Be endson, Rica do Mo el; Gassmann, F anziska; Ma o ano, B uno; Ti i ayi,
Nyasha J.; Kamau, John
Wo king Pape
The impac s o he mic o inance mul iplied app oach on
seasonal ood insecu i y: E idence om a high- equency
panel su ey in Uganda
UNU-MERIT Wo king Pape s, No. 2024-014
P o ided in Coope a ion wi h:
Maas ich Economic and Social Resea ch Ins i u e on Inno a ion and Technology (UNU-MERIT),
Uni ed Na ions Uni e si y (UNU)
Sugges ed Ci a ion: Be endson, Rica do Mo el; Gassmann, F anziska; Ma o ano, B uno; Ti i ayi,
Nyasha J.; Kamau, John (2024) : The impac s o he mic o inance mul iplied app oach on seasonal
ood insecu i y: E idence om a high- equency panel su ey in Uganda, UNU-MERIT Wo king
Pape s, No. 2024-014, Uni ed Na ions Uni e si y (UNU), Maas ich Economic and Social Resea ch
Ins i u e on Inno a ion and Technology (UNU-MERIT), Maas ich
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/326910
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by-nc-sa/4.0/
#2024-014
The impac s o he Mic o inance Mul iplied app oach on seasonal
ood insecu i y: E idence om a high- equency panel su ey in
Uganda
Rica do Mo el, F anziska Gassmann, B uno Ma o ano, Nyasha Ti i ayi
and John Kamau
Published 19 June 2024
Maas ich Economic and social Resea ch ins i u e on Inno a ion and Technology (UNU-MERIT)
email: in o@me i .unu.edu | websi e: h p://www.me i .unu.edu
Boschs aa 24, 6211 AX Maas ich , The Ne he lands
Tel: (31) (43) 388 44 00
UNU-MERIT Wo king Pape s
ISSN 1871-9872
Maas ich Economic and social Resea ch Ins i u e on Inno a ion and Technology
UNU-MERIT
UNU-MERIT Wo king Pape s in end o dissemina e p elimina y esul s o esea ch ca ied
ou a UNU-MERIT o s imula e discussion on he issues aised.
1
The impac s o he Mic o inance Mul iplied app oach on seasonal ood insecu i y:
E idence om a high- equency panel su ey in Uganda
Rica do Mo el a, F anziska Gassmann b, B uno Ma o ano b, Nyasha Ti i ayi c, and John Kamau d
June 2024
Abs ac
We s udy he impac o an inno a i e p og am ha combines mic o inance wi h a ming ex ension se ices
on ood secu i y ou comes in u al Sou h-Wes e n Uganda. Fo his pu pose, we use expe imen al da a and
mon hly panel da a collec ed o e wo yea s o moni o seasonal changes. The esul s sugges ha nei he he
combined app oach o mic o inance wi h a ming ex ension se ices no s andalone mic o inance
demons a ed signi ican e ec i eness in educing ood insecu i y h oughou seasons in he pe iod o analysis.
Households in bo h ea men g oups expe ienced a educ ion o die a y di e si y mainly du ing land
p epa a ion app oxima ely wo yea s a e he s a o he in e en ions. He e ogeneous analysis e ealed ha
households ecei ing MFM se ices and ha ing be e access o ma ke s expe ienced occasional imp o emen s
in ood secu i y. Finally, households wi h highe ood po e y le els in he MFM g oup expe ienced some
imp o emen s in ood secu i y, while hose in he Mic o inance g oup encoun e ed spo adic nega i e
ou comes in e ms o die a y di e si y.
JEL codes: G21, I3, Q16, Q18
Keywo ds: die a y di e si y, ood secu i y, mic o inance, ag icul u e
a Inno a ions o Po e y Ac ion (IPA). Con ac : .mo el@ou look.com
b UNU-MERIT – Maas ich Uni e si y
c UNICEF Innocen i-Global O ice o Resea ch and Fo esigh
d Low-Income Financial T ans o ma ion (L-IFT)
2
1. In oduc ion
Ru al popula ions in Sub-Saha an A ica ace signi ican challenges due o wea he and economic shocks associa ed wi h
seasonal ag a ian cycles, leading o a ia ions in income, ood access, and consump ion. Seasonal po e y, pa icula ly
du ing he lean season, ad e sely a ec s ood secu i y, die a y in ake, and nu i ional s a us (Hoddino and Yohannes
2002; Sen and D èze 1989; Jan y e al. 2016). Add essing hese issues is a c i ical policy p io i y o na ional
go e nmen s and he in e na ional communi y, e lec ed in ini ia i es like he Sus ainable De elopmen Goals and he
'ze o hunge ' agenda (FAO e al. 2020; FAO 1996; Uni ed Na ions 2017). Achie ing ood secu i y equi es consis en
access o su icien , sa e, and nu i ious ood ha mee s die a y equi emen s and p e e ences while conside ing he
challenges o seasonali y (FAO 2002).
Using an expe imen al design, his s udy aims o measu e he impac o an inno a i e app oach ha in eg a es
Mic o inance p oduc s wi h Ag icul u e, Poul y, and Li es ock (APL) ex ension se ices on ood secu i y ac oss
a ming seasons. This bundled app oach, known as Mic o inance Mul iplied (MFM), posi s ha in eg a ing p og ams
add essing complemen a y ou comes can yield g ea e impac s han implemen ing hem sepa a ely. MFM a ge s poo
u al households ha a e g appling wi h seasonali y cons ain s and can enhance household p oduc i i y h ough
di e en mechanisms. Mic o inance can imp o e seasonal income du ing he lean season, he eby inc easing
households’ liquidi y o in es men s and ( ood) spending as well as sa ings capaci y, which helps o smoo hen
consump ion o e hese seasonal shocks (Khandke e al.2015; 2010). The APL p og am is di ec ly linked o a ming
ac i i ies, and i o e s ag icul u e, poul y, and li es ock ex ension se ices, including aining on mode n a ming
me hods in combina ion wi h dis ibu ing p oduc i e echnologies, such as imp o ed seeds o accines o chickens and
li es ock. Ex ension se ices can help boos he p oduc i i y and di e si y o c ops and animal p oduc s, and he su plus
can be sold in he ma ke s o sa ed o consump ion in he lean season (Hawkes and Ruel 2006). While hei
mechanisms a e di e en , bo h p og ams ha e he po en ial o complemen each o he o ensu e ood secu i y. Cash
liquidi y can be used immedia ely o smoo h consump ion while lea ning mode n a ming p ac ices and using imp o ed
p oduc i e inpu s can inc ease p oduc i i y in he longe e m. Addi ionally, c edi can be injec ed in o a ming
ac i i ies o boos p oduc i i y and comme cialize p oduc s, which can lead o highe income, sa ings, and ood o
be e cope wi h seasonal a ia ions.
Despi e he po en ial complemen a i y o hese p og ams in ensu ing ood secu i y, he e is limi ed e idence ega ding
hei syne gies in smoo hing consump ion and mi iga ing he nega i e impac s o seasonal shocks. To ill his e idence
gap, his s udy employs a ac o ial andomised design ha compa es he impac o he bundled MFM app oach and
s andalone Mic o inance o a con ol g oup. The aim is o e alua e how hese in e en ions p o ec households om
seasonal a ia ions in ood secu i y. The esea ch assesses ends in ood secu i y and die a y di e si y using a ich panel
da ase collec ed mon hly o e 24 mon hs in u al Sou h-Wes e n Uganda, p edominan ly comp ising smallholde
a me s engaged in ain- ed ag icul u e.
3
Resul s sugges ha nei he he bundled MFM app oach no s andalone mic o inance showed signi ican e ec i eness
in educing ood insecu i y ac oss seasons in he sho e m. Howe e , he e we e no iceable nega i e impac s on
die a y di e si y du ing he ha es ing and land p epa a ion seasons a he end o he s udy pe iod. The lack o signi ican
e ec s on ood insecu i y ou comes and he nega i e e ec s on die a y di e si y could be due o ins i u ional and socio-
economic ac o s. The analysis shows ha households ha esided close o ma ke s and ecei ed MFM se ices
expe ienced spo adic imp o emen s in ood secu i y du ing ha es ing, while die a y di e si y emained unchanged.
Households wi h highe ood po e y le els in he MFM g oup expe ienced some imp o emen s in e ms o ood
secu i y sco e in he i s mon h and du ing land p epa a ion phases in he second yea , while hose in he Mic o inance
g oup expe ienced a dec ease in he die a y di e si y sco e du ing one mon h o he second ha es ing o yea one.
This s udy add esses a gap in he li e a u e by comp ehensi ely examining he impac o bundling mic o inance and
ex ension se ices on ood secu i y ac oss ag icul u al seasons. The exis ing e idence on he e ec i eness o
mic o inance and a ming p og ams in add essing seasonal ood insecu i y and die a y di e si y is inconclusi e, and
he e is a lack o esea ch on he combined e ec s o hese in e en ions. This pape makes a aluable con ibu ion o
he s udy o seasonal ood secu i y by conduc ing an in-dep h analysis based on a comp ehensi e panel su ey co e ing
ou plan ing and ha es ing seasons. Unlike many impac e alua ions in he ield o de elopmen economics ha ely
on limi ed da a poin s – usually, wo o occasionally h ee i he e is a midline su ey – his unique panel o mon hly
su ey ounds o e 24 mon hs allows o a ho ough explo a ion o he po en ial bene i s o combining mic o inance
and echnology adop ion p og ams in educing seasonal ood insecu i y. Tes ing he disagg ega ed impac s o a bundled
app oach is ano he con ibu ion o his pape . The ac o ial andomised design allows o di e en ia ing e ec s o
mic o inance se ices and an in eg a ed mul iplied app oach ha adds on ag icul u e and li es ock ex ension se ices.
