The Master of Art in Economics Thesis on The Impact of FISP on the benefiting Farmers - The case of Gwembe District. An Oral Presentation.

1. Impacts of Farmer Input Support Programme on beneficiaries
The case of Gwembe District.
Alfred Sianjase & Professor Venkatesh Seshamani
School of Humanities & Social Sciences
Department of Economics

2. PRESENTATION OUTLINE
1.0 INTRODUCTION
2.0 LITERATURE REVIEW
3.0 METHODOLOGY
4.0 FINDINGS/DISCUSSIONS
5.0 CONCLUSION & RECOMMENDATIONS
 References
Acknowledgements

3. 1.1 Background
 Agriculture is a critical sector of any economy, especially developing economies.
 Increased agro-production is critical to increased HH food security & reduced
vulnerabilities.
 Zambia, then needs to implement the pro-poor economic policies targeting
majority rural poor through promotion of increased agricultural production.
 The Zambian Government promoted the private sector participation in input
supply to increase crop production.
 Market failure resulted through the failure of the private sector in fertilizer and
seed distribution to the small scale farmers.
 Hence access to agro-inputs such as certified seed and fertilizers a big challenge to
smallholder farmers.
 Government then initiated a fertilizer subsidy programme in 2001/2002
agricultural season to help increase access to inputs and increase crop production.
1.0 INTRODUCTION

4. 1.2 Statement of the Problem
Despite the continued provision of subsidized agro-inputs largely for maize
production to smallholder farmers, the Programme has not helped to improve
maize production among its target group.
1.3 Purpose of the Study
Study is to be used as a partial fulfilment of the requirements of the degree of
Master of Arts in Economics and offer subsidy policy advise as need may arise.
1.4 Objectives
o General Objective
The general objective is to investigate the impact of Farmer Input Support
Programme (FISP) on benefiting households.
o Specific Objectives
To find out the impact of input subsidies on maize output,
To find out the effect of input subsidies on households’ dependence on
subsidies in maize production,
To draw policy implications on the need to continue or to discontinue with input
subsidies from the empirical findings.

5. 1.5 Research Questions / Hypothesis
 Null hypothesis: there is no significant impact of FISP on the farmers’ maize
crop production,
 Alternative hypothesis; there is a significant impact,
 segregated into two hypotheses, namely;
Subsidized seed and fertilizer have no significant impact on maize output,
Subsidized seed and fertilizer have significant effect on the subsidy
dependence.
1.6 Significance of the Study
Government, has been funding maize inputs subsidy programme, under MAL
since 2001
This has been taking the larger portion of the Ministry’s budget than its core
functions as shown in figure 1 below.
 Hence the need to determine its impact on maize production by the benefiting
farmers.

6. Figure 1: Proportion of MAL Expenditure on FISP Compared to Department of Agriculture – 2001 to 2010 - Zambia
1.7 The Study Area
 The study was done in Gwembe district where 570 smallholder
farmers were sampled and the questionnaire administered to collect data.
1.8 Limitations of the Study
 Owing to limitations of time and finances, the data collected and
analyzed in this study was only for Gwembe district. As such, the
results obtained may not be representative of the country as whole.

7. 2.0 LITERATURE REVIEW
2.1 Conceptual Framework
 characterisation of subsidies suggests concepts based on the economic
principles of efficiency, equity, sustainability and political economy of input
subsidies in SSA
Efficiency
Agro-inputs subsidies are inefficient if they merely encourage the adoption of
use of costly inputs than benefits.
Agro-subsidies may be efficient as they help farmers overcome the market
distortions generated by the market failures.
Equity
 Countries implementing pro-poor policies promote use agro-input subsidies as
a tool for resource distribution by targeting subsidies on the poorest smallholder
farmers.

8. Sustainability
 Subsidy programmes if can be maintained over the long term without draining
the public resources,
 or if the outcomes in terms of adoption of use of improved inputs and methods
continue after programme.
Political economy of input subsidies in SSA
 Ideally, policies implemented to maximize national welfare, although personal
political motivations play a role.
2.2 Theoretical Framework

9. 2.3 Relevant Literature
 Rich empirical literature on the analysis of impact of seed and fertilizer
subsidies.
Survey done by Chibwana et al (2010) on impact of fertilizer subsidy in Malawi
suggests an increase in maize yields of recipient farmers by 249 kg/ha.
Survey conducted by World Bank (2010) at end of the 2007/2008 season
showed that participants achieved an average yield of around 2 metric tons
(MT) per hectare.
 World Bank’s aggregate estimate on maize production increased by
146,000MT in the same season, corresponding to 89% growth in output.
However, the surveys did not cluster farmers in their various production
distributions to determine the responsiveness of each of the clusters to input
subsidies
Survey also did not consider to determine the dependence subsidies created
among the benefiting farmers.
Hence need for this research to address these gaps in the previous surveys.

