The Impact of Technology Adoption on Income and Food Security Of Smallholder Cassava Farmers: Empirical Evidence From Indonesia

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The Impact of Technology Adoption on Income and Food Security Of Smallholder Cassava Farmers: Empirical Evidence From Indonesia

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ISSN: 2005-4238 IJAST 699

The Impact of Technology Adoption on Income and Food Security Of Smallholder Cassava Farmers: Empirical Evidence From Indonesia

Abdul Wahib Muhaimin¹, Hery Toiba², Dwi Retnoningsih^3,, Lis M.Yapanto⁴

1

Department of Soci-Economics, Faculty of Agriculture, University of Brawijaya, Malang 65145, Indonesia.

2

Department of Agribusiness, Faculty of Agriculture, University of Tribhuwana Tunggadewi, Malang 65114,

3

Department of Soci-Economics, Faculty of Agriculture, University of Brawijaya, Malang 65145, Indonesia.

4

Department of Soci-Economics, Faculty of Fishery and marine science, Gorontalo State University, 96128, Indonesia

Abstract

The purpose of this study is to analyze the relationship between adoption of new technologies, income and food security of small farmers in East Java. Data from a survey of 300 cassava farmers from three districts:

Malang, Blitar, and Trenggalek, East Java Province were analyzed to explain this problem. Matching tendency scores are used to analyze the impact of adoption of new technologies such as the selection of

“Varieties Malang 4" cassava varieties for corn flour to affect income and food security in cassava farmers.

The results of econometric analysis reveal that there is an impact on heterogeneity of adoption. We find that adoption has a positive effect on agricultural income and diversity of household diets. However, the adoption has a negative impact on smallholder management strategies for food insecurity. The results show that improving technology can improve the welfare of small farmers.

Keywords: Adoption, food security, propensity score matching, Indonesia.

Introduction

Besides rice and corn, cassava is one of the important commodities in most Indonesian. Cassava is both consumed directly as a staple food by household and used as raw materials for the food industry. Data from the Ministry of Agriculture in 2012 showed that cassava needs as a staple food are 12.3 million tons.

These figures decreased by 11% compared to those in 2007. Meanwhile, cassava needs as raw materials for the food industry were 12.91 million tons. Although, in aggregate, the needs for cassava as staple food tends to relatively decrease annually (Ministry of Agriculture, 2012) because of an escalating income of the residents, demand for raw materials, especially for the tapioca flour industry tends to increase annually.

Economic development literature state that one of several ways to alleviate farmers’ poverty in the rural area is to encourage the adoption of new technologies (Feder, Just, & Zilberman, 1985). Also, several results of researches in various countries have proved that the adoption of new technologies can alleviate the poverty rate of rural populations (Ali and Abdulai, 2010, Canavire-Bacarreza & Hanauer, 2013;

Cavatassi, Salazar, González-Flores, & Winters, 2011; Del Carpio, Loayza, & Datar, 2011; E. Duflo, Kremer, & Robinson, 2008; Mendola, 20; Nkonya, Phillip, Mogues, Pender, & Kato, 2012; Awotide et al, 2013). Technology adoption is expected to increase productions, efficient uses of inputs, and farmers' income (Ali and Abdulai, 2010; Yi, 2014). From this multiplier effect, it is expected to increase income which results in an increase of consumption and food security, and alleviate poverty in rural areas (Ali and Abdulai, 2010; Vigani and Magrini, 2014).

Effects of Technology Adoption on Income, Food Security and Poverty

New technology adopting process is commonly expected to be able to improve production, income and, as such, improve food security and poverty. Many studies on technology diffusion have confirmed that

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farmers adopted new technologies has been able to promote farm production (Ali and Abdulai, 2010, Awotide, 2013). In addition, several studies have proven that the technology adoption of new varieties tends to escalate the demand for production inputs (Ali and Abdulai, 2010), and labour input (Ali and Abdulai, 2010). As a result, farmers’ farming production increased along with an increase in farmers’ income and consumption (Bezu, 2014; Khonje et al, 2015). Several studies conducted in several developing countries have found that an increase adoption done by farmers can also promote food security of farmers’ household, for instance, Sheferaw’s study (2014) in Ethiopia, and Kabunga et al. study (2014) in Kenya.

