metadata: authors: 0: author: Deininger,Klaus W.
1: author: Xia,Fang
count: Malawi
docna: 0: docna: Assessing effects of large-scale land
            transfers: challenges and opportunities in Malawi's
            estate sector
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Agricultural Economics,ICT Applications,Food Security
teratopic: Agriculture,Gender,Macroeconomics and Economic Growth
abstracts: cdata!: This study uses data from the complete
            computerization of agricultural leases in Malawi, a
            georeferenced farm survey, and satellite imagery to document
            the opportunities and challenges of land-based investment in
            novel ways. Although 1.5 million hectares, or 25 percent, of
            Malawi's agricultural area is under agricultural
            estates, analysis shows that 70 percent has expired leases
            and 140,000 hectares are subject to overlapping claims. This
            reduces revenue from ground rent by up to US$35 millon per
            year or 5 percent of public spending and, by decreasing
            tenure security, may affect the productivity of land use.
            Indeed a 2006/07 survey shows large farms underperforming
            small ones in yield, productivity, and intensity of land
            use, while failing to generate positive spillovers. Recently
            passed Land Acts create opportunities to clarify the
            boundaries and lease status for existing estates as a first
            step toward systematic demarcation of customary estates.
            Failure to follow this sequence could exacerbate insecurity,
            with undesirable effects on productive performance.
geo_reg_key: 517195,517191
repnme: repnme: Assessing effects of large-scale land
            transfers: challenges and opportunities in Malawi's
            estate sector
display_title: Assessing effects of large-scale
            land transfers: challenges and opportunities in
            Malawi's estate sector
listing_relative_url: /research/2017/09/27993827/assessing-effects-large-scale-land-transfers-challenges-opportunities-malawis-estate-sector
topicv3: Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Land Tenure m1326631 888,Responsible Agricultural Investment
            m1327121 1963,Digitization m1505678 3078
publishtoextweb_dt: 2017-09-20T00:00:00Z
historic_topic: Agriculture,Gender,Macroeconomics and Economic Growth
no_of_pages: 35
author_keys: 0: author_key: 96410
1: author_key: 1109580
model_extractions: WPS8200 Policy Research Working Paper 8200 Assessing Effects of Large-Scale Land Transfers Opportunities in Malawi’s Estate Sector _Klaus Deininger Fang Xia_ Development Research Group Agriculture and Rural Development Team September 2017
Policy Research Working Paper 8200

# **Abstract**

This study uses data from the complete computerization of agricultural leases in Malawi, a georeferenced farm survey, and satellite imagery to document the opportunities and challenges of land-based investment in novel ways. Although 1.5 million hectares, or 25 percent, of Malawi’s agricultural area is under agricultural estates, analysis shows that 70 percent has expired leases and 140,000 hectares are subject to overlapping claims. This reduces revenue from ground rent by up to US$35 millon per year or 5 percent of public spending and, by decreasing tenure security, may affect the productivity of land use. Indeed a 2006/07 survey shows large farms underperforming small ones in yield, productivity, and intensity of land use, while failing to generate positive spillovers. Recently passed Land Acts create opportunities to clarify the boundaries and lease status for existing estates as a first step toward systematic demarcation of customary estates. Failure to follow this sequence could exacerbate insecurity, with undesirable effects on productive performance.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or xia.fang.fx@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Assessing Effects of Large-Scale Land Transfers: Challenges and Opportunities in Malawi’s Estate Sector**

Klaus Deininger<sup>a</sup> Fang Xia<sup>b</sup> a World Bank, Washington DC b Research Institute for Global Value Chains, University of International Business & Economics, Beijing JEL Codes: J20, J21, J22, J23, J24, Q10, Q11, Q12 This paper would not have been possible without the support from the Ministry of Lands in particular by Atupele Muluzi, Ivy Luhanga, Charles Msosa, and Davie Chilonga as well as digitization of estate leases under Henry Kankwamba and Dan v.Setten. We also thank Daniel Ali, Blessings Botha, Thabbie Chilongo, Mercy Chimpokosero-Mseu, Alejandro de la Fuente, Time Fatch, Thea Hilhorst, Maxwell Mkondiwa, Valens Mumvaneza, Sam Katengeza, Richard Record, and two anonymous reviewers as well as the editor of this journal for helpful comments that helped to greatly improve the quality of the paper. Funding support from DFID and the German Government (BMZ via GIZ) is gratefully acknowledged. The views presented are those of the authors and do not necessarily represent those of the World Bank, its Executive Directors or the member countries they represent.
# **Assessing Effects of Large-Scale Land Transfers: Challenges and Opportunities in Malawi’s Estate Sector**

# **1. Background**

Since the 2007/08 commodity price boom, transfers of large tracts of land for agricultural production have been a key issue in policy debates on African agriculture (Collier and Dercon 2014; Cotula 2014; Deininger and Byerlee 2011). Yet, while there has been enormous interest in the size (Dell’Angelo _et al._ 2017; Holmen 2015), causes (Arezki _et al._ 2015), and the aggregate impact (Davis _et al._ 2014) of such transfers, actionable assessment of the extent to which transferred land is being used, the efficiency of such use, and potential impacts on neighboring smallholders has been limited. Evidence on these would be of importance for governments to manage public land transfers in ways that can reduce risks and maximize positive socioeconomic impacts. Latin America shows the advantages of combining administrative with remotely sensed data for real-time monitoring by the public (Assuncao _et al._ 2015) and private sectors (Gibbs _et al._ 2016), but use of such methods in Africa is still in its infancy (Lemoine and Rembold 2016).

In this paper, we show that land registries contain a wealth of information but that lack of maintenance and failure to use these data, partly because they were locked up in analog form, affected economic performance by reducing tenure security and the ability to harness land’s economic potential. We note that digitizing such data and combining them with survey information allow making inferences on the longer-term impact of large farm investment in Africa and drawing policy relevant conclusions for Malawi, a country where, as described in more detail in section 2.2 below, large areas had been transferred to estates in the 1980s and early 1990s (Mandondo and German 2015).

With some 1.35 million ha or about 25% of the country’s arable area, agricultural estates are an important part of Malawi’s rural economy. But their contribution to public revenue is negligible, as 70% of agricultural estate leases have expired and failure to index to inflation levels of the ‘ground rent’ that was supposed to be paid to the government for using the land has reduced revenue even for non-expired leases. Associated losses are large: charging half the market price for land rental would increase public revenue by US$35 million or 5% of total public spending a year. It also undermines incentives for record maintenance, possibly setting in motion a downward spiral whereby low rent collection makes record maintenance difficult while low quality of records implies that for any individual estate owner, spending resources on updating of records is not a desirable strategy. As a result, investment and productivity in the large farm sector remain low, smallholders fail to benefit from technology- or market-driven spillovers emanating from large farms, and an estate sector that could -with proper records to ensure management and integration into the broader rural economy- potentially have acted as a motor for rural change instead becomes an obstacle to progress.

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Spatial records are also of poor quality: 28% of agricultural estates have at least 20% of their area overlap with another estate, an issue affecting a total of 137,064 ha; and less than 5% of estates have a remaining lease term of more than 10 years, and thus a time horizon long enough to make longer-term investments, which may also reduce productivity directly. Linking spatial records to georeferenced farm survey data and satellite imagery allows us to show that weak record maintenance also affects performance. First, survey data suggest that for all crops with the exception of cassava, smallholders’ yields are significantly above those by estates. As estates use consistently more inputs than smallholders, this implies a negative relationship between farm size and productivity on the land area actually cultivated. Second, overlaying recorded estate boundaries with land use categories from supervised classification of medium-resolution satellite imagery implies that only about 40% of estate land is used for crop cultivation. While we lack data on soil quality, the fact that estate land was the best makes this rather surprising (and of course they could rent out to smallholders). Finally, and not surprisingly in light of the above, estates fail to act as a motor for the rural economy and a source of positive spillovers for neighboring smallholders, a function they were expected to perform when established.

Our findings are particularly policy relevant because in late 2016, after protracted debate, Malawi passed a series of Land Bills that aim to increase security of customary land users’ rights and overcome the dualism of the country’s post-independence tenure system among others by allowing sporadic registration of customary land under so-called ‘customary estates’. Literature suggests that low-cost, participatory, and systematic land tenure regularization can encourage investment and effectiveness of land use (Fenske 2011; Lawry _et al._ 2016), empower women (Ali _et al._ 2016a; Newman _et al._ 2015), and improve scope for lease markets to transfer land to more efficient operators (Ali _et al._ 2014). A sporadic approach that fails to first clarify the boundaries of land currently under estates; the status of rights to such land; and the ultimate owner of unutilized estate land (i.e. if it is government land that can be transferred to investors or reverts to the traditional domain) may -contrary to intentions- increase tenure insecurity, conflict, and inequality.

The paper is organized as follows: Section two situates the paper in the debate on large scale agricultural investment by highlighting the challenge of assessing productive efficiency by large agricultural enterprises and provides background on the evolution of Malawi’s estate sector. Section three discusses administrative and remotely-sensed data sources, using them to quantify the evolution of Malawi’s agricultural estates, to identify challenges to the quality of the textual and spatial land records, and to draw out implications in terms of public revenue and intensity of land use. Section four compares productivity between smallholders and estates and explores the extent to which the presence of estates benefits neighboring small farmers via technology or market access spillovers. Section five concludes with implications for policy and research.