The indings highligh he impo ance o enhancing p og am design o inco po a e ood secu i y and nu i ion
componen s a i s co e, s eng hening and adap ing mic o inance se ices o u al popula ions and seasonali y, and
conside ing local ma ke access and dynamics.
2. Li e a u e e iew
Seasonali y – unde s ood as in a-annual, seasonal luc ua ions in he ag a ian cycle – is inhe en o smallholde a ming
households and common in u al Sub-Saha an A ica. This dynamic means ha income and consump ion a e p one o
luc ua e depending on he season. In he g owing season, when mos o he ood is p oduced, a m labou is in demand,
ood p ices a e cheape , and, in gene al, mo e income is gene a ed. This pe iod is ollowed by he lean season when
ood s ocks a e deple ed, labou is sca ce, ood p ices a e highe , and income ends o be lowe . This seasonal a ia ion,
he e o e, aises he oppo uni y cos o p ices, labou , wages, and e en mig a ion pa e ns (De con and K ishnan
2007; De e eux, e al. 2011; Kaminski, e al. 2014; S ephens and Ba e 2011; Basu and Wong 2012; B yan, e al.
2014). The e is a la ge body o e idence ha indica es ha seasonal a ia ion in household consump ion is mainly due
4
o luc ua ions in income, p ices, labou , un eliable ma ke s, and access o c edi (Rosenzweig and Binswange 1993;
Chaudhu i and Paxson 2002; Paxson 1993). One o he mos c i ical isks o his seasonal dynamic is ha households
ha do no ha e he necessa y coping mechanisms – o example, s o ing ood, sa ings, asse s, c edi – will consume
mo e calo ies and nu ien s du ing he g owing season bu be unable o mee hei an adequa e die a y in ake du ing
he lean season, nega i ely a ec ing hei ood secu i y and nu i ional s a us, a key dimension o human wellbeing
(Hoddino and Yohannes 2002; De con and K ishnan 2007; Ch is ian and Dillon 2016; Jan y e al. 2016).
Achie ing e ec i e consump ion smoo hing is challenging o many poo u al households due o limi ed esou ces and
ex e nal ac o s such as p ice luc ua ions, labou demand, and heal h and clima e shocks. As a esul , u al households
use di e en isk mi iga ion mechanisms o smoo hen consump ion and ensu e ood secu i y, complemen ing p oac i e
s a egies (be o e seasonal shocks occu ) wi h eac i e esponses (a e seasonal shocks occu ). Fo example, households
can s o e ood, use sa ings, accumula e asse s, bo ow money, o engage in o - a m labou (Mo duch 1995; Fink,
Jack, and Masiye 2020; Chambe s, e al. 1981; De con and K ishnan 2007; Collins e al. 2009). Howe e , deg ees o
success a y because o he unce ain y o se e al ex e nal ac o s, such as ood p ice luc ua ion, a ailabili y o o - a m
wo k, and e en he incidence o heal h and clima e shocks, du a ion o he lean season, among o he s (Ch is ian and
Dillon 2016). The li e a u e also emphasizes ha he poo es households a e mo e ulne able o seasonal dep i a ion
as hey canno a o d mos o hese mechanisms, such as li es ock and g ain s o age, signi ican sa ings, o access o
lexible c edi . As a esul , hei me hods o smoo hening consump ion a e inadequa e and ine icien (Khandke , e
al. 2015). E en i expec ed, seasonal a ia ions p esen cons an challenges o poo u al households as hey ha e o
cons an ly sa e and bo ow money o ha e enough ood o ea du ing he yea (Collins e al. 2009).
Ce ain in e en ions such as mic o inance and a ming ex ension se ices may ha e he po en ial o o e come hese
cons ain s and empowe households o manage isks and shocks, enabling hem o main ain ood secu i y and s abilize
hei consump ion. The exis en e idence ela ed o hese in e en ions is u he discussed below.
Mic o inance. Access o c edi p o ides liquidi y and can be a ool o consump ion smoo hening du ing seasonal shocks
(Collins e al. 2009; Mo duch 1995; Zelle and Sha ma 2000; Angelucci, e al. 2015). Mic o inance can also help
p omo e o - a m income-gene a ing ac i i ies (Bandie a, e al. 2022; Bane jee, e al. 2015). These ac i i ies a e less
ola ile du ing seasonal shocks and, in p inciple, p o ide households wi h a s eadie income du ing he lean season,
allowing mo e spending on ood and helping o smoo h consump ion du ing c i ical momen s (Pi and Khandke 2002;
Zaman 1999; 2004; Khandke 2012). Howe e , he e idence base does no con incingly suppo his claim. Se e al
expe imen al s udies in di e en con ex s show null e ec s on income, sa ings, o consump ion (Meage 2019;
Bandie a, e al. 2022; Bane jee, e al. 2015; Dahal and Fiala 2020).
1
Schola s also poin ou ha s anda d mic o inance
1
The analysis by Bane jee, Ka lan, and Zinman co e ed Bosnia (Augsbu g e al. 2015), E hiopia (Ta ozzi, Desai, and Johnson 2015), India
(Bane jee, Ka lan, and Zinman 2015), Mexico (Angelucci, Ka lan, and Zinman 2015), Mo occo (C épon e al. 2015), and Mongolia (A anasio
e al. 2015) be ween 2003 and 2012. Meage ’s esea ch (Meage 2019) included he same s udies and added an RCT by Ka lan and Zinman
(2011) in he Philippines. Dahal and Fiala (2020), inally, added a s udy by Fiala (2018) in Uganda.
5
schemes, equi ing egula epaymen s ega dless o he income seasonali y, can be de imen al o bo owe s, o
example, by leading o o e -indeb edness (Mo duch 1999; Schicks 2014; Rahman 1999). Mic o inance ins i u ions
ha e s a ed o ailo hei p oduc s a ge ed o a me s o he seasonal a ia ions o he ag a ian cycle and he e idence,
in gene al, shows ha lexible p oduc s ha e mo e po en ial han he s anda d schemes (Field e al. 2013). E idence o
he impac o lexible c edi p oduc s in Bangladesh and Zambia inds imp o emen s in ood secu i y (Khandke ,
Khalily, and Samad 2015; Fink, e al. 2020), especially among he poo es . Loans a e used as a mechanism o
consump ion smoo hing du ing he lean season and hen epaid in small ins almen s o e he yea when income and
ood access inc ease again (Be g and Em an 2020).
Fa ming ex ension se ices. Ru al households in low-income se ings ace a ious challenges such as limi ed esou ces, high
ansac ion cos s, and ma ke ' ailu es' like he lack o es ablished c edi and sa ing mechanisms. Acco ding o he
ag icul u al household model heo y, hese households make decisions abou hei p oduc ion and consump ion ha
a e in e dependen , and non-sepa able om hei esou ce alloca ion decisions (Jan y and Sadoule 2006; Conceição
e al. 2016; Jack 2013; Ka lan e al. 2014; Mo is e al. 2007; Muza i, e al. 2012; Ud y 2010; Jan y e al. 2016).
To add ess his challenge, a ming ex ension se ices o e aining on mode n a ming echniques and access o highly
p oduc i e echnologies, such as imp o ed seeds o animal accina ion, o di e si y c opping sys ems and boos he
p oduc i i y o smallholde a me s, de eloping, a he same ime, hei capaci y o espond o shocks and isks by
smoo hing consump ion and di e si ying die s po en ial o igh ood insecu i y (Hawkes and Ruel 2006; Ba e ,
Rea don, and Webb 2001; Loison 2015). Howe e , he empi ical e idence is inconclusi e and he e is s ill an impo an
gap in he e ec s o seasonali y. A me a-analysis on he associa ion be ween he di e si ica ion o c op p oduc ion and
die di e si y shows ha , while indings end o be gene ally posi i e, ew s udies epo consis en esul s ac oss
indica o s (Sibha u and Qaim 2018). One o he challenges highligh ed is he high numbe s o addi ional c ops o animal
species equi ed o p oduc ion by smallholde a me s in o de o achie e he desi ed le els o die a y di e si y
(Sibha u and Qaim 2018). A sys ema ic e iew o he e ec i eness o ag icul u al p og ams on child nu i ion in
de eloping coun ies shows o e all posi i e impac s on p oduc i i y and ood consump ion, howe e , hese e ec s did
no lead o a subs an i e imp o emen in he die o poo households (Masse e al. 2012). Howe e , a ecen e alua ion
in Uganda shows ha c op di e si ica ion inc eased household die di e si y and consump ion (Tes aye and Ti i ayi
2020). Addi ionally, he e is s ill li le causal e idence o he link be ween ex ension se ices and ood secu i y.
