The Effect of Education and Productivity to Poverty

Putri Ulfa Kamalia

Postgraduate Student of Universitas Negeri Surabaya

Keywords: Effect, Education, Productivity, Poverty.

Abstract: The purpose this research is to analyze the effect of education and productivity to poverty in Madura Island

partially and simultaneously. In this research, there are two independent variables are education (X1) and

productivity (X2). Then, poverty (Y) is a dependent variable. The population in this research are four districts

in Madura Island namely Bangkalan, Sampang, Pamekasan and Sumenep. The data used is secondary data

from 2011-2015. The data collection technique used is purposive sampling. The method of analysis using

pan-el data regression with SPSS application. The results of this research showed that education and

productivity have a significant effect to poverty in Madura Island. As for suggestions, the population should

be given the widest opportunity to take education to a higher level and to increase productivity should be

increased the development of industrial infrastructure because most residents in Madura Island is still

livelihood as farmers and fishermen.

1 INTRODUCTION

Suramadu bridge connecting Madura Island

(Bangkalan) with Java Island (Surabaya) along 5.438

meters was inaugurated by the President of the Re-

public of Indonesia, Susilo Bambang Yudhoyono on

Tuesday, June 10, 2009. With the construction of the

longest bridge in Indonesia is expected to improve the

economy of Madura facilitate the flow of

transportation. Suramadu bridge is also expected to

in-crease industrial expansion in Madura.

However, based on The Central Bank of

Indonesia, the potential for poverty in Indonesia is

largely located in Eastern Indonesia. East Java is

ranked 15th with the largest percentage of poor

people. High economic growth in fact leads to

decrease in poverty. In East Java, the highest poverty

is predominantly in the northern regions of East Java

and the island of Madura with a subsistence economy.

Central Bureau of Statistics (BPS) in analyzing

poverty using the concept of basic needs approach.

The poor are residents who spend per capita per

month for food and non-food less than the poverty

line. Poverty line in the city is higher than in the

village be-cause the prices of goods in the city tend to

be higher than in the village.

Indeed, economic growth is key in the

development of a region. Increased economic growth

will increase people's income and purchasing power

so that per capita expenditure per month increases and

the poorest categorized population is reduced.

Madura Island became part of East Java Province

experiencing unfavorable conditions. The pace of

economic growth is slow and per capita income lags

be-hind. This is evidenced by the data from The

Central Bank of Indonesia in 2014 on East Java about

welfare rate shows the highest poverty mostly located

in the northern region of East Java and the island of

Madura namely Sampang, Bangkalan, Probolinggo,

Sumenep and Pamekasan are the five poorest areas in

East Java. This data is also supported by research

conducted by Soejoto (2016) shows the classification

of Madura regional development pattern as follows:

Table 1: Classification of regional development patterns.

Classification

District

Forward but Depressed

Sumenep

Relatively

Disadvantaged

Pamekasan,

Sampang

Very Disadvantaged

Bangkalan

Source: Soejoto (2016)

Poverty alleviation commitments must be

accompanied by government social expenditure

support, especially for productivity activities and

community empowerment. In addition to

productivity, high quality of education and health are

534

Kamalia, P.

The Effect of Education and Productivity to Poverty.

In Proceedings of the 2nd International Conference on Economic Education and Entrepreneurship (ICEEE 2017), pages 534-539

ISBN: 978-989-758-308-7

formed on composite index of HDI, then the capital

to access the economy becomes easier, so that poverty

can be sup-pressed. Based on The Central Bank of

Indonesia, the Red Zone (quadrant 4) is low HDI,

high% of poor people are in Sampang, Bangkalan,

Probolinggo, Sumenep, Pamekasan, Situbondo,

Bondowoso. The low quality of Human Resource

society and the high poverty are mostly located in

Madura Island and horseshoe area.

Based on the above description, the authors are

interested to researching the effect of education and

productivity to poverty in Madura Island. Thus, the

research formulation in this research are: 1) Does

education affect poverty in Madura Island? ; 2) Does

productivity affect poverty in Madura Island? ; 3) Are

education and productivity simultaneously affecting

poverty in Madura Island?. The purpose of this study

was to analyze the effect of education and

productivity to poverty in Madura Island either

partially and simultaneously.

