Human Development Index in Indonesia: Are We in Line with SDGs

and How Much Have We Grown?

Tery Setiawan

, Indah S. R. Kuntari

and Indah Puspitasari

Department of Psyhology, Maranatha Christian University, Prof. Drg. Surya Sumantri 65, Bandung, Indonesia

Keywords: Human Development, Indonesia, Well-Being, Sustainable Development Goals, Latent Growth.

Abstract: Human development index encapsulates the attainment of health, education, and living standards of one nation.

We posit that the progress towards the 2030 sustainable development goals is vital in ensuring positive

achievement in HDI, especially in a developing country such as Indonesia. In this study, we aim to investigate

the relation between the current fulfilment of the 2030 agenda and HDI in Indonesia and examine the growth

Indonesia has made in 2016 thru 2020. By using national data gathered by Statistics Indonesia, we carefully

selected relevant indicators of the 2030 agenda to run regression model on HDI. Additionally, we employed

latent growth modelling to show the growth of HDI in Indonesia. Controlling for provincial minimum wage,

our regression analyses show that partnership for the goals, represented by the percentage of individuals using

internet, is the strongest predictor for positive attainment of HDI. Further, there is a small positive increment

of HDI annually within each province as well as between provinces. On average, the growth model

demonstrates a significant difference in measurement years between provinces, with year 2020 being the peak

among other years. These novel findings shed light on Indonesia’s human development, which echoes the

progress of national development.

1 INTRODUCTION

Human development index (HDI) was put forth in the

human development report in 1990 and since then

marked the shift in perspective of human

development; involving not only economic terms but

also health and education (Conceição, 2019). In brief,

HDI is a composite index composed of life

expectancy at birth, educational attainment (via

expected years of schooling and the mean years of

schooling), and gross national income (GNI) per

person (in US dollar) at purchasing-power parity

(Lind, 2019). Based on these four country-specific

statistics, HDI then classifies a country into three

ranks of development: developed, still developing,

and under developed (Hou, Walsh, & Zhang, 2015).

Although development rank is not the solely primary

objective of HDI, this rank is useful to indicate the

progress of national development of a country and

allows for cross-country comparison.

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HDI treats humans as the source of development

of a country. The United Nations Development

Programme (UNDP) expressed a clear view on this,

“People are the real wealth of a nation” (UNDP, 1990,

p. 9). Further, HDI was much needed amid a

country’s financial growth. According to UNDP, a

nation’s development, should create a conducive

environment for its citizens to flourish and enjoy

“long, healthy, and creative lives” (p. 9). By

providing such environment, people are expected to

live with dignity, lead a productive work life, and

maintain a positive well-being. It is important to note,

however, that HDI is a simplified measure of human

development and limited to only objective well-

being, such as income and health. Other dimensions

which explain individuals’ well-being such as social

connections are not as straightforward because of its

subjectivity. Although both categories are necessary

to provide the overall quality of individual’s life, this

article specifically is focused only on the objective

470

Setiawan, T., Kuntari, I. and Puspitasari, I.

Human Development Index in Indonesia: Are We in Line with SDGs and How Much Have We Grown?.

DOI: 10.5220/0010754500003112

In Proceedings of the 1st International Conference on Emerging Issues in Humanity Studies and Social Sciences (ICE-HUMS 2021), pages 470-480

ISBN: 978-989-758-604-0

dimensions of well-being captured through the HDI

measure.

One of the main weaknesses in HDI revolves

around its statistical calculations. First, the

combination of expected years of schooling and mean

years of schooling makes it difficult to analyse the

individual impact on HDI (Herrero, Martínez, &

Villar, 2012). Second, employing geometric mean

poses a two-fold risk in the interpretation. On the one

hand, geometric mean normalizes values given in the

dataset. Thus, it is difficult to justify a country’s

educational attainment when such index is obtained

through the combination of two variables. On the

other hand, geometric mean clears out the scaling

issue. But it is heavily affected by the choice of

minima for each HDI dimension (Klugman,

Rodríguez, & Choi, 2011). If any of the dimension

falls at the minimum bound, the whole index then

approaches to zero bringing down the other

dimensions. Third, from its birth HDI undermines the

importance of ecological domain (Biggeri & Mauro,

2018). During the current pandemic times that we live

in, this issue has become more apparent and urgent

than ever before. Even in the 2020 human

development report, UNDP argues that human

development should also address the assurance to

protect the planet to ensure sustainable development

for future generations (UNDP, 2020).

