World Economics - Insight , Analysis and Data

World Economics - Insight , Analysis and Data

Agricultural Statistics: Who benefits from distortions?

Morten Jerven - January 2013

This paper explores the political economy of the production of agricultural output data using the recent history of Malawi as an example. There an agricultural census (2006/2007) indicated a maize output of 2.1 million tonnes, compared to the previously widely circulated figure of 3.4 million tonnes which had been widely acclaimed as proof of the success of the government’s agrarian support policies. It is widely recognised that in developing economies the production standards of agricultural statistics are weak and unreliable mainly because of resource constraints in statistical agencies. Data are also subject to political pressure. The experience of Malawi suggests that ‘data’ are themselves a product of agricultural policies.

The focus of this paper is the reliability, or rather the lack of it, of agricultural statistics in emerging markets. This subject is particularly important given the importance of the agricultural sector in GDP in many of these countries. The agricultural sector’s significance as a provider of income and employment in both monitored and informal economies is also reflected in the attention paid to it in the policies of national governments and the concerns of multinational bodies like the World Bank. Much of the interest is concerned with the need to raise levels of agricultural productivity, but in order to monitor the success of policies aimed at this objective it is necessary to be able to measure agricultural outputs and inputs as accurately as possible. Unfortunately, the political economy of the process of the creation and dissemination of agricultural datasets often results in published data reflecting more the exigencies of national governments than underlying reality.

These problems can be illustrated by the recent experience of the use of fertilizer subsidies in Malawi. The results from the most recent agricultural census, published in 2010, indicate that the maize crop output for 2006/2007 was 2.1 million metric tonnes. This compares with the previously reported 3.4 million metric tonnes, thus implying that the total output of the main food crop in Malawi was only 60 percent of what was previously thought. This is a striking discrepancy, but it begs the question of why were previous estimates of maize output so high? Was it simply due to incompetence or lack of resources in the relevant statistical agencies responsible for data collection or was it something more insidious? The answer perhaps lies in the political use made by statistics by governments to demonstrate the success of controversial policies aimed at raising output and labour productivity in the agricultural sector such as subsidising inputs.

This paper dissects the recent Malawian experience on fertilizer subsidies, but it is noted that measuring the success of agricultural policies becomes almost impossible when there are political and economic incentives to distort reported aggregate agricultural data. The unreliability of the data in many emerging markets casts doubt on the reliability of premature assessments on the cost effectiveness of interventionist policies.[1]  


Subsidizing Agricultural Inputs

Subsidizing agricultural inputs such as fertilizer, seeds or fuel are often justified either as corrections of a ‘market failure’ or as a means of breaking out of the ‘vicious circle of underdevelopment’ (Nurkse: 1953).[2] The argument is that there is a potential high return on investment in fertilizer, but this potential is not met because of capital shortage. Low initial capital thus results in underinvestment in agriculture. In turn, this may provide justification for government intervention and/or official development assistance.[3] Indeed, fertilizer subsidies were an integral part of the state-led development push in the 1960s and 1970s, but were then scaled down as part of a larger trend of cuts in state spending during the structural adjustment programmes of the 1980s and 1990s.


The Malawian government decided to break with the anti-subsidy consensus that had emerged at the IMF and the World Bank by re-introducing fertilizer subsidies. This move reignited the debate on the efficacy of such interventionist policies in which there are two sides in the controversy. The two main protagonists representing these sides are Jeffrey Sachs and those who support his ‘big push approach’ and William Easterly and his supporters who are sceptical of top–down, aid-financed development schemes. Sachs strongly supported the Malawian government’s reintroduction of subsidies. Writing in the New York Times, he applauded the country’s President Bingu wa Mutharika who “broke old donor-led shibboleths by establishing new government programs to get fertilizer and high-yield seeds to impoverished peasant farmers who could not afford these inputs. Farm yields soared once nitrogen got back into the depleted soils” (Sachs, 2010).


The broader importance of the Malawian experiment and Mutharika’s policies in terms of the debate on input subsidies is that they have seemingly succeeded in changing the World Bank’s stance on the issue.[4] The 1997 World Bank Country Report for Malawi targeted the removal of input subsidies. By contrast, in 2011 the Bank states on its website that it “strongly supports Malawi’s efforts to improve smallholder production. The national input subsidy has made an important contribution to this objective” (World Bank web post). Easterly explains the change proposing that the development debates on subsidies are re-occurring in a pendulum-like fashion (Easterly 2009). However, such debates are not driven purely by policy agendas and fashions; empirics and evidence are also important.


