a mini review of the soft skills literature.

• 1 Department of Education, Centre on Skills, Knowledge and Organisational Performance, University of Oxford, Oxford, United Kingdom
• 2 Department of Psychology, Panteion University of Social and Political Sciences, Athens, Greece

There has been a progressively heightened preoccupation with soft skills among education stakeholders such as policymakers, educational psychologists, and researchers. Soft skill curricula have been considered these days and developed not only for graduates and as on-the-job training programs but also for students across all levels of education. However, different people mean different things when referring to soft skills. This review presents evidence to suggest that the use of the term “soft skills” has expanded to encompass a variety of qualities, traits, values, and attributes, as well as rather distinct constructs such as emotional labor and lookism. It is argued here that these infinite categories of things can be skills because soft skills research is primarily focused on what are the needs and requirements in the world of work. This approach is problematic because it assigns characteristics to soft skills, which in turn affect the design of the soft skills curricula. For example, soft skills are often construed as decontextualized behaviors, which can be acquired and transferred unproblematically. The paper proposes that an in-depth and embedded approach to studying soft skills should be pursued to reach a consensus on what they are and how to develop them because otherwise they will always be expanded before restricted (as they have become ambiguous) in their meaning and definition.

Introduction

Suppose you are present in a communication encounter between two men, Joe and Martin. Joe looks upset and literally screams while recounting an incident that has happened to him:

“Can you believe this?,” , Joe starts, “CAN YOU BELIEVE HIM? THE NERVE (.) he actually ended up ordering me “shut up, already, and do as I say!” (sounds infuriated) Joe is breathing heavily.

Martin nods thoughtfully.

(0.7) “As if he was in charge of me (.) as if he owned me … Where does he come off telling me what to do? Who does he think he is?” Joe continues.

Martin nods again, pushing his glasses up the bridge of his nose.

(0.9) Joe seems lost in his thoughts.

“I still can’t believe that this happened to me (.) It still makes me furious … ”

Martin’s nodding continues.

< “Can you understand now why I acted the way I did?” Joe asks. “I was badly provoked = = What would you do if you were me?” >

Martin responds with a nod.

Throughout Joe’s outburst, Martin keeps nodding. Nodding, in this instance, is an expressed form of active listening ( Pasupathi et al., 1999 ; Browning and Waite, 2010 ). The behavior of listening as a unified whole, moreover, features most prevalently in the lists of communication skills encountered in the soft skills literature – educational, medical, management, policy, or other (see for example, Jain and Anjuman, 2013 ). However, is Martin’s behavior a communication skill as these lists inform us? Let us consider two alternative scenarios providing context for this exchange, which, hopefully, can help us decide.

In the first scenario, Martin is a clinical psychologist, and Joe is his client. Martin has been treating Joe for the past 2 years; he is, therefore, aware that Joe suffers from bipolar disorder and that, as part of this, he experiences, periodically, manic episodes, like the one he recounts in the aforementioned exchange taking place in a supermarket between him and a stranger. In this communication encounter, Martin’s nodding and listening are the expressive form of his active processing of the contents of this narrative. It can be argued that it realizes Martin’s relation to Joe (i.e., he is Joe’s therapist) and his intention to encourage him to let it out and that it is enacting Martin’s knowledge of Joe’s condition, the relevant symptoms, and the techniques to deal effectively with it. In line with this, it seems fair to suggest that Martin’s listening behavior is an effective communication strategy and that it can, therefore, be construed as a “communication skill.”

In the second scenario, Joe has just started working as an employee in his uncle’s business. He works along with other seven employees under a team leader (Jacob). The team reports to Martin, the line manager. Joe is difficult to work with and has been constantly reporting problems to Martin with either his team leader or other team members. In the above excerpt, he recounts a recent episode between him and his team leader, Jacob, when the former refuses to follow the agreed strategy during a negotiation meeting. The encounter between Joe and Martin is one of many within the past few weeks. In this instance, and contrary to what one might have expected from a line manager, Martin’s behavior toward Joe fails to articulate the sensible aim of reasoning with Joe and taking actions to ensure such fights come to an end. Martin’s behavior, therefore, seems to be guided by something different; a possible explanation could be that he fears his behavior might displease the boss, so he remains silent instead. If that is the case, could still the behavior of listening be construed as a “communication skill” within the context of this scenario?

These episodes aim to illustrate how meaningless it is to call listening – as a random sample of any of the behaviors commonly featured in the different soft skills lists – a communication skill, before having access to all contextual information that would allow making an informed judgment. However, as the review of the literature that follows highlights this is the norm conceptualization of soft skills: any behavior mobilized in a communication encounter can be taken out of context and find its place to a list of communication skills without any formal and scientific criterion for doing so. The review starts first with the norm approach in the conceptualization and use of the term “skill” – itself.

Sources and Search Strategy

The literature review for this mini-review article was undertaken at two separate points in time: in the first instance looking at the literature up to and including 2011 and later for years 2011–2020. During the first period (up to 2011), a review of the term soft skills formed part of the literature review undertaken as part of a doctorate thesis ( Touloumakos, 2011 ). During this period, (a) keyword searches using the term “soft skills” (but also “soft skills” AND “characteristics,” “soft skills” AND “nature,” “soft skills” AND “development”) were conducted through the scientific databases: Google Scholar, Web of Science, and Scopus; (b) specific journals focusing on education, management, and the labor market were targeted and searched to meet the criteria of the doctoral research that looked at the difference between soft skills conceptualization in practice and in educational policy.

During this second period, the previous steps were reiterated to produce an up-to-date list of papers in which the term was used and defined. The author acknowledges that this article does not follow the methodology of a systematic review and that there is certainly scope for a thorough and systematic review on this topic in the future.

What Do We Mean by Skill?

The first known use of the term “skill” dates back in the 13th century (Merriam-Webster’s, 2019). Skill is considered as the “dexterity or coordination…in the execution of tasks” (typically of physical nature), as the “ability to use one’s knowledge effectively and readily in execution and performance,” and as “learned power of doing something competently.” The practical disposition of skill is acknowledged in these definitions. It is also highlighted in the work of Ryle (1949) and Polanyi (1962) , according to who skill is construed as what knowledge sets in action (know-how and know-that, respectively), and, therefore, the two (knowledge and skill) are seen as “reciprocally constitutive” ( Orlikowski, 2002 ). For the purposes of this paper, I adopt the view of skill as what knowledge sets in action (know-how).

The Expansion of Skills: The Emergence of the Soft Skills Category

Contributing factors in the expansion of the term skill.

Over the years, the term skill has expanded considerably, to the point that its meaning became vague. In recent discourse, especially, it has taken on a range of meanings, and as a result, it refers, frequently, to “what is not skill” ( Hart, 1978 ). Indicatively, the term often refers to attitudes, traits, volitions, and predispositions ( inter alia , Payne, 2004 ; Clarke and Winch, 2006 ) and is sometimes confused, and even interchangeably used, with terms such as expertise and competence ( Payne, 2000 ; Pring, 2004 ; Eraut and Hirsch, 2007 ). Its gradual expansion has meant (and is reflected in) the emergence of new skills categories and subcategories (indicatively: generic, soft, interpersonal, etc.). Key contributing factors toward such gradual expansion of the term skill are identified at three levels. The first is at the rhetorical level; the second is at the definitional level; and the third level is at the dispositional character of term itself within different scientific fields.

Focusing on the rhetorical level, in recent years, there has been a linguistic transition from terms such as “skilled work” and “skilled labor” to “skills.” Payne’s paper highlights this shift:

Whereas the Carr Report of 1958 (HMSO 1958: 10), for example, could still talk of “ skilled craftsmen ” [my emphasis] as being the “backbone of industry,” 40 years on, The Learning Age (Department for Education and Employment 1998: 65) was employing a much wider discourse of “basic skills,” “employability skills,” “technician skills,” “management skills,” and “key skills” ( Payne, 2000 , p. 353).

As is evident, the term “skill” (i.e., a noun) began to be used as an independent concept and replaced the use of the term as a characteristic referring to people and professions for example “skilled craftsmen,” “skilled labor,” and “skilled trades” (i.e., an adjective) in the policy rhetoric. The consistency of the use of “skill” in the literature reflects a tendency to turn the abstract notion of a “skilled craftsman” into something more concrete. In this transition, one can identify a reified conceptualization of skill, according to which “skill” is an entity – often a property of an individual (see Sfard, 1998 ; Clarke and Winch, 2006 ).

At the definitional level, the criteria of what counts as skill expanded considerably, which naturally meant the expansion of the term “skill” as well. Relevant here is the ongoing debate around which jobs should be placed on the high skills end of the spectrum (see Lloyd and Payne, 2008 ). In Marx’s work (1970), for example, distinguishing criteria for skilled job included the high wages and low levels of physical labor at the same time. More recent seminal theoretical work summarizes the criteria distinguishing “unskilled” and “skilled” jobs ( Lloyd and Payne, 2008 ). The thinking behind such distinction is quite different from that of Marx. The authors discuss as an example (p. 1–2) the emergence of categories such as “emotional labor” ( Hochschild, 1979 , 1983 ) as a form of skilled labor “ requiring a range of quite complex and sophisticated abilities (see Bolton, 2004 , 2005 ; Korczynski, 2005 ).” The additional criteria for “what count as skill” in this work suggest a progressively ambiguous use of skill, which destined to term “skill” itself to ambiguity.

Third was the versatility of the term rendering it useful within the context of a range of scientific disciplines. Research on skills is rampant in the international literature, for example in cognitive studies – since many decades now – ( Anderson et al., 1996 , 1997 ), in education ( Clarke and Winch, 2006 ; Eraut and Hirsch, 2007 ; Ritter et al., 2018 ), in policy-making ( Wolf, 2004 , 2011 ; Ewens, 2012 ; World Economic Forum [WEF], 2015 ; OECD, 2016 ; LINCS, 2020 ), in labor market studies ( Meager, 2009 ; Kok, 2014 ), in management ( Kantrowitz, 2005 ; Stevenson and Starkweather, 2010 ), or in medicine ( Maguire and Pitceathly, 2002 ; Kurtz et al., 2005 ), to name a few. This evidence corroborates the multi-currency of “skill”,” which operationalizes cognitive mechanisms , human capital (the worker) , and jobs and tasks , depending on the discipline. It is because of this that we tend to speak of people and work in terms bundles of “skills” ( Darrah, 1994 ). The problem is this seems hard to avoid considering that the “deeper one looks into any activity the more knowledge and skill one is likely to find” ( Lloyd and Payne, 2009 , p. 622 drawing from Attewell, 1990 ).

Taken together, this evidence suggests not only the “ conceptual equivocation ” ( Payne, 2000 ) of the term as it is, but also the potentially perpetual emergence of new skills categories, a “galaxy of “soft,” “generic,” “transferable,” “social,” and “interactional’ skills ” (p. 354).

