ORIGINAL PAPER
Human capital and economic growth across regions: a case study in Indonesia
Yoga Affandi1 · Donni Fajar Anugrah1 · Pakasa Bary1
Received: 12 March 2018 / Revised: 27 July 2018 / Accepted: 7 August 2018
© Eurasia Business and Economics Society 2018
Abstract
This paper investigates the role of human capital on economic growth in both a quanti- tative context as well as a qualitative one. The empirical assessment focuses on a subna- tional Indonesian dataset with wide spatial diversity by using two different approaches:
(1) production function estimates using panel data and (2) conditional convergence equation estimates of cross-sectional observations, with national examination results used as a proxy of education quality. The study’s results suggest that human capi- tal is a main driver for economic growth. This conclusion explains major variations in medium-term economic growth across subnational units and also underscores the importance of investing in human capital. The empirical estimates indicate the follow- ings: (1) elasticity of human capital to economic growth varies across regions, which may be related to the share of the manufacturing sector, and (2) quantitative measures of human capital (e.g., years of schooling) are important for economic growth. How- ever, cognitive skills are a more significant factor because they are derived from the quality of human capital investment. Therefore, while we suggest that school enroll- ment is important, high-quality educational infrastructure and a curriculum that focuses on enhancing cognitive skills are key to ensuring higher economic growth.
Keywords Economic growth · Education · Human capital JEL codes O15 · O47
The views expressed on this paper are those of the authors and do not necessarily represents the views of Bank Indonesia.
* Pakasa Bary [email protected]
Yoga Affandi [email protected]
Donni Fajar Anugrah [email protected]
1 Economic and Monetary Policy Department, Bank Indonesia, Syafruddin Prawiranegara Tower, 19 Fl., Jl. MH Thamrin No. 2, Jakarta 10350, Indonesia
1 Introduction
Since becoming a middle-income country in 2004, Indonesia has opened up new opportunities to develop into a high-income country.1 As labor is a factor of produc- tion, Indonesia’s demographic bonus may help the country achieve this goal.2 How- ever, the opportunity to achieve high economic growth will only be realized if labor is productive. Therefore, human capital development in Indonesia is necessary for long-term economic growth as indicated by Affandi and Anugrah (2016). This study extends Anugrah and Affandi’s research.
Without increased labor productivity, it will be difficult for Indonesia to escape the middle-income trap.3 Facing this challenge, the government attempted to improve human resources through its structural reform agenda, notably by provi- sioning higher government spending in the health and education sectors. In educa- tion, the improvement took two forms: (1) a compulsory education program that has added years of school for most Indonesian children over the past few decades and (2) a better curriculum and higher-quality teachers.
Many studies have emphasized the importance of human resource development or human capital to increase economic output. Lucas (1988) and Romer (1990) were among the first to model this relationship. Mankiw et al. (1992) asserted a sepa- ration between labor variables and human capital variables in the Solow growth model, stating that human capital has a positive effect on output. In the 1990s, many empirical studies on economic growth (e.g., Barro 1991) found that human capital influenced economic growth. In Indonesia, a study by Tjahjono and dan Anugrah (2006) noted that an increase in human capital was followed by an increase in out- put. Hanushek and Kimko (2000) and Hanushek (2013), who studied the quality of education, found that it has a significant effect on increasing output. However, no studies have addressed the quality of Indonesian education using subnational-level data. In addition, no Indonesian study on human capital development has used a spa- tial dataset of subnational units.
This study aims to analyze human capital development’s influence on Indone- sian economic growth. The relationship between education and output is elaborated upon, both in terms of quantity as well as quality. In Indonesia, one should also study the robustness of relationships across subnational units. More than 30 subna- tional units (provinces) exist in Indonesia with notable spatial differences, including large differences in development and economic growth. Subnational or provincial- level data, which can also include larger groups, consists of Sumatra, Java, Kalim- antan, and Eastern Indonesia (KTI). To the best of our knowledge, this study is the
1 Indonesia’s per capita GDP in 2004 was 1080 U.S. dollar, which is within income range of lower mid- dle-income countries category based on World Bank groupings.