In detail, in 2015 all members of the United

Nations (UN) have agreed to pursue 17 sustainable

development goals (SDGs) to end poverty, protect the

planet, and enable people live with peace and

prosperity by 2030 (Conceição, 2019; UNDP, n.d.).

The goals include eliminating poverty in all forms,

providing good health and well-being, and ensuring

gender equality. These SDGs are directly (and

indirectly) related to human development,

represented by HDI. Although the relation seems

obvious (see Conceição, 2019), there is still lack of

evidence to support a consistent relationship between

SDGs and HDI. Indonesia provides such interesting

case.

The fourth largest country in population is ranked

high in human development index (UNDP, 2020).

However, Indonesia is still struggling with many

societal as well as ecological issues. In terms of civil

and political rights, Indonesia is ranked at 64 globally

and thus, considered a nation with flawed democracy

(Prasetia, 2021). In terms of health, (World Health

Organization, 2017) reports there is still high

inequality in health access in the country. Moreover,

in terms of ecology, Indonesia is the world’s largest

concentration of seagrass (UNDP, 2020). However,

most of their seagrass is unhealthy. In addition, there

are numerous SDGs’ indicators that are indirectly

related to the HDI but might play a big role in

enhancing HDI. For instance, given the importance of

internet in financial and education sectors, having

access to the internet may predict a higher level of

HDI than not having it (Setiawan & Suhartomo,

2019). In addition, although there exist many reports

of HDI in Indonesia from time to time, there is none

that offers growth prediction. Even though having

such information gives an overview of the nation’s

human development over the years. Moreover, the

knowledge provides a vital insight for policy makers

and scholars in proposing the direction of the

country’s development. All this echoes the necessity

to carefully look at the relation between the

achievement of SDGs and the HDI level of a nation,

in this case Indonesia.

In addition, currently a large number of research

focused solely on improving the assessment of SDGs

and the HDI measure (Klugman et al., 2011;

Nguefack-Tsague et al., 2011), whereas few to none

studies the empirical link between the two concepts.

Even though the theoretical link between some of the

SDG’s indicators, e.g., built environment, and the

HDI, e.g., heath indicator, has been drawn by many

research, the investigation has not involved the real

index obtained from the SDG’s indicators

operationalization and the HDI measures. In this

study, we use the figures obtained from the SDGs and

the HDI assessments available by Statistics

Indonesia.

By using real figures, we aim for a two-fold goal.

One, providing evidence for the relation between

SDGs and the HDI level. Two, providing a better

estimate for the relation between decent living

environment and empowerment, on the one hand, and

objective well-being on the other. Living

environment has been shown by Dean and colleagues

(2018) to affect mental health of the residents. More

importantly, this investigation demonstrates that

studying the theoretical link of interest should never

address environment as only the physical form, and

instead should take into account all the necessary

components, such as food availability for the

residents, water and energy availability, and housing

options (Sayles et al., 2019). Further, empowerment

is equally important mainly because it indicates that

people have an option and the freedom to choose.

This knowledge gives valuable insight into the role of

empowerment in developing a sense of unity among

the people and later can turn into a collective action

to promote better welfare for everyone in the

community (Drury et al., 2015).

Human Development Index in Indonesia: Are We in Line with SDGs and How Much Have We Grown?

471

Taken together, we aim to investigate to what

extent the relevant SDGs indicators are related to the

HDI in Indonesia and to what extent the HDI in

Indonesia have progressed in the last five years

(2016-2020). As echoed earlier, providing a secure

and healthy place for individuals to grow allows

people to expand their capabilities (Conceição, 2019;

UNDP, 1990, 2020). Therefore, we expect that the

progress towards SDGs is related to the growth of

HDI. Moreover, with the achievement of SDGs in

Indonesia in the past five years, we also expect to see

an increment of the HDI within that period.

