Giving Informal Learning the Recognition it Deserves

Annex A. Quantitative analysis of informal learning

This annex provides a detailed description of the quantitative analysis on both benefits and drivers of informal learning. It outlines the data sources and sample composition, explains the methodology, and summarises the key results.

Overview of data sources and sample composition

This analysis draws on two major international surveys: the OECD Survey of Adult Skills (PIAAC) and the EU Adult Education Survey (AES). Together, they provide complementary insights into informal learning across demographic and labour market contexts.

The 2023 PIAAC wave covers 29 countries and 151 711 adults aged 16‑65, of whom around two‑thirds were employed. It is well suited to examining workplace learning, measuring behaviours such as learning by doing, updating knowledge on products and services, and learning from colleagues, with data collected on weekly frequency.

AES, co‑ordinated by Eurostat and conducted in over 30 countries in 2022, surveys adults aged 18‑64 and distinguishes between formal, non-formal and informal learning. Informal learning is defined as intentional, non-institutionalised activity, with greater emphasis on non-workplace contexts and detailed typologies of learning modes.

Both surveys provide rich contextual data but differ in scope, recall periods, and coverage of incidental learning. Their breadth and comparability make them uniquely valuable for this study.

Methodology

Variables included in the analysis (PIAAC)

The main outcome variable in the drivers analysis is a binary indicator capturing whether an individual engages in any form of informal learning at work at least once per week. This is derived from three distinct PIAAC questions that ask how frequently respondents:

• How often does your current job involve learning new things? (H2_D09a)

• How often does your current job involve learning-by-doing from the tasks you perform? (H2_D09b)

• How often does your current job involve keeping up to date with new products or services? (H2_D09c)

The other variables included in our analysis are summarised in Table A A.1.

Table A A.1. PIAAC Indicators included in the analysis

Category	Indicator
Individual Background and Cognitive Skills	Age: Four categorical dummies (25‑34, 35‑44, 45‑54, 55‑64) Gender: Binary variable (female = 1). Immigration Status: Binary indicator for foreign-born individuals. Parental Education: Highest education level among parents, coded into three levels (low/medium/high). Books at Home (Age 14): Categorical measure capturing the number of books in the household at age 14; serves as a proxy for early learning environment. Education Level: Respondent’s highest attained level, grouped as low (up to lower secondary), medium (upper secondary), and high (tertiary). Literacy Skills: Standardised PIAAC literacy levels (based on scores – plausible values), used as a continuous control.
Learning Motivation and Behaviours	Non-formal learning participation and motivation: Four-category variable combining participation and intention. This is based on whether the respondent participated in non-formal training in the past 12 months (question B2_Q08a) as well as reason for participating (question B2_Q12) and not having participated despite wanting to (question B2_Q12): Did not participate and did not want to Did not participate but wanted to Participated but did not want to (reason for participation=was obligated to participate) Participated and wanted to (not necessarily obligated). Reading at Home: Binary variable for reading books, newspapers, etc. at least once per week. Accessing Information Online at Home: Binary variable for seeking information online at least once per week.
Job Complexity and Requirements	Simple and Complex Problem Solving: Frequency-based dummies derived from how often workers perform each task at work. Low Skills for Job: Binary variable based on self-reported perception of having too few skills for one’s job. Job Requirements Index: Composite score (standardised) aggregating education and experience required for the job, based on self-reports.
Workplace Context and Organisation	Company Size: Categorical variable with six groups: 1‑10, 11‑49, 50‑249, 250‑499, 500‑999, and 1 000+ employees. Tenure at Employer: Continuous variable measuring years with current employer. Change in Work Environment: Binary indicator capturing any recent change in work content, technologies, or client interaction.
Workplace Learning Environment and Skill Use	High-Performance Work Practices (HPWP): Two factors constructed using principal component analysis (PCA): Autonomy: Index summarising workers’ control over how they perform their tasks and organise their time. Including: how often a worker chooses own tasks, chooses how to work, chooses work speed, chooses work hours, organises own time, and plans own activities. Teamwork: Index summarising teamwork practices, reflecting shared goals, and peer learning. Include: working in team, ability to influence team targets and leader, getting assistance from colleagues when needed
Learning Environment	Collaboration with Colleagues: Continuous index based on reported frequency of collaboration with other colleagues. Sharing Information and Teaching Others: Indexes derived from frequency of peer-learning behaviours.
Skill Use at Work: Binary indicators for weekly use of:	Reading. Writing. Numeracy. Physical skills. Hands and fingers (manual dexterity)
Social-emotional skills/personality types (for subset of countries)	For countries with available data (Canada, Chile, Czechia, Estonia, Germany, Italy, Korea, New Zealand, Norway, Portugal, the Slovak Republic, Spain), Big Five personality traits are derived using factor analysis from 17 behavioural items: Emotional stability. Compassion. Conscientiousness. Extraversion. Openness
Benefits of informal learning	Three outcome variables are used to assess the benefits of informal learning, capturing both economic and subjective well-being: Hourly Wages (log-transformed): The main outcome is the natural log of gross hourly wages. This allows interpretation of results as percentage differences in earnings. Job Satisfaction: A self-reported measure on a 1‑5 scale, where 1 is “extremely dissatisfied” and 5 is “extremely satisfied.” This captures how content individuals are with their current job. Life Satisfaction: A self-reported measure on a 0‑10 scale reflecting overall satisfaction with life. It provides a broader view of well-being beyond the workplace.