2 A demographic bonus occurs when the dependency ratio is low or the working-age ratio is high (e.g., when the working-age population is high when compared with the dependent population in relation to other countries. Because labor is a factor of production, the demographic bonus indicates an opportunity to achieve relatively high economic growth.
3 The middle-income trap occurs when a country fails to increase its per capita income after reaching middle-income status.
first to contribute to the literature by adding empirical evidence on both the quality as well as the quantity of human capital using a subnational dataset. The findings are as follows: (1) human capital has a positive impact on economic growth, with its magnitude depending on the share of the manufacturing industry, and (2) cognitive abilities constitute the most significant element that boosts economic growth.
The remainder of this paper is as follows. Section 2 reviews theories underlying the models that estimate the impact of human capital on economic growth. Section 3 analyzes the results while also providing additional data and anecdotal information to support the analysis. Conclusions and recommendations are provided in the final section.
2 The role of human capital
Human capital is generally considered to be a quantitative element, often measured by a variable such as years of schooling. The relationship between human capital and economic growth has been explained by the Mankiw–Romer–Weil model (1992), which extends Solow’s exogenous growth theory. The relationship between human capital development and education quality was empirically examined by Hanushek and Kimko (2000) and Hanushek (2013). Here, education quality refers to cognitive skills, measured by students’ scores in science and mathematics. Many other studies in several countries, including Indonesia, deal with the relationship between human capital and economic growth.
2.1 The Mankiw–Romer–Weil model (an extended Solow growth model)
The Mankiw–Romer–Weil (1992) model, abbreviated as the MRW Model, is an expansion of the Solow growth model. It adds human capital as an additional factor of production and sees technology as labor-augmented. The human capital variable itself is an extension of labor, where labor is counted not only in units but also in the level of skills. More skilled labor, often gained from education, can result in higher output when compared with a nonskilled workforce. This model starts with a pro- duction function as follows:
Equivalently, an intensive form can be represented as follows:
where y= Y
AL.
An individual is assumed to invest a fraction of their income in human capital sH and a fraction in physical capital sK . In addition, it is assumed that human capital and physical capital depreciate at the same rate, 𝛿 . As in the standard Solow model, (1) Yt=Kt𝛼Ht𝛽(AtLt)1−𝛼−𝛽.
(2) yt =k𝛼th𝛽t,
population and technology grow at the constant rates of n and g , respectively. There- fore, physical capital increases as follows:
On the other hand, the equation below shows the increase of human capital.
By substituting (2) and (3), the steady state value of physical capital is as follows:
Next, by substituting (2) and (4), the steady state value of human capital is as follows:
Referring to Eq. (6) and considering Eq. (2) at a steady state, greater investment in human capital increases the steady state level of output and leads to greater long- term growth.
2.2 Adding qualitative aspects
Aside from output-level specifications, Mankiw et al. (1992) also estimated growth rates that underscore output convergence processes between countries, which are conditional on the levels of human capital. However, they only measured quantita- tive aspects of human capital, such as years of schooling.
While Hanushek (2013) stated that developing countries made considerable progress in closing the educational gap with developed countries, recent research has emphasized the importance of cognitive skills for economic growth. Without improving the quality of schools, developing countries will find it difficult to boost economic performance over the long term.
Hanushek (2013) also suggested that using educational achievement to measure human capital in multicountry (international) analyses may cause great difficul- ties because, first, a comparison of human capital necessitates the assumption that schools in different countries provide identical education quality, and second, it assumes that schools are the only source of human capital and skills.
Hanushek (2013) compared specifications of the convergence equation, implying that developed countries grow more slowly than do developing countries. In general, the empirical specification of a (conditional) convergence equation is as follows:
(3) k̇t=sKyt− (n+g+ 𝛿)kt.
(4) ḣt=sHyt− (n+g+ 𝛿)ht.
(5) kt∗=
( s1−𝛽K s𝛽H n+g+ 𝛿
) 1
1−𝛼−𝛽
.
(6) h∗t =
( s𝛼Ks1−𝛼H n+g+ 𝛿
) 1
1−𝛼−𝛽
.
(7) Δln yi= 𝛽0+ 𝛽yln y0i+
n
∑
j=1
𝛽jZi,j+ 𝜀i.
