Impact of the Global Economic Crisis on Vulnerable Households in MalawiA consultancy report submitted to:

Malawi Economic Justice Network (MEJN),P.O. Box 20 135Lilongwe2by

Charles B.L. Jumbe, PhDFrederick B.M. MsiskaCentre for Agricultural Research and Development,P.O. Box 219,Lilongwe.charlesjumbe@yahoo.com;frederickmsiska@yahoo.com

January 2010

Table of Contents

List of Tables.......................................................................................................................................... iiiList of Figures......................................................................................................................................... iiiList of boxes........................................................................................................................................... iiiList of Annexes....................................................................................................................................... ivAcronyms.................................................................................................................................................vAcknowledgements.................................................................................................................................viExecutive Summary...............................................................................................................................vii1.0 INTRODUCTION......................................................................................................................1

1.1 Background..............................................................................................................................11.2 Objectives and Scope of the Study..........................................................................................31.3 Study Limitations.....................................................................................................................31.4 Organization of the Report......................................................................................................4

2.0 METHOLOGY OF THE STUDY...............................................................................................42.1 Literature Review....................................................................................................................42.2 Study Approach Design...........................................................................................................4

2.2.1 Study Tools Development...............................................................................................42.2.2 Recruitment of Research Assistants................................................................................42.2.3 Review of Study Tools and Training of Research Assistants..........................................5

2.3 Sampling and Data Collection Process....................................................................................52.3.1 Sampling Processes..........................................................................................................52.3.2 Field Data Collection.......................................................................................................5

2.4 Data Entry, Analysis and Report Writing................................................................................62.4.1 Field Reports by Research Assistants..............................................................................62.4.2 Data Entry........................................................................................................................62.4.3 Data Analysis and report writing.....................................................................................6

3.0 FINDINGS, RESULTS AND ANALYSIS.................................................................................63.1 Household Demographic Characteristics.................................................................................6

3.1.1 Household size.................................................................................................................63.1.2 Age of household head...................................................................................................73.1.3 Household Literacy Levels..............................................................................................83.1.4 Marital Status of Household Heads.................................................................................93.1.5 Main Occupation of Household Heads..........................................................................10

3.2 Household Access and Demand for Social Services.............................................................113.2.1 Access to Social Services..............................................................................................113.2.2 Demand for Social Services...........................................................................................15

4.0 IMPACT OF GLOBAL FINANCIAL CRISIS ON INPUT AND OUTPUT PRICES...........174.1 Impact on Agricultural Input prices.......................................................................................174.2 Impact on commodity prices..................................................................................................18

5.0 IMPACT OF FINANCIAL CRISIS ON INCOMES, WAGES, REMITANNCES & GIFTS..215.1 Impact on Household Incomes.............................................................................................21

5.1.1 Changes in household economic activities and incomes...............................................215.1.2 Changes in Household Income Levels...........................................................................23

5.2 Recovery from Income Shocks..................................................................................................235.3 Impact on unskilled wage rates..............................................................................................25

5.3.1 Inter-temporal dynamics in the wage rates for unskilled labour...................................255.4 Impact on Remittances...........................................................................................................26

5.4.1 External Remittances.....................................................................................................265.4.2 Internal remittances........................................................................................................295.4.3 Contribution of remittances to household income.........................................................315.4.3 Impact on Gifts..............................................................................................................32

6.0 IMPACT OF GLOBAL FINANCIAL CRISIS ON LIVELIHOOD ASSETS.........................346.1 Household Disposal of Assets...............................................................................................346.2 Impact on livestock assets......................................................................................................36

7.0 HOUSEHOLD EXPENDITURE AND CONSUMPTION.......................................................388.0 CONCLUSIONS AND IMPLICATIONS FOR POLICY........................................................40

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References..............................................................................................................................................43List of Annexes......................................................................................................................................44

List of TablesTable 1: Household Size........................................................................................7Table 2: Age of household head............................................................................8Table 3: Age Groups of Household Members at National level...........................8Table 4: Main occupation of Household Head....................................................10Table 5: Access to educational facilities............................................................11Table 6: Distance and time taken to reach education facility.............................12Table 7: Quality of education facilities................................................................12Table 8: Access to health facilities......................................................................13Table 9: Distance and time taken to the health facility......................................13Table 10: Access to safe water............................................................................14Table 11: Distance and time to access water points...........................................15Table 12: Services or programs provided to the communities...........................15Table 13: Activities to improve livelihood...........................................................16Table 14: Demand for services by communities.................................................16Table 15: Comparison of observed changes in farm input prices between 2008 and 2009.............................................................................................................17Table 16: Comparison of fertilizer supply on local markets between 2008 and 2009.................................................................................................................... 18Table 17: Changes in price of maize between 2008 and 2009............................18Table 18: Comparison of household income between 2007, 2008 and 2009......22Table 19: Household perceptions on changes in incomes, 2008 and 2009........24Table 20: Perception of recovery from income decline.......................................24Table 21: Whether the Household has a Member living outside the Country....26Table 22: Amount and frequency of external remittances..................................27Table 23: Changes in Flow of External Remittances..........................................28Table 24: Household response to the decreased external remittances..............29Table 25: Internal remittances............................................................................29Table 26: Share of remittances in total incomes in 2008....................................31Table 27: Types of gifts given in years 2008 and 2009.......................................32Table 28: Disposed of household assets over 2008.............................................34Table 29: Reasons for Disposal of Household Assets..........................................35Table 30: Changes in Livelihood Assets over 2008.............................................35Table 31: Annual expenditure among rural households.....................................38List of FiguresFigure 1: Marital status........................................................................................9Figure 2: Average Maize Price Trends 2007-09..................................................18Figure 3: Ranking of important source of livelihood from 2007 to 2009............21Figure 4: Channels for sending remittances.......................................................27Figure 5: Description of change in the flow of internal remittances..................30Figure 6: Reasons for decline in internal remittances........................................31Figure 7: Reasons for maintaining the assets.....................................................36Figure 8: Reasons for disposing livestock...........................................................37Figure 9: Description of change in consumption/eating habits..........................39List of boxesBox 1: Commodity Prices and Livelihood: The case of Nabwenje Village, Mchinji district.................................................................................................................20Box 2: Tobacco prices and household income: Case of Chimenya village, Phalombe district................................................................................................25

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Box 3: Case Study on Remittances in Lufita, Chitipa District.............................28

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List of Annexes

Annex 1: List of Sampled Villages.......................................................................44Annex 2: Household Age Groups.........................................................................45Annex 3: Quality of health services.....................................................................46Annex 4: Water quality........................................................................................47Annex 5: Reasons for the observed changed in farm input prices......................47Annex 6: General perception about commodity prices.......................................48Annex 7: Comparison of months with highest price for maize, 2008 and 2009..48Annex 8: District Level Paired Statistical Tests for Income Difference..............48Annex 9: Regression Results for Determinants of Changes in Household Incomes...............................................................................................................50Annex 10: Changes in unskilled wage rates........................................................51Annex 11: Reasons for Increase in unskilled wage rate.....................................52Annex 12: Amount and frequency of receiving internal remittances..................52Annex 13: Types and sources of gifts..................................................................53Annex 13: Trends in livestock holding for 2007, 2008 and 2009........................54Annex 15 Household expenditure shares 2008 and 2009...................................55Annex 16: Coping mechanisms in the face of crisis............................................56

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AcronymsADMARC : Agricultural Development and Marketing CooperationAPIP : Agricultural Productivity Investment ProgrammeGDP : Gross Domestic ProductFGD : Focus Group DiscussionsMEJN : Malawi Economic Justice NetworkMSCE : Malawi School Certificate of EducationODI : Overseas Development InstituteRA : Research AssistantSAP : Structural Adjustment ProgrammeSSA : Sub Saharan AfricaSPSS : Statistical Package for Social ScientistsTA : Traditional AuthorityTOR : Terms of ReferenceUNICEF : United Nations Children’s Fund

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AcknowledgementsWe would like to take this opportunity to express deep appreciation to various institutions and individuals who have made invaluable contributions towards this study. Our special thanks go to the Malawi Economic Justice Network for the technical assistance provided to the study team and also for working tirelessly in mobilizing the financial resources without which this study would not have been possible. In same vein, we are indebted to UNICEF Malawi office for timely provision of the requisite financial support which made this study a reality.All the research assistants who participated in the study by collecting data from scores of rural households and small scale businesses deserve our heartfelt recognition for the job well done. These are Misheck Mtaya, Ms Grace Banda, Ms Brenda Mwagomba, Stater Magombo, Martin Likongwe and Austin Chimbiya. It was not easy to endure through the hot days of work across the five districts covered by the study.To the nearly five hundred respondents in the rural Malawi who spared their precious time and effort to provide us with the valuable data upon which this study is based, we say, thank you a million times. Surely, the study would not have accomplished if you had decided to ignore us or refuse to grant us the interviews, and for that we shall always remain deeply indebted. In spite of the valuable contributions to this report from various stakeholders, the consultants, however, remain responsible for all the errors, omissions and mistakes therein.

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Executive SummaryThis study was commissioned by the Malawi Economic Justice Network to establish the impact of the current global economic crisis on the Malawi economy with particular focus on its implications on the poor and vulnerable groups and propose ways of mitigating the impacts. The study recognizes that the financial crisis which is the focus of this analysis was preceded by the food crisis characterized by high food prices which were beneficial to net producers while impoverishing consumers. It comes against the background of the fact that while several international analyses of the causes, impact and policy responses have been undertaken that shed light on such issues, there is little or no system research information on the Malawi economy particularly relating to the rural households.The study was conducted in fifteen villages from five districts of Chitipa in the North, Salima and Mchinji in the Centre and Phalombe and Mangochi in the South. In each village, thirty households were sampled for household interviews thus totaling four hundred and fifty households. In order to triangulate the results of the household interviews, interviews were held with the small scale traders in the nearby trading centre of each village and focused groups of men and women separately. The 2008 global economic crisis, with its serious negative implications on the Malawi economy especially the vulnerable households, follows the footsteps of other previous shocks that have hit the country such as the declines in the international prices of tobacco in the 1990s, upsurge in petroleum products in the late 1970s, and slippages in the exchange rates in the 1990s. The results show that the Malawi economy, just like other Sub- Saharan Africa economies, benefited from global food price increases in 2007-08 seasons. Analysis of household income patterns for the past three years shows that rural households experienced an average income earnings increased by MK15,230.50, representing a 33 percent increase, between 2007 and 2008 owing to global commodity prices increases. These positive gains have been quickly negated by global financial meltdown leading to a decline in average income earnings of MK 9,000.00, representing a decline of 15 percent between 2008 and 2009 for the same rural households. In any case, this means that the 2008 economic crisis would have had much more serious negative implications had it been that it were not preceded by increases in household incomes in the year before . A spatial view of the impact of global economic crisis shows significant variations in impact of the crisis on the different districts of the country. This analysis shows that Chitipa district is the least affected by the global economic crisis as evidenced by the fact that it registered significant income increases not only within the 2007-08 period but also when we compare the 2007 and 2009 income levels. On the other hand, Mangochi district is the most negatively affected by the crisis with double income decreases for 2008-09 period and even when comparing 2007 and 2009 household income levels.Lack of proper produce markets and inadequate access to farm inputs such as fertilizer have been established as the major causes of household income declines. This calls for speedy government action in terms of ensuring stable and accessible input and output markets.

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With respect to remittances, the study shows that 25 percent of the households have a member working outside the country (external migrants) while 38 percent of the households have members working and living outside the district of origin (internal migrants). For those households that receive external remittances, they receive on average an amount of MK 16,500 per year which represents 1.2 percent of total household income. This clearly indicates that Malawi’s rural economy is not strongly linked to the flow of external remittances. However, internal remittances are relatively more important than external remittances as they account for more than 2.5 percent of the total household income. Overall, internal and external remittances account for only 3.5 percent of total household income with female headed households have a higher share of remittances (6.2%) to total household income.Both forms of remittances have substantially declined in 2009 and this calls for introduction and/or scaling up of safety net programmes for households who have been negatively affected by declines in remittances especially in the districts that have high proportions of rural households who do rely on remittances such as Mangochi district. In terms of household assets, the study has established that about 70 percent of the rural households had their asset stocks either remaining constant or reduced between 2008 and 2009. Lack of money to purchase additional households assets was given as the major reason. In addition, cases of theft, natural depreciation of assets and some few sales of assets to raise incomes for food were also reported. In this regard, it can be argued that since global economic crisis has not yet really resulted into forcing most rural households to sell their assets, there is need to come up with social and economic support measures such as public work programmes that would prevent this from happening at a large scale. In the face of financial crisis, people devise different coping measures such as reducing the amount of food (14%) and shifted to cheaper foods (7%). However, our results indicate that the global financial crisis had little impact on the livelihoods of the local communities. Although the poor and vulnerable households devote more than 25 percent of budget on food, the food prices in 2009 were not that high despite the financial crisis. The effect of the economic crisis could have worsened if the government had not intervened in the agricultural production through the agricultural inputs subsidy programme. In other words, the global financial crisis was internally mitigated by the good harvest obtained in 2008 and 2009 following good rains and the increased maize productivity through the use of hybrid seeds and application of fertilizers under the government subsidy program.The study has established that the provision of formal credit opportunities and reduction in agricultural input prices are essential services in boosting incomes of rural communities through improved crop productivity and participation in business opportunities. Essentially, such interventions would be instrumental in cushioning rural communities against the effect of economic shocks other than the provision of short-term safety nets such as food assistance. The study findings call for more long term development-oriented than relief interventions in order to effectively empower Malawi’s vulnerable households against any economic shocks.

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The study has shown that rural incomes in Malawi are to a larger extent integrated with the dynamics in the global economy, such that price changes in the world economy which are likely to affect the welfare of rural communities in Malawi. In particular, analytical results indicate that a 1 percent increase in selling prices faced by the households leads to a corresponding increase in household income by 0.24 percent. Essentially, what this means is that market oriented public policies are critical for sustainable socio-economic empowerment of the rural populace. These results call for shift in policy focus away from supply side policy interventions in favor of interventions that effectively link farmers to reliable markets.

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1.0 INTRODUCTIONThe world economy has over the past few years undergone a few economic crises that have had significant implications for the developing countries including Malawi. The major crises include: the fuel, food and financial crises from 2007 to mid 2008. Of these, the financial crisis is the most recent and with the most negative implications in terms of slowing down the global economic growth and investments.The food and financial crises have different underlying causes. The high food prices largely emanated from a surge in consumption of agriculture products due to the income and population growth, rising energy prices and subsidized biofuels production, all of which took place against a background of stagnating agricultural productivity and output growth (von Braun, 2008). Further, von Braun (2008) observes that the financial crisis which emerged from the subprime lending and flawed regulatory regimes in the developed world, resulted in the collapse of financial markets and hence economic slowdown and downward pressure on prices of agricultural products. Admittedly, the sequencing of these two crises has placed complex challenges on policy makers worldwide. While policy makers started developing policy measures to contain the inflationary and macroeconomic effects of high food prices, they had to deal with the emergent challenge of dampening food and agriculture prices due to the financial crisis.The financial crisis has had several implications for the developing countries. These range from declines in foreign direct investments, declines in export revenues due to shrinking demand in the developed world, downturns in remittances, rise in unemployment due to job losses (e.g. 30 000 in Zambia), declines in foreign aid, increase in food insecurity and malnutrition, increase in poverty, amongst others (Overseas Development Institute (ODI), 2009; von Braun, 2008).Analyses of the policy responses to the two major crises reveal wide variations in the approaches to deal with the crises even in the developing world. According to the ODI (2009), policy responses range from continuing business as usual to using proactive approaches. In some countries, steps were made to implement growth accelerating policies or even implementing fiscal stimuli, yet in others, there have only been minor adjustments to the monetary policies. In terms of social policy responses, ODI (2009) further observes that some countries such as Nigeria and Zambia significantly reduced their budgetary allocations to the social sector, whereas as Cambodia and Indonesia have extended their social protection provisions. In general, a country’s capacity to respond to the crises depends upon extent of revenue contraction, the ability of the government to access resources to finance the fiscal deficit, and the preexistence of social protection systems (ODI, 2009).The foregoing synopsis shows that a wealth of information exists on the causes, impact and policy responses of the global economic crisis on world economies including Sub -Saharan Africa (SSA). However, little is known about the implications of the phenomenon on the Malawi economy particularly the vulnerable rural households. It is for this reason that this study was commissioned to unravel the extent to which vulnerable households in Malawi

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have been impacted by the global economic crisis and the responses to various aspects of the crisis.1.1 BackgroundThe Malawi economy has since independence in 1964 relied on the agricultural sector for economic development and the well-being of Malawians. The sector contributes about 36-39 percent of the gross domestic product (GDP), accounts for over 90 percent of national export earning and 85 of employment opportunities. To confirm the importance of the agricultural sector to the national economy, household studies have shown that about 70 percent of the rural households earn their incomes from sale of agriculture products. The agricultural sector itself is dualistic, comprising the estate and smallholder sub-sectors. Besides agriculture, the other important sectors of the national economy are: wholesale and retail trade (14%), manufacturing (8%), financial and insurance activities (7%), and construction (5%) (MDPC, 2009).A look at Malawi’s economic performance vis-à-vis the various policy regimes it has undergone shows that the economy performed differently under various policy regimes. For instance, between 1964 and 1979, national policies were biased towards the estate sub-sector. During this period, the economy grew at an average annual growth rate of about 6 percent, well above the population growth rate of 2.9 percent (Sena and Chinkunda 2002). However, due to rising energy prices, disrupted external trade routes and influx of refugees from the wars in neighboring Mozambique, drought and the declining terms of trade from the late 1970s to the 1980s, the country witnessed economic slowdown of 3 percent annual growth rates (ibid).The economic performance was not any better in the 1990s when the country was in the midst of implementing its structural adjustment programmes. This was so because the adjustment programmes did little to counteract the effects of intermittent droughts that mostly affected smallholder agriculture, nor the continued declining terms of trade challenges. Interestingly, during the post SAPs era, between 1995 and 2000, economic growth registered slight improvement with an average annual growth rate of around 5 percent. This was due to government interventions that aimed at cushioning against the effects of the SAPs. These included programmes such as Starter Packs programme1 and the Agricultural Productivity Investment Programmes (APIP)2 amongst others.Although the country has undertaken a number of reforms in the agricultural sector, the economy remains vulnerable to the external shocks. This is largely due to inadequate economic diversification, supply side rigidities and the poor management of the reform process. In the same vein, Logfren et al. (2001) observes three major external shocks that have had impact on the Malawi economy, and these are: (i) changes in the international prices of tobacco (ii) changes in petroleum products and (iii) variations in the real exchange rate.With respect to tobacco prices, the major historical price shocks include a price decline by 50 percent between 1991 and 1994, an increase by 66 percent 1 Under the programme, almost all vulnerable households were given free fertilizer and seeds for 0.1 hectare of land. 2 This was a soft credit programme administered by the Ministry of Agriculture and Food Security with the financial and technical support from the European Union.

