MALAWI AGRICULTURAL SNAPSHOT 2004/5

Transcription

1 Malawi Agricultural Snapshot 2004/5 MALAWI AGRICULTURAL SNAPSHOT 2004/5 Roberts, Cleophelia; Azzarri, Carlo MARCH, 2014 HARVESTCHOICE - INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE (IFPRI) harvestchoice.org

2 Abstract The purpose of this series of country briefs is to provide a comprehensive and standardized view of agricultural characteristics integrated with socioeconomic variables across countries. In the Malawi Agricultural Snapshot, we illustrate the state of agriculture in Malawi with reference to both household characteristics and the national context. Graphs and tables are generated using data from the Malawi Second Integrated Household Survey (IHS-2) Income and expenditure variables, including expenditure deciles and income shares, are based on the expenditure and income aggregates constructed by Harvest Choice and the Rural Income Generating Activities (RIGA) team at FAO. The Government of Malawi conducted the IHS-2 over a twelve-month period in 2004 and Whereas IHS-1 in 1997/1998 relied on monthly diaries kept by respondents, IHS-2 included enumerator-administered questions to capture information about expenditures. Two-stage stratified sampling from the 1998 census was used to select the sample for the IHS-2. The sample is divided into three regions: north, center, and south, and further divided into rural and urban areas. The urban strata include the major metropolitan areas. Within the rural stratum, the sample is further divided into sub-strata based on 26 districts of Malawi (Likoma District is excluded). Just as each district was considered a separate sub-stratum of the rural stratum, each of the four metropolitan areas was considered its own sub-stratum of the urban stratum. Therefore, the sample includes thirty total strata: four urban centers and 26 rural districts. In the first stage of sampling the sampling unit, 564 enumeration areas (EAs) were selected within each stratum based on probability proportional to size from the 1998 census. The second stage of sampling involved the random selection of 20 households within each EA. Suggested Citation Roberts, Cleophelia; Azzarri, Carlo (2014) "Malawi Agricultural Snapshot 2004/5," Working Paper, HarvestChoice - International Food Policy Research Institute (IFPRI). 1

3 Contents Abstract... 1 Suggested Citation... 1 List of Tables and Figures Introduction The Country at a Glance Farm Assets, Farming Systems and Agricultural Input Use Crop Production Livestock Nutrition Conclusion References List of Tables and Figures Table 1: Characteristics of Malawi s Population, Table 2: Characteristics of Malawian Households... 6 Table 3: Household Income Composition in Urban and Rural Households, by Income Decile... 8 Table 4: Household Income Composition in Male- and Female-headed Households, by Income Decile... 9 Table 5: Agricultural Input Use among Farm Households, by Region...12 Table 6: Income Sources and Seasonal Cultivation...13 Table 7: Agricultural Input Use by Expenditure Quintile...14 Table 8: Income Sources and Seasonal Cultivation by Expenditure Quintile...14 Table 9: Agricultural Input Use by Education Level...15 Table 10: Income Sources and Seasonal Cultivation by Household Head Education...16 Table 11: Agricultural Input Use by Cultivated Land Quintile...16 Table 12: Income Sources and Seasonal Cultivation by Cultivated Land Area Quintile...17 Table 13: Top Input Combinations for Plots in the Rainy Season...17 Table 14: Top Input Combinations for Plots in the Dimba Season...18 Table 15: Prevalence of Livestock Ownership and Mean Number of Animals Owned, by Region, Expenditure Quintile, and Land Quintile...21 Table 16: Stunting Prevalence among Children under Five Years, by Age and Gender...23 Table 17: Underweight Prevalence among Children under Five Years, by Age and Gender...24 Figure 1: Poverty and Agricultural Engagement by District... 5 Figure 2: Shares of Household Income in Urban and Rural Households, by Income Decile... 7 Figure 3: Household Income Composition in Male- and Female-headed Households, by Income Decile... 9 Figure 4: Distribution of Hectares Planted by Season, for Agricultural Households...11 Figure 5: Top 15 Crops by Average Production Value per Household...19 Figure 6: Average Production Value per Household, by Expenditure Quintile...19 Figure 7: Top Crop Production Combinations...20 Figure 8: Average Annual Sale Value of Livestock Products...22 Figure 9: Nutritional Outcomes for Children under Five Years Old...23 Figure 10: Stunting Prevalence among Children under Five Years, by Age and Gender...25 Figure 11: Underweight Prevalence among Children under Five Years, by Age and Gender...25 Figure 12: Wasting Prevalence among Children under Five Years, by Age and Gender