Δln yi is per capita economic growth for country i; ln y0i is the log of per capita income in country i for a base year (or initial condition); Zi,j is j-th additional vari- able that affects economic growth; and 𝜀i is the error term. For education-related studies, Zi,j is generally a measure of human capital or changes in human capital.
Some alternative indicators used in previous studies include school enrollment, lit- eracy rate, and years of schooling.
Hanushek (2013) estimates this function with several alternative Zi,j variables, namely, the length of schooling, the quality of education, and a combination of the two (school duration and quality of education). His observations covered a long period—between 1960 and 2000—for 50 countries and incorporated data on eco- nomic growth, years of schooling, and Programme for International Student Assess- ment (PISA) test results as a proxy of cognitive ability.
Hanuschek’s (2012, 2013) estimated results show a clear relationship between cognitive skills and economic growth. Estimates of cognitive abilities also have higher R2 statistics, which indicate a better fit of his models to the data when com- pared with models that only include years of schooling.
When including only years of schooling as an educational control variable, the estimation result indicates that years of schooling positively relate to economic growth. However, when quality of education is added to the model, years of school- ing no longer affect growth because the coefficient “years of schooling” is very close to zero. Conversely, quality of education very significantly influences economic growth. Overall, the results of these estimates indicate that the quality of education impacts economic growth differentials between countries.
2.3 Other empirical studies
Barro’s (1991) findings for the 1960–1985 period using samples from 98 countries prove that primary and secondary school enrollment rates in the 1960s were sig- nificantly and positively related to economic growth in subsequent years until 1985.
Barro (1991) also found that a higher level of human resources is associated with lower fertility rates and more capital investment.
Barro and Sala-i-Martin (1995) added variables to the regression, in particular the interaction between per capita income in the initial year and a composite measure of education and health. These interaction terms capture human capital’s improved ability to absorb new technologies. As the parameter values of these interaction terms were found to be significant and positive, Barro and Sala-i-Martin (1995) concluded that convergence is indeed faster when human capital levels are high. In other words, human capital causes a faster convergence process; therefore, income in lower-income countries tends to catch up with those of more developed countries.
Several subsequent studies assumed that human capital accrues not only from the duration of education or school enrollment rates. Hanushek and Kimko (2000) were the first researchers to add a cognitive ability test variable to indicate quality of education.
Their study indicated that cognitive ability had a more significant effect on economic growth and that the inclusion of these variables decreased the contribution of educa- tion. Hanuschek and Wausmann (2011, 2012) and Hanushek (2013), among others,
extended Hanushek and Kimko (2000) study by adding countries and testing several sample groups. The findings of these follow-up studies generally confirmed the impor- tance of education in promoting economic growth.
The level of education required to affect economic growth depends on an economy’s developmental state. Gemmell (1996) opined that basic education is most important for developing countries; secondary education is most important for middle-income devel- oping countries; and higher education is most important for OECD countries. In addi- tion, Gemmell (1996) found that human capital has a significant and positive effect on economic growth in both the base year as well as later years.
In line with Gemmell (1996), Zhang and Zhuang (2011) suggested that the compo- sition of human capital in China is of the utmost importance because higher education in this county affects economic growth more than primary education and secondary education do. More advanced provinces in China benefit more from higher education, while less advanced ones are more dependent on primary and secondary education.
In support of economic growth, Su and Liu (2016), using urban data, noted an interaction between human capital and Foreign Direct Investment (FDI). They found that FDI has a positive effect on per capita GDP growth, with the rate mirroring the level of human capital in each city. They explained that human capital contributes to growth by facilitating technology transfers derived from FDI, further noting that the complementary effect of FDI and human capital is stronger for FDI when it is based on technology rather than when it is based on labor-intensive industries.
From a policy perspective, the empirical findings of De Ree et al. (2015) indi- cated that unconditional salary increases for teachers have no effect on the quality of education. This conclusion provides the first experimental evidence of a unique policy change in Indonesia—the permanent doubling of teachers’ salaries during late 2000s. This study’s survey was conducted using a large-scale, random sam- pling method in Indonesian schools, involving more than 3000 teachers and 80,000 students.
