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_148_ _American Economic Journal: applied economics_ _april 2013_

**VI. Conclusions**

Prior to the intervention, owners of unregistered firms were either ignorant of, or vastly overestimated the costs of registration. We might therefore have expected that simply informing firms about the costs of registration would be sufficient to induce registration. Such a finding would have been consistent with the view, often attributed to de Soto, of informal firms being excluded from the formal sector by the perception, if not the reality, of high entry barriers. But in fact we find that information and reimbursement for the modest direct costs do not result in any increase in registration. Instead, registration is spurred only when the information is combined with incentive payments.
The incentive required to induce registration is modest compared to reported profit levels of the sample enterprises. A payment of two month’s profit is sufficient to induce half of the firms to register. The willingness of firms to register for a modest payment suggests that they perceive modest costs but even more modest benefits from being formal. The relatively high rates of registration induced by these payments are consistent with firms taking a rational cost-benefit approach to the decision to formalize, as suggested by Levy 2008 . Among those not registering after receiving ( ) an offer of this magnitude, more than half took some steps toward registering. They stopped only when learning that issues of land ownership would prevent them from registering without paying significant fees to landlords, temples, or the government.
Thus, in the absence of land issues, the net costs of formality appear to be modest for almost all of the informal firms represented by our sample—though the minority of firms affected by land issues may be seen as excluded from the formal sector.
The net benefit of formality also appears modest for most firms. While we do find an increase in profits after formalization, the mean treatment effect seems to be driven by a successful upper tail. Firms that formalize begin advertising and using receipt books more often, but don’t appear to get the more touted benefits of formalizing, such as increased access to credit, obtaining government contracts, or participating in government programs. Most firms seem therefore to be rationally refraining from formalizing, while a few seem to be suboptimally informal.
This finding is important for two reasons. First, while governments clearly should not mimic our experiment with a policy of direct payments, the results do suggest that modest increases in the perceived benefits of being formal could be expected to dramatically increase the demand to formalize among firms currently operating informally. Whether tax collections increase enough to pay for these increased benefits or, alternatively to pay for increased enforcement efforts is beyond the scope ( ) of this paper. Our data do suggest, though, that given the current tax code, the additional tax collections would not be large among this sample of firms. This, combined with the fact that the spillovers to growth are modest, suggests that near-term gains to the government of increased formality are limited. Second, despite the pervasive interest of governments around the world in trying to increase the size of their formal sector, our results overall suggest little in the way of pent-up demand to become license rather than municipal license as proof of formality in many countries, there may be less ex ante reason to expect a positive impact on firms of municipal registration.


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_Vol. 5 No. 2_ _de Mel et al.: Demand for and Consequences of Formalizing_ _149_ formal among existing firms. Nevertheless, our results do show an increase in trust in the government as a result of formalizing. It is possible that formalizing a large number of firms would offer broader benefits to society in terms of trust.
Finally, it should be noted that our study measures the benefits of formalizing for firms already in business. There may well be benefits from the simplification of registration at the extensive margin, if high-ability entrepreneurs not currently operating a business are induced to enter. Measuring such impact remains a key area for future research.

References

**Alcázar, Lorena, Raúl Andrade, and Miguel Jaramillo.** 2010. “Panel Tracer Study on the Impact of
/ Business Facilitation Processes on Enterprises and Identification of Priorities for Future Business Enabling Environment Projects in Lima, Peru–Report 5: Impact Evaluation after the Third Round.” Unpublished.
**Bertrand, Marianne, and Francis Kramarz.** 2002. “Does Entry Regulation Hinder Job Creation?

Evidence from the French Retail Industry.” _Quarterly Journal of Economics_ 117 4 : 1369–1413.
( )
**Bruhn, Miriam.** 2011. “License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico.” _Review of Economics and Statistics_ 93 1 : 382–86.
( )
**Bruhn, Miriam, and David McKenzie.** 2009. “In Pursuit of Balance: Randomization in Practice in
Development Field Experiments.” _American Economic Journal: Applied Economics_ 1 4 : 200–232.
( )
**de Mel, Suresh, David J. McKenzie, and Christopher Woodruff.** 2009. “Measuring Microenterprise
Profits: Must We Ask How the Sausage Is Made?” _Journal of Development Economics_ 88 1 : ( ) 19–31.
**de Mel, Suresh, David J. McKenzie, and Christopher Woodruff.** 2012. “Sri Lanka–Formalization
Experiment Data 2008–2011.” World Bank Microdata Catalog. LKA_2008_SLFED_v01_M.
[http://microdata.worldbank.org/index.php/catalog/1063](http://microdata.worldbank.org/index.php/catalog/1063) accessed February 11, 2013 .
( )
**de Mel, Suresh, David McKenzie, and Christopher Woodruff.** 2013. “The Demand for, and Consequences of, Formalization among Informal Firms in Sri Lanka: Dataset.” _American Economic Jour-_
_nal: Applied Economics_ [. http://dx.doi.org/10.1257/app.5.2.122.](http://dx.doi.org/10.1257/app.5.2.122)
**De Soto, Hernando.** 1989. _The Other Path: The Economic Answer to Terrorism_ . New York: Harper and
Row Publishers.
**Di Tella, Rafael, Sebastian Galiani, and Ernesto Schargrodsky.** 2007. “The Formation of Beliefs: Evidence from the Allocation of Land Titles to Squatters.” _Quarterly Journal of Economics_ 122 1 :
( ) 209–41.
**Djankov, Simeon.** 2002. “The Regulation of Entry.” _Quarterly Journal of Economics_ 117 1 : 1–37.
( )
**Fajnzylber, Pablo, William F. Maloney, and Gabriel V. Montes-Rojas.** 2011. “Does Formality Improve
Micro-firm Performance? Evidence from the Brazilian SIMPLES Program.” _Journal of Develop-_ _ment Economics_ 94 2 : 262–76.
( )
**Hsieh, Chang-Tai, and Peter J. Klenow.** 2009. “Misallocation and Manufacturing TFP in China and
India.” _Quarterly Journal of Economics_ 124 4 : 1403–48.
( )
**Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto.** 2011. “Unpacking the Black Box
of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.” _American Political Science Review_ 105 4 : 765–89.
( )
**International Finance Corporation.** 2009. _Doing Business 2010: Reforming through Difficult Times_ .
International Finance Corporation IFC . Washington, DC, September.
( )
**Jaramillo, Miguel.** 2009. “Is There Demand for Formality among Informal Firms? Evidence from
Microfirms in Downtown Lima.” German Development Institute Discussion Paper 12 2009.
/
**Kaplan, David S., Eduardo Piedra, and Enrique Seira.** 2011. “Entry Regulation and Business StartUps: Evidence from Mexico.” _Journal of Public Economics_ 95 11–12 : 1501–15.
( )
**Klapper, Leora, Luc Laeven, and Raghuram Rajan.** 2006. “Entry Regulation as a Barrier to Entrepreneurship.” _Journal of Financial Economics_ 82 3 : 591–629.
( )
**Klapper, Leora, and Inessa Love.** 2010. “The Impact of Business Environment Reforms on New Firm
Registration.” World Bank Policy Research Working Paper 5493.
**Levy, Santiago.** 2008. _Good Intentions, Bad Outcomes: Social Policy, Informality and Economic_
_Growth in Mexico_ . Washington, DC: Brookings Institution Press.

Sri Lanka–Formalization Experiment Data 2008–2011

Sri Lanka–Formalization Experiment Data 2008–2011

Sri Lanka–Formalization
Experiment Data

World Bank Microdata Catalog


Input:
_150_ _American Economic Journal: applied economics_ _april 2013_

**Maloney, William F.** 2004. “Informality Revisited.” _World Development_ 32 7 : 1159–78.
( )
**McKenzie, David.** 2010. “Dimensions of Informality in Bangladesh.” Unpublished.
**McKenzie, David.** 2012. “Beyond Baseline and Follow-up: The Case for More T in Experiments.”
_Journal of Development Economics_ 99 2 : 210–21.
( )
**McKenzie, David, and Yaye Seynabou Sakho.** 2010. “Does It Pay Firms to Register for Taxes? The
Impact of Formality on Firm Profitability.” _Journal of Development Economics_ 91 1 : 15–24.
( )
**McKenzie, David, and Christopher Woodruff.** 2012. “What are we learning from business training and
[entrepreneurship evaluations around the developing world?” http://ipl.econ.duke.edu/bread/papers/](http://ipl.econ.duke.edu/bread/papers/working/358.pdf)
[working/358.pdf.](http://ipl.econ.duke.edu/bread/papers/working/358.pdf)
**Monteiro, Joana and Juliano Assunção.** 2012. “Coming Out of the Shadows? Estimating the Impact
of Bureaucracy Simplification and Tax Cut on Formality in Brazilian Microenterprises.” _Journal of_ _Development Economics_ 99 1 : 105–15.
( )
**Perry, Guillermo E., William F. Maloney, Omar S. Arias, Pablo Fajnzylber, Andrew D. Mason, and**
**Jaime Saavedra-Chanduvi.** 2007. _Informality: Exit and Exclusion_ . Washington, DC: World Bank.
**World Bank.** 2009. _Increasing Formality and Productivity of Bolivian Firms_ . World Bank. Washington DC, July.


Input:
**This article has been cited by:**

1. Miriam Bruhn, David McKenzie. 2013. Using administrative data to evaluate municipal reforms:
an evaluation of the impact of Minas Fácil Expresso. _Journal of Development Effectiveness_ **5** :3,
[319-338. [CrossRef]](http://dx.doi.org/10.1080/19439342.2013.813959)

administrative data


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World Development Vol. 34, No. 7, pp. 1287–1300, 2006

- 2006 Elsevier Ltd. All rights reserved
0305-750X/$ - see front matter www.elsevier.com/locate/worlddev doi:10.1016/j.worlddev.2005.12.004

# The Role of Opinion Leaders in the Diffusion of New Knowledge: The Case of Integrated Pest Management

GERSHON FEDER and SARA SAVASTANO [*] Development Research Group, The World Bank, USA Summary. — The paper reviews the literature on the characteristics and impact of opinion leaders on the diffusion of new knowledge, concluding that there is no clear evidence on whether opinion leaders are more effective if they are similar in socio-economic attributes to the other farmers rather than superior to would-be followers. A multivariate analysis of the changes in integrated pest management knowledge in Indonesia among follower farmers over the period 1991–98 indicates that opinion leaders who are superior to followers, but not excessively so, are more effective in transmitting knowledge. Excessive socio-economic distance is shown to reduce the effectiveness of diffusion.
The paper then derives operational implications of the empirical results.

