The effect of household wealth on the adoption of improved maize varieties in Zambia

Food Policy 33 (2008) 550–559

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The effect of household wealth on the adoption of improved maize varieties in Zambia Augustine S. Langyintuo a,*, Catherine Mungoma b a b

International Maize and Wheat Improvement Center (CIMMYT), P.O. Box MP 163, Mount Pleasant, Harare, Zimbabwe Ministry of Agriculture and Cooperatives, Zambia Agriculture Research Institute, C/O Golden Valley Agricultural Research Trust, P.O. Box 54, Fringilla, Zambia

a r t i c l e

i n f o

Article history: Received 4 June 2007 Received in revised form 20 March 2008 Accepted 4 April 2008

Keywords: Wealth index Improved high yielding maize variety Technological change Double-hurdle model Zambia

a b s t r a c t Production and price risks that could render input use unprofitable sometimes prevent rural households from benefiting from input technological change. The household’s ability to cope with such risks and hence benefit from input technological change is often positively related to its wealth or stock of productive assets. Empirical evidence, however, suggests a non-linear relationship between wealth and adoption of new agricultural technologies so that within a rural community, households on the lower wealth continuum behave differently from those on the higher level. Using farm level data collected from 300 randomly selected households in three districts of Zambia in 2004/2005 crop season, this paper first stratifies households into poorly- and well-endowed households based on their access to productive assets and estimates separate double-hurdle models for the adoption of improved, high yielding maize (IHYM) varieties for each group. The results show that factors influencing the adoption and use intensity of IHYM varieties differ between the two groups. This draws attention to the need for recommending wealth group-specific interventions to increase the adoption and use intensity of such varieties and their subsequent impacts on food security and general livelihoods of the households. The explicit testing for the possibility that differences in household wealth affect the way in which other variables influence adoption decisions is the paper’s unique contribution to the adoption literature. Ó 2008 Elsevier Ltd. All rights reserved.

Introduction Input technological change is fundamental to rural transformation (Arndt et al., 1977) but it sometimes by-passes some rural populations because of production and price risks that could render the input use unprofitable (Kelly et al., 2003). Throughout the developing world where input technology has made less dramatic changes in agricultural productivity, the incidence of rural poverty and food insecurity is pervasive (Rosegrant and Hazell, 2000; Renkow, 2000). It is common knowledge that resource poor farmers are often reluctant to invest in any untried input due mainly to their limited cash resources and/or access to credit. As economic theory would predict, relatively wealthier (or more resource-endowed) households have a better ability to cope with production and price risks and consequently more willing to adopt new technologies than their poorer (or less resource-endowed) counterparts (Hardaker et al., 1997). This study demonstrates that the adoption decisions of improved, high yielding maize (IHYM) varieties in selected districts in Zambia differ between well- and poorly-endowed households.

* Corresponding author. Tel.: +263 4 301 807; fax: +263 4 301 327. E-mail address: [email protected] (A.S. Langyintuo). 0306-9192/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2008.04.002

The use of improved, high yielding crop varieties by rural households can mean a difference between improved livelihoods and staying trapped in poverty and hunger. There is a proliferation of improved crop varieties on the market yet farmers continue to use traditional, low yielding varieties. Drawing mainly on three paradigms, namely, the innovation-diffusion (Feder and Slade, 1984), the adopters’ perception (Kivlin and Fliegel, 1967), and the economic constraints (Aikens et al., 1975) models either individually or in combinations, past studies (e.g. Adesina and Zinnah, 1993; Smale et al., 1994; Morris et al., 1999; Doss, 2006; Langyintuo and Mekuria, 2005) have shown the significant influence of access to cash (or credit), among other factors, on the adoption of improved agricultural technologies by smallholder farmers in developing countries. Access to cash (or credit), which promotes adoption of risky technologies through the relaxation of liquidity constraints (Bhalla, 1979) as well as boosting the household’s risk bearing ability (Hardaker et al., 1997) is hardly available to resource poor farmers for varied reasons (Lowenberg-DeBoer et al., 1994). It is argued that the profitability of a scale neutral technology such as improved seed will induce farmers to sell their productive assets (e.g. motorcycles, bicycles, radios, etc.) to generate sufficient cash to purchase the necessary inputs (Feder et al., 1985). Primarily due to the disproportionate distribution of productive assets among households within a community, one would expect

A.S. Langyintuo, C. Mungoma / Food Policy 33 (2008) 550–559

adoption behaviors to differ across socioeconomic groups. In his ‘‘middle-class conservatism” model showing the relationship between wealth and technology adoption, Cancien (1967) used data from several different countries to prove that within any given farming community, households on the upper part of the wealth continuum are most likely to adopt new technologies because of their secure economic positions. Those on the lower wealth continuum, on the other hand, may be willing to adopt because of their greater desire for upward mobility in the economic group but unable to invest in new opportunities and therefore lowest in terms of adoption of new techniques. The model recognizes the existence of a small group between the two that is unwilling to invest in new techniques that may fail leading them to loose their relatively favorable economic positions and thus shows non-linearity between wealth and technology adoption. Further empirical evidence of the non-linearity between wealth and adoption is shown by DeWalt (1975) using data from Mexican farmers. To provide a clearer understanding of the factors determining the adoption of IHYM varieties in selected districts in Zambia, this paper first stratified households into two wealth groups before modeling group-specific adoption decisions. In general, households are endowed with varying levels of different assets each of which could potentially contribute to their wealth statuses (Moser, 1998; Freeman et al., 2004; Ellis and Bahiigwa, 2003). This poses a potential problem in any effort to stratify them based on wealth. Following Filmer and Pritchett (1998, 2001), and Zeller et al. (2006), this paper uses their productive assets to construct wealth indices by a principal components analysis (PCA) method. As detailed later, the mean index of the sample is zero. Households with indices above the sample mean are classified well-endowed while those below poorly-endowed. Separate double-hurdle models are then estimated for each group after statistically testing for a break in the wealth index about the sample mean. The rationale for the choice of the double-hurdle model is that farmers take two steps in their decision to adopt and use an IHYM variety. The first step (or hurdle) is a decision on whether or not to adopt the improved variety. Once the first hurdle is crossed, the second hurdle of how much to adopt (or intensity of adoption) must be crossed before a positive outcome can be observed (Blundell and Meghir, 1987). By stratifying households into poorly- and well-endowed categories before modeling adoption decisions, the paper addresses one of the major weaknesses in the adoption studies alluded to by Feder et al. (1985) and Doss (2006) in their review of adoption literature in developing countries. The results show that factors conditioning the adoption and use intensity of IHYM varieties differ between the two groups. This draws attention for the need to design wealth group-specific interventions to increase the adoption and use intensity of IHYM varieties and their subsequent impacts on food security and general livelihoods of rural households in the target areas. Whereas the non-linearity between wealth and technology adoption is common in the adoption literature (See, for example, Cancien, 1967; DeWalt, 1975; Kristjanson et al., 2003; Doss and Morris, 2001), allowing for the coefficients on other variables in adoption models to vary between wealth groups is not. By testing for the possibility that differences in household wealth affect the way in which other variables influence adoption decisions is, therefore, the paper’s unique contribution to the adoption literature. The rest of the paper is organized as follows. The Section ‘Empirical methods’ discusses the PCA method used in estimating wealth indices and modeling adoption using the double-hurdle model. This is followed by a description of the survey locations and data collected in Section ‘Survey locations and data’ . The empirical results and discussions are presented in Section ‘Results and discussions’. The paper ends with some concluding remarks and policy implications of the findings in Section ‘Concluding remarks and policy implications’.