2 CONCEPTUAL FRAMEWORK

2.1 Human Development

Human development is an effort to expand

opportunities of the population to achieve a decent

life (UNDP, 1990). At the practical level, increasing

the basic capacity involves increasing the

productivity of the population through enhancing

knowledge, a decent standard of living and a decent

health so that they can live a healthy long life. In

detail, human development should be based on the

following basic premises: (1) human as the centre of

growth, (2) human development is intended to

enlarge the options available to citizens, not only to

increase their income, (3) human development is

intended to improve human capabilities, (4) human

development involves productivity, equity,

sustainability and empowerment, and (5) human

development is the basis to determine development

goals and ways to achieve them.

Based on the premises, we can conclude that

human development is about people’s quality of life.

Indeed, human development has now progressed

from solely economic indicators to people’s well-

being indicators, in which economic is one of them

(Stiglitz, Sen, & Fitoussi, 2009). The notion of well-

being encompasses a wide array of human

necessities. Several studies show the importance of

well-being in various life domains, such as health

(Marcinko, 2015; Steptoe, Deaton, & Stone, 2015),

education (Chen, 2011), and society (MacIlvaine,

Nelson, Stewart, & Stewart, 2013). Over time, the

measurement of well-being evolved with the

inclusion of psychological indicators. Individual's

subjective perception of their well-being is an

important indicator of the quality of human life.

Thus, the notion of well-being should be treated

as multi-dimension concept. Standing on the shoulder

of previous scholars, well-being should, at least,

include economic living standards (i.e., income,

consumption), health, education, personal activities

(e.g., work, leisure activities), political participation,

social life and interpersonal relationships, living

environment, and insecurity (e.g., economically,

physically, or psychologically). Dimensions such as

living standards, health, and education can be

objectively captured through the notion of human

development proposed by the UNDP (1990). It is

important to reiterate that this study relies on the HDI

measure to capture individual’s well-being and

therefore, only accounts for the objective nature of

well-being.

2.2 The Relation between SDGs and

HDI in Indonesia

HDI is put forth as a composite single-number

indicator that captures the basic dimensions involved

in human development, which are health, education,

and income (Conceição, 2019). Meanwhile, all UN

member countries, including Indonesia, have agreed

to set up goals or development targets to pave the way

for a positive human development. These

development targets are summarized in 17 SDGs with

169 targets and 232 indicators. The goals are ending

poverty, having zero hunger, ensuring good health

and well-being for the population, providing quality

education, ensuring gender equality, providing clean

water and sanitation, affordable and clean energy,

decent work and economic growth, industry,

innovation, and infrastructure, reduced inequalities,

sustainable cities and communities, supporting

responsible consumption and production, climate

action, life below water, life on land, and finally

peace, justice and strong institutions (UNDP, n.d.).

SDGs officially replaced the Millennium

Development Goal's (MDG's) concept which ended

in 2015.

As a follow-up action by Indonesia, in 2017, the

government passed the Presidential Regulation

(Perpres: Peraturan Presiden) No. 59 of 2017 with

the tagline "no one left behind". If the goals can be

carried out comprehensively, it is believed that there

will be a positive change in individuals’ well-being as

well as their behaviours in caring the environment.

Thus, the progress towards the fulfilment of SDGs

can ensure the improvement of HDI (Haryanto,

2018).

Efforts to achieve the SDGs target are a priority

for Indonesia's national development, requiring

synergy of planning policies at the national level and

at the provincial and district/city levels. Several

SDGs indicators worth mentioning to support HDI

ICE-HUMS 2021 - International Conference on Emerging Issues in Humanity Studies and Social Sciences

472

include an increase in minimum consumption below

1,400 kcal/capita/day, increase in number of

individuals having a birth certificate, and decrease in

unmet need for health services. Due to these

improvements, a person is expected to be in good

health and thus, can survive longer. If they are sick,

they can easily make arrangements with the health

service provider to speed up their recovery. Further,

having a birth certificate is the first step towards a

decent living in Indonesia (Badan Pusat Statistik,

2021a). With the certificate, individuals can be

registered officially to obtain social security (known

as BPJS), to be registered in the tax department,

school, to work in the formal sector, to buy property

or land, to be eligible for voting, and to be able to

obtain a passport. Thus, ensuring the achievement of

SDGs can help improve the rank of HDI of Indonesia.

Most importantly, achieving SDGs means allowing

Indonesian citizens to lead a productive and healthy

life in a sustainable environment, both ecologically as

well as institutionally.

Given numerous indicators of SDGs, we carefully

select relevant indicators to be included in the study.