Methods

2.1. Theoretical Model

2.1.1 Empirical Model Specifications of Technology Adoption

We can begin the technology adoption model of cassava by assuming that the adoption of new cassava technology is dichotomous, in which new technology will be adopted when the net benefits of adopting the technology are bigger than not adopting the technology. Suppose the difference between the net benefits of adopting the technology and not are symbolized by I *, where I *> 0 indicates that the net benefits of adoption exceed non-adoption. Referring to Ali and Abdulai, (2010), although I * cannot be observed, I * can be formulated as a function of the observed elements in the following latent variable models:

𝐼_𝑖^∗=∝ 𝑍_𝑖+ 𝜇_𝑖, 𝐼_𝑖 = 1[𝐼_𝑖^∗> 0] (1)

Where 𝐼_𝑖 is a binary variable in which 1 for households if they adopt new technologies and 0 if they do not adopt new technologies. ∝ is a vector of parameters to be expected, 𝑍_𝑖 is a vector of household characteristics and other characteristics. 𝜇_𝑖 is an error term which is assumed to be normally distributed.

Further, the probability of households adopting new technologies can be formulated as follows:

Pr(𝐼_𝑖= 1)= Pr(𝐼_𝑖^∗> 0)= Pr(𝜇_𝑖 > −𝛼𝑍_𝑖)= 1 − 𝐹(−𝛼𝑍_𝑖) (2)

Where F is the cumulative distribution for function 𝜇_𝑖. A logit model is obtained from the assumption that the model is formulated from a function distribution of F. Since the objective of the present study was to investigate the impact of adoption on production, input demands, consumption, income, and as such, effect on food security and poverty, the adoption of technology is expected to affect production, input demands, consumption and income. To know the correlation between adoption decision and possible potential outcomes, it can be formulated with the equation of farmer household utilities. We assume that households are risk-neutral, and farmer households maximize their profits, 𝜋, on the other hand, they are confronted with the constraints of input and output in perfectly competitive markets, and also a single-output technology is presumably quasi-concave on input variable, W. Therefore, the mentioned correlation can be formulated in equation as follows:

𝑀𝑎𝑥 𝜋 = 𝑃𝑄(𝑊, 𝑍)− 𝑌^′W (3)

Where P is input price, Q is the level of expected output, and Y is the column vector of the input price.

Whereas, W is a vector of input quantities and Z is the farming characteristics, household characteristics and other economic characteristics. From equation (3), profit (𝜋) can be described as a function of farmers’

technology choice I, output prices, input variables, and characteristics of farmer households:

𝜋 = 𝜋(𝐼, 𝑌, 𝑃, 𝑍) (4)

Using Hotelling Lemma from equation (3) generates a reduced equation for the input demand and output supply:

𝑊 = 𝑊(𝐼, 𝑌, 𝑃, 𝑍) (5) 𝑄 = 𝑄(𝐼, 𝑌, 𝑃, 𝑍) (6)

From equation 4 to 6, it clearly shows that the input demand and output supply from farmer households are affected by technology choice variables (I), input prices (Y), output prices, and also characteristics of farmer households.

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2.1.2. Problems on the Evaluation of Adoption Program

The problems arising in evaluating the impact of policy intervention or program in many developing countries is reducing the bias in heterogeneity between program recipients and non-recipients.

Although the impact can be assessed from the difference of the outcome between the presence and the absence of the program, it is, in fact, difficult to get information on different conditions at the same time (Widyanti and Sumarto, 2007). Therefore, the outcome indicators of policy interventions can be seen, but without any counter-factual intervention, they are difficult to observe (Suharyadi, 2007). Suharyadi (2007) elaborates well about the illustration of the difficulties in evaluating the impact of interventions. Suppose that there is an intervention program carried out by the government, named program X. The conditions of a situation before the intervention of the program (𝑡₀) is 𝑋₀. If the program is done until 𝑡₁, the outcome indicator of program changes or rises 𝑋1. However, the outcome difference between the initial indicators (𝑋₀) and (𝑋₁) is not necessarily the result of the program because there are counter-factual indicators that are difficult to observe. In figure 1, the counter-factual results are illustrated in 𝑋^′. Thus, the impact of the policy/program intervention is the difference between the outcome indicator after the program (𝑋₁) and outcome indicator of counter-factual (𝑋^′).