10. 3.0 METHODOLOGY
3.1 Study Design
 study adopted the survey research design in which primary data was collected.
3.2 Research Instruments
 A questionnaire was used in data collection.
3.3 Data Collection Procedure
 A questionnaire was administered to 600 small scale farmers in 8 randomly sampled
agricultural camps in Gwembe district.
 Cluster sampling method was used since population is dispersed over a wide
geographical area.
 Due to missing responses to some items, the final sample dropped to 570 farmers,
giving 95% participation.
3.4 Data Analysis
 Quantile regression, first developed by Koenker and Bassett (1978) was used.
 Research estimates the equation; (Yijt = β0 + β1Sijt + β2Xijt + β3Tt + Ci + μijt) for maize
production as a linear model via quantile regression.

11. Data Analysis (Cont’d)
 compares those results with conditional mean estimates from OLS.
 Quantile regression allows seeing how subsidized inputs affect maize
production.
 This helps in addressing the question of whether or not input subsidy
programs can significantly boost maize production for those at the bottom of
the maize production distribution.

12. 4.0 FINDINGS AND DISCUSSION
4.1 Results
 Varying results were observed on subsidized inputs and demographic variables
across maize production distribution.
 HHs at 5th percentile only gain 0.69kg of maize, 1.10Kg at 10th
,3.11Kg at the 50th
and
are all statistically significant at 1%.
 HHs at the 90th
percentile gain 2.58Kg per Kg of subsidized inputs and it is statistically
significant at 5%.
 HHs at 5th
percentile gain 5.58Kg for each additional year of schooling of head of HH,
2.61Kg for male-headed HH, 6.32Kg for additional larger HH and 3.21Kg for HHs
whose head was once employed and loses only 0.47Kg MP of for each additional
year of age of the head HH.
 HHs at 10th and 50th percentiles gain by 9.24Kg and 29.78Kg respectively for each
additional year of schooling by the head of the HH and both are statistically
significant at 1% level.
 Households at the 90th percentile gain a marginal product of 69.65Kg from an
additional year of schooling by the head.

13. COVARIATES
POOLED OLS
CONDITIONAL
MEAN
ESTIMATION
POOLED QUANTILED REGRESSION
5%
10% 50% 90%
Coeff. P-Value Coeff. P-Value Coeff. P-Value Coeff. P-Value Coeff. P-Value
Kg subsidized Inputs acquired by hh 3.77*** (0.00) 0.87 (0.00) 1.78*** (0.00) 2.87*** (0.00) 3.91*** (0.00)
total land cultivated for maize in ha 133.2*** (0.00) 29*** (0.00) 48*** (0.01) 112*** (0.00) 437*** (0.00)
log of Age of hh head in each year -4.94 (0.78) -0.47 (0.89) -0.71 (0.93) -0.92 (0.96) -0.63 (0.98)
=1 if household head attended school 22*** (0.00) 5.58 (0.19) 9.24*** (0.00) 29.78*** (0.00) 69.65*** (0.00)
=1 if household is male headed 62*** (0.00) 2.61 (0.28) 7.36 (0.39) 8.60 (0.49) 37.13*** (0.00)
Household Size 26.14 (0.71) 6.32 (0.67) 9.49 (0.86) 15.18 (0.51) 21.38 (0.89)
=1 if hh head was once employed 16.2** (0.03) 3.21 (0.16) 4.78 (0.21) 9.29 (0.74) 11.27** (0.02)
Average annual rainfall over past 4 growing seasons in ml 0.61*** (0.00) 0.10*** (0.01) 0.29*** (0.00) 0.17** 0.02) -0.36 (0.18)
cumulative rainfall over the current growing season in ml 0.04 (0.81) 0.03 (0.36) 0.01 (0.73) 0.00 (0.80) 1.02 (0.44)
Std deviation of the average long run rainfall -0.06 (0.74) 0.06 (0.49) 0.08 (0.17) 0.12 (0.15) -0.19 (0.58)
Intercept -1.93*** (0.00) -114*** (0.00) -214*** (0.00) -692*** (0.00) -1362*** (0.00)
Soil quality dummy variables included Yes Yes Yes Yes Yes
Num of Observations 570 570 570 570 570
R2
0.41 0.06 0.18 0.26 0.31
COVARIATES
FIRST DIFFERENCE,
CONDITIONAL MEAN
ESTIMATION
CORRELATED RANDOM EFFECTS QUANTILE REGRESSION
5% 10% 50% 90%
Coeff. P-value Coeff. P-value Coeff. P-value Coeff. P-value Coeff. P-value
Kg subsidized Inputs acquired by hh 2.24*** (0.00) 0.69*** (0.00) 1.10*** (0.00) 3.11*** (0.00) 2.58** (0.02)
total land cultivated for maize in ha 241*** (0.00) 35*** (0.00) 55*** (0.00) 98*** (0.00) 337*** (0.00)
log Age of hh head in each year NA NA -1.41 (0.88) 1.56 (0.83) 4.69 (0.61) -2.63 (0.94)
=1 if household head attended school NA NA 10.08 (0.24) 24*** (0.00) 31.40*** (0.00) 49.27* (0.08)
=1 if household is male headed 51 (0.45) 18 (0.49) -15 (0.50) -18 (0.58) -56.10 (0.63)
Average annual rainfall over past 4 growing
seasons in ml -0.54*** (0.00) -0.09*** (0.00) -0.12** (0.05) -0.16*** (0.01) -0.34 (0.11)
Household Size 18.11*** (0.00) 4.38 (0.29) 7.45 (0.49) 10.61 (0.82) 12.19 (0.14)
=1 if hh head was once employed 11.36* (0.01) -1.89* (0.01) 3.46 (0.51) 5.26* (0.03) 9.18 (0.21)
cumulative rainfall over the current growing
season in ml -0.02 (0.63) 0.06** (0.03) 0.05** (0.02) 0.04 (0.27) 0.13 (0.31)
Std deviation of the average long run rainfall -0.22 (0.23) 0.03 (0.25) 0.05 (0.41) 0.07 (0.58) -0.16 (0.13)
Intercept -8.79 (0.96) -23 (0.80) -44 (0.76) 385 (0.34) -1,004 (0.52)
Soil quality dummy variables included Yes Yes Yes Yes Yes
Num of Observations 228 570 570 570 570
R2
0.21 0.09 0.17 0.28 0.36
Table 1: Pooled Quantile Regression Results for Maize Production (in Kg)
Table 2: Correlated Random Effects (CRE) Quantile Regression Results for Maize Production (in Kg)

