(February draft)

Transcription

1 For an International NGO Background statistics, cross tabs, summaries, graphs, t-tests and regression analysis for Nepal response survey data (February draft) Contents Confidence level/statistical relevance Background statistics a. Demographics and disability Red/distribution card received a_i. Cross-tab: Red card/caste a_ii. Regression analysis: Red card/caste b_i. Cross-tab: Red card/male or female of head of household b_ii. Regression analysis: Red card/male or female headed household c_i. Cross-tab: Red card/location type c_ii. Regression analysis: Red card/location type d_i. Cross-tab: Red card/ward d_ii. Regression analysis: Red card/ward e_i. Red card/damage to house e_ii. Regression analysis: Red card/damage to house f_i. Cross-tab: Red card/where are you living now? (across two tables) f_ii. Regression analysis: Red card/where are you living now g. Overall regression analysis for receipt of red card and the variables in section House or partially a_i. Cross-tab: House /where are you living now (across two tables) a_ii. Regression analysis: Damage to house/where are you living now b_i. Cross-tab: House /house structure now (over two tables) b_ii. Regression analysis: House / house structure now c. Cross-tab: Damage to house by Caste d_i. Cross-tab: Damage to house by male or female head of household d_ii. Regression analysis: House /male or female head of household e_i. Cross-tab: Damage to house by location type e_ii. Regression analysis: House /location type

2 3f. Overall regression analysis for house and the variables in section House construction before and after a_i. Cross-tab: House construction before/now (over two tables) a_ii. Regression analysis for house before/house now House improved a_i. Cross-tab: House improved/location type a_ii. Regression analysis for house improved/location type b_i. Cross-tab: House improved/caste b_ii. Regression analysis for house improved/caste c_i. Cross tab: House improved/male or female head of household c_ii. Regression analysis for house improved/male or female headed household d. Regression analysis for section Land ownership a. Summary: Land ownership overview b_i. Cross-tab: Land ownership/caste b_ii. Regression analysis for land ownership/caste c_i. Cross-tab: Land ownership by male or female head of household c_ii. Regression analysis for land ownership by male or female head of household d_i. Cross-tab_i: Land ownership/location type d_ii. Regression analysis for land ownership/location type e_i. Cross-tab: Land ownership by damage to house e_ii. Regression analysis for land ownership/damage to house

3 Confidence level/statistical relevance The confidence level is throughout the findings is assumed to be 95% confidence level and 5% margin of error, unless otherwise stated. Where findings are disaggregated in the document, this may be described as being 90% confidence level and 10% margin of error. 1. Background statistics 1a. Demographics and disability Average household size: 4.5 Position Age Sex* Disability Count Mean age Min Max Male % Female % Before After Change Position 1 (head) Position 2 Position 3 Position 4 Position 5 Position 6 Position 7 Position % 27.30% 8.38% 9.56% 1.18% incr % 71.50% 3.72% 5.19% 1.47% Incr % 44.28% 2.93% 3.37% 0.44% incr % 56.08% 2.64% % % 45.50% 3.08% 1.68% 1.4% decr % 53.68% 0.52% % decr % 51.68% 5.61% 3.37% 2.24% decr % 58.53% 1 1 0% 8 Position % 40% 0 0-3

4 9 Position % 36.36% Position % 100% Position % 50% Position % 0% *Where the percentage of males and females does not add up to 100%, this is because there a small number of respondents who replied other to this question and this is not included in the results as it accounted for a very small proportion of respondents. 2. Red/distribution card received 2a_i. Cross-tab: Red card/caste Red card received: 1 = Indigenous/Janajati (Gurung, Magar, Tamang, Newar, Chepang,) 2 = Caste group (Brahman, Chhetri, Sanyasi 3 = Dalits Total Yes count No count Don t know count Total count

5 Percentage: Yes % No % Don t know % a_ii. Regression analysis: Red card/caste Comment: the p value of and R2 value of 0.11% shows that there is no statistically relevant relationship between receipt of a red card and Caste. Source SS df MS Number of obs = F(1, 752) = 0.82 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] caste _cons b_i. Cross-tab: Red card/male or female of head of household 1 =male 2 = female Total Red card received: Yes count No count

6 Don t know count Total count Percentage Yes % No % Don t know % Total b_ii. Regression analysis: Red card/male or female headed household Comment: the p value of and R2 value of 0.29% shows that there is no statistically relevant relationship between receipt of a red card and male/female headed household. Source SS df MS Number of obs = F(1, 754) = 2.17 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] sex_head_household _cons c_i. Cross-tab: Red card/location type 6

