Smallholder Land Access in Post-War Northern Mozambique

World Development Vol. 37, No. 8, pp. 1379–1389, 2009 Ó 2009 Elsevier Ltd. All rights reserved 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

doi:10.1016/j.worlddev.2008.08.016

Smallholder Land Access in Post-War Northern Mozambique ¨ CK and KATI SCHINDLER * TILMAN BRU German Institute for Economic Research (DIW Berlin), Berlin, Germany Humboldt University of Berlin, Germany Households in Conflict Network (HiCN) Summary. — This paper analyzes the inequality and determinants of flexibility in smallholder land access in post-war northern Mozambique. This paper demonstrates that high land endowments in aggregate do not imply equal access to cultivated or fallow land at the household level, even if land access has some flexibility across time. A formal test establishes the low extent of flexibility in land access at the household level in the study site. The econometric analysis further reveals that some groups of households such as female-headed households and those with low asset endowments or weak social institutions suffer from significant rigidities in land access. Ó 2009 Elsevier Ltd. All rights reserved. Key words — land access, agriculture, institutions, inequality, Africa, Mozambique

of land for current productive use (represented below by self-reported cultivated area per household) or as fallow and hence future productive—or current regenerative—use (represented below by self-reported fallow area per household). Fallow land entails an option value due to the potential of agricultural productivity increases derived from the future cultivation of current fallow land. Inequality of land distribution may exist for land endowment and land access. These definitions are adopted here to account for some key features of the study site; for this reason, they differ from the widely used definition of land access promoted by Bruce (1998). Not covered in this paper are land access issues related to the commercial land sector; we focus on the smallholder (or family farm or peasant) sector. 1 The analysis reveals the high degree of unequal smallholder land access in post-war northern Mozambique, with different factors restricting smallholders from establishing comparable farm sizes. Household composition, the war-time destruction of assets, and a household’s social standing within the community all had large impacts on land access, with female-headed households being extremely constrained in their access to land. The empirical results of this case study emphasize that high land endowments in aggregate do not equalize land access at the household level, which hence requires rural development and social protection policies for some groups of smallholders. The paper complements the other papers presented in this special issue in three regards. First, Mozambique was characterized by a weak legal framework of land tenure in the postwar period, despite the land reform that was being legislated in that period. The Mozambican case hence contrasts with an environment of more clearly defined and enforced land tenure

1. INTRODUCTION The aim of this paper is to analyze the flexibility of smallholder land access in an environment characterized by a low population density and weak formal institutions. In particular, we want to (A) assess the scale of the unequal distribution of land at the household level, (B) evaluate the flexibility of land access at the household level, and (C) identify the determinants of land access at the household level. The paper uses household survey data collected in the period 1994–96 in Mozambique, an extremely poor developing nation facing a daunting post-war reconstruction challenge while moving towards a market-oriented economy. Smallholder land access in this setting is the result of conflicting forces. On the one hand, the low population density suggests that land access may be comparatively flexible. On the other hand, weak social institutions, weak markets, and low asset endowments imply that there are significant barriers at the household level to land access. The empirical setting hence provides an opportunity to identify from a multitude of possible influences the key determinants of the flexibility of smallholder land access. The concept of the flexibility of land access builds on the insights of Binswanger and McIntire (1987) and Chayanov (1966/1925). In an economy with perfectly flexible land access, on the one hand, households can easily access further area for cultivation and farm households will maintain a constant land to labor ratio for a given household composition, all other things being equal. In an economy with perfectly rigid land access with no land markets of any type, on the other hand, farm households would control a given area of land each throughout their life cycles. An increase in household size would cause the land to labor ratio to drop. A distinguishing feature of this paper is the differentiation of land endowment as an aggregate concept and land access as a household-level concept. In our paper, we consider land endowment to represent the overall availability (or existence) of land relative to the population, measured by population density per square kilometer. We consider land access to be the ability of a household to convert such general land endowment into current and future land use, subject to household-level or community-level constraints. In other words, land access represents the ability of a household to claim a plot

* The data were generously made available by the Food Security Project of the Mozambican Ministry of Agriculture and Michigan State University. Financial support from the United States Institute of Peace is gratefully acknowledged. We thank the editors of this special issue, three anonymous referees, the participants of conferences in Berlin and Washington DC, and Valpy FitzGerald for their helpful comments. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Unites States Institute of Peace or the other above-mentioned institutions. Final revision accepted: August 7, 2008. 1379

