metadata: authors: 0: author: Eozenou,Patrick Hoang-Vu
1: author: Madani,Dorsati H.
2: author: Swinkels,Robertus A
count: Mali
docna: 0: docna: Poverty, malnutrition and vulnerability in Mali
docty: Policy Research Working Paper
subtopic: Livestock and Animal Husbandry,Reproductive Health,Early Child and Children's Health,Nutrition,Educational Sciences,Inequality
teratopic: Macroeconomics and Economic Growth,Industry,Poverty Reduction
abstracts: cdata!: This paper provides new insight into the
            poverty, malnutrition and vulnerability issues in Mali,
            using existing household survey data. First, it presents a
            profile of households that are poor, "food poor,"
            or have malnourished children. Second, it explores the
            impact of recent weather and price shocks on household
            welfare and identifies those affected most by the shocks.
            Finally, it estimates vulnerability to poverty by modeling
            both households' expected consumption and their
            consumption volatility, and by distinguishing between
            idiosyncratic and covariate risks. The basic results of the
            analysis match conventional knowledge about poverty, food
            poverty, and malnutrition. The prevalence of chronic
            malnutrition is high in Mali, with 44 percent of Malian
            households and 66 percent of food poor Malian households
            having at least one stunted child. A 25 percent increase in
            cereal prices and a 25 percent decrease in cereal production
            are estimated to increase the number of food poor by 610,000
            people. An estimated US$ 5.4 million of extra aid per year
            will be needed to lift the newly food poor above the food
            poverty line. About US$ 182 million is needed to do this for
            all existing and new food poor. Vulnerability incidence is
            in general two to three times higher among the poor than the
            non-poor, except in urban areas and in the region of Sikasso
            where the vulnerability incidence is five to six times
            higher among the poor. Overall, vulnerability is mostly
            driven by poverty induced vulnerability, except in the
            capital, Bamako, where vulnerability is more driven by risk
            induced vulnerability.
geo_reg_key: 1927320,517191
repnme: repnme: Poverty, malnutrition and vulnerability in Mali
display_title: Poverty, malnutrition and
            vulnerability in Mali
listing_relative_url: /research/2013/08/18091518/poverty-malnutrition-vulnerability-mali
topicv3: Poverty m1326687 695,Education m1327048 470,Stunting m1327504 1445,Livestock m1504490 4024,Malnutrition m1627088 4737
publishtoextweb_dt: 2013-08-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Poverty Reduction
no_of_pages: 40
author_keys: 0: author_key: 1175217
1: author_key: 127245
2: author_key: 390341
model_extractions: WPS6561 Policy Research Working Paper 6561 Poverty, Malnutrition and Vulnerability in Mali _Patrick Eozenou Dorsati Madani Rob Swinkels_ The World Bank Africa Region Poverty Reduction and Economic Management Department August 2013
Policy Research Working Paper 6561

# **Abstract**

This paper provides new insight into the poverty, malnutrition and vulnerability issues in Mali, using existing household survey data. First, it presents a profile of households that are poor, “food poor,” or have malnourished children. Second, it explores the impact of recent weather and price shocks on household welfare and identifies those affected most by the shocks. Finally, it estimates vulnerability to poverty by modeling both households’ expected consumption and their consumption volatility, and by distinguishing between idiosyncratic and covariate risks. The basic results of the analysis match conventional knowledge about poverty, food poverty, and malnutrition. The prevalence of chronic malnutrition is high in Mali, with 44 percent of Malian households and 66 percent of food poor Malian households having at least one stunted child. A 25 percent increase in cereal prices and a 25 percent decrease in cereal production are estimated to increase the number of food poor by 610,000 people. An estimated US$ 5.4 million of extra aid per year will be needed to lift the newly food poor above the food poverty line. About US$ 182 million is needed to do this for all existing and new food poor. Vulnerability incidence is in general two to three times higher among the poor than the non-poor, except in urban areas and in the region of Sikasso where the vulnerability incidence is five to six times higher among the poor. Overall, vulnerability is mostly driven by poverty induced vulnerability, except in the capital, Bamako, where vulnerability is more driven by risk induced vulnerability.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at peozenou@worldbank.org, dmadani@worldbank.org, and rswinkels@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Poverty, Malnutrition and Vulnerability in Mali Patrick Eozenou, Dorsati Madani and Rob Swinkels**<sup>**1**</sup>

**July 2013**

# **Abstract**

This paper provides new insight into the poverty, malnutrition and vulnerability issues in Mali, using existing household survey data. First, it presents a profile of households that are poor, “food poor,” or have malnourished children. Second, it explores the impact of recent weather and price shocks on household welfare and identifies those affected most by the shocks. Finally, it estimates vulnerability to poverty by modeling both households’ expected consumption and their consumption volatility, and by distinguishing between idiosyncratic and covariate risks. The basic results of the analysis match conventional knowledge about poverty, food poverty, and malnutrition. The prevalence of chronic malnutrition is high in Mali, with 44 percent of Malian households and 66 percent of food poor Malian households having at least one stunted child. A 25 percent increase in cereal prices and a 25 percent decrease in cereal production are estimated to increase the number of food poor by 610,000 people. An estimated US$ 5.4 million of extra aid per year will be needed to lift the newly food poor above the food poverty line. About US$ 182 million is needed to do this for all existing and new food poor. Vulnerability incidence is in general two to three times higher among the poor than the non-poor, except in urban areas and in the region of Sikasso where the vulnerability incidence is five to six times higher among the poor. Overall, vulnerability is mostly driven by poverty induced vulnerability, except in the capital, Bamako, where vulnerability is more driven by risk induced vulnerability.