- 2006 Elsevier Ltd. All rights reserved.

Key words - Asia, Indonesia, opinion leaders, diffusion, innovations

1. INTRODUCTION

The subject of the diffusion of innovations received much attention in a variety of professional disciplines over the past few decades: Sociologists, economists, communications specialists, and education researchers proposed various theories on the factors and processes which underpin the observed patterns of information diffusion and the adoption of innovations (Rogers, 1995; Sunding & Zilberman, 2001). The interest in this topic stems from the importance of innovative technologies, organizations, and contractual arrangements in improving the well being of societies and the environment. A better understanding of the processes by which new knowledge diffuses within and across societies and communities can suggest actions and investments that can be undertaken by governments and firms that aim to promote innovations. The large volume of empirical evidence that accumulated over the years clarified many aspects of diffusion processes and their characteristics in various contexts.
One key observation highlighted in many studies is the role of social links and community structure in the diffusion process. Communications and information relating to new knowledge were shown to be embedded within the more general fabric of social interactions among individuals. The pattern of information flows received and transmitted by individuals is thus related to their social environment, the network of their contacts, and their status within that network (see the literature cited in Brock & Durlauf, 2001; Rogers, 1995). Recent empirical works by economists focused on a more precise definition of the social and communication networks of individuals whose technology decisions were analyzed (e.g., Bandiera & Rasul, 2003; Conley & Udry, 2003). The purpose of the present paper is to provide insights on

- Gershon Feder and Sara Savastano are, respectively, Research Manager and consultant, Development
Research Group, The World Bank. The views expressed in this paper are those of the authors only, and do not necessarily represent the World Bank and its affiliated organizations. The first author had supervisory authority, on the World Bank’s behalf, over the implementation of the IPM training project in Indonesia in the years 1995–97. Final revision accepted: December 8, 2005.

1287


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1288 WORLD DEVELOPMENT an issue that is still not fully resolved in the empirical innovation diffusion literature, namely, the extent to which the attributes that make opinion leaders stand out in a community (social status, wealth, skills) can hamper or enhance their ability to disseminate information to an audience that is of a lesser status, lesser wealth, and lesser skills. The analysis utilizes data describing changes in knowledge regarding integrated pest management (IPM) among farmers in three provinces of Java, Indonesia, to demonstrate that leadership properties actually help in the diffusion of knowledge, but as the social and wealth disparity between leaders and would-be followers increases, the ability to disseminate knowledge diminishes at the margin, and may in fact decline.
The program of the paper is as follows: The next section reviews the relevant literature and derives the conceptual framework underpinning the empirical work in the paper. This is followed by descriptions of the promotion of IPM in Indonesia, and the data. A subsequent section outlines the analytical methodology followed by the empirical analysis. The last section provides conclusions and implications.

2. LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK The seminal work of Rogers (1995) provides a coherent theory, as well as empirical evidence, of many aspects of innovations diffusion. One of the themes elaborated upon is the role and characteristics of ‘‘opinion leaders.’’ Such individuals have the status, expertise, links to external sources of knowledge, or experience that enable them to provide information and advice about innovations to others within their community.
Opinion leadership is thus reflected in the ability to influence others’ attitudes and knowledge (Chatman, 1987; Valente & Davis, 1999). Such leadership may be informal rather than formal, but many scholars observed that opinion leaders tend to have higher social status than ‘‘followers’’ (Bandura, 1986, p. 151). Researchers also noted that opinion leaders are often more exposed to external sources of information, such as mass media or change agents (e.g., extension workers), have higher formal education, higher levels of literacy, a more cosmopolitan orientation, and higher income and wealth (Chatman, 1987; Rogers, 1995, p. 92; Valente, 1996; Weimann, 1994, p. 217).

The notion that opinion leaders, who stand out within the community in terms of their socio-economic and human capital characteristics, are key to information diffusion contrasts with the common observation (verified empirically) that most people tend to have communication networks composed mostly of individuals who are like them in social status and other respects. Sociologists and communications scholars define the tendency for communications to flow mostly among similar individuals as homophily, which is contrasted with heterophily, whereby individuals communicate with those who are different from them.
The prevalence of homophily in communication networks leads to an alternative characterization of opinion leaders as being similar in their socio-economic characteristics to the followers. Thus, Weimann’s (1994) comprehensive study of opinion leadership concludes that ‘‘The almost single possible generalization on the socio-demographic level is the tendency towards similarity of the influential–influencee dyad. The trickledown model, suggesting a vertical flow from upper classes to lower classes, from more educated to less educated, or from higher income groups to lower income groups, was rejected in almost all domains. The more frequent flow was from leader to follower from the same social group. This tendency resulted in a homogeneity of the leader–followers groups in terms of most socio-demographic measures. People turn to seek advice from their peers, from individuals of the same background, interests, and values. The flow of information and influence is likely to be rather horizontal.’’ (p. 88) Rogers (1995) offers a possible middle ground between the two views. He observes that homophily may act as a barrier to the diffusion of new knowledge because ‘‘new ideas usually enter a system through higher status and more innovative members’’ (Rogers, 1995, p. 288). But while he identifies a general tendency for followers to seek information and advice from opinion leaders who are perceived as more technically competent than themselves, he emphasizes that those leaders are selected so that they are not too much better. Thus, according to Rogers (1995, p. 294), ‘‘a follower typically seeks an opinion leader of somewhat higher status.’’ Similarly, Valente and Davis (1999, p. 59) argue that ‘‘learning occurs most efficiently when individuals are trained by their ‘‘near peers,’’ whom they have chosen as their models.’’ These views reflect the notion of ‘‘optimal heterophily’’ (Alpert & Anderson, 1973; Rogers & Shoemaker, 1971),


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THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1289 implying that excessive similarity, or excessive differences among individuals, could hinder the effectiveness of communication flows between them.
The measurement of capacity for opinion leadership attracted considerable attention in the literature. Early work by Rogers and Cartano (1962) describes three approaches: (i) the sociometric approach, whereby many members of a group are asked to identify those whose opinions are influential; (ii) a key informant approach, whereby a selected small group of individuals who are perceived as knowledgeable about the community are asked to identify the group’s opinion leaders; (iii) the self-designating approach, where respondents are asked questions to determine the degree to which they perceive themselves to be opinion leaders. More complex approaches labeled as ‘‘network analysis’’ were developed in subsequent years, relying on formal methods for measuring who talks to whom in a community (Valente & Davis, 1999; Valente & Foreman, 1998). All these approaches allow a score-based continuous index of leadership, but require access to respondents who can be interviewed directly on matters of opinion leadership, provide information on the groupings and channels of communications, and serve as a basis for counts of the number of ‘‘nominations’’ a presumed opinion leader received from community members.
The empirical evidence on the impact of opinion leaders’ socio-economic traits, as compared to the traits of other community members, on their effectiveness in diffusing information is not conclusive. For example, several studies highlight difficulties in the flow of information from higher status rural groups to lower strata (e.g., Roling, Ascroft, & Wa Chege, 1976; Van de Fliert, 1993). Similarly, Weimann (1994) cites studies indicating that opinion leaders are similar in socio-demographic characteristics to followers. Yet the study by Gibbons, de Koninck, and Hasan (1980) documents the effective role of prominent and wealthier leaders as sources of information about innovations in rural settings. Sen (1969, p. 26) notes that ‘‘the direction of influence from leaders to followers is then vertical, from high to low strata of the village society.’’ Similarly, Bose and Saxena (1966) found that rural opinion leaders, who were identified by other farmers as the most important source for agricultural information, were more literate, more competent, and superior in socio-economic standing.

For the purposes of framing the analysis in this paper, we formulate, based on the discussion in the literature, three alternative propositions that can be tested empirically: (a) People are more likely to seek and acquire information from those who are similar to them in various socio-economic respects.
(b) People are more likely to seek and acquire information from those who have a higher status as opinion leaders, and who are thus superior to them in socio-economic attributes.

A third proposition reflects the ‘‘optimal heterophily’’ concept and its implication that effective opinion leaders will tend to be superior to others, but within bounds.

(c) People are more likely to learn from opinion leaders who are superior to them in socio-economic aspects, but who are not too different.

These propositions will be tested using data pertaining to the introduction of IPM knowledge in Indonesia through an intensive training program to selected farmers. As will be argued, these farmers were typically selected for their opinion leadership status or potential. Analyzing the extent to which other farmers’ knowledge of IPM relates to the opinion leaders (who received intensive IPM training) in their communities will allow insights on the veracity of the three propositions.