551

Empirical methods The PCA method used in computing household wealth indices is first justified and then discussed in this section. This is then followed by a presentation of the double-hurdle model employed in the empirical analysis of the factors influencing the adoption and use intensity of IHYM varieties in selected districts in Zambia. Estimating wealth indices As noted earlier, the productive assets (or asset indicators) owned by households can potentially contribute to their wealth but their ownership varies tremendously between households. This makes it very difficult to rank households based on their economic statuses without normalizing (or weighting) the assets in a manner that avoids distortions due to different measurement scales. Once normalized, indices can then be constructed and aggregated to facilitate ranking. A challenge here is the identification of the relevant weights to give to each asset indicator. Filmer and Pritchett (1998) observed four possible options as follows: (1) Assigning weights based on qualitative or subjective judgment, (2) Constructing a set of weights based on a common factor which can be applied to all the indicators (for example, market or shadow prices), (3) Avoiding the need for weights by running a multivariate regression analysis with all the indicators as unconstrained variables, or (4) Allowing the weights to be determined mathematically, using PCA method. Option one is inappropriate in this analysis because households own assets ranging from human capital (e.g. household labor) to physical assets (e.g. radio sets) that makes it impossible to find a common factor which could meaningfully be applied to all the assets (Filmer and Pritchett, 2001). Option two is not suitable either given the highly imperfect markets for most commodities and services in the study area (as in most parts of the developing world) to allow the use of shadow pricing (Sadoulet and de Janvry, 1995). The third option, multivariate regression, is statistically unsatisfactory because the variables to be included are not independent of each other suggesting that the resulting multicollinearity would produce misleading regression coefficients. The fourth option, PCA, a technique for extracting from a set of variables those few orthogonal linear combinations of the variables that capture the common information most successfully, was used to construct an overall index of household wealth (Filmer and Pritchett, 2001; Zeller et al., 2006). In PCA1, the first principal component of a set of variables is the linear index of all the variables that captures the largest amount of information that is common to all of the variables (Filmer and Pritchett, 2001). Suppose we have a set of K variables, a1j to aKj , representing the ownership of K assets by each household j. Each asset is normalized by its mean and standard deviation. For example, a1j ¼ ðaij  ai Þ=si , where a1 is the mean of a1j across households and s1 is its standard deviation. These selected variables are expressed as linear combinations of a set of underlying components for each household j:

1 The PCA is carried out after normalizing the assets (or scaling them from 0 to 1) as xl xmin follows: i ¼ xmax xmin where i is the index, xl is the level, while xmin and xmax are the minimum and maximum values of x, respectively taken from the actual data collected. Once normalized, the indicators can be added together without the element of distortion which would be introduced by widely differing value ranges.

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a1j ¼ m11 A1j þ m12 A2j þ    þ m1K AKj 

8j ¼ 1; . . . ; j

ð1Þ

aK1j ¼ mK1 A1j þ mK2 A2j þ    mKK AKj where the As are the components and the vs the coefficients on each component for each variable (and do not vary across households). The solution for the problem is indeterminate because only the left-hand side of each line is observed. To overcome this indeterminacy, PCA finds the linear combination of the variables with maximum variance, usually the first principal component A1j, and then a second linear combination of the variables, orthogonal to the first, with maximal remaining variance, and so on. Technically the procedure solves the equations (R  kI)vn = 0 for kn and vn, where R is the matrix of correlations between the scaled variables (the as) and vn is the vector of coefficients on the nth component for each variable. Solving the equation yields the eigenvalues (or characteristic roots) of R, kn and their associated eigenvectors, vn. The final set of estimates is produced by scaling the vns so the sum of their squares sums to the total variance. The ‘‘scoring factors” from the model are recovered by inverting the system implied by Eq. (1), and yield a set of estimates for each of the K principal components: A1j ¼ f11 a1j þ f12 a2j þ    þ f1K aKj AK1j

 ¼ fK1 a1j þ fK2 a2j þ fKK aKj

8j ¼ 1; . . . ; j

ð2Þ

The first principal component, expressed in terms of the original (un-normalized) variables, is therefore an index for each household based on the expression: A1j ¼ f11 ða1j  a1 Þ=ðs1 Þ þ    þ f1K ðaKj  aK Þ=ðsK Þ

