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THE SUCCESS OF RASKIN PROGRAM IN CENTRAL LOMBOK REGENCY USING PATH ANALYSIS DECOMPOSITION Desy Komalasari , Mustika Hadijati, Lailia Awalushaumi and Nurul Fitriyani

Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mataram University E-mail: [email protected]

Abstract

The aims of this research were to determine the path model decomposition and the variables that impact the granting rice program for poor households (Raskin) in Central Lombok Regency, West Nusa Tenggara Province, Indonesia. The methods used were path decomposition and regression analysis. The primary data were collected in 16 villages using questionnaire. The endogenous variable was the success of the program, and the six exogenous variables involved the Precise of Time, the Precise of Quality, the Precise of Administration, the Precise of Price, the Precise of Quantity, and the Precise of Households. The variable that impacted the success of Raskin program in Central Lombok Regency was the Precise of Rice Quality. Keywords: Path Analysis Decomposition, Regression Analysis, Raskin Program Central Lombok. 1. Introduction Central Lombok Regency is one of the regency in West Nusa Tenggara Province, are always implement the Raskin Program (granting rice program for poor households). The number of households Raskin recipient determined based on an integrated data base for the Social Protection Program in the data by BPS (Central Bureau of Statistics) and managed by TNP2K (National Teams Acceleration of Poverty Reduction) Indonesia. During the year, each of households received 15 kilograms of rice for each month, and they were paid the price of Rp. 1.600,- per kilograms. The number of households Raskin recipient is 94.745, consist of 12 of districts, there is Pujut (11.417 households), Praya Barat (8.855 households), Praya Barat Daya (7.001 households), Praya Tengah (5.280 households), Praya (8.098 households), Praya Timur (7.465 households), Janapria (7.696 households), Kopang (8.176 households), Batukliang (6.776 households), Batukliang Utara (6.191 households), Pringgarata (7.975 households) and Jonggat district (9.815 households). Raskin is a form of public policy of the Indonesian government to distribute special rice for poor households (Hastuti et al, 2012). The objectives of Raskin are to strengthen the food security of poor families, improve the quality of Human Resources (HR), support rice farming and increase the economic empowerment of the region. In addition, Raskin has direct impact on the stability of rice prices, which also play a role in the stability of the national economy. The factors influencing the success of the Raskin program include "Six Criteria ", Precise of Time, Precise of Quality, Precise of Administration, Precise of Price, Precise of Quantity, and the Precise of Households (Hastuti et al, 2012). Path analysis decomposition is a statistical method that can be used to analyze the success of the Raskin program in Central Lombok Regency. It can be used for development of regression models to test the suitability of the correlation matrix on two or more models (Matjick and Sumertajaya, 2011). The Path model in the path analysis diagram illustrates the relationship between independent variables (exogenous variables), mediation variables (intervening variables), and dependent variables (endogenous variables.

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International Social Science Conference, Mataram University, Lombok, Indonesia (24 - 26 November, 2015) Constructing Coherence and Sustainable Social Development

This study aims to determine the model and variables that influence the success of Raskin program using path decomposition and regression analysis in Central Lombok Regency. 2. Path Analysis Path analysis is usually performed for continuous variables by using linear regression equations, and the basic idea is applied to the analysis of causal systems (Eshima et al, 2001). Path analysis is one of the statistical methods that used to measure the direct and indirect relationships between variables in a model. The causality in path analysis model usually indicated with circles and arrows. The predicted value obtained by regression models compared with the correlation matrix of observation variables (Mattjick and Sumertajaya, 2011). Decomposition is one of the Path analysis methods. This method determine the coefficients of the variables. Sudaryono (2010) said that path analysis can be formulated as a coefficients estimate of a set of structural linear equation that describes the relationship (cause and effect relationships). The pattern of causal relationships between variables displays by images, known as the path diagram. There are a lot of models in the path analysis that can be used, such as Multiple Regression Model, Mediation Model, Combination Model of Multiple Regression and Mediation, Complex Models, Recursive Model and Non Recursive Model (Sarwono, 2007). 2.1. Path Coefficient

Path coefficient is the regression model coefficient that can be obtained after all variables, both exogenous and endogenous variable, are standardized by transformation. The coefficient also called by weighted beta or standardized beta coefficients (Ahn, 2002). Path coefficient e path

(Roflin, 2009). The other formula to calculate of path coefficients with least squares method (Lumenta, et al, 2012). There are several stages in the path analysis determination are as follows. a. Draw the complete track diagram with structural equation and explain the research hypothesis, from the exogenous and endogenous variables. b. Identify the structure that will calculate the coefficients track. Suppose that there are k exogenous variables (X) and one endogenous variable (Y) that expressed by the equation: c.

(1)

Calculate the inverse correlation matrix of exogenous variables. (2)

d.