To illustrate how the issue of selection bias emerges in policy interventions or technology dissemination programs, it can be described by the reduced form equation of the correlation between technology selection and the outcome variable as follows:

𝑂_𝑖 = 𝛼₀+ 𝛼_𝑖𝐼_𝑖+ 𝛼₂𝑍_𝑖+ 𝜖_𝑖 (7)

Where 𝑂𝑖 is a vector of outcome variable from farmer household i, such as output, input demand, income, consumption pattern, food security, and poverty status. While 𝐼_𝑖 is technology choice, 𝑍_𝑖 is characteristics variable of household and cassava farming, and 𝜖_𝑖 is error term. According to Ali dan Abdulai, (2010), the issue of selection bias will appear when unobserved variables or error term (𝜇_𝑖) on technology choice equation (1) correlate with the error term (𝜖_𝑖) in equation (7).

If the correlation between 𝜇_𝑖and 𝜖_𝑖 is unequal to zero, using classic method Ordinary Least Square (OLS), it will generate the estimate parameters which tends to be biased (Ali dan Abdulai, 2010). In these conditions, some authors suggest to use Heckman two-step methods or use instrumental regression variable (IV). The use of Heckman two-step methods is often constrained by the strict normality assumptions of the error term distribution. While the use of regression IV is often also limited by the difficulty of finding strong instrumental variables.

3.1.3. Propensity Score Matching Method

Propensity Score Matching (PSM) method is a statistical method of matching. It estimates the effect of the treatment, program, or policy by calculating the estimated covariance of treatment. PSM is used to reduce bias because confounding variable obtained from the estimate of the effect of treatment is gained from a simple comparison between the outcomes of the treatment and the control groups. The PSM method seeks to capture the influence of the covariate X in treatment groups which are observed differently on the propensity score or single index (Khandker, Koolwal, and Samad, 2009). The outcome from the program and non-program participants with the same propensity score will then be compared to determine the impact of a program. While unmatching households will be dropped.

Statistically, the propensity score is a conditional probability for treatment groups referring to characteristics in pre-intervention (Rosenbaum and Rubin, 1983)

𝑃(𝑍)= 𝑃𝜏{𝐷 = 1|𝑍}= 𝐸{𝐷|𝑍} (8)

Where D={0,1) is a binary variable showing different group, D=1 indicates adopter groups, and D=0 indicates non-adopter groups. While Z is an indicator of pre-adoption policy. The conditional distribution of Z, given p(X) is identical for both adopters and non-adopters.

According to Ali and Abdulai, 2010), the advantage of using PSM compared to other parametric econometric approaches is that PSM does not require certain assumptions from the function form of relationship model between outcome and outcome indicator.

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3.1.4 Average Treatment Effects

When propensity score 𝑃(𝑍) has been known, the value of Average Treatment effect (ATT) can be estimated with the following formula:

𝜏 = 𝐸{𝑅₁− 𝑅₀|𝐼 = 1}

= 𝐸{𝐸{𝑅₁− 𝑅₀|𝐼 = 1, 𝑝(𝑍)}}

= 𝐸{𝐸{𝑅₁|𝐼 = 1, 𝑝(𝑍)}− 𝐸{𝑅₀|𝐼 = 0, 𝑝(𝑍0}𝐼 = 0} (9)

Where the outer expectation is the distribution of (p(Z)|I=1), and 𝑅₁ and 𝑅₀ are a potential outcome of counterfactual condition from treatment (Becker dan Ichino, 2003)

There are several methods that can be used to match the propensity score between adopter and non- adopter groups. Those methods include Nearest Neighbor Matching, Radius Matching, Radius Matching, Kernel Matching, and Stratification Matching.

