Human capital and leadership: the impact of cognitive and noncognitive abilities

ABSTRACT

We conduct an economic analysis about the impact of human capital on an individual’s potential of becoming a leader based on data from the Programme for the International Assessment of Adult Competencies Survey (PIAAC). Our human capital indicators include not only traditional measures such as education and experience, but also various measures of cognitive and noncognitive ability. Our cognitive ability measures include numeracy, literacy, and problem solving abilities, and noncognitive ability measures include perseverance, motivation to learn, and social trust. We specifically investigate the effect of measurement error and reverse causality on the estimation results. We find that problem-solving ability is the most important in affecting leadership among cognitive ability measures, and perseverance shows the strongest impact among noncognitive ability measures. As a leader supervises more employees, the role of cognitive and noncognitive ability becomes more critical.

KEYWORDS:

• Human capital
• leadership
• cognitive ability
• noncognitive ability

JEL CLASSIFICATION:

• I21
• J24

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ There is a large literature about leadership in the area of psychology and organizational behaviour literatures (See Zaccaro 2007; Yukl 2008,; Dinh et al. 2014 for a detailed review).

² The link provides access to the PIAAC data: http://www.oecd.org/site/piaac/publicdataandanalysis.htm.

³ We exclude individuals in agriculture industry and the military. These industries involve different mechanisms of becoming a leader, and are not considered in the current study.

⁴ Individuals are defined as managers if they belong to one of these occupational groups: administrative and commercial managers (ISCO = 12), production and specialized services managers (ISCO = 13), hospitality, retail, and other services manager (ISCO = 14).

⁵ These two measures are constructed based on two PIAAC questions: ‘Do you manage or supervise other employees?’ and ‘How many employees do you supervise or manage directly or indirectly?’ The response options for the first question include ‘Yes’ and ‘No’ and for the second question include ‘1 to 5 people’, ‘6 to 10 people’, ‘11 to 24 people’, ‘25 to 99 people’ and ‘100 or more people’.

⁶ When presenting descriptive statistics and regression analyses, we divide all cognitive ability scores by 100. Thus, the numeracy, literacy, and problem solving scores range from 0 to 5 in the analyses that we apply.

⁷ Numeracy tasks require, for instance, calculating the number of layers of tea candles packed in a box given other information or calculating the cost of a trip from a motor-vehicle logbook.

⁸ The literacy test contains questions that require finding the right contact information in a simulated website, identifying the name of the author of a particular book in a simulated library website, and extracting certain information from given paragraphs or tables.

⁹ Problem-solving questions include tasks such as reserving a meeting room on a particular date using a reservation system, organising a family get together, and locating information on a spreadsheet and then e-mailing the requested information.

¹⁰ Other studies use similar questions to measure perseverance such as ‘I have achieved a goal that took years of work’ and ‘I have overcome setbacks to conquer an important challenge’ (Duckworth et al. 2007).

¹¹ Because three cognitive ability measures are highly correlated, incorporating them together in the model causes significant multicollinearity issues. The coefficient of problem solving ability remains to be positive and significant, but literacy becomes negative and significant.

¹² If we include the noncognitive measures in the model separately, the results are similar. That is, perseverance is the only significant predictor out of the noncognitive abilities.

¹³ The Multiple Indicator approach is closer to classic approach of errors-in-variables but is less restrictive. More specifically, for the MI approach, in equation $\mathrm{y}={\alpha }_0+\mathbf{x}\beta +\gamma \mathrm{q}+\mathrm{v}$, where $\mathrm{q}$ represents the omitted ability variable. Suppose we have multiple indicators of $\mathrm{q}$ from $q_1$ to $q_n$, and they are highly correlated. Indicator $q_1$can be written as $q_1={\delta }_0+{\delta }_{1}q+a_1$, where $\mathrm{c}\mathrm{o}\mathrm{v}\left(\mathrm{q},a_1\right)=0$ and $\mathrm{c}\mathrm{o}\mathrm{v}\left(\mathbf{x},a_1\right)=0$. If we rewrite $q$ as a function of $q_1$ and then substitute it back into the original equation, we then have $\mathrm{y}={\alpha }_1+\mathbf{x}\beta +{\gamma }_1{\mathrm{q}}_1+\mathrm{v}-{\gamma }_1{a}_1$, where ${\gamma }_1=\frac{\gamma }{{\delta }_1}$ . Then, we express each of the rest of the indicators (i.e. $q_2$ to $q_n$) as a function of $q$, so we obtain an error term for each indicator of q (i.e. $a_1$ to $a_{\mathrm{n}}$). Since all the common component in indicators has been controlled by q, MI approach assumes that the error terms are uncorrelated, then $q_2$ to $q_n$ become valid instruments for ${\mathrm{q}}_1$.

¹⁴ Because of data limit, we do not have useful information in investigating the potential endogeneity issue for noncognitive ability measures.

¹⁵ The average age of leaders in our sample is 42.

Additional information

Funding

This study was funded by the National Natural Science Foundation of China (71773151) and the Fonds National de la Recherche Luxembourg (ATTRACT "ASKI21").