Monitoring the success of agricultural policies

It is widely acknowledged that the change from recurring famines to more recent relative affluence is due to an increase in land yields which has been helped by the increased use of subsidized agricultural inputs (Lea and Hanmer, 2009: 8).[5]  While the lack of reliable evidence to monitor the optimal use of subsidies is a problem, for some governments, the paucity of data provides an opportunity to ensure that the evidence that does exist supports their policies! Statisticians are vulnerable to this pressure because the data basis itself is weak and any data series covering developing countries rests on questionable assumptions, especially those regarding food production.[6]

A suggested solution to this problem has been the use of randomized trials, thus countering what Banerjee calls “the resistance to knowledge” (2007: 16). The issue of fertilizer subsidies has been subject to such randomized trials by Duflo et al. (2008). Set in Kenya, the study conducted some demonstration experiments in which treatment and control plots were randomly selected. Unsurprisingly, it was found that “fertilizer, when used in appropriate quantities, is highly profitable” (Duflo et al., 2008: 487). However, well promoted, randomized laboratory-like studies reveal little understanding of how the political dimensions of provision affect agricultural production in the aggregate or how the returns to fertilizer are distributed.


Unfortunately aggregate output data provided by national statistics offices are constructions that are subject to political influence. In this case in order to answer the question: what do we know about the effects of state intervention in providing agricultural inputs a central issue is how the political system will respond to and manage a fertilizer subsidy programme. Studying these issues in laboratory-like experiments ignores the political dimension. In Malawi President Mutharika’s political success has been intimately linked to the perceived results of his agricultural policies. The fertilizer subsidy programme was proposed as part of his larger Malawi Economic Growth Strategy during the election campaign in 2004 and it is thought that this contributed significantly to his electoral victory. The agricultural focus of the implemented development agenda targeted smallholders generating broad support among the electorate (Cherwa et al., 2006).  Mutharika was subsequently re-elected to the Presidency in 2009, obtaining 66 percent of the popular vote.


The Malawian Experience

The voting figures show the psephological [IS1] evidence demonstrating the political importance of the government’s agricultural policies, but the available evidence on the economic impact of the subsidies is conflicting. Dorward and Chirwa (2011: 237) explain the scale of the fertilizer programme:

This involved in 2008/9 the selection from over 2.5 million farm households of more than 1.5 million fertiliser coupon beneficiaries, the printing and distribution of 5.9 million coupons, and purchase and distribution of over 3.4 million bags of fertiliser. All of this was done to tight deadlines, to widely dispersed farmers across the country (some in poorly accessible areas), with fraud and theft a major temptation and threat (the value of all subsidised commodities was approximately US$220 million, and of each fertiliser coupon was greater than 10% of annual household income for more than 40% of the population).


The authors estimate that the programme accounted for about 6, 8, 9 and 16 percent of the national budget in 2005/6, 2006/7, 2007/8, 2008/9 respectively. In turn, direct donor support for the programme was 9, 7 and 38 US$ million, which accounted for 9, 10 and 14 percent of the total financing for the programme. The allocation of national resources to the fertilizer subsidy programme were significant, therefore, and totalled 4.6 percent of GDP in 2008/2009, or approximately one-third of the aid inflows (Lea and Hanmer, 2009).


The impressive growth data reported from Malawi, and re-reported in the New York Times, following the re-introduction of the fertilizer subsidies was based on crop data collected by the Ministry of Agriculture published in the Malawi Annual Economic Report. These reported crop data were based on the last census, in 1992/1993, and the annual projections of agricultural production were built up by using yield and acreage observations from agricultural extension officers (Malawi, 2009).