Variables included in the analysis (AES)

The main indicator for informal learning is the following question “During the past 12 months, have you deliberately tried to learn anything on a particular topic or area, or are you currently doing it”. Respondents are then presented with five informal learning activities and can indicate “yes” or “no” for each of them.

1. Learning from a family member, a friend or a colleague,

2. Learning by using printed material (books, professional magazines, etc.),

3. Learning by using electronic devices (online or offline),

4. Learning by guided tours in museums, historical or natural or industrial sites,

5. Learning by visiting learning centres (including libraries).

The other variables included in our analysis are summarised in Table A A.2.

Table A A.2. AES Indicators included in the analysis

Category	Indicator
Individual Background	Age: Four categorical dummies (25‑34, 35‑44, 45‑54, 55‑64). Gender: Binary variable (female = 1). Education Level: Respondent’s highest attained level, grouped as low (up to lower secondary), medium (upper secondary), and high (tertiary). Labour market status: grouped as employed, unemployed and inactive. Occupation: at ISCO level 1. Household type: grouped as One‑person household, single parent, couple without children, couple with children.
Learning indicators	Participation in non-formal learning: distinguishing the following types Participation in courses at the workplace or in your free time Participation in workshops or seminars at the workplace or in your free time Participation in guided on-the‑job training, i.e. planned periods of training, instruction or practical experience, using normal tools of work, either at the immediate place of work or in a work-situation, with the presence of a tutor Participation in private lessons with the aid of a teacher or tutor for whom this is a paid activity Participation in formal education: based on the question “During the last 12 months, have you participated (as a student or apprentice) in formal education or training?”

Benefits of informal learning

The analysis examines whether engaging in informal learning at work is associated with higher earnings, greater job satisfaction and improved life satisfaction, using the PIAAC database given its wide range of indicators. To address concerns of selection and omitted variable bias, three models of increasing robustness are estimated: ordinary least squares (OLS), propensity score matching (PSM), and a Heckman selection model.

• Ordinary Least Squares (OLS). The baseline specification regresses log hourly wages on a binary indicator of weekly informal learning, controlling for demographic background, education, cognitive skills, workplace context, and fixed effects for country, industry and occupation. This provides an initial estimate of the wage premium, though it cannot account for unobserved characteristics such as motivation.

• Propensity Score Matching (PSM). PSM compares learners with non-learners who share similar observable characteristics, thereby reducing bias from selection on observables. While PIAAC offers a rich set of controls, the method cannot adjust for unobserved traits or ensure full matching.

• Heckman Selection Model. This model is based on the ones used in “Returns to different forms of job related training” (Fialho, Quintini and Vandeweyer, 2019[1]). To address selection on unobservables, a two‑step model is applied. The first step estimates the probability of engaging in informal learning, using exclusion restrictions such as at-home learning behaviours, before incorporating the Inverse Mills Ratio into the wage regression. This approach corrects for potential bias but relies on strong distributional assumptions and the validity of the restrictions.

