Assessing the Impact of Agricultural Technology Adoption on Farmers ...

Asian Economic Journal 2010, Vol. 24 No. 2, 141–160

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Assessing the Impact of Agricultural Technology Adoption on Farmers’ Well-being Using Propensity-Score Matching Analysis in Rural China* Haitao Wu, Shijun Ding, Sushil Pandey and Dayun Tao Received 10 February 2009; Accepted 16 March 2010

The present paper assesses the impact of improved upland rice technology on farmers’ well-being. The study uses propensity-score matching to address the problem of ‘self-selection,’ because technology adoption is not randomly assigned. It applies this procedure to household survey data collected in Yunnan, China in 2000, 2002 and 2004. The findings indicate that improved upland rice technology has a robust and positive effect on farmers’ well-being, as measured by income levels and the incidence of poverty. The effect of technology on well-being shows a diminishing impact on producers’ incomes. This implies that newer innovations are continuously needed to replace older technologies that have reached their saturation points. asej_2033

141..159

Keywords: technology adoption, poverty alleviation, propensity score matching. JEL classification: O33, P36. doi: 10.1111/j.1467-8381.2010.02033.x

I.

Introduction

The study of how individuals or households escape poverty has been a central issue of development economics research (Ravallion, 1997; World Bank, 2000). Of the world’s 1.2 billion extremely poor people surviving on less than US$1 a day (IFAD, 2001), 75 percent live in rural areas. For the most part, they depend on agriculture and related activities for survival (Ravallion et al., 2007). Improved agricultural technology plays a critical role in promoting agricultural growth and improving the food security of the poor (Pandey, 2000). The current published * Wu: School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430074, China. Email: [email protected]. Ding (corresponding author): School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430074, China. Email: [email protected]. Pandey: Social Sciences Division, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines. Email: [email protected]. Tao: Crops Research Institute, Yunnan Academy of Agriculture Science, Kunming 650205, China. Email: [email protected]. We would like to acknowledge financial support from the National Science Foundation of China (70573122 and 70773120) and the Rockefeller Foundation (2005 SE 003). We are grateful to Yuping Chen, W. Robert Reed, Craig Parsons, Huaiyu Wang, Fengyi Hu, Peng Xu, Jiawu Zhou, Jing Li, Xianneng Deng, Lu Feng, Lu Wen, Jian Li, Yun Li, Lourdes E. Velasco and anonymous referees for helpful comments and suggestions. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.

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literature has established that the spread of new technology helps reduce poverty directly by raising farmers’ incomes, and indirectly by lowering the price of staple food (Fan, 1991; Feder and Umali, 1993; Huang and Rozelle, 1996; Janvry and Sadoulet, 2002). Yunnan, located in southwestern China with 94 percent of its territory occupied by mountains, is one of the poorest provinces in China. There are 2.28 million people living below the state poverty line in Yunnan, accounting for 10.6 percent of the total poor population of China. In 2004, the overall poverty ratio in Yunnan was 10.7 percent, compared to the national poverty ratio of 2.8 percent (NBS, 2004). The majority of the poor resides in the southern mountainous area. Despite increasing commercialization of agriculture in Yunnan, upland rice production is still a primarily subsistence-oriented activity. In recent decades, the local government has taken a series of measures to improve agricultural productivity and to promote improved agricultural technologies. These technologies include higher yielding rice varieties and the use of chemical fertilizers. They have been widely used in upland areas, and are credited with the recent rise in upland rice productivity in these areas. Anecdotal evidence and farm surveys indicate that these technologies have had a positive impact on poverty reduction (Wu, 2009), However, to date, formal quantitative analyses are wanting. A major difficulty in assessing the impact of a specific technology, such as the improved upland rice technologies in southern Yunnan, consists of establishing a suitable counterfactual against which the impact can be measured. The impact of technology adoption must be separated from that of other socioeconomic factors that simultaneously determine the well-being of the households. Failure to do so will cause the corresponding impact estimates to be biased. A powerful econometric procedure for removing this bias is propensity-score matching (PSM). In the present paper, a non-parametric PSM method is used to evaluate the effect of improved upland rice variety adoption on farmers’ well-being using household data from southern Yunnan, China. II.

