Effect of Public Place for Urban Poor’s Benefit in Kind

1. Introduction

The development of cities was marked by the amount of people living in them. The demand of descent infrastructure services was fulfilled with the construction of infrastructure that fulfills the needs of low, middle and upper class people in the society. Nevertheless, the phenomenon that happened in Indonesia cities is with the construction of large scale infrastructures, there are more poor people living near them.

With the rapid growth of city development, poverty is globally moving into the cities. Few conditions that caused by poverty are: people who don’t have a sustainable access, created slum area; almost everyday, cities are filled with vagrants and beggars; a large gap in education and health services between the rich and the poor; the formation of slum area caused by the population growth from the rapid flow of urbanization or migration. Until 2010, there are dozens of public infrastructures like health, education, trade and open space facilities gave attractions and opportunities for poor people multiplier effects.

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According to Jung et al (Jung, S et al, 2009), government expenditures for public facilities was purposed to eradicate poverty level in cities. However, the development of public facilities as one of city attractions can cause the migration of poor people to the cities. Globalisation, migration and social exclusion are often the keywords employed to explain this process of spatial concentration of especially long-term unemployed and immigrant and ethnic minority communities. The availability of public facilities as an attraction factor for poor people activities are analyzed using Geoda to identify spatial effect (Anselin, et al, 2004).

2. Poverty in the City

According to the World Bank Institute (2005), poverty is a lack of well-being where the poor can be defined as someone who does not have enough income or minimum adequate consumption levels. Poverty can be defined based on the monetary value, the type of consumption, housing, or the poor health. The macro approach on poverty and well-being focused on individual’s ability to function in society, such as income, education, health, powerlessness, and lack of freedom in politics. According to Vincen (Vincen, 2009), poverty is a multidimensional problem that goes beyond economic issues as it relates to social, political, and cultural. Poverty is a form of human conflicts resulting from reactions due to lack of basic needs, biological, and psychological.

Characteristics of urban poverty can be reviewed based on three-dimensional indicators of poverty. Based on the national poverty reduction strategy by Bappenas (2004), the three-dimensional nature of poverty can be use to measured in-kind benefits such as:

• Income. Income are affected by poor people’s livelihood that has shortcomings in terms of skill and willingness resulting non-permanent work. Most of the income derived from employment in the informal sector, because the formal sector is not able to absorb low-skilled labor. Other occupations carried out by the urban poor are high risk occupations where there are no guarantees of sustainability. The impacts derived from the low income are problems in the ownership of land and basic services.
• Education. The urban poor have limited access to educational services caused by the gap of education costs, limited educational facilities, cost of education, limited access to education; high education costs both direct and indirect costs (Bappenas, 2004). The impact of these problems are the poor people are not able to get a steady job, lack of constructive activities to improve the skills of young people of school age, and gender inequalities.
• Health. Poor people who work in the informal sector are at risk of having a low income. This causes an inability to reach basic services such as decent housing. The urban poor are dominated by people who do not have a chance urbanization decent tenure thus creating a slum area. The physical condition of housing and income limitations will lead to low purchasing power for accessing health and preserving the environment.

3. Infrastructure Services

Utoro (Utoro, 2006) suggested that services embody the organization of to the community as the fulfillment of needs and interests. Public services fulfill primary need which includes service levels, patterns of distribution, outreach, and the tendency of the level of need. Most of the infrastructure can be regarded as a public facility, the facilities provided by the government or private managed in order to meet the needs of the community are typically in the form of roads, bridges, buildings, open spaces, and so on.

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Social activities and economic activities characterize the development of a city. One indicator of the dynamic development of the city can be seen from the economic conditions of the city (urban economic). In general, the characteristics of urban development can be determined by the capacity of infrastructure and facilities in a city. That condition indicates that the infrastructure and facilities are very vital part in the development of a city.

Infrastructure is a key foundation in social and economic activities. According to Dardak (Dardak, 2008), infrastructure services are part of the public sector to enable private sector and household consumption activities. The dynamics of the economy of a city is determined by how much the efficiency of the use of space or land-use patterns for the activity of the economic infrastructure in the city. The economic development of the city will be determined by the dynamics of trading systems that exist in the city and also in the surrounding area.

4. Klojen District

Klojen District located in Malang urban center area has the most complete public facilities services and the most densed population in Malang urban area. According to Klojen District Detailed Plan year 2010-2030, Klojen District is planned as the center for regional service for Malang city. Klojen District functioned as the center for education, trade, public service and public administration. According to Malang Statistic Biro (BPS) data year 2011, there are 10.328 poor household living in Klojen District.

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5. Methods

To identify whether there are neighbouring spatial effect between benefit in kind for the poor in public space and public space services using Geoda, the Klojen District is divided into 37 blocks as analysis unit, using physical boundary (road and river), administration boundary and the distribution of public space. The public space characteristics used in this research are: accesibility, service level, capacity, sidewalk availability, sidewalk pavement, parking availability, open space availability, lighting, security, visitor and activities. Whilst public space benefit in kind for the poor are divided into economic, education and health benefits.