Soft Skills, Categories of Soft Skills, and Links Between Them

Soft skills were among the skills categories resulting from such expansion. While the emergence and use of the category of “soft skills” signified an important division between those skills that were cognitive and technical in nature – now frequently referred to as hard/technical skills – and those that were not, a unified view of the term in the literature has not been achieved. The genesis and use of the term are traced as far back as 1972 in training documents of the US Army (see Caudron, 1999 ; Moss and Tilly, 2001 ). Since then, the term has been expanded itself to comprise categories (in the various lists of soft skills) that include (but not exhaust to):

(a) Qualities (some of which one can see in the emotional intelligence literature) including adaptability, flexibility, responsibility, courtesy, integrity, professionalism, and effectiveness, and values such as trustworthiness and work ethic (see indicatively Wats and Wats, 2009 ; Touloumakos, 2011 ; Robles, 2012 ; Ballesteros-Sánchez et al., 2017 );

(b) Volitions , predispositions , attitudes like good attitude , willingness to learn , learning to learn other skills , hardworking , working under pressure, or uncertainty (see indicatively Stasz, 2001 ; Stasz et al., 2007 ; Andrews and Higson, 2008 ; Cinque, 2017 );

(c) Problem solving , decision making, analytical thinking/thinking skills , creativity/innovation , manipulation of knowledge , critical judgment (see indicatively Cimatti, 2016 ; Succi, 2019 ; Succi and Canovi, 2019 ; Thompson, 2019 );

(d) Leadership skills and managing skills (see indicatively Crosbie, 2005 ; Lazarus, 2013 ; Ballesteros-Sánchez et al., 2017 ), as well as self -awareness , managing oneself/coping skills (see Cimatti, 2016 ; Cinque, 2017 ; Thompson, 2019 );

(e) Interpersonal savvy/skills , social skills , and team skills , effective, and productive interpersonal interactions (see indicatively Kantrowitz, 2005 ; Bancino and Zevalkink, 2007 ; Succi and Canovi, 2019 ; Thompson, 2019 );

(f) Communication skills (see indicatively Wats and Wats, 2009 ; Mitchell et al., 2010 ; Stevenson and Starkweather, 2010 ; Robles, 2012 ; Cinque, 2017 ) including elements of negotiation , conflict resolution , persuasion skills , and diversity (see, in addition, Bancino and Zevalkink, 2007 ; Majid et al., 2012 ; Cinque, 2017 ; Succi and Canovi, 2019 ) as well as articulation work – that is orchestrating simultaneous interactions with people, information, and technology (see Hampson and Junor, 2005 ; Hampson et al., 2009 ); but also going as far as.

(g) Emotional labor (originally from Hochschild, 1983 ), and even in some cases (in service jobs for example).

(h) Aesthetics , professional appearance , and “ lookism ” (see Nickson et al., 2005 ; Warhurst et al., 2009 ; Robles, 2012 ); finally,

(i) Other areas covered included cognitive ability or processes (see Cimatti, 2016 ; Ballesteros-Sánchez et al., 2017 ; Thompson, 2019 ), ability to plan and achieve goals (see Cimatti, 2016 ).

Next to the expansion of the categories comprising soft skills, the hierarchical relationships between the different categories of soft skills, as featured in the literature, added to its ambiguity. An example is the relationship between communication and interpersonal skills. In some places, the two terms are used as interchangeable; in some other cases, they are seen as two distinct categories forming alongside other categories of the construct of soft skills ( Halfhill and Nielsen, 2007 ; Anju, 2009 ; Selvalakshmi, 2012 ; Jain and Anjuman, 2013 ). Finally, elsewhere, a hierarchical relationship exists between the two, namely the former is seen a part (a subcategory) of the latter ( Rungapadiachy, 1999 ; Hayes, 2002 ; Harrigan et al., 2008 ). The simultaneous overlap, submerging, vicinity, and yet disparity of terms such as communication and interpersonal skills is just one of the many in the skills literature (cf. Kinnick and Parton, 2005 , for discussion about overlap between communication and leadership). It becomes evident, accordingly, that these terms, much like the term soft skills has often become so stretched that their limits have become, in turn, vague. Their expansion meant actually that they became polysemous and, because of that, hard to grasp in a unified and organized way and therefore restricted in meaning and use.

This mini- review unveiled two important aspects in relation to the research and the conceptualization of soft skills. The first is that the rampant categories and lists of soft skills seem to be either the outcome of empirical work focusing on breaking down work activities (paraphrasing Lloyd and Payne, 2009 ) in addressing skills requirements, or recycled lists drawing from this work. This is the approach typically encountered in papers focusing on training graduates, training programes within organizations, and employers skills demands (for example Schulz, 2008 ; Constable and Touloumakos, 2009 ; Chamorro-Premuzic et al., 2010 ; Majid et al., 2012 ; Ballesteros-Sánchez et al., 2017 ; Succi and Canovi, 2019 ). This, however, can only be taken to be a veneer of an evidence-based approach to soft skills conceptualization, which is key for their understanding and development for two reasons:

(a) Because same categories mean different things and different categories mean same things to stakeholders (researchers, participants, policymakers), and

(b) Because the aim of researching skills requirements is very different to the aim of researching soft skills characteristics and their nature (soft skills conceptualization).

It is at the level of the conceptualization, characterization, and definition, therefore, that we need to pursue an evidence-based approach, so as to achieve a common language and avoid getting lost in translation in the use of the various soft skills terms.

The second aspect is that, in line with the way the literature features soft skills, they encompass such a wide and diverse range of categories (for example qualities, traits, values, predispositions, etc.) that makes it impossible to think about them as a coherent whole. Arguably, the warehousing approach of soft skills categories development, abstracts behaviors from the context of their enactment and call them skills. This approach, by definition, has ramifications for our understanding of soft skills characteristics, which in turn affects the thinking that underpins their development. For example, given that skills in line with this view are seen as actions toward tasks, it brings to the center the person who acts ( Matteson et al., 2016 ) and, by extension, construes them as personal properties of a generic nature that can be first acquired and transferred uncomplicatedly across contexts ( Touloumakos, 2011 ). Given that this (much like any other) conceptualization of soft skills affects the way we think about their development and their inclusion in education curricula, it is clear that a more inclusive, bottom—up and embedded view would provide a more pragmatic and meaningful alternative in their study.

Author Contributions

This work has been undertaken in its entirety by AT.

Part of the work presented here was undertaken as a Ph.D. research.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The Ph.D. was funded by the Economics and Social Research Council and the State Scholarship Foundation. The author would like to thank Dr. Alexia Barrable for her thoughtful comments and insights on this work.

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Keywords : soft skills, skill, soft skills conceptualization, soft skills development, curriculum design

Citation: Touloumakos AK (2020) Expanded Yet Restricted: A Mini Review of the Soft Skills Literature. Front. Psychol. 11:2207. doi: 10.3389/fpsyg.2020.02207

Received: 31 May 2020; Accepted: 06 August 2020; Published: 04 September 2020.

Reviewed by:

Copyright © 2020 Touloumakos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Anna K. Touloumakos, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

The future of soft skills development: a systematic review of the literature of the digital training practices for soft skills

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Soft skills are becoming increasingly important in the workplace. Due to their interpersonal nature and experiential face-to-face reality, they are often touted as nearly impossible to develop online; our study finds that an increasing body of literature is offering evidence and solutions to overcome impediments and promote digital technologies use in soft skills training. This review aims to perform a state of the art on the research on digital solutions for soft skills training using a systematic review of literature.

A systematic literature review following the PRISMA statement was conducted on the ISI Web of Science, where from 109 originally collected papers, 37 papers were held into consideration for the in-depth analysis.

This paper aims at bringing clarity for both research and practice to facilitate and promote more effective online training initiatives as well as innovative solutions for training in different areas.

In recent years, the global economy has been facing structural changes, rapidly evolving into the world of digital transformation. The unpredictability of the nature and pace of the changes will make it crucial that individuals in groups, organizations and societies alike develop skills for dealing with all kinds of situations, especially soft skills and in particular emotional and social competencies. In this work we look into the literature in a systematic way in order to understand the types of competences most addressed, most commonly used techniques and positive and negative results of the training...

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Soft skills, do we know what we are talking about?

• Review Paper
• Published: 02 June 2021
• Volume 16 , pages 969–1000, ( 2022 )

Cite this article

• Sara Isabel Marin-Zapata   ORCID: orcid.org/0000-0001-6885-895X 1 ,
• Juan Pablo Román-Calderón   ORCID: orcid.org/0000-0002-4352-8513 1 ,
• Cristina Robledo-Ardila   ORCID: orcid.org/0000-0001-7205-2326 1 &
• Maria Alejandra Jaramillo-Serna   ORCID: orcid.org/0000-0003-3033-8494 1  

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During the last decade, individual competencies and soft skills have reached a position of paramount importance among scholars in different fields. Yet, there seems to be a lack of consensus around the meaning of both concepts to the extent that they are sometimes used interchangeably. In response to this, we conducted a systematic review aimed at shedding light on the meaning of competencies and soft skills in business literature. The theoretical perspectives and methodological choices used by scholars were also accounted for as they are closely related to the definitions of these concepts. The systematic review presented in this paper addressed three specific research questions: (a) how are soft skills and competencies conceptualized in the reviewed literature?, (b) what are the main theories used in the study of soft skills and competencies?, and (c) methodologically, what are the main characteristics of those studies? The results indicate that there is still lack of consensus regarding the definitions of both terms. We also found that a large portion of the papers lacked a solid theoretical foundation, while the rest of the papers evidenced that business studies on competencies and soft skills suffer from theoretical dispersion. With regards to the methods used, we conclude that improvements must be made to help develop an understanding of competencies and soft skills. Consequently, a theoretical model explaining the relationships between these concepts was developed taking into account the sounder theoretical perspectives found in the literature review.

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Marin-Zapata, S.I., Román-Calderón, J.P., Robledo-Ardila, C. et al. Soft skills, do we know what we are talking about?. Rev Manag Sci 16 , 969–1000 (2022). https://doi.org/10.1007/s11846-021-00474-9

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Hard evidence on soft skills ✩

James j. heckman.

a Department of Economics, University of Chicago, 1126 E. 59th Street, Chicago, IL 60637, USA

b American Bar Foundation, 750 North Lake Shore Drive, Chicago, IL 60611, USA

c University College Dublin, Ireland

This paper summarizes recent evidence on what achievement tests measure; how achievement tests relate to other measures of “cognitive ability” like IQ and grades; the important skills that achievement tests miss or mismeasure, and how much these skills matter in life.

