The Adoption and Impact of Soil and Water Conservation Technology

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ABSTRACT. This paper identifies the factors that af- fect farmers' decisions to adopt soil and water con- servation technology in Africa and how this technol-.
The Adoption and Impact of Soil and Water Conservation Technology: An Endogenous Switching Regression Application Awudu Abdulai and Wallace Huffman ABSTRACT. This paper identifies the factors that affect farmers’ decisions to adopt soil and water conservation technology in Africa and how this technology impacts farm yields and net returns. This technology is important because it improves efficiency of water use from rainfall—a critical issue in waterdeficient Sub-Saharan Africa. An analysis of new data from a survey of 342 rice farmers in northern Ghana shows that farmers’ education, capital and labor constraints, social networks and extension contacts, and farm soil conditions mainly determine adoption of field ridging, and the adoption of this technology increases rice yields and net returns significantly. (JEL Q15, Q24)

I. INTRODUCTION

Farmers in the Sahel zone of Sub-Saharan Africa normally face unpredictable weather that often results in production uncertainty and unforeseen hardships for farm households. The low and erratic rainfall in many parts of the region often leads to water shortages for optimal crop growth. Lower yields or complete harvest failure resulting from droughts remains a serious problem for farm households in the region, since it often leads to severe food shortages and welfare losses. Water scarcity in the region has therefore remained a major concern for both policy makers and international organizations, with several attempts to address the problem through the development of irrigation systems and improved cultivation practices. Farm households exposed to unpredictable weather conditions and potential harvest failure often adopt drought-resistant and improved cultivation

Land Economics • February 2014 • 90 (1): 26–43 ISSN 0023-7639; E-ISSN 1543-8325 䉷 2014 by the Board of Regents of the University of Wisconsin System

measures as ex ante risk management strategies (Barrett 2011; Kato et al. 2011). The adoption of more efficient farming practices and technologies that increase agricultural productivity and promote environmental sustainability remains crucial to achieving the goals of food security and poverty alleviation in Sub-Saharan African countries (Ersado, Amacher, and Alwang 2004). Hence, understanding the determinants of technology adoption rates has clear implications for agricultural and environmental policy design (Huffman 2001; Lewis, Barham, and Robinson 2011). Many governments in the region, with financial and technical support from international organizations, have therefore invested in developing and disseminating soil and water conservation technologies aimed at enhancing agricultural productivity and environmental sustainability. One such effort was the initiation of the Lowland Rice Development Project (LRDP) in Ghana, which was implemented with the aim of developing a profitable and sustainable intensive rice production system in the country. Given the low and erratic nature of rainfall in the region, a major goal of the project was to introduce the construction of earthen bunds and ridge channels as a water conservation method, which reduces production uncertainty. Despite the efforts put in by the LRDP through extension agents to encourage the adoption of this technology, the adoption rate

The authors are, respectively, professor, Department of Food Economics and Consumption Studies, University of Kiel, Germany; and C. F. Curtiss Distinguished Professor of Agriculture and Life Sciences, and professor, Department of Economics, Iowa State University, Ames.

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remains quite low (FSRPOP 2005).1 The low adoption rate probably indicates that while the technology is generally effective in promoting optimal crop growth and improving yields, constraints such as capital, labor, and other factors may serve as barriers to adoption. In particular, because the technology is labor intensive, poor households facing labor or liquidity constraints may have difficulty in adopting it. Moreover, soil conditions such as fertility, permeability, or water holding capacity, as well as the gradient of the land, may be important factors that farmers consider in their adoption decisions. The empirical literature on adoption of new agricultural technology provides several explanations for low adoption, ranging from credit constraints, information barriers, risk aversion, and environmental and institutional factors to local costs and benefits (Huffman and Mercier 1991; Barrett et al. 2004). While the empirical literature on adoption and diffusion of technology in Sub-Saharan Africa appears voluminous, the literature on impact of soil and water conservation technologies remains scanty (Kassie, Shiferaw, and Muricho 2011). Given the low and unreliable nature of rainfall in this region, studies that address these issues would definitely be useful to policy makers in designing agri-environmental policies. For example, although Ghana has been identified as a country with a comparative advantage in the production of paddy rice in the subregion, rice productivity and output have remained low because of periodic water shortages (MOFA 2001). This paper identifies the factors that affect farmers’ decisions to adopt soil and water conservation technology in Africa and how this technology impacts farm outcomes such as net returns and yields. This technology is important because it improves the efficiency of water use from rainfall—a critical issue in water-deficient Sub-Saharan Africa. The study utilizes data from a new survey of 342 rice farmers in the Northern Region of Ghana. 1

Similar low levels of adoption of water conservation technologies have been observed in other African countries. For example, Perret and Stevens (2006) have argued that although researchers in southern Africa have developed water conservation technologies, adoption rates have been low.

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In this region, farmers annually construct earthen bunds, or small ridges created by mounding up soil on each side of the ridge. The resulting channels on each side of the ridge serve to collect water and diffuse it laterally and aid water absorption. Although these bunds are quite labor intensive to construct, they aid water conservation and boost rice yields.2 The issue of technology adoption and its impact on farm outcomes has received considerable attention in the theoretical and empirical literature (Bravo-Ureta et al. 2006; Posthumus, Gardebroek, and Ruben 2010; Kassie, Shiferaw, and Muricho 2011; Amare, Asfaw, and Shiferaw 2012). In their investigation of the impact of soil conservation on farm income in Central American hillside farming, using a two-stage least squares method, Bravo-Ureta et al. (2006) found that the adoption of agroforestry systems has a positive and statistically significant association with farm income. However, in this study, the behavior of adopters cannot be directly compared to nonadopters because of selfselection, such that soil conservation measures are likely to be adopted by farmers who find it useful and not by those who do not. In their recent study on Uganda, using a propensity score matching approach, Kassie, Shiferaw, and Muricho (2011) also found that the adoption of improved groundnut varieties significantly increases crop income. However, the drawback of the PSM is its unconfoundedness assumption, which implies that once observable characteristics are controlled for, technology adoption is random and uncorrelated with the outcome variables. As argued by Smith and Todd (2005), there may be systematic differences between adopters’ and nonadopters’ outcomes even after conditioning, because selection is based on unmeasured characteristics. Our study differs from these studies in terms of the technologies considered and the 2 It has been shown for inland valleys in West Africa that the “period of positive water balance can be extended by about 20 days in wet years” in Bida, Nigeria, or by about 50 days in both wet and dry years in Makeni, Sierra Leone, by properly constructed bunds (Gunneweg, Evers, and Huizing 1986).

