HOUSEHOLD DIETARY DIVERSITY AND SECURITY

Household Dietary Diversity
Lacking products

Household Dietary Diversity

# Table 6: Food groups consumed by ≥50% of households by dietary diversity
# HDDS.pdf pag.31
dfSEtmp <- inner_join(dfSE %>% mutate(city=paste(country,city,sep="_")),
                   dfSE %>% mutate_at(.vars = 116:133,~ifelse(.=="Yes",1,0)) %>% 
                     rowwise() %>% 
                     # mutate(HDDS_Tot = sum(c_across(HDDS_01:HDDS_18))) %>% 
                     mutate(HH_size=adult14plus+child3to13+child0to2) %>% 
                     mutate(HD01.Cereals=HDDS_01,
                            HD02.Green.leafy.vegetables=ifelse((HDDS_04+HDDS_03)>0,1,0),
                            HD03.Vitamin.A.rich.fruit=HDDS_06,
                            HD04.Oil=HDDS_15,
                            HD05.Other.vegetables=HDDS_05,
                            HD06.Fish=HDDS_11,
                            HD07.Legumes.nuts.seeds=ifelse((HDDS_12+HDDS_13)>0,1,0)) %>% 
                     select(unique_id,HD01.Cereals:HD07.Legumes.nuts.seeds),
                   by="unique_id") %>% 
  mutate(HD7.Tot=HD01.Cereals+HD02.Green.leafy.vegetables+HD03.Vitamin.A.rich.fruit+HD04.Oil+HD05.Other.vegetables+HD06.Fish+HD07.Legumes.nuts.seeds)

Food.group	variable.name	question	descr
Cereals	HDDS_01	Q17.1	Cereals
Green leafy vegetables	HDDS_04	Q17.4	Dark green leafy vegetables
	HDDS_03	Q17.3	Orange fleshed roots/tubers or vegetables
Vitamin A rich fruit	HDDS_06	Q17.6	Orange fleshed fruits
Oil	HDDS_15	Q17.15	Oils/Fats
Other vegetables	HDDS_05	Q17.5	Other vegetables
Fish	HDDS_11	Q17.11	Fish
Legumes, nuts and seeds	HDDS_12	Q17.12	Pulses
	HDDS_13	Q17.13	Nuts and seeds

variable	KE	MO	TN	TZ	UG	Total
HD01.Cereals	91.1	95.0	85.3	94.9	88.3	90.6
HD02.Green.leafy.vegetables	86.6	64.3	68.0	90.0	65.5	75.2
HD03.Vitamin.A.rich.fruit	50.8	46.8	17.4	51.1	50.2	44.4
HD04.Oil	68.4	89.6	80.4	77.9	74.2	76.7
HD05.Other.vegetables	89.7	61.9	75.0	78.2	90.3	81.2
HD06.Fish	22.7	28.4	30.3	51.3	33.4	32.4
HD07.Legumes.nuts.seeds	58.5	53.9	66.9	71.5	74.6	65.5

variable	KE_Kisumu	KE_Kitui	KE_Nyeri	MO_Beni Mellal	MO_Meknes	TN_Sousse	TN_Tunis	TZ_Daressalaam	TZ_Morogoro	UG_Kalerwe	UG_Kampala	UG_Kapeeka	Total
HD01.Cereals	89.0	97.4	86.7	96.5	93.8	80.7	90.0	92.6	97.2	87.0	91.7	86.3	90.6
HD02.Green.leafy.vegetables	99.8	83.1	77.4	54.5	72.2	72.1	63.9	89.8	90.2	68.6	70.4	57.5	75.2
HD03.Vitamin.A.rich.fruit	41.8	60.2	50.1	47.8	46.0	33.5	1.4	64.8	37.7	51.0	41.9	57.7	44.4
HD04.Oil	67.1	71.3	66.9	85.5	92.8	79.9	80.8	83.8	72.1	63.2	83.2	75.9	76.7
HD05.Other.vegetables	89.5	97.0	82.7	60.2	63.2	76.1	73.9	80.2	76.2	87.4	90.5	93.0	81.2
HD06.Fish	52.5	10.6	6.0	19.0	36.0	38.8	21.8	50.1	52.5	36.2	32.8	31.1	32.4
HD07.Legumes.nuts.seeds	40.8	71.3	62.8	44.0	61.8	68.1	65.7	68.1	74.9	78.4	70.2	75.1	65.5

variable	1.Low	2.Medium	3.High	Total
HD01.Cereals	67.2	94.2	98.6	90.6
HD02.Green.leafy.vegetables	27.7	78.6	98.3	75.2
HD03.Vitamin.A.rich.fruit	11.8	34.6	82.2	44.4
HD04.Oil	46.7	76.9	94.9	76.7
HD05.Other.vegetables	40.5	85.8	98.0	81.2
HD06.Fish	8.6	22.7	64.4	32.4
HD07.Legumes.nuts.seeds	24.4	64.5	92.6	65.5

dfSE <- inner_join(dfSE,
                  dfSE %>% mutate_at(.vars = 116:133,~ifelse(.=="Yes",1,0)) %>% 
                    rowwise() %>% 
                    # mutate(HDDS_Tot = sum(c_across(HDDS_01:HDDS_18))) %>% 
                    mutate(HH_size=adult14plus+child3to13+child0to2) %>% 
                    mutate(H01.Cereals=HDDS_01,
                           H02.White.tubers.roots=HDDS_02,
                           H03.Vegetables=ifelse((HDDS_03+HDDS_04+HDDS_05)>0,1,0),
                           H04.Fruits=ifelse((HDDS_06+HDDS_07)>0,1,0),
                           H05.Meat=ifelse((HDDS_08+HDDS_09)>0,1,0),
                           H06.Eggs=HDDS_10,
                           H07.Fish=HDDS_11,
                           H08.Legumes.nuts.seeds=ifelse((HDDS_12+HDDS_13)>0,1,0),
                           H09.Milk=HDDS_14,
                           H10.Oils.fats=HDDS_15,
                           H11.Sweets=HDDS_16,
                           H12.Spices.condiments.beverages=ifelse((HDDS_17+HDDS_18)>0,1,0)) %>% 
                    mutate(HDDS12.Tot=H01.Cereals+H02.White.tubers.roots+H03.Vegetables+H04.Fruits+H05.Meat+H06.Eggs+H07.Fish+H08.Legumes.nuts.seeds+H09.Milk+H10.Oils.fats+H11.Sweets+H12.Spices.condiments.beverages) %>% 
                    select(unique_id,HH_size:HDDS12.Tot),
                  by="unique_id")

aggregate variables

educ in 3 classes, hh_head_employ in 3 classes and presence of children

dfSE <- dfSE %>% mutate(educ3 = factor(ifelse(is.na(educ),NA,
                                            ifelse(educ %in% levels(dfSE$educ)[1:2],educ,3)),
                                     levels=1:3,
                                     labels=c(levels(dfSE$educ)[1:2],"Secondary or more"))) 
dfSE <- dfSE %>% mutate(hh_head_employ_3=ifelse(hh_head_employ %in% c("Unemployed","Casual worker (paid by the day)"),1,
                                        ifelse(hh_head_employ %in% c("Sole trader","Businessman / self-employed","Other"),2,
                                               ifelse(hh_head_employ %in% c("Clerk (formal employment)","Manager","Regular worker"),3,NA))))
dfSE$hh_head_employ_3 <- factor(dfSE$hh_head_employ_3,levels = 1:3,labels = c("Unemployed.Casual","self.employed","Clerk.Regular.Manager"))