Key Words: Mali, poverty, malnutrition, vulnerability, impact of shocks JEL code: D00, I30, I32, I38.

Sector Board: Poverty Reduction and Economic Management (PREM)

> 1 All three authors are economists at the World Bank. Authors may be contacted at <u>peozenou@worldbank.org, dmadani@worldbank.org,  and rswinkels@worldbank.org.   We thank Ousmane</u> Diagana, Setareh Razmara and Eric Bell for their support during the preparation of this paper.   The findings, interpretations and conclusions expressed herein do not necessarily reflect the views of the Board of the Executive Directors of the World Bank or the governments they represent.
|Introduction...............................................................................................3|
|---|
|I.<br>The characteristics of the poor, the food poor and the malnourished ............................. 4|
|A. Household characteristics .....................................................................5|
|B. Location characteristics........................................................................6|
|C. Occupational activity..........................................................................7|
|D. Assets and Welfare.............................................................................8|
|E. Transfers.........................................................................................8|
|II. Impact of recent cereal price and production shocks on the food-poor..................10|
|A. The welfare effect of the current political/security crisis on Malian<br>households...........................................................................................13|
|III. Vulnerability to poverty ................................................................................................ 14|
|A. Concept and methodology....................................................................14|
|B. Results..........................................................................................15|
|**I**V. Analytical extensions .................................................................................................... 24|
|References ............................................................................................................................ 25|
|Annex 1: Methodology to measure vulnerability to poverty ................................... 26|
|A. Baseline model................................................................................26|
|B. Multilevel random effect model...............................................................27|
|Annex 2: Graphs ...................................................................................................... 29|
|Annex 3: Tables. ....................................................................................................... 31|

2
# Introduction

The objective of this paper is to shed light on the poverty, malnutrition and vulnerability profile in Mali before the March 2012 coup and assess the impacts of some production and price shocks the country has sustained recently. The Malian economy was buffeted by a partial drought in 2011. Furthermore, by summer 2012, the coup and internal strife had led to some 320,000 refugees, of which some 40-45% was internally displaced refugees. That is about 27% of the population of the North of Mali. The disruption of the normal life and production capacity of a large number of Malians are expected to have considerable effects on their vulnerability and ability to cope.

The paper identifies the poor, food poor and malnourished in Mali, providing a short profile of their characteristics and where they are. It assesses the impact of the recent food price and food production shocks on these different groups. We also estimate the impact of the internal refugees staying with relatives, finding that this arrangement affects the welfare of host families. The study then analyzes the vulnerability of Malians, looking at whether this vulnerability is poverty induced (low mean consumption) or risk induced (high consumption volatility), as well as whether it is induced by idiosyncratic risk or covariate risk. A final section provides suggestions for further analytical extensions. The data used for this analysis are from the household survey ELIM/MICS 2009/2010, the latest available for such an analysis.

A household is considered poor if its per capita consumption is below the national poverty line. This was set at 165,431 FCFA (approx. 346 USD) per capita per year for the ELIM/MICS 2010 survey. A household is said to be ‘food poor’ (or extremely poor) if its level of total expenditure per capita is below the national food poverty line. In 2010, this food poverty line was set at 118,173 FCFA per person per year (approx. 247 USD). This food poverty line is defined as the minimum consumption expenditure needed to meet the cost of consuming 2,450 kilocalories per day, reflecting Malian consumption habits.

Malnutrition indicators are based on nutritional outcomes (weight and height) measured for all children aged 0-59 months in the ELIM-MICS 2009/10 survey. Three basic malnutrition indicators are considered here (stunting, wasting and underweight). Stunting is based on a child’s height and age. It is a measure of chronic nutritional deficiency.<sup>i,ii</sup> Wasting is based on a child’s weight and height. It is a measure of short-term nutritional deficiency.<sup>iii</sup> Underweight is based on a child’s weight and age. It is a composite measure of chronic and acute nutritional deficiency.<sup>iv</sup> 3
# I. The characteristics of the poor, the food poor and the malnourished

In this section we look at different groups of vulnerable households and we compare them across common dimensions such as: household characteristics, location characteristics, activity, assets owned, welfare, and transfers received. We compare the general Malian population to (1) the poor, (2) the food poor and (3) malnourished (households with at least a ‘stunted’ child; stunting is also referred to as chronically malnourished).

For the analysis of malnourishment, we focus on the subsample of households for which information on children’s nutritional outcomes is available. These are all the households with at least one child under the age of 5 years old. Using population weights, this subsample is representative for about 937,400 households or 10.8 million people (the total population of Mali was 14.5 million in 2010).

