JRSSEM 2022, Vol. 01, No. 9, 1413 – 1421

E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494

DOI : 10.36418/jrssem.v1i9.156 https://jrssem.publikasiindonesia.id/index.php/jrssem/index

THE EFFECT OF ECONOMIC GROWTH ON

MULTIDIMENSIONAL POVERTY

Desliyani Tri Wandita

Toto Gunarto

Arivina Ratih

1,2,3

Master Program in Economics, University of Lampung, Indonesia

e-mail: desliyani101[email protected]

, totogunarto2[email protected]

, arivinaratih@gmail.com

*Correspondence: desliyani101[email protected]

Submitted: 24 March 2022, Revised: 05 April 2022, Accepted: 16 April 2022

Abstract. Poverty is a complex problem, so a more appropriate approach is needed to represent

this complex phenomenon and multidimensional reality. This study aims to analyze the effect of

GRDP per Capita on multidimensional poverty. The method used to measure multidimensional

poverty in this study is the Alkire Foster method. The multidimensional poverty index that was built

refers to the index developed by Alkire and Santos, as well as several previous studies with several

changes that were adjusted to the availability of data. The results of this study resulted in panel

data regression analysis and involved the HDI control variable. It was found that partially, economic

growth as measured by gross regional domestic product per capita at constant market prices in

2010 had no significant effect on the multidimensional poverty rate in Lampung Province,

Indonesia 2017-2019.

Keywords: economic growth; poverty; and multidimensional.

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih                                                               | 1414
 
 
DOI : 10.36418/jrssem.v1i9.156      https://jrssem.publikasiindonesia.id/index.php/jrssem/index 
INTRODUCTION 
 
Poverty is a serious problem that must 
solve immediately. This  is  reflected in the 
first  goal  in the Sustainable Development 
Goals  (SDGs),  which  was  sparked  at 
countries globally on September 25, 2015, 
namely eliminating poverty anywhere and 
in  any  form.  This  goal  at  least  gives  a 
message to all countries globally, including 
Indonesia,  to  focus  more  on  overcoming 
poverty  from  one  side  and  from  various 
sides. Thus, by 2030, poverty is expected to 
have  been  overcome  (Hapsari,  2019); 
(Khalifah et al., 2017).  
Statistics Indonesia views poverty as an 
inability  to  meet  basic  needs,  so  in  its 
measurement,  Statistics  Indonesia  uses  a 
household  expenditure  approach  in  line 
with the measures used by the World Bank. 
However, the calculation of poverty has not 
been able to consider people who are not 
poor,  but  in  certain  circumstances,  the 
expenditure is indeed small. The calculation 
method  with  this  expenditure  approach 
also  cannot  describe  the  poor  who  are 
prone to illness, lack access to education or 
public facilities, live in slum environments, 
or have an  inadequate standard of living. 
The  expenditure  approach  has  not  been 
able to fully answer the first objective of the 
SDGs, namely, eliminating poverty in all its 
forms. 
Another  approach  to  measuring 
poverty  is  emphasizing  the  monetary 
dimension, and the results are easy but not 
always satisfactory. (Rogan, 2016) revealed 
that the poverty approach using monetary 
analysis could only capture a small part of 
poverty.  According  to  Sen,  the  issue  of 
poverty  is  not  only  related  to  purchasing 
power parity, income, or consumption, but 
there  is  a  broader  dimension  of  poverty. 
Apart  from  income  and  consumption, 
people  who  have  limited  access  to  basic 
education  or  health  services  due  to 
economic  limitations  can  also  be  poor. 
Communities  with  poor  sanitation 
conditions, sources of lighting, and cooking 
fuel  are  not  suitable;  the  condition  of 
houses with earth floors is also said to be 
poor (Conti et al., 2010); (Conti & Heckman, 
2010). 
The study of multidimensional poverty 
prompted  (Yu,  2013)  to  estimate 
multidimensional  poverty  in  China,  a 
country with a high disparity rate between 
provinces and  rural and urban areas.  Yu's 
research  found  that  the  rapid  rate  of 
economic  growth  had  an  impact  on 
reducing poverty in China over the past few 
years,  both  from  a  monetary  and 
multidimensional perspective. 
Studies on multidimensional poverty in 
Indonesia  have  also  been  carried  out  by 
several researchers, such as . These studies 
were conducted at the provincial level with 
different  methods  and  dimensions  of 
poverty.  Although  some  researchers  have 
carried  out  studies  of  multidimensional 
poverty in Indonesia, similar research is still 
needed, especially studies of poverty at a 
smaller level, namely districts/cities, such as 
those  conducted  by  (Artha  &  Dartanto, 
2018), (Alkire & Santos, 2014); (Beycan et 
al., 2019); (Alkire et al., 2018). 
The  complexity  of  poverty  is  quite 
visible in Lampung Province. At first, glance, 
if we look at economic growth and poverty 
reduction in Lampung Province, it tends to 
run  quite  well.  However,  it  should  be  re-
examined  that  in  addition  to  the 

