Discussion Papers In Economics
And Business

An Update of the Returns to Education in Kenya:
Accounting both endogeneity and sample selection biases

Kentaro Shimada, Zeba Khan, Suguru Mizunoya and Ayako Wakano

Discussion Paper 16-18

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANAn Update of the Returns to Education in Kenya:
Accounting both endogeneity and sample selection biases

Kentaro Shimada, Zeba Khan, Suguru Mizunoya and Ayako Wakano

Discussion Paper 16-18

May 2016

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANAn Update of the Returns to Education in Kenya:
Accounting both endogeneity and sample selection biases
Kentaro Shimadaa, Zeba Khanb, Suguru Mizunoyac and Ayako Wakanod

Abstract: The study is latest to estimate returns to education after the introduction of free primaryeducation in 2003 in Kenya, simultaneously addressing two sources of biases due to endogeneity ofschooling and earnings, and sample selection. Using the 2005-2006 Kenya Integrated Household andBudget Survey, the paper finds that (a) returns to additional year of schooling are 14.9% for males and13.5% for females with a continuous education variable, but the returns to females are consistently higher
than males when returns are estimated by level of education, (b) returns to education increases for higher
levels of education i.e., the classical pattern of diminishing return to schooling does not hold true for bothmales and females in Kenya, and (c) the use of joint IV-Heckman method adjust the endogeneity andsample selection biases introduced by OLS and IV.

JEL code: I26, I25, O55
Key Words: Returns to Education; Education Policy; Human Capital; Gender; Kenya

a- Ph. D candidate, Graduate School of International Cooperation Studies, Kobe University

Email address: shimaken629@gmail.com
b- Research Assistant, Faculty of Social Science, Chinese University of Hong Kong

Email address: khanzeba@hotmail.com
c- Assistant Professor, Faculty of Social Science (Global Studies), Chinese University of Hong Kong

Email address: suguru777@gmail.com
d- Ph. D candidate, Graduate School of Economics, Osaka University

Email address: ayakowakano@gmail.com

1. Introduction
The positive impact of additional schooling on earnings, captured by the “private” rate of returnon education, is widely accepted as a measure of educational productivity, and incentive for households toinvest in human capital. The rate of return on education is useful in (i) explaining patterns of educational
demand, (ii) guiding how best to prioritize the distribution of public resources in education, and (iii)
identifying areas which need incentives and promotion for private investment. The compilation of work by
Psacharopoulos (1973,1981,1985,1994) and Psacharopoulos and Patrinos (2002) have found rates ofreturn to education to be consistently higher in developing regions (Sub-Saharan Africa, Latin America
and the Caribbean) compared to the world average, and the returns tend to decline with increasing levelsof education. As these returns to education, especially at the primary level, exceed returns obtained frominvestments in physical capital, Psacharopoulos’s inference led to a paradigm shift in global focus on
expanding access to primary education in developing countries, resulting in considerable flow of donormoney in these nations.
As one of five such countries included in Psacharopoulos’ initial international comparative
account of returns to education (1973) – Kenya has a comparatively longer history of return to education
studies. Private and social returns to education in Kenya have been estimated to additional years ofschooling in various ways, and also according to the discrete cycles of primary, secondary, and tertiary
education.
Kenyan education has witnessed significant expansion and change after gaining independence in
1963. In the 1960s and 70s, apart from overseeing a rapid increase in primary enrollment, Kenya’s first
President Kenyatta also called for a grassroots-driven harambee movement, which led to a parallel
enlargement of secondary education. Later in 1985 a shift from the previously British colonial-style 7-4-23 educational system to a 8-4-4 structure under President Moi extended primary schooling by one year inorder to implement a more practical, vocational curriculum (Buchmann, 1999). The overhauling of theeducation system in Kenya with a purpose to achieve “Education for All”, led to the introduction of free
primary education in January 2003, which resulted in gross primary enrollment to touch approximately
107%in 2003, in comparison to 92% in 2002 (World Bank, 2015). According to Tooley, Dixon andStanfield (2008), the enrolment in primary education in 2003, increased to 7.2 million from 5.9 million.
Following the establishment of free primary education framework, the Kenyan government in 2008,
implemented the free day secondary education programme, which aimed to make secondary education
more accessible. Predictably, therefore, returns to education in Kenya should vary over time (Appleton,-1-

Bigsten, and Manda, 1996) due to structural changes arising from policy interventions. Updating theseestimates based on comparatively recent data thus should be of policymaking interest.
Furthermore, in recent years, evidence has emerged indicating the classic pattern of diminishingrates of return to education by level suggested by Psacharopoulous and Patrinos (2002) may no longer
hold true for the majority of developing countries. Rather, analysis of data from the 1990s and early
2000s suggests that the slope of the earnings function increases with education level (Colclough, Kingdon,
and Patrinos, 2010).
It is noteworthy to state that existing study on return of education in Kenya suffer from data andmethodological deficiencies. They include lack of gender disaggregation, and limited sample coverage(e.g., only workers in Nairobi were covered). For example, Patrinos and Psacharopoulos (2002) exhibit anextensive list of returns of education from various sources for different countries, but the returns in Kenya
are not disaggregated by sex; Manda, Mwabu, and Kimenyi (2002) did not account for endogeneity issue;
and Johnson (1972), Knight and Sabot (1987), and Armitage and Sabot (1987) used samples only fromNairobi. As the large amount of public budget is spent for the education sector and the education is one of
the key national strategies, estimating the returns of education using nationally representative data for both
sex is critical to evaluate and plan effective education and labour market policies.
Returns to an additional year of schooling at both, overall and discrete levels of education havebeen estimated for many developing countries using the semi-logarithmic earnings function (Mincer,1974), which has become ubiquitous for its undemanding data requirements. However, the scarcity ofquality data introduces biases to the estimated returns to education with the use of ordinary least squares(OLS) methods. None of the previously published studies on return to education in Kenya addressed theissue of endogeneity arising from the correlation between education and earnings, and nonrepresentativeness of the wage-working sample compared to the overall population.