ð3Þ

According to Filmer and Pritchett (2001) the critical assumption of PCA is that the undefined ‘common information’ is in fact determined by the underlying phenomenon that the index is trying to measure (in this case, wealth), which unfortunately cannot be statistically verified since it depends on the correct identification of the relevant variables or indicators, and is therefore largely a matter of judgment. One of the advantages of PCA apart from the objectivity of the weights is that it estimates the contribution of each variable to the underlying common phenomenon, and thus enables the ranking of indicators according to their importance in determining a household’s level of wealth. Specification of the double-hurdle model As noted in the literature, defining adoption can be problematic (Feder et al., 1985; Doss, 2006). To allow for the application of the empirical model developed here, this study adopts two definitions: (i) whether or not a farmer adopted an IHYM variety (a dichotomous choice), and (ii) the extent of adoption or the proportion of maize area under the IHYM variety once adopted (a continuous variable). In the survey year not all households adopted IHYM varieties thereby resulting in some observations being zero. This implies that the standard Tobit model originally formulated by Tobin (1958) and commonly used in adoption modeling, can be conveniently adopted. However, the Tobit model attributes the censoring to a standard corner solution thereby imposing the assumption that non-adoption is attributable to economic factors alone (Cragg, 1971). A generalization of the Tobit model as a double-hurdle model overcomes this restrictive assumption by accounting for the possibility that non-adoption are due to non-economic factors as well. Originally formulated by Cragg (1971) and recently applied in expenditure studies by Jones (1992), Burton et al. (1996), Jensen and Yen’s (1996), and Yen and Jones (1997), the double-hurdle model assumes that households make two sequential decisions

with regard to adopting and using IHYM varieties. Each hurdle is conditioned by the household’s socio-economic characteristics and variety-specific attributes (e.g. yield, disease resistance and taste). Whereas non-economic factors alone can condition the attainment of the first hurdle, economic factors are important in determining a positive outcome of the second (Blundell and Meghir, 1987). In other words, rural household wealth is essential in determining their level of participation in the improved seed market. Subject to their wealth profiles, households may be willing to make trade-offs in their choice for varietal attributes. For example, a well-endowed, market-oriented household may be interested in maize varieties suitable for the market (or industrial use), compromising on attributes such as taste desirable for home consumption that a poorly-endowed, subsistent-oriented household may be interested in. In the double-hurdle model, a different latent variable is used to model each decision process, with a probit model to determine the probability that a household will adopt an IHYM variety and a Tobit model to determine the extent of adoption, viz: yi1 ¼ w0i a þ mi yi2 ¼ x0i b þ li

decision to adopt an IHYM variety

yi ¼ x0i b þ li

if yi1 > 0 and yi2 > 0

extent of adoption

ð4Þ

where yi1 is a latent variable describing the household’s decision to adopt an IHYM variety, yi2 a latent variable describing the extent of adoption (or area planted to IHYM seed), and yi is the observed proportion of maize area planted to IHYM varieties (or dependent variable). wi and xi are vectors of variables explaining the adoption and use intensity, respectively, while mi and li are the respective error terms assumed to be independent and distributed as mi  Nð0; 1Þ and li  Nð0; r2 Þ. Allowing for heteroscedasticity and a non-normal error structure (Jensen and Yen, 1996; Yen and Jones, 1997), the model is estimated using maximum likelihood of the form:   0  xb Lða; b; h; hÞ ¼ P0 1  Uðw0i aÞU i ri    Tðhyi Þ  x0i b 2 2 1=2 ð5Þ  P1 ð1 þ h yi Þ ÞUðw0i aÞr1 i / ri To assess the impact of the regressors on the extent of adoption, it is necessary to analyze the marginal effects of the selected variables. According to Jensen and Yen (1996), and Yen and Jones (1997), the extent of adoption conditional on adoption is 0 1   0 1 Z 1    0 xb Tðhyi Þ  xi b C yi  B qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Eðyi yi > 0Þ ¼ U i / @ Adyi :  ri ri 0 ri 1 þ h2 y2i ð6Þ Survey locations and data The data used in this analysis were collected from the Katete, Sinazogwe and Mkushi districts randomly selected from the Eastern, Central and Southern Provinces of Zamba which were also randomly selected from the nine provinces in the country. These provinces, which represent a wide range of ecological variability, lie at an altitude of between 600 and 1500 m above mean sea level and generally characterized by hot and dry spells. The rainy season is between November and March with an average annual rainfall ranging from 600 to 700 mm. The main soils types are Acrisols, Lithosols, Cambisols and Fluvisols with low water holding capacity, shallow rooting depth, rapid physical deterioration, erosion hazard and poor workability (Langyintuo et al., 2005). In each district, 10 villages and 10 farmers per village were randomly selected. In all 300 farm households (25% female headed) participated in the survey as part of a region-wide farm level sur-

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Results and discussions Descriptive statistics of households Discussion on the typologies of households is based on two wealth groups, poorly- and well-endowed households, stratified on the basis of their wealth indices that were computed using the PCA detailed below. Computing wealth indices by the PCA method The PCA was run2 on 14 selected asset indicators based on households’ assessment of assets generally perceived to be important in defining wealth status in the communities in the three districts (Table 1). Fourteen components were extracted in the first stage of the PCA but only the first six were significant (based on the Kaiser criterion3 of an eigen-value greater than one). The first component was used in constructing the index because it explained about 18% of the total variance in the 14 indicators and gave a positive weight for all of them. The assigned weights were used to construct an overall standardized composite wealth index. Households were then ranked from the least to the highest composite wealth index (Fig. 1). By design, the sample mean of the standardized wealth index is 0. Households (57%) with negative indices are considered ‘‘poorly-endowed” while those (43%) with non-negative indices ‘‘well-endowed”. Dividing the score by the standard deviation generates an impact factor, which indicates the relative adjustment of the wealth index by acquiring the corresponding asset. The four4 top assets with the largest impact factors are (i) pair of bullocks, (ii) radio set, (iii) bicycle, and (iv) access to mechanical labor. The relative impacts of these assets on household wealth are consistent with household’s choice of asset accumulation (Langyintuo et al., 2005). When a household has money, the first investment is in mechanical labor to ease the seasonal labor bottleneck during land preparation. This supposedly leads to improved yields and incomes. With the increased incomes, the second item purchased is a bicycle for facilitate mobility before a radio set is purchased to listen to the news. If sufficiently rich, the household then buys a pair of bullocks for its own use and hiring out. A household’s ownership of these assets vis à vis its position in the wealth ranking suggests that owning at least any two of the four assets classifies the household as well-endowed otherwise it is poorly-endowed (Table 2). Only 10 out of the 300 households (or 3%) fail to meet this selection criterion suggesting that the method could be used as a ‘‘rule of thumb” to categorize households into the two wealth groups with a 95% confidence. Note that this is applicable only in similar socio-economic circumstances be2

