Inequality of Opportunity in Educational Achievements Cross-Country - - PowerPoint PPT Presentation

Inequality of Opportunity in Educational Achievements

Cross-Country and Intertemporal Comparisons P . Luongo

University of Bari

Inequality – Measurement, trend, impacts and policies UNU-WIDER Conference September 2014

P . Luongo (University of Bari) Inequality of Educational Opportunity September 2014 1 / 28

Outline

1

Aim & Motivation

2

Model

3

Data

4

Results

5

Conclusions

6

Appendix

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Aim & Motivation

Research questions

1

Does the country ranking change when we switch the focus of the analysis from average test scores to fairness?

2

Is there any country that outperform in both the level and the degree of fairness?

3

There has been any change in the strength of the association between socio-economic characteristics and students’ performances?

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Aim & Motivation

Motivations

Education influences labour market participation, civic engagement, health status, earnings, social mobility, etc. (Blau & Kahn, 2005; Hanushek & Woessmann, 2010; among others). Intergenerational persistence in educational achievements (Marks, 2005; Macdonald et al. 2010; Ermisch et al. 2012) Inequality in educational attainments (Thomas et al. 2001; Morrison & Murtin, 2007) Inequality in educational achievements (Brown et al., 2007; Micklewright et al. 2007)

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Aim & Motivation

IEOp

Existing evidences de la Vega & Lekuona (2013): PISA 2009 Gamboa & Waltenberg (2011) PISA 2006 & 2009, LAC Ferreira & Gignoux (2011) PISA 2006 What’s new?

1

PISA 2012

2

Changes over time (PISA 2003, 2006, 2009, 2012)

3

How do the less advantaged students perform?

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Model

Model

Adapt to our framework the idea of measuring fairness through an

• rdered pair (Roemer, 2013):

EduOpp = (W EEOp, IEOp) W EEOp: focuses on worst-off students IEOp: looks at the whole sample

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Model

Outcome function

Test scores (s) depend only on circumstances (c) and effort (e) si = f(c, e) c used to partition students into K (j = 1, ..., K) types e correspond to the rank π occupied by each student in its own type distribution of test scores vj(π): level of s for individuals in type j occupying the rank π

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Model

W EEOp

W EEOp = 1 min

j (π)dπ

(1) Class-ranked situations: W EEOp corresponds to the average score

• f the worst-off students (Roemer, 2013)

Not class-ranked situations: W EEOp corresponds to the left-hand envelope of the distribution of CDFs (Roemer, 2013) Empirically this involves the estimation, for each country, of each type-specific CDF and their envelopes

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Model

IEOp

Ex-Ante Approach: IEOp measured as between type inequality in mean outcome Parametric procedure si = βki + εi Index of Inequality IEOp = var(k,ˆ

β) var(y)

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Data

Dataset

OECD PISA 2003: 41 countries PISA 2006: 57 countries PISA 2009: 74 countries PISA 2012: 65 countries Domains Mathematics Science Reading

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Data

Sample

2 stages sampling procedure Students aged between 15 years and 3 months and 16 years and 3 months enrolled in grade 7 or higher Raw test scores (s) scaled by using IRT and then standardized si = ˆ µ + ˆ

σ σ(xi − µ)

where xi is the test score of student i, ˆ µ = 500 and ˆ σ = 100 are the arbitrary (final) grand mean and SD

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Data

Available Data

PISA contains information on: Schools’ policies and practices Students’ background Students’ motivation Students’ learning style

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Data

Some empirical issues

EduOpp = (W EEOp, IEOp) W EEOp focuses on the worst-off type: the omission of relevant circumstances determines a measure of “social welfare” UPWARD biased

Intuition: when a new circumstance is added there is at least one additional type-distribution, conditional to a given value of the new circumstance, which is going to be at its left

IEOp looks at the whole population: the omission of relevant circumstances determines a measure of inequality which is DOWNWARD biased; some variation is left unexplained and attributed to effort. As # of K ↑ ⇒ W EEOp ↓ and IEOp ↑

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Data

Variables

Students’ circumstances Gender Parental level of education

1

ISCED ≤ 2

2

3≤ ISCED ≤ 4

3

ISCED ≥ 5

Parental job classification

1

White collar

2

Blue collar

12types

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Results

W EEOp in Reading, 2012

213 232 295 301 302 302 323 331 332 335 339 344 345 346 350 354 355 358 360 365 371 371 377 378 384 389 390 390 393 399 399 400 403 411 412 419 421 424 425 426 427 430 432 433 435 435 437 439 439 440 442 445 446 446 447 448 449 449 452 453 468 470 471 472 510 516