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# **2. Background and justification**

A key obstacle to a more evidence-based and rational debate on large land-based investment has been the difficulty of obtaining systematic data to document large land transfers and measuring farms’ performance. We describe how absence of such information has made large farm transfers a politically highly contested issue in Malawi, where a policy to establish estates as a driver of rural change that was adopted in the 1980s was quickly stopped and led to protracted policy debate that resulted in the 2016 passage of a Land Bill, though regulations and implementation arrangements remain to be drafted.

# **2.1 The challenge of assessing large farm performance**

Almost a decade after concerns about large scale agricultural investment first appeared in the literature, there seems agreement that, beyond any direct benefits, e.g. in terms of lease fees, transfer of land to investors has the potential to generate positive indirect effects (Collier and Venables 2012). Such effects may be realized by ‘pioneer investors’ helping with discovery of agro-ecological suitability and provision of access to technology and markets for credit, input, labor, and output for local smallholders. The desire to harness such effects led to formation of agricultural investment promotion agencies all over the world.

In African countries with often large land areas not all of which is deemed to be fully utilized,<sup>1</sup> low quality and weak maintenance of analog records, weak technical capacity, and lack of transparency often limited the ability to satisfy conditions to generate benefits (Deininger and Byerlee 2011). This may result in uncoordinated or poorly recorded land transfers, weak or non-existent business plans and a promotion of speculators and urban elites (Sitko and Jayne 2014 ; Jayne _et al_ . 2016) rather than pioneers. Together with the high risk of such investments (Tyler and Dixie 2013), this often dashed high expectations. It also created a danger of unsuccessful investors trying to use political channels to affect factor prices, e.g., by trying to keep down labor cost or constrain access to capital, with potentially unfavorable long-term consequences.<sup>2</sup> Yet, although a large number of studies assessing the impact of specific investments now available provide valuable insights regarding the dynamics of establishment and performance of large farms in specific cases, the extent to which these are representative of the sector at large is difficult to ascertain. Addressing this issue would require dealing with two issues. First, data on the universe of land transfers are needed to avoid that results are due to sample or case selection.<sup>3</sup> Second, to be able to assess how policies affect outcomes,

> 1 Most of the land available for expansion in Africa is concentrated in few countries (Deininger and Byerlee 2012), with poor access to infrastructure and low levels of profitability (Chamberlin _et al._ 2014), and often also weak governance (Arezki _et al._ 2015).

> 2 The importance of this issue is demonstrated by the many historical examples where accumulation of large tracts of land by large but relatively inefficient farms led to rent-seeking behavior and, using their locally dominant position, to monopolize input or output markets (Binswanger _et al._ 1995), subvert provision of public goods such as education (Nugent and Robinson 2010; Vollrath 2009), undermine financial sector development (Rajan and Ramcharan 2011), or restrict political participation (Baland and Robinson 2008).

> 3 If only one agency can transfer land and records are good, a complete transaction record is sufficient. If multiple agencies are involved, a fieldbased sample frame, ideally constructed and maintained by the national statistical agency is needed.Ali _et al._ (2017) illustrate this for Ethiopia.

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time series information will be desirable. Traditionally this has come from censuses or sample surveys. Linking georeferenced surveys to digital administrative data opens up new possibilities for analysis. Also, routine availability of satellite imagery has opened up new avenues for analysis (Donaldson and Storeygard 2016). Machine learning algorithms using medium-scale imagery at rather high frequency that is now available freely on cloud-based platforms have been shown to generate information on land use and potentially even crop type or yield at field level as long as fields are of a minimum size (Lobell _et al._ 2015). Combining such data with administrative records could help address many of the issues that traditionally impeded routine monitoring of large investments’ performance and we use the case of Malawi to illustrate how this could be done in practice.

Malawi is of interest due to the scale of large farm investments and the length of time for which these have been in operation. Some 20-25% of the country’s land was leased to commercial farms in the late 1980s to help commercialize the sector and partly to overcome shortcomings in regulatory regimes for customary tenure. The time elapsed since then allows discerning longer-term impacts and identifying challenges not yet apparent in cases where land transfers have happened more recently.

Bringing together administrative data with those from other sources allows us to contribute methodologically to describe gaps in such data and to assess how they may have affected the extent to which benefits from estates did materialize. As Malawi has just passed new land laws, the implementation of which still needs to be regulated, insights from such analysis can directly feed into the policy debate. In particular, efforts to implement new policies without first resolving pending issues with estate leases or substantially improving the quality of record keeping risk adding just another layer of unconnected ‘rights’ that could increase complexity and conflict potential.

# **2.2 The evolution of Malawi’s estate sector**

Malawi has traditionally been characterized by a dualistic land tenure structure geared towards cash crop production. In colonial times, cultivation of tobacco, the country’s main cash and export crop, was restricted to white settlers who had preferential access to land, labor, and credit (Binswanger _et al._ 1995), and guaranteed market access via a quota system (Mataya and Tsonga 2001). After independence in 1964, estate land was transferred to Malawians (Jaffee 2003) with direct and indirect public support: Until 1994, only estates were allowed to produce tobacco and smallholders had to sell their output to the marketing board at low prices. The surplus thus generated was funneled to estate owners in the form of soft loans, thus providing an implicit subsidy that reinforced the dualistic structure of the country’s agriculture (Kydd 5
and Christiansen 1982).<sup>4</sup> Thereafter, tobacco quotas were gradually extended to smallholders by licensing clubs of 10-30 members. Rapid take-up led to marked improvements in socio-economic indicators (Jaffee 2003) and soon brought small farmers’ share in tobacco production to some 70% (Lea and Hanmer 2009).

Yet these reforms did little to improve smallholders’ tenure security under customary tenure that historically allowed egalitarian land access and high levels of security by community members (Bruce and MigotAdholla 1994) but over time came under increasing stress. Land scarcity due to population growth, migration, and urban expansion, increased the frequency of land transactions with outsiders (Ricker-Gilbert _et al._ 2014). As these are liable to <mark>challenges</mark> (van Donge 1999 <mark>), often after long p</mark> eriods of dormancy <mark>(JulLarsen and Mvula 2007), p</mark> erceived tenure insecurity increases (Lovo 2016; Place and Otsuka 2001) with negative impact on output, especially by females (Deininger _et al._ 2017).

To boost commercial crop production, 21-year leases to a large number of estates, most sized from 10 to 30 hectares were, in the late 1980s, carved out of what was deemed unutilized customary land and transferred to aspiring farmers (Devereux 1997; Mandondo and German 2015).<sup>5</sup> The formal process to obtain a lease comprised four steps (van Setten 2016): An application stating size, intended use, and location of the desired piece of land (normally a sketch map), together with a ‘no objection’ document by the chief certifying that neither chief nor village headman object to the proposed transfer had to be submitted. Having validated the application, the government issued an offer that details the length of the lease, permitted land use, assessed fees, and annual ground rent, ideally accompanied by a survey plan that describes the property’s location more precisely. Acceptance transformed the offer letter into a preliminary lease contract. The lease contract would then be formalized by a deed that is formally registered. As each step normally required side payments, the process followed in reality was often quite different or remained incomplete.<sup>6</sup> Dissatisfaction with the results of such a strict distinction between estates and the customary sector led to a moratorium on lease issuance in 1994 together with the launch of a more comprehensive land policy reform process.<sup>7</sup> In 2016, this culminated in Parliamentary approval of a series of Land Bills, key provisions of which are discussed below. The new Land Act limits the land rights of non-nationals and classifies land into public (government or unallocated customary land) or private (freehold, leasehold, and customary estates). ‘Customary estates’ are defined as all land owned, held or occupied as private land within a

> 4 Transactions were directly supported through loans from the Farmers Marketing Board (FMB), a successor to the Native Tobacco Board, later transformed into the Agricultural Development and Marketing Cooperative (ADMARC). Indirect support came from restricting tobacco cultivation by smallholders and from establishing ADMARC as the sole marketing option with a power to fix prices (Mandondo and German 2015). 5 As access to a minimum of 12 ha of land was required to access tobacco marketing quotas, an unknown number of so-called ‘ghost estates’ was established, often in office-based processes without corresponding to actual land on the ground.

> 6 Failure of having certain processes completed may imply that formal or informal transaction costs of doing so are too high or perceived benefits from doing so too low, possibly as a result of the government not maintaining records properly. A more detailed assessment of the extent to which processes can be simplified or informal (transaction) cost reduced may be needed to identify specific policy recommendations.

> 7 A Presidential Commission had been established in 1996 and submitted a report (Saidi 1999) that prompted adoption of a National Land Policy and implementation strategy in 2002. Draft legislation was submitted to Parliament in 2006.

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traditional land management area (TLMA). The Customary Land Act defines mechanisms for registration of customary estates, formalizes the role of chiefs in land allocation and conflict resolution, and mandates establishment of land committees and land tribunals at the TA, district, and national level to perform this role.<sup>8</sup> It allows for systematic identification and recording of parcel boundaries to be followed by adjudication of rights that is impossible without key policy decisions having been made about renewal/cancellation of leases for existing estates.<sup>9</sup> The Survey Act creates opportunities to use general boundaries and modern technology, opening the door for using low-cost (US$ 5-6 per parcel) approaches as in neighboring countries (Nkurunziza 2015). It also provides for surveying of TLMAs as part of national spatial data infrastructure. Also, the Registration Act decentralizes registries to district level, and stipulates filing requirements including provision of registry maps to chiefs. The Physical Planning Act expands the reach of planning beyond urban areas.