Overall, the prevalence of malnutrition in Mali is high. This counts especially for chronic malnutrition. 44 percent of households have at least one child that is chronically malnourished. This prevalence of malnutrition is even higher among the poor (59%) and among the food-poor (66%).<sup>v</sup> In 2010, the proportion of people in Mali who were food poor (lived in a household whose consumption per person was below the food poverty line) was 22 percent (or some 3.1 million people).

**Table 1:  Prevalence of malnutrition among the whole population, the poor and the food poor (%)**

|**Households with at least:**|**Whole population **|**Poor **|**Food poor **|
|---|---|---|---|
|one wasted child|18|23|25|
|one stunted child|44|59|66|
|Both|11|17|20|

**_Source_** _: Authors’ calculations using ELIM –MICS 2010_

Food poverty is positively associated with malnutrition. The graph plots in Annex 1 show that there is a positive association between food poverty and malnutrition at the district level in Mali. This relation is strongest for chronic malnutrition. The correspondence is however not perfect, and we find households who are food poor but not malnourished (upper-left part of the graph plots) as well as households who are not food-poor but who are malnourished (lower-right part of the plots). Furthermore, it is noteworthy that while food poverty affects malnutrition outcomes by restricting access to food resources, lack of access is not the unique channel to malnutrition. A proper utilization of the resources, as well as other environmental factors such as sanitation and access to health services, also influence nutritional outcomes.

Looking at the intersection between food poverty and chronic malnutrition, we find that 12 percent are both malnourished and food poor. Six percent are food poor but not chronically malnourished and 32 percent are malnourished but not food poor at the time of the survey. The remaining 50 person are neither food poor nor malnourished.

4
## **Figure 2.1:  Distribution Between Food Poverty And** **<u>Chronic Malnutrition</u>**

<!-- Start of picture text --> Food Poor<br><!-- End of picture text --> <!-- Start of picture text --> 413<br> 6 %<br><!-- End of picture text --> <!-- Start of picture text --> 746<br> 12 %<br><!-- End of picture text --> <!-- Start of picture text --> 3256<br> (50 %)<br><!-- End of picture text --> <!-- Start of picture text --> Malnourished<br><!-- End of picture text --> <!-- Start of picture text --> Sample of<br> N = 6449<br><!-- End of picture text --> <!-- Start of picture text --> 2034<br> 32 %<br><!-- End of picture text --> _Source: June 5, 2012_

**.**

# **A. Household characteristics**

Poor, food poor and malnourished households are larger than the median household size in Mali. The median household in Mali is composed of 9 members. In comparison, the food poor households have 12 members and malnourished households have 15 members. The poor and vulnerable households are also characterized by a larger average number of dependent members (1.5-1.6 dependent members for every active member compared to 1.4 on average for the whole population). In addition, poor and food poor households are also more likely to have an older head of household (51-52 years old of median age compared to 48 for the whole country) and they are more likely to be headed by members with no primary level education. On the other hand, households with heads that have secondary or more education are much less affected by poverty, food poverty and stunting. Female headship does not appear to affect households negatively, as fewer of these households are food poor. This phenomenon is seen often in poverty analysis and follows largely from our definition of welfare<sup>vi</sup> (Table 2).

5
**Table 2:  Household** **<u>characteristics of the poor, the food poor and the malnourished</u>**

|**Household characteristics**|**Overall**<br>**population **|**Poor**|**Non-poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Household size (median)|9|11|12|10|
|Age dependency ration|1.4|1.6|1.6|1.5|
|Female headed (proportion of column)|7%|6%|4%|7%|
|Head’s age (median)|48|51|52|50|
|Head: no education|77%|87%|90%|85%|
|Head: primary education only|11%|9%|9%|10%|
|Head: secondary education or more|12%|4%|2%|6%|
|**Total**|**100%**|**100%**|**100%**|**100%**|
|Total number of sample households|6449|2335|1159|2095|
|Population estimate- individuals|10.8|5.1|2.6||
|(millions)|||||
|Population estimate – households|0.9|0.35|0.16|0.31|
|(millions)|||||

_Source: authors’ calculations using ELIM –MICS 2010_

# **B. Location characteristics**

While 77 percent of the overall Malian population lives in rural areas, nearly all the poor (90%) and the food poor (93%) are concentrated in rural areas and (table 3). Most (84%) of the malnourished live in rural areas. Households with malnourished children are spread across the country in proportion to the population. The only exception perhaps is Sikasso where the chronically malnourished are more overrepresented (19% of the chronically malnourished live there while only 16% of the population does) (table 3). While 11 percent of our population resides in the northern regions of Mali (Timbuktu, Gao, and Kidal) the share of poor and food-poor households living in the North is actually smaller than the national average. This is because the northern regions are the least populated in Mali. Poverty rates in the Sikasso region are fairly high despite despite good agricultural conditions and cotton production. This so-called ‘Sikasso Paradox’ is partly caused by the relatively high poverty line that is set for this region (Delarue etal. 2009, Dury and Bocoum 2012, World Bank, 2012).

