population, tenure, and natural resource management: the case of customary land area in malawi

Ž .Journal of Environmental Economics and Management 41, 13�32 2001doi:10.1006�jeem.2000.1134, available online at http:��www.idealibrary.com on

Population, Tenure, and Natural Resource Management:The Case of Customary Land Area in Malawi1

Frank Place

International Centre for Research in Agroforestry, P.O. Box 30677, Nairobi, Kenya

and

Keijiro Otsuka

International Food Policy Research Institute, Washington, DC 20036; and Faculty of Economics,Tokyo Metropolitan Uni�ersity, Tokyo 192-0397, Japan

Received June 25, 1997; revised May 17, 1999; published online October 24, 2000

This paper analyzes the factors determining conversion of woodlands to agricultural land,changes in tree cover, and maize yield for the last two and a half decades by using aerialphotos and field survey data in 57 communities in Malawi. The econometric analysis revealedthat population pressure led to greater land conversion but had no effect on yield or treecover changes. Areas where greater changes in traditional tenure systems have occurred areassociated with reduction in tree cover and expansion of agricultural land. These resultssuggest the importance of strengthening institutions to manage natural resources. � 2000

Academic Press

1. INTRODUCTION

Malawi, as elsewhere in sub-Saharan Africa, has experienced a significantchange in its landscape cover. Although reliable figures are hard to come by, theForestry Department estimates the annual deforestation rate to be 1.3% per year

� �in the 1980s 38 . This has raised concern about the future supply of fuelwood and� �other tree products and environmental services 14, 16, 23 . Much of the deforesta-

tion is believed to be linked to conversion of communally owned miombo wood-lands into agricultural land. This involves expansion onto steep slopes and other

� �fragile lands in many cases. Bojo 4 presents data suggesting that the consequenteffects on soil erosion create serious costs to the Malawian economy, which is

� �heavily agriculturally based 37 .Malawi policymakers, having few resources at their disposal, must make critical

choices concerning the type of land use patterns that will prevail in the future.Unfortunately, while much information has been generated about current land use,

1 The authors thank colleagues in Malawi for fine work: Vincent Mkandawire of the Ministry ofAgriculture and Bob Green for leading the remote sensing interpretation; Ken Neils and RedgeMasupayi of the Agricultural Policy Research Unit at Bunda College for ground surveys and datahandling. Many thanks also to Towa Tachibana of IFPRI for technical assistance and to participants atworkshops held at IFPRI and Michigan State University, in particular Peter Hazell, Sherman Robinson,Carl Eicher, Tom Reardon, and John Straus. Lastly, the authors are grateful to three anonymousreferees for making substantial suggestions for improvement of the paper.

13

0095-0696�00 $35.00Copyright � 2000 by Academic Press

All rights of reproduction in any form reserved.

PLACE AND OTSUKA14

there is little understanding of the dynamic process leading up to the current landutilization pattern nor to related effects on productivity and the stock of naturalresources. This study will provide new evidence as to how communities havemanaged their land and tree resources and what factors seemed to be mostimportant in their decisions. This information is valuable to policymakers whocontinue to struggle with the twin objectives of alleviating poverty in the short runand in preserving the natural resource base in the long run so that futuregenerations may have access to high quality income generating assets.

Information on factors related to tree cover change has only recently beengenerated. Cross-country studies using data from the 1970s and 1980s found thatchanges in land area under forest were negatively related to population growth and

� � � �insecure ownership rules 13 , level of debt and initial forested area 24 , export� �value of wood products, and per capita income 6 . Community level studies are

scant, especially with respect to sub-Saharan Africa. A study of two contrastingsites in Nigeria showed that deforestation is neither inevitable nor linearly related

� �to population growth 9 . In a study of 64 sites in Uganda between 1960 and 1995,� �Place and Otsuka 34 found that tree cover change differed significantly between

Ž . Ž .agricultural positive and non-agricultural land negative . In turn, factors such astenure system and access to roads had differing impacts on tree cover changeconditional on location of trees in agricultural and non-agricultural land.

In this study we are concerned with the influence of population pressure andland tenure on land use and tree resource management in Malawi. Our studyfocuses on the customary sector which occupies about 70% of land area in Malawi� �35 . In this sector, cultivators are small peasants with few exceptions and many ofthem suffer from severe poverty. Uncultivated land is usually owned by thecommunity and the permission from the village headman is required to clear the

� �land for agricultural production 21, 30, 32 . While the use rights over agriculturalland are well established, the rights to transfer are limited, as in the case of other

� �areas in Africa 1 . Hence, no land market exists and transfer of agricultural land isprimarily through inheritance. Traditionally, matrilineal inheritance was practicedby the majority of the rural population, but the patrilineal system has increasinglybeen adopted in many areas. The objective of this study is to identify statisticallyhow the customary land tenure institutions and their changes affect land use andmanagement of woodland resources in the context of the customary sector ofMalawi.

The remainder of the paper is organized as follows. We begin with a descriptionof customary land tenure in our study sites which leads into our major hypotheses.This is followed by the presentation of the theoretical and econometric model usedto test the hypotheses and then a brief description of data collection methods. Theremaining sections describe and discuss the results of our analysis first at adescriptive level and then based on our econometric analyses. Finally, the conclu-sions and policy implications are presented to end the paper.

2. CUSTOMARY LAND TENURE SYSTEMS IN MALAWI

The customary sector consists mainly of smallholders and a key distinction hereis between matrilineal and patrilineal ethnic groups. When land was abundant bothsystems vested land in chiefs and village headmen. The village headmen in turn

RESOURCE MANAGEMENT IN MALAWI 15

would cede rights over specific tracts of land to families and family leaders. Newlands could be opened through requests to family leaders and village headmen.