Such an unconditional increase in salaries significantly increased teachers’ sat- isfaction regarding their income, reduced their motives to seek additional employ- ment, and diminished stress related to their financial condition. However, after 2–3 years, the unconditional salary increase did not result in any improvement in teaching or in students’ exam results. Thus, while the salary increase significantly improved teachers’ welfare, it did not significantly alter education quality.
In comparison with an unconditional salary increase for existing teachers, Dolton and Marcenaro-Gutierrez (2011) suggested that recruiting individuals with higher teaching proficiency, as well as providing faster pay increases, would have a positive effect on students’ exam results.
3 Analysis
3.1 The level of human capital in Indonesia
Based on 2014 United Nations Development Programme (UNDP) data, the aver- age duration of schooling in Indonesia for individuals 25 years of age and over is
7.6 years, which is lower than the world’s average of 7.9 years (Fig. 1). This 7.6-year figure places Indonesia between the medium range and the high range of human development. However, current participation in education has improved in Indone- sia, allowing an expectation of 13 years of schooling in the future, which is higher than the anticipated world average of 12.2 years. Indonesia’s present investment in education is a valid strategy; however, analysis is required to assess whether the quality of education across countries effectively promotes economic growth.
Figure 2 (below) shows that mean years of schooling correlate positively with per capita income levels. Years of schooling in Indonesia are above average, which indi- cates that its per capita income level is higher than that predicted by the relationship between education level and Per Capita Gross National Income (GNI). Therefore, Indonesia may find it difficult to raise per capita income levels further without an increase in mean years of schooling.
Although higher than the test scores in 2012, results of Indonesia’s 2015 Inter- national Student Assessment (PISA) test were lower than those in many countries, notably, Indonesia’s PISA results were below those in other ASEAN countries.
These statistics cause concern as test results can be a proxy for future human capital.
Low Indonesian results in the areas of mathematics, reading, and science suggest that the education policy in Indonesia needs to be taken to the next level, with more focus on improving quality. This is important for Indonesia’s students to compete with students in other countries (Table 1).
In particular, the type of education also needs to be analyzed in order to deter- mine whether it is well diversified and consistent with available jobs. Comparing the skill diversification of Indonesian workers with those in Malaysia, which has the best skill diversification in the ASEAN-6 according to UNDP data (Fig. 3), indi- cates that Indonesia has too many students enrolled in social sciences, business and law, agriculture, and education. Lower comparative enrollments are seen in health and welfare, humanities and arts, engineering, manufacturing and construction, and natural sciences.
According to International Labor Organization (ILO) (2015), employee skill lev- els in Indonesia are generally insufficient (see Table 2). Notably, 89% of workers in the agricultural sector have skills lower than expected. The same is true for crafts- men, parliamentarians, high-ranking officials, operators, and technicians. However, worker skills for office employees and those in elementary occupations tend to be higher than expected. This indicates that the information is still insufficient to match employers with applicants and underlines the importance of more training geared to specific fields.
3.2 Quantity of education
This section estimates augmented human capital (Eq. 1), which is a production func- tion also assumed in Mankiw et al. (1992). The panel observations cover the period 1985–2014 for 30 provinces in Indonesia. In addition, estimation results for the geo- graphical areas of Sumatra, Java, Kalimantan, and Eastern Indonesia are provided.
