ࡱ>  ;=0123456789:q` ~bjbjqPqP 2::  ( RĘ8vlNr.2F MMMMMMM$PhR7M {{{7M MIII{F  MI{MIIV  0c\E ,HM<NQxSSS R0I, [RRR7M7M3RRRN{{{{3 FN    Agricultural extension system reform and agent time allocation in China* Ruifa HUa, b, (, Zhijian YANGa, b, c, Peter KELLYd, Jikun HUANGa, b a Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Jia 11, Datun Road Beijing 100101, China b Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Jia 11 Datun Road, Beijing 100101, China c Graduate University of Chinese Academy of Sciences, Beijing, 100039, China d University of California, Berkeley, Department of Agricultural and Resource Economics ( Corresponding author. Tel: 86-10-64889836Fax86-10-64856533 E-mail address:  HYPERLINK "mailto:rhu.ccap@igsnrr.ac.cn" rhu.ccap@igsnrr.ac.cn (R. Hu). Abstract We conducted a nationally representative survey to measure the impact of Chinas institutional reforms in public agricultural extension on the time allocation of its one million agricultural extension agents. We found that Chinese agents spent much less time than their titles would suggest on providing agricultural extension services, and that agents whose base salaries were funded fully or partially by commercial activities spent substantially less time serving farmers. The institutional incentives associated with the source of funding have a much larger effect on agent time allocation than do the levels of funding. We conclude that the recent government policy to separate commercial activities from extension services is a step in the right direction and should be expanded. The results also suggest that, at least for agricultural extension, the goal of many national governments and international donors to develop locally financing institutions to sustain development projects may be misguided. 1. Introduction A major ideological debate in development centers focuses on whether the provision of local public goods in developing countries should be made financially self-sustaining. Should external international or national-government donors indefinitely fund activities that generate positive externalities, or can these activities be funded more effectively by establishing institutions that rely on local cost-recovery? Replacing dependency with self-sufficiency is attractive across the ideological spectrum, but empirical evidence from a number of contexts suggests that local financing may be ineffective in delivering public goods. For example, Kremer and Miguel (2007) observed that cost recovery measures for de-worming drugs in Kenya reduced uptake by 80%, and Meuwissen (2002) found disappointing results from cost recovery for health services in Niger. Morduch (1999) noted that microfinance institutions, when asked to be self-sustaining, focused less on the poor. Few studies of local financing institutions have focused specifically on agriculture, which has been newly recognized as crucial to development and poverty alleviation (World Bank 2007), or on China, where fiscal responsibilities have been decentralized on a massive scale (Zhang 2006). A few papers (e.g., Anderson and Feder, 2004) do discuss the role of private contractors or fee-for-service in agricultural extension, a crucial institution for farmers. However, to our knowledge none have used an econometric analysis of primary survey data, and none have used data from a transition economy, as we do in this paper. Reforms of Chinas agricultural extension system represent one of the largest-scale examples of a shift to local financing of public goods in a developing country. In 1985, Chinas central government encouraged public agricultural extension (PAES) stations to earn their own income through commercial activities (Wang, 1994; Lu, 1999), in order to cover their budgets and offer appropriate input technologies. In the early 1990s, the Chinese government formalized the commercial reforms by classifying agents by their source of funding: fully funded agents (government payroll), partially funded agents (government pays part of base salary), and self-funded agents (base salary comes from commercial activities and grants). Counties had flexibility in how to implement the reforms, and in some counties that had been less able to finance agricultural extension, all of the stations took at least some agents off of the government payroll. Cuts in funding for the PAES have affected the day-to-day operations of the system (Ke et al., 2005; Hu et al., 2004). Several studies have seen greatly reduced services since the early 1990s (Hu et al., 2004; Qiao et al., 1999). In this paper, we examine the effects of financing institutions on the time allocation of Chinas approximately one million agricultural extension agents. We find that agents on average spend only 81 days per year in the field and doing extension work. In particular, those whose salaries are financed through commercial activities (or by grants) spend much less time in the field serving farmers than those who are on a government payroll. The institutional incentives associated with the source of financing appear to be crucial -- increasing the total amount of financing improves service provision only slightly, with every 1,000 yuan (11%) annual increase in financing only increasing time in the field and doing extension work by 0.19%, but that agents who are on a government payroll spend more time in the field providing agricultural extension services (AES) than those whose salaries are completely funded by commercial activities and grants. 