This study

simultaneously addresses these two problems by adopting IV and Heckman method for computation of
returns to additional years of schooling.
Thus this study aims to update the returns of education in Kenya whose latest estimates weremade using the Welfare and monitoring Survey 1994 through the lenses of gender. The paper addresses
both endogeneity and selectivity biases, by employing instrumental variable with Heckman’s two-stepprocedure based on the suggestion of Wooldridge (2002). There is paucity of studies on return ofeducation in Kenya undertaken after the Millennium Development Goals was envisaged by the United
Nations in 2000, and after the implementation of key education policies in Kenya i.e., the free primaryeducation policy. The study is, to the best of our knowledge, the first time that the approach which
addresses both biases has been used to estimate returns to education for both sexes in a developing Africancountry; noteworthy to mention that previous authors have limited its use to married women in
Kazahkstan (Arabsheibani and Mussurov, 2007) and in China (Chen and Hamori, 2009).
The paper is structured as follows. Section 2 provides a review of previous literature examining
rates of return to education in Kenya. Section 3 explains the Mincerian framework used to estimate therate of return to an additional year and level of schooling in Kenya, and the empirical strategy adopted tosimultaneously correct for endogeneity of schooling and sample selection bias. Section 4 describes thedataset and variables used in the analysis. The main results and discussion are presented in Section 5, andSection 6 gives some concluding remarks.

2. Literature Review on Returns of Education in Kenya
Several studies have been undertaken to analyze the returns to additional years of schooling in
Kenya. Among the earliest studies is the World Bank sponsored study conducted by Thias and Carnoy in
1969, with few following in 1970’s while the recent studies initiated around 2000. A detailed list of thestudies is presented in the Table A1. Rates of return obtained from various studies cannot be directlycompared due to variations in methodology and data coverage. However it is useful to review theliterature in order to understand previous empirical strategy and trends of the results.
A World Bank cost-benefit analysis by Thias and Carnoy (1969) estimated returns to levels of
education using cross-sectional urban earnings data collected in a 1968 Labor Force Survey of private andpublic sector employees in the cities of Nairobi, Mombasa, and Nakuru. Calculating the rate of return that
set the discounted time value of education costs equal to the stream of its benefits, they found overall
returns to education for urban males to be quite high: 32.7% for primary, 36.1% for lower secondary,
23.8% to higher secondary, and 27.4% to university education, respectively. For females, returns toprimary education were much lower at primary level i.e., 9.5%, but returns to lower secondary education
were comparable to that of males (33.6%). Johnson (1972) estimated continuous returns to education
based on earnings function of log hourly wages on categorical form of years of education expressed inquadratic, by using data from a survey of 1970 wage activity for low- to middle-income Africanhouseholds in Nairobi. Controlling for potential human capital accumulated through experience, union
membership, type of employment (government or self), gender, and major tribal variables, Johnson foundincreasing marginal returns to education, with a base return of 1%, and each additional year of schoolingincreasing it further to 2.2. Using 1980 data on wage-workers in Nairobi, Armitage and Sabot (1987)
estimated far lower private returns to the completion of secondary education of 14.5% for governmentsupported institutions, and just 9.5% for community-funded harambee (Kiswahili for “let’s pull together”)
schools.

Using data from three labor force surveys conducted in 1978, 1986, and 1995, Appleton, Bigsten
and Manda (1999) found fairly high private returns. They estimated returns to education using both
Mincerian regression and CBA. For example, returns in 1995 are 25%, 7%, and 35%, for primary,secondary, and university education respectively. The study also shows that there is a trend that the returnto the tertiary education is higher and increasing over the years whereas the returns to the primaryeducation is decreasing. Manda, Mwabu & Kimenyi (2002), another recent study which emphasized onincorporating human capital externality, also show that the returns to tertiary education are estimatedhighest in 1995, but returns to primary and secondary education are much lower.
The latest study so far -Manda, Mwabu & Kimenyi (2002) computed returns to education in 1994
using Welfare Monitoring Survey of Kenya, and further disaggregated according to location and sex ofworkers. The study showed that the returns to education are higher for males across all education levels;
the returns to schooling for males are 11.0%, 17.8%, 35.2% and females stand at 5.7%, 15.8%, 32.2%, forprimary, secondary and tertiary education. This study so far provides the most comprehensive returns to
education by level of education and sex. Other studies did not provide such detailed estimates.
There are two major insights from the literature review. First, earlier studies such as Thias andCarnoy (1969), Johnson (1972), some results using old data in 1978 and 1986 of Appleton, Bigsten and
Manda (1999) showed the diminishing pattern of the rates of return to education. However more recent
results such as the results using 1995 data from Appleton, Bigsten and Manda (1999) and Manda, Mwabu& Kimenyi (2002) suggest increasing returns to higher levels of education in Kenya, as primary education
becomes a social norm for the majority of Kenyans. Second, the studies undertaken to assess returns to
education in Kenya differ according to three dimensions namely- geographical coverage, disaggregationby the sex variable, and control for endogeneity and selection biases. The latest study was conducted using
1994 data, prior to major education reforms to achieve universal primary education in Kenya. Except
Manda, Mwabu & Kimenyi (2002) previous studies did not differentiate rates of returns to education andthe varying rates of retunes to different levels of education. It is important to evaluate returns to additional
year of schooling according to sex so that policymakers are better equipped to chart strategies to educate
girls, as women in Kenya have dearth of employment opportunities arising due to lack of education(World Bank, 2004). Finally, none of the published studies adopts methods to correct the estimates ofreturns to education for endogeneity and selection bias.