dfSE$children0_2 <- factor(ifelse(is.na(dfSE$child0to2),NA,ifelse(dfSE$child0to2>0,1,0)),levels = 0:1,labels = c("No","Yes"))
dfSE$children3_13 <- factor(ifelse(is.na(dfSE$child3to13),NA,ifelse(dfSE$child3to13>0,1,0)),levels = 0:1,labels = c("No","Yes"))

variable aggregation on innovation

a single summary variable was created between local_food_interest and nutri_food_interest

some reasons for choosing innovative products and some obstacles were then aggregated (see tables below)

methodology used: confirmatory factor analysis

group.variable	group.code	group.descr	ref.variable	variable	descr
Reason	1	wellness	local_food_interest	local_envfriend	It would be environmentally-friendly
nutri_food_interest	nutri_healthy	It could make my household diet healthier
2	availability	local_food_interest	local_afford	I could afford it
local_food_interest	local_find	I could easily find it where I buy food
nutri_food_interest	nustri_afford	I could afford it
3	culture	local_food_interest	local_tradition	It would be in line with my culture / tradition
nutri_food_interest	nutri_lifestyle	It would be in line with my lifestyle / culture
4	farmers	local_food_interest	local_helpfarmer	It could help local farmers
nutri_food_interest	nutri_trust	I trust the farmer / seller
5	trust	local_food_interest	local_friends	It has been suggested by friends / people I trust
nutri_food_interest	nustri_doctor	I would trust my doctor’s recommendation
6	status	nutri_food_interest	nutri_sstatus	It could enhance my social status

group.variable	group.code	group.descr	ref.variable	variable	descr
Obstacle	1	taste	local_food_interest	obst_loc_familar	I would not be familiar with its taste
nutri_food_interest	obst_nutri_fam	I would not be familiar with its taste
2	habits	local_food_interest	obst_loc_change	I don’t want to change my food practice/habits
nutri_food_interest	obst_nutri_fpractice	I don’t want to change my food practice/habits
nutri_food_interest	obst_nutri_time	I would not have enough time to prepare it
3	price	local_food_interest	obst_loc_comparab	I would need to compare its price with other food products
nutri_food_interest	obst_nutri_price	I would need to compare its price with other food products
4	healthy	local_food_interest	obst_loc_healthy	I don’t know if it is healthy
nutri_food_interest	obst_nutri_safe	A new food product would make me feel less safe
5	trust	local_food_interest	obst_loc_friend	My friends / people I know do not eat it
local_food_interest	obst_loc_trust	I don’t trust the farmer / seller
nutri_food_interest	obst_nutri_friend	My friends / people I know do not eat it

# variables in analysis
cbind(names(dfSE.HD)[1:39])

##       [,1]                 
##  [1,] "HDDS12.Tot"         
##  [2,] "country"            
##  [3,] "city"               
##  [4,] "gender"             
##  [5,] "age"                
##  [6,] "HH_size"            
##  [7,] "children0_2"        
##  [8,] "children3_13"       
##  [9,] "educ3"              
## [10,] "income_avg"         
## [11,] "hh_salary"          
## [12,] "hh_head_employ_3"   
## [13,] "income_food"        
## [14,] "imp_availability"   
## [15,] "imp_nutrition"      
## [16,] "imp_provider"       
## [17,] "imp_price"          
## [18,] "imp_envfriend"      
## [19,] "imp_divers"         
## [20,] "imp_tradition"      
## [21,] "imp_local"          
## [22,] "imp_product"        
## [23,] "healty_diet"        
## [24,] "local_food"         
## [25,] "meet_food_needs"    
## [26,] "lacking"            
## [27,] "innov_food_interest"
## [28,] "Obstacle.habits"    
## [29,] "Obstacle.healthy"   
## [30,] "Obstacle.price"     
## [31,] "Obstacle.taste"     
## [32,] "Obstacle.trust"     
## [33,] "Reason.availability"
## [34,] "Reason.culture"     
## [35,] "Reason.farmers"     
## [36,] "Reason.status"      
## [37,] "Reason.trust"       
## [38,] "Reason.wellness"    
## [39,] "yest_celebration"

% of respondents who ate the different types of food by country
variable	KE	MO	TN	TZ	UG	Total
H01.Cereals	91.0	95.0	87.5	94.9	88.3	91.2
H02.White.tubers.roots	38.7	66.6	62.0	44.9	76.6	58.2
H03.Vegetables	95.3	83.4	87.7	93.4	95.4	92.0
H04.Fruits	76.0	78.3	72.1	77.0	71.9	74.9
H05.Meat	38.6	70.6	76.6	46.7	40.5	50.7
H06.Eggs	39.7	62.4	59.3	20.1	37.7	41.8
H07.Fish	25.4	28.4	30.1	51.3	33.3	33.5
H08.Legumes.nuts.seeds	57.8	53.9	68.1	71.3	74.5	65.6
H09.Milk	79.2	65.7	86.4	36.3	46.3	60.7
H10.Oils.fats	69.9	89.6	83.2	77.7	74.2	77.5
H11.Sweets	62.1	81.4	72.7	63.9	78.9	71.8

HDDS by country
country	n	avg.HDDS	sd.HDDS	median.HDDS
KE	1,291	7.646785	2.180416	8
MO	900	8.718889	2.189031	9
TN	674	8.772997	2.552355	9
TZ	976	7.722336	2.202039	8
UG	1,515	8.157096	1.896010	8
Total	5,356	8.126774	2.205626	8

Test	Statistic	df	p.value	Signif
Kruskal-Wallis rank sum test	292.89	4	3.703e-62	***

HDDS by income
income_avg	n	avg.HDDS	sd.HDDS	median.HDDS
lower than average	1,207	7.590721	2.294961	8
somewhat lower than average	1,978	8.180991	2.098631	8
about average	847	8.409681	2.162664	9
somewhat higher than average	684	8.277778	2.065268	8
higher than average	640	8.434375	2.381871	9
Total	5,356	8.126774	2.205626	8

