# ! # # # ! # # # ! # # ! # Child Anthropometrics and # #Geographic Differences in "#$%#&!'()#$*(+$*!,+! Malnutrition- ERSS Wave 1 S.+9."F#6+7)/3#-";#6)-)26#/4#9/3<#6*/96#)*-)# -(.#/(0++%*!($!1(2#&(3! Malnutrition E+"# -3+# E/3+# 0.<+0@# )/# 8+# 4-3E."F# )*-"# In 2011/2012, Ethiopiaʼs Central Statistical 9/E+"(#9*/#-3+#E/3+#0.<+0@#)/#8+#9/3<."F#."# Looking at the data further, we can identify !"#$%&%'$%&&(#)*+#,-)./"-0#123+-2#/4#5)-).6).76#."# Agency, in collaboration with the World Bank, -# */26+*/0;# +")+3I3.6+G#M/3+# )*-"# *-04#/4#-00# 7/00-8/3-)./"#9.)*#)*+#:/30;#1-"<#7/";27)+;#)*+# geographic areas with particularly high rates of conducted the first wave of the Ethiopia Rural 9/3<."F# 9/E+"# -3+# +"F-F+;# ."# -# "/"C4-3E# =+"+3-0# >/26+*/0;# 523?+@# A-"+0# B=>5CA-"+0D(# malnutrition. The data show that areas */26+*/0;# 826."+66(# 9*.7*# .6# 262-00@# ?+3@# face a Socioeconomic Survey (ERSS), which collects higher burden of malnutrition than do small town 9*.7*# 7/00+7)6# ;+)-.0+;# ;-)-# /"# ."7/E+# 9.)*# -# 6E-00C67-0+#-";#."4/3E-0G#:-F+#9/3<#4/3#8/)*# detailed data 4/726# /"# on household F+";+3G# welfare .6# H*+# =>5CA-"+0# and income- -# "-)./"-00@# areas. Forty-eight percent of under-5 children F3/2I6# .6# I3+;/E."-")0@# /2)6.;+# -F3.720)23+(# in generating activity. The ERSS ʼ sample includes 3+I3+6+")-).?+# 623?+@# 9.)*# -# 6-EI0+# /4# J(%%%# -";# ruralE+"# E/3+# -3+#are Ethiopia )9.7+# -6# to )*-"#compared stunted, 0.<+0@# -6# 29 percent */26+*/0;6K# 4,000 households that6+7/";# )*+# 9-?+# 32"6# are representative 43/E# of small 9/E+"# )/# *-?+# 9-F+# of children in small towns. +EI0/@E+")# 9*+"# Similarly, the $%&$'$%&LG# towns H*.6# and rural "/)+# areas; I3+6+")6# wave 2 will6+0+7)+;# 6)-).6).76# be expanded to 9/3<."FG## underweight prevalence of 26 percent in rural /"#F+";+3#-";#0.?+0.*//;6#."#,.F+3.-G## include urban areas and will run from 2013 to #areas is almost double the small town estimate, 2014. This note summarizes the anthropometric !"#$%&"'()*+),)-.*,(/*+'0)%1*%2*3%14** !"#$%&"'()*+',)%-*.&*/'(0'- ! which stands at 15 percent (see Figure 1). data and resulting malnutrition indicators from ;5 ;5?( M+"# -";# ERSS-wave 1 .9/E+"# 1 ."# )*+# =>5CA-"+0# 3+I/3)# )*+.3# :5 :5?( E-."# +EI0/@E+")# 6+7)/3(# 9*.7*# 7/20;# 8+# 9-F+# 951: Figure 95?( Malnutrition estimates, by rural and small town 85 Background on Child 9/3<(#*/26+*/0;#+")+3I3.6+#-7).?.).+6(#/3#4-3E."FG# 85?( 75 75?( NF3.720)23+(# )3-;+(# -";# I+36/"-0# 6+3?.7+6# -3+# Anthropometrics .EI/3)-")#+EI0/@E+")#6+7)/36#4/3#8/)*#E+"#-";# 65 60   65?( 5 5?( <,& Rural   9/E+"G#:*.0+#-F3.720)23+#.6#)*+#0-3F+6)#6+7)/3#4/3# =%>,& The three anthropometric indicators most often 50   Small  town   E+"(# .)# .6# referenced )3-;."F# for )*-)# malnutrition monitoring ;/E."-)+6# in4/3# 9/E+"# children BLOPD# 9.)*# -F3.720)23+# ."# 6+7/";# I0-7+# BLLPDG# !"# 40   are: stunting, or low height-for-age; underweight, 7/EI-3.6/"(# E+"# -3+# E27*# 0+66# 0.<+0@# )/# 8+# low weight-for-age; and wasting, low weight-for- )3-;."F#B&%PD#)*-"#."#/)*+3#6+7)/36G#:/E+"#9*/# 30   * %   height. More specifically, these figures represent 9/3<#-3+#-06/#E/3+#0.<+0@#)/#8+#."#E-"24-7)23."F# 20   * children whose height-for-age, weight-for-age, and )*-"#-3+#E+"#9*/#9/3-KL'$)*!