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empirical strategy employed. We model adoption as a selection process, where the expected benefits to the technology drive farmers’ adoption decisions. Specifically, we employ an endogenous switching regression approach to account for selectivity bias, and to capture the differential impact of adoption on adopters and nonadopters of the technology. The approach thus allows us to examine the determinants of adoption of the technology, as well as the impact of the adoption decision on rice yields and net returns.

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several episodes of rainfall levels as low as 600 mm per year and as high as 1,800 mm per year were recorded, representing both periods of severe droughts and floods. These unpredictable conditions normally occur at critical stages of plant growth, resulting in considerable crop yield losses. Technologies such as earthen bunds have therefore been developed to help conserve water for optimal plant growth as well as to reduce soil erosion from run-off, thereby increasing productivity. Data

II. BACKGROUND AND DATA Background

Rice is a staple food in Ghana, and its importance has increased significantly over the last three decades. Yearly per capita consumption of rice increased from 7.4 kg in 1982 to 25 kg in 2006, with great differences between rural and urban areas. Estimates indicate that yearly per capita consumption is about 38 kg in the urban areas. This trend is attributed to rapid urbanization, increased incomes, and factors of convenience such as good storability and ease of cooking (Garbers, Hirsch, and Paasch 2007). This increasing demand for rice occurs in the face of stagnating productivity and output on the supply side. After peaking at 280,000 tons in 1998, domestic production declined to 236,540 tons in 2005. Average yields of paddy rice increased marginally from 0.9 MT/ha in the 1970s to 1.97 MT/ha in 2005. According to the Ministry of Food and Agriculture, these yield levels are extremely low, since potential yields of 6.5 MT/ ha could be achieved under more effective extension and use of recommended technologies (MOFA 2001). As indicated earlier, a major constraint to rice production, which is mainly in the northern part of the country, is the low and unreliable rainfall. The unimodal rainfall begins in May and ends in September. A recent study by Amikuzuno and Donkoh (2012) covering the period between 1976 and 2010 shows that the seasonal variability in the pattern of rainfall in the region is quite high, with rainfall levels alternating between peaks and troughs, with a mean annual rainfall level of about 958.84 mm. Within their period of analysis,

The data used in the present study were collected from a survey during the 2006 cropping season in 24 communities located in four neighboring districts in the Northern Region, and covering three river valleys (Kulda-Yarong valley, Sillum valley, Zuwari valley and Tamale). Using information from a survey conducted by the FSRPOP in 2005, and a preliminary focus group study among farmers in the study area, a multistage sampling procedure with purposive selection of villages and random selection of households was employed to select 342 farmers for the survey. This sample size compares very well with similar farm surveys in Sub-Saharan Africa, where smaller sample sizes have been used in empirical analyses. The four districts in which the survey was conducted were chosen to ensure representation of adopters and nonadopters of the technology, as well as different landholdings and household types. The sample therefore adequately represents farm types found in the region. Information from the farmers was gathered through interviews. Enumerators who speak both the local language and English were hired to conduct the interviews. Farmers were asked to provide detailed information on specific farm activities. Additional information was obtained from the Northern Regional office of the Ministry of Food and Agriculture. The data covered information on production systems, input use, costs, socioeconomic characteristics of farmers, as well as plot-level characteristics. Table 1 presents descriptive statistics for the survey households. It can be observed from the table that 49% of farmers adopted bund technology for the 2006 farming season. Farmers in the sample are smallholders with

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TABLE 1 Variable Names, Definitions, and Descriptive Statistics for the Sample Variable YIELDS NET RETURN BUNDS AGE FSIZE EXTENSION PERCEPTION BULLOCK CREDIT NONFARM EDUCATION LABOR WAGERATE FERTAPLIC FERTPRICE ILLNESS FGROUP HYBID DISTFARM TAMALE KULDA SILLUM ZUWARI FERTILE PERMEABLE SLOPE

Description (bags/ha)a

Rice output Revenue minus input and hired labor costs per hectare (GHS) 1 if farmer adopted bunds, 0 otherwise Age of respondent (years) Total rice area (ha) 1 if farmer got information from extension agent, 0 otherwise 1 if farmer perceives bunds as highly effective soil and water conservation method, 0 otherwise Number of bullocks owned by the farmer 1 if farmer is not liquidity constrained, 0 otherwise 1 if farmer participated in off-farm work, 0 otherwise Number of years of schooling of farmer Labor application (person days) GHS per hour Application of nitrogen fertilizer (kg/ha) Average price of nitrogen fertilizer (GHS/kg) Household member labor time spent ill (person days) 1 if farmer was member in farmers’ group, 0 otherwise 1 if farmer used hybrid variety, 0 otherwise Mean distance from compound to plot by bike 1 if farmer is located in Tamale, 0 otherwise 1 if farmer is located in Kulda-Yarong, 0 otherwise 1 if farmer is located in Sillum, 0 otherwise 1if farmer is located in Zuwari, 0 otherwise Share of land on highly fertile soil (%) Share of land on permeable soil (%) Share of land on steep slope (%)

Sample Mean

Std. Dev.

18.03 184.5

10.47 162.9

0.49 37.31 3.19 0.44

0.50 10.82 2.52 0.39

0.52

0.50

0.15 0.40 0.36

0.40 0.49 0.48

6.43 68.38 0.40 31.06 1.9 7.27

3.81 4.75 0.12 14.27 0.42 4.56

0.56

0.50

0.91 16.12 0.42 0.13 0.33 0.12 0.51 0.52 0.10

0.29 14.36 0.38 0.19 0.26 0.32 0.49 0.48 0.13

Note: GHS, Ghana cedis. a One bag is equivalent to 82 kg.

an average farm size of 3.19 ha. Net returns are measured as the difference between the value of rice yield and per hectare costs of inputs, hired labor, and costs of land preparation and harvesting. The inputs included chemical fertilizer, pesticides, and seeds. The costs of land preparation included tractor and bullock services. Hired labor was included for land and bund preparation, sowing, weeding, and harvesting. The wage rates paid by the farmers were used in the calculation. As shown in Table 1, the average net return per hectare was GHS 184.5.3

Environmental and soil conditions was captured through soil fertility, soil permeability, and the gradient of the land (Di Falco and Chavas 2009). The fertility of the individual farms was measured through farmers’ perception of land fertility. Farmers were specifically asked to indicate the fertility of their cultivated land, either as fertile or infertile.4 To obtain the share of land categorized as fertile, the area of cultivated land that was ranked as fertile was divided by the total area of land. Education is measured as the number of years of schooling completed among those

3 The exchange rate of the Ghana cedi (GHS) to the U.S. dollar is $1 = GHS 1, after the new currency was launched in 2007.