4 1 Introduction Malawi is an exceptionally poor and unequal country (Whiteside 2000; Orr 2000). It also has one of the densest populations in sub-saharan Africa. Because of high population growth outpacing gains in agricultural productivity, food production per capita is declining. These factors make Malawi particularly susceptible to food insecurity. (Gladwin 2000; Zant 2011; Ricker-Gilbert et al. 2014). Malawian farmers are especially vulnerable to several types of shocks. Because agriculture is mainly rainfed, farmers have few defenses against climatic shocks. Furthermore, they face threats from plant and animal diseases, maize price volatility and health shocks (Mussa and Pauw 2011). At the same time, Malawi is landlocked, so imports and exports carry high costs. This situation leaves Malawians extremely vulnerable to international food price shocks (Babu 2000; Whiteside 2000; Mussa and Pauw 2011). In less than a decade following 2000, the country suffered two major food shortages. In 2001 and 2002 Malawi experienced a famine that caused significant mortality, then in 2005 and 2006 low maize harvests and high prices produced extreme food scarcity (Zant 2011; Ellis and Manda 2012). The lean season of late 2013 and early 2014 is also projected to be particularly tough. Although there will likely be a surplus of maize at the national level, 21 of 28 districts will face food scarcity due to dry spells and insufficient harvest (Sabola 2013; MVAC 2013). Despite lagging productivity (Orr 2000), agriculture is an important part of the Malawian economy. Not only does agriculture account for 30% of gross domestic product (GDP), it employs 80% of the Malawian workforce and accounts for 90% of export revenues (Mussa and Pauw 2011; Wood et al 2013). Tobacco is undisputable the main crop, accounting for more than 70% of agricultural exports, nearly 15% of GDP, and one quarter of the country s total tax base (Mkwara and Marsh 2014). This heavy reliance on tobacco places a substantial burden on the economy as world prices decline and the international community increases pressures to limit tobacco production. To combat food insecurity, the government of Malawi has instituted the Farm Input Subsidy Program (FISP). It provides reduced cost fertilizer for maize and tobacco, improved seeds for maize and other legumes, and pesticides (Chibwana and Fisher 2011). Evidence on the program s impact is mixed and inconclusive (Pauw and Thurlow 2014). Whereas national production estimates suggested dramatic increases in maize production and productivity during the years of the FISP, farm-level studies found relatively modest increases in maize production and yields over the same period.. Moreover, poor households were less likely than rich households to receive any voucher (Lunduka et al. 2013). With a program cost of US$ million per year during 2005/ /10 (Arndt at al. 2013), costbenefit analysis results vary depending on the study and assumptions made ranging from negative to positive returns. In 2008, the program accounted for 70 percent of the agricultural budget and 16 percent of the government's budged leaving very little room for spending on other important areas of agricultural spending such as extension services and rural infrastructure (Karamba 2013). To cope with uncertainty, maintaining multiple livelihood strategies is both common and necessary in Malawi (Gladwin et al 2001). Traditionally, women are subsistence food producers, while men grow cash crops. Off-farm women brew beer, sell snacks, produce vegetables, and sell ganyu labor. On the other hand, men rent oxen for ploughing, make bricks, build homes, sell firewood, run shops, buy and sell items to trade, repair bicycles and shoes, and sell milk (Gladwin et al 2001). In lean times, households income is earned not from their own land, but from working others land (Harrigan 2003). To earn cash, lower-income men traditionally rent their land for cash to better-off farmers or outsiders. They would also sell their labor or parts of their crops in exchange for money or fertilizers. Women would try to sell their labor (Bryceson and Fonseca 2006). According to previous research, where there are viable non-farm livelihoods in Malawi, there are fewer people living in poverty (Ellis, Kutengule, and Nyasulu 2003; Benson, Chamberlin, and Rhinehart 2005; Mussa and Pauw 2011). Mussa and Pauw (2011) argue that both participating in agriculture and having access to non-agricultural livelihoods decrease households likelihood of being poor. Benson, Chamberlin, and Rhinehart (2005) note that because of intra-national diversity, a national approach to poverty is unlikely to succeed. Rather, strategies should be made at smaller geographical levels (Benson, Chamberlin, and Rhinehart 2005). However, Wood et al (2013) and Diao et al (2012) find that even in diverse areas, growth in the agricultural sector disproportionately benefits the poor. To support development in agriculture, Harrison (2003) suggests that rural infrastructure must be improved and Gladwin et al (2001) argues that women s multiple roles inside and outside the household must be considered. In the next sections we first illustrate the demographic and socio-economic characteristics of Malawian households. We then describe farm assets, farming systems and agricultural inputs used, followed by a description of crop production and livestock. Lastly, we illustrate the nutritional situation of children. 3

5 2 The Country at a Glance Table 1 illustrates the importance of agriculture to the Malawian population, in terms of participation and income generation. It also shows the severity of poverty in Malawi in general and in rural areas in particular. Because poverty is so prevalent in rural areas and agriculture is so important to rural livelihoods, developing robust political strategies on investment and innovation in agriculture present an opportunity for poverty alleviation. Developing sustainable agricultural technologies and small-holder agriculture is vital to help vulnerable households. Table 1: Characteristics of Malawi s Population, Number of Observations Total Population Percentage of Population Poor at National Poverty Line Households Engaged in Agriculture Net Income from Agriculture Region North Rural North Urban Center Rural Center Urban South Rural South Urban Gender of Household Head Male Female Agricultural Engagement Non-farm Farm Malawi The population of Malawi is most concentrated in the south of the country (Table 1). In all three regions (north, center, and south), the population is overwhelmingly rural. More than 40% of all Malawians reside in the rural south and 38% live in the rural center. The districts with the greatest percentages of households engaged in agriculture are in the south, while those least engaged in agriculture are in the north (Figure 1). This is largely a consequence of less urban households engaging in agriculture in the north relative to the south (Table 1). Of all Malawians, 91.1% live in farming households, and 89.1% of all households are engaged in agriculture. These farming households obtain 76.5% of their income from agriculture and even non-farm households obtain 20.9% of their income from agriculture. Yet 52.4% of Malawians live below the national poverty line. A far greater percentage of rural households (55.8%) live in poverty than urban households (25.4%). Figure 1 demonstrates that the districts with the greatest percentage of people living below the poverty line are concentrated in the south, while those with the lowest percentage are found mostly in the center. The rural south has the greatest percentage of persons below the poverty line at 64.4%. In contrast, the urban south and center have the lowest percentage of persons below the poverty line at 24.3% and 24.6%, respectively. Despite being the lowest percentages in the country, these levels of poverty are elevated and demand attention. 4