3.2 Estimation Strategy

In this section, we present the specifications model of the econometric analysis used and the hypothesis of the correlation between the variables. There are 3 econometric models that are presented in this section. The first is the logit. The second is the Average Treatment Effect (ATE). The third is propensity score matching (PSM). To estimate the impact of the adoption of new technology on cassava farming using PSM, the logit equation are firstly estimated to obtain the propensity score. A common model of the logit equation used in this study is as follows:

Prob_adopsi(𝐼_𝑖 = 1)= 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + 𝛼𝑍_𝑖^′+ 𝛽𝑋_𝑖^′+ 𝜇_𝑖

Where 𝑍 is a vector of farmers' socio-economic characteristic. X is the characteristic of farming management and 𝛼 is a parameter vector that will be estimated. While 𝜇_𝑖is error variable of the logit model.

The Dependent variable, Prob_adopsi, is a dummy variable representing farmers' decision to adopt new technology (𝐼_𝑖 = 1), and not adopt technology (𝐼_𝑖 = 0). Vector 𝑍_𝑖^′ represents the socio-economic variables of farmer households, including age, education, number of family members, land area, income, land ownership status, dummy livestock asset, dummy credit access. Regression coefficients of age, number of family members and time to reach the nearest agricultural public service are expected to negatively affect on the adoption of new technologies in agriculture (Ali and Abdulai, 2010; Abdoulaye and Sanders 2013;

González-Flores et al . 2014). While, education, land area, income, and dummy credit access are expected to have a significant and positive effect on technology adoption (Diagne and Demont, 2007; Ali and Abdulai, 2010; Abdoulaye and Sanders 2013).

Characteristics of farming management are shown by vector X, which represents the dummy land ownership status, distance from land to agricultural markets. Variable of dummy land ownership is hypothesized to have a positive effect on the adoption of new technology. It is engendered by the possibility of access will be greater toward financial access. Meanwhile, the dummy variable of land distance to commodity market is hypothesized to have a positive effect on adoption. It means that the closer the farming land to the agricultural commodity market, the greater the probability of farmers adopting new technology (Diagne and Demont, 2007).

The second step to use PSM is to estimate the Average Treatment Effect on Treated (ATT) of a program intervention in each population using equation 8 mentioned above:

𝑃(𝑍)= 𝑃𝜏{𝐷 = 1|𝑍}= 𝐸{𝐷|𝑍} (8)

The present study used modern estimation treatment effects from Rosenbaum and Rubin (1983): Angrist et al (1996); Heckma and Vytlachil (2007) and Woodridge (2002). This present study used a framework of counter-factual outcomes in which each farmer in the population receives two potential outcomes, namely adopter and non-adopter. Given 𝑦_𝑖1 is the potential outcome from adopters and 𝑦_𝑖0 is the potential outcome from non-adopters. Hence, the expected value from adopters can be presented by the mean difference of 𝐸(𝑦_𝑖1− 𝑦_𝑖0). The hypothesis was that adoption gives a positive effect on outcome: gross income, food security and poverty status. For outcome, gross income was measured in Indonesian Rupiah (IDR) / ha.

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While the income total variable was measured by family income in IDR /year. Food security was measured by using The Coping Strategies Index (Maxwell and Caldwell, 2008; Maxwell and Caldwell, and Longworthy, 2008; Maxwell, Vaitla, and Coates, 2014) and Food Consumption Score (Hoddinott and Yohannes, 2002; Coates et al., 2007; IFPRI, 2008; WFP, 2012). Poverty status was measured using the headcount index.