2. Agricultural extension in China The Chinese government re-established its PAES at the end of the 1970s. By the end of the 1980s, the system employed an extension staff of more than one million (Zang, 1989); about 70% had graduated from technical high schools or colleges (Zang, 1989; Lu, 1999). More than 90% of them worked at PAES stations at the county and township levels, with most agents at the township level (Table 1). By the mid-1980s, China had established stations in every rural county and township, even in remote regions, and this large system provided high-quality AES. The PAES stations became overstaffed, in part because of the proliferation of specialized stations (Huang et al., 2000), creating a financial burden for local governments (Hu et al., 2004). To improve efficiency, many counties restructured their PAES stations by merging different stations and establishing new agricultural extension service centers. For example, most counties have merged their crop-management technology station, plant protection station, and soil- and fertilizer-technology station to establish a single crop-technology extension service center (ESC). Apart from locally initiated mergers, Chinas central government has also carried out a series of agricultural extension reforms since the mid-late 1980s. These reforms have included a decentralization of administrative responsibilities from counties to townships, as well as the commercial reforms that are the focus of this paper. On a macro level, Chinas public agricultural research and extension system has been key to one of the nations great achievementsrapidly increasing agricultural production and feeding the worlds largest population with limited land and water resources (Huang et al. 2003; Fan and Pardey 1992). The Chinese Academy of Agricultural Sciences found that technology accounted for at least 40% of the increase in Chinas agricultural production between 1978 and 1995 (Zhu 1997), and independent researchers have confirmed that technology has been crucial to total factor productivity growth in China (Huang et al. 2000; Fan and Pardey 1992; Jin et al. 2002). However, Chinas agricultural extension system suffers from a variety of problems related directly or indirectly to poor funding and training, as well as incentive and mission problems typical of government bureaucracy internationally. Public agricultural extension in China receives little central government funding, less than 10% of the total extension budget (Huang et al. 2000; Fock 2003; Huang et al 2003). Except at a handful of national extension centers, most funding comes from county and township governments, and is often highly inadequate. Our 2003 survey found that 77% of stations had no project grants, and 25% were sometimes unable to pay extension agents on time. Those that did have project grants often diverted them to pay regular salaries. Many stations relied heavily on commercial activities for income, which accounted for an average of 35% of revenues at township stations and 20% at county stations. Total annual government funding averaged only 6136 yuan per extension agent at township stations and 13467 per agent at county stations. Chinas million-plus agricultural extension agents are poorly trained as well as poorly funded. In our survey, 68% of county-level stations had not hired a single agent with a B.S. degree or higher between 1996 and 2002, and only 6 out of 1245 agents we interviewed had attended a short course. Agents are frequently called on for administrative duties that have nothing to do with agricultural extension, including family planning, budget management, elections, fire protection, legal matters, and so on, especially at the township level. (Given the ever-changing nature of central government mandates, specific duties outside of agricultural extension can be idiosyncratic at any particular time, including everything from studying ideology after a major political event to combating a disease outbreak.) They also engage in commercial activities to raise funds for the station. According to our survey, in 2002, extension agents spent an average of only 81 days per year actually providing agricultural extension services. Commercial activities at extension stations affect the quality as well as quantity of extension service. Many agents have become salespeople, promoting greater use of expensive marketable technologies like seeds, fertilizers, pesticides, and machinery than what farmers really need (Huang et al., 2001). Private business owners complain that extension stations compete directly with them and use public funds to cover overhead costs. More importantly, agents provide few public goods, such as information about farm management or conservation. Our interviews revealed that the commercial reforms have not aligned the interests of extension agents and farmers. As was the case before the commercial reforms, agents tend to be more concerned about satisfying their superiors than about serving farmers, and it remains very difficult for these superiors to observe the performance of agents in the field. Information still flows primarily from top to bottom, as opposed to involving farmers in a modern participatory process. The difference is that now agents are evaluated more on the basis of raising funds for the local government, and less on providing quality service. Commercial activities at extension stations can also conflict with and weaken the regulatory oversight role of local agricultural bureaus. 3. Survey design Our data come from a nationwide survey conducted by the authors at the end of 2002 and in early 2003. Stations were chosen according to a multi-stage cluster design with regionally stratified random sampling. From each of Chinas seven major geographic regions (northeast, northwest, north, east, south, central, and southwest), we randomly selected one province. All of the counties in each sampled province were divided into two groups according to farmers per capita net income, and two counties were randomly selected from each group. Finally, all townships in the sampled counties were divided into three groups based on farmers per capita net income, and one township was randomly selected from each group. In each county/township, we randomly selected three stations (or all stations where there were fewer than three). The sample includes 7 provinces, 28 counties, 84 townships, and 363 extension stations (198 at the county level and 165 at the township level). We asked the station leader (or deputy leader) to participate in the survey, as well as a randomly selected one-third of the agents at each agricultural extension station in the sampled counties and townships. In total, 1,245 extension staff members (45 per county on average) were interviewed, including 423 station leaders (239 at the county level and 184 at the township level) and 822 agents (531 at the county level and 291 at the township level). The interviews included questions about personal characteristics and time allocation. We asked staff members how many days in 2002 they spent on administration, providing AES, commercial activities; and non-work activities (days they did not go to work). Most administrative days were spent on duties unrelated to agricultural extension; most days spent providing AES were xiaxiang, or going to the field, but we also counted days spent providing or receiving AES training under providing AES. We also collected information on government investments (specifically, operational budgets and project grant funding); on staff size; on the types of funding (full, partial, or self), on whether township governments had been granted the three rights (personnel, financial and asset management); and on other station characteristics (such as specific station policies and regulations). 