3. THE INTRODUCTION OF IPM KNOWLEDGE IN INDONESIA IPM in agriculture is an ecologically based approach to pest management. It aims to minimize the use of chemical pesticide by enhancing the abilities of natural enemies of pests, the crop itself, and various factors in the field environment, to control pests presence and reduce the loss that they can cause. IPM was introduced intensively in 1985 following a severe outbreak of pests in rice (a key strategic commodity in a country where rice is the staple food). The pest problem was traced to excessive use of chemical pesticides, which destroy pests and their predators alike, but following which certain pests can reemerge in much larger numbers in the absence of natural enemies. Following the government’s acceptance of IPM as a desirable objective, training to farmers in IPM concepts was introduced in the 1990s through a large scale and intensive training program for farmers called the ‘‘Farmer Field School’’


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1290 WORLD DEVELOPMENT (FFS) that was first piloted in 1989. This program utilizes participatory methods ‘‘to help farmers develop their analytical skills, critical thinking, and creativity, and help them learn to make better decisions’’ (Kenmore, 1997).
The typical FFS enhances participating farmer’s knowledge on agroecosystems dynamics, within a framework of integrated pest and crop management. A great emphasis of the program has been on sensible pest management, safety with regard to chemical pesticides, and understanding of the interactions between pest insects and beneficial insects that limit the numbers and the impact of pests. With the knowledge gained in the FFS training, it was expected that a lower and safer use of chemical pesticides would be practiced by farmers.
Participatory training and hands-on experimentation are a key principle of the FFS, and the purpose of the training is to make the graduates ‘‘confident pest experts, self-teaching experimenters, and effective trainers of other farmers’’ (Wiebers, 1993). The group of trainees includes 20–25 participants from the village.
The duration of an FFS is about 12–14 weeks within a full crop-growing season. A facilitator trainer leads the program, conveying knowledge on, and facilitating discussion of, ecologically based approaches in pest management, and overall good crop management decision procedures and practices. The training aims not only to sharpen the participants’ decisionmaking abilities, but also to empower them for leadership and community activism (Scarborough, Killough, Johnson, & Farrington, 1997, p. 102).
The selection of farmers for FFS training was not random, and sought to purposely include those with agricultural expertise, as well as others with high social status such as rich farmers and religious leaders (Winarto, 1994, p. 153). A detailed study from the early 1990s points out that those selected tended to be more affluent farmers, better educated and informed, who enjoyed higher status in the community. A relatively significant proportion of these selected farmers served also as official and semi-official village leaders (Van de Fliert, 1993, pp. 130– 132). In fact, presumed or proven ability to spread information was a specific selection consideration at the local level, where selection was done by farmer group leaders in consultation with extension workers and village officials (Scarborough et al., 1997, p. 105; Van de Fliert, 1993, p. 203). Of course, there were situations where relatively young, lower status farmers were selected, although they needed to be perceived by the organizers of the training as being of high farming expertise (Winarto, 1994, 2004).
The cultural context certainly has an impact on the direction of communication flows and the perception of opinion leadership. The Javanese farming communities in the study areas are all Moslem, and tend to be hierarchical, and male dominated, with respect typically accorded to older and wealthier men, particularly if they achieved religious merit through pilgrimage to Mecca. [1] While the donor-funded program attempted to purposefully select a larger number of women than would otherwise be proposed by local officials and village leaders, the actual proportion of women trained was rather small, and in many cases nonfarming women were included in the training simply to satisfy gender targets (World Bank Implementation Completion Report, 2000, p. 5). As there are no women in the sample, the study pertains to diffusion of knowledge among men only.

4. DATA The data underlying this study were obtained through a panel survey of Javanese farm households conducted by the Indonesian Center for Agrosocio-economic Research (CASER) in April/May 1991 and again in June 1999. The baseline sample included rice-growing villages that had already been covered by the program, as well as villages that were not yet covered by the program, but were in areas where the program was planned to be implemented. All villages were visited in the repeat survey in 1999, but our analysis focuses only on those villages that had not yet been exposed to an FFS at the time of the baseline survey in 1991. In most of these villages, the village sample contains randomly selected farmers who participated in the training in various years throughout the period covered, as well as farmers from the same village who did not participate. Of the 268 sample households from the 22 sample villages where a field school had been implemented during 1991–99, only 112 had actually participated in the training while the remaining 156 households had not attended a program, but had been potentially exposed to some of its (new knowledge) benefits through informal communications and joint activities with graduates of the program who lived in their village.

panel survey of Javanese farm households

panel survey


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THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1291 The 1991 survey collected information on households’ farm operations and characteristics for the 1990–91 wet rice season and on their household attributes, activities, and assets. It also documented the farmers’ knowledge of specific aspects of pest management that were to be included in the training program, through a set of questions on specific curriculum components. The 1999 survey repeated the same questions and collected additional data regarding the household and the village participation in FFS training, and more information about the community. The farmers’ responses to the identical knowledge questions in both 1991 and 1999 were scored, and the number of correct answers relative to the total number of pest management questions serves as an indicator of pest management knowledge. [2] More detailed information on the construction of the knowledge score is provided in Appendix A.

5. ANALYTICAL METHODOLOGY The data do not contain information on the specific communication patterns, self-perceptions on leadership status, or numbers of nominations from peers received by farmers selected for training. It is thus not feasible to develop measures of opinion leadership for these farmers along the lines of work by Chatman (1987) or Valente and Davis (1999). However, it is possible to rely on the fact that the selection of farmers by program officers and village leaders to participate in the intensive IPM training was related to their observed or presumed opinion leadership potential, and on the availability of a number of socio-economic indicators related to opinion leadership for every farmer in the sample. In support of this approach, it is noted that studies of FFS training programs indicated that trained farmers were expected to proactively disseminate the knowledge gained in the course of the training to other members of the community (Scarborough et al., 1997, p. 105; Simpson & Owens, 2002; Van de Fliert, 1993; Winarto, 1994). Obviously, not all potentially strong opinion leaders were selected for training. Similarly, some people with low opinion leadership capacity were included in the training in some villages. But it is reasonable to expect that persons with higher opinion leadership capacity had a higher probability of being selected for training. While the capacity for opinion leadership is not observable to the researcher, the literature suggests that such capacity is related to observed or measurable characteristics of the farmers, such as education, pre-training pest management knowledge, and wealth. One can thus utilize a dichotomous choice model such as probit to estimate, based on a sample of program-trained and untrained farmers, the parameters associated with observable indicators associated with opinion leadership: P 1⁄4 f ðb [0] X Þ; ð1Þ where P is the probability of being selected, X is a vector of observed leadership characteristics, and b is a vector of parameters. The imputed value of the probability of selection [P [b] 1⁄4 f ðb [^][0] ; X Þ where b [^] is an estimate of b] can be viewed as an index of opinion leadership capacity, as it reflects this unobserved (to the researcher) capacity. This index can be calculated for every farmer in the sample, whether actually selected or not, once the parameter vector b is estimated.
Because the data contain for each community in the sample several randomly selected trained farmers, these selected farmers may be viewed as a representative sample of the group of trained farmers in the specific village community. The average imputed index of opinion leadership capacity of the trained (selected) farmers of a given village (say, P j) is an indica
[b] tor of the extent of the IPM-trained opinion leadership available to disseminate the knowledge gained in the training in that village.
The difference in the imputed probability of selection of any two farmers is a measure of how different these farmers are in terms of their capacity for opinion leadership. A farmer with a lower probability is more likely to be a follower, compared with a farmer who has a higher probability. The ability to calculate the difference in capacities for opinion leadership provides us with a means for clarifying the effects of Rogers’ concepts of homophily and heterophily on the communication of new knowledge between trained farmers and untrained farmers. If farmers tend to learn more from those better than themselves (i.e., from those who have a higher capacity for opinion leadership than themselves), then measurable changes in knowledge about IPM among farmers who did not attend the training will be positively related to the differences in capacities for opinion leadership (as represented by differences in the imputed probabilities for selection for training) between themselves and those

1999 survey

1991 survey


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1292 WORLD DEVELOPMENT who actually did attend the training. Given that in each community several farmers attended the training, the relevant reference in a given village j is the average capacity for opinion leadership among the trained farmers in that village, as represented by P j. That is, for any farmer i

[b] (in village j) who did not attend training, the degree of similarity (homophily) with the trained group (denoted by Dij) is approximated as Dij 1⁄4 P j � f ðb [^][0] X ijÞ; ð2Þ

[b] where f ðb [^][0] X ijÞ is the imputed probability of selection of untrained farmer i in village j. If Dij is very close to zero, then the untrained farmer i of village j is very similar to the trained group from that village in the attributes that reflect capacity for opinion leadership. If Dij is positive, then the group of trained farmers of village j is ‘‘better’’ (has on average a higher capacity for opinion leadership) than the untrained farmer i of the same village.
Using the variable Dij as a measure of dissimilarity (socio-economic ‘‘distance’’) between the untrained farmers and the trained farmers in their villages enables the testing of the three alternative hypotheses formulated in Section 2: If untrained farmers are more likely to seek and learn IPM knowledge from opinion leaders who are similar to them (hypothesis (a)), then the gains in IPM knowledge by any farmer i from village j during the period 1991–98 covered by the two surveys will be negatively related to the absolute value of Dij (or to transformations of Dij which increase in value; the larger is the absolute value of Dij, such as the square of Dij). If untrained farmers are more likely to seek advice and learn from farmers who have strong opinion leadership characteristics and who are of higher socio-economic standing (hypothesis (b)), then the gains in untrained farmers’ IPM knowledge during 1991–98 would be positively related to Dij. If untrained farmers tend to learn from opinion leaders who are better than themselves, but not too much better (hypothesis (c)), then the relationship between gains in IPM knowledge and Dij will follow a nonlinear pattern such as a quadratic, where the linear term is positive, but the square term is negative (implying that at the margin, as the opinion leaders become too dissimilar to the followers, their impact on knowledge diffusion diminishes). Because hypothesis (b) is nested within hypothesis (c), the empirical formulation will test both hypotheses simultaneously.

More formally, if we denote the level of IPM knowledge in 1991 and 1999 as K(0) and K(1), respectively, then the following multivariate equations will facilitate the testing of hypothesis: K ijð1Þ � K ijð0Þ 1⁄4 a þ cjDijj þ d [0] Zij þ l [0] V jþeij; ð3aÞ K ijð1Þ� K ijð0Þ 1⁄4 a þ cDij þ gD [2] ij [þ] [d][0][Z][ij][ þ] [l][0][V] [j][þ][e][ij][;] ð3bÞ where Zij is a vector of characteristics of untrained farmer i from village j which may affect knowledge acquisition aside from interactions with the trained farmers, and Vj is a vector of village characteristics which can affect changes in knowledge of residents of that village. The parameter a is a constant, while the parameters k, g, d, and l are associated with the various explanatory variables. The term eij is a stochastic error. Eqn. (3a) is associated with hypothesis (a), while Eqn. (3b) is associated with hypotheses (b) and (c).