Achievement tests miss, or perhaps more accurately, do not adequately capture, soft skills —personality traits, goals, motivations, and preferences that are valued in the labor market, in school, and in many other domains. The larger message of this paper is that soft skills predict success in life, that they causally produce that success, and that programs that enhance soft skills have an important place in an effective portfolio of public policies.

1. Introduction

Contemporary society places great value on standardized achievement tests to sift and sort people, to evaluate schools, and to assess the performance of nations. Admissions committees use tests like the SAT, the ACT, and the GRE (Graduate Record Examinations) to screen applicants. In the United States, the No Child Left Behind (NCLB) Act stipulates that government-run schools must administer standardized achievement tests in order to be eligible for federal funding. 1 The Programme for International Student Assessment (PISA) evaluates student performance in math, science, and reading across countries. The results attract media attention and influence policy. The year 2000 PISA test results caused Germany to re-evaluate its educational system and introduce a variety of educational reforms ( Grek, 2009 ).

Despite the widespread use of standardized achievement tests, the traits that they measure are not well-understood. This paper summarizes recent evidence on what achievement tests capture; how achievement tests relate to other measures of “cognitive ability” like IQ and grades; the important skills that achievement tests miss or mismeasure, and how much these other skills matter in life.

Achievement tests miss, or more accurately, do not adequately capture, soft skills —personality traits, goals, motivations, and preferences that are valued in the labor market, in school, and in many other domains. The larger message of this paper is that soft skills predict success in life, that they produce that success, and that programs that enhance soft skills have an important place in an effective portfolio of public policies. 2

Measurement of cognition and educational attainment has been refined during the past century. Psychometricians have shown that cognitive ability has multiple facets. 3 This progress is not widely appreciated. Many social scientists—even many psychologists—continue to use IQ tests, standardized achievement tests, and grades interchangeably to proxy “cognitive ability.” 4 Even though scores on IQ tests, standardized achievement tests, and grades are positively correlated with each other, the recent literature shows that they measure different skills and depend on different facets of cognitive ability. Recent research also shows that all three measures are associated with personality, but to different degrees across various cognitive measures.

Standardized achievement tests were designed to capture “general knowledge” produced in schools and through life experiences. Such knowledge is thought to be relevant to success inside and outside of the classroom. However, achievement tests are often validated using other standardized achievement tests or other measures of cognitive ability—surely a circular practice.

A more relevant validity criterion is how well these tests predict meaningful outcomes, such as educational attainment, labor market success, crime, and health. No single measure of cognitive ability predicts much of the variance in these outcomes, and measurement error does not account for most of the remaining variance, leaving much room for other determinants of success. 5

Success in life depends on personality traits that are not well captured by measures of cognition. Conscientiousness, perseverance, sociability, and curiosity matter. While economists have largely ignored these traits, personality psychologists have studied them over the last century. 6 They have constructed measures of them and provide evidence that these traits predict meaningful life outcomes.

Many scholars—inside and outside of psychology—have questioned the existence of stable personality traits, arguing that constraints and incentives in situations almost entirely determine behavior. These scholars claim that people are like chameleons—they adapt to any situation. 7 A substantial body of evidence shows that stable traits exist. People tend to behave in the same fashion across a wide range of situations. 8 Evidence from genetics and neuroscience provides a biological basis for the existence of such traits, suggesting that something tied to the person, not the just the situation, affects behavior. 9

Throughout this paper we use the term “personality traits” to describe the personal attributes not thought to be captured by measures of abstract reasoning power. These attributes go by many names in the literature, including soft skills, personality traits, noncognitive skills, noncognitive abilities, character, and socioemotional skills. These different names connote different properties. The term “traits” suggests a sense of permanence and possibly also of heritability. The terms “skills” and “character” suggest that they can be learned. In reality, the extent to which these personal attributes can change lies on a spectrum. Both cognitive and personality traits can change and be changed over the life cycle but through different mechanisms and to different degrees at different ages. To avoid confusion, throughout this paper we use the term “trait” to capture the set of personal attributes we study. 10

Psychological traits are not directly observed. There is no ruler for perseverance, no caliper for intelligence. All cognitive and personality traits are measured using performance on “tasks,” broadly defined. Different tasks require different traits in different combinations. Some distinguish between measurements of traits and measurements of outcomes, but this distinction is misleading. Both traits and outcomes are measured using performance on some task or set of tasks.

Psychologists sometimes claim to circumvent this measurement issue by creating taxonomies of traits and by applying intuitive names to responses on questionnaires. These questionnaires are not windows to the soul. They are still rooted in task performance or behavior. Responding to a questionnaire is itself a task. Additionally, many of the questionnaires inquire directly about behavior, e.g., a measure of Agreeableness used in the German Socioeconomic Panel asks the extent to which a respondent “is sometimes somewhat rude to others.” 11 How else can one answer that question but reflect on one's behavior? IQ tests and standardized achievement tests also measure performance on different “cognitively loaded” tasks.

Performance on most tasks depends on effort, personality traits, cognitive ability, and incentives, although the importance of each differs by task. This dependence creates a fundamental problem in measuring traits. Most studies in psychology devise a set of measures to capture a trait but do not standardize for incentives in the situation in which the trait is being measured or for other traits. Measured cognitive ability and measured personality depend on a constellation of factors. The identification problem arising from the multiple determinants of performance on tasks is empirically important, even for measures of cognitive ability. Incentives can affect performance on IQ tests. Multiple traits affect performance on cognitive tasks. For example, personality traits affect achievement test scores and grades. 12 Caution is required in taking the measures developed by psychologists too literally.

Nonetheless, measures of personality traits predict meaningful life outcomes. Conscientiousness—the tendency to be organized, responsible, and hardworking—is the most widely predictive of the commonly used personality measures. It predicts educational attainment, health, and labor market outcomes as strongly as measures of cognitive ability. 13

Most studies in psychology only report correlations between measured traits and outcomes without addressing whether the traits cause the outcomes and without controlling for the other traits and incentives that determine performance on the tasks used to measure the traits. While traits are relatively stable across situations, they are not set in stone. They change over the life cycle. On average, Agreeableness and Conscientiousness tend to grow with age. Different facets of cognitive ability peak at different ages. Interventions, education, and parenting can affect traits in lasting ways.

This paper summarizes recent evidence that personality causally affects life outcomes. We review some of the literature from psychology and economics and then focus on two particularly compelling examples. 14

First, we show how an achievement test, the General Educational Development (GED) test, fails to capture important traits that affect success in life. High school dropouts can take the GED to certify to employers and post-secondary institutions that their skills are equivalent to those of high school graduates who do not attend college. After accounting for differences in pre-existing cognitive ability, GED recipients perform much worse in the labor market than high school graduates and much more like other high school dropouts. GED recipients lack important personality traits (see Heckman et al., 2011a , 2012a ).

Second, we show how an early childhood intervention, the Perry Preschool Program, improved the lives of disadvantaged children, even though the program did not permanently change the IQ of its participants. The program changed their personality traits in a lasting way (see Heckman et al., 2012b ). Other interventions and observational studies provide supporting evidence that early-childhood investments improve outcomes through their effects on personality. 15

2. Defining and measuring personality traits

2.1. history and measurement of cognitive ability.

Modern intelligence tests have been used for just over a century, beginning when a French minister of public instruction wished to identify retarded pupils in need of specialized education programs. In response, Alfred Binet created the first IQ test. 16 IQ scores were interpreted as measuring a stable trait. The standardized achievement test was created in the wake of the perceived success of IQ tests as an objective and cost-effective measure of acquired skills. In contrast to IQ tests, standardized achievement tests were designed to measure “general knowledge” that could be acquired in schools and through life experiences and was widely applicable beyond the classroom to workplace and social functioning. 17

Achievement tests are typically validated on other achievement tests, IQ tests, and grades, rather than on tasks or outcomes in the labor market and in social functioning. Table 1 shows correlations among scores on standardized achievement tests, IQ tests, and grades. Standardized achievement tests are correlated with IQ tests, but the correlation depends on the subject area of the standardized achievement test. Hartlage and Steele (1977) find that the arithmetic portions of standardized achievement tests are the most highly correlated with IQ. Grades and scores on IQ tests and standardized achievement tests are far from perfectly correlated, suggesting that they measure different aspects of “cognitive functioning.” 18

Cognitive ability validities.

Denitions: WISC —Wechsler Intelligence Scale for Children, WISC-R —Wechsler Intelligence Scale for Children—Revised, WAIS - Wechsler Adult Intelligence Scale, Raven's IQ — Raven's Standard Progressive Matrices, GED —General Educational Development, DAT —Differential Aptitude Tests, WIAT —Wechsler Individual Achievement Test, CAT —California Achievement Test, WRAT —Wide Range Achievement Test, AFQT – Armed Forces Qualification Test.

Psychologists distinguish between fluid intelligence (the rate at which people learn) and crystalized intelligence (acquired knowledge). 19 Achievement tests are heavily weighted towards crystallized intelligence, 20 whereas IQ tests like Raven's progressive matrices (1962) are heavily weighted toward fluid intelligence. 21 , 22 Many psychologists do not recognize the differences among these measures and interchangeably use IQ, achievement tests, and grades to measure “cognitive ability” or “intelligence,” and this practice is also wide-spread in economics. 23

2.2. Defining and measuring psychological traits

Validating one measure of cognitive ability using other measures of cognitive ability is circular. More relevant is how well these measures predict important life outcomes. Table 2 shows the extent to which IQ, standardized achievement tests, and grades explain the variance of outcomes at age 35 in the National Longitudinal Survey of Youth, 1979 (NLSY79) data. The three groups of columns under each category show results for different sub-samples based on the availability of the different cognitive measures. For each category, the first column shows the explained variance using only the designated measure of cognitive ability. Achievement tests and grades are more predictive than IQ. But none of these measures explains much of the variation of any outcome, leaving considerable room for other determinants. As noted in the introduction, it is unlikely that measurement error accounts for all of the remaining variance.

Predictive validities in outcomes that matter (adjusted R-squared).