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rights in the Brazilian Amazon (Araujo et al.; Ludewigs et al.; Pacheco) and with case study sites with much higher population density (Berry; Bouquet; Fay). Second, the paper addresses land-related behavioral choices by smallholder farmers and their villages as well as implications for national land legislation (Ludewigs et al.; Place; Upton). Third, our analysis reveals the critical role of social institutions and gender relations for land tenure (Bruce and Knox). This paper is structured as follows. Section 2 summarizes the pertinent literature on land access and identifies gaps. Section 3 provides a brief overview on the case study site and discusses the early post-war land reform in Mozambique up to 1995. Section 4 presents the data and methodological issues. Section 5 discusses the empirical findings. Section 6 concludes. 2. LITERATURE REVIEW This paper expands two strands of the literature (see also Sikor & Mu¨ller, 2009). First, the diversity and unequal distribution of land access within the smallholder farm sector are discussed. Research on the diversity of this sector has been shaped by the work of Chayanov (1966/1925) and Singh, Squire, and Strauss (1986), while a more recent empirical foundation is provided by Jayne et al. (2001). In his original model, Chayanov argues that the cultivated area is adopted according to a household’s stage in the life cycle, reflecting changing ratios of dependent to non-dependent resident household members. Binswanger and McIntire (1987) argue that weak institutional environments may be land abundant with high endowments of land in the aggregate and Chayanov-style equalization of land access at the household level (i.e., ‘‘flexible” land access in the language of this paper) for comparable households. More recently, the literature has pointed out the impact of community factors on the size of land under cultivation. For example, Newell, Pandya, and Symons (1997) show that output per hectare and cultivated land are inversely related due to variations in land fertility and labor supply across villages in Gujarat. Holden and Yohannes (2002) find that in rural Ethiopia the relationship between land access and tenure insecurity is specific to the study site and local characteristics, such as access to markets. Second, a large body of academic studies assesses the implementation and design of land reforms in Sub-Saharan Africa, which has not resulted in univocal conclusions (Deininger & Feder, 1999; Jayne, Mather, & Mghenyi, 2006; Quan, 1998; Sikor & Mu¨ller, 2009). Pinckney and Kimuyu (1994) report that communities in Kenya ignored state-led land titling programs that did not fit their needs. Several studies emphasize that while women have limited but recognized land rights under customary tenure systems their rights may be totally lost under a private property regime (Gengenbach, 1998; Lastarria-Cornhiel, 1997). Several gaps in the literature remain. First, relatively little attention has been paid to the issue of institutional change and land reform in more land-endowed regions of less-developed countries. Most studies on tenure security and land investments implicitly assume that land is a scarce factor of production. In addition, several studies specifically focus on areas with high population density that bear the potential of land-related conflicts, such as Rwanda (Andre´ & Platteau, 1998), some regions in Uganda (Deininger & Castagnini, 2004), and Zimbabwe (Bruce, Fortmann, & Nhira, 1993). It is commonly assumed that high land endowments correspond with an equal distribution of land (Binswanger & McIntire, 1987; Sjaastad & Bromley, 1997), making land reform in those

circumstances a low priority. This paper will demonstrate that even high endowments of land at the aggregate level can imply inequality in land access at the household level. Second, only few studies examine exogenous causes beyond the household composition. For example, Baland, Gaspart, Place, and Platteau (1999) show that initial endowments are a determining factor in risk-coping strategies of Ugandan smallholders. Holden and Yohannes (2002) analyze the correlation between land use and investment in land and identify a poverty trap for small farmers, whose ability to intensify production is limited. Our paper contributes to this literature by exploring econometrically the constraints to unequal land access both as a result of voluntary behavioral choices and of exogenous factors. Third, most studies that have analyzed land access either rely on descriptive statistics only or regard its correlation with agricultural yields and productivity. One of the few studies that address the determinants of land access with econometric techniques is done by Marule (1998) on Mozambique, using the same household survey data as this paper. However, Marule utilizes a narrow choice of variables in his model. As a consequence, the variance in the data explained by his model is very limited, with adjusted R-squared values below 0.15. Another study by Jayne et al. (2003) explores land allocation patterns and their relation to income poverty, using cross-country data from five countries including Mozambique. Again, the models used in that paper explain less than one third of the variation of land access in Mozambique. Hence, this paper complements former research by conducting a more comprehensive econometric analysis on the determinants of land access. A number a topics that have already been covered extensively by other researchers will not be discussed again in this paper. These include tenure security (Marule, 1998; Myers, 1994; Unruh, 1998), the impact of large, privately owned cotton producers on the smallholder sector (Pitcher, 1998; Strasberg, 1997; Strasberg & Kloeck-Jenson, 2002), and the linkage between land tenure and household welfare (Jayne et al., 2003). 3. THE CASE OF NORTHERN MOZAMBIQUE Mozambique experienced a low intensity war of destabilization from 1976 till 1992 (Geffray, 1991; Hume, 1994; Saul, 1999; Vines, 1996). It severely damaged or destroyed rural infrastructures and services, including irrigation systems, trading posts, mechanized agricultural production, and extension programs (Comissa˜o Nacional do Plano, 1993; World Bank, 1989, 1990). This led to many farm household endowments being exogenously determined. For example, the number of cattle in Mozambique declined from over 1.3 million in 1982 to 0.25 million in 1992 (Ministe´rio da Agricultura, 1994). The presence of land mines and the return of six million refugees and internally displaced persons put further pressure on the infrastructure (Unruh, 2002; World Food Programme, 2001). To compensate at least partially, international donors and non-governmental organizations provided agricultural tools and seeds to selected villages during the last years of the war (Hanlon, 1991). Yet productivity in the smallholder sector in the post-war period continued to remain well below regional averages (Tschirley & Weber, 1994), per capita food production only reached 90% of its pre-war level by 1996 and GDP per capita was as low as 140.55 USD in 1992 (World Bank, 2005). The north of Mozambique is often considered the ‘‘green belt” of the nation, where agricultural production is in large