13.0 7.6 024681012141618 Years
Expected years of schoolingMean years of schooling Fig. 1 Average and expected years of schooling Source: UNDP
Norway AustraliaNetherlandsGermanyIrelandUnited StatesSingapore Hong Kong, China (SAR)
Liechtenstein IcelandIsrael Luxembourg JapanFranceAustriaFinland SpainItaly Czech RepublicGreece
Brunei Darussalam Cyprus Qatar AndorraSaudi ArabiaUnited Arab Emirates PortugalBahrain LatviaCroaa
Kuwait MontenegroBelarus
Oman RomaniaUruguay BarbadosBulgariaPalauPanamaMalaysiaTrinidad and Tobago Cuba Costa RicaTurkey Sri LankaBrazil GeorgiaGrenadaJordan UkraineFijiMongoliaThailandLibya Tunisia Jamaica TongaBelize
Maldives Samoa
EgyptTurkmenistan Indonesia Paraguay Palesne, State ofUzbekistanPhilippinesEl Salvador Viet Nam Kyrgyzstan Iraq Micronesia (Federated States of)
GuyanaMoroccoNamibia Guatemala Tajikistan
India Honduras
Bhutan Timor-Leste Vanuatu
Congo Kiriba
Equatorial Guinea ZambiaGhana BangladeshCambodia Nepal
MyanmarNigeria MadagascarZimbabwe
Mauritania Papua New Guinea Comoros
YemenLesotho TogoHaiRwandaUgandaBenin Sudan Senegal EthiopiaGambia Congo (Democrac Republic of the)Liberia
Mali MozambiqueGuineaBurkina Faso Burundi Chad Eritrea Central African Republic
Niger
y = 0.3002x + 6.7032 R² = 0.6027 67
8
910
11
12 0.02.04.06.08.010.012.014.0
Log Gross Naonal Income per
Capita Mean Years of Schooling Fig. 2 Years of schooling and per capita income Source: UNDP, plotted by the authors
Table 1 2015 PISA test results
Source: OECD
Math Reading Science
OECD average 490 493 493
Indonesia 386 397 403
Malaysia 446 431 443
Thailand 415 409 421
Vietnam 495 487 525
0 10 20 30 40 50 Agriculture60
Educaon
Engineering, Manufacturing,
Construcon
Health and Welfare Humanies and
Arts Sciences
Services Social Sciences,
Business, Law
Indonesia Malaysia
Fig. 3 Registration of university students in Indonesia and Malaysia by field of study Source: World Eco- nomic Forum
Table 2 Qualifications across occupations
Source: ILO (2015)
Occupation Underqualified Well-matched Overqualified
Legislators, senior officials and managers 49.0 51.0 NA
Professionals 22.7 77.3 NA
Technicians and associate professionals 52.5 47.5 NA
Office clerks 6.5 54.3 39.1
Service workers and market and sales workers 58.7 35.7 5.5
Skilled agricultural and fishery workers 88.9 10.3 0.8
Craft and selected trade workers 72.4 25.9 1.6
Plant and machine operators and assemblers 55.5 42.0 2.5
Elementary occupations NA 78.0 22.0
All Occupations 56.0 37.0 7.0
The data are annual, province-level statistics for 1985–2014 for the following variables: regional GDP, capital stock, labor, and human capital. Regional GDP and labor are defined as provincial GDP and provincial labor, respectively. Regional capital stock is obtained by calculating the proportion between provinces and the nation. Labor data is defined as the number of laborers in a particular province.
Human capital data in each province is based on the number of school years.
To examine the effect of human capital on output, we develop panel models using the OLS method (provincial fixed effects).4 As can be seen in Table 3, we have problems with cross-sectional dependence as indicated by Pesaran cross-sectional dependence (Pesaran CD) test results. Accordingly, we apply a weighted method, using the Seemingly Unrelated Regression (SUR) methodology. SUR is also applied to obtain estimation efficiency (Moon and Perron 2006).
The result, using Panel SUR weighting (Table 4), suggests that three factors of production (capital, labor, and human capital) have a significant and positive impact on output. This finding is consistent with the results of several studies, such as Tjah- jono and dan Anugrah (2006), who found capital, labor, and human capital have positive effects on output in Indonesia from 1985 to 2004.
The results suggest that the impact of human capital on output is notably positive in Sumatera and Java (see Table 4). The effect of human capital on output is higher in Java than it is in other regions. For full observations on Indonesia, the estimated elasticity of human capital to output is 0.08. This coefficient is higher than the figure of 0.05 derived in previous research by Tjahjono and dan Anugrah (2006), which indicated an increasing influence of human capital over the past decade.