However, a National Census of Agriculture and Livestock funded by the Norwegian Agency for Development Cooperation (NORAD) conducted by the National Statistical Office (NSO) was undertaken in 2006/2007, but not released until 2010. The reporting delay was an issue of concern (Malawi, 2009), but when it was finally published, the results were not accepted by the Ministry of Agriculture.[7] The problem was that the census showed remarkably lower figures for the total output of all crops (see Table), including the prestigious maize crop. Notably, the maize figures reported are much closer to national food needs, while the official figures in 2006/2007 would imply that either huge stockpiles of maize were accumulating or that the average Malawian was consuming something in excess of 4,000 calories a day, compared to the commonly assumed figure of around 1,500 to 2,000 (personal communication IMF, 2010).


Table: Census data and Ministry of Agriculture data compared,
2006/2007 crop estimates 
(metric tonnes)

(1) Figure cited in NACAL p xii; note that tables 3.8 and 3.9 show identical figures for sweet potato and groundnuts, so there is an error somewhere.

(2) Figure cited in NACAL pg. xii for beans, pulses, and groundnuts.

(3) Figure in MEPD report for pulses alone.


The main difference between the two reports stems not from average yield figures, but from how the data are aggregated. The issue of disagreement is the number of agricultural households, where the Ministry of Agriculture used a figure nearly 1 million higher than that used by the census. Statistical officers diplomatically stated that this may have been due to the different definitions of the household being employed (NSO, personal communication, 2010). Less diplomatically, there were hints that some farmers might have been invented in some cases in order to qualify for subsidies (NORAD, IMF personal communication, 2010). There was an economic incentive for both farmers and agricultural extension officers to ‘increase’ the numbers of households since the vouchers themselves have a market value. There is some evidence and growing concern that the vouchers are not reaching the right recipients, and that officials and local authorities are able to profit from them (Africa Research Institute, 2007; International Food Policy Research Institute, 2009).


There is no direct evidence of tampering with the data, but the indications that it happened are strong, as were the incentives to do so. Domestically, the President and the Ministry desired good, consistent performance in order to keep the electorate convinced of the continued success of the agricultural development strategy. Externally, the government needed to be able to convince donors that the fertilizer and seed programmes were working, to ensure that political and financial support would be forthcoming. However, donors have grown increasingly weary of the Mutharika government in recent years, and following political suppression of political protest during the summer 2011, donors, and central among them, the UK Department for International Development (DfID) have suspended aid to Malawi (for a discussion of the events see Wroe 2012, 135). This may in turn mean less funding for a fertilizer programme, which has undoubtedly been important for farmers’ livelihoods in Malawi, but which aggregate success has been overstated because of its political importance.[8]


Concluding Thoughts

The situation of agricultural data in Malawi fits into an established pattern of strong executive pressure on statistical authorities to get the particular data that the leadership needs, where the motivation is not to monitor the economy, but to affirm success (personal communication, Malawi Reserve Bank, NSO).[9]

The case of Malawi illustrates a general point that the state provision of subsidies and inputs is embedded in political economies marked by weak evidence across locations with varying political priorities. This provides ample room for a negotiation of the agricultural data used in monitoring the success of programmes. Pressures from above to ‘cheat’ are strong if politicians need to justify their policies. However, the causation does not only run from top to bottom. The very existence of subsidies, particularly in the form of per capita vouchers, does provide an incentive for the agricultural sector to expand spontaneously. It is in the interest of peasants and agricultural extension officers to ‘increase’ the numbers of farming households, not only to please superiors, but also because the vouchers themselves have a market value. The example from Malawi discussed above shows how the re-introduction of agricultural subsidies from 2005 onwards created a perpetual political demand for high growth rates, which was spontaneously met by peasants who oversubscribed to fertilizer vouchers.


The research for this paper has been funded by the Social Sciences and Humanities Research Council of Canada (3-year Standard Grant). The author would like to thank those who were willing to share their information during the field research conducted in Malawi in 2010. While writing the paper I got useful advice from Colin Poulton, Ronald J. Herring, Andrew Dorward and Brian Cooksey. An earlier draft of this paper was presented at the African Economic History Workshop held at the Graduate Institute in Geneva, May 2011, and at the African Studies Association Meetings in Washington DC, November 2010. The author would like to thank participants in these events for helpful comments. The author is also thankful to John Harriss who read an earlier version of this manuscript and provided very useful comments. The usual disclaimer applies.


Morton Jerven has recently published a book Poor Numbers:
how we are misled by African development statistics and
what to do about published by Cornell University Press.

The book is in the Cornell catalogue and from Amazon.