Across all models, simultaneity remains a limitation: PIAAC measures both learning and outcomes at a single point in time, making it difficult to disentangle cause and effect. Results should therefore be interpreted as robust associations rather than causal effects. Nevertheless, consistency across specifications strengthens confidence that the observed relationships are meaningful.

Drivers of informal learning

To examine the drivers of informal learning at work, we estimate linear probability models (OLS and logit) using PIAAC and AES data. The outcome is a binary indicator of whether individuals engage in informal learning. In PIAAC, country, industry and occupation fixed effects control for structural differences, while some specifications also include measures of social-emotional skills (Big Five traits).

Robustness was tested through alternative outcome definitions (individual behaviours, hours spent learning), by adding and excluding variable blocks (e.g. personality, workplace practices), and by estimating models separately by gender, education and country. All models apply replicate weights and appropriate variance estimation procedures.

The models incorporate extensive controls on individual characteristics, workplace context, skill use and learning behaviours. Nonetheless, several limitations remain. First, the cross-sectional design identifies associations rather than causal relationships, and self-selection into supportive jobs may bias estimates. To mitigate this, indicators of learning motivation and non-formal learning participation were included, and in some countries, results were tested with personality measures. Second, some predictors overlap conceptually with the outcome (e.g. skill use and teamwork), though factor analysis and diagnostic checks reduced collinearity risks. Finally, reliance on self-reports may introduce perception biases, yet consistent findings across specifications and subgroups lend confidence to the robustness of results.

Results

Benefits of informal learning

The analysis demonstrates that informal learning is consistently and significantly associated with higher job satisfaction. In the OLS model, learners report 0.09 points higher satisfaction (on a 1‑5 scale), rising to 0.11 points in the PSM model (see Table A A.3). The Heckman model confirms a significant 0.09‑point effect. Alignment across methods strengthens confidence that informal learning enhances job satisfaction, likely through greater engagement and personal development.

For life satisfaction, the evidence is also positive though effects are smaller. OLS estimates a 0.13‑point increase (on a 0‑10 scale), significant at the 10% level, while PSM yields a larger effect of 0.19 points and Heckman a 0.12‑point increase. These consistent results suggest that informal learning is moderately linked to greater overall well-being.

By contrast, the analysis finds no consistent evidence that informal learning at work leads to higher wages. Findings across the different models are not statistically significant and the direction of the association varies across estimation methods. Overall, any apparent wage advantage seems to reflect underlying differences between learners and non-learners rather than a genuine return to informal learning.

Additional tests for broader outcomes, such as social trust and self-reported health, show no robust associations. Although some models suggest modest positive effects, they are not consistent across specifications. Thus, the benefits of informal learning appear strongest for job and life satisfaction, with weaker or more uncertain links to other social outcomes.

Table A A.3. Results – benefits of informal learning

Outcome	Model	Estimate	R-squared	N
Log hourly wage	OLS	0.025 (0.022)	0.335732	63 429
	PSM (ATT)	‑0.005 (0.01)		63 173
	Heckman correction	0.024 (0.023)	0.344656	68 885
Job satisfaction	OLS	0.092 (0.027)**	0.110	67 114
	PSM (ATT)	0.106 (0.012)***	–	66 833
	Heckman	0.089 (0.019)***	0.066	27 982
Life satisfaction	OLS	0.132 (0.063) *	0.137	67 075
	PSM (ATT)	0.190 (0.026) ***	–	66 793
	Heckman	0.118 (0.069) *	0.134	66 803

Note: All models include controls for individual background (age, gender, migration status, parental education, books at age 14), education and cognitive skills (PIAAC literacy level, own education), job and workplace context (firm size, tenure, job complexity, job-skill mismatch, high-performance work practices, and workplace learning culture), and fixed effects for country, industry, and occupation. PSM uses one‑to‑one nearest neighbour matching with a calliper of 0.01 and 500 bootstrap replications. Heckman selection correction is based on a two‑step model: The selection equation (first step) includes all variables above, plus several exclusion restriction variables hypothesised to affect the likelihood of participating in informal learning but not directly related to job satisfaction: frequency of reading at home, frequency of accessing information online. These variables proxy a person’s intrinsic learning interest or style, which is assumed to influence participation in informal learning but not to directly affect wages or satisfaction once other factors are controlled.

p < 0.1=*, p < 0.05 = ** p < 0.01 = ***

Drivers of informal learning

Regression results (see Table A A.4) reveal several clear patterns. Age is a strong predictor: participation in informal learning declines steadily over the working life, likely reflecting reduced demands or opportunities as roles stabilise. Literacy proficiency is negatively associated, suggesting that lower-skilled workers may rely on informal learning to adapt or fill gaps.