SMALLHOLDER LAND ACCESS IN POST-WAR NORTHERN MOZAMBIQUE

part undertaken by the smallholder sector (Pitcher, 1998). This part of the country has several characteristics that make it distinct from the southern and central parts. First, northern Mozambique was quite isolated for many months each year both during the war and the post-war period due to the historically weak infrastructure, the destruction of the war and bad weather (Dinerman, 2006; World Bank, 1990). Second, there were few formal employment opportunities in the north and few migrant workers, unlike in southern Mozambique. Offfarm employment opportunities were mostly limited to extremely informal labor sharing arrangements called ganho-ganho, done on a reciprocal basis or paid in kind (Bias & Donovan, 2003). Only 11% of all rural households in the north occasionally or regularly employed agricultural labor in the mid-1990s (UNDP, 1999). Third, the population density in northern Mozambique ranges from 12 to 50 inhabitants per square kilometer in the observed districts (FSP survey) compared to national averages of 20, 34, and 47 inhabitants per square kilometer in Mozambique, Tanzania, and Kenya, respectively (World Bank, 2005). Fourth, there are practically no landless households in rural northern Mozambique (see Tables 2 and 3 below). Fifth, judging from the farm household data of the FSP survey, from UNDP evidence at the district level and from own fieldwork, local agricultural crop markets were the most important and, occasionally, the only existing output markets in the north in the early post-war period (UNDP, 1996). Lastly, the dominant ethnic group of the north, the Macua, has a matrilineal kinship system and practice matrilocality as their preferential residence patterns after marriage (Arnfred, 2001; Pitcher, 1996). The early post-war period in Mozambique was characterized by competing claims to land (Myers, 1994). Following the peace accord of 1992, a rush to land began, as the urban elites were seeking capital security and senior military personnel were rewarded with land for their co-operation in the peace process. Government officials at various levels of the political hierarchy granted land concessions to commercial enterprises, often without proper documentation and investigation of whether or not the land was occupied by local smallholders. Land had been a major issue both during the war (Geffray, 1991) and in the debates leading up to the 1990 constitution and the peace accord of 1992 (Hume, 1994); it remained an important topic in the following years. However, several land reforms in the early post-war period, all top-down in their nature, fell short of providing a clearly defined institutional framework for the allocation of land (Quan, 1998, p. 24). The 1990 constitution defined land to be a state property, allowing only use rights to individuals. The privatization of land was debated intensively at several points of time, but was not approved by the parliament, hence land remains state property. In 1995, a land policy was enacted which both recognized customary rights over land and allowed for the formal registration of areas that had been occupied through customary practices (de Quadros, 2003, p. 177f). Also, the registering of use rights was allowed, though the cadastral mapping in some provinces was outdated or inadequate. Social institutions at the community level, who were historically involved in allocating land, remained intact at least in some areas. In the pre-colonial era, land was allocated by ‘‘traditional” leaders, locally called mwene, who also resolved local disputes and fulfilled rituals with respect to the land (Bowen, 2000; Pitcher, 1998, p. 121f). The Portuguese colonial rulers nominated another authority at the village level, re´gulos, who had duties in their function as the lowest tier of the colonial administration, such as tax collection and the distribution of farming land to smallholders. In some cases, individuals

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held both mwene and re´gulo positions, with both enjoying some popular legitimacy. After independence, Frelimo considered both authorities to be undemocratic and inequitable and replaced them with party secretaries, who were selected by community members with Frelimo’s acceptance. This allowed for the possibility of selecting a relative of the former mwene as party secretary, thus sustaining the chieftaincy informally (Blom, 2002, p. 108). Furthermore, at least during the early period of the civil war, Frelimo aimed at expelling cultural traditions and rituals in the rural areas (Geffray, 1991). However, in the post-war period, all three kinds of local institutions, ‘‘traditional,” colonial, and Frelimo party secretaries were present in some areas of Mozambique. According to Pitcher (1996, p. 139), both mwenes and re´gulos continued to be the respected moral authorities in some villages despite their long oppression. While access to land was possible through both formal titling processes and a customary allocation system, a structure to link both systems was missing, thus creating uncertainty about the process of acquiring land (Myers, 1994). To conclude, in the post-war period, there was no single way to access land: ‘‘Land is acquired through systems that vary from region to region, and . . . these systems include means of access such as inheritance, marriage, donations, authorizations by local entities, sale and purchase, and renting or lending” (de Quadros, 2003, p. 190). Furthermore, there was a considerable ambiguity over the role of individuals, communities, and the state in land allocation (Unruh, 2002, p. 3).

4. DATA AND METHODS To answer our three research questions, we will calculate and compare measures of the inequality of land access from all available household surveys from the first five years of post-war northern Mozambique and we will analyze econometrically one of these surveys in more detail. In addition to using available household survey data, the authors conducted interviews with farm households in Nampula (also at the time the FSP and CARE data were being collected) and key informant interviews in Nampula and Maputo in 1995, 1999, and 2008. The FSP survey data used to address econometrically research questions B and C were collected by the Food Security Project at the Ministry of Agriculture, Maputo, in collaboration with the Michigan State University and the Land Tenure Centre in five waves from June 1994 to January 1996 (MAP/ MSU Research Team, 1996; Strasberg, 1997; Unruh, 1997). Parts of the questionnaire include recall questions so that land access can be analyzed descriptively, but not in a multi-variate context, for two subsequent agricultural years, namely 1994– 95 and 1995–96. The cleaned survey includes 371 randomly selected farm households in 16 villages of three districts in Nampula and Cabo Delgado provinces in northern Mozambique. Villages are the primary sampling units (PSU). The survey was stratified according to households’ cotton growing status, with four mutually exclusive categories. The sample is broadly representative of the more accessible parts of Nampula and Cabo Delgado provinces in northern Mozambique in 1995. Note that in comparison to other study sites analyzed in this special issue, the sample area still represents some of the least developed and most isolated parts of Sub-Saharan Africa in the 1990s. The main weakness of the survey is that it, naturally, misrepresents the history of the war by focusing on surviving individuals and households while not recording war-related deaths.

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Other surveys collected in northern Mozambique during the war or in the first five years after the war, which are used descriptively to answer research question A, include a survey of 343 households conducted by the same team as the FSP in 1991 (i.e., during the war) which in many ways was an early pilot study for the FSP survey (Tschirley & Weber, 1994), a survey of 238 households funded by the NGO CARE in 1994–95 (Care Mozambique, 1996; Strasberg, 1997), the Nampula sub-sample of the national smallholder survey TIA covering 685 farm households in 1996 (Ministe´rio da Agricultura, 1996; Ministe´rio da Agricultura e Pescas, 1998) and the first national living standard measurement survey conducted in 1996 covering 701 households in Nampula (Instituto Nacional de Estatı´stica, 1998). 2 Note that the TIA adopted

a more random sampling and stratification strategy compared to FSP, not aiming to capture potential cotton growing areas. The CARE and FSP datasets were collected by the same research teams but they differ in regional focus, sampling techniques, timing of data collections, and questionnaire design (Strasberg, 1997). Hence the samples were not pooled. Based on our fieldwork experience, we believe that the FSP is perhaps the most carefully designed, collected, and cleaned rural household survey from the early post-war period in northern Mozambique. The variables used in the analysis below are summarized for reference and transparency in Table 1, which lists variable names, definitions, means, standard deviations, minimum and maximum values for all variables. For convenience, the

Table 1. Summary statistics of the FSP survey Mean

SD

Min

Max

Land access (dependent variables) AREACACE Natural log of cultivated area per adult equivalent per household in hectare AREAFACE Natural log of fallow area per adult equivalent per household in hectare

Variable

0.82 1.59

0.65 0.84

3.07 5.08

1.31 0.62

Household labor characteristics ACEHHL DEPEND AGEHEAD AGEHEAD2 EDUMAX

1.63 0.45 0.35 0.12 39.9 12.59 1749.1 1096.9 3.50 2.20

0 0 18 324 0

2.56 0.78 82 6724 12

War legacies FEMALE FEMHEAD VASSET ANIMAL TOOL TOOLTYPE

Definition

Natural log of adult equivalent household members Ratio of dependent to non-dependent household members Age of household head in years Square of age of household head in years Maximum years of education, includes all household members

Ratio of females over total number of members per household in 1994–96 Is this a female-headed household? Natural log of value of assets owned in 1992 in 1996 US$ per household Does the household own at least one large animal in 1992? Number of tools per capita per household Number of types of tools per household

0.46 0.01 2.92 0.11 0.92 2.89

0.17 0.18 2.67 0.30 0.57 1.18

0 0 0 0 0 0

1 1 7.81 1 4 5

Does the household have a very high soil quality? Distance to fields in minutes per household

0.40 40.58

0.48 27.11

0 2.00

1 191.25

Total number of days ill per household in 1994–95 Proportion of cultivated area per household with lack of rain Is the household affected by cyclone Nadia in 1994? Do most of the households’ crops suffer from pests?

46.06 0.29 0.33 0.40

70.32 0.31 0.45 0.49

0 0 0 0

433 1 1 1

Social institutions AUTHTP Is main person of household in a position of traditional authority (mwene, piamwene)? AUTHCP Is main person of household in a position of colonial authority (regulo, cabo)? AUTHMP Is main person of household in a position of modern authority (secretario/presidente or assistant)? AUTHTR Is main person of household related to traditional authority (mwene, piamwene)? AUTHCR Is main person of household related to colonial authority (regulo, cabo)? AUTHMR Is main person of household related to modern authority (secretario/presidente or assistant)? ORIGINM Origin of main man of household is this village? ORIGINF Origin of main woman of household is this village?

0.01 0.01 0.03 0.28 0.17 0.16 0.67 0.64

0.12 0.14 0.19 0.42 0.36 0.35 0.50 0.50

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

Market institutions PRICEVAR Variance of Paasche price index for home-produced food crops in 1994–96 LABOR Natural log of hours of labor hired for household farm work per capita per village in 1994–96 MARKET Natural log of total crop sales by all village households in US$ MILL Is there a grain mill in the village (subjective view)?

0.40 1.40 7.53 0.19

0.36 0.98 1.56 0.47

0 0.32 4.99 0

1.33 3.40 10.04 1

District specific effects DIS1 DIS2 DIS3

0.50 0.11 0.38

0.50 0.36 0.47

0 0 0

1 1 1

Field characteristics AREAFER DISTANC Risk variables ILLDAYS RAIN CYCLONE PEST

Montepuez district Meconta district Monapo district

Source: FSP household survey. Notes: The data are weighted. Categorical variables are coded to answer the questions shown above with no = 0 and yes = 1. Variables refer to the agricultural season 1994–95 unless stated otherwise.

SMALLHOLDER LAND ACCESS IN POST-WAR NORTHERN MOZAMBIQUE

variables are listed by thematic groups starting with the land access variables measured in natural logs (as used in the regression analysis below). Note that Table 2 discusses these variables measured in levels, as this makes their interpretation in the summary statistics more intuitive. Land access is measured both as cultivated and fallow area in hectare per adult consumption equivalent (ACE), where the conversion factors are those used by Rose and Tschirley (2000). 3 We adopt the ACE specification throughout our analysis as this accounts—as a first approximation—for observable difference in consumption needs and production capacities across households (Atkinson, 1991; Lanjouw & Ravallion, 1995). Household labor characteristics capture the size of the household measured in ACE units, its dependency ratio, the age of the head of household, and the number of years of education that the best educated household member possesses. The latter variable was chosen as education levels in northern Mozambique are very low, thus suggesting that selected individuals with relative high education will adopt different coping strategies with an impact on household land access. Variables representing the legacy of the war (at least in part) include the ratio of females to total household size, which may have been affected by relatively higher war-induced male mortality, the gender of the household head (where many female heads may be war widows as argued below), and several asset variables indicating ownership of assets, large animals and tools at the end of the war (hence ensuring that these variables are beyond the control of households in the period of analysis). Field characteristics control for the quality of the plots by indicating households which have land with very high soil quality (which is a self-reported characteristic) and measuring the average distance to plots in minutes walking time per household. Risk variables capture some measurable risks facing farm households. These include the total number of ill days per year reported by households, the share of land reported to have suffered a lack of rain, an indicator variable for households badly affected by the cyclone Nadia in 1994, and an indicator variable measuring if most of a household’s crops suffered from pests. As social institutions can matter for land access, several social institution variables are included in the analysis. These capture if a household is in a position of traditional, colonial, or modern authority or if the household is related to one such authority. Furthermore they capture if the main man or woman of a household was born in the current village of residence. Finally, and given the potential importance of community level and market access effects reviewed above, the analysis includes a variable measuring the variance of a Paasche price index (Deaton & Zaidi, 1999) for home-produced food crops which captures otherwise unobservable agricultural shocks