Data Description

Data for this paper was derived from household surveys conducted in southern Yunnan in 2000, 2002 and 2004. Seven counties from southeast, south and southwest Yunnan were selected as sample counties, and two villages were selected from each county for data collection. Clusters of 30 households from each village were randomly selected for a structured questionnaire survey. A total of 508 households in 14 villages were interviewed during the three surveyed years. Information was collected on household demographic characteristics, resources, income and crop production activities, with attention focused on upland rice production. A small number of households migrated during the sample period where surveyors were unable to maintain conduct. Households unavailable for all three interviews were deleted from the sample, leaving 473 households for analysis in this paper. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.

IMPACT ASSESSMENT OF TECHNOLOGY Table 1

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Characteristics of adopter and non-adopter: Summary statistics (2004)

Number of households Household size (person) Number of labor force (person) Age of household head (year) % of households with female head % of household members with high school education attainment Land area (ha) % of slope land area % of terrace land area % of irrigated land area Crop sown area (ha) % of lowland rice area % of upland rice area % of maize rice area % of other crops area Number of draft animals

Average

Non-adopter

Adopter

Difference (%)

473 4.7 2.5 41.9 6.1 11.8

220 4.7 2.6 42.2 6.4 12.3

253 4.7 2.4 41.7 5.9 11.5

-0.7 7.3* 1.4 7.3 7.0

1.7 60.1 14.6 6.2 21.0 7.7 29.8 30.9 31.6 2.0

1.5 60.2 11.5 7.0 19.0 9.4 26.3 34.1 30.2 2.4

1.9 60.0 17.3 5.5 22.8 6.2 32.8 28.1 32.8 1.6

-26.7*** 0.4 -33.6*** 28.8* -16.9*** 52.0*** -20.0*** 21.2*** -8.0 50.2***

Note: t-test is used for differences in means. *and ***indicate that difference between adopter and non-adopter are statistically significant at 10 and 1% level, respectively.

II.1 Farmers’ basic characteristics From 2000 to 2004, farmers increasingly adopted improved upland rice technologies in the surveyed areas. In 2000, 29 percent of farmers adopted improved upland rice technology, and this increased to 38 and 53 percent in 2002 and 2004, respectively. Adopters are defined as those households who adopted at least one component of the improved upland rice technology. Table 1 provides a comparison of some of the major characteristics of adopters and non-adopters. Non-adopters’ land area is significantly smaller than that of adopters’. The share of terrace land area of non-adopters is significantly lower than that of adopters.1 The share of irrigated land area of non-adopters is significantly higher than that of adopters. Lowland rice is grown mainly in irrigated areas and farmers with irrigated fields planted proportionately larger areas of lowland rice. The shares of lowland rice area and maize areas of non-adopters are significantly higher than those of adopters, whereas the opposite trend can be seen for the share of upland rice areas. II.2 Farmers’ well-being Four indices are used for analyzing farmers’ well-being. These are average household income, the incidence of poverty (the head-count ratio), the poverty gap and 1. Terrace land, contrary to slope land, is defined as land with a slope of less than 25 degrees, and is mostly manually constructed. Crops growing in terrace land are of higher yield compared to slope land. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.

