A Gendered Approach to Credit Demand: Evidence from Marsabit District, Kenya Anne Gesare, Megan Sheahan, Andrew Mude, Rupsha Banerjee ADRAS IBLI Academic Workshop ILRI Nairobi, 11 th June 2015

Background Poor households in the ASALs face a multitude of shocks which are both covariant and idiosyncratic in nature The shocks have a negative impact on the socio and economic welfare of the households which increases their vulnerability to poverty Women headed households are highly vulnerable to the negative household shocks due to social norms, intra-household inequalities and unsupportive institutions: – ownership and control over resources, – decision making, – Low literacy levels, – Lack of information and poor access to credit

Background Access to a variety of financial instruments can help households manage the multiple shocks they face Savings Credit Insurance Interventions in the area; IBLI, HSNP, saving groups (CARE, BOMA) Index insurance; help pastoralists manage the impact of climate related shocks but does not help in household specific losses. Credit acts as an ex-post response strategy to shocks Credit services directed towards poor households can help households respond to shocks through Consumption smoothing Adoption of technologies Productive investment

Knowledge gap Past studies  Interconnection between credit access and household welfare (Guirkinger and Boucher, 2007)  Coexistence of formal and informal credit markets (Diagne, et.al., 2000, Bell, et al., 1997)  Market imperfections and intra-household dynamics affect rural women’s access and demand for credit (Quisumbing 2003) Gaps  Limited knowledge about the functioning of the credit markets in the pastoral context  Factors influencing female HH demand for credit in the pastoral context

Objectives Explore patterns of credit demand among men and women in Marsabit District Understand gender differentiated determinants of credit demand in Marsabit District Assessing if shocks influence male and female headed households decision to borrow differently Research questions Do shocks affect male and female headed households decision to borrow differently? Do risk aversion affects household decision to borrow and how much to borrow?

 Marsabit District in Eastern Region  16 sub-locations purposively selected  924 households panel data  5 years panel data  2.9 attrition rate per year  We use 820 balanced panel households` Study area and data

Empirical Strategy Credit demand; We ask if households borrow credit in the past year, with the expectation of repayment, from whom they borrowed, amount borrowed and the interest rate Cash credit  Formal sources – Banks (Equity, KCB, Cooperative bank) – Sacco (Mwalimu/Mkulima/livestock Traders Sacco)  Informal sources – Friends/neighbours – Family – Merry-go-rounds – Trader (monetary) Goods on credit – Trader (in kind)

Empirical Strategy

Household Characteristics (Gender) Total (N=820)Female (N=308)Male (N=512)T-test Age (Years) *** Dependency ratio (Ratio) Education level (years) *** Annual income (‘000 Kshs) ** Herd size (TLU) ** Savings income (‘000 Kshs) * Income from livestock (Ratio) High risk aversion ** Lost animals to drought (%) *** Sick HH members (%) Financial literacy Number of network groups (count) *** HSNP recipient (%) *

Characteristics of FHH Never marriedMarriedDivorcedWidowed Proportion (%) Age (years) Receive HSNP (% Herd size (TLU) Income from livestock (ratio) Savings ('000 Kshs) Total Income ('000 Kshs) Livestock losses to drought (%) Borrowed goods on credit (%) Borrowed formal credit (%)0111 Borrowed informal credit (%)615811

Credit Applications and Sources ( ) Gender Bought goods on credit Formal creditInformal creditAll sources Total number of HH 2009M F M F M F M F M F PooledM F total

Amount of Credit Borrowed

Model 1; Factors influencing demand for Credit VariableDemandVariableDemand FHH0.029Savings Animal loss-0.270**Income-0.001** High risk aversion0.370***Ratio of income from livestock-0.03 Ill health0.225**Livelihoods0.090** Animal loss*FHH0.396***Number of children in school0.026 Ill health*FHH0.138Low financial literacy-0.423* High risk aversion*FHH-0.264Range land below normal TLU-0.011***Age HSNP0.006**Household size0.06 Fully settled0.065Dependency ratio Partially settled-0.001Education Networks0.077*

Model 2: Determinants of Amount Borrowed VariableCoefficientVariableCoefficient FHH-0.385Savings0.005* Animal loss0.124Income0.001 Ill health0.022Proportion of income from livestock High risk aversion0.020Livelihoods0.038 Animal loss*FHH0.075Number of children in school Ill health*FHH0.299*Low financial literacy High risk aversion*FHH-0.404*Range land below normal-0.210* IBLI purchase0.020Age Fully settled0.514*Household size0.002 Partially settled0.571*Dependency ratio0.449 TLU0.001Education0.018 HSNP recipients0.006Networks-0.007

Conclusion  Households in Marsabit borrow for consumption smoothing  Credit demand in Marsabit is lower than demand in rural areas of other developing countries  Effects of risk aversion on credit demand and amount borrowed differ within MHH and FHH  The relationship between shocks and credit demand differs between MHH and FHH,  HH liquidity reduces demand for credit but increases amount borrowed Implications  Conducive systems in place to enable continuous provision of credit especially during shock aftermaths; tailored to suit pastoralists in terms of accessibility, lending terms and repayment schedules  Encourage use of mobile based money lending systems; Mshwari

Moving forward Qualitative study: Understanding the concept of credit demand and the process which goes into the profiling of an individual as credit worthy or not Understanding the intra-household gender dynamics around IBLI, who makes the decision to purchase, who receives the payout, how is the money from payout spent after the payout

Thank you!