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and market risks, two indicators for the density of labor and output markets at the village level, and a variable indicating the presence of a local grain mill. Two district dummies are also included to capture remaining local fixed effects. The second and third research questions are tested empirically using survey linear regressions for these dependent variables, where the estimation accounts for stratification, clustering and weights matching the survey design of the data leading to appropriate adjustments to the standard errors of the estimates. The estimations include all independent variables expected to be of relevance, irrespective of their actual significance. This leads to the following reduced form equation following Feder and Onchan (1987) and Place and Hazell (1993), which will be tested empirically below: Land accessi ¼ f ðLabor characteristicsi ; War legaciesi ; Field characteristicsi ; Risk variablesi ; Social institutionsi ; Market institutions; District controlsÞ; where the subscript i denotes household-specific effects of each of the vectors. Contrary to the land literature, the equation does not include credit and yield variables, as our field research indicated that formal credit transactions were negligible in rural areas of northern Mozambique and no suitable data for plot-level yields are available in the dataset. This is mainly due to households practicing intercropping for all crops except for cotton, which is not grown by all households in the sample. In addition, the analysis applies to only one year, making the time frame too short to consider household size as an endogenous variable.

5. RESULTS AND DISCUSSION (a) Inequality of land access Table 2 provides descriptive statistics of the two key land variables calculated using the FSP household survey for the agricultural years 1994–95 and 1995–96. Note that 122 households do not have any fallow land. The data indicate that even when correcting for different household structures, there are significant inequalities in land access. In fact, while cultivated area is quite unequally distributed in both years with Gini coefficients of 0.34 and 0.40 in 1994–95 and 1995–96, respectively, the inequality of the fallow area is much higher (with respective Gini coefficients of 0.63 and 0.51). Reductions in area cultivated seem to correspond to increases in fallow land—both measured in mean area and using the Gini coefficients. Most importantly, perhaps, the data show that there is flexibility of land access for a given set of households across years. Note that both years include the same households so

Table 2. Distribution of land access Cultivated area

ha/ACE

1994–95 1995–96

Gini

1994–95 1995–96

Source: FSP household survey. Note: The data are weighted.

Fallow area

Min

Mean

Max

Min

Mean

Max

0.05 0.03

0.54 0.46

3.70 5.47

0 0

0.22 0.34

1.86 2.20

0.34 0.40

0.63 0.51

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that we can be confident that these changes are not due to changes in the composition of the households across years. In fact, the FSP data indicate that land access at the plot level occurs mainly through clearing (94% of all households— with multiple answers possible per household), through informal institutions (45%), through cotton companies (26%), through formal institutions such as administration, mwene or re´gulo (4%), and through purchases (1%). Expressed differently, 44% of all households relied on one land access channel, 38% on two channels, 16% on three channels, and 1% on four channels (the maximum). The average number of channels per household used across villages ranges from 1.29 in Linde to 2.48 in Mepine. Land clearing remains the most important mechanism of land access in 14 of the 16 surveyed villages. Table 3 summarizes all available household surveys conducted in northern Mozambique in the period 1991–96. The table compares quintiles of land holdings per adult consumption equivalent (ACE). The results indicate that land inequality exists despite the high land endowment in the region and that land inequality varies across space and time. However, no clear trend can be identified from the surveys, none of which is in itself representative of Nampula province or northern Mozambique generally. Note that the ratio of the highest to the lowest quintile Q5/Q1 is less variable for the two FSP and CARE surveys (each of which covers two years). This may be due to the fact that households in the potential cotton growing areas of the FSP and the CARE surveys were also able to obtain land from the cotton companies, hence helping to avoid large land access inequalities. If true, this feature would bias downwards our econometric estimates of land access rigidities using FSP data below in comparison to other areas in northern Mozambique. 4 (b) Flexibility of land access The formal test of rigidity in land access at the household level is presented in column (1) in Table 5. The key variable of interest is ACEHHL and its coefficient. Theoretically, perfect rigidity in land access implies a land access per ACE elas-

ticity of household size of minus one (in other words there is no change in land per household for a change in household size) while perfectly flexible land access per ACE implies an elasticity of zero (each increase in labor endowments is fully matched by an increase in land access per household—thus resulting in an unchanged land access per ACE). Note that if both the dependent and the independent variables are measured in logs, then the coefficient can hence be interpreted as an elasticity. In fact, the estimated coefficient for cultivated area is 0.725 and it is highly significant. There is hence a strong decline in land access per ACE unit with changing household size measured in ACE units. The corresponding coefficient in column (2) confirms this result: households with more ACE also have less access to fallow land per ACE unit. Combined, these are strong results, which indicate that households do not have very flexible land access, for a given land endowment at the aggregate level. It would be useful to have similar calculations for other settings in Mozambique or in northern Mozambique at other times or indeed in other countries. However, as we believe this to be the first formal test of rigidities in land access, there are no comparable studies yet. The following analysis aims to indicate the nature of these restrictions at the household level. (c) Determinants of land access A land access profile based on FSP data is presented in Table 4. This is an unconditional analysis, with the purpose of assessing correlations between land access and various independent variables. A land poverty line is set at the mean total area per adult consumption equivalent per household in mid1994 in hectare, which is 0.75 ha. This line thus divides the sample broadly into relatively more and less land-poor households and accounts for both cultivated and fallow areas. Given the uni-modal, bell-shaped distribution of the land variable, the results are not sensitive to the choice of the poverty line as the mean. The table shows the population share of the sample in each subgroup, the headcount index, and the land access gap index (Foster, Greer, & Thorbecke, 1984). The latter

Table 3. Land access in Northern Mozambique Survey

Period

Number of households

Cultivated land per household in ha/ACE (%)

Q1

Q2

Q3

Q4

Q5

1991

343

0.55 (101)

0.16 (7)

0.29 (11)

0.44 (17)

0.67 (23)

1.28 (43)

8.00

1993–94

238 238

0.33 (7) 0.37 (7)

0.53 (12) 0.62 (12)

0.74 (17) 0.85 (17)

1.03 (24) 1.17 (22)

1.80 (40) 2.14 (42)

5.45

1994–95

0.89 (100) 1.03 (100)

1994–95

371 371

0.22 (11) 0.16 (8)

0.38 (17) 0.31 (14)

0.54 (19) 0.42 (23)

0.73 (25) 0.64 (23)

1.34 (28) 1.22 (31)

6.09

1995–96

0.54 (100) 0.46 (99)

TIA

1996

685

0.51 (101)

0.12 (5)

0.25 (10)

0.39 (16)

0.58 (24)

1.21 (46)

10.08

LSMS

1996

701

0.75 (100)

0.23 (6)

0.39 (11)

0.56 (15)

0.86 (23)

1.71 (45)

7.43

FSP CARE

FSP

Quintiles of cultivated land in ha/ACE (%)

Q5/Q1

5.78

7.63

Sources: Various surveys as explained in Section 4. Notes: The data for the quintiles Q1 to Q5 have been ranked by hectare per adult consumption equivalent (ha/ACE) and show the mean ha/ACE per household per quintile and the share of total land held by each quintile. Some percentages do not add up to 100 due to rounding. CARE 1993–95: Data are paired. FSP 1994–96: Data are paired. LSMS 1996: Data refer to rural Nampula province.

SMALLHOLDER LAND ACCESS IN POST-WAR NORTHERN MOZAMBIQUE

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Table 4. Land access profile

All households Ratio of dependent to non-dependent household residents Is head of household in any position of authority? Is head of household related to any position of authority? Origin of head of household is this village? Is this a female-headed household? Number of tools per capita Days ill per year Does the household’s land have a very high soil quality? Distance to fields (in minutes) Is there a grain mill in the village?

Low High No Yes No Yes No Yes No Yes Few Many Few Many No Yes Short Long No Yes

Population share (%)

Total area per household in ha/ACE

100 90 10 93 7 68 32 21 79 99 1 89 11 74 26 59 41 87 13 81 19

0.75 0.75 0.75 0.73 0.99 0.79 0.65 0.67 0.77 0.75 0.63 0.67 1.31 0.80 0.58 0.70 0.81 0.76 0.65 0.75 0.72

Land poverty Headcount ratio

Land access gap

0.62 0.63 0.58 0.63 0.52 0.59 0.70 0.65 0.61 0.62 0.62 0.68 0.19 0.56 0.80 0.67 0.55 0.60 0.79 0.62 0.64

0.24 0.24 0.22 0.24 0.11 0.23 0.25 0.28 0.22 0.23 0.27 0.26 0.06 0.20 0.33 0.26 0.20 0.22 0.35 0.23 0.26

Source: FSP household survey. Note: The data are weighted and refers to mid-1994.

indicates the average shortfall of the land poor from the land poverty line, thus providing a measure of the depth of land poverty. The data suggest that some key household-level variables are correlated with land access. For example, if a household’s head is in a position of authority, total area is 0.24 ha/ACE more than the average household. This is also reflected in the land access gap, which is much lower for households holding a position of authority. Likewise households with many tools per capita have much higher land access with 0.56 ha/ ACE more land than the average household. The most landpoor subgroups are very ill and female-headed households. However, female-headed households only account for a small share of the weighted sample. Interestingly, the dependency ratio and the presence of a grain mill do not correspond with changes in land access, questioning the importance of either Chayanov-style life cycle effects or those indicated by villagelevel institutions as found by Holden and Yohannes (2002). The results of the two regressions on the determinants of land access measured with cultivated area (AREACACE) and fallow area (AREAFACE) are summarized in columns 1 and 2 of Table 5, respectively. Both dependent variables exhibit well-behaved bell-shaped curves. The two regressions, which were implemented using Stata’s survey commands, have relatively high R-squared values, thus indicating a good fit. While some coefficients are not significant individually at the usual levels of significance, each group of exogenous variables as grouped in Table 5 is jointly significant (data not shown) with the exception of the district-specific controls, which in turn confirms that important local effects are already captured by the market institution variables. The correlation coefficients of the independent variables are generally quite low (below 0.3 in absolute terms), with a few larger coefficients among risk variables and district dummies as well as market institution variables and the district dummies (data not shown). These correlations are not surprising, as rainfall and exposure to cyclones as well as market development are likely to correlate

spatially. However, none of these coefficients of correlation exceeds 0.6, which suggests that the specification still captures distinct effects. Furthermore, a variance-inflation-factor analysis conducted in Stata using regression commands did not reveal large correlations between independent variables either (all VIF values were below 5.4, with most values being close to 1). A robustness test of the models was conducted using per household and per capita values of the two dependent land variables (data not shown). These further estimates yield similar coefficients and t-values except for household composition variables (which should vary between per household, per capita, and per ACE specifications by definition), which confirms the robustness of the regression models. Household labor characteristics such as the age of the household head (AGEHEAD) and education (EDUMAX) have significant impacts on land access (especially cultivated area). The fact that even at the low observed levels of education, households with a higher level of education have more land access suggests that expanding education may contribute to increased agricultural production (not increased off-farm production). Having a higher dependency ratio in a household (DEPEND) raises cultivated area per ACE (i.e., the pressure to produce increases) and lowers the fallow area per ACE, though the former effect is not significant. Of those variables representing the legacies of the recent war, households with a higher share of women (FEMALE) have no difference in cultivated area but they control significantly more fallow land. Female-headedness (FEMHEAD) turns out to have a significant negative impact on land access (especially cultivated area), confirming the initial evidence of Table 4. Female-headed households were either widowed or divorced, having an average age of the female household head of 51 years. Our qualitative fieldwork in northern Mozambique suggests that these households were strongly affected by the war, as they may have been victim to rape during the war or may have lost their kin who otherwise would have integrated these females into their own households, following

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WORLD DEVELOPMENT Table 5. Determinants of land access (1) AREACACE

(2) AREAFACE

Coefficient

t-Statistic

Coefficient

t-Statistic

Household labor characteristics

ACEHHL DEPEND AGEHEAD AGEHEAD2 EDUMAX

0.725 0.270 0.049 0.000 0.036

6.37*** 0.99 3.47*** 2.74*** 2.56**

0.876 0.958 0.038 0.000 0.016

4.60*** 2.81*** 1.60 1.44 0.71

War legacies

FEMALE FEMHEAD VASSET ANIMAL TOOL TOOLTYPE

0.263 0.917 0.055 0.095 0.264 0.066

1.57 4.78*** 4.51*** 0.92 3.33*** 2.39**

0.611 0.068 0.049 0.064 0.029 0.045

2.32** 0.33 2.47** 0.61 0.14 0.68

Field characteristics

AREAFER DISTANC

0.190 0.001

3.22*** 1.35

0.173 0.005

1.57 2.66**

Risk factors

ILLDAYS RAIN CYCLONE PEST

0.001 0.017 0.263 0.026

2.49** 0.19 2.53** 0.42

0.001 0.061 0.306 0.008

1.26 0.39 1.51 0.07

Social institutions

AUTHTP AUTHCP AUTHMP AUTHTR AUTHCR AUTHMR ORIGINM ORIGINF

0.228 0.354 0.088 0.153 0.012 0.033 0.053 0.155

1.56 3.73*** 0.71 2.67** 0.22 0.60 1.36 3.10***

0.380 0.611 0.099 0.375 0.073 0.303 0.146 0.118

1.99* 1.56 0.49 2.38** 0.47 2.39** 1.24 1.18

Market institutions

PRICEVAR LABOR MARKET MILL

0.072 0.038 0.077 0.161

0.75 0.68 2.15** 2.12**

0.034 0.065 0.051 0.157

0.28 0.77 0.99 0.93

District controls

DIS1 DIS2 Constant Observations R-squared

0.194 0.082 1.925

1.33 1.06 3.46***

0.060 0.154 1.333

0.29 1.30 2.37**

371 0.62

249 0.42

Source: FSP household survey. Notes: All regressions were estimated as survey linear regressions. There are 122 households without fallow land in regression (2). * Significantly different from 0 at the 10% level. ** Significantly different from 0 at the 5% level. *** Significantly different from 0 at the 1% level.

Macua custom. Other variables which represent the war legacy include household assets held at the time of the end of the war (VASSET), which impact positively on both measures of land access. This suggests complementarities in asset and land endowments in the post-war period. Animals owned at the end of the war (ANIMAL) on the other hand have no significant effects on land access—perhaps because most larger animals had been killed in the war (if local conditions permitted the raising of large livestock in the first place). Additional tools owned by the household (TOOL) increase land access (measured in cultivated area). As Hanlon writes: ‘‘most peasant farmers are presently limited to what they can cultivate entirely by hand, with a hoe” (2002, p. 21). Our fieldwork had shown that tool ownership was largely determined by the experience of the war, with many tools having been looted or having depreciated without replacement during the war. The former traders of tools and other items (often of Asian ethnicity) were displaced by the war and an absence of black-

smiths in Macua communities implies that household tool endowments are given and a real constraint to increased land access. Owning an additional type of tool (TOOLTYPE) slightly decreases cultivated area, probably because households with a set of different tools undertake coping strategies other than agriculture. The total number of days spent ill per household per year (ILLDAYS) reduces land access (for cultivated area), as expected. Households affected by cyclone Nadia (CYCLONE) have higher land access. This may in part be due to the definition of the variable, which captures among other things the destruction of cashew trees by cyclone Nadia. Households more affected by the cyclone are hence households with higher tree stocks and correspondingly larger farms. The variables reflecting lack of rain (RAIN) and crop pests (PEST) are not significant, which is unexpected. This result indicates that households experiencing lack of rain may opt for non-agricultural coping strategies to insure against risk instead of