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Farmers’ well-being: adopter and non-adopter 2000

Number of households Income per capita (US$) Incidence of poverty (%) Poverty gap (%) Poverty severity (%) Incidence of poverty, Yunnan Incidence of poverty, China

2002

2004

Nonadopter

Adopter

Nonadopter

Adopter

Nonadopter

Adopter

336 165.2 46.3 19.0 10.4 14.6 3.5

137 295.5 21.0 7.4 3.9 13.1 3.0

294 218.3 31.5 11.7 5.9 10.7 2.8

179 326.0 12.7 4.2 2.1

220 270.7 18.2 5.5 2.5

253 344.4 7.5 1.8 0.7

the severity of poverty. The latter three indices are calculated from the commonly used Foster–Greer–Thorbecke poverty index, p: α

z − yit ⎞ if yit < z p ( yit ) = ⎛⎜ ⎝ z ⎟⎠ = 0 otherwise, where z is the ‘poverty line,’ yit is the income of the ith farmer in the tth year, and a is a parameter. When a = 0, the index is simply a binary indicator of whether or not the farmer is below the poverty line. When a = 1, the index is a measure of the ‘poverty gap.’ When a = 2, p equals the squared poverty gap, which is used as a measure of the severity of poverty. The three indices reflect different characteristics of poverty. The poverty line is defined by the state-level poverty line for Yunnan for the respective year. The associated values for 2000, 2002 and 2004 are US$89, 90 and 95, respectively. Table 2 compares, among other things, the incidence of poverty, the poverty gap and the poverty severity of adopters and non-adopters for the respective years of our sample. Although the incidence of poverty in rural China is officially reported at 3.5, 3.0 and 2.8 percent in 2000, 2002 and 2004, the corresponding values for Yunnan are 14.6, 13.1 and 10.7 percent. The incidence of poverty declined from 2000 to 2004, and this coincides with the period when improved upland rice technologies were actively promoted. On average, the gross income of adopters is much higher than that of nonadopters. The incidence of poverty is lower among adopters and so are the indices of poverty gap and poverty severity. This observation suggests that improved upland rice technologies might have contributed to an increase in income and a reduction in poverty. III.

Analytical Framework

If technology was randomly assigned to farmers, one could assess the causal effect of technology adoption on farmers’ income by comparing the difference © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.

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in income between adopters and non-adopters. In fact, technology is not randomly assigned. Instead, it is a process of farmers’ ‘self-selection.’ Whether a farmer adopts the technology or not is determined by a set of socioeconomic variables. Socioeconomic variables influence technology adoption, and these might also directly affect farmers’ well-being (or incomes, in this case). If technology adoption shows a positive correlation with farmers’ well-being, one cannot distinguish whether the adoption promotes farmers’ well-being or whether better-off farmers have a higher probability of adopting technology. Using the framework of Rosenbaum and Rubin (1983), we define the ‘treatment effect’ as the difference between the well-being farmer i receives in the two states of the world: (i) the farmer adopts technology, T = 1, and (ii) the farmer does not adopt technology, T = 0:

τ i = Yi (1) − Yi ( 0 ) .

(1)

The average treatment effect (ATE) is defined as the expectation of the treatment effect across all farmers. A problem arises when using non-experimental data because only one of these states is actually observed; that is, either Yi(1) or Yi(0) is observed for each farmer i, but not both. The unobserved well-being is called the counterfactual well-being. Accordingly, it is convenient to express the ATE as:

E (τ i ) = P ⋅ [ E (Y 1 T = 1) − E (Y 0 T = 1)] + (1 − P ) ⋅ [ E (Y 1 T = 0 ) − E (Y 0 T = 0 )] , (2) where P is the probability of observing a farmer adopting improved upland rice technology. In Equation (2), the ATE for the whole sample is the weighted average of the technology (treatment) effect for adopters and non-adopters. In estimating the ATE, both counterfactual well-beings, E(Y0|T = 1) and E(Y1|T = 0), should be constructed. Because of the associated complications, many studies focus on one or the other of the respective counterfactuals. The most prominent evaluation parameter is the so-called ‘average technology effect on the treated’ (adopted) (ATT) and is given by:

τ ATT = E (τ T = 1) = E [Y (1) T = 1] − E [Y ( 0 ) T = 1] .