4 Recent evidence shows that African farmers’ subjective reporting of soil conditions has been quite accurate (Barrett et al. 2004; Suri 2011).

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with formal schooling. Education is hypothesized to have a positive impact on technology adoption (Huffman 2001). Households having a larger labor endowment may be more likely to adopt water conserving practices, since that may reduce seasonal labor constraints (Moser and Barrett 2003). In particular, because the construction of earthen bunds is labor intensive, higher labor availability could have positive effects on adoption of the technology, while households facing seasonal labor constraints may opt not to adopt the technology. As noted by Ersado, Amacher, and Alwang (2004), illness saps a household’s available labor supply and can be expected to reduce the probability of adopting labor-intensive productivity-enhancing practices. Particularly in the study area, where guinea worm and malaria diseases are quite common, affected households may spend more time caring for the sick, rather than working on the fields. We therefore gathered information on time spent sick by family members during the farming season. The variable representing the number of sick family members is measured in household member labor time spent ill in person-days during the farming season. Wealth and differential access to capital are often-cited factors explaining differential rates of adoption. Possession of machinery and farm size are used to capture the impact of household wealth on adoption. Farmers in many rural areas of developing countries face credit constraints as a result of imperfections in agricultural and financial markets, and this tends to affect the adoption of new technologies, especially where capital investments are required. Adoption behavior may not be affected by a credit constraint, if farmers simply use other sources of finance, such as own equity or income from nonfarm activities. Hence, the formulation of credit constraint appears to be tricky (Sunding and Zilberman 2001). Farmers were therefore classified as liquidity constrained if they received some credit but expressed interest in borrowing more at the prevailing interest rate, or if credit was unavailable because their request was rejected, or there was no access at all to formal or informal lenders. Income from nonfarm activities may reduce financial constraints, particularly for

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resource-poor farmers, enabling them to purchase productivity-enhancing inputs. However, the net effect of off-farm activities is a priori ambiguous, since participation in offfarm activities may restrict production and decision-making activities (Wozniak 1984). Information provided by extension workers also serves as one important source of information on how and when to use a new technology. Moreover, membership in a farmer’s organization constitutes a social network through which farmers can obtain information about new technologies. Farmers’ adoption decisions are influenced by their network of family and friends (Bandiera and Rasul 2006). However, social effects can be negative when networks are very large, perhaps due to strategic delays (Bouma, Bulte, and van Soest 2008).5 Table 2 presents differences in the characteristics of adopters and nonadopters, with their t-values.6 The table highlights a number of important issues. The t-values suggest that there are some differences between adopters and nonadopters of the technology, with respect to farm-level and household characteristics. The average output for adopters of bund technology was 4.38 bags/ha higher than that of nonadopters, indicating significant differences in yields. This difference suggests that adoption of the technology plays a significant role in enhancing agricultural productivity growth. The average differences presented appear to mask the actual differences between adopters and nonadopters. For example, when soil fertility is considered, the average yield for adopters of bund technology on fertile soils was 29.75 bags/ha, while the corresponding amount for nonadopters on fertile soils was 23.25 bags/ha, showing a difference of 6.5 bags/ha. This result reveals that when 5 For example, Miguel and Kremer (2004) find the probability of an individual taking deworming drugs decreases in the number of those who have taken the drug earlier, indicating negative network social effects. 6 The t-statistic employed to show the differences between adopters (yA) and nonadopters (yN) is computed as t = (yA − yN) /冪(var(yA)/nA) + (var(yN)/nN), where nA and nN are the numbers of adopters and nonadopters, respectively; yA and yN are the sample means for adopters and nonadopters, respectively; with degrees of freedom given as nA + nN – 2.

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TABLE 2 Farm and Household Characteristics of Adopters and Nonadopters of Bunding Technology: Summary Statistics Variable

Nonadopters

Adopters

Diff.

17.10 184.24 37.78 5.21 7.61 0.12 0.85 2.75 28.41 38.23 61.26 8.15 46.28 0.32 0.09 53.54 58.49 5.20 176

21.48 197.10 36.81 6.30 9.47 0.19 0.95 3.83 53.01 33.33 75.5 6.90 65.47 0.58 0.472 46.55 46.60 8.41 166

4.38** 12.83* − 0.97 1.09 1.86** 0.07 10.15*** 1.08** 24.60*** − 4.90** 14.24*** − 1.25** 19.19*** 0.26** 0.38*** − 7.00* − 11.89** 3.21*

(bags/ha)a

Rice output Net returns per hectare (GHS) Age of the respondent Schooling (years) Household size Bullock possession Hybrid variety Farm size Access to credit Off-farm activity Labor application Illness (person days) Membership in farmers’ group Information from extension agent Perception index Share of land on high fertile soil Share of land with good permeability Share of land on slope Number of observations

Note: GHS, Ghana cedis. a One bag is equivalent to 82 kg. * Significant at the 10% level; **significant at the 5% level; *** significant at the 1% level.

land is fertile, the contribution of such a technology toward increasing crop productivity becomes stronger. Adopters of the bund technology also exhibit higher net returns, with net returns of GHS 197.10 per hectare, compared to GHS 184.24 per hectare for nonadopters, with a difference of GHS 12.83 per hectare, which is statistically significant. There appears to be significant differences between adopters and nonadopters in labor endowments, access to credit, as well as membership in farmers’ organizations. Moreover, adopters generally own more land and have better access to formal credit than nonadopters. While only 28% of nonadopters of bund technology had access to credit, as much as 53% of adopters had access to credit. There are also differences between adopters and nonadopters in terms of number of years of schooling and access to extension agents, with adopters being more educated and having more contacts to extension agents, compared to nonadopters. Although the comparisons discussed above reveal some significant differences between adopters and nonadopters in terms of yields and net returns, knowledge of average differ-

ences is not enough to explain the adoption decisions across sample farmers, since they do not account for the effect of other characteristics of farmers. In the next section, we model adoption as a selection process, where the expected benefits to the technology drive farmers’ adoption decisions. III. CONCEPTUAL FRAMEWORK Theoretical Model

We model the adoption of bunding technology, under the assumption that farmers choose between construction of bunds and nonconstruction. We assume here that farmers are risk neutral, and take into account the net benefit derived from the technology in the decision-making process. Farmers are therefore assumed to choose the technology that provides maximum net benefits. Under the assumptions, let us represent the net benefit farmer j derives from adopting the technology as YjA and the net benefit from nonadoption represented as YjN, with net benefits representing wealth. The two regimes can be specified as

