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Digital Poverty Conditionsfor East Java in Fourth Industrial Revolution

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Digital Poverty Conditionsfor East Java in Fourth Industrial Revolution International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 Digital Poverty Conditionsfor East Java in Fourth Industrial Revolution Dyah Anisa Permatasari, Lilik Sugiharti, Ahmad Fudholi Understanding digital poverty according to some experts is Abstract: In terms of the use of information and the inability to use information technology, either because of communication technology (ICT), the East Java government lack of access or due to lack of skills in using technology. actually needs to identify the groups most affected by the lack of Lack of information about the benefits of goods/ services digital inclusion. This is in accordance with one of the nawa related to technology or illiteracy, but also can be interpreted bhakti satya's visions, namely the strengthening of people's basic rights and social disruption brought about by the 4.0 technological as a lack of income to get digital access [5]. The inability of a revolution. Budgetary wastage will occur if we do not know the person to use technology that occurs due to deprivation of size of digital poverty in urban districts, especially if various public capabilities, namely the freedom to use ICT and freedom of services are in digital form, but not everyone who uses these public choice relating to the purpose of using ICT for passive or services understands digital.In this study, individual micro data active [6].Economic demand is a concept of demand that is from the 2015 and 2017 of National Socioeconomic Survey influenced by purchasing power, without purchasing power a districts/ cities in East Java was used which will be aggregated with Podes data for 2018. This is seen as strong enough to analyze requirement is not a demand. Purchasing power is affected by the factors affecting district/ city digital poverty in East Java. To consumer income, with inadequate income that demand can map the conditions of digital poverty, classifies districts/ cities into be canceled or reduced if the need is urgent. The demand or four quadrants as seen from digital poverty and economic poverty, purchasing power of ICT arises from consumer preferences. while to find out what factors influence digital poverty are carried To set consumer preferences, consumers must know the out testing the structural relationship between variables using the benefits and disadvantages (costs) of the ICT. Poverty circles ordinal logistic regression method.This study is analyze the condition of digital poverty along with the factors that influence it will occur if those who do not have access/ do not have the both from economic conditions, demographic conditions, and the purchasing power of ICT so that they will never have an ICT availability of infrastructure in districts/ cities in East Java. In demand and then will never know the benefits of the ICT, and addition, it is hoped that the analysis in this study will be able to so on.Therefore information in the economy is very provide consideration for policy makers in dealing with digital important because it relates to asymmetric information, poverty issues, especially pro-poor telecommunications namely the difference in information between one party and communication policies. another party in economic activity. One that affects Index Terms: Digital poverty index, economic poverty index, information asymmetry is technology, especially ICT. ICTs quadrant analysis, logit ordinal regression are needed to overcome assimetric information. Example of the use of technology in overcoming asymmetric information I. INTRODUCTION in agriculture, it is necessary to develop a production information system and an internet-based food commodity Information and Communication Technology (ICT) in the market as food information from production to market so that digital era place more emphasis on the use of fixed there is no price distortion among producers and consumers. telephones, cellular telephones, and internet usage because Diagne, A., & Ly [7] conducting research with a sample of they are seen as technologies that are two-way 17 African countries and showing that digital poverty occurs communication [1].The use of ICTs is very important due to lack of access and use of ICT by households in certain especially in the Industrial Revolution 4.0 where there are geographical areas. This study also mentions that the level of massive changes to the way to produce, distribute and wealth and education is the dominant factor in reducing consume goods and services in more efficient ways [2, 3]. digital poverty. The social demographic conditions of a According to Alkire [4], poverty is multidimensional and person also influence the conditions of digital poverty. The depends on the perspectives and capacities of individuals social demographic conditions that are seen to influence who experience it. The poor are not only those who earn less digital poverty are age, gender, education. Education and than $ 1 per day