FACTORS AFFECTING LAND ACCESS FOR RESIDENTS IN CAO LÃNH CITY, ĐỒNG THÁP PROVINCE

FACTORS AFFECTING LAND ACCESS FOR RESIDENTS IN CAO LÃNH CITY, ĐỒNG THÁP PROVINCE

FACTORS AFFECTING LAND ACCESS FOR RESIDENTS IN CAO LÃNH CITY, ĐỒNG THÁP PROVINCE

Ngô Thạch Thảo Ly

La Văn Hùng Minh

Đồng Tháp University

Abstract:

The study aims to identify the factors influencing the land access of residents in Cao Lãnh City, Đồng Tháp Province. Data collected from citizen surveys and consultations with management officials have identified four main groups of factors affecting land access: regulations and policies, administrative procedures on land, land finance, and customs. The regression analysis model was used to evaluate the influence of each of these factors on residents’ land access.

The results indicate that three groups of factors—”regulations and policies,” “land finance,” and “customs”—all have a positive correlation with residents’ land access, with “customs” being identified as the most influential factor and land finance as the least influential factor.

Keywords: Đồng Tháp, land access, land use rights.

I. INTRODUCTION

The effective use and management of land play a crucial role in maximizing the potential benefits of successful and sustainable socio-economic development. An efficient land market is a necessary condition to achieve this goal. The condition for an efficient land market is that land access must be cost-effective, easy, and swift [1]. Cao Lãnh City, with its many potentials and advantages in natural conditions, is very conducive to the development of commerce, services, and tourism. In recent years, the locality has attracted many investment sources, along with the urbanization process and infrastructure projects being implemented, which have boosted the demand for land access and the exercise of land use rights.

Improving the land access index aims to create a competitive advantage for the locality in promoting investment, attracting economic sectors to implement production and business investment plans in the City. Identifying the factors affecting land access aims to improve the land access index while serving as a basis for proposing solutions to enhance the land access index and perfecting the legal basis regarding land use rights.

II. RESEARCH METHODS

Data Collection Method: Primary data was collected through survey questionnaires from households and individuals using land in Cao Lãnh City, Đồng Tháp Province. For the exploratory factor analysis model, according to Hair et al. (2009) [3], the sample size is determined based on: (1) the minimum (min =50) and (2) the number of variables. The ratio of the sample size to the number of variables analyzed is 5:1 or 10:1.

This means that each observed variable needs a minimum of 5 samples, preferably 10 samples. For the applied model, the minimum observed sample size is 160. To ensure minimum accuracy and exclude ineligible samples, the selected sample size will be 300. The questionnaire includes four independent variables: (1) State policies; (2) Administrative procedures on land; (3) Land finance; (4) Customs, with a Likert scale from 1 (strongly disagree) to 5 (strongly agree). The study also includes direct consultations with 20 land management officials from related agencies in Đồng Tháp to better understand the factors affecting land management and use.

Data Processing Method: The collected data is compiled in Excel and processed using R software with the following analyses and statistical tests: Cronbach’s Alpha coefficient analysis to exclude unsuitable variables and limit unnecessary variables during the research process. After analyzing Cronbach’s Alpha reliability coefficient, the scales are further evaluated by Exploratory Factor Analysis (EFA) to condense and merge variables into factors. Finally, linear regression analysis is conducted to evaluate the impact level of factors on land access.

III. RESEARCH RESULTS AND DISCUSSION

1. Scale Construction

The scale and observed variables evaluating the land access of households and individuals using land in Cao Lãnh City are inherited and selected from Michael Taylor’s (2009) research report and FAO’s (2012) [2] guidelines on responsible land governance. The study also consulted with 20 local experts and land management officials. The research has identified four main groups of factors affecting land access: State policies (CS), Administrative procedures on land (TTHC), Land finance (TC), and Customs (TQ).

Table 1. Scale and observed variables evaluating the land access of residents

2. Scale Quality Assessment

The quality of the scale was assessed through Cronbach’s Alpha reliability coefficient to exclude unnecessary variables. Variables with a total correlation coefficient of less than 0.3 were considered unnecessary variables, and the standard for selecting the scale was a Cronbach’s Alpha reliability coefficient of 0.6 or higher [6]. The results of the scale quality assessment showed that all four scales (CS, TTHC, TC, and TQ) met the requirements for the exploratory factor analysis model, with Cronbach’s Alpha coefficients all greater than 0.6 and the variables having total correlation coefficients greater than 0.3.

Table 2. Summary of scale quality assessment results

3. Factor Analysis

Assessment of Data Suitability

Table 3. KMO and Bartlett’s Test Results

The KMO test result of 0.86 satisfies the condition for applying the exploratory factor analysis model, and the Bartlett’s test shows a p-value of 0.000. This indicates that the four factors (CS, TTHC, TC, and TQ) are closely correlated with the land access of land users.

3.1. Identification of Main Factors

In EFA analysis, the basis for identifying main factors is the use of Eigenvalue. According to Kaiser’s criterion, the main factors must have an Eigenvalue > 1 [1].

Table 4. Eigenvalue and Variance Explained by Factors

According to Kaiser’s criterion, four factors were identified. The first factor, with an Eigenvalue of 6.47, explains 6.47/16 = 40.43% of the total variance. The second factor, with an Eigenvalue of 1.798, explains 1.798/16 = 11.24% of the total variance. Similarly, the fourth factor, with an Eigenvalue of 1.167, explains 1.167/16 = 7.29% of the total variance. These four factors together explain 67.83% of the total variance of the model.