From a subnational perspective, this study indicates regional differences in the impact of human capital. Java’s human capital elasticity level of 0.20 is followed by those of Sumatera (0.12), Kalimantan (0.02), and East Indonesia (0.02). How- ever, for Kalimantan and East Indonesia, the parameter is not significant. Java’s relatively greater human capital influence on output can be explained by the large
Table 3 Estimation results (panel OLS, provincial fixed effects)
Standard errors are in parentheses; ***, **, and * denote significance at the 99, 95, and 90% confidence levels, respectively
Indonesia Sumatera Java Kalimantan Eastern Indonesia
Capital 0.35*** (0.02) 0.28*** (0.03) 0.20*** (0.04) 0.47*** (0.04) 0.41*** (0.03) Labor 0.76*** (0.05) 0.69*** (0.14) 1.38*** (0.16) 0.63*** (0.10) 0.77*** (0.07) Human capital 0.08*** (0.03) 0.07 (0.09) 0.29*** (0.08) 0.03 (0.05) 0.04 (0.04)
Adjusted R2 0.98 0.97 0.99 0.98 0.94
S.E.R 0.17 0.18 0.13 0.12 0.18
No. of Obs. 780 780 780 780 780
Pesaran CD 33.33*** 4.70*** 9.79*** 5.18*** 6.06***
4 We also applied the OLS method, with fixed effects controlling for year, and found results relatively similar to provincial fixed effects.
number of manufacturing industries in the region. In general, the manufacturing industry needs a skilled workforce to increase its output. While Sumatra has less manufacturing than does Java, it has more than that in Kalimantan, as well as East Indonesia. Therefore, the coefficient of human capital in Sumatra is higher than those in Kalimantan and East Indonesia, the regions of which are dominated by mining, agriculture, and fisheries.
East Indonesia has a particularly strong agricultural sector (including fisher- ies as a subsector) and a mining sector that does not require many highly skilled laborers. Thus, additional skilled workers would not increase output significantly in this region. Similarly, in Kalimantan, the level of human capital elasticity is not significant.
Some studies, such as those by Miller and Upadhyay (2002) and Castiglionesi et al. (2005), highlight the important role played by human capital in improv- ing output in manufacturing industries. These authors provided evidence that human capital has a positive impact on output, particularly if measured by total factor productivity (TFP). A study by Ciccone and Papaioannou (2006) found that industries, particularly export-oriented ones, grow faster with more educated employees. Similarly, Adejumo et al. (2013) showed that human capital sup- ported the manufacturing sector in Nigeria by improving industry value-added.
As Java is dominated by manufacturing, the role of human capital is more signifi- cant than those of other factors. This is in line with Su and Liu’s (2016) findings that human capital facilitates technological transfer from FDI, thereby adding to economic growth. These studies indicate that human capital may have different effects on different regional economies.
Su and dan Liu’s (2016) findings thus support that of Eicherngreen et al.
(2013), who suggest that strengthening the manufacturing sector can avoid the middle-income trap. They emphasized industries that exported high-tech prod- ucts. In this case, improving the quality of human resources becomes very important. More educated and skilled workers are necessary to strengthen the manufacturing sector, particularly if a country wants to develop its medium- or high-technology-based industries. As an example, South Korea has successfully avoided the middle-income trap by supporting a strong industry sector and pro- viding workers with a high level of education.
Table 4 Estimation results (panel SUR)
Standard errors are in parentheses; ***, **, and * denote significance at the 99, 95, and 90% confidence levels, respectively
Indonesia Sumatera Java Kalimantan Eastern Indonesia
Capital 0.35*** (0.00) 0.28*** (0.01) 0.20*** (0.03) 0.53*** (0.04) 0.41*** (0.01) Labor 0.76*** (0.00) 0.53*** (0.05) 1.14*** (0.12) 0.36*** (0.08) 0.67*** (0.03) Human capital 0.08*** (0.00) 0.12*** (0.04) 0.20** (0.08) 0.02 (0.02) 0.02 (0.01)
Adjusted R2 0.99 0.99 0.99 0.99 0.99
S.E.R 1.02 0.99 0.98 0.97 0.99
No. of Obs. 780 780 780 780 780
3.3 Quality of education
By comparing estimation results with three (conditional) convergence specifi- cations, this subsection replicates Hanuschek’s (2012, 2013) method of obtain- ing provincial-level data in Indonesia. The first specification uses an educational quantity, S , as a control variable in a convergence equation as follows:
The second specification replaces S with quality of education or cognitive skills, Q , as a control variable. Hence, the following holds true:
In the third specification, these two control variables are included as follows:
Hanuschek (2012, 2013) used PISA test results for quality of education.