7 Red card received: Urban (For Ktm) Semiurban (For Ktm) Rural (For Ktm) Lowland (For other districts) Midland (For other districts) Highland (For other districts) Total Yes count No count Don t know count Total count Percentage: Yes % No % Don t know % c_ii. Regression analysis: Red card/location type Comment: the p value of and R2 value of 0.17% shows that there is no statistically relevant relationship between receipt of a red card and location type. Source SS df MS Number of obs = F(1, 755) = 1.28 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] location_type _cons

8 d_i. Cross-tab: Red card/ward Please note, there was no ward 13. Red card recei ved:: Yes count No count Don t know count Total count Tot al Percentage: Yes % No % Don t know % d_ii. Regression analysis: Red card/ward 8

9 Comment: the p value of and R2 value of 0.00% shows that there is no statistically relevant relationship between receipt of a red card and ward. Source SS df MS Number of obs = F(1, 755) = 0.00 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] ward_no -5.43e _cons e_i. Red card/damage to house Red card received: Total/fully (not safe) Minor damage Not Total Yes No Don t know Total count Percentages: Yes No

10 Don t know e_ii. Regression analysis: Red card/damage to house. Comment: the p value of and R2 value of 0.04% shows that there is no statistically relevant relationship between receipt of a red card and damage to house. Source SS df MS Number of obs = F(1, 755) = 0.28 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] damage_to_house _cons f_i. Cross-tab: Red card/where are you living now? (across two tables) Red card received: Same place as before (not repaired) Own house (new) Renting Living with relatives Second home Yes No Don t know Total

11 Percentages: Yes No Don t know Red card received: Living with another family Temporary shelter Camp Same place as before (repaired) Other Grand total Yes No Don t know Total Percentages: Yes No Don t know f_ii. Regression analysis: Red card/where are you living now Comment: the p value of and R2 value of 0.05% shows that there is no statistically relevant relationship between receipt of a red card and where are you living now. Source SS df MS Number of obs = F(1, 754) = 0.40 Model Prob > F = Residual R-squared =

12 Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] living_now_change _cons g. Overall regression analysis for receipt of red card and the variables in section 2 Comment: regression analysis does not show a statistically significant relationship between receipt of a red card and the other variables in this section. This was the case when considering each variable separately (as above) and is reflected when they are considered together in one model (as below). The P value does not show significance and, in addition, the R2 value has been low for each variable (less than 1%), meaning that this model explains less than 1% of the variance in the data for receipt of a red card. Source SS df MS Number of obs = F(6, 745) = 0.84 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = red_card Coef. Std. Err. t P> t [95% Conf. Interval] caste sex_head_household location_type ward_no -6.97e damage_to_house living_now_change _cons

13 3. House or partially 3a_i. Cross-tab: House /where are you living now (across two tables) Damage to house Same place as before (not repaired) Own house (new) Renting Living with relatives Second home Total/Fully (not safe) Minor damage Not Total Percentages: Total/Fully

14 (not safe) Minor damage Not Damage to house Living with another family Temporary shelter Camp Same place as before (repaired) Other Grand total Total/Fully (not safe) Minor damage Not Total Percentages: Total/Fully

15 (not safe) Minor damage Not a_ii. Regression analysis: Damage to house/where are you living now Comment: the p value of shows that there is a statistically relevant relationship between damage to house and where the respondent is living now. The R2 value is 2.27% which is relatively low - the more variance that is accounted for by the regression model, the closer the data points will fall to the fitted regression line giving a higher R squared %. Note, R 2 does not indicate whether the independent variables are a cause of the changes in the dependent variable. The R2 value in this case shows that this model accounts for a relatively small variation in the results (the data points do not fit closely to the fitted regression line). Source SS df MS Number of obs = F(1, 754) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = damage_to_house Coef. Std. Err. t P> t [95% Conf. Interval] living_now_change _cons

16 3b_i. Cross-tab: House /house structure now (over two tables) Damage to house Brick with thatched Mud with thatched Brick with tin Mud with tin Stone wall with stone Stone wall with tin Total/Fully (not safe) Minor damage Not Total Percentages: Total/Fully (not safe) Minor damage Not

17 Damage to house Reinforced Cement Concrete Brick with tarpaulin Mud with tarpaulin Tent Other Grand total Total/Fully (not safe) Minor damage Not Total Percentages: Total/Fully (not safe) Minor damage Not

18 3b_ii. Regression analysis: House / house structure now Comment: the p value of shows that there is a statistically relevent relationship between damage to house and the house structure now. The R2 value of 16.30% shows that this accounts for 16% of variance in the results, which is reasonable impact (normally look for at least 25% of variance for survey data of this nature). Source SS df MS Number of obs = F(1, 755) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = damage_to_~e Coef. Std. Err. t P> t [95% Conf. Interval] house_now _cons c. Cross-tab: Damage to house by Caste Damage to house 1 = Indigenous/Ja najati (Gurung, Magar, Tamang, Newar, Chepang,) 2 = Caste group (Brahman, Chhetri, Sanyasi 3 = Dalits 4 = Prefer not to say 5 = other Total Total/Fully