Calculate the path coefficient

. (3)

ISBN : 978-979-8911-91-0

Desy Komalasari , Mustika Hadijati, Lailia Awalushaumi and Nurul Fitriyani: THE SUCCESS OF RASKIN PROGRAM IN CENTRAL LOMBOK REGENCY USING PATH ANALYSIS DECOMPOSITION

2.2. Examining the path coefficient

Examining the path coefficient with two testing significance, either partially or simultaneously. The steps for examining are as follows: Determine the statistical hypothesis , Examine the partially testing of each path coefficient with t-test. (4)

which k is a number of exogenous variables, is the path coefficients, is the determination coefficient, is the inverse correlation matrix of exogenous variables and nk-1 is the number of degrees. Examine the simultaneously testing of all path coefficient with F-test.

Reject the hypothesis if

, where

(5)

.

2.3. Regression Analysis

Regression analysis is concerned with predicting the mean value of dependent variable from known values of one or more independent variable . The variable model with a dependent variable , and independent variables can be written as: (6)

In equation (9), denotes the intercept, while regression), and is the residual term (Mishra and Min, 2010).

the slope coefficients (partial

3. Research Method The sampling method used Stratified Random Sampling, and the number of sample households using Slovin technique with alpa 5%. Primary data for this study were collected using questionnaires and interviews. Questionnaires were distributed to 404 households in 16 villages and personal interviews were conducted, from May to July 2015. The 16 villages involved Aik Darek, Mantang, Mekar Bersatu, Kopang Raya, Darmaji, Muncan, Semparu, Ubung, Jelantik, Puyung, Nyerot, Tanak Awu, Ketare, Sengkol, Rembitan, and Kuta Villages. The procedure used by decomposition of path analysis, and determined the significant variables using regression analysis. The completely procedure involves a) determining the correlation matrix, such as exogenous variables, intervening variables and endogenous variables; b) Calculating the path coefficients; c) Calculating the residual or error term; d) Determining the model; e) Examining the simultaneously and partially model; f) Determining the affected variables; g) Give the interpretation of the model.

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4. Results and Discussion The path decomposition diagram from six exogenous variables and one endogenous variable of granting rice program for poor households (Raskin) in Central Lombok Regency are represented in this section. It diagram has been showed by Figure 1. Z1 P1

r12 Z2

r23

P2

Z3

P3

r34

P4

Z4

r45

Zy

P5

Z5

r56

P6

Z6

Figure 1. Path diagram for Raskin Program Path analysis decomposition model in Central Lombok Regency composed from six exogenous variables (six precise) and one endogenous variable (success program) was represented by:

where z was the standardized variable, and exogenous variable, while summaries described in each of the following sub-sections. a.

were the path coefficients of each

Pearson correlation values obtained between the exogenous variables summarized in Table 1.

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X1_Precise of Time

Table 1. Correlations Coefficients X3 X6 X1 X2 Precise of X4 X5 Precise of Precise Precise of Administrati Precise of Precise of Household of Time Quality on Price Quantity s 1

.087

.037 1

X2_Precise of Quality

.087 .037

.322

X4_Precise of Price

.052

X6_Precise of Households

.000

X3_Precise of Administration

X5_Precise of Quantity

b.

.183

.000

.070

.344

.114

.322

-.034

-.034

.070

1

.265

.005

.035

.114

.005

.126

1

.218

.344

.265

.218

.035

1

.126

Path coefficients of each exogenous variable summarized in Table 2. Table 2. Coefficients

Model 1

.183

1

.052

Unstandardized Coefficients B

Std. Error

.046 .456 .083

.038 .065 .045

.057 .342 .095

(Constant)

1.515

X4_Precise of Price

.040

.027

X6_Precise of Households

.050

.074

X1_Precise of Time X2_Precise of Quality X3_Precise of Administration X5_Precise of Quantity

Standardized Coefficients

.026

Dependent Variable: Y_The Success Program

.214

.047

Beta

t

Sig.

7.069

.000

.072

1.507

.133

.032

.684

.494

.029

1.222 6.971 1.862 .550

.222 .000 .063 .582

The path analysis decomposition value described by the Standardized Coefficients Beta. Thus, the path coefficient values obtained were , , , , , and . c.

Residual Value

The residual value obtained from the 0.171 value of determination coefficient (R Square) of the model. The residual value of the model was: therefore model equation represented by:

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d.

Path Decomposition Test

Model testing simultaneously conducted by the F-test analysis of variance (ANOVA) while the partial test conducted by the t-test. Table 3 showed the test results. Model 1

Table 3. ANOVA

Regression Residual Total

Sum of Squares

68.309

df

331.905 400.214

6

394 400

Mean Square

11.385

.842

F

13.515

Sig.