3.2. Identification of Constituent Variables of Extracted Factors

After assessing the reliability of the scales, exploratory factor analysis was conducted using Principal Component Analysis extraction and Varimax rotation methods to identify the constituent variables of the extracted factors. The extraction method only retains variables with a factor loading coefficient greater than 0.55 [3]. The results of factor extraction and the identification of constituent variables for each factor are detailed in Table 5.

Through the rotated factor matrix, the initial factors were rearranged with 15 variables (excluding variable TC1 due to a factor loading coefficient less than 0.55) into 4 factors. The variables in each factor were arranged in descending order from top to bottom and from left to right according to the importance of each variable and each factor.

+ Factor Group 1 (RC1) consists of 7 variables: CS1, CS2, CS3, CS4, TTHC1, TTHC3, and TTHC4. Since these variables do not follow the initial scale arrangement, the scale quality must be reassessed before conducting multivariate regression analysis. The reassessment results showed that the scale’s Cronbach’s Alpha reliability coefficient was 0.84, and no variable had a total correlation coefficient less than 0.3. Thus, this scale meets the requirements for the exploratory factor analysis model. The variables in this scale relate to state land policies and administrative procedures on land. Therefore, the first factor is called “Regulations and Policies” (CS).

Bảng 5. Kết quả trích nhân tố

+ Factor Group 2 (RC3) consists of 3 variables: TC2, TC3, and TC4. These variables are related to individual financial capability in the survey. This factor is named “Land Finance” (TC).

+ Factor Group 3 (RC2) consists of 3 variables: TQ2, TQ3, and TQ4. These variables are related to customs in land use. This factor is named “Customs” (TQ).

+ Factor Group 4 (RC4) consists of 2 variables: TQ1 and TTHC1. These variables are related to customs and administrative procedures on land. This factor is named “Other Factors” (KH).

4. Linear Regression Analysis

The research model proposes four factors affecting land access: CS, TC, TQ, and KH. The model and hypotheses are tested using linear regression analysis to examine the importance of each factor group influencing the dependent variable (land access).

The linear regression analysis results for the four factors RC1 (CS), RC2 (TQ), RC3 (TC), and RC4 (KH) are detailed in Table 6. From Table 6, R2 = 0.6836 and adjusted R2 = 0.678 indicate a good fit of the model. However, factor RC4 is not statistically significant (t = 0.896 < 2 and p = 0.371). As a result, RC4 is excluded from the multivariate regression model.

Table 6. Regression Analysis Results of the Four Variables RC1, RC2, RC3, and RC4

After excluding factor RC4 from the model, linear regression analysis was conducted on the remaining three variables to find the variance explained by the model. The analysis results are presented in Table 7.

Table 7. Regression Analysis Results of the Three Variables RC1, RC2, and RC3

The regression analysis results for the three variables RC1, RC2, and RC3 also yield an R2 (R2 = 0.6825) similar to the previous model. This indicates that the three independent variables CS, TQ, and TC explain approximately 68% of the variance in the dependent variable. In other words, 68% of the variation in land access and the exercise of land use rights by residents in Cao Lãnh City, Đồng Tháp Province is determined by three factors: Regulations and Policies, Land Finance, and Customs. All three variables are statistically significant, with t-values greater than 2 and p-values close to zero. The linear equation predicting land access is:

LV = -0.9368 + 0.3902CS + 0.4015TQ + 0.3782TC

The analysis of regression coefficients indicates that all three independent factors—”Regulations and Policies,” “Land Finance,” and “Customs”—have a positive correlation (positive β coefficients) with the level of land access for land users, with very small p-values for all three variables and the constant being statistically significant with α coefficient = -0.9368. The β coefficients indicate the influence of the independent variables on the dependent variable. Comparing the β values shows that “Customs” has the most significant impact on land access and the exercise of land use rights (β = 0.4015), followed by “Regulations and Policies” (β = 0.3902), and finally “Land Finance” (β = 0.3782).

IV. CONCLUSION

The study identified three main groups of factors: “Regulations and Policies,” “Land Finance,” and “Customs.”

All three factors play an important role in influencing land access for residents in Cao Lãnh City, Đồng Tháp Province, with “Customs” being the most influential factor.

To improve land access for residents, the locality needs to adjust policies and administrative procedures, review financial obligations related to land exploitation and use, and implement solutions to limit land speculation. Specifically, understanding and respecting local customs will be key to enhancing land access for residents.

References

1. Courtney, M. G. R. (2013). Determining the number of factors to retain in EFA: Using the SPSS R-menu v2.0 to make more judicious estimations. Practical Assessment, Research & Evaluation.
2. FAO (2012). Voluntary guidelines on the responsible governance of tenure of land, fisheries and forests in the context of national food security. Food and Agriculture Organization of the United Nations, Rome.
3. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Pearson Prentice Hall.
4. Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark iv. Educational and Psychological Measurement.
5. World Bank (2014). Comprehensive Report on Land Management Information Disclosure in Vietnam. Hồng Đức Publishing House, Hanoi.
6. Hoàng Trọng, Chu Nguyễn Mộng Ngọc (2008). Data analysis in research with SPSS (volume 2). Hồng Đức Publishing House, Ho Chi Minh City.

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