4. Descriptive statistics We found that funding levels per agent at Chinas agricultural extension stations were low, and that government-funded operating expenses represented the largest share of station budgets. In 2002, agricultural extension stations at the township and county levels had an average budget per agent (inclusive of salaries and extension expenditures) of only 14,304 yuan (Table 2), with county-level stations only a bit higher at 16,496 yuan. This included 11,197 yuan of government funding (13,467 yuan for county stations and 6,136 for township stations), 78% of the total. Income from commercial activities averaged 3,107 yuan (3,029 at the county level and 3,280 at the township level), accounting for 22% of the total. Of the government funding, an average of 8,990 yuan funded the operating budget (OB) and 2,031 yuan funded project grants (PG), 63% and 14% of total funding, or 80% and 18% of government funding, respectively. Agents spent a relatively small share of their time offering AES in 2002 (Table 3). Agents averaged 273 working days per year, or slightly more than 5 days per week after vacations, sick days, and other leave. In most counties, the hours worked by extension agents were comparable to those worked by other local government employees. However, extension agents spent only 135 days per year, or 49% of their working days, on administrative work. Most of this administrative work was unrelated to agricultural extension, particularly for township-level extension agents. Commercial activities consumed an additional 56 days per year (21% of working days). This left only 85 days (31% of working days) for providing AES. Extension agents, whose title suggests that their primary duty is to provide agricultural extension services to farmers, in fact spent less than 1/3 of their working time doing so. In contrast, in the 1980s, agents spent almost all of their time on extension-related activities (Authors survey). Supervisory time in the office cannot explain the limited time that agents spend in the field providing AES. In fact, more senior and better educated agents spend more time providing AES and less time on administration and commercial activities. Our survey found that time spent providing AES increased with seniority, from 71 days for junior staff, to 94 days for mid-level staff, to 107 days for senior staff. Likewise, commercial activities decreased from 63, to 50, to 29 days, respectively. More highly educated staff members also spent more time on AES than did less educated staff members. Time spent on delivering AES increased from 73 days for those with a secondary vocational-school or lower education, to 86 days for those with two to three years of technical college, to 95 days for those with a BS degree or above. Time spent on commercial activities decreased with education, from 75, to 41, to 34 days, respectively. Time spent on AES increased with staff position, from 70 days for ordinary agents, to 95 days for station leaders, though it was slightly lower for ESC leaders (88 days). But station leaders spent nearly the same time on commercial activities (57 days) as did other agents (60 days), or actually slightly higher as a percentage of working days. Both station leaders and ordinary agents spend much longer on commercial activities than county agricultural bureaus customarily plan for. Time allocation differed greatly for agricultural agents at different administrative levels (Table 3). County-level agents spent 86 days providing AES, compared to only 73 days for township-level agents. County-level agents spent only 39 days on commercial activities, far less than did the township agents. This illustrates one of the problems resulting from the reform of agricultural extension stations: the township agents, who should be working most closely with farmers actually spent less time providing AES. This is the main reason why farmers often complained that they had not seen an extension agent for many years (Shi et al., 2003). Fully or partially funded agents spent more time offering AES (85 and 87 days, respectively) than did self-funded agents (58 days) (Table 3). In practice, stations where all of the agents were self-funded were operating virtually as private enterprises, in which the agents spent an average of 190 days on commercial activities. The comparable figures for the fully funded and partially funded agents were 25 and 64 days, respectively. Administrative time also varied: The fully funded agents spent more time on administration (160 days) than did either partially funded (116 days) or self-funded agents (48 days). Thus, based on prevailing institutional practices, fully funded agents spent more time on administration, but about the same time on delivering AES as did partially funded agents. Government investment in agricultural extension funds the OB (core funding), PG, and capital construction. OB funding is used primarily for staff salaries, office expenses, and routine extension activities; PG funding is used for special technical extension projects. Table 3 suggests that government investment is significantly associated with agents time allocations. As per capita OB and per capita PG funding increase, agents allocate more time to AES and to administration, and less time to commercial activities. For example, when per capita OB funding was less than 5,000 yuan, agents spent only 74 days on AES and 95 days on administration, but 115 days on commercial activities. When per capita OB funding was more than 22,000 yuan, agents spent 98 days on providing AES and 166 days on administration, but only 2 days on commercial activities. It should be noted that when per capita OB funding increased from 5,000 yuan to 22,000 yuan, the time agents spent on offering AES increased only 6 percentage points (from 20% to 26% of total time). The time spent on commercial activities decreased 32 percentage