.000a

a. Predictors: (Constant), X6_Precise of Households, X1_Precise of Time, X4_Precise of Price, X2_Precise of Quality, X3_Precise of Administration, X5_Precise of Quantity Dependent Variable: Y_The Success Program

Based on the Table 3, we can be obtained 13.515 value of F-test, and 0.000 value of the probability. Due to the probability value that smaller than alpha (0.05), it could be done to reject the null hypothesis. It could be concluded that the model was obtained significant. So that the exogenous variables (X1, X2, X3, X4, X5, and X6) jointly affected significantly to the endogenous variable (Raskin program was success in Central Lombok Regency). The partial test were used the t-test. The partial test of each coefficient showed in Table 2. The Precise of Time Variable (X1) had no effect to the endogenous variable, because the significant value (0.222) was greater than alpha (0.05). The Precise of Quality Variable (X2) significantly effected the endogenous variable, because the significant value (0.000) was less than alpha (0.05). The Precise of Administration Variable (X3) had no effect to the endogenous variable, because the significant value (0.063) was greater than alpha (0.05). The Precise of Price Variable (X4) had no effect to the endogenous variable, because the significant value (0.133) was greater than alpha (0.05). The Precise of Quantity Variable (X5) had no effect to the endogenous variable, because the significant value (0.582) was greater than alpha (0.05). The Precise of Households Variable (X6) had no effect to the endogenous variable, because the significant value (0.582) was greater than alpha (0.05). It could be concluded that the only variable that significantly affect the successful of Raskin program in Central Lombok was X2 (Precise of Quality Variable). The direct, indirect, and total effect of each variables were calculated after the test were summarized in Table 4.

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Table 4. The effected of exogenous variables on endogenous variable

Variables Z1 Z2 Z3 Z4 Z5 Z6

Direct effect

0.057 0.342 0.095

0.072 0.029 0.032

Z1

0.000 0.005 0.002 0.003 0.010 0.000

Z2

0.013 0.000 0.110 0.012 0.075 0.012

Indirect effect

0.004 0.031 0.000

0.004 -0.002 0.005

0.005 0.006 0.010

Total Indirect 0.000 0.026 0.001 0.041 0.004 0.131

0.033 0.011

0.019 0.000

0.000 0.004

0.004 0.000

Z3

0.007

Z4

0.000

Z5

0.008

Z6

0.001

0.007 0.141 0.027

Total effect

0.083 0.383 0.226 0.079 0.170 0.059

% of Total effect 8.30 38.30 22.60 7.90 17.00 5.90

Table 4 represented the influence of direct, indirect, and total effect on each variable. From Tabel 4, the direct effect from X1 was 0.057, and the indirect effect 0.026, hence the total effect from X1 was 0.083 (8.3 %). The direct effect from X2 was 0.342, and the indirect effect 0.041, hence the total effect from X2 was 0.383 (38.3 %). The direct effect from X3 was 0.095, and the indirect effect 0.131, hence the total effect from X3 was 0.226 (22.6 %). The direct effect from X4 was 0.072, and the indirect effect 0.007, hence the total effect from X4 was 0.079 (7.9 %). The direct effect from X5 was 0.029, and the indirect effect 0.141, hence the total effect from X5 was 0.170 (17.0 %). The direct effect from X6 was 0.032, and the indirect effect 0.027, hence the total effect from X6 was 0.059 (5.9 %). 5. Conclusions Based on the result, it can be concluded that the exogenous variable that influence the success of Raskin program in Central Lombok Regency was the precise of rice quality variable, with the 0.383 (38.3 %) path coefficient value. 6. References Ahn, J. (2002). Beyond Single Equation Regression Analysis: Path Analysis and Multi-Stage Regression Analysis. American Journal of Pharmaceutical Education Vol.66. Spring.

Eshima, N., Tabata, M., and Zhi, G. (2001). Path analysis with logistic regression models: Effect analysis of fully recursive causal systems of categorical variables. Journal Japan Statist Social. Volume 31. No.1. Page 1 14.

Hastuti., Sulaksono B., Mawardi S. (2012). Implementation Effectiveness Overview Raskin Achieving Six Right. Smeru Research. Lumenta, C. Y., Kekenusa, J. S., Hatidja, D. (2012). Path Analysis of Factors Causes of Crime in Manado. Scienties Journal. Volume 12 No. 12. October.

Mishra, D. P., and Min, J. (2010). Analyzing The Relationship Between Dependent and Independent Variables In Marketing: A Comparison of Multiple Regression With Path Analysis. Journal Innovative Marketing. Volume 6. Issue 3. Roflin, E. (2009). Trimming Method on Path Analysis Causal Model Determining the General Allocation Fund City in the South Sumatra Province. Science Research Journal. A Edition. 0912- 01-2. December. Sarwono, J. (2007). Path Analysis For Business Research With SPSS. Andi. Yogyakarta.

Sudaryono. (2010). Application Path Analysis By Order Variable Placement. Journal of Educational, Evaluation. Volume 1. No 1. June.

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