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between 1994 and 1998, and another decline by 25 percent between 1998 and 2000 (World Bank 1998, p. 35 quoted in Logfren, et al, 2001). The international price of petroleum products, which in 1998 accounted for 7 percent of total imports, also fluctuated substantially such that between 1996 and 1998, the average price fell by more than 40 percent whereas between 1998 and 2000, it more than doubled (ibid). In terms of exchange rates, Logfren et al (2001) observes that relative to other African countries, Malawi’s real exchange rate fluctuations are among the highest: the year-to-year changes in the index for the real effective exchange rate were above 25 percent in 1994, 1996, and 1998. The causes of these sharp variations include budgetary crises, pegging of the nominal exchange rate at unsustainable levels, the seasonality of Malawi’s export earnings, and unpredictable foreign aid flows (IMF 1997, p. 14; World Bank 2000a, p. 220 quoted in Logfren et al, 2001).The external shocks on Malawi’s economy could be classified as global and idiosyncratic. Global crises refer to those crises that affect many countries including Malawi such as the oil price shocks of the 1970s, while idiosyncratic shocks refer to shocks that are peculiar to the country such as the withdrawal of international development support in early 1990s due to poor governance, and the intermittent droughts of 1980s and 1990s. It is worth mentioning that most recently the Malawi economy has once again been subjected to significant global shocks such as the rising fuel, fertilizer and food prices and the financial crisis.While studies of the impact of previous crises on the macro-economy have been conducted in Malawi, there has been minimal in-depth analysis of how the global crises impacts on the livelihoods of the local communities at the micro level. Such an analysis is critical in providing insights for devising effective short, medium and long-term responsive measures not only to address the just gone-by crises, but to develop strategies to help local communities respond to future shocks.1.2 Objectives and Scope of the StudyThe overall objective of the study as indicated in the original Terms of Reference (TORs) was to assess the impact of the current global economic crisis on the Malawi economy with particular focus on its implications on the poor and vulnerable groups and to propose ways of mitigating the impact. The following were the specific objectives of the study:i. Investigate the impact of the global economic crisis on trade, capital flows, aid flows, remittance and likely budgetary implications;ii. Assess impact on poverty statistics especially as it relates to the poor and vulnerable groups such as women and children;iii. Examine the monitoring systems in place to track the impact of the financial crisis;iv. Assess the social protection implications of the crisis;v. Provide recommendations on how to mitigate the effect of the crisis on the poor and vulnerable people.However, during the inception meeting, the scope of work and focus of the study were re-casted by the client only to assess the impact of the crises on

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vulnerable households. As such, the macro-economic analysis components of the assignment were not to be tackled as originally indicated in the original TORs. 1.3 Study LimitationsFew challenges were encountered while undertaking the study that affected the data collection process. The decision to increase the sample size from 200 households as originally planned to 450 households to avoid small sample biases, though well appreciated by both parties, had significant budgetary implications. Yet no budgetary adjustments were made to accommodate this adjustment. This means that the study team had to operate with tight budget and within the set time frame while covering a more than double the original sample size. Furthermore, much as the enumerators recruited for the survey were of different cultural backgrounds, language barrier was still encountered in some remote parts of Chitipa and Mangochi districts where some respondents especially the elderly could not comfortably communicate in national language, Chichewa. This challenge was overcome by identifying someone within the village who could understand the national language better and help in translating some of the critical words.Since the survey was taking place at the time when the rural households had just been registered for the national farm inputs subsidy distribution exercise, in some villages, households thought the study team was registering beneficiaries of the subsidy program. The study team was well prepared for this and they clearly explained the purpose of the study that it had nothing to do with the subsidy program. Another challenge was fatigue among respondents. This was common in some districts such as Mangochi where some of the respondents that had been sampled for this study had been exposed to similar interviews in previous studies. These households including village chiefs lamented that they do not see any benefits emanating from such studies. In response, the study team had to explain to the respondents the importance of not only this study but also any other study that may come in the future from any organization. Finally, it is also worth pointing out the data collection encountered some challenges emanating from fuel shortage especially towards the end of the survey. During data entry, there were also frequent power outages that led to the delays in data entry and cleaning. Despite these challenges, the data collected provide useful information and reliable data from which to derive realistic estimates of the impact of the global economic crisis on the vulnerable households in Malawi.1.4 Organization of the ReportThis report is organized as follows. The first Section provides the background to the study. Section 2 presents detailed methodology, whereas Section 3 presents the main findings on demographic characteristics. Section 4 presents findings on the impact of the global crisis on input and output prices while Section 5 presents the findings on household incomes, wage rates, remittances and gifts. Section 6 presents findings the impact of the global financial crisis on livelihood assets while Section 7 discusses household expenditure and consumption. Conclusions and policy implications are presented in Section 8.

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2.0 METHOLOGY OF THE STUDY2.1 Literature ReviewThe very first stage of the study involved collection of literature related to the study agenda which, included research papers on food security, rural livelihoods and impact of the global food and financial crises on the developing countries.2.2 Study Approach Design2.2.1 Study Tools DevelopmentFor purposes of triangulation of the study findings, three data collection tools were used in this study, namely household questionnaire, checklists for traders and focus group discussions (FGD). The household questionnaire had major components of: social economic characteristics; household incomes; remittances; livelihood assets; input, output and food prices, household expenditure and consumption; access to social services and amenities, coping strategies, responses and priorities. The traders’ checklists focused on issues such as: product range and storage capacity; demand, supply and buyer behavior, and credit assess. The FGD checklist had the following components: livelihoods and incomes; employment and wages; remittances; prices; expenditure and consumption; coping strategies, responses and priorities. 2.2.2 Recruitment of Research AssistantsRecruitment of Research Assistants (RAs) was done concurrently with the design of study tools. Recruitment criteria involved education levels and previous similar working experience. The minimum academic education qualification was the Malawi School Certificate of Education (MSCE). Based on these criteria, the consultants managed to recruit three graduates from the University of Malawi to be part of the research team. This was done to ensure effective and efficient data collection, recording and production of field reports.2.2.3 Review of Study Tools and Training of Research AssistantsAfter getting feedback from MEJN on the data collection tools, on 14th October 2009, the consultants reviewed the tools and proceeded to mobilize Research Assistants for training. Training of the Research Assistants took place from17th to 19th October, 2009. The training focused on data procedures such as how to conduct household and focused group discussions interviews using the tools, recording of data, and writing of individual FGD reports.Pre-testing of the study tools was conducted in Ndalama village, Traditional Authority Malili in Lilongwe district on 18th October, 2009. Thereafter, the data tools were finalized taking into account the pre-testing field experiences.2.3Sampling and Data Collection Process2.3.1 Sampling ProcessesA total of five districts were sampled for the study namely Chitipa in the North, Salima and Mchinji in the Centre, Phalombe and Mangochi in the South. These districts were sampled based on the differences in poverty rates as indicated in

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national publications. In addition, since a number of social protection programmes have been implemented in some of these districts, the study sought to establish the extent to which such programmes have assisted in mitigating the impact of the global financial crisis on the livelihoods of the rural households.In each district, one (1) TA and three (3) villages were randomly sampled. In each village, 30 households were also randomly sampled based on the village register obtained from the village headman. Thus a total of 15 villages and 450 households were sampled for the study (see Annex 1).2.3.2 Field Data CollectionData collection was done from 21st October 2009 to 6th November 2009 in 15 villages across the sampled five districts of study. In each village, the process of data collection was always preceded with a courtesy call and briefing session for the village heads on the objectives and scope of the study. Thereafter, community members were sensitized about the study with the assistance of the village heads before the study team proceeded to conduct the interviews and an FGD in the villages. Where available, an interview was conducted with one trader in the nearest trading centre. The household questionnaire was administered to 450 households. To ensure reliability of data, interviews were conducted with either a household head or spouse or both.Interviews with the traders or small scale businessmen and women were done at the trading centre where the trader was conducting his/her business. This was done so to ensure that realistic and reliable data was collected on the trading transactions by the concerned respondent. However, the number of traders that were interviewed during this study was very few to warrant a detailed statistical analysis.In terms of FGDs, a total of 30 group interviews were conducted across the 15 villages. In each village, the study team conducted separate male and female FGDs comprising about 8 members. Separation of male and female groups was done to ensure that each gender group freely expresses their views on issues affecting their lives and livelihoods. In other words, this was done in order to overcome challenges of gender barriers to expression of one’s views.At the end of each day, review sessions were held to look at weaknesses that were addressed and strengths that had to be built upon. In so doing, the data collection process improved on a daily basis hence the confidence this study collected and analyzed reliable data.2.4Data Entry, Analysis and Report Writing2.4.1 Field Reports by Research AssistantsAfter the field work, the RAs produced field reports detailing the key findings and experiences. These reports basically focused on the critical information that was not captioned on the data collection tools. As such, the study has a wealth of information besides what is contained in the formal data collection tools.

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2.4.2 Data EntryData from all the three tools was input into Statistical Package for Social Scientist (SPSS) by three data entry clerks hired for the exercise. A data entry template was developed from 9th November 2009 to 2nd December 2009 by the data entry manager who was also responsible for quality assurance during field data collection. 2.4.3 Data Analysis and report writingData analysis was undertaken from 3rd to 11th December, 2009. The process involved analyzing data based on the focus of the study. The SPSS tables were exported into word for ease of presentation and thereafter discussion of the insights obtained from each analysis made. In so doing, comparisons were made with findings from other similar national and international studies.3.0 FINDINGS, RESULTS AND ANALYSISThe following sections discuss the findings of the baseline survey, based on the information collected through the household questionnaires from the five sampled districts backed by focus group discussion data.3.1 Household Demographic CharacteristicsThe study collected data on household demographic and socio-economic characteristics focusing on variables such as, household size, age of household head and members, literacy levels of the household members, sex of household head, main occupation and marital status of the household head, amongst others. These data provide insights on the ability of the households to engage in the various economic activities and cope with the economic shocks. In fact, according to the World Bank (2007), low education levels, access to land, and increasing dependency ratios have significant bearing on poverty levels especially in the rural Malawi. 3.1.1 Household sizeAccording to a study on determinants of poverty in Malawi by National Economic Council et. al. (2001), size of a household was found to have significant impact on the household welfare implying there may be economies of scale of household welfare derived from increasing household size. In this regard, this study collected data on household size and Table1 presents the results of the study for both male and female headed households.

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Table 1: Household SizeDistrict Gender of household head Mean Std. Deviation NChitipa Male 5.13 1.531 79 Female 3.75 1.982 8 Total 5.00 1.614 87Mchinji Male 5.23 1.622 81 Female 4.78 1.641 9 Total 5.19 1.621 90Salima Male 5.08 2.039 75 Female 4.36 1.906 14 Total 4.97 2.025 89Mangochi Male 5.41 1.996 78 Female 5.14 1.875 14 Total 5.37 1.971 92Phalombe Male 5.00 1.607 80 Female 5.00 1.673 11 Total 5.00 1.606 91Total Male 5.17 1.764 393 Female 4.66 1.822 56 Total 5.11 1.777 449Table 1 shows that the average household size in rural Malawi is 5 members. Our estimates compares well with the mean household size of 4.5 persons per household across the country from the integrated household survey (NSO, 2004). Male headed households on average have 5 household members compared to 4.7 for female headed households. Much as the difference in household size between male and female headed households is minimal, the fact that male headed households are slightly bigger than their female counterparts means that female headed households are disadvantaged in terms of production labor. However, from a consumption perspective, one would argue that the study results show higher consumption burdens in male headed households than in their female counterparts. 3.1.2 Age of household headAge of a household head is also critical in determining household welfare. The NEC, et al (2001) study found that in Malawi, households with older household heads tend to be poorer than those with younger heads. The data on age of a household head in this study is presented in Table 2. Results show that on average, the age of a household head is 40.2. With a standard deviation of 15, this means that older household heads were around 55 years of age with youngest being around 25 years of age. In terms of age distribution by gender, the results show that female headed households are slightly older (42 years) than the male headed households (40 years). These study findings agree with the NSO (2008) Welfare Monitoring Survey findings which found that female household heads were generally older than their male counterparts, such that 43 percent of the female heads were 50 years or older as compared to 27 percent for their male counterparts.

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Table 2: Age of household head District Gender of household head Mean (years) Std. Deviation NChitipa Male 37.7 13.5 78Female 38.3 9.7 9Total 37.8 13.1 87Mchinji Male 40.0 15.0 80Female 48.8 15.8 9Total 40.9 15.2 89Salima Male 38.0 15.5 76Female 44.3 21.9 12Total 38.9 16.5 88Mangochi Male 45.4 15.9 77Female 41.2 14.4 15Total 44.7 15.6 92Phalombe Male 38.6 14.9 79Female 37.8 13.8 12Total 38.5 14.7 91Total Male 39.9 15.1 390Female 41.9 15.7 57Total 40.2 15.2 447We further analyzed the demographic characteristics of the sampled households into different age categories as presented in Table 3 below and Annex 2. Results show that children aged 0-14 years constitute 49 percent of the total household sizes in the rural Malawi. On the other hand, the economically active age group of 15-64 years constitutes the other half of the rural population. The situation is not any different between male and female headed households. These study results indicate that the rural population is dominated by a youthful age group. Although the study did not examine why the situation is like that, this means that Malawi’s socio-economic interventions must target the youth who constitute the larger proportion of the country’s populace. Table 3: Age Groups of Household Members at National levelAge Group Sex of Household Head Totalmale femaleUnder five children (0- 5 years) 22.2 20.6 21.4Primary school going age (6-14 years) 26.5 28.8 27.6Secondary & tertiary school going age (15-24 years) 19.0 19.7 19.4Economically active youths (25-39 years) 17.8 18.3 18.1Economically active adults (40-64 years) 11.3 11.0 11.2The elderly (>65 years) 3.1 1.5 2.3Total (%) 100.0% 100.0 100N 1156 1135 22913.1.3 Household Literacy LevelsStudies have shown that literacy levels of a household head have significant impact on the socio-economic status or welfare of the household (Word Bank, 2007; National Economic Council et al, 2001). For instance, the determinants of poverty study by NEC et al (2001) found that raising the education levels of a household head from primary education to Junior Certificate level, raises household incomes by 22 percent. The National Statistical Office (2008) defines

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literacy as a situation where a person is able to read and write a simple sentence in any language. Analysis results are presented in Table 4.

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Table 4: Household Literacy Level

Gender of household head Literacy levelDistrict Total(%)

Chitipa(%)Mchinji(%)

Salima(%)Mangochi(%)

Phalombe(%)Male Cannot read & write 2.6 25.9 31.6 55.1 17.7 26.5Can read only 2.6 4.9 7.9 6.4 3.8 5.1Can read and write 94.9 69.1 60.5 38.5 78.5 68.4Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 78 81 76 78 79 392Female Cannot read & write 11.1 77.8 69.2 46.7 41.7 50.0Can read only 0.0 0.0 0.0 13.3 8.3 5.2Can write only 0.0 0.0 0.0 0.0 8.3 1.7Can read & write 88.9 22.2 30.8 40.0 41.7 43.1Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 9 9 13 15 12 58Analysis results in Table 4 shows that of the 450 sampled households interviewed in the survey, 392 or 87 percent were male headed households while 13percent were female headed households. In terms of literacy levels, 68 percent of the male headed households could read and write whereas only 43 percent of the female headed households demonstrated such literacy levels. These estimates are close to the estimates from the integrated household survey where 64 percent of the population in Malawi was found to be literate where 76 percent among male-headed households was literate while 50 percent among females were literate (NSO, 2004). Recent estimated by NSO (2008) showed that the national male literacy rate was 79 percent compared to 59 percent for females. Comparative analysis of district literacy levels shows significant variations amongst districts. Chitipa district had the highest literacy levels with 95 and 89 percent of the male and female headed households respectively being able to read and write. The lowest literacy levels were registered in Mangochi especially for male headed households (39%) whereas Mchinji had the lowest literacy levels for female headed households (22%). The study did not establish why there are such variations in the literacy levels amongst the districts or between male and female headed households.3.1.4 Marital Status of Household HeadsAnalysis of data on marital status of household heads in the five sampled districts is presented in Figures 1 below. The marital statuses reported include: single (never married before), married, polygamist, widowed, divorced and separated.

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Figure 1: Marital status According to Figure 1, nearly 80 percent of the households in the sample are married with one spouse. About 6- 8 percent reported to have polygamous marriages. The other forms of marital status had very few reported cases. Highest incidences of divorce were reported in Phalombe district with 11 percent of the households while Chitipa district had the least cases of 2 percent. The single (never married before) cases were very few amongst sampled household in the five districts.3.1.5 Main Occupation of Household HeadsEmployment, especially formal wage employment is a major determinant welfare (NEC, et al, 2001). In this study, respondents were asked to state their income sources and results are in Table 4. Essentially, the findings show that farming is the major economic activity of the sampled households. This applied to both male and female headed households. For instance, 86 and 79 percent of the male headed and female households, respectively, indicated farming as their major occupation. Petty trading and wage employment were the second and third most important occupations identified in the study.Table 4: Main occupation of Household Head

Gender Main occupationDistrict

Total(%)Chitipa(%)Mchinji(%) Salima(%)

Mangochi(%)Phalombe(%)Male Farming 83.3 95.1 96.1 67.9 86.1 85.7Wage employment 6.4 .0 2.6 2.6 5.1 3.3Artisan (carpentry bricklaying) 1.3 .0 .0 9.0 5.1 3.1Charcoal/firewood selling 1.3 .0 .0 5.1 1.3 1.5Beer brewing .0 .0 .0 5.1 .0 1.0Petty trading 7.7 4.9 1.3 10.3 2.5 5.4Total 100.0 100.0 100.0 100.0 100.0 100.0Sample 78 81 76 78 79 392Female Farming 55.6 88.9 84.6 73.3 91.7 79.3Wage employment 11.1 .0 15.4 13.3 .0 8.6Artisan (carpentry bricklaying) 11.1 .0 .0 .0 .0 1.7Charcoal/firewood selling .0 .0 .0 6.7 .0 1.7Petty trading 22.2 11.1 .0 .0 8.3 6.9

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Other inc' none .0 .0 .0 6.7 .0 1.7Total (%) 100 100 100 100 100 100Sample (N) 9 9 13 15 12 58Comparisons amongst districts show no major differences except that Mangochi and Chitipa registered the highest proportions of male headed households involved in petty trading (10% and 8%, respectively). For female headed households, 22 percent of the female headed households in Chitipa reported to be engaged in petty trading followed by 11 percent in Mchinji district.SummaryThe analysis of household characteristics focused on demographic and socio-economic characteristics covering variables such as, household size, age of household head and members, literacy levels of the household members, sex of household head, main occupation and marital status of the household head. The analysis results show that family sizes of male headed households are slightly bigger than their female-headed counterparts implying that there are differences in terms of household labor supply as well as the burdens of feeding household members. Analysis of the age structures of the sampled households show that Malawi’s rural population is dominated by the youth. This means that Malawi’s socio-economic development interventions must target the youth who constitute the larger proportion of the country’s populace. In terms of occupation, the study finds that farming and trading are the major livelihood sources, with 86 percent of male headed and 79 percent of the female headed households indicating that farming was a major occupation. The sampled populace demonstrated significant literacy variations between male and female headed households such that 68 percent of the male headed households could read and write whereas only 43 percent of the female headed households could do that. The study findings are consistent with findings from other studies. 3.2 Household Access and Demand for Social Services 3.2.1 Access to Social Services3.2.1.1 Access to Education FacilitiesAccess to education facilities is a key for the attainment of social development. From Table 5 below, kindergarten or nursery schools are not readily available across all districts with only 5 percent of the households in Phalombe reporting having kindergarten. Availability of primary education facilities is generally high across all districts with the highest number of households (75%) reported in Phalombe and the lowest is Chitipa, (59%). Nearly 20 percent of the households in our sample have a secondary school within their vicinity. Table 5: Access to educational facilities

District nameEducation facility Total(%) Sample(N)Kindergarten(%)

Primary education(%)secondary education(%)

tertiary institution(%) Chitipa 22.6 58.1 18.5 .8 100.0 124 Mchinji 20.6 67.0 12.4 .0 100.0 97 Salima 10.4 66.7 22.9 .0 100.0 96 Mangochi 21.3 61.5 17.2 .0 100.0 12213

Phalombe 4.9 74.8 20.4 .0 100.0 103Total (%) 16.4 65.1 18.3 .2 100.0 542We investigated the level of accessibility of these education facilities in terms of distance and time taken to reach these facilities. In general, all education facilities are located between 1 and 1.5 km from homesteads except for secondary education facilities that are located more that 1.5 km from the homesteads. Children from Mchinji travel the longest distance to a secondary facility (3 km). Children take less than 1 hour to reach the education facilities. Interestingly, where there are kindergarten facilities, the children travel less than 30 minutes with the exception of Phalombe where kids take 1 hour to reach the facility (Table 6).

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Table 6: Distance and time taken to reach education facilityEducation Facility District name Distance from homestead (km) Time taken to reach social amenity (minutes)Kindergarten

Chitipa 0.33 (0.289) 12.85 (7.76)Mchinji 1.24 (1.65) 16.60 (18.06)Salima 1.09 (1.27) 23.40 (35.6)Mangochi 0.56 (0.64) 14.12 (13.23)Phalombe 1.74 (1.42) 60.00 (44.20)Total 0.77 (1.1) 18.0 (21.9)Primary education

Chitipa 1.31 (1.2) 31.5 (26.3)Mchinji 0.80 (0.92) 16.9 (17.4)Salima 0.84 (0.8) 17.7 (18.3)Mangochi 0.76 (0.85) 20.2 (15.3)Phalombe 0.79 (1.1) 18.4 (18.2)Total 0.90 (1.0) 21.0 (20.1)Secondary education Chitipa 1.6 (3.0) 27.3 (35.7)Mchinji 3.0 (5.4) 43.6 (15.7)Salima 2.7 (6.2) 45.3 (21.1)Mangochi 2.5 (5.1) 54.3 (41.5)Phalombe 1.51 (1.6) 45.6 (49.4)Total 2.20 (4.5) 43.2 (36.8)The numbers in brackets are standard deviations

In general, results In Table 6 above suggest that most children have access to education facilities in all the districts. However, we further investigated the perception of respondents on the quality of education facility. As shown in Table 7 below, kindergarten facilities are rated as being average or above average by the majority of households (more than 80% of the households) across all districts except for Phalombe and Salima. There could be fewer kindergarten facilities in Phalombe and Salima compared to the other districts.Table 7: Quality of education facilitiesEducation facility District Quality of service Total(%)Poor(%)below average(%)

Average(%)above average(%)

Excellent(%)Kindergarten

Chitipa 3.7 81.5 7.4 7.4 100.0Mchinji 5.0 90.0 5.0 100.0Salima 10.0 50.0 40.0 100.0Mangochi 80.0 20.0 100.0Phalombe 20.0 40.0 40.0 100.0Average 2.3 2.3 77.0 16.1 2.3 100.0Primary education

Chitipa 3.0 7.5 67.2 10.4 11.9 100.0Mchinji 1.5 60.0 36.9 1.5 100.0Salima 6.3 57.8 34.4 1.6 100.0Mangoc 2.7 64.9 29.7 2.7 100.15

hi 0Phalombe 59.7 33.8 6.5 100.0Average .6 3.5 62.0 29.1 4.9 100.0Secondary education

Chitipa 9.5 66.7 19.0 4.8 100.0Mchinji 66.7 33.3 100.0Salima 13.6 63.6 18.2 4.5 100.0Mangochi 81.0 19.0 100.0Phalombe 14.3 71.4 14.3 100.0Average 8.2 70.1 19.6 2.1 100.03.2.1.2 Access to health facilitiesWe collected data on access to health facilities in all the districts. Results in Table 8 indicate that the majority of households obtain their health services from the hospital and health centre. Interestingly, some households consult traditional healers for medical help especially in Chitipa where 9 per cent of the households rely on traditional healers.Table 8: Access to health facilities District

Social services Total(%) Sample (N)Hospital(%)

Health centre(%)Private clinic(%)

traditional healers(%) Chitipa 55.0 32.0 4.0 9.0 100.0 100 Mchinji 35.4 42.5 20.4 1.8 100.0 Salima 34.9 44.3 19.8 .9 100.0 106 Mangochi 44.4 36.8 14.5 4.3 100.0 117 Phalombe 20.2 68.3 7.7 3.8 100.0 104Total 38.0 44.6 13.5 3.9 100.0 540In terms of distance covered by households to access health services, Table 9 shows that households take more than 1 hour to reach a health facility located about 4.5 km from homesteads. Households in Mangochi travel relatively longer distances to a health facility (6.5km) taking more than 1.5 hours. Households in Mchinji cover the shortest distance of 2.6km to the health facility. Table 9: Distance and time taken to the health facilitySocial services District name Distance from homestead (km) Time taken to reach health facility (minutes)hospital Chitipa 4.7 71.2 Mchinji 2.7 53.1 Salima 3.5 75.1

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Mangochi 7.45 104.5 Phalombe 4.8 85.7 Total 4.8 78.3health centre Chitipa 2.78 43.4 Mchinji 3.2 49.7 Salima 8.1 96.3 Mangochi 7.3 104.4 Phalombe 4.3 81.9 Total 5.2 77.3Private clinic Chitipa 1.3 58.8 Mchinji 1.6 55.0 Salima 3.0 73.8 Mangochi 2.7 54.6 Phalombe 1.6 54.4 Total 2.2 60.4traditional healers Chitipa 4.2 46.7Mchinji 0.8 45.0Salima 11.0 90.0Mangochi 4.8 67.4Phalombe 4.0 78.8Total 4.3 59.6

In terms of services, the hospital services are generally rated above average by 57 percent of the households in our sample (Annex 3). However, the majority of households in Phalombe (48%) rated the services offered in hospital as average. The services offered in health centers are rated above average except in Salima where the majority of households (47%) rated the services offered in health centers as average. Private clinics and traditional healers are not that popular across all the sampled districts.3.2.1.3 Access to safe waterIn terms of access to safe water, Table 10 below shows that more than 60 percent of the households rely on boreholes as their main sources of water supply across all districts. Results further show that households in Phalombe have the highest percentage of households with access to safe water from the tap (41%) followed by Chitipa (10%). Mchinji has the highest proportion of households (54%) that rely on water from wells (mostly unprotected wells) and 84 percent of the households in Mangochi depend on boreholes.Table 10: Access to safe water

DistrictWater points Total Sample (N)Borehole(%) tap water(%) River(%) Well(%) (%) Chitipa 60.7 10.3 19.6 9.3 100.0 107 Mchinji 43.6 .0 2.1 54.3 100.0 94 Salima 84.4 1.0 .0 14.6 100.0 96 Mangochi 57.9 2.6 29.8 9.6 100.0 114 Phalombe 56.7 41.0 .7 1.5 100.0 134Total 60.4 12.8 10.6 16.1 100.0 545

Interestingly, households in all the districts travel a distance of less than half a kilometer to the water collection points except households in Chitipa who cover nearly a kilometer to the water source (see Table 11). Most households in 17

Mangochi who draw their water from taps do so from water kiosks located more than a kilometer away and spend more than 40 minutes to reach the collection point. However, the number of households who rely on tap water is very small. In Chitipa, some households draw their water from rivers located more than 1.5 km away from their homesteads. These households take more than 30 minutes to the river. Those that draw their water from wells spend less than 20 minutes (round trip) to the wells that are located between 20 and 70 meters from their homesteads.In terms of water quality, the majority of households who rely on boreholes (55%) rated the quality of water as being average while 40 percent rated the boreholes as having above average quality. As expected, tap water is considered to be above average by 61 percent of the households. However, the problem is that sometimes the taps run dry and the water pressure is often low especially during peak periods (morning, noon and evening). More than 40 percent of the households that draw their water from rivers consider the quality of the water as below average while 44 percent that draw their water from wells consider the water quality as being average (see Annex4). In general, the quality of water from unprotected wells and boreholes exposes the community to water-related diseases such as dysentery, diarrhea and cholera.

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Table 11: Distance and time to access water points

Water source District Distance from homestead (km) Time taken to reach social amenity (minutes)Borehole Chitipa 0.74 (1.98) 17.6 (22.4) Mchinji 0.36 (0.31) 7.21 (8.20) Salima 0.50 (0.36) 14.4 (16.3) Mangochi 0.47 (0.68) 14.6 (30.2) Phalombe 0.42 (0.31) 9.99 (8.5) Total 0.50 (0.96) 13.13 (19.4)Tap water Chitipa 0.84 (0.68) 31.8 (27.2) Salima 0.10 (1.93) 5.0 (10.7) Mangochi 1.93 (2.66) 40.67 (9.01) Phalombe 0.37 (0.31) 11.0 (9.75) Total 0.51 (0.69) 14.19 (15.68)river Chitipa 1.62 (1.32) 33.81 (22.74) Mchinji 0.50 (0.00) 4.00 (1.41) Mangochi 0.53 (0.93) 17.15 (16.55) Phalombe 0.10 (0.0) 5.0 (0.0) Total 0.92 (1.18) 22.61 (20.59)well Chitipa 0.69 (0.58) 17.0 (14.0) Mchinji 0.22 (0.21) 4.88 (4.27) Salima 0.22 (0.25) 6.42 (7.75) Mangochi 0.22 (0.13) 12.0 (15.5) Phalombe 0.20 (0.14) 6.5 (4.95)0 Total 0.27 (0.31) 7.46 (9.24)3.2.2 Demand for Social ServicesBesides access to social services, the study also investigated the households’ demand for services and programs that could improve their livelihood. More than 70 percent of households had received a number of services from different services providers. Table 12 below summarizes beneficiaries of different programs across all districts.Table 12: Services or programs provided to the communities

Type of supportDistrict Total(%)Chitipa(%) Mchinji(%) Salima(%) Mangochi(%) Phalombe(%)Cash transfer 8.2 1.2 3.3 2.6 0.9 3.2Inputs subsidy 69.1 73.5 67.0 61.0 68.4 68.0Credit facility   1.2 1.1 1.3 0.9 0.9Food relief 3.1 1.2 7.7 6.5 11.1 6.2Other inc' medical 19.6 22.9 20.9 28.6 18.8 21.7Total (%) 100 100 100 100 100 100Sample (N) 15 316 4 29 101 465

The most popular intervention implemented across all districts is the agricultural inputs subsidy program. Other interventions include cash transfer programs especially in Chitipa, food relief in Phalombe and Salima and health services across all districts. The study sought to establish interventions which rural communities consider as critical for improving their livelihoods. Results are summarized in Table 13 below

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Table 13: Activities to improve livelihood District Total(%) Sample (N)Activity

Chitipa(%)Mchinji(%)

Salima(%)Mangochi(%)

Phalombe(%) Cash crop production 42.7 46.1 44.6 32.6 50.4 43.3 280Business 40.3 35.9 37.7 38.8 31.1 36.7 237Employment 2.4 1.6 1.5 4.7 4.4 2.9 19 Livestock rearing 4.0 .8 4.6 4.7 3.0 3.4 22 Food production 8.9 14.1 10.0 14.7 8.1 11.1 72Others (loan, remittances, education, ganyu) 1.6 1.6 0.8 4.7 3.0 1.9 20Total 100.0 100.0 100.0 100.0 100.0 100.0 646Across all districts, Table 13 indicates that the growing of cash crops and business ventures are the two most important interventions that would help to boost rural incomes and thereby lead to the improvement of their welfare. In order to economically empower the communities to attain better living standards, the study investigated the services demanded by the communities. The interventions demanded by the local communities are summarized in Table 14 below.Table 14: Demand for services by communities

List of activitiesDistrict Total(N)

Sample(N)Chitipa(%)

Mchinji(%)Salima(%)

Mangochi(%)Phalombe(%) Reduced inputs prices 39.9 39.2 35.6 38.7 34.7 37.6 320 Increase output prices 11.7 5.5 12.6 7.4 10.0 9.4 80 Job creation 4.9 2.8 1.1 4.3 4.1 3.4 29 Skills development 9.8 6.1 5.7 4.3 10.0 7.2 61 Provision of formal credit opportunities 31.9 43.1 40.8 43.6 38.2 39.6 337

Other (food assistance and farm input coupons) 1.8 3.3 4.0 1.8 3.0 2.8 24Total 100.0 100.0 100.0 100.0 100.0 100.0 851As shown in Table 14 above, between 35 and 40 percent of the households consider reduced input prices and provision of credit facilities as key for improving their well being. It is interesting to note that the provision of farm input coupons and food assistance are not that important. This shows that rural households are aware that provision of free food and inputs are not long term solutions to their long-term socio-economic development. SummaryThe foregoing analysis on access to the various social services shows that in terms of education, most children have access to education facilities in all the districts. In general, all education facilities are located between 1 and 1.5 km from homesteads except for secondary education facilities that are located more

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than 1.5 km from the homesteads. On health services, the study finds that households take 1 hour to reach a health facility located at about 4.5 km from homesteads. However, mixed ratings were reported for quality of the health services. With regard to water, much as households have multiple water sources, 60 percent of the responses indicated that households rely on borehole as their main source of water, and that most households travel less than half a kilometer to access water collection points. Natural rivers still act as a reliable source of water for a good proportion of households (40 %). Essentially the analysis of access to social services show positive picture for most in rural households in Malawi though the quality of such services remains unsatisfactory to most households. Needless to say, this calls for measures to improve the quality of most essential social services to the rural populace. Analysis of demand for socio-economic services, household responses shows that provision of formal credit opportunities (40%) and reduction in agricultural input prices (38%) are critical services required in boosting incomes of rural communities. Such services are essential for improved crop productivity and participation in business opportunities hence have the potential to economically empower rural communities thereby reduce their vulnerability to effects of economic shocks. Worth noting is the fact that the study results show that rural households appreciate the fact that hand-outs and other related safety nets, are not the reliable mechanisms for improvement of their livelihoods especially in the long term. 4.0 IMPACT OF GLOBAL FINANCIAL CRISIS ON INPUT AND OUTPUT PRICESThe impact of the global financial crisis on different economies, including Malawi, has been the focus of most recent studies. Studies by the Economic Justice Network (2009) and Briancon and Lightfoot (2009) on the impact of the global economic crisis on Malawi found that the crisis has had both macroeconomic and sectoral effects. This is reflected in the economic growth slowdown from 9.7 percent in 2008 to a projected growth of 6.2 in 2009, the reduction in donor inflows, foreign exchange pressures and the decline in export prices for different export commodities such as tobacco, coffee and cotton (Economic Justice Network, 2009 and Briancon and Lightfoot, 2009). Since the focus of this study has been on the impact of the global financial crisis on vulnerable households in the rural Malawi, the following section analyzed changes in agriculture input and commodity prices.4.1 Impact on Agricultural Input prices The study investigated how the global financial crisis has affected prices of inputs, commodities and how this affected rural livelihoods in all the five districts. In terms of farm inputs (fertilizers, seed and chemicals), we compared household responses about changes in general input prices between 2008 and 2009 as reported by nearly 98 percent of the households in our sample. Results are presented in Table 15 below. Table 15: Comparison of observed changes in farm input prices between 2008 and 2009 Observed changes in farm input prices between 2008 and 2009 Total(%)District Increase Reduced Remained the same Don't know

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d(%) (%) (%) (%) Chitipa 17.4 80.2 2.3 0 100.0 86 Mchinji 12.6 70.1 0 17.2 100.0 87 Salima 6.8 68.2 1.1 23.9 100.0 88 Mangochi 12.5 55.7 2.3 29.5 100.0 88 Phalombe 11.0 82.4 2.2 4.4 100.0 91Average 12.06 71.32 1.58 15.0 100.0 440In general, the majority of farmers (71%) reported that input prices have significantly reduced in 2009 compared to 2008. The prime reason for the reduction reported by the majority of respondents (69%) is government interventions in the input prices through the subsidy program. In addition, the effects of global economic slowdown, resulting in the general price reduction of imported inputs such as fertilizer (See Annex 5). In terms of availability of farm inputs on the market, results in Table 16 shows that compared to 2008, most farm inputs (seed, fertilizer and chemicals) were relatively available in most markets. In recent times, there has been an upsurge of private sector participation in both input and output markets that make chemical fertilizers to be available even in rural areas. For example, results in Table 16 show that more than 40 percent of the households in our sample indicated an increase in the supply of chemical fertilizers across the study sites even in remote district of Chitipa where 67 percent of the respondents indicate an increase in the availability fertilizers.Table 16: Comparison of fertilizer supply on local markets between 2008 and 2009District Compared to 2008, how has been the supply of fertilizer on the market? Total(%)

Sample (N) Increased(%) Decreased(%) No change(%) Don’t know(%) Chitipa 67.4 24.4 4.7 3.5 100.0 86 Mchinji 41.4 25.3 11.5 21.8 100.0 87 Salima 30.7 20.5 15.9 33.0 100.0 88 Mangochi 28.4 19.3 9.1 43.2 100.0 88 Phalombe 42.9 26.4 14.3 16.5 100.0 91Average 42.16 23.18 11.1 23.6 100.0 4404.2 Impact on commodity pricesThe study further investigated whether commodity prices were affected by the global recession especially how this has impacted on the vulnerable communities in rural areas. Since maize is the major staple food crop in Malawi, the study further investigated the dynamics in the industry in 2008 and 2009. Table 17 below has the analysis results. Table 17: Changes in price of maize between 2008 and 2009Responses on maize price changes Total Sample

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District (%) (N)Increased(%) Decreased(%) No change(%) Chitipa 21.8 69.0 9.2 100.0 87 Mchinji 11.2 86.5 2.2 100.0 89 Salima 4.5 94.3 1.1 100.0 88 Mangochi 17.6 73.6 8.8 100.0 91 Phalombe 5.5 90.1 4.4 100.0 91Total 12.1 82.7 5.2 100.0 446

Table 17 shows that maize prices have generally been lower in 2009 compared to 2008. More than 80 percent of the respondents reported that prices of maize have drastically reduced over this period. In fact, across the districts our results show price offered in 2009 were lower than those for 2008 (see Annex 6). For instance, the average maize price in October 2009 was MK51.40 per kg compared to MK44.00 per kg in October 2008, representing a 14 percent decline (see Annex 7). To confirm the responses from the households, the study analyzed the price trends for 2007-2009 as reported the Ministry of Agriculture market information system (see Figure 2).

Figure 2: Average Maize Price Trends 2007-09.(source: Minisry of Agriculture and Food Security)According to Figure 2, a comparison average national maize prices for October 2008 and October 2009 shows a 27 percent decline in maize prices from MK54.30 to MK 39.90 per kg. These results confirm the analysis results from the data collected in this study. The decline in maize prices can be explained by increased production of maize due to the agriculture inputs subsidy program and conducive weather conditions. In addition, according to Von Braun (2008) and Briancon and Lightfoot (2009), this could also be attributed to the global downward pressure on prices of agricultural products as a result of the collapse of financial markets. However, the actual impact of the global downfall of commodity prices on Malawian maize and other agricultural commodities can be best established through a price transmission analysis, which was not the scope of this study.

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This notwithstanding, the study sought the views of the rural households on the key factors that led to the decline in maize prices. More than 50 percent of the households attributed the decline to increased maize production as farmers are now growing hybrid seed and apply chemical fertilizers through the government-supported subsidy program. Other reasons reported by households include absence of output markets for maize (11.6%), availability of maize stocks in the households (8.2%) and high maize supply on the market (6.5%). These reasons were also highlighted during the several focus group discussions we had with the rural communities. As summarized in Box 1, the communities attributed the low prices to the fact that the private sector in the country does not have financial resources to purchase maize from them at reasonable prices and hence they are selling at very low prices. During focus group discussions with the communities in Chitipa, they clearly attributed the decline in prices of maize and other commodities to the fact that ADMARC did not open their markets to buy the commodities from farmers, and where that was done; the ADMARC depots did not have adequate financial resources. In any case, as argued by EJN (2009) this situation translates into loss of incomes for the rural communities. It was further reported that in 2008/09 season, most households had devoted their land to tobacco and other cash crops production at the expense of maize as a response to higher prices that was in that year. This was particularly experienced in Salima where most farmers grew cotton in anticipation of higher revenue from cotton sales with which to purchase maize. Unfortunately, the expected higher prices for these commodities were not realized in 2009. They argued that collapse in cotton prices forced them to sell their maize and other produce at lower prices to compensate for the cotton income losses. The commodity price variations observed in this study have significant implications for net producers and consumer of commodities. Since seasonal maize price variations come against the background of high maize production levels the country has been registering for the past few years, this situation reveals inefficiencies in marketing systems that lead to loss of income among those producing for the market in 2009 if compared with 2008 prices. However, from a welfare perspective, these results suggest that the livelihoods of local communities would have worsened if maize prices were very high. In other words, net consumers would have suffered a budgetary squeeze in the face of high maize prices that could have forced the local communities to change their consumption behavior or sale of household assets to cope with the high maize prices.

Box 1: Commodity Prices and Livelihood: The case of Nabwenje Village, Mchinji district.For most community members in Nabwenje village in Mchinji district, they rate 2008 as one of their best years because prices for every agricultural commodity were good. This year (2009), agriculture commodity prices have been going down and yet the other 24

commodities sold on the market such as soap, sugar, amongst others, remain expensive. Some members of the village said that they understand that the world is going through some financial hardships but somehow they feel discouraged by their own government. They argued that if Government had done something, agricultural commodity prices wouldn’t have gone this low.One community member argued that she thinks that this year (2009) the Government was so busy with campaigning for elections that they forgot to negotiate better prices for the buyers. She said that “mavutowa adza kamba koti macompany abwino oti atigule zokolola pamtengo okwera sanapezeke. Iwo anali otangwanidwa ndi zisankho zapitazi.” (These problems we are having now have come about because of government failure to encourage private companies to buy farmers’ produce. The government was busy with political campaigns for the just ended elections instead of looking for markets). This failure to secure reliable buyers meant that farmers were at the mercy of vendors who dictated prices offered to farmers. Another respondent echoes her friends’ sentiments saying that “timangogulitsabe popeza tavutika ndife kuti tipeze ndalama, koma kuyankhula mwachilungamo ife tabeledwa ndipo palibenso chifukwa cholimbikira kulima poti sitiwonapo cholowa.’ (We still sell as we are the ones looking for money, but quite honestly, there is no need for working hard in the field as buyers are stealing from us and we are not making profits). This shows that most farmers in 2009 had no means to sustain their livelihoods. The other problem is that most farmers took loans with the hope that they will pay back when they sell their harvests. This has increased their problems because most of them have failed to pay back; some have paid with the little they had made. This has prompted them to even sell what they were keeping for their own consumption with the hope of lessening the loan that they took in 2008.For some respondents they are not sure as to why they are getting lesser than what they got in 2008. They neither blame government, the private companies that buy their produce nor the vendors. All they know is that this year (2009) there is no profit from farming. One community member said “mitengo yatsika chifukwa chovuta ndi kusowa kwa ndalamayi.’ (Prices are low because there is no money). They are feeling the same effects their fellow community members are feeling and wishes if things could go back to the way they were in 2008.SummaryThis analysis on the impacts of the global financial crisis on vulnerable households in the rural Malawi has focused on key economic factors such as agriculture input and commodity output prices. The analysis results show that the decline in inputs prices in 2009, compared to 2008, as accessed by the rural communities can be attributed to both the continued government interventions in the input markets as well as effects of the global financial crisis. On commodity output prices, lower output prices were reported in 2009 compared to 2008 due to increased production levels coupled with the global downward pressure on prices of agricultural products as a result of the collapse of financial markets and hence economic slowdown. Inter and intra-seasonal price variations have been noted across the districts in 2008 and 2009 in this study. The seasonal price variations reveal inefficiencies in marketing systems characterized by inadequate commodity flow between market centers within the country. It also implies income differentials between those who are net producers of maize, who benefited from the rise in the output prices, and the net consumers who lose incomes in such situations.

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5.0 IMPACT OF FINANCIAL CRISIS ON INCOMES, WAGES, REMITANNCES & GIFTS 5.1 Impact on Household Incomes5.1.1 Changes in household economic activities and incomesHouseholds were requested to state the major annual economic activities they have been involved from 2007 to 2009 and rank each activity in terms of its importance to the household livelihood. Three ranking options were provided namely, first most important, second most important and third most important. Results in Figure 3 below shows that farming has been the major economic activity during the study period across all the five districts as evidenced by about 70percent of the responses. In addition, petty trading remains the second most important economic activity. With farming and petty trading consistently remaining the first and second most important economic activities of the sampled rural households over the past three years, this implies that the country’s rural economic structure remain unchanged despite the changes in the global economic conditions.

Figure 3: Ranking of important source of livelihood from 2007 to 2009.In order to establish the impact of the global crisis on household incomes in rural Malawi, a number of simple statistical tools were used. A comparison of income levels were conducted on the income levels earned by the households over the three years to assess differences in incomes earned between 2007 and 2008, between 2008 and 2009 and finally between 2007 and 2009.Comparative income analyses for 2007 and 2008 were undertaken to establish the impact of the global agricultural commodity price increases in 20083 just prior to the financial meltdown. On the other hand, a comparative analysis of 2008 and 2009 incomes was undertaken to examine the impact of economic crisis, characterized low agricultural commodity prices and consequently low incomes, on the sampled rural households. Finally, a comparative analysis of 2007 and 2009 was undertaken to ascertain the results from the other two 3 The global commodity prices were also noticeable in Malawian economy especially the agricultural sector and were quite beneficial to net producers.

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analyses. These analyses were undertaken at national levels as well as district levels.Analysis results at the national level are presented in Table 18. The district-level comparative analyses of the income differentials are presented in the Annex 8. Table 18: Comparison of household income between 2007, 2008 and 2009Income Obs Mean(MK) Changes in income differential (%)2008 income 425 61, 200.422007 income 425 45, 969.92 Income differential 425 15, 230.50 33.132009 income 449 50, 157.042008 Income 449 59, 094.61Income differential 449 -8,937.57 -15.122009 income 424 50, 699.322007 income 424 45, 653.81Income differential 424 5, 045.50 11.05Table 18 depicts comparative analyses of average annual incomes earned by sampled rural households between 2007 and 2009. Analysis of the 2007 and 2008 incomes shows that the average incomes earned by households in 2007 rose from MK 45969.92 to MK 61200 in 2008 reflecting an increase of 33 percent increase over the 2007 incomes. In fact, the income differential of MK 15230.50 is statistically significant at 1 percent. This means that rural households had benefited from the general increase in world commodity demand and prices. Further comparison of the 2008 and 2009 incomes show that incomes had declined by MK 8937.57 which reflects is a 15 percent decrease over the this period. The decline is significant at 1 percent. In fact, such a decline in household incomes is not surprising considering that the commodity price analysis established that over the same period, households faced a 10 percent output price decline. Further analysis of income earnings for the years 2007 and 2009 show that household income earned in 2009 is 11 percent higher than those for 2007. These results have significant implications on the extent to which households have been affected by the global financial crisis. In particular, the 2008/09 significant income declines imply that the economic gains obtained year before have been wiped out by the global economic crisis. However, the situation was different for the income differential between 2007 and 2009 of MK 5045.50 which was slightly higher than that for 2007 and statistically significant at 5 percent level. It is evident from the foregoing analysis results that if the 2008 economic crisis had not been preceded by some positive developments in 2007 that had led to increase in household incomes of the rural households, the situation would have been devastatingly worse for the country’s rural economy. Apparently results from this analysis together with those from a study by Briancon and Lightfoot (2009) show that output commodity prices have an important bearing on the economic welfare of the rural communities. To confirm such findings, we conducted a regression analysis on determinants of changes in household incomes for the 2008-09 period. Analysis results in Annex 4 confirm our earlier findings that changes in producer prices faced by rural households and incomes changes in the 2007-08 period are amongst the major factors that determined the observed changes in the 2008-09 incomes. This is evidenced by the fact that the analysis shows that a 1 percent increase in selling

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prices faced by the households leads to a 0.24 percent increase in household income in rural Malawi (see Annex 9). The study results on household incomes show that rural incomes in Malawi are to a larger extent integrated with the dynamics in the global economy, such that price changes in the world economy are likely to affect the welfare of rural communities in Malawi in one way or the other. Essentially, what this means is that market oriented public policies are critical for sustainable socio-economic empowerment of the rural populace. These results call for shift in policy focus away from a supply side policy orientation in favor of policy interventions that link farmers to reliable markets. 5.1.2 Changes in Household Income LevelsComparative analyses of average income earnings across the five districts over the past 3 years shows that all sampled districts registered significant increases in income between 2007 and 2008 (Annex 8). For instance, in Chitipa district, households had an income increase of more than 50 percent between 2007 and 2008 whereas for Mchinji district an income increase more than 60 percent and was also significant at 1 percent. In the case of Phalombe, households had a significant average income increase by 19 percent. Salima and Mangochi districts had positive income increases which were not significant. In section 3.1.5, it was established that the economic activities of households in Salima and Mangochi districts are well integrated to the market where petty trading preoccupy most household activities. As such, households in these two districts are vulnerable even to the slightest economic shocks. All the districts registered decreases in income levels for the 2008-09 period except that the income decreases in Chitipa and Phalombe districts were not that significant. Results for Mchinji district show that households had an average income decrease by 24 percent. Salima and Mangochi had both an average income decrease of 18 percent. The results for Salima and Mangochi districts show that households in these districts are more vulnerable to economic shocks than from the other districts. From the focus group discussions in both districts, this was attributed to the decline in effective demand and low prices for critical commodities such as cotton as in the case of Salima (where most households rely on cotton as a cash crop) and Mangochi where they rely on petty trading. Phalombe had an average decrease of MK 4135 representing an 8 percent decline. Households in Chitipa experienced a 12 percent decline in income. Households in Chitipa district rely on cross-border trading of agricultural commodities with neighboring countries especially Tanzania. As indicated during one focus group discussion, households sell some of their products in Tanzania at reasonable prices compared to the prices offered in Malawi on the same commodities. This means that households in Chitipa districts have not felt the economic crisis effects as has been the case with other four districts as the effects of the global crisis is mitigated by cross-border trade.A comparative analysis of average incomes earned in the years 2007 and 2009 show mixed patterns. In general, there were no statistical differences in incomes household earned between 2007 and 2009 across all districts except Chitipa that had the highest income increase by 34 percent in 2009 over 2007. Mchinji, Salima and Phalombe had a positive increase but not significant. In the case of Mangochi district, households earned more income in 2007 than in