(3)

Given Equation (3), the problem of selection bias is straightforward to see, because the second term on the right-hand side is unobservable. If E[Y(0)|T = 0] = E[Y(0)|T = 1], the non-adopter can be used as an adequate comparison group. However, as discussed above, this condition is rarely satisfied with nonexperimental data. A possible way forward is to use a ‘matching’ approach. The basic idea is that one infers the behavior of a given adopter by matching them with an observationally equivalent non-adopter. In so doing, the researcher hopes to create a situation similar to what would have been observed in a random experiment. Two assumptions need to be satisfied to apply this approach. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.


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The first is the assumption of ‘conditional independence’: given a series of observable covariate Xs, which are not affected by technology adoption, the potential well-being is independent of technology assignment:

Y 0 , Y 1 T X , ∀X ,

where denotes independence. If the assumption is fulfilled, holding the observable covariates constant, the non-adopter’s well-being has the same distribution that adopters would have experienced had they not adopted the technology. Accordingly, technology can be deemed as randomly assigned. It follows that the ATT can be expressed as follows:

τ ATT = E (τ T = 1) = E [Y (1) T = 1, X ] − E [Y ( 0 ) T = 1, X ] .

(4)

A problem arises when there are many observable covariates that determine farmers’ well-being. For example, if X contains s covariates that are all dichotomous, the adopter must be matched with a non-adopter having identical values for all 2S variables. This is obviously a problem when S is relatively large and/or the sample size is relatively small. To deal with this dimensionality problem, Rosenbaum and Rubin (1983) suggest using a balancing scores method. The method shows that if potential well-being is independent of technology adoption conditional on covariates X, it is also independent of technology adoption conditional on a balancing score. The propensity score, defined as the possibility of farmer adopting technology conditional on covariates X, is one possible balancing score:

Pi ( X ) = Prob (T = 1 X ) .

(5)

This conditional probability is the propensity score, which allows one to identify similar farmers. The second assumption relates to the common support or overlap condition. It rules out perfect predictability of T given X, so that

0 < P (T = 1 X ) < 1.

(6)

This condition guarantees that farmers with the same X values have a positive probability of being both an adopter and a non-adopter. Accordingly, ATE is only defined in the region of this common support. Farmers falling outside this region are not included in the estimation of ATE. The common support assumption improves the matching quality by excluding farmers at the tails of the propensity score distribution. It ensures that characteristics observed in the technology adoption group can also be observed among the non-adopters (Bryson et al, 2002). A downside of the common support assumption is that it reduces the sample size. If the proportion of lost farmers is too large, this might raise concerns that the remaining farmers are insufficiently representative of the population, thus casting doubt on the associated ATE estimates. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.


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Assuming that both the conditional independence and common support conditions hold, the propensity score matching estimator for ATT can be expressed as follows: PSM τ ATT = E P ( X ) T =1 {E [Y (1) T = 1, P ( X )] − E [Y ( 0 ) T = 0, P ( X )]} .

(7)

Equation (7) reveals that the propensity score matching estimator is simply the mean difference in the well-being of the two groups (adopter and non-adopter) over the common support area. IV.

Propensity-Score Matching Procedures

This section describes in greater detail the propensity score matching procedure that we use in assessing the impact of upland rice technology on farmers’ well-being. IV.1 Estimating propensity scores Logit and Probit are standard approaches for estimating models with limited dependent variables. Both approaches produce similar results when estimating the probability of an individual farmer being an adopter or a non-adopter (Caliendo et al., 2005). In this paper, we use the Probit model to estimate propensity scores. Because matching is based on the assumption of conditional independence, variables included in the model should satisfy the ‘balance requirement.’ We use statistical significance and the pseudo-R2 test to check for differences in average propensity scores between adopters and non-adopters, conditional on X. If there is no significant difference in the propensity scores of the two groups, we conclude that the respective variables satisfy the balance requirement. IV.2 Choosing a matching algorithm Once propensity scores have been calculated, one needs an algorithm to match farmers in the adopter group with farmers in the non-adopter group, based on the closeness of their propensity scores. Several matching algorithms, such as nearest neighbor matching (NNM), caliper matching and kernel matching (Heckman et al, 1998; Smith and Todd, 2005), have been suggested in the published literature. Each of the three matching methods has some shortcomings. The NNM matches each farmer from the adopter group with the farmer from the nonadopter group having the closest propensity score. NNM faces the risk of bad matches if the closest neighbor is far away. This risk can be reduced by using caliper matching, which imposes a maximum tolerance on the difference in propensity scores. Finally, kernel matching uses a weighted average of all farmers in the adopter group to construct a counterfactual. A major advantage of this © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.