points (from 32% to less than 0.5% of total time), and the time on administration increased 19 percentage points (from 26% to 46% of total time). Thus, based on the prevailing institutional practices, increasing government investment did not effectively stimulate agents to spend more time bringing AES to farmers; it mainly induced agents to shift from commercial activities to administration. The same pattern can be seen in the per capita PG funding. Of the 1,245 extension staff we surveyed, 953 worked at stations with no PG funding; these agents spent 77 days delivering AES, 137 days in the office, and 60 days on commercial activities. When per capita PG funding was more than 5,000 yuan, agents spent 100 days on AES, 142 days on administration, and 22 days on commercial activities. Raising either the OB or PG funding decreased the time spent on commercial activities, though increasing PG funding may be more effective at directing the time savings toward AES. 5. Model and variable description The administrative process that determines how much time agents spend providing AES is complex and not directly observable, so we used a reduced-form model to measure the determinants of time allocation and to isolate those of most interest. To measure the impacts of government investment and reforms on the time agents spend offering AES, we propose the following agent-time-allocation model. Proportion of time spent on AES delivery = f (government investment, institutional structure and reforms, agents personal characteristics, regional characteristics) The following variables represent the concepts of this model: AES delivery time: proportion of time spent on providing technical advice to farmers and on conducting demonstrations and managing experimental field sites. This includes time in the field, as well as time spent providing and receiving training. Government investment (continuous variables): per capita OB (Operating Budget funding) and per capita PG (Project Grants funding). Institutional structure and reforms: administrative level of the station (0=county-level, 1=township-level) and type of funding (dummy variables for partially and self-funded agents, with fully funded as the omitted category). Personal characteristics: Age; years of employment; sex (0=male, 1=female); leadership (two dummy variables for ESC leader/deputy leader and station leader/deputy leader, with non-leadership position as the omitted category); seniority (two dummy variables for senior and mid-level staff, with junior staff as the omitted category); educational level (two dummy variables for 2-3 years of technical college, and for bachelors and above, with specialized secondary school or below as the omitted category); specialization (seven dummy variables for plant protection, horticulture, soil fertility, agricultural machinery, animal husbandry, agricultural economics, and other, with agronomy as the omitted category); and whether the job is suited to the agents training (0=no, 1=yes). Regional characteristics: A dummy variable for southern China, which captures differences in cropping systems between the north and south. 6. Results and discussion The model was estimated as a cross-sectional tobit (Table 4). Most of the signs of the estimated coefficients on the control variables representing personal characteristics are as expected. For example, when an agents specialization does not match his training, he spends less time bringing AES to farmers. Likewise, higher levels of education and seniority are associated with greater AES delivery. Most importantly, the coefficients for both government investment variables, for per capita OB and per capita PG, are positive (Rows 1 and 4), indicating that when the government increases its investment in the PAES, it stimulates the agents to spend more time delivering AES. However, the magnitude of this impact is small: The coefficient on per capita OB is statistically significant at the 5% level, but the estimated value is only 0.2 in the baseline specification (Row 1 Column 1). Controlling for other factors, each 1,000 yuan increase in per capita annual OB will prompt agents to increase their time spent delivering AES by only 0.17% (0.6 days per year). Even at the upper limit of the 95% confidence interval, agents increased their time spent on AES by only 1.2 days annually for every 1000 yuan of annual increase in per capita OB. The coefficient on per capita PG is even smaller and is not statistically significant, with a small confidence interval around zero (Row 4). These results suggest that, based on prevailing institutional arrangements, increasing extension project investment will not substantially increase the time agents spend providing AES. Column 2 suggests that project grants may increase time in the field for self-funded agents, but this coefficient is not significant under alternative specifications. Under current institutional practices, increases in government investment in PAES may be effective, but their impact is very limited. The coefficients for type of government funding reveal the most notable results of the analysis (Rows 13 and 14). Compared to fully funded agents, controlling for levels of investment and other factors, partially funded or self-funded agents spend smaller proportions of their time delivering AES6.0% (22 days) and 9.5% (35 days) of total time, respectively (Column 2). We see that the PAES commercial reforms significantly reduced the time that agents spend offering AES. This result is stable even when not controlling for the amount of expenditures (Column 3). Because the above results rely on cross-sectional data, they may be subject to selection bias, since many of the same unobserved variables are likely to underlie differences in budgets, institutions, and personnel characteristics. To address this concern, we ran a placebo test, including 1996 funding levels in the regression. If current funding is simply a proxy for unobserved fixed-station characteristics, the coefficient for past funding should be similar to that for current funding, and introducing both variables into the same regression might make both coefficients insignificant due to multicollinearity. Similarly, if past funding is a proxy for unobservable past station characteristics that go into determining present institutions and agent characteristics, past funding would be expected to have a significant correlation with the time that agents currently spend on AES. Including coefficients for past funding (Columns 4 and 5) makes the results less significant, and calls into question the effectiveness of current project grants in increasing AES time for self-funded agents, but does not have a large effect on the coefficients or standard errors for current funding. In all specifications, the confidence intervals on the estimates of the impact of funding levels on time allocation form narrow confidence intervals close to zero. In all specifications, including one that excludes coefficients on funding levels entirely (Column 3), the effect of funding sources is large and significant. The above results are also robust under a wide variety of other specifications (see Appendix). 7. Conclusions and policy implications In 2002, Chinese agricultural extension agents spent an average of only 81 days actually bringing AES to farmers, and the township agents, who ought to be in closest contact with the farmers, spent even fewer days. This study determined that the main factor determining how much time Chinese agricultural extension agents spent serving farmers was whether their agents salary came from the government or from commercial activities. Increasing the total amount of financing, whether for the operational budget or for specific projects, improved service provision only slightly. But partially funded agents provided 27 fewer days of AES to farmers than did fully funded agents, and self-funded agents provided even less controlling for other factors. In contrast, increasing either operating or project budgets by 1000 yuan per agent annually, an increase of approximately 11%, would increase time spent on AES by less than one day per year. Likewise, the international literature has chronicled disappointing attempts to create local institutions that make the provision of public goods financially sustainable. In Chinese agricultural extension context, when commercial reforms encouraged stations to become more locally funded, the agents were distracted by profit incentives, and spent their time on commercial activities rather than on AES delivery. The present study measures only the quantity, not the quality, of services, but other studies have suggested that the commercialization of extension stations has also reduced the quality of their services by creating conflicts of interest between agents and farmers. Chinas Ministry of Agriculture is aware of the problem of agents spending little time bringing AES to farmers, but in our opinion the reforms (i.e., merging stations and centralizing administrative rights at the county level) do not go far enough. We conclude that to improve the service of the PAES in delivering AES to farmers, the recent government policy to separate commercial activities from extension services is a step in the right direction and should be expanded. Chinas agricultural extension system reforms also reveal a more general lesson for governments and international development agencies. It is unwise to expect miracles from either local institutions or additional funding for public services, and prudent to proceed cautiously when contemplating the privatization or local financing of basic public services. Appendix Robustness of results We ran six types of robustness checks on the data: 1. Relaxing assumptions of the tobit specification. Two assumptions of our tobit specification are not precisely satisfied by the data: the statistical independence of the observations and the constant variance of the residuals. Because our data are derived from multi-stage cluster sampling, observations within each cluster may not be statistically independent, and therefore the standard errors in the tobit model may be biased. Correcting the tobit model for clustering and heteroskedasticity increased the standard errors slightly when clustering on provinces, but actually decreased the errors when clustering on counties (a result of a negative intra-county correlation of the independent variables), and neither procedure changed whether any coefficients of interest are significant. 2. Relaxing the implicit assumption of the non-concavity of the effect of funding levels on time allocation. An alternative to our linear model of the effect of funding on time allocation is a concave model, i.e., that increasing funding levels has a diminishing effect on time allocation. The coefficient for the square of funding is significant and negative (Appendix Table 1), implying concavity, but is no longer significant when we exclude large outliers (per capita funding more than three standard deviations above the mean). 3. Dropping control variables. Unconditional results without any control variables were similar to those shown with controls, as were results with selected control variables dropped. Dropping the south China dummy variable weakened the estimated impact of commercialization because stations in southern provinces were more likely to implement the commercial reforms, and agents in southern provinces spent more time offering AES. We speculate that this is a result of geography; stations in southern provinces have found more opportunities to commercialize stations that focus on the high-value specialty crops that grow in warmer climates, and their agents spent more time on AES because of a longer growing season. We believe that including the south China dummy variable is appropriate to produce unbiased estimates of the impact of commercial reforms and funding on AES time allocation. 4. Running the analysis on subsets of the data. To check the robustness of the results, the data were subdivided by province, type of station, type of position, or specialization, and the analysis was run on one category at a time. No such subset of the data yielded results that were of the opposite sign as the pooled results and statistically significant. 