The Statistical Package for Social Scientists (SPSS) was used to run the PCA. The eigenvalue is a measure of standardised variance, with a mean of 0 and standard deviation of 1. Each standardised variable (i.e. each of the 14 indicators in our case) contributes at least the variance of 1 to the principal components extraction. The Kaiser criterion states that unless a principal component extracts at least as much as one of the original variables (i.e. has a standardised variance equal to or greater than 1), it should be dropped from further analysis (Filmer and Pritchett, 2001). 4 The number of top assets chosen is based purely on judgement. We have found out that three or four work very well. 3

Table 1 Total variance explained using principal components extraction method using standardized values of variables Component/ variable

Initial eigen values

Human capital Household labor capacity Non-family labor Access Natural capital Total farm Total cropped area Total TLU

Standard deviation

Scoring factor

Impact factora

18.378

3.743

0.161

0.043

1.668

11.911

0.498

0.080

0.161

1.478 1.271

10.556 9.076

6.564 2.580

0.083 0.223

0.013 0.086

Total

Percent of variance

2.573

1.182

8.440

7.963

0.284

0.036

Physical capitalb Own bullocks Own bicycle Own radio Own television Mechanical labor access

1.085 0.760 0.663 0.613 0.544

7.746 5.427 4.736 4.380 3.889

0.438 0.499 0.501 0.180 0.501

0.251 0.204 0.225 0.016 0.192

0.575 0.409 0.448 0.087 0.383

Financial capitalb Access to cash credit

0.436

3.113

0.451

0.119

0.263

0.423

3.022

0.492

0.011

0.023

0.357

2.548

0.495

0.073

0.148

Social capitalb Association membership Input program benefactor a

The impact factor is calculated as the score divided by the standard deviation. Binary variable with 1 if household owns or has access to the asset and 0 otherwise. b

4.5 3.5 2.5

Wealth index

vey undertaken by the International Maize and Wheat Improvement Center (CIMMYT). Structured questionnaires designed to capture information on a range of indicators related to household livelihood strategies were administered between August 2003 and July 2004 by trained enumerators under the direct supervision of research scientists from the Zambian Agricultural Research Institute and CIMMYT. The analytical tools used in addition to descriptive statistics in this study are those discussed in the preceding section.

1.5

"Poorly-endowed"

"Well-endowed"

(Wp = -0.6647)

(Ww = 0.8877)

0.5 -0.5

Wi = 0 (Sample mean)

-1.5 -2.5 1

30

59

88 117

146

175

204

233

262

291

Household Fig. 1. Distribution of households according to wealth groups. Note: Wp = 0.6647 is the mean index of households falling below the sample mean of 0; Ww = 0.8877 is the mean index of households with indices above the sample mean. Source: field data.

cause the wealth index computation is relative to the community. For non-representative communities, separate wealth indexing is required to identify assets that have the greatest impact on wealth to facilitate their stratification. Distribution of assets by wealth group As expected the well-endowed households own significantly larger total farm sizes than their poorly-endowed counterparts (Table 3). The average cultivated farm size for the latter is less than half of that for the former. But the areas under maize (1.9 ha and 1.3 ha, respectively) do not differ significantly. Information on the area planted to IHYM varieties showed that among the 197 adopters, only eight planted IHYM varieties to 100% of their maize

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A.S. Langyintuo, C. Mungoma / Food Policy 33 (2008) 550–559

Table 2 Simple tool for assigning households into wealth categories using impacts of selected assets Asset combination levels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Ownership of (or access to) the asseta Pair of bullocks (0.575) p p p p

Total impact

Radio set (0.448) p p p

Bicycle (0.409) p p

p p p

p p p

p p

p p

Mechanical labor (0.383) p

p p p p

p p p

p p p p

p

Percent households

1.815 1.432 1.406 1.367 1.240 1.023 0.984 0.958 0.857 0.831 0.792

8.0 4.0 3.0 2.3 8.5 2.7 2.4 3.0 4.4 2.3 2.7

0.575 0.448 0.409 0.383 0.000

0.7 12.0 14.5 8.7 20.8

Wealth categoryb

Well endowed (43%)

Poorly endowed (57%)

p Impact in parenthesis and ( ) implies access to the asset. Percent distributions of households into wealth groups are in parenthesis. By physically checking the actual data, only three percent of households were wrongfully classified as ‘‘well-endowed”. a

b

Table 3 Selected households wealth indicators by wealth category in Zambia Wealth categorya Poorly-endowed (n = 172) Household size (number) Household labor force (number) Total farm land (ha) Cultivated land (ha) Adoption rate (proportion of maize area) Adoption rate (proportion of farmers) Access to off-farm income (proportion) Association membership (proportion) Extension visits (number per year)

Well-endowed (n = 128)

Whole sample (n = 300)

6.99 (4.07) 4.95 (2.85)

9.59** (6.39) 6.62** (4.52)

8.10 (5.34) 5.66 (3.74)

4.54 (5.48) 2.06 (1.35) 0.15 (0.15)

6.30* (7.70) 4.31** (3.21) 0.49** (0.32)

5.29 (6.56) 3.02 (2.58) 0.23 (0.18)

0.58 (0.50)

0.77** (0.43)

0.66 (0.48)

0.65 (0.47)

0.87** (0.34)

0.72 (0.45)

0.43 (0.50)

0.38 (0.49)

0.41 (0.49)

1.58 (1.27)

1.64 (1.17)

1.61 (1.23)

0.78** (0.42) 0.04 (0.19)

0.54 (0.50) 0.03 (0.18)

Ownership of selected physical assets A bicycle (proportion) 0.36 (0.48) A television set 0.03 (0.17) (proportion) A radio set (proportion) 0.31 (0.46) Access to mechanical 0.27 (0.45) labor (proportion) Pair of bullocks 0.04 (0.20) (proportion) Cattle (number) 0.24 (1.10) Small ruminants (number) 0.91 (2.83) Fowls (number) 6.72 (11.12) Tropical livestock units 1.44 (2.47) (number) Economic status (or mean 0.66 (0.38) wealth index)

**

0.79 (0.41) 0.78** (0.42)

0.51 (0.50) 0.49 (0.50)

0.55** (0.50)

0.26 (0.44)