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approach is that it produces ATE estimates with smaller lower variance, because it utilizes greater information. The present paper uses all three approaches to estimate the impact of technology adoption on Yunnan’s farmers.2 IV.3 Checking Overlapping and Common Support A standard approach for checking common support is to compare variable minima and maxima. Observations whose propensity scores are smaller than the minimum and/or larger than the maximum in the opposite group are removed from the sample (Caliendo and Kopeinig, 2005). A possible problem with this approach is that there might be limited overlap between the two groups’ propensity scores, so that a large number of observations are discarded. Accordingly, Smith and Todd (2005) suggest using a trimming procedure whereby the region of common support is defined where P has positive density within both the T = 1 and T = 0 distributions. IV.4 Assessing match quality Because the matching procedure conditions on the propensity score but does not condition on individual covariates, one must check that the distribution of variables are ‘balanced’ across the adopter and non-adopter groups. Rosenbaum and Rubin (1985) recommend that standardized bias (SB) and t-test for differences be used to check matching quality. In addition, Sianesi (2004) suggests re-estimating the propensity scores using only adopters and matching nonadopters. If the covariates X are randomly distributed across adopter and nonadopter groups, the value of the associated pseudo-R2 should be fairly low. IV.5 Estimation of standard errors Although there is little formal evidence to justify bootstrapping (Imbens, 2004), the approach has been widely applied. This study follows suit by using a bootstrapping methodology to calculate the standard error for the estimate of the technology impact. V.

Results and Discussion

V.1 Variables selection and result of Probit analysis A Probit model is used to predict the probability of adoption of improved upland rice technology for each year of the study. With a view to the conditional independence assumption, one should select explanatory variables that are significant determinants of well-being and also correlated with technology adoption. Table 3 reports the associated Probit estimates. 2.

Three caliper scales were used: 0.01, 0.1 and 0.5.

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Table 3 Results of Probit estimation of propensity scores Variable

2000 Coefficient

Household size Age of household head Age of household head squared Sex of household head =1, if female Education level of household head % of labor force Land area % terrace land % irrigated land Number of draft animals Distance from market Altitude of residence Village with extension program County dummy Menglian Lancang Cangyuan Menghai Jinghong Wenshan Pinbian Constant Balancing Observation Pseudo R2

-0.079 0.032 0.000 0.376 0.135 -0.502 0.001 0.022 -1.098 0.064 0.004 -0.003 0.016 Reference -0.188 -1.070 -0.673 -0.272 1.499 1.650 3.608 Yes 473 0.312

2002 Z-value -1.21 0.81 -0.76 1.27 1.24 -1.21 0.19 0.05 -1.24 1.39 0.19 -4.5 0.06

Reference -0.58 -2.05** -1.09 -0.74 2.55** 2.94*** 2.78*** Yes 473 0.346

2004

Coefficient

Z-value

Coefficient

Z-value

-0.062 0.064 -0.001 0.001 0.012 -0.112 0.015 0.911 -2.676 0.017 0.040 -0.002 0.474

-0.99 1.55 -1.57 0.00 0.11 -0.28 2.58*** 1.98** -2.78*** 0.40 2.07** -3.18* 1.90***

-0.066 -0.065 -0.001 -0.061 -0.093 -0.742 0.018 0.713 -2.512 -0.007 0.085 -0.002 1.211

-1.11 1.58 -1.55 -0.21 -0.89 -1.97** 3.31*** 1.51 -3*** -0.22 4.25*** -2.29** 4.74***

-3.90*** -3.45*** -3.00*** -1.36 0.5 0.85 0.87

-1.979 -2.326 -4.165 0.277 -1.919 -2.448 -3.444

-6.37*** -6.21*** -6.39*** 0.68 -3.51*** -4.65*** -2.58***

Reference -1.151 -1.437 -1.818 -0.544 0.265 0.434 1.101 Yes 473 0.331

Note: *, ** and ***indicate significance at the 1, 5 and 10% level, respectively.