and are unable to meet their needs, but also economic capacity have a negative relationship with digital those who work without skills, without political access, poverty. The higher education and economic capacity, the people with low literacy levels less likely to become digital poor [8-10], besides the location Revised Manuscript Received on November 15, 2019 of residence, availability of BTS, signals are also seen to Dyah Anisa Permatasari, Department of Economics, Faculty of influence the conditions of digital poverty [11-13].Kponou Business and Economics, Universitas Airlangga, Surabaya, Indonesia. [14] found that digital poverty conditions had a negative Lilik Sugiharti, Department of Economics, Faculty of Business and Economics, Universitas Airlangga, Surabaya, Indonesia. effect on the success of universal telecommunications Ahmad Fudholi, Solar Energy Research Institute, Universiti services in Africa. The higher education and economic Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia. capacity, the less likely to become digital poor [8, 9, 12]. Published By: Retrieval Number: D9249118419/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.D9249.118419 12450 & Sciences Publication Digital Poverty Conditionsfor East Java in Fourth Industrial Revolution The objectives of this study is to analyze the condition of Y = 4, if included in the extreme digital poor. digital poverty along with the factors that influence it both The magnitude of the probability for each category can be from economic conditions, demographic conditions, and the written in the form of ordinal logistical transformation in availability of infrastructure in districts/ cities in East Java. general as follows: 푝푗 퐾 푙푛 = 훽푗 + 푘 훽푗푘 푥푘 +ε (1) II. RESEARCH METHODS 푝0 where: The digital poverty measure in this study adopts the digital j = 1,2, ..., j is the number of categories of the dependent poverty measure used by Barrantes [5] with modification in variable accordance with the Susenas data household conditions, k = 1,2, ..., k is the number of independent variables. divided into 4 categories: (i) digital rich (category 1), General model of this equation as follow: connected to the internet(Category 2), poor digital(Category 2 퐷푖푔푃표푣푖 = 훽0 + 훽1 퐸푥푝 + 훽2퐴푔푒 + 훽3퐴푔푒 + 훽4퐽푎푟푡 + 3), and (iv) extreme digital poor (Category 4). 훽 퐸푑푢 + 훽 퐽푘푒푙 + 훽 퐾푒푔푡푛 + 훽 퐿푎푝푈푠 + 훽 푆푡퐾푎푤푖푛 + Second step is to classify extreme digital poor and digital 5 6 7 8 9 훽 퐷푎푒푟푇푇 + 훽 푇표푝표 + 훽 푊푎푟푛푒푡 + poor (categories 3 & 4) into the digital poor category. The 10 11 12 훽 푆푖푛푦푎푙푇푒푙푝 + 훽 퐽퐵푡푠 + 푒 (3) formula for calculating the digital poverty index is based on 13 14 where : the Headcount Index (P ) poverty measure. In mathematical 0 DigPov = Digital poverty form the Headcount Index is written as: 푁 Exp = average monthly expenditure per capita 푃 = 푝 (1) 0 푁 Jart = Number of household members Edu = Education where, Jkel = Sex P = Headcount Index 0 Kegtn = Most activities carried out a week ago N = number of poor population (categories 3 and 4) p LapUs = Business field N = total population (residents aged 5 years and over) StKawin = Marital status Third step of this study is analyzing digital poverty using DaerTT = Area of residence quadrant analysis, as shown in Fig. 1. Topo = Topography Warnet = Place to access internet Digital Poverty Index (X) SignalTelp = Telephone signal Economic Poverty Index (Y) JBts = Number of BTS Quadrant II: Quadrant I 훽0, 훽2, . 훽13 = Coefficients District/ city with a low Regencies/cities with high 푒 =Error digital poverty index and digital poverty index and high economic poverty The data used in this study were sourced from the Central high economic poverty index. Statistics Agency (BPS), namely Socio-Economic Survey index data (Susenas) and podes data. Podes 2018 data and Susenas data for 2015 and 2016 are used to map digital poverty using Quadrant IV Quadrant III GIS and compare digital poverty with economic poverty into District/ city with a low Regencies/ cities with high four quadrants, whereas to analyze the factors that influence digital poverty indexand digital poverty index and digital poverty only use Podes 2018 data and Susenas data for low economic poverty index low economic poverty 2015. The sample of this study is individual regencies/ cities index in East Java who are aged 5 years and over (5+). The independent variables used in this study include the average expenditure per capita per month. Age variable is the Fig 1. Digital poverty using quadrant analysis age of an individual measured in years. The age squared variable is the square value of the age variable, the number of The Equation (2)was used to calculating the economic household members is the number of household members. poverty index [9]: The variable level of education becomes uneducated, graduated elementary/ junior high, graduated high school and 1 푧−푦 ∝ graduated from tertiary education. The gender variable is 푃 = 푞 푖 (2) ∝ 푛 푖=1 푧 male or female. Most activity variables are work, school, where, household care and other activities. Business field variables α = 0 are divided into primary and non-primary.
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