6. EMPIRICAL SPECIFICATION AND ANALYSIS (a) Factors affecting the selection of participants in IPM training The parameters of Eqn. (1) can be estimated as a probit, where, while the probability of selection is not observed, we define a dummy variable taking the value 1 when the data pertain to a farmer selected for training, and zero otherwise. The variables comprising the vector X are suggested by the literature cited in Section 2, and include the following: (i) Total rice land owned in 1991: The area of land owned is an indicator of wealth, and is related to status. Therefore, this indicator is expected to increase the probability of selection for training.
(ii) Area of unirrigated rice land owned in 1991: While holding total area owned constant, larger amounts of unirrigated land are negatively related to wealth and status and, therefore, will affect negatively the probability of selection.
(iii) Spending on pesticide per hectare of rice in 1991 (measured in natural logarithm): The volume of spending on pesticides is related to wealth, as well as to the potential interest


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THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1293 in IPM. The probability of selection is, therefore, expected to be positively related to this indicator.
(iv) Number of household members in 1991: Larger households imply lower land per person (holding land area constant), and thus less wealth per capita, and lower status. It is, therefore, expected that household size will be negatively related to the probability of selection for training.
(v) Rice yield per hectare in 1991 (measured in natural logarithm): The yield is taken as an indicator related to farming skills. Higher yields are, therefore, expected to contribute positively to the probability of selection.
(vi) Education of household head in 1991: Farmers’ education has been shown to positively affect their productivity (Jamison & Lau, 1982) and is, therefore, another indicator of farming skills and status affecting the probability of selection positively.
(vii) Highest education level in the household in 1991: Families with more highly educated members are typically of higher status even if the selected farmer himself is not the most educated in the household.
(viii) Score on knowledge of IPM in 1991: The level of familiarity with IPM concepts is an indicator of a farmer’s knowledge and skill in farming, and it is hypothesized to affect the probability of selection positively.
Because all variables reflected values in 1991, prior to the actual training (which took place in the 7 years after 1991), there is no endogeneity in the values observed. The means and standard deviations of the variables used in the analysis of the probability of selection are displayed in Table 1.
While the means of the two groups are not statistically different due to large standard er rors, the data suggest that farmers selected for training tended to be larger landowners, with more irrigated land, higher spending on pesticides, higher yields, from families with more formal education, and better initial knowledge of IPM. These characteristics are all consistent with the profile of opinion leaders who are wealthier, better educated, and more skilled in farming. The probit analysis results presented in Table 2 essentially confirm these observations.
All variables have the expected sign and all, except for household size and farmer education, are statistically significant at least at the 10% level. About two-thirds of the observations would be correctly predicted using the estimated parameters (i.e., the sum of the percentage of trained farmers with imputed probability higher than 50%, and untrained farmers with imputed probability of less than 50%).
The parameters presented in Table 2 were used to impute the probabilities of selection for each farmer in the sample. An important issue is whether the factors affecting the selection of farmers into the training program have changed over time, in light of the observations on difficulties in horizontal communications made by early observers such as Van de Fliert (1993).
To examine this hypothesis, the sample of trained farmers was subdivided into two groups: (i) early trained (those who were trained prior to the initiation of the large-scale World-Bank-funded program in 1994), and (ii) those who were trained later, starting in 1994 (there were 57 and 55 farmers, respectively, in the two groups). A probit analysis was applied to the sample of 112 trained farmers, in which those selected later received the value 1, while those selected early received the value zero.
The explanatory variables were those used in Table 1. Variables used in the analysis of the probability of selection for IPM training [a] Variable Unit Untrained farmers Trained farmers Sample size persons 156 112 Total rice land (1991) ha .60 .93 Unirrigated rice area (1991) ha .19 .07 Spending on pesticides ’000 Rp/ha 93 111 Size of household persons 4.7 4.7 Rice yield kg/ha 5757 6116 Education of farmer years 5.1 5.7 Highest education in family years 7.9 9.0 Score on IPM knowledge in 1991 1–6 2.52 2.65 a Training took place after 1991.


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1294 WORLD DEVELOPMENT Table 2. Probit analysis of factors affecting selection of IPM training Variable (1991 values) Estimated parameter [a] Total rice land (ha) .112 [*] (1.93) Unirrigated rice land (ha) �1.054 [***] (2.60) Spending on pesticides .087 [*] (1.77) (1998 ’000 Rp/ha) Size of household (persons) �.067 (1.27) Rice yield (kg/ha) .884 [**] (2.19) Education of farmer (years) .040 (1.51) Highest education .081 [**] (2.15) in family (years) Score on IPM .196 [*] (1.71) knowledge (integer 1–6) Constant �9.232 [***] (2.64) Sample size 268 % Predicted correctly 66% Pseudo-R [2] .11 Log. likelihood �162.99 a Numbers in parentheses are Z values. Standard errors were corrected for clustering in villages.

- Significant at 10%.
** Significant at 5%.
*** Significant at 1%.

Table 2. None of the explanatory variables (nor the constant term) had statistically significant coefficients, implying that there is no significant difference in the actual selection criteria for the program between the earlier and later years.
These probabilities serve as indicators of farmers’ opinion leadership capacity. For each village, the imputed probabilities for the subsample of trained farmers were summed up and divided by the number of trained farmers, yielding an estimate of the average opinion leadership capacity of IPM-trained farmers in that village (P [b] j). [3] Because in four villages the sample included only untrained farmers (no trained farmers from these villages were interviewed, although the training program was implemented in these villages), the subsample of untrained farmers from villages for which there was an estimate of P j was reduced to

[b] 117. For each one of these 117 farmers, the value of Dij was calculated following Eqn. (2).
As explained in Section 5, the Dij values represent the extent of difference between the opinion leadership profile of the 117 untrained farmers and the average opinion leadership profile of the selected farmers from the same village who actually received intensive IPM training. The trained farmers could potentially serve as nodes of knowledge diffusion to the untrained farmers.

(b) Analysis of the role of opinion leadership in IPM knowledge diffusion The diffusion of knowledge about IPM can be inferred by comparing the scores on IPM-related questions in 1991, before the implementation of the training program, and in 1999. By that time, the training program had already been administered to selected farmers in the villages covered under the present study, and the knowledge that the trained farmers gained was expected to diffuse to their neighbors and friends. The data indicate that the mean knowledge score of the 117 nonparticipants increased only slightly during 1991–99 from 2.59 to 2.72.
This figure masks a considerable degree of variation, and further analysis can clarify the role of opinion leadership in the different villages in explaining the variation. However, farmers have various sources of information, and the analysis needs to account for variation among farmers in terms of their exposure to alternative sources of information, as well as differences among them in the effort that they exert to proactively seek information (Feder & Slade, 1984). To account for these differences, the analysis needs to include variables representing such differences, in addition to the variable Dij, which represents the interaction between the untrained farmers and those who have received intensive training.
One set of farmer-specific attributes, which may be related to differences in inclination to search for knowledge, are the indicators used to characterize opinion leadership, as displayed in Table 1. These variables, which are related to wealth, farming skills and education, can be hypothesized to relate also to general proclivity to seek information, regardless of the implementation of the intensive IPM training in the village.
Additional sets of variables, which can characterize the access to other sources of information, are defined at the village (rather than individual) level. These include the following: (i) The number of sales kiosks in the village (1991): Kiosks are points of informal interactions among farmers, where inputs and household goods are bought and information can be exchanged. A larger number of such gathering spots can increase the opportunities for information acquisition.
(ii) Whether there is an active agricultural cooperative in the village (1991): Cooperatives played a role in pesticide distribution, and their employees may have promoted


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THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1295 the use of pesticides in contravention of IPM concepts (Van de Fliert, 1993, pp. 203, 233).
On the other hand, the cooperative office is also a place where farmers meet and information is exchanged. The impact of this indicator on knowledge diffusion is, therefore, uncertain.
(iii) The change in travel time to subdistrict townships, 1991–99: Subdistrict townships are the local market centers where farmers buy and sell products, and where they are exposed to additional sources of information. The longer the travel time to the township, the larger the transaction cost of accessing the market, and the less frequent are the trips to the market. This indicator is, therefore, negatively related to information diffusion.
(iv) Change in availability of Pest Observers in the village: Pest Observers are officials of the Plant Protection Department, assigned to monitor pest attacks and coordinate responses. With the advent of the intensive IPM training program, the number of Pest Observers was increased, and their role as sources of IPM information was enhanced (in fact, many of them functioned also as trainers in Farmer Field Schools). By 1999, each of our sample villages reported the presence of such an Observer. The change in this presence compared to the situation in 1991 is expected to be positively related to the diffusion of IPM knowledge.
(v) Change in the extent of village irrigation: This variable is a proxy to village overall level of wealth, and hence it is also an indicator of other village infrastructure and access to mass media. It is expected, therefore, that this variable is positively related to the diffusion of knowledge.
In addition to these village level variables, the analysis includes dummy variables for the three Java provinces (one of which serves as reference) where study villages are located, to reflect any aggregate differences in infrastructure changes such as transportation and communication that could affect diffusion.
Three different specifications of Eqns. (3a) and (3b) are used in order to verify that the results are not dependent on arbitrary formulations. The first two specifications, two-sided tobit and ordered probit, are standard methods of analyzing dependent variables which are censored and discreet. The dependent variable in the ordered probit specification is a categorical variable where three possible outcomes are de fined: (i) ‘‘no improvement in knowledge score;’’ (ii) ‘‘small improvement in knowledge score (one point);’’ and (iii) ‘‘large improvement in knowledge score (two or more points).’’ One additional specification utilizes a transformation of the knowledge data into a continuous variable, through a logistic specification. The transformation entails conversion of the knowledge scores (which are integers in the range 0–6) into fractions by dividing them into 6 (the maximum number of correct answers). Denoting this fraction by k, one can define the logarithm of the odds ratio ln[k/ (1 - k)], which is a variable in the range (�1, 1). The higher the knowledge score, the larger the odds ratio. The difference in the logarithms of the odds ratio during 1991–99 is similarly in the range (�1, 1). If it is assumed that the logarithm of the odds ratio is dependent linearly on the set of explanatory variables defined above, the analysis can be carried out by means of ordinary least squares. [4] Table 3 presents alternative estimates of Eqn.
(3a), allowing a test of hypothesis (a). As is evident from the results, the parameter associated with the absolute difference between the opinion leadership capacity of the untrained farmers and that of the trained farmers is not significantly different from zero. Therefore, the results provide no support to the hypothesis that farmers tend to learn more from those who are very similar to them. Alternative estimates of Eqn. (3b), designed to test hypotheses (b) and (c), are presented in Table 4.
Both the OLS estimate of the difference in logarithms of odds ratio and the ordered probit estimate confirm hypothesis (c) that both the linear term and the quadratic term are statistically significant. [5] The two-sided tobit estimate is similar in magnitude, except that the quadratic term is not significant even at a 10% level. However, while the standard errors for the ordered probit and the OLS estimate were corrected for clustering of observations at the village level, such a correction is not feasible in the tobit procedure. It is possible, however, that the estimates of the standard errors in the tobit equation are biased upwards in the absence of the clustering correction, as it is observed that most of the test statistics (z and t) in the other equations have higher values. The three alternative estimates yield a fairly similar critical value of Dij beyond which any additional superiority of the opinion leaders detracts from their communication effectiveness: such a critical value maximizes the terms