Explanatory variables include household size, age of the household head, gender of the household head, educational attainment of the household head, number of farm laborers in the household, land area (or farm size), percentage of terraced land area, percentage of irrigated land area, number of draft animals, distance to market, altitude of the farm, a dummy variable representing whether or not improved upland rice technology has been promoted in the village and a regional (county) dummy. The pseudo-R2s of the Probit model estimates are 0.312, 0.346 and 0.331 in 2000, 2002 and 2004, respectively. The combination of variables satisfies the balance requirement. The results of the Probit model illustrate the impact of socioeconomic indicators on farmers’ decisions to adopt improved upland rice technology. Taking the 2004 estimates as an example, share of farmer labor force has a significant negative effect on improved upland rice technology adoption. Farmers with more land area are more likely to adopt new technology. Similarly, farmers with more terraced land area have a higher probability of technology adoption. The percentage of irrigated lowland area has a negative effect on the upland rice technology © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.


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adoption. Farmers far away from the market have a higher probability of adoption. This finding is consistent with Pandey et al. (2002) who conclude that farmers more closely linked with the market in northern Vietnam are less likely to grow upland rice. The improved upland rice extension program has a significant positive effect on technology adoption. Taking Menglian County as a reference, farmers in Jinghong county have a higher probability of adopting improved upland rice, whereas farmers in other counties in our sample have a lower probability of adoption. The reason might be the greater availability of improved upland rice varieties in Menglian and Jinghong counties. V.2 Influence of different matching algorithms on the result of estimation Different matching algorithms, including NNM matching, caliper matching and kernel matching, are used to estimate the effects of upland rice technology on farmer well-being. The different matching algorithms produced different quantitative results, but the qualitative findings are similar. For example, when estimating the effect of technology on the poverty gap, the kernel approach produces an ATE of -0.010, whereas the NNM without replacement approach estimates an ATE of -0.032. The two matching algorithms have different common support regions, leading to the selection of different observations. Therefore, they are based on somewhat different samples. Table 4 shows the sample loss in the adopter group under different matching algorithms. The result indicates that the NNM with replacement has the least sample loss because the farmers in the non-adopter group can be used for more than one match. In 2004, there was only a 2.77 percent sample loss. The NNM without replacement and the kernel approach have the lowest sample losses. The different caliper approaches generate the largest sample losses. Hence, the flip side of the attempt to produce better matches is that it results in a substantial reduction in sample size. Table 4 Number of matching farmers lost due to common support requirement 2000

NNM without replacement Caliper = 0.01 Caliper = 0.1 Caliper = 0.5 NNM with replacement Kernel

2002

2004

Before matching

After matching

Loss in %

Before matching

After matching

Loss in %

Before matching

After matching

Loss in %

137

129

5.84

179

170

5.03

253

220

13.04

82 86 112 129

40.15 37.23 18.25 5.84

88 96 109 170

50.84 46.37 39.11 5.03

85 91 121 246

66.40 64.03 52.17 2.77

117

14.60

158

11.73

212

16.21

Note: NNM, nearest neighbor matching.