Gender differences are modest. Women are slightly more likely than men to engage overall, particularly through learning by doing and keeping up to date, though not necessarily by learning new things.

Workplace context is central. Team-based practices, recent organisational changes, and skill mismatch are robust positive predictors, while longer tenure reduces learning opportunities. Voluntary participation in non-formal training strongly increases informal learning, with smaller effects from mandatory training. Skill-rich jobs and frequent collaboration also foster learning, whereas firm size and online information seeking show little effect.

In countries with data on personality traits, compassion, conscientiousness and emotional stability are positively associated with informal learning, while extraversion is not significant and openness shows a small negative link (see Table A A.5). Including these traits modestly improves model fit but does not alter the main findings. Overall, workplace environment and motivation emerge as the strongest drivers, with personality traits playing a complementary role.

Table A A.4. Drivers of informal learning – main results

Variable Block	Variable	Learning new things	Leaning by doing	Keeping up to date	Any type
Background characteristics	age (25‑34)	‑0.060 (0.030)**	‑0.063 (‑0.063)**	‑0.022 (0.027)	‑0.071 (0.022)***
	age (35‑44)	‑0.090 (0.030)***	‑0.099 (‑0.099)***	‑0.040 (0.028)	‑0.092 (0.021)***
	age (45‑54)	‑0.120 (0.030)***	‑0.124 (‑0.124)***	‑0.046 (0.030)	‑0.128 (0.024)***
	age (55‑64)	‑0.110 (0.040)***	‑0.127 (‑0.127)***	‑0.033 (0.034)	‑0.104 (0.024)***
	Gender (female)	0.010 (0.010)	0.028 (0.028)**	0.036 (0.013)***	0.022 (0.012)*
	Immigrant	0.010 (0.020)	‑0.035 (‑0.035)	‑0.026 (0.023)	‑0.024 (0.021)
	Number of books at 14	0.010 (0.010)	0.002 (0.002)	‑0.002 (0.005)	0.006 (0.005)
	Parental education (high)	‑0.030 (0.020)	‑0.019 (‑0.019)	‑0.015 (0.019)	‑0.001 (0.019)
	Parental education (medium)	‑0.010 (0.020)	‑0.015 (‑0.015)	0.000 (0.018)	0.008 (0.016)
Cognitive skills	Education(high)	‑0.030 (0.030)	‑0.004 (‑0.004)	‑0.030 (0.028)	‑0.022 (0.026)
	Education(medium)	‑0.040 (0.030)	‑0.004 (‑0.004)	‑0.020 (0.028)	‑0.028 (0.028)
	Literacy PIAAC level	‑0.030 (0.010)***	‑0.034 (‑0.034)***	‑0.038 (0.009)***	‑0.025 (0.007)***