Resea che s ound ha he adop ion o echnologies ha equi e low up on in es men s achie ed ood secu i y in
Uganda (Pan, e al. 2018). In Tanzania, esea che s ound ha a me ield schools encou aged a me s o adop
echnologies ha helped smoo h p oduc ion o e seasons (i.e., p oduced ood ac oss all seasons) and educed hunge
(La sen and Bie Lilleø 2014). Howe e , in Came oon, an e alua ion ound ha ex ension se ices did no imp o e
he household die a y di e si y sco e (Ngomi e al. 2020).
6
3. Uganda and he BRAC p og am
Desc ip ion o he con ex . Uganda is pa icula ly ulne able o ood secu i y as ag icul u e is mainly ain- ed and hus,
suscep ible o seasonal and wea he shocks (Tes aye and Ti i ayi 2020). Uganda anks 95 h ou o 113 coun ies in he
Global Food Secu i y Index and, acco ding o he Ugandan Bu eau o S a is ics, 37% o households a e ood-poo
whe eas u al households we e as double as likely o be unde his ca ego y (The Economis 2022). Ag icul u e accoun s
o he la ges sha e o employmen wi h nea ly 40% o he wo king popula ion, o which 43% epo ed ha subsis ence
a ming was hei main sou ce o income. Howe e , he adop ion o p oduc i e a ming inpu s and echnologies
emains low (Ba ua 2011; Wo ld Bank 2016; UBOS 2018). Access o c edi was apidly inc easing a he ime o he
s udy, eaching app oxima ely a qua e o households, bu mos o hem in u ban se ings. The la ges p opo ion o
c edi sou ces we e s ill in o mal wi h exo bi an in e es a es, pa icula ly in u al a eas (Chaia e al. 2009; FSD
Uganda 2018; UBOS 2014; 2010; 2018).
The loca ion o he s udy is in Sou h-Wes e n Uganda, in he dis ic s o Kabale and Rukungi i (Figu e 1, Panel A).
This se ing has a bimodal seasonal calenda , wi h wo ainy seasons (usually be ween Ma ch and June and mid-Augus
o Decembe ) and d y seasons in be ween (WFP 2013). This seasonali y means ha he e a e also wo main ag icul u al
seasons e e y yea ; he i s is ypically be ween Janua y o July, and he second is om Augus o Decembe . Each
season includes pe iods o land p epa a ion, c op cul i a ion, weeding, and ha es ing. The i s ag icul u al season o
he yea ends o be longe and, he e o e, he peak lean season usually akes place a ound he middle o his pe iod,
ha is om Ap il o May.
Bo h Kabale and Rukungi i dis ic s ha e ag icul u e as a main sou ce o ood and income, bu hey di e in he main
c ops hey p oduce. In Kabale, he main c ops a e po a oes, so ghum, and beans, while in Rukungi i a e bananas, beans,
and cassa a. Mos households in bo h dis ic s manage hei ood and cash needs du ing he plan ing and weeding pe iod
h ough a combina ion o s a egies. They u ilize hei ca y-o e s ocks om p e ious seasons, engage in li es ock
ea ing, sell hei labou ei he locally o by mig a ing o lowland a eas, o seek suppo om ela i es (B owne and
Glaese 2010). F om 2011 o 2013, he egion expe ienced gene ally posi i e pa e ns o ag icul u e and li es ock
p oduc ion. Du ing his ime, bimodal a eas in Uganda, including he Sou h-Wes , o e all expe ienced a ou able
ain all, and abo e-a e age c op p oduc ion, leading o eplenished ood s ocks, s abilized p ices, and no o minimal
ood insecu i y. In 2014, he bimodal egions saw sho d y spells and poo ain all dis ibu ion ac oss bo h ainy
seasons, howe e , he o e all ha es s we e close o a e age, and, in gene al, households we e able o mee hei ood
needs h ough ma ke pu chase and seasonal ac i i ies, such as c ops sales, casual labou in ha es and pos -handling
ac i i ies, and pe y ade. Th oughou he pe iod o his s udy, bimodal a eas emained in Phase 1 o he In eg a ed
Phase Classi ica ion (IPC),
2
indica ing minimal acu e ood insecu i y (FEWS NET 2023).
2
The IPC sys em was de eloped by a conso ium o in e na ional o ganiza ions including FAO, WFP, UNICEF, and WHO, among o he s.
13
Figu e 3. Household Die a y Di e si y Sco e (HDDS) – OLS panel analysis by mon h
No e: Labels nex o he x-axis in each igu e e e o he mon h numbe and he season. "Lean" e e s o he lean season; "ha "
s ands o ha es ing; "p ep" e e s o land p epa a ion.
Robus ness check. Resul s on he main ou comes o in e es a e u he explo ed using a di e ence-in-di e ence
(DID) es ima ion. The DID model assesses e ec s o he ea men g oups o e ime compa ed o he con ol g oup.
The model compa es he di e ences o mon h 1 wi h each o he subsequen mon hs. In o he wo ds, he eg ession is
epea ed o he in e ac ion on mon h 1 wi h each ollowing mon h, s a ing om mon h 2 un il mon h 24. This
app oach allows iden i y whe he and how he ea men s ha e an impac ha a ies ac oss di e en ime pe iods
ela i e o he i s mon h o he panel.
The DID analysis yielded simila esul s o he OLS eg ession analysis in e ms o he lack o signi ican impac s on he
HFIAS sco e (Table A5). Rega ding die a y di e si y, he only impac obse ed in he DID analysis was a highe sco e
du ing he i s land p epa a ion pe iod, jus be o e he lean season. This inding con as s wi h he esul s o he OLS
analysis, whe e nega i e signi ican e ec s we e obse ed on adequa e die a y di e si y in he las h ee mon hs o he
panel, du ing he las land p epa a ion season in he s udy pe iod. While some di e ences we e obse ed be ween he
DID analysis and he main OLS analysis by panel mon h, he o e all ends we e no subs an ially di e en . These
14
indings p o ide addi ional obus ness o he main esul s and suppo he conclusion ha he in e en ions did no ha e
signi ican impac s on ood secu i y and die a y di e si y.
5.2 In e media y Ou comes
Explo a ion o he o impac on in e media y ou comes – namely bo owing, sa ings, and labou – do no show clea
ends o signi ican di e ences be ween he ea men g oups and he con ol (Table A6 o Table A10). The s udy
indings show no signi ican changes in o mal bo owing and sa ings o e seasons, in line wi h he indings o mul iple
expe imen al s udies in di e se con ex s (Bane jee, e al. 2015; Meage 2019; Dahal and Fiala 2020; Bandie a, e al.
2022).The only excep ion a e households in he MFM g oup who bo owed less, on a e age, han hose in he con ol
g oup du ing one mon h o he las ha es season (mon h 22), which coincides wi h he signi ican dec ease in die a y
di e si y. As discussed in he in e p e a ion o ood secu i y ou comes, s uc u al and p og amma ic ba ie s, such as a
s anda d epaymen model ha o e ed no lexibili y o u al households may ha e educed households' abili y o engage
(Mo duch 1999; Schicks 2014; Rahman 1999). Addi ionally, he e we e no sus ained changes in labou pa icipa ion,
bu hose in he mic o inance g oup wo ked mo e on someone else’s land du ing he lean weeding season (mon h 13)
and inc eased en ep eneu ial engagemen du ing he i s land p epa a ion season (mon hs 5 and 6).
6. He e ogenous analysis
This sec ion ocuses in o he e ogenous ea men e ec s by examining he in luence o dis ance o he nea es ma ke
and weal h. By conside ing hese ac o s, he s udy aims o unco e nuanced insigh s in o he a ying impac s o he
in e en ions on ood secu i y and die a y di e si y among di e en subg oups wi hin he s udy popula ion.
6.1. Dis ance o he nea es ma ke
P oximi y o ma ke s can shape oppo uni ies and cons ain s o households as i p o ides oppo uni ies o access
p oduc i e inpu s, inancial p oduc s, comme cial in o ma ion as well as mo e and di e se oods. Households close o
ma ke s may also be be e posi ioned o access o - a m income sou ces, educing ag icul u e dependence. Con e sely,
a he households ace ele a ed ansac ion cos s, diminishing ma ke hese oppo uni ies (Su i 2011; Abay and Jensen
2020; Madzo e a e al. 2021; Kuss, Gassmann, and Mugumya 2022). The e o e, analysing he impac o in e en ions
such as mic o inance and ag icul u e ex ension p og ams by dis ance o ma ke s can p o ide insigh s in o whe he
di e en households expe ience a ying impac s, which is c ucial o designing mo e a ge ed and e ec i e
de elopmen policies. The dis ance was calcula ed by asking esponden s in he baseline su ey o he ime i ook
hem o walk o he nea es ading cen e o ma ke . Using he median ime a he communi y le el, hese we e hen
di ided in o wo g oups based on hei dis ance o he ma ke , wi h he median dis ance se ing as he cu -o poin .
We c ea e a dummy a iable wi h a alue o one o households close o ma ke s (i.e. below he median epo ed
dis ance) and in e ac his a iable wi h he ea men a iables o es ima e he di e en ial impac o he p og ams due
o he dis ance o he nea es ma ke .