The Average Treatment Effect on Treated (ATT) value was estimated using the Nearest Neighbor Matching and Kernel Matching. The Nearest Neighbor Matching method was used to get the closest propensity score between the adopter groups (treatment) and the non-adopter groups (control group). This method paired the adopter with non-adopter groups. After they were paired, the different outcome between the treatment and control group can be computed. Yet, there is a flaw from this method, namely the opportunity to obtain an extreme difference in the propensity score, and it, then, contributes independently to the estimation of ATT (Tarigan, et. al, 2008). Another alternative method to supplement the flaw of this method is the Kernel Matching method. This method matches all adopters with a weighted average of all non-adopters with weights that are inversely proportional to the distance between the propensity scores of adopters and non-adopters. This Kernel Matching formula is as follows:

𝜏^𝐾=_𝑁¹_𝑇∑ {𝑌_𝑖^𝑇−^∑ ^𝑌^𝑗

𝐶𝐺(^{𝑝𝑗−𝑝𝑖}

ℎ𝑛 )

𝑗∈𝐶

∑ 𝐺(^{𝑝𝑗𝑘−𝑝𝑘𝑖}

ℎ𝑛 )

𝑘∈𝑐

𝑖∈𝑇 } (11)

From the analysis results of the propensity score matching, we can hypothesize that there was a different causal impact on production, input, income, food security and poverty status from adopters and non- adopters on cassava farming.

3.3. Data

300 samples of cassava farmer households in 3 districts, namely Malang, Blitar and Trenggalek were selected by multistage sampling. A purposive sampling location was used because they were both a centre of cassava production and a distribution area of new farming technology in East Java. Each district was randomly selected one sub-district. Next, 2 villages were randomly selected in each of those sub- districts. Respondents from this study were cassava farmers who adopted new technology, and farmers who did not adopt new technology. A simple random sampling method was used to determine respondents.

Firstly, a census was conducted to the list of farmers who plant cassava in two villages in each predetermined districts, so that the research sampling was obtained. Secondly, from a list of farmers in each village, 50 farmers were chosen randomly resulting in100 farmers in each district or 300 farmers in the three districts. The research was conducted from June 2018 to August 2018. The surveys were conducted via face-to-face interviews with the person responsible for managing their horticultural Farming using a structured questionnaire.

Results and Discussion

Characteristics Comparison between Adopters and non-adopters

Outcome indicators of the impact of new technology adoption exhibited that there are significant differences between adopters and non-adopters in which adopters have gross income and more diverse food group intake (see food consumption scores). However, if you look at the difference in the mean score of a food cope strategy, it implied that non-adopters’ ability to deal with the limitations or lack of access to food is higher than the adopters.

The Estimation Results of Factors Affecting Technology Adoption and Its Impact on Income, Food Security and Welfare of Cassava Farmers

The probit model in the present study was used to elaborate on the effect of several variables on farmers' decisions in adopting new technologies i.e. high yield products. Table 3 showed that there were four

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variables that significantly affected farmers' decisions in adopting namely age, age2, agricultural market and land certificate. Of those variables, age significantly affects farmers in adopting new technology. Age had a negative effect on farmers' decisions in adopting but age2 had a positive effect. At a certain age, farmers had a higher tendency to adopt and when farmers are getting older, the tendency will be lower.

access to agricultural markets positively affect farmers' decisions in adoption, higher farmers' access to agricultural markets increase farmers' opportunities to be adopters. These results were in line with Feder and Zilberman (1985), Feder and Umami (1993), Ali, and Abdulai, (2010) Becerril, J., Abdulai, A., 2010 and Coromaldi et al. (2015). The only family asset that had a positive effect on the adoption of new technology was the legal status of land ownership. While the other variables like a number of family members, the number of family dependents and education did not significantly affect farmers to be an adopter. One of the reasons was that access to technological information along with its benefits was relatively easier to obtain than those, in the past, which usually depended on the level of education. It was in line with Wu et al (2010) study in China who found the same phenomenon. Farmers who had land certificate tend to have a higher chance of adopting new cassava varieties compared to non-adopter farmers.