3. Methodology
The empirical framework adopted to estimate the private returns to education use the Mincerian semilogarithmic basic earnings function (Mincer, 1974),

ln 𝑊𝑖 = 𝛼 + 𝛽𝑆𝑖 + 𝛾1 𝐴𝑖 + 𝛾2 𝐴𝑖 2 + 𝛿𝑿𝑖 + 𝜀𝑖(1)where ln 𝑊𝑖 is the natural logarithm of the hourly wage reported by each individual 𝑖 ; 𝛼 is a constant; 𝑆𝑖is years of schooling; 𝐴𝑖 is a measure of worker experience, entered in linear and quadratic forms; 𝑿𝑖 is avector of other observed exogenous explanatory variables, and an error term, 𝜀𝑖 . The wage-earning
specification is examined for males and females separately. The coefficient is interpreted as the private
rate of return to education, that is, the relative change in wages for each additional year of schooling,
averaged across all sampled individuals and levels of education.
It is a recognized fact that returns obtained from simple ordinary least squares (OLS) estimation of
the Mincerian earnings function may be biased due to endogeneity of the educational variable. As an
example, if years of schooling are positively correlated with an unobserved or otherwise omitted ability
factor which also affects earnings in a positive way, OLS will tend to overestimate, since high-ability
individuals both complete more years of schooling and earn higher wages in the labor market. Here, thepotential endogeneity of years of schooling is addressed by adopting a conventional instrumental variables(IV) approach, where an observable covariate that affects schooling but not earnings is used to instrument
for schooling in the following two-equation model:

𝑆𝑖 = 𝜁𝒁𝑖 + 𝜇𝑖

(2)

ln 𝑊𝑖 = 𝛽𝑆𝑖 + 𝛾1 𝐴𝑖 + 𝛾2 𝐴𝑖 2 + 𝛿𝑿𝑖 + 𝜈𝑖 (3)where 𝒁𝑖 is a vector of the instrument and other observed exogenous explanatory variables 𝐴𝑖 , 𝐴𝑖 2 , andthose already in 𝑿𝑖 ; and 𝜇𝑖 and 𝜈𝑖 are error terms.
This study chooses the commonly-used instrument of maternal education (e.g., Trostel, Walker,
and Woolley, 2002). In low-income countries where women are mostly poorly educated, unemployed andhave comparatively little standing or bargaining power over household decisions such as educational
expenditure and enrollment, while mothers with more education are suggested to enhance their children’s
educational outcomes largely directly. They do so by improving the time children spend on educational
activities outside school, helping children with schoolwork, and encouraging educational assistance fromother family members (Andrabi, Das, and Khwaja, 2012). Indeed, Kabubo-mariara and Mwabu (2007)
found maternal education to be an important determinant of both educational enrollment and overall grade
attainment in Kenya. Paternal education is not selected as a second instrument, because fathers’ educationand its related outcomes (his socioeconomic status and occupation) are far more likely to influence theirchildren’s employment status as well as education, especially as African youths frequently find it
necessary to utilize familial social capital and contact networks during the job-hunting process (Filmer andFox, 2014).

In practice, IV estimates of returns to education in the literature typically exceed those obtained
from OLS by a magnitude of 20% of more. Measurement error may bias returns to education either
downwards. If the overall effect of the measurement error outweighs, it leads to overestimation of returns
to schooling using OLS. Nonetheless, Card (2001) estimates the impact of measurement bias to berelatively of the order of 10%, and suggests that the large gap reflects downward bias in OLS estimates
due to heterogeneous returns to education, where individuals with high discount rates choose to complete
less schooling (Lang, 1993).
OLS estimates of returns to education may also be biased due to sample selectivity, if the wageworking sample is not fully representative of the working population. Accounting for females in
estimation of returns to education addresses concerns of selectivity bias, as it is observed that education
does influence employment of females in a positive way and as better-educated individuals earn higher
salaries, returns to education for females are expected to be biased upwards. In Kenya, however, where
there is substantial labor market heterogeneity, possibly owing to male workers in the informal or smallscale agricultural sectors who generally earn less than their formally-employed counterparts (Nyaga,
2010) and do not report an official wage, such that OLS returns to education for males are also biased
upwards. Consequently, this potential sample selection bias is corrected for by applying Heckman’s twostep method (Heckman, 1979) on both males and females, wherein individual’s participation in wageearning activity is modelled as being determined by a selection equation

