Elsevier

Food Policy

Volume 33, Issue 6, December 2008, Pages 550-559
Food Policy

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

https://doi.org/10.1016/j.foodpol.2008.04.002Get rights and content

Abstract

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.

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.

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 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, Filmer and Pritchett, 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, 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’.

Section snippets

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.

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

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.

Concluding remarks and policy implications

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

Acknowledgements

This publication was made possible through financial support provided by the Rockefeller Foundation. The ideas expressed here are those of the authors’ and do not necessarily reflect those of the Rockefeller Foundation. The authors are grateful to three anonymous reviewers for their constructive comments and suggestions.

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