A podcast where he discusses his work on economic
statistics is also available.


Africa Research Institute (2007, December) Making Fertiliser Subsidies Work in Malawi. Briefing Note 0703.

Banerjee, A. and Duflo E. (2011). Poor economics: a radical rethinking of the way to fight global poverty. New York: Public Affairs 2011.

Banerjee, A. (2007) Making Aid Work. Cambridge, MA: MIT Press.

Chirwa, E.W., Kydd, J., and Dorward, A. (2006) ‘Future Scenarios for Agriculture in Malawi: Challenges and Dilemmas’. The Future Agricultures Consortium. Research Paper 3.

Collier, P. (2007) The Bottom Billion. Why the Poorest Countries Are Failing and What Can Be Done About It. New York: Oxford University Press.

Conroy, A. C., M. J. Blackie, A. Whiteside, J. C. Malewezi and J. D. Sachs (2006) Poverty, Aids and Hunger. Breaking the Poverty Trap in Malawi. New York: Palgrave Macmillan.

Dorward, A. and Chirwa E. (2011) ‘The Malawi Agricultural Input Subsidy Programme: 2005-6 to 2008-9.’ International Journal of Agricultural Sustainability, 9 (1). pp. 232-247.

Duflo, E., M. Kremer and J. Robinson (2008) ‘How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya’. American Economic Review, 98(2), 482-488.

Easterly, W. (2009) ‘Can the West Save Africa?’ Journal of Economic Literature, 47(2), 372–447.

International Food Policy Research Institute (2009) Fertilizer Subsidies in Africa. Are Vouchers the Answer? IFPRI Issue Brief 60.

Jerven M. (2013) Poor Numbers: How we are misled by African development statistics and what to do about it. Ithaca, NY: Cornell University Press.

Jerven, M (2012) “The Political Economy of Agricultural Statistics: Evidence from India, Nigeria and Malawi”, Simons Papers in Security and Development, No. 18, School for International Studies, Simon Fraser University, Vancouver, March 2012.

Lea, N., and L. Hanmer (2009) Constraints to Growth in Malawi. Policy Research Working Paper 5097, World Bank, Africa Region: Southern Africa Poverty Reduction and Economic Management Unit.

Malawi, National Statistics Office (2010) National Census of Agriculture and Livestock (NACAL). Zomba: NSO.

Malawi, “Peer Review of Malawi National Statistical System,” January 2009, Republic of Malawi and Partnership in Statistics for Development in the 21st Century, 26–30.

Nurkse, R. (1953) Problems of Capital Formation in Underdeveloped Countries. London: Oxford University Press.

Sachs, J. (2009, April 8) Homegrown Aid. New York Times, p. A23.

World Bank. (1997) Accelerating Malawi’s Growth: Long-Term Prospects and Transitional Problems. Washington, D.C.: World Bank Southern Africa Department.

World Bank. Malawi, Fertilizer Subsidies and the World Bank [Web post, last accessed January 2012]. Retrieved from

Wroe D. (2012) ‘Donors, dependency, and political crisis in Malawi’, African Affairs 111(442), pp. 135-144.



[1] For a full version of this argument, see Jerven (2012).

[2] Now frequently referred to as ‘poverty traps’ as in for instance (Collier 2007).

[3] For a basic introduction for how this old debate is most recently rephrased, see Banerjee and

Duflo (2011).

[4] For policy prescriptions on how to break the ‘poverty trap’ in Malawi with interventions such as fertilizer subsidy, see Conroy, Blackie, Whiteside, Malewezi and Sachs (2006).

[5] In addition to better weather conditions.

[6]  For an argument on how this relates to a broader range of statistics on African economies, see Jerven (2013).

[7] This information was obtained during interviews conducted at the National Statistical Office, the IMF Office, the Norwegian Agency for Development Cooperation and at the Reserve Bank Malawi in Lilongwe, Malawi in November 2010.

[8] President Mutharika died of a heart attack in Lilongwe on 5 April 2012, and the former vice-president, Joyce Banda was sworn in as the President of the Republic of Malawi; on 7 April. How this will affect the fertilizer programme, which has undoubtedly been important for farmers livelihoods in Malawi, but which aggregate success has been overstated because of its political importance, is as of yet unknown.

[9] See Jerven (2013).

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