I mpact of education on inequality along the wage distribution - - PowerPoint PPT Presentation
I mpact of education on inequality along the wage distribution profile in Cameroon: 2005-2010 Presented at the UNU-WI DER Conference on I nequality Measurement, Trends, I mpacts, and Policies, Helsinki 56 September 2014 by Fra
Presented at the UNU-WI DER Conference on ‘I nequality – Measurement, Trends, I mpacts, and Policies’, Helsinki 5‒6 September 2014
by
Fra rancis Menj o Bay aye Faculty of Economics and Management University of Yaoundé II, Cameroon
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Introduction Research questions Objectives/hypotheses Methodology Empirical Results Policy Implications
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Popular responses by citizens to lack of fairness
has recently been at the root of regime change
These voices typically call for more social
inclusion and fair chances for everybody in society
as ingrained in the concepts of equity, fairness and
social justice (UNDP 2011)
3
Inequality typically stifles both macro and
household economic growth, yet having both fair and unfair components
Measured inequality is basically a function of two
major components:
comprising inequality of circumstances, to which an
individual may not be held responsible; and
inequality of effort, to which an individual can largely
be held responsible
(Roemer 1998; Bourguignon et al. 2007; Baye and Epo 2013)
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Education is viewed essentially as an effort-
related determinant of individual wages
it complements with or substitutes for exogenous
circumstances that enhance or constrain individual labour market opportunities
Education increases the skills and productivity of
poor households, enhances their employability and earnings, as well as their welfare.
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Resolving deficiencies in access and returns to
education is, therefore,
expected to be instrumental in augmenting the
standard of living of the poor.
Generally, educational expansion is expected to
lead to an increase in the labour market participation opportunities opened to economic agents and
thus an essential catalyst for the fight against
inequality and poverty.
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Education is viewed as the single most important
determinant of income.
Yet, exploring literature relating education to income
inequality reveals mixed results.
While, some find a positive relation between
schooling and inequality (Chiswick 1971); Winegarden 1979),
Others find a negative association between
school enrolment and income inequality (Ahluwalia 1976; Sylwester 2005).
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However, from a state of unequal distribution
we believe that investments in education and
related infrastructures would increase labour market opportunities
relatively more for those at the bottom than for
those at the top of the income distribution profile.
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In this context, a key question arises: Is smoothening education more
inequality reducing at lower than upper tails of the income distribution profile?
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The related objectives are: To evaluate the determinants of employment
sector choices;
To examine the nature of change in returns
to formal education between 2005 and 2010 along the wage distribution; and
To evaluate the impact of education on
measured inequality along the wage distribution.
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Other things being equal: Education is relatively more important in
sanctioning wages and allocation of workers to various employment sectors;
Returns to education were inclusive in the
Cameroon labour market between 2005 and 2010; and
Smoothening education is more inequality
reducing at lower than upper percentiles in the distribution of wages.
11
The role of education in causing or mitigating
wage inequalities has been explained theoretically using
the human capital theory (Mincer 1958, 1996;
Schultz 1960; Becker 1964)
the dual labour market theory discrimination theory, and screening and signalling theory (Spencer 1973) 12
The acquisition of human capital determines
the productive characteristics of individuals and relate positively to productivity (Mincer 1958, 1996; Schultz 1960; Becker 1964).
Differences in the degree of human capital
accumulated by individuals is likely to differentiate their marginal productivities.
And if workers are rewarded according to
their marginal productivities, this generates wage inequalities
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the marginal productivity theory, therefore,
constitutes a potential lens in explaining wage inequalities because
those at the bottom of the wage profile are
perceived to have lower productivity due to their lower human capital attainment compared to those at the top.
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Another lens to view inequalities in the
distribution of wages is
the dual labour market theory that divides the
market into the primary labour market (formal sector), which is more organized and
the secondary labour market (informal
sector), which is rather spontaneous.
Wages in the primary market are typically
higher than those in the secondary market.
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Alternatively, screening and signalling are
competing theories about the value of education
because they assume that formal education rather
helps only in sorting out potential productive workers.
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Conducting analyses based on pooled individual
records from the 2005 and 2010 Cameroon LFSs;
Correcting for potential employment sector-
selection bias in the structural wage equation;
Running conditional quantile wage regressions;
and
Designing factual and counterfactual experiments
to elicit the impact of education on inequality along the wage distribution.
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To study the effects of education on wages,
we exploit the 2005 and 2010 Cameroon labour force surveys by pooling them together.
This enables the testing of how the effect of
education on occupational choices and wages changed in the period 2005-2010.
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By way of methodology, we followed:
a two-step econometrics estimation
procedure and conducted factual and
counterfactual experiments for inequality
impact assessment.
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In terms of econometrics,
the first step regression involves the estimation of
a multinomial probit model of employment sector choices (children below six years and other wage earners).