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V.3 Assessing the matching quality Table 5 lists the values of the three indicators before and after matching. Detailed matching effects can be seen from the figures attached in the Appendix. Before matching, the average standardized biases for all observed variables are 31.7 in 2000, 31.8 in 2002 and 25.9 in 2004. The decreasing trend indicates that the matching procedures produced better balance over time. As indicated by the average standardized bias measure, the caliper approach has the best matching quality, whereas NNM without replacement has the worst. The average t-test results for all variables before matching were 58.51 in 2000, 63.18 in 2002 and 53.55 in 2004. This has a similar decreasing trend, as indicated by SB. The more the t-value decreases, the better is the matching quality. By re-estimating the propensity score based on only the adopter group and the matching non-adopter group and comparing the pseudo-R2 values of before and after matching, the result shows that pseudo-R2 values for the caliper approach and the kernel approach are fairly low after matching. This implies that the covariates X are distributed randomly in the adopter group and the non-adopter groups. In general, NNM causes the least sample loss, but its matching quality is the worst. The caliper approach has the best matching quality despite a high sample loss during matching. The kernel approach has a lower sample loss and a better matching quality. Table 5

Before matching

After matching NNM without replacement

Caliper = 0.01 Caliper = 0.1 Caliper = 0.5

NNM with replacement

Kernel

Matching quality indicators

Quality indicators

2000

2002

2004

Pseudo R2 Average standardized bias t-test

0.312 31.706 58.51

0.345 31.762 63.18

0.331 25.878 53.55

Pseudo R2 Average standardized bias t-test Pseudo R2 Average standardized bias t-test Pseudo R2 Average standardized bias t-test Pseudo R2 Average standardized bias t-test Pseudo R2 Average standardized bias t-test Pseudo R2 Average standardized bias t-test

0.088 15.828 24.13 0.036 6.872 9.54 0.058 10.747 15.26 0.048 11.278 16.53 0.106 10.385 18.45 0.049 8.040 12.24

0.186 18.626 32.87 0.028 6.782 9.38 0.057 9.215 12.98 0.033 10.152 14.61 0.081 10.435 20.7 0.035 6.742 13.09

0.288 21.970 44.29 0.049 7.793 10.53 0.041 6.569 9.03 0.038 6.289 10.57 0.076 11.022 25.78 0.042 9.391 19.49

Note: NNM, nearest neighbor matching. © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.


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V.4 Effect of technology on farmers’ income and poverty status Table 6 shows the effect of improved upland rice technology on farmers’ incomes. Using different matching algorithms, the effect ranges from 0.325 to 0.480 in 2000, from 0.270 to 0.356 in 2002, and from 0.139 to 0.317 in 2004. Using 50 times bootstrapping to test the statistical significance, the results show that the improved upland rice technology has a robust and positive effect on farmers’ income. Comparing the effects estimated by the same matching algorithm in the three years under study reveals a decreasing trend from 2000 to 2004. Taking the caliper approach with a caliper of 0.01 as an example, the average income of adopters is estimated to be 1.53, 1.32 and 1.26 times higher than that of nonadopters in 2000, 2002 and 2004, respectively. The effects of improved upland rice technology on the poverty gap are listed in Table 7. The estimates indicate that improved upland rice technology has a robust and negative effect on farmers’ poverty gap in each of the three years. Depending on the specific matching algorithm used, the estimated impact of technology adoption is estimated to range from -0.087 to 0.050 in 2000, from -0.061 to -0.040 in 2002 and from -0.032 to -0.010 in 2004. In words, the poverty gap among adopters is estimated to be 5.0–8.7 percent lower than the corresponding value for non-adopters in 2000, 4.0–6.1 percent lower in 2002, and 1.0–3.2 percent lower in 2004. This indicates a decreasing effect on poverty reduction over time. We conclude that the adoption of improved upland rice technology has had a significant positive effect on farmers’ well-being. In addition, there has been a trend towards decreasing impact over time. This can be explained by decreasing prices resulting from the continued increase in supply that facilitates the spread of technology. The incremental income gains of producers decreases because of the dampening price effect. This means that the price effect results in the transfer of technology benefits from producers who benefit initially, to consumers who become the ultimate beneficiaries. This is a general finding regarding the impact of agricultural technologies (Hazell and Ramasamy 1991, Hazell 2008). Furthermore, the finding provides some evidence to support the technology diffusion theory that agricultural technology diffusion goes through an early adoption period, takeoff period, saturation period and, finally, a decline period (Rogers, 1962). VI.