HPWP	Autonomy at work	0.040 (0.020)**	0.017 (0.017)	0.016 (0.018)	0.018 (0.016)
	Team practices at work	0.020 (0.010)**	0.010 (0.010)	0.027 (0.007)***	0.015 (0.007)**
Job complexity	Complex problem solving at work				0.001 (0.000)***
Job requirements	0.020 (0.010)**	‑0.005 (‑0.005)	‑0.009 (0.011)	0.001 (0.010)
Low skills for Job	0.110 (0.020)***	0.102 (0.102)***	0.006 (0.020)	0.094 (0.018)***
Simple problem solving at work				0.000 (0.000)***
Job context	Change in work environment	0.020 (0.010)**	0.027 (0.027)	0.062 (0.015)***	0.056 (0.014)***
	Company size (1000 or more)	0.040 (0.030)	‑0.012 (‑0.012)	0.002 (0.026)	0.008 (0.023)
	Company size (11‑49)	0.000 (0.020)	‑0.004 (‑0.004)	‑0.023 (0.022)	‑0.004 (0.018)
	Company size (250‑499)	0.030 (0.030)	‑0.003 (‑0.003)	0.004 (0.028)	0.010 (0.024)
	Company size (500‑999)	0.000 (0.030)	‑0.012 (‑0.012)	‑0.014 (0.030)	0.007 (0.027)
	Company size (50‑249)	‑0.010 (0.020)	0.015 (0.015)	‑0.009 (0.020)	0.011 (0.017)
	Tenure at employer		‑0.005 (‑0.005)***	‑0.002 (0.001)**	‑0.004 (0.001)***
Learning behaviour	Access information online	‑0.020 (0.030)	0.025 (0.025)	‑0.007 (0.029)	0.006 (0.027)
	Non formal (didn’t participate but wanted)	0.020 (0.020)	0.012 (0.012)	0.002 (0.027)	0.029 (0.024)
	Non formal (Participated and wanted to)	0.050 (0.020)**	0.039 (0.039)***	0.028 (0.016)*	0.059 (0.014)***
	Non formal (participated but didn’t want)	0.020 (0.020)	0.012 (0.012)	‑0.003 (0.025)	0.040 (0.021)*
	Read at home (at least once a week)	0.000 (0.020)	0.016 (0.016)	0.027 (0.017)	0.016 (0.014)
Learning environment	Collaboration with colleagues	0.020 (0.010)**	0.019 (0.019)***	0.021 (0.005)***	0.017 (0.005)***
Sharing information with colleagues				0.000 (0.000)**
Teaching others at work				0.000 (0.000)***
Skills use at work	Hands and finger use		0.013 (0.013)***	0.010 (0.004)**	0.010 (0.004)**
	Numeracy skills	0.020 (0.010)**	0.013 (0.013)	0.028 (0.008)***	0.019 (0.008)**
	Physical skills	0.010 (0.010)	0.013 (0.013)***	0.010 (0.005)**	0.008 (0.004)**
	Reading	0.050 (0.010)***	0.033 (0.033)***	0.050 (0.008)***	0.033 (0.007)***
	Writing	0.010 (0.010)	0.001 (0.001)	0.012 (0.010)	0.011 (0.009)
R2		0.25	0.214	0.225	0.265
FE for: country, industry, occupation		V	V	V	V