Because of the absence of PISA test results in Indonesian provinces, this study uses provincial averages of the Junior Secondary School National Examination (Ujian Nasional) to measure the quality of education. We incorporate two alterna- tive proxies, math scores and scores on the Natural Sciences package (consisting of mathematics, physics, chemistry, biology, English, and languages). Since this national examination is identical across all provinces, it provides a reasonable proxy for education quality. We selected junior high school exam results, rather than those of any other school level mainly because these targeted students are approximately the same age as students taking the PISA exam, which assesses the ability of 15-year-old students.
Average provincial data from the National Examination represents cross-sec- tional data from 34 provinces in Indonesia, obtained from the Ministry of Educa- tion. Meanwhile, macroeconomic data, particularly real, per capita GDP, is taken from the Central Bureau of Statistics (BPS). For the initial GDP, this paper uses real, per capita GDP from 2010, whereas real, per capita income growth uses two alternatives: the growth of real, per capita GDP for 2014 and the average growth rate of real, per capita GDP between 2011 and 2014. In addition, quantity of edu- cation uses the log of the participation rate in senior secondary schools, which is also obtained from BPS. The school participation rate follows Barro (1991). We use senior secondary schools because of their important role, particularly for vocational high schools, in supplying labor to manufacturing industries in Indonesia. Robust- ness tests on several alternative proxies for schooling are reported in Table 5; how- ever, no significant difference from the principal estimation results is observed.
In general, the estimation results are consistent with the convergence equation hypothesis, where the per capita economic growth rate is inversely related to the initial per capita income level. This relationship is significant for all estimation results. In addition, no difference in interpretation is found between two alternative dependent variables, namely, average per capita economic growth for 2011–2014 (Table 6) and per capita economic growth for 2014 (Table 7).
(8) Δln yi= 𝛽0+ 𝛽yln y0i+ 𝛽sSi+ 𝜀i.
(9) Δln yi= 𝛽0+ 𝛽yln y0i+ 𝛽QQi+ 𝜀i.
(10) Δln yi= 𝛽0+ 𝛽yln y0i+ 𝛽sSi+ 𝛽QQi+ 𝜀i.
Table 5 Robustness tests for schooling (dependent variable: 2014 per capita income growth)
Standard errors are in parentheses; ***, **, and * denote significance at the 99, 95, and 90% confidence levels, respectively
S = Senior secondary
(main result) S = Junior secondary S = elementary
Log (2010) 1.07** (0.48) − 1.24** (0.48) − 1.24*** (0.47)
Log(s) 3.55*** (1.18) 3.58*** (1.08) 3.53*** (1.08)
Adj. R2 0.11 0.15 0.16
S.E.R 1.62 1.58 1.52
No. of Obs. 33 33 33
Table 6 Estimation results of convergence equations, 5-year average (dependent variable: per capita income growth average for 2011–2014)
Standard errors are in parentheses; ***, **, and * denote significance at the 99, 95, and 90% confidence levels, respectively
School Math Natural sci-
ences School + math School + natural sciences Log (2010) − 0.94* (0.52) − 0.74* (0.41) − 1.30***
(0.53) − 1.03 (0.66) − 1.28** (0.55)
Log(s) 3.33** (1.29) 1.83 (1.91) − 1.35 (3.69)
Log(math) 2.91*** (1.05) 1.76* (0.93)
Log(natural
sciences) 2.93** (0.91) 3.84* (2.18)
Adj. R2 0.07 0.08 0.15 0.07 0.13
S.E.R 1.76 1.76 1.69 1.76 1.71
No. of Obs. 33 33 33 33 33
Table 7 2014 Estimation results of convergence equations (dependent variable: 2014 per capita income growth)
Standard errors are in parentheses; ***, **, and * denote significance at the 99, 95, and 90% confidence levels, respectively
School Math Natural sciences School + math School + natural sciences Log (2010) − 1.07** (0.48) − 0.86** (0.40) − 1.44***
(0.48) − 1.17*
(0.60) − 1.43*** (0.49)
Log(s) 3.55*** (1.18) 1.96 (1.47) − 1.26 (2.63)
Log(math) 3.11*** (1.01) 1.88* (1.01)
Log(natural
sciences) 3.11*** (0.82) 3.953* (1.96)
Adj. R2 0.11 0.12 0.21 0.13 0.19
S.E.R 1.62 1.61 1.52 1.60 1.54
No. of Obs. 33 33 33 33 33
Referring to the estimation of the “school” model, increasing participation rates in education cause per capita economic growth, with a statistically significant 99%
confidence level. In the “mathematics” and “natural science” models, an increase of national exam results in mathematics or science lessons (quality measures) raise the rate of growth with a statistically higher significance than that in the “school”
model.