6
# ~~<u>ry</u>~~
# **D. Assets and welfare**<sup>**ix**</sup>

Food-poor households appear to have few assets (worth 10.107 FCFA per capita). This implies that in addition to being extremely poor they also tend to be very vulnerable. In case of weather or health shocks they have limited assets that they can sell to meet emergency needs. The food-poor and the malnourished are over-represented among livestock owners: 35 percent of the poor and 36 percent of the food-poor own livestock while 27 percent of all Malian households do (Table 5). However in case of shocks such as droughts, livestock prices often tend to drop dramatically reducing the ability of these households to turn them into cash to purchase emergency needs.

**Table** **<u>5:  Assets of the poor the food poor and the chronically malnourished</u>**

||**Whole**<br>**population **|**Poor**|**Food poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Mean consumption expenditure per<br>capita (FCFA)|206,901|121,025|95,092|177,942|
|Value of owned durable goods|18,571|11,062|10,107|15,408|
|(median per capita, FCFA)|||||
|Owns livestock (cows, sheep, goat,<br>camels) (% of column total that|27%|35%|36%|32%|
|does)|||||
|Value of livestock(median,FCFA)|393,519|450,640|477,897|433,088|

_Source: authors’ calculations using ELIM –MICS 2010 household survey data_ We analyze the characteristics of the food poor and malnourished households with low levels of assets (Table A 2 in Annex). Results suggest that the group with low assets tend to have smaller households and a higher ratio of dependent/ active household members compared to those with high assets. Malnourished households with low assets are more likely to be headed by a member with less than primary education. Furthermore, the amount of assets held does not reveal any geographical pattern, except for Sikasso. While this region houses only 16% of the Malian population, it has 36% of the food-poor with low-asset holdings. Malnourished households with low levels of assets are only overrepresented in farming and primary sector (respectively 84% and 85%).

# **E. Transfers**

Transfers such as remittances from household members or support from the state or NGOs can improve household resilience. We find that the poor food poor and malnourished households are as likely as other households to receive domestic transfers (21-22%) (Table 6). The median value of these transfers (in per capita terms) is however about 2500, 1875 and 3333 FCFA per capita for the poor, the food poor and the malnourished respectively, less than other households , who receive 4166 FCFA per capita. Fewer poor and food-poor households receive foreign remittances compared to all households (12-14% against 19%) but the median value of the transfers received is about the same (about 5000 FCFA). The 8
poor and food poor households who do not receive family assistance are over-represented in Sikasso and Segou, while the malnourished without family remittances are overrepresented in Sikasso, Segou and Mopti. The incidence of public transfers is marginal with less than 1% of the population receiving transfers.

**Table** **<u>6:  Transfers received by the poor, food poor and the malnourished</u>**

||**Whole**<br>**population**|**Poor**|**Food poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Proportion receiving domestic remittances|22%|22%|21%|22%|
|Value (median per capita, FCFA)|4167||||
|Proportion receiving foreign remittances|19%|14%|12%|19%|
|Value ((median per capita, FCFA)|10,000|5,556|5,000|9,339|
|Proportion receiving public transfers|0.8%|0.7%|0.3%|0.6%|
|Value((medianper capita,FCFA)|13,636|9,545|11,604|8,438|

_Source: authors’ calculations using ELIM –MICS 2010 household survey data Note: percentages are across columns, i.e. 22 % of the whole population receives domestic remittances_ **_._** 9
# II. Impact of recent cereal price and production shocks on the food-poor

Mali has experienced shocks to cereal prices due to the international rise in cereal prices in recent years (2008/2009 food price rises as well as 2011 price rises). Also, the country experienced partial drought in 2011, affecting production of cereals, especially rice. In February 2012 the government of Mali assessed 111 communes at risk of food crisis while another 85 were identified as communes in economic difficulty due to the drought. Globally, there was need to assist some 3.575 million Malians. In this section we use the ELIM-MICS 2010 data to simulate the impact on the food poor of such cereal price and production shocks.

The food-poor are highly dependent on purchases and self-production of cereals in their total consumption. Nationwide, cereals form on average about a quarter of household consumption, half of which are purchased grains with the other half being self-produced grains. The share of purchased grain in overall consumption is highest in Timbuktu and Gao (22%) and lowest in Sikasso (6%). In Timbuktu and Gao (Northern Mali) cereals form about a third of total household consumption indicating the overall vulnerability of this region to changes in cereal prices and production.

**Table 7.** **<u>Share of purchased and self-produced cereals in total consumption by region</u>**

|**Region **|**Share of**<br>**purchased**<br>**cereals in total**<br>**consumption**<br>**(%)**|**Share of self-produced**<br>**grain in total**<br>**consumption (%)**|**Share of both purchased**<br>**and self-produced grain**<br>**in total consumption**<br>**(%)**|
|---|---|---|---|
|Kayes-Koulikourou|11|11|22|
|Sikasso|6|17|23|
|Mopti-Ségou|11|16|27|
|Tomboucto-Gao|22|10|32|
|Bamako|15|1|16|
|Total|12|12|24|

_Source: authors’ calculations using ELIM –MICS 2010_ We assess the impact of recent cereal price and production shocks on the number of food poor, that is, the people that have a total consumption level that is below the food poverty line of 118 173 FCFA per person per year. We do this by looking first at the amount of cereals households purchased in 2010 using the ELIM survey of that year and the share of purchased and self-produced cereals in total consumption. We then calculate the extra amount those households would have to pay to purchase that same amount of cereals, given the post price increase. We assume that quantities of cereals bought do not change 10
and that there is no substitution into other food items. We subsequently reflect the loss of purchase power by reducing household consumption expenditure by the extra costs they would incur to buy the same amount of cereals at higher prices. For self-produced cereals we calculate the value of the reduction in cereal production following the drought and adjust total consumption expenditure accordingly. We then assess the effect these changes in consumption expenditure have on food poverty rates across the country.