Two main criteria for selection are the immediate

impact on the people’s welfare and the availability of

the latest data provided by the Statistics Indonesia

(Badan Pusat Statistik). The following SDGs

indicators are involved in the study: the calory intake

below 1400 kcal/day (zero hunger SDG), unmet need

for health services (good health and well-being),

knowledge and understanding about modern

contraception (gender equality SDG), level of

unemployment (decent work and economic growth

SDG), access to decent and affordable housing

(sustainable cities and communities SDG), having a

birth certificate (peace, justice and strong institutions

SDG), democracy index (peace, justice and strong

institutions SDG), and access to the internet

(partnership for the goals SDG).

Based on the aforementioned above, we

hypothesize that (H1) calory intake below 1400

kcal/day, (H2) unmet need for health services and

(H4) unemployment level are expected to be

negatively related to the HDI. Whereas (H3) the

knowledge and understanding about modern

contraception, (H5) access to decent and affordable

housing, (H6) having a birth certificate, (H7)

democracy index, and (H8) access to internet are

positively related to the growth of HDI. Furthermore,

with the current achievement of SDGs we expect to

see a yearly positive growth of HDI from the period

of 2016 to 2020 (H9).

3 METHOD

This study used secondary data provided by Statistics

Indonesia. The data was laid out according to

province, enabling us to do cross-province

comparison in later analysis. In specific, we obtained

the HDI numbers from the Statistical Yearbook of

Indonesia from 2016 thru 2020. For the SDGs

indicators, since the latest record of the achievement

of SDGs in 2020 has yet been published, we obtained

the latest SDGs indicators numbers from the official

site of Statistics Indonesia. The quality data were

gathered through reliable surveys, such as National

Labor Force Survey (Sakernas: Survei Angkatan

Kerja Nasional) and National Socioeconomic Survey

(Susenas: Survey Sosial Ekonomi Nasional). The

followings are measures used in this study:

3.1 Dependent Variable

HDI is a composite measure consisted of three

dimensions, which are (1) life expectancy at birth for

assessment, (2) the education uses number of years of

schooling for adults aged 25 years and above and the

expected number of years of schooling for children of

school-entering age, and (3) the standard of living by

gross national income per capita (Klugman et al.,

2011). Each dimension is assigned equal weighting

and normalized to attain a range from zero to one. The

HDI is then obtained by calculating a geometric mean

of all three dimensions.

3.2 Independent Variables

There are eight SDGs indicators that were treated as

independent variables and the following descriptions

were taken from the 2019 report of SDGs progress by

the Statistics Indonesia (Badan Pusat Statistik, 2019).

Calory intake below 1,400 kcal/day is a

straightforward measure capturing the proportion of

individuals with calory intake of individuals

minimum below 1,400 kcal/day. This number is set

as a minimum suggested for each individual that is

adjusted to their age range (Badan Pusat Statistik,

2019).

Unmet need for health services is the percentage

of individuals who suffer from health complaints but

do not seek outpatient treatment (Badan Pusat

Statistik, 2021d). In order to get the percentage, BPS

compares the number of people who experience

health complaints and their activities that are

disturbed but do not seek outpatient treatment to the

number of the outpatient treatment. The activities that

are disturbed can involve work, school or other daily

Human Development Index in Indonesia: Are We in Line with SDGs and How Much Have We Grown?

473

activities. Reasons why people do not seek outpatient

treatment can vary from having no medical saving to

having no means of transportation. This data was

gathered by the Statistics Indonesia itself.

Knowledge and understanding about modern

contraception measure the percentage of knowledge

and understanding of couples of childbearing age

about modern contraception, such as birth-control pill

(Badan Pusat Statistik, 2019). This data was provided

by Indonesian Demographic and Health Survey

(Survei Demografi dan Kesehatan Indonesia).

Level of unemployment measures the percentage

of the total unemployed to the total labour force. BPS

(Badan Pusat Statistik, 2021c) extended the concept

of unemployment to accommodate the revision of the

term proposed by the international labour

organization (ILO). Currently, unemployment term

covers those who are looking for work, those who are

starting up a new business/firm/establishment, those

who are not looking for work due to the discouraging

presumption, and those who are not looking for work

because having already secured a job but yet to start

(future starter). The data was gathered by Sakernas.

