Table 3.

Summary Statistics for Comparable Variables Used in Model to Impute Consumption, by Survey

(1)(2)(3)(4)(5)(6)
2018/19 NLSS (%)2018/19 GHS (%)Difference 2018/19 (percentage points)2018/19 GHS post-planting (%)2018/19 GHS post-harvesting (%)Difference (percentage points)
North-Central14.0714.0714.0814.07−0.01
North-East8.288.288.298.27−0.02
North-West19.5119.5119.5019.530.03
South-East13.1313.1313.2013.07−0.13
South-South17.9017.9017.9117.88−0.03
Gender81.1780.17−1.0080.4379.90−0.53
Dependency Ratio42.4443.090.6642.8843.300.42
Employed: waged19.7617.21−2.5542.6436.90−5.74
Employed: nonfarm37.1839.742.5716.2418.171.93
Main floor material: cement70.3071.391.0960.5260.620.10
Main cooking fuel: wood59.0660.571.5171.3771.420.05
No toilet25.0729.033.9615.0716.341.27
Imported rice43.8547.133.2846.9247.340.42
Beef45.3543.91−1.4442.3745.443.07
Fish-fresh18.1415.71−2.4329.0928.97−0.12
Recharge cards85.1385.07−0.0684.2485.881.64
Air conditioner2.571.68−0.891.691.66−0.03
Washing machine2.161.96−0.201.981.95−0.03
Cars and other vehicles8.479.991.5210.009.98−0.02
Generator24.9625.490.5225.5025.48−0.02
Microwave2.471.74−0.731.761.73−0.03
TV Set48.0347.73−0.2947.7147.760.05
Computer4.714.17−0.554.194.15−0.04
(1)(2)(3)(4)(5)(6)
2018/19 NLSS (%)2018/19 GHS (%)Difference 2018/19 (percentage points)2018/19 GHS post-planting (%)2018/19 GHS post-harvesting (%)Difference (percentage points)
North-Central14.0714.0714.0814.07−0.01
North-East8.288.288.298.27−0.02
North-West19.5119.5119.5019.530.03
South-East13.1313.1313.2013.07−0.13
South-South17.9017.9017.9117.88−0.03
Gender81.1780.17−1.0080.4379.90−0.53
Dependency Ratio42.4443.090.6642.8843.300.42
Employed: waged19.7617.21−2.5542.6436.90−5.74
Employed: nonfarm37.1839.742.5716.2418.171.93
Main floor material: cement70.3071.391.0960.5260.620.10
Main cooking fuel: wood59.0660.571.5171.3771.420.05
No toilet25.0729.033.9615.0716.341.27
Imported rice43.8547.133.2846.9247.340.42
Beef45.3543.91−1.4442.3745.443.07
Fish-fresh18.1415.71−2.4329.0928.97−0.12
Recharge cards85.1385.07−0.0684.2485.881.64
Air conditioner2.571.68−0.891.691.66−0.03
Washing machine2.161.96−0.201.981.95−0.03
Cars and other vehicles8.479.991.5210.009.98−0.02
Generator24.9625.490.5225.5025.48−0.02
Microwave2.471.74−0.731.761.73−0.03
TV Set48.0347.73−0.2947.7147.760.05
Computer4.714.17−0.554.194.15−0.04

Source: Authors’ analysis based on data from 2019/19 Nigerian Living Standards Survey and 2018/19 General Household Surveys

Note: the table shows the average value of nonmonetary indicators used in the consumption model for the purpose of survey-to-survey imputations. Data are from the 2018/19 NLSS and2018/19 GHS. For GHS data the average reflects the average value between two visits (post-planting and post-harvesting). For 2018/19 GHS zone weights are adjusted to match 2018/19 official NLSS zone population shares and ensure comparability. Columns 3–6 show the summary stats for the two visits in the 2018/19 GHS, to check whether nonmonetary indicators are subject to seasonal variation over the year and could bias the imputed estimates.