In 2010 the total number of these food poor people was 3.2 million (22% of the population), of which a majority lived in Sikasso, Mopti and Segou. Following the 2011 drop in cereal production (estimated at 25% at end December 2011) due to localized drought, the number of the food poor is estimated to have increased by 363,000 people. If we also take into account the rise in cereal prices and the consequent drop in purchase power, the total amount rises by another 174,000 people, _increasing the number of foodpoor Malians from 22% to 26% of the population or an additional 610.000 people._ The largest absolute increase is in Mopti-Segou where 227,000 more people become food poor as consequence of the combined shocks. The largest relative increases are witnessed in northern regions of Mali where the proportion of people living in food poor households increases by 41% (in total an additional 58,000 households) (See Table 8). This implies that even before the March 2012 coup, the northern regions of Mali was already witnessing the largest relative increase in the number of food poor households in the country, following the price and production shocks of 2011.

**Table** **<u>8. Number of people (in ‘000) per region under the fod poverty line without and with shocks</u>**

||**No of**<br>**existing**<br>**foodpoor**|**Food poor**<br>**if cereal**<br>**prod**<br>**reduces by**<br>**25%(a)**|**Food poor if**<br>**cereal prices**<br>**rise by 25%**<br>**(b)**|**Both**<br>**shocks (a)**<br>**+(b)**|**Number of**<br>**additional**<br>**food poor**<br>**people**|**%**<br>**increas**<br>**e in**<br>**food**<br>**poor**<br>**people**|
|---|---|---|---|---|---|---|
|Kayes-Koulikoro|578|674|636|744|166|29%|
|Sikasso|1504|1619|1543|1653|149|10%|
|Mopti-Ségou|977|1122|1013|1204|227|23%|
|Tombouctou-Gao-Kidal|141|159|173|199|58|41%|
|Bamako|51|47|66|66|14|28%|
|Total|3210|3574|3385|3821|610|19%|
|Additional (no)||363|174|610|||
|Additional (% of total||2.5%|||||
|population)|||1.2%|4.2%|||

_Source: Authors’ calculations using ELIM –MICS 2010._ Those who have become food poor following both shocks are overwhelmingly located in rural areas. However, the rise in cereal prices also increases the number of the food poor 11
households in urban areas by some 30,000 people. Most of the newly food poor are farmers or livestock owners (88%) while 6% are self-employed and 4% without employment. The elderly are somewhat overrepresented: 40% of the newly food poor live in a household headed by someone older than 60 years, whereas this group represents only 32% of the population.

Asset ownership among those who have fallen below the food poverty line due to these shocks is relatively low. The median worth of these household assets is on average about 10,100 FCFA per capita compared to the median of 18,400 FCFA for the rest of the population (table 9). In contrast, livestock ownership tends to be higher among the group that has fallen below poverty line (a median of 27,000 FCFA per capita compared to 18,000 FCFA among the rest of the population).. The median worth of household assets and livestock combined is 48,000 FCFA per capita among this group, or about half the food poverty line. So if they fall 10% below the food poverty line, selling all their assets would enable them to get back to the food poverty line for just six months. This clearly would be nothing more than a very temporary solution.

**<u>Table 9. Value of assets per capita in FCFA for the new food poor (following shocks)</u>**

||**The new food poor**<br>**following production and**<br>**price shocks**|**Rest of the population**|
|---|---|---|
|Median value of household assets|10,100|18,400|
|Median value of livestock assets|27,104|17,934|
|Median value of both household and livestock|47,800|57,200|
|assets|||
|Mean family remittances|2,400|10,400|
|Median transfers from NGOs orgovernment|8,000|12,500|

_Source: Authors’ calculations using ELIM –MICS 2010 household survey data. Note: The "new food poor" are those that will fall below the food poverty line following the production and price shocks._ Remittances and support by NGOs and the government can play an important role in cushioning shocks. Households can be identified using the ELIM 2010 survey. Using this data we estimate that of the 610,000 people who have become food poor following the two shocks about 33% receives family remittances. But only about 0.1% have received any transfers from NGOs or the government The amounts received per person per year are small. Average levels per capita of these remittances are FCFA 2400 per year for those that have fallen under the food poverty line following the shocks as compared to FCFA 10.400 for the rest of the population.