Access to decent and affordable housing measures

the percentage of households with access to decent

and affordable housing. According to BPS (Badan

Pusat Statistik, 2021b), a decent housing is the one

that meets the following criteria: 1) sufficient area of

at least 7.2 m2 per capita 2) available access to proper

drinking water, 3) available access to proper

sanitation, and 4) durable housing.

Having a birth certificate refers to the percentage

of individuals aged 0-17 with a birth certificate. This

birth certificate should be issued by the Civil Registry

in each region, and not the one from a hospital or

doctor (Badan Pusat Statistik, 2021a). This document

holds high importance in ensuring the recognition of

a child lawfully and thus, protecting their rights. On

the contrary, children without an official birth

certificate will face difficulties in accessing health

and education services.

Democracy index measures the index of free and

fair elections, the role of Regional People's

Representative Assembly, the role of the political

parties, local government bureaucracy, and

independent judiciary (UNDP, 2010). It ranges from

0-100, with higher score indicating higher index.

Access to internet measures the proportion of

individuals using the internet. The use includes

accessing social media, such as facebook, twitter, and

whatsapp.

3.3 Control Variable

We employed province minimum income as a control

variable to ensure that any relation found was not

spurious due to the difference of province minimum

income.

3.4 Strategy for Analyses

First, we performed multilinear regression in a

stepwise fashion to test hypothesis 1 thru 8 on SPSS

Table 1: Bivariate Correlations Between Variables.

Measure 1 2 3 4 5 6 7 8 9

1. HDI -

-.52

(.002)

-.29

(.094)

.59

(.000)

.48

(.004)

.38

(0.03)

.72

(.000)

.48

(.004)

.85

(.000)

2. Calory Intake - -

-.18

(.305)

-.56

(.000)

-.09

(.617)

-.22

(.221)

-.58

(.000)

-.43

(.011)

-.51

(.002)

3. Unmet need for

health services

- - -

.26

(.136)

-.37

(.030)

.08

(.631)

-.11

(.532)

.03

(.875)

-.30

(.080)

4. Modern

contraception

- - - -

.17

(.339)

.44

(.009)

.74

(.000)

.42

(.014)

.48

(.004)

5. Unemployment

level

- - - - -

-.10

(.572)

.11

(.520)

-.01

(.964)

.61

(.000)

6. Access to housing - - - - - -

.38

(.025)

-.01

(.953)

.21

(.225)

7. Birth certificate - - - - - - -

.49

(.004)

.62

(.000)

8. Democracy index - - - - - - - -

.44

(.009)

9. Access to internet - - - - - - - - -

Bold indicates significance at level p < .05.

ICE-HUMS 2021 - International Conference on Emerging Issues in Humanity Studies and Social Sciences

474

25. To do this, we took the latest HDI figures (year

2020) and test it on the latest SDGs indicators

numbers provided by Statistics Indonesia. Each

model is built upon each SDG indicator following the

order of SDGs according to UNDP, e.g., as we did not

include SDG 1, calory intake as SDG 2 became the

first indicator to be included in the model, unmet need

health services became the second and continued until

the last SDG (UNDP, n.d.). Prior to calculation, we

performed preliminary tests to ensure that all

predictors have linear relationship with the HDI and

that there is no multicollinearity between predictors.

We ran normality tests by calculating the residuals,

that is difference between the measured value and the

predicted value, for each predictor in the regression

model. The Shapiro-Wilk for normality test indicated

that access to housing, knowledge about modern

contraception, the level of unemployment, and unmet

needs for healthcare services deviate from normality.

However, their skewness and kurtosis values did not

exceed 2 and 7, respectively. Therefore, as suggested

by Kim (2013), we may still consider the data to be

acceptable. Next, we found that all predictors were

linearly related to the HDI. Further, all predictors had

variance inflation factor (VIF) values ranging from

1.58 to 4.26 with tolerance statistics above .2, which

indicated no multicollinearity (Field, 2009). The

correlation matrix in Table 1 also supports the

finding.

Looking closely at Table 1, we found that the HDI

level is correlated with all of the predictors, except

with the percentage of people with unmet needs for

healthcare services. The strongest correlation found

with the percentage of people having a birth

certificate. While, surprisingly, the lowest coefficient

was found between the HDI level and access to

housing and democracy index.