# **2.3 Earlier evidence on estate sector performance**

The 1997 Estate Lands Utilisation Study or ELUS remains a key source of information on the estate sector (Ministry of Lands and Valuation 1997). The fact that records were incomplete and paper-based made drawing a sample difficult. Eventually the study sample was drawn listing all estates in 59 10x10 km blocks in 9 districts which, according to official records, had the highest concentration of estates.<sup>10</sup> On this basis, the universe was estimated to comprise 29,000 estates with an area of 916,815 ha. Some 57% of estate land was found to have been newly cleared with the remainder having been used as customary land before; in fact a sizeable share of estate owners seem to have converted land they previously farmed under customary tenure, either to be able to grow tobacco (the most prevalent reason in the Center) or to increase tenure security (the most prevalent reason given in the North and South). Despite Malawi’s relative land scarcity, 75% of estate owners reported to have suitable land that they did not utilize. In tobacco estates, 29% of suitable land was not utilized, a share that varied between 50% in the North and 25% in the Center. Economic performance in terms of yield per ha was best in the size groups below 20 ha or above 500 ha. Interestingly, good performance was strongly positively correlated with land use intensity. About half of owners are absentee ones and 25% indicated that they rarely visited their estates. This may encourage

> 8 So-called Traditional Land Management Areas (TLMAs) at Group Village Headman (GVH) level, as identified in a certificate and map of customary land (CCL), are established as basic spatial units. In each TLMA, a customary Land Committee (CLC) with six elected members (half women) and chaired by the GVH chair will be supported by a Land Clerk, an employee of the local assembly. The CLC, in collaboration with the TA, can grant individuals customary estates of indefinite duration and register rights to these.

> 9 This requires policy decisions on (i) how to define an estate, how to define idle land, and what to do with land that had been leased to estates but is no longer used as an estate (e.g. subsistence farming as a result of sub-division or transfer); (ii) what action to take in case of lessees’ failure to comply with lease conditions (either in terms of non-compatible land uses or failure to pay ground rent); (iii) how to adjust estate boundaries in case of imprecise original surveys and expansion or contraction of the originally leased area; and (iv) lease terms including levels of ground rent to be charged for renewal of leases on land that is lawfully occupied by estates; and (v) procedures for re-allocating unused estate land, in particular the role of TAs and other local institutions in this process.

> 10 These districts are Rumphi, Mzuzu, Kasungu, Dowa, Lilongwe, Nkhotakhota, Mangochi, Machinga, and Zomba. The listing yielded a total of 3,908 estates out of which some 500 were chosen for a more detailed survey.

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encroachment which was an issue on 52% of estates above 500 ha, though on a much smaller share (5%) of estates below 20 ha. Tenancy was widespread, with some 72% reporting to employ tenants who were estimated to account for 52% of estates’ labor force. Finally, public land records were often incomplete or of low quality: in about one-third of cases, estates identified in the field could not be located on maps by Ministries of Lands or Agriculture and that 45% had not completed the prescribed process to obtain a registered deed.

# **3. Using new data to describe land rights and use in Malawi’s estate sector**

Digitization of lease contracts allows us to trace the evolution of Malawi’s estate sector. Contracts’ textual components highlight that most leases have expired so that foregone public revenue is large and land may no longer be used as designated. Leases’ spatial components point towards significant overlaps that may reduce tenure security, undermine investment incentives, and discourage intensive land use, a notion that is indeed supported by categorization of land use based on overlays with medium resolution imagery.

# **3.1 Assessing historical evolution and revenue potential of the estate sector**

A major reason for the difficulties in effectively managing estate leases was that even textual data were stored on paper, distributed among three registries, and thus very difficult to access. To make data available for analysis, computerization of all documents, supported by a World Bank project,<sup>11</sup> was thus an essential first step. Using the original establishment data, figure 1 illustrates the changes in estate numbers and the area under agricultural estates. Table 1 illustrates that from a basis of 16,725 ha registered estates in the preindependence period (155 estates with average size of 124 ha), large scale land transfers accelerated considerably after independence in three main phases.<sup>12</sup> First, in the period to 1986, 2,277 new leases with a total area of 237,322 ha were awarded, i.e. 104 leases with an average of 105 ha implying a total transfer to leasehold of some 10,800 ha each year. A second phase, from 1986 to 1994, saw the number of leases issued each year multiply more than 25 times to 2,626 per year but the average size declined to some 25 ha, implying a total transfer to leasehold of some 65,000 ha per year.<sup>13</sup> In the period following the 1994 moratorium, overall issuance of new agricultural leases dropped sharply to 176 leases or transfer of 7,800 ha per year. The sub-period before 2007 saw issuance of slightly more but smaller leases while after 2006, the average size of leases increased but fewer new leases were issued. While the majority was issued in 1988-95, issuance of leases continued apace for non-agricultural estates.

> 11 Leases were digitized by a team from Lilongwe University of Agriculture and Natural Resources (LUANAR). Given the limited number of documents and the lack of staff with the relevant experience, the cost of digitizing textual and spatial data was about US$ 3 per lease. 12 These figures exclude a limited number of freehold estates that had been established before independence. Records for these are in a separate registry the digitization of which is planned jointly with that of the deeds registry.

> 13 With a mean size of 6.6 ha, ranging from 16 ha in the North to 2.5 in the Center, urban leases seem more akin to layouts, and computerization of deeds could yield interesting details on subsequent transactions.

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Descriptive statistics based on the digitized leases show how, by making available administrative data that thus far had been locked up on paper, computerization can expand transparency and opportunities for policy action and analysis. Focusing on textual data only,<sup>14</sup> table 2 shows that, with some 1.5 million ha (1.35 and 0.14 in agricultural and non-agricultural estates, respectively) in 58,733 leases (35,140 and 23,593 for agricultural and non-agricultural land), total area under estates is larger than had been estimated by ELUS. Agricultural estates measure 39.8 ha on average, with the largest ones in the South. While most agricultural estates are in the 10-30 ha group, 6% (952,847 ha) and 0.6% (603,705 ha) of estate area is in estates larger than 50 or 500 ha, respectively. Data suggest that the prescribed process for obtaining a lease was not always completed; in fact, only 36% of all leases (42% of agricultural ones) are supported by a deed. And 34% (37% of agricultural ones) have only a letter of offer and 30% (21% of agricultural ones) remained at the application stage. The quality of spatial documentation varies; while 2% of leases for agricultural estates (and 18% for non-agricultural ones) are surveyed and accompanied by a deed plan, 52% (and 66% for nonagricultural ones) have not advanced beyond the sketch plan whereas for 46% (and 16% for non-agricultural ones) the sketch was redrawn by the survey department.<sup>15</sup> For 7,819 agricultural estates with a total area of 404,584 ha, documents lack data on lease duration.<sup>16</sup> With a mean annual rent of less than US$ 1/ha for agricultural estates and US$ 27/ha for non-agricultural ones, the value of public revenue from such rents eroded over time, implying that yield may be below the cost of collection. To illustrate the potential revenue from agricultural leases, we note that, according to the 2010/11 Living Standards Measurement Survey (LSMS-ISA), the mean price of an existing lease is US$ 58/ha and the price at which respondents would be willing to lease in additional land is somewhat above $50/ha.<sup>17</sup> Even a compliance rate of 50% could generate annual lease revenue of some US$ 35 million in addition to providing strong incentives for effective land use.

The potential for collecting ground rent is further eroded by the fact that, in 2016, leases for 70% of agricultural estates had expired and 22% were indeterminate (compared to 9% and 48% for non-agricultural ones, respectively). In fact, with 3% due to expire in less than 10 years, only 5% of agricultural estates (vs. 41% of non-agricultural ones) had remaining lease terms beyond 10 years. This could negatively affect productivity by increasing tenure insecurity and undermining investment incentives and also by limiting

> 14 We report differences in estate sizes between the lease record and the spatial analysis of mapped boundaries in appendix table 1. 15 Sketch Plans are plans that have been validated by a licensed surveyor, most of them private companies, but are generally of low quality and accuracy. Survey Drawn (SD) sketch plans normally just involve reproduction of the information provided in application sketch plans by the Survey Department in a homogeneous format without conducting a (re-)survey in the field. Deed plans are resurveyed by the Surveys Department and thus of much higher geographical accuracy.

> 16 Discussions suggest that many individuals might have believed that omission of the start date or duration of a lease would imply that their lease was de facto of unlimited duration and in some cases such omission may have been associated with fraudulent practices.

> 17 Please note that the LSMS-ISA sample intentionally excludes the estate sector, thus the lease rate refers to what is paid outside the estate sector.

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the scope for efficiency-enhancing transfers of land to operators with higher levels of ability. Data on estate performance could allow assessing the extent and the incidence of such insecurity and policy implications.

# **3.2 Assessing overlapping rights using spatial data**

Beyond the textual information discussed above, complete digitization allows us to use spatial data to assess record quality by exploring overlaps among records. The most basic way of doing so is to check for overlaps in the data, which, if records are correct, would imply that land was simultaneously transferred to two different owners. Figure 2 illustrates this by displaying (in black lines) recorded boundaries for all estates as per the registry in one district. Even cursory inspection reveals a large number of substantial overlaps that are unlikely to be due to limited precision of the survey technology used when issuing leases.

District-level figures from analysis of the spatial part of estate leases in table 3 show that 28% of agricultural estates have at least 20% of their area registered to two different owners. Such double-registration affects 10.2% of the area under agricultural estates or 137,064 ha.<sup>18</sup> The share of double-registration varies across districts: figures are highest in Balaka (55%), Kasungu (18%), and Mzimba (9%). The table also highlights cross-district variation in the share of leased area that has expired, a figure that is highest in Dowa (84%), Mzimba (70%), and Mangochi (43%), with a national average of 48%. Double-registration of agricultural estates by lease validity suggests that the problems are slightly more frequent for expired leases (appendix table 2). If this reduces tenure security and incentives for investment or effective land use, a systematic process of ground verification may be needed.

An expanding literature highlights the potential of using remotely sensed imagery for crop forecasting and early warning (Basso _et al._ 2013), including the assessment of cultivation status and possibly yields at field level based on machine learning (Lobell 2013). To capitalize on these advances, medium resolution SPOT imagery from 2013-14 was used to obtain an estimate of the share of registered estate land under different types of land cover (Van Setten _et al._ 2014).<sup>19</sup> Subject to the caveats regarding quality of spatial data noted earlier, these estimates suggest that a sizeable share of estate land seems to be not used for crop production.

The figures in table 4 show that, with some 42% of land under crops in the aggregate, intensity of land use in the estate sector has not increased compared to what had been found by ELUS, implying that farms fail to comply with original plans of full land utilization but at the same time fail to lease out their land. Only about 18% of estates are estimated to use 70% or more of their land for crops. Intensity of land use is highest in the size group below 20 ha, lowest in the 50-500 ha group, and then again increases slightly in the above 500 ha group, similar to what was found by ELUS and in line with the narrative of significant amounts of

> 18 We chose the 20% cutoff to exclude small and non-substantive overlaps that may be due more to the accuracy of mapping. 19 Categories used were maize, other crops, grassland, savannah/shrubs, forest, and built up area including bare land and waterbodies.

10
‘idle’ estate land. Obtaining a more reliable estimate of the extent to which land currently assigned to estates is unused or underused, though beyond the scope of this paper, would be desirable given the size of estimated economic impacts and the fact that such analysis is no longer too difficult.<sup>20</sup> Such information would be an important basis for policy decisions, e.g. whether (or when) to let estate land that is not used revert to customary authorities.

# **4. Exploring estates’ contribution to agricultural productivity**

We use georeferenced survey data from NACAL to assess whether estates help to increase the productivity of land use either directly or indirectly. Direct effects are approximated by comparing levels of yield, input use, and land use intensity between estates and smallholders. Indirect effects are identified by exploring if smallholders’ location on or in close proximity to estates affects their levels of input use, output, or profit.

# **4.1 Comparing land use and productivity between smallholders and estates**

While administrative data on estate boundaries allow a rough assessment of land under crops via overlays with satellite imagery, information on production and yields requires survey-based information. We use the 2006/07 National Census of Agriculture and Livestock (NACAL).<sup>21</sup> This survey contains information for the 12-month period starting in October 2006 for both smallholders and estates: estates were drawn from a nation-wide list and the survey identified smallholder farms in a two-stage process. Enumeration areas (EAs) were first randomly selected by district with stratification by agro-ecological zone. In selected EAs, a listing was then undertaken and farm households were drawn randomly from the list aiming for 10 small (< 2 acres) and 5 medium sized (≥ 2 acres) farms per EA.

For smallholders, information on household composition, assets, and plot-level production as well as GPS coordinates was collected. Useable data on GPS coordinates are available for 20,677 observations. Information on socio-economic characteristics and production in appendix tables 3 and 4 shows that nationally about 9% -from 16% in the Center to 3% in the South- live as tenants or squatters on an estate. Compared to the rest, the latter cultivate a slightly larger area (1.05 vs. 0.67 ha) and devote a higher share of their land to tobacco, but there is little evidence of differences in terms of intensity of input use, and profits for maize are actually slightly lower.

Table 5 illustrates that on average the 868 estates in the sample had an age of 19 years with largest estates the oldest. Most (73%) are owned by Malawian persons, 11% by ‘others’ -most likely legal entities- and

> 20 Availability of free imagery (sentinel 1/2) at higher temporal and spatial resolution, together with algorithms that can be run on platforms such as Google Earth Engine (GEE) makes analysis much easier.

> 21 The main reason for using the NACAL is that, in contrast to other surveys such as the recent Integrated Household Surveys, it covers estates and smallholders. While we were unable to access the frame used to draw the estate sample (or estate coordinates), we assume that only functioning estates were interviewed, implying that there is some selection that needs to be accounted for when interpreting the results. Although the age of the survey and these shortcomings suggest that a new survey of estates’ productive performance would be highly desirable, fieldwork conducted in a number of regions leads us to believe that the general conclusions derived here are still valid.

11
10% by expatriates. The ownership share of expatriates and government peaks at 100-500 ha and that of ‘others’ in the > 500 ha group. About a third of estates have tenants; the share of estates with tenants peaks at close to 50% in the 10-100 size category. In contrast to other countries where large farms produce bulk commodities and often generate little employment (Ali _et al._ 2015), many of Malawi’s estates are labor intensive. Permanent or temporary male (female) labor is hired by 64% (27%) and 70% (56%) of estates respectively. Demand for permanent labor per ha increases with size to about 0.9 males and 0.6 females in the largest category though the pattern for temporary labor is more volatile.

Comparing smallholders to estates provides interesting insights in a number of respects (table 6). First, for estates, 15% of allocated land is operated, a share that decreases from 88% in the group below 5 ha, a figure that is comparable to the intensity of land utilization by smallholders, to 12% in the above 500 ha group (table 6). _Prima facie_ this provides some support for claims about unused or underused estate land that have been a recurrent theme in Malawi’s policy debate (Holden _et al._ 2006). Second, production structure and cropping patterns differ between smallholders and estates: 42% of estate area is devoted to tobacco, followed by maize (39%), groundnuts (7%), and other crops.

The data also suggest that for all crops except cassava smallholders’ yields are significantly above those by estates. Non-parametric regressions for yields of tobacco, maize, groundnuts and cassava against the log of farm size using the pooled sample of smallholders and estates in figure 3 graphically illustrate that, with the possible exception of cassava, adding large farms to the sample of smallholders does not lead to a reversal of the negative relationship between farm size and yields on land area actually cultivated; to the contrary the relationship is robust and rather tightly estimated. While these are yields rather than profits, the share of estates using purchased inputs and the mean per hectare value of such inputs by those who use them is significantly above the equivalent figure for smallholders. This suggests that the relationship between farm size and profits is unlikely to be positive. It would be of great interest to examine if profits or land use intensity (by the estate owner or tenants) are higher on estates with no overlapping registered claims or valid lease documents to explore if, say, tenure insecurity reduced productivity or prevented estates from enhancing income and overall production by leasing out part of their land to smallholders or increasing the number of tenants they employ. Unfortunately, estate data are not georeferenced, making overlays with administrative records that would be needed to conduct such analysis impossible.

# **4.2 Assessing impacts of estates on nearby smallholders**

If access to modern technology is limited or factor markets are imperfect, commercial farm establishments may benefit neighboring smallholders by improving their knowledge of improved techniques and allowing easier access to factor and output markets. The rationale for the latter is that if the volume of potential transactions in any given location is limited, high transaction costs may well ration smallholders out of such 12
markets (Key et al. 2000) even if they had working capital and would not depend on credit. To the extent that they use certain inputs or produce outputs for the market, estates can then provide market access to neighboring smallholders, potentially on implicit credit. An additional source of positive spillovers is through employment on estates that can increase smallholders’ demand and potentially relieve their borrowing constraints. Small farmers who work on estates as casual workers may also acquire knowledge about new techniques or pick up specific skills that will be useful on their own farms. Beyond such beneficial effects, the literature has long pointed out that large farms may compete with local smallholders for resources, most prominently land (German et al. 2013; Schoneveld 2014) but also water (von Braun and Meinzen-Dick 2009; Rulli et al. 2013).

Spatial proximity as a channel for transmission of spillover effects between investors and neighboring households has been used to investigate economic and social impacts of mine openings or closings (Chuhan-Pole _et al._ 2015), including on female empowerment (Kotsadam and Tolonen 2015). Although more limited, evidence from Zambia (Ahlerup and Tengstam 2015; Sipangule and Lay 2015), Nigeria (Adewumi _et al._ 2013), Mozambique (Deininger and Xia 2016) and to some extent Ethiopia (Ali _et al._ 2016b) suggests that a similar framework can be used to assess the impacts of large farm investment on neighboring small farmers. While for the case at hand lack of panel data on smallholders makes it impossible to identify causal impacts<sup>22</sup> , we can use simple regressions as a descriptive device to assess whether, after controlling for other factors, smallholders’ location on or distance to an estate, with or without a valid lease, affects their production outcomes. To do so, we estimate where _Yijk_ is the variable of interest, i.e. either the value of inputs or crop output and profit by household _i_ in village _j_ of district _k_ ; _αk_ is a vector of district fixed effects; _Sijk_ is an indicator variables for smallholders located within an agricultural estate; _Dijk_ is the distance to the boundary of the next agricultural estate for those not located within an agricultural estate.<sup>23</sup> To distinguish by validity of estates’ leases, we further add interactions between indicator variables for validity of leases and _Sijk_ and _Dijk_ . _β_ and _γ_ are the parameters to be estimated. ε _ij_ is an error term clustered by the closest agricultural estate.

Results in table 7 suggest that, largely as a result of larger area cultivated, location on or proximity to an agricultural estate is associated with higher levels of output (col. 6). This does, however, not translate into higher levels of productivity; in fact for squatters on agricultural estates, output and profit per hectare (cols. 1 and 2) are negative and significant and per-hectare profits (col. 1) are higher only for smallholders in

> 22 For example, selective immigration or outmigration may explain the differences between smallholders located within and outside an agricultural estate.

> 23 The distance is set to be zero for smallholders located within an agricultural estate.

13
closer proximity to the boundary of estates with non-expired leases. While further exploration of this issue with better data would be warranted, this suggests that any indirect benefits from estates will be quite limited. Non-parametric regressions for profits in maize and tobacco in figure 4 also support this notion, although wide confidence intervals point towards enormous heterogeneity especially far from estate boundaries.

# **5. Conclusion and policy implications**

A decade after the emergence of high demand for large scale agricultural land acquisition, no consensus has yet emerged on how to analytically tackle the issue of large farm investment. Our paper contributes to this debate from a methodological and a substantive point of view. Methodologically, we show how combining administrative records with georeferenced survey data and remotely sensed imagery can help address many of the issues that traditionally impeded routine monitoring of large agricultural investments’ performance that could then trigger swift action in case of deviations. Substantively, we find that Malawian estates, most formed in the late 1980s to ‘commercialize’ the agricultural sector, failed to live up to their potential using 2006/07 data: With few exceptions yields were below and input use above those of smallholders who as a result seem to have derived few spillover benefits, either in terms of technology or market access. Detailed investigation of land records suggests that loss of public revenue and tenure insecurity emanating from expired lease records, most of which were of low quality to start with, may be a key contributing factor by making it difficult for government to collect ground rent revenue, encouraging speculative instead of productive land use, and -via tenure insecurity- lower intensity and productivity of land use. A failure to maintain and use administrative data not only undermines generation of the revenue to deal with the issue but may also give rise to a collective action problem whereby each private lessee will not have an incentive to keep records up to date although society would greatly benefit from it.

To unleash the potential of properly run estates to contribute to the diversification of Malawi’s agricultural sector, there is need to renew, cancel, or renegotiate existing estate leases in a systematic process that could then form the basis for continued monitoring of lease performance in near real time. Policy decisions on procedures for lease renewal, in particular setting levels of ground rent that are realistic and procedures on how to deal with accumulated ground rent arrears are one precondition for such a process to be feasible. A second precondition is that a clear hierarchy of evidence among competing claims be established and procedures developed to ascertain and adjudicate rights in a way that deals with discrepancies, or overlaps through administrative mechanisms be developed.

The recent approval of a set of Land Acts creates enormous opportunities to link clarification of estate leases to securing smallholder land rights, but also holds a risk of unintended consequences whereby, instead of improving tenure security, sporadic adjudication processes will add yet another layer of complexity to a set of land records that already contains numerous overlaps and thus exacerbate tenure 14
insecurity and increase the level of disputes. To avoid this, it will be essential to allow sporadic adjudication processes only in areas where TLMAs have been determined and estate leases have been clarified in a process immediately followed by adjudication and registration of customary land ownership in an integrated and systematic process.

Future research, ideally building on more recent data, would be desirable in a number of areas, in particular by (i) exploring the link between land and other factor markets; (ii) ascertaining more carefully current and past land use on estates that continue or have ceased to function as estates; and (iii) exploring heterogeneity of outcomes among estates as well as smallholder producers in greater detail. This can be linked to more careful documentation of administrative processes, the associated transaction costs, and how these link to the political economy at the local level. All of these would provide important input into the design and possibly evaluation of a forward-looking program of tenure regularization to improve the productivity and resilience of Malawi’s agricultural sector and, given this sector’s importance, its overall economic performance.

15
**<u>Table 1: Evolution of number and area under agric. and non-agric. estate leases</u>**

|||**1909-64**|**1965-86**|**1987-94**|**1995-2016**|**By sub-**|**period**|
|---|---|---|---|---|---|---|---|
|||||||**1995-2006**|**2007-16**|
|**Panel A: Cumulat**<br>_Total_|**ive figures**|||||||
|Area transferred|1000 ha|17.95|259.12|779.05|960.06|864.62|960.06|
|No. of leases|No.|648|5,281|27,282|39,695|33,252|39,695|
|_Agric._||||||||
|Area transferred|1000 ha|16.73|254.05|772.85|944.18|853.34|944.18|
|No. of leases|No.|155|2,432|23,439|27,321|26,202|27,321|
|_Non-agric._||||||||
|Area transferred|1000 ha|1.23|5.08|6.21|15.89|11.29|15.89|
|No. of leases|No.|493|2,849|3,843|12,374|7,050|12,374|
|**Panel B: Period in**|**crements**|||||||
|_Total_||||||||
|Area transferred|1000 ha|17.95|241.17|519.93|181.01|85.57|95.44|
|No. of leases|No.|648|4,633|22,001|12,413|5,970|6,443|
|Mean lease size|ha|29.24|52.80|23.79|14.87|14.84|14.89|
|_Agric._||||||||
|Area transferred|1000 ha|16.73|237.32|518.82|171.33|80.49|90.84|
|No. of leases|No.|155|2,277|21,007|3,882|2,763|1,119|
|Mean lease size|ha|123.90|105.15|24.73|44.13|29.43|81.47|
|_Non-agric._||||||||
|Area transferred|1000 ha|1.23|3.85|1.13|9.68|5.08|4.60|
|No. of leases|No.|493|2,356|994|8,531|3,207|5,324|
|Mean lease size|ha|2.56|1.67|1.30|1.16|1.67|0.87|
|**Panel C: Annual i**<br>_Total_|**ncrements**|||||||
|Area/year|1000 ha|0.32|10.96|64.99|8.23|7.13|9.54|
|Leases/year<br>_Agric._|No.|12|211|2,750|564|498|644|
|Area/year|1000 ha|0.30|10.79|64.85|7.79|6.71|9.08|
|Leases/year<br>_Non-agric._|No.|3|104|2,626|176|230|112|
|Area/year|1000 ha|0.02|0.18|0.14|0.44|0.42|0.46|
|Leases/year|No.|9|107|124|388|267|532|

_Source:_ Own computation from the National Geographical Estates Database.

16
**<u>Table 2: Descriptive statistics of estates by lease status</u>**

||**Total**||**Non-agric**|**. estates**|||**Agric.**|**estates**||
|---|---|---|---|---|---|---|---|---|---|
|||**All**|<br>**North **|<br>**Center**|**South **|**All**|<br>**North **|<br>**Center**|**South **|
|**General characteristics**||||||||||
|Total area (1,000 ha)|1,487.44|138.68|45.52|20.30|72.86|1,348.76|230.63|871.61|246.52|
|Mean area (ha)|27.10|6.60|15.98|2.54|7.17|39.80|39.49|35.12|76.23|
|Signed before 1988 (%)|18.29|27.71|26.48|37.46|20.03|13.82|11.30|13.74|18.91|
|Signed 1988 to 1995 (%)|56.25|7.59|8.79|7.14|7.66|79.31|81.39|81.99|52.67|
|<br>Signed after 1995 (%)|25.47|64.70|64.74|55.40|72.32|6.88|7.31|4.27|28.41|
|<br>Length of lease (years)|40.71|76.77|81.09|64.19|86.24|24.35|24.52|23.41|32.46|
|Lease length <=21 years (%)|47.88|9.55|5.85|15.35|5.80|73.62|65.85|77.92|55.40|
|Lease length >21 years (%)|19.79|43.04|35.27|38.47|49.73|4.19|3.67|3.24|12.27|
|Size less than 10 ha (%)|42.60|97.83|97.05|98.44|97.57|8.37|7.98|5.86|28.29|
|Size 10 - 30 ha (%)|45.62|1.09|1.72|0.90|1.05|73.22|71.92|78.21|37.20|
|<br>Size 30 - 50 ha (%)|5.90|0.25|0.28|0.16|0.31|9.40|9.81|8.98|11.90|
|<br>Size 50 - 100 ha (%)|3.21|0.26|0.32|0.18|0.32|5.04|5.91|4.11|10.58|
|Size 100 - 500 ha (%)|2.12|0.33|0.14|0.24|0.45|3.24|3.65|2.22|10.30|
|Size above 500 ha (%)|0.55|0.25|0.49|0.09|0.31|0.74|0.74|0.61|1.73|
|**Formal documentation**||||||||||
|Has deed (%)|35.80|26.51|18.72|25.66|29.97|42.03|43.33|40.07|54.49|
|<br>Has offer (%)|65.49|49.51|39.06|49.33|53.34|76.22|68.76|79.53|64.93|
|Has offer but no deed (%)|34.47|30.56|26.61|33.09|29.76|37.09|27.70|41.91|17.86|
|Lease indeterminate (%)|32.41|47.54|58.91|46.38|44.57|22.25|30.50|18.90|32.45|
|Sketch plan (%)|56.76|65.81|58.18|67.65|65.81|51.65|52.07|52.85|41.94|
|SD plan (%)|35.53|16.14|23.08|16.73|14.42|46.47|46.61|45.94|50.23|
|<br>Deed plan (%)|7.71|18.05|18.74|15.63|19.77|1.87|1.32|1.21|7.83|
|<br>Annual rent (US$/ha)|10.69|26.66|23.18|23.22|30.35|0.79|0.37|0.53|3.59|
|_Lease term in 2016_||||||||||
|Lease expired (%)|45.35|9.16|5.15|15.03|5.46|69.65|63.13|74.64|43.98|
|Lease term <= 10 a (%)|2.42|2.09|2.66|2.46|1.57|2.64|1.55|2.43|6.27|
|Lease term > 10 a (%)|19.82|41.21|33.29|36.13|48.41|5.46|4.82|4.04|17.30|
|No. of obs.|58,733|23,593|3,728|9,236|10,629|35,140|6,181|25,560|3,399|

_Source:_ Own computation from the National Geographical Estates Database.

17
**<u>Table 3: Extent, expiration status, and double registration for agricultural estates by district</u>**

|||**Number of lea**|**ses**||**Area**|**under le**|**ases**||
|---|---|---|---|---|---|---|---|---|
||**Total**|**Expired**|**Overlap**<br>**>20%**|**Total**<br>**ha**|**Expire**<br>**ha**|**d**<br>**%**|**Overlap**<br>**ha**|**> 20%**<br>**%**|
|**North**|||||||||
|Chitipa (CH)|219|93|29|6,825|3,009|44.1|584|8.6|
|Karonga (KA)|245|49|7|23,433|1,331|5.7|49|0.2|
|Mzimba (MZ)|3,886|2,689|824|128,002|89,229|69.7|11,274|8.8|
|<br>Nkhata Bay (NB)|418|98|26|40,588|2,567|6.3|302|0.7|
|<br>Rumphi (RU)|1,413|973|215|31,785|20,491|64.5|2,549|8.0|
|<br>_Subtotal North_|6,181|3,902|1,101|230,633|116,629|50.6|14,758|6.4|
|**Center**|||||||||
|Dedza (DZ)|224|88|19|10,815|3,061|28.3|1,121|10.4|
|Dowa (DA)|4,563|3,535|1,361|90,638|75,835|83.7|11,346|12.5|
|Kasungu (KU)|9,521|7,266|4,129|339,668|182,148|53.6|62,634|18.4|
|<br>Lilongwe (LL)|<br>540|<br>224|<br>69|<br>20,780|<br>3,451|16.6|<br>427|2.1|
|Mchinji (MC)|4,223|3,397|1,200|109,948|66,957|60.9|14,460|13.2|
|<br>Nkhotakota (KK)|2,389|1,748|611|109,932|63,401|57.7|11,518|10.5|
|Ntcheu (NU)|363|179|43|49,800|5,570|11.2|1,521|3.1|
|Ntchisi (NT)|1,991|1,564|371|43,621|32,271|74.0|3,320|7.6|
|Salima (SL)|1,746|1,076|345|96,405|34,410|35.7|5,217|5.4|
|_Subtotal Center_|25,560|19,077|8,148|871,606|467,105|53.6|111,565|12.8|
|**South**|||||||||
|Balaka (BK)|50|-|9|1,190|-|-|656|55.1|
|Blantyre (BT)|215|31|16|2,317|314|13.5|31|1.3|
|<br>Chikwawa (CK)|200|15|27|29,806|364|1.2|166|0.6|
|Chiradzulu (CZ)|51|15|4|768|101|13.2|19|2.4|
|<br>Machinga (MA)|503|292|63|42,307|10,261|24.3|1,144|2.7|
|Mangochi (MI)|1,530|878|242|104,871|44,607|42.5|7,729|7.4|
|<br>Mulanje (MJ)|165|49|5|23,833|760|3.2|8|-|
|<br>Mwanza (MN)|148|30|25|10,709|1,569|14.7|705|6.6|
|Neno (NE)|10|-|1|232|-|-|18|7.6|
|<br>Nsanje (NJ)|68|5|9|5,198|140|2.7|40|0.8|
|Phalombe (PE)|9|-|-|87|-|-|-|-|
|<br>Thyolo (TO)|111|19|7|4,390|118|2.7|35|0.8|
|<br>Zomba (ZA)|339|161|19|<br>20,813|2,267|10.9|192|0.9|
|<br>_Subtotal South_|3,399|1,495|427|246,523|60,502|24.5|10,741|4.4|
|**Total Malawi**|||||||||
|_Total Malawi_|35,140|24,474|9,676|1,348,763|644,236|47.8|137,064|10.2|

_Source:_ Own computation from the National Geographical Estates Database.

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**<u>Table 4: Land use status for agricultural estates</u>**

||**Total area**<br> **(1,000 ha)**|**Share of land under**<br>**crops (%)**|**Share of estates with at least**<br>**70% of area under crops (%)**|**No. of**<br>**obs.**|
|---|---|---|---|---|
|Total|683.83|42.07|18.09|24,823|
|**Region**|||||
|North|101.04|34.97|11.34|3,758|
|Center|455.38|44.51|20.59|18,526|
|South|127.41|38.99|9.85|2,539|
|**Lease duration/validity**|||||
|Expired/indet. lease|569.86|42.36|18.24|23,034|
|Valid lease|113.97|40.65|16.21|1,789|
|Valid lease > 10 years|102.20|40.28|15.37|1,171|
|Has deed|400.49|41.22|17.02|12,259|
|Has SD plan|382.79|41.89|17.58|12,637|
|**Time of transfer**|||||
|Before independence|0.43|45.88|8.33|12|
|1964-1985|76.52|40.02|17.44|1,193|
|After 1985|502.53|43.96|18.23|19,814|
|**Size**|||||
|<10 ha|9.04|51.10|23.56|1,957|
|10-20 ha|205.42|48.32|21.77|15,224|
|20-50 ha|169.06|40.57|10.83|5,828|
|50-100 ha|77.82|34.96|4.78|1,151|
|100-500|108.68|35.50|4.24|590|
|>= 500 ha|113.82|43.43|6.85|73|

_Source:_ Own computation National Geographical Estates Database overlaid with SPOT imagery. _Note:_ Crop use is defined as maize and other crops. Figures are reported only for estates for which satellite imagery is available.

19
**<u>Table 5: Estate characteristics by size</u>**

||**All**|||**Size categ**|**ory in ha**|||
|---|---|---|---|---|---|---|---|
|||**<=5**|**5-10**|**10-50**|**50-100**|**100-500**|**>500**|
|**Estate ownership**||||||||
|Years run by the current owner|18.99|13.14|12.54|15.28|21.13|19.84|30.77|
|Owner is Malawian (%)|72.58|75.00|82.76|92.42|80.21|50.52|29.75|
|Owner is expatriate (%)|10.48|12.50|0.00|1.18|4.17|29.17|20.66|
|Owner is other (%)|10.94|12.50|13.79|4.50|6.25|12.50|33.88|
|Owner is government (%)|2.19|0.00|0.00|0.00|3.13|5.21|4.96|
|Owner is NGO (%)|3.23|0.00|3.45|1.90|6.25|2.60|6.61|
|**Labor demand**||||||||
|Hired perm. male labor (%)|64.40|37.50|58.62|52.13|52.08|82.29|91.74|
|No. of perm. male labor|28.76|1.50|4.92|3.66|7.17|49.43|108.24|
|No. of perm. male labor per ha|0.60|0.50|0.93|0.40|0.45|0.89|0.88|
|Hired perm. female labor (%)|27.19|25.00|17.24|12.09|23.96|47.92|52.07|
|No. of perm. female labor|13.05|0.83|0.96|1.26|1.87|13.67|65.84|
|No. of perm. female labor per ha|0.19|0.28|0.19|0.11|0.05|0.19|0.58|
|Hired temp. male labor (%)|70.28|37.50|65.52|63.51|68.75|76.56|88.43|
|No. of temp. male labor|45.05|154.67|7.08|13.06|15.48|72.80|139.95|
|No. of temp. male labor per ha|1.51|31.28|1.96|1.48|0.96|1.14|1.03|
|Hired temp. female labor (%)|55.76|12.50|44.83|46.92|54.17|64.58|79.34|
|No. of temp. female labor|23.58|2.00|8.15|6.86|10.91|34.73|79.53|
|No. of temp. female labor per ha|0.71|0.50|1.65|0.86|0.50|0.54|0.45|
|Total wage bill per ha (US$)|131.95|249.76|144.42|133.60|174.62|138.21|77.64|
|**Tenancy**||||||||
|Have tenants (%)|33.64|12.50|10.34|43.84|44.79|25.00|9.92|
|Number of tenants|4.98|0.63|1.03|4.65|6.36|4.90|6.36|
|No. of obs.|868|8|29|422|96|192|121|

_Source:_ Own computation from 2006/07 NACAL.

20
**<u>Table 6: Comparing production and yields between estates and smallholders</u>**

||||**Esta**|**tes by siz**|**e in ha**||||**Smallhold**|**ers by si**|**ze in ha**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**All**|**<=5 **|**5-10**|**10-50**|**50-100**|**100-500**|**>500**|**All**|**<=1 **|**1-5 **|**5-10**|**>10**|
|**Land use**|||||||||||||
|Area owned|433.86|4.00|8.52|21.61|74.28|272.36|2,544|1.06|0.43|1.72|7.07|29.34|
|Area operated|66.98|3.50|5.77|10.27|27.42|80.21|294.53|0.70|0.38|1.35|5.10|8.07|
|**Share of area by crop**|||||||||||||
|Tobacco (%)|42.07|31.94|15.89|39.71|42.48|47.93|47.15|1.77|1.22|3.65|4.55|2.36|
|Maize (%)|38.86|56.94|66.95|44.30|41.75|30.83|22.93|60.06|60.51|58.90|60.13|49.55|
|<br>Beans (%)|0.73|0.00|1.28|0.76|1.68|0.28|0.51|1.86|1.94|1.62|1.40|1.44|
|Rice (%)|1.08|0.00|0.00|1.13|0.07|1.75|0.89|2.34|2.27|2.65|3.40|0.53|
|Cassava (%)|1.84|0.00|0.64|1.80|3.46|2.48|0.10|4.65|4.28|5.88|2.37|9.40|
|Ground nuts (%)|7.02|5.56|12.99|9.95|6.78|3.38|1.38|4.86|4.06|7.46|5.69|11.20|
|<br>Tea (%)|3.67|0.00|0.00|0.00|0.00|5.92|16.97|0.02|0.01|0.07|0.00|0.00|
|Other crops (%)|4.73|5.56|2.24|2.35|3.79|7.43|10.07|12.87|12.86|13.11|9.06|11.95|
|**Yield (kg/ha) by crop**|||||||||||||
|<br>Tobacco|960|854|1,047|905|1,089|1,010|1,000|1,129|1,233|1,073|674|491|
|Maize|1,585|1,313|<br>1,874|1,685|<br>1,286|<br>,1385|<br>1,606|<br>1,765|<br>1,911|<br>1,367|505|1,199|
|Beans|355||75|207|432|1,200|615|427|455|334|115|1,043|
|Rice|1,123|||1,310||750|750|2,143|2,333|1,835|188|673|
|Cassava|<br>3,058|||<br>3,692|1,417|2,140||<br>2,742|<br>2,914|<br>2,366|1,681|3,200|
|Ground nuts|765||440|840|439|783|550|1,199|1,298|1,087|720|821|
|Tea|648|||||1,537|370|2,992|1,004|3,489|||
|**Purchased inputs**|||||||||||||
|<br>Purchased fertilizer (%)|93.66|75.00|82.76|95.73|89.58|93.75|93.39|60.91|58.69|70.03|56.32|53.27|
|Cost of fertilizer (US$/ha)|192.09|161.50|118.11|149.68|175.01|270.21|250.15|41.05|48.54|17.09|3.97|1.07|
|<br>Purchased pesticides (%)|65.78|50.00|37.93|56.64|65.63|78.13|85.95|7.98|7.06|11.07|11.05|11.21|
|<br>Cost of pesticides (US$/ha)|27.70|26.17|3.30|8.48|12.49|60.84|62.45|1.74|2.13|0.47|0.30|0.04|
|Purchased seed (%)|65.09|50.00|62.07|67.77|70.83|60.94|59.50|44.82|44.02|48.07|44.21|44.39|
|Cost of seed (US$/ha)|9.71|1.79|10.00|5.27|10.32|13.19|19.92|15.42|19.01|3.71|1.29|0.28|
|<br>Purchased other inputs (%)|13.13|25.00|27.59|14.45|15.63|10.42|6.61||||||
|<br>Cost of other inputs (US$/ha)|1.79|0.00|19.47|1.30|1.56|1.66|0.02||||||
|**Sample distribution**|||||||||||||
|<br>North (%)|23.96|25.00|3.45|33.89|30.21|13.02|6.61|16.46|15.64|19.83|8.95|4.67|
|<br>Center (%)|57.83|37.50|72.41|61.61|57.29|49.48|56.20|37.35|34.03|47.22|60.00|69.63|
|South (%)|18.20|37.50|24.14|4.50|12.50|37.50|37.19|46.20|50.33|32.96|31.05|25.70|
|No. obs|868|8|29|422|96|192|121|20,677|15,946|4,327|190|214|

_Source:_ Own computation from 2006/07 NACAL.

_Note:_ Other crops for estates include cotton, paprika, soya beans, coffee, macadamia nut, sugar cane, sorghum, peas, and grams. Other crops for smallholders include sorghum, millet, soya beans, ground beans, pigeon peas, cow peas, sun flower, sweet potato, irish potato, cotton, sugar cane, and coffee.

21
|**Table 7: Smallholders’ profits, output, an**|**d input use r**|**elative to their**|**location to a**|**gricultural es**|**tates**||
|---|---|---|---|---|---|---|
||**Profit**<br>**(US$/ha)**|**Output val.**<br>**(US$/ha)**|**Fertilizer**<br>**(US$/ha)**|**Manure**<br>**(US$/ha)**|**Seed**<br>**(US$/ha)**|**Output**<br>**(US$)**|
|**Panel A: No controls**|||||||
|Squatter|-9.612|-6.170|-1.628|-2.159***|-3.076|47.456***|
||(7.236)|(7.275)|(5.181)|(0.807)|(3.068)|(14.504)|
|Non-squatter * dist. to|<br>-1.107|<br>-1.281|<br>-0.915**|<br>-0.060|<br>0.557*|<br>-4.706***|
|<br>next agric. estate|(1.382)|(1.278)|(0.452)|(0.065)|(0.284)|(1.008)|
|<br>Observations|<br>16,439|<br>17,568|<br>20,225|<br>17,124|<br>19,568|<br>17,568|
|R-squared|0.031|0.031|0.146|0.022|0.075|0.020|
|**Panel B: With controls**|||||||
|Squatter|-15.871*|-9.795|-1.639|-2.463**|-5.719***|48.675***|
||(8.290)|(7.997)|(3.353)|(0.992)|(1.535)|(14.647)|
|Non-squatter * dist. to|<br>-2.016|<br>-0.362|<br>-0.108|<br>-0.028|<br>0.789**|<br>-3.723***|
|next agric. estate|(1.550)|(1.305)|(0.471)|(0.098)|(0.333)|(0.924)|
|<br>Observations|<br>13,100|<br>14,074|<br>16,155|<br>13,714|<br>15,596|<br>14,074|
|R-squared|<br>0.047|<br>0.054|<br>0.160|<br>0.035|<br>0.088|<br>0.061|
|**Panel C: Distinguishing lease validity**|||||||
|<br>Squatter (β0)|-41.057*|-39.113**|1.718|-2.306|-3.448|-5.440|
||(21.187)|(19.171)|(10.263)|(1.412)|(4.286)|(17.158)|
|Squatter|<br>28.090|<br>32.673|<br>-3.946|<br>-0.184|<br>-2.618|<br>60.890**|
|* ag. estate w/ invalid lease (β1)|(22.546)|(20.555)|(10.699)|(1.532)|(4.541)|(23.902)|
|Non-squatter * dist. to next ag. estate (γ0)|-8.244***|-3.527|-0.132|0.113|0.968*|-3.000|
||(3.147)|(3.451)|(0.794)|(0.163)|(0.511)|(1.953)|
|Non-squatter * dist. to next ag. estate|<br>7.321**|<br>3.744|<br>0.059|<br>-0.169|<br>-0.207|<br>-0.957|
|* ag. estate w/ invalid lease (γ1)|(3.386)|(3.703)|(0.954)|(0.203)|(0.594)|(2.262)|
|<br>Ag. estate w/ invalid lease|<br>-12.616|<br>-18.185|<br>-5.368|<br>0.121|<br>-1.391|<br>6.223|
||(14.968)|(12.779)|(5.174)|(1.120)|(2.509)|(9.816)|
|Observations|<br>13,100|<br>14,074|<br>16,155|<br>13,714|<br>15,596|<br>14,074|
|R-squared|<br>0.048|<br>0.054|<br>0.160|<br>0.035|<br>0.088|<br>0.061|
|<br>**Tests:**|||||||
|Test β0+ β1= 0|2.17|0.57|0.41|5.46**|13.89***|11.51***|
|<br>Testγ0+γ1= 0|0.35|0.03|0.02|0.23|4.15**|14.23***|

_Note:_ Profits are for maize, rice & tobacco. Regressions in panels B and C include village-, household-, and parcel level controls. Village controls include access to all season road and inheritance regimes; household controls include the number of children, adults, and old; head’s characteristics (gender, age, education, birth place); ownership of durable goods, housing conditions, the value of livestock and agricultural assets; parcel controls include topography and district fixed effects are included throughout. In panel C, β0 (γ0) is the estimated coefficient for squatters (non-squatters) for agricultural estates with valid leases and the sum of β0 and β1 (γ0 and γ1) tests effects on squatters (non-squatters) for agricultural estates with invalid lease whereas β1 (γ1) tests for the difference of effects for squatters (non-squatters) on estates with invalid leases.

Standard errors in parentheses are clustered by the closest agricultural estate. *** p<0.01, ** p<0.05, * p<0.1.

22
**Figure 1: Cumulative density of the number agricultural leases issued and covered after independence**

<!-- Start of picture text --> Year<br>Number of agricultural estates<br>Area under agricultural estates (1,000 ha)<br>30000 1000<br>800<br>20000<br>600<br>400<br>10000<br>200<br>Number of agricultural estates<br>0 0<br>Area under agricultural estates (1,000 ha)<br>1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> _Source:_ Own computation from the National Geographical Estates Database.

23
**Figure 2: Graphical part of Malawi’s estate lease database overlaid on Google Earth**

_Source:_ Spatial data from the National Geographical Estates Database overlaid with google earth.

24
**Figure 3: Non-parametric regressions of yield for main crops for smallholders and estates**

<!-- Start of picture text -->
-4 -2 0 2 4 6 -4 -2 0 2 4 6<br>ln (tobacco area in ha) ln (maize area in ha)<br>95% CI lpoly smooth: ln (tobacco yield in kg) 95% CI lpoly smooth: ln (mazie yield in kg)<br>8<br>8<br>6<br>6<br>4<br>4<br>2<br>2<br>0<br><!-- End of picture text -->

**Figure 3a: Tobacco**

**Figure 3b: Maize**

<!-- Start of picture text -->
-4 -2 0 2 4 -4 -2 0 2 4<br>ln (ground nuts area in ha) ln (cassava area in ha)<br>95% CI lpoly smooth: ln (ground nuts yield in kg) 95% CI lpoly smooth: ln (cassava yield in kg)<br>7 10<br>8<br>6<br>6<br>5<br>4<br>4<br>2<br>3 0<br><!-- End of picture text -->

**Figure 3c: Ground nuts**

**Figure 3d: Cassava**

_Source:_ Own computation from 2006/07 NACAL. _Note:_ As explained in the text, both smallholder and estate samples are included.