1415 | The Effect of Economic Growth on Multidimensional Poverty

percentage of the number of poor people,

which far exceeds the national figure, it

turns out that Lampung's economic

growth, which reached 4.30 percent in

2019, occupies a fairly good position,

namely the third-highest among other

provinces in the Sumatra Island region.

However, this is not in line with the

percentage of poor people in Lampung

Province, which ranks fourth highest in

Sumatra.

The quality of human resources in

Lampung Province can be reflected in the

HDI. From year to year, Lampung HDI

shows an increase. However, when

compared to other provinces on the island

of Sumatra, the HDI of Lampung Province

is in the last rank. If you look at the

percentage figures for the poor and the

HDI for the provinces throughout Sumatra

Island, it seems at first glance that there is

no relationship between poverty rates and

HDI. Several provinces on the island of

Sumatra have a higher percentage of poor

people than the percentage of poor people

in Lampung Province. Still, Lampung

Province has the lowest HDI compared to

other provinces.

The phenomenon that occurs in

conditions of poverty in Lampung shows

that the problem of poverty is not just an

economic problem (Martin & Petersen,

2019); (Font & Maguire-Jack, 2020).

Demographic factors such as education

level or family structure can also influence

poverty ("Growth Has Been Good for

Decades," nd). Multidimensional poverty

means seeing poverty from various aspects

and dimensions, not only one side. Poverty

from this point of view is more in line with

the goals of the SDGs because, from this

point of view, all kinds of poverty will be

detected.

Poverty seen only from one side is

thought to have not been able to capture

the impact of economic growth, which

tends to be broader. And secondly, there is

a large enough inequality so that economic

growth can only be felt by the upper-

middle class and not the lower middle class.

To answer this conjecture, an approach to

calculating poverty is needed to cover all or

more aspects. Therefore, this study aims to

analyze the effect of GRDP per Capita on

multidimensional poverty.

METHODS

The early stages of research, an

approach was taken to measure

multidimensional poverty in Lampung

Province. The method used to measure

multidimensional poverty in this study is

the Alkire Foster method. The built

multidimensional poverty indicators refer

to the indicators developed by (Welker et

al., 2013) and several previous studies with

some modifications adapted to the

availability of data.

Inferential analysis was conducted to

test the hypothesis to prove whether there

is an effect of GRDP per capita and HDI on

the multidimensional poverty level, using

panel data regression to select the best

model. Time series data is from 2017-2019,

and cross-sectional data are districts/cities

in Lampung Province.

This study uses secondary data. The

data used to calculate the multidimensional

poverty of districts/cities in Lampung

Province in this study is secondary data

derived from the National Socio-Economic

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih | 1416

Survey (Susenas) collected by the Statistics

Indonesia of Lampung Province in 2017-

2019.

The secondary data needed to build the

regression model is GRDP per capita based

on constant prices for the base year 2010

for all regencies/cities in Lampung GRDP

per capita, and HDI data used are from

publications published by Statistics

Indonesia.

The Alkire Foster method is used in

calculating multidimensional poverty

variables. The calculation of

multidimensional poverty using the Alkire

Foster method uses several dimensions and

indicators and determines the amount of

weight for each indicator. This study uses

three types of dimensions, and these three

dimensions are divided into ten indicators.