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2009, a decline by 13 percent, although the income differential was not significant. Of all the districts, Chitipa district is the least affected by the global economic crisis since it registered significant income increases not only within the 2007-08 period but also when comparing the 2007 and 2009 incomes. The 2008-09 income differential was not significant. On the other hand, Mangochi district is the most negatively affected by the crisis with double significant income decreases for 2008-09 and 2007 and 2009. According to the focus group discussions with communities in Chitipa district, there is good market integration between the households in the district with the traders from the neighboring countries such as Tanzania and Zambia. In section 3.1.5, most households in Chitipa especially among female headed households (22%) are involved in petty trading. Cross-border trade helps these households to cope with the financial crisis. This confirms our earlier finding that market integration is important for improving the incomes and welfare of the rural households. 5.2 Recovery from Income ShocksBesides collecting quantitative data on the actual income values realized by households, the study also sought to documents the experiences of the respondents on their income changes in 2009 compared to the previous year. Analysis results in Table 19 show that 64 and 59 percent of male and female headed households respectively reported that their incomes had declined between 2008 and 2009. Increased income earnings were reported by 33 percent of both male and female headed households. At the district level, 47 percent of the male headed households in Chitipa district reported to have had increased incomes in 2009 compared to 2008. This is in sharp contrast with the proportion of respondents from Mchinji (21%) who reported to have had increased incomes over the period. These results agree with statistical test analyses which showed that Chitipa district was not as badly affected by global economic slowdown as was the case with other districts. However, according to results, Mchinji is the most affected of all the districts.Table 19: Household perceptions on changes in incomes, 2008 and 2009Gender Changes in household incomes between 2008 and 2009 Total (%)Male

Response Chitipa(%) Mchinji(%) Salima(%) Mangochi(%) Phalombe(%)Increased 47.4 21.0 26.3 30.0 39.2 32.7Decreased 51.3 76.5 72.4 60.0 57.0 63.5No change 1.3 2.5 1.3 10.0 3.8 3.8Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 78 81 76 80 79 394Female Increased 66.7 22.2 38.5 30.8 16.7 33.9Decreased 22.2 66.7 53.8 69.2 75.0 58.9No change 11.1 11.1 7.7 .0 8.3 7.1Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 9 9 13 13 12 56From Table 19, those households that reported to have had an increase in incomes, the major reasons given include: profit from produce sales, the high commodity prices, and high crop production levels, amongst others. The first two major reasons are actually similar in that the high producer or market

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prices naturally lead to increased profit margins for the sellers. Further, many households had reported to have had decrease in incomes than those with increase or remaining constant. Major reasons given for decrease in incomes are: inadequate proper produce markets (58%) and poor access to fertilizer (15%). Evidently, the foregoing major results on causes of decline in household incomes are market related. Having established that the majority of households had experienced a decline in their incomes over the past year, the study found out perceptions regarding time for recovery from the reduction in household incomes. Results are presented in Table 20.Table 20: Perception of recovery from income decline

Estimated time to recover from income shocksDistrict Total(%)Chitipa(%)

Mchinji(%)Salima(%)

Mangochi(%)Phalombe(%)1-3 months from now 7.1 .0 3.2 1.8 1.9 2.53-6 months from now 19.0 8.8 4.8 7.0 7.4 8.86-12 months from now 21.4 57.4 50.0 38.6 55.6 46.3>12 months from now 33.3 22.1 27.4 22.8 22.2 25.1Never 16.7 5.9 4.8 21.1 13.0 11.7n/a 2.4 5.9 9.7 8.8 .0 5.7Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 42 68 62 57 54 283

According to Table 20, more than 45 percent of the households who experienced income decline indicated that they expect to recover from some unfortunate scenario in 6-12 months time. About 25 percent of the households indicated that economic recovery would take more than 12 months, whereas 12 percent reported that they may never recover at all.Much as the study did not collect data that could be used to ascertain the recovery time frames by the households, the fact that the majority of households indicated that full recovery take 6-12 months or even more suggests severity of the impact of global economic crisis on the livelihoods of rural households in Malawi. With some households indicating that their economic recovery may take more than 12 months, this means economic activities of one agricultural season may not be adequate for full recovery. However, if deliberate public supply side and demand side interventions were put in place to assist in the economic recovery of the rural households, the stated recovery periods could be reduced. Box 2: Tobacco prices and household income: Case of Chimenya village, Phalombe district.Households in Chimenya village engage in a number of economic activities for their livelihoods. These activities include production and sale of agricultural commodities such as tobacco, livestock, and participating in piece works (ganyu). During discussions with the community members in the village, it was learnt that production and sale of tobacco was considered the major income source for the people of the village.Apparently, community members in the village are amongst those who have stories about the extent of the decline in tobacco and its implications on their welfare. They could clearly compare the average tobacco prices from the 2007/08 season with those of 2009. When asked to substantiate their claims, they indicated that during the 30

2007/08 growing season, most farmers sold their tobacco at price ranges of $1.86 to $2.40 per kg at the auction floors. During the 2008/09 season, tobacco prices had plunged to as low as $0.64 to $0.78 per kg. This is almost half the prices they sold in 2008. When asked whether they know why tobacco prices have plunged like this, they indicated that they do not know the reason. However, they noted that private traders came from faraway places and impose prices on them without negotiations. The decline in tobacco and other commodity prices has had a wide range of implications for members of Chimenya village. They indicated that due to low incomes from price decline, they have not been able to purchase new household assets, nor could afford to enjoy dietary diversification as was the case in 2008. These things are now considered as luxuries in the face of income declines. Interestingly, a direct impact of the decline in tobacco on food security has established. They complained of cases of food insecurity owing to reduction in tobacco prices. They explained that due to the failure of the households to access enough money from tobacco sales, they had resorted to selling the little maize they produced as their food crop. The maize sales were meant to generate income to compensate for the low tobacco incomes. As expected, this has led to food shortages by most households in their community which they believe will not suffice them throughout the next harvest season. They believe that cases of food insecurity will be high during the months of January to March 2010.When further questioned on how they think they are surviving, responses given pointed at sale of assets like bicycles, radios and chickens as an option. One community member summed it as follows: “Timatogulitsako kaya ndi ka nkhuku, kaya kambuzi kuti tipezepo tindalama togulira chakudya pakhomo, nthawi zina timatosinthanitsa ndi kaufa” (we sell our chickens and goats to gain money to access food or even exchange these with maize flour) said one of the respondents.5.3 Impact on unskilled wage rates Since rural wages constitute an importance source of rural livelihoods in Malawi, the study sought to establish whether there have been any changes in wage rates, and if so, the implications of such changes. We focused on unskilled wage labor because this is a reflection of the labor situation for majority of rural populace in rural Malawi.5.3.1 Inter-temporal dynamics in the wage rates for unskilled labourThe analysis on rural labor wage rates is undertaken to capture effects of the global economic crisis on labor dynamics in rural Malawi. Results of such an analysis are presented in Annex 10. From the Annex, results show that the mean monthly wage rate for the whole sample in 2008 was MK 1,723.20 and increased to MK 1,959.00 in 2009. The change in wage rates of MK 236.40 represents a 14 percent increase. Similar trend is observed across all the districts with the monthly wage rates increased between 9 to 16 percent. Even if after taking into account inflation rate of about 7.5 percent, these results still mean that rural communities were in 2009 earning more than in 2008. This translates to improved livelihoods among workers although the increase in Mchinji is the lowest (8%) compared to the rest of the districts. Having established that significant increase in wage rates have been registered between 2008 and 2009 in rural Malawi, we further examine the reasons for such a trend. Results on the reasons are presented in Annex 11. Results show that the major types of unskilled work in the rural Malawi include molding bricks and on-farm ganyu activities. Of the two, on-farm ganyu registered more

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responses than the other. Reasons given for changes in wage rates include: goodwill of the one providing the ganyu employment opportunities, increase in price of commodities on the market (i.e. response to inflation pressures), scarcity of the ganyu labor on the market, if the type of work is labor demanding, yearly increase for those that regularly undertake such work, lack of money especially on the part of laborers hence demanding more pay, amongst others.Of all the reasons given for the reported increase in unskilled labor wage rate, increase in price of commodities on the market in response to inflation pressures and lack of money especially on the part of laborers hence demanding more pay, were reported to be first and second reasons respectively. This was the case for both molding bricks and on farm ganyu activities. In any case, the reported increase in wage rates is supply side driven in a direct response to the decline in other economic opportunities, in which the rural masses could have been involved in. This, therefore, raises questions of how and why the demand side has been able to accommodate such changes in the wage rates considering that those who engage laborers have also been negatively affected by the same economic crisis. Since this analysis has shown that unskilled wage rate has significantly increased between 2008 and 2009, one may assume that this has acted as a cushion for the decline in household incomes from main livelihood sources such as farming and petty trading. This is the case for those who participate in ganyu labor. Realizing that our earlier analysis shows that only 4 - 5 percent of the rural households depend on ganyu as a source of livelihood, increases in unskilled labor market is not likely to compensate for the decrease in incomes from the major sources.5.4 Impact on Remittances Flow of remittances and gifts in the rural economy was one of the major issues investigated in this study. The analysis on remittances focused on whether a household had a member outside the country or the district, amounts and frequency of money being remitted to the households, channel of remittance, whether there have been changes to the amounts of remittance flows and why, and household response to the changes in remittance flows.5.4.1 External RemittancesEstablishing whether or not the household has a member living outside the country was the starting point of the study on external remittance. Findings on the responses to the question are in Table 21.Table 21: Whether the Household has a Member living outside the CountryDo you have a household member living outside Malawi?

ResponseDistrict

TotalChitipa Mchinji Salima Mangochi PhalombeYes Count 26 28 5 45 9 113% 29.9 31.1 5.6 48.4 9.9 25.1No Count 61 62 84 48 82 337% 70.1 68.9 94.4 51.6 90.1 74.932

Total Count 87 90 89 93 91 450Table 21 shows that 25 percent of the sampled households indicated to have a member living outside the country. Of these, Mangochi district has the highest proportion of households living outside the country (49%) followed by Mchinji (31%) and Chitipa (30%). Linking outside country migration trends to income levels presents a rather mixed picture. Although the study established that Mangochi district is one of the districts with lower income levels and Chitipa as one of the districts with high income levels, outside migration does not substantially enhance household income. Much as it would have been insightful to know what exactly these households are doing outside the country, this information was difficult to collect from the sampled households. However, what the study managed to collect and analyze was the data on whether or not the migrants assist the households back home and amount of money being remitted. From the results, only 22 percent of the households get support from the migrated members. Mangochi district has the highest proportion of households with migrated members supporting the relations back home. In terms of frequency of receiving remittances, on average, households receive remittances three (3) times a year except in Mchinji where households receive remittances 12 times a year (see Table 22). The amount of money received ranged from MK 1,500 to MK50, 000 with a mean of MK 16,500. Table 22: Amount and frequency of external remittances

District Amount received (MK) Frequency of receipt per yearChitipa Mean 15000.00 3.43 Std. Deviation 9354.14 3.36Mchinji Mean 3000.00 12.00 Std. Deviation 1250.98 .0.67Mangochi Mean 16000.00 2.36 Std. Deviation 14392.23 1.99Phalombe Mean 30000.00 1.50 Std. Deviation 28284.27 0.80Total Mean 16500.00 3.00 Std. Deviation 14490.47 3.04Analysis of disbursement channels of remittances seeks to establish the most commonly used mechanisms for the flow of the remittances in the rural Malawi. To this effect, households were asked to state how they got the money as presented in Figure 4.

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Figure 4: Channels for sending remittances

Figure 4 above shows that most remittances reach rural Malawi households through three channels. These include returning residents, commercial banks and electronic money transfer. The most popular means of sending money in Chitipa, Mchinji and Mangochi is through returning residents. Box 3: Case Study on Remittances in Lufita, Chitipa DistrictA female group from Lufita village revealed that there are about 8 households in the village who have members working and living outside Malawi. The distribution was as follows: four are in Zambia, three in South Africa, and one in Japan. The community members are aware that households in the village with relations living outside Malawi do receive some financial assistance from those relatives. They know this because those who receive the money usually share the information to the close relations within the village. Bank transfer (using the banks available at Chitipa Boma) was major channel used, much as other sources such as sending friends who are visiting home from the concerned countries and bring the money themselves when they come during holiday were also indicated. Besides money, clothes and shoes were mentioned as other items being remitted from outside the country into the village. While actual amount of money received is not made public, it was rumored that a son working in Zambia sent his mother about MK 200,000.00 in 2008 for building a house (the exact month could not be recalled). In most cases, external remittances are used for household consumption and purchase of farm inputs. So far, there have been no remittances for village development projects such as schools, clinics, roads and boreholes. It was also learnt that much as the remittances are meant for parents of siblings of the households, in some cases, they end up being shared amongst the close relations. A good case in point is about this lady who is in Japan - who sent money to her parents who reside in Mzuzu City for them to enjoy the 2008 Christmas. The parents in Mzuzu decided to share some of the remittances to one of the aunts at home in Lufita village, Chitipa also for her to partake in enjoying Christmas. Apparently, it was indicated that there is a decline in the remittances both in terms of frequency as well as the actual amounts being sent in 2009 compared to 2008. However, they did not know exactly why this is the case. Whatever the case, this decline in the remittances to the village has negative effects on the livelihoods of the concerned households especially as they also face lower producer prices for their commodities.

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For those households benefiting from external remittances, the study sought to establish if they have experienced any change in the amounts and frequency of remittances. Analysis details are in Table 23.Table 23: Changes in Flow of External Remittances

Description of changeDistrict

Total(%)Chitipa(%) Mchinji(%) Mangochi(%)Phalombe(%)Increased 28.6 100.0 40.0 50.0 40.0Reduced 57.1 .0 60.0 50.0 56.0Remained the same 14.3 .0 .0 .0 4.0

According to Table 23, 56 percent households who receive remittances had a decrease in the flow of the remittances. Households could not though associate the decline in the flow of remittances to the global financial crisis. However, more than 40 percent of the beneficiary respondents indicated to have had increased remittances. From the focus group discussions, beneficiary households indicated that the increase in remittances implies that their relatives do appreciate the need to assist their families despite the global economic crisis.Realizing that some households do depend on the external remittances, the study sought to assess the effects of changes in financial flows on their livelihoods. In response, some household indicated to have had reduced access to the basic necessities, while others said there was no real change on their livelihoods- especially those that receive very little amounts of external remittances.The study also sought to establish how remittance-recipient households cope up with the reductions in the flow of external remittances. Focus was on the households who have had a decrease in the external remittances. Analysis details are in Table 24.Table 24: Household response to the decreased external remittances

Household response to the changeDistrict Total(%)Chitipa(%) Mangochi(%) Phalombe(%)Hunger (food insecurity) 25.0 14.3 .0 16.7reduce amount of fertilizer 25.0 14.3 .0 16.7no change 25.0 .0 .0 8.3find other alternatives .0 71.4 100.0 50.0Not sure 25.0 .0 .0 8.3Total 100.0 100.0 100.0 100.0

According to Table 24, of the few household who have experienced declines in external remittances, 50 percent of them have resorted to finding domestic alternative income sources to the reduction in flow of the external resources. About 17 percent of household affected by the decline in external remittances indicated that the reduction has resulted in household food insecurity as well as the reduction in the fertilizer use in their gardens. With such negative implications of the reduction in the external remittances on the rural households, it can be safely concluded that if Malawi’s rural economy were heavily integrated with the global economy where the flow of remittances could have had negative implications on rural livelihoods.35

5.4.2 Internal remittancesSince there are a lot of similarities between external and internal remittances, analysis of internal remittances has largely focused on issues such as, the amount and frequency of receiving internal remittances, whether there have been changes in frequency and amounts received, types of flow changes encountered, and reason for decline in remittances. The study sought to establish if households who have household members living and working outside the district received some remittances in 2008. Results are summarized in Table 25 below.Table 25: Internal remittances Did you receive internal remittances in 2008

ResponsesDistrict

TotalChitipa Mchinji Salima Mangochi PhalombeYes Count 9 1 0 14 2 26% 34.6 3.8 .0 53.8 7.7 100.0No Count 0 2 0 1 1 4% .0 50.0 .0 25.0 25.0 100.0N/A Count 78 87 89 78 88 42018.6 20.7 21.2 18.6 21.0 100.0Total Count 87 90 89 93 91 450

According to the Table 25 above, only 30 households were able to provide data on frequency and amounts of internal remittances from household members working and living outside their district. However, the study was not able to establish why this is so, nor to which districts the migrants from the five districts were working.Of the 30 households with members working outside the district, 26 (or 87%) of them do receive the remittances from their relatives. The analysis results in Table 32 show that Mangochi district has the highest proportion of households (54%) receiving internal remittances from relatives living and working outside the district, followed by Chitipa district (35%). This corresponds with our findings on external remittances which show that Mangochi district also has the highest proportion of households living outside the country who send remittances back home. Much as the study was not able to establish the reasons for the differences in flow of internal and external remittances across the districts, we can assume that this is related to levels of income sources and levels in the districts from which migrants come. Essentially, this implies that districts such as Mangochi, with low income levels, provide propensity for relations outside the districts or country to send remittances home.The study collected information about the amount and frequency of receiving remittances by those with a relation leaving outside their district of residence. Annex 12 resents the results of the analysis. From Annex 12, the average amount of remittances range between MK2250 to MK19, 000. Compared to external remittances, the average amount of internal remittances is half the average for external remittances. The average amount of internal remittances is MK8598 compared to MK16500 for external remittances. On average, households receive remittances once to twice a year. With respect to changes in flow of internal remittances, three major changes has been established, namely increase, decrease and constant levels as shown in Figure 5.

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Figure 5: Description of change in the flow of internal remittancesAccording to the analysis, 38 percent of total sample had experienced changes in flow of internal remittances. This is higher than 25 percent of the households who indicated to have had an external migrant. Of the households with internal migrants, most of them (53%) had experienced reduced internal remittance flows over 2008 which compares well with 56 percent of the households in the case of external remittances. Nearly 33 percent of the internal remittance beneficiaries indicated to have had increased flow of remittances, compared to 14 percent of the households who did not experience any change in their internal remittance flows.Having established the different patterns of internal remittance flows, the study further sought to examine the reasons behind the observed trends. Particular focus on whether the recipient households know the reasons for the decline in flow of internal remittances from their relatives. The various reasons given by the respondents are summarized in Figure 6.

