Mig igrant Remit ittances an and Gender in in Zim imbabwe Julie Litchfield, Pierfrancesco Rolla, Farai Jena, Upenyu Dzingirai,Kefasi Nyikahadzoi and Patience Mutopo University of Sussex and University of Zimbabwe 1
Mot otiv ivatio ion “I have observed that the sending patterns of men and women are influenced by the social obligations that society places on them. Women are more organised and send food and clothes regularly. My son does not remit any goods during the year; he sends us money and groceries during the Christmas holiday as he argues that life is also difficult for him. I have interpreted this behaviour as rather being irresponsible and forgetting his African roots ” Fieldwork quote from 2013 2
Con onceptual Fram amework • The NELM literature considers motives to remit as being driven by • Pure altruism • Enlightened self-interest (e.g. co-insurance) • Exchange (e.g. To secure inheritance rights) • Difficult to test convincingly in empirical work but generally supports some form of self-interest or exchange • Gap is around gender • Do women remit less? Orozco et al (2013) suggest that women remit less than men in 18 countries; Niimi and Reilly (2011) find same for Vietnam; differences due largely to poorer economic opportunities for women at destination. Yet Abrego (2009) on Salvadorian migrants suggest women remit more • Are motives the same for men and women in contexts where institutions (e.g.. inheritance norms, income-sharing within villages) are highly gendered? 3
Overview • Data and evidence on remittances and gender • The data gap: under-reporting; in-kind remittances • Migrating out of Poverty Migration surveys • Remittance decisions: incidence, amount, composition or mix • Empirical approach and preliminary results • Once we control for characterstics of migrants, there is no difference between men and women in either how likely they are to remit or in how much they remit, but there is a difference in what they remit. • Discussion 4
Th The Data Gap • Data on cash remittances, especially international, is under-reported • Money carried by friends/associates; bills paid; hawala • Not collected or reported by gender (either of the sender or recipient) • Data on in-kind remittances are not often collected • When they are, often not included in official reports • When they are, don’t always capture the most common forms of in-kind remittances such as food and clothing • What we know about remittances may be under- reported by anything between 10 and 50%, and particularly so for women 5
Cas ash is is im important but t under-estimates total remittances : : Fij Fiji i an and Ton onga 20 2005 6
Ken enya 20 2009 Migratio ion Su Survey: Wom omen les ess likely ly to o sen send cash ash bu but valu alue of of in- kind nd rem emit ittances mak akes up up gap ap Cash and inkind remittances Estimate of cash and inkind comparison by gender remittances by gender 60.00% 160000 140000 50.00% 120000 40.00% 100000 30.00% 80000 20.00% 60000 10.00% 40000 20000 0.00% Percentage of migrants Percentage of migrants 0 sending cash sending inkind Average estimate value of Average estimate value of cash remittances inkind remittances Male Female Male Female Similar in Burkina Faso 2009 survey 7
Senegal 20 2009 Mig igration Survey Women remit le less ss an and se send le less ss cas ash an and in in-kind remittances Cash and inkind remittances comparison Estimate of cash and inkind remittances by by gender gender 80.00% 600000 70.00% 500000 60.00% 400000 50.00% 300000 40.00% 200000 30.00% 100000 20.00% 10.00% 0 Male Female 0.00% Percentage of migrants Percentage of migrants Average estimate value of cash remittances sending cash sending inkind Average estimate value of inkind remittances Male Female Similar in Nigeria 2009 8
So South Afr fric ica 2009 Mig igration Su Survey • Preference among women for sending in- Cash and inkind remittances comparison by gender kind remittances 44.00% 43.00% • No data on values 42.00% 41.00% 40.00% 39.00% 38.00% 37.00% 36.00% Percentage of migrants Percentage of migrants sending cash sending inkind Male Female 9