Test	Statistic	df	p.value	Signif
Kruskal-Wallis rank sum test	124.28	4	6.498e-26	***

model 1

mod.HD <- lm(HDDS12.Tot~.,data=dfSE.HD)
tbm1a <- lm2df(mod.HD)
tbm1b <- Anova2df(mod.HD)

my.flextb(tbm1a,title = "model 1 - coefficients") %>% autofit()

model 1 - coefficients
variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	2.5198	0.7000	3.5999	3.2e-04	***
cityKE.Kitui	0.2334	0.1614	1.4463	0.1482
cityKE.Nyeri	0.0778	0.1691	0.4604	0.6453
cityMO.Beni Mellal	0.9297	0.1592	5.8393	5.6e-09	***
cityMO.Meknes	1.6319	0.1532	10.6524	3.1e-26	***
cityTN.Sousse	0.7668	0.1634	4.6927	2.8e-06	***
cityTN.Tunis	1.4224	0.1742	8.1631	4.0e-16	***
cityTZ.Daressalaam	-0.0366	0.1595	-0.2295	0.8185
cityTZ.Morogoro	0.1451	0.1651	0.8790	0.3794
cityUG.Kalerwe	0.7684	0.1520	5.0563	4.4e-07	***
cityUG.Kampala	0.9306	0.1554	5.9894	2.2e-09	***
cityUG.Kapeeka	0.6186	0.1534	4.0313	5.6e-05	***
genderFemale	0.1451	0.0592	2.4523	0.0142	*
age	0.0022	0.0024	0.9199	0.3577
HH_size	0.0101	0.0140	0.7158	0.4741
children0_2Yes	0.1403	0.0705	1.9894	0.0467	*
children3_13Yes	0.1943	0.0681	2.8542	0.0043	**
educ3Primary	0.5015	0.1121	4.4728	7.9e-06	***
educ3Secondary or more	0.5376	0.1121	4.7953	1.7e-06	***
income_avgsomewhat lower than average	0.3459	0.0816	4.2402	2.3e-05	***
income_avgabout average	0.4559	0.1018	4.4786	7.7e-06	***
income_avgsomewhat higher than average	0.5574	0.1131	4.9292	8.5e-07	***
income_avghigher than average	0.7676	0.1285	5.9724	2.5e-09	***
hh_salary	0.0869	0.0348	2.4982	0.0125	*
hh_head_employ_3self.employed	0.2134	0.0775	2.7522	0.0059	**
hh_head_employ_3Clerk.Regular.Manager	0.1510	0.0799	1.8909	0.0587	.
income_foodLess than half (from 25% to 50%)	0.0153	0.1076	0.1422	0.8870
income_foodAbout half (50%)	0.1324	0.1017	1.3019	0.1930
income_foodMore than half (from 50% to 75%)	0.1328	0.1082	1.2275	0.2197
income_foodAlmost all (from 75% to 100%).	-0.3285	0.1347	-2.4395	0.0147	*
imp_availability	-0.0224	0.0263	-0.8514	0.3946
imp_nutrition	0.0022	0.0289	0.0771	0.9386
imp_provider	0.1055	0.0255	4.1367	3.6e-05	***
imp_price	-0.0994	0.0297	-3.3436	8.3e-04	***
imp_envfriend	-0.0067	0.0252	-0.2652	0.7909
imp_divers	-0.0057	0.0299	-0.1909	0.8486
imp_tradition	-0.0361	0.0237	-1.5248	0.1274
imp_local	-0.0021	0.0231	-0.0926	0.9262
imp_product	-0.0646	0.0271	-2.3794	0.0174	*
healty_diet	0.2204	0.0292	7.5434	5.4e-14	***
local_food	0.0170	0.0238	0.7135	0.4756
meet_food_needs	0.1149	0.0298	3.8625	1.1e-04	***
lacking	0.2369	0.0435	5.4503	5.3e-08	***
innov_food_interest	-0.0537	0.3517	-0.1527	0.8786
Obstacle.habits	0.2333	0.2974	0.7844	0.4328
Obstacle.healthy	-0.3243	0.2970	-1.0919	0.2749
Obstacle.price	-0.3292	0.3010	-1.0936	0.2742
Obstacle.taste	0.6376	0.4320	1.4758	0.1401
Obstacle.trust	-0.2155	0.1909	-1.1291	0.2589
Reason.availability	0.1869	0.2034	0.9186	0.3583
Reason.culture	-0.6052	1.0029	-0.6034	0.5463
Reason.farmers	0.2067	0.4613	0.4480	0.6542
Reason.status	0.0094	0.0594	0.1585	0.8740
Reason.trust	0.5599	0.4743	1.1805	0.2379
Reason.wellness	0.2699	0.4210	0.6410	0.5216
yest_celebrationYes	0.2716	0.0944	2.8767	0.0040	**
r.squared	0.1536
adj.r.squared	0.1448
fstatistic	17.4841	55.0000	5,300.0000	0.0e+00
n.obs	5,356.0000

my.flextb(tbm1b,title = "model 1 - anova") %>% autofit()

model 1 - anova
variable	Sum Sq	Df	F value	p.value	signif
city	923.7763	11	20.1854	1.6e-40	***
gender	25.0204	1	6.0139	0.0142	*
age	3.5207	1	0.8462	0.3577
HH_size	2.1318	1	0.5124	0.4741
children0_2	16.4662	1	3.9578	0.0467	*
children3_13	33.8933	1	8.1466	0.0043	**
educ3	100.3434	2	12.0593	6.0e-06	***
income_avg	174.6901	4	10.4972	1.8e-08	***
hh_salary	25.9661	1	6.2412	0.0125	*
hh_head_employ_3	31.8903	2	3.8326	0.0217	*
income_food	82.4848	4	4.9565	5.5e-04	***
imp_availability	3.0161	1	0.7250	0.3946
imp_nutrition	0.0247	1	0.0059	0.9386
imp_provider	71.1944	1	17.1124	3.6e-05	***
imp_price	46.5121	1	11.1797	8.3e-04	***
imp_envfriend	0.2925	1	0.0703	0.7909
imp_divers	0.1516	1	0.0364	0.8486
imp_tradition	9.6734	1	2.3251	0.1274
imp_local	0.0357	1	0.0086	0.9262
imp_product	23.5536	1	5.6614	0.0174	*
healty_diet	236.7388	1	56.9028	5.4e-14	***
local_food	2.1181	1	0.5091	0.4756
meet_food_needs	62.0695	1	14.9191	1.1e-04	***
lacking	123.5864	1	29.7054	5.3e-08	***
innov_food_interest	0.0970	1	0.0233	0.8786
Obstacle.habits	2.5600	1	0.6153	0.4328
Obstacle.healthy	4.9605	1	1.1923	0.2749
Obstacle.price	4.9759	1	1.1960	0.2742
Obstacle.taste	9.0610	1	2.1779	0.1401
Obstacle.trust	5.3041	1	1.2749	0.2589
Reason.availability	3.5109	1	0.8439	0.3583
Reason.culture	1.5149	1	0.3641	0.5463
Reason.farmers	0.8349	1	0.2007	0.6542
Reason.status	0.1046	1	0.0251	0.8740
Reason.trust	5.7976	1	1.3935	0.2379
Reason.wellness	1.7094	1	0.4109	0.5216
yest_celebration	34.4288	1	8.2753	0.0040	**
Residuals	22,050.1585	5,300

model 1 - stepwise BIC

mod.HD.s <- my.stepwise(mod.HD,direction = "both",crit = "bic")
lmb.s <- lm.beta(mod.HD.s)

tbm1a <- lm2df(mod.HD.s)
tbm1a$Std.Estimate <- NA
tbm1a$Std.Estimate[1:29] <- round(lmb.s$standardized.coefficients,4)
tbm1b <- Anova2df(mod.HD.s)
my.flextb(tbm1a,title = "model 1 - stepwise BIC - coefficients") %>% autofit()