3.67** 4.67** 28.14** 9.93**

(5.39) (6.85) (101.85) (9.97)

0.89** (0.88)

1.71 2.51 15.86 5.06

(3.99) (5.29) (67.75) (7.96)

In contrast, 58% of the poorly-endowed, adopt IHYM varieties on only 15% of their maize area. The well-endowed own far more livestock5 (ruminants and chickens) than the poorly-endowed. In addition, proportionately more well-endowed households own physical assets such as pairs of bullock, bicycles, and radio sets. To supplement their farm income, some household members engage in off-farm activities such as petty trading, employment in the formal and informal sectors (e.g. bicycle fitting work, artisanal work, local beer brewing, etc) as well as selling labor for cash. An estimated 61% of all households have at least a member engaged in one or the other formal or informal employment. To compensate for their poor agricultural base, proportionately more poorly-endowed households have access to off-farm income than their well-endowed counterparts, consistent with existing literature (Reardon et al., 2006). The proportional expenditure on farm inputs (including improved seed) is larger for the well-endowed households compared with the ‘‘poorly-endowed” (Fig. 2). The opposite is true for food expenditure. Choice of variables for the empirical adoption model There is no firm economic theory that dictates the choice of which explanatory variables to include in the first and second hurdles of the double-hurdle model. Therefore, the variables in Table 4 reflecting (1) farm and farmer specific characteristics, (2) organizational affiliation, and (3) technology specific attributes, are selected based on adoption literature. Farm and farmer specific characteristics

0

a

Means or proportions statistically different between the poorly- and wellendowed households at 1% (**) and 5% (*) levels of error probability. In parenthesis are the standard deviations.

areas suggesting that the majority (96%) of adopters also plant traditional unimproved maize varieties. At the whole sample level, about 65% of the households plant IHYM varieties on an average of 23% of their cropped area. Possibly as a result of their relatively better risk bearing ability, 78% of the well-endowed households adopt IHYM varieties on an estimated 49% of their maize areas.

The farm and farmer specific characteristics are important in evaluating whether human capital (age of household head, educational level, and household labor force), fixed social bias (i.e. gender of household head) and total farm size are important in the adoption decision process. The general belief that older farmers are less amenable to change and therefore unwilling to change 5 Livestock are measure in tropical livestock unit (TLU). A TLU is an animal unit that represents an animal of 250 kg liveweight, and used to aggregate different species and classes of livestock as follows: Bullock :1.25; cattle: 1.0; goat, sheep and pig: 0.1; guinea fowl, chicken and duck: 0.04 and turkey: 0.05 (Runge-Metzger, 1988).

A.S. Langyintuo, C. Mungoma / Food Policy 33 (2008) 550–559

Farm inputs 50 40 30 20 10 Miscellaneous

0

Food

Clothes Poorly-endowed (Total expenditure: ZK0.64Mil)

555

2003). During such visits farmers get exposed to new technologies and their interactions with extension staff stimulate communication thereby reducing the information asymmetry often associated with new technologies. As mentioned earlier, research has shown that lack of access to cash (or credit) does significantly limit the adoption of improved high yielding crop varieties but the choice of appropriate variable to measure access to credit remains problematic. In a discussion of the limitations, challenges and opportunities for improving technology adoption using micro-studies, Doss (2006) outlines the different measures often used but cautions the inherent problems of these methods, especially their endogeneity. The use of farmers’ access to non-farm income in this study avoids this problem because it is expected to be less related to the adoption decision than farm income (Herath and Takeya, 2003; Thirtle et al., 2003). Distance to output markets is expected to have a negative impact on adoption because the farther away farmers are from output markets the less likely they would be willing to purchase improved, high yielding crop varieties that allow them to produce large quantities of output for which they may not find markets.

Well-endowed (Total expenditure: ZK1.06Mil) Fig. 2. Expenditure profiles of households by wealth category. Note: The Zambian currency is the Zambian Kwacha (ZK). The exchange rate in May 2005 was 1US$ = ZK 4850. Source: field data.

from their old practices to new ones (Adesina and Zinnah, 1993) will be tested against the alternative argument that age is positively related to adoption especially prior to the consolidation period in the producer’s life cycle. Undeniably, the introduction of a new technology into a farming system results in disequilibrium suboptimal use of resources thus requiring the farmer’s entrepreneurial ability to perceive, interpret and respond to the change in the context of risk to ensure equilibrium optimal use of their meager resources (Schultz, 1964). Moreover, being able to read affords the farmer the opportunity to benefit from research literature (such as bulletins) and hence better informed and more willing to adopt improved technologies than otherwise. An improved variety is a scale neutral technology and would barely have an impact on labor use. Nevertheless, household labor force is hypothesized to have a positive impact on adoption because improved high yielding varieties may increase the seasonal demand for labor, so that adoption is less attractive for those with limited family labor or those operating in areas with less access to labor markets (Feder et al., 1985; Doss, 2006). The relationship between total farm size and the adoption of improved high yielding crop varieties often depends on such factors as fixed adoption costs, risk preferences, credit constraints and labor requirements (Just and Zilberman, 1983; Feder et al., 1985). The adoption of an improved, high yielding variety entails initial set up costs in terms of learning, locating and developing markets, etc and if considered fixed costs, they discourage adoption by small farms (Feder et al., 1985). Moreover, farmers with larger farms will be more willing to devote portions of the land to an untried variety compared with those with smaller ones. Therefore, farm size is expected to have a positive impact on adoption. Organizational affiliation Farmers must have information about the intrinsic characteristics of IHYM varieties before they can consider adopting them or not. Within the context of the study area, extension services are important sources of farmers’ access to such information especially the frequency of visits by the extension officer or by the farmer to the extension officer (Boahene et al., 1999; Herath and Takeya,