knowledge data


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1296 WORLD DEVELOPMENT Table 3. Test of the hypothesis that diffusion is faster if opinion leaders are similar to other farmers Variable [a] Two-sided tobit Ordered probit [b] (N = 117) (N = 117) probit [b] OLS of log. odds ratio [b] (N = 117) (N = 117) (N = 117) Absolute difference in opinion �0.062 (0.05) [c] 0.377 (0.52) [d] �0.058 (0.08) [c] leadership capacity jDijj Total rice land (ha) �0.007 (0.05) �0.006 (0.04) �0.005 (0.06) Unirrigated rice area (ha) �0.357 (1.14) �0.335 (0.75) �0.243 (0.85) Spending on pesticides (’000 Rp/ha) �0.013 (0.20) 0.010 (0.18) �0.008 (0.16) Size of household (persons) 0.111 (1.59) 0.103 [**] (1.97) 0.081 [*] (1.71) Rice yield (kg/ha) 0.420 (0.89) 0.009 (0.02) 0.306 (1.22) Education of the farmer (years) �0.012 (0.25) 0.049 (0.91) �0.009 (0.28) Highest years of education (years) 0.016 (0.35) 0.031 (0.75) 0.018 (0.70) No. of sale kiosks (numbers) 0.482 [***] (2.87) 0.274 [**] (2.03) 0.334 [***] (3.13) Cooperative (numbers) 0.342 (0.86) 0.064 (0.18) 0.196 (0.58) Change in travel time to town (time) �0.062 [***] (2.71) �0.040 [**] (2.24) �0.046 [***] (3.81) Change in availability of pest observer (numbers) 0.609 [*] (1.96) 0.447 [*] (1.90) 0.431 [***] (2.95) Change in village irrigation (%) �0.078 (0.16) 0.031 (0.07) �0.019 (0.07) Observations 117 117 117 R [2] /pseudo-R [2] 0.06 0.07 0.18 a Regressions also include constant and dummy variables for province.
b Standard errors adjusted for clustering at village level.
c Numbers in parentheses are t-statistics.
d Numbers in parentheses are Z-statistics.

- Significant at 10%.
** Significant at 5%.
*** Significant at 1%.

Table 4. Test of the hypothesis of diffusion through opinion leaders who are superior to the followers Variable [a] Two-sided tobit Ordered probit [b] (N = 117) (N = 117) probit [b] OLS of log. odds ratio [b] (N = 117) (N = 117) (N = 117) Difference in opinion leadership capacity (Dij) 4.788 [***] (4.28) [c] 4.311 [***] (3.48) [d] 3.503 [***] (4.51) [c] Square of the difference in opinion �4.700 (1.56) �4.998 [**] (2.34) �3.492 [**] (2.20) leadership capacity ðD [2] ij [Þ] Total rice land (ha) 0.145 (1.02) 0.116 (0.97) 0.106 [*] (2.00) Unirrigated rice area (ha) �1.082 [***] (3.26) �0.907 [**] (1.98) �0. 773 [**] (2.67) Spending on pesticides (’000 Rp/ha) 0.104 (1.60) 0.106 [*] (1.83) 0.077 (1.66) Size of household (persons) �0.013 (0.18) �0.002 (0.04) �0.010 (0.21) Rice yield (kg/ha) 1.523 [***] (3.03) 0.925 [*] (1.93) 1.113 [***] (3.72) Education of the farmer (years) 0.030 (0.64) 0.083 (1.59) 0.022 (0.64) Highest years of education (years) 0.101 [**] (2.13) 0.102 [**] (2.20) 0.080 [**] (2.72) No. of sale kiosks (numbers) 0.501 [***] (3.20) 0.346 [**] (2.22) 0.348 [***] (3.78) Cooperative (numbers) 0.423 (1.14) 0.214 (0.59) 0.252 (1.00) Change in travel time to town (time) �0.057 [***] (2.66) �0.043 [**] (2.07) �0.042 [***] (3.26) Change in availability of pest observer (numbers) 0.965 [***] (3.23) 0.819 [**] (2.47) 0.691 [***] (3.77) Change in village irrigation (%) 0.370 (0.79) 0.471 (0.93) .308 (0.81) Observations 117 117 117 R [2] /Pseudo-R [2] 0.11 0.11 0.29 a Regressions also include constant and dummy variables for province.
b Standard errors adjusted for clustering at village level.
c Numbers in parentheses are t-statistics.
d Numbers in parentheses are Z-statistics.

- Significant at 10%.
** Significant at 5%.
*** Significant at 1%.


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THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1297 cDij þ gD [2] ij [, and is calculated as the value of][ D][ij] where the first derivative of the latter term is zero. The parameter estimates of c and g in Table 4 indicate that the critical value is in the range .43–.50, and thus when the leadership capacity index exceeds that range, the ability to diffuse information declines.
The estimation results for other factors contributing to knowledge diffusion are mostly as expected, and quite consistent regardless of the estimation specification. In particular, higher yield levels, larger irrigated areas, and higher education in the household are farmer characteristics significantly related to knowledge gains. Farmers located in villages with more sales kiosks, closer to market towns, and with increased presence of pest protection government officials were more likely to increase their knowledge of IPM, in part through access to alternative sources of information, that is facilitated by these characteristics.

7. CONCLUSIONS AND IMPLICATIONS The results in the preceding section confirm that differences in the opinion leadership capacity of farmers who were trained intensively in IPM field schools explain, among other factors, the variation in IPM knowledge gains among farmers who did not participate directly in the training. The effectiveness of opinion leaders in diffusing knowledge that was specifically targeted to them through intensive training was shown to depend positively on the extent of leaders’ superiority compared to the socioeconomic and farming skill attributes of the would-be followers. However, the results indicate that if the selected opinion leaders are excessively superior to the others in the community, their effectiveness actually diminishes and they may become essentially irrelevant to the diffusion of knowledge beyond a small circle of those higher status individuals who are closely associated with them.
These conclusions have implications for the operations of extension and information programs seeking to spread new knowledge widely across large populations, particularly in areas where mass media sources are not accessible to the majority of the population. Such programs have traditionally focused the direct interactions of change agents (extension work ers, trainers) on selected individuals, as budget and manpower constraints would not allow for direct interaction with every member of the target population. For example, the Training and Visit extension program instructed extension agents to select permanent groups of ‘‘contact farmers’’ for their bi-weekly village visits (Benor & Harrison, 1977). The Farmer Field School training approach focused similarly on selected groups of participants in intensive training. Several other knowledge enhancement programs described in Scarborough et al. (1997) followed similar selection and diffusion strategies.
As is evident from the results of the present analysis, it is possible to err in two opposite directions: (i) selecting diffusion agents who are too ‘‘average’’ to the point that they are not much respected as leaders, and (ii) selecting opinion leaders who are too prominent and atypical to the point that most members of the community do not interact with them, or view their knowledge as being likely irrelevant.
Obviously there are circumstances where programs include representatives of the latter group for political reasons, but the analysis suggests that such representation should not be excessive. Similarly, there may be circumstances where lower-status members of the community who are not currently opinion leaders should be included for purposes of empowerment and development of their potential leadership capacity. In such cases, one needs to recognize that these are the main purposes of such individuals’ inclusion, rather than the diffusion of knowledge.
Striking the right balance in the selection of program participants, as implied by the results of the present paper, is conceptually appealing, but not easy to implement in practice, as a combination of attributes (with unknown aggregation weights) is relevant, and some of the traits may not be readily observed by outsiders and researchers (although often known to members of the communities involved).
Community participation in selecting those to be targeted for roles as opinion leaders can overcome some of the challenges (Valente & Davis, 1999). It is likely that different types of programs may require different types of leaders (e.g., reflecting gender or ethnic aspects), and additional analysis is required to clarify the contextual factors that need to be considered.


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1298 WORLD DEVELOPMENT NOTES

1. A detailed description of the cultural aspects
of communication flows in Indonesia, in the context of the IPM training program, is provided in Winarto (2004).

2. While the pest management questions asked (six in number) cover only part of the pest management training, they are viewed as a good indicator of the IPM knowledge gained in the course of training. This is confirmed by Feder, Murgai, and Quizon (2004) who reported an analysis of the relation between pest management knowledge scores in this dataset and the utilization of chemical pesticides by the farmers. Their results indicated that farmers with higher IPM knowledge scores utilized statistically significantly less pesticides, although the extent of the change was modest.

3. As explained in Appendix B, data limitations compel us to conduct the analysis at the village level, while the ideal case would focus on the hamlet (sub-village) level.
This implies strong assumptions spelled out in Appendix B.