Second, we ran growth curve modelling using

SPSS 25 to test whether there is a positive growth of

HDI in Indonesia in the past five years, from 2016

thru 2020 (H9). In doing so, we started with a null

model using provinces to predict HDI over the five-

year period. Next, we set up a model using time as a

predictor for the fixed effect. By this, we allowed the

province’s intercepts to randomly vary but the slope

for the time is fixed. Subsequently, we set up a model

allowing time to vary at level 2. Here, we allowed the

province’s intercepts and the slope for the time to

randomly vary. By doing this we can identify

increment of HDI across provinces as well as across

years.

Table 2: Regression model of HDI on independent variables.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Intercept

74.32

(.000)

79.85

(.000)

-10.15

(.657)

-3.71

(.867)

10.10

(.658)

46.50

(.085)

44.37

(.076)

38.51

(.091)

38.52

(.110)

Calory Intake

-31

(.002)

-.35

(.000)

-.17

(.040)

-.17

(.034)

-.18

(.025)

-.11

(.140)

-.08

(.251)

-.03

(.676)

-.03

(.682)

Unmet need for

health services

-.98

(.007)

-1.21

(.000)

-.95

(.004)

-.88

(.006)

-.44

(.202)

-.39

(.219)

-.31

(278)

-.31

(.301)

Modern

Contraception

.90

(.000)

.79

(.001)

.61

(.017)

.10

(.742)

-.00

(.987)

.10

(.699)

.10

(704)

Unemployment

Level

.45

(.064)

.55

(.026)

.70

(.004)

.78

(.001)

.32

(.245)

.31

(.256)

Access to

Housing

.06

(.088)

.06

(.083)

.08

(016)

.06

(.054)

.06

(.074)

Birth Certificate

.12

(.026)

.10

(.053)

.06

(.240)

.06

(.074)

Democracy

Index

.16

(.024)

.10

(.165)

.10

(.175)

Access to

Internet

.15

(.017)

.15

(020)

Province

Minimum

Income

.00

(.974)

F test 11.69 11.28 16.43 14.36 12.95 13.46 14.41 16.12 13.76

Adjusted R

.24 .38 .58 .62 .64 .69 .74 .79 .77

Note. Bold indicates significance at level p < .05.

Human Development Index in Indonesia: Are We in Line with SDGs and How Much Have We Grown?

475

4 RESULTS

In Table 2, we start with the proportion of individuals

with calory intake below 1400 kcal/day into account.

The results if Model 1 confirm hypothesis 1, in which

the proportion of individuals with 1400 kcal/day

calory intake is negatively related to the level of HDI

(b = -.31, p = .002). In Model 2, we also expected that

there would be a negative relation between the

percentage of individuals with unmet need for health

services and the HDI. The results supported

hypothesis 2 (b = -.98, p = .007). Next, we expected

that the knowledge and understanding of young

productive couples on modern contraception would

be positively related to the HDI. The results in Model

3 support hypothesis 3 confirming a positive relation

between variables of interest (b = .90, p = .000).

Further, Model 4 takes into account the level of

unemployment. Contrary the expectation, the results

disconfirm hypothesis 4 that there is no significant

relationship between the percentage of the total

unemployed to the total labour force and the HDI.

Model 4, however, still demonstrates significant

relationships between the previous indicators and the

HDI. This indicates that by keeping deficiency of

calory intake and unmet need at low level and with

the help of knowledge of the use of modern

contraception among young couples, human

development can progress in the right track even

though unemployment may seem competitive. Model

5 tests hypothesis 5 that is the percentage of

households with access to decent and affordable

housing would be positively related to the HDI. Here,

we find that there is no significant relationship

between the variables of interest. However, we find

that by including the variable of access to housing

makes the knowledge and understanding of the use of

modern contraception significant (b = .61, p = .017)

Next, Model 6 includes birth certificate to test

hypothesis 6. As expected, the percentage of

individuals aged 0-17 with a birth certificate is

positively related to the HDI (b = .12, p =.026).

Considering the importance of such document, this

shows that registration of a child’s birth paves the

way for a growth of human development.

Furthermore, Model 7 includes democracy index

to promote positive human development. The results

confirm hypothesis 7 that the higher the democracy

index the higher the level of HDI (b = .16, p = .024).