Technology specific attributes Regarding the technology specific attributes, each farmer compared the available IHYM varieties with their choice of best local variety in terms of seed cost, availability in local retail stores, consumer acceptability, yield potential, resistance to field and storage pests, and grain palatability. Improved seeds are often generally more expensive than the local ones. Therefore, perceptions on cost of seed is thus hypothesized to have a negative impact on adoption. Seed companies are often reluctant to deliver seeds to remote areas due to high transaction costs (Langyintuo, 2004). Seed availability in local retail stores managed by agro-dealers or other retail outlets is expected to have a positive impact on adoption. The perceived superiority of IHYM varieties over the local ones in terms of yield and resistance to field and storage pests are expected to be positively related to adoption. If farmers perceive that the improved variety is more palatable than the local ones, or acceptable to consumers, adoption rates will increase. Empirical results and discussion The hypothesized non-linearity between wealth and adoption of IHYM varieties (and use intensity) is verified using locally weighted bivariate regression (LOWESS in Stata) analysis. Similar to the observations by Cancien (1967) and DeWalt (1975), the lowess smoothing graph (Fig. 3) supports the hypothesis. The graph also shows an apparent break (or kink) in the wealth indices about the sample mean. A polynomial smoothing (Baum, 2006) on defined segments6 of the data series did not show any other significant breaks. To statistically rationalize the use of separate adoption models for the poorly- and well-endowed households, a restricted model based on the assumption that all coefficients are uniform across each sub-sample and a full unrestricted model are ran. At a 99% confidence level, the calculated F-statistic (with 19 and 262 degrees of freedom) of 3.004 compared with the tabulated F-statistic of 2.490 suggests a rejection of the null hypothesis of no structural break. Separate double-hurdle models are then estimated for each

6 A polynomial smoothing using linear spline (Baum, 2006) is a mathematical function that enforces continuity between adjacent segments. Four segments identified in this analysis included: (i) those below the mean of the poorly-endowed, (ii) those above the mean of the poorly-endowed but below the sample mean, (iii) those equal to and above the sample mean but below the mean of the well-endowed, and (iv) those equal to and above the mean of the well-endowed.

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Table 4 Descriptive statistics of selected variables in empirical models Variablea

Definition

A. Farm and farmer specific characteristics 1 if household head is a male and 0 otherwise Gender(±) Age Age of household head (years) Education Years of formal education of household head Labor force Household labor force (man-equivalents)b Farm size Total farm size (ha) B. Organizational affiliation Extension visits Number of extension visits received per year (number) Off-farm 1 if household have had access to off-farm income and 0 otherwise income Wealth index Household wealth index Distance to output markets (km) Distance() C. Technology specific attributes Seed cost() 1 if farmer perceives the improved seed to be more costly than the local one and 0 otherwise Availability 1 if the improved seed more readily available than local one in local retail stores and 0 otherwise Acceptability 1 if it is easier to sell grain from improved seed compared with the local one and 0 otherwise Yield potential Value of 1 if the improved variety to yield more than the local one and 0 otherwise Pests resistance 1 if the improved variety to be more resistant to field pests than the local one and 0 otherwise Storability 1 if the improved variety perceived more resistant to storage pests than local one and 0 otherwise Palatability 1 if the improved variety perceived to be more palatable than the local one and 0 otherwise a b

Poorly-endowed (n = 172)

Well-endowed (n = 128)

0.73 (0.44) 43.7 (14.6) 1.96 (0.67) 4.95 (2.85) 1.48 (1.45)

0.78 (0.41) 40.3 (12.4) 2.02 (0.52) 6.62 (4.52) 1.80 (1.49)

1.58 (1.27) 0.65 (0.47)

1.64 (1.17) 0.55 (0.50)

0.66 (0.38) 31.0 (37.2)

0.89 (0.88) 18.7 (23.3)

0.84 0.15 0.58 0.56 0.44 0.36 0.12

(0.37) (0.36) (0.49) (0.50) (0.50) (0.48) (0.33)

0.88 0.12 0.67 0.74 0.35 0.26 0.05

(0.33) (0.33) (0.47) (0.44) (0.48) (0.44) (0.22)

A priori signs are positive unless otherwise stated. Man-equivalents were calculated after Runge-Metzger (1988) as follows: household members less than 9 years = 0; 9–15 years or above 49 years = 0.7; and 16–49 = 1.

wealth group and compared with one for the whole sample (Table 5). Results of the first hurdle (the probability of adopting an IHYM variety) are presented in the upper portion of Table 5 while those of the second hurdle (i.e. the intensity of adoption once adopted) in the lower portion. Factors influencing the probability of adopting an IHYM variety The empirical results indicate that across both wealth groups, age, education and grain yield potential are statistically significant in influencing the probability of adopting an IHYM variety. Similar to previous findings by Gerhart (1975), Rosenzweig (1978), and Adesina and Zinnah (1993), the results suggest that the more educated the farmer is, the more likely he/she will adopt an IHYM variety possibly because they can better process information more rapidly than otherwise (Shultz, 1975). Contrary to expectations, the probability of adopting an IHYM variety is positively influenced by age. This might be driven primarily by the farmers’ entrepreneurial experience proxied by the number of years the farmer has been the main decision maker on farming activities, which is observed to be positively and highly correlated (0.73) with age. The data indicates that a typical household head is 42 years old on average and for about 16 years has been responsible for making major farming decisions. Clearly, he/ she gains experience each passing year, which positively influences his/her choice of IHYM varieties to plant. Whether producing maize mainly for home consumption or the market, yield potential plays a crucial role in the farmer’s decision to plant a given variety (Adesina and Zinnah, 1993). It is, therefore, not surprising that the probability of adopting an IHYM variety will increase once a farmer perceives that the yield potential of the given variety is higher than that of the existing local ones. Three factors, namely, access to off-farm income, availability of seed in local retail outlets, and wealth status of household have positive and significant impacts on the probability of adopting an IHYM variety among the poorly-endowed households but not among their well-endowed counterparts. The first two factors are also significant in the whole sample model. These results seem to suggest that relaxing an apparent liquidity binding constraint among the poorly-endowed households through access to off-farm income or wealth accumulation that shifts the household’s wealth