32 Y jA = X j βA + u jA

Land Economics

[1]

and Y jN = X j βN + u jA,

[2]

where Xj is a vector of variable factor prices, fixed factors, and farm and household characteristics; βA and βN are vectors of parameters; ujA and ujN are iids. The farmer will normally choose the technology if the net benefits obtained by doing so are higher than obtained by not using the technology, that is, YjA > YjN (Pitt 1983). Although the preferences of the farmer, such as perceived net benefits of adoption, are unknown to the researcher, the characteristics of the farmer and the attributes of the technology are observed during the survey period. We can therefore represent the net benefits derived from adoption of the technology by a latent variable D j∗, which is not observed but can be expressed as a function of the observed characteristics and attributes, denoted as Z, in a latent variable model as follows: D j∗ = γ ′Z j + ε j, D j = 1 [ D j∗ > 0 ] ,

[3]

where D j is a binary variable that equals 1 for farmers who adopt the technology, and zero otherwise, with γ denoting a vector of parameters to be estimated. Thus, the farmer adopts the technology only if the perceived net benefits are positive. The error term ε with mean zero and variance σ 2ε captures measurement errors and factors unobserved to the researcher but known to the farmer. Variables in Z include factors that influence the adoption decision, such as farm-level and household characteristics. Empirical Specification

The previous discussion shows that farmers normally take into account outcomes such as potential net benefits when making decisions on the adoption of new technologies. Thus, the technologies employed by farmers need to be taken into account when analyzing outcomes such as yields or net returns. Failure to do so may result in selection bias. The bias arises because farmers who would obtain

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lower than average net returns from the new technology, given prices and fixed factors, choose not to adopt and as such truncate the observed technology profit distribution (Pitt 1983). In particular, selection bias occurs if unobservable factors influence both error terms in the technology choice equation (ε) and the outcome equation (u), thus resulting in correlation of the error terms of outcome and choice equations, with corr(ε,u) = ρ. Such unobservable factors may include (1) the innate managerial and technical abilities of the farmers in understanding and using new agricultural technologies; (2) the types of social networks formed by farmers that are not captured, such as the kind of neighbors the farmer speaks to and whether such neighbors have adopted the technology; and (3) transaction costs that can be incurred through the adoption of soil and water conservation measures, or costs incurred by farmers as a result of poor access to input suppliers because of infrastructure constraints. As noted by Suri (2011), knowledge of such constraints can allow for targeted policy interventions to alleviate their constraints and as such enable them to adopt new technologies to improve yields. When such unobservables are not measured by the researcher, then there would be a correlation between the regressors and the error term, resulting in ρ ≠ 0. In such cases, standard regression techniques such as ordinary least squares would yield biased results. Moreover, in examining the impacts of new agricultural technologies, it is normally difficult to simply attribute the differences in yields and net returns between adopters and nonadopters of the technology to adoption. Where experimental data are available through randomized control trials, information on the counterfactual situation would normally be provided, and as such the problem of causal inference can be resolved (Miguel and Kremer 2004). However, when the data available are only from a cross-sectional survey, as the one employed in the current study, there would be no information on the counterfactual situation. As argued by Dehejia and Wahba (2002), an effective way of addressing the problem is to resort to an investigation of the direct effect of technology adoption by analyzing the differences in outcomes among

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farm households. The propensity matching approach proposed by Rosenbaum and Rubin (1983) has been widely employed to examine the impacts of technology adoption on farm outcomes and household welfare, particularly when self-selection is an issue (e.g., Amare, Asfaw, and Shiferaw 2012).7 However, a major objective of the propensity score estimation is to balance the observed distribution of covariates across the groups of adopters and nonadopters. Thus, the probit or logit estimates obtained in the estimation cannot be considered as determinants of adoption. Given our interest in examining the determinants of adoption, as well as the impacts of adoption, we employ the endogenous switching regression model to account for selection bias in our estimation of the impact of adoption on farm outcomes. The endogenous switching regression approach was developed by Lee (1982) as a generalization of Heckman’s selection correction approach. It accounts for selection on unobservables by treating selectivity as an omitted variable problem (Heckman 1979). In contrast to the Heckman model, farm outcomes such as yields and net returns can be observed for the whole sample of adopters and nonadopters. Thus, in the switching regression approach, the farmers are partitioned according to their classification as adopters and nonadopters in order to capture the differential responses of the two groups. Given that farmers choose to either adopt the technology or not adopt it, the observed net benefits take the following values:

tors, and farm-level and household characteristics. The vectors β in [4] and γ in [3] are the associated parameters that have to be estimated. Although the variables in the vectors X in equation [4] and Z in equation [3] may overlap, it is important to note that proper identification requires that at least one variable in Z does not appear in X. Self-selection into the adopters or nonadopters categories may lead to nonzero covariances between the error terms of the adoption decision equation and the outcome equation.8 The three error terms ε, uA, uN are assumed to have a trivariate normal distribution with mean vector zero and the following covariance matrix:



Y jA = X ′β jA + u jA if D j = 1,

[4]

where YjA and YjN are the outcome variables for adopters and nonadopters, respectively, X′ is a vector of variable factor prices, fixed fac7 A well-known drawback of the PSM approach is the unconfoundness assumption, also known as the conditional independence assumption, which implies that once observable factors are controlled for, technology adoption is random and uncorrelated with the outcome variables. As argued by Smith and Todd (2005), there may be systematic differences between adopters’ and nonadopters’ outcomes even after conditioning, because selection is based on unmeasured characteristics.

σ2A

σAN σAε

cov(uA,uN,ε) = Σ = σAN σ2N σAε

σNε



σNε , σ2ε

[5]

where var(u A) = σ 2A, var(u N) = σ 2N, var(ε) = σ 2ε, cov(u A,u N) = σ AN, cov(u A,ε) = σ Aε, and cov(u N,ε) = σ Nε. For this reason, the error terms in equation [4], conditional on the sample selection criterion, have nonzero expected values, and ordinary least squares estimates of coefficients βA and βN suffer from sample selection bias (Lee 1982). According to Johnson and Kotz (1970), the expected values of the truncated error terms (u A⎪D = 1) and (u N⎪D = 0) are then given as E(uN⎪D = 0) = E(uN⎪ε ≤ − Z ′γ) = σNε

Regime 0 (Nonadopters): Y jN = X ′β jN + u jN if D j = 0, Regime 1 (Adopters):

33

− φ(Z ′γ/σ)

≡ σNε λN

[6]

≡ σAε λ A,

[7]

1 − Φ(Z ′γ/σ)

and E(uA⎪D = 1) = E(uA⎪ε > − Z ′γ) = σAε

φ(Z ′γ/σ) Φ(Z ′γ/σ)

where φ and Φ are the probability density and cumulative distribution function of the stan8 That is, some unobservable factors that influence the probability to adopt the technology could also influence yields and net returns.