𝐷𝑊 𝑖 = 1[𝜃𝑻𝑖 + 𝜂𝑖 > 0](4)where the dummy variable indicating selection 𝐷𝑊 𝑖 equals 1 if wage-earning is observed (𝑊𝑖 > 0), and0 otherwise; 𝑻𝑖 is a vector of additional observed exogenous explanatory variables for participation. In
2addition to 𝑆𝑖 , 𝐴𝑖 , 𝐴𝑖 and the explanatory variables already in 𝑿𝑖 , this study includes as selectivity
variables the natural logarithm of the individual’s household expenditure, ln 𝐻𝐻𝐸𝑖 ; and drawing on thedeterminants of Kenyan participation in employment reported by Nyaga (2010), household size,
disaggregated into the number of children in the household aged below 6 years (primary-school age),

HHChildren6-𝑖 , and the number of elderly in the household aged over 65 (above the working-age
threshold), HHAdults65+𝑖 ; and dummy variables indicating whether an individual is household head,

𝐷headship ; and the household owns its present dwelling, 𝐷HHownshouse 𝑖 . 𝜂𝑖 is an error term.
𝑖Using the parameter 𝜃̂ estimates obtained from the probit 𝑃(𝐷𝑊 𝑖 = 1|𝑻𝑖 ) = Φ(𝜃𝑻𝑖 ) over theentire working-age subsample, the inverse Mills ratio 𝜆𝑖 (𝜃̂𝑻𝑖 ) =̂ 𝑻𝑖 )𝜙(𝜃̂ 𝑻𝑖 ) is computed for each observation
Φ(𝜃and included as an additional exogenous explanatory variable in the selectivity-corrected Mincerian

ln 𝑊𝑖 = 𝛽𝑆𝑖 + 𝛾1 𝐴𝑖 + 𝛾2 𝐴𝑖 2 + 𝛿𝑿𝑖 + 𝜄𝜆𝑖 (𝜃̂𝑻𝑖 ) + 𝜈𝑖 ′(5)estimated for the selected subsample, where the coefficient 𝜄 measures the covariance of the residuals inthe selection and earnings equations 𝜎𝜂𝑖 ,𝜀𝑖 , and its statistical significance and sign indicates the existence
and, if so, direction of the sample selectivity bias, which is expected to be negative.
To adjust for both endogeneity of education and sample selectivity simultaneously requires the
combination of the Heckman and IV procedures. Following Wooldridge (2002), 𝑻′𝑖 in the joint HeckmanIV first-stage selection probit 𝑃(𝐷𝑊 𝑖 = 1|𝑻′𝑖 ) = Φ(𝜃′𝑻′𝑖 ) estimated over the entire working-age
subsample incorporates all exogenous explanatory variables, i.e., the instrument and those already in 𝑻𝑖 ,omitting 𝑆𝑖 . Similarly, 𝒁′𝑖 in the second-stage IV equation for 𝑆𝑖 is a vector of the newly estimated̂ 𝑻′𝑖 ) and 𝑻′𝑖 for all observations in the selected subsample:
𝜆′𝑖 (𝜃′𝐷𝑊 𝑖 = 1[𝜃′𝑻′𝑖 + 𝜂𝑖 > 0]

(6)

𝑆𝑖 = 𝜁𝒁′𝑖 + 𝜇𝑖

(7)

̂ 𝑻′𝑖 ) + 𝜈𝑖ln 𝑊𝑖 = 𝛽𝑆𝑖 + 𝛾1 𝐴𝑖 + 𝛾2 𝐴𝑖 2 + 𝛿𝑿𝑖 + 𝜄𝜆′𝑖 (𝜃′(8)

OLS, IV, Heckman-corrected, and joint IV-Heckman-corrected returns to education are estimatedon the entire sample of wage-workers. This overall sample is then disaggregated into subsamples of wageworkers whose highest grade was bounded by primary (i.e., those born in or before 1971 who hadcompleted up to Standard 7, or born in or after 1972 and had completed up to Standard 8), secondary(those who had completed from Standard 8 to Secondary 4), and tertiary education (those who hadcompleted Secondary 4 or higher). Doing so permits the slope of the earnings function (the rate of returnto education) to vary across the three levels of education.

4. Data
The study uses the 2005-2006 Kenya Integrated Household Budget Survey (KIHBS), collected
information from a nationally representative sample of 13,430 households on a wide range ofsocioeconomic indicators relating to demographics, education, employment, expenditure, andconsumption. The analysis is restricted to wage-earners of working-age (15 to 65 years) at the time of thesurvey, excluding full-time students. The sample sizes of male and female workers are 5,406 and 3,146,
respectively.
The labor module in the KIHBS household questionnaire asked household members their average
daily working hours and earnings for the previous month. Assuming 20 working days per month, thisinformation is used to calculate each wage-worker’s hourly wage. Age is substituted as a proxy forpotential work experience 𝐴𝑖 , primarily because years of prior working experience or job tenure were notdirectly surveyed. However, as noted by Barouni and Broecke (2014), Mincer’s traditional expression for

𝐴𝑖 , age minus schooling minus primary entry age, is also less relevant in African countries where lateprimary matriculation, repetition, and dropping out are relatively common. Educational capital wasrecorded as the highest grade completed, from which the continuous variable for years of schooling 𝑆𝑖 wascomputed, adjusted for the different systems pre- and post-1985 educational reform. 𝑆𝑖 is subsequently
used to define subsamples of wage-workers by highest participatory education level. The descriptive
statistics is presented in Table A2.