6 Figure 1: Poverty and Agricultural Engagement by District In the average Malawian household there are 4.5 total members, of whom 52.8% are female (Table 2). Non-farm households have fewer members (3.7) and a lower proportion of women and girls (45.8%) than the national average. More than 73% of households have married heads. Among non-farm households only 66.5% of heads are married, while 73.9% of household heads among farm households are married. The greatest prevalence of married heads is in the north, where 78.3% of rural households and 82.1% of urban households have married heads. Female-headed households make up 22.9% of all households. Only 9.7% of female household heads are married, while 92% of male household heads are married. In all three regions of Malawi, there are more households headed by women in rural areas than in urban areas, just as there are more female-headed farm households than female-headed non-farm households. The average dependency ratio in Malawi is 1.1 (Table 2). However, in urban households in all regions of the country and in nonfarm households the ratio is less than one, indicating that there are more potentially active adults than dependents in those households. Households headed by women have a greater dependency ratio (1.4) than those headed by men (1.1). Female household heads have also attained less education than male household heads, another factor which contributes to their disadvantage. 5

7 Mussa and Pauw (2011) find that poor households are larger than non-poor households. Moreover, they have higher dependency ratios 1, their heads have little or no formal education, and are unlikely to participate in salaried employment. More female-headed households than male-headed households are poor and poor households tend to be dependent on ganyu labor. These findings are consistent with Table 2, which shows that household size varies strikingly with expenditure quintile. 2 Households in the lowest expenditure quintile include 6.2 persons, on average, while those in the highest expenditure quintile include just 3.3 persons. Also, the lowest percentages of households engaged in agriculture, headed by women and with married heads, are all in the highest expenditure quintile at 76.8%, 19.4%, and 65.6%, respectively. The difference in agricultural engagement is most striking as the percentage of households engaged in agriculture is over 9% for all but the highest expenditure quintile. Years of adult household members education also vary by expenditure quintile. The average number of years of education that adults in the lowest quintile have attained (3.0) is just half that of adults in households in the highest expenditure quintile (6.3). Conversely, the dependency ratio in households in the lowest quintile (1.7) is more than twice the ratio in households in the highest quintile (0.7). Table 2: Characteristics of Malawian Households Engaged in Agriculture Household Size (#) Dependency Ratio Married Head Femaleheaded Households Female Household Members Years of Adults' Education Region North Rural North Urban Center Rural Center Urban South Rural South Urban Agricultural Engagement Non-farm Farm Gender of Household Head Male Female Expenditure Quintile Lowest Highest Malawi In Figure 2 and Table 3 we see that among both rural and urban households, agricultural and fishing wages make up a smaller and smaller portion of income in successive income deciles. Conversely, in both rural and urban strata non-agricultural wages make up a larger and larger share of income with successively higher income deciles. While the share of non-agricultural wages in the highest income decile is 21.6% in rural areas, it is 78.5% in urban areas. Rather than drawing the conclusion that participation in agriculture leads 1 The dependency ratio is defined as the ratio of children under 15 and adults over 60 to adults aged In other words, it is the ratio of household members expected to be dependent to adults of prime working age. 2 To show the relationship between different levels of household income and other household characteristics, we first calculate household expenditures. Household income is notoriously difficult to calculate accurately in developing countries, so we use household expenditures as a proxy, which are generally seen as easier to recall and less prone to error (RIGA 2010). We then divide households into expenditure quintiles. We first identify the minimum and maximum levels of annual expenditures among households in our sample. Between these minimum and maximum expenditures, the number of households are divided into five parts or quintiles. Because the division into five parts is done by number of households rather than the amount of expenditures, the difference between top and bottom consumers may vary by quintile. 6

8 to lower income, it may be more correct to suggest, as Mussa and Pauw (2011) do, that more diverse income-generating options are associated with greater wealth. Figure 2: Shares of Household Income in Urban and Rural Households, by Income Decile As with agricultural and fishing wage, transfers comprise a smaller share of households income with successive income deciles in both rural and urban areas. Among urban households, crop production comprises the lowest portion of income for the lowest and highest deciles. Livestock production and non-agriculture self-employment do not exhibit a clear relationship with income level for urban households. Among rural households, those in the lowest and highest income deciles obtain a greater share of their income from nonagricultural self-employment than the middle deciles. Although the relationship is not as clear as with non-agricultural wage work, there is a general trend of livestock production comprising a larger share of income for larger income deciles. There is no clear relationship between income shares from crop production and income decile in rural households, although crop production is an important source of income for households in all income deciles, ranging from 37.6% to 63.2% of income. 7

9 Table 3: Household Income Composition in Urban and Rural Households, by Income Decile Agricultural and Fishing Wage Nonagricultural Wage Crop Production Source of Income Livestock Production Nonagricultural Selfemployment Transfers Table 4 and Figure 3 show differences in the contribution of different activities to income between households headed by men and those headed by women. As in the previous comparison between urban and rural households, there is a clear relationship between income shares from agricultural wages, non-agricultural wages, and transfers and income deciles. Agricultural wages and transfers contribute less to greater income deciles, while non-agricultural wages contribute more to higher income deciles. Although the direction of these relationships is the same in both male- and female-headed households, the magnitudes of the contributions are different. Femaleheaded households obtain smaller shares of their income from wages than male-headed households and more from transfers. These differences suggest that female-headed households are more dependent on others than male-headed households. Furthermore, there is no clear relationship between income deciles and either crop production or non-agricultural self-employment s contribution to income for either male- or female-headed households. Other Urban Income Decile Lowest Highest Rural Income Decile Lowest Highest

10 Table 4: Household Income Composition in Male- and Female-headed Households, by Income Decile Agricultural and Fishing Wage Nonagricultural Wage Crop Production Source of Income Livestock Production Nonagricultural Selfemployment Transfers Figure 3: Household Income Composition in Male- and Female-headed Households, by Income Decile Other Male-headed Income Decile Lowest Highest Female-headed Income Decile Lowest Highest