Concluding Remarks

There exists a complicated relationship between agricultural technology adoption and farmers’ well-being. Although it is widely accepted that diffusion of agricultural technology can increase well-being, assessing the effect of agricultural technology at the micro household level is challenging due to the difficulties in separating out the technology effects from the effects of other socioeconomic characteristics. The econometric approach used in the present paper addresses this selfselection bias. Accordingly, we find that upland rice technology has a robust and positive effect on farmers’ well-being in southern Yunnan, as measured by © 2010 The Authors Journal compilation © 2010 East Asian Economic Association and Blackwell Publishing Ltd.

0.345 0.425 0.402 0.325 0.456 0.480

0.101 0.115 0.120 0.084 0.104 0.098

Bootstrap error

2000

6.45*** 5.71*** 5.46*** 7.75*** 6.30*** 6.68***

Z-value

0.270 0.275 0.308 0.315 0.356 0.297

Effect

0.084 0.079 0.097 0.091 0.072 0.084

Bootstrap error

2002

6.08*** 6.50*** 5.25*** 5.62*** 7.06*** 6.06***

Z-value

0.317 0.230 0.216 0.139 0.293 0.246

Effect

Effect of improved upland rice technology on farmers’ income: matching estimates 2004

0.071 0.082 0.072 0.069 0.074 0.079

Bootstrap error

Note: Log income is used; bootstrapping test, 50 replications. ***indicates significance at the 1% level. NNM, nearest neighbor matching.


Effect

Table 6

4.34*** 3.77*** 4.29*** 4.46*** 4.17*** 3.91***

Z-value

IMPACT ASSESSMENT OF TECHNOLOGY 153


0.023 0.023 0.025 0.025 0.024 0.022

-0.060 -0.087 -0.082 -0.063 -0.050 -0.060 4.98*** 5.01*** 4.70*** 4.62*** 4.86*** 5.42***

Z value

-0.051 -0.047 -0.051 -0.061 -0.040 -0.040

Effect

0.017 0.015 0.015 0.018 0.013 0.016

Bootstrap error

2002

4.21*** 4.72*** 4.87*** 3.95*** 5.75*** 4.64***

Z value

Note: Bootstrapping test, 50 replications. ***indicates significance at the 1% level. NNM, nearest neighbor matching.


Bootstrap error

Effect

2000

-0.032 -0.025 -0.028 -0.030 -0.014 -0.010

Effect

Table 7 Effect of improved upland rice technology on farmers’ poverty gap: matching estimates

0.012 0.010 0.013 0.011 0.012 0.010

Bootstrap error

2004

2.59*** 3.05*** 2.36*** 2.85*** 2.57*** 3.20***

Z value

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increases in income and decreases in poverty. Interestingly, the effect of technology on well-being shows a diminishing impact over time. This suggests that there are limits to the impact on farmers’ incomes induced by specific technologies. The policy implication of the findings is that agricultural technology innovations need to be generated and promoted continuously to replace older technologies that have reached their saturation point. Appendix I

0.005 0.010 0.015 0.020 0.025

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Figure Density of the propensity scores before and after matching: (a) before matching, 2004; (b) after nearest neighbor matching without replacement matching, 2004; (c) after kernel matching, 2004; (d) after caliper (0.01) matching, 2004; (e) before caliper matching, 2002; (f) after nearest neighbor matching without replacement matching, 2004; (g) after kernel matching, 2002; (h) after caliper (0.01) matching, 2002; (i) before matching, 2000; (j) after nearest neighbor matching without replacement matching; (k) after kernel matching, 2000; and (l) after caliper (0.01) matching, 2000. ---, not adapters; —, adapters

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