4. One can show that this formulation is consistent with a model whereby the knowledge score is related to the household and village characteristics through a logistic equation K t=6 1⁄4 1=1⁄2e [�][h][�][k][0][Q] �, where h and k are parameters, and Q is the vector of explanatory variables.

5. The possibility of time effects was checked by including an interaction term between the variable Dij and the length of time since training was provided in the village. The parameter of the interaction term was not statistically significant.

REFERENCES Alpert, M., & Anderson, T. (1973). Optimal heterophily and communication effectiveness: Some empirical findings. Journal of Communication, 23(3), 328– 343.
Bandiera, O., & Rasul, I. (2003). Complementarities, social networks, and technology adoption in Mozambique. Unpublished manuscript, London School of Economics.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall.
Benor, D., & Harrison, J. (1977). Agricultural Extension: The Training and Visit System. Washington, DC: World Bank.
Brock, W., & Durlauf, S. (2001). Interaction based Models. In J. Heckman, & E. Leomer (Eds.).
Handbook of econometrics (Vol. 5, pp. 3297–3380).
Amsterdam: North Holland.
Bose, A. B., & Saxena, P. C. (1966). Opinion leaders in a village in Western Rajasthan. Man in India, 46(2), 121–129.
Chatman, E. (1987). Opinion leadership, poverty, and information sharing. RQ, 26(3), 341–353.
Conley, T., & Udry, C. (2003). Learning about new technology: Pineapple in Ghana. Unpublished Manuscript, Yale University.
Feder, G., & Slade, R. (1984). The acquisition of information and the adoption of new technology.
American Journal of Agricultural Economics, 66(3), 312–320.
Feder, G., Murgai, R., & Quizon, J. (2004). The acquisition and diffusion of knowledge: The case of pest management training in farmer field schools, Indonesia. Journal of Agricultural Economics, 55(2), 217–239.
Gibbons, D., de Koninck, R., & Hasan, I. (1980).
Agricultural modernization, poverty and inequality: The distributional impact of the green revolution in regions of Malaysia and Indonesia. Westmead, Farnborough: Teakfield LTD.
Jamison, D., & Lau, L. (1982). Farmer education and farm efficiency. Baltimore, MD: Johns Hopkins University Press.
Kenmore, P. (1997). A perspective on IPM. Center for Information on Low External Input and Sustainable Agriculture, Newsletter, 13(4).
Rogers, E. (1995). Diffusion of innovations (4th ed.). New York, NY: The Free Press.
Rogers, E., & Cartano, D. (1962). Methods of measuring opinion leadership. Public Opinion Quarterly, 26(4), 435–441.
Rogers, E., & Shoemaker, F. (1971). Communication of innovations: A cross cultural approach. New York: The Free Press.
Roling, N., Ascroft, J., & Wa Chege, F. (1976).
Diffusion of innovations and the issue of equity in rural development. Communications Research, 3(2), 155–171.
Scarborough, V., Killough, S., Johnson, D., & Farrington, J. (1997). Farmer-led extension: Concepts and practices. London: Intermediate Technology Publications.
Sen, L. K. (1969). Opinion leadership in India: A study of interpersonal communication in eight villages. Hyderabad: National Institute of Communication Development.
Simpson, B., & Owens, M. (2002). Farmer field schools and the future of agricultural extension in Africa.
Sustainable Development Dimensions, Food and Agriculture Organization, Rome.
Sunding, D., & Zilberman, D. (2001). The agricultural innovation process: Research and technology adoption in changing agricultural sector. In B. Gardner, &


Input:
THE ROLE OF OPINION LEADERS IN THE DIFFUSION 1299 G. Rausser (Eds.). Handbook of agricultural economics (Vol. 1a, pp. 207–226). Amsterdam: North Holland.
Valente, T. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89.
Valente, T., & Davis, R. (1999). Accelerating the diffusion of innovations using opinion leaders.
Annals of the American Academy of Political and Social Science, 566(1), 55–67.
Valente, T., & Foreman, R. (1998). Integration and radiality: Measuring the extent of an individual’s connectedness and reachability in a network. Social Networks, 20(1), 89–105.
Van de Fliert, E. (1993). Integrated pest management: Farmer field schools generate sustainable practices.
A case study in Central Java evaluating IPM training (304 pp). Wageningen Agricultural University Paper No. 93-3. Wageningen, The Netherlands: PUDOC.
Weimann, G. (1994). The influentials. Albany, NY: State University of New York Press.
Wiebers, U. C. (1993). Integrated pest management and pesticide regulation in developing Asia. World Bank Technical Paper No. 211. Asia Technical Department Series, Washington, DC.
Winarto, Y. (1994). Encouraging knowledge exchange: Integrated pest management in Indonesia. In I.
Scoones, & J. Thomson (Eds.). Beyond farmers first: Rural people’s knowledge, agricultural research, and extension practice (pp. 150–154). London: Intermediate Technology Publications.
Winarto, Y. (2004). Seeds of knowledge: The beginning of integrated pest management in Java. New Haven, CT: Yale Southeast Asia Studies, Monograph 53.
World Bank (2000). Implementation completion report for the Indonesia integrated pest management training project, June 2000.

APPENDIX A. CONSTRUCTION OF THE KNOWLEDGE VARIABLE The farmer interviews held in 1991 and in 1999 sought to establish farmers’ familiarity with key elements of the pest management principles promoted in the FFS curriculum.
The questions were not necessarily posed in a direct ‘‘Yes’’ or ‘‘No’’ format, and often involved some preliminary conversation on the topic and clarifications. After the farmer responded to themes brought up by the interviewer, the latter was in a position to judge whether the farmer was ‘‘knowledgeable’’ or not regarding the specific theme.
The six themes that were the specific focus of the interview on pest management were: (i) Whether it is advisable to have a pre-set schedule of spraying against rice pests or to spray at the early stage of plant growth, if insects are observed or if insect damage is noticed. IPM principles suggest that preset spraying schedules, and spraying in reaction to pest or pest damage in the early phases are not useful.
(ii) Whether there are nonchemical measures (biological or mechanical) that can be utilized to minimize the presence of, or vulnerability to, pests. The training establishes a number of nonchemical alternatives.
(iii) Whether the respondent could identify correctly at least 70% of pests from a list of candidates.
(iv) Whether the respondent could identify at least 70% of beneficial insects and living organisms, which actually suppress pests.
(v) Whether the respondent is aware of the benefits of synchronized planting of crops so as to minimize the opportunities for pest damage and proliferation.
(vi) Whether the respondent is aware of the benefits of practicing crop rotations so as to minimize pest resurgence and proliferation.
The number of correct answers relative to the total (i.e., the proportion of correct responses) defines the score of knowledge for each farmer.
Farmers with a higher knowledge score are expected to be less vulnerable to pest attacks or pest damage, and are likely to use less chemical pesticides.

APPENDIX B. DATA LIMITATIONS OF THE ANALYSIS AT THE VILLAGE LEVEL In the Indonesian context, a distinction needs to be drawn between the term village (desa), which is an administrative unit, and hamlet (dusun), which is a social community. A desa typically contains several geographically proximate or adjacent dusuns. The sample was drawn from 22 villages containing 42 hamlets.
In eight of the hamlets, the sample contained only untrained farmers, while in six hamlets the sample contained only trained farmers.
Therefore, while the analysis of the selection of trained farmers pertains to all 22 villages and their 42 hamlets, the analysis of diffusion pertains to 17 villages (and their 29 hamlets) in which the sample contains both trained and untrained farmers. In four villages, there was


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1300 WORLD DEVELOPMENT only one hamlet each in the sample. The number of sampled trained farmers from any given hamlet is in most cases too small (sometimes only two or three) to form a unit of observation for the present paper’s diffusion analysis, as the model requires the calculation of a mean leadership profile for each communication network.
The hamlet is a much more cohesive, social, and communication network than the village, and ideally one would conduct the analysis at the hamlet level. The analysis in the present paper uses the village, rather than the hamlet, as the communication network frame, under the strong assumptions that the social structure in all the hamlets of one village is similar, and the selection process of opinion leaders for training was similar in these hamlets. Under such assumptions, the aggregated samples drawn randomly from the hamlets of a given village are unbiased representations of the social profiles of trained and untrained farmers, within each hamlet of that village, and the estimated average opinion leadership index at the village level represents the type of opinion leadership that an untrained farmer from a hamlet in that village was exposed to.
An analysis that utilized data at the hamlet level (omitting all hamlets where there were only three or less graduates or three or less untrained farmers) yielded results that were qualitatively similar, but because a smaller sample (only 84 untrained farmers compared to 117) could be utilized, the statistical significance of several of the variables was low, although the signs and the magnitudes of the key parameters were similar to that of the analysis at the village level, and the key result of the paper holds. That is, even with the smaller sample, the parameter of Dij in Table 4 is significant, although the parameter of the square of Dij is not significant (t = 1.51), but negative.


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# Growing together or growing apart? A village level study of the impact of the Doha round on rural China

Marijke Kuiper & Frank van Tongeren Agricultural Economics Research Institute (LEI) – Wageningen UR Paper prepared for the 7 [th] Annual Conference on Global Economic Analysis, _Trade, Poverty and the Environment_, Washington D.C. June 17-19, 2004 First draft, May 2004 Abstract Most studies of the opening of the Chinese economy focus on the national level. The few existing more disaggregated analyses are limited to analyzing changes in agricultural production. In this paper we employ an innovative village equilibrium model which accounts for nonseparability of household production and consumption decisions. This allows us to analyze the impact of trade liberalization on household production, consumption and off-farm employment, the interactions among these three aspects of household decisions, as well as the interactions among households in a village economy. Analyzing the impact of trade liberalization we find changes in relative prices of inputs and outputs and outside village employment to have opposite impacts on household decisions. Price changes affect the labor intensity of rice production as households switch between intensive two-season and on-season rice in response to changes in their shadow wages. Outside village employment opportunities induce a less labor intensive rice cropping. The consequent drop in demand for traction services reduces cash income for those households that do not have access to migration and thus economic growth in coastal areas affects them indirectly through the village factor market. In terms of aggregate village agricultural supply response we find the impact of the increase in employment to dominate aggregate response. As a result the marketed surplus of case study village is found to be shifting from rice to livestock.

