This document analyzes the gender dynamics of technology adoption using panel data from Uganda. It finds that: 1) Female-headed households are less likely to adopt technology initially compared to male-headed households. 2) Female-headed households' adoption decisions are more dynamic, with their likelihood of continuing or discontinuing technology use depending more on their prior experiences. 3) For both male-headed and female-headed households, prior technology use increases the likelihood of continued use, indicating adoption decisions have path dependence and are dynamic rather than static.

1. Gender and Dynamics of Technology Adoption
Evidence from Uganda
Khushbu Mishra1 Abdoul G. Sam1 Mario J. Miranda1
Gracious M. Diiro2
1Department of Agricultural, Environmental, and Development Economics
The Ohio State University
2College of Agricultural and Environmental Sciences
Makerere University
AAEA & WAEA Joint Annual Meeting, July 26-28, 2015
Mishra et al. (OSU & Makarere) Gender and Dynamics AAEA & WAEA 2015 1 / 15

2. Motivation
Hunger kills more people every year than AIDS, malaria and
tuberculosis combined (WFP 2015)
SSA has the highest prevalence: 25% of the population malnourished.
Agricultural outputs have reduced over the last decade in this region
(Suri 2011).
The adoption of modern technology has been slow in SSA, especially
among FHHs.
Females are 50% of the agricultural labor force (FAO 2011).
Mishra et al. (OSU & Makarere) Gender and Dynamics AAEA & WAEA 2015 2 / 15

3. Literature
Adoption: Credit constraints, resource access, informational barriers,
differences in agroecological conditions, commitment & learning
(Conley & Udry 2010; Duflo et al. 2008; Foster & Rosenzweig 1995)
Gender gap: farm size, asset ownership, input access (land, labor,
extension services, market) (Peterman et al. 2011; Thapa 2008; Doss
& Morris 2000).
Dominant approach: static adoption decision.
Recent studies show the decision to be dynamic (Ma & Shi 2015;
Dercon & Christiaensen 2011; Suri 2011).
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4. Our Contribution
Rsearch Questions
Is technology adoption decision dynamic?
How does it differ over male and female headed households?
Motivate our empirical analysis with a stochastic dynamic model.
Incorporate gender using four waves of panel data from Uganda.
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5. Instituional Context - Uganda
Agricultural sector contributes 22.5% to the GDP, employs over 66%
of the working population, of which over 55% are female labor
(MAAIF 2011).
The growth in productivity of agriculture sector is 0.9% (2010/11)
compared to 2.4% (2009/10), & the gvt-set target of 6%.
Low technology use: only 7-8% used fertilizers in 2009, compared to
17-31% in neighboring Kenya (Suri 2011).
An average Ugandan farmer applies about 2.1 kg/ha. fertilizer vs 32.4
kg/ha. by a Kenyan farmer (Greensand 2011)
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6. Data - Uganda
One wave of UNHS 2005/06 & three waves of UNPS 2009/10 -
2011/12 by UBOS
Interviewed household heads with a detailed agricultural module.
6,688 observations, with 1,672 observations per year.
Dependent variable: one if used any of hybrid seed, inorganic fertilizer
& pesticides, zero otherwise.
Independent variables: lagged technology adoption, technology
adoption in the first period, other covariates.
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7. Gender & Dynamics of Technology Adoption
Figure: Fraction of households adopting technology by wave and gender
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8. Dynamic Model
The household maximizes the present value of current & expected
future utility of consumption over an infinite horizon such that its
dynamic decision problem is a Bellman equation:
V (w) = max
j=0,1
0≤x≤bg
{u(w + x − κj ) + δE˜˜zV (˜zg − (1 + r)x + ˜yj )} (1)
Here,
j = binary technology adoption choice
u = household’s utility
w = current liquid wealth
x = amount borrowed by the household
κj = cost of adopting farming technology j
δ ∈ (0, 1) = household’s per-period discount factor
˜zg = off-farm income depends on gender of the household head
˜yj = farm income, depending on technology j
Mishra et al. (OSU & Makarere) Gender and Dynamics AAEA & WAEA 2015 8 / 15

9. Empirical Model
Yit = α + ρYi0 + λYit−1 + Xitθ + Xi δ + ηi + it (2)
Here,
Yit = binary technology adoption decision
Yi0 = technology adoption in the first period
Yit−1 = lagged technology adoption
Xit = time variant individual household covariates
Xi = time invariant individual household covariates j
ηi = time invariant error
it = time variant error
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10. Results - Solving the Bellman Equation Value Function
Figure: Action contingent value function of households versus Wealth
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11. Results - Combined Household Level
VARIABLES DynamicProbit CML
Lagged technology .0653*** .0252
Baseline technology — .0496**
Lagged ln farminc .0071*** .0071***
HH head sex .0715*** .0843*
Region/Year dummy Yes Yes
Table: Marginal effects from Dynamic Probit & CML of technology adoption
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12. Results - Gender Disaggregated Household level
VARIABLES DynamicProbit CML
Male HHs
Lagged technology 0.0495* .0136
Baseline technology — .0467***
Female HHs
Lagged technology .1150*** .0705*
Baseline technology — .0465
Region/Year dummy Yes Yes
Table: Marginal effects from Dynamic Probit & CML of technology adoption
Mishra et al. (OSU & Makarere) Gender and Dynamics AAEA & WAEA 2015 12 / 15

13. Discussion and Conclusion
Baseline technology is the primary determinant of technology
adoption at the combined household level.
Mainly driven by MHH (71% of the sample).
Female households are less likely to adopt technology in the first
period.
FHH are more likely to move in and out of adoption status depending
on their most recent experience.
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14. Policy Implications
Access to farming plots to marginal households (e.g., FHH).
FHH and decision makers should be trained and informed about
modern technology varieties
Complimentary environment that overcome barriers to adoption:
1 Off-farm income sources
2 Insurance mechanism
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15. Thank you!
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