Source: National Longitudinal Survey of Youth 1979. Table Description: The table shows the adjusted R-squared from regressions of later-life outcomes on measures of personality and cognition. For each cognitive measure, the first column shows the explained variance using only the measures of cognitive ability, the second column shows the explained variance from using only the measure of personality (Personality), and the third column shows the explained variance from using both the measures of personality and cognition (Both). The last row shows the adjusted R-squared from a regression of each cognitive measure on the personality measures. Measures of Personality and Cognition: The measures of personality include minor illegal activity in 1979 (vandalism, shoplifting, petty theft, fraud and fencing), major illegal activity in 1979 (auto theft, breaking/entering private property, grand theft), participation in violent crime in 1979 (fighting, assault and aggravated assault), tried marijuana before age 15, daily smoking before age 15, regular drinking before age 15, and any intercourse before age 15. It also includes measures of self-esteem and locus of control. Self-esteem is measured using the ten-item Rosenberg scale administered in 1980. Locus of control is a measure of how much control an individual believes they have over their life and is measured using the 4-item Rotter scale. IQ and grades are from high school transcripts. IQ is pooled across several IQ tests using IQ percentiles. GPA is the individual's core-subject GPA from 9th grade. The Armed Forces Qualification Test (AFQT) was adjusted for schooling at the time of the test conditional on final schooling as described in Hansen et al. (2004) . Outcomes: Due to the biennial nature of the survey after 1994, some respondents are not interviewed at age 35, for these individuals age 36 is used. Earnings include zero-earners and excludes observations over $200,000 (2005 dollars). Hourly wage excludes observations less than $3 or over $200 (2005 dollars). Hours worked excludes observations less than 80 or more than 4000. Jail by age 35 indicates whether the respondent had listed residing in a jail or prison at some point before age 35. Welfare at age 35 indicates whether the respondent received any positive amount of welfare at age 35. Married at age 35 indicates whether the responded was currently married. B.A. degree by age 35 indicates whether the respondent received a B.A. degree (or higher) by age 35. Depression in 1992 is based on the 7-item Left for Epidemiologic Studies Depression Scale (CES-D). Sample: The sample excludes the military oversample. The samples differ across the IQ, AFQT, and GPA due to missing measures across the samples.

Personality is one missing ingredient. The second columns in each category preview our later discussion of the explanatory power of personality. They show the variance explained by measures of personality. 24 In many cases, the variance explained by personality measures rivals that explained by measures of cognitive ability. The relative importance of personality depends on the outcome. The third column for each subsample shows the variance explained when both the cognitive and personality measures are used as predictors. In many cases, including the measures of personality in a regression with cognitive measures explains additional variance. The correlations between the set of measures of personality and the measures of cognition are positive, but not especially strong (see the bottom row of each table). Each set of traits has an independent influence on the outcomes in the table.

Even though economists have largely ignored personality traits, the pioneers of the original IQ tests recognized their importance. 25 Alfred Binet the creator of the first IQ test (the Stanford-Binet test), noted that:

“[Success in school]…admits of other things than intelligence; to succeed in his studies, one must have qualities which depend on attention, will, and character; for example a certain docility, a regularity of habits, and especially continuity of effort. A child, even if intelligent, will learn little in class if he never listens, if he spends his time in playing tricks, in giggling, in playing truant.” -( Binet and Simon, 1916 , p. 254)

Since the middle of the 19th century, personality psychologists have studied these traits. One leading personality psychologist defines personality traits in the following way:

“Personality traits are the relatively enduring patterns of thoughts, feelings, and behaviors that reflect the tendency to respond in certain ways under certain circumstances.” -( Roberts, 2009 , p. 140)

Personality traits are manifested through thoughts, feelings, and behaviors, and therefore, must be inferred from measures of performance on “ tasks ,” broadly defined. Under this definition, performance on IQ tests is a personality trait because it is an enduring pattern of behavior (how one “behaves” or “performs” on an IQ test). 26

Personality psychologists primarily measure personality traits using self-reported surveys. They have arrived at a relatively well-accepted taxonomy of traits called the “Big Five,” which includes Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Table 3 defines these traits and their multiple facets. 27 Some argue that the Big Five are the longitude and latitude of personality, by which all more narrowly defined traits may be categorized (see Costa and McCrae, 1992a ). While the Big Five measures are now the most widely used, there are several other taxonomies, including the Big Three, the MPQ, and the Big Nine. They are conceptually and empirically related to the Big Five. 28 Other taxonomies, including psychopathology as measured by the DSM IV and measures of temperament, have also been related to the Big Five. 29 Almlund et al. (2011) show that economic preference parameters are not all that closely related to psychological traits and apparently represent different traits that, along with the psychological traits, govern behavior.

The Big Five domains and their facets.

Notes: Facets specified by the NEO PI-R personality inventory( Costa and McCrae, 1992b ).Trait adjectives in parentheses from the Adjective Check List ( Gough and Heilbrun, 1983 ).

Source: Table adapted from John and Srivastava (1999) .

A deeper issue, as yet not systematically investigated in the literature in economics or psychology, is whether the traits captured by the alternative measurement systems are the manifestation of a deeper set of preferences or goals. Achieving certain goals requires certain traits, e.g., a surgeon has to be careful and intelligent; a salesman has to be outgoing and engaging and so forth, etc. Under this view, traits are developed through practice, investment, and habituation. The deeper traits may be the preference parameters that generate the manifest traits. The apparent stability of expressed traits across situations may be a consequence of the stability of the goals and incentives to achieve these goals. 30

2.3. Identification problems in measuring traits

Measuring traits is difficult, because, as suggested by Roberts' definition of personality, all psychological measurements are calibrated on measured behavior, and the behaviors used to measure one trait can be influenced by incentives and other traits. To infer traits from behaviors requires standardizing for all of the other contributing factors that produce the observed behavior. The inability to parse and localize behaviors that depend on a single trait or ability gives rise to a fundamental identification problem that is typically ignored in empirical research investigating how psychological traits affect outcomes. 31

There are two primary issues. First, behavior depends on incentives created in situations. Different incentives elicit different amounts of effort on the tasks used to measure traits. Accurately measuring personality traits requires standardizing for the effort applied in any task. Second, behavior in one task can depend on multiple traits. Not standardizing for incentives and other traits can produce misleading estimates of any trait.

These identification problems are empirically important when measuring any given trait. For example, incentives partly determine scores on IQ tests, even though some have argued that performance on IQ tests reflects maximal effort. 32 A series of studies conducted over the past 40 years shows that incentives, like money or candy, can increase IQ scores, particularly among low-IQ individuals. The Black-White gap in IQ can be completely eliminated by incentivizing students with M&M candies. 33 The incentives in one test do not affect performance on future tests.

The recent literature shows that personality traits are associated with standardized achievement test scores, which many analysts use interchangeably with IQ scores. 34 Figs. 1 and ​ and2 2 show how the variance in the scores on two achievement tests, the Armed Forces Qualification Test (AFQT) and the closely related Differential Aptitudes Tests (DAT), 35 are decomposed into IQ and personality measures. Personality traits explain a substantial portion of the variances in both AFQT scores and DAT scores. 36 The personality traits are incrementally valid in that they explain the variance above and beyond the variance that IQ explains in a regression. These findings caution the interpretation that standardized achievement tests only measure cognitive ability. They are bundled with personality traits. In data from the Stella Maris secondary school in Maastricht, Holland, Openness to Experience is strongly correlated with IQ. 37

Decomposing achievement tests and grades into IQ and personality [NLSY79]. Source: Borghans et al. (2011a) , National Longitudinal Survey of Youth 1979 (NLSY79). Notes: Rotter is a measure of locus of control designed to measure the extent to which individuals believe that they have control over their lives through self-motivation or self-determination as opposed to the extent to which individuals believe that the environment controls their lives ( Rotter, 1966 ). Rosenberg is a measure of self-esteem designed to measure the degree of approval or disapproval toward oneself ( Rosenberg, 1965 ). The Armed Forces Qualification Test (AFQT) score is constructed from the Arithmetic Reasoning, Word Knowledge, Mathematical Knowledge, and Paragraph Comprehension Armed Services Vocational Aptitude Battery (ASVAB) subtests. Rotter was administered in 1979. The ASVAB and Rosenberg were administered in 1980. IQ and GPA are from high school transcript data. AFQT, Rosenberg, and Rotter have been adjusted for schooling at the time of the test conditional on final schooling, as described in Hansen et al. (2004) . IQ is pooled across several IQ tests using IQ percentiles. GPA is the individual's core subject GPA from 9th grade. Sample excludes the military oversample.

Decomposing achievement tests and grades into IQ and personality [Stella Maris]. *Grit is a measure of persistence on tasks ( Duckworth et al., 2007 ). Source: Borghans et al. (2011a) .

Further complicating identification, not everyone responds to incentives in the same way. Borghans et al. (2008b) show that adults spend substantially more time answering questions on IQ tests when rewards are higher. Subjects high in Emotional Stability and Conscientiousness are less affected by rewards. Similarly, Segal (2008) shows that introducing cash incentives for performance on the coding speed test of the Armed Services Vocational Battery (ASVAB) increases performance substantially, particularly for men with lower levels of Conscientiousness.

2.4. Are there stable personality traits?

Many have questioned whether personality traits exist. The publication of Walter Mischel's (1968) book, Personality and Assessment , gave rise to a heated “personality-situation” debate within psychology, which pitted the social psychologists who favored situational factors as explaining behavior against those who considered stable personality traits as more consequential. Mischel argued that aspects of situations overshadow any effect of personality on behavior. 38

A large body of evidence reviewed in Almlund et al. (2011) shows that stable personality traits exist and are predictive of many behaviors. 39 An important paper by Epstein (1979) presents compelling evidence that, averaging over tasks and situations at a point in time, people act in a predictable fashion with a high level of reliability ( R 2 of 0.6–0.8) of average behavior (“measured personality”) across situations. The incentives in any situation also matter. Heritability studies show that measures of personality traits tend to be about 40%–60% heritable, suggesting that something tied to the person, rather than the situation, influences behavior ( Bouchard and Loehlin, 2001 ). 40 Evidence in neuroscience suggests that expression of traits is related to regions of the brain (see Canli, 2006 ; DeYoung et al., 2010 ).

2.5. The evolution of personality traits over the life cycle

Even though personality traits are relatively stable across situations, they are not set in stone. They change over the life cycle. Fig. 3 shows that Conscientiousness tends to increase monotonically over the life cycle. Other traits change in different ways over the life cycle. 41 Crystallized intelligence tends to increase monotonically for most of the life cycle, whereas fluid intelligence tends to peak in early adulthood and then decline. 42

Cumulative mean-level changes in personality across the life cycle. Note: Cumulative d values represent total lifetime change in units of standard deviations (“effect sizes”).

Source: Figure taken from Roberts et al. (2006) and Roberts and Mroczek (2008) . Reprinted with permission of the authors.

This evidence does not address whether these changes occur naturally (“ontogenic change”) or whether they are due to changes in the environments commonly experienced over the life cycle (“sociogenic change”). No evidence is available in the published literature on the distributions of these profiles over the life cycle. Almlund et al. (2011) review the evidence on how parental investment and interventions promote changes in personality.