5. Results
Average returns to an additional year of schooling for the overall sample of wage-workers are
statistically significant at the 1% level for both sexes and at mean value of independent variables. OLS
returns to schooling for males (14.0%) and females (13.8%) are nearly equivalent. However, IV returns toschooling sharply diverge between males (11.5%) and females (14.2%). The large Cragg-Donald Fstatistic (218.8, males; 93.9, females) and high F-statistics of the first stages (77.0 males; 34.7 females)
and Shea Partial statistics (0.218.8 males; 0.197, females) suggest mother’s education to be an adequate
instrument for years of schooling for both males and females. In contrast, returns to schooling estimatedby the Heckman two-step method (12.7%, males; 11.2%, females) show evidence of an expectedsignificant upward selectivity bias in the OLS returns to schooling, although the magnitude of theselectivity bias is comparatively small. The Heckman returns to schooling are 10% and 23% lower than
OLS estimates for males and females.
The estimates of returns to education yielded from the joint IV-Heckman procedure are 14.9% and13.5% for males and females, respectively. The estimate of male workers using IV-Heckman procedure isslightly higher than the OLS estimates, but that of female worker is almost same as the OLS estimate. In
fact, the selectivity term is statistically insignificant in the joint IV-Heckman estimate of returns toschooling for females (cf. エラー! 参照元が見つかりません。A5, column 4), which is indicativethat the sample selection bias would not be a serious issue in estimation returns of schooling in Kenya.
Table 1 below summarizes the findings from different methods.
Table 1. Private Returns to an Additional Year of Schooling by Means of Estimation, Overall
SampleMale
Female14.0%***
13.8%***
OLS
11.5%***
14.2%***
IV

12.7%***
Heckman
14.9%***
Joint IV-Heckman
Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.
aselectivity term not statistically significant.
Source: Table A3

11.2%***
13.5%***The previous analysis using a continuous education variable as the dependent variable shows that
there is little gender difference in returns to additional year of schooling across all the methods. However,
the analysis to estimate varying returns to education by the level of education unpacks the nature of
returns and shows a significantly different picture i.e., the returns to education vary significantly between
different levels of education, between male and female workers, and the analysis using a continuous
education dependent variable may lead to wrong policy implications. The results are summarized in Table
2. Major findings are the following. First, the rates of return at primary level are statistically insignificant
for both male and female samples when both biases are controlled. Second, the returns to an additional
year of schooling increases progressively i.e., the return of the secondary education is higher than that ofprimary education, and the return of the tertiary education is higher than that of secondary education. The
coefficients of rate of return at secondary and tertiary levels are statistically significant at the 1% levelacross sexes and means of estimation. Using the IV-Heckman approach, the rates of return of secondary
and tertiary education for males are estimated as 20.1% and 71.2%, respectively. For female workers, they
are 36.8% and 87.6%, respectively.

Last, the rates of return of schooling among female workers arehigher than male workers consistently, except the primary education where the return for female workers
is insignificant. These results unpack the nature of returns to education and seem to provide a better
understanding such as increasing returns. The results in the previous section show that the return toeducation among male workers is higher than female, but this analysis shows that the female’s returns ofsecondary and tertiary education are higher than males ones by 59.0% and 53.5%. These results would
rather encourage the government to take more active roles in promoting girl’s secondary and postsecondary education. Consistently, OLS returns to schooling are biased in both upwards and downwards
with respect to IV estimates while the Heckman returns demonstrate the presence of statisticallysignificant upward selectivity bias in the OLS estimates.
Table 2. Private Returns to an Additional Year of Schooling Disaggregated by Level of Educationand Estimation Methodology
Male
FemalePrimary
Secondary
Tertiary
Primary Secondary
Tertiary
6.3%*** 18.2%***
23.3%***
0.90%
23.2%***
33.3%***
OLS
0.90%
14.7%
90.1%***-0.94%
36.4%***
91.9%***
IV
4.5%*** 16.5%***22.3%***-1.1%
18.1%***
31.1%***
Heckman
0.60%
20.1%***
71.2%***-0.73%
36.8%***
87.6%***
Joint IV-Heckman