Under the scenario of the country experiencing a 25% drop in cereal production and a 25% cereal price increase, and assuming perfect targeting of aid, an estimated 2.7 billion FCFA (US$5.4 million) per year of additional assistance will be needed to raise the level of consumption of the households who have fallen below the food poverty line back up to the 12
food poverty line. 39 percent of these funds are needed in Mopti-Segou, as the largest number of people who have fallen below the food poverty line are located there. About 12 percent of this amount is needed in northern Mali. These are not very large amounts. If we include the households that already were below the food poverty line before the shocks, the total amount is much higher: about 182 million USD per year.

# **A. The welfare effect of the current political/security crisis on Malian households**

The March 2012 coup and consequent events have led to the vulnerabilization of an additional number of Malians. The 87 communes forming the three regions of north Mali (Timboucou, Gao, Kidal) and the 17 communes of the neighboring Mopti are now in humanitarian crisis. The government places the preliminary number of individuals affected at an additional 1.76 million Malians. Some the 320,000 Malians have fled. Some 55% have taken refuge in neighboring countries (Mauritania, Niger, Burkina and Algeria) while some 45% are domestically displaced persons. The domestically displaced Malians have moved to all southern regions, but an important number have remained in the Mopti – Segou area, region that is immediately adjacent to the conflict area due to language and familial affinities. This influx of displaced persons is reportedly placing economic pressure on both host and guest households. Food prices since then have increased even further and the increase in the number of food poor household is therefore expected to be even higher than estimated above.

Welfare loss for the northern household is large, regardless of whether they have been dislocated or not. It is associated with loss of current assets and revenues as well as earning potentials in the future, not to mention the health and educational consequences of the crisis on the welfare of all northern households. Undertaking an estimation of this welfare loss is not feasible at this stage as it requires a thorough survey.

The southern households are also bound to be affected by the political, security and economic uncertainties. One element of this impact is the welfare effect of the dislocation of a typical northern household on a typical southern host household. This will likely increase the vulnerability of the recipient households. We estimate this welfare loss as follows. We note that the median household size is 6 in the Northern regions and 8 in the South. On average, an increase in household size from 8 to 14 (+75%) is associated with a welfare loss of about 27% if the host household belongs to the first wealth tercile, 25% if it belongs to the second tercile, and 16% if it belongs to the third (see figure 2). These welfare changes are rough estimates for short term effects and do not take into account any welfare contribution by the displaced household.<sup>x</sup> The security situation in the north of Mali and continued political uncertainty in the south will affect the prospects for economic recovery as the transitional government prepares for reunification of the country and elections in 18 to 24 months. This uncertainty suggests 13
further downside risks for the welfare of the Malians, but especially the sizeable vulnerable groups.

**Figure 2.1:  Poverty Rate And Vulnerability Incidence At The District Level**

<!-- Start of picture text --> (Cercles)<br><!-- End of picture text --> <!-- Start of picture text --> Welfare Effects of Increasing Household Size<br>By Wealth Category (Terciles of Durables Owned)<br>0 8 10 14 20 25<br>Household Size<br>1st Tercile of Wealth 2nd Tercile of Wealth 3rd tercile of Wealth<br>Source: ELIM 2009/10 and authors' calculations.An average increase in household size from 8 to 14 is associated with a welfare loss of27% for households in the first wealth tercile, 25% for households in the second wealth tercile and 16% for households in the third wealth tercile<br>200<br>150<br>122<br>90<br>Consumption Expenditure<br> per Household per year (1000 FCFA)<br>0<br><!-- End of picture text -->

# III. Vulnerability to poverty

## **A. Concept and methodology**

Household income in developing countries is generally very volatile because poor households are often exposed to household specific and community level shocks. Moreover, available insurance mechanisms to protect income from the influence of these shocks are often absent or imperfect. Therefore, poverty status observed at some point in time might not be the best guide to inform about households’ long term wellbeing.

In this section, we assess the households’ ex-ante vulnerability to poverty. We use Chaudhury (2003) methodology to estimate vulnerability based solely on cross-sectional data. We measure vulnerability as the conditional probability of a household’s income to fall below the poverty line in the next period. A household will be classified as vulnerable if its predicted vulnerability level is above a defined threshold. Usually a threshold of 0.5 is chosen in the literature. Under this framework, the source of vulnerability can be decomposed into high volatility (risk induced vulnerability) and low mean consumption (poverty induced vulnerability).

This approach is extended by introducing idiosyncratic and covariate risks. This is done by introducing a multilevel random effect model (Gunther and Harrtgen (2009)). 2 The consumption risk faced by households is defined by household specific risks (idiosyncratic

> 2 This approach has also been taken more recently by Imai etal. (2011) and Imai (2011).

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risks) and community level risks (covariate risks). Distinguishing between idiosyncratic and covariate risks is important for two reasons. First, informal risk sharing mechanisms taking place at the community level can be effective in partially insuring idiosyncratic risk but will remain vulnerable to the realization of aggregate risks (covariate risks). Second, making this distinction is relevant from a policy perspective to design the most appropriate safety net mechanisms. The targeting of the vulnerable population for example will differ according to whether shocks are dominantly idiosyncratic or covariate. The details of this methodology are presented in the annex.