Ž .In a matrilocal system practiced by the Chewa and Yao in our study sites , acommon practice was for the male to take up residency in the wife’s villageŽ .uxorilocal marriage . In this system, land had traditionally been passed from

� �mother to daughter or from family leader to female family members 30 . Thecouple often resided permanently in the wife’s village, though this was not alwaysrequired. This system is akin to matrilineal systems observed in some parts of Asia,

� �such as Sumatra 33 . In matrilineal systems where the couple moves to orotherwise resides in the husband’s village, the husband traditionally acquired landfrom a village headman or family leader in his village. This land would then pass

Ž .from uncle to nephew or niece perhaps through the family leader . Precious littleevidence is available on how these processes have changed over time and thispaper hopes to document the extent of this change.

A patrilineal system, common in the north of Malawi, is similar to thoseelsewhere in sub-Saharan Africa in that men claim land ownership and can pass onall land developments to his children. It has been customary among the Ngoni andTumbuka at our sites to favor sons over daughters in inheritance practices.

The various modes of transfer and tenure arrangements might have different� �incentives on farm and tree management due to differences in tenure security 2 .

Inheritance is by far the most common land transfer mechanism and even in amatrilocal system whereby the transmission is from mother to daughter householdsseemingly have incentives to manage the land wisely for their family heirs.However, because males are the key decision makers, this superficial descriptionmasks important disincentives that may arise for males within the matrilocalsystem. Three types of situations are of concern. The first is where husbands reside

Ž . � �in the wife’s village uxorilocal marriage only on a temporary basis 31 . Thisclearly creates disincentives for long-term investment, particularly in the caseswhere the husband has rights to land in his own village. A second case concernsrights to land following death or divorce of a spouse. In the case where the widowor widower resides in the deceased’s village, continued rights to land are not at allguaranteed. In the case of matrilocal marriage, these situations can reduce theincentives for a man to work in his wife’s village. Where either a death or divorcebecomes more likely, the non-resident spouse may likely increase activities thatenhance short-term returns at the expense of long-term returns. A third situation iswhere land passes from uncles to nephews or nieces, bypassing the children. Thesearguments do not imply that the husband does not care about the welfare of hiswife and children. Yet, he will behave more myopically under the matrilocal cummatrlineal system than under the patrilineal system, even if he has affection for hisfamily.

The customary sector includes both privately managed and communally managedland. The process of privatization in the case of Malawi mainly concerns thepermanent allocation of land to households primarily for cropping purposes. As aconsequence, farm holdings in much of Malawian customary land are small. Thereare some examples of larger farms containing non-cultivated areas within thecustomary sector of the northern part of Malawi, but data on whether these arefallowed land or pristine woodlands are poor.

There are also communally held lands, held by the clan or village headman,which appear to be virtual open-access resources, with few rules on user group

PLACE AND OTSUKA16

Ž � � � �.membership or use rates see Coote et al. 10, 11 and Lowore et al. 27 . Onenotable exception is the Village Forest Area system initiated in the 1920s, rekin-dled recently by the Forestry Department, in which communities demarcate wood-

Žland areas to be placed under special management rules which are always.conservation oriented .

A first empirical test will be of the hypothesis that increased population pressurewill lead to conversion of woodlands to privately held lands, resulting in loss of treecover. However, the relationship may not be linear and some woodland area maybe maintained even at very high population densities. The speed of conversion may

Žbe conditioned by other factors such as the value of woodlands e.g., values might.be higher where livestock holdings are large or the possibilities and incentives for

intensification on existing agricultural land.A second area of examination involves the effect of the land acquisition

mechanism and associated incentives on land use, tree planting, and other naturalresource investments. Improved tenure security is expected to lead to moresustainable management of resources. This may be demonstrated by greater

Ž .investment in tree planting and other investments on agricultural land or bypreserving woodland resources. If males are the key decision makers over naturalresources, better management of woodlands and tree resources is expected inpatrilineal rather than in matrilineal systems and in non-matrilocal rather than inmatrilocal systems. The opposite could be the case, however, if women have greaterdecision-making power.2

3. THE THEORETICAL ECONOMETRIC MODEL

While the management of land and tree resources in Malawi is affected by amultitude of decision makers, we focus our model on choices made by householdswho ultimately are the major users of resources. Variables such as population and

Žtenure that are affected largely by institutions e.g., migration systems and ethnic.group cultural norms are treated as exogenous to household decisions.

We consider a two-period model in which rural households maximize expectedprofits by choosing to allocate their labor between agricultural production andcollection of resources directly from woodlands or forests. The household may alsochoose to convert a certain area of woodland into agricultural land. We capturethis decision through labor allocated for conversion in the first period. In this way,the farmer clearly trades current period consumption for future consumption wheninvesting in land conversion. If the household decides to convert land, it is assumed

Ž A.to be in addition to an initial endowment of agricultural land a . At the time of1this conversion, the household also benefits from the exploitation of forest prod-ucts found on the cleared land. The household can further exploit forest productsfrom the remaining village forest area in period 2. Agricultural production occursin both the first and second periods.

Let us begin with a description of household determination of agriculturalproduction relations. Agricultural production is determined by households through

2 � �Mkandwire 31 considers that males are heads of households in the matrilineal communities of theChewa ethnic group.


allocation of agricultural labor in both periods and through decisions to increase itsagricultural land holding over its initial endowment.

q A � f a A , l A , � 1Ž .Ž .1 1 1

q A � f a A , l A , � , 2Ž .Ž .2 2 2

where q A is production of agriculture in period t, a A is agricultural land in periodt tt, l A is current season labor for agriculture assumed not to have spillover effect intperiod t � 1, and � is an agricultural sustainability parameter. This parameter hasa negative effect if labor is used in an unsustainable manner, which may decreasesoil fertility. We assume this is the case in Malawi where hardly any profitableagricultural investments were available to farmers.3 The function f is assumed tobe twice differentiable in the arguments with positive but diminishing marginalproducts.

Agricultural land in period 2, a A, can be larger than a A by clearing woodland in2 1period 1, and the difference is designated as �a. Thus,

a A � a A � �a. 3Ž .2 1

Since woodland is an open access resource to community members, the amount ofland the household can convert to farm land depends not only on its own behaviorbut also on the behavior of other community members. Following the tradition of

Ž � �.common property literature e.g., 12 , it is likely that the the amount of landconverted by the household will be a function of not only its own labor but alsolabor of other households expended for land conversion. It is well known that if thehousehold determines its own labor input while assuming no reaction to its choicefrom other households, socially excessive land conversion is likely to occur.

Households also have access to village forests and they will expend labor tocollect forest resources from their stock in the second period. The stock is the statevariable and is assumed to evolve according to the equation

WF � F 1 � � � � A�A , 4Ž .Ž .2 1

where F and F are the stock of forest resources in the first and second periods,1 2respectively, � is the natural regeneration rate, AW is the area of woodland in

� W �period 1, and � � A�A is the rate of reduction of forest area.The function describing the resource extraction activity by the household is given

by

q F � g l F , F , 5Ž .Ž .2 2 2

where q F represents the amount of forest resources extracted and l F stands for2 2the labor input for resource extraction. This function, like that for agriculturalproduction, is assumed twice differentiable and well behaved.

It is assumed that households maximize expected profits over the two periods.Households will maximize over their labor allocation for agricultural production,

Ž C . Ž .woodland extraction, and land conversion l . We assume wages w and prices1

3 If there were profitable investments, such as tree crops, we could have explicitly added a variable tocapture investment labor.

PLACE AND OTSUKA18

Ž A.for agricultural output P are fixed over the two periods. This appears reason-able in Malawi where maize was the dominant crop throughout the country andfaced a monopolistic buyer throughout most of the period. The constant outputprice across villages will tend to keep wages equalized across villages. We further

F Žassume that the price of forest resources, P , is fixed over time though there maybe more justification to assume forest products are traded only locally and

. 4therefore the price would be related inversely to F . When households convert2Ž .land area �a for a given density of forest d in period 1, they earn a gross returnF

of P F*�a*d .5 Profits in periods 1 and 2 are then given byF

Ł � P A f a A , l A , � � P F�ad � w l A � lC 6Ž .Ž . Ž .1 1 1 F 1 1

Ł � P A f a A , l A , � � P Fg l F , F � w l A � l F . 7Ž .Ž . Ž . Ž .2 2 2 2 2 2 2

We assume that second period profits are discounted by interest rate r. Further,we assume that second period profits are subject to tenure insecurity, �, which asdescribed above reflects the probability of losing rights of residence and land in thevillage. This is higher for male decision makers under the matrilocal tenure systemwhere they reside in their wives’ villages. Thus, the expected present value of

Ž .profits V is expressed as

V � Ł � 1 � � � 1 � r Ł . 8Ž . Ž . Ž .1 2

Assuming an interior optimum, the first order conditions can be expressed as

P A � f�� l A � w � 0 9Ž .Ž .t

P F � g�� l F � w � 0 10Ž .Ž .2

F C�w � P d ��a�� l � 1 � � � 1 � rŽ . Ž .Ž .F 1

� ��a�� lC P A � f�� a A � P F � g�� F � F ��a � 0. 11Ž . Ž . Ž .� 4Ž . Ž .1 2 2 2

Ž .It is obvious from Eq. 9 that labor allocation for agriculture and, hence, yield offood crop production critically depend on the sustainability parameter. In this

Ž .simplified formulation, Eq. 10 shows that the amount of labor allocated to forestŽ .resource extraction does not depend directly upon tenure insecurity � . Yet, so

long as the marginal product of labor depends on the amount of forest resources,there is indirect effect of land tenure institutions on labor allocation. Since thelarger stock of forest resources leads to larger marginal product of extraction labor,the larger initial stock of forest resources will lead to larger depletion of theseresources, ceteris paribus. Therefore, under open-access, where relatively large

4 Even if determined locally, one could assume that household decisions over land conversion do notconsider the effect of conversion on the price of forest products.

5 Here the presumption is that the household collects a sufficient amount of forest resources as abyproduct of land conversion in the first period, so that no additional resource extraction activity iscarried out in this period. To incorporate such behavior and the imperfection of market of forestresources, it may be more realistic to assess the value of these resources in terms of utility functionsubject to declining marginal utility.


land conversion would be expected to occur in period 1, forest stocks would bediminished and labor for forest extraction in period 2 would be relatively low.6

While the effect of labor allocation for forest clearance has a positive effect onŽ Ž ..agricultural profit see the first term in curly bracket in Eq. 11 , its effect on

profit from forest resource extraction is unclear since the last term in the curlybracket is negative. We expect that the combined effect is positive, given thatagricultural sustainability is likely negative, so that the marginal impact of bringingnew land into agriculture is expected to be high, and that the effect of landconversion by an individual household on the total stock of forest resources is

Ž . Ž .likely to be small. Given these, Eq. 11 shows that greater tenure insecurity �will lead to less land conversion. In both matrilocal and patrilineal cases, malesgain from clearing land through first period forest products. However, those inpatrilineal systems have a higher probability of reaping rewards from subsequentagricultural production.

4. ECONOMETRIC MODELLING OF HOUSEHOLD DECISIONS

In order to test these predictions on observations on yield change, tree coverchange, and land use change, it would be ideal to have information on profits fromagriculture and extraction, explicit measures of tenure security, prices, and wages,and the sustainability parameters. Since these are not available, in actual estima-tion we use proxies and variables which are expected to affect those key variables.First, we assume that the profitability of farming per unit of land in the base periodis positively and closely correlated with yield of maize, which was estimated to

Ž .occupy over 72% of smallholder cultivated area in purestands and mixes in� �7Malawi in 1980 28 . Second, we assume that the density of tree cover in woodland

in the base period can be captured by the estimate of tree cover density at the timegenerated from the aerial photo analysis. Third, we assume that � and � areexogenous and not directly observable, but their effects can be captured by avariety of exogenous variables such as soil type and the proportion of trees thatcoppice well. We note that population pressure on land may affect � and � bychanging the scarcity of foods and woodland products. Finally we assume that � isconditioned by the land tenure system, which is reflected in the prevalence of thematrilineal cum matrilocal system or the proportion of land received from wife’s

Ž .family and the village headman of the wife’s village M .Assuming that each household maximizes the net benefit from labor allocation

and that all households in the community have common expectations about � and� , the aggregate area converted from woodland to agricultural land at the commu-nity level is the sum of the converted areas by households. Thus, we postulate that

6 If open-access did not prevail, then it would be reasonable to assume that households could devotelabor in period 1 to the protection or enrichment of woodlands for future benefits and in this casetenure security would matter.

7 Prices of maize and other crops were virtually uniform throughout Malawi because of a monopolyparastatal operating only recently. We do not have data on 1971 prices, but the coefficient of variationon 1995�96 maize prices at the sites was a paltry 0.08.

PLACE AND OTSUKA20

the land conversion at the community level is affected by the set of explanatoryvariables

� A�A � F Y , G , F , G , M , N �A , G , D , A A�A , 12Ž .Ž .1 Y 1 F 1 N 1

Ž .where the dependent variable is normalized by total area of community A toadjust for differences in the total size of communities; Y and F denote yield and1 1woodland tree cover in the initial period, respectively; G and G represent theirY Fexpected growth rates; N �A is population density in the initial period and G1 Nstands for the population growth rate; D is distance to urban centers; and A A�A is1the share of agricultural land in the initial period. While Y , F , N �A, D, A A�A,1 1 1 1G and M are either predetermined or exogenous to the household decision, GN Yand G are considered as endogenous variables at the household level. Since theFrate of land conversion is assumed to be determined in the base period with theexpectations of future growth rates of some key variables, the initial conditionsplay prominent roles.

If the Boserupian hypothesis of intensification of the farming system takes place� �in response to population pressure 5 , or if the Hayami�Ruttan thesis of induced

� �technological innovation takes place in response to changing factor scarcities 19 ,G will depend on population density and on access to output and input markets.YThere are indications that adoption of high yielding maize technologies had

� �occurred in some areas 36 and we may expect that the green revolution wasparticularly successful in land scarce areas close to urban markets. Land tenurewould matter in the growth rate of maize yield to the extent that yield-enhancinginvestment plays a significant role, though our evidence suggests this is not thecase. Thus, we assume the functional relationship in the determination of yieldgrowth

G � G Y , F A , M , N �A , D , A A�A , E, G , � A�A, G , 13Ž .Ž .Y Y 1 1 1 1 N F

where F A is tree cover on agricultural land in the initial period and E is a vector1of exogenous environmental factors, such as rainfall, which would affect yieldgrowth.

Woodland management may depend on the homogeneity of community mem-bers, since this affects the cost of organizing community actions and enforcing

� �agreements 8, 29 , and on the prevailing land tenure, as this affects the extent ofmyopic behavior. Aside from the cost of mobilizing collective action, the rate ofresource extraction will depend on the demand for woodland resources, which isaffected by population density and access to markets, and the supply, which isgoverned by the initial endowment of woodland and the regeneration capacity ofwoodland. It is also important to note that the Malawi government has attemptedto promote tree cover by designating village forest areas and by implementing treeplanting projects. These considerations lead to the formulation of the functionalrelationship

G � G Y , F , M , N �A , D , A A�A , EH, CA, GP, G , � A�A, G , 14Ž .Ž .F F 1 1 1 1 N Y

where EH represents the degree of homogeneity of community members, whichmay be measured by the proportion of the major ethnic group and the proportionof cattle-owning households using woodland for grazing; CA represents an ability


for woodland regeneration, measured by the prevalence of tree species with goodcoppicing ability; and GP corresponds to government policies, such as area undermanaged village forest areas and implementation of tree planting projects. In theestimation, we use changes in proportion of land under tree cover for G .F

Population growth and the extent of the matrilineal cum matrilocal system aretreated as exogenous to the household decisions over labor for agriculture, landclearing, and forest extraction. They are, however, endogenous at a village levelwhere they are affected by an interaction of local and supra-local institutions anddriving forces. Though the determination of these variables could be examined in arecursive model, we exclude this analysis here as it would detract from the mainfocus of this paper.8

Ž . Ž .As shown in Eqs. 12 to 14 , the three household choice variables are hypothe-sized to depend on many of the same explanatory variables. For the coefficients ofthe models to be identified, it is important to include explanatory variables that areunique to one of the equations. For tree cover change, variables satisfying thiscriterion are initial period size of village forest areas, the ability of trees to regrowfollowing cutting, and trees planted by externally driven projects. For yield change,identifying variables were soil type, initial period fertilizer use, and average rainfall,which are deemed more important for crop production than for other decisions.

ŽFor the change in agricultural land equation, we used altitude e.g, hills are more.difficult to convert and total tree cover on the landscape.

5. DATA COLLECTION METHODS AND DESCRIPTION

5.1. Sampling Strategy

The data for this study come from 57 enumeration areas throughout Malawi andthese are the smallest units for which boundaries are known and can thus betransferred onto maps with relative ease.9 Enumeration areas are designed to haveroughly the same population and thus are different in size depending on thedensity of population. Our sample had a range of size from 180 to 5580 hectares,

Ž .with the mean being 1271 hectares or about 4 km by 3 km . Excluded from theŽsample were enumeration areas in the extreme north and south Karonga and

.Shire Valley , and those corresponding to towns and gazetted state land. Theremaining enumeration areas were selected by a stratified random process in which15 each from the north, north-central, south-central, and south of Malawi wereselected. This stratification was made to ensure a variation in population densityand tenure type, both of which are known to vary from north to south in Malawi.