2 LITERATURE REVIEW

2.1 The Theory of the Vicious Poverty

Circle

According to Samuelson (2006: 440) the vicious

circle in developing countries is low average income;

low savings and investment; slow capital

accumulation and low productivity. Barriers to

development often get heavy. Low levels of income

make it difficult to create savings, so capital is

difficult to collect. As a result, productivity cannot

increase so that in-come is unlikely to increase.

Successful development must break the chain in some

places. If the country succeeds simultaneously to

invest more, develop skills and reduce population

growth, it can break the vicious cycle of poverty and

an angel circle will lead to rapid economic

development.

Very low community revenues and an

underdeveloped banking system in the early stages of

the economic growth process do not allow a

developing country to address the underlying capital

shortage. Vicious circle theory illustrates the

difficulties facing a poor country to realize

development (Sukirno, 2006: 439).

2.2 Productivity: Roles and

Determining Factors

The term productivity refers to the amount of goods

or services that a worker can produce every hour of

work. The key role of productivity in determining the

standard of living prevailing in a country is the same

as that of a sailor. Look again that the Gross Domestic

Product of a country's economy measures two things

at once the total income that each resident gains in

economic activity and the total cost incurred to

produce goods and services (Mankiw, 2014: 42).

According Mankiw (2014: 43-44) factors that

determine the productivity of physical capital, human

capital, natural resources and technological insights.

The completeness of the equipment and structures

used in producing goods and services is called

physical capital. Then, knowledge and skills acquired

by workers through education, training and

experience. Like physical capital, human capital also

enhances a country's ability to produce goods and

services. Hu-man capital also produces factors of

production. Furthermore, natural resources are inputs

in production activities provided by nature such as

land, rivers and mineral deposits. Then, that can affect

productivity is the mastery of science and technology

is an under-standing of the best ways to produce

goods and services.

It is necessary to understand the difference be-

tween the mastery of science and technology with

human capital although both are closely related, but

there are important differences. Mastery of science

and technology refers to people's understanding of

how things work. Human capital refers to resources

that are expected to transform that understanding to

the workforce. In other words, if likened to a book

then science is the quality of the content of a book,

while human capital is the amount of time used by

someone to read the book (Mankiw, 2014: 45).

The special characteristic possessed by human

capital is that it cannot be lost or diminished if the

factors of production are used, utilized or sold. Of-ten

more used human capital is not the measure de-

creases but its value becomes higher (Irawan, 2002:

120).

Thus, Human Capital Theory and in a different

sense Correspondence Theory both provide a set of

implications for policies to alleviate poverty. Broadly

speaking, the former implies that an effective anti-

poverty strategy should incorporate the enhancement

of education and skills amongst poor households.

This will enhance their productivity in the informal

urban and rural economy, and it will also increase

their eligibility for paid employment in the formal

The Effect of Education and Productivity to Poverty

535

sector and for advancement once they are employed.

Correspondence Theory similarly implies that in-

creasing levels of schooling in the labour force are

likely to be functional to the process of employment

growth. However it does not necessarily imply a

benign impact for those school leavers who fail to

secure access to the formal sector (Oxaal, 1997).

2.3 Amartya Sen's Capability

Approach

Amartya Sen, the winner of the Nobel Prize in eco-

nomics in 1998, stated that the capability to function

is the most important thing to determine the status of

poor or not. Sen further argues that poverty cannot be

measured properly on the basis of income or even

with utility as it is understood so far; the most dizzy

is not what a person has or can be what he is and what

he does and can do. This is referred to as

functionality. Sen defines capability as one's own

freedom, according to their personal characteristics

and control over commodities. This view helps to

explain why development economists strongly

emphasize the importance of education and health.

They conclude that countries with high income levels

but low health and education standards are a growing

but undeveloped country (Todaro, 2014: 19).

2.4 Education and Poverty

There are many, various and interconnected causes of

poverty, and we can't use a magic formula to eradicate

it. But, we can consider education as a reducing risk

element of high poverty, which may pre-vent the

occurrence of another generation, much poorer. In the

underdeveloped countries and developing countries,

people instinctively know that education is a good

thing for their children, and in developed countries,

we have a lot to learn and to re-learn about the

importance of education. People who live in poverty

are aware of the fact that sending their children to

school will give them opportunities that they didn't

have. Even if education is not sufficient, due to the

multidimensional nature of poverty. Educational

systems, both at the micro and macro-level, have an

important role in supporting social upward mobility.