model 1 - stepwise BIC - coefficients
variable	Estimate	Std.Error	t.value	p.value	signif	Std.Estimate
(Intercept)	3.6624	0.2803	13.0675	2.0e-38	***
cityKE.Kitui	0.1576	0.1538	1.0249	0.3055		0.0192
cityKE.Nyeri	-0.0905	0.1569	-0.5769	0.5640		-0.0106
cityMO.Beni Mellal	0.7035	0.1502	4.6849	2.9e-06	***	0.0839
cityMO.Meknes	1.4807	0.1446	10.2380	2.3e-24	***	0.1953
cityTN.Sousse	0.6739	0.1562	4.3142	1.6e-05	***	0.0747
cityTN.Tunis	1.2332	0.1590	7.7554	1.0e-14	***	0.1348
cityTZ.Daressalaam	-0.1919	0.1438	-1.3348	0.1820		-0.0250
cityTZ.Morogoro	-0.0032	0.1442	-0.0220	0.9825		-0.0004
cityUG.Kalerwe	0.6588	0.1423	4.6298	3.7e-06	***	0.0869
cityUG.Kampala	0.7548	0.1435	5.2591	1.5e-07	***	0.0999
cityUG.Kapeeka	0.5651	0.1441	3.9225	8.9e-05	***	0.0753
children3_13Yes	0.2338	0.0614	3.8085	1.4e-04	***	0.0515
educ3Primary	0.5137	0.1118	4.5939	4.5e-06	***	0.1042
educ3Secondary or more	0.5527	0.1081	5.1122	3.3e-07	***	0.1208
income_avgsomewhat lower than average	0.3754	0.0807	4.6524	3.4e-06	***	0.0821
income_avgabout average	0.4799	0.1001	4.7934	1.7e-06	***	0.0794
income_avgsomewhat higher than average	0.5927	0.1111	5.3349	1.0e-07	***	0.0897
income_avghigher than average	0.8171	0.1253	6.5234	7.5e-11	***	0.1202
hh_salary	0.1011	0.0316	3.1936	0.0014	**	0.0432
imp_provider	0.1012	0.0239	4.2391	2.3e-05	***	0.0584
imp_price	-0.1155	0.0291	-3.9648	7.4e-05	***	-0.0543
healty_diet	0.2282	0.0286	7.9902	1.6e-15	***	0.1111
meet_food_needs	0.1307	0.0295	4.4374	9.3e-06	***	0.0647
lacking	0.2221	0.0427	5.1995	2.1e-07	***	0.0766
Obstacle.price	-0.2179	0.0624	-3.4903	4.9e-04	***	-0.0768
Obstacle.taste	0.1812	0.0537	3.3745	7.4e-04	***	0.0656
Reason.availability	0.2578	0.0579	4.4527	8.7e-06	***	0.0746
yest_celebrationYes	0.2766	0.0939	2.9450	0.0032	**	0.0387
r.squared	0.1419
adj.r.squared	0.1374
fstatistic	31.4698	28.0000	5,327.0000	0.0e+00
n.obs	5,356.0000

my.flextb(tbm1b,title = "model 1 - stepwise BIC - anova") %>% autofit()

model 1 - stepwise BIC - anova
variable	Sum Sq	Df	F value	p.value	signif
city	1,129.6923	11	24.4741	5.7e-50	***
children3_13	60.8643	1	14.5045	1.4e-04	***
educ3	112.2618	2	13.3765	1.6e-06	***
income_avg	211.9784	4	12.6291	3.1e-10	***
hh_salary	42.7989	1	10.1993	0.0014	**
imp_provider	75.4071	1	17.9702	2.3e-05	***
imp_price	65.9627	1	15.7195	7.4e-05	***
healty_diet	267.9017	1	63.8432	1.6e-15	***
meet_food_needs	82.6246	1	19.6902	9.3e-06	***
lacking	113.4454	1	27.0350	2.1e-07	***
Obstacle.price	51.1195	1	12.1822	4.9e-04	***
Obstacle.taste	47.7836	1	11.3872	7.4e-04	***
Reason.availability	83.1977	1	19.8267	8.7e-06	***
yest_celebration	36.3940	1	8.6730	0.0032	**
Residuals	22,353.3809	5,327

tbm1c <- ols_vif_tol(mod.HD.s)
my.flextb(tbm1c,title = "model 1 - stepwise BIC - VIF") %>% autofit()

model 1 - stepwise BIC - VIF
Variables	Tolerance	VIF
cityKE.Kitui	0.4585218	2.180921
cityKE.Nyeri	0.4771503	2.095776
cityMO.Beni Mellal	0.5027575	1.989030
cityMO.Meknes	0.4425395	2.259685
cityTN.Sousse	0.5371740	1.861594
cityTN.Tunis	0.5329579	1.876321
cityTZ.Daressalaam	0.4578217	2.184257
cityTZ.Morogoro	0.4548312	2.198618
cityUG.Kalerwe	0.4571326	2.187549
cityUG.Kampala	0.4462202	2.241046
cityUG.Kapeeka	0.4373596	2.286448
children3_13Yes	0.8818977	1.133918
educ3Primary	0.3130226	3.194657
educ3Secondary or more	0.2887049	3.463745
income_avgsomewhat lower than average	0.5166474	1.935556
income_avgabout average	0.5872204	1.702938
income_avgsomewhat higher than average	0.5697702	1.755094
income_avghigher than average	0.4745943	2.107063
hh_salary	0.8786503	1.138109
imp_provider	0.8490350	1.177808
imp_price	0.8596357	1.163283
healty_diet	0.8333207	1.200018
meet_food_needs	0.7567280	1.321479
lacking	0.7428316	1.346200
Obstacle.price	0.3323803	3.008602
Obstacle.taste	0.4268361	2.342820
Reason.availability	0.5737739	1.742847
yest_celebrationYes	0.9312493	1.073826

lm.resid.plots(mod.HD.s)

tbm1d <- test2df(ks.test(mod.HD.s$residuals, 'pnorm'),test="s")
my.flextb(tbm1d,title = "model 1 - stepwise BIC - residuals normality test") %>% autofit()

model 1 - stepwise BIC - residuals normality test
Test	Statistic	df	p.value	Signif
One-sample Kolmogorov-Smirnov test	0.17		0	***

model 1 - stepwise AIC

mod.HD.sA <- my.stepwise(mod.HD,direction = "both",crit = "aic")
lmb.sA <- lm.beta(mod.HD.sA)
tbLM <- lm2df(mod.HD.sA)
tbLM$Std.Estimate <- NA
tbLM$Std.Estimate[1:41] <- round(lmb.sA$standardized.coefficients,4)
tbAN <- Anova2df(mod.HD.sA)
my.flextb(tbLM,title = "model 1 - stepwise AIC - coefficients") %>% autofit()