Observation

544

412

162

229

252

110

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Table A4
6. Conclusion
The study calculates returns to additional year of schooling based on gender, almost five decades
after the first study of this kind; Thias and Carnoy (1968) undertook analysis, and after 13 years since thelatest estimates were made by Manda, Mwabu and Kimenyi (2002). The paper extends its analysis forcalculation of returns to education, by using various other approaches such as IV, Heckman and joint IVHeckman with a purpose to simultaneously correct for two potential sources of bias in OLS estimates ofthe Mincerian earnings function: the endogeneity of schooling and earnings; and potential nonrepresentativeness of the wage-working sample, which only contains individuals who reported wages andmay exclude workers employed in the informal or small-scale agricultural sectors with different earnings
profiles. The returns to schooling on the overall sample of wage-workers estimated by the IV andHeckman methods separately demonstrate statistically significant biases as expected and in line withprevious literature, with OLS returns biased both upwards and downwards compared to IV results
depending on the level of education, and biased slightly upwards from Heckman results, due to sample
selectivity. The paper makes a case for employing IV and Heckman methods simultaneously to estimate
returns to education more precisely. In addition the returns of education are estimated at the overall andby the level of education to unpack the nature of returns of education. The study finds that the joint IVHeckman estimation finds the returns to an additional year of schooling as 14.9% for males and 13.5% forfemales, using a continuous education dependent variable.
The estimation of the returns to additional years of primary, secondary, and tertiary education
reveals the situation to be more nuanced. In particular, the joint IV-Heckman estimates of returns toprimary education are not statistically significant for both males and females, demonstrating a minimal
wage differential between workers with and no primary education. The relatively small selectivity
correction to OLS returns to schooling implies that wage structures for Kenyan workers with primary or
less than primary education have become fairly similar. The spurt in primary enrollment in Kenya1 seems
to have led to a phenomenal increase in number of individuals with primary schooling thereby causing
decline in returns to primary education. It would also be worthwhile to undertake future efforts toevaluate the selection bias over time in order to explore trends in Kenya which has a sizable informal
employment sector.
1

According to the UNESCO Institute of Statistics (various), the national gross enrollment rate in
Kenya was 107.7% in 2005.

Conversely, the returns to additional years of schooling increase from secondary (20.1%, males;
36.8%, females) to tertiary education (71.2%, males; 87.6%, females), and the returns to education of
females workers are consistently higher than those of male workers. The study renders support toincreasing returns to education with increasing levels of education, as presented in previous studies,
thereby reinforcing the importance of secondary and tertiary education in Kenya and defying the classicalpattern. The study also indicates that measures to enhance access to post-primary levels of education forfemales might open up windows to avail financially rewarding employment opportunities andconsequently reducing their financial vulnerability as females are poorer than men in Kenya as suggestedby the World Bank (2004).
The study concludes that implementation of educational policies and investment in education
sector in Kenya should facilitate access, and enhance the quality of secondary and tertiary education withspecial focus to integrate the female population into the education system. Policy measures should not be
restricted to raising primary enrollment, but instead, should aim to improve the quality of primary schools
in order to curtail factors which give rise to any form of exclusion of children from education. Perhaps
such policy measures will maximize students’ chances of progressing onwards to post-primary levels of
education, only after which they begin to accrue significant returns to additional years of schooling. The
results of the paper strongly suggest that education overall remains a favorable sector for public andprivate investment in Kenya, and that particular attention should be paid to post-primary and girls’
education.

Annex
Table A1: Summary of studies undertaken to estimate returns to education in Kenya
Data
Returns to Education
Analytical
Study
MethodYear
Coverage
Sex
Primary
Secondary
Tertiary
Lower Sec:
OLS
36.1%
Male
32.7%
27.4%
Thias andNairobi,
Mincerian1968 Labour Force
Higher Sec:
CarnoyMombasa, and Equation andSurvey23.8%(1969)
NakuruCost-Benefit
Lower Sec:
Analysis
Female
9.5%
n.a.
33.6%
Various percentage increments are calculated(e.g., 8.5% from 0 years to 2 years of education).
OLS
Johnson
1971
Nairobi
MincerianOverall Marginal effect of additional year of education is a(1972)
wage equation
convex function of year of education (i.e., 1.0% +
2.2%*year of education)
Knight and1980
Nairobi
Overall
n.a.
16%
n.a.
OLS
Sabot (1987)
Mincerian
The Kenya Survey
Government:
Equation andArmitage andof Wage
14.5%
Cost-Benefit
Nairobi
Overalln.a.
n.a.
Sabot (1987)
Employment andAnalysis
Harambee: 9.5%
Education 1980
Mincerian
1978: 8%42%
15%
The 1978 Labour
1986: 9%26%
30%
Force Survey, The National (1978
OLS
1995: 2%
12%
69%
Appleton,
1986 Urban Labour and 1986), andMincerian
Cost-Benefit
Bigsten andForce Survey, The
Nairobi,wage equation
Overall
Lower Sec:23%,
Manda
1995 Regional
Mombasa,and Cost
1978:24%
13%
Higher
Sec: 28%(1999)
Programme on
Nakuru, andBenefit1986:
Lower Sec:17%,
Enterprise
Eldoret (1995)
Analysis31%
22%
Higher Sec:20%
Development survey
1995:
Lower Sec:7%,35%
25%
Higher Sec:n.a.
Manda,Overall
7.9%
17.2%
32.5%
The Welfare
OLS
Mwabu &
Male
11.0%
17.8%
35.2%
Monitoring Survey
National
Mincerian
Kimenyi1994
wage equation
Female
5.7%
15.8%
32.2%
(2002)