2.6. The predictive power of personality

Table 2 shows that personality traits predict many later-life outcomes as strongly as measures of cognitive ability. Conscientiousness—the tendency to be perseverant and hardworking—stands out as the most predictive of the Big Five traits across many outcomes. Fig. 4 presents for males correlations between the Big Five and educational attainment, adjusting and not adjusting for fluid and crystalized intelligence. Conscientiousness predicts educational attainment more than either of the facets of intelligence. 43 Similar patterns appear for many other outcomes, including labor market performance, grades, and health. 44

Association of the Big Five and intelligence with years of completed schooling. Notes: The figure displays standardized regression coefficients from a multivariate regression of years of school attended on the Big Five and intelligence, controlling for age and age squared. The bars represent standard errors. The Big Five coefficients are corrected for attenuation bias. The Big Five were measured in 2005. Years of schooling were measured in 2008. Intelligence was measured in 2006. The measures of intelligence were based on components of the Wechsler Adult Intelligence Scale (WAIS). The data are a representative sample of German adults between the ages 21 and 94. Source: Almlund et al. (2011) , German Socio-Economic Panel (GSOEP), waves 2004–2008.

A recurrent finding in the literature is that measured IQ is highly predictive of performance on complex tasks and jobs ( Gottfredson, 1997 ). The importance of IQ increases with job complexity, defined as the information processing requirements of the job: cognitive skills are more important for professors, scientists, and senior managers than for semi-skilled or unskilled laborers ( Schmidt and Hunter, 2004 ). In contrast, the importance of Conscientiousness does not vary much with job complexity ( Barrick and Mount, 1991 ), suggesting that it pertains to a wider spectrum of jobs.

The literature in economics establishes that the same bundle of traits has different productivity in different tasks. People also differ in their endowments of traits. These two features lead to sorting in the tasks people pursue in life and are a manifestation of the general principle of comparative advantage in the labor market and in life (see Almlund et al., 2011 ; Borghans et al., 2008a ; Cattan, 2012 ; Heckman et al., 2006a , 2011b ).

Achievement test scores are crude, low-dimensional summaries of high-dimensional vectors of traits that operate in conjunction with effort. It is unlikely that these summaries capture the precise combinations of traits required for success in specific life tasks. The thrust of recent research in personality and economics is to isolate the traits that determine life outcomes and to understand how those diverse traits determine choices of tasks.

Most of the evidence in personality psychology is correlational. The reported correlations do not prove that personality traits cause higher educational attainment although it is consistent with it. For example, the reported pattern in Fig. 4 could arise if educational attainment increased Conscientiousness. We next present causal evidence.

3. Causal evidence

3.1. problems with establishing causality.

Most studies in personality psychology do not address the question of causality, i.e., whether measured traits cause (rather than just predict) outcomes. Empirical associations are not a reliable basis for policy analysis. In this section, we discuss difficulties in establishing causality. We also summarize several studies that provide evidence that personality traits cause outcomes.

We introduce a simple framework to analyze the effect of traits on outcomes and how traits evolve over time. 45 Eq. (1) shows how an outcome at age a , T a , which is the performance on a task, depends on cognition C a , personality P a , other acquired skills such as education and job training K a , and the effort allocated to the task e T a :

Eq. (2) shows how the effort allocated to task T a depends on cognition C a , personality P a , other acquired skills K a , incentives R T a , and preferences γ a

The effort applied to a task is the outcome of a choice problem that depends on traits, preferences, and incentives, much like a supply equation in the standard theory of consumer choice. Preferences can be thought of as additional traits. 46 Some psychological theories posit that people have limited effort that they can divide among different tasks (see, e.g., Baumeister and Tierney, 2011 ).

Eqs. (1) and (2) formalize the difficulty in establishing a causal relationship between outcomes and traits. Multiple traits, effort, and acquired skills generate performance in a given task. Many studies in psychology and economics do not control for these inputs and equate measurement of a set of outcomes with the trait the analyst is trying to measure. 47 This practice can lead to a substantial bias in inference about any particular trait.

An additional point is that most studies assume a linear relationship between outcomes and traits. This practice is particularly problematic for measuring personality traits, where the effect of a trait on an outcome is not always linear or even monotonic. Too much of a good thing can be bad ( ∂ ϕ a ∂ P a < 0 for P a > P ̄ for threshold P ̄ . For example, extreme levels of traits are associated with psychopathologies. High levels of Conscientiousness are associated with Obsessive Compulsive Disorder, which hinders task performance ( Samuel and Widiger, 2008 ). Nonlinearities can also arise when traits and incentives interact, as in the analyses of Borghans et al. (2008b) and Segal (2008) who show that people with different personality traits respond differently to incentives on tests. 48

The traits and other acquired skills evolve over time through investment and habituation. Eq. (3) shows that traits at age a +1 are age-dependent functions of cognitive ability, personality traits, other acquired skills, and investment I a at age a . In this way, previous levels of traits and acquired skill affect current levels of traits and acquired skill. Eq. (3) formalizes the notion that the traits and skills governing performance at a point in time are themselves the outcome of investment and habituation:

In conjunction with resource constraints, a “deeper” set of preference parameters at age a may govern investment decisions and effort allocated to tasks.

3.2. Extreme Examples of Personality Change

Laboratory experiments and brain lesion studies provide some of the most compelling evidence that personality traits can change and that the change affects behaviors. The most famous example is that of Phineas Gage, a railway construction foreman whose head was impaled by a metal spike. Miraculously he retained his problem solving abilities, but he changed from being polite and dependable to being rude and unreliable. His personality change caused him to lose his job and alienate family members ( Damasio et al., 2005 ). Laboratory experiments show that expressed traits can be manipulated temporarily. Magnetic disruption of the left lateral prefrontal cortex can increase experimentally elicited discount rates ( Figner et al., 2010 ) and nasal sprays of oxytocin increase trust ( Kosfeld et al., 2005 ).

3.3. Evidence from the GED testing program

The GED is a standardized achievement test that serves as an alternative to a high school diploma. High school dropouts can take the seven-and-a-half-hour GED exam to certify that they have the “general knowledge” of a high school graduate. The test is widely used. The GED testing program currently produces 12% of high school certificates each year in the United States. We draw on the analysis of Heckman et al. (2012a) and first present results for males. The GED program provides insight into the effects of personality traits on outcomes. GED recipients have the same cognitive ability as high school graduates, but differ in their personality traits.

Table 4 shows the correlations between GED test scores and other achievement test scores. GED test scores are strongly correlated with scores on other standardized achievement tests. The correlations range from 0.61 with the General Aptitude Test Battery (GATB) to 0.88 with the Iowa Test of Educational Development, the progenitor of the GED.

Validities of GED test.

GED recipients are smarter than other dropouts. Fig. 5 shows the distributions of a factor extracted from the components of the ASVAB for male high school dropouts, GED recipients, and high school graduates. 49 The sample excludes people who attend post-secondary education. The distribution of the scores of GED recipients is much more like that of high school graduates than that of high school dropouts.

Cognitive ability by educational status. Source: Reproduced from Heckman et al. (2011b) , which uses data from the National Longitudinal Study of Youth 1979 (NLSY79). Notes: The distributions above represent cognitive ability factors estimated using a subset of the Armed Services Vocational Aptitude Battery (ASVAB) and educational attainment as laid out in Hansen et al. (2004) . The sample is restricted to the cross-sectional subsample for both males and females. Distributions show only those with no post-secondary educational attainment. The cognitive ability factors are normalized by gender to be mean zero standard deviation one.

If they have the same cognitive ability as high school graduates, then why do they drop out of high school? Success in school requires other traits. On a variety of other dimensions, GED recipients behave much more like other dropouts. Fig. 6 shows measures of early adolescent drug use, crime, sex, and violence extracted from three data sources. 50 Male high school graduates perform better on all measures than high school dropouts or GED recipients. GED recipients are much more similar to dropouts, but in several cases are statistically significantly more likely to engage in risky behaviors than other dropouts. On no outcome measure in that figure are dropouts statistically significantly more likely to engage in risky behaviors compared to GED recipients. Fig. 7 summarizes these adolescent behaviors using a single factor and shows that unlike the cognitive summary measures, the distribution of the non-cognitive (personality) summary measure of GED recipients is much closer to that of dropouts than to that of high school graduates.

Measures of adolescent behaviors for male dropouts, GED recipients, and high school graduates. Sources: Heckman et al. (2012a , Chapter 3). National Longitudinal Survey of Youth 1979 (NLSY79), National Longitudinal Survey of Youth 1997 (NLSY97), National Educational Longitudinal Survey (NELS). Notes: Minor crime includes vandalism, shoplifting, petty theft, fraud, holding or selling stolen goods. Major crime includes auto theft, breaking/entering private property, grand theft. Violent crime includes fighting, assault, aggravated assault. Tests of Significance: The estimates for GED recipients and high school graduates are statistically significantly different at the 5% level for all variables. The estimates for dropouts and high school graduates are statistically significantly different at the 5% level for all variables, except for “Minor Crime (NLSY79)” and “Drinks by 14 (NLSY97).” The estimates of “Smokes by 14 (NLSY97),” “Drinks by 14 (NLSY97),” and “Theft by 14 (NLSY97)” between GED recipients and dropouts are statistically significantly different at the 5% level.

Distribution of a summary measure of noncognitive ability by education group. Source: Reproduced from Heckman et al. (2011b) , which uses data from the National Longitudinal Study of Youth 1979 (NLSY79). Notes: The distributions above represent noncognitive ability factors estimated using measures of early violent crime, minor crime, marijuana use, regular smoking, drinking, early sexual intercourse, and educational attainment as in Hansen et al. (2004) . Sample restricted to the cross-sectional subsample for both males and females. Distributions show only those with no post-secondary educational attainment. The noncognitive ability factors normalized to be mean zero standard deviation one.

The traits that cause GED recipients to drop out of high school manifest themselves in many other life outcomes. One potential benefit of the GED certificate is that it opens doors to post-secondary education. Fig. 8 shows post-secondary educational attainment for GED recipients and high school graduates. About 40% of GED recipients enroll in a 2- or 4-year college. Nearly half drop out within the first year. Fewer than 5% earn a B.A. degree and fewer than 10% earn an A.A. degree (associate degree typically completed in two years).

Post-secondary educational attainment across education groups through age 40 males. Sources: Heckman et al. (2012a , Chapter 4). National Longitudinal Survey of Youth 1979 (NLSY79). Notes: The graph shows post-secondary educational attainment of GED recipients and high school graduates. Variable Definitions: “Some College” represents people who entered any post-secondary institution ever. “Some College, More Than a Year” represents people who completed at least a year of some post-secondary education ever. “A.A.” represents people who obtained associate degrees ever. “B.A.” represents people who obtained bachelor's degrees ever. “B.A.” also includes people with higher education: M.A. Ph.D and professional degrees. Tests of Significance: The estimates for GED recipients and high school graduates are statistically significantly different at the 5% level for all but attainment of the A.A. degree.