5. Dropping outliers. Among the variables of most interest in the model, only the per capita OB variable for 2002 contains a larger number of outliers than would be found in a normal distribution. If outliers in the per capita OB represent measurement error, attenuation may bias downwards the coefficient on this variable. Although the per capita OB data were derived from actual administrative records and therefore are not subject to measurement error, outliers in per capita OB that were created by a different data-generating process than the rest of the sample could also bias the coefficient downwards. Running the analysis without outlying values for 2002 per capita OB (more than 3 standard deviations from the sample mean) doubled its estimated coefficient to 0.39% per 1000 yuan annually, and had no meaningful effect on the levels or significance of any other estimated coefficients. Although the magnitude of this coefficient is somewhat sensitive to outliers, this sensitivity does not affect our general conclusion, for three reasons: First, neither quantitative nor qualitative evidence supports the idea that a different data-generating process for the outliers in 2002 per capita OB actually exists. Although 8 out of 1,245 observations lie further than 3 standard deviations from the mean, the proportion of outliers is not high enough to reject the null hypothesis that only 0.3% of observations will lie outside of 3 standard deviations from the mean (equivalent to a normal distribution), even at the 10% level. In addition, a closer examination of the specific stations generating outlying values reveals no obvious qualitative evidence of a different data-generating process. Second, a quantile regression, an alternative method of reducing the influence of outliers, yields a coefficient on per capita OB of only 0.25%, similar to the original result of 0.17%. Third, even if 0.39% is a better estimate of the coefficient, increasing per capita OB is still not a very effective means of improving the delivery of AES, relative to reforming financing institutions. 6. Adding controls for per-capita financial income. Although our unit of analysis is the agent, who cannot control his source of funding, estimates of the effects of sources of funding are potentially subject to omitted variables bias and simultaneity bias. As a more exogenous measure of government support, we used per-capita financial income (total public employees divided by total local revenue). When we ran our regressions including per-capita financial income, we found that the coefficients on partial and self funding remained highly significant (see Appendix Tables 2 and 3). References Anderson, J. R., & Feder, G. (2004). Agricultural Extension: Good Intentions and Hard Realities. World Bank Research Observer. 19, 41-60. Fan S., & Pardey P. (1997). Research productivity and output growth in Chinese agriculture. J. Dev. Econ, 53, 115-137 Hu, R., & Huang, J. (2001). Agricultural Technology Extension Investment and Its Impact in China. Strategy and Management, 3, 25-31. Hu, R., Huang, J., & Li, L. (2004). Zhongguo nongji tuiguang: xianzhuang, wenti ji jiejue duice. Guanli shijie, 5, 50-57. Hu, R., Huang, J., Jin, S., & Rozelle, S. (2000). Assessing the contribution of research system and CG genetic materials to the total factor productivity of rice in China. Journal of Rural Development, 23, 33-70 Huang, J. & Rozelle, S. (1996). 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Fiscal Decentralization and Political Centralization in China: Implications for Growth and Inequality. Journal of Comparative Economics, 34 (4), 713-726. Zhu, X. (1997). Woguo nongye keji jinbu gongxianlv cesuan fangfa. Beijing, China: Zhongguo nongye chubanshe. 3-28. Table 1. Distribution of extension agents in China, 1996-2006 By administrative level (%)By specialization (%)YearTotalAbove countyaCounty levelTownship level0CropsLivestockAgricultural machineryAquatic productsAgricultural economics19961006.736.656.741.132.413.62.410.619971006.537.356.341.130.815.92.99.319981005.733.860.538.531.917.33.29.019991006.334.459.339.731.816.33.29.020001007.034.858.141.031.615.13.29.120011007.335.757.142.032.213.63.38.920021007.336.756.042.932.012.83.29.020031007.737.554.741.134.112.63.38.920041007.938.553.641.435.111.43.58.720051008.839.451.839.534.912.63.89.220061009.340.450.441.433.812.33.68.9 a Above county level refers to prefectural, provincial, or national level agricultural extension units and agents. Source: Ministry of Agriculture. Table 2. Per capita budget for agricultural extension units, 2002Yuan/agent/year%MeanTownshipCountyMeanTownshipCountyBudget itemTotal14,3049,41616,496100100100Government funds11,1976,13613,467786582Operating budget8,9904,87110,837635266Project grants2,0311,1112,443141215Other176154186121Commercial3,1073,2803,029223518Source: Authors survey of 363 stations in 28 counties. Table 3. Time allocation of agricultural extension agents by personal characteristics, 2002Obs.Days per yearAdmin.AES deliveryComm. workNon-workTotalOverall1245135815692365Employment statusSenior staff841381072990365Mid-level staff424129945092365Junior and other737139716393365Academic credentialsBS and above192138953497365 2 or 3 years Technical College464143864195365 Secondary Specialized School and below589128737588365PositionESC leader86168881891365Station leader488130955782365Other6711357060100365ProvinceHebei188137874794365Heilongjiang2651884629101365Zhejiang1281498923104365Hubei1887877100110365Guangdong109869710479365Sichuan1751101195778365Gansu192159805273365Administrative levelCounty7701368639103365Township475134738375365Government fundingFully funded823160852596365Partially funded244116876497365Self funded178485819070365Government investment for operating budget (per capita OB: 1000 Yuan / year) b per capita OB<=5369957411580365 52255166982100365Government investment for extension projects (per capita PG: 1000 Yuan / year)cper capita PG=095313777609136505.09714210022101365a. Government investment funds the OB (core funding), PG, and capital construction Any capital construction or cash-basis retirement-related expenses are excluded from this paper. b. The average per capita OB for the 1,245 extension staff sampled was 9,313 yuan in 2002. c. The average per capita PG for the 1,245 extension staff sampled was 2,040 yuan in 2002. n=1,245; random sampling standard error=1.4 percentage points or 5 days, larger for sub-groups. Source: Authors survey.  