15
Resul s show ha households close o ma ke s expe ience a signi ican dec ease in he household ood insecu i y sco e
in he MFM g oup du ing he i s ha es ing season o he second yea (mon h 15) (Figu e 4, Table A11). By con as ,
he analysis o he die a y di e si y sco e does no show signi ican changes based on he dis ance o ma ke s (Figu e 5,
Table A12). Hence, he obse ed imp o emen s in ood secu i y ou comes among households close o ma ke s in he
MFM g oup seem o be spo adic, only appea ing in speci ic mon hs bu ha dly c ea ing a clea end.
Figu e 4. Household Food Insecu i y Access Scale (HFIAS) sco e by dis ance o ma ke (Close o he ma ke )
No e: Labels nex o he x-axis in each igu e e e o he mon h numbe and he season. "Lean" e e s o he lean season; "ha "
s ands o ha es ing; "p ep" e e s o land p epa a ion.
16
Figu e 5. Household Die a y Di e si y Sco e (HDDS) by dis ance o ma ke (Close o he ma ke )
No e: Labels nex o he x-axis in each igu e e e o he mon h numbe and he season. "Lean" e e s o he lean season; "ha "
s ands o ha es ing; "p ep" e e s o land p epa a ion.
6.2 Food po e y
Food po e y is measu ed by a dummy a iable based on he numbe o ypes o ood consumed a baseline. Households
who consume less han he a e age numbe o ood ypes, indica ing highe ood po e y, ge a alue o one. We
in e ac his a iable wi h he ea men a iables o es ima e he di e en ial impac o he p og ams due o hei le el
o ood po e y.
The analysis e eals ha ood-poo households in he MFM g oup expe ience a signi ican dec ease in he ood
insecu i y sco e in he i s mon h and du ing he land p epa a ion phase in he second yea (mon hs 17 and 23),
(Figu e 6, Table A13). Howe e , signi ican changes a e no consis en o e ime and, he e o e, le els o ood
consump ion do no seem o ha e subs an ial and sus ained impac s in ood secu i y ou comes.
17
Figu e 6. Household Food Insecu i y Access Scale (HFIAS) sco e by ood consump ion (Food po e y)
No es: Labels nex o he x-axis in each igu e e e o he mon h numbe and he season. "Lean" e e s o he lean season; "ha "
s ands o ha es ing; "p ep" e e s o land p epa a ion.
Resul s also indica e a dec ease in he die a y di e si y sco e among ood-poo households in he Mic o inance g oup
du ing he second ha es ing o yea one (mon h 10) (Figu e 7, Table A14). The obse ed dec eases in die a y di e si y
among ood-poo households a e sca e ed and inconclusi e. A possible in e p e a ion o hese changes, howe e , could
be a ibu ed o he ac ha hese households aced limi a ions in accessing a a ie y o oods due o hei inancial
cons ain s. Despi e he p o ision o mic o inance, he a ailable inancial esou ces may s ill be insu icien o suppo
di e se ood pu chases. Mo eo e , asse -poo households may ha e limi ed access o ma ke s and may be cons ained
by highe ood p ices and limi ed a ailabili y o di e se ood i ems. I is impo an o no e ha while mic o inance
in e en ions may imp o e access o inancial esou ces, addi ional ocused suppo ac i i ies on enhancing ood access
and nu i ion educa ion may be necessa y o add ess he speci ic challenges aced by asse -poo households in imp o ing
hei die a y di e si y.
18
Figu e 7. Household Die a y Di e si y Sco e (HDDS) by ood consump ion (Food po e y)
No e: Labels nex o he x-axis in each igu e e e o he mon h numbe and he season. "Lean" e e s o he lean season; "ha "
s ands o ha es ing; "p ep" e e s o land p epa a ion.
7. Conclusions
The s udy implemen s a andomised e alua ion o examine he impac s o Mic o inance and an in eg a ed app oach ha
combines Mic o inance wi h Ag icul u e, Poul y, and Li es ock ex ension p og ams on ood secu i y ou comes and
die a y di e si y o e a ming seasons. The esul s indica e ha he in e en ions did no ha e signi ican e ec s on ood
secu i y ou comes, as measu ed by he Household Food Insecu i y Access Scale (HFIAS). Mo eo e , he analysis on
die a y di e si y indica es ha households in bo h he MFM app oach and he s andalone Mic o inance p og am
expe ienced a signi ican ly lowe Households Die a y Di e si y Sco e (HDDS) du ing he las ha es ing season and
subsequen land p epa a ion mon hs in he s udy pe iod. He e ogeneous analysis employing dis ance o ma ke s and
le els o ood po e y p oduced in e es ing esul s. The analysis e eals ha households close o ma ke s in he MFM
g oup expe ienced posi i e ood secu i y ou comes, pa icula ly du ing he i s ha es ing season o he second yea .
Mo eo e , households wi h highe ood po e y le els in he MFM g oup expe ienced some imp o emen s in e ms o
19
ood secu i y in he i s mon h and du ing land p epa a ion phases in he second yea ; howe e , households wi h highe
ood po e y in he Mic o inance g oup expe ienced spo adic nega i e ou comes in e ms o die a y di e si y.
The indings o his s udy p o ide aluable insigh s o policymake s and p ac i ione s aiming o enhance he
e ec i eness o inancial inclusion in e en ions in add essing ood secu i y and die a y di e si y. One key akeaway is
he impo ance o enhance he design o inancial inclusion p og ams by building in componen s ha explici ly a ge
ood secu i y and nu i ion ou comes. I is no enough o simply bundle i wi h ano he p og am – in his case, APL
se ices – i i does no in eg a e mechanisms ha di ec ly enhance he in ake o di e se and nu i ious oods in o he
p og am design. In o he wo ds, he ecommenda ion is o ake a comp ehensi e s and by ocusing no only on income
gene a ion and p oduc i i y bu also on eng aining o he dimensions in he p og am design, such as ood- ela ed aspec s
ha can mo e e ec i ely con ibu e o imp o ing ood secu i y and nu i ional well-being among ulne able
popula ions. Addi ionally, i is c ucial o ailo mic o inance p oduc s and se ices o he speci ic needs o households,
pa icula ly o u al households wi h ola ile income due o ag icul u al seasons (e.g., including lexible epaymen
e ms) while aking in o conside a ion he dynamics o local ma ke s. Mo e g anula indings also sugges ha p oximi y
and access o ma ke s can play a c ucial ole in mi iga ing ood insecu i y. Mo eo e , a ge ing poo e sec ions o he
popula ion is ele an o ackle ood secu i y- ela ed ou comes.
These ecommenda ions can con ibu e o he imp o emen o inancial inclusion in e en ions in add essing ood
secu i y and die a y di e si y. By enhancing p og am design, s eng hening mic o inance se ices, conside ing local
ma ke dynamics, e ec i ely a ge ing less-weal hy popula ions, and p io i izing long- e m measu emen ,
policymake s and p ac i ione s can e ine in e en ions and p omo e sus ainable imp o emen s in ood secu i y and
nu i ional well-being among ulne able popula ions.
20
Re e ences
Abay, Kib om A., and Na haniel D. Jensen. 2020. “Access o Ma ke s, Wea he Risk, and Li es ock P oduc ion
Decisions: E idence om E hiopia.” Ag icul u al Economics 51 (4): 577–93.
h ps://doi.o g/10.1111/agec.12573.
Angelucci, Manuela, Dean Ka lan, and Jona han Zinman. 2015. “Mic oc edi Impac s: E idence om a Randomized
Mic oc edi P og am Placemen Expe imen by Compa amos Banco.” Ame ican Economic Jou nal: Applied
Economics 7 (1): 151–82. h ps://doi.o g/10.1257/app.20130537.
A anasio, O azio, B i a Augsbu g, Ralph De Haas, Emla Fi zsimons, and Heike Ha mga . 2015. “The Impac s o
Mic o inance: E idence om Join -Liabili y Lending in Mongolia.” Ame ican Economic Jou nal: Applied Economics 7
(1): 90–122. h ps://doi.o g/10.1257/app.20130489.
Augsbu g, B i a, Ralph De Haas, Heike Ha mga , and Cos as Meghi . 2015. “The Impac s o Mic oc edi : E idence
om Bosnia and He zego ina.” Ame ican Economic Jou nal: Applied Economics 7 (1): 183–203.
h ps://doi.o g/10.1257/app.20130272.
Balla d, Te i, Jenni e Coa es, Anne Swindale, and Megan Dei chle . 2011. “Household Hunge Scale: Indica o
De ini ion and Measu emen Guide.”
Bandie a, O iana, Robin Bu gess, E ika Dese anno, Rica do Mo el, Im an Rasul, Munshi Sulaiman, and Jack Thiemel.
2022. “Mic o inance and Di e si ica ion.” Economica 1. h ps://doi.o g/10.1111/ECCA.12424.
Bandie a, O iana, Robin Bu gess, E ika Dese anno, Rica do Mo el, Munshi Sulaiman, and Im an Rasul. 2022. “Social
Incen i es, Deli e y Agen s and he E ec i eness o De elopmen In e en ions.” Jou nal o Poli ical Economy
Mic oeconomics 1 (1): 162–224. h ps://doi.o g/10.1086/722898.
Bane jee, Abhiji , Dean Ka lan, and Jona han Zinman. 2015. “Six Randomized E alua ions o Mic oc edi : In oduc ion
and Fu he S eps.” Ame ican Economic Jou nal: Applied Economics 7 (1): 1–21.