19 (not safe) Minor damage Not Total Percentages: Total/Fully (not safe) Minor damage Not d_i. Cross-tab: Damage to house by male or female head of household Damage to house Male Female other Total 19

20 Total/Fully (not safe) Minor damage Not Total Percentages: Total/Fully (not safe) Minor damage Not d_ii. Regression analysis: House /male or female head of household Comment: the p value of shows that there is a not a statistically relevant relationship between damage to house and male/female headed household. 20

21 Source SS df MS Number of obs = F(1, 754) = 1.43 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = damage_to_house Coef. Std. Err. t P> t [95% Conf. Interval] sex_head_household _cons e_i. Cross-tab: Damage to house by location type Damage to house: Urban (For Ktm) Semiurban (For Ktm) Rural (For Ktm) Lowland (For other districts) Midland (For other districts ) Highland (For other districts) Total Total/Fully (not safe) Minor damage Not Total

22 Percentages: Total/Fully (not safe) Minor damage Not e_ii. Regression analysis: House /location type Comment: the p value of shows that there is a statistically relevant relationship between damage to house and the house structure now. The R2 value of 15.09% shows that this model accounts for 15.09% of variance in the results (this relates to how close to the data points fit to the regression line (not that closely) and does not mean how much the dependant variable relates to the independent variable). Source SS df MS Number of obs = F(1, 755) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = damage_to_h~e Coef. Std. Err. t P> t [95% Conf. Interval] location_type _cons

23 3f. Overall regression analysis for house and the variables in section 3 Multinomial regression analysis for section 3 show a statistically significant relationship between damage to house and house now (e.g. brick with thatched, mud with thatched, brick with tin etc) and also location type (i.e. urban, semi-urban and rural for Ktm and lowland, midland and highland for other districts). There is not a statistically significant relationship with damage to house and living now change, caste and sex of the head of household (position 1). When considering damage to house with the variable house now the statistical significance remains the same, with a p value of The R2 value is 16%, meaning that house now accounts for 16% of variance in the results for damage to house, which is not that high. House now does have a statistically significant impact but it is not a relatively big impact (it is recommended to look for at least 25% of impact on the results for survey data such as this). Regarding the regression of damage to house with the variable location type, when regressing these with the other variables, the P value is When looking at these two variables on their own, the P value remains the same at Although this is a statistically significant result (with urban districts of Ktm having the influence over the results), the R2 value is 15%, again this is showing that although the variable location type is having a statistically significant impact on the results for damage to house but it is not that big an impact. Source SS df MS Number of obs = F(5, 750) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = damage_to_house Coef. Std. Err. t P> t [95% Conf. Interval] living_now_change house_now caste sex_head_household location_type _cons

24 House construction before and after 4a_i. Cross-tab: House construction before/now (over two tables) House construction after: House construction before: Brick with thatched Mud with thatched Brick with tin Mud with tin Stone wall with stone Stone wall with tin Brick with thatched Mud with thatched Brick with tin Mud with tin Stone wall with stone Stone wall with tin Reinforced Cement Concrete Brick with tarpaulin

25 Mud with tarpaulin Other Total Percentages: Brick with thatched Mud with thatched Brick with tin Mud with tin Stone wall with stone Stone wall with tin Reinforced Cement Concrete Brick with tarpaulin Mud with tarpaulin Other House construction after: 25

26 House construction before: Reinforced Cement Concrete Brick with tarpaulin ( Mud with tarpaulin Tent Other Grand total Brick with thatched Mud with thatched Brick with tin Mud with tin Stone wall with stone Stone wall with tin Reinforced Cement Concrete Brick with tarpaulin Mud with tarpaulin Other Total Percentages: Brick with thatched Mud with thatched Brick with tin

27 Mud with tin Stone wall with stone Stone wall with tin Reinforced Cement Concrete Brick with tarpaulin Mud with tarpaulin Other a_ii. Regression analysis for house before/house now Comment: the p value of shows that no significant relationship was found between house before and house after. Source SS df MS Number of obs = F(1, 755) = 5.64 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = house_before Coef. Std. Err. t P> t [95% Conf. Interval] house_now

28 _cons House improved 5a_i. Cross-tab: House improved/location type House improved: Urban (For Ktm) Semi-urban (For Ktm) Rural (For Ktm) Lowland (For other districts) Midland (For other districts) Highland (For other districts) Total Yes No Don t know N/A not living in a house Total Percentages: Yes No Don t know N/A not living in a house