Table 1. Dimensions, Indicators, Deprivation Cutoffs and Weights for

Multidimensional Poverty Calculation

Dimension

Indicator

Cut off deprivation

Weights

(1)

(2)

(3)

(4)

Education

Length of school

No household member

who has completed

nine years of education

(junior high school or

equivalent)

1/6

School participation

There are school-age

children (7-15 years)

who have dropped out

of junior high school or

the equivalent

1/6

Health

Household calorie

consumption

Household calorie

consumption is less

than 70 percent of the

Nutrition Adequacy

Rate (2019)

1/12

Household Protein

Consumption

Household protein

consumption per day is

less than 80 percent of

the Nutrition Adequacy

Rate (2019)

1/12

Drinking water

Deprived if households

do not have access to

clean drinking water or

access to clean water

1/12

1417 | The Effect of Economic Growth on Multidimensional Poverty

Sanitation

Deprived of the

household does not

have proper sanitation

or if the toilet is shared

with other households

1/12

Standard of

living

Electricity

Deprived of the

household does not

have access to PLN

electricity

1//12

Floor

Deprived of the

household lives in a

building with a dirt or

sand floor

1/12

Fuel

Deprived of the

household cooks using

firewood or charcoal

1/12

Floor area

Deprived of the

household lives in a

house with a floor area

per capita of less than

or equal to 7.2m2

1/12

Source: Alkire Foster, modified

This multidimensional poverty gauge

uses a nested weighting system, unit of

analysis, selection of indicators,

dimensions, first cutoff point, and second

cutoff point used by UNDP in calculating

MPI. A household or individual can be

categorized as a household or individual

experiencing multidimensional poverty if

the total weight per individual or

household is greater than or equal to 33.33

percent. Each individual is given a score

according to the deprivation experienced in

the household, based on the ten

constituting indicators. The maximum

score for all indicators is 100 percent, so

each dimension has the same weight,

namely 33.33 percent, as well as the weight

of each indicator.

Economic growth is the influence of the

value of income expressed in unit

price/nominal amount. Economic growth

also describes an increase in the physical

production of goods or services within a

certain period. Economic growth reflects

the economic development of a country or

region, which can be measured by national

income or Gross Domestic Product (GDP),

or Gross Regional Domestic Product

(GRDP). The economic growth variable in

this study was measured by the 2010 GRDP

per capita approach. The GRDP per capita

is useful for knowing the real per capita

economic growth of the population of an

area. HDI is a measure of development

performance formed using three

dimensions, namely long and healthy life,

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih | 1418

knowledge, and a decent living.

The effect of GRDP per Capita and HDI

on multidimensional poverty, regression

analysis was used. Regression analysis in

this study uses panel data. Panel data

(pooled data or longitudinal data)

combines cross-section data and time-

series data. Regarding the purpose of this

study, the model used is as follows.

logMPIit = 0 + 1logPDRBcapit +

2logIPMit + it (1)

where:

i: each district/city;

t: year t;

: error

MPI: percentage of the

multidimensional poverty level;

GRDPkap: GRDP per capita 2010;

HDI: Human Development Index;

Regression analysis was used to see the

effect of GRDP per Capita and HDI on

multidimensional poverty. Regression

analysis in this study uses panel data. Panel

data (pooled data or longitudinal data)

combines cross-section data and time-

series data.

RESULTS AND DISCUSSION

A. Multidimensional Poverty in

Lampung Province

Table 2. Multidimensional Poverty Profile of Lampung Province, 2017 – 2019

Year

MPI (%)

Intensity (%)

2017

29.99

11.79

2018

29.29

11.45

2019

26.06

11.03

Source: Socio-Economic Survey, processed

Table 2, shows the multidimensional

poverty profile in Lampung Province from

2017 to 2019. The table contains

information about the number of poor

people, poverty rates, poverty intensity,

and multidimensional poverty index. The

number of multidimensional poor

people is the total number of

individuals classified as poor

multidimensionally. MPI is the proportion

of multidimensional poor people to the

total population. Poverty intensity is the

average proportion of the weighted

indicators in which the poor are deprived

(A). In 2019, 26.06 percent of the

population was categorized as poor

multidimensionally. These

multidimensional poor people are deprived

of at least a third of the indicators in the

dimensions of health, education, and living

standards (Weziak-Bialowolska, 2016);

(Migala-Warchol & Pasternak-Malicka,

2018). These multidimensional poor people

may not be eligible, for example, in terms

of sanitation, school sustainability, cooking

fuel, and so on. From 2017 to 2019, MPI in

Lampung Province experienced a decline.