Collecting data is used questionnaires to obtain information from the respondents and field observations to obtain data of infrastructure services. The multiple spatial regression is used to create a model of relationship of infrastructure services and benefit in kind the poor. In this research, Geographic Information System (GIS) by ArcMap is used as basic data to analisys in spatial statistic program. Using computer program known as GeoDa, spatial autocorrelation, Moran’s I values, and spatial regression for each variable was able to be calculated. The results of the analysis presented in following:

• Spatial weights, which describes the relationship between the neighborhood polygons with another polygon. In this analysis will note the number of blocks that are affected and will be included in the model equations;
• Formula spatial model with the multiple regression model as follows

Y₁= A.W+ B + a.X₁ + b. X₂ + c.X₃ + d.X₄ + e.X₅ + f. X₆ + …..+k.X₂₇₍₁₎

Y₂= A.W+ B + a.X₁ + b. X₂ + c.X₃ + d.X₄ + e.X₅ + f. X₆ + …..+k.X₂₇₍₂₎

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Y₃= A.W+ B + a.X₁ + b. X₂ + c.X₃ + d.X₄ + e.X₅ + f. X₆ + …..+k.X₂₇₍₃₎

Y_1: Economic Benefit (Rp)

Y_2: Education Benefit (Rp)

Y_3: Health Benefit (Rp)

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A: Lambda

W: Spatial Weight

B: Constants

a-k: Variabel Coeffisient

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X_1-11: Independent Variables

Spatial multiple regression analysis performed spatial weight and the value of Lagrange Multiplier (LM) Lag and Lagrange Multiplier (LM). The spatial model based on the results of statistical tests that showed the significant value and also it can be seen by the largest value of determinant coefficient (R²).

Table 1. Blocks code in Klojen, Malang

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19	Kasin	65117-1

6. Benefit in Kind

According to Suwandi (Suwandi, 2004), the poor in urban and rural areas should be able to obtain basic services consisting of economic, educational, and health.

• Economic benefits, the amount of the benefit that is obtained directly from the income received each month by the community so that it can be used as a savings or investment.
• Benefits of Education. In the economic benefit, education can be used as one of the indirect benefits received. Educational benefits are the amount of rupiahs set aside for educational purposes such as schools, courses, equipment purchases, and so on.
• Health Benefits. In addition to education, health can be used as one of the indirect benefits received. Health benefits are the amount of rupiahs set aside for health reasons such as health insurance, the benefit of treatment, and so on.

Value of benefit in kind and infrastructure variables in each block is represented by highest value, lowest value, and average value. The minimum and maximum value show the benefit from services that in each blocks, while the average value is the general description of services provided by the blocks.

7. Results
   1. Accessibility

Accessibility is measured by distance (in meters) between the poor settlements and public facility. A maximum accessibility value ​​is 22.000 meters, while the minimum value is 50 m. This phenomenon suggests that there were a lot of different accessibility characteristics. Poor peoples that worked in the infrastructure services are not only lived in Klojen, but also have been coming from outside of Malang.

2. Level of infrastructure

Level of infrastructure is measured by scale of services. Hierarchy of infrastructure level is divided into three levels (districts, cities, and regional). Maximum value of infrastructure level is located in block 65112-2 because there are facilities which serve districts, cities, and regional scale.

3. Capacity of facilities

Capacity of facility is measured by area (in square meter) where the activity of poor people conducted in each blcoks. The maximum capacity or ​​the largest facilities is 29,100 m², while the minimum value of the variable is 300 m².

4. Capacity of facilities

Capacity of facility is measured by area (in meter square) where the activity of poor people happened for each blocks. The maximum capacity or ​​the largest facilities is 29,100 m², while the minimum value of the variable is 300 m².

5. Pavementt

Pavement is measured by the types of pavement of the pedestrian way where the activity of poor people conducted in each block. Pavement variable are divided into 4 types: cement, paving, soil, and without pedestrian way. The highest score is located in block 65117-2 where there are full of cement pedestrian way that supports and facilitates people activities.

6. Open space area

Open space area is measured by the area (in square meter) of open space where the activity of poor people happened for each block. Maximum value is 2500 m² and it’s located in 65119-8, while the minimum value is located in blocks without open space facilities.

7. Number of lighting

Lighting is measured by the number of lighting facilities where the activity of poor people conducted in each block. The maximum value of variable is 24 lightings and located in block 65111-1 and 65111-4. The minimum value is located in blocks without lighting facilities.

8. Number of security

Security variable is measured by the number of security posts where the activity of poor people conducted in each block. A maximum value ​​is 6 security posts, while the minimum values are located in block without security facilities.

9. Number of Visitor

Visitor is measured by the number of visitors per day to the facility where the activity of poor people conducted in each block. Maximum value of this variable is 5,000 visitors per day, while the minimum value of the variable is 25 visitors per day. The number of visitors is related to the infrastructure scale.