12

Table A2: Descriptive Statistics by Region and Sex
VARIABLES

Male

Female
Mean

SD

Min

Max

N

Mean

SD

Min

Max

LnW

5406

3.29

1.24

-4.09

8.36

3146

2.90

1.30

-4.94

7.71

Eduyear

5406

8.27

4.04

0.00

18.00

3146

7.80

4.22

0.00

18.00

Married

5406

0.68

0.47

0.00

1.00

3146

0.52

0.50

0.00

1.00

Age

5406

34.76

11.07

15.00

65.00

3146

33.44

10.85

15.00

65.00

Age2

5406

1331

842

225

4225

3146

1236

804

225

4225

Mother's education

789

2.90

3.69

0.00

17.00

389

3.93

4.17

0.00

18.00

LnHHExp

17166

9.75

1.15

0.00

15.71

17844

9.71

1.06

0.00

15.10

HHChidren6-

17166

1.01

1.14

0.00

9.00

17844

1.17

1.18

0.00

9.00

HHAdults65+

17166

0.16

0.42

0.00

3.00

17844

0.17

0.43

0.00

3.00

Headship

17166

0.47

0.50

0.00

1.00

17844

0.18

0.39

0.00

1.00

HHownhouse

17166

0.62

0.49

0.00

1.00

17844

0.61

0.49

0.00

1.00

Urban

5406

0.50

0.50

0.00

1.00

3146

0.53

0.50

0.00

1.00

Central

5406

0.12

0.33

0.00

1.00

3146

0.15

0.36

0.00

1.00

Coast

5406

0.12

0.33

0.00

1.00

3146

0.11

0.32

0.00

1.00

Eastern

5406

0.16

0.37

0.00

1.00

3146

0.16

0.37

0.00

1.00

Northeastern

5406

0.02

0.14

0.00

1.00

3146

0.01

0.10

0.00

1.00

Nyanza

5406

0.16

0.37

0.00

1.00

3146

0.18

0.39

0.00

1.00

Rift valley

5406

0.24

0.43

0.00

1.00

3146

0.21

0.41

0.00

1.00

Western

5406

0.09

0.29

0.00

1.00

3146

0.08

0.26

0.00

1.00

Regional DummyN

Note: LnW: Log Hourly Wage; Eduyear: Years of Schooling; LnHHExp: Log Household Expenditure

13

Table A3. OLS and IV, Heckman and Joint IV-Heckman Estimations of Returns to Schooling
OLS
Male
[1]
VARIABLESSi
Ai
Ai2
Marriedi

IV

Female[2](eq. 1)ln Wi

0.140***[0.004]0.091***[0.009]-0.001***[0.000]0.131***[0.038]0.138***[0.005]0.083***[0.012]-0.001***[0.000]0.064[0.042]Male[3](eq. 3)ln Wi

Female[4](eq. 3)ln Wi

Heckman
Male
Female[5][6](eq. 5)(eq. 5)
ln Wi
ln Wi

Joint IV-Heckman[7](eq. 8)ln Wi

0.115***[0.023]0.064**[0.031]0.000[0.000]0.133[0.110]0.142***[0.030]-0.037[0.045]0.001[0.001]
0.015[0.313]0.127***[0.004]0.040***[0.012]0.000[0.000]0.083**[0.039]0.112***[0.008]0.018[0.019]0.000[0.000]0.203***[0.054]0.149***[0.027]-0.128[0.094]0.002*[0.001]-0.215[0.196]-0.410***[0.068]1.476***[0.285]-0.531***[0.122]2.001***[0.507]

17116
11710

17844
14698

0.212***[0.014]-0.003***[0.000]0.387***[0.077]0.077***[0.023]0.005[0.020]0.004[0.042]0.297**[0.122]-0.017[0.045]-0.021***[0.006]-1.034**[0.483]5.079**[2.142]
789

789

0.126

0.287

HHChidren6-i
HHAdults65+i
Headshipi
HHownhousei
MSi (instrument)

Constant

0.027[0.148]-0.032[0.193]0.566[0.412]

1.771***[0.558]Observations
Censored
R2
Pseudo R2
Cragg-Donald F-statistic
Shea Partial R2
Wald chi2
F-test

5,406

3,146

789

389

0.319

0.247

0.151

0.089

[9]
(probit eq. 6)
Dwi

0.219
[1.184]-0.003[0.016]0.366[2.047]-0.074[0.442]-0.190*[0.113]-0.194[0.224]-0.514[1.415]-0.161[0.270]0.521***[0.121]-2.499[7.085]6.897[33.891]

LnHHExpi

λi (selectivity term)

Male
[8]
(eq. 7)Si[10](eq. 8)ln Wi
0.135***[0.026]-0.031[0.186]0.001[0.003]0.01[0.332]

Female[11](eq. 7)Si

0.297***[0.022]-0.004***[0.000]-0.227[0.186]-0.02[0.027]-0.004[0.025]-0.027[0.059]0.022[0.201]-0.123**[0.058]0.006[0.007]-5.130***[0.304]0.005[0.76]
1.719[4.206]-10.088***[2.867]0.136***[0.039]7.264***[2.292]
1.048***[0.211]-0.310**[0.147]0.981**[0.39]-2.931***[0.94]
4.236***[1.181]
0.093[0.076]-44.867***[11.774]230.087***[62.643]

6492

389

389

4835

0.098

0.337

0.135
28.33
0.179
1067
633.5

257.9

30.53

[12]
(probit eq. 6)
Dwi-5.608***[0.399]