The inclusion of democracy index makes birth

certificate no longer plays a significant role in the

HDI progress. Subsequently, Model 8 considers the

proportion of individuals using the internet. We find

that there is a significant relation between using the

internet and the HDI (b =.15, p =.020). Interestingly,

the role of the internet use makes the other significant

relations in the previous model insignificant. The

results seem to be in line with the increasing

embeddedness of internet use in individual’s daily

life. Finally, we included province minimum wage in

Model 9 to test whether previously found relations

were spurious due to the inclusion of province

minimum wage. We found that there was no

significant relationship between province minimum

Note. .00 = 2016; 1.00 = 2017; 2.00 = 2018; 3.00 = 2019; 4.00 = 2020

Figure 1: Predicted growth of the HDI throughout 2016 to 2020.

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Table 3: Latent growth model of HDI from 2016-2020.

Model 0 Model 1 Model 2

Intercept 70.28 (.000) 69.26 (.000) 69.14 (.000)

Level 1 (fixed effect)

Year .51 (.000) .62 (.000)

Variance of the residuals (within province across time) .72 (.000) .06 (.000)

Level 2

Year 2016 .01 (.105)

Year 2017 .00 (.274)

Year 2018 .00 (.035)

Year 2019 .01 (.024)

Year 2020 .36 (.000)

Variance of the means (between provinces) 15.43 (.000) 15.56 (.000) 16.61 (.000)

Variance of the means (between years) .01 (.001)

Note. Bold indicates significance at level p < .05.

wage and the HDI and, moreover, the relations

found in Model 8 remained the same in Model

9.Furthermore, we conducted growth curve model to

test hypothesis 9 that there is increment of the HDI in

Indonesia from 2016 to 2020. Figure 1 plots the

growth of HDI over the five years. As expected, DKI

Jakarta province shows the highest growth of all

provinces while Papua is shown to have the lowest

growth.

In relation to the predicted growth across

provinces and years, we first start with Model 0 or

null model (see Table 3). Here, the grand mean of the

HDI is 70.28 (p = .000). We find that there is a

significant variance of the HDI within provinces

across time (b = .72, p = .000). In addition, there is

also a significant variance of the HDI between

provinces (b = 15.43, p = .000), which indicates a

possible random effect of provinces. Therefore, in

Model 1 we set time as a fixed-effect predictor and

allow the province’s intercepts to randomly vary. The

results show that, at level 1, time is positively related

to the HDI (b = .51, p = .000). Although reduced, the

variance of the HDI within province also remains

significant (b = .06, p = .000). Whereas the variance

of the HDI between provinces is slightly changed (b

= 15.56, p = .000). To further test the increment

during the five years, we set time as a level 2 random-

effect predictor. The results show that, at level 1,

time’s relation with the HDI is slightly increased (b =

.62, p = .000). At level 2, we find a low variance of

the HDI across years (b = .01, p = .001). Taken

together, these results show that there is a slight

significant growth of the HDI in the period of 2016 to

2020. However, the difference of the HDI between

provinces across time is still significantly substantial

(b = 16.61, p = .000).

5 DISCUSSION AND

CONCLUSION

The current study explores the progress of the HDI in

Indonesia by testing the relations between SDGs

indicators and the HDI in Indonesia and looking at the

growth of HDI from 2016 to 2020. Being ranked at a

high development country, Indonesia seems to be

struggling with its own societal and ecological

problems (see BPS, 2019; UNDP, 2020). Some of our

findings are consistent with the expectations, while

others provide alternative explanations.

First, we find that the fulfilment of minimum daily

calory intake is related to the growth of HDI in

Indonesia. This finding shows that by eliminating

hunger, one of which through minimizing the

percentage of individuals with calory intake below

1,400 kcal/day, helps individuals to have a healthy

life and support their education. The fulfilment of

calory intake does not stand alone in providing a

healthy life. Our finding also shows that by lowering

the number of people with unmet health services

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477

strongly helps the increment of the HDI in Indonesia.

This is somewhat corroborates the report of the state

of health inequality in Indonesia (World Health

Organization, 2017). Due to health inequality that still

widely exists across the nation, the rank of HDI in

Indonesia will be most likely to increase only slightly

from year to year.

Similarly, the knowledge and understanding

among couples of childbearing ages about modern

contraception highly supports the growth of HDI.