GED recipients lack persistence in a variety of tasks in life. Fig. 9 shows the survival rates in employment, marriage, and in the condition of not having been incarcerated. GED recipients tend to exit employment, become divorced, and enter jail at rates similar to those of high school dropouts, while high school graduates are much more persistent.

Survival rates in various states for male dropouts, GED recipients, and high school graduates. Source: Heckman et al. (2012a , Chapter 4). National Longitudinal Survey of Youth 1979 (NLSY79), nationally representative cross sectional sample. Notes: The "Survival Rate in Marriage" is based on the first marriage spell that began after 1979. People who were already married or divorced in 1979 were excluded from the sample because the length of their spell is unknown. The "Survival Rate in Employment" is based on all employment spells that began after 1979 and after the respondent was 16. People are excluded from the employment estimates if they have been to jail. The "Survival Rate in Non-Incarcerated State" is based on the time until first incarceration starting at age 22 (the youngest age for which the jail status is available for all respondents). Respondents who were already in jail at age 22 were excluded from the sample. Tests of Significance: The estimates for GED recipients and high school graduates are statistically significantly different at the 5% level in all cases except for the first year of the "Survival Rate in Employment" and the first year of the "Survival Rate in Marriage." The estimates for dropouts and high school graduates are statistically significantly different at the 5% level in all cases. The estimates for drop-outs and GED recipients are significantly different at the 5% level for years three through five of the "Survival Rate in Employment."

Adjusting for their differences in cognitive ability, male GED recipients perform virtually the same as high school dropouts in the labor market. Fig. 10 shows the hourly wages and annual earnings of male GED recipients and high school graduates compared to high school dropouts for different age groups. The first set of bars shows the outcomes after adjusting for age, race, year, and region of residence. The second set of bars shows the effects after additionally adjusting for AFQT scores. The third set of bars shows the effects after additionally adjusting for standard measures of family background. GED recipients and high school graduates outperform dropouts in regressions that only adjust for age, race, year, and region of residence. After adjusting for cognitive ability, GED recipients are indistinguishable from dropouts, whereas high school graduates earn more and have higher hourly wages. Controlling for family background characteristics does not change the story.

Labor market outcomes differences. By age—NLSY79—males. Source: Heckman et al. (2012a , Chapter 3). National Longitudinal Survey of Youth 1979 (NLSY79). Controls: “Raw”—age, race, year, and region of residence; “ Abil ”—age, race, region of residence, and Armed Forces Qualification Test (AFQT) adjusted for schooling at time of test; “ BG ”—age, race, year, region of residence, mother's highest grade completed, urban status at age 14, family income in 1978, broken home status at age 14, South at age 14, AFQT, and factors based on adolescent behavioral measures, crime and school performance. Regressions exclude those reporting earning more than $300,000 or working more than 4000 hours. Notes: All regressions allow for heteroskedastic errors and when appropriate clustering at the individual level.

Most of the patterns found for women parallel those found for men. However, there are some important differences. 51 While female GED recipients share similar cognitive and personality traits as male GED recipients, their outcomes differ. After accounting for differences in cognitive ability, female GED recipients do not earn higher hourly wages than other dropouts, but unlike men they have higher annual earnings because they are more likely to participate in the labor force. 52

3.4. Evidence from the Perry Preschool Program and other interventions

Evidence from the Perry Preschool Program shows how personality traits can be changed in ways that produce beneficial lifetime outcomes. The Perry preschool Program enriched the lives of 3- and 4-year-old low-income, Black children with initial IQs below 85 at age 3. 53

Participants were taught social skills in a “plan-do-review” sequence where students planned a task, executed it, and then reviewed it with teachers and fellow students. They learned to work with others when problems arose. 54 In addition, home visits promoted parent–child interactions. The program ended after 2 years of enrollment and both treatments and controls entered the same school. The program was evaluated by the method of random assignment.

The program did not improve IQ scores in a lasting way. Fig. 11 shows that, by age ten, treatment and control groups had the same average IQ scores. Many critics of early childhood programs seize on this finding and related evidence to dismiss the value of early intervention studies.

Perry Preschool Program: IQ, by age and treatment group. Notes: IQ measured on the Stanford-Binet Intelligence Scale ( Terman and Merrill, 1960 ). The test was administered at program entry and at each of the ages indicated. Source: Cunha et al. (2006) and Heckman and Masterov (2007) based on data provided by the High Scope Foundation.

Nevertheless, the program improved outcomes for both boys and girls, resulting in a statistically significant rate of return of around 6–10% per annum for both boys and girls (see Heckman et al., 2010a , b ). These returns are above the post-World War II, pre-2008 meltdown in stock market returns to equity estimated to be 5.8% per annum. 55

The Perry Preschool Program worked primarily through improving personality traits. Participants had better direct measures of personal behavior (a weighted average of “absences and truancies,” “lying and cheating,” “stealing,” and “swears or uses obscene words” measured by teachers in the elementary school years). Participants of both genders improved their “externalizing behavior,” a psychological construct related to Agreeableness and Conscientiousness. For girls, the program improved Openness to Experience (proxied by academic motivation). The program also improved scores on the California Achievement Test (CAT). This evidence is consistent with the evidence presented in the previous section that shows that performance on achievement tests depends on personality traits.

Other studies are broadly consistent with the evidence from the Perry Preschool study. Analyses of data from Project STAR, a program that randomly assigned kindergartners and teachers to classes of different sizes, yields results similar to the Perry Program. Using data from Project STAR, Dee and West (2011) find that assignment to a small class is associated with positive changes in personality. In a follow-up analysis, Chetty et al. (2011) examine the Project STAR program and find that students placed in higher quality kindergarten classes—as measured by their peer's average performance on a Stanford Achievement Test—had significantly higher earnings in early adulthood.

The curriculum of Promoting Alternative Thinking Strategies (PATHS) teaches self-control, emotional awareness, and social problem-solving skills and is aimed at elementary school children (see Bierman et al., 2010 ). A recent random-assignment, longitudinal study demonstrates that the PATHS curriculum reduces teacher and peer ratings of aggression, improves teacher and peer ratings of prosocial behavior, and improves teacher ratings of academic engagement. 56 PATHS is an exemplar of school-based social and emotional learning (SEL) programs. A recent meta-analysis shows that the program improved grades by 0.33 standard deviations and achievement test scores by 0.27 standard deviations ( Durlak et al., 2011 ). 57

Likewise, several random assignment evaluations of Tools of the Mind , a preschool and early primary school curriculum targeting development of self-control, show that it improves classroom behavior as well as executive function, defined as higher-level cognitive skills including inhibitory control, working memory, and cognitive flexibility ( Barnett et al., 2006 , 2008 ; Bodrova and Leong, 2001 , 2007 ; Diamond et al., 2007 ; Lillard and Else-Quest, 2006 ). 58 Positive findings are reported for the Montessori preschool curriculum ( Lillard and Else-Quest, 2006 ). Unlike the Perry study, these studies do not have long-term followups.

There is evidence that targeted intervention efforts can improve aspects of Conscientiousness. In contrast to the multi-faceted curricula described above, studies targeting improvement in this trait are designed to isolate a particular mechanism producing behavioral change. For instance, Rueda et al. (2005) designed a set of computer exercises to train attention in children between 4 and 6 years of age. Children in the intervention group improved in performance on computer tasks of attention relative to children who instead watched interactive videos for a comparable amount of time. Similarly, Stevens et al. (2008) designed a 6-week computerized intervention and showed that it can improve selective auditory attention (i.e., the ability to attend to a target auditory signal in the face of an irrelevant, distracting auditory signal). As is typical of much of the literature, all of these programs have only short-term follow-ups.

Several studies suggest that personality can be remediated in adolescence. Martins (2010) analyzes data from EPSIS, a program developed to improve student achievement of 13–15 year-olds in Portugal by increasing motivation, self-esteem, and study skills. The program consists of one-on-one meetings with a trained staff member or meetings in small groups. The intervention was tailored to each participant's individual skill deficit. Overall, the program was successful and cost-effective, decreasing grade retention by 10 percentage points.

Other life experiences, like employment, can improve personality. Gottschalk (2005) analyzes evidence from a randomized control trial that working at a job can improve locus of control, a trait related to Neuroticism that measures the extent to which individuals believe that they have control over their lives through self-motivation or self-determination as opposed to the extent that the environment controls their lives ( Rotter, 1966 ). 59 He uses data from the Self-Sufficiency Project (SSP) in which some welfare recipients were randomly offered substantial subsidies to work. The subsidy more than doubled the earnings of a minimum wage worker. People in the experimental group worked about 30% more hours than those in the control group. After 36 months, those who received the subsidy were more likely to have an improved locus of control.

3.5. Additional evidence

Studies that account for the endogeneity of investment and education provide further evidence of the causal effect of education and cognitive and personality traits on outcomes. Heckman et al. (2011b) estimate a sequential model of education to study the effects of education on a variety of outcomes. Correcting for selection into education, they find that early cognitive and personality traits affect schooling choices, labor market outcomes, adult health, and social outcomes and that increasing education promotes beneficial labor market, health, and social outcomes.

Heckman et al. (2006b) estimate a version of Eq. (3) to analyze the effects of increases in education on measured cognition and personality measures. 60 Controlling for the problem of reverse causality that schooling may be caused by traits, they find that schooling improves both personality and cognitive traits and that these traits, in turn, boost outcomes. 61

Cunha et al. (2010) estimate a model of the technology of skill formation using longitudinal data on the development of children with rich measures of parental investment and child traits. They control for the endogeneity of investment using shocks to family income along with other instruments. Their model is a version of Eq. (3) . Traits are self-productive and exhibit dynamic complementarity—current values of traits affect the evolution of future traits through direct and cross effects. A leading example of a cross effect is that more motivated children are more likely to learn. They estimate parameters that summarize how past personality traits affect future cognitive traits.

They find that self-productivity becomes stronger as children become older, for both cognitive and personality traits. The elasticity of substitution for cognitive inputs is smaller later in life. This means that it is more difficult to compensate for the effects of adverse environments on cognitive endowments at later ages than it is at earlier ages. This finding is consistent with the high rank stability of cognition over ages past 10–12 reported in the literature. It also helps to explain the evidence on the ineffectiveness of cognitive remediation strategies for disadvantaged adolescents documented in Cunha et al. (2006) , Knudsen et al. (2006) and Cunha and Heckman (2007) .

Personality traits foster the development of cognition but not vice versa. It is equally easy at all stages of the child's life cycle to compensate for early disadvantage in endowments using personality traits. (Elasticities of substitution for these traits are essentially the same at different stages of the life cycle.) The most effective adolescent interventions target personality traits. 62

This paper reviews recent evidence on the importance of personality in economic and social life. It shows that success in life depends on many traits, not just those measured by IQ, grades, and standardized achievements tests. Personality traits predict and cause outcomes.

