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LSMS Skills M easurement T raining

LSMS Skills M easurement T raining. Rachid Laajaj (Paris School of Economics - INRA). Outline. What are skills and which skills? Cognitive, non cognitive and technical skills Why does it matter to measure skills? How to asses the quality of measurement instruments?

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LSMS Skills M easurement T raining

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  1. LSMSSkills Measurement Training RachidLaajaj (Paris School of Economics - INRA)

  2. Outline • What are skills and which skills? • Cognitive, non cognitive and technical skills • Why does it matter to measure skills? • How to asses the quality of measurement instruments? • What are the gaps and challenges? • The LSMS-ISA attempt to fill some of these gaps

  3. Definition of the skills by type • COGNITIVE SKILLS (Hard skills) • NON-COGNITIVE SKILLS (Soft skills) • TECHNICAL SKILLS (task specific) • “Nature vs nurture” is not a correct distinction but cognitive skills are affected more at early ages and non-cognitive skills more at later ages. • Comples interaction in the generation of these skills

  4. Cognitive Skills • The terms IQ, general intelligence, general cognitive ability, general mental ability, or simply intelligence are often used interchangeably, reflecting the ability to solve abstract problems. • Since the different aspects of cognition are highly correlated, a general intelligence factorlabeled “g” is a variable that summarize positive correlations among different cognitive tasks. • Early 20th century, psychologist Charles Spearman noticed positive correlation across similarly unrelated school subject and attributed it to general intelligence.

  5. Measurement of Cognitive Skills in the field: Examples of tests • Digit Span test: forward or backward. Can be auditive or visual. • Raven’s Progressive Matrices • Math questions • Reading tests • Vocabulary tests From more to less pure cognitive test

  6. Non-Cognitive Skills • Non cognitive skills are personality traits that are weakly correlated with measures of intelligence, such as the IQ index (very broad) • Heckman highlights the importance of perseverance, motivation, timepreference, risk aversion, self-esteem, self-control, preference for leasure • Five factor model is a broadly accepted taxonomy • Renewal of interest for entrepreneurship, locus of control, aspirations, etc.

  7. Technical skills • Technical skills are the basic knowledge required to perform a task: very field specific by definition. • Why using proxies rather than the knowledge that really matters? Example with the use of seeds and fertilizer in Mozambique.

  8. Does it Matter? What are the evidence? Cognitive skills • Good body of evidence in cognitive skills, mostly but not exclusively on developed countries. • Most evidence on the cognitive side: Hanushek and Kimko (2000) use math and science test scores, and find it to predict growth much better than years of education. • Numerous studies establish that measured cognitive ability is a strong predictor of schooling attainment and wages, conditional on schooling (Cawley, Heckman, and Vytlacil 2001). • Increasing number of studies in developing countries.

  9. Does it Matter? What are the evidence?Non-Cognitive skills • Heckman, Stixrud & Urzua find that a change in non-cognitive skills from lowest to highest level has an effect on behavior comparable to or greater than a corresponding change in cognitive skills. • The marshmallow experiment: • The Abecedarian project Chicago Child-Parent Center • Trainings > limited success

  10. What are the gaps? • More evidence on cognitive studies, less on non-cognitive ones, and very little in technical skills. • Very little in developing countries and rural areas, where less data is available • Virtually nothing on intra-household skills • Very limited data that is comparable across developing countries

  11. Technical difficulties • Related to the measurement: • Measurement errors • Imperfect proxies • Applicability to field conditions • Related to evidence of its importance • Omitted variables • Reverse causality • Challenges in the use of instruments (isolate one improvement, change in the short run)

  12. What is a good measurement? • Consistency across time: High correlation if you replicated the measure within a period short enough that it should not have change • Validity: Are you measuring what you intend to measure? Should correlate well with other measures of the same skill. Should predict well related behaviors. Ex: back to the locus of control

  13. Toward the improvement of skills measurement in developing countries • Our LSMS-ISA initiative: • Test and re-test of questions for consistency • Identify the best tradeoff between time and precision of the measurement. • Will investigate intra-household skills and their complementarities • Combine it with a randomized technology adoption intervention: predictive role of the skills: • The importance of “observing the unobservables” • Which one matters for technology adoption • Which one matters for productivity

  14. Feedback from intervention to behavior? • Interesting new work on how interventions shapes behaviors. • Examples: • Macours and Vakis (2008): Aspirations and leadership • Laajaj (2012): Endogenous time horizon More coming soon!

  15. Back IQ

  16. Digit Span Test • Memory span: longest list of items that a person can repeat back in correct order immediately after presentation on 50% of trials. Called digit span when numbers are used. Measures the short term memory. Back

  17. Raven’s Progressive Matrices (1) • 60 Multiple choice questions in order of difficulty, test the reasoning ability.

  18. Raven’s Progressive Matrices (2)

  19. Raven’s Progressive Matrices (3)

  20. Raven’s Progressive Matrices (4) Back

  21. The Big Five Personality traits

  22. Big Five personality traits and their facets Back

  23. Locus of Control Back1 Back2

  24. Technical Knowledge Questions for Seeds and Fertilizer Use Back

  25. Returns of cognitive abilitie in Developing countries (Hanusheck & Woesman 2008)

  26. Back

  27. The precision vs time tradeoff Back

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