29 5a_ii. Regression analysis for house improved/location type Comment: the p value of shows that no significant relationship was found between house improved and location type. Source SS df MS Number of obs = F(1, 755) = 2.84 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = house_impro~d Coef. Std. Err. t P> t [95% Conf. Interval] location_type _cons b_i. Cross-tab: House improved/caste House improved: 1 = Indigenous/Janajati (Gurung, Magar, Tamang, Newar, Chepang,) 2 = Caste group (Brahman, Chhetri, Sanyasi 3 = Dalits Total Yes No

30 Don t know NA not living in a house Total count Percentage: Yes No Don t know NA not living in a house b_ii. Regression analysis for house improved/caste Comment: the p value of shows that no significant relationship was found between house improved and Caste. Source SS df MS Number of obs = F(1, 752) = 2.79 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = house_impr~d Coef. Std. Err. t P> t [95% Conf. Interval]

31 caste _cons c_i. Cross tab: House improved/male or female head of household House improved: Male Female Total Yes No Don t know 98 NA not living in a house Total Percentages: Yes No Don t know 98 NA not living in a house

32 5c_ii. Regression analysis for house improved/male or female headed household Comment: the p value of shows that no significant relationship was found between house improved and male/female headed household. Source SS df MS Number of obs = F(1, 754) = 0.01 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = house_improved Coef. Std. Err. t P> t [95% Conf. Interval] sex_head_household _cons d. Regression analysis for section 5 Comment: there is no statistically significant relationship between house improved and the variables within section 5 above F(3, 749) = 1.72 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = house_improved Coef. Std. Err. t P> t [95% Conf. Interval] location_type caste

33 sex_head_household _cons Land ownership 6a. Summary: Land ownership overview Land ownership: Frequency Percentage Cumulative Never owned Yes before the and still own Yes now own but didn t before the No did own before the but not now Total b_i. Cross-tab: Land ownership/caste Land ownership: Indigenous/Janajati (Gurung, Magar, Tamang, Newar, Chepang,) Caste group (Brahman, Chhetri, Sanyasi Dalits Total 33

34 Never owned 0 Yes before the and still own 1 Yes now own but didn t before the 2 No did own before the but not now Total count Percentage: Land ownership: Indigenous/Janajati (Gurung, Magar, Tamang, Newar, Chepang,) Caste group (Brahman, Chhetri, Sanyasi Dalits Total Never owned Yes before the and still own Yes now own but didn t before the No did own before the but not now b_ii. Regression analysis for land ownership/caste 34

35 Comment: the p value of shows that there is a statistically relevent relationship between land ownership and Caste. However, the amount of variance in the results for land ownership is low at 1.30%. Source SS df MS Number of obs = F(1, 751) = 9.92 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = land_owner~p Coef. Std. Err. t P> t [95% Conf. Interval] caste _cons c_i. Cross-tab: Land ownership by male or female head of household Land ownership: Male Female Total Never owned Yes before the and still own Yes now own but didn t

36 before the No did own before the but not now Total Percentages: Never owned Yes before the and still own Yes now own but didn t before the No did own before the but not now c_ii. Regression analysis for land ownership by male or female head of household Comment: the p value of shows that there is not a statistically relevant relationship between land ownership and male/female headed household. Source SS df MS Number of obs = F(1, 753) = 0.02 Model Prob > F = Residual R-squared = Adj R-squared =

37 Total Root MSE = land_ownership Coef. Std. Err. t P> t [95% Conf. Interval] sex_head_household _cons d_i. Cross-tab_i: Land ownership/location type Land ownership: Urban (For Ktm) Semi-urban (For Ktm) Rural (For Ktm) Lowland (For other districts) Midland (For other districts) Highland (For other districts) Total Never owned Yes before the and still own Yes now own but didn t before the No did own before the but not now Total Percentages: Never owned

38 Yes before the and still own Yes now own but didn t before the No did own before the but not now d_ii. Regression analysis for land ownership/location type Comment: the p value of shows that there is statistically relevant relationship between land ownership and location type. The amount of variance seen in the land ownership results as a result of this regression with location type is 10.41%. Source SS df MS Number of obs = F(1, 754) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = land_owners~p Coef. Std. Err. t P> t [95% Conf. Interval] location_type cons

39 6e_i. Cross-tab: Land ownership by damage to house Land ownership: Total/Fully (not safe) Minor damage Not Total Never owned Yes before the and still own Yes now own but didn t before the No did own before the but not now Total Percentages: Never owned Yes before the and still own Yes now own but didn t before the No did own before the

40 but not now 6e_ii. Regression analysis for land ownership/damage to house Comment: the p value of shows that there is statistically relevent relationship between land ownership and location type. The amount of variance seen in the land ownership results as a result of this regression with damage to house is 6.22%. Source SS df MS Number of obs = F(1, 754) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = land_ownership Coef. Std. Err. t P> t [95% Conf. Interval] damage_to_house _cons