11 3 Farm Assets, Farming Systems and Agricultural Input Use Malawian agriculture is characterized by a single rainy season (Whiteside 2000; Ellis and Manda 2012), uncertain rainfall and low technology adoption (Babu 2000). Moreover, the population density makes land a scarce resource (Ellis, Kutengule, and Nyasulu 2003; Harrigan 2003). Small land holdings, in turn, make it impossible for most households to resolve food insecurity through increased productivity alone (Whiteside 2000; Ellis, Kutengule, and Nyasulu 2003; Gladwin 2003). As land is scarce and there is only one rainy season in Malawi, intercropping is impractical for many farmers (Gladwin et al 2001). Therefore, mono-cropped maize dominates cultivation, even as soil fertility declines (Harrigan 2003). Households that grow tobacco typically hold more land than maize growers (Wood et al 2013). In the north, center, and south of Malawi, the mean number of hectares cultivated by households is greater in rural areas than in urban areas (Figure 4). In all geographic strata, households cultivate more land, on average, in the rainy season than in the dimba (dry) season. While households cultivate between 0.7 and 1.2 hectares of land in the rainy season, on average, they cultivate 0.2 hectares in the dimba season (Table 5). 10

12 During the rainy season in Malawi farm households cultivate 0.97 hectare on average (Table 5). As Figure 4 shows, rural farming households cultivate more land than urban farm households in all three regions of the country. However, in many cases greater shares of urban farm households use farming inputs than rural farm households. Urban households in the center and south spend more on this fertilizer than rural households. Only in central Malawi do more urban households than rural households use organic fertilizer. Central Malawi is also the only region where a greater share of rural households use inorganic fertilizer than urban households. In all three regions, more urban households use purchased seeds and ganyu labor than rural households. Figure 4: Distribution of Hectares Planted by Season, for Agricultural Households In the dimba (dry), season the share of urban households using various inputs falls behind the share of rural farm households. Exceptions include the share of urban households in the north using inorganic fertilizer, purchased seeds, and ganyu labor. Additionally, the share of urban households employing ganyu labor exceeds the share of rural households employing ganyu labor in all regions of the country. 11

13 Table 5: Agricultural Input Use among Farm Households, by Region Land Cultivated Area (Ha) Malawi Kwacha in 2004/2005 is equal to US$1 PPP Ganyu labor is seasonal, hired labor Organic Fertilizer Inorganic Fertilizer Average Expenditure (2005 US$ PPP) To earn cash for food, the most vulnerable households forgo their own production for wages (Bryceson 2006; Whiteside 2000; Mkandawire 2011). Traditionally, men rent household land for cash while women sell their labor to other households in an exchange called ganyu (Bryceson and Fonseca 2006). The term ganyu describes a large number of temporary work arrangements (Whiteside 2000). It is the second most important source of cash for households, after their own production (Whiteside 2000). Ganyu is the main mode of inter-household labor exchange (Bryceson and Fonseca 2006) and has existed since at least the beginning of the 20 th century (Whiteside 2000). Such labor often fetches low wages (Whiteside 2000) paid in cash or in kind (Bryceson and Fonseca 2006). Because ganyu diverts households labor from their own production, many are left with idle land and insufficient labor for themselves (Whiteside 2000; Ellis, Kutengule, and Nyasulu 2003; Bryceson 2006). Households perform ganyu most commonly during the peak agricultural season (Bryceson and Fonseca 2006) after food stores from the last agricultural season have run out and before the next harvest (Whiteside 2000). Households that appear secure in their consumption command the most respect (Bryceson and Fonseca 2006). Therefore, ganyu labor is performed reluctantly as it is an admission of poverty (Whiteside 2000). While it is common for offerers of work to give preferential ganyu wages to friends and relatives, laborers sometimes travel far from home to work ganyu where no one knows them (Whiteside 2000; Mkandawire 2011). However, doing ganyu for strangers is a source of exploitation (Mkandawire, Luginaah, and Baxter 2013), especially for women and orphaned children. In addition to labor, sexual favors are increasingly demanded of women and adolescent 12 Purchased Seeds Average Expenditure (2005 US$ PPP) Ganyu Labor Average Expenditure (2005 US$ PPP) Rainy Malawi Region North Rural North Urban Center Rural Center Urban South Rural South Urban Gender of Household Head Male Female Dry Malawi Region North Rural North Urban Center Rural Center Urban South Rural South Urban Gender of Household Head Male Female