# **B. Results**

We estimate two specifications for our empirical model of log consumption per capita. The set of explanatory variables Xij includes household demographics (household size, age dependency), head of household’s characteristics (gender, education level, activity), some indicators of wealth (livestock ownership, wealth index 3 ) and rainfall shocks experienced at the commune level in 2008 and in 2009.<sup>4</sup> The first specification is a simple OLS model (column 1 in Table 10). The sizes of the household and female headship each has a negative marginal contribution to welfare and is associated on average with 4% lower consumption per capita. Households where the head has completed primary education have on average a 3% premium on consumption per capita compared to households where the head has no education. For households where the head has completed secondary education, this premium is close to 12%. Wealth is positively associated with total consumption expenditure per capita, whether we look at livestock ownership or at measures based on durable goods ownership and housing.

Farming households living in districts where cotton production is an important activity are significantly worse off compared to the other households.<sup>5</sup> Consumption per capita is almost 30% lower for these households, everything else held equal. This is not a new finding since – as mentioned above - many studies have already documented the so-called “Sikasso paradox” situation in which poverty rates are surprisingly high in a fertile region dominated by cash crop production (Delarue etal. 2009, Dury and Bocoum 2012, World

> 3 The wealth index is based on a principal component analysis based on durable goods ownership and on housing conditions.

> 4 Rainfall data come from the NASA Tropical Rainfall Measuring Mission (TRMM) which provides a narrowly gridded (0.25x0.25 degrees of longitude and latitude) dataset of accumulated precipitation. Monthly data at aggregated for 2008 and 2009, and commune level rainfall is imputed from the 5 closest grid points using an Inverse Distance Weighting (IDW) algorithm. Rainfall shocks are then constructed based on the yearly standardized deviation from the 1998-2011 commune-specific precipitation average: zct = Rctσ−Rc,1998−2011R�����������������c,1998−2011 where Rct is the accumulated rainfall in commune c and

> year t, R���������������c,1998−2011 is the average yearly rainfall for commune c between 1998 and 2011, and σc,1998−2011R is the standard deviation of yearly accumulated rainfall for commune c between 1998 and 2011.

> 5 The ELIM 2010 survey did not ask directly to the respondents whether households were explicitly engaged in cotton production. Our cotton variable is constructed as the intersection between the set of households engaged in agriculture and the set of households living in districts where the Malian state owned cotton company (CMDT) operates (http://www.maliagriculture.org/services_tech/cmdt/page-cmdt.html).

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Bank, 2012). However, to our knowledge, this is the first study looking at the issue with a regression analysis.

Finally, rainfall shocks experienced at the commune level in 2008 and 2009 are positively associated with welfare. Households experiencing a one standard deviation drop in local precipitation in 2009 have on average a 6% lower consumption per capita (-5% for a similar magnitude shock experienced in 2008).

We also estimate a multilevel random effect model (second column in Table 10) with a cluster level specific effect. The estimated coefficients for the household and the cluster level variables remain in the same order of magnitude. We are now able however to decompose the unexplained variance into a between cluster component and a within cluster component. The unexplained variance in consumption accounted for by the covariate (between cluster variance) accounts for about 28% of the total unexplained variance. The remainder is explained by idiosyncratic risk only (within cluster variance).

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|Dependent: Log consumption per capita|||
|---|---|---|
|VARIABLES|als|Multilevel<br>Random Effect|
|Household size|-0.0457***|0.0427***|
||(0.001)|(0.001)|
|Household size squared|0.0005***|0.0005***|
||(0.000)|(0.000)|
|Age dependency over size|-0,322***|-0.350***|
||(0.024)|(0.021)|
|Female Head|-0.0418***|-0.0636"**|
||(0.015)|(0.014)|
|Primary education|0.0334**|0.0463***|
||(0.015)|(0.013)|
|Secondary+ education|o.115***|0.129"**|
||(0.015)|(0.014)|
|Secondary sector|-0.0431**|-0.0360**|
||(0.019)|(0.018)|
|Tertiary sector|-0.00087<br>|0.00919<br>|
||(0.015)|(0.015)|
|Public sector|0.0013|0.0031|
||(0.016)|(0.015)|
|Cotton district|-0.354***|-0.229***|
||(0.014)|(0.020)|
|Small cow herder (=10)|0.0334***|0.0495***|
||(0.012)|(0.011)|
|Large cow herder (>10)|0.122***|0.136***|
||(0.021)|(0.019)|
|Small sheep herder (<10)|0.0394***|0.0306***|
||(0.011)|(0.010)|
|Large sheep herder (>10)|0.113***|0.0722***|
||(0.020)|(0.019)|
|Wealth index Q1|-0.207***|0.158***|
||(0.015)|(0.014)|
|Wealth index Q2|-0.0837***|0.0754***|
||(0.014)|(0.013)|
|Wealth index Q4|0.102***|0,140***|
||(0.015)|(0.015)|
|Wealth index5|0.390***|0.404***|
||(0.018)|(0.018)|
|Yearly rainfall shock (z-score) 2008|0.0569***|0.0623***|
||(0.004)|(0.008)|
|Yearlyrainfallshock(z-score)2009|0.0625***|0.0833***|
||(0.011)|(0.020)|
|Constant<br>|12.73%**|12.66***<br>|
|9.0<br>|1|)<br>|
|cluster variance||0.051***<br>(0.003)|
|Within cluster variance||0.129***|
|||(0.002)|
|Observations<br>Standard errorsin parentheses<br>*** 9<0.01, ** p<0.05, * p<0.1|9,036|9,036|