8 The analysis can become quite cumbersome and lengthy depending on what assumptions are maderegarding the role of yield change, land use change, and tree cover change on population and landtenure.

9Aerial photographs for 60 sites were analyzed for land use and tree cover change, but 3 were notvisited for field surveys due to logistical difficulties.

PLACE AND OTSUKA22

5.2. Natural Resource Management Indicators

Land Use and Its Change

For each enumeration area, aerial photographs were obtained for 1971�73 and1995. Both sets are at a scale of 1:25,000 which allows for a minimum mapping unitŽ .i.e., area which can be effectively differentiated and given boundaries of aboutone hectare. Land use was differentiated into agriculture, wetland, grassland,

Ž .forests, plantations, woodlands, woody regrowth i.e., bushland , rocky areas,marshes, built-up areas, and surface water. Within agriculture, further differentia-tion was made according to tree canopy cover with separate classes identified forareas under 2% cover, 2�5% cover, 5�10% cover, 10�20% cover, and 20�40%

Ž .cover. Forests were differentiated by normally stocked averaging of 80�100% andŽ .depleted average of 50�60% . Woodlands and regrowth were each differentiated

into five tree cover classes: 2�20%, 20�40%, 40�60%, and 60�80% cover. Accord-ing to our definitions, other land use categories were largely devoid of trees.10

Table I displays some statistics on broad land use categories in the 1970s and inthe 1990s using simple averages across the 57 enumeration areas.11 Agriculture hadthe largest share of the enumerated land area in both years, averaging 0.52 in 1972and 0.68 in 1995.12 While most sites had an increase in agricultural land, there wasconsiderable variation. The change in share of agricultural land per enumerationarea ranged from �0.32 to �0.73 across the study sites. Most of the increase inland under agriculture was accommodated by a reduction in woodland and bush-land. This broad category of land use had a mean share land area of 0.33 in 1972which dwindled down to 0.19 in 1995. Nonetheless, about one-fifth of all enumera-tion areas retained a woodland area comprising at least a 0.40 share of land area in

TABLE ILand Use and Land Use Change across Study Sites

Sharechangeinterval Most positive Most negative

Average Average which absolute absoluteLand use share across share across includes 80% change in change incategory sites in 1971 sites in 1995 of sites share share

Agriculture 0.515 0.677 �0.02 to 0.41 0.73 �0.32Woodland & 0.330 0.185 �0.38 to 0.00 0.10 �0.66

bushlandForest & 0.013 0.002 0.00 to 0.00 0.07 �0.63

plantationsOther 0.142 0.136 �0.12 to 0.08 0.35 �0.18

10 ŽIf there was significant tree cover, then the area would have been reclassified e.g., from grassland.to woodland .

11 It is worthy to note that the 1995 land use estimates from our enumeration areas correlate very� �closely to those from a larger study designed to be representative of Malawi 18 .

12 ŽNote that agriculture is a small proportion of land in the larger enumeration areas in northern.Malawi . Thus, the weighted share of land in agriculture is much lower, 0.50 in 1995, for example.


Ž .1995 mainly areas in the north . There were very few natural forests or plantationsin the study sites. The number of enumeration areas with at least 1.0 percent forestcover was only five in both 1972 and 1995.

Tree Co�er and Its Change

Table II displays some summary statistics pertaining to tree canopy cover acrossthe enumeration areas. In 1972, the average canopy cover was estimated to be17.3%. Over half of the enumeration areas had a tree cover of under 10.0%, butabout one-fourth of enumeration areas had a canopy cover of over 30.0%. By 1995,tree cover had fallen to an estimated cover of 10.1%.

There are significant differences in tree cover according to whether the primaryland use is agriculture or not. Table II shows that tree canopy cover in agricultureland is very low in the sites, estimated to be only around 2% in both years. Thislevel of cover has remained constant across most enumeration areas as noted bythe fact that the change in absolute tree cover was between �0.02 and �0.01 for80% of the sites. Tree cover in non-agricultural areas is much higher, but hasshown a more marked decline. From a level of about 24.3% in 1971, tree cover innon-agricultural areas has dropped to 16.7%, on average, across the study sites.13

Despite that fact that 44 sites experienced a decrease in non-agricultural land treecover, there was significant variation of tree cover change across sites. Our data do

� �not seem to support the contention of Hyde and Seve 23 that ‘‘the level ofdeforestation still exceeds the level of reforestation, but the rate of reforestation israpid.’’

Crop Yield and Its Change

Data on average yields of important crops in 1971 and 1996 were collectedthrough group interviews. Maize is the most important crop grown by smallholdersin Malawi and we received data on maize yields for 52 of 57 sites.14 Reported yields

TABLE IITree Cover Pattern across Study Sites

MostCover change Most positive negative

Average tree Average tree interval which absolute absoluteBroad land use cover across cover across includes 80% change in change incategory sites in 1971 sites in 1995 of sites cover cover

Agriculture 0.023 0.020 �0.02 to �0.01 0.05 �0.07Non-agriculture 0.243 0.167 �0.18 to 0.03 0.14 �0.61Average 0.173 0.101 �0.20 to 0.00 0.06 �0.57

13Again, note that because the northern enumeration areas are larger and have a greater treedensity, the weighted average tree cover for Malawi would be much higher than indicated by our simpleaverages across sites.

14 Maize yield estimates were not obtained for five enumeration areas. For these, averages calculatedfrom nearby enumeration areas were used.

PLACE AND OTSUKA24

Ž .for 1996 are considered to be fairly accurate mean of 1.1 tons per hectare , whilethose for some 25 years back are less so, as this stretches recall capabilities ofrespondents. Nonetheless, we feel that the relative changes in yields across sitesare not systematically biased in any significant way and therefore provide at theminimum a useful ordinal ranking. All but three enumeration areas saw maizeyields decline over the study period and the mean decline in yield was 54.5%.These data support other conclusions that Malawi’s agricultural development

� �strategy neglected the development of smallholder agriculture 15, 25, 26, 35 .

5.3. Explanatory Factors

Data for the explanatory variables in our model come mainly from group andhousehold surveys conducted in each of the enumeration areas. Our field surveysconsisted of group interviews combined with surveys of 10 randomly selectedhouseholds in each site. Secondary data sources were used for information onpopulation, climate, and soils. Table III displays information on many of the factorshypothesized to lead to changes in the management of natural resources, accordingto broad geographical zone. The north has a low population density and predomi-nance of patrilineal ethnic groups. The north-central represents Malawi’s frontier

� �area where the greatest number of migrants have settled 7 . The south is moreheavily populated and largely by matrilineal groups.

We report a select few highlights among the explanatory variables. First, quite anumber of variables differ across geographical location. Among these includepopulation density and growth. Another is land transfer institutions. The preva-lence of matrilocal inheritance systems is strongest in the matrilineal south,weakest in the patrilineal north, and is moderate in the transitional north-centralzone. Overall, about half of all plots were acquired through the wife’s family.Lastly, several variables were obtained which are directly related to tree cover andits change. One-fourth of the sites had external tree planting projects whileone-third reported to have had village forest areas in the early 1970s.

TABLE IIIDescription of Important Explanatory Variables

North North-central SouthVariable region region region Average

1971 population density 25.4 48.7 93.8 65.2Annual population growth 1971�95 3.91 5.24 3.44 4.10Percentage of plots through matrilocal 13.4 33.5 68.9 46.0

systemŽ .Distance to tarmac km 33.8 13.9 28.1 24.8

Distance between closest tarmac road 76.8 115.2 89.7 95.1Ž .and a major city km

Ž .1971 maize yield kg�ha 2278 5192 2726 3410Average percentage change in maize �44.4 �65.5 �51.5 �54.5

yield 1971�95Percentage of households with cattle in 11.3 13.2 9.9 11.3

1971Percentage of households using fertilizer 30.0 35.5 24.9 29.3

in 1971Ž .Average annual rainfall mm 1004 867 907 915


6. ECONOMETRIC ANALYSIS

6.1. Estimation Method

Ž . Ž .The model expressed in Eqs. 12 through 14 contains three endogenousvariables: land use change, woodland tree density change, and yield change. Land

Žuse change is reflected by the absolute change in the share of agricultural land of.total enumeration area land . As indicated above, this change is nearly equal to the

Ž .change in the opposite direction of woodland and bushland, so that the inclusionof one land use change in the model is sufficient. Theoretically, tree density changeon agricultural land and non-agricultural land ought to be considered in the model.However, in practice, our data indicate very little change in tree cover density onagricultural land; in both the early 1970s and mid 1990s, a very low tree density isfound. Due to the limited number of sample observations and the difficulties inidentifying each of the equations, it was decided to exclude tree cover on agricul-tural land from the formal analysis. A third natural resource management variableis the change in yield. Since maize is the most commonly grown crop in Malawi, thechange in yield of maize is used as the measure of yield change.

Our theoretical model shows that each of these dependent variables is linked tothe others because of trade-offs of labor allocation. Therefore, one specification ofthe model is where each dependent variable is a function of the remaining twoendogenous variables. Other specifications that eliminated one or both of theendogenous variables from specific equations were also tried. The results, however,are very robust over these alternative specifications. We also tried two estimationmethods, single-equation two-stage least squares and three stage least squares.Again, both estimations produced nearly identical results for the same specifica-tions. We selected the two-stage method to report here because we were then ableto easily correct for possible heteroscedasticity and it is the corrected results thatwe report. As for specification of right-hand side variables, we tried many alterna-tive sets of regressors. We include the full set in the model presented, but note thechanges in key variables when restricted models are run.

Finally, we investigated the effect of tenure in two alternative ways. The first wasthe proportion of plots acquired through the wife’s family. This turned out to beinsignificant in all regression models. We then interacted the variable with theregional dummies to determine whether more subtle relationships existed.15 Suchrelationships may be hypothesized since dominant ethnic groups and traditionaltenure practices differ according to geographical region. The models including theregional-specific tenure variables are the ones reported in the discussion below.

6.2. Results

The results from a 2SLS model are presented in Table IV. The R-squares werehigh in all of the equations and F-statistics were highly significant in the tree coverchange and land use change regressions. F-tests to evaluate alternative specifica-tions found that the full land use change model reported in Table IV is superior toothers. This is not true of the tree cover change and yield change models where wecannot reject the hypothesis that some coefficients are zero. The direction and

15 We are grateful to an anonymous referee for this suggestion.

PLACE AND OTSUKA26

TABLE IV2SLS Regressions for Household Model of Land Use Change, Tree Cover Change,

and Yield Change, Corrected for Heteroscedasticity

Change in shareTree cover Yield of agriculture

Variable change change land

Constant 0.406 3.004 �0.919Ž . Ž . Ž .1.74 3.62 �1.84

North-central zone �0.441 0.555 �0.099Ž . Ž . Ž .�3.46 2.24 �0.70

Southern zone �0.423 0.090 �0.081Ž . Ž . Ž .�4.78 0.40 �0.56

Pct of population of main ethnic group 0.004Ž .0.93

1970 population density 0.002 �0.008 0.011Ž . Ž . Ž .0.46 �0.87 1.95

1970 pop. density squared �0.00001 0.00004 �0.00005Ž . Ž . Ž .�0.50 0.79 �1.65

Log of distance to tarmac 0.003 0.041 �0.011Ž . Ž . Ž .0.25 1.40 �0.61

Log of distance from tarmac to major �0.026 �0.162 0.028Ž . Ž . Ž .city �2.52 �3.60 1.14

1970 log of yield �0.326 0.108Ž . Ž .�4.43 2.01

1970 share of land in agriculture �0.109 �0.137 �0.588Ž . Ž . Ž .�0.93 �0.60 �3.00