14 orphans (Bryceson 2006; Mkandawire 2011). According to Mkandawire, Luginaah, and Baxter (2013), orphan laborers are especially subservient in ganyu exchanges. Rather than bargain for wages they might say, Just pay me anything (Mkandawire, Luginaah, and Baxter 2013). While it is not uncommon for children to perform ganyu labor (Whiteside 2000; Mkandawire 2011), orphans are clearly vulnerable because of their deference to employers and their availability during school hours (Mkandawire, Luginaah, and Baxter 2013). Female-headed households tend to have less labor available to them than male-headed households do (Whiteside 2000). In addition, female-headed households participate in ganyu labor more often than male-headed households, although women work for lower wages than men (Whiteside 2000). Households affected by HIV/AIDS are especially dependent on ganyu (Mkandawire, Luginaah, and Baxter 2013). Offerers of ganyu work may see hiring others as a favor to the less fortunate (Whiteside; Mkandawire 2011). However, when shocks take place, it is unlikely that enough better-off farmers exist to support all ganyu laborers (Whiteside 2000). Table 6 illustrates that rural households obtain a greater share of their incomes from crop and livestock production than urban households do and that urban households obtain a greater share of their incomes from non-agricultural wages and self-employment than rural households do. This situation may reflect a greater range of income-generating opportunities in urban areas. In both the rainy and dimba seasons a greater percentage of rural households have plots than urban households, except in the north during the dimba season. While 34.6% of rural households have plots in the dimba season in the north, 35.0% of urban households do. Not only do fewer households cultivate land in the dimba season than in the rainy season, they cultivate less of their land in the dimba season (1.0 ha) than in the rainy season (0.2 ha). Rural households in central Malawi receive the highest share of their income from crop production at 63.1%. In northern Malawi livestock production contributes 16.3% of income--almost twice what it contributes in other rural areas. Urban households in the south receive more of their income from non-agricultural wages (50.7%) than urban households in the other regions. This is largely driven by Blantyre (located in the south), which is the industrial and financial center of Malawi. Urban households in the center obtain a smaller percentage of their income from non-agricultural self-employment (18.5%) than those in the north and south, who obtain 25.4% of their income from this activity. The percentage of households with plots in the dimba season (32.5%) is less than half the percentage of households with plots in the rainy season (87.3%). Table 6: Income Sources and Seasonal Cultivation Crop Production Share of Income from Livestock Production Nonagricultural Wage Nonagricultural Selfemployment Table 7 demonstrates that the amount of land cultivated, the share of households using various inputs and the amount that households spend on these inputs are all greater in higher expenditure quintiles during the rainy season. For the dry season, this pattern is true for most inputs. As Table 5 also suggests, households in all expenditure cultivate around 0.2 hectares in the dry season. The shares 13 Households with Plots in Rainy Season Dry Season Malawi Region North Rural North Urban Center Rural Center Urban South Rural South Urban Agricultural Engagement Non-farm Farm

15 of households in the highest expenditure quintile using manure and compost (19.5%) and purchased seeds (20.7%) is lower than for households in the next lower expenditure quintile (20.1% and 21.2%, respectively). For ganyu labor, the expenditures by households in the lowest expenditure quintile are higher ($15) than for the next higher quintile ($13). The greatest divide between the lowest and highest quintiles is in use of ganyu labor during the rainy season. The share of households in the lowest quintile using ganyu labor (7.2%) is less than a third of the national share (23.7%), while the share of households in the highest quintiles using ganyu labor (41.4%) is almost twice the national share. This imbalance may arise because more households in the lowest quintile supply ganyu labor than hire it. Table 7: Agricultural Input Use by Expenditure Quintile Land Cultivated Area (Ha) Organic Fertilizer Inorganic Fertilizer Purchased Seeds Ganyu Labor Average Expenditure (2005 US$ PPP) Table 8 demonstrates that the share of income households obtain from agriculture is not necessarily higher or lower with increasing expenditure quintiles. However, both non-agricultural wages and non-agricultural self-employment make up greater shares of income for households in higher expenditure quintiles. Crop and livestock production make up greater shares of income for households in the middle expenditure quintiles than for households in the highest or lowest quintiles. Although smaller shares of households cultivate plots in the rainy season in higher expenditure quintiles, in the dry season, the share of households cultivating plots is highest in the middle expenditure quintile. Table 8: Income Sources and Seasonal Cultivation by Expenditure Quintile Average Expenditure (2005 US$ PPP) Average Expenditure (2005 US$ PPP) Rainy Malawi Expenditure Quintile Lowest Highest Dry Malawi Expenditure Quintile Lowest Highest Crop Production Share of Income from Livestock Nonagricultural Production Wage Nonagricultural Selfemployment Households with Plots in Rainy Season Dry Season Malawi Expenditure Quintile Lowest Highest

16 Table 9 illustrates that during the rainy season the shares of households using fertilizer, purchased seeds, and ganyu labor, and the amount of money that these households spend on inputs are larger with higher levels of household head education. The amount of land cultivated is also larger with higher levels of education. Only the share of households using organic fertilizer presents an exception. A smaller share of households whose heads have attained tertiary education use organic during the rainy season than the share of households whose heads have attained lower levels of education. Most notable among the differences in input usage during the rainy season are average expenditures on inorganic fertilizer and purchased seeds and the share of households using ganyu labor. In the rainy season, households whose heads have no formal education spend less than the national average on inorganic fertilizer ($59) and seeds ($7), but households whose heads have achieved some tertiary education spend almost twice the national average on fertilizer ($155) and purchased seeds ($19). For ganyu labor, the share of households employing others is a little more than half the national share (12.5%) for households whose heads have no formal education, but more than three times the national share (78.4%) for households whose heads have achieved tertiary education. Table 9: Agricultural Input Use by Education Level Land Cultivated Area (Ha) Organic Fertilizer Inorganic Fertilizer Purchased Seeds Ganyu Labor Average Expenditure (2005 US$ PPP) Average Expenditure (2005 US$ PPP) Average Expenditure (2005 US$ PPP) Rainy Malawi Education Level None Primary Secondary Higher Dry Malawi Education Level None Primary Secondary Higher During the dry season, the shares of households using various inputs and the amounts they spend on those inputs vary less by education level. However, the amount of land cultivated in the dry season varies by education level. Households whose heads have achieved tertiary education cultivate 0.5 ha on average rather than the 0.2-ha average for all other levels of household head education. Table 10 shows a general trend of lower shares of income from crop and livestock production for higher levels of household head education and higher shares of non-agricultural wages for higher levels of household head education. In general, greater percentages of households cultivate plots in both the rainy and dry seasons with lower levels of household head education. However, the share of income from livestock production is lower for households with heads who have no formal education than for households with heads who have primary education, and the share of income from non-agricultural self-employment is lower for households whose heads have tertiary education than for those whose heads have achieved secondary education. Income shares among households whose heads have tertiary education diverge most greatly from national averages. The share of these households income from crop production (14.4%) is less than a third of the national average (50.7%); for livestock production, their income share (4.1%) is less than half the national average (8.5%); for non-agricultural self-employment their income share (4.2%) is less than half the national average (10.3%), and for non-agricultural wages, their income share (63.9%) is more than five times the national share (11.8%). 15