|~~Ct~~|
|---|
**Figure 3:**

<!-- Start of picture text --> Vulnerability Distribution (Expected Poverty)<br>0 10 20 30 40 50 60 70 80 90 100<br>Estimated Probability to Fall Below the Poverty Line<br>Source: ELIM 2010<br>Density<br><!-- End of picture text --> While poverty and vulnerability to poverty are closely related, the two concepts are not the same and the set of vulnerable households does not overlap exactly with the set of poor households (Figure 4). Poor households are on average more vulnerable (61.8%) than nonpoor households (28.7%).

**Figure 4**

<!-- Start of picture text --> Predicted Vulnerability<br>Among the Poor and the Non-poor<br>0 20 43.2 50 60 80 100<br>Predicted Vulnerability (Covariate + Idiosyncratic Risk)<br>Poor Non-poor<br>Source: ELIM 2010 and authors'calculations.<br>The vertical line represents the aggregate level of vulnerability (43.2%).<br>Density<br><!-- End of picture text --> However, if we choose 0.5 for our vulnerability threshold (i.e. a household will be classified as vulnerable if its probability to fall below the poverty line is higher than 50%), we note that not all the poor households are classified as vulnerable (34% are not vulnerable), and some non-poor households are classified as vulnerable (19%). Figure 5 18
below also shows how the vulnerability incidence varies between poor and non-poor when we vary the threshold above which households are classified as vulnerable.

**Figure 5**

<!-- Start of picture text --> Vulnerability Incidence<br>0 10 20 30 40 50 60 70 80 90 100<br>Vulnerability Threshold<br>Overall Poor Non-poor<br>Source: ELIM 2010 and authors'calculations.<br>The vertical line represents the chosen threshold for our vulnerability analysis.<br>100<br>80<br>60<br>40<br>20<br>Vulnerability Incidence (%)<br>0<br><!-- End of picture text --> At the regional level, average vulnerability also tends to be higher where poverty rates are higher, but again, there is no one to one mapping between poverty and vulnerability. Sikasso appears to be the region where both poverty and vulnerability are the highest in the country (Figure 6).

**Figure 6**

<!-- Start of picture text --> Poverty Rate and Vulnerability Incidence<br>By Region<br>Vulnerability Poverty<br>Source: ELIM 2010.<br>The horizontal line represent the average poverty (43.6%) rate and the aggregate vulnerability level (43.2%).<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>80<br>60<br>40<br>20<br>Poverty Rate and Vulnerability Incidence (%)<br>0<br><!-- End of picture text --> From Figure 7 and 8, we see however, that the high vulnerability incidence in Sikasso is mostly a concern of the households who are already poor. Among households with levels of welfare above the poverty line, vulnerability is not higher in Sikasso than other regions. One implication is that to reduce vulnerability in a region like Sikasso, designing safety net 19
mechanisms such as cash transfers targeting the poor seems to be a valid option. In other regions, like Segou for example, where vulnerability seems to be driven as much by high consumption volatility as by low mean consumption, introducing insurance mechanisms may also be a viable policy to consider.

**Figure 7**

<!-- Start of picture text --> Vulnerability Incidence Among the Poor<br>By Region<br>Idiosyncratic Risk Covariate Risk<br>Source: ELIM 2010.<br>The horizontal line represent the average vulnerability incidence among the poor (56.4%).<br>Figure 8<br>Vulnerability Incidence Among the Non-Poor<br>By Region<br>Idiosyncratic Risk Covariate Risk<br>Source: ELIM 2010.<br>The horizontal line represent the average the vulnerability level among the non-poor (32.7%).<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>Kayes Koulikoro Sikasso Ségou Mopti Tombouctou Gao Kidal Bamako<br>80<br>60<br>40<br>Vulnerability Incidence (%) 20<br>0<br>40<br>30<br>20<br>Vulnerability Incidence (%) 10<br>0<br><!-- End of picture text --> Figure 9 suggests that poverty and vulnerability in Sikasso are equally high in all districts. Plotting the poverty rate against the vulnerability incidence at the district (cercle) level, we 20
see that all the districts of Sikasso are in the upper right quadrant (high poverty-high vulnerability).

**Figure 9**

<!-- Start of picture text --> Poverty and Vulnerability<br>Provincial Level (Cercles)<br>Baroueli Kita Dioila KangabaSan KadioloBlaYanfolilaSikassoKoutiala Yorosso<br>Banamba<br>Yelimane BaDiema f ou l abe KayesNioroMacina D Bankassj enne TominianSegouKoulikoro Koro BougouniKolondieba<br>AbeibaraTin-EssakoTenenkouKidal YouwarouKatiTombouctouTessalitAnsongoBoueremNionoDireGaoMenakaNiafunkeMoptiKeniebaGourma-RharousGoundamBandiagara Nