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Gender and the dynamics of technology adoption: empirical evidence from a
household-level panel data
Khushbu Mishraa,1, Abdoul G. Samb, Gracious M. Diiroc, Mario J. Mirandab
aAssistant Professor, Stetson University, 421 N. Woodland Blvd, DeLand, FL 32723, USA bOhio State University cMakerere University
Abstract
Very few empirical studies account for the dynamic nature of the agricultural technology
adoption decision and none of these explores if this dynamic nature depends on the gender of the
decision maker. Using four waves of a household level Ugandan panel data, this is the first
empirical analysis to account for self-learning (one’s own adoption experience) in explaining
current adoption decision in a developing country context, and the first to study the interaction
between self-learning and gender. Technology adoption is defined as the adoption of either hybrid
seed, or inorganic fertilizer, or pesticides. Our results indicate that the dynamic panel data Probit
model is superior to its static counterpart in the sense that self-learning, captured by lagged
technology adoption indicators, is by far the most important determinant of technology adoption.
We also find a weaker impact of self-learning for female-headed households than male-headed
households. Female-headed households face fewer learning opportunities, which produces a
lower self-learning impact in later periods, further exacerbating the gap in technology adoption
among male- and female-headed households.
JEL classifications: O1, O3, Q16
Keywords: technology adoption, panel data, dynamic estimation, Uganda
1 (Corresponding author). The authors are thankful to the participants of Agricultural and Applied Economics Association Annual Meeting and seminars at the Ohio State University and University of Florida and two anonymous referees. The usual disclaimer applies.
0
Gender and the dynamics of technology adoption: empirical evidence from a
household-level panel data
Abstract
Very few empirical studies account for the dynamic nature of the agricultural technology
adoption decision and none of these explores if this dynamic nature depends on the gender of the
decision maker. Using four waves of a household level Ugandan panel data, this is the first
empirical analysis to account for self-learning (one’s own adoption experience) in explaining
current adoption decision in a developing country context, and the first to study the interaction
between self-learning and gender. Technology adoption is defined as adoption of either hybrid
seed, or inorganic fertilizer, or pesticides. Our results indicate that the dynamic panel data Probit
model is superior to its static counterpart in the sense that self-learning, captured by lagged
technology adoption indicators, is by far the most important determinant of technology adoption.
We also find a weaker impact of self-learning for female-headed households than male-headed
households. Female-headed households face fewer learning opportunities, which produces a
lower self-learning impact in later periods, further exacerbating the gap in technology adoption
among male- and female-headed households.
JEL classifications: O1, O3, Q16
Keywords: technology adoption, panel data, dynamic estimation, Uganda
1
1. Introduction
Hunger kills more people every year than AIDS, malaria, and tuberculosis combined
(World Food Programme (WFP), 2015). Among regions most suffering from hunger, Sub-
Saharan Africa (SSA) has the highest prevalence of food insecurity, with 25% of the population
malnourished (WFP, 2015). Agricultural yields (output per acre) have fallen over the last decades
in many SSA economies (Suri, 2011), further exacerbating the food insecurity situation in the
region. There is widespread evidence that adoption of improved agricultural production
technologies can increase yields significantly (Nziguheba et al., 2010)
Despite the apparent advantages of modern agricultural technology, its adoption has been
rather slow in SSA. For example, only 5% of households use modern tractors, 16% use agro-
chemicals, and the use of nutrient application varies from 17% in Niger and Tanzania to virtua lly
non-existent in Uganda (Barrett, 2018; Christiaensen, 2017). Credit constraints, access to
resources, informational barriers, taste preferences, differences in agro-ecological conditions, and
lack of effective commitment devices are among the reasons typically advanced to explain low
rates of technology adoption (Dercon & Christiaensen, 2011).
Moreover, technology adoption rates in the region are lower among female farmers in
comparison to their male counterparts (Fisher & Kandiwa, 2014; Peterman, Behrman, &
Quisumbing, 2014). The reasons of the gender gap in technology adoption have been attributed to
differences between male and female farmers in farm size, asset ownership, social capital, and
access to labor, training, and extension services (Kondylis, Mueller, Sheriff, & Zhu, 2015;
Magnan, Spielman, Gulati, & Lybbert, 2015; Ndiritu, Kassie, & Shiferaw, 2014; Peterman et al.,
2014; Peterman, Quisumbing, Behrman, & Nkonya, 2011; Ragasa, Berhane, Tadesse, &
Taffesse, 2013). This imbalance undermines agricultural efficiency in SSA and by extension food
2
security given that females make up 40% of the agricultural labor force, significantly contributing
to the production process (World Bank, 2019). Leveling the field of access to agricultura l
resources for male and female could increase the total output up to 4%, reducing malnutrition and
lifting hundreds of thousands of farming households out of poverty (World Bank, 2015).
For most of the studies conducted on agricultural technology adoption and gender gaps in
Africa, the dominant approach has been to assume that the technology adoption decision is static
(Abebaw & Haile, 2013; Asfaw & Admassie, 2004; Katungi, 2007; Kondylis et al., 2015; Pamuk,
Bulte, & Adekunle, 2014; Ragasa et al., 2013; Tanellari, Kostandini, Bonabana-Wabbi, &
Murray, 2014). While these models provide important information on determinants of technology
adoption, they disregard the possibility that the adoption decision can be dynamic in that farmers
may base their current adoption decision on their past adoption experiences (Besley & Case, 1994;
Foster & Rosenzweig, 1995; Ma & Shi, 2015). Ignoring the dynamic nature of technology
adoption decision can introduce substantial biases, painting an inaccurate picture of the factors
that affect adoption decisions. For example, using structural theoretical models, Ma and Shi
(2015) find that the dynamic model fits their data better than the static model as the former is
more consistent with farmers’ underlying technology decision process. They also find that
farmers learn more from their own experiences than their neighbors’ experiences in a dynamic
setting. This is because differences in individual farm and farmer characteristics imply that
experiences of one farmer may not apply to others in that true returns from the adoption can only
be learned by farmers’ own experiences. However, their study uses data on US soybean farmers,
hence the findings may not explain the dynamics of technology adoption decisions in the
developing world. In that regard, Dercon and Christiaensen (2011) build a theoretical dynamic
model of how a household’s capacity of protecting itself ex-post from falling consumption affects
3
its ex-ante risk taking from technology adoption decisions. They use data from Ethiopia to test
their model but the empirical estimation does not account for decision making as a dynamic
process. An earlier study by Moser and Barrett (2006) attempt to estimate the dynamics of
smallholder technology adoption and find that learning effects encourage adoption among
farmers. However, their study is based on pseudo-panel data constructed from a cross-sectional
dataset due to lack of a true longitudinal panel dataset. In fact, the lack of household- leve l
longitudinal data has been a major obstacle to a better understanding of the dynamics of
agricultural technological adoption process in developing countries (Moser and Barrett 2006). We
address this gap in the literature by empirically accounting for the dynamic adoption process by
including self-learning for the first time in a developing country context, to the best of our
knowledge, by taking advantage of four waves of a household level national dataset from Uganda.
We define technology adoption as adoption of either hybrid seeds, or inorganic fertilizer, or
pesticides.
Furthermore, the literature has shown a lower rate of adoption among female farmers due
to less favorable environment for technology adoption pertaining to farm and farmer
characteristics (Fisher & Kandiwa, 2014; Ndiritu et al., 2014; Peterman et al., 2014; Quisumbing
et al., 2014; Ragasa et al., 2013; Tanellari et al., 2014). We speculate that the lower rate of
adoption can imply a lower opportunity for learning. This would indicate that female-headed
households have a lower impact from learning than male-headed households. Therefore, we
further investigate if the dynamics of technology adoption decisions differ by the gender of the
household head. To the best of our knowledge, we are the first to do so.
The empirical estimation follows three stages. First, we fit our data using a static Probit
model. Second, we use a Conditional Maximum Likelihood Estimator (CMLE) for dynamic
4
estimations. For the CMLE, we add the lagged dependent variable to account for the dynamic
nature of technology adoption. Third, we investigate the dynamics of technology adoption
disaggregated by gender of the household head. Some interesting results emerge from the
econometric analysis. First, dynamic models are superior than the static models. Second, lagged
technology adoption is the main driver of current adoption, indicating that adoption decisions are
highly determined by farmers’ own experimentation. Third, for the gender-disaggrega ted
analysis, the lagged technology adoption coefficient is significant and positive for female-headed
households but, it is lower than that of male-headed households, indicating a lower self-learning
effect for the female decision-makers. These results have important implications. First, studies
on technology adoption should include self-learning as it is an important determinant of adoption.
Second, female-headed households face fewer learning opportunities earlier, which produces a
lower self-learning impact later. This phenomenon reinforces the gap in technology adoption
among male- and female-headed households.
The remainder of this paper is structured as follows. Section 2 discusses hypotheses and
methodology. Section 3 provides a discussion of the data and descriptive statistics. Section 4
presents the results and discussion. Section 5 concludes.
2. Hypotheses and methodology
There is a widespread research on agricultural technology adoption. Following Griliches
(1957) and Feder, Just, and Zilberman (1985), earlier studies focused on how farmer
characteristics and farmland heterogeneity affect adoption decisions under a static setting. The
more recent literature acknowledges that the adoption decision is an inherently dynamic process
and incorporates learning into adoption models (Baerenklau, 2005; Besley & Case, 1994; Dercon
5
& Christiaensen, 2011; Foster & Rosenzweig, 1995; Ma & Shi, 2015; Moser & Barrett, 2006).
Using myopic (i.e., static) as well as learning (i.e., dynamic) models, Besley and Case (1994)
find that the learning model performs better in predicting the technology diffusion path of the
Green Revolution in India. Using a theoretical model that incorporates learning by doing and
learning spillovers, Foster and Rosenzweig (1995) test their predictions using the same Indian
dataset and find that adoption increases with farmers’ experience with technology. They further
find that farmers’ own experience has a stronger effect on adoption than neighbors’ experience
or formal public information dissemination sources such as government extension agents.
Similarly, Baerenklau (2005) builds an adoption model with a focus on risk preferences,
endogenous learning, and peer-group influence. Testing his model with a group of Wisconsin
dairy farmers, he finds that self-learning is more important than peer-group influence. Moser and
Barrett (2006) model dynamics of farmer technology choice by allowing for farmer
experimentation of technology (learning by doing), exposure to extension educators or other
farmers in the community (learning from others), and one’s education which may affect the rate
of learning. Using data from Madagascar, they find that both self-learning and learning from
others are important determinants of adoption. Lastly, Ma and Shi (2015) construct a dynamic
model to capture forward-looking farmers’ experimental behavior and find that farmers’ own
leaning experience matter more than their neighbors’ as the latter may carry additional noise
either due to information loss during communication or due to inapplicability of the information
given heterogeneous characteristics of farmers and farm plots. Therefore, based on these
theoretical studies, we formulate our first hypothesis as follows:
Hypothesis 1: Technology adoption increases with self-learning.
6
While the above-mentioned theoretical studies are silent on the issue of gender and the
dynamics of technology adoption, we can use their general framework to draw some inferences
about how adoption dynamics may vary between female and male-headed households (FHH and
MHH hereafter). For example, per Moser & Barrett (2006)’s findings, we can infer that FHH are
likely to experience less learning due to lower rates of technology adoption (learning by doing),
lesser exposure to extension educators or other farmers in the community (learning from others),
and lower education. Likewise, per Dercon and Christiaensen (2011), we can conjecture that
lower ex-post coping capacity among FHH in case of a negative shock means they invest in input
technology at a lower rate. In fact, studies have found that FHH have higher barriers to technology
adoption (due to lower income, reduced access to inputs and extension services, and poor quality
of social networks) and heterogeneity in production experiences post adoption (due to smaller
farm size, lower soil quality, and labor) (Kondylis et al., 2015; Magnan et al., 2015; Ndiritu et
al., 2014; Peterman et al., 2014, 2011; Ragasa et al., 2013). The predictions from the theoretica l
dynamic models combined with the findings from static empirical models suggest that FHH have
less learning opportunities due to their own lower experiences in adoption as well as higher
barriers to learn from social and institutional networks than MHH. Therefore, we formulate our
second hypothesis as follows:
Hypothesis 2: The impact of self-learning is weaker for FHH than MHH.
To test these hypotheses, we conduct our econometric analysis in three phases: a static
Probit model, a dynamic Probit CMLE model, and the CMLE model disaggregated by gender of
the household head. As commonly done, we include a lag of the dependent variable as an
additional explanatory variable to account for the possibility of dynamics in the decision to adopt.
7
This lag captures self-learning effect from state dependence, i.e., the impact on technology
adoption in the present period due to adoption in the past period (own experience).
However, incorporating a lagged dependent variable in the context of Probit estimation
with panel data leads to the violation of strict exogeneity which produces inconsistent estimates
(Wooldridge, 2000). For example, suppose our observations start at a date 𝑡 = 0, so that 𝑌𝑖0 is
the first observation of technology adoption 𝑌. For 𝑡 = 1, . . . , 𝑇, we are interested in the dynamic
fixed effects model:
𝑃(𝑌𝑖𝑡 = 1|𝑌𝑖𝑡−1 ,… , 𝑌𝑖0, 𝒙𝑖 , 𝑐𝑖) = Ф(𝒙𝑖𝑡𝜽 + λ𝑌𝑖𝑡−1 + 𝑐𝑖) (1)
where 𝒙𝑖𝑡 = (𝒙𝑖1, … , 𝒙𝑖𝑇) is a vector of contemporaneous explanatory variable, and Ф is the
Probit link function. Equation 1 simply captures the fact that technology adoption behavior
depends on past behavior, farm and household characteristics 𝒙𝑖 , and fixed (unobserved)
effects 𝑐𝑖. The joint distribution of the data stemming from Equation 1 is:
𝑓(𝑌1, 𝑌2, … , 𝑌𝑇 = 1|𝑌0, 𝒙,𝑐; 𝜽, λ) = ∏ 𝑓(𝑌𝑡|𝑌𝑡−1, … , 𝑌1,𝑌0 , 𝒙𝑡 , 𝑐; 𝜽, λ)
𝑇
𝑡=1
(2)
With fixed-T asymptotics, the parameters of primary interest 𝜃 and λ cannot be
consistently estimated due to the unobserved effects 𝑐𝑖 (Wooldridge, 2010). To circumvent this
incidental parameters problem, we need to integrate 𝑐𝑖 out of the likelihood function, which
requires further assumptions about the baseline period observation 𝑌𝑖0. This is known as the init ia l
condition problem. One way to get around it is to assume a density for the ci conditional on some
elements of the covariates 𝒙𝑖𝑡 (as in Chamberlain (1980)) and the initial adoption period 𝑌𝑖0 in
the framework of Wooldridge (2010)’s conditional maximum likelihood estimator (CLME) for
dynamic models. This allows for correlation between the initial condition 𝑌𝑖0 and the unobserved
heterogeneity 𝑐𝑖 in that 𝑐𝑖 = 𝜓 + 𝜉0𝑌𝑖0 + �̅�𝑖𝜉 + 𝜂𝑖 where 𝜂𝑖 ~Normal(0, 𝜎𝜂𝑖) is independent of
8
(𝑌𝑖0, �̅�𝑖) and �̅�𝑖 is the averages of 𝒙𝑖𝑡 , 𝑡 = 1,… , 𝑇. By the assumption of the distribution of 𝑐𝑖, the
latent version of the dynamic Probit model can now be written as:
𝑌𝑖𝑡∗ = 𝜓 + λ𝑌𝑖𝑡−1 + 𝒙𝑖𝑡𝜽 + 𝜉0𝑌𝑖0 + �̅�𝑖𝜉 + 𝜂𝑖 + 𝜀𝑖𝑡 (3)
where 𝑌𝑖𝑡∗ represents the latent net benefit of technology adoption and 𝑌𝑖𝑡 is the indicator variable
of modern technology adoption by household i at time t. In practice, this means that in addition
to contemporaneous explanatory variable 𝒙𝑖𝑡 , we add lagged technology adoption 𝑌𝑖𝑡−1, baseline
technology adoption 𝑌𝑖0, and the averages of explanatory variables �̅�𝑖. Region and time fixed
effect dummies which do not vary across time are omitted from �̅�𝑖.We refer to Equation 3 as
CMLE and use it as our main equation for dynamic analysis. To compare our dynamic CMLE
with a static model, we simply drop the two variables 𝑌𝑖𝑡−1 and 𝑌𝑖0.
In addition to the self-learning component, the literature has also pointed out the
importance of learning externalities from other mechanisms such as from the success of their
neighbors, membership to farmer groups, social networks, formal information dissemination
programs, extension services, and education (Diiro, Ker, & Sam, 2015; Knight, Weir, &
Woldehanna, 2003; Magnan et al., 2015; Moser & Barrett, 2006; Ndiritu et al., 2014; Ragasa et
al., 2013; Schultz, 1963; Smale, Assima, Kergna, Thériault, & Weltzien, 2018). To account for
these learning externalities, we incorporate the household head’s membership to a farmer’s group,
household’s participation in National Agricultural Advisory Services (NAADS) training
program, number of extension visits, and education (proxied by aggregate literacy of the
household members). The NAADS program started in 2001 with the primary objective of
providing information on inputs, production, and market (Kasirye, 2013). Moreover, we add
several controls that have been found to impact technology adoption: credit constraints, proxied
by number of plots cultivated (as in Dercon and Christiaensen (2011)), farm income (as in Moser
9
and Barrett (2006)), and off-farm income (as in Diiro and Sam (2015)), ability to cope with
changes in income proxied by livestock (as in Dercon and Christiaensen (2011)), and land
ownership (proxied by land certificate as in Gao et al. (2018)). We use lags of these variables in
our empirical model to mitigate possible endogeneity bias due to reverse causality. We also
control for soil quality (as in Ma and Shi (2015), labor availability proxied by household size and
number of adult male members in the household (as in Doss and Morris (2000)), and age of the
household head. Studies have found that since taking on risky behavior--adoption of a new
technology in our case--requires astute memory and learning, reduction in cognitive abilities due
to aging is associated with diminished tolerance for risky rewards (Albert & Duffy, 2012; Grubb,
Tymula, Gilaie-Dotan, Glimcher, & Levy, 2016). Furthermore, households may not have access
to risk coping mechanisms post production, which implies that their adoption decisions may be
influenced by risks related to shocks. Therefore, we include weather shocks (i.e., drought, flood,
and landslides), health shocks (i.e., death and illness), and other shocks (i.e., job loss, theft, fire,
violence, crop pests, and livestock pests) in our empirical model. Finally, we also include region
and year fixed effects to account for any local agro-climatic conditions (rainfall and temperature
variation) that vary across geographic areas and other shocks that vary over time but are common
to all regions (Griliches, 1957; Ouma et al., 2002; Smale et al., 2018). While these are also the
factors that are generally heterogeneous over MHH and FHH, we include marital status as an
additional control to account for those females who became household heads in FHH either due
to the death of their spouse, or divorce and separation, or never married. Over 66% of the FHH
are in this category across the years on average.
10
3. Data and descriptive analysis
About 70% of the female and 58% of the male working population are engaged in
agriculture, making an overall contribution of 25% to the Ugandan national GDP in 2017
(Ministry of Agriculture, Animal Industry, and Fisheries (MAAIF), 2019). Recognizing the need
for deeper insight into factors affecting this sector, the government added a detailed agricultura l
module to its Uganda National Household Survey (UNHS) 2005/06. The module collects data on
land, crop area, inputs, outputs, livestock, poultry, and agricultural extension services and
technologies. The UNHS 2005/06 surveyed 7,417 households nationally of which 3,123
households were selected for panel surveys known as Uganda National Panel Survey (UNPS)
(Uganda Bureau of Statistics (UBOS), 2012). The UNPS 2009/10, 2010/11, and 2011/12
successfully retained 83, 82, and 75% of the original sample and replaced the rest with split-off
tracking.1 Our study utilizes roughly 74% of the surveyed households from one wave of the
UNHS 2005/06 and three waves of UNPS 2009/10, 2010/11, and 2011/12 as these households
are engaged in agriculture (UBOS, 2012). After data cleaning, we end up with a total of 8,293
and 7,904 observations across all four years for the first and second cropping seasons,
respectively. Uganda has two cropping seasons, the first season runs from January through June
and the second runs from July through December. Annual crops, predominantly maize, beans,
and cassava, are grown in both seasons since rainfall is available in both, with shorter and more
intense (3 months) versus longer and spread out (4 months), respectively (Orlove, Roncoli,
1 The reasons for attrition cited are migration to unknown locations, natural causes such as death, and
refusal. UBOS generates a 20% sample of the households from each enumeration area selected for the UNPS to adjust the size and composition of sample that maybe impacted by attrition (UBOS 2013). This is a national level study that is not primarily conducted to study technology adoption; hence, we should not be concerned about attrition for the purpose of our work. However, to rule out any possible correlation between technology adoption and attrition, we construct an attrition variable equal to one if the household was not present in all waves and zero otherwise and find no correlation between technology adoption and attrition (see Table A1 in Appendix).
11
Kabugo, & Majugu, 2010). Usually more households cultivate plots in the first than the second
season, for example, 77% versus 72% in 2009/10 (Roberts & Azzarri, 2014). Therefore, we use
the former as our primary analysis and the latter as robustness check.2
In principle, technology adoption could be measured as a continuous variable in terms of
quantities of inputs used but, due to unreliable data on input quantities, we take technology
adoption to be binary throughout the paper as often done in the literature (Suri, 2011). The UNPS
collected data on three kinds of technology adoption: hybrid seed, inorganic fertilizer, and
pesticides. Due to very low rates of adoption of these inputs individually and by gender, we define
technology adoption to be one if the farmer used any of the three sources of technology, and zero
otherwise. Figure 1 presents the dynamics of technology adoption over time. Overall, a total of
25% of Ugandan households use technology over the four waves of data collection. Looking at
adoption over the waves, we note a general pattern that households that adopted (did not adopt)
technology in any particular period are more (less) likely to adopt technology the following period
(with few exceptions). This suggests that the nature of technology adoption is dynamic and should
be modeled accordingly.
[Figure 1 about here]
Table 1 presents tests of equality of means of the variables (reviewed earlier) used in our
empirical analysis between adopters and non-adopters by wave. The differences between the
households that adopt and those that do not adopt generally follow the economic intuition for all
the waves. For example, the means of factors that are associated with learning externalities (total
2 Results from both seasons exhibit the same pattern (see Results section for details).
12
household literacy, number of extension visits, participation in NAADS training, and farmer group
membership) are higher for adopting households. Likewise, the means of variables proxied for
credit access and labor availability are higher for adopters than non-adopters; for example, number
of plots, indicator for land certificate, livestock value, household size, and total adult males (with
an exception off-farm income for 2009/10). Furthermore, technology adopters tend to have
younger household heads (with an exception of 2005/06) and a higher proportion are married.
When it comes to experiencing shocks, we generally do not find any pattern of significant
differences between the adopter and non-adopter households.
[Table 1 about here]
Figure 2 presents the technology adoption proportion of MHH versus FHH; on average,
the adoption rates are 28 and 19%, respectively, across all waves. The adoption rates are generally
increasing for both MHH and FHH (with an exception of UNPS 2009/10) and are higher for MHH
than FHH for all waves. This later pattern can be inferred from the stark difference in initia l
adoption, i.e., first period of available panel data in UNHS 2005/06. Since FHH have lower
adoption rate initially, this translates into lower learning opportunity and consequently lower rate
of adoption in the later periods.
[Figure 2 about here]
In order to delve further into the heterogeneity between MHH and FHH that may have
caused the differences in adoption, we conduct a pairwise mean ttest comparison of variables used
13
in our analysis by wave and gender of the household head. The differences between FHH and
MHH generally follow the economic intuition for all the waves (see Table A2 in Appendix for
details).
4. Results and discussion
To test our hypotheses, our econometric analysis employs three phases. Phase one
employs the static Probit model. This is a correlated random effects model which controls for
individual heterogeneity and time fixed effects. However, this estimation fails to account for
adoption decision as a dynamic process which is problematic given that farmers update their
decision making over time based on their prior experiences. Therefore, phase two employs CMLE
which allows for the dynamic process of technology adoption and unobserved farmer
heterogeneity. To build more robust results, the estimations within each phase control for region
and time effects. Phase three employs the most robust CMLE (with region and time fixed effects)
to investigate the dynamics of technology adoption across MHH and FHH.
Table 2 presents marginal effects of the technology adoption from static Probit and
dynamic CMLE models. Our results provide support for Hypothesis 1 as evidenced by large and
significant coefficient on lagged technology adoption variable. The CMLE model shows that
households that adopt technology in the previous period are more likely to adopt in the following
period by about 18 percentage points per the preferred specification (that controls for time and
region fixed effects). This impact is by far the largest among the other significant marginal effects.
We also find a positive and significant estimate of over 4 percentage points on the baseline
adoption variable. Together, these results underscore the importance of self-learning on
technology adoption decisions of farmers. The pseudo R-squared and AIC/BIC measures of
14
goodness of fit (Posada & Buckley, 2004) for the preferred dynamic model are 0.2056 and
3186/3457 compared to 0.0282 and 3873/4136, respectively for the preferred static model,
providing clear statistical evidence that the dynamic model is a superior way to model adoption
decision. For learning externalities, extension visits, participation in NAADS training, farmer
group membership, and aggregate household literacy, we do not find any statistically significant
impact. These results concur with literature on the importance of learning (Besley & Case, 1994;
Foster & Rosenzweig, 1995; Ma & Shi, 2015) and more specifically with the literature that
emphasizes the importance of learning by doing over learning from others (Baerenklau, 2005;
Conley & Udry, 2010; Munshi, 2004). Among the proxies for credit access, only farm income is
statistically significant. We also note the negative and significant sign on land certificate which
seems puzzling initially. A test of comparison of mean off-farm income for those with and
without land certificate shows that the mean for the former is over three times higher than the
latter. This may indicate that households that own formal certificates are not primarily invested
in agriculture and hence are less likely to adopt agricultural technology. Moreover, age of the
household head is negative and significant, which substantiates previous research that aging
reduces tolerance for risky rewards (Albert & Duffy, 2012; Grubb et al., 2016). Finally, the
coefficients of shock variables are also negative, but statistically insignificant.
[Insert Table 2 about here]
Next, we present gender-disaggregated analysis of the dynamics of technology adoption
decision using the preferred CMLE model (that controls for time and region fixed effects) in
Table 3. Our results provide support for Hypothesis 2 as evidenced by significant but lower
15
coefficient on lagged technology adoption variable for FHH than MHH. These coefficients are
14 and 20 percentage points, respectively. While these findings reiterate the role of self-learning
in technology adoption, the coefficients on lagged technology adoption for FHH is significantly
lower than those for MHH at 5% level. We further match FHH and MHH by propensity score
(PS) method to account for possible differences that could arise due to non-linear effects of one
or more control variables in our model (Rosenbaum & Rubin, 1983). The propensity score is
based on all the observables used as controls in our analysis except for region and time fixed
effects which we include additionally in our PS method (see Mishra and Sam (2016) for
application details). Lagged technology adoption coefficients still remain significantly lower for
FHH than MHH. We speculate these learning differences occur for two reasons. First, FHH adopt
at a lower rate than MHH. The lower adoption creates reduced self-learning opportunity which
then translates into a relatively lower rate of adoption in later periods. Moreover, adoption is a
binary decision in our study whereas in reality there are differences in intensity of adoption (Ali,
Bowen, Deininger, & Duponchel, 2015) which can further translate into learning differences
between FHH and MHH. Although there are no explicit dynamic models of FHH versus MHH
adoption behavior in the literature, studies have found that lower ex-post coping capacity in case
of a bad shock lowers adoption in the current period, therefore lowering their net marginal gain
and consequently causing lower adoption in later periods (Dercon & Christiaensen, 2011; Ma &
Shi, 2015; Suri, 2011). Second, previous work has found that females have lower level of trust
and higher risk perception than males implying lower adoption rates of new technologies among
FHH (Siegrist, Gutscher, & Earle, 2005) and lower associated learning from adoption.
Unfortunately, we cannot test this speculation directly as data limitation neither allows us to
examine the intensity of adoption nor provides us with measures of trust and risk perception
16
pertaining to agricultural households. Overall, the results imply that current gaps in adoption can
propagate later gaps in adoption due to associated gaps in learning opportunities.
[Insert Table 3 about here]
Lastly, we repeat these three estimation phases with data for the second season; results
follow the same pattern as those from the first season (see Table A3 in Appendix). However, we
note that the coefficients are smaller than those in the first season. Given that less households
farm in the second season and have lower rate of adoption than the first season (Roberts &
Azzarri, 2014), the results further bolster our findings that lower current adoption implies a lower
self-learning generating a lower future adoption.
5. Conclusion and policy implications
Several studies in the literature investigate the determinants of technology adoption. Yet,
most of these studies assume the technology adoption decision to be static and therefore do not
account for learning from past experience. While there are theoretical papers that model
technology adoption as a dynamic process, very few empirical studies do so owing, until recently,
to the lack of longitudinal panel dataset from developing countries (Moser & Barrett, 2006).
Besides, none of the few dynamic studies considers whether the dynamic nature of the adoption
decision varies by gender. Therefore, using four waves of household level longitudinal panel data
from Uganda, we empirically investigate the dynamics of technology adoption at pooled and
gender-disaggregated household levels in this paper. By doing so, we make a significant
contribution to the limited literature on empirical estimation of technology adoption decision as a
17
dynamic process and shed light on a yet to be explored topic of adoption dynamics and gender.
Using static and dynamic Probit models, we have three major findings. First, the dynamic
models perform markedly better than the competing static model highlighting the need for
modeling technology adoption decision as a dynamic process. We find that recent experience
(technology adoption) in the previous period is the primary determinant of technology adoption
in the current period. We also find that the positive influence of adoption experience lingers over
time with a positive and significant impact of adoption in the initial/baseline adoption year on all
future period adoption decisions. Unlike self-learning, the variables proxying learning
externalities are not statistically significant. Together, the results imply that learning from self-
experience is the primary determinant of adoption of the technologies considered in this study
and therefore static models can present an incomplete story of adoption decisions. In future, we
hope to see more studies that can take advantage of more recently available longitudinal panel
datasets and present a more accurate empirical evidence on technology adoption decision when
modeled as a dynamic process.
Finally, we find that the positive and significant estimates of lagged technology adoption
are lower for female-headed households than male-headed households. These results imply that
the experience of having adopted in the past carries a lesser weight for future adoption decisions
for female- relative to male-headed households. We speculate that current adoption differences
(both in rate and intensity) cultivate differences in self-learning opportunity which can reinforce
later differences in adoption. Since modern agricultural technologies are accompanied by a higher
yield variance (Miranda & Farrin, 2012; Traxler, Falck-Zepeda, Ortiz-Monasterio R., & Sayre,
1995), lower ex-post risk coping capacity among female farmers may relatively weaken their
commitment to technology adoption (Dercon & Christiaensen, 2011; Suri, 2011). For example,
18
Magnan et al. (2015) find that women have poorer quality of social networks in the form of poorer
households compared to men, indicating a weak social insurance post-adoption. Likewise,
heterogeneity among the decision makers such as trust and risk perception that are associated
with adoption of new technologies could also spur this difference (Cai, Chen, Fang, & Zhou,
2014; Cole et al., 2013). This result has important policy implications. Since learning by doing is
important for continuity in adoption, policies should be in place to provide complimentary
environments (e.g., insurance through groups as in Hill, Hoddinott, and Kumar, (2013)) so that
initial barriers to adoption are broken for female-headed households making learning more
effective for future periods. The reduction in learning gap can encourage stickiness to technology
adoption in the later periods, boost the agricultural productivity, and reduce malnutrition which
is much needed in SSA.
19
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Tables and Figures
Fig. 1. Dynamics of technology adoption for pooled households
28
Fig. 2. Fraction of households adopting technology by wave and gender
29
Table 1
Mean ttest comparisons of variables by survey year and technology status
2005-06 2009-10 2010-11 2011-12
Variables Not Adopt Adopt Not Adopt Adopt Not
Adopt
Adopt Not
Adopt
Adopt
Number of plots 6.03 8.28*** 5.87 7.17*** 6.40 7.29*** 5.67 6.32***
Land certificate 0.07 0.12*** 0.21 0.24* 0.17 0.24*** 0.21 0.23
Soil quality 1.72 1.61*** 1.55 1.51 1.47 1.44 1.44 1.40*
Farm income/USh 173,797 528,933*** 853,214 1,194,701 637,723 1,313,836*** 930,461 1,877,419
Off-farm income/USh 150,376 213,722 561,199 540595 563,905 1,467,292** 663,489 1,122,478
Livestock value/USh 618,385 976,052** 1,038,583 1,538,971* 321,507 500,594*** 797,579 1,340,530
HH head sex 0.72 .81*** 0.69 .81*** 0.67 0.79*** 0.68 0.75***
HH head age/years 43 45*** 47 46 48 46* 49 48
HH head married 0.78 .84*** 0.73 .84*** 0.74 0.81*** 0.74 0.80***
30
HH size 5.8 6.8*** 6.2 7.0*** 6.84 7.6** 7.48 8.21***
Total adult males 1.3 1.6*** 2.8 3.2*** 2.14 2.42* 1.49 1.64**
Total HH literacy 2.11 2.37** 3.29 4.09*** 3.64 4.40*** 4.25 5.23***
Extension visits 0.15 0.59*** 1.02 2.56*** 0.60 1.68*** 0.75 1.17***
Participation in training 0.07 0.12*** 0.14 0.27*** 0.16 0.25 0.19 0.34***
Farmer group member 0 0 0.11 0.22*** 0.12 0.22 0.15 0.28***
Weather shocks 0.36 0.34 0.19 0.19 0.11 0.14 0.10 0.10
Other shocks 0.09 0.09 0.02 0.03** 0.01 0.01 0.01 0.01
Health shocks 0.09 0.11* 0.08 0.07 0.06 0.06 0.04 0.02**
*** p<0.01, ** p<0.05, * p<0.1 denote the significant differences in means between households that adopt and do not adopt
technology.
31
Table 2
Marginal effects of technology adoption from static and dynamic models
Variables Static Probit Static Probit Dynamic CMLE Dynamic CMLE
Lagged technology 0.2307*** 0.1807***
(0.0138) (0.0141)
Baseline technology 0.0729*** 0.0471***
(0.0158) (0.0158)
Number of plots -0.0015 -0.0019 -0.0051 -0.0045
(0.0042) (0.0041) (0.0042) (0.0042)
Land certificate -0.0296 -0.0320 -0.0560** -0.0600**
(0.0274) (0.0266) (0.0266) (0.0261)
Soil quality -0.0005 -0.0046 0.0063 0.0059
(0.0165) (0.0159) (0.0158) (0.0154)
Ln farm income/USh 0.0031 0.0031 0.0047* 0.0049**
(0.0024) (0.0023) (0.0025) (0.0024)
Ln off-farm income/USh -0.0000 -0.0004 0.0012 0.0008
(0.0029) (0.0028) (0.0031) (0.0030)
Ln livestock value /USh -0.0009 -0.0008 -0.0024 -0.0023
(0.0028) (0.0028) (0.0029) (0.0029)
HH head sex 0.0458 0.0406 0.0792 0.0666
(0.0562) (0.0537) (0.0509) (0.0494)
HH head age/years -0.0039* -0.0048** -0.0038* -0.0047**
32
(0.0024) (0.0023) (0.0021) (0.0020)
HH head married -0.0140 0.0152 -0.0075 0.0219
(0.0613) (0.0581) (0.0552) (0.0536)
HH size 0.0097*** 0.0012 0.0106*** 0.0035
(0.0028) (0.0043) (0.0027) (0.0044)
Total adult males -0.0074 -0.0041 -0.0098 -0.0069
(0.0109) (0.0107) (0.0105) (0.0104)
Total HH literacy 0.0010 -0.0000 0.0041 0.0026
(0.0047) (0.0045) (0.0046) (0.0045)
Extension visits -0.0011 -0.0015 -0.0023 -0.0025
(0.0025) (0.0024) (0.0023) (0.0022)
Participation in training 0.0082 0.0162 0.0115 0.0210
(0.0322) (0.0314) (0.0309) (0.0304)
Farmer group member 0.0352 0.0236 0.0411 0.0286
(0.0359) (0.0351) (0.0353) (0.0348)
Weather shocks 0.0320 -0.0314 0.0345 -0.0204
(0.0497) (0.0483) (0.0470) (0.0464)
Other shocks -0.2775 -0.2666 -0.2829 -0.2387
(0.2076) (0.1999) (0.1910) (0.1862)
Health shocks -0.0277 -0.0234 -0.0130 -0.0138
(0.0584) (0.0563) (0.0567) (0.0552)
Region/year dummy No Yes No Yes
Observations 3,818 3,818 3,334 3,334
33
Number of HHID 2,058 2,058 1,770 1,770
Log likelihood -2003 -1895 -1628 -1549
Pseudo R2 0.0282 0.2056
AIC/BIC 4083/4320 3873/4136 3338/3582 3187/3456
*** p<0.01, ** p<0.05, * p<0.1; standard errors in parentheses.
34
Table 3
Marginal effects of dynamic technology adoption for gender disaggregated households
CMLE CMLE PS PS
Variables MHH FHH MHH FHH
Lagged technology 0.1993*** 0.1398*** 0.2295*** 0.1714***
(0.0168) (0.0259) (0.0215) (0.0309)
Baseline technology 0.0440** 0.0492* 0.0547** 0.0511
(0.0191) (0.0282) (0.0258) (0.0353)
Region/year dummy Yes Yes Yes Yes
Observations 2,310 1,024 2,102 942
Number of HHID 1,246 578 1,078 498
Log likelihood -1119 -419
*** p<0.01, ** p<0.05, * p<0.1; standard errors in parentheses; both CMLE and PS methods account for
the eighteen control variables on income, farm and farmer characteristics, learning externalities, and shocks
as in Table 2; for brevity, we exclude them from the table.
35
Appendix
Table A1
Probit model regression on attrition
Variables Probit
Lagged technology 0.0767
(0.2445)
Number of plots -0.0810**
(0.0361)
Land certificate -0.0624
(0.2553)
Soil quality -0.0126
(0.1580)
Ln farm income/USh -0.0000
(0.0000)
Ln off-farm income/USh 0.0000
(0.0000)
Ln livestock value /USh 0.0000
(0.0000)
Hh head sex 0.2699
(0.2850)
HH head age/years -0.0321***
(0.0089)
HH head married -0.4082
(0.2866)
HH size -0.0865
36
(0.0583)
Total adult males 0.0531
(0.1134)
Total HH literacy 0.0252
(0.0462)
Extension visits -0.0071
(0.0403)
Participation in training -0.1102
(0.3898)
Farmer group member -0.1533
(0.5082)
Weather shocks -0.3438
(0.4494)
Other shocks -0.6270
(1.3371)
Health shocks -0.0158
(0.5292)
Region/year dummy Yes
Observations 8,293
Log likelihood -1603
*** p<0.01, ** p<0.05, * p<0.1; standard
errors in parentheses
37
Table A2
Mean t-test comparison of variables by survey wave and gender of the household head
2005-06 2009-10 2010-11 2011-12
Variables FHH MHH FHH MHH FHH MHH FHH MHH
Number of plots 6.00 6.75*** 5.64 6.42*** 6.16 6.80*** 5.35 6.08***
Land certificate 0.08 0.08 0.22 0.22 0.18 0.20 0.19 0.22*
Soil quality 1.75 1.67*** 1.53 1.54 1.50 1.44** 1.49 1.40***
Farm income/USh 103,853 311,935*** 882,650 964,188 485,900 923,744*** 697,382 1,427,187***
Off-farm income/USh 138,112 174,942 257,158 671,049** 429,535 916,752 679,186 848,894
Livestock value/USh 475,195 783,191* 968,862 1,244,709 246,334 412,233*** 589,022 1,114,614
HH head age/years 48 42*** 52 45*** 51 46*** 52 47***
HH head married 0.40 0.94*** 0.30 .93*** 0.34 0.93*** 0.35 0.94***
HH size 5.4 6.2*** 5.5 6.7*** 6.23 7.35*** 6.96 8.01***
Total adult males 0.9 1.5*** 2.4 3.1*** 1.75 2.41** 1.10 1.72**
Total HH literacy 1.74 2.32*** 2.87 3.74*** 3.40 3.98*** 4.05 4.74***
38
Extension visits 0.25 0.26 0.86 1.64*** 0.53 0.98*** 0.58 1.00***
Participation in training 0.06 0.09* 0.13 0.20*** 0.13 0.20*** 0.18 0.26***
Farmer group member 0 0 0.10 0.15*** 0.10 0.16*** 0.14 0.21***
Weather shocks 0.35 0.36 0.20 0.19** 0.11 0.12* 0.10 0.10
Other shocks 0.09 0.09 0.02 0.03 0.01 0.01 0.007 0.01**
Health shocks 0.12 0.09*** 0.09 0.07* 0.07 0.06 0.04 0.03**
*** p<0.01, ** p<0.05, * p<0.1 denote significant differences in means between female- and male-headed households.
Table A3
Marginal effects of dynamic technology adoption from Probit and CMLE models for second season
Variables Static Probit Dynamic CMLE MHH CMLE FHH CMLE
Lagged technology 0.1346*** 0.1433*** 0.1053***
(0.0150) (0.0177) (0.0290)
Baseline technology 0.0528*** 0.0656*** -0.0009
(0.0151) (0.0178) (0.0306)
Number of plots -0.0059* -0.0098*** -0.0073* -0.0160**
(0.0031) (0.0034) (0.0041) (0.0063)
Land certificate -0.0278 -0.0396* -0.0472 -0.0268
(0.0230) (0.0241) (0.0299) (0.0418)
Soil quality 0.0234 0.0224 0.0283 0.0028
(0.0143) (0.0148) (0.0184) (0.0264)
Ln farm income/USh 0.0004 0.0002 0.0002 0.0010
(0.0021) (0.0024) (0.0030) (0.0041)
Ln off-farm income/USh 0.0029 0.0034 0.0067* -0.0026
(0.0026) (0.0029) (0.0037) (0.0051)
Ln livestock value /USh -0.0003 -0.0014 -0.0073* 0.0051
(0.0026) (0.0029) (0.0038) (0.0047)
HH head sex 0.0037 -0.0091
(0.0485) (0.0486)
HH head age/years -0.0026 -0.0023 0.0003 -0.0045
(0.0021) (0.0020) (0.0045) (0.0036)
HH head married -0.0488 -0.0721 -0.0773 -0.0289
(0.0523) (0.0517) (0.0931) (0.0760)
40
HH size 0.0003 0.0020 0.0025 0.0037
(0.0038) (0.0041) (0.0051) (0.0071)
Total adult males -0.0115 -0.0069 -0.0090 -0.0153
(0.0098) (0.0102) (0.0122) (0.0195)
Total HH literacy 0.0021 0.0041 0.0071 -0.0059
(0.0040) (0.0043) (0.0051) (0.0079)
Extension visits -0.0004 0.0004 0.0011 -0.0078
(0.0021) (0.0021) (0.0023) (0.0083)
Participation in training 0.0138 0.0025 0.0171 -0.0885
(0.0275) (0.0285) (0.0335) (0.0615)
Farmer group member 0.0055 0.0142 0.0061 0.0872
(0.0308) (0.0323) (0.0375) (0.0704)
Weather shocks 0.1546*** 0.1297*** 0.1488*** 0.1082
(0.0415) (0.0427) (0.0514) (0.0805)
Other shocks 0.3143* 0.2822 0.2564 0.3596
(0.1698) (0.1748) (0.2070) (0.3516)
Health shocks -0.0172 -0.0266 -0.0222 -0.0381
(0.0500) (0.0527) (0.0647) (0.0969)
Region/year dummy Yes Yes Yes Yes
Observations 3,385 2,938 2,093 845
Number of HHID 1,835 1,570 1,132 481
Log likelihood -1390 -1150 -1625 -1548
AIC/BIC 2865/3122 2387/2650
*** p<0.01, ** p<0.05, * p<0.1; standard errors in parentheses.