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Land Economics

dard normal distribution, respectively. The ratio of φ and Φ evaluated at Z′γ is referred to as the inverse Mills ratio λA, λN (selectivity terms).9 The selectivity terms are incorporated into equation [4] to account for selection bias. The estimation of the model proceeds in two stages. The first stage involves a probit regression to determine the probability of adoption and thus estimation of the parameter γ given in equation [3]. These estimates are then used to calculate the selectivity terms (λA, λN) according to equations [6] and [7]. The drawback of this two-step approach is that it generates residuals that are heteroskedastic and as a result cannot be used to obtain consistent standard errors without cumbersome adjustments (Lokshin and Sajaia 2004). The full information maximum likelihood method suggested by Lokshin and Sajaia (2004) overcomes the problem through a simultaneous estimation of the two equations, that is, the adoption and outcome equations. Of particular interest are the signs and significance levels of the correlation coefficients (ρ) from the estimates. As indicated previously, these are the correlations of the error terms of the outcome and choice equations (corr(ε, u) = ρ). Specifically, there is endogenous switching, if either ρAε(σAε/σAσε) or ρNε(σNε/σAσε) is significantly different from zero, which would result in selection bias. If ρ > 0, this would imply negative selection bias, indicating that farmers with below average yields and net returns are more likely to adopt the technology. On the other hand, ρ < 0 implies positive selection bias, suggesting that farmers with above average yields and net returns are more likely to adopt the technology. Of significant interest in the present study is the effect of adoption of the technology on farm outcomes. This can be examined by first specifying the expected values of the outcome (Fuglie and Bosch 1995). For an adopter of the technology with characteristics X and Z,

9 The notion of applying the two-stage method to estimate the switching simultaneous-equations models has been discussed by Lee (1982). Other estimators than Heckman’s make even stronger distributional assumptions; they are more efficient than the Heckman estimator.

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the expected value of the outcome, YjA, is given as E(Y jA⎪D = 1) = Xβ jA − σAε λA.

[8]

Sample selection is taken into account in the last term, indicating that farmers that have adopted the technology may behave differently from an average farmer with identical characteristics due to unobserved factors (Maddala 1986). Now the expected value of the same farmer had he chosen not to adopt the technology is given as E(Y jN⎪D = 1) = Xβ jN − σNε λA.

[9]

The change in the outcome due to adoption can then be specified as the difference between adoption and nonadoption. Thus, the expected outcomes from equations [8] and [9] are employed to obtain unbiased estimates of adoption effects. These estimates are termed the average treatment effect on the treated (ATT) in the impact assessment literature (Lokshin and Sajaia 2004):10 ATT = E [ Y jA⎪D = 1 ] − E [ Y jN⎪D = 1 ] = X(β jA − β jN) + (σAε − σNε)λA,

[10]

where σ represents the covariance of the error terms and λ the inverse mills ratios. In estimating farm outcomes, we will consider not just net returns, but also yields. That is, the impact of adoption on yield and net returns per hectare will be estimated. It is important to note that if self-selection is based on comparative advantage, σAε − σNε would be positive, indicating that adoption would result in higher yields and net returns than under random assignment (Maddala 1986). As indicated earlier, propensity score matching approach has been widely employed to estimate the average treatment effect. However, its 10 Since λ is included in both equations [8] and [9], A unobserved vectors are taken into account. The approach simply assumes that unobserved factors have differential effects on adopters and nonadopters. Thus, taking the differences in effects, σAε – σNε, while holding λA constant ensures that the effects of unobserved factors are canceled out. In this case, the estimated yield and net returns differences would be solely due to adoption impacts, without any unobserved effects.

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strong assumption of confoundedness makes it a restrictive approach. An issue that needs to be addressed in estimating the adoption specification is the potential endogeneity problem that may arise with variables such as extension services and nonfarm work. In particular, households may have to decide between allocating labor to nonfarm work and to construction of earthen bunds. Especially during the farming season when labor is in high demand for farm work, farmers may choose to allocate more labor to farm activities, including the construction of bunds, resulting in less time being allocated to nonfarm work. Although extension agents normally disseminate new technologies to farmers, leading to the adoption of the technologies, it may also happen that farmers who have adopted new technologies without previous access to extension agents would have the agents visiting them to provide them with additional information on the best use of the new technologies, as well as information on interrelated technologies. Thus, both the nonfarm work and extension services variables may be jointly determined with the decision to adopt in the adoption specification. To account for these potential endogeneity problems, we employ the Rivers and Vuong approach (1988), since the dependent variable is dichotomous. The estimation is carried out by first specifying the potential endogenous variables (nonfarm work and extension services) as functions of all other explanatory variables given in the adoption equation, in addition to a set of instruments in the firststage regressions. That is, the specification used is T i = γZ ij + ψV ij + ζ ij,

[11]

where Ti is vector of the two potential endogenous variables (nonfarm work and extension services), Z is as described previously, and Vij is a vector of instruments that is correlated with the given endogenous variable but uncorrelated with the error term, εj in equation [3], and is therefore excluded in estimating equation [3]. Rather than using the predicted values from the first-stage equation as in a usual two-stage estimation approach, the ap-

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proach involves specifying the adoption equation given in equation [3] as D ij∗ = βZ ij + φT i + R ij + υ ij,

[12]

where Zij is as defined previously, Ti is a vector of the observed potential endogenous variables (nonfarm work and extension services), and Rij is a vector of the residual terms from the first-stage regressions of the endogenous variables. The probit estimates of the potential endogenous variables in Z are then consistent (Wooldridge 2002). To ensure identification in the estimation of the adoption specification, some of the variables included in the firststage estimation in equation [11] are excluded from the adoption equation in [12]. A suitable identification strategy is to employ a variable that strongly influences the endogenous variable but does not influence the decision to adopt the technology. In the extension service equation, we employed as instrument the distance from the nearest extension outlet, which affects the access to extension services, but not the decision to adopt the technology. In the nonfarm equation we used distance to the regional capital as instrument, which has been identified as a variable affecting participation in nonfarm employment, but not the decision to adopt the technology (Abdulai and Huffman 2005). IV. EMPIRICAL RESULTS

The estimates of the determinants of adoption and the impact of adoption on net returns and rice yields are presented in Tables 3 and 4.11 As indicated previously, the full information maximum likelihood approach estimates both the adoption and the outcome equations jointly. Thus, the selection equations, representing determinants of adoption, are given in the second columns of Tables 3 and 4, providing two different sets of results due to slightly different specifications. Given that these coefficients can be interpreted as normal probit coefficients, we will discuss the results from the selection equations in both 11 The full information maximum likelihood method was estimated using the econometric software Stata version 12 (StataCorp 2011).

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TABLE 3 Endogenous Switching Regression Results for Adoption and Impact of Adoption on Net Returns Net Returns Variable CONSTANT AGE EDUCATION EXTENSION FSIZE FGROUP CREDIT MACHINERY NONFARM ILLNESS IMPROVED FERTAPLIC FERTPRICE LABOR WAGERATE FERTILE PERMEABLE SLOPE KULDA ZUWARI TAMALE PERCEPTION NONFARMRES EXTENSIONRES ln σN ρNε ln σA ρAε Log likelihood v2-Statistic for overidentification Likelihood ratio test of independent equations v2(1)

Selection

Nonadopters

Adopters

− 0.126 ( − 0.428) − 0.105 ( − 1.580) 0.476** (2.298) 0.246** (2.253) 0.498* (1.896) 0.378** (2.362) 0.403** (2.465) 0.131** (2.178) − 0.249* ( − 1.835) − 0.148* ( − 1.872) 0.314* (1.924) 0.425 (1.156) 0.158 (1.278) 0.062 (1.405) − 0.186 (1.297) 0.234** (2.316) 0.149** (2.185) 0.186** (2.021) − 0.337* ( − 1.861) − 0.138* ( − 1.689) 0.189 (1.536) 0.107** (2.386) 0.034 (0.879) 0.157 (1.598)

1.257* (1.814) − 0.138 ( − 1.359) 5.167** (2.487) 2.374* (1.864) − 0.532** (2.104) 3.570** (2.246) 3.697** (2.282) 0.077 (1.361) 0.384* (1.775) − 0.157** ( − 2.151) 4.634** (2.374) 0.387 (1.573) − 0.541** ( − 2.377) 0.049 (1.382) − 1.782** ( − 2.282) 3.618** (2.181) 0.083* (1.724) − 1.162* (0.761) − 0.324 ( − 1.528) 2.867* (1.929)* 0.475 (1.019)

− 1.097 ( − 0.292) 0.095 (1.019) 3.825* (1.932) 4.698** (2.095) 0.423 (1.546) 5.563** (2.179) 6.347** (2.194) 1.262** (2.297) 0.676 (1.543) − 2.183** ( − 2.091) 5.758** (2.360) 0.168 (1.620) − 0.269** ( − 2.613) 0.074 (1.603) − 1.871** ( − 2.446) 5.982** (2.327) 0.206** (2.313) − 2.267* (1.884) − 0.415 ( − 1.267) 2.649** (2.283)* 0.376 (0.9691)

0.454*** (3.293) − 0.087 ( − 1.428) − 521.76 0.859 [0.62]

0.562*** (4.615) − 0.529*** (2.831)

23.16***

Note: Absolute t-values in parentheses and p-value in square brackets. NONFARMRES and EXTENSIONRES denote residuals from the first-stage regressions for nonfarm work and extension agents, respectively. * Significant at the 10% level; **significant at the 5% level; *** significant at the 1% level.

tables together. In both specifications, the coefficients of the two variables representing the residuals derived from the first-stage regressions for the potential endogenous variables that include extension visits and nonfarm work are also presented. The estimates show that the residuals of both the extension contacts and the nonfarm work are not significantly different from zero at conventional levels, suggesting that the coefficients have been consistently estimated (Wooldridge 2002). The v2 statistics presented in Tables 3 and 4

for the validity tests of the overidentifying restrictions also fail to reject the exclusion restriction that the instruments employed affect adoption only through extension and nonfarm work variables.12 The empirical results for the probability of adopting the technology are generally in 12 Given that they are not the focus of our study, and in the interest of brevity, the first-stage regressions are not reported in the paper but are available upon request from the authors.

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37

TABLE 4 Endogenous Switching Regression Results for Adoption and Impact of Adoption on Rice Yields Rice Yields Variable CONSTANT AGE EDUCATION EXTENSION FSIZE CREDIT MACHINERY NONFARM ILLNESS IMPROVED FERTAPLIC WAGERATE LABOR FERTPRICE FERTILE PERMEABLE SLOPE KULDA ZUWARI TAMALE FGROUP PERCEPTION NONFARMRES EXTENSIONRES ln σN ρNε ln σA ρAε Log likelihood v2-Statistic for overidentification Likelihood ratio test of independent equations v2(1)

Selection

Nonadopters

0.276 (0.629) − 0.172 ( − 1.592) 0.328* (1.825) 0.348** (2.114) 0.183** (2.104) 0.291* (1.874) 0.149* (2.331) − 0.179** ( − 2.073) − 0.167** ( − 2.392) 0.069** (2.184) 0.286 (1.493) − 0.072 (1.307) 0.032 (1.528) 0.062 (1.037) 0.209** (2.265) 0.088* (1.898) 0.058 (1.252) 0.196 (1.442) 0.115* (1.851) 0.126 (1.527) 0.247** (2.258) 0.213** (2.664) 0.026 (0.829) 0.107 (1.036)

1.143** (2.249) − 0.326 ( − 0.683) 3.274** (2.521) 2.657** (2.355) − 2.264*** (3.162) 2.583** (2.416) 0.863 (1.507) 0.794** (2.051) − 0.516* (1.783) 0.045** (2.409) 0.113** (2.238) − 1.148 ( − 1.282) 0.875** (2.063) 0.142 (1.362) 0.179* (1.779) 0.037* (1.938) − 1.538** ( − 2.146) − 1.942* ( − 1.768) 0.561** (2.279) 0.285 (1.563)

Adopters − 1.682 ( − 0.582) 0.087 (1.472) 1.948* (1.682) 5.631** (2.472) − 0.185 ( − 0.748) 5.790** (2.526) 2.235** (2.469) 1.098 (1.538) − 0.624** (2.162) 0.028* (1.742) 0.096** (2.477) − 0.784 ( − 1.546) 0.489* (1.821) 0.869 (1.012) 0.417** (2.586) 0.492** (2.390) − 0.287 (1.183) − 0.304 (0.279) 2.469** (2.671) 0.513 (1.438)

0.316*** (4.681) − 0.066 ( − 1.381) − 483.62 0.715 [0.52]

0.516*** (4.258) − 0.426*** ( − 2.892)

28.73***

Note: Absolute t-values in parentheses and p-value in square brackets. NONFARMRES and EXTENSIONRES denote residuals from the first-stage regressions for nonfarm work and extension agents, respectively. * Significant at the 10% level; **significant at the 5% level; *** significant at the 1% level.