Ba e , C B, T Rea don, and P Webb. 2001. “Non a m Income Di e si ica ion and Household Li elihood S a egies
in Ru al A ica: Concep s, Dynamics, and Policy Implica ions.” Food Policy 26: 315–31.
Ba ua, P oloy. 2011. “Assessmen o he Sho -Run Impac o BRAC’s Ag icul u e and Li es ock P og amme in
Uganda.” BRAC Wo king Pape Se ies 1.
Ba ua, P oloy, and Munshi Sulaiman. 2006. “Ta ge ing he Poo es in Mic o inance: Po e y Ou each o BDP Ul a
Poo P og amme.” CFPR/TUP Wo king Pape Se ies 13.
21
Basu, Ka na, and Maisy Wong. 2012. “E alua ing Seasonal Food Secu i y P og ams in Eas Indonesia.” Munich Pe sonal
RePEc A chi e Pape 51219.
Beaman, Lo i, Dean Ka lan, B am Thuysbae , and Ch is ophe Ud y. 2014. “Sel -Selec ion in o C edi Ma ke s:
E idence om Ag icul u e in Mali.” NBER Wo king Pape Se ies 20387: 1–37. h ps://doi.o g/10.3386/w20387.
Be g, Claudia, and M. Shahe Em an. 2020. “Mic o inance and Vulne abili y o Seasonal Famine in a Ru al Economy:
E idence om Monga in Bangladesh.” Jou nal o Economic Analysis and Policy 20 (3).
h ps://doi.o g/10.1515/bejeap-2018-0359.
B owne, S ephen, and Lau a Glaese . 2010. “Li elihood Mapping and Zoning Exe cise: Uganda.” Famine Ea ly Wa ning
Sys em Ne wo k.
B yan, Gha ad, Shyamal Chowdhu y, and Ahmed Mush iq Moba ak. 2014. “Unde in es men in a P o i able
Technology: The Case o Seasonal Mig a ion in Bangladesh.” Econome ica 82 (5): 1671–1748.
h ps://doi.o g/10.3982/ECTA10489.
Chaia, Albe o, Apa na Dalal, Tony Goland, Ma ia Jose Gonzalez, Jona han Mo duch, and Robe Schi . 2009. “Hal
he Wo ld Is Unbanked.” Financial Access Ini ia i e F aming No e Oc obe .
Chambe s, Robe , Richa d Longhu s , and A nold Pacey. 1981. Seasonal Dimensions o Ru al Po e y. London: F ances
Pin e L d.
Chaudhu i, Subham, and Ch is ina Paxson. 2002. “Smoo hing Consump ion unde Income Seasonali y :” Columbia
Depa men o Economics Discussion Pape Se ies 0102 (54).
Ch is ian, Paul, and B ian Dillon. 2016. “Long Te m Consequences o Consump ion Seasonali y.” A ican De elopmen
Bank Wo king Se ies 241.
Coa es, Jenni e , Anne Swindale, and Paula Bilinsky. 2007. “Household Food Insecu i y Access Scale (HFIAS) o
Measu emen o Food Access: Indica o Guide.” Washing on, D.C.: Food and Nu i ion Technical Assis ance
P ojec .
Collins, Da yl, Jona han Mo duch, S ua Ru he o d, and O landa Ru h en. 2009. Po olios o he Poo : How he Wo ld’s
Poo Li e on $2 a Day. P ince on: P ince on Uni e si y P ess.
Conceição, Ped o, Sebas ian Le ine, Michael Lip on, and Alex Wa en-Rod íguez. 2016. “Towa d a Food Secu e
Fu u e: Ensu ing Food Secu i y o Sus ainable Human De elopmen in Sub-Saha an A ica.” Food Policy 60: 1–
9. h ps://doi.o g/10.1016/j. oodpol.2016.02.003.
22
Coppel, Emily, and Munshi Sulaiman. 2015. “Key Findings om Randomized E alua ion o Mic oc edi and
Ag icul u e Se ices in Uganda.” BRAC Resea ch Re iew 2.
C épon, B uno, Flo encia De o o, Es he Du lo, and William Pa ien é. 2015. “Es ima ing he Impac o Mic oc edi
on Those Who Take I up: E idence om a Randomized Expe imen in Mo occo.” Ame ican Economic Jou nal:
Applied Economics 7 (1): 123–50. h ps://doi.o g/10.1257/app.20130535.
Dahal, Mahesh, and Na han Fiala. 2020. “Wha Do We Know abou he Impac o Mic o inance? The P oblems o
S a is ical Powe and P ecision.” Wo ld De elopmen 128.
h ps://doi.o g/doi.o g/10.1016/j.wo ldde .2019.104773.
De con, S e an, and P amila K ishnan. 2007. “Vulne abili y, Seasonali y and Po e y in E hiopia.” The Jou nal o
De elopmen S udies 36 (6): 25–53. h ps://doi.o g/10.1080/00220380008422653.
De e eux, S ephen, Rachel Saba es-Wheele , and Richa d Longhu s . 2011. Seasonali y, Ru al Li elihoods, and
De elopmen . London: Rou ledge.
D esche , La issa S, Silke Thiele, and Ge B M Mensink. 2007. “A New Index o Measu e Heal hy Food Di e si y
Be e Re lec s a Heal hy Die Than T adi ional Measu es.” The Jou nal o Nu i ion 137: 647–51.
FAO. 1996. “Rome Decla a ion on Wo ld Food Secu i y.” Wo ld Food Summi . 1996.
h p://www. ao.o g/3/W3613E/W3613E00.h m.
———. 2002. “Repo o he Wo ld Food Summi : Fi e Yea s La e .” 2002.
h ps://www. ao.o g/3/Y4172M/y4172m01e.h m.
FAO, IFAD, UNICEF, WFP, and WHO. 2020. The S a e o Food Secu i y and Nu i ion in he Wo ld 2020. T ans o ming
Food Sys ems o A o dable Heal hy Die s. Rome: FAO. h ps://doi.o g/10.4060/ca9692en.
FEWS NET. 2023. “Uganda - Food Secu i y Ou look.” Famine Ea ly Wa ning Sys ems Ne wo k. Feb ua y 13, 2023.
h ps:// ews.ne /eas -a ica/uganda.
Fiala, Na han. 2018. “Re u ns o Mic oc edi , Cash G an s and T aining o Male and Female Mic oen ep eneu s in
Uganda.” Wo ld De elopmen 105: 189–200. h ps://doi.o g/10.1016/j.wo ldde .2017.12.027.
Field, E ica, Rohini Pande, John Papp, and Na alia Rigol. 2013. “Does he Classic Mic o inance Model Discou age
En ep eneu ship Among he Poo ? Expe imen al E idence om India.” Ame ican Economic Re iew 103 (6): 2196–
2226. h ps://doi.o g/10.1257/ae .103.6.2196.
Fink, Gün he , B Kelsey Jack, and Felix Masiye. 2020. “Seasonal Liquidi y, Ru al Labo Ma ke s, and Ag icul u al
P oduc ion †.” Ame ican Economic Re iew 110 (11): 3351–92. h ps://doi.o g/10.1257/ae .20180607.