model 1 - stepwise AIC - coefficients
variable	Estimate	Std.Error	t.value	p.value	signif	Std.Estimate
(Intercept)	3.1060	0.3199	9.7101	4.2e-22	***
cityKE.Kitui	0.2354	0.1567	1.5023	0.1331		0.0287
cityKE.Nyeri	0.0567	0.1616	0.3507	0.7258		0.0066
cityMO.Beni Mellal	0.9240	0.1568	5.8944	4.0e-09	***	0.1101
cityMO.Meknes	1.6328	0.1502	10.8735	3.0e-27	***	0.2154
cityTN.Sousse	0.7549	0.1595	4.7318	2.3e-06	***	0.0837
cityTN.Tunis	1.4033	0.1647	8.5231	2.0e-17	***	0.1534
cityTZ.Daressalaam	-0.0515	0.1513	-0.3403	0.7337		-0.0067
cityTZ.Morogoro	0.1321	0.1542	0.8564	0.3918		0.0172
cityUG.Kalerwe	0.7487	0.1461	5.1252	3.1e-07	***	0.0988
cityUG.Kampala	0.8944	0.1507	5.9367	3.1e-09	***	0.1184
cityUG.Kapeeka	0.5893	0.1474	3.9973	6.5e-05	***	0.0785
genderFemale	0.1385	0.0587	2.3594	0.0183	*	0.0312
children0_2Yes	0.1500	0.0669	2.2424	0.0250	*	0.0294
children3_13Yes	0.2149	0.0614	3.4997	4.7e-04	***	0.0473
educ3Primary	0.4925	0.1115	4.4187	1.0e-05	***	0.0999
educ3Secondary or more	0.5086	0.1087	4.6804	2.9e-06	***	0.1111
income_avgsomewhat lower than average	0.3433	0.0812	4.2265	2.4e-05	***	0.0751
income_avgabout average	0.4515	0.1014	4.4524	8.7e-06	***	0.0747
income_avgsomewhat higher than average	0.5531	0.1126	4.9142	9.2e-07	***	0.0837
income_avghigher than average	0.7613	0.1280	5.9497	2.9e-09	***	0.1120
hh_salary	0.0973	0.0316	3.0814	0.0021	**	0.0416
hh_head_employ_3self.employed	0.2216	0.0772	2.8688	0.0041	**	0.0490
hh_head_employ_3Clerk.Regular.Manager	0.1546	0.0796	1.9418	0.0522	.	0.0334
income_foodLess than half (from 25% to 50%)	0.0203	0.1072	0.1896	0.8496		0.0039
income_foodAbout half (50%)	0.1379	0.1012	1.3626	0.1731		0.0300
income_foodMore than half (from 50% to 75%)	0.1442	0.1077	1.3392	0.1806		0.0275
income_foodAlmost all (from 75% to 100%).	-0.3215	0.1337	-2.4054	0.0162	*	-0.0398
imp_provider	0.1031	0.0245	4.1995	2.7e-05	***	0.0595
imp_price	-0.1019	0.0291	-3.5045	4.6e-04	***	-0.0479
imp_tradition	-0.0391	0.0217	-1.7960	0.0726	.	-0.0273
imp_product	-0.0667	0.0268	-2.4850	0.0130	*	-0.0345
healty_diet	0.2207	0.0288	7.6686	2.1e-14	***	0.1075
meet_food_needs	0.1152	0.0297	3.8827	1.0e-04	***	0.0570
lacking	0.2334	0.0430	5.4319	5.8e-08	***	0.0805
Obstacle.price	-0.1573	0.0561	-2.8057	0.0050	**	-0.0555
Obstacle.taste	0.2602	0.0661	3.9371	8.4e-05	***	0.0942
Reason.trust	0.2158	0.0643	3.3547	8.0e-04	***	0.0603
yest_celebrationYes	0.2710	0.0940	2.8827	0.0040	**	0.0380
innov_food_interest	0.1595	0.0573	2.7844	0.0054	**	0.0468
Obstacle.trust	-0.0790	0.0560	-1.4116	0.1581		-0.0283
r.squared	0.1526
adj.r.squared	0.1462
fstatistic	23.9285	40.0000	5,315.0000	0.0e+00
n.obs	5,356.0000

my.flextb(tbAN,title = "model 1 - stepwise AIC - anova") %>% autofit()

model 1 - stepwise AIC - anova
variable	Sum Sq	Df	F value	p.value	signif
city	1,059.7440	11	23.1953	3.7e-47	***
gender	23.1214	1	5.5668	0.0183	*
children0_2	20.8854	1	5.0285	0.0250	*
children3_13	50.8706	1	12.2478	4.7e-04	***
educ3	95.9145	2	11.5464	9.9e-06	***
income_avg	173.2103	4	10.4257	2.1e-08	***
hh_salary	39.4372	1	9.4951	0.0021	**
hh_head_employ_3	34.5029	2	4.1535	0.0158	*
income_food	84.0683	4	5.0602	4.5e-04	***
imp_provider	73.2502	1	17.6361	2.7e-05	***
imp_price	51.0097	1	12.2813	4.6e-04	***
imp_tradition	13.3969	1	3.2255	0.0726	.
imp_product	25.6485	1	6.1752	0.0130	*
healty_diet	244.2529	1	58.8075	2.1e-14	***
meet_food_needs	62.6153	1	15.0756	1.0e-04	***
lacking	122.5501	1	29.5057	5.8e-08	***
Obstacle.price	32.6967	1	7.8722	0.0050	**
Obstacle.taste	64.3809	1	15.5006	8.4e-05	***
Reason.trust	46.7434	1	11.2542	8.0e-04	***
yest_celebration	34.5148	1	8.3099	0.0040	**
innov_food_interest	32.2004	1	7.7527	0.0054	**
Obstacle.trust	8.2760	1	1.9926	0.1581
Residuals	22,075.5039	5,315

tbVIFa <- ols_vif_tol(mod.HD.sA)
my.flextb(tbVIFa,title = "model 1 - stepwise AIC - VIF") %>% autofit()

model 1 - stepwise AIC - VIF
Variables	Tolerance	VIF
cityKE.Kitui	0.4372201	2.287177
cityKE.Nyeri	0.4451279	2.246545
cityMO.Beni Mellal	0.4566546	2.189839
cityMO.Meknes	0.4063464	2.460954
cityTN.Sousse	0.5096985	1.961944
cityTN.Tunis	0.4919540	2.032710
cityTZ.Daressalaam	0.4088204	2.446062
cityTZ.Morogoro	0.3936746	2.540169
cityUG.Kalerwe	0.4293111	2.329313
cityUG.Kampala	0.4008233	2.494865
cityUG.Kapeeka	0.4134606	2.418610
genderFemale	0.9114096	1.097202
children0_2Yes	0.9280124	1.077572
children3_13Yes	0.8725968	1.146005
educ3Primary	0.3118500	3.206670
educ3Secondary or more	0.2828856	3.534998
income_avgsomewhat lower than average	0.5047296	1.981259
income_avgabout average	0.5663255	1.765769
income_avgsomewhat higher than average	0.5494636	1.819957
income_avghigher than average	0.4501593	2.221436
hh_salary	0.8741883	1.143918
hh_head_employ_3self.employed	0.5474619	1.826611
hh_head_employ_3Clerk.Regular.Manager	0.5377755	1.859512
income_foodLess than half (from 25% to 50%)	0.3862238	2.589173
income_foodAbout half (50%)	0.3281121	3.047739
income_foodMore than half (from 50% to 75%)	0.3787205	2.640470
income_foodAlmost all (from 75% to 100%).	0.5816809	1.719156
imp_provider	0.7945425	1.258586
imp_price	0.8540054	1.170953
imp_tradition	0.6910013	1.447175
imp_product	0.8285757	1.206890
healty_diet	0.8120751	1.231413
meet_food_needs	0.7391340	1.352935
lacking	0.7265165	1.376431
Obstacle.price	0.4079383	2.451351
Obstacle.taste	0.2787729	3.587149
Reason.trust	0.4933228	2.027070
yest_celebrationYes	0.9198172	1.087173
innov_food_interest	0.5636276	1.774221
Obstacle.trust	0.3969378	2.519286