agreement with predictions from the analytical model. In both specifications, variables having the same name have statistically similar effects on adoption. The variable representing education of the farmer is positive and significantly different from zero, suggesting that more-educated farmers are more likely to construct earthen bunds on their land, a finding that is consistent with the notion that education is important in helping farmers in their decisions on adopting new innovations and technologies (Huffman 2001). The extension

variable is also positive and statistically significant in both specifications, indicating that farmers with contacts to extension agents are more likely to adopt the technology. This result confirms the importance of information as a means of reducing uncertainty about the technology. As argued previously, agricultural extension tends to be a major source of information on technological improvements in the sector in many parts of the developing world and as such can be quite crucial to their adoption. Landholding, which is the main resource

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Land Economics

of farmers, has a positive and significant impact on the probability of adopting the bund technology, a finding that is consistent with previous studies using farm size as a determinant of technology adoption. The coefficient of the household size variable is also positive and significantly different from zero, suggesting that larger households, with more labor endowments, are more likely to adopt this labor-intensive technology. A larger number of days spent sick by family members and not working tends to reduce the probability of adopting the technology, suggesting that illness distracts farmers from adopting the technology, probably because it is labor intensive (Ersado, Amacher, and Alwang 2004). The variable for access to credit is positive and significantly different from zero, suggesting that farmers that are not liquidity constrained are more likely to adopt the bund technology, confirming the significance of access to formal credit for farmers in the adoption process. Belonging to a farmer’s organization increases the probability of adopting the technology, a finding that concurs with the notion that social networks facilitate the flow of information, which tends to enhance the adoption of new agricultural technologies (Bandiera and Rasul 2006). Quite interesting are the effects of the land quality variables, which are all significantly different from zero. Specifically, the variables for soil fertility and permeability are both positive and significantly different from zero, indicating that bunds are more likely to be constructed on more fertile and more permeable soils. Bunds are also more likely to be constructed on land with steeper slopes, apparently to slow down water flow in order to reduce soil erosion and also conserve water. These findings suggest that farmers consider land quality in the adoption decisions of such soil and water conservation technologies. Farmers engaged in nonfarm activities appear to be less likely to adopt the technology, suggesting that participation in nonfarm activities may be restricting the allocation of labor to farm activities, which in turn, negatively impacts the adoption of labor-intensive technologies. As indicated previously, nonfarm employment is an income diversification strategy among farm households that have the pos-

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sibility to sell their labor on the nonfarm labor market (Huffman 2001). Hence, farmers who engage in these activities may simply be doing so in order to hedge against lower incomes in the event of lower crop yields. The village dummies are significantly different from zero, indicating significant cluster effects, and probably revealing agroclimatic variation and differences in access to infrastructure. The results regarding the impact of adoption on net returns and yields are presented in the third and fourth columns of Tables 3 and 4, for nonadopters and adopters, respectively. The estimates generally show the impact of the household and farm-level characteristics on farm productivity and net returns for adopters and nonadopters. As indicated previously, identification of the model requires that there is at least one variable in the selection or adoption equation that does not appear in the outcome equations. In the yield specification, the variable representing perception of the effectiveness of the bunding technology by farmers and members in farmers’ organization are used as identifying instruments. While perception of the effectiveness is expected to affect adoption decisions, it should not affect farm productivity directly. Similarly, membership in a farmer’s organization could affect adoption decisions, but not yields. The perception variable is also used to identify the net benefits equation because it is not expected to influence net benefits, given adoption or nonadoption. The likelihood ratio tests for joint independence of the three equations are also reported in Tables 3 and 4, and the tests show that the equations are dependent. An interesting finding in both tables is the signs and significance of the covariance terms (ρAε and ρNε). The results show that the covariance terms for the adopters in Tables 3 and 4 are all statistically significant, indicating that self-selection occurred in adoption. Thus, adoption of bunding technology may not have the same effect on the nonadopters, if they choose to adopt (Lokshin and Sajaia 2004). Moreover, the negative sign for ρAε indicates a positive selection bias, suggesting that farmers with above-average yields and net returns have a higher probability of adopting the technology. Thus, comparative advantage tends to play a critical role in the determination of

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yields, net returns, and adoption decisions. This finding is consistent with earlier studies by Barrett et al. (2004) and Abdulai and Binder (2006) but contrasts with the findings by Kabunga, Dubois, and Qaim (2012). The statistically insignificant covariance estimate for nonadopters (ρNε ) suggests that in the absence of adoption of the technology, there would be no significant difference in the average behavior of the two categories of farmers caused by unobservable factors.13 The results in Tables 3 and 4 indicate that education is an important factor in explaining higher rice yields and higher net returns among adopters of the technology. The positive and significant coefficients of the variable suggest that good knowledge and firm understanding of the technology may increase the benefits of bund construction in terms of yields and net returns. This finding is in line with the findings of Kassie, Shiferaw, and Muricho (2011) for Uganda. Age of the household head does not seem to have any significant impact on net returns and rice yields of both adopters and nonadopters. Farm size appears to have differential impacts on adopters and nonadopters. The negative and significant coefficients for nonadopters indicate that for this group of farmers, larger farms obtained significantly lower yields and net returns than smaller farms, a finding that supports the inverse farm size– productivity relationship, which posits that small farms are more productive than large farms (Binswanger, Deininger, and Feder 1995; Chen, Huffman, and Rozelle 2011). For adopters, farm size did not significantly influence yields or net returns. Variables indicating wealth and capital constraints were found to influence outcomes, although at varying levels. The possession of farm machinery such as tractors tends to have positive and significant impacts on yields and net returns of adopters, but no significant impact for nonadopters. In particular, tractor plowing is more beneficial for adopters of bund technology, as 13 The necessary conditions for consistency are also fulfilled, since ρNε > ρAε, indicating that adopters of the technology obtain higher yields and net returns than they would if they did not adopt the technology (Lokshin and Sajaia 2004).