17 Table 10: Income Sources and Seasonal Cultivation by Household Head Education Crop Production During the rainy season, higher quintiles of land are associated with greater amounts of land cultivated, greater shares of households using fertilizer, purchased seeds, and ganyu labor, and greater amounts spent on these inputs (Table 11). While households in the lowest land quintile cultivate just 0.32ha of land, those in the highest quintile of land cultivate 2.17ha on average. Expenditures on inorganic fertilizer are less than half the national average in households in the lowest land quintile ($37) and almost twice the national average among households in the highest land quintile ($143). Table 11: Agricultural Input Use by Cultivated Land Quintile Share of Income from Livestock Nonagricultural Production Wage Nonagricultural Selfemployment Households with Plots in Rainy Season Dry Season Malawi Education Level None Primary Secondary Higher Land Cultivated Area (Ha) Organic Fertilizer Inorganic Fertilizer Purchased Seeds Ganyu Labor Average Expenditure (2005 US$ PPP) Average Expenditure (2005 US$ PPP) Average Expenditure (2005 US$ PPP) Rainy Malawi Land Quintile Lowest Highest Dry Malawi Land Quintile Lowest Highest In the dry season, the share of households using organic and inorganic fertilizer and purchased seeds does not clearly vary by land quintile. However, the amount of land cultivated varies greatly by quintile. In the lowest quintile, the average household cultivates no land at all, while households in the highest land quintile cultivate 0.61ha. The shares of households hiring ganyu labor are higher in successive land quintiles, with just 4.3% of households in the lowest land quintile hiring laborers and 20.8% of households in the highest land quintile hiring laborers. 16

18 Table 12: Income Sources and Seasonal Cultivation by Cultivated Land Area Quintile Crop Production Households that cultivate larger areas of land in the rainy season obtain more of their income from crop and livestock production and less of their income from non-agricultural wages and self-employment (Table 12). Nearly 100% of households in all land quintiles cultivate plots in the rainy season, but greater shares of households in higher land quintiles cultivate plots in the dry season. Among households that cultivate land in the dry season, income shares from different sources have no clear relationship to the amount of land cultivated. Tables Table 13 and Table 14 show that the largest shares of households forgo inputs in both the rainy (25%) and dry (12.1%) seasons. However, as the previous tables have shown, uptake of inorganic fertilizer and purchased seeds is quite high, possibly due to the FISP. Following no input use, the most common input combinations during the rainy season are inorganic fertilizer by itself (21.5%) and inorganic fertilizer coupled with purchased seeds (12.3%). During the dry season, the most common input combinations after no inputs are organic fertilizer with inorganic fertilizer, purchased seeds, and irrigation (11.9%) and irrigation by itself (10.8%). Table 13: Top Input Combinations for Plots in the Rainy Season Share of Income from Livestock Nonagricultural Production Wage Nonagricultural Selfemployment Households with Plots in Rainy Season Dry Season Malawi Rainy Season Land Quintile Lowest Highest Dry Season Land Quintile Lowest Highest Cultivated Area No Inputs 25.0 Inorganic Fertilizer 21.5 Inorganic Fertilizer, Seeds 12.3 Purchased Seeds 11.4 Inorganic Fertilizer, Ganyu Labor, Seeds 6.0 Inorganic Fertilizer, Ganyu Labor

19 Table 14: Top Input Combinations for Plots in the Dimba Season Cultivated Area No Inputs 12.1 Organic and Inorganic Fertilizer, Seeds, Irrigation 11.9 Irrigation 10.8 Organic Fertilizer, Irrigation 9.5 Organic Fertilizer, Seeds, Irrigation 8.9 Inorganic Fertilizer, Irrigation 8.0 Inorganic Fertilizer, Seeds, Irrigation 8.0 Organic Fertilizer, Inorganic Fertilizer, Irrigation 7.5 Purchased Seeds Crop Production At the turn of the 20 th century, drought-resistant staples such as millet and sorghum were replaced in Malawian cultivation by maize and soybeans (Smale 1995; Babu 2000). Maize is now the main crop grown in Malawi and the main staple for households (Ellis, Kutengule, and Nyasulu 2003; Zant 2011; Ellis and Manda 2012; World Bank 2013). Yet most households do not produce enough maize for subsistence (Smale 1995). Poor access to land and having only one cropping season for maize each year pose a constraint to maize production (Whiteside 2000; Ellis and Manda 2012). While official statistics on maize yields show a sharp increase since the introduction of FISP, these are inconsistent with other sources data and have been questioned (Beck et al. 2013). In general, households cultivate as much maize as they can to avoid dependence on purchased maize (Ellis, Kutengule, and Nyasulu 2003), because when households must depend on maize from the market, they are vulnerable to price fluctuations (Babu 2000). However, high yields alone do not halt poverty. Benson, Chamberlin, and Rhinehart (2005) finds that areas with the highest maize yields have the highest rates of poverty. Cash cropping is one means of increasing household income (Gladwin et al 2001). Burley tobacco is Malawi s main cash crop (Ellis, Kutengule, and Nyasulu 2003; Wood et al 2013). It accounts for most of Malawi s exports (Orr 2000; Wood et al 2013). However, growing tobacco remains too risky an endeavor for most households (Wood et al 2013). Among the land-poor, cash crops must be many times more profitable than subsistence crops to induce smallholders to switch (Gladwin et al 2001). Because of smallholders land constraints, they can cultivate either tobacco or maize, not both (Wood et al 2013). They also face high costs with uncertain returns, as well as challenges from land, labor, credit, and staple market fluctuations. The households that grow tobacco tend to have higher incomes, more land, and younger household heads (Wood et al 2013). The effects of cash cropping on household welfare depend on intra-household dynamics. While cash crops are dominated by men (Wood et al 2013), women are responsible for household food (Gladwin et al 2001). Cash cropping leaves smallholders vulnerable to shocks in staple food prices and purchased staples tend to crowd out supplementary foods (Wood et al 2013). In the event that staples become expensive and households hold no stores, they suffer from hunger. For these reasons, risk-averse households avoid not only cash crops, but hybrid maize (Smale 1995; Harrigan 2003). Figures Figure 5 and Figure 6 demonstrate that maize is the top crop in Malawi in terms of average production value. Hybrid maize provides more than $80 (PPP) to each household and local maize provides around $70 (PPP) per household on average. In contrast, the crop with the next highest average production value is the groundnut, whose average production value is only around $30. In Figure 6 we see the diversity of average production values of various crops by household expenditure quintiles. For hybrid maize, the average production value for households in the lowest expenditure quintile is around $50, while it is more than $130 for households in the highest expenditure quintile. The difference in average production value is less striking for local maize and other crops. Households in the lowest expenditure quintile earn almost $60 from local maize, on average, while households in the highest expenditure quintile earn almost $80 from local maize. 18