3.3 The Impact of New Technology Adoption on Total Income, Food Security and Welfare of Cassava Farmers

In the analysis of PSM, four matching methods were used in this present study including NNM, radius matching, kernel matching and stratification methods. The following is a discussion of the effect of adoption on total revenue, Food Consumption Score and Food Cope Strategies, and income respectively. Table 2 showed the impact of adoption on the total income of adopters and non-adopters. The ATT results of those four matching methods are as follows: NNM (11100000), radius matching (10300000), kernel matching (10600000) and stratification method (10600). Those results generally show that adoption of new technologies by adopter farmers have a positive impact on farmers total income. The farmer who adopted to earn a higher total revenue ranged from Rp. 10,300,000 to Rp. 11,100,000 than farmers who did not adopt new technology. These results were in line with researches conducted by Wu et al (2010) in China and Becerril, and Abdulai (2010) in Mexico. These results were in line with Some previous studies (Salazar L., et al., 2016; Khonje M., et al., 2015), The results of the ATT analysis with other food security indicators i.e. Food Cope Strategies showed the opposite results; the adopters had a lower level of cope strategies than non-adopters. The analysis of ATT results is as follows: NNM (-2.04 million), radius matching (-621000), kernel matching (-29600) and stratification method (-55500). Although in absolute terms, the ATT value of various methods showed differences in the mean value of the family total income, there was no a significant difference in the mean of family income between adopters and non-adopters. In other words, the impact of technology adoption has no significant effect on different income or poverty status of cassava farmers in the three districts. This result is consistent with previous findings (Ghimire R., & Huanh, 2016), explaining the negative correlation between technology adoption and farm income in Nepal.

Conclusion

The results of econometric analysis reveal that there is an impact on heterogeneity of adoption. We find that adoption has a positive effect on agricultural income and diversity of household diets. However, the adoption has a negative impact on smallholder management strategies for food insecurity. The results show that improving technology can improve the welfare of small farmers.

Therefore, efforts to prioritize market infrastructure are very important.

References

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A. Table 1. The Mean Difference of characteristics variable between adopters and non-adopters

Variable Adopters (n=113) Non-Adopters (n=187)

Mean Std. Dev. Mean Std. Dev. Significance

Gross Income 13600000 14400000 3316979 2790601 -7.51***

Food Cope Strategies 40.434 12.75 50.38 21.106 5.09***

Food Consumption Scores 32.65 3.42 28.28 3.771384 -10.31***

Total Income 21500000 21200000 21600000 14500000 0.07

The number of the neighbourhood 3.27 1.16 3.21 1.09 -0.45

Age 55.15 11.9 57.02 10.25 1.38

Age2 3182.19 1376.46 3355.42 1184.25 1.11

Respondent Education 9.61 2.84 9.856 3.14 0.70

Dummy livestock Asset 0.39 0.49 0.34 0.47 -1.06

Public facilities of agriculture 14.95 10.53 9.75 10.71 -4.12***

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Agricultural Market 8.41 6.66 5.67 5.75 -3.63***

Certificate 0.43 0.50 0.27 0.44 -2.92***

Land Area 0.42 0.067 0.38 0.59 -1.03

Dummy credit access 0.27 0.44 0.29 0.45 0.44

Income 21500000 21200000 21600000 14500000 0.07

Note; *,**,*** denote significance on 10%, 5%, and 1 % respectively

B. Table 2. The adoption impact of new technologies on the income of cassava farming Matching Method Treatment Control ATT Std. Err t

NNM 113 69 11100000 1390000 8.026***

Radius Matching 112 182 10300000 1090000 9.483***

Kernel Matching 113 182 10600000 1030000 10.345***

Stratification method 113 182 10600000 1680000 6.316***

Note; *,**,*** denote significance on 10%,5% dan 1% respectively

C. Table 3. The impact of varieties Malang 4 on Food Consumption Scores

Matching Method Treatment Control ATT Std. Err t

NNM 113 69 3.646 0.653 5.584***

Radius Matching 112 182 4.345 0.511 8.497***

Kernel Matching 113 182 4.359 0.398 10.938***

Stratification method 113 182 4.298 0.392 10.95***

Note; *,**,*** denote significance on 10%,5% dan 1% respectively

Figure 1. Evaluation using comparison varieties Malang 4 before and after the program X1

X1* X0

t = 0 t = 1 time

(observed) (counter-factual)

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