Abstract
During the past decades, land regularization and administration programs have become part of the development tool box to improve the welfare of rural farmers. However, these programs cannot produce the intended economic benefits if people choose not to enroll in them. In this context, this paper investigates how household, plot, and community characteristics, as sources of informal property rights, explain the low take-up (less than 15%) of land regularization under the National Rural Land Information and Management System Program (Sigtierras Program) in Ecuador. Using newly collected survey data combined with census tract-level data and administrative project data, we assess how different household-, plot-, and community-level characteristics affect the likelihood of take-up of legal support to regularize tenancy offered by Sigtierras. Our results suggest that some household-specific and community-level characteristics that enhance a household’s ownership claim are important determinants of take-up. Our empirical results provide insights to inform targeting strategies to enhance the effectiveness and efficiency of future land regularization efforts in Ecuador.
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Notes
Cantons are the second-level subdivisions of Ecuador below provinces and are equivalent to municipalities. There are 226 cantons in the country. Cantons are further subdivided into parishes, which are classified as urban or rural.
In order to do this, a cadastral sweep collects information from each parcel using a unique parcel record card (ficha predial).
The plot legal status is self-reported by the household’s head. Thus, it is possible that some households opted not to provide information or that the status was unknown.
The data from SIOL contains information from the cadastral sweep and the number of times and dates when plot holders contacted SIOL.
The household survey was collected as baseline data for the purposes of an impact evaluation of the Sigtierras Program. For the impact evaluation design, the universe of treated and control cantons was 58 and 105, respectively. Eighteen cantons belonging to 8 provinces (Azuay, Chimborazo, Imbabura, Loja, Pichincha, Esmeraldas, Morona Santiago, and Santo Domingo) were selected for data collection: 9 as control and 9 as treated. To select the households that were surveyed from the 9 treated cantons, 110 rural census tracts were selected (from a universe of 1224) through different sampling methods (stratification, clustering, and multi-stage). Finally, once the census tracts were identified, 1360 households were selected using random sampling.
The 2010 Population and Housing Census included 40,650 census tracts, yielding an average of 94 households per census tract.
This does not pose any bias in our analysis given that in our sample, regardless of the number of plots under informal legal tenancy, a household decision is always binary, that is when it requested legal support to regularize, it did so for all of its plots with tenure irregularities.
Appendix 1 Table 10 presents a distribution of the households and plots among the 9 cantons per census tracts.
Typically, this is land held by a family that has been subdivided among its members, but that it has not been properly regularized.
This will be our dependent variable in the econometric model to be estimated.
Personal communication from Antonio Bermeo, former Executive Director of Sigtierras, (April 4th, 2019).
We calculate the effect using the equation below:
head_female – (head_fem*years)*(18.3) – (head_female*age)*(52.1) - (head_female*social_program)*(.64) – (head_female*ethnic_group)*(.12) – (head_female*tot_notitle1)*(.5) -.(head_female*tot_notitle2)*(.34) – (head_female*tot_notitle3)*(.08) + (head_female*tot_unreg)*(.13)
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Acknowledgments
The authors would like to thank Marisol Inurritegui for providing institutional and project specific knowledge, as well as Hector Valdes Conroy, Rosangela Bando, Jose Martinez and Giulia Zane for helpful comments. The authors are grateful for the support of the Rural Development, Environment and Disaster Risk Management Division of the Inter-American Development Bank and the Sigtierras program personnel in conducting the study. Finally we thank two anonymous reviewers and the editor for their helpful comments.
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Appendices
Appendix 1
Appendix 2
Principal Component Analysis
PCA is an unsupervised machine learning technique that provides a low-dimensional set of features from a larger dataset. More technically, PCA will give a new set of uncorrelated, orthogonal components that maximize the variation of the original data. In the context of linear regression, PCA is useful because it lessens possible issues that could arise from multicollinearity, including imprecise estimates. Given that the principal components will account for a certain percentage of the original observations, we can keep up to a certain number of components and then obtain a varimax rotation of each variable associated with each of the first principal components.
We applied the PCA to a total of 65 observations. It is important to mention that originally, the census data consisted in 80 variables; however, we dropped variables with zeros and ones because this technique is not suitable for categorical variables. Also, some pairwise variables showed either a perfect or an extremely high correlation (close to 1); thus, we keep just one of these variables to avoid redundancy in the data. As is common practice, we standardize each variable to have mean zero and standard deviation one.
Choosing how many principal components two retain is difficult as there is no well-established rule of thumb. In this study, we opted to use two methods. First, we use the standard scree plot method. To choose a cut-off, a visual inspection of the scree plot is typically done to determine the moment where the variance starts to decrease considerably. This is known as the elbow in the plot (see James et al. 2013).
Figure 1 shows the scree plot from our PCA. As we mentioned before, the first and second principal components explain 27 and 14.5% of the data, respectively. After the second component, the variance drops off forming an elbow. From the third principal component on each additional component explain less of the variance of the data until the percentage explained by the last components is close to zero. For instance, the tenth principal component only accounts for 2.7% of the variation of the data. Based to the scree plot, we could retain the first two principal components as predictors. The main concern with this approach is that a visual analysis could still constitute an ad hoc decision. According to James et al., we can retain more principal components if we find interesting features in them and they have a clear interpretation.
Scree plot from PCA
The loading vectors for the first eight principal components explain 75% of the cumulative variance of the data. Only loadings which are equal or greater than 0.22 for each principal component are included. It should be noted that, together, the first three principal components explain close to 49% of the total variation, that is, almost half of the variation of the data. The first component accounts for 27% of the total variation of the data, while the second principal component explains 14.5%. Principal component three accounts for approximately 8% of the variation of the data. The rest of the principal components account individually for less than 8% of the variation of the data. Therefore, we keep the first three principal components because they are the ones that have a more straightforward interpretation.
Appendix 3
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Corral, L.R., Montiel Olea, C.E. What Drives Take-up in Land Regularization: Ecuador’s Rural Land Regularization and Administration Program, Sigtierras. J Econ Race Policy 3, 60–75 (2020). https://doi.org/10.1007/s41996-019-00041-1
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DOI: https://doi.org/10.1007/s41996-019-00041-1