status to a higher level will significantly increase their probability of adopting IHYM varieties. Some seed companies have developed simplistic extension materials7, which are either embossed on the bag or in pamphlets to educate farmers when they visit input (seed) stores. This strategy might be the reason for the increase in the probability of adoption if seed is available in local retail stores. Or the increase in the probability of adoption is simply due to the proximity of the seed. The reason for its significance among the poorly-endowed households may be related to immobility. Bicycles are the main sources of transport in the study area but only 36% of the poorly-endowed households own them compared with 78% of the well-endowed households. This indirectly influences the proportion of households in either wealth category whose mobility is limited within their homesteads. In other words, the majority of the poorly-endowed households rely on retail shops within their vicinity to have access to the seed and/or educational materials developed by seed companies. Factors determining the intensity of use of an adopted IHYM variety Discussion in this section will refer to the second hurdle model results (the intensity of adoption) presented in the bottom portion of Table 5 and the percent changes or elasticities derived from the marginal effects in Table 6. Note that the computed marginal effects are used to calculate percent changes in the dependent variable when the exogenous variable shifts from zero to one for categorical variables and elasticities at the sample means for continuous variables. After adoption both age and education no longer play significant roles in determining how much area is put to the IHYM variety. Two technology specific variables that significantly influence the intensity of use of IHYM variety adopted irrespective of wealth status are grain yield and resistance to field pests (Table 5). If the poorly- and well-endowed farmers perceive that the new variety is superior to the best local ones in terms of yield, they will increase their intensity of use by 18% and 26%, respectively, and by 7% and 4% in terms of resistance to field pests (Table 6). The strikingly different impacts of the yield variable on the two wealth

7 Simplistic extension materials include for example the picture of a zebra symbolizing the earliness of the maize variety.

557

1

Table 5 Maximum likelihood estimates of the double-hurdle model

.2

.4

.6

.8

Variable

0

Adoption rate of IHYM varieties (proportion of maize area)

A.S. Langyintuo, C. Mungoma / Food Policy 33 (2008) 550–559

-2

0

2

4

Household wealth index

Fig. 3. Lowess smoothing graph of adoption rate of IHYM varieties against wealth index. Source: field data.

groups are possibly linked to the differential liquidity statuses of the groups. Unlike the well-endowed households with greater ability to cope with risk, the poorly-endowed households possibly because of their relatively higher risk aversion nature are unwilling to invest in an untried variety even if it appears superior in terms of yield to the local ones they know. It might also be that they lack cash to purchase large quantities of the seed or complementary inputs such as fertilizers to exploit the potential of the improved varieties. Whereas total farm size, access to off-farm income, wealth index, seed availability in local retail stores and perceptions on seed cost are statistically significant in influencing the intensity of use of IHYM varieties in the whole sample and poorly-endowed household models, distance to markets and consumer acceptability significantly influence the intensity of use among the well-endowed households. The significant and negative relationship between farm size and adoption, similar to the findings of Barker and Herdts (1978) and Adesina and Zinnah (1993) suggests that any additional land increase is diverted to other crops other than IHYM varieties. For a unit increase in farm size, the intensity of use of IHYM varieties decreases by 0.4%. As a result of their relatively limited access to transport, the poorly-endowed farmers will intensify their use of IHYM varieties by 11% if seeds are sold in local retail outlets. If the price of the IHYM variety is perceived by the poorly-endowed farmers to be higher than that of the local varieties, adoption and use intensity of such a variety will decrease by 5%. It is not surprising, therefore, that both access to off-farm income and increase in wealth index are statistically significant. Moving a poorly-endowed farmer from a position of lack of access to access to off-farm income or from a lower to a higher wealth ranking will increase his/her use intensity by 13%. Although not significant in either the whole sample or well-endowed model, palatability significantly influences the use intensity of an IHYM variety among poorly-endowed households. If the adopted variety is perceived to be more palatable than the existing, local varieties, its intensity of use will increase by 2%. The well-endowed households likely to be market-oriented are willing to trade-off palatability for consumer acceptability, which incidentally is not statistically significant in the other two models. If the variety is perceived by farmers to meet the preferences of consumers, its use intensity will increase by 5%. In other words, varietal characteristics with market rather than home consump-

Whole sample (n = 300)

Poorly-endowed (n = 172)

First hurdle: probability of adopting an IHYM varietya; dependent not farmer adopted an IHYM variety (dichotomous) Gender 0.010 (0.056) 0.008 (0.053) 0.004** (0.002) Age 0.005** (0.002) 0.144** (0.032) Education 0.131** (0.034) Labor force 0.005 (0.008) 0.012 (0.008) Total farm size 0.001 (0.004) 0.004 (0.005) Extension visits 0.008 (0.021) 0.002 (0.021) * 0.127* (0.060) Off-farm income 0.123 (0.062) Wealth index 0.004 (0.033) 0.127* (0.065) Distance 0.000 (0.001) 0.001 (0.001) 0.105* (0.060) Availability 0.123* (0.060) Seed cost 0.027 (0.076) 0.048 (0.088) Acceptability 0.027 (0.049) 0.038 (0.049) 0.177* (0.081) Yield potential 0.266** (0.090) Pests resistance 0.079 (0.053) 0.066 (0.052) Storability 0.005 (0.056) 0.006 (0.057) Palatability 0.041 (0.072) 0.023 (0.071) Katete dummy 0.035 (0.084) 0.072 (0.074) Sinazongwe 0.027 (0.090) 0.052 (0.079) dummy

Well-endowed (n = 128) variable: whether or 0.016 (0.075) 0.004* (0.002) 0.117** (0.047) 0.003 (0.008) 0.001 (0.005) 0.017 (0.026) 0.065 (0.080) 0.022 (0.039) 0.001 (0.002) 0.123 (0.079) 0.055 (0.071) 0.047 (0.060) 0.260** (0.108) 0.037 (0.068) 0.036 (0.069) 0.097 (0.090) 0.050 (0.112) 0.001 (0.108)