100 200 300 400 500 WEOp Reading

SVK QRS BGR MNE KAZ QAT PER ARE JOR ROU CZE SRB ARG ISR MYS TUN SVN URY COL BRA LVA IDN CHL LTU RUS SWE HUN MEX CRI THA GRC NOR HRV POL QUB BEL LUX ISL FRA DNK QUA ITA TUR FIN LIE EST ESP JPN NZL CHE PRT KOR NLD QUC CAN GBR TAP USA DEU AUS IRL MAC VNM SGP HKG QCN Author's elaborations on OECD PISA 2012

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Results

IEOp in Reading, 2012

.0536 .0631 .0725 .0733 .0808 .0851 .0876 .0926 .0931 .101 .102 .104 .108 .108 .11 .113 .114 .115 .116 .117 .117 .119 .12 .121 .122 .123 .125 .125 .125 .127 .129 .133 .134 .134 .134 .135 .136 .138 .14 .142 .147 .149 .15 .151 .153 .157 .158 .161 .165 .166 .166 .172 .172 .176 .177 .18 .185 .19 .19 .192 .196 .196 .213 .213 .215 .269

.1 .2 .3 IEOp Reading

MAC HKG KOR JPN QUA IDN GBR NLD CAN NOR ISL MEX MYS ESP TUN IRL SRB USA QRS ITA KAZ RUS COL LIE AUS ARG EST VNM SWE SGP QUC NZL BRA QCN CZE TAP QUB CRI DNK CHE FIN HRV FRA LUX BEL QAT TUR GRC PRT POL URY ROU DEU ARE PER CHL HUN ISR LTU SVN LVA MNE JOR SVK THA BGR Author's elaborations on OECD PISA 2012

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Results

Average performance and IEOp in Reading, 2012

ARE ARG AUS BEL BGR BRA CAN CHE CHL COL CRI CZE DEU DNK ESP EST FIN FRA GBR GRC HKG HRV HUN IDN IRL ISL ISR ITA JOR JPN KAZ KOR LIE LTU LUX LVA MAC MEX MNE MYS NLD NOR NZL PER POL PRT QAT QCN QRS QUA QUB QUC ROU RUS SGP SRB SVK SVN SWE TAP THA TUN TUR URY USA VNM OECD

400 450 500 550 600 Average score in Reading .05 .1 .15 .2 .25 IEOp Reading

Author's elaboration on OECD PISA 2012

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Results

Is there any “outperforming” country?

ARE ARG AUS BEL BGR BRA CAN CHE CHL COL CRI CZE DEU DNK ESP EST FIN FRA GBR GRC HKG HRV HUN IDN IRL ISL ISR ITA JOR JPN KAZ KOR LIE LTU LUX LVA MAC MEX MNE MYS NLD NOR NZL PER POL PRT QAT QCN QRS QUA QUB QUC ROU RUS SGP SRB SVK SVN SWE TAP THA TUN TUR URY USA VNM OECD

a b c d 200 300 400 500 WEOp Reading .05 .1 .15 .2 .25 IEOp Reading

Author's elaboration on OECD PISA 2012

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Results

Geographical Pattern

.05 .1 .15 .2 .25 IEOp Reading 2012 200 300 400 500 WEEOp Reading 2012 North Africa and Asia Eastern Europe North America South America Western Europe

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Results

Changes over time, 2003 – 2012

AUS BEL BRA CAN CHE CZE DEU DNK ESP FIN FRA GBR GRC HKG HUN IDN IRL ISL ITA JPN KOR LIE LUX LVA MAC MEX NLD NOR NZL POL PRT RUS SWE THA TUN TUR URY USA OECD

• 50

50 100 WEOp Reading

• .05

.05 .1 .15 IEOp Reding

Author's elaboration on OECD PISA 2003 - 2012 IEOp Reduction IEOp Increase WEOp Increase WEOp Reduction

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Conclusions

Limits of the analysis

Due to the omission of relevant circumstances the two components risk to be biased, so caution is necessary in interpreting the results. PISA involves only students who do not drop out and have not repeated too many grades. With these caveats in mind. . .

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Conclusions

Conclusion

High heterogeneity across countries in terms of both levels and degree of fairness in education The strength of the association between parental background and students’ test scores tends to be higher in Reading than in Math and Science This association is, on average, lower in countries that perform better in average test scores There aren’t countries that outperform in both dimensions of fairness W EEOp tends to be higher and IEOp lower in some Asiatic countries, in North America and in Western European countries where also variability is lower

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Conclusions

Conclusions (ctd.)