+#",'=1&2'4#;+%'   /0'!"#$"%&'()#'*"%'1%'&2"'3)4&25'' +#"+3'JC?K'+%,'@/KL<'=21:"'%)%F(+#*'"%&"#!#13"3' Table 2: Stunting and Wasting, by region +%,'=+9"'=)#G'+#"'*4$2'*)#"'$)**)%'1%'4#;+%' Table 3: Stunted, underweight, wasted- by 7556 Stunted  (%)   Wasted  (%)   +#"+35' household (HH) headʼs gender Region   ?56 (Std.  Error)   (Std.  Error)   !%(1%+@ HH  head  is  male   HH  head  is  female   >56 >5 >56 =56 53   7     (Std.  error)     (Std.  error)   Tigray   =5 =56( <56 (5)   (3)   A3+$0*'#*+, Stunted   47%                 ( 2)   52%                    (5)   ;56 <5 <56( A3+$0*'#*+, 52   10   Underweight   ;5 27%                 ( 2)   21%                    (4)   Amhara   :56 ;56( 956 (4)   (2)   B%*/,C%'D Wasted   :5 :56( 12%                 ( 1)   6%                    (2)   856 43   10   ,&#,+E+$/, 95 956( B%*/,C%'D Oromia   756 We used household consumption to create welfare (3)   (2)   F"3,(1%+@ 85 856( ,&#,+E+$/, 56 quintiles. 75 756( The poorest households are grouped 53   14   $&("3 F"3,(1%+@ SNNP   into the 56( 1 5st quintile and the richest households fall (4)   (2)   thH"', I,J"', H"', I,J"', F"3,(1%+@ into the 5 quintile. Overall, stunting and All  other   39   10   $&(&%&G"3 regions   (4)   (2)   underweight B,"D prevalence B,"D B,"D B,"D decreases as one moves K+L"& K+L"& M*+"' M*+"' from ' the poorest to the top quintile. Interestingly, ) Identifying Vulnerable Sub- we do )not observe the same trend for wasting (see Populations 62"%'(+#*'+$&171&1"3'+#"'"8+*1%",')%'+'#"91)%+:' Figure5%-(%1)*-()3"14)67)816*-9:#1*') 4). ! ;+313<' 2)="7"#<' &2"' #"34:&3' +9+1%' #"(:"$&' *+:"' countries, 1%' In developing,)*1%+%$"' boys typically exhibit M))G1%9' +&' 4#;+%' +%,' #4#+:' :)$+&1)%3' 3"!+#+&":E<' (+#*1%9' =1&2' >?' !"#$"%&'Figure#"34:&3' 4: Malnutrition32)=' +'estimates, by welfare quintile 2192"#' !+#&1$1!+&1)%' 1%' =+9"' higher rates of malnutrition !+#&1$1!+&1)%' than girls. +*)%9' Ethiopia *"%' 7"#343' @A' !"#$"%&' 80   "*!:)E*"%&' 1%' 4#;+%' 7"#343' #4#+:' I19"#1+<' =1&2' proves to be no +*)%9' =)*"%' exception; 291%'percent &2"' %)#&25' of B2"' ,1(("#"%$"3' 1%' under-5 =+9"'"*!:)E*"%&'1%'4#;+%'+#"+3'#"!)#&",'+&'CA' Stunted   &2"'3)4&2'+#"'%)&'+3'9:+#1%9<'=1&2';)&2'*"%'+%,' boys are underweight, compared to only 23 !"#$"%&' $)*!+#",' &)' @?' !"#$"%&' 1%' Underweight   #4#+:' +#"+35' =)*"%' percent of under-5 girls. Similarly, +#)4%,' #"!)#&1%9' as shown CA' in !"#$"%&' 60   N"9+#,:"33' )(' 9")9#+!21$+:' :)$+&1)%<' *"%' +#"' Wasted   Figure 3, there!+#&1$1!+&1)%'#+&"3'1%'+9#1$4:&4#"5' are 1.5 times as many wasted boys *)#"' :1G":E' &)' ;"' 1%7):7",' 1%' =+9"' "*!:)E*"%&' than girls, with male and female wasting estimates 40   &2+%' =)*"%5' O#;+%' +#"+3' #"!)#&' *+:"' %   falling at 13 and!"#$%&"'()!%*($&+,)*-().%/0"1) 9 percent, respectively. !+#&1$1!+&1)%' +&' /P' !"#$"%&' +%,' ("*+:"' +&' C@' "2)3"14'' 20   !"#$"%&5' N4#+:' +#"+3' #"!)#&' *+:"' !+#&1$1!+&1)%' +&' D8+*1%1%9' Figure 3: Malnutrition !+#&1$1!+&1)%' estimates, +&' &2"' 2)43"2):,' by gender @-'!"#$"%&'+%,'("*+:"'+&'0'!"#$"%&5'' :"7":' =2"#"' +&' :"+3&' )%"' 2)43"2):,' *"*;"#' 0   !+#&1$1!+&"3' 1%' &2"' +$&171&E<' #"34:&3' 32)=' &2+&' Q+#&1$1!