14. 4.2 Discussion
 At 5th percentile, 36.2% of the interviewed households showed
dependence on FISP.
At 90th percentile, only 4.3% of the respondents showed dependence on
FISP
 At 10th and 50th percentiles, 29.1% and 16.7% respectively of the
respondents showed dependence on FISP.
 Observations from the results is that HHs at the lower and upper end of the
maize production distribution obtain a significantly lower response to
subsidized inputs than HHs at the median of the distribution.
 Demographic variables give positive responses to subsidized inputs other
than age which has a negative response.
Higher end has lower response probably because HHs at the top (90th
percentile) are most likely engaged in production of cash crops like cotton
and also may be involved in other income generating activities other than
crop production
Hence may not be interested in management effort to obtain high marginal
output.

15. 5.0 CONCLUSION & RECOMMENDATIONS
5.1 Conclusion
 HHs at the 50th
percentile in the maize production have a high positive response
(3.11Kg of maize per Kg of subsidized inputs than those at the lower end (5th
) and
the upper end (90th
) with responses of 0.69Kg and 2.58Kg of maize per Kg of
subsidized inputs.
5.2 Recommendations
• For use of agricultural inputs subsidies to increase maize crop production, it is
recommended to target HHs at the 50th percentile which obtain a positive higher
return (HHs with 1 and 2 hectares and have enough family labour).
• For HHs at the 5th
and 10th
percentile, social cash transfer may be more feasible as
they are less responsive.
• There is also value in extending this study to other districts in the country before
arriving at a national policy on agricultural input subsidies.

16. References
1. Abrevaya, J. and C.M. Dahl. (2008) “The Effects of Birth Input on Birthweight:
Evidence form Quantile Estimation on Panel Data.” Journal of Business & Economic
Statistics 26(4): 379-397.
2. Buchinsky, M. (1994) “Changes in the U.S. Wage Structure 1963-1987: Applications
of Quantile Regression.” Econometrica 62:405-458.
3. Chibwana, C., M. Fisher, G. Shively. 2010. “Land Allocation Effects of Agricultural
Input Subsidies in Malawi.” (in press) World Development.
4. Dennis Chiwele, Martin Fowler, Ed Humphrey, Alex Hurrell, Jack Willis (December,
2010) “Agriculture Case Study – Evaluation of Budget Support in Zambia”, Oxford
Policy Management: 27
5. Ministry of Agriculture and Livestock (2012), “Implementation Manual 2012/2013
Agricultural Season”, Famer Input Support Programme (FISP), Mulungushi House,
Lusaka, Zambia.
6. Wooldridge, J.M. (2011) Econometric Analysis of Cross Section and Panel Data, 2nd
Edition. London: MIT Press.
http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1000023,
23/11/2012.

17. Acknowledgements
wish to express my gratitude to the following people;
 My supervisor, Professor Venkatesh Seshamani, for his tireless guidance offered to
me while I was writing this dissertation.
 My wife Mrs. Lillian Muntanga Mwiinga Sianjase for her encouragement and support
during the development and completion of this dissertation.