Table 4. Tobit estimation of the agent time allocation modelDependent variable: % of days spent providing AES Standard deviations in parentheses(1)(2)(3)(4)(5)Government investment Per capita OB 2002 (1000 yuan / person)0.167 0.110 0.126 0.064 (0.08)** (0.08) (0.09) (0.09)  Per capita OB 2002 X Partially funded-0.019 0.238 (0.28) (0.34)  Per capita OB 2002 X Self funded-0.609 -0.076 (0.43) (0.55)  Per capita PG 2002 (1000 yuan / person)0.076 0.031 0.066 0.035 (0.06) (0.06) (0.07) (0.07)  Per capita PG 2002 X Partially funded0.178 0.143 (0.20) (0.21)  Per capita PG 2002 X Self funded2.227 -0.778 (0.91)**(1.52)  Per capita OB 1996 (1000 RMB / person)0.122 0.152 (0.11) (0.11)  Per capita OB 1996 X Partially funded-0.491 (0.36)  Per capita OB 1996 X Self funded-1.176 (0.87)  Per capita PG 1996 (1000 RMB / person)0.003 -0.021 (0.06) (0.06)  Per capita PG 1996 X Partially funded0.562 (0.91)  Per capita PG 1996 X Self funded5.897 (2.42)** Institutional structure and reform Nature of government funding (fully funded = 0) Partially funded-6.039 -6.798 -5.189 (2.91)**(1.97)***(3.08)* Self funded-9.540 -11.567 -7.856 (3.03)***(2.47)***(3.30)**  Administrative level (county-level = 0): -3.143 -2.003 -2.446 -3.073 -1.985  Township-level (1.68)* (1.71)(1.68)(1.69)* (1.75)Agent personal characteristics  Position (no managerial position = 0) ESC leader0.813 -0.039 0.619 0.874 0.309 (2.96)(2.94) (2.92) (2.97)(2.94) Station leader3.708 3.318 3.667 3.734 3.488 (1.60)** (1.59)** (1.58)** (1.60)**(1.59)** Employment status (Junior and other = 0) Senior staff6.767 6.295 5.972 6.618 6.107 (3.19)** (3.16)**(3.17)*(3.19)** (3.16)*  Mid-level staff2.669 2.750 2.605 2.600 2.647 (1.73)(1.71)(1.72)(1.73)(1.71)  Education (secondary specialized school and below = 0) BS and above5.036 3.331 3.585 5.117 3.522 (2.34)** (2.35)(2.35) (2.34)**(2.35) 2 or 3 years technical college4.559 3.582 3.716 4.641 3.715 (1.71)***(1.70)** (1.71)** (1.71)*** (1.70)**  Working specialization (crop management=0) Plant protection-0.535 -0.238 0.034 -0.453 -0.042 (2.90) (2.88)(2.89)***(2.90)(2.87) Horticulture10.437 10.712 10.819 10.517 10.644 (3.41)*** (3.38)***(3.39)***(3.41)*** (3.37)***  Soil fertility-6.088 -4.824 -4.594 -6.048 -4.972 (3.88)(3.86)(3.86)(3.88)(3.85) Agricultural machinery-15.481 -15.027 -14.800 -15.689 -15.209 (2.39)*** (2.37)*** (2.37)*** (2.39)***(2.38)*** Animal husbandry-9.336 -7.217 -6.397 -9.342 -7.451 (2.06)***(2.15)***(2.14)*** (2.06)***(2.17)*** Agricultural economics-11.828 -12.450 -11.916 -12.049 -12.727 (2.99)***(2.96)***(2.96)***(2.99)***(2.95)*** Other-11.427 -10.929 -10.342 -11.544 -10.983 (2.80)*** (2.78)***(2.77)***(2.81)***(2.79)*** Work specialization not matches training (yes=0)-3.138 -2.765 -2.802 -3.291 -2.866 (1.71)*(1.70)*(1.70)*(1.72)*(1.70)*  Years of employment0.190 0.199 0.218 0.205 0.203 (0.13)(0.13)(0.13)*(0.13)(0.13) Female (male = 0)-6.232 -6.176 -6.418 -6.079 -5.891 (1.79)***(1.77)***(1.78)***(1.79)*** (1.77)*** Age -0.033 -0.036 -0.057 -0.035 -0.034 (0.13) (0.13)(0.13)(0.13)(0.13)Regional dummy (North=0): south11.013 13.220 14.153 11.148 13.265 (1.53)*** (1.68)***(1.63)***(1.53)***(1.69)***Constant20.524 22.203 23.387 19.822 21.282 (4.45)***(4.43)***(4.34)***(4.48)***(4.47)***Pseudo-R20.0260.0290.0280.0260.030Number of observations12451245124512451245Note: The symbols, ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. Appendix Table 1. Tobit estimation of the agent time allocation model (OB and PG squared)Dependent variable: % days spent providing AES Standard deviations in parentheses(1)(2)(3)(4)(5)Government investmentPer capita OB 2002(1000RMB/person) square0.0010.0000.0000.000(0.001)(0.001)(0.001)(0.001) per capita OB 2002 square X partially funded-0.0010.005(0.011)(0.012)per capita OB 2002 square X Self funded-0.0250.012(0.030)(0.035)Per capita PG 2002(1000RMB/person) square0.0000.0000.0000.000(0.000)(0.000)(0.000)(0.000) Per capita PG 2002 square X partially funded0.0010.001(0.002)(0.002)Per capita PG 2002 square X self funded0.203-0.177(0.067)***(0.215)Per capita OB 1996(1000RMB/person)square0.0030.002(0.001)**(0.001)** per capita OB 1996 square X partially funded-0.011(0.008)per capita OB 1996 square X Self funded-0.111(0.081)Per capita PG 1996(1000RMB/person) square-0.000-0.000(0.000)(0.000) Per capita PG 1996 square X partially funded0.055(0.094)Per capita PG 1996 square X self funded0.911(0.498)*Institutional structure and reformNature of government funding(fully funded=0)Partially funded-6.580-6.798-6.404(2.191)***(1.966)***(2.223)***Self funded-11.361-11.567-10.289(2.708)***(2.466)***(2.855)***Administrative level(county-level=0)Township-level-3.770-2.176-2.446-3.840-2.295(1.665)**(1.694)(1.679)(1.667)**(1.715)Agent personal characteristicsPosition(no managerial position=0)ESC leader1.3640.2800.6191.6490.837(2.951)(2.926)(2.922)(2.959)(2.932)Station leader3.5363.2363.6673.5293.401(1.597)**(1.586)**(1.584)**(1.593)**(1.581)** Employment status(Junior and other=0) senior staff6.7546.2515.9726.4795.978(3.196)**(3.163)**(3.173)*(3.189)**(3.155)*Mid-level staff2.8202.8732.6052.6242.681(1.729)(1.714)*(1.717)(1.726)(1.711)Education(secondary specialized school and blow=0)BS and above5.5683.4163.5855.8323.774(2.337)**(2.350)(2.351)(2.334)**(2.346)2 or 3years technical college4.6863.4883.7164.9193.738(1.710)***(1.704)**(1.707)**(1.708)***(1.702)**Working specialization(crop management=0)Plant protection-0.590-0.0270.034-0.4780.182(2.906)(2.875)(2.886)(2.899)(2.865)Horticulture10.60710.87210.81910.62310.786(3.415)***(3.375)***(3.389)***(3.406)***(3.362)***Soil fertility-6.041-4.602-4.594-6.089-4.713(3.884)(3.846)(3.858)(3.875)(3.832)Agricultural machinery-15.304-14.834-14.800-15.519-14.788(2.389)***(2.363)***(2.372)***(2.385)***(2.371)***Animal husbandry-9.306-6.861-6.397-9.475-7.104(2.063)***(2.141)***(2.138)***(2.059)***(2.150)***Agricultural economics-11.401-12.126-11.916-11.656-12.285(2.986)***(2.951)***(2.965)***(2.979)***(2.940)***Others-10.749-10.564-10.342-10.820-10.546(2.798)***(2.774)***(2.767)***(2.803)***(2.775)***Work specialization not matches training (yes=0)-3.242-2.869-2.802-3.574-3.170(1.710)*(1.695)*(1.696)*(1.711)**(1.695)*Years of employment0.1900.2000.2180.2050.204(0.127)(0.126)(0.126)*(0.127)(0.126)Female(male=0)-6.488-6.305-6.418-6.210-6.007(1.789)***(1.768)***(1.776)***(1.788)***(1.763)***Age-0.044-0.040-0.057-0.039-0.033(0.127)(0.125)(0.126)(0.126)(0.125)Regional dummy(North=0): south11.04113.65614.15311.26213.793(1.520)***(1.650)***(1.633)***(1.519)***(1.653)***Constant22.51623.31423.38721.81322.521(4.374)***(4.336)***(4.338)***(4.371)***(4.330)***Observations12451245124512451245Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1% Appendix Table 2. Tobit estimation of the agent time allocation model with per capita financial income (2002)Dependent variable: % days spent providing AES Standard deviations in parentheses(1)(2)(3)Government