Education in all its forms, in my opinion, is one of the

most important factors in breaking the vicious circle

of intergenerational transmission of poverty.

Investments in this area are profitable over the long

term and bring the most reliable profits. At the same

time, investment in education of children, especially

those who are at the be-ginning of the road, represents

a safe start in life. Heading to this, nations are creating

for themselves both or education and training systems

more inclusive at all levels and for all ages, whether

we speak of primary and secondary school levels,

higher education or vocational training and education

for adult person (Mihai, 2015).

Based on Diaz's (2008) research analyzes both the

monetary and non-monetary effects of the education

level of the head of the household on poverty. He

propose that schooling returns should not be thought

as a single number - usually the schooling coefﬁcient

in an income equation - but as a set of elements whose

length depends on the number of identiﬁed poverty

dimensions. He also found interesting dis-similarities

by gender and urban-rural location. Exploring the

non-pecuniary returns, he found that the education of

the head positively inﬂuences family health and

housing conditions.

Lelkels research (2010) showed that for a

majority of countries, labour market-related factors

(employment status and work intensity) and

education are more important in explaining

inequalities than are age or household structure.

Income differences be-tween education group’s

account for the largest share of total inequality in

Southern European countries.

3 METHODS

This study used a quantitative approach with the type

of associative research. This research is an associative

research because it is a research that aims to know the

relationship between two variables or more and know

its affect (Sujarweni, 2014: 11).

In this research, there are two independent

variables are education (X1) and productivity (X2).

Then, poverty (Y) is a dependent variable. Here is a

re-search design:

Figure 1: Research design.

Population of this research is Madura Island

namely Bangkalan, Sampang, Pamekasan Sumenep.

The data collection technique by purposive sampling

are data of education, productivity and poverty from

2011-2015 in the four districts. The type of data is

secondary data obtained from the Central Bureau of

Statistics (BPS).

Data analysis technique used in this research is

panel data regression by using SPSS application. To

ICEEE 2017 - 2nd International Conference on Economic Education and Entrepreneurship

536

measure education (X1) that is with percentage of

educational level of high school graduates*(Source:

BPS). To measure productivity (X2) that is with

percentage data of Gross Regional Domestic Product

according to business field ** (Source: BPS). Then,

to measure poverty by percentage of poverty level***

(Source: BPS).

4 RESULTS AND DISCUSSION

Based on the balance of data, in this study is a panel

of balanced data (balanced panel). The data panel is

balanced if each cross section unit has the same

amount of time series observation (Suliyanto, 2011:

229). In this study there are four units of cross section,

each district has observation time series for five years.

In this study, the researchers wanted to study the

effect of education (X1) and productivity (X2)

variables to poverty (Y) in Madura Island namely

Bangkalan, Sampang, Pamekasan and Sumenep for

the period of 2011-2015.

The assumptions used in the analysis of this re-

search data are intercept and constant slope

coefficient over time. According to Ghozali (2014:

294) we assume intercept and slope coefficients are

constant over time and space, while error term reflects

differences over time and individuals. Assuming this

means ignoring the time and space dimension, so

direct the Ordinary Least Square regression. In this

study there are four districts that have five series da-

ta so that researchers have observations of 20.

Regression equation in this research is as follows:

𝑌

𝑖𝑡

= 𝛽

+ 𝛽

𝑋

1𝑖𝑡

+ 𝛽

𝑋

2𝑖𝑡

+ 𝜇

𝑖𝑡

(1)

Where i = unit cross section; t = time period.

Based on data processing using SPSS application

obtained the following results:

Table 2: Autocorrelation test.

Source: Data processed by researchers, 2017

Based on the output the value of R is 0.878 means

the correlation between productivity and education

variables against poverty of 0.878. This means a tight

correlation because the value of 0.878 approaching to

1. Then, the value of R Square (R2) of 0.772 means

the percentage of the contribution of productivity and

education variables to poverty is 77.2%, while the rest

influenced by other variables that are not included in

this model. Standard error of the estimate of 1.78656.

Durbin Watson's value is used to see whether or not

an autocorrelation is in the regression model. The DW

value of that output is 2.057. Then for the dL and dU

values in the DW table at 0.05 significance with n=20

and k=2 the dL value is 1.100 and the dU value is

1.537. Thus, the value 4- dL is 2.9 and the value 4-dU

is 2.463. Thus dU<DW<4-dU is 1.537<2.057<2.463

then there is no positive or negative autocorrelation.