39

bunds constructed with tractors during plowing are more robust than bunds that are constructed manually. The ownership of bullocks has a positive effect on yields, albeit statistically significant only for adopters. As bunds that are constructed using bullocks are more robust than those constructed manually and have a higher quality, rice yields of farmers owning bullocks tend to be higher. Access to credit tends to have a positive effect on productivity and net returns for both adopting and nonadopting farmers. Participation in nonfarm activities tends to have a significant and positive effect on productivity for nonadopters, but no impact for adopters. To the extent that nonadopters are more credit constrained compared to adopters, the significant impact on nonadopters may be due to the fact that income accruing from these activities is used to purchase productivity-enhancing inputs like labor and fertilizer to increase yields. Participation in farmers’ organizations and access to extension services have positive and significant impacts on net returns for both adopters and nonadopters. As argued by Durlauf and Fafchamps (2005), social networks may be crucial in reducing search and information costs associated with both the adoption of new technologies and marketing of agricultural products. Thus, farmers’ organizations are useful in helping farmers identify markets to sell their products at lower costs. The coefficients of the price of fertilizer and wage rate in Table 3 have the expected negative signs and are significantly different from zero, indicating that higher input prices reduce net benefits. On the other hand, the coefficients of the variables for fertilizer application and labor in Table 4 are positive and significantly different from zero, suggesting that farms that apply fertilizer and employ more labor obtain higher yields. The results also reveal that location fixed effects may be significant in explaining differences in farm outcomes. In particular, farmers located in Kulda valley tend to have lower rice yields and net returns, while those located in the Tamale area and Zuwari are found to have higher yields and net returns. As expected, plot-level characteristics also tend to affect yields and net returns. In particular, a higher proportion

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TABLE 5 Impact of Bunding Technology Adoption on Rice Yields and Net Returns Mean Outcome

(bags/ha)a

Yields Net returns (GHS/ha)

Adopters

Nonadopters

ATT

t-Value

26.85 237.33

21.58 205.17

5.27*** 32.16***

7.38 6.52

Note: ATT, average treatment effect on the treated; GHS, Ghana cedis. a One bag is equivalent to 82 kg. *** Coefficient significant at the 1% level.

of very fertile rice cropping area has a positive and significant influence on rice yields and net returns among adopters of the technology. The significant influence of soil variables suggests that productivity estimates may be biased if environmental variables are omitted, a finding that is in line with the argument put forward by Sherlund, Barrett, and Adesina (2002). The estimates for the average treatments effects (ATT), which show the impact of adoption on rice yields and net returns, are presented in Table 5. Unlike the mean differences presented in Table 2, which may confound the impact of technology adoption on yields and net returns with the influence of other characteristics, these ATT estimates account for selection bias arising from the fact that adopters and nonadopters may be systematically different. The results reveal that adoption significantly increases yields and net returns. Specifically, the causal effect of adoption of the technology is 5.27 bags (about 448 kg) per hectare, representing a 24% increase in yields. Similarly, the adoption increased net returns by 16%, from GHS 205 per hectare to GHS 237 per hectare. These findings are consistent with the view that adoption of new agricultural technologies can improve farm productivity and household incomes (Minten and Barrett 2008). V. CONCLUSIONS AND IMPLICATIONS

This paper uses farm-level data to examine the factors that influence the adoption of soil and water conservation technology, as well as the impact of adoption on yields and net returns among rice farmers in northern Ghana. Comparisons of average yields and net returns between adopters and nonadopters of the tech-

nology revealed some significant differences. However, knowledge of average differences is not enough to understand the adoption decisions across a sample of farmers, since they do not account for the effect of other characteristics of farmers. We therefore modeled adoption as a selection process, where the expected benefits to the technology drive farmers’ adoption decisions. Specifically, we employ an endogenous switching regression approach to account for selectivity bias, and to capture the differential impact of adoption on adopters and nonadopters of the technology. The results showed that sample selection bias would result if the outcome equations (yields and net returns) were to be estimated without considering the adoption decision. Thus, adoption of bunding technology may not have the same effect on the nonadopters if they adopt. Furthermore, a positive selection bias was observed for both rice yields and net returns, suggesting that more-productive farmers tend to adopt the technology. Thus, comparative advantage tends to play a critical role in the determination of yields, net returns, and adoption decisions. As in other studies, we also find that farm households with lower labor endowments appear to be less likely to adopt the soil and water conservation technology. The findings also indicate a positive and significant influence of education on adoption, as well as impact on yields and net returns, confirming the importance of provision of schools in the rural areas. Moreover, households that lose labor due to illness of members have a lower probability of adopting the technology, indicating that provision of health services in the area could promote the adoption

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of these technologies. While the incidence of guinea worm disease has declined in recent years, malaria still remains a serious disease that keeps many farmers at home during the farming season. Our findings also confirm the significance of social networks and land quality in determining which farmers adopt the technology. To the extent that networks of farmers’ organizations can reduce information barriers to adoption, encouraging the formation of such farmers’ organizations, particularly in rural areas, could be effective in promoting the adoption of new technologies. It is significant to mention that generalizing these results will be case specific for each situation. Nevertheless, the findings reveal that besides variations in wealth and related factors such as land holdings, differences in soil conditions need to be accounted for in accurately explaining adoption patterns of new agricultural technologies in Sub-Saharan Africa. On the impact of adoption on farm outcomes, the results showed that the causal effect of adoption was to increase rice yields by about 24% and net return by 16%, suggesting that soil and water conservation technologies in areas facing low and erratic rainfall patterns can contribute significantly to productivity and farm income increases. The causal effect of positive and significant impact of the technology on yields and household income reaffirms the potential role of new agricultural technology in raising farm productivity and directly reducing rural poverty through higher farm household incomes. Overall, our findings do have policy implications for the adoption of agricultural technologies and increasing farm productivity. In particular, they suggest that effective policy measures to promote the adoption of new technologies should include the improvement of farmers’ education, information channels such as extension services, and social networks. To ensure effective dissemination and adoption of new conservation technologies, government and extension services could take the lead in promotion and dissemination at the initial stages and in the process create an enabling environment for the effective participation of the private sector. The positive influence of access to credit suggests that policies that enhance farmers’ access to credit

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would go a long way to facilitate the adoption of new agricultural technologies, and increase yields and farm income. Hence, policy makers need to help farmers overcome financial and information barriers that are crucial in enhancing the adoption of new soil and water conservation technologies. The negative impact of poor health on adoption, yields, and net returns underscores the importance of efforts by policy makers and nongovernmental organizations to improve sanitation and health conditions in the rural areas via the provision of primary health care facilities such as clinics, and in some places hospitals. In the northern Ghana case, the Ministry of Health and the Association of Church Development Project have set up small health computer centers to develop and integrate materials for health education and health information exchange within the primary health care programs to improve health conditions in rural areas of the region (Apoya 2012). Acknowledgments The authors would like to thank, without implicating, the journal editor and two anonymous reviewers for valuable comments and suggestions that have substantially improved the paper. They would also like to thank Rosamond Naylor and Wally Falcon, as well as seminar participants at Stanford University, for useful comments and discussions. They also thank Liane Faltermeier and James Kombiok, who collected the data used in the study. They acknowledge financial support from the German Research Foundation.

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