29
Table A2. Balance check a baseline
(1)
(2)
(3)
Va iables
All ea men s
MFM
Mic o inance
Age o esponden
0.00182
-0.00109
0.00291*
(0.00133)
(0.00122)
(0.00128)
Ma i al s a us
-0.0149
-0.00120
-0.0137
(0.0144)
(0.0126)
(0.0143)
P ima y educa ion comple ed
-0.0285
-0.000315
-0.0281
(0.0519)
(0.0525)
(0.0512)
Bo owed om o mal sou ces in he las yea
-0.00583
0.0211
-0.0270
(0.0372)
(0.0403)
(0.0359)
Engaged in ag icul u e o li es ock in he las yea
-0.0506
0.0118
-0.0624
(0.0740)
(0.0780)
(0.0811)
Numbe o sleeping ooms in he house
-0.0161
0.00786
-0.0240
(0.0194)
(0.0189)
(0.0205)
A e age numbe o meals pe day
0.0220
0.0190
0.00299
(0.0288)
(0.0281)
(0.0310)
Kep cash sa ings in he las yea
-0.0985
-0.0487
-0.0498
(0.0725)
(0.0665)
(0.0709)
Wo ked in someone else’s a m (wages)
0.0765
0.0558
0.0207
(0.0799)
(0.0799)
(0.0868)
Wo ked o - a m (sel -employed)
0.101*
-0.00536
0.107
(0.0506)
(0.0468)
(0.0578)
Wo ked o - a m (wages)
-0.0239
-0.0540
0.0301
(0.0890)
(0.0755)
(0.0924)
P e alence o household ood insecu i y access (based on HFIAS)
-0.0835
0.0391
-0.123
(0.0680)
(0.0592)
(0.0681)
Insu icien ood access (based on HFIAS)
0.0146
0.0474
-0.0328
(0.0549)
(0.0562)
(0.0512)
Inadequa e die a y di e si y (based on HDDS)
-0.00149
0.0209
-0.0224
(0.0836)
(0.0923)
(0.0829)
Cons an
0.779**
0.237*
0.542**
(0.152)
(0.118)
(0.155)
Obse a ions
792
792
792
R-squa ed
0.007
0.013
0.019
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
30
Table A3. HFIAS: Household Food Insecu i y Scale Sco e (0-27) – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
0.4766
[0.5730]
0.2364
[0.6072]
774
0.112
7.516
7.794
7.607
mon h 2
ha es
0.2834
[0.5583]
0.3688
[0.6000]
771
0.104
7.115
7.408
7.527
mon h 3
ha es
-0.1426
[0.5338]
0.5384
[0.5800]
754
0.112
6.107
5.885
6.688
mon h 4
p epa a ion
-0.1140
[0.5890]
0.7379
[0.6152]
738
0.099
6.126
5.906
6.917
mon h 5
p epa a ion
-0.0023
[0.5875]
0.6106
[0.5982]
715
0.095
6.228
6.192
6.793
mon h 6
p epa a ion
0.3827
[0.6012]
0.1288
[0.5759]
706
0.094
6.251
6.597
6.469
mon h 7
lean
0.4740
[0.5210]
0.4393
[0.5367]
712
0.079
6.093
6.529
6.645
mon h 8
lean
0.4484
[0.5396]
0.4685
[0.5555]
751
0.119
5.834
6.169
6.323
mon h 9
ha es
0.7185
[0.5438]
0.8833
[0.5515]
773
0.152
5.099
5.684
5.905
mon h 10
ha es
0.3333
[0.5493]
0.5894
[0.5890]
764
0.178
5.364
5.601
5.916
mon h 11
p epa a ion
0.2936
[0.5388]
0.7895
[0.6009]
768
0.191
5.046
5.178
5.714
mon h 12
p epa a ion
0.5538
[0.5083]
0.9846
[0.5545]
767
0.199
4.850
5.167
5.644
mon h 13
lean
0.0784
[0.5568]
0.1677
[0.5528]
627
0.147
5.797
5.797
6.057
mon h 14
ha es
0.2996
[0.5154]
0.5392
[0.5215]
772
0.113
5.267
5.526
5.763
mon h 15
ha es
0.0793
[0.4902]
0.3116
[0.4835]
778
0.103
4.830
4.926
5.162
mon h 16
p epa a ion
-0.0900
[0.5374]
0.2540
[0.5328]
778
0.112
5.003
4.912
5.234
mon h 17
p epa a ion
-0.1775
[0.5662]
0.0941
[0.5669]
768
0.146
5.245
5.072
5.387
mon h 18
p epa a ion
-0.5490
[0.5231]
0.1829
[0.5556]
776
0.117
5.475
4.898
5.579
mon h 19
lean
0.1713
[0.5495]
0.2697
[0.5319]
759
0.141
5.777
5.813
5.943
mon h 20
lean
-0.0993
[0.5100]
-0.2906
[0.5113]
782
0.105
5.372
5.327
5.076
mon h 21
ha es
0.4086
[0.5374]
-0.3767
[0.5311]
780
0.126
5.303
5.728
4.917
mon h 22
ha es
0.2058
[0.5944]
-0.5532
[0.5633]
780
0.097
5.339
5.492
4.649
mon h 23
p epa a ion
0.5110
[0.5970]
-0.4276
[0.5507]
772
0.139
5.589
5.988
5.023
mon h 24
p epa a ion
0.5752
[0.6466]
-0.2698
[0.5894]
687
0.174
5.353
5.511
4.767
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
31
Table A4. HDDS: Household Die a y Di e si y Sco e (0-12) – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
-0.1916
[0.2358]
-0.2434
[0.2231]
791
0.068
5.782
5.651
5.545
mon h 2
ha es
0.1072
[0.2096]
0.1914
[0.2126]
788
0.101
6.301
6.463
6.466
mon h 3
ha es
-0.2044
[0.2681]
-0.0251
[0.2436]
789
0.100
6.752
6.665
6.805
mon h 4
p epa a ion
-0.1692
[0.2716]
-0.2545
[0.2287]
782
0.101
6.418
6.395
6.295
mon h 5
p epa a ion
-0.1961
[0.2874]
0.1164
[0.2397]
774
0.119
6.436
6.293
6.560
mon h 6
p epa a ion
-0.2019
[0.3254]
0.3741
[0.2675]
779
0.122
6.372
6.260
6.839
mon h 7
lean
-0.1442
[0.2651]
0.0851
[0.2210]
765
0.085
6.242
6.207
6.392
mon h 8
lean
-0.2558
[0.2532]
-0.0327
[0.2253]
775
0.093
6.493
6.341
6.530
mon h 9
ha es
-0.3817
[0.2622]
0.0925
[0.2203]
774
0.140
6.741
6.456
6.846
mon h 10
ha es
-0.0976
[0.2574]
-0.0155
[0.2184]
763
0.108
6.543
6.448
6.444
mon h 11
p epa a ion
-0.2208
[0.3274]
-0.0434
[0.2473]
768
0.103
6.755
6.530
6.744
mon h 12
p epa a ion
-0.4317
[0.2866]
0.0656
[0.2259]
767
0.135
6.686
6.299
6.832
mon h 13
lean
-0.4242
[0.3298]
-0.0815
[0.2516]
627
0.160
6.356
5.934
6.295
mon h 14
ha es
-0.2721
[0.3064]
-0.0748
[0.2402]
772
0.156
6.719
6.455
6.662
mon h 15
ha es
-0.6054
[0.3238]
-0.3163
[0.2445]
778
0.134
7.018
6.527
6.812
mon h 16
p epa a ion
-0.5595
[0.3033]
-0.3377
[0.2502]
778
0.126
7.028
6.458
6.734
mon h 17
p epa a ion
-0.3983
[0.2939]
-0.3239
[0.2394]
768
0.128
6.918
6.618
6.693
mon h 18
p epa a ion
-0.3503
[0.3106]
-0.3378
[0.2463]
776
0.171
6.975
6.675
6.675
mon h 19
lean
-0.4961
[0.3176]
-0.1758
[0.2419]
759
0.157
6.652
6.217
6.582
mon h 20
lean
-0.4459
[0.3162]
-0.2123
[0.2473]
782
0.125
6.872
6.512
6.747
mon h 21
ha es
-0.6055
[0.3136]
-0.1719
[0.2489]
780
0.161
6.952
6.461
6.890
mon h 22
ha es
-0.6756*
[0.3015]
-0.0620
[0.2509]
780
0.169
7.014
6.417
7.010
mon h 23
p epa a ion
-0.6811*
[0.3308]
-0.4134
[0.2672]
772
0.157
7.354
6.797
7.066
mon h 24
p epa a ion
-0.8456*
[0.3285]
-0.4661
[0.2601]
687
0.177
7.378
6.689
7.030
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
32
Table A5. Food secu i y indica o s – DID analysis
G oup
HFIAS Sco e
HDD Sco e
es
MFM
mon h_2
-0.2271
0.2691
[0.5508]
[0.1944]
M i
mon h_2
0.1327
0.4071
[0.5499]
[0.2129]
MFM
mon h_3
-0.6325
-0.0470
[0.5967]
[0.2479]
M i
mon h_3
0.2808
0.2224
[0.6102]
[0.2345]
P epa a ion
MFM
mon h_4
-0.6349
-0.0098
[0.6307]
[0.2719]
M i
mon h_4
0.5503
-0.0116
[0.6361]
[0.2652]
MFM
mon h_5
-0.5301
-0.0378
[0.6151]
[0.2874]
M i
mon h_5
0.4334
0.3680
[0.6026]
[0.2494]
MFM
mon h_6
-0.1043
-0.0376
[0.6202]
[0.3203]
M i
mon h_6