Figure 6: Reasons for decline in internal remittances37

According to Figure 6, responses indicating that the recipient households do not know the reasons across the districts- ranging from 30 percent in Chitipa to 59 percent in Mchinji. Interestingly, some households indicated to know that their relations did not have resources to send them, and responses to that effect ranged from 26 to 41 percent. The other reasons include: commitments of the migrants in terms of building houses; that the recipient households had more money and improved living standards. As such, internal remittances are not needed much.5.4.3 Contribution of remittances to household incomeWe further analyzed the contribution of remittances to household income. Results of the analysis are presented in Table 26 below.Table 26: Share of remittances in total incomes in 2008Share of remittances in total incomeDistrict Gender

Total income (MK)External remittances (%)

Internal remittances (%)Total remittances (%)Chitipa Male 107,154.21 0.9 1.9 2.7  Female 56,077.78 4.2 7.6 11.9  Total 101,870.44 1.2 2.5 3.7Mchinji Male 56,459.88 0.2 3.8 3.9  Female 59,294.44 - 3.5 3.5  Total 56,743.33 0.1 3.7 3.9Salima Male 38,780.53 - 1.7 1.7  Female 93,000.00 - 6.7 6.7  Total 46,700.22 - 2.5 2.5Mangochi Male 44,984.03 3.3 1.1 4.4  Female 103,896.15 1.2 8.3 9.5  Total 53,308.57 3.0 2.1 5.1Phalombe Male 59,378.48 2.0 2.5 4.5  Female 39,075.00 - - -  Total 56,701.10 1.7 2.2 3.9

Full sampleMale (MK) 61,382.28 1.3 2.2 3.5Female (MK) 72,623.21 0.9 5.3 6.2

Table 26 above shows that internal and external remittances together account for only 3.5 percent of the total household income. Further results show that internal remittances contribute 2.6 percent of the total household income compared to only 1.2 percent for external remittances. This shows that most migrants do not remit back home significant amounts of money. Across the districts, results indicate that remittances contribute about 5 percent to total household income in Mangochi which is the largest share among the five districts. Results also indicate that female-headed households receive more internal remittances than male-headed households where remittances accounts for 6.2 percent to total household income compared to only 3.5 percent among male-headed households. An interesting analysis would have been to establish how the recipients use the remittances to assess whatever these remittances are used for development or social purposes.38

5.4.3 Impact on GiftsThe study investigated the types of gifts received, sources of gifts, and whether there have been changes in the number of households receiving gifts between 2008 and 2009. Table 27 below summarizes the types of gifts that households received between 2008 and 2009.Table 27: Types of gifts given in years 2008 and 2009

District YearType of gift

Total(%)Sample(N)

Cash(%) Food(%)Clothing(%)

Other inc' cell phone, bicycle (%) Soap(%)Chitipa 2008 29.0 32.3 27.4 8.1 3.2 100.0 62 2009 27.3 34.1 29.5 9.1 100.0 44Mchinji 2008 27.3 52.3 13.6 6.8 100.0 44 2009 30.6 47.2 16.7 5.6 100.0 36Salima 2008 46.4 32.1 19.6 1.8 100.0 56 2009 52.3 31.8 15.9 100.0 44Mangochi 2008 49.1 34.0 11.3 5.7 100.0 53 2009 37.1 34.3 25.7 2.9 100.0 35Phalombe 2008 31.3 43.8 17.2 7.8 100.0 64 2009 30.8 50.0 13.5 5.8 100.0 52From Table 27 above, the most common gifts that households receive include food, clothes and cash. In Malawi, gifts are part of the Malawi culture and a critical component that supports rural livelihoods. Often, these gifts are given during hard times when food stocks are low during the lean periods. Other gifts such as cash are given during special functions such as weddings, funeral and initiation ceremonies. These gifts help recipients to survive hardships.Analysis of types and sources of different gifts received were undertaken to establish the extent of social networks in the rural populace as summarized in Annex 6 below. The major types of gifts received include: cash income, food, clothing, phones, bicycles and soap. Most gifts are received from relative or friend in town within the locality and abroad. Interestingly, politicians were mentioned as sources of the various gifts. This is perhaps not surprising considering that the study was conducted a few months after general elections whose campaign process included distribution of gifts to the prospective voters. Friends and relatives within the locality are the major source of gifts in all the sampled districts and across all the types of gifts, followed by friends and relatives in town. This means that friends and relatives provide a good source of safety net in times of need.The analysis further sought to assess the trends in gifts from different sources for the years 2008 and 2009. This was done to establish if there have been

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changes in the flow of gifts between the two years. Results are presented in annex 13. According to the results, most households received gifts in 2008 compared to 2009. This applies to all the five districts and all types of gifts received by the respondents which include cash, food, clothing and other necessities such as bicycles and phones, and soap. For instance, for the two year accumulative 177 responses acknowledging receipt of cash gifts, 58 percent were in 2008 compared to 42 percent in 2009. If viewed in the context of other economic variables that define rural livelihoods such as income earnings and remittances, the decline in numbers or proportions of households receiving gifts in 2009 relative to 2008 shows that the global economic crisis, which has affected Malawi as a country, has resulted in reduction of gifts amongst the rural populace. Needless to say, this has strained social-cultural fabric of the country. However, it was not within the scope of this study to examine the social-cum-cultural implications of the decline in gifts owning to the global economic crisis.SummaryThis section has analyzed the impact of the global financial crisis on household incomes, wages, remittances and gifts. On incomes, a comparative analysis of the household incomes across the years shows that from 2007 and 2008 incomes show that the average incomes earned by households in 2008 by MK15,230.50 which is a 33 percent increase over the 2007 incomes. This means that rural households had benefited from the general increase in world commodity demand and prices. For 2008 and 2009, household incomes had declined by MK 8,937 which reflects is a 15 percent decline. In fact, such a decline in household incomes agrees with fact that within the same period households had experienced a 10 percent output price decline. A look at the income earnings of the years 2007 and 2009 show that households incomes earned in 2009 are 11 percent higher than those of 2007. The serious nature of the impact of the crisis on household incomes is demonstrated by the expected recovery times which range from 6 months to more than a year. This, therefore, calls for both demand and supply side interventions if the livelihoods of the rural households are to return to normalcy as soon as possible. A look at household income earnings across the districts shows that Chitipa district has surpassed other districts in times of economic boom in 2007-08 and even during the economic crisis period. On the other hand, the analysis shows that Mangochi district is the worst affected in terms of income earnings. The variations in income earnings which take place against a background of similar household economic activities imply significant differences in economic vulnerability across the districts, and this is attributable to the differences in the depth of market integration households in such districts have. On rural wages, the analysis shows that the major types of unskilled labor are ganyu on farm and molding bricks. For those involved in unskilled labor, a 14 percent increase wages in 2009 over the previous year was reported. This increase is commendable development, in that it would act as a cushion for the decline in household incomes from key livelihood sources such as farming and petty trading for those who provided unskilled labor services. However, realizing that only 4 - 5 percent of the rural households depend on ganyu as a source of livelihood, increases in unskilled wage rate market is not likely to compensate for the decrease in incomes from the major sources.

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On remittances, the study finds that Mangochi district has the highest proportion of households living outside the country (49%) followed by Mchinji (31%) and Chitipa (30%). However, the rural households do not get significant amounts of remittances from both internal and external migrants. The average amount of internal remittances is MK8,598 compared to MK16,500 for external remittances. On external remittances, the analysis shows that they constitute only 2.2 percent of the household incomes. Significant declines have been recorded for both external and internal remittances in 2009 over the 2008 receipts. While the rural recipient households may not know the reasons for such a decline, this reflects the impact of global financial crisis on the migrant household members wherever they are. The situation is not any different in the case of gifts as the analysis results show that fewer households received gifts in 2009 compared to 2008. This applies to both cash and in kind gifts. Since gifts are provided out of what one has, the decline in the gifts reflects the economic hardships the rural communities face in view of the global financial crisis. While this may have had strained social-cultural fabric of the country, the extent to which this may have happened is yet to be established. 6.0 IMPACT OF GLOBAL FINANCIAL CRISIS ON LIVELIHOOD ASSETSThe study investigated the livelihoods assets of the households in the different districts to establish if there has been any change in the ownership of productive and personal assets. The objective in this analysis was to establish the causes of such changes and if there is direct and indirect linkage to the global economic crisis.6.1 Household Disposal of AssetsAmongst the critical issues investigated in this study is whether or not a household had disposed of its assets over the past year. This was followed by a further investigation into the given reasons for the disposal decision. Table 28 presented details of the response analysis.Table 28: Disposed of household assets over 2008If a household disposed of some of its assets in 2008

Response DistrictTotalChitipa Mchinji Salima Mangochi PhalombeYes No. 20 30 37 39 37 163% 23.0 33.3 41.6 41.9 40.7 36.2No No. 67 60 52 54 54 287% 77.0 66.7 58.4 58.1 59.3 63.8Total No. 87 90 89 93 91 450% 100.0 100.0 100.0 100.0 100.0 100.0

From the table above, about 36 percent of the sampled rural households indicated to have disposed of their assets over the previous year. The majority (64%) did not dispose of their assets. A spatial analysis of the responses show that Chitipa district has the least proportion of household disposing assets (23%) while Mangochi and Salima districts had the highest number of households (42%). The results are in line with patterns of income earnings demonstrated in the previous sections which show that Chitipa districts was the 41

least affected by the global economic crisis. In other words, the study results show that there is a positive relationship between income earnings and propensity to dispose of household assets. The study further sought to establish the factors behind household decisions to dispose of their assets. Results of such an investigation are presented in Table 29.According to Table 29, theft of household assets (38%) and depreciation of assets (38%) were the major responses given by the sampled households as factors explaining the disposal process. Selling the households assets was the third most important reason with 16 percent of the respondents. Surprisingly, Chitipa district registered theft as the major form of household assets disposal with 45 percent of the responses and selling assets as business (20%) the highest across the districts. The fact that Chitipa district, which registered high average income earnings despite the global economic crisis, had the highest responses on theft of household assets reveals another ugly facet of the global economic crisis which pushes community members into stealing as a means of survival although the magnitude is very low. Since it was revealed during the focus group discussions in Chitipa has a lot of traders from the neighboring countries, we assume that some of them are responsible for theft instances being registered in the district. Table 29: Reasons for Disposal of Household AssetsReason for the disposal decision

District Total(N)Chitipa(%) Mchinji(%) Salima(%) Mangochi(%) Phalombe(%)Sold as business 20.0 .0 2.7 .0 2.8 3.8Sold as a coping strategy 20.0 25.0 16.2 13.2 11.1 16.4Given out as Gift .0 3.6 .0 .0 11.1 3.1Theft (it was stolen) 45.0 28.6 40.5 42.1 36.1 38.4Other reasons including broken down 15.0 42.9 40.5 44.7 38.9 38.4Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 20 28 37 38 36 159

Further to establishing the household decisions on disposal of their assets over the past year, the study also investigated the changes in stocks of household assets over the past year, that is, from 2008 to 2009. Table 30 presents the results.Table 30: Changes in Livelihood Assets over 2008 Whether households experienced changes in their assets in 2008 ResponsesDistrict TotalChitipa Mchinji Salima Mangochi PhalombeIncreased No. 43 19 16 23 31 132% 50.0 21.1 18.0 24.7 34.1 29.4Decreased No. 8 17 23 24 18 90% 9.3 18.9 25.8 25.8 19.8 20.0Remained the same No. 35 52 49 45 40 221% 40.7 57.8 55.1 48.4 44.0 49.2Not sure No. 0 2 1 1 2 6% .0 2.2 1.1 1.1 2.2 1.3Total No. 86 90 89 93 91 449

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% 100.0 100.0 100.0 100.0 100.0 100.0Table 30 shows that the major changes experienced by households include remaining the same, an increase and decrease. Half of the sampled rural households from the five districts did not have changes in their assets (i.e. the assets remained the same), while about 30 percent had increase in the household assets. One fifth of the households had their asset levels reduced over the period. In addition, about 70 percent of the rural households had their asset stocks either remaining constant or reduced. In fact, this is not surprising considering that analysis of incomes in the previous sections have shown that most households had experienced income decline of the same period. In fact, this is further demonstrated by the fact that a comparative analysis of response on changes in stocks of household assets shows that 50 percent of the households in Chitipa had an increase in household assets, whereas only 18 percent of the household in Salima had a similar experience. Since the majority of households (about 50%) indicated to have had constant asset stocks over 2008, we further provide an analysis of the reasons behind such a development. Figure 7 provides the details.

Figure 7: Reasons for maintaining the assetsAs shown in Figure 7, of the households that indicated to have had no change in assets, about 90 percent of them indicated that this was due to lack of funds to procure new assets. The other reasons had responses less than 5 percent and most of these responses show an array of factors that had both positive and negatively implications on the assets stocks. The former largely constitute the responses which show that households had some money, and managed to buy new assets, whereas the latter is represented by responses such as no better economic activity; the assets had broken down, got stolen or had been put to other uses.6.2 Impact on livestock assetsSince livestock are critical household assets for rural livelihood, the study took specific interest to investigate the extent to which global financial crisis implicitly affected livestock ownership among rural households by comparing the stocks between 2007, 2008 and 2009, respectively as shown in Annex 14.

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Results above show that households in Chitipa and Mangochi posses different types of livestock compared to the other three districts (Mchinji, Salima and Phalombe). The average stock in 2007, 2008 and 2009 are 9.4, 11 and 6.9 livestock units per household. This shows that between 2007 and 2008, there was a 17 percent increase in the number of livestock per households and a decline by 37 percent in 2009. Between 2007 and 2009, the average livestock holding declined by 27 percent. We find similar trend in all the districts except Mangochi. In this district, the average number of livestock units per household was 13 in 2007. The number of livestock holding per household fell to 11.7 in 2008 then further to 7.9 in 2009.A number of households depleted their livestock over the study period for various reasons. Figure 8 below shows the reasons why households depleted their livestock.

Figure 8: Reasons for disposing livestockThe Figure above shows that most households sold their livestock to cope with different shocks they faced especially in Mchinji and Phalombe where 37 percent of the households sold their livestock as a coping strategy. In Salima, more than 40 percent of the households consumed their livestock that led to the reduction in the number of livestock. Livestock diseases also contributed to the decline in livestock across all the districts with Chitipa and Mchinji being the most affected. Few households sold their livestock across all the districts as an income source. There are also incidences of theft. In general, livestock plays an important role in filling gaps in household livelihood security. SummaryAnalysis of the impact of the global financial crisis on household livelihoods has focused on general household assets and specifically on livestock assets. While national level analysis shows that the majority of households (64%) did not dispose of their assets, a spatial analysis shows district variations of the 44

responses with Chitipa district having the least proportion of household disposing assets while Mangochi and Salima districts had the highest number of households. Essentially these results show that there is a positive relationship between income earnings and disposal of household assets which means that if incomes have been negatively affected by the global economic meltdown, the same applies to the ownership of assets. With respect to livestock, the study finds between 2007 and 2008, there was a 17 percent increase in the number of livestock per households and a decline by 37 percent in 2009. Factors such as sales as a coping strategy, own consumption, and effects of diseases are the major determinants of depletion of livestock assets over the 2008-09 period.

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7.0 HOUSEHOLD EXPENDITURE AND CONSUMPTIONThe study investigated the extent to which the global financial crisis affects the welfare of vulnerable communities in the study areas. In this regard, welfare is mainly defined in terms of household consumption and expenditure on food in relation to other household needs. Although there are variations in terms of share of household budget on food across the sampled districts, on aggregate, food accounts for the bulk of household expenditure as shown in Table 31 below. Table 31: Annual expenditure among rural households Expenditure item

Average expenditure in 2008 (MK)Average expenditure in 2009 (MK

Share of expenditure in 2008(%)Share of expenditure in 2009 (%)

Changes in expenditure share (%)Health 2,743.95 (4,031.52) 2,674.70 (4,559.05) 4.54 4.89 0.36Education 3,737.59 (7,882.40) 4,292.77 (9,678.43) 6.18 7.85 1.67Transport 4,279.24 (7,337.85) 4,099.13 (6,945.75) 7.07 7.50 0.42Food 13,447.06 (14,031.86) 14,351.65 (15,809.70) 22.23 26.25 4.02Rent and utilities 7,825.71 (10,869.91) 7,427.03 (8,520.19) 12.94 13.58 0.65Groceries 5,959.43 (7,809.44) 6,149.06 (7,341.90) 9.85 11.24 1.39Other(gifts, clothes) 9,506.67 (13,538.89) 7,688.71 (11,789.77) 15.71 14.06 -1.65Household assets 13,000.00 (1,414.21) 8,000.00 (1,708.35) 21.49 14.63 -6.86Average 6,722.93 (10,531.99) 6,703.75 (11,207.90 12.50 12.50 0.00Brackets are standard deviations

According to the study, food accounted for more than 26 percent of total household expenditure in 2009 compared to 22 percent in 2008. Other expenditure items critical to the households are household assets gifts, rentals and utilities. Annex 15 provides detailed expenditure pattern of households across all the districts. Results indicate that more than a third of household income in all districts except Chitipa and Mchinji is spent on food. The analysis results further show that Chitipa and Mchinji districts registered no changes in share of food expenditures in 2008 and 2009; On the other hand, Salima, Mangochi and Phalombe districts registered increased food expenditures shares of 33 to38 percent, 29 to 38 percent and 27 to 32 percent, respectively.Since increase in household expenditures on food items is usually seen as an indicator of household economic hardships, the results for Chitipa district agree with our findings in section 5.3.2 on district income changes which show that the district did not have significant income declines over the 2008-09 period. However, for Mchinji, it is not clear why the reported significant declines in incomes are not reflected in the household food consumption shares. Probably the consumption shares for two districts could be attributed to the fact that unlike the other districts, Chitipa and Mchinji are bordered with Zambia and

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Tanzania districts where there is movement of food items across both countries. This helps households to meet their food gaps. From the welfare perspective, cross border movement of food reduce household vulnerability to food insecurity. The study further investigated if households had to change their eating or consumption habits in response to economic hardships faced. Nearly 70 percent of the households did not change their eating habits neither did they change the types of food they consume as shown in Figure 9 below.