Table 3.

Summary Statistics for Comparable Variables Used in Model to Impute Consumption, by Survey

(1)(2)(3)(4)(5)(6)
2018/19 NLSS (%)2018/19 GHS (%)Difference 2018/19 (percentage points)2018/19 GHS post-planting (%)2018/19 GHS post-harvesting (%)Difference (percentage points)
North-Central14.0714.0714.0814.07−0.01
North-East8.288.288.298.27−0.02
North-West19.5119.5119.5019.530.03
South-East13.1313.1313.2013.07−0.13
South-South17.9017.9017.9117.88−0.03
Gender81.1780.17−1.0080.4379.90−0.53
Dependency Ratio42.4443.090.6642.8843.300.42
Employed: waged19.7617.21−2.5542.6436.90−5.74
Employed: nonfarm37.1839.742.5716.2418.171.93
Main floor material: cement70.3071.391.0960.5260.620.10
Main cooking fuel: wood59.0660.571.5171.3771.420.05
No toilet25.0729.033.9615.0716.341.27
Imported rice43.8547.133.2846.9247.340.42
Beef45.3543.91−1.4442.3745.443.07
Fish-fresh18.1415.71−2.4329.0928.97−0.12
Recharge cards85.1385.07−0.0684.2485.881.64
Air conditioner2.571.68−0.891.691.66−0.03
Washing machine2.161.96−0.201.981.95−0.03
Cars and other vehicles8.479.991.5210.009.98−0.02
Generator24.9625.490.5225.5025.48−0.02
Microwave2.471.74−0.731.761.73−0.03
TV Set48.0347.73−0.2947.7147.760.05
Computer4.714.17−0.554.194.15−0.04
(1)(2)(3)(4)(5)(6)
2018/19 NLSS (%)2018/19 GHS (%)Difference 2018/19 (percentage points)2018/19 GHS post-planting (%)2018/19 GHS post-harvesting (%)Difference (percentage points)
North-Central14.0714.0714.0814.07−0.01
North-East8.288.288.298.27−0.02
North-West19.5119.5119.5019.530.03
South-East13.1313.1313.2013.07−0.13
South-South17.9017.9017.9117.88−0.03
Gender81.1780.17−1.0080.4379.90−0.53
Dependency Ratio42.4443.090.6642.8843.300.42
Employed: waged19.7617.21−2.5542.6436.90−5.74
Employed: nonfarm37.1839.742.5716.2418.171.93
Main floor material: cement70.3071.391.0960.5260.620.10
Main cooking fuel: wood59.0660.571.5171.3771.420.05
No toilet25.0729.033.9615.0716.341.27
Imported rice43.8547.133.2846.9247.340.42
Beef45.3543.91−1.4442.3745.443.07
Fish-fresh18.1415.71−2.4329.0928.97−0.12
Recharge cards85.1385.07−0.0684.2485.881.64
Air conditioner2.571.68−0.891.691.66−0.03
Washing machine2.161.96−0.201.981.95−0.03
Cars and other vehicles8.479.991.5210.009.98−0.02
Generator24.9625.490.5225.5025.48−0.02
Microwave2.471.74−0.731.761.73−0.03
TV Set48.0347.73−0.2947.7147.760.05
Computer4.714.17−0.554.194.15−0.04

Source: Authors’ analysis based on data from 2019/19 Nigerian Living Standards Survey and 2018/19 General Household Surveys

Note: the table shows the average value of nonmonetary indicators used in the consumption model for the purpose of survey-to-survey imputations. Data are from the 2018/19 NLSS and2018/19 GHS. For GHS data the average reflects the average value between two visits (post-planting and post-harvesting). For 2018/19 GHS zone weights are adjusted to match 2018/19 official NLSS zone population shares and ensure comparability. Columns 3–6 show the summary stats for the two visits in the 2018/19 GHS, to check whether nonmonetary indicators are subject to seasonal variation over the year and could bias the imputed estimates.

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