In a combined model that combines the quantity of education with the quality of education (cognitive ability), while the measure of educational quality is still significant, the proxy of educational quantity becomes insignificant. This indicates that cognitive skills, or educational participation that enhances cognitive skills, affect economic growth, not merely the general level of educational participation (Tables 6, 7).
4 Concluding remarks
The relevant literature shows that human capital affects income and economic growth. To take advantage of Indonesia’s demographic growth for long-term eco- nomic and welfare improvement, a natural increase of productivity and wages is necessary through the improvement of human capital. This paper discusses the rela- tionship of human capital with output in Indonesia, incorporating both national and subnational data.
This paper also analyzes the role of education with respect to quantity and quality by employing SUR panel regressions for a production function model and cross- sectional regressions for the Hanuschek model. In general, the empirical tests show results in accordance with the hypothesis that the role of human capital significantly affects output development in Indonesia. On the spatial side, we also find a posi- tive effect of human capital on output, particularly in Java’s dominant industrial sec- tor. Meanwhile, empirical results from the Hanuschek model show that to promote higher and sustainable economic growth, the quality of education matters.
A clear imbalance between regions occurs at the provincial level, where Java’s industrial sector has the highest level of human capital elasticity. The industrial sector employs a large number of people as labor and requires workers with skills or high levels of education (This conclusion is supported by the many educational facilities, schools, and colleges located in Java.). Outside Java, in regions heav- ily invested in agriculture (including fisheries as a subsector) and mining, workers with a lower level of skills predominate. In East Indonesia, for example, the role of human capital is still relatively small and educational facilities are very limited.
Although the average number of years of schooling in Indonesia remains rela- tively low, current school participation indicates growth in education poised to exceed world averages in the future. Nevertheless, the quality of education in Indo- nesia lags as evidenced by the relatively poor performance of Indonesian students in mathematics, reading, and natural science examinations. This indicates relatively low cognitive abilities. Although school enrollment and average school attend- ance are standard ways of measuring human capital, cognitive skills are even more important for long-term growth than are quantitative measures of education, such
as the number of years of schooling. The empirical results show that investment in human capital effectively supports economic growth in Indonesia. Therefore, educa- tion needs to focus not only on the number (quality) of students but also on the qual- ity and effectiveness of education to improve cognitive skills.
Along with improving the amount of education, it is necessary to support the government’s compulsory education program. Additional education spending pro- vides educational facilities, resources, physical infrastructure, and home education (including free books), while also improving social welfare. Skill diversity in the workforce can be increased by more education and by incentives to enroll in the fields of health and welfare studies, engineering, manufacturing, construction, and science. This involves gradually adjusting educational levels and targeting high- and mid-level job skills, including providing incentives for appropriate FDI.
Improving education evenly in all regions is important, particularly outside Java.
In Java itself, educational development should be directed at subjects directly benefi- cial to industry. Because a curriculum can be tailored to meet industry needs, indus- try should be given an active role in its design. In addition, specific vocational train- ing is required in accordance with market needs. A more diversified high school or diploma education, such as with the establishment of vocational schools, can link to specific industry needs. Some vocational schools already have been opened, with specifications tailored to the chemistry, automotive, and textiles industries. This effort should be extended to other sub-industries as well.
Better education quality can be achieved by improving the national curriculum, subjects studied, and teaching quality itself, with a focus on improving cognitive ability. In addition, to improve teaching quality, it is necessary to increase the quali- fications required of teachers or lecturers. A strategic focus should be adopted to attract the best teachers, including performance-based pay raises that can be meas- ured objectively.
Acknowledgements The authors are grateful to the anonymous reviewer, the editor, and participants of 24th EBES Conference in Bangkok for their useful comments.
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