-0.0684
0.6323*
[0.5758]
[0.2849]
Lean
MFM
mon h_7
-0.0806
0.0110
[0.6426]
[0.3027]
M i
mon h_7
0.2017
0.3153
[0.6120]
[0.2928]
MFM
mon h_8
-0.0784
-0.0797
[0.6820]
[0.3033]
M i
mon h_8
0.2512
0.1960
[0.6497]
[0.3017]
Ha es
MFM
mon h_9
0.1552
-0.2069
[0.7126]
[0.3214]
M i
mon h_9
0.6045
0.3230
[0.6502]
[0.3046]
MFM
mon h_10
-0.2501
0.0647
[0.7272]
[0.3021]
M i
mon h_10
0.3281
0.2133
[0.6697]
[0.2765]
P epa a ion
MFM
mon h_11
-0.2579
-0.0796
[0.6959]
[0.3676]
M i
mon h_11
0.5669
0.1753
[0.6488]
[0.3038]
MFM
mon h_12
0.0127
-0.2751
[0.6590]
[0.3370]
M i
mon h_12
0.6862
0.2934
[0.6891]
[0.2954]
Lean
MFM
mon h_13
-0.4851
-0.2594
[0.7589]
[0.3859]
M i
mon h_13
-0.0345
0.1476
[0.7901]
[0.3200]
Ha es
MFM
mon h_14
-0.2703
-0.1340
[0.7208]
[0.3705]
M i
mon h_14
0.2675
0.1815
[0.7158]
[0.3149]
MFM
mon h_15
-0.4836
-0.4534
[0.7136]
[0.3785]
M i
mon h_15
0.0473
-0.0582
[0.7215]
[0.3138]
33
G oup
HFIAS Sco e
HDD Sco e
P epa a ion
MFM
mon h_16
-0.6278
-0.3833
[0.7017]
[0.3649]
M i
mon h_16
0.0106
-0.0745
[0.6987]
[0.3329]
MFM
mon h_17
-0.7468
-0.2327
[0.7838]
[0.3552]
M i
mon h_17
-0.1742
-0.0612
[0.7459]
[0.3166]
MFM
mon h_18
-1.1088
-0.1904
[0.7352]
[0.3625]
M i
mon h_18
-0.1166
-0.0773
[0.7471]
[0.3099]
Lean
MFM
mon h_19
-0.3748
-0.3383
[0.7336]
[0.3886]
M i
mon h_19
-0.0028
0.0731
[0.7759]
[0.3362]
MFM
mon h_20
-0.6302
-0.2975
[0.6906]
[0.4099]
M i
mon h_20
-0.5354
0.0313
[0.7318]
[0.3491]
Ha es
MFM
mon h_21
-0.1329
-0.4444
[0.7291]
[0.3853]
M i
mon h_21
-0.6518
0.0904
[0.7571]
[0.3264]
MFM
mon h_22
-0.3371
-0.5195
[0.7561]
[0.3763]
M i
mon h_22
-0.8044
0.1803
[0.7959]
[0.3321]
P epa a ion
APL
mon h_23
-0.0810
-0.5207
[0.7745]
[0.3802]
MFM
mon h_23
-0.6836
-0.1512
[0.7843]
[0.3178]
M i
mon h_24
0.0251
-0.6817
[0.7956]
[0.3919]
MFM
mon h_24
-0.5673
-0.1969
[0.8388]
[0.3243]
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
34
Table A6. Bo owing om o mal sou ces – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
-0.0337
[0.0445]
-0.0841
[0.0451]
792
0.071
0.364
0.337
0.292
mon h 2
ha es
-0.0411
[0.0421]
-0.0818
[0.0423]
792
0.097
0.309
0.267
0.224
mon h 3
ha es
-0.0014
[0.0368]
-0.0341
[0.0373]
791
0.093
0.213
0.191
0.175
mon h 4
p epa a ion
-0.0113
[0.0380]
-0.0251
[0.0371]
792
0.075
0.220
0.194
0.185
mon h 5
p epa a ion
-0.0125
[0.0431]
-0.0601
[0.0392]
790
0.078
0.275
0.242
0.205
mon h 6
p epa a ion
0.0051
[0.0397]
-0.0131
[0.0372]
791
0.089
0.224
0.218
0.198
mon h 7
lean
-0.0073
[0.0476]
-0.0455
[0.0396]
791
0.045
0.248
0.249
0.195
mon h 8
lean
0.0656
[0.0471]
-0.0244
[0.0376]
791
0.087
0.245
0.315
0.218
mon h 9
ha es
0.0554
[0.0467]
0.0037
[0.0405]
790
0.123
0.263
0.315
0.260
mon h 10
ha es
0.0518
[0.0501]
-0.0344
[0.0438]
791
0.095
0.307
0.354
0.256
mon h 11
p epa a ion
0.0418
[0.0491]
-0.0553
[0.0443]
790
0.089
0.297
0.340
0.237
mon h 12
p epa a ion
0.0289
[0.0468]
-0.0712
[0.0445]
791
0.083
0.293
0.319
0.224
mon h 13
lean
-0.0390
[0.0447]
-0.0690
[0.0443]
791
0.116
0.269
0.230
0.198
mon h 14
ha es
-0.0774
[0.0438]
-0.0398
[0.0484]
789
0.073
0.343
0.254
0.289
mon h 15
ha es
0.0080
[0.0429]
0.0465
[0.0444]
791
0.061
0.259
0.265
0.289
mon h 16
p epa a ion
-0.0019
[0.0414]
0.0253
[0.0434]
791
0.043
0.269
0.265
0.273
mon h 17
p epa a ion
-0.0072
[0.0443]
0.0188
[0.0438]
791
0.052
0.290
0.280
0.286
mon h 18
p epa a ion
-0.0301
[0.0452]
0.0165
[0.0467]
790
0.079
0.324
0.292
0.306
mon h 19
lean
-0.0509
[0.0439]
-0.0524
[0.0443]
791
0.086
0.309
0.272
0.257
mon h 20
lean
-0.0590
[0.0436]
-0.0304
[0.0476]
791
0.096
0.326
0.276
0.300
mon h 21
ha es
-0.0450
[0.0417]
-0.0359
[0.0447]
791
0.094
0.326
0.276
0.293
mon h 22
ha es
-0.0808*
[0.0404]
-0.0287
[0.0451]
790
0.093
0.334
0.249
0.300
mon h 23
p epa a ion
-0.0443
[0.0379]
0.0028
[0.0453]
786
0.081
0.284
0.240
0.280
mon h 24
p epa a ion
-0.0690
[0.0409]
0.0024
[0.0456]
721
0.107
0.269
0.191
0.258
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
35
Table A7. Kep cash sa ings – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
-0.0325
[0.0591]
-0.0494
[0.0606]
792
0.047
0.787
0.764
0.734
mon h 2
ha es
0.0794
[0.0552]
0.0362
[0.0549]
792
0.052
0.753
0.837
0.795
mon h 3
ha es
0.0290
[0.0470]
0.0007
[0.0480]
791
0.070
0.825
0.856
0.805
mon h 4
p epa a ion
0.0025
[0.0524]
-0.0111
[0.0506]
792
0.063
0.770
0.779
0.750
mon h 5
p epa a ion
-0.0627
[0.0542]
-0.0189
[0.0554]
790
0.067
0.780
0.719
0.747
mon h 6
p epa a ion
-0.0129
[0.0548]
-0.0196
[0.0549]
791
0.050
0.790
0.798
0.766
mon h 7
lean
0.0022
[0.0553]
-0.0170
[0.0588]
791
0.051
0.766
0.770
0.734
mon h 8
lean
0.0275
[0.0458]
0.0066
[0.0442]
791
0.079
0.790
0.813
0.786
mon h 9
ha es
-0.0221
[0.0526]
-0.0245
[0.0469]
790
0.060
0.779
0.755
0.747
mon h 10
ha es
0.0110
[0.0497]
0.0526
[0.0425]
791
0.102
0.762
0.774
0.805
mon h 11
p epa a ion
-0.0439
[0.0498]
0.0452
[0.0425]
790
0.092
0.803
0.762
0.834
mon h 12
p epa a ion
-0.0515
[0.0514]
0.0216
[0.0460]
791
0.065
0.790
0.739
0.802
mon h 13
lean
-0.0716
[0.0380]
-0.0494
[0.0308]
791
0.533
0.707
0.611
0.640
mon h 14
ha es
-0.0271
[0.0489]
0.0227
[0.0413]
789
0.099
0.782
0.766
0.818
mon h 15
ha es
-0.0215
[0.0467]
0.0048
[0.0433]
791
0.090
0.797
0.786
0.805
mon h 16
p epa a ion
0.0072
[0.0402]
0.0507
[0.0393]
791
0.092
0.814
0.813
0.851
mon h 17
p epa a ion
-0.0279
[0.0488]
0.0289
[0.0440]
791
0.064
0.793
0.770
0.825
mon h 18
p epa a ion
0.0337
[0.0441]
-0.0182
[0.0440]
790
0.123
0.797
0.856
0.805
mon h 19
lean
-0.0678
[0.0566]
-0.0230
[0.0548]
791
0.036
0.790
0.747
0.785
mon h 20
lean
-0.0553
[0.0471]
0.0067
[0.0435]
791
0.117
0.804
0.770
0.824
mon h 21
ha es
-0.0393
[0.0444]
0.0097
[0.0432]
791
0.183
0.777
0.759
0.798
mon h 22
ha es
-0.0518
[0.0463]
0.0130
[0.0429]
790
0.152
0.779
0.751
0.798
mon h 23
p epa a ion
0.0127
[0.0450]
0.0331
[0.0433]
786
0.157
0.765
0.768
0.788
mon h 24
p epa a ion
-0.0090
[0.0549]
0.0488
[0.0478]
721
0.097
0.762
0.753
0.801
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
36
Table A8. Wo ked on someone else's land (wages) – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
0.0121
[0.0316]
-0.0046
[0.0304]
792
0.058
0.0859
0.105