Figure 9: Description of change in consumption/eating habitsFor those that changed their consumption or eating habits, the majority of the food insecure households adopted coping mechanisms such as reducing the amount of food (14%) and shifted to cheaper foods (7%). In contrast, 9 percent of the richer households shifted to more expensive food. In general, the economic meltdown did not change household food consumption habits of the households. The effect of the economic crisis was internally mitigated by the good harvest that households obtained in 2008 and 2009 following good rains and the increased maize productivity through the use of hybrid seeds and application of fertilizers under the government subsidy program. Other coping mechanisms that household deploy in times of temporary shocks are summarized in Annex 16. As shown Annex 16, piece work (ganyu) is the mostly important coping strategy among households across all districts 47

especially in Salima and Mchinji districts. For those with livestock particularly in Chitipa and Mangochi, the effects of the economic crisis were cushioned by the sale of livestock assets among 18 and 9 percent of the households, respectively.Summary

An investigation into the extent to which the global financial crisis affects the welfare of vulnerable communities in the study areas from the expenditure point of view, shows that an increase in share of household expenditure on food in 2009 (26%) compared to 2008 (22%). At district level, the analysis results show that Chitipa and Mchinji districts registered no changes in share of food expenditures in 2008 and 2009 while increases have been registered for the other three districts of Salima (33 to38%), Mangochi (29 to 38%) and Phalombe (27 to 32%). The differences in food expenditure shares could be explained by the level of economic vulnerability such those households who are more vulnerable spend more on food items than those who are not. With respect to consumption habits, the analysis shows that most households (about 70%) did not change the amount of food consumed per day nor the type of food they consume. This implies that the effects of the economic meltdown were mitigated by the factors such as good harvest that most rural households obtained in 2008 and 2009 following good rains and the increased maize productivity through the use of inputs from the agriculture inputs subsidy programme. 8.0 CONCLUSIONS AND IMPLICATIONS FOR POLICYThe study has investigated the impact of the global economic crisis on Malawi’s vulnerable households with focus on issues such as trends in household incomes; remittances and gifts; livelihood assets; input, output and food prices; expenditures and consumption; access to social services and amenities, coping strategies and responses. Vulnerable households from five districts of the study are the primary source of information in this study. An analysis of issues emanating from this study brings to light a number of critical insights which have policy implications. It must be recognized that economic shocks are not new to the Malawi economy. At the national level, these could be summarized as shocks relating to changes in the international prices of tobacco, changes in petroleum products and those relating to variations in the real exchange rates. At the household levels, the major shocks include area specific dry spells and droughts, sudden closure of ADMARC markets, amongst others. Analysis of household income patterns for the past three years shows that most rural households benefited from the global commodity price increase as evidenced by an average income increase by 33 percent between 2007 and 2008. However, these positive gains have been undone by economic crisis which has led to an average income decline by 15 percent between 2008 and 2009. This situation implies that if the 2008 economic crisis had not been preceded by some positive developments that had led to increase in household incomes of the rural households, the situation would have been devastatingly worse for the country’s rural economy. The analysis has also established that most rural households were worse off in 2009 than in 2008, they are, however, still better than they were in 2007.

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According to the households themselves, the major reasons given for decrease in incomes are lack of proper produce markets and inadequate access to farm inputs such as fertilizer. From the results, Chitipa district is the least affected by the global economic crisis since it registered significant income increases not only within the 2007-08 period but also when comparing the 2007 and 2009 incomes. On the other hand, Mangochi and Salima districts are the most negatively affected by the crisis with double significant income decreases for 2008-09 and 2007 and 2009. This is mainly due to the decline in effective demand and low prices for critical commodities such as cotton (in the case of Salima) while most households in Mangochi rely on petty trading. In Chitipa, most households are well linked to neighboring Zambia and Tanzania with whom they trade with. Such trade helps them to counter most economic hocks households face.In any case, the results call for government policy measures that would ensure stability in incomes levels of rural households especially considering that income poverty has implications for household nutritional status which is usually associated with food intake which, in turn, is taken to be dependent on household income. The study shows that 25 percent of the households have a member working outside the country (external migrants) while 38 percent of the households have members working and living outside the district of origin (internal migrants). Analysis shows that the mean annual external remittance income received by rural households is MK 16,500 which represents 1.3 percent of the total household income. This clearly indicates that Malawi’s rural economy is not strongly linked to the flow of external remittances though internal remittances were found to be relatively more important considering that they account for more than 2.5 percent of the total household income.During the 2008-09 period, a good proportion of remittance beneficiary households have experienced declines thus being unable to purchase food from the market as well as farm inputs for their gardens resulting in household food insecurity challenges. This notwithstanding, Mangochi district has the highest proportion of households receiving both external and internal remittances from relatives living and working outside the district and the country. However, the study revealed that there was a decline in the remittances from their relatives living abroad. This reflects the financial difficulties which these migrants are facing in different countries they are working and living. Of course, 40 percent of the beneficiary respondents indicated to have had increased remittances. This implies that household members outside the country do appreciate the negative implications of global economic crisis on their relations back home and hence undertook to increase the support levels to cushion off such effects. About 17 percent of households affected by the decline in external remittances indicated that the reduction has resulted in household food insecurity as well as the reduction in the fertilizer use in their gardens. With such negative implications of the reduction in the external remittances on the rural households, it can be safely concluded that if Malawi’s rural economy were heavily integrated with the global economy where the flow of remittances could have had negative implications on rural livelihoods.To avert the suffering by the remittance receiving households, there is need for introduction and scaling up of safety net programmes for household who have

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been negatively affected by declines in remittances. However, such programmes should be designed to become developmental so that these households reduce their dependence of remittances. About 70 percent of the rural households had their asset stocks either remaining constant or reduced between 2008 and 2009. Lack of money to purchase additional households assets was given as the major reason. In addition, cases of theft and natural depreciation of assets were also reported which did offset attempts to purchase new assets. Since global economic crisis has not yet really resulted into forcing few households selling assets, there is need to come up with social and economic support measures that would prevent this from happening. Public works programmes and other safety net programmes that have had a proven record of assisting rural households in asset accumulation for sustainable and improved livelihoods could be of use in this regard. In the face of financial crisis, people devise different coping measures such as reducing the amount of food (14%) and shifted to cheaper foods (7%). However, our results indicate that the global crisis has little impact on the local communities. For example, the financial crisis did not affect household food consumption habits of the households. Although the poor and vulnerable households devote more than 25 percent of budget on food, the food prices in 2009 were not that high despite the financial crisis. The effect of the economic crisis was internally mitigated by the good harvest that households obtained in 2008 and 2009 following good rains and the increased maize productivity through the use of hybrid seeds and application of fertilizers under the government subsidy program.In terms of interventions, the study has shown that most households prefer interventions that would lead to long term sustainable development. Other than providing them with subsidized farm inputs or food assistance, more than 40 percent of the households consider reducing input prices and provision of credit facilities are important for improving their well being. This shows that rural households are aware that provision of free food and inputs are temporary solutions to their long-term socio-economic development. The study has shown that rural incomes in Malawi are to a larger extent integrated with the dynamics in the global economy, such that price changes in the world economy which are likely to affect the welfare of rural communities in Malawi. In particular, analytical results indicate that a 1 percent increase in selling prices faced by the households leads to a corresponding increase in household income by 0.24 percent. These results call for shift in policy focus from supply side policy interventions in favor of interventions that effectively link farmers to reliable markets.

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ReferencesBriancon, C., and Lightfoot, C., 2009. Malawi: Impact of the Global Economic Crisis, September 2009. Economic Justice Network (EJN), 2009. The Social Impact of the Global Financial Crisis on the SADC Region. Emmett, B., 2009. Paying the Price for the Economic Crisis, Oxfam International Discussion Paper, www.oxfam.orgFosu, K.A., and Naude, W., 2009. Policy Responses to the Global Economic Crisis in Africa, United Nations University, Policy Brief Number 3, 2009Ministry of Economic Planning and Development, 2009. Annual Economic Report, Lilongwe.Lofgren. H., Chulu, O., Sichinga, O., and Simtowe, F., 2001. External Shocks and Domestic Poverty Alleviation: Simulations with A CGE Model of Malawi, International Food Policy Research Institute, Washington, D.C Musau, S., undated. Impact of Global Economic Crisis on Health in Africa, USAID Africa’s Health in 2010/AED, Washington D.C.National Economic Council, National Statistical Office and International Food Policy Research Institute, 2001. Determinants of Poverty In Malawi. An analysis of the Malawi Integrated Household Survey, 1997-98, Lilongwe. National Statistical Office, 2005. Integrated Household Survey 2004-05, Zomba. National Statistical Office, 2008. Welfare Monitoring Survey for 2008, Zomba.Overseas Development Institute, 2009. The Global Financial Crisis and Developing Countries: Preliminary Synthesis of Ten Draft Country Report.Von Braun, J., 2008. Food and Financial Crises: Implications for Agriculture and the Poor: A Food Policy Report by International Food Policy Research Institute. UNICEF, 2009. The Impact of the Food and Economic Crisis on Child Health and Nutrition, Draft working paper prepared for UNICEF conference, East Asia and the Pacific Islands, Singapore. World Bank, 2007. Malawi Poverty and Vulnerability: Investing in Our Future, Report 36546-MW, December 2007. Washington.

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List of AnnexesAnnex 1: List of Sampled Villages District Traditional Authority Village Sample1. Chitipa Mwabulambia Lufita 29Mwamukumbwa 31Nkangwa 27Sub-total 872. Mchinji Mavwere Mkusa 30Mkwelera 30Nkhwazi 30Sub-total 903. Salima Khombedza Khombedza 29Msosa Kamphinda 30Mwaza Chiunjiza 30Sub-total 894. Mangochi Mponda John Sawadi Njeleza 31Manjawila 31Mkungumbe 31Sub-total 935. Phalombe Mkhumba Chimenya 28Mumbo 33Kaduya Ulolo 30Sub-total 91Full sample 450

52

Annex 2: Household Age Groups

District Age Category Gender of household head Total(%) Male(%) Female(%)Chitipa Under five children (0- 5 years) 22.8 19.1 20.9 Primary school going age (6-14 years) 25.1 25.5 25.3 Secondary & tertiary school going age (15-24 years) 16.7 21.8 19.3 Economically active youths (25-39 years) 23.3 23.6 23.4 Economically active adults (40-64 years) 9.3 9.5 9.4 The elderly (>65 years) 2.8 .5 1.6 Total 100.0 100.0 100.0Sample 215 220 435Mchinji Under five children (0- 5 years) 25.5 22.0 23.8 Primary school going age (6-14 years) 23.5 30.5 26.8 Secondary & tertiary school going age (15-24 years) 19.3 16.1 17.8 Economically active youths (25-39 years) 16.9 17.9 17.4 Economically active adults (40-64 years) 11.9 12.6 12.2 The elderly (>65 years) 2.9 .9 1.9 Total 100.0 100.0 100.0Sample 243 223 466Salima Under five children (0- 5 years) 18.8 22.3 20.5 Primary school going age (6-14 years) 29.7 26.1 28.0 Secondary & tertiary school going age (15-24 years) 21.8 21.3 21.6 Economically active youths (25-39 years) 17.5 17.1 17.3 Economically active adults (40-64 years) 9.2 10.0 9.5 The elderly (>65 years) 3.1 3.3 3.2 Total 100.0 100.0 100.0Sample 229 211 440Mangochi Under five children (0- 5 years) 17.9 18.8 18.4 Primary school going age (6-14 years) 31.1 33.1 32.1 Secondary & tertiary school going age (15-24 years) 19.1 18.1 18.6 Economically active youths (25-39 years) 12.8 17.3 15.2 Economically active adults (40-64 years) 14.9 11.2 12.9 The elderly (>65 years) 4.3 1.5 2.8 Total 100.0 100.0 100.0Sample 235 260 495Phalombe Under five children (0- 5 years) 26.1 21.3 23.7 Primary school going age (6-14 years) 23.1 28.1 25.5 Secondary & tertiary school going age (15-24 years) 17.9 21.7 19.8 Economically active youths (25-39 years) 19.2 15.8 17.6 Economically active adults (40-64 years) 11.1 11.8 11.4 The elderly (>65 years) 2.6 1.4 2.053

Total 100.0 100.0 100.0Sample 234 221 455

54

Annex 3: Quality of health servicesHealth Facility District

Quality of serviceTotal (%)

Sample(N)below average(%) Average(%)

above average(%)Excellent(%) hospital

Chitipa 1.9 31.5 55.6 11.1 100.0 54 Mchinji 5.0 22.5 72.5 .0 100.0 40 Salima 2.7 37.8 59.5 .0 100.0 37 Mangochi 7.8 37.3 54.9 .0 100.0 51 Phalombe 19.0 47.6 33.3 .0 100.0 21Total 5.9 34.0 57.1 3.0 100.0 203

health centre

Chitipa 3.1 28.1 53.1 15.6 100.0 32 Mchinji 2.1 46.8 51.1 .0 100.0 47 Salima 10.6 46.8 40.4 2.1 100.0 47 Mangochi 4.7 23.3 62.8 9.3 100.0 43 Phalombe 5.7 38.6 47.1 8.6 100.0 70Total 5.4 37.7 50.2 6.7 100.0 239

Private clinic

Chitipa .0 25.0 75.0 .0 100.0 4 Mchinji 8.7 39.1 47.8 4.3 100.0 23 Salima 4.8 85.7 9.5 .0 100.0 21 Mangochi 6.3 62.5 31.3 .0 100.0 16 Phalombe .0 75.0 25.0 .0 100.0 8Total 5.6 61.1 31.9 1.4 100.0 72

traditional healers

Chitipa 22.2 44.4 22.2 11.1 100.0 9 Mchinji .0 100.0 .0 .0 100.0 2 Salima .0 100.0 .0 .0 100.0 1 Mangochi

20.0 40.0 40.0 .0 100.0 5

55

Phalombe 25.0 75.0 .0 .0 100.0 4 Total 19.0 57.1 19.0 4.8 100.0 21

56

Annex 4: Water qualityWater source DISTRICT

Quality of water Total(%) Sample (N)Poor(%)below average(%)

Average(%)above average(%)

Excellent(%)borehole Chitipa 7.8 4.7 62.5 25.0 .0 100.0 64 Mchinji .0 2.6 46.2 46.2 5.1 100.0 39 Salima 2.5 2.5 60.5 33.3 1.2 100.0 81 Mangochi .0 .0 64.1 35.9 .0 100.0 64 Phalombe .0 .0 40.8 57.9 1.3 100.0 76Total 2.2 1.9 55.2 39.5 1.2 100.0 324

tap water Chitipa 9.1 9.1 9.1 54.5 18.2 100.0 11

Salima .0 .0 .0 100.0 .0 100.0 1 Mangochi .0 .0 33.3 66.7 .0 100.0 3 Phalombe .0 .0 20.0 61.8 18.2 100.0 55Total 1.4 1.4 18.6 61.4 17.1 100.0 70

river Chitipa 47.6 42.9 4.8 4.8 100.0 21

Mchinji .0 50.0 50.0 .0 100.0 2 Mangochi 33.3 45.5 21.2 .0 100.0 33Phalombe .0 100.0 .0 .0 100.0 1Total 36.8 45.6 15.8 1.8 100.0 57

well Chitipa 40.0 20.0 30.0 10.0 100.0 10 Mchinji 7.8 39.2 47.1 5.9 100.0 51 Salima 14.3 42.9 42.9 .0 100.0 14 Mangochi 18.2 27.3 54.5 .0 100.0 11Phalombe .0 50.0 .0 50.0 100.0 2Total 13.6 36.4 44.3 5.7 100.0 88

Annex 5: Reasons for the observed changed in farm input pricesDistrict Reasons for the observed changesTotal (%) Sample (N)

Don't know(%)Few traders in the area(%)

Low import prices of fertilizer(%)Government intervention (subsidy)(%)

Strategy to encourage farmers to produce more57

(%) Chitipa 23.8 2.5 12.5 60.1 1.3 100.0 80 Mchinji 3.3 1.6 37.7 57.4 0 100.0 61 Salima 12.1 0 13.6 71.2 0 100.0 66Mangochi 23.4 0 6.7 70 0 100.0 60 Phalombe 0 0 6.5 84.4 2.6 100.0 77Sample (N) 12.52 0.82 15.4 68.62 0.78 100.0 344