All psychological traits are measured by performance on tasks. Psychological traits have different productivities in different tasks. Performance on tasks depends on incentives and multiple traits, giving rise to a fundamental identification problem when measuring any single trait. This identification problem is empirically important even for measures of cognitive traits.

The importance of cognitive ability increases with the complexity of the task. Given their endowments of traits and the incentives they face, people sort into tasks in life in pursuit of their comparative advantage.

Traits are stable across situations, but their manifestation depends on incentives to apply effort in the situations where they are measured and also depends on other traits and skills. However, traits are not set in stone. They change over the life cycle and can be enhanced by education, parenting, and environment to different degrees at different ages.

Scores on achievement tests capture both cognitive and personality traits. Children who are more academically motivated and more curious learn more and have higher test scores. More motivated children also try harder on achievement tests.

The evidence in this paper should give pause to analysts and policy makers who rely solely on achievement tests to monitor school performance and school systems. Standardized achievement tests do not adequately capture many skills that matter in life. GED recipients perform about as well as high school graduates on achievement tests but perform much worse in many aspects of life because they lack important personality traits. Categorizing GED recipients as high school graduates misrepresents national statistics on educational attainment. 63 The Perry Preschool Program improved the lives of its participants without increasing their IQ scores, demonstrating why it is problematic to focus curricula exclusively on improving cognitive test scores.

Monitoring school progress and creating programs to enhance skills requires a broader framework of measurement. Interventions that promote beneficial changes in personality have an important place in a portfolio of public policies to foster human development.

✩ This paper was presented as the Adam Smith Lecture at the Annual Meeting of the European Association of Labour Economists held in Cyprus, September 2011. This research was supported in part by the University of Chicago, A New Science of Virtues: A Project of the University of Chicago, the American Bar Foundation, a conference series from the Spencer Foundation, the JB & MK Pritzker Family Foundation, Susan Thompson Buffett Foundation, the Geary Institute, University College Dublin, Ireland, NICHD R37 HD065072 and R01 HD054702, a European Research Council grant hosted by University College Dublin, DEVHEALTH 269874, a grant to the Becker Friedman Institute for Research and Economics from the Institute for New Economic Thinking (INET), and an anonymous funder. The views expressed in this paper are those of the authors and not necessarily those of the funders or commentators mentioned here. We thank Linor Kiknadze for research assistance. Paul Devereux made helpful comments that are incorporated in this paper.

1 Sales of achievement tests have increased by nearly 400% between 1959 and 2005 ( Digest of Education Statistics, various years ; The Bowker Annual: Library and Book Trade Almanac, various years ).

2 This paper draws on and supplements Borghans et al. (2008a) , Almlund et al. (2011) , and Heckman et al. (2012a) .

3 See Carroll (1993) and Ackerman and Heggestad (1997) for a discussion.

4 Many call this “IQ”, e.g., Flynn (2007) , Nisbett (2009) , and Nisbett et al. (2012) .

5 On the magnitudes of measurement error on a variety of economic measures, see Bound et al. (2001) . These authors report that at most 15–30% of earnings variance is due to measurement error.

6 Some early studies in economics are Bowles and Gintis (1976) , and Bowles et al. (2001) . An important study in sociology is Jencks (1979) . Work in psychology going back to Terman et al. (1925) shows that personality traits matter ( Murray, 1938 ; Terman et al., 1947 ; and the discussion in Gensowski, 2012 ).

7 See Mischel (1968) . Some behavioral economists share this view. See, e.g., Thaler (2008) .

8 See Epstein (1979) for an early paper showing that personality traits are stable across multiple situations. See the special issue of Journal of Research in Personality (43),“ Personality and Assessment at Age 40 ,” for a more recent discussion.

9 See Bouchard and Loehlin (2001) for estimates of the heritability of traits. See Canli (2006) and DeYoung et al. (2010) for evidence that regions of the brain are associated with different traits.

10 Drawing on the literature in psychology, Borghans et al. (2008a) present one definition of cognitive traits.

11 Throughout this paper, we adopt the convention of capitalizing traits from the "Big Five" personality taxonomy. See Table 3 for a description of the Big Five.

12 See Borghans et al. (2011a) .

13 See the evidence collected in Almlund et al. (2011) , Borghans et al. (2008a) , and Roberts et al. (2007) .

14 Borghans et al. (2008a) and Almlund et al. (2011) present extensive surveys of this literature.

15 The “Tools of the Mind” intervention is designed to promote “executive functioning,” which has both cognitive and personality components. Barnett et al. (2006 , 2008 ), Bierman et al. (2010) , Bodrova and Leong (2001 , 2007 ), Dee and West (2011) , Diamond et al. (2007) , Durlak et al. (2011) , Lillard and Else-Quest (2006) report success of this intervention. For a contrary view, see the study by Farran et al. (2011) .

16 In 1904, La Société Libre pour l'Etude Psychologique de l'Enfant appointed a commission to create a mechanism for identifying these pupils in need of alternative education led by Binet. See Herrnstein and Murray (1994) for an overview of Binet's life and work.

17 See Lindquist (1951) .

18 It is an irony of the testing literature that high school grades are more predictive of first year college performance than SAT scores ( Bowen et al., 2009 ). The SAT and related tests are thought to be more objective measures of student quality than high school grades ( Lemann, 1999 ).

19 See, e.g., Nisbett et al. (2012) .

20 See Roberts et al. (2000) .

21 See Raven et al. (1988) . The high correlation between intelligence and achievement tests is in part due to the fact that both require cognitive ability and knowledge. Common developmental factors may affect both of these traits. Fluid intelligence promotes the acquisition of crystallized intelligence.

22 Carroll (1993) and Ackerman and Heggestad (1997) discuss more disaggregated facets of cognitive ability.

23 See Flynn (2007) and Nisbett et al. (2012) . For examples in economics, see Benjamin et al. (2006) .

24 They include measures of adolescent risky behavior, self-esteem and locus of control (the extent to which people feel they have control over their lives). For precise definitions of the measures used, see the notes to Table 2 .

25 Lewis Terman, who created the Stanford-Binet test, even collected data on personality traits of a high-ability sample. In this sample, Conscientiousness is highly predictive of health and earnings ( Savelyev, 2011 ; Gensowski, 2012 ).

26 Studies of test-retest reliability of IQ tests show that scores are highly correlated across repeated testing occasions (see, e.g., Niolon, 2005 ).

27 See, e.g., Borghans et al. (2008a) .

28 See Borghans et al. (2008a) and Almlund et al. (2011) for a comparison of these taxonomies.

29 See, e.g., Cloninger et al. (1999) .

30 McAdams and Pals (2006) adds goals to the list of possible traits. Almlund et al. (2011) develop a model in which preferences and traits determine the effort applied to tasks.

31 See Borghans et al. (2011a) and Almlund et al. (2011) .

32 A leading psychometrician, Carroll (1993) , does not accept the notion that IQ captures maximal effort.

33 See Ayllon and Kelly (1972) , Borghans et al. (2008b) , Breuning and Zella (1978) , Clingman and Fowler (1976) , Edlund (1972) , Holt and Hobbs (1979) , Larson et al. (1994) , Segal (2008) . This evidence is summarized in Borghans et al. (2008a) and Almlund et al. (2011) .

34 See, e.g., Nisbett (2009) .

35 The correlation between DAT and AFQT scores in the National Longitudinal Study of Youth 1979 (NLSY79) is 0.76 to 0.80 ( Borghans et al., 2011b ). Friedman and Streicher (1985) estimate correlations between 0.65 and 0.82 for in a sample of high school sophomores and juniors. Kettner (1976) estimates correlations between DAT and the AFQT subtests of 0.76 to 0.89 in a sample of juniors and seniors

36 The lower explained variance in the sample with DAT is likely a consequence of restriction on range. The DAT data come from a single school, whereas the AFQT data come from a national sample.

37 See Borghans et al. (2011b) for information on the Stella Marris secondary school and the analysis described in the text.

38 This theme has been picked up in behavioral economics. See Thaler (2008) .

39 See the special issue of Journal of Research in Personality (43), entitled “ Personality and Assessment at Age 40 ” for a recent discussion.

40 Devlin et al. (1997) suggest that traditional estimates of the heritability of IQ may be inflated because they fail to take into account the effect of the environment on conditions in the maternal womb. See also Rutter (2006) and an emerging literature on epigenetics.

41 See the evidence collected in Borghans et al. (2008a) and Almlund et al. (2011) for a variety of other traits.

42 See McArdle et al. (2000) .

43 Results are similar for women (see Almlund et al., 2011 ).

44 See Almlund et al. (2011) , Borghans et al. (2008a) , and Roberts et al. (2007) for comprehensive reviews of the evidence.

45 This framework draws on Almlund et al. (2011) .

46 The empirical relationship between measured preference parameters and Big Five measures is weak (see Almlund et al., 2011 ).

47 Selecting measures and verifying them is part of the sometimes mysterious and in-herently subjective process of “construct validity” in psychology. For a discussion, see Borghans et al. (2008a) .

48 Formally, this occurs when ∂ 2 ψ T a ∂ P a ∂ R T a ≠ 0 .

49 Similar results are found for females.

50 The data sets are the National Longitudinal Survey of Youth 1979 (NLSY79), National Longitudinal Survey of Youth 1997 (NLSY97), and National Educational Longitudinal Survey (NELS). For discussion of these data sets, see Heckman et al. (2012a) .

51 See Heckman et al. (2012a) .

52 The increased labor supply response is largely due to female GED recipients who attain some post-secondary education or who have dropped out of high school due to pregnancy. See Heckman et al. (2012a) for a full discussion of the evidence on the performance of GED recipients.

53 We draw on the analysis of Heckman et al. (2012b) .

54 Sylva (1997) describes the Perry program as a Vygotskian program fostering personality traits. Vygotsky developed a psychology of child development in structured social settings that emphasized development of social and personality skills. The Vygotskian approach strongly influences the Tools of the Mind program (see Bodrova and Leong, 2001 , 2007 ).

55 See DeLong and Magin (2009) .

56 See Bierman et al. (2010) .

57 Note, however, that the largest federal study to date on character education programs, including PATHS, failed to find evidence for improvements in behavior or academic performance (see Social and Character Development Research Consortium, 2010 ).

58 However, amore recent large-scale study ( Farran et al., 2011 ) does not find any effect of the program on self-regulation or literacy, language, and mathematics achievement.

59 The relationship between locus of control and the Big Five trait of Neuroticism is discussed in Almlund et al. (2011) .

60 They estimate the effect of schooling on self-esteem and locus of control, personality traits related to Neuroticism. The Rosenberg Self-Esteem Scale attempts to assess the degree of approval or disapproval of oneself ( Rosenberg, 1965 ). The relationship between these measures and the Big Five traits of Neuroticism is discussed in Almlund et al. (2011) .