0.0779
mon h 2
ha es
0.0188
[0.0275]
0.0394
[0.0298]
792
0.054
0.0687
0.0891
0.104
mon h 3
ha es
-0.0143
[0.0338]
-0.0041
[0.0357]
791
0.042
0.117
0.0973
0.0974
mon h 4
p epa a ion
-0.0144
[0.0327]
-0.0159
[0.0343]
792
0.053
0.124
0.112
0.0974
mon h 5
p epa a ion
-0.0072
[0.0323]
-0.0016
[0.0333]
790
0.060
0.127
0.121
0.107
mon h 6
p epa a ion
-0.0028
[0.0339]
-0.0194
[0.0333]
791
0.070
0.131
0.136
0.101
mon h 7
lean
0.0520
[0.0324]
0.0186
[0.0329]
791
0.058
0.124
0.171
0.130
mon h 8
lean
-0.0304
[0.0359]
0.0277
[0.0441]
791
0.060
0.159
0.132
0.179
mon h 9
ha es
0.0050
[0.0341]
0.0345
[0.0397]
790
0.038
0.107
0.113
0.133
mon h 10
ha es
-0.0196
[0.0356]
0.0657
[0.0436]
791
0.067
0.128
0.105
0.175
mon h 11
p epa a ion
0.0238
[0.0286]
0.0450
[0.0338]
790
0.051
0.107
0.133
0.143
mon h 12
p epa a ion
-0.0073
[0.0351]
0.0449
[0.0396]
791
0.066
0.155
0.152
0.179
mon h 13
lean
-0.0282
[0.0315]
0.0779*
[0.0376]
791
0.107
0.145
0.117
0.201
mon h 14
ha es
-0.0084
[0.0361]
0.0422
[0.0405]
789
0.057
0.166
0.156
0.182
mon h 15
ha es
-0.0091
[0.0337]
0.0436
[0.0401]
791
0.045
0.152
0.136
0.172
mon h 16
p epa a ion
-0.0212
[0.0312]
0.0311
[0.0371]
791
0.060
0.172
0.144
0.172
mon h 17
p epa a ion
-0.0146
[0.0374]
-0.0098
[0.0374]
791
0.066
0.186
0.167
0.156
mon h 18
p epa a ion
-0.0502
[0.0346]
0.0025
[0.0371]
790
0.075
0.210
0.160
0.189
mon h 19
lean
-0.0316
[0.0380]
-0.0292
[0.0372]
791
0.069
0.206
0.183
0.153
mon h 20
lean
-0.0206
[0.0352]
-0.0148
[0.0363]
791
0.071
0.168
0.148
0.134
mon h 21
ha es
-0.0076
[0.0357]
-0.0391
[0.0356]
791
0.046
0.172
0.167
0.111
mon h 22
ha es
-0.0190
[0.0367]
-0.0264
[0.0350]
790
0.061
0.159
0.148
0.114
mon h 23
p epa a ion
-0.0318
[0.0380]
-0.0262
[0.0374]
786
0.077
0.176
0.146
0.134
mon h 24
p epa a ion
-0.0565
[0.0396]
-0.0404
[0.0395]
721
0.078
0.185
0.119
0.122
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
37
Table A9. Wo ked o - a m (sel -employed) – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
0.0329
[0.0338]
0.0862*
[0.0427]
792
0.172
0.206
0.256
0.315
mon h 2
ha es
0.0199
[0.0349]
0.0739
[0.0446]
792
0.172
0.223
0.252
0.289
mon h 3
ha es
0.0360
[0.0345]
0.0750
[0.0414]
791
0.165
0.210
0.261
0.299
mon h 4
p epa a ion
0.0196
[0.0378]
0.0716
[0.0408]
792
0.145
0.227
0.256
0.308
mon h 5
p epa a ion
0.0210
[0.0359]
0.0959*
[0.0415]
790
0.161
0.206
0.230
0.315
mon h 6
p epa a ion
0.0475
[0.0366]
0.1133**
[0.0420]
791
0.132
0.190
0.233
0.308
mon h 7
lean
-0.0130
[0.0351]
0.0076
[0.0381]
791
0.141
0.228
0.218
0.240
mon h 8
lean
0.0235
[0.0361]
0.0046
[0.0382]
791
0.167
0.224
0.249
0.237
mon h 9
ha es
0.0246
[0.0367]
0.0210
[0.0372]
790
0.142
0.215
0.245
0.240
mon h 10
ha es
0.0175
[0.0403]
0.0233
[0.0404]
791
0.148
0.224
0.257
0.256
mon h 11
p epa a ion
-0.0027
[0.0400]
0.0157
[0.0386]
790
0.155
0.238
0.238
0.263
mon h 12
p epa a ion
0.0094
[0.0377]
0.0288
[0.0376]
791
0.166
0.221
0.226
0.260
mon h 13
lean
-0.0163
[0.0324]
0.0154
[0.0344]
791
0.120
0.179
0.152
0.192
mon h 14
ha es
0.0039
[0.0393]
0.0404
[0.0404]
789
0.148
0.225
0.227
0.269
mon h 15
ha es
0.0056
[0.0405]
0.0342
[0.0423]
791
0.136
0.234
0.233
0.276
mon h 16
p epa a ion
-0.0089
[0.0418]
0.0428
[0.0417]
791
0.140
0.241
0.226
0.286
mon h 17
p epa a ion
-0.0125
[0.0384]
0.0604
[0.0410]
791
0.134
0.228
0.206
0.292
mon h 18
p epa a ion
-0.0071
[0.0397]
0.0477
[0.0419]
790
0.148
0.238
0.222
0.287
mon h 19
lean
-0.0317
[0.0422]
0.0228
[0.0440]
791
0.140
0.247
0.210
0.267
mon h 20
lean
-0.0274
[0.0406]
0.0596
[0.0433]
791
0.154
0.251
0.218
0.303
mon h 21
ha es
-0.0207
[0.0407]
0.0433
[0.0435]
791
0.152
0.258
0.230
0.300
mon h 22
ha es
-0.0363
[0.0407]
0.0335
[0.0456]
790
0.141
0.266
0.226
0.300
mon h 23
p epa a ion
-0.0237
[0.0407]
0.0367
[0.0442]
786
0.144
0.263
0.236
0.300
mon h 24
p epa a ion
-0.0563
[0.0423]
0.0355
[0.0447]
721
0.140
0.265
0.217
0.310
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
38
Table A10. Wo ked o - a m (wages) – OLS panel analysis by mon h
Mon h
Season
MFM
[S.E.]
M inance
[S.E.]
Obs
R2
Con ol a g
MFM a g
M i a g
mon h 1
weeding
-0.0235
[0.0233]
-0.0129
[0.0241]
792
0.060
0.0722
0.0543
0.0584
mon h 2
ha es
0.0019
[0.0245]
-0.0003
[0.0240]
792
0.027
0.0584
0.0659
0.0617
mon h 3
ha es
0.0088
[0.0185]
0.0373
[0.0224]
791
0.055
0.0309
0.0428
0.0747
mon h 4
p epa a ion
-0.0170
[0.0173]
0.0027
[0.0224]
792
0.054
0.0447
0.0349
0.0584
mon h 5
p epa a ion
-0.0255
[0.0148]
-0.0061
[0.0207]
790
0.071
0.0447
0.0195
0.0422
mon h 6
p epa a ion
-0.0312
[0.0174]
0.0060
[0.0230]
791
0.060
0.0552
0.0272
0.0714
mon h 7
lean
-0.0342
[0.0190]
-0.0117
[0.0218]
791
0.049
0.0586
0.0272
0.0487
mon h 8
lean
-0.0197
[0.0149]
0.0049
[0.0218]
791
0.064
0.0414
0.0233
0.0487
mon h 9
ha es
0.0073
[0.0162]
0.0107
[0.0194]
790
0.064
0.0311
0.0350
0.0455
mon h 10
ha es
-0.0179
[0.0164]
-0.0181
[0.0173]
791
0.054
0.0448
0.0272
0.0325
mon h 11
p epa a ion
0.0045
[0.0173]
-0.0039
[0.0189]
790
0.067
0.0414
0.0391
0.0390
mon h 12
p epa a ion
-0.0150
[0.0208]
-0.0182
[0.0211]
791
0.073
0.0552
0.0350
0.0390
mon h 13
lean
-0.0146
[0.0155]
-0.0206
[0.0158]
791
0.075
0.0379
0.0195
0.0227
mon h 14
ha es
0.0053
[0.0174]
0.0097
[0.0186]
789
0.085
0.0311
0.0391
0.0455
mon h 15
ha es
-0.0167
[0.0175]
-0.0010
[0.0206]
791
0.090
0.0414
0.0311
0.0487
mon h 16
p epa a ion
-0.0043
[0.0154]
0.0102
[0.0186]
791
0.090
0.0310
0.0311
0.0455
mon h 17
p epa a ion
0.0016
[0.0173]
0.0026
[0.0174]
791
0.076
0.0345
0.0389
0.0487
mon h 18
p epa a ion
0.0006
[0.0172]
0.0060
[0.0172]
790
0.086
0.0310
0.0389
0.0554
mon h 19
lean
0.0022
[0.0164]
0.0142
[0.0177]
791
0.075
0.0275
0.0350
0.0554
mon h 20
lean
-0.0009
[0.0165]
0.0068
[0.0180]
791
0.075
0.0344
0.0350
0.0521
mon h 21
ha es
-0.0200
[0.0173]
-0.0006
[0.0187]
791
0.093
0.0447
0.0272
0.0489
mon h 22
ha es
-0.0243
[0.0177]
-0.0041
[0.0189]
790
0.091
0.0483
0.0272
0.0489
mon h 23
p epa a ion
-0.0135
[0.0176]
0.0033
[0.0188]
786
0.094
0.0415
0.0315
0.0489
mon h 24
p epa a ion
-0.0069
[0.0167]
0.0033
[0.0186]
721
0.081
0.0346
0.0340
0.0453
* p<0.05, ** p<0.1 Robus s anda d e o s in pa en heses.