58

Annex 6: General perception about commodity prices

Annex 7: Comparison of months with highest price for maize, 2008 and 2009District Months Average price in 2008 Average price in 2009 Price change (%)Chitipa January 49.7 (16.4) 49.1 (8.1) -1.2 February 60.0 (14.1) 37.5 (10.6) -37.5 September 35.0 (7.1) 45.0 (7.1) 28.6 October 35.0 (0.0) 20.0 (0,0) -42.9 Average 47.8 (15.6) 44.1 (11.3) -7.7Mchinji January 42.5 (24.7) 51.0 (26.9) 20.0 August 50.0(0.0) 42.0 (0.0) -16.0 September 41.0 (7.4) 47.0 (9.1) 14.6 October 25.0 (0.0) 35.0 (0.0) 40.0 Average 40.6 (12.1) 46.0 (12.5) 13.3Salima October 58.5 (23.3) 52.5 (10.6) -10.3Mangochi January 125.0 (21.2) 75.0 (21.1) -40.0 August 100.0 (0.0) 60.0 (0.0) -66.7 October 80.0 (0.0) 60.0 (0.0) -25.0 Average 107.5 (25.0) 67.5 (15.0) -37.0Phalombe January 80.0 (0.0) 65.0 (0.0) -18.9 March 100.0 (0.0) 120.0 (0.0) 20.0 Average 90.0 (14.1) 92.5 (38.9) 2.8Total January 63.6 (34.0) 55.1 (15.8) -13.4 February 60.0 (14.1) 37.5 (10.6) -37.5 March 100.0 (0.0) 120.0 (0.0) 20.0 August 75.0 (35.4) 51.0 (12.7) -32.0 September 39.9 (7.3) 46.4 (8.0) 16.3 October 51.4 (24.6) 44.0 (17.1) -14.4 Average 57.4 (28.2) 51.8 (19.1) -9.8Annex 8: District Level Paired Statistical Tests for Income DifferenceChitipa District Obs Mean income (MK)

Std. Dev. (MK) [95% Conf. Interval] (MK) Income change (%)2008 Income 85 99522.35 53105.60 44928.75 59

154116.02007 Income 85 65738.82 05253.80 43036.11 88441.53Income differential 85 33783.53** 70781.70 -3053.23 70620.29 51.4%2009 income 86 86278.49 138747.60 56530.93 116026.002008 income 86 97870.93 251628.30 43921.70 151820.20 Income differential 86 -11592.44NS

159369.70 -45761.39 22576.50 -11.8%2009 income 84 85955.36 139597.50 55660.83 116249.902007 income 84 64378.57 105131.60 41563.61 87193.53 Income differential 84 21576.79*** 59228.44 8723.43 34430.15 33.5%Mchinji District2008 Income 82 59882.32 112924.20 35070.15 84694.482007 Income 82 37163.17 64062.42 23087.11 51239.23Income differential 82 22719.15*** 62024.72 9090.821 36347.47 61.1%2009 income 90 41983.33 109457.10 19057.98 64908.692008 income 90 55415.00 108702 32647.80 78182.20 Income differential 90 -13431.67***

49859.93 -23874.63 -2988.71-24.2%2009 income 82 42276.83 113102.80 17425.42 67128.242007 income 82 37163.17 64062.42 23087.11 51239.23 Income differential 82 5113.66NS 64777.87 -9119.60 19346.92 13.8%Salima District2008 Income 87 42741.61 64606.40 28972.12 56511.102007 Income 87 34554.83 53856.30 23076.49 46033.16Income differential 87 8186.78NS 66228.09 -5928.34 22301.90 23.7%2009 income 89 34527.19 78794.99 17928.85 51125.542008 income 89 41966.52 64086.66 28466.51 55466.52 Income differential 89 -7439.33** 34010.60 -14603.74 -274.92 -17.7%2009 income 87 34970.34 79650.02 17994.62 51946.072007 income 87 34554.83 53856.30 23076.49 46033.16 Income differential 87 415.52NS 77429.79 -16087.01 16918.04 1.2%Mangochi District2008 Income 82 47892.80 60215.55 34662.00 61123.612007 Income 82 45190.82 67003.82 30468.46 59913.17Income differential 82 2701.99NS 24947.11 -2779.49 8183.47 6.0%2009 income 92 38716.30 50259.68 28307.82 49124.79

60

2008 income 92 47027.28 61429.28 34305.64 59748.93 Income differential 92 -8310.98** 34827.31 -15523.51 -1098.45 -17.7%2009 income 82 39547.56 52279.85 28060.42 51034.72007 income 82 45190.82 67003.82 30468.46 59913.17 Income differential 82 -5643.26NS42857.36 -15060.05 3773.54 -12.5%Phalombe District2008 Income 89 56120.22 72048.75 40942.99 71297.462007 Income 89 47080.00 77278.9 30801.02 63358.98Income differential 89 9040.23*** 30808.46 2550.352 15530.10 19.2%2009 income 92 50948.26 61794.64 38150.95 63745.572008 income 92 55083.70 71255.23 40327.15 69840.24 Income differential 92 -4135.44NS 40820.12 -12589.04 4318.173 -7.9%2009 income 89 50834.16 62519.30 37664.32 64003.992007 income 89 47080.00 77278.90 30801.02 63358.98 Income differential 89 3754.157N

S 54682.78 -7764.896 15273.21 8.0%*** p<0.001, *** p<0.05

61

Annex 9: Regression Results for Determinants of Changes in Household Incomes Explanatory variables Log of Average Income Changes in the 2009-08 periodLog of Age of household head -0.007*(0.003)Gender of household head ( male =1, female=0) 0.012(0.119)Log of Years of Residence in the Village by the Household head 0.003(0.003)Literacy of the household head (literate =1, illiterate=0) -0.009(0.090)Household size 0.030(0.022)Log of Average Prices changes in 2009 over 2008 0.239***(0.062)Log of Income changes in 2008 over 2007 0.236***(0.053)Constant -0.121(0.248)Observations 288R-squaredF (7,280)Prob>FAdj- R Squared 0.1295.930.0000.1074Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

62

Annex 10: Changes in unskilled wage rates Chitipa Obs Monthly wage (MK)Std. Err.(MK)

Std. Dev.(MK)[95% Conf. Interval](MK)

Change in wages (%)Monthly wage rate in 2008 63 1675.16 180.25 1430.69 1314.84 2035.47Monthly wage rate in 2009 63 1950.33 225.22 1787.63 1500.13 2400.54Wage differential 63 275.17*** 72.77 577.58 420.64 129.71 16.43MchinjiMonthly wage rate in 2008 73 1713.61 149.16 1274.40 1416.27 2010.95Monthly wage rate in 2009 73 1862.49 155.34 1327.22 1552.83 2172.15Wage differential 73 148.88** 75.82 647.79 300.0179 2.26 8.69SalimaMonthly wage rate in 2008 72 1689.66 134.59 1142.07 1421.29 1958.04Monthly wage rate in 2009 72 1946.49 164.43 1395.26 1618.62 2274.36Wage differential 72 256.83*** 68.49 581.16 393.39 120.26 15.20MangochiMonthly wage rate in 2008 66 1615.88 148.41 1205.70 1319.48 1912.28Monthly wage rate in 2009 66 1863.89 176.00 1429.84 1512.40 2215.39Wage differential 66 248.02*** 74.33 603.89 396.47 99.56 15.35PhalombeMonthly wage rate in 2008 64 1929.84 152.54 1220.34 1625.01 2234.67Monthly wage rate in 2009 64 2193.00 178.63 1429.05 1836.03 2549.97Wage differential 64 263.16*** 107.09 856.71 477.16 49.16 13.64Full sampleMonthly wage rate in 2008 338 1723.20 68.11 1252.28 1589.22 1857.19Monthly wage rate in 2009 338 1959.61 80.03 1471.31 1802.19 2117.03Wage differential 338 236.41*** 35.75 657.21 306.73 166.09 13.72***p<0.000 **P<0.05

63

Annex 11: Reasons for Increase in unskilled wage rate Type of Unskilled workReason for change in wage rate District Total(%)

Chitipa(%)Mchinji(%)

Salima(%)Mangochi(%)

Phalombe(%)Molding bricks Depends on the one proving the ganyu 12.5 7.7 .0 12.5 .0 7.3Increase price of commodities on market 31.3 15.4 69.2 .0 60.0 34.5Labor involving 25.0 23.1 23.1 12.5 .0 20.0Yearly increase 12.5 7.7 .0 37.5 40.0 14.5Lack of money 18.8 46.2 7.7 37.5 .0 23.6Total (%) 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 16 13 13 8 5 55Ganyu on farm Depends on the one proving the ganyu 9.3 8.0 5.8 8.9 7.5 7.8Increase price of commodities on market 53.5 38.0 30.8 48.9 41.5 42.0scarcity of ganyu 14.0 2.0 .0 .0 7.5 4.5Labor involving 4.7 8.0 19.2 6.7 9.4 9.9Yearly increase 9.3 2.0 1.9 11.1 9.4 6.6Lack of money 9.3 42.0 42.3 24.4 24.5 29.2Total 100.0 100.0 100.0 100.0 100.0 100.0Sample (N) 43 50 52 45 53 243

Annex 12: Amount and frequency of receiving internal remittancesDistrict Amount received (MK) Frequency of receipt per yearChitipa Mean 6,411.13 1.9 Std. Deviation 9,936.84 2.1Mchinji Mean 2,250.00 1.5 Std. Deviation 1,962.78 1.3Salima Mean 19,078.95 1.4 Std. Deviation 6,8152.98 1.0Mangochi Mean 4,657.14 1.7 Std. Deviation 8,355.93 1.0Phalombe Mean 4,275.00 1.5 Std. Deviation 5,323.21 0.7Total Mean 8,597.54 1.6 Std. Deviation 35,976.29 1.3

64

Annex 13: Types and sources of giftsType of gift Source of gift received

District Total(%)Chitipa(%)

Mchinji(%)Salima(%)

Mangochi(%)Phalombe(%)Cash Relative/friend in town 38.5 54.5 54.5 23.5 35.7 43.2Relative/friend within the locality 53.8 45.5 42.4 70.6 64.3 53.4Relative/friend abroad .0 .0 .0 5.9 .0 1.1Others including politicians 7.7 .0 3.0 .0 .0 2.3

Total 13 11 33 17 14 88100.0 100.0 100.0 100.0 100.0 100.0Food Relative/friend in town 20.0 6.3 40.0 40.0 13.0 20.3Relative/friend within the locality 80.0 93.8 60.0 60.0 82.6 78.1Other including politicians .0 .0 .0 .0 4.3 1.6Total 5 16 15 5 23 64100.0 100.0 100.0 100.0 100.0 100.0Clothing Relative/friend in town 46.7 20.0 22.2 .0 30.0 26.9Relative/friend within the locality 46.7 80.0 77.8 75.0 50.0 63.5Relative/friend abroad 6.7 .0 .0 25.0 10.0 7.7Others including politicians .0 .0 .0 .0 10.0 1.9Total 15 10 9 8 10 52100.0 100.0 100.0 100.0 100.0 100.0Other inc' cell phone, bicycleRelative/friend in town 60.0 .0 .0 .0 18.8Relative/friend within the locality 20.0 100.0 75.0 80.0 62.5Relative/friend abroad .0 .0 25.0 .0 6.3Others, including politicians 20.0 .0 .0 20.0 12.5Total 5 2 4 5 16100.0 100.0 100.0 100.0 100.0Soap Relative/friend in town 100.0 100.0Total 2 2100.0 100.0

65

66

Annex 14: Trends in livestock holding for 2007, 2008 and 2009

District Type of livestock owned Number of livestock 2007 Number of livestock 2008 Number of livestock 2009Chitipa Cattle 5.4 (3.3) 6.3 (4.5) 7.6 (6.3) Rabbits 17.3 (8.4) 35.0 (21.2) 24.0 (0.0) Goats 3.1 (1.7) 4.4 (2.7) 5.7 (4.8) Pigs 2.6 (1.4) 4.9 (2.6) 4.6 (3.6) Chicken 13.7 (13.8) 15.2 (12.6) 11.7 (11.2) Guinea fowl 14.0 (0.0) 11.0 (0.0) 8.0 (0.0) Total 9.6 (11.5) 11.4 (11.5) 9.5 (9.5)Mchinji Cattle 3.8 (2.1) 2.8 (1.0) 2.8 (1.0) Goats 3.9 (3.1) 4.7 (2.7) 3.0 (1.6) Pigs 4.6 (4.9) 8.2 (2.8) 4.3 (1.3) Chicken 10.0 (8.4) 13.3 (10.9) 4.5 (3.0) Total 7.8 (7.5) 10.4 (9.8) 3.9 (2.5)Salima Cattle 3.00 (1.7) 2.7 (1.2) 2.3 (0.6) Goats 3.4 (4.0) 4.9 (5.3) 3.9 (5.3) Pigs 2.0 (0.0) 7.0 (0.0) 3.5 (0.7) Chicken 8.5 (8.3) 13.3 (12.1) 4.9 (3.1) Total 6.5 (7.3) 9.9 (10.7) 4.3 (3.9)Mangochi Cattle 1.0 (1.0) 1.0 (0.0) 0 Rabbits 30.0 (.0) 0 0 Goats 6.2 (5.3) 6.6 (4.0) 6.4 (4.8) Pigs 1.0 (0.0) 2.0 (0.0) 2.0 (0.0) Sheep 16.0 (0.0) 25.0 (0.0) 15.0 (0.0) Chicken 17.5 (40.69 15.3 (19.5) 9.0 (8.4) Guinea fowl 12.0 (0.0) 8.0 (0.0) 3.0 (0.0) Duck 8.0 (10.0) 6.0 (5.7) 5.0 (0.0) Pigeon 2.0 (0.0) 6.0 (0.0) 7.0 (0.0) Total 13.0 (31.4) 11.7 (15.7) 7.9 (7.2)Phalombe Cattle 4.0 (0.0) 4.0 (0.0) 2.0 (0.0) Goats 5.4 (3.7) 5.5 (2.9) 3.9 (2.2) Pigs 4.3 (2.7) 6.7 (3.0) 2.3 (1.4) Chicken 12.2 (9.3) 15.2 (10.7) 8.2 (7.7) Total 9.2 (8.2) 11.2 (9.6) 6.2 (6.4)Total Cattle 4.6 (3.0) 4.9 (3.9) 5.8 (5.6) Rabbits 20.5 (9.3) 35.0 (21.2) 24.0 (0.0) Goats 4.6 (4.0) 5.3 (3.6) 4.6 (4.1) Pigs 3.5 (2.8) 6.3 (1.9) 3.5 (2.5) Sheep 16.0 (0.0) 25.0 (0.0) 15.0 (0.0) Chicken 12.7 (20.59 14.6 (13.4) 8.4 (8.6) Guinea fowl 13.0 (1.4) 9.5 (2.1) 5.5 (3.5) Duck 8.0 (9.9) 6.0 (5.7) 5.0 (0.0) Pigeon 2.0 (0.0) 6.0 (0.0) 7.0 (0.0) Total 9.4 (16.4) 11.0 (11.6) 6.9 (7.3)

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Annex 15 Household expenditure shares 2008 and 2009

District Expenditure categoryAverage expenditure 2008

Average expenditure 2009Expenditure share 2008(%)

Expenditure share 2009(%)Change in expenditure share (%)Chitipa Health 2,893.02 2,577.78 5.51 4.27 -1.24Education 4,870.98 5,731.09 9.28 9.50 0.22Transport 6,946.08 7,679.55 13.24 12.73 -0.51Food 11,285.25 13,314.04 21.51 22.08 0.57Rent utilities 7,950.00 7,886.67 15.15 13.08 -2.08Groceries 2,270.00 3,438.18 4.33 5.70 1.37Other payments(e.g., gifts) 3,250.00 11,680.00 6.19 19.37 13.17Household assets 13,000.00 8,000.00 24.78 13.27 -11.51Total 52,465.33 60,307.29 100.00 100.00 0.00Mchinji Health 2,675.07 2,317.53 4.46 4.62 0.16Education 5,408.86 5,345.14 9.01 10.66 1.64Transport 3,643.64 2,830.33 6.07 5.64 -0.43Food 14,493.14 11,733.88 24.15 23.39 -0.76Rent utilities 12,000.00 12,000.00 20.00 23.92 3.92Groceries 3,280.00 5,540.00 5.47 11.04 5.58Other payments(e.g., gifts) 18,500.00 10,391.67 30.83 20.72 -10.12Total 60,000.70 50,158.54 100.00 100.00 0.00Salima Health 2,348.55 2,976.06 5.17 6.80 1.63Education 3,480.36 5,260.00 7.67 12.02 4.35Transport 2,953.06 2,542.61 6.51 5.81 -0.70Food 14,825.37 16,515.44 32.66 37.74 5.08Rent utilities 10,000.00 5,000.00 22.03 11.42 -10.60Groceries 6,788.46 7,092.31 14.95 16.20 1.25Other payments(e.g., gifts) 5,000.00 4,380.00 11.01 10.01 -1.01Total 45,395.80 43,766.41 100.00 100.00 0.00Mangochi Health 2,980.60 3,355.48 6.22 8.01 1.78Education 3,075.45 3,699.33 6.42 8.83 2.40Transport 3,318.11 3,212.07 6.93 7.66 0.73Food 13,890.75 15,815.48 29.00 37.73 8.73Rent utilities 4,780.00 5,100.00 9.98 12.17 2.19Groceries 8,846.15 7,000.00 18.47 16.70 -1.77Other payments(e.g., gifts) 11,000.00 3,733.33 22.97 8.91 -14.06Total 47,891.06 41,915.70 100.00 100.00 0.00Phalombe Health 2,957.66 1,827.63 6.45 4.03 -2.42Education 2,049.09 2,069.15 4.47 4.57 0.10Transport 4,633.33 5,096.98 10.11 11.25 1.14Food 12,601.33 14,661.36 27.49 32.36 4.87

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Rent utilities 8,335.71 7,600.00 18.18 16.77 -1.41Groceries 6,475.45 7,016.36 14.13 15.48 1.36Other payments(e.g., gifts) 8,786.15 7,041.67 19.17 15.54 -3.63Total 45,838.74 45,313.14 100.00 100.00 0.00

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Annex 16: Coping mechanisms in the face of crisis

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