+&1)%' 1st   (poorest)   1%'3rd   )((F(+#*' 5th   3":(F"*!:)E*"%&' (richest)   13' 60   Girls   *+:"F2"+,",' 2)43"2):,3' +#"' *)#"' :1G":E' &)' 2192"#' +*)%9' =)*"%' &2+%' Welfare  QuinRles   *"%' 1%' ;)&2' 4#;+%' 50   !+#&1$1!+&"' 1%' +::' +$&171&1"3' $)*!+#",' &)' +%,' #4#+:' +#"+35' R%' 4#;+%' +#"+3<' )((F(+#*' 3":(F ("*+:"F2"+,",' 2)43"2):,35' D%9+9"*"%&' Boys   1%' Analysis "*!:)E",'!+#&1$1!+&1)%'+*)%9'=)*"%'3&+%,3'+&' 40   of the ERSS wave 1 anthropometric data 2)43"2):,' "%&"#!#13"' 13' &2"' *)3&' $)**)%' suggests 00'!"#$"%&'$)*!+#",'&)'-0'!"#$"%&'()#'*"%<'+%,' Ethiopia that +#"+3' a very high face)((F(+#*' burden of %   30   +$&171&E' ()#' ;)&2' *+:"F' +%,' ("*+:"F2"+,",' 1%' #4#+:' ("*+:"' 3":(F"*!:)E*"%&' malnutrition, a conclusion that matches that of the 3&+%,3'+&'-/'!"#$"%&'$)*!+#",'&)'@P'!"#$"%&'()#' 20   2)43"2):,3H'0@'+%,'>-'!"#$"%&'#"3!"$&17":E5' current literature. *"%5' S)="7"#<' Identifying ()#' (+#*1%9<'particularly vulnerable &2"' #"7"#3"' 13' &2"' ' 10   groups, $+3"H'such 1%'as4#;+%'children +#"+3<' in rural *"%' +#"'areas, !#",)*1%+%&' boys, and =1&2' =5 those living in households with illiterate heads, can 0   =56( ( @C' !"#$"%&' $)*!+#",' &)' .' !"#$"%&' ()#' =)*"%5' <5 <56( Stunted   Underweight   Wasted   help policyB213' 13' +:3)' &2"' makers target$+3"' 1%' #4#+:' nutrition +#"+3<' =1&2' programs more >.' ( ;5 ;56( !"#$"%&' effectively. )(' *"%' !+#&1$1!+&1%9' 1%' (+#*1%9' ( A3+$0*'#*+, $)*!+#",'&)'/-'!"#$"%&')('=)*"%5' We also find that :5 household head characteristics :56( This brief is based on data collected by the Central B%*/,C%'D Statistical B213'Agency ;#1"(' 13'as part )%' of the Living Standards ( play a role in childhood 95 956( malnutrition. On average, ;+3",' ,+&+' $)::"$&",' ;E' &2"' children living (under household heads that can ,&#,+E+$/, Measurement Study – Integrated I+&1)%+:'T4#"+4')('U&+&13&1$3'+3'!+#&')('&2"'M171%9' Surveys on 85 856( read and write( in at least one language exhibit Agriculture (LSMS-ISA) project. U&+%,+#,3' V"+34#"*"%&' U&4,E' ²' R%&"9#+&",' The full dataset is F"3,(1%+@ available better outcomes 75for all three indicators. Table 3 756( U4#7"E3' )%' W9#1$4:&4#"' for download JMUVUFRUWL' at CSA !#)X"$&5'via B2"' ( outlines the differences in malnutrition for children (4::',+&+3"&'13'+7+1:+;:"'()#',)=%:)+,'+&'IWYW'71+' http://www.csa.gov.et. 56( 5 with male vs. female household ===5%19"#1+%3&+&59)75%95'' A'' H"',heads. I,J"',Although children with male household heads B,"D B,"D exhibit lower The findings outlined in this brief are drawn from… ' ' I%+(J%+,($&-%+J"#$%&N(E',"/,(4$/$#O( rates of stunting, they are more likely to be Revisiting z-scores: A review of LSMS-ISA 1112&$3,+$"&/#"#23%42&3( underweight and wasted. The wasting prevalence Anthropometric ' Data, The World Bank, as ( I)&' 34#!#131%9:E<' &2"' #4#+:' +%,' 4#;+%' ,171,"' P+(0%&#"0#O( for children in female-headed households is half presented at the LSMS-ISA Annual Workshop 32)=3' *)#"' +9#1$4:&4#+:' 2)43"2):,3' 1%' &2"' #4#+:' !"#$%&"'()*+,"*(%-(.#"#$/#$0/( that of male-headed households (6 vs. 12 percent). +#"+3' ()#' ;)&2' *+:"F' +%,' ("*+:"F' 2"+,",' 2013, Addis Ababa, Ethiopia -,,DL"0@Q&$3,+$"&/#"#23%42&3(   !"#$%&"'()*+,"*(%-(.#"#$/#$0/()+$,-((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((1112&$3,+$"&/#"#23%42&3( (