Eastern European countries occupy an intermediate position in terms of IEOp Between 2003 and 2012 ↑ in W EEOp has been accompanied by ↑ in IEOp Few countries moved toward lower degree of IEOp all the while improving the performances of the less advantaged students. Most of them, with the exceptions of Indonesia and Mexico, are Western European.

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Conclusions

THANK YOU!

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Appendix

W EEOp in Mathematics, 2012

257 287 301 309 319 322 336 340 341 344 349 353 354 354 356 357 361 367 369 370 371 377 379 385 386 387 389 391 402 403 407 409 410 411 412 414 417 417 420 422 423 426 428 428 430 437 438 440 441 443 444 446 447 448 450 452 456 458 459 464 479 486 486 509 514 522 554

200 400 600 WEOp Math

SVK MNE QRS PER QAT JOR ISR COL SRB BGR ARG CZE TUN BRA IDN KAZ ROU CHL ARE CRI URY ALB HUN LTU RUS MYS MEX GRC HRV NOR THA QUB SVN POL QUA FRA LVA TUR SWE QUC LUX DNK USA NZL GBR LIE IRL BEL ITA ESP AUS FIN ISL CAN CHE PRT JPN EST DEU NLD TAP VNM KOR MAC SGP HKG QCN Author's elaborations on OECD PISA 2012

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Appendix

W EEOp in Science, 2012

279 283 297 305 311 327 336 343 352 356 357 359 367 368 369 369 374 375 377 383 393 394 394 399 399 403 403 405 407 413 415 418 419 421 421 422 424 427 429 430 433 433 434 434 437 441 443 447 451 452 452 453 459 459 459 460 462 465 475 476 486 494 495 507 527 532

100 200 300 400 500 WEOp Science

QRS MNE KAZ QAT CZE PER BGR JOR SRB ARE ISR ROU IDN TUN ARG COL BRA ALB URY MYS SVN CRI MEX LTU CHL GRC NOR RUS HUN SWE LUX DNK QUB QUA FRA THA LVA HRV ISL LIE QUC POL CHE USA BEL TUR GBR NZL CAN ITA JPN NLD PRT IRL ESP AUS FIN TAP KOR DEU SGP MAC EST VNM HKG QCN Author's elaborations on OECD PISA 2012

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Appendix

IEOp in Mathematics, 2012

.043 .0507 .0516 .0518 .0546 .0567 .0656 .0656 .0659 .0677 .0682 .0697 .0718 .0724 .0774 .0803 .082 .0822 .0868 .0876 .09 .0943 .0953 .0958 .0962 .0968 .0974 .0976 .0987 .0993 .103 .104 .105 .106 .106 .108 .109 .114 .115 .117 .121 .122 .122 .123 .123 .129 .13 .131 .132 .132 .132 .134 .137 .137 .139 .14 .155 .162 .168 .174 .174 .184 .189 .197

.05 .1 .15 .2 IEOp Math

ISL FIN QRS RUS NOR EST KOR SRB CAN HKG SWE GBR IDN NLD HRV MEX QUA MYS JPN ITA MNE QAT TUR CZE AUS LVA VNM IRL CHE JOR LTU SGP THA DNK QCN USA ARG GRC ESP SVN TUN QUC COL TAP ARE DEU ROU BEL BRA FRA NZL POL LIE CRI PRT QUB HUN URY SVK LUX PER BGR CHL ISR Author's elaborations on OECD PISA 2012

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Appendix

IEOp in Science, 2012

.0383 .0419 .044 .0461 .0484 .0509 .0532 .0547 .0557 .0578 .0598 .0672 .0692 .0694 .0697 .072 .0736 .0739 .0759 .0769 .0778 .0819 .085 .086 .088 .0909 .0917 .0919 .0923 .0928 .0953 .096 .102 .104 .105 .105 .106 .109 .11 .11 .111 .116 .116 .118 .118 .118 .12 .12 .126 .128 .13 .131 .132 .134 .134 .137 .138 .142 .144 .151 .156 .173 .175 .181 .203

.05 .1 .15 .2 IEOp Science

KOR ISL HKG KAZ SRB EST NOR CAN FIN HRV QRS IDN VNM MYS JPN SWE TUR TUN ITA MEX RUS NLD QUA CZE GBR LVA THA IRL AUS ESP LTU COL USA MNE DNK SVN ARG QCN QAT CRI BRA CHE QUC POL GRC SGP TAP DEU NZL ROU PRT BEL FRA ARE JOR QUB LIE URY HUN CHL PER ISR LUX SVK BGR Author's elaborations on OECD PISA 2012

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