Table 3: Multicolinearity test.

From the above output, obtained the tolerance value of

both independent variables greater than 0.1 is 0.980 and

VIF value less than 10 that is 1.020 so there is no

multicollinearity.

Table 4: Partial test.

Coefficients

Model

Standardized

Coefficients

Beta

Sig.

(Constant)

16.35

.000

Education

.878

7.502

.000

Productivity

.056

2.997

.003

a. Dependent Variable: Poverty

Source: Data processed by researchers, 2017

From these outputs, the education variables have

a t-value of 7.502 with a significance level of 0.000.

The productivity variable has a t value of 2.997 with

a significance level of 0.003. T table can be seen in

the statistical table on the significance of

0.05/2=0.025 with df=n-k-1 or 20-2-1=17, the results

obtained for table t of 2.110. Thus, t arithmetic

Model Summary

Mod

Square

Adjust

ed R

Square

Std. Error of

the Estimate

Durbin-

Watson

.878

.772

.745

1.78656

2.057

a. Predictors: (Constant), Productivity, Education

b. Dependent Variable: Poverty

Model

Collinearity Statistics

Toleran

VIF

(Constant)

Education

.980

1.020

Productivity

.980

1.020

a. Dependent Variable: Poverty

Source: Data processed by researchers, 2017

The Effect of Education and Productivity to Poverty

537

education variables greater than t table (7.502>2.110)

and level of significance 0.000< 0.05. This means that

education has a partial effect on poverty. Then, t

calculate the productivity variable is greater than t

table (2.997>2.110) and significance level

0.003<.0.05. This means that productivity partially

affects poverty.

Table 5: Simultaneous test.

ANOVA

Model

Sig.

Regression

28.709

.000

Residual

Total

a. Predictors: (Constant), Productivity, Education

b. Dependent Variable: Poverty

Source: Data processed by researchers, 2017

From the output, obtained F count equal to 28.709

and significance value equal to 0.000. F table can be

seen in table F at the 0.05 significance level with

df1=2 and df2= (n-k-1) is (20-2-1=17) to obtain F

table of 3.592. Thus, F arithmetic> F table

(28.709>3,592) and a significance level of

0.000<0.05. Thus, education and productivity have a

significant effect simultaneously to poverty.

The results obtained in accordance with the grand

theory. According to Samuelson (2006: 440) the

vicious circle in developing countries is low aver-age

income; low savings and investment; slow capital

accumulation and low productivity.

There is a correlation between educations to

poverty. In general, if a person's education is low, he

will work as a hSired laborer or work for a low wage.

When the wages received are low, then the income is

low. When income is low, then productivity is low

and will create poverty. It will go on like a cycle

called the vicious cycle of poverty.

And then, Lelkels research (2010) showed that for

a majority of countries, labour market-related factors

(employment status and work intensity) and

education are more important in explaining

inequalities than are age or household structure.

Based on the result of the analysis, education

factor can reduce poverty level. When a person has a

high education, he / she will get a decent job in

accordance with the competence of his field; As well

as the wages received. This will increase income and

increase consumption. As consumption increases,

productivity will increase so that economic growth

will also increase and will reduce poverty slowly but

surely. In accordance with the theory, education is a

capital investment of human capital which if always

used will not be exhausted, but will improve the

ability and usefulness that will increase productivity.

5 CONCLUSIONS

Based on the results of research, the education

partially affect to poverty in Madura Island.

Productivity partially affect to poverty in Madura

Island. Then, education and productivity

simultaneously affecting poverty in Madura Island.

As for suggestions for the results of this study is due

to the education effect on poverty in Madura Island

then the population should be in Madura given the

widest opportunity to take education to a higher level

because education is the investment of human cap-

ital. Likewise to increase productivity in Madura Is-

land should be increased the development of

industrial infrastructure because most residents in

Madura Island is still livelihood as farmers and

fishermen. The implications of this study are for

further research that is to improve certain things that

have not been reached by this research. For example,

by adding other variables that affect poverty that has

not been studied in this study, that are capital, income

and health. Furthermore, research can also be

expanded within the scope of a province or country.

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