SMALLHOLDER LAND ACCESS IN POST-WAR NORTHERN MOZAMBIQUE

adopting their total farm area to the expected rainfall. Alternatively, especially the rainfall risk may be correlated spatially (its correlation coefficient with DIS1 is 0.346) so that cultivating larger areas does not offer insurance from rain risks, thus discouraging more land access in a given area. Of the variables reflecting social institutions that are relevant for the allocation of land at the local level, the three variables indicating that the head is in any position of traditional, colonial, or modern authority all have positive and large coefficients, some of which are significant. These results hence confirm the unconditional analysis of Table 4 while further differentiating the effect, identifying the colonial authority as being of high relevance for land access. In contrast, all variables indicating that the household is related to a traditional, colonial, or modern authority have negative but smaller coefficients for cultivated area (with AUTHTR being significant) and a more mixed evidence for fallow area (with AUTHTR being negative and significant and AUTHMR positive and significant). Being in a household where the main woman was born locally (ORIGINF) also raises cultivated area as may be expected in the matrilinear Macua culture. Perhaps these households are better able to mobilize labor, for example through informal work sharing such as ganho–ganho, and thus increase the area under cultivation. The data also show that wives who are married according to matrilocality have a higher probability of being in a position of local authority than men living in their village of origin. However, the practice of matrilocality was interrupted during the war, as mobility was severely restricted in the rural areas. As a consequence, only 9.8% of the surveyed households had followed residential rules according to matrilocality while in 45.3% of households both spouses descended from the village they lived in during the survey. In 9.8% of all households only the husband descended from the village and in 35.3% of households none of the spouses had their origin in this village, possibly indicating a history of internal displacement. The positive effect of kinship ties on land access in northern Mozambique was also confirmed by Marule (1998), Pitcher (1996, p. 99) and Strasberg and Kloeck-Jenson (2002, p. 18f.). Variables depicting market institutions are not significant with two exceptions. Living in a village with more crop sales (MARKET) raises area farmed, probably pointing to the strong incentive effect of the existing local markets for agricultural outputs. The self-reported existence of a local grain mill (MILL) lowers land access, in contrast to Table 4. This may be due to household seeking benefits of down-stream value-added activities, with households relying less on growing and storing subsistence crops. Finally, regional differences not captured by the variables discussed above appear to be limited, as neither of the district dummies (DIS1, DIS2) is significant in either regression (DIS3 being the omitted variable). The analysis of land access using two indicators suggests that indeed there are differences between accessing area for cultivation and for fallow. The latter is not driven by risk variables nor by market institutions. It appears also from our fieldwork that accessing fallow land is more related to profound, often war-related, shortages of labor and to long-term coping strategies, both beyond the control of the household in the short-term. As is succinctly summarized in a report by the Ministry of Agriculture on a case study of one district in Manica province where the relationship between land endowments, land access, and fallow land was also an issue (Shumba, Navalha, Miquisse, & Amos, 1995, p. i): ‘‘Field results show that the family sector agriculture is characterized by low external inputs cropping benefiting from natural soil regeneration and with feeble linkages to the poorly developed livestock sector

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[sic]. However the option of letting land lie fallow is no longer open for some farmers particularly those who are in areas around the old protected villages. There are signs of degradation in the form of all types of erosion ultimately leading to a decline in yields.”

In summary, there appear to be constraints to land access from both within and beyond the control of households. On the one hand, the technologies of the smallholder sector play an important role, such as economies of scale in agricultural production. For example, the yields per hectare may be increased by intensified weeding and watering, thus making the clearing of new fields not a necessary condition to supply larger households. This is reinforced by household-specific transaction costs in searching for, acquiring, clearing, and planting new land. Also, extending cultivated area goes along with diminishing returns of extending area with seasonality. On the other hand, not all households that wish to expand their land may be able to do so. For example, having kinship ties to local authorities is an exogenous factor in the medium term. Similarly, being a female-headed household may well not be an outcome of individual choice in the context of a recent war. Access to land was shaped by the household experience of war in several ways. First, and as discussed in Section 3, the war destroyed many assets, such as tools, thus depriving households of important complementary production inputs. This was aggravated by markets for agricultural inputs being severely handicapped by poor infrastructure in the early post-war period. Second, the war had led to a high mortality rate due to both direct acts of war and the virtual break down of the health care system, hence inducing uncertainty about future household sizes. These factors may reduce incentives to expand the scale of agricultural production with changes in the household composition. This also explains the sluggish aggregate agricultural supply response to peace in post-war rural Mozambique discussed in Section 3. Third, having a good standing in a village—either being in a position of authority or being related to authority holders—is another characteristic that has a significant impact on land access, which in turn was shaped by war (through death, displacement, and political struggles over local authority). Hence both household-level variables and factors exogenous from a household perspective seem to favor land access for some households and disadvantage others, explaining at least in part the observed inequality of land access.

6. CONCLUSIONS This paper explores land inequality, rigidity in land access, and the determinants of land access for smallholders in postwar northern Mozambique. The results indicate that, first, high land endowments at the aggregate level do not equalize land access at the household level. Second, land access to cultivated and fallow areas is severely restricted on average even when controlling for household endowments and composition. Third, the restrictions to land access are partly specific to households and partly beyond their control. Among these variables, female-headedness, household composition, asset endowments, and not being in a position of local authority are key restrictions to land access, which are unlikely to diminish rapidly over time. We demonstrate econometrically that there are hence clear differences across households based on observable characteristics in their ability to access land. The design of rural development and social assistance policies may wish to account for these differences.

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WORLD DEVELOPMENT

NOTES 1. Note that, in this paper, the term ‘‘village” refers to physical settlements while larger groups of households form ‘‘communities”. In some cases, villages and communities may be synonymous but the former will be used mostly in a physical and the latter in a social sense in this paper. Empirically, it can be hard to delineate communities (Kepe, 1999). In practice, the FSP survey variables are calculated at the household level or at the village level. 2. These sample sizes correspond to the available and cleaned number of cases used in the analysis of Table 3.

3. For two households with a per ACE household size of below 1, the final value of ACEHH was set to 1 to be able to calculate log values of ACEHH for these observations as well. Repeating the regression analysis without these two observations resulted in only minor differences (data not shown). We conclude that the regression results are robust to changes in these two observations. 4. We are grateful to an anonymous referee for suggesting this interpretation.

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