1970 woodland tree cover �0.748Ž .�6.06

1970 census area tree cover �0.272Ž .�0.59

Proportion of trees that coppice well 0.045Ž .1.37

1970 percentage of village forest 0.002Ž .area to total area 2.83

Log of trees planted by external �0.001Ž .projects �0.23

1970 pct of households with cattle 0.004 �0.0005 0.002Ž . Ž . Ž .0.84 �0.25 1.66

1970 pct of households using �0.001Ž .fertilizer �1.24

Average annual rainfall �0.001Ž .�1.13

Mean altitude 0.00009Ž .0.98

Average distance to plots 0.013Ž .0.27

Whether sandy soils prevail �0.170Ž .�2.08

1970 agriculture land tree cover �0.170Ž .�0.05

Percentage of plots acquired by �0.569 0.809 �0.419Ž . Ž . Ž .women in north zone �4.35 1.48 �1.15

Percentage of plots acquired by 0.179 �0.167 �0.011Ž . Ž . Ž .women in north central zone 1.89 �0.85 �0.07

Percentage of plots acquired by 0.044 0.360 �0.158Ž . Ž . Ž .women in south zone 0.63 1.21 �1.66


Ž .TABLE IV� Continued

Population growth 0.003 �0.017 0.020Ž . Ž . Ž .0.15 �0.44 0.67

aChange in woodland tree cover 0.311 �0.780Ž . Ž .0.83 �1.99

aChange in yield �0.112 0.107Ž . Ž .�2.82 1.18

aChange in share of agricultural land �0.143 0.011Ž . Ž .�1.25 0.03

Significance of F-statistic 0.0000 0.0370 0.0002

Note: Figures in parentheses are t-values.a Fitted values from first stage instrumental variable regression used.

significance of the population pressure coefficient estimates are robust under thealternative regressions. This not true for the results on tenure variables. Where theresults are not robust, we note these in the discussion.

Change in Non-agricultural Land Tree Density

The change in woodland tree density is found to be related to tenure, but not inthe manner hypothesized earlier. First, when entered as a single variable, theinheritance system was not related to tree cover change. Thus our main hypothesisis not supported. However, when interacted with geographical location, morematrilineal cum matrilocal systems led to faster decline in tree cover in the

Ž .northern region robust across all specifications tried . Further, we found in somemodels that the opposite trend was found in the north-central region. A consistentexplanation for this is that faster loss of trees is associated with the influx ofmigrant groups, the matrilineal groups into the patrilineal north and the patrilinealgroups into the Chewa dominated north-central. These types of households maywell have different long-term strategies than indigenes and make the creation andenforcement of conservation rules more difficult.

Ž .We found that tree cover change was higher mainly smaller decreases wherevillage forest areas occupied a larger share of the area. These were identified byrespondents as specially managed areas, implemented with the assistance of theForest Department. The change in tree density was strongly and negatively relatedto the initial period level, suggesting that change is more rapid when the scarcityvalue was lower. Tree cover loss was found to be more severe in areas further frommajor cities. This is contrary to expectations, but may simply mean that woodlands

Žnearer to cities were cleared prior to 1970 this would be expected under virtual.open-access conditions . Tree cover loss was also greater where yield change was

Ž .higher less negative . This is not self-evident, but it may be that yield losses areŽnot as great in areas where tobacco is more prevalent residual impact of fertilizer

.use in which case we would expect greater removal of trees for tobacco drying.Finally, tree cover change is related to region. There has been greater tree densityloss in the north-central region, characterized by estate development and highwood demands for tobacco drying and curing, and in the southern region, wheredemand for wood for fuel and shelter by a sweltering population is acute.

PLACE AND OTSUKA28

Change in Crop Yields

The strongest relationship appears to be the initial period yield which shows anegative relationship. This may simply reflect the fact that respondents were much

Žmore aware of current day yields which were relatively similar across enumeration. Ž .areas than they were of past yields which varied much more . Yield decline was

steeper the further away from a major city. The latter result may capture thehigher ratio of input costs to output prices leading to lower use of inorganicnutrient inputs in areas far away from major cities, especially in recent years whereliberalization has led to significant reductions in fertilizer use. The rate of yield

Ž .decline was less in areas of better soils i.e., where sandy soils were not prevalentwhich is the expected result. Lastly, yield decline was not as severe in thenorth-central region which might be expected given that it is now the majortobacco growing area and thus is the heaviest user of fertilizer and relatively largefarms remain allowing for some fallowing.

Change in Share of Agricultural Land

Population density has an important positive effect on conversion of land intoagriculture. The negative sign on the squared term indicates that the positive effectof population density declines with population density and is maximized at a

Ž .population density of 114 per square kilometer exceeded by 19% of sites .Population growth was, however, not significantly related to conversion. The effectof tenure is weaker. As in the case of tree cover change, the single tenure variablehad no significant impact on land use change, but became more significant wheninteracted with geographical zone. Conversion of woodlands into agricultural landwas accelerated in patrilocal areas relative to matrilocal areas, but in the southernregion only. The result is only just statistically significant and the effect doesdisappear under some alternative specifications. The direction of the effect isconsistent with the nature of the results on tree density where areas experiencingmore profound changes to the traditional system were associated with greater treecover decline.

The initial share of agricultural land was negatively related to conversion asexpected. The log of yield was significant and had a positive effect on conversion,also as expected. Greater conversion to agriculture is associated with faster loss ofnon-agricultural tree cover. This would be anticipated if the resulting loss in treecover lowered profits of woodlands relative to profits from conversion to agricul-ture. Finally, conversion to agricultural land is associated with a greater percentageof households with cattle in the initial period. This is contrary to the notion thathouseholds with cattle would prefer to retain more woodland cum grazing land.But since cattle raising was never as prominent an activity in Malawi as in other

� �places, the result may reflect a wealth effect 3 .