A gradual integration in the global economy, combined with far-reaching domestic reforms, has led China to be a showcase of attaining rapid economic growth through market reforms. The rapid economic growth during the past decades, however, has been accompanied by an increasing disparity between coastal and interior regions and between rural and urban areas. The coastal cities benefited most from the increasing export opportunities, due to a combination of geographical factors and deliberate policies (Démurger _et al._, 2002).

The extent of reform in combination with the size of the Chinese economy, has resulted in a body of literature with a growth rate rivaling China’s GDP growth. Most studies of the opening of the Chinese 1


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economy to the rest of the world focus at the national level. One exception is a study by Diao _et al._ (2003) on the regional impact of China’s recent WTO accession. As in other national level studies, they find a positive aggregate effect for China as a whole. This aggregate effect obscures differences across regions. Reflecting past trends of diverging growth between coastal and inland provinces (see for example Démurger _et al._, 2002; Jones _et al._, 2003), income gaps between provinces are found to widen following WTO accession. They also find rural-urban migration providing an important mechanism for transmitting urban growth to the rural areas. Especially central provinces bordering the booming coastal provinces (Anhui, Jiangxi, Hubei and Hunan), benefit from an increase in ruralurban migration.

A second exception to the common use of a national level analysis is a study by Huang _et al._ (2003) analyzing the impact of WTO accession on farm households. They find that despite a small positive aggregate impact of the WTO, the distribution of benefits gives cause for concern. Households in richer coastal areas benefit most, having higher yielding lands and cultivating internationally competitive crops. Another interesting finding of their household level analysis is an increase in agricultural production, despite a decrease in the agricultural price index. Farmers respond to the changes in relative prices induced by the WTO by shifting to more competitive activities (livestock, fish, vegetables and rice), resulting in a net increase in agricultural production.

These two studies show that a disaggregated analysis of the impact of trade reform yields insights that cannot be attained from national level studies. Our objective in this paper is to contribute a village level perspective on trade reform in China. Adjustment responses of different household types and within-village interactions are central to this analysis. This study complements earlier studies by zooming in on the differential impact of trade liberalization on households within a village. A new methodology that accounts for family-farm production specifics in a village-economy setting gives us a unique perspective on the impact of trade reform. Going beyond the household level study of Huang _et al_ . (2003) we account in addition for the impact of farm income on rural household consumption.
We furthermore account for the impact of rural-urban migration, thus not limiting the analysis to developments in the agricultural sector.

In this study we combine a macro level analysis of trade reform, with a village level general equilibrium model of a rice-producing village in Jiangxi Province. To be able to combine the macro and village level analysis of trade reform, we need to ascertain how trade reform affects households.
Prices and labor demand form the key transmission mechanisms through which macro level trade reform affects rural households (Winters, 2002). We therefore focus on changes in prices for agricultural inputs and outputs, and on the increased demand for labor by labor-intensive sectors in which China has a comparative advantage.

Our analyses of further trade reform builds on a baseline that encompasses China’s recent WTO accession, phasing out of export quota for textiles under the Agreement on Textiles and Clothing. We use the GTAP model to generate macro-level impacts of further reform under a WTO –Doha round and feed these into the village-level model.

The village equilibrium model incorporates farm-household models, allowing us to analyze the impact of changes in farm production, off-farm employment and household consumption, as well as the interactions among these three components of household decisions. Since households differ in their 2


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endowments and access to off-farm employment, household response to changes in the socioeconomic environment differ.

Analyzing the impact of changing in agricultural input and output prices we find an increased village supply of rice and other livestock, which corresponds with the findings of national level studies and with the results of the household analysis in Huang _et al._ (2003). This increased supply of rice, however, is the net result of two household groups increasing rice production, and two household groups reducing rice production. Analyzing the impact of an increase in outside village employment following trade liberalization, we find it matters whether economic growth is limited to the coastal areas or spread more evenly throughout the country because of differential access to outside province migration. The net impact of an evenly spreads increase in employment on supply response is a decrease in rice supply, caused by an increasing scarcity of labor. Again we find diverging household response, with some households increasing rice production due to lower costs of animal traction rented within the village. The combined impact of price and employment changes following trade liberalization is a decrease in rice supply from the village. In terms of the village supply response, the impact of the increased employment thus dominates the impact of the change in prices.

When analyzing the income effects of trade liberalization we find it matters considerably whether we account for reduced household consumption due to migrants. Ignoring the consumption of migrants, trade liberalization would reduce within-village income inequality; accounting for the consumption impact we find that trade liberalization worsens within-village income distribution.

This paper is structured as follows. We start by providing some background on non-separability of household decisions and how this affects the structure of the village equilibrium model. The second section discusses the data and models used in this study. The third section focuses on the impact of price changes, employment and the combination of these two aspects of trade liberalization on household agricultural production decisions. Section four then analyzes the aggregate village supply response as well as the income and welfare impact of trade liberalization. Section five concludes.

1 A household perspective on general equilibrium modeling As illustrated above, a disaggregated perspective can lead to new insights in the impact of trade reform. Analyzing the household level impact of trade reform, the study of Huang _et al._ (2003) comes closest to the approach taken in this study. Due to data limitations their analysis is limited to analyzing changes in agricultural production, ignoring both changes in household consumption decisions and off-farm employment. Separating farm production decisions from household consumption decisions is standard in general equilibrium models, both macro as well as existing village general equilibrium models. Ignoring the interdependency of household production and consumption decisions, however, can be misleading when market imperfections render these two aspects of household decisions nonseparable.

Nonseparability of household production and consumption decisions occurs when the effective price of a commodity used both in production and consumption is not exogenous to the household, but is determined endogenously by household demand and supply. In this case decisions at the production 3


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side will affect demand and supply of the commodity, which affects consumption decisions, and vice versa. Such nonseparability occurs if households are not price-takers in a market, if markets are missing, or if there is a gap between buying and selling prices (Löfgren and Robinson, 1999). The seminal work of de Janvry, Fafchamps and Sadoulet (1991) shows how rational behavior of farmers in combination with market failures may give rise to sluggish or counterintuitive household responses.

To illustrate a number of points regarding nonseparable household models a simple model is postulated. The household is assumed to maximize utility derived from the consumption of an agricultural commodity _Ca_ ( _e.g._ a food crop produced by the household) and leisure _Cl_ . The household faces a production technology producing the food crop ( _Qa_ ) with family labor ( _Lf_ ), hired labor ( _Lh_ ), and a fixed input ( ~~_q_~~ ). The household may also work off-farm ( _Lo_ ), but total labor use cannot exceed the family time-endowment ( _T_ ). The cash constraint provides a third constraint on utility maximization; goods can only be bought if money is earned. In this simple model without savings and transfers this constraint translates to requiring the inflow of money to equal to the outflow.

This very basic household model can mathematically be described as: max _u_ ( _Ca_, _Cl_ ) utility function (1) _Ca_, _Cl_, _L_ _f_, _Lh_ subject to, _Qa_ [=] _f_ ( _L_ _f_, _Lh_, ~~_q_~~ ) production function (2a) _L_ _f_ + _Lo_ + _Cl_ = _T_ time constraint (2b) _pQa_ + _wLo_ = _pCa_ + _wLh_ cash constraint (2c) where, in addition to the variables defined before, _p_ denotes the price of the food crop and _w_ the wage rate. The three constraints may be collapsed into a single full income constraint, by substituting the production function and time constraint in the cash constraint, and rearranging: _p_ [ _f_ ( _L_ _f_, _Lh_, _q_ ) - _Ca_ ] = _w_ [ _Lh_ - ( _T_ - _L_ _f_ - _Cl_ )] full income constraint (2) Maximization of the utility function (1) subject to the full income constraint (2) yields input demand, output supply and consumption decisions of the household.

In the mathematical description the interaction between production and consumption decisions is not immediately obvious. Due to its simplicity the model can also be solved graphically, which does allow a direct view on (non-)separability of household decisions (Taylor and Adelman, 2003). The mathematical description assumes perfect markets for food and labor, which can be purchased and sold at fixed prices _p_ and _w_ . No distinction is made between own produced and purchased food, nor between family and hired labor. Due to the fixed prices, production and consumption decisions can be separated, as is illustrated in Figure 1.

The household faces a trade-off between producing food crop and leisure, depicted by the production possibility frontier (PPF). The optimal production level with perfect markets (PM [P] ) is at the point where the slope of the PPF equals the price-ratio between labor and food. Optimal consumption with perfect markets (PM [C] ) is at the point where the marginal rate of substitution equals the price ratio.
These are standard micro-economic results from producer and consumer theory, reflecting the 4

production possibility frontier


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neoclassical nature of the model. The graphical representation illustrates that first the production problem is solved (moving the line representing the price-ratio until it just touches the PPF), after which the solution to the consumption problem is found (finding the highest indifference curve (I) still touching the relative price line).

**Food crop**
( _Qa_, _Ca_ ) _Qapm_ _Qanm_ = _Canm_ _Capm_

**PM** **[P]**

**NM**

**PM** **[C]**

**I** _**[pm]**_

**I** _**[nm]**_

_-w/p_ _Clnm_ _Clpm_ _Lpm_ = _Lhpm_ + _T_ - _Clpm_

- ω / ρ

**PPF**

_T_ **Leisure** ( _Cl_ ) Source: adapted from Taylor and Adelman (2003)

**Figure 1:** **Household production and consumption decisions with perfect markets (pm) and no markets (nm)**
**for food and labor**

The opposite extreme from perfect markets is an absence of all markets. In this case prices are no longer given, but determined by the interplay of production and consumption decisions. In terms of Figure 1 this implies finding the point (NM) where the indifference curve I [nm] touches upon the PPF.
In this point the household consumes all the output it produces, and the amount of labor it uses equals the time-constraint minus leisure. In this case the prices of food and labor are no longer exogenous.
Instead they are endogenous shadow prices ( ω and ρ) that cannot be observed in the absence of trade 5


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with the outside world. The graphical representation illustrates the nonseparability: the optimal point is now found by simultaneously dealing with consumption decisions represented by the indifference curve and production decisions represented by the production possibility frontier.