0.151
22.31
0.262

359.2

11.03

Notes: standard errors in parenthesis; *** p < 0.01, ** p < 0.05, * p < 0.1.; Si: Year of schooling; Ai: Age; Ai2: Age squared; Married: Marital status (1=Married); LnHHExp: Log household total expenditure; HHChildren6-: Living
in household with children under age 6; HHAdults65+: Living in household with adults over 65 years old; Headship: Relation to household head (1=head); HHownhouse: household head’s ownership of house (1=owned house);
MSi: Mother’s years of schooling; and λi: inverse mill’s ratio (heckman’s lambda) ; Results of first-stage estimation and probit estimations for column [1] – [6] are available upon request

Table A4. OLS and IV, Heckman and Joint IV-Heckman Estimations of Returns to Schooling, Level of Education Sub-Sample

14

Male
OLS
[1]
VARIABLES
Si
Ai
Ai 2
Marriedi

0.063***[0.007]0.077***[0.011]-0.001***[0.000]0.200***[0.048]λiConstant
Observations
Censored
R2
Wald Chi2
F-testFemale0.810***[0.179]
2,855

544

0.1

0.066

Ai
Ai 2
Marriedi

78.73

0.009
[0.009]0.084***[0.012]-0.001***[0.000]-0.088*[0.052]λi
Constant
Observations
Censored
R2
Wald Chi2
F-test10,796
7,941

IV-Heckman
[4]

OLS
[5]

SecondaryHeckman[7](eq. 1) ln W
0.147
0.165***[0.102][0.009]0.05
0.034**[0.043][0.016]0.000
0.000[0.001][0.000]0.028-0.008[0.152][0.050]-0.524***[0.072]0.203
1.224***[1.109][0.347]
IV
[6]

0.006
[0.051]0.178**[0.086]-0.002*[0.001]
0.095[0.160]0.314[0.431]-0.886[1.907]0.182***[0.009]0.105***[0.012]-0.001***[0.000]0.091**[0.046]

544

3,488

412

0.067

0.203

0.101

-0.723***[0.215]

96.68

OLS
[13]
VARIABLESSi

Primary
Heckman[3](eq. 1) ln Wi
0.009
0.045***[0.054][0.008]0.122***
0.011[0.037][0.017]-0.001**0.000[0.001][0.000]0.022
0.213***[0.124][0.050]-0.523***[0.101]0.468
2.643***[0.439][0.399]IV[2]0.931***[0.213]

9.189
Primary
IV
Heckman[14][15](eq. 1) ln Wi-0.094-0.011[0.064][0.011]0.049
0.019[0.050][0.022]-0.0010.000[0.001][0.000]-0.3090.124[0.370][0.079]-0.590***[0.164]2.068***
2.959***[0.609][0.603]

1,747

229

0.027

-0.024

12693
10946

1.641

OLS
[9]

Tertiary
Heckman[11](eq. 1) ln W
0.901***
0.223***[0.250][0.018]0.055
0.080***[0.126][0.030]-0.0010.000[0.002][0.000]0.431-0.124[0.365][0.084]-0.516***[0.106]-8.651***-0.532[2.406][0.593]IV[10]0.201**[0.092]-0.059[0.095]0.001[0.001]-0.198[0.237]-0.58[0.460]2.401[2.139]0.233***[0.017]0.152***[0.026]-0.001***[0.000]0.031[0.075]

412

1,179

162

0.085

0.408

-0.126-2.437***[0.439]

467.5
7.447
IV-Heckman
[16]

221.2
OLS
[17]
0.232***[0.013]0.057***[0.016]0.000[0.000]0.071[0.051]

9.908
SecondaryIV
Heckman[18][19](eq. 1) ln Wi
0.364***
0.181***[0.124][0.017]-0.048-0.037[0.071][0.025]0.001
0.001***[0.001][0.000]0.19
0.250***[0.433][0.064]-0.680***[0.135]-0.5852.134***[0.977][0.659]-0.073[0.048]0.098[0.113]-0.001[0.002]-0.355[0.379]0.24[0.454]0.776[2.515]-0.830***[0.284]

229

1,933

252

0.002

0.248

0.065

12.14
12.1

9,303
5,815

IV-Heckman
[8]

8383
6450

159.3

6.055

0.712***[0.210]-0.074[0.154]0.000[0.002]-0.264[0.488]-1.487*[0.817]-1.787[3.608]
162
0.065

486.3
8.403
IV-Heckman
[20]

201.9
OLS
[21]
0.333***[0.025]0.106***[0.032]-0.001*[0.000]-0.025[0.082]8.085
Tertiary
IV
Heckman[22][23](eq. 1) ln Wi
0.919***
0.311***[0.312][0.028]0.227
0.000[0.189][0.048]-0.0030.001[0.004][0.001]
0.055[0.089]-0.610***[0.199]-11.754**-0.219[5.219][1.096]

0.368***[0.088]-0.237[0.206]0.004[0.003]0.204[0.433]-0.748[0.815]
3.61[4.788]-3.013***[0.575]

252

693

110

0.062

0.395

-0.163

283
1.539

2,705
1,526

IV-Heckman
[12]

2207
1514

7.398
IV-Heckman[24]
0.876***[0.280]0.043[0.194]-0.001[0.003]-1.051**[0.480]-6.558[5.103]110-0.058

214.6
7.448

112.2

9.126

8.523

Notes: standard errors in parenthesis; *** p < 0.01, ** p < 0.05, * p < 0.1.; Si: Year of schooling; Ai: Age; Ai2: Age squared; Married: Marital status (1=Married); Results of first-stage estimation and probit estimations are available
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