Second hurdle: adoption intensitya; dependent var % maize area under an IHYM variety Gender 0.255 (0.266) 0.224 (0.347) 0.468 (0.674) Age 0.012 (0.008) 0.010 (0.010) 0.029 (0.018) Education 0.144 (0.172) 0.133 (0.222) 0.571 (0.408) Labor force 0.039 (0.036) 0.001 (0.048) 0.111 (0.070) 0.094* (0.051) 0.064 (0.053) Total farm size 0.054** (0.019) Extension visits 0.044 (0.096) 0.022 (0.130) 0.059 (0.205) 0.886** (0.325) 0.028 (0.716) Off-farm income 0.729** (0.256) Wealth index 0.534** (0.161) 0.409** (0.471) 0.918 (0.422) 0.007 (0.006) 0.021* (0.010) Distance 0.010* (0.005) ** ** 0.642 (0.501) 0.559 (0.775) Availability 0.261 (0.357) 9.148** (1.201) 0.763 (0.710) Seed cost 1.631** (0.524) Acceptability 0.001 (0.233) 0.087 (0.307) 0.173* (0.507) 1.518** (0.301) 2.751** (0.567) Yield potential 1.747** (0.230) 0.631* (0.313) 1.282* (0.592) Pests resistance 0.692** (0.252) Storability 0.248 (0.263) 0.149 (0.368) 0.546* (0.507) 0.681 (0.872) Palatability 0.440 (0.378) 0.243* (0.497) 1.071* (0.469) 2.193* (1.072) Katete dummy 1.206** (0.373) 1.386** (0.491) 3.205** (1.127) Sinazongwe 1.652** (0.391) dummy Constant 1.288 (0.920) 8.654 (0.980) 0.607 (1.725) Censored 103 73 30 observations Log likelihood 104.659 64.129 32.334 1382** 5929** 1288** Wald chi2 (36) a ** *

Standard errors are in parentheses. Significant at 1%. Significant at 5%.

tion appeal have a significant impact on the intensity of use among well-endowed households. Related to consumer acceptability is market access. The wellendowed households are willing to adopt and expand the cultivation of an IHYM variety if they have access to markets. Among this group of farmers, for every kilometer decrease in distance to market, they are willing to adopt and expand the IHYM variety area by 8%. Because of their propensity to produce marketable surpluses, the well-endowed households logically will prefer to expand the cultivation of varieties that store well (i.e. resistant to storage pests) by 4% if they could store the grains for extended periods before sale at least storage costs (in terms of storage losses to pests damage). Although the probability of adopting an IHYM variety is invariant to district, the use intensity of such a variety is. A plausible explanation for the reason why farmers in Katate and Sinanzogwe districts are less willing to intensify the use of IHYM varieties compared to those in the base district of Mkushi may be linked

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Table 6 Marginal effects of adoption intensity after double hurdle estimation Variable

Whole sample (n = 300)

Poorly-endowed (n = 172)

Gender Age Education Labor force Total farm size Extension visits Off-farm income Wealth index Distance Availability Seed cost Acceptability Yield potential Pests resistance Storability Palatability Katete dummy

0.010 0.005 0.131 0.005 0.001**

0.008 0.004 0.144 0.012 0.004*

0.016 0.004 0.117 0.003 0.001

0.008

0.002

0.017 *

0.065

**

0.127 0.001 0.105** 0.048** 0.038 0.177**

0.022 0.081* 0.123 0.055 0.047* 0.260**

0.079**

0.066*

0.037*

0.005 0.041 0.035**

0.006 0.023* 0.072*

0.036* 0.097 0.050*

0.123

*

Well-endowed (n = 128)

*

.004 0.000* 0.123** 0.027** 0.027 0.266**

0.127

to access to off-farm income. By disaggregating statistically significant explanatory variables by district, only access to off-farm income differed significantly between Katate or Sinanzogwe district and Mkushis district. In both Katate and Sinazongwe, 53% each of the households have access to off-farm income compared with 76% of district those in Mkushi. Eliminating the access to off-farm income from the model as a sensitivity analysis (results not presented) shows that neither district dummy is significant in both first and second stages of the model. All other variables assumed similar signs and significance except wealth with its significance level reduced to 5%.

much to adopt (or intensity of adoption) must be crossed before a positive outcome can be observed. The empirical results suggest that factors influencing the adoption and use intensity of IHYM varieties differ between the poorlyand well-endowed households. This draws attention to the need for recommending wealth group-specific interventions to increase the adoption and use intensity of such varieties and their subsequent impacts on food security and general livelihoods of the households. For instance, among the poorly-endowed households, relaxation of the apparent cash/credit constraint through improved access to viable off-farm income generating activities (e.g. petty trading, local beer brewing, and artisenal work), can potentially increase the probability of adoption and use intensity of IHYM varieties. Existing literature suggests positive spillover effects of offfarm income on agriculture by substituting for credit when credit markets fail (Thirtle et al., 2003). Similar impacts are expected if households increase their wealth statuses through trading up of their productive assets. An effort by development agents, governments and seed companies to provide widely distributed seed retail outlets will benefit all farmers but especially the poorlyendowed. IHYM varieties that target the market may have a direct benefit to the well-endowed households who are more likely than their poorly-endowed counterparts to produce marketable surpluses. Investment in formal and non-formal (e.g. adult literacy) education that allows farmers to benefit from research and extension bulletins, on the other hand, will increase the probability of adopting IHYM varieties among both the poorly- and well-endowed farmers. Additionally, widely distributed field demonstrations within the vicinities of farmers by governments, NGOs and seed companies to show the superiority of IHYM varieties over the local ones in terms of yield can serve as educational tools to increase the adoption rates of such varieties. In conclusion, it may be stressed that stratifying households into meaningful wealth categories and testing for the possibility that differences in household wealth affect the way in which other variables influence adoption decisions is a significant step towards improving our understanding of factors that influence farmers’ IHYM varieties adoption decisions.

Concluding remarks and policy implications

Acknowledgements

There is sufficient empirical evidence suggesting a significant impact of access to cash or credit on the adoption and use intensity of improved technologies but often lacking among small holder farmers in developing countries. Households, therefore, rely on their wealth that can be generated from their productive assets to chart a route out of poverty. A non-linear relationship exists between wealth and technology adoption within a rural community. Those on the lower end of the wealth continuum behave differently from those on the upper end due primarily to their risk bearing abilities Using PCA, this paper generated wealth indices for 300 selected households interviewed in three districts in Zambia. An estimated 57% of the households had indices below the mean index of zero and classified as poorly-endowed and the rest with indices above zero well-endowed. The non-linear relationship between wealth and technology adoption was confirmed by running a locally weighted bivariate regression analysis. A chow test suggested the rejection of the null hypothesis of no structural break about the sample wealth mean. This justified the estimation of separate double-hurdle models for each wealth group. The rationale for the choice of the double-hurdle model is that farmers take two steps in their decision to adopt and use an IHYM variety. The first step (or hurdle) is a decision on whether or not to adopt the improved variety. Once the first hurdle is crossed, the second hurdle of how

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