lm.resid.plots(mod.HD.sA)

tbm1da <- test2df(ks.test(mod.HD.sA$residuals, 'pnorm'),test="s")
my.flextb(tbm1da,title = "model 1 - stepwise AIC - residuals normality test") %>% autofit()

variable	KE.Estimate	KE.p.value	KE.signif	KE.St.Estimate	MO.Estimate	MO.p.value	MO.signif	MO.St.Estimate	TN.Estimate	TN.p.value	TN.signif	TN.St.Estimate	TZ.Estimate	TZ.p.value	TZ.signif	TZ.St.Estimate	UG.Estimate	UG.p.value	UG.signif	UG.St.Estimate
(Intercept)	0.0767	0.9145			4.7173	6.8e-25	***		4.3699	9.2e-04	***		3.0654	9.1e-05	***		4.8579	2.7e-28	***
cityKE.Kitui
cityKE.Nyeri
cityMO.Beni Mellal
cityMO.Meknes					0.5796	3.0e-05	***	0.1316
cityTN.Sousse
cityTN.Tunis									0.9968	8.4e-05	***	0.1954
cityTZ.Daressalaam
cityTZ.Morogoro
cityUG.Kalerwe
cityUG.Kampala
cityUG.Kapeeka
genderFemale									0.4277	0.0354	*	0.0823
age	-0.0072	0.0869	.	-0.0441					0.0207	0.0090	**	0.1112
HH_size													0.0912	0.0025	**	0.0909	0.0506	9.2e-04	***	0.0843
children0_2Yes	0.2199	0.0955	.	0.0424
children3_13Yes	0.3049	0.0158	*	0.0622					0.3832	0.0513	.	0.0740
educ3Primary					0.8634	5.2e-05	***	0.1604	1.7100	0.0336	*	0.2192					0.3461	0.0184	*	0.0841
educ3Secondary or more					0.9105	2.1e-06	***	0.2029	1.7040	0.0313	*	0.2304					0.3803	0.0063	**	0.1000
income_avgsomewhat lower than average	1.1067	1.5e-06	***	0.1559					0.5766	0.1656		0.1077					0.3095	0.0165	*	0.0785
income_avgabout average	0.8743	1.4e-05	***	0.1497					0.7442	0.0830	.	0.1344					0.4515	0.0125	*	0.0791
income_avgsomewhat higher than average	0.9267	1.0e-06	***	0.1876					1.2308	0.0086	**	0.1887					0.7225	0.0053	**	0.0778
income_avghigher than average	1.2284	1.4e-09	***	0.2606					1.3204	0.0111	*	0.1507					0.4801	0.2246		0.0316
hh_salary	0.2203	0.0012	**	0.0839					0.2168	0.0389	*	0.0830
hh_head_employ_3self.employed	0.2250	0.1270		0.0473	0.6281	0.0011	**	0.1126									0.2233	0.0503	.	0.0582
hh_head_employ_3Clerk.Regular.Manager	0.2832	0.0397	*	0.0630	0.3714	0.0180	*	0.0840									0.0681	0.5979		0.0159
income_foodLess than half (from 25% to 50%)	0.7502	0.0122	*	0.1398									0.1474	0.5246		0.0261	-0.2201	0.1552		-0.0510
income_foodAbout half (50%)	0.6723	0.0180	*	0.1518									0.0924	0.6506		0.0205	0.1243	0.4186		0.0297
income_foodMore than half (from 50% to 75%)	0.6538	0.0248	*	0.1283									0.4300	0.0545	.	0.0818	0.1397	0.3969		0.0295
income_foodAlmost all (from 75% to 100%).	0.0627	0.8441		0.0085									-0.3585	0.2881		-0.0366	-0.2771	0.1567		-0.0444
imp_availability	0.1883	0.0043	**	0.0750					-0.1481	0.0727	.	-0.0707
imp_nutrition													0.1125	0.0566	.	0.0675
imp_provider	0.1469	0.0035	**	0.0840									0.0914	0.1079		0.0563	0.1079	0.0047	**	0.0753
imp_price													-0.1635	0.0381	*	-0.0654	-0.1223	0.0017	**	-0.0792
imp_envfriend
imp_divers	-0.1005	0.0692	.	-0.0484	0.2371	0.0018	**	0.1122
imp_tradition					-0.1851	0.0080	**	-0.0946					-0.1794	9.9e-05	***	-0.1372
imp_local	0.1011	0.0379	*	0.0540									0.0989	0.0594	.	0.0693	-0.0850	0.0067	**	-0.0694
imp_product	0.0841	0.1043		0.0437	-0.3037	3.2e-05	***	-0.1444	-0.2249	0.0212	*	-0.0913	-0.1321	0.0758	.	-0.0597
healty_diet	0.3819	7.0e-11	***	0.1920									0.2807	2.6e-05	***	0.1431	0.1410	0.0036	**	0.0785
local_food									-0.1462	0.0884	.	-0.0675	0.2146	6.2e-04	***	0.1154
meet_food_needs	-0.0891	0.1595		-0.0393	0.3155	6.0e-07	***	0.1665					0.2813	5.2e-04	***	0.1104	0.1220	0.0046	**	0.0761
lacking	0.1893	0.0298	*	0.0646					0.1926	0.1407		0.0578	0.4019	1.7e-04	***	0.1244	0.2799	6.0e-04	***	0.0900
innov_food_interest	0.6159	1.5e-04	***	0.1591	0.3736	0.0391	*	0.1167	-0.6033	0.0132	*	-0.1548
Obstacle.habits									-0.8531	0.0021	**	-0.2522	0.6043	0.0109	*	0.2410
Obstacle.healthy					0.2768	0.0094	**	0.1021	-1.1224	0.0637	.	-0.2996
Obstacle.price																	-0.2197	0.0200	*	-0.0865
Obstacle.taste									1.5790	0.0101	*	0.4657	-0.2958	0.0352	*	-0.1224	0.1537	0.0612	.	0.0581
Obstacle.trust													-0.4417	0.0307	*	-0.1703
Reason.availability	0.3935	0.0281	*	0.0880	-0.4758	0.0153	*	-0.1520					0.2544	0.1018		0.0568	0.2719	0.0021	**	0.1012
Reason.culture	0.7652	3.3e-04	***	0.2149
Reason.farmers	-0.5237	0.0399	*	-0.1279	0.5785	0.0025	**	0.1755
Reason.status													-0.0684	0.1237		-0.0501
Reason.trust									0.6937	0.0050	**	0.1654
Reason.wellness	-0.7690	2.0e-06	***	-0.1632					0.7125	0.0052	**	0.1621
yest_celebrationYes					0.2913	0.0955	.	0.0520	0.7104	0.0353	*	0.0799					0.3131	0.0171	*	0.0599
r.squared	0.2312				0.2250				0.1178				0.1653				0.1126
adj.r.squared	0.2148				0.2128				0.0880				0.1478				0.0989
fstatistic	14.0696	0.0e+00			18.3543	0.0e+00			3.9519	5.5e-09			9.4581	0.0e+00			8.2251	0.0e+00
n.obs	1,291.0000				900.0000				674.0000				976.0000				1,515.0000

variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	1.9234	0.7459	2.5787	0.0099	**
cityKE.Kitui	0.2140	0.1662	1.2877	0.1979
cityKE.Nyeri	0.1077	0.1738	0.6199	0.5353
cityMO.Beni Mellal	0.9562	0.1679	5.6957	1.3e-08	***
cityMO.Meknes	1.5294	0.1661	9.2063	5.0e-20	***
cityTN.Sousse	0.6862	0.1740	3.9443	8.1e-05	***
cityTN.Tunis	1.3940	0.1811	7.6955	1.7e-14	***
cityTZ.Daressalaam	-0.0002	0.1681	-0.0012	0.9991
cityTZ.Morogoro	0.0916	0.1734	0.5282	0.5974
cityUG.Kalerwe	0.7608	0.1629	4.6693	3.1e-06	***
cityUG.Kampala	0.8121	0.1657	4.9023	9.8e-07	***
cityUG.Kapeeka	0.6289	0.1629	3.8608	1.1e-04	***
genderFemale	0.1349	0.0627	2.1506	0.0316	*
age	0.0006	0.0026	0.2169	0.8283
HH_size	0.0241	0.0149	1.6174	0.1059
children0_2Yes	0.1497	0.0746	2.0071	0.0448	*
children3_13Yes	0.1832	0.0720	2.5427	0.0110	*
educ3Primary	0.5027	0.1197	4.1985	2.7e-05	***
educ3Secondary or more	0.4846	0.1196	4.0508	5.2e-05	***
income_avgsomewhat lower than average	0.3849	0.0871	4.4179	1.0e-05	***
income_avgabout average	0.4445	0.1098	4.0491	5.2e-05	***
income_avgsomewhat higher than average	0.5641	0.1193	4.7301	2.3e-06	***
income_avghigher than average	0.7763	0.1348	5.7600	8.9e-09	***
hh_salary	0.1091	0.0373	2.9278	0.0034	**
hh_head_employ_3self.employed	0.2210	0.0820	2.6952	0.0071	**
hh_head_employ_3Clerk.Regular.Manager	0.1482	0.0849	1.7464	0.0808	.
income_foodLess than half (from 25% to 50%)	0.0181	0.1183	0.1530	0.8784
income_foodAbout half (50%)	0.1668	0.1121	1.4875	0.1370
income_foodMore than half (from 50% to 75%)	0.1591	0.1182	1.3459	0.1784
income_foodAlmost all (from 75% to 100%).	-0.3667	0.1455	-2.5200	0.0118	*
imp_availability	-0.0225	0.0281	-0.8031	0.4219
imp_nutrition	0.0243	0.0305	0.7963	0.4259
imp_provider	0.1058	0.0270	3.9121	9.3e-05	***
imp_price	-0.1158	0.0319	-3.6242	2.9e-04	***
imp_envfriend	-0.0030	0.0268	-0.1122	0.9107
imp_divers	-0.0030	0.0317	-0.0960	0.9236
imp_tradition	-0.0424	0.0252	-1.6822	0.0926	.
imp_local	0.0099	0.0244	0.4054	0.6852
imp_product	-0.0499	0.0289	-1.7290	0.0839	.
healty_diet	0.2113	0.0310	6.8235	1.0e-11	***
local_food	0.0187	0.0251	0.7435	0.4572
meet_food_needs	0.1205	0.0324	3.7197	2.0e-04	***
lacking	0.2432	0.0461	5.2771	1.4e-07	***
innov_food_interest	-0.1909	0.3759	-0.5077	0.6117
Obstacle.habits	0.2496	0.3203	0.7792	0.4359
Obstacle.healthy	-0.5892	0.3153	-1.8690	0.0617	.
Obstacle.price	-0.4474	0.3222	-1.3887	0.1650
Obstacle.taste	1.0245	0.4592	2.2309	0.0257	*
Obstacle.trust	-0.2459	0.2050	-1.1995	0.2304
Reason.availability	0.2381	0.2180	1.0922	0.2748
Reason.culture	-1.0316	1.0732	-0.9612	0.3365
Reason.farmers	0.4602	0.4941	0.9313	0.3518
Reason.status	0.0046	0.0635	0.0730	0.9418
Reason.trust	0.7819	0.5054	1.5472	0.1219
Reason.wellness	0.4788	0.4494	1.0654	0.2867
r.squared	0.1547
adj.r.squared	0.1451
fstatistic	16.0322	54.0000	4,729.0000	0.0e+00
n.obs	4,784.0000

variable	Sum Sq	Df	F value	p.value	signif
city	724.5259	11	15.7277	1.1e-30	***
gender	19.3693	1	4.6251	0.0316	*
age	0.1971	1	0.0471	0.8283
HH_size	10.9552	1	2.6159	0.1059
children0_2	16.8703	1	4.0283	0.0448	*
children3_13	27.0755	1	6.4652	0.0110	*
educ3	79.2383	2	9.4604	7.9e-05	***
income_avg	163.6559	4	9.7696	7.2e-08	***
hh_salary	35.8980	1	8.5718	0.0034	**
hh_head_employ_3	30.5397	2	3.6462	0.0262	*
income_food	101.0372	4	6.0315	7.7e-05	***
imp_availability	2.7012	1	0.6450	0.4219
imp_nutrition	2.6555	1	0.6341	0.4259
imp_provider	64.0930	1	15.3044	9.3e-05	***
imp_price	55.0072	1	13.1348	2.9e-04	***
imp_envfriend	0.0527	1	0.0126	0.9107
imp_divers	0.0386	1	0.0092	0.9236
imp_tradition	11.8510	1	2.8298	0.0926	.
imp_local	0.6884	1	0.1644	0.6852
imp_product	12.5191	1	2.9893	0.0839	.
healty_diet	194.9904	1	46.5605	1.0e-11	***
local_food	2.3148	1	0.5527	0.4572
meet_food_needs	57.9450	1	13.8363	2.0e-04	***
lacking	116.6237	1	27.8478	1.4e-07	***
innov_food_interest	1.0796	1	0.2578	0.6117
Obstacle.habits	2.5426	1	0.6071	0.4359
Obstacle.healthy	14.6286	1	3.4931	0.0617	.
Obstacle.price	8.0757	1	1.9284	0.1650
Obstacle.taste	20.8437	1	4.9771	0.0257	*
Obstacle.trust	6.0258	1	1.4389	0.2304
Reason.availability	4.9960	1	1.1930	0.2748
Reason.culture	3.8695	1	0.9240	0.3365
Reason.farmers	3.6320	1	0.8673	0.3518
Reason.status	0.0223	1	0.0053	0.9418
Reason.trust	10.0253	1	2.3939	0.1219
Reason.wellness	4.7537	1	1.1351	0.2867
Residuals	19,804.5468	4,729

variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	3.2036	0.3088	10.3754	5.9e-25	***
cityKE.Kitui	0.1330	0.1535	0.8664	0.3863
cityKE.Nyeri	-0.0730	0.1577	-0.4631	0.6433
cityMO.Beni Mellal	0.7814	0.1574	4.9635	7.2e-07	***
cityMO.Meknes	1.4117	0.1570	8.9908	3.5e-19	***
cityTN.Sousse	0.6617	0.1662	3.9800	7.0e-05	***
cityTN.Tunis	1.2628	0.1626	7.7675	9.7e-15	***
cityTZ.Daressalaam	-0.0627	0.1495	-0.4192	0.6751
cityTZ.Morogoro	0.0186	0.1490	0.1249	0.9006
cityUG.Kalerwe	0.6472	0.1504	4.3037	1.7e-05	***
cityUG.Kampala	0.6434	0.1533	4.1965	2.8e-05	***
cityUG.Kapeeka	0.5702	0.1524	3.7413	1.9e-04	***
children3_13Yes	0.2513	0.0651	3.8604	1.1e-04	***
educ3Primary	0.5430	0.1193	4.5525	5.4e-06	***
educ3Secondary or more	0.5418	0.1154	4.6946	2.7e-06	***
income_avgsomewhat lower than average	0.4213	0.0862	4.8858	1.1e-06	***
income_avgabout average	0.4781	0.1081	4.4235	9.9e-06	***
income_avgsomewhat higher than average	0.6098	0.1173	5.1995	2.1e-07	***
income_avghigher than average	0.8604	0.1316	6.5367	6.9e-11	***
hh_salary	0.1348	0.0341	3.9481	8.0e-05	***
imp_provider	0.1031	0.0254	4.0670	4.8e-05	***
imp_price	-0.1292	0.0306	-4.2240	2.4e-05	***
healty_diet	0.2300	0.0300	7.6702	2.1e-14	***
meet_food_needs	0.1437	0.0320	4.4908	7.3e-06	***
lacking	0.2468	0.0451	5.4669	4.8e-08	***
innov_food_interest	0.2759	0.0507	5.4450	5.4e-08	***
r.squared	0.1404
adj.r.squared	0.1359
fstatistic	31.0944	25.0000	4,758.0000	0.0e+00
n.obs	4,784.0000

variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	8.2345	0.1411	58.3545	0.000	***
PGG.1	0.0039	0.0023	1.6568	0.0979	.
PGG.2	-0.0018	0.0022	-0.8126	0.4166
r.squared	0.0029
adj.r.squared	0.0010
fstatistic	1.5228	2.0000	1,049.0000	0.2186
n.obs	1,052.0000

HOUSEHOLD DIETARY DIVERSITY AND SECURITY

fabrizio

2022-11-08

Household Dietary Diversity

aggregate variables

educ in 3 classes, hh_head_employ in 3 classes and presence of children

variable aggregation on innovation

model 1

model 1 - stepwise BIC

model 1 - stepwise AIC

model 1 - by country

model 2 - yest_celebration = “No”

model 2 - yest_celebration = “No” - stepwise

model 3 - HDDS and experiments

Lacking products

model 1

model 1 - stepwise AIC

model 1 - stepwise BIC

country	n	avg	sd	median
KE	1,291	3.395019	0.7438695	3.479202
MO	900	4.046933	0.7810282	4.146254
TN	674	3.872472	0.7661246	3.967603
TZ	976	4.190486	0.6813964	4.291344
UG	1,515	3.749216	0.6097538	3.756556
Total	5,356	3.809789	0.7602628	3.876982

HDDS - risk
variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	8.5247	0.1373	62.0972	0.00	***
risk	-0.0158	0.0239	-0.6615	0.5084
r.squared	0.0003
adj.r.squared	-0.0004
fstatistic	0.4376	1.0000	1,568.0000	0.5084
n.obs	1,570.0000

HDDS - time
variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	8.2569	0.0958	86.1493	0.00	***
time	0.0127	0.0138	0.9259	0.3546
r.squared	0.0004
adj.r.squared	-0.0001
fstatistic	0.8574	1.0000	2,016.0000	0.3546
n.obs	2,018.0000

HDDS - trust
variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	8.4968	0.1218	69.7829	0.000	***
Trust.1	-0.0002	0.0045	-0.0484	0.9614
Trust.2	0.0002	0.0033	0.0509	0.9594
Trust.3	-0.0019	0.0021	-0.8968	0.3700
Trust.4	-0.0009	0.0016	-0.5461	0.5851
r.squared	0.0064
adj.r.squared	0.0037
fstatistic	2.3420	4.0000	1,448.0000	0.0530
n.obs	1,453.0000

variable	Estimate	Std.Error	t.value	p.value	signif
(Intercept)	1.4700	0.2199	6.6848	2.5e-11	***
cityKE.Kitui	0.1555	0.0508	3.0592	0.0022	**
cityKE.Nyeri	-0.0895	0.0533	-1.6797	0.0931	.
cityMO.Beni Mellal	0.6594	0.0495	13.3184	7.9e-40	***
cityMO.Meknes	0.5973	0.0481	12.4208	6.1e-35	***
cityTN.Sousse	0.4709	0.0512	9.2002	5.0e-20	***
cityTN.Tunis	0.2993	0.0551	5.4327	5.8e-08	***
cityTZ.Daressalaam	0.6841	0.0494	13.8540	6.7e-43	***
cityTZ.Morogoro	0.6867	0.0512	13.4225	2.0e-40	***
cityUG.Kalerwe	0.4762	0.0476	10.0149	2.1e-23	***
cityUG.Kampala	0.5209	0.0486	10.7192	1.5e-26	***
cityUG.Kapeeka	0.4237	0.0481	8.8137	1.6e-18	***
genderFemale	0.0160	0.0187	0.8562	0.3919
age	0.0019	0.0008	2.4533	0.0142	*
HH_size	-0.0036	0.0044	-0.8073	0.4195
children0_2Yes	0.0080	0.0222	0.3584	0.7200
children3_13Yes	0.0206	0.0215	0.9594	0.3374
educ3Primary	-0.0106	0.0354	-0.2981	0.7656
educ3Secondary or more	0.0557	0.0354	1.5745	0.1154
income_avgsomewhat lower than average	0.0477	0.0257	1.8549	0.0637	.
income_avgabout average	0.0799	0.0321	2.4883	0.0129	*
income_avgsomewhat higher than average	0.1285	0.0357	3.6028	3.2e-04	***
income_avghigher than average	0.0927	0.0406	2.2834	0.0224	*
hh_salary	0.0175	0.0110	1.5988	0.1099
hh_head_employ_3self.employed	0.0662	0.0244	2.7078	0.0068	**
hh_head_employ_3Clerk.Regular.Manager	0.0269	0.0252	1.0670	0.2860
income_foodLess than half (from 25% to 50%)	0.0735	0.0339	2.1694	0.0301	*
income_foodAbout half (50%)	0.0583	0.0320	1.8209	0.0687	.
income_foodMore than half (from 50% to 75%)	-0.0090	0.0341	-0.2631	0.7925
income_foodAlmost all (from 75% to 100%).	0.0000	0.0425	0.0010	0.9992
HDDS12.Tot	0.0235	0.0043	5.4503	5.3e-08	***
meet_food_needs	0.0866	0.0093	9.2918	2.2e-20	***
imp_availability	0.0139	0.0083	1.6790	0.0932	.
imp_nutrition	0.0126	0.0091	1.3894	0.1648
imp_provider	0.0215	0.0080	2.6775	0.0074	**
imp_price	0.0371	0.0094	3.9617	7.5e-05	***
imp_envfriend	0.0191	0.0079	2.4041	0.0162	*
imp_divers	0.0499	0.0094	5.3098	1.1e-07	***
imp_tradition	0.0126	0.0075	1.6853	0.0920	.