Note: The dependent variables are binary indicators for whether the individual engages at least once a week in each of the following behaviours: (1) learning new things at work, (2) learning by doing, (3) keeping up to date with changes in products/services, and (4) any of the three. Robust standard errors in parentheses.

*p < 0.1, **p < 0.05, ***p < 0.01.

Source: OECD (2025[2]), OECD Survey of Adult Skills (PIAAC), www.oecd.org/skills/piaac/.

Table A A.5. Drivers of informal learning – with and without Big 5 personality types

Effects of drivers on participating in any type of informal learning at least once a week

	variable	With Big 5	Without Big 5	Without Big 5 for Big 5 sample
Background characteristics	age (25‑34)	‑0.089 (0.021)***	‑0.071 (0.022)***	‑0.089 (0.021)***
	age (35‑44)	‑0.110 (0.020)***	‑0.092 (0.021)***	‑0.111 (0.020)***
	age (45‑54)	‑0.135 (0.023)***	‑0.128 (0.024)***	‑0.135 (0.023)***
	age (55‑64)	‑0.108 (0.022)***	‑0.104 (0.024)***	‑0.103 (0.023)***
	Gender (female)	0.020 (0.010)**	0.022 (0.012)*	0.025 (0.009)***
	Immigrant	‑0.014 (0.012)	‑0.024 (0.021)	‑0.012 (0.013)
	Number of books at 14	0.001 (0.004)	0.006 (0.005)	0.003 (0.004)
	Parental education (high)	‑0.011 (0.013)	‑0.001 (0.019)	‑0.011 (0.013)
	Parental education (medium)	‑0.008 (0.013)	0.008 (0.016)	‑0.008 (0.013)
Cognitive skills	Education(high)	‑0.006 (0.021)	‑0.022 (0.026)	‑0.004 (0.021)
	Education(medium)	‑0.021 (0.020)	‑0.028 (0.028)	‑0.022 (0.020)
	Literacy PIAAC level	‑0.024 (0.006)***	‑0.025 (0.007)***	‑0.025 (0.005)***
HPWP	Autonomy at work	0.036 (0.010)***	0.018 (0.016)	0.035 (0.010)***
	Team practices at work	0.021 (0.005)***	0.015 (0.007)**	0.023 (0.005)***
Job complexity	Job requirements	0.003 (0.008)	0.001 (0.010)	0.003 (0.008)
	Low skills for Job	0.113 (0.011)***	0.094 (0.018)***	0.106 (0.011)***
Job context	Change in work environment	0.043 (0.010)***	0.056 (0.014)***	0.047 (0.010)***
	Tenure at employer	‑0.003 (0.001)***	‑0.004 (0.001)***	‑0.003 (0.001)***
	Company size (11‑49)	0.013 (0.014)	‑0.004 (0.018)	0.011 (0.014)
	Company size (50‑249)	0.012 (0.014)	0.011 (0.017)	0.010 (0.014)
	Company size (250‑499)	0.029 (0.017)*	0.010 (0.024)	0.033 (0.018)*
	Company size (500‑999)	0.013 (0.021)	0.007 (0.027)	0.015 (0.021)
	Company size (1000 or more)	0.044 (0.018)**	0.008 (0.023)	0.044 (0.018)**
Learning behaviour	Access information online (at least once a week)	0.041 (0.020)**	0.006 (0.027)	0.047 (0.021)**
	Non formal (didn’t participate but wanted)	0.025 (0.015)*	0.029 (0.024)	0.028 (0.015)*
	Non formal (Participated and wanted to)	0.051 (0.010)***	0.059 (0.014)***	0.054 (0.010)***
	Non formal (participated but didn’t want)	0.048 (0.016)***	0.040 (0.021)*	0.049 (0.015)***
	Read at home (at least once a week)	0.031 (0.010)***	0.016 (0.014)	0.036 (0.010)***
Learning environment	collaboration with colleagues (index)	0.017 (0.004)***	0.017 (0.005)***	0.019 (0.004)***
Skills use at work	Hands and finger use	0.014 (0.003)***	0.010 (0.004)**	0.016 (0.003)***
	Numeracy skills	0.018 (0.005)***	0.019 (0.008)**	0.020 (0.005)***
	Physical skills	0.000 (0.003)	0.008 (0.004)**	0.000 (0.003)
	Reading	0.028 (0.005)***	0.033 (0.007)***	0.028 (0.005)***
	Writing	0.006 (0.006)	0.011 (0.009)	0.008 (0.006)
Social-emotional skills	Compassion	0.027 (0.005)***	#N/A	#N/A
	Conscientiousness	0.011 (0.005)**	#N/A	#N/A
	Emotional stability	0.028 (0.004)***	#N/A	#N/A
	Extroversion	‑0.001 (0.005)	#N/A	#N/A
	Openness	‑0.014 (0.004)***	#N/A	#N/A
R2		0.24	0.265	0.233
FE for: country, industry, occupation	V	V	V	V

Note: The “With Big 5” model includes five personality traits (compassion, conscientiousness, emotional stability, extraversion, openness) for a subset of countries where this data is available. “Without Big 5” refers to the full-sample model excluding these traits, while “Without Big 5 for Big 5 sample” applies the same specification to the restricted sample for comparability.

*p < 0.1, **p < 0.05, ***p < 0.01.

Source: OECD (2025[2]), OECD Survey of Adult Skills (PIAAC), www.oecd.org/skills/piaac/.

References

[1] Fialho, P., G. Quintini and M. Vandeweyer (2019), “Returns to different forms of job-related training: Factoring in informal learning”, OECD Social, Employment and Migration Working Papers, No. 231, OECD Publishing, Paris, https://doi.org/10.1787/b21807e9-en.

[2] OECD (2025), Survey of Adult Skills (PIAAC), http://www.oecd.org/skills/piaac/ (accessed on 25 February 2019).