10. The economic benefits

The economic benefits are measured by the value of income (in rupiahs) that was earned every month because of the poor’s working activities in infrastructure services in each block. A maximum economic benefit is Rp12.000.000 per month and it is located in 65119-5, while the minimum value is Rp300.000 per month.

11. Educational benefits

Educational benefits are measured by the value of income which can be saved to education purpose (in rupiahs) that was collected every month because of the poor’s working activities in infrastructure services in each block. Maximumt educational is Rp 1.500.000 per month, while the minimum value is Rp 0.

12. Health Benefits

Health benefits are measured by the value of income which can be saved to health purpose (in rupiahs) that was earned every month because of working activities in infrastructure services in each block. Maximum value of the health benefits is Rp500.000 per month, while the minimum value is only Rp3000 per month.

Spatial autocorrelation is the correlation of a variable to itself through space. This means that spatial autocorrelation quantifies everything are related to everything else, but nearer things are more related than distant things. By investigating spatial autocorrelation, it is possible to test the strength of spatial autocorrelation throughout a map. Meanwhile, Moran’s I is the statistical standard for determining spatial autocorrelation. The strength of autocorrelation is based on a range from -1 to 1. As the resulting product of the Moran’s I calculation approaches 1, the stronger the spatial correlation.

Based on the analysis, Moran’s I value of 0.2782, 0.2397 and 0.1152 for all dependent variables, the amount of spatial autocorrelation is minimal. This suggests that where economic, education, and health benefits are located is a function of randomness. Meanwhile, benefits value in the nearest neighbouring blocks is not much affect the high value of benefits in each block.

Using Geoda spatial regression, Moran’s I test and Local Indicator Spatial Autocorrelation (LISA), obtained neighbouring spatial correlation model between urban public space characteristics with benefit in kind for urban poor.

Table 1 Benefit in Kind Spatial Regression Model

Spatial Regression Model
Y1 = 1435434+ 0,2837605.W + 483262,9.X10 + 167479,6.X18
Y2 = -2600942 – 0,3221031.W + 20,94021.X1 + 15,33539.X5 + 3581828.X10 + 158529.X11 + 145914,6.X18+ 212624,8.X19 – 304595,4.X20– 368676,7.X21 + 654824,5.X23
Y3 = 22567,75 + 0,1570038.W + 2026,002.X14 + 385,74.X15 + 51283,1.X18 +69346,33.X19– 99900,86.X20 + 230,9778.X24 +248,4346.X25
Y1 : Maximum Economic Benefit Y2: Maximum Education Benefit Y3: Maximum Health Benefit W: Spatial Weight (Neighbouring effect) X1: Maximum Accesibility X5: Maximum Capacity X10: Average Sidewalk Width X11: Sidewalk pavement	X14: Average Parking Space X15: Maximum Open Space X18: Maximum Lighting X19: Minimum Lighting X20: Average Lighting X21: Maximum Security Post X23: Average Security Post X24: Maximum Visit X25: Minimum Visit

8. Conclusion

Cluster Map of Local Indicator Spatial Autocorrelation (LISA) shows that the value of each benefit in kinds is not concentrated in a particular region based on the autocorrelation value. This suggests that economic, education, and health benefits are located is a random function. Whereas, benefits value in the nearest neighbour block is not much affecting the high value of benefits in block. Implicitly, the models suggests that the poor act rationally in determining the location of work based of infrastructure services that provide advantages more than the groups of nearest infrastructure in neighboring blocks.

9. Bibliography

Anselin, et al. 2004. Geoda: An Introduction to Spatial Data Analysis. USA: Urbana Champaign

Badan Pusat Statistik.2012. Perkembangan Beberapa Indikator Utama Sosial-Ekonomi Indonesia. Jakarta: Badan Pusat Statistik Indonesia

Bappenas.2004. Strategi Nasional Penanggulangan Kemiskinan Bab II. Jakarta: Bappenas

Dardak, H. 2008. Pembangunan Infrastruktur secara Terpadu dan Berkelanjutan Berbasis Penataan Ruang. Direktorat Jendral Penataan Ruang

Jung, S et al. 2009. Public Expenditure and Poverty Reduction in Southern United States. Presented at the Southern Agriculture Economics Association Annual Meeting, Atlanta January 31-February

Suwandi. 2004. Perencanaan dan Strategi Penanggulangan Kemiskinan di Daerah.Jakarta: SMERU

Utoro, R.I. 2006. Kajian Optimalisasi dan Tingkat Pelayanan Sarana Dasar di Kota Kecamatan Jalancagak-Subang. Tesis Dipublikasikan. Semarang: Universitas Diponegoro.

Vincen, B. 2009. The Relationship between Poverty, Conflict, and Development. Journal of Sustainable Development. 2(1): 15-28

World Bank Institute. 2005. Introduction to Poverty Analysis: Poverty Manual.

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