20 Figure 5: Top 15 Crops by Average Production Value per Household Figure 6: Average Production Value per Household, by Expenditure Quintile Figure 7 shows that maize is included in seven of the top ten cropping systems in Malawi. In fact, the top two crop combinations are maize with groundnuts and maize alone. Both of these combinations are cultivated by more than 130,000 households, whereas the next most common crop combination (beans alone) is cultivated by fewer than 50,000 households. 19

21 Figure 7: Top Crop Production Combinations 5 Livestock Livestock are considered valuable assets in Malawi. A household with around five heads of cattle and three to five goats is considered well-off. Most households, however, own few animals. Theft of livestock is common, so the risks of owning animals can outweigh the gains (Ellis, Kutengule, and Nyasulu 2003). The prevalence of livestock ownership varies by region, expenditure quintile, and land quintile (Table 15). Largely due to climatic conditions and vegetation, the percentage of households that own cattle poultry, sheep, and pigs is highest in the rural north. However, goat ownership is most prevalent among rural households in central Malawi (25.9% of households own goats). In the rural north the share of households that own cattle (17.1%) is more than three times the national share (4.9%). Similarly, the share of households that own pigs (12.4%) is more than twice the share of national households that own pigs (6.1%). Although the share of households owning many types of animals is highest in the north, the number owned is not always highest in the north. For example, the average number of cattle owned is highest among urban households in the center (6.0). Also, the number of poultry owned in the urban center (16.3) is almost twice the national average (9.0). 20

22 Table 15: Prevalence of Livestock Ownership and Mean Number of Animals Owned, by Region, Expenditure Quintile, and Land Quintile Cattle Poultry Goats Sheep Pigs % Owners Number Owned % Owners Number Owned % Owners Number Owned % Owners Number Owned % Owners Number Owned Malawi Region North Rural North Urban Center Rural Center Urban South Rural South Urban Gender of Household Head Male Female Expenditure Quintile Lowest Highest Land Quintile Lowest Highest The shares of households owning cattle are higher with higher expenditure quintiles. In contrast, the shares of households owning sheep are lower with higher expenditure quintiles. Smaller shares of households in the highest and lowest quintiles own goats and pigs than households in the middle quintiles. Greater shares of households own each type of animal except sheep with greater land quintiles. The share of top quintile households owning poultry (72.5%) is almost twice the share of households in the lowest land quintile (37.1%). For goats, sheep, and pigs the shares of households in the highest land quintile owning each type of animal (39.7%, 2.0%, and 13.3%, respectively) are close to twice as high as the national average (20.8%, 0.7%, and 6.1%, respectively) and almost four, ten, and six times the shares of households in the lowest quintiles owning goats, sheep, and pigs (11.5%, 0.2%, and 2.1%, respectively). Except for pigs, whose husbandry is affected by religion, the greater the land quintile, the greater the number of animals held. In Figure 8, we see that the livestock product with the highest average annual sale value is milk at around $180 per household per year. Milk s average sale value per household is more than twice that of the next most lucrative product, meat. 21