This basic household model and its graphical solution illustrate a number of points that are essential to farm household modeling. First, standard economic rules for production and consumption remain valid. Differences with a separate analysis of production and consumption decisions occur because of endogenous prices, not because of different behavior by the household. This result implies that standard approaches to modeling production and consumption decisions may be also followed.
Although the analytical framework remains the same, endogenous household prices complicate the development of an applied model. The household shadow prices are an analytical construct and thus cannot be directly observed. This complicates the estimation of demand and supply functions for nonseparable household models.

Second, household models tend to generate ambiguous result and easily become analytically intractable. Ambiguous results may already occur with perfect markets. In the graphical example given above, assume that prices for food increase The price-ratio line will flatten and the point of tangency to the PPF will be at a higher level of food production. The increase in production and prices increases income of the household. The household will be able to shift to a higher indifference curve and increases its food consumption. The increase in food consumption may outweigh the increase in food production, depending on the preferences of the household. The food price increase then does not lead to an unequivocal increase in marketed surplus. If an analytical solution of the household model can be derived, it will generally be difficult to sign because of counteracting effects on the production and consumption sides of the household. In models with multiple missing markets interdependencies between variables will prevent derivation of an analytical solution. Problems in deriving an analytical solution and ambiguous results of analytical solutions, if they can be derived, are important reasons for the development of quantitative household models.

Third, accounting for different levels of market integration of households has clear policy relevance.
As the graphical solution in Figure 1 illustrates, integration in markets increases the welfare of the household. Apart from the welfare implications for the household, interaction between producer and consumer decisions may result in unexpected results. For example, if the objective of a food price policy is to increase the marketed surplus, limited market integration of households may yield adverse results. Household models help in understanding counterintuitive response to policy changes, as well as identifying possible complementary policies that promote the desired response.

Nonseparability has become an important feature of household models, but is absent in existing general equilibrium models. A study by Löfgren and Robinson (1999) provides a stylized application of including farm-household models in a general equilibrium model, but this approach has not yet been implemented in an empirical analysis.

Given the recent transformations in China, markets are still developing and imperfections can be expected to be abound. Studies of factors influencing migration decisions (Hare, 1999; Murphy, 2000; Rozelle _et al._, 1999a; Rozelle _et al._, 1999b) and of patterns in inequality (Benjamin and Brandt, 1999), refer to imperfect land, labor and credit markets as being relevant in the Chinese context. Such 6


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a partial integration in markets may give rise to nonseparability of household decisions, or may create (thin) local markets through which household decisions affect each other.

If households interact with each other in local village markets, and these markets are not integrated with markets outside of the village, local general equilibrium effects occur. Studies of market integration in China find villages to be integrated in markets for major outputs (Huang _et al._, 2003) and for fertilizer (Qiao _et al._, 2003). While villages may thus be assumed to be integrated in agricultural input and output markets, integration of factor markets is limited. Labor markets are highly segmented (Gilbert and Wahl, 2003), resulting in a rural labor surplus (Cook, 1999). Village labor markets, however, can be expected to be limited. Due to the collective ownership of land, all households have access to land. Lacking landless households and generally having similar agricultural production patterns, there is little scope for local labor markets.

While limiting the prospect for local labor markets, collective ownership of land results in village land markets. Land rights remain ambiguous, in spite of land tenure reforms that have granted household user rights for 30 years, and recently were changed to permit inheritance (Huang and Rozelle, 2004).
Land is allocated on the basis of demographic criteria, and readjustments occur to adjust for changes in household size, despite formal household user rights. The result is an ambiguous land tenure situation (Ho, 2001) in which households have an incentive to keep their land cultivated to avoid loosing it during a next readjustment. Households that migrate to urban areas therefore rent their land to other households, to maintain their claim to the land in case they are unable to secure a living in the urban areas. Given the ambiguity of land tenure, land rental markets are restricted to villagers, resulting in local land markets.

Village interactions may also arise through informal credit markets. Government intervention in the formal banking sector remains strong. Regulated interest rates are well-below market clearing levels, while soft loans to state enterprises seize a large share of available funds. Rural households are thus rationed out of credit markets. In the late 1980s, rural cooperative funds developed, targeted at rural households. These funds proofed too successful competitors of existing rural credit cooperatives and were dissolved in 1999 (Park _et al._, 2003). As a result of the lack of formal credit options, household have to rely on loans from friends and relatives, generating local informal credit markets.

Despite the integration in agricultural input and output markets, local markets for land and capital are thus expected to exist, and imperfect labor markets are expected to result in nonseparability of household production and consumption decisions. In this study we therefore use a village level general equilibrium model to account for the interactions among households in local markets, while paying due attention to nonseparability of household decisions.

Taylor and Adelman (1996) pioneered the use of general equilibrium models at village level. Their model closely follows the structure of macro level models, for example by modeling production at sector level, which misses the impact of nonseparability of household decisions. We take a different approach to village modeling, placing differences in household response due to nonseparability at the center of the model. This household perspective on general equilibrium modeling results in a different model structure than used in macro level general equilibrium models and in existing village level models. We model production activities as being household-specific. This allows for idiosyncratic household response consistent with nonseparability of production and consumption decisions. A 7


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second major difference is the nesting structure used for modeling production decisions. For each activity the structure is calibrated on the household survey data [1] . As a result we have household specific production functions, capturing differences in household access to inputs [2] .

By placing households at the center of the model, the village model used in this study is able to capture differences in production decisions reflecting differences in access to the inputs, interactions between household production and consumption decisions, and interactions among different households within a village economy.

2 Models and data 2.1 Macro level analysis The macro economic analyses are performed using the GTAP model, with some important modifications to the version 5 database. Van Tongeren and Huang (2004) describes the changes made and the baseline simulation which is the reference for the scenarios used in the present study.
Specifically, we have updated policy information on China from the original version 5 GTAP database to include recent changes in domestic policies and changes in border policies. In addition we have adjusted the Chinese input-output table to incorporate microdata information on primary factor shares. We also made adjustments to the data on livestock production and – consumption. Using macro-economic information, and incorporating known imminent policy changes, we project the database forward in time to the year 2010 and beyond. The policy changes along the baseline include the full implementation of China’s WTO accession and the phasing out of export quota under the Agreement on Textiles and Clothing. This baseline is the reference for simulations to assess the effects of further trade liberalization under the WTO Doha round.

Our experiment reduces multilaterally the support to agriculture grouped under the three classical pillars, market access, export competition and domestic support, and it reduces border distortions in non-agricultural sectors. The simulation for agriculture follows closely the original modalities proposal tabled in 2002 by the European Commission (European Commission, 2002). This proposal contained a certain asymmetry between the treatment of OECD countries and developing countries.
Specifically, we model a reduction of import tariffs by –36% for trade amongst OECD countries and total elimination of import barriers against LDCs, while LDCs themselves are not reducing their import barriers. In the area of export competition, we reduce the value of subsidies by -45% in OECD countries only, and eliminate fully the export subsidies on wheat and oilseeds. In the area of domestic support, the AMS ceiling is lowered by -55%, but the blue box payments are maintained. All reductions are made from the final Uruguay Round commitments. For Non-agricultural Market access, we apply a straight –50% reduction of applied rates. It should be noted that this simulation is illustrative for the possible effects of a successful round. Within the spectrum of proposals offered prior to the ministerial meeting in Cancun, September 2003, the EC proposal is rather conservative.
An assessment of the more far-reaching proposals tabled by the CAIRNS group and the US are discussed in Van Tongeren and van Meijl (2004).

1 This calibration also rejected the commonly assumed separability of factors and intermediate inputs, and this assumption was therefore dropped in the village equilibrium model.

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version 5 database

GTAP database

Chinese input-output table

household survey data


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The main findings for China from the Doha simulation can be summarized as follows. Processed food, labor intensive horticulture and textiles obtain the highest production gain. In these sectors, China has already a comparative advantage, and exports are an important part of total demand. For processed food tariffs cuts are relatively high and exports are important. We observe a modest output growth in rice. For textiles tariff cuts are modest but exports are very important, so export growth leads to growth production. We also observe an increase in livestock output.

While exports are estimated to grow in some products, the further reduction of remaining import barriers also enables China to import more products. Imports in such commodities as coarse grains, sugar, cotton, dairy and intensive livestock products are expected to rise.

Table 1: Shocks administered to village model (% change with respect to base) _Trade_ _Price changes_ _Employment_ _liberalization_
**Agricultural outputs** One season rice 2 - 2
Two season rice 2 - 2 Other crops 2 - 2 Cattle 2 - 2 Pigs 2 - 2 Other livestock 2 - 2
**Agricultural inputs** Fertilizer 0 - 0
Herbicides 0 - 0 Pesticides 0 - 0 Seed 2 - 2 Purchased feed 2 - 2 Other inputs 1 - 1
**Consumption goods** Food 2 - 2
Processed food 2 - 2 Nonfood 1 - 1 Durables 1 - 1 Other expenditures 1 - 1

**Wages** Non-agricultural employment - 3 3
Migration, inside province - 3 3 Migration, outside province - 3 3
**Outside employment** Non-agricultural employment - 9 9
Migration, inside province - 9 9 Migration, outside province - 7 7 It can be concluded that China can gain a lot from a new WTO round, but most from liberalization in manufacturing. Around 60% of the potential income gains can be attributed to improved market access for manufacturing goods into OECD countries. Labor intensive manufacturing and food processing industries have the potential to absorb labor and generate incomes in activities outside 2 Detailed description of the village equilibrium model and calibration procedures can be obtained from the 9

Doha simulation