investmentPer capita financial income 2002(1000RMB/person)0.2580.1360.188(0.109)**(0.112)(0.123)Per capita financial income 2002 X partially funded-1.108(0.381)***Per capita financial income 2002 X self funded0.746(0.454)Institutional structure and reformNature of government funding(fully funded=0)Partially funded-6.1911.745(2.027)***(3.507)Self funded-11.036-16.771(2.502)***(4.455)***Administrative level(county-level=0)Township-level-4.013-2.592-3.145(1.661)**(1.682)(1.682)*Agent personal characteristicsPosition(no managerial position=0)ESC leader1.7720.7470.878(2.934)(2.922)(2.907)Station leader3.7353.7573.591(1.594)**(1.584)**(1.578)** Employment status(Junior and other=0) senior staff6.2315.7685.144(3.196)*(3.175)*(3.169)Mid-level staff2.6902.5192.290(1.727)(1.717)(1.709)Education(secondary specialized school and blow=0)BS and above5.0463.3793.898(2.346)**(2.356)(2.355)*2 or 3years technical college4.7023.7663.841(1.706)***(1.706)**(1.700)**Working specialization(crop management=0)Plant protection-0.972-0.194-0.598(2.905)(2.890)(2.879)Horticulture9.48710.2299.713(3.442)***(3.422)***(3.410)***Soil fertility-6.364-4.918-5.385(3.879)(3.864)(3.851)Agricultural machinery-15.913-15.187-15.183(2.402)***(2.392)***(2.379)***Animal husbandry-9.343-6.599-6.082(2.057)***(2.143)***(2.137)***Agricultural economics-11.834-12.144-12.093(2.988)***(2.969)***(2.953)***Others-11.418-10.862-11.232(2.814)***(2.799)***(2.790)***Work specialization not matches training (yes=0)-3.241-2.849-2.763(1.704)*(1.695)*(1.687)Years of employment0.1870.2120.228(0.127)(0.126)*(0.125)*Female(male=0)-6.662-6.486-6.727(1.786)***(1.775)***(1.767)***Age-0.043-0.052-0.046(0.126)(0.126)(0.125)Regional dummy(North=0): south10.36213.51313.620(1.553)***(1.714)***(1.735)***Constant21.21422.52921.870(4.411)***(4.393)***(4.411)***Observations124512451245Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1% Appendix Table 3. Tobit estimation of the agent time allocation model with per capita financial income (2002) and province dummiesDependent variable: % days spent providing AES Standard deviations in parentheses(1)(2)(3)Government investmentPer capita financial income 2002(1000RMB/person)0.4860.4550.514(0.182)***(0.181)**(0.192)***Per capita financial income 2002 X partially funded-1.060(0.373)***Per capita financial income 2002 X self funded0.512(0.450)Institutional structure and reformNature of government funding(fully funded=0)Partially funded-7.1640.557(2.081)***(3.510)Self funded-13.488-17.291(2.589)***(4.496)***Administrative level(county-level=0)Township-level-3.624-2.147-2.651(1.637)**(1.648)(1.649)Agent personal characteristicsPosition(no managerial position=0)ESC leader3.7022.6242.730(2.891)(2.868)(2.856)Station leader3.2473.0672.960(1.570)**(1.556)**(1.550)* Employment status(Junior and other=0) senior staff4.9434.1863.680(3.169)(3.140)(3.133)Mid-level staff2.7832.5942.436(1.726)(1.710)(1.703)Education(secondary specialized school and blow=0)BS and above5.8014.1674.691(2.309)**(2.305)*(2.307)**2 or 3years technical college5.3844.3194.408(1.678)***(1.673)***(1.668)***Working specialization(crop management=0)Plant protection-0.6580.287-0.144(2.860)(2.837)(2.828)Horticulture6.8317.8127.346(3.397)**(3.367)**(3.359)**Soil fertility-7.007-5.504-5.973(3.809)*(3.781)(3.770)Agricultural machinery-16.265-15.112-15.107(2.364)***(2.351)***(2.340)***Animal husbandry-10.618-7.142-6.681(2.034)***(2.124)***(2.120)***Agricultural economics-11.698-11.919-11.904(2.930)***(2.901)***(2.888)***Others-10.896-10.204-10.551(2.764)***(2.741)***(2.733)***Work specialization not matches training (yes=0)-4.087-3.797-3.726(1.681)**(1.667)**(1.660)**Years of employment0.2240.2430.256(0.124)*(0.123)**(0.123)**Female(male=0)-7.101-7.132-7.300(1.764)***(1.748)***(1.741)***Age-0.064-0.064-0.059(0.125)(0.124)(0.123)Province dummy (Hebei province=0)Heilongjiang 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h~h@CJOJQJ^JaJ#h~h@CJOJQJ^JaJo(LI2$If`gd@lkd:/$$Ifl   \d t0  644 lalp(/kd/$$Ifl   \d t0  644 lalp($$If`a$gd@l$$If`a$gd@l$IfWD `gd@lI2$If`gd@lkd.0$$Ifl   \d t0  644 lalp(/kd0$$Ifl   \d t0  644 lalp($$If`a$gd@lCJQX$$If`a$gd@l$If`gd@lXYZI2$If`gd@lkd"1$$Ifl   \d t0  644 lalp(Zdnxy/kd1$$Ifl   \d t0  644 lalp($$If`a$gd@ly$$If`a$gd@l$If`gd@lI2$If`gd@lkd2$$Ifl   \d t0  644 lalp(/kd2$$Ifl   \d t0  644 lalp($$If`a$gd@l$$If`a$gd@l$If`gd@lI2$If`gd@lkd 3$$Ifl   \d t0  644 lalp(/kd3$$Ifl   \d t0  644 lalp($$If`a$gd@l  $$If`a$gd@l$If`gd@l  !")*129:;\]`avw~xxxxxxx"x#xѱўU hQh@CJOJQJ^JaJh@CJOJQJ^JaJo( hh@CJOJQJ^JaJhh@CJaJ h~h@CJOJQJ^JaJ#h~h@CJOJQJ^JaJo(> !"I2$If`gd@lkd3$$Ifl   \d t0  644 lalp("*2:;/kdx4$$Ifl   \d t0  644 lalp($$If`a$gd@l;]^_`$$If`a$gd@l$If`gd@l`awI0$IfWD,`gd@lkd4$$Ifl   \d t0  644 lalp(w/kdl5$$Ifl   \d t0  644 lalp($$If`a$gd@l$$If`a$gd@l$If`gd@lI0$IfWD,`gd@lkd5$$Ifl   \d t0  644 lalp(/kd`6$$Ifl   \d t0  644 lalp($$If`a$gd@l$$If`a$gd@l$If`gd@lxI0$IfWD,`gd@lkd6$$Ifl   \d t0  644 lalp(Hubei province5.13210.28710.457(2.764)*(2.962)***(2.962)***Guangdong province6.53810.19810.640(3.180)**(3.250)***(3.242)***Sichuan province10.29211.76311.755(2.797)***(2.811)***(2.799)***Gansu province0.349-1.453-1.105(2.819)(2.815)(2.816)Constant24.58326.05225.105(5.032)***(4.993)***(5.045)***Observations124512451245Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1% * This research is jointly funded by the National Natural Science Foundation of China (70325003), Chinese Academy of Sciences (KSCX2-YW-N-039), and the Canadian International Development Agency (CIDA).  The response rate was 100% except for a small number of agents who were traveling in other counties. Sampled agents who were in the field when the survey team arrived were called back to the office. A possible concern with this type of questionnaire is that respondents may fear a breach of confidentiality or otherwise be reluctant to report time allocations that are not in accordance with their employers expectations. However, this did not appear to be a problem. There was no difference in the reported time allocation between agents at stations that had formalized time allocation expectations and those that had not (Hu & Huang, 2001).  In 2002-03, 1US$=8.27 yuan. The current rate is approximately 1US$=7 yuan.  A difference-in-difference analysis with panel data is not possible because there was very little variation in time allocations in the 1980s before the reforms. 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