61 Both Heckman et al. (2011b) and Heckman et al. (2006b) use an identification strategy based on matching on proxies for unobserved traits that corrects for measurement error and the endogeneity of schooling.

62 Cunha et al. (2006) report that 16% of the variation in educational attainment is explained by adolescent cognitive traits, 12% is due to adolescent personality (socio-emotional traits), and 15% is due to measured parental investments.

63 See Heckman and LaFontaine (2010) .

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The Essence of Soft Skills

Appropriate soft skills play an important role in getting employment, to lead a successful career as well as during social interactions in the society. Soft skills are also called as - Interpersonal skills, Life skills, People skills- are the abilities and skills that help to become a complete professional especially in corporate sectors around the globe. The students aspiring to become Professionals not only need academic and technical skills but also Soft-skills to meet the goals of the organization. The professionals who want to lead successful career are not getting expected jobs or promotions because of soft skills deficit. A considerable number of people are not reaching their destinations because of not having required soft skills in social interaction. Soft skills are required to all human beings to lead the life smoothly. They are required in all professions whether it is business, employment, Politics, Administration or service. This paper throwslight on the questions how important softskills are and which skills can be learned and acquired in educational institutes and workplaces. This paper also emphasizes the necessity of incorporating soft skills training programs in curriculum, highlighting the objectives of soft skills and various teaching methods to be applied. This paper is divided into seven parts. First part is introduction, Second part is meaning and definition, third one Review of Literature, Fourth one objectives of soft skills training program, fifth one Most important Soft skills, Sixth one personality development and soft skills and the last one is conclusion.

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Soft skills are the new demand of HRs in all the corporate and educational organizations. As it includes the behavioral, interpersonal, communication, leadership, problem solving, managerial and other characteristics traits of personality. These are the skills which represents an organized and well-mannered reflection of human. Previously soft skills were found understood but now as population is increasing, it is decreasing the employability skills and so the job opportunities. People are busy in collecting degrees and experience certificates of various jobs and they have underestimated behavioral and social skills. Such skills have now become the most expected skills in each and every interview as well as in professional working. This paper will help professionals to understand the importance and significance of learning these soft skills.

Nguyễn Hoàng Phúc

This paper is an attempt to communicate the importance of soft skills in students' lives both at secondary school level as well as at the college or Higher education stage. The paper is also extract of true experience in 12 years of teaching students at under graduate and post graduate level, and how soft skills complement hard skills, which are the technical requirements of a job the student is trained to do. Having worked on both side of the fence as a teacher as well currently working as consultant for recruiting and training young graduates, the paper is a true attempt to highlight how soft skills can improve various aspect of a student in communication skill as well behavioural aspect. It is the prime responsibility of educators and educational institutions to take special responsibility regarding imparting of soft skills, because during students' university time, educators have major influence on the development of their students' soft skills. Blending the training of soft skills into hard skills can make teaching fun for the teacher while it would be fun for the students breaking the monotony of theory lectures. Through various stories and interesting real time learning an attempt has been made to impart soft skills through animated movies which are for 2 to 5 minute duration. Over 100000 young students have been imparted these soft skills and have been successfully offered gainful employment in retail, apparel and textile sector where the author was engaged in working.

Nataliia Zinukova

mani raj rokz

Gilang Permana

Lbstract: With the changing educational trends, versatility in educational courses, availability of masses of qualifed per-v'hile remaining 85ok is made by soft skills' Most employers dependable, resourceful, ethical, effective oiit d". With the onset of econo e Indian are to be imbibed by Indian youth to show their real potential a institutions in India have initiated programmes' proiecls and as undertaken in the most dynamic city of India' Chandigarh' I J'acilities and iob lls in students, tYPe equipped with relev(,nt soft skills. EW TECHNOLOGIES HAVE been driving the emergence of a new 'knowledge economy' with ever increasing proportion of national GNPs dedicated to new technology based industry and high level services sector' The changes in occupational structure and required skills as countries adopt new technologies, therefore apply to emerging 'knowledge economy' as well, along with their implications for higher education and training system which is the backbone for the gowth and development of any country. The 'knowledge economy' is not just high technology industry or lCT. It can be broadly described as the one that hamesses and uses new and existing knowledge to improve the productivity of various sectors such as agriculture, industry servrces etc' and increases overall welfare. The central theme of the emergence of a knowledge economy revolves around the knowledge which may be in the form of complex problem solving, innovation and creativity, visualization of new markets, understanding social and global implications, working in new environments and with people of different cultures and countries, developing new products and services etc" ln this scenario the contemporary workplace is also characterized by (rather than merely subject to) continuous change. One impact of this transformation is that lifelong learning has been identified as a means of sustaining employability as well as professional and career growth. It is widely believed that such dimensions of performance as learning, innovation and enterprises can be enhanced by placing greater emphasis on developing soft skills within the education and training processes. While hard skills are discipline specific skills needed to performbasic duties at work, soft skills are the ones that define an individual's approach towards work and life problems etc. In other words, it can be said, while hard skills are the academic skills, experience and level of expertise as generally described in a resume and indicate the knowledge of concepts, principles, methods, procedures and techniques needed for performing jobs, soft skills are those sought out behaviours and characteristics that employees demonstrate unconsciously and routinely on the job.

International Journal of Trend in Scientific Research and Development

rajeswari surisetty

Universal Journal of Educational Research

Horizon Research Publishing(HRPUB) Kevin Nelson

Nowadays, as a result of economic changes, employers are eager to look for employees with applied skills rather than cognitive skills. Diplomas are not being considered as a definitive indicator of students' professional intelligence as it used to be before. Fourth Industry Revolution has dramatically changed labor market and knowledge is now acknowledged as strength of productivity and economic growth, and information technology has acquired great importance. In such a situation, whether employees start their career or change a job, they can show successful performance, if they can change and update themselves continuously, build positive interpersonal relationships in the workplace, learn and win in the unpredictable challenges of the work place. Accordingly, today the graduates of Higher Education need to master not only professional skills of their job, but also various soft skills, including the ability to communicate, coordinate, work under pressure, and solve problems. The purpose of this article is to provide literature on the connection of soft skills with employability and world labour market, the role of soft skills in career success for future professionals. The article also examines the definition and selection of the most demanding soft skills from the perspective of different literature. The role of Higher Education in forming and developing soft skills will then be discussed. The article illustrates the results of survey carried out with the aim to analyze the degree of integration of soft skills in Study Skills program that is taught for bachelor students in Uzbek State World Languages University and academic curricula.

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Richa Vishwakarma

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6 Ways to Sharpen Your Skills As a Medical Professional

by Jasmine Pennic 07/19/2024 Leave a Comment

For medical professionals, maintaining high-quality care in a field that is developing quickly is a constant struggle. To keep ahead of the ever-evolving landscape, they must constantly hone their talents. Lifelong learning is more than just a trendy idea; it’s a necessity for keeping up with technology improvements and satisfying patients’ ever-growing expectations.

This should not be just about clinical skills; soft skills are equally important, reflecting how well you can connect with and relate to your patients. Beyond just being a trend that is gaining popularity, lifelong learning is crucial to keeping up with technology improvements and satisfying the ever-growing needs of patients. Here are six approaches to upgrading your professional skills.

1. Continuously Improve Clinical Skills

New medical technologies are constantly being developed, improving the methods used in treating patients and enhancing their experience. For efficiency, you should continue studying and getting experience in taking on new tasks. Programs for Continuing Medical Education (CME), created especially to assist medical practitioners in staying up to date with developments in their field, may be something you want to think about. Through conferences, workshops, and seminars, CMEs offer organized learning opportunities that guarantee you acquire current knowledge and skills.

Continue to search for relevant online courses and certifications in various areas like anesthesia, cardiology, radiology, or some other specialty appropriate to your training. For instance, you could consider finding out about the use of ketamine, a medication at first created as a sedative, which is currently being investigated for its expected advantages in dealing with mental conditions. By taking specific courses at a ketamine clinic training on ketamine’s administration, safety, and effects, you can extend your treatment choices and enhance patient care.

2. Hands-On Experience and Simulation Training

Simulation exercises, such as emergency response drills and mock surgeries, are highly immersive and effective for learning new techniques in a controlled environment. The sessions will always have a mimicking touch on real-life situations that goes a long way to train one on habits of developing problem-solving abilities while adapting to most circumstances. The more one participates in it, the more confidence and better practice in time is well developed.

You could aim for several simulation exercises, incorporating trauma simulations, VR, and ACLS. That will give you a picture of the actual medical scenarios, hence the reason you know how to work under pressure to make some of the critical decisions.

3. Networking and Mentorship

Developing a network with other industry players has become an essential way to exchange expertise. Seek for opportunities to network with peers and industry leaders through online forums, professional association memberships, conferences, and other means.

One can also grow one’s career by channeling more insights to improve service delivery. For instance, a mentor closes the gap between practical execution and theoretical knowledge, increasing productivity.

4. Staying current with Medical Research

Evidence-based care is relevant in stipulating that a patient receives effective services. Medical studies and advances have made this explicit by increasing one’s knowledge of the changing industry issues. Take grounds on online resources that set the mark by using aids such as Google Scholar and PubMed to familiarize oneself with best practices and treatments.

Work efficiency calls for wide-based dissemination of new research results within the operations before implementation. It encompasses the assessment of the relevance and validity of new research, with sufficient weight placed upon the study design and sample size. As a health professional, this gives you the best impression of serving your patient effectively using the most current therapy.

5. Enhancing Soft Skills

While enhancing your technical prowess, you should invest in soft skills like empathy, teamwork, and great expression. For example, mastery in your communication is fundamental to ensuring your patients get all the information they would like, from diagnosis to treatment options and follow-ups. Being able to express the right words at the right time is going to be of utmost importance as regards the recovery and assurance of the patient.

The healthcare sector involves several professionals who must unite to ensure the offering of suitable, timely services to patients. Teamwork is a very vital attribute that each professional must possess.  By virtue, such working environments are very cohesive, and so the chances of miscommunication or even conflict of any kind are really reduced, all adding up to great customer care.

6. Join Professional Medical Associations

Many associations offer numerous benefits, from more access to primary medical associations’ resources, events, and journals to unique networking and professional development opportunities. Keep on joining more groups and committees to get even more exposure.

These associations advocate for the welfare of medical professionals and put you together with like-minded professionals in one voice during policy-making. You discover more about regulatory issues, which others still find inspiringly challenging when thinking of refined ways to beat the push for change.

In medicine, service quality will have a dependency on how sharp one is in service and techniques. You have to think of how best you would improve your care by adopting various techniques and knowledge with time. Search for opportunities for learning, either by taking one’s courses or attending particular events in the industry.

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