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

PRESENTATION OUTLINE

1.0 INTRODUCTION2.0 LITERATURE REVIEW3.0 METHODOLOGY4.0 FINDINGS/DISCUSSIONS5.0 CONCLUSION & RECOMMENDATIONS

ReferencesAcknowledgements

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

1.2 Statement of the ProblemDespite 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 StudyStudy 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 Objectiveso General ObjectiveThe general objective is to investigate the impact of Farmer Input Support Programme (FISP) on benefiting households.o Specific ObjectivesTo 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.

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 StudyGovernment, 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.

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.

2.0 LITERATURE REVIEW2.1 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.

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

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.

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.

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.

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.

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-ValueKg 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 YesNum of Observations 570 570 570 570 570R2 0.41 0.06 0.18 0.26 0.31

COVARIATES

FIRST DIFFERENCE, CONDITIONAL MEAN

ESTIMATIONCORRELATED 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 YesNum of Observations 228 570 570 570 570R2 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)

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.

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.

References1. 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.

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.