The employment sectors were public, private,
informal and small-scale agriculture - the reference category.
After the multinomial probit model, we generated
three inverse Mills ratios a la Heckman (1979).
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In the second step, structural wage equations
correcting for employment sector-selectivity bias were estimated at the mean and across selected quantiles of the wage distribution.
Using estimates of the selectivity-corrected
wage equations, factual and counterfactual experiments were designed.
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In particular,
counterfactual distributions were simulated in
which wage inequalities within selected quantiles were independent of variations in years of schooling.
Inequalities computed by
the Gini and the Generalized Entropy class of
measures using the simulated factual and counterfactual distributions were compared to elicit the impact of education on inequality overall and along the wage distribution profile
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Wage equations, Factual and Counterfactual Experiments
v C S E d E d LnW
k m m k k m k k k k k k
+ + + + + + + =
∑ ∑ ∑
+ = = =
λ α α α α α α α
' 1 7 6 4 3 2 1
* 2010 2010 (5)
k m m k k m k k k k k k
C S E d E d W Ln λ α α α α α α α
∑ ∑ ∑
′ + = = =
+ + + + + + =
1 7 6 4 3 2 1
ˆ ˆ ˆ * 2010 ˆ ˆ 2010 ˆ ˆ ˆ (8) ) ˆ ˆ ˆ ˆ * 2010 ˆ ˆ 2010 ˆ ˆ exp(
1 7 6 4 3 2 1
v C S E d E d W
k m m k k m k k k k k k
+ + + + + + + =
∑ ∑ ∑
′ + = = =
λ α α α α α α α (9) ) ˆ ˆ ˆ ˆ * 2010 ˆ ˆ 2010 ˆ ˆ exp(
1 7 6 4 3 2 1
v C S E d E d W
k m m k k m k k k k k k q q Eq
+ + + + + + + =
∑ ∑ ∑
′ + = = =
λ α α α α α α α (10)
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I mpact of Education on I nequality I = Gini or Generalized Entropy Class
) ( ) ( ) ( W I W I W I
q
E I
− = Θ (11) If Θ > 0, education is inequality augmenting in the factual distribution. If Θ = 0, education is inequality neutral in the factual distribution. If Θ < 0, education is inequality reducing in the factual distribution.
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1.72 1.26
0.0938
1.4
1.8138 1.2431 0.27
1 2 3 4
Public Private Informal
Marginal effects of + year of schooling: Baseline, ∆(2005-2010) and Total effect
Baseline 2005 ∆(2005-2010) Total effect
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10.27 7.19 8.82 9.86 10.75 11.5 12.13 12.41 6.73 5.47 4.35 6 6.39 7.95 9.79 10.74 2 4 6 8 10 12 14 Overall 5th Quant 10th Quant 25th Quant 50th Quant 75th Quant 90th Quant 95th Quant
Distribution of log wage and years of schooling by selected percentiles: 2005- 2010
Log of wages Education
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5.66 2.03 3.47 4.17 6.26 7.14 8.85 9.61 1.52 8.46 9.15 2.4
7.18 10.49 12.62 6.57 5.818 6.392 8.166 8.45
5 10 15 20 25 30
Overall Qreg(0.05) Qreg(0.1) Qreg(0.25) Qreg(0.5) Qreg(0.75) Qreg(0.9) Qreg(0.95)
Logwage Estimates:
Baseline, incremental and total returns of education: 2005-2010
Base returns Incremental returns Total returns
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0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 Overall Quantile (0.05) Quantile (0.10) Quantile (0.25) Quantile (0.50) Quantile (0.75) Quantile (0.90) Quantile (0.95)
Gini Inequalities from factual and counterfactual distributions
Factual Counterfactual
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6.67 3.58 3.58 5.67 6.69 7.08 7.65 7.59
1 2 3 4 5 6 7 8 9
Overall Qreg(0.05) Qreg(0.1) Qreg(0.25) Qreg(0.5) Qreg(0.75) Qreg(0.9) Qreg(0.95)
Gini inequality: impacts of education on wage inequalities: 2005-2010
Absolute Impact Relative Impact
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12.93 7.82 7.72 11.16 12.83 13.49 14.51 14.35
2 4 6 8 10 12 14 16
Overall Qreg(0.05) Qreg(0.1) Qreg(0.25) Qreg(0.5) Qreg(0.75) Qreg(0.9) Qreg(0.95)
Generalized entropy inequality: impacts of education on wage inequalities (for θ=0): 2005-2010
Absolute Impact Relative Impact
Levelling the playing field in terms of
schooling opportunities would be an important public policy when trying to reduce inequality and poverty.
In this context, a more balanced schooling
profile would result in a more or less balanced distribution of labour market
Our findings indorse public policies that
favour investments leading to educational expansion.
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