Mig igratin ing ou out of of Poverty Mig igratio ion Su Surveys • Five household surveys in Bangladesh, Indonesia, Ghana, Ethiopia and Zimbabwe, (2013-2015) • All but Indonesia cover multiple regions of the country • Common approach to sampling • Households selected randomly from village lists stratified into households with and households without migrants • Common definition of migration with spatial and temporal element • A member of the household who is currently away living outside the community*, has been away for at least 3 months and left within the last 10 years • All data is publically available on our MOOP web-site http://migratingoutofpoverty.dfid.gov.uk/themes/migration- data 10
Zim imbabwe 2015 su survey • Three districts Chivi, Gwanda and Hurungwe • Two wards in each district, 18 villages in total • 1200 households, 70% have at least one migrant • 1463 individual migrants 11
Table 1: Household sample by district and migrant status Households Households Households Households Total with Internal with with both with no migrants International Internal and migrants migrants International migrants District Chivi 85 190 27 98 400 Hurungwe 202 74 24 99 399 Gwanda 52 151 53 138 394 Total 339 415 104 335 1,193 12
What ar are in in-kind remit ittances? • World Bank African Migration • But not food or clothing. Surveys collect data on: • Survey of Netherlands to Suriname • Household appliances remittances suggests food and clothing are most common in-kind • refrigerators, deep freezers, TV, remittances HiFi system, Washing Machine, Stove/cooker, Microwave, air- conditioners, furniture, DVD/Video players, Mobile phones, • Business equipment • Computers and accessories, sewing machines, hair-dressing equipment; • Tractor and agricultural equipment • Transport • Motorbike, cars, buses, trucks 13
How we measure remit ittances What type of goods were received? (%) • At the individual level Food 74.3 • Remittances sent by Clothing 17.4 each migrant in last 12 School items 1.9 months Household utensils 1.6 Mobile phone 1.5 • In cash with value Blankets 1.1 reported by HH Ag inputs 0.4 respondent Computers 0.4 • In-kind by type and Business equipment 0.3 value reported by HH Building materials 0.3 respondent Bicycles and motor cycles 0.2 Other electronic equipment 0.1 Others 0.5 14
Remittances s in in Zim imbabwe 20 2015 in in-kin ind rem emit ittances pa part rtia iall lly mak ake up up the the gap ap be between men en an and wom omen Cash and in-kind remittances: comparison by Estimates of cash and in-kind remittances by gender gender 500 50 40 400 30 300 20 200 10 100 0 0 % sending cash % sending in-kind $ value of cash $ value of in-kind $ value of cash and in-kind Men Women Men Women 15
Em Empir iric ical l ap approach • We estimate three econometric models • remittance incidence : the probability that a migrant sends remittances • remittance amount : the $ value of total cash and in-kind remittances • remittance mix : the % of total remittances that are cash • 𝑆𝑓𝑛 𝑗 = 𝑏 + 𝑐𝐺𝑓𝑛𝑏𝑚𝑓 𝑗 + 𝑑 𝑁𝑗𝑠𝑏𝑜𝑢 𝑑ℎ𝑏𝑠𝑏𝑑𝑢𝑓𝑠𝑗𝑡𝑢𝑗𝑑𝑡 + 𝑒 𝐼𝐼 𝑑ℎ𝑏𝑠𝑏𝑑𝑢𝑓𝑠𝑗𝑡𝑢𝑗𝑑𝑡 + 𝑣 • Test if there are differences in remittance behaviour by gender and possible sources of those differences in gender- specific models • Cluster by HH as some households have more than one migrant • Selection bias in modelling amount and mix so we use Tobit 16
Do Do women remit le less ss aft fter con ontrolling for or mig igrant an and HH ch characteristics? Incidence Amount Mix No statistically No statistically Yes: Cash as % of significant significant total is on average difference difference 12.5% points lower than for men 17
Do factors that influence remittance behaviour differ by gender? Incidence Amount Mix Ethnicity Sotho women less likely to Women from all Ndebele men remit compared to women groups remit less and women send from other ethnic groups; no than Shona women; lower % cash differences between men of than other diff ethnicities. groups Age of migrant Older women more likely to Older men and No correlations remit than younger; women remit more; Time away Remittance decay among men Remittances decline No correlations migrants; not among women among men by $2 for every month migrant has been away Dependent Positive relationship for Remittances $200 No correlations children left women; no effect for men higher among men behind in HH with dep kids; HH Wealth No correlations Weak positive No correlations correlation for men; no link for women Education of No correlations No correlations No correlations Migrant 18
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