25
**Figure 4: Non-parametric regressions of profit for maize, rice and tobacco**

<!-- Start of picture text --> 0 2 4 6 8 10<br>Distance to the next agricultural estate in km<br>95% CI lpoly smooth: maize profit US$/ha<br>Figure 4a: Maize<br>0 2 4 6 8 10<br>Distance to the next agricultural estate in km<br>95% CI lpoly smooth: tobacco profit US$/ha<br>Figure 4b: Tobacco<br>240<br>220<br>200<br>180<br>160<br>140<br>1500<br>1000<br>500<br>0<br><!-- End of picture text --> _Source:_ Own computation from 2006/07 NACAL.

26
|**Appendix table 1: Discrep**|**ancy in agricultura**<br>|**l estate sizes betw**<br>**% of l**<br>|**een lease records a**<br>**eases with discrepa**<br>|**nd calculation from**<br>**ncy between**<br>|**mapped boundaries**<br>|
|---|---|---|---|---|---|
||**-1% to 1%**|**-5% to 5%**|**-10% to 10%**|**-20% to 20%**|**-50% to 50%**|
|**Total Malawi**||||||
|Valid leases|32.44|60.63|74.70|85.23|93.01|
|Expired leases|23.22|68.18|81.76|90.80|97.58|
|Indeterminate leases|16.08|47.31|62.35|74.71|86.60|
|**North**||||||
|Valid leases|33.05|57.63|71.19|80.93|91.95|
|Expired leases|16.77|52.80|68.11|83.03|96.37|
|Indeterminate leases|9.27|31.14|46.90|67.93|90.27|
|**Center**||||||
|Valid leases|31.70|59.52|73.82|84.15|92.16|
|Expired leases|24.02|70.54|84.13|92.27|97.94|
|Indeterminate leases|16.92|50.84|66.10|76.09|85.31|
|**South**||||||
|Valid leases|33.61|63.92|77.76|88.96|95.06|
|Expired leases|29.86|78.78|87.84|92.81|96.26|
|Indeterminate leases|23.10|56.78|69.60|79.14|86.74|

_Source:_ Own computation from the National Geographical Estates Database.

27
**<u>Appendix table 2: Extent of double registration for agricultural leases by lease validity</u>**

||**Number**|**% of**|**overlap >20**|**% with**|**Area under**|**% of o**|**verlap >20**|**% with**|
|---|---|---|---|---|---|---|---|---|
||**of leases**|**Valid**|**Expired**|**Indet.**|**leases (1000 ha)**|**Valid**|**Expired**|**Indet.**|
|**Total Malawi**|||||||||
|Valid leases|2,847|6.25|14.01|6.32|300|2.80|3.65|1.30|
|Expired leases|2,4474|2.93|22.02|6.51|644|1.73|9.93|3.05|
|Indeterminate leases|7,819|2.66|18.75|6.46|405|0.96|5.25|2.28|
|**North**|||||||||
|Valid leases|394|3.30|5.58|1.52|32|0.39|0.83|0.18|
|Expired leases|3,902|0.90|14.81|4.25|117|0.31|7.99|1.80|
|Indeterminate leases|1,885|0.74|10.77|4.88|82|0.09|2.99|1.15|
|**Center**|||||||||
|Valid leases|1,652|6.78|20.94|8.54|176|4.33|5.63|1.32|
|Expired leases|19,077|3.31|24.90|7.13|467|2.09|11.53|3.41|
|Indeterminate leases|4,831|3.35|25.05|6.89|229|1.00|7.48|2.41|
|**South**|||||||||
|Valid leases|801|6.62|3.87|4.12|92|0.73|0.86|1.66|
|Expired leases|1,495|3.28|4.14|4.48|61|1.71|1.31|2.71|
|Indeterminate leases|1,103|2.90|4.81|7.25|94|1.64|1.75|2.95|

_Source:_ Own computation from the National Geographical Estates Database.

28
# **<u>Appendix table 3: Smallholders’ socio-economic characteristics</u>**

||**Total**|**North **|**By region**<br>**Center **|**South **|
|---|---|---|---|---|
|**Household composition and head’s characteristics**|||||
|Number of children|2.23|2.48|2.26|2.12|
|Number of adults|2.36|2.72|2.38|2.23|
|Number of old people|0.18|0.19|0.18|0.19|
|% of female head|28.07|23.73|25.63|31.58|
|Head’s age|43.09|43.84|42.42|43.36|
|% of heads no schooling at all|26.12|11.33|28.38|29.52|
|% of heads with primary 1-5|25.99|20.04|27.37|26.98|
|% of heads with primary 6-8|28.56|37.41|27.83|26.02|
|% of heads with sec.& above|16.79|28.53|14.08|14.82|
|% of head born in own village|52.73|55.28|55.56|49.53|
|% with hh members did wage job|15.48|16.63|15.40|15.14|
|**Household assets**|||||
|Value of livestock (2006 US$)|99.00|241.30|76.17|67.32|
|Value of agric. assets (2006 US$)|84.14|333.72|44.78|27.79|
|% owned radio|64.11|70.32|61.45|64.06|
|% owned cell phone|12.87|19.53|11.19|11.89|
|% of grass roof|74.11|67.41|80.95|70.93|
|% of iron sheets roof|25.05|31.70|18.21|28.24|
|% of sand floor|4.26|0.89|3.46|6.10|
|% of smoothed mud floor|78.07|74.06|82.67|75.76|
|% of smoothed cement floor|17.43|24.67|13.71|17.88|
|% of mud walls|8.95|10.58|9.97|7.55|
|% of compacted earth walls|17.47|23.95|33.86|1.91|
|% of mud brick walls|33.48|10.46|26.66|47.16|
|% of burnt brick walls|37.50|51.10|27.42|40.86|
|**Distance to estates and community characteristics**|||||
|Km to the nearest ag. estate|2.10|2.16|1.44|2.61|
|% within ag. estate|8.72|7.61|16.26|3.03|
|No. of obs.|20,677|3,379|7,732|9,566|

_Source:_ Own computation from 2006/07 NACAL.

29
**<u>Appendix table 4: Characteristics of smallholder production</u>**

||**Total**||**By region**||**Locat**|**e on ag. e**|**state**|
|---|---|---|---|---|---|---|---|
|||**North **|**Center **|**South **|**No**|**Yes**|**Ttest**|
|**Land endowment and topography**||||||||
|Land area (ha)|1.06|0.86|1.52|0.76|0.99|1.78|***|
|% of mountain slope|15.16|23.23|13.74|13.56|15.52|11.37|***|
|% of dregs|8.00|6.02|6.42|9.91|8.16|6.25|***|
|% of plain|72.47|64.69|76.62|71.80|71.79|79.71|***|
|% of other topography|4.37|6.06|3.22|4.72|4.53|2.67|***|
|**Land use**||||||||
|Cultivated area (ha)|0.70|0.65|0.90|0.56|0.67|1.05|***|
|% of land under maize|69.25|60.01|72.05|70.25|69.18|69.90||
|% of land under rice|2.70|5.63|1.40|2.73|2.74|2.31||
|% of land under sorghum|1.90|0.04|0.11|4.10|2.05|0.32|***|
|% of land under beans|2.14|1.81|3.06|1.49|2.25|1.05|***|
|% of land under pigeon peas|3.63|0.09|0.06|7.95|3.89|0.97|***|
|% of land under ground nuts|5.60|3.74|8.84|3.54|5.32|8.51|***|
|% of land under cassava|5.36|18.85|4.05|1.54|5.46|4.40|**|
|% of land under tobacco|2.04|3.79|2.92|0.66|1.63|6.15|***|
|% of land under other crops|7.37|6.04|7.51|7.74|7.47|6.40|**|
|**Input and labor use**||||||||
|Value of fertilizer (2006 US$/ha)|39.44|62.22|50.50|47.24|50.75|52.83||
|Value of improved maize seeds (2006 US$/ha)|11.07|11.15|17.17|14.26|15.19|11.25||
|Value of manure (2006 US$/ha)|2.78|1.20|5.97|1.63|3.21|1.45||
|Value of other seeds (2006 US$/ha)|4.33|3.64|4.77|6.18|5.46|2.75|*|
|% attended extension activities|19.19|31.48|17.44|16.23|19.20|19.08||
|% used exchange labor|20.54|29.45|24.18|14.43|20.31|22.92|***|
|Value of ag. assets (2006 US$/ha)|318|996|233|144|327|223||
|**Output and profit**||||||||
|<br>Total crop yield (2006 US$/ha)|253|263|247|254|253|252||
|<br>Maize profit (2006 US$/maize ha)|199|171|187|218|201|179|***|
|<br>Rice profit (2006 US$/rice ha)|397|368|404|409|403|329|*|
|<br>Tobacco profit (2006 US$/tobacco ha)|636|749|553|737|646|606||
|No. of obs|20,677|3,379|7,732|9,566|18,873|1,804||

_Source:_ Own computation from 2006/07 NACAL. _Note:_ Other crops includes millet, soya beans, ground beans, cow peas, sun flower, sweet potato, irish potato, cotton, tea, sugar cane, and coffee.

30
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