This finding corroborates the notion that personal

autonomy, captured through gender equality,

provides options that expand people’s capabilities

(Stiglitz et al., 2009). In addition, having a birth

certificate and high democracy index also support

growth of the HDI. This shows that people’s well-

being is largely determined by access to vital

knowledge, sense of security in the community, and

more importantly, being able to choose freely

(UNDP, 1990).

In contrast, our finding shows that unemployment

level is not associated with the growth of HDI.

Furthermore, access to housing is also found not to be

a significant predictor to the HDI. These findings

contradict the notion that economic indicators,

through means of employment and housing, support

individuals’ well-being. However, given the

condition that other indicators, e.g., minimum daily

calory intake and healthcare service are met, this

finding validates the claim by Stiglitz et al. (2009)

that the measure of subjective well-being should also

be considered to accompany the objective well-being

captured in the current HDI.

Further, the number of people having access to the

internet is found to be associated with the HDI. Most

interestingly, the inclusion of this predictor weakens

all the other variables. Given the high importance of

internet in people’s daily lives, this finding suggests

that being able to access the internet empowers

people with knowledge necessary to improve their

health and socio-economic conditions. A program

such as e-health has been shown to promote

information exchange among health professionals,

even in the war zones (Eyesenbach, 2007). In

addition, internet enables people in less developed

areas to receive the same knowledge as others in more

developed areas. Thus, internet empowers people

with the same quality of education and skills needed

for higher education and labour opportunities

(Johnson, 2016). In sum, having access to

information and communications technology (ICT),

part of Goal 9, will significantly affect the choices

and opportunities available to people. Inexpensive

and reliable access to the internet enables other

capabilities in the areas of education, work, and

political participation, among others. However, it is

worth noting that the use of internet can also bring

detrimental impact on human development. It is

shown that higher internet use is related to higher

crime incidence in Indonesia (Setiawan &

Suhartomo, 2019).

Regarding the growth of HDI in Indonesia, we

find that there is a slight increment of the HDI from

2016 to 2020. A plausible explanation for this is due

to the apparent variance of the HDI across provinces.

Indonesia still experiences widespread unequal

development in many domains. Take internet for

instance, although internet has given positive impacts

on human development, the existence of internet

divide exacerbates the inequality in education and

human capital in rural and remote islands in Indonesia

(Sujarwoto & Tampubolon, 2016).

There are, at least, two limitations notable in this

study. One, due to the availability of data this study

cannot include subjective well-being measure.

Therefore, the current study solely relies on the HDI

measure, although we are aware that human

development cannot be captured by only four

measures and simplified into one index measure.

Two, SDG indicators employed in this study may

oversimplify the real problems. Indicators such as

political participation and religious freedom are

rarely captured perfectly in a democracy index.

Therefore, future studies are encouraged to explore

human development further by including other

relevant indicators claimed to associate with

subjective well-being. Three, due to the availability

of the data our statistical analysis cannot compute the

progress of SDGs in 2016 to 2020 and the growth of

HDI altogether in the same model. However, our

findings can still provide a nuanced description of the

relation between SDGs and the HDI as well as the

growth of HDI in Indonesia.

In conclusion, this study shows that relevant SDG

indicators are related to the growth of HDI in

Indonesia. Given the embeddedness of internet in

people’s daily life, having access to the internet is

shown to be the strongest predictor for the growth of

HDI (see Puspitasari & Ishii, 2016; Setiawan &

Suhartomo, 2019; Sujarwoto & Tampubolon, 2016

for internet impacts and divides in Indonesia).

Additionally, we show a glance of optimism in the

growth of HDI in Indonesia. Although the disparity

between provinces is still substantial, the HDI in

Indonesia has shown to keep growing. Overall,

despite criticisms directed towards the measure of

HDI and the operationalization of SDGs, both

concepts still provide a useful yardstick to gauge the

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human development of a country relative to others

and allow for cross-province comparison within one

country (Adam, Kammas, & Lapatinas, 2015; Blum,

2013; Hák, Janoušková, & Moldan, 2016). Moreover,

the link between SDGs and the HDI paves the way

for further investigation on the importance of well-

being towards the development of a nation,

considering its objective and subjective nature. This

study is expected to encourage future research to

build upon the current impact of the Covid-19

pandemic on the growth of HDI in the next few years.

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