23 Figure 8: Average Annual Sale Value of Livestock Products 6 Nutrition According to Babu (2000), uncertain rainfall and low adoption of agricultural technology result in low food availability in Malawi. Hunger is perceived as a greater and more immediate threat than even HIV/AIDS (Bryceson and Fonseca 2006). In fact, a significant marker of wealth is the ability of a household to feed its members year-round (Ellis, Kutengule, and Nyasulu 2003). Not only is the prevalence of undernourishment high (Zant 2011), micronutrient deficiencies are common. Monotonous diets only exacerbate these deficiencies (Ecker and Qaim 2010). There is little diversity in the typical Malawian diet (Ecker and Qaim 2010). Most calories come from maize (Zant 2011; Wood et al 2013) and meals include very few fruits and vegetables (Ecker and Qaim 2010). Despite poor diets, most of Malawian households expenditures are on food (Benson, Chamberlin, and Rhinehart 2005). Two recent food crises left a great number of households hungry. The famine resulted in significant mortality (Zant 2011; Ellis and Manda 2012). Ecker and Qaim (2010) find that higher income and lower maize prices are associated with more diverse diets. However, Wood et al (2013) show that the nutritional outcomes of children from commercialized rather than subsistence households are not necessarily different. In fact, nutritional outcomes for children in commercialized households with higher incomes may be worse than those in subsistence households. Tobacco production by a household during the year or year after a child s birth is associated with an increased incidence of stunting. This result may be due to commercial households purchases of much of their food, which leaves them exposed to food price shocks (Wood et al 2013). Figure 9 shows that child nutritional outcomes follow no clear pattern in Malawi s three regions. The share of children who are stunted 3 is between 18% and 35% in the least affected districts and 51% to 61% in the most affected districts. Neither wasted (low weight for height) nor underweight (for age) children are as prevalent as stunted children. Rates of underweight children in areas least affected are between 7% and 15% and in places most affected they are between 22% and 28%. For wasting, levels are between 0% and 1% for the least affected districts and 2% to 5% in the most affected districts. There are only three districts that have either elevated or very low rates of stunted, wasted, and underweight children. Nkhotakota and Salima Districts in the eastern portion of central Malawi, near Lake Malawi, have among the lowest rates of all three of these indicators of poor child nutritional health. Nkhata Bay, just to the north of these is another district with elevated levels of stunted, wasted, and underweight children. 3 Stunting (low height for age) measures long-term childhood nutrition levels (Wood et al 2013). Children who suffer from stunting face a higher probability of early death, lower physical capabilities, and lower mental capacity than children without stunting (Wood et al 2013). 22

24 Figure 9: Nutritional Outcomes for Children under Five Years Old Malawi has an extremely high rate of stunting (43.9%, Table 16). A study looking at stunting determinants in Malawi concluded that the strongest variable associated with stunting was small birth size. Other important factors included inappropriate infant feeding and a high number of illness episodes amongst the infants (Espo et al. 2002). The prevalence of stunting is higher in rural areas than in urban areas. It is also more pronounced in farm households than in non-farm households. Rates of underweight children are also higher in farm households and most rural areas than in non-farm and urban areas. However, in the north, the prevalence of underweight children is 15.7% in rural areas and 20.1% in urban areas. Greater shares of children face wasting in rural households and in farm households than in urban and non-farm households, except in the south. There, the rate of wasting among children is 2.2% in both rural and urban areas. Table 16: Stunting Prevalence among Children under Five Years, by Age and Gender Stunted Underweight Wasted Boys Girls Boys Girls Boys Girls Region North Rural North Urban Center Rural Center Urban South Rural South Urban Agricultural Engagement Non-farm Farm Malawi

25 Rates of stunting, wasting, and underweight children vary by the age of the child (Table 17). Stunting and underweight children are least prevalent between six months and one year old (23.9% and 13.4%, respectively). Stunting is most prevalent among children between three and four years old (50.8%), while being underweight is most common between one and two years of age (23.1%). Wasting is least prevalent between four and five years (0.8%) and most prevalent between one and two years (3.7%). Stunted and underweight children are more prevalent in households headed by men (44.3% and 18.4%, respectively) than in those headed by women (41.8% and 17.6%, respectively). On the other hand, the prevalence of wasting is slightly higher in female-headed households (2.2%) than in maleheaded households (2.1%). While stunting, being underweight, and wasting do not exhibit a strong relationship with expenditure quintiles, they do vary with the education level of the household head. All three are least common in households whose heads have achieved tertiary education, at 34.9%, 11.2%, and 1%, respectively. While underweight children and wasting are most common among households whose heads have no formal education (19.9% and 2.6%, respectively), stunting is most common among households whose heads have primary education (45.1%), although households whose heads have no formal education also have a high prevalence of stunting at 44%. Table 17: Underweight Prevalence among Children under Five Years, by Age and Gender Stunted Underweight Wasted Boys Girls Boys Girls Boys Girls Age of Child, in Months Expenditure Quintile Lowest Highest Education Level None Primary Secondary Higher Gender of Household Head Male Female Malawi As Figure 10 demonstrates, boys and girls have nearly the same stunting rate. There are sharp increases in the share of stunted children between the ages of 6 to 12 months, and between 24 and 36 months. Between 12 and 24 months the share of stunted children remains fairly flat, slightly decreasing. A greater share of boys than girls are underweight from six months to two years (Figure 11). However, by ages three to four years, the rate of underweight girls is higher than the rate of underweight boys. Rates of wasting vary little by gender (Figure 12). A slightly greater share of boys are more wasted than girls between one and two years, while a slightly greater share of girls are more wasted than boys between ages three and four. 24

26 Figure 10: Stunting Prevalence among Children under Five Years, by Age and Gender Figure 11: Underweight Prevalence among Children under Five Years, by Age and Gender 25

27 Figure 12: Wasting Prevalence among Children under Five Years, by Age and Gender 7 Conclusion As the foregoing analysis demonstrates, poor Malawian households face myriad challenges. Poor households obtain most of their income from crop production, but are severely land-constrained. Furthermore, while they contain more household members, they include fewer active adults relative to dependents than wealthy households. The adults in these poor households have little formal education with which to obtain non-farm income relative to their wealthy counterparts and their opportunities remain limited. Female-headed households earn less in wages than male-headed households, but depend more on transfers from others. Although FISP is ongoing, crop intensification continues to be an opportunity for Malawian households to obtain more food from their land. As soil fertility is already on the decline, responsible intensification that improves rather than depletes the soil is necessary. Furthermore, financing to improve access to inputs would be necessary for the poorest households to intensify their crop production. Irrigation could help blunt the impact of droughts or dry spells on households production and food prices. However, the intensification of agriculture can resolve only some poverty. General land scarcity indicates that most households need non-farm income sources in addition to agriculture. 26