Home » Topics » Wealth Index » Calculating a Representative Wealth Index for Clusters Using DHS Sample Weights
| Calculating a Representative Wealth Index for Clusters Using DHS Sample Weights [message #30598] |
Mon, 06 January 2025 01:10  |
Rean
Messages: 4
Registered: January 2025
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Dear DHS team and community members,
I have a question regarding applying sample weights (hv005) in the DHS datasets, particularly when calculating a representative Wealth Index (WI) for each cluster.
As I understand, the sample weights are provided at the cluster level, meaning all households within the same cluster share the same weight. However, this presents a challenge when trying to calculate a single WI value to represent the cluster. Without specific information about the sampling strategy or the relative importance of each household within the cluster, it seems that a simple arithmetic mean of the household WI values is the most straightforward approach.
However, this method assumes equal importance for all households within the cluster and does not account for potential variance within the cluster itself. As a result, it might overlook important intra-cluster disparities and may not fully represent the overall socioeconomic context of the cluster.
Given these limitations, I would like to ask:
1. Is there a recommended approach to aggregate household WI values into a single, representative cluster value while respecting the sampling design?
2. Are there additional resources or considerations regarding how intra-cluster variance can be incorporated into such calculations?
I would greatly appreciate any guidance or advice on this matter. Thank you for your time and for providing such valuable data for research.
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| Re: Calculating a Representative Wealth Index for Clusters Using DHS Sample Weights [message #30632 is a reply to message #30616] |
Sun, 12 January 2025 04:14  |
Rean
Messages: 4
Registered: January 2025
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Member |
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Dear DHS Team and Community Members,
Thank you for your response and the helpful guidance regarding calculating the Wealth Index (WI) at the cluster level. Based on some challenges I encountered during my analysis, I have a couple of follow-up questions.
1. Overlapping Cluster Areas:
Given that GPS coordinates provided for clusters can have up to a 10km positional error, we often use a 10km × 10km square area centered around the given coordinates to ensure that the actual sampling points fall within this boundary. However, after defining these 10km squares, I observed that many clusters have spatially very close coordinates, resulting in significant overlap--sometimes as high as 80-90% between their corresponding areas. Despite this overlap, the mean WI values for these overlapping clusters often differ significantly. This raises concerns about the representativeness of using the mean WI as the cluster-level indicator. I would like to ask if DHS considered such overlapping cluster areas during the survey design, and if so, how such scenarios are typically handled to ensure the validity of cluster-level WI values.
2. Weighted vs. Unweighted Mean:
In my work on predicting cluster-level WI using remote sensing data, I noticed that using the weighted mean of WI values often leads to better results than the unweighted mean. However, you mentioned previously that the unweighted mean is the recommended approach for cluster-level WI calculations. Could this observation be a coincidence, or does it suggest that the weighted WI might still have some relevance or utility at the cluster level, despite being theoretically less representative in this context?
I greatly appreciate any insights or advice you could provide on these issues. Thank you for your time and support in helping researchers like me better understand and utilize DHS data.
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| Re: Calculating a Representative Wealth Index for Clusters Using DHS Sample Weights [message #30633 is a reply to message #30616] |
Sun, 12 January 2025 04:14 |
Rean
Messages: 4
Registered: January 2025
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Member |
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Dear DHS Team and Community Members,
Thank you for your response and the helpful guidance regarding calculating the Wealth Index (WI) at the cluster level. Based on some challenges I encountered during my analysis, I have a couple of follow-up questions.
1. Overlapping Cluster Areas:
Given that GPS coordinates provided for clusters can have up to a 10km positional error, we often use a 10km × 10km square area centered around the given coordinates to ensure that the actual sampling points fall within this boundary. However, after defining these 10km squares, I observed that many clusters have spatially very close coordinates, resulting in significant overlap--sometimes as high as 80-90% between their corresponding areas. Despite this overlap, the mean WI values for these overlapping clusters often differ significantly. This raises concerns about the representativeness of using the mean WI as the cluster-level indicator. I would like to ask if DHS considered such overlapping cluster areas during the survey design, and if so, how such scenarios are typically handled to ensure the validity of cluster-level WI values.
2. Weighted vs. Unweighted Mean:
In my work on predicting cluster-level WI using remote sensing data, I noticed that using the weighted mean of WI values often leads to better results than the unweighted mean. However, you mentioned previously that the unweighted mean is the recommended approach for cluster-level WI calculations. Could this observation be a coincidence, or does it suggest that the weighted WI might still have some relevance or utility at the cluster level, despite being theoretically less representative in this context?
I greatly appreciate any insights or advice you could provide on these issues. Thank you for your time and support in helping researchers like me better understand and utilize DHS data.
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| Re: Calculating a Representative Wealth Index for Clusters Using DHS Sample Weights [message #30640 is a reply to message #30616] |
Mon, 13 January 2025 00:14 |
Rean
Messages: 4
Registered: January 2025
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Member |
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Thank you so much for your response and the helpful guidance regarding calculating the Wealth Index (WI) at the cluster level. Based on some challenges I encountered during my analysis, I have a couple of follow-up questions.
1. Overlapping Cluster Areas:
Given that GPS coordinates provided for clusters can have up to a 10km positional error, we often use a 10km × 10km square area centered around the given coordinates to ensure that the actual sampling points fall within this boundary. However, after defining these 10km squares, I observed that many clusters have spatially very close coordinates, resulting in significant overlap--sometimes as high as 80-90% between their corresponding areas. Despite this overlap, these clusters' mean WI values often differ significantly. This raises concerns about the representativeness of using the mean WI as the cluster-level indicator. I want to ask if DHS considered such overlapping cluster areas during the survey design. If so, how such scenarios are typically handled to ensure the validity of cluster-level WI values?
2. Weighted vs. Unweighted Mean:
In my work on predicting cluster-level WI using remote sensing data, I noticed that using the weighted mean of WI values often leads to better results than the unweighted mean. However, you mentioned previously that the unweighted mean is the recommended approach for cluster-level WI calculations. Could this observation be a coincidence, or does it suggest that the weighted WI might still have some relevance or utility at the cluster level, despite being theoretically less representative in this context?
I greatly appreciate any insights or advice you could provide on these issues. Thank you for your time and support in helping researchers like me better understand and utilize DHS data.
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