7. SUMMARY AND CONCLUSIONS

First of all, it should be made clear that it is difficult to generalize our findingsfor all of Malawi, because of the considerable variation from north to south. In ourdiscussions below, we have in mind the situation facing the central and southern


regions, which are home to the majority of the population and which are facingmore critical natural resource management choices. The north, with its relativelylow population density, cannot be lumped in with the others in the followingdiscussion, although it may face similar issues in the near future.

We found several trends in natural resource management which portend hardtimes ahead for rural households in Malawi. First, we found that in nearly all sites,yields of maize, the staple food crop, were perceived by respondents to havedeclined over the past 25 years. Second, while families and communities havehitherto been able to compensate for this by expanding agricultural areas, newuncultivated areas are becoming scant. In nine enumeration areas land once underwoodlands has completely disappeared and in most others, the area under wood-land has decreased markedly. This supports the hypothesis that woodland has beenexcessively converted to farm land. Third, the conversion of land has had adramatic and negative impact on aggregate tree density since tree cover is many

Žtimes greater on non-agricultural land than on agricultural land 16.7 compared to.2.0% in 1995 . At the same time, we found little evidence of significant tree

planting, though in some of the sites, tree density on agricultural land inchedupward. Fifth, exacerbating the effect of land conversion on tree cover is the

Ž .decrease in tree density on remaining woodlands by about 33% .In sum, the increasing scarcity of woodlands and their products has not yet

manifested itself in sufficiently attractive incentives for their sustainable manage-ment. Given the high degree of poverty among rural households and the poorremuneration from maize production, it is unlikely to be high profits from agricul-tural production that have driven the conversion process. Instead, one must look atweaknesses in the management of woodlands themselves as contributing theseemingly low valuation attributed to their preservation and management.

It is difficult to assess the degree to which this apparent natural resourcedegradation is undesirable. However, considering the lack of woodland resourcemanagement and acceleration effect in areas where mixed tenure systems areoperating, the pace of natural resource degradation almost certainly exceeds thesocially optimal level. The rather likely possibility that resources have beendegraded beyond the sub-optimal rate implores us to then ponder how these trendscan be reversed.

For this, we turn to our econometric analyses for some insights. Populationdensity was found to have its anticipated positive effect on the conversion of landinto agriculture. However, interestingly, neither population density nor growthnecessarily led to decreased yields or tree cover. This is encouraging and suggeststhat to some extent, there are some induced responses occurring within communi-

Ž .ties. It is also encouraging to see that tree cover change is negatively and stronglyrelated to initial tree cover. This again points to some locally recognized increase

� �in the scarcity value of tree resources 22 . Lastly, we found no evidence that anyparticular customary tenure system was providing insufficient incentives for long-term management of woodland or farm land resources.

Connectivity to markets could play a role in raising long-term benefits fromresources and improving household incentives to manage them. We found thatproximity to major urban areas was positively associated with yield change and atthe same did not face the same magnitude of woodland tree cover loss as did moreremote areas over the study period. Such a finding is similar to that found for

� �Uganda 34 where market integration was linked to significant tree planting

PLACE AND OTSUKA30

activities by households. The fear that infrastructure development will have delete-rious effects on the environment is unwarranted. This is notably different than

� �evidence pertaining to virgin forest areas in the Amazon 17 , suggesting thatproperty rights systems in more settled areas such as Malawi are sufficient toforestall wanton resource depletion. There is clearly a role for governments to playhere and increased market connection can be a strategy to reduce poverty whilesimultaneously promoting natural resource management.

While tree planting on farms has shown little movement in the aggregate, thereseems to be better scope for increasing tree density on farms than on woodlands,where areas and tree densities have displayed a rapid decline. Aside from improvedincentives to plant and manage trees on individual farms, the simple fact that most

Ž .of the landscape the north excepted is already under agriculture leads us to thisconclusion.

But growing trees on farms in Malawi does not seem to be straightforward as� �noted by Dewees 14 . This is no doubt linked to institutional constraints on the

supply side, such as lack of a seed production and distribution system. However,our study has found evidence to suggest that tenure factors play an important roleon the demand side of the equation. We found that woodland conversion is higherand tree density in remaining woodlands is lower in areas where there appear to besome changes to traditional tenure systems whether from local processes orthrough migration. There are likely to be more of these types of areas in the futureas extreme population densities in some areas force people to migrate. Thus therewill be an increased need for local institutions to develop or strengthen to ensurethat natural resources are managed for the social good.

APPENDIX: LIST OF SYMBOLS

q A production of agriculture in period tta A agricultural land in period ttl A current season labor for agriculture assumed not to have spillover effectt

in period t � 1� an agricultural sustainability parameterf , H function parameters�a household agricultural land that is converted from woodlandlC household labor for clearing of woodland1LC total labor expended for land conversion in the community1AW area of woodland at community level in period 1F stock of forest resources at time tt� natural regeneration rate of tree resources in woodlands� A area of woodlands converted at community levelq F amount of forest resources extracted by the household in period 22l F labor input for resource extraction by the household in period 22w wage rateP A price of agricultural output

F Ž .P price of woodland tree outputd density of tree cover in woodlandFŁ household profits in period ttr rate of time preference


p probability of husband losing rights to residency in wife’s villageV expected present value of profits� partial derivativeM prevalence of matrilocal system or the proportion of land received from

wives’ familiesA total area in communityN �A population density in the initial period1D distance to urban centersA A�A proportion of community land area under agriculture in the initial1

periodY crop yield in the initial period1G growth rate of crop yieldYG growth rate of woodland tree coverFG population growth rateNF AN tree cover on agricultural land in the initial period1E a vector of exogenous environmental factorsCA the proportion of common tree species with good coppicing ability

ŽGP government policy measured by proportion of protected village forest.area

EH dummies for ethnic groups distinguished by matrilineal and patrilinealtraditions

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population, tenure, and natural resource management: the case of customary land area in malawi

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