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih | 1419

DOI : 10.36418/jrssem.v1i9.156 https://jrssem.publikasiindonesia.id/index.php/jrssem/index

B. The Effect of Economic Growth on

Multidimensional Poverty

To examine the effect of economic

growth as measured by the GRDP per

capita approach to the

multidimensional poverty level, the

model used refers to equation 1.

Considering that the data used in this

study is panel data, it is necessary to

conduct three tests to determine the

most appropriate technique for

estimating panel data regression,

namely the Chow test, Hausman test,

and Breusch-Pagan Lagrange Multiplier

(LM). After carrying out these three

tests, it can be concluded that the fixed

effects model or FEM is the best model

that can be used to estimate the effect

of economic growth using the GRDP

per capita approach to

multidimensional poverty.

A simultaneous test for the

random-effects model using reviews

can be seen from the probability value

2. The simultaneous test was conducted

to determine whether the independent

variables in the model, namely:

economic growth with the GRDP per

capita approach and HDI, and the

probability value 2 = 0.000 are smaller

than the significance level = 5 percent

for each regression model. Thus, it can

be concluded that there is at least one

or all independent variables that

statistically influence the

multidimensional poverty variable.

Table 3. Variable Estimation Results with Fixed Effect Random (FEM)

Source: Output Results Eviews

After conducting a simultaneous

test of the fixed effects model, the next

step is to test the coefficient of

determination. This test determines

how much the independent variables

can explain poverty in the regression

model. The R2 value for the fixed effects

model can use an adjusted R2 value of

0.894667 for the multidimensional

poverty model, which means that

the diversity or variance of the

multidimensional poverty level variable

can be explained by independent

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih                                                               | 1420
 
variables such as economic growth with 
the GRDP per capita and HDI of 97.36 
percent. In comparison, other variables 
outside  the  model  explain  the 
remaining 2.64 percent. 
Based on the parameter estimation 
results  in  Table  4.1,  the  general 
equation for the two models using the 
fixed effects model (FEM) approach can 
be written as follows. 
Log M PIit = 64,065 – 2,170 log GDP 
capit – 2,875 log I PMit.  In  the 
multidimensional  poverty  model,  the 
coefficient of economic growth with the 
GRDP  per  capita  approach  is  -2.170. 
Based on the output eviews, the partial 
probability  value  of  the  economic 
growth  variable  with  the  GRDP  per 
capita approach is 0.156, greater  than 
the significance level of = 5 percent, so 
it  does  not  significantly  affect  the 
growth of the multidimensional poverty 
rate. This indicates that the increase in 
value-added  from  the  productive 
sectors in Lampung has not been fully 
accompanied  by  the  capability  of  the 
population  to  meet  the  needs  of 
nutrition  and  health,  education,  and 
living standards. The estimates in Table 
3, produce the probability value of the 
HDI  variable  for  the  model  of  0.986, 
which  is  greater  than  the  significance 
level of = 5 percent. Therefore, it can be 
concluded that the growth of the HDI 
variable does not significantly affect the 
multidimensional poverty variable. 
 
CONCLUSIONS 
 
The conclusions that can be drawn from 
the results of the multidimensional poverty 
analysis in Lampung Province during 2017-
2019 are as follows. 
1.  Descriptively,  the  percentage  of  the 
multidimensional poor, the intensity of 
multidimensional  poverty,  and  the 
multidimensional  poverty  level  at  the 
Lampung  Province  level  show  a 
downward trend. 
2.  Based on the partial test results, it was 
found  that  economic  growth  with  the 
ADHK  GRDP  per  capita approach  had 
no  significant  effect  on  the 
multidimensional  poverty  level  in 
Lampung.  This  indicates  that  the 
increase  in  value-added  from  the 
productive sectors in Lampung has not 
been  fully  accompanied  by  the 
capability of the population in terms of 
nutrition  and  health,  education,  and 
living standards. 
 
 
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© 2022 by the authors. Submitted  
for possible open access publication  
under  the  terms  and  conditions  of  the  Creative 
Commons  Attribution  (CC  BY  SA)  license 
(https://creativecommons.org/licenses/by-sa/4.0/). 

Desliyani Tri Wandita, Toto Gunarto, Arivina Ratih | 1422

DOI : 10.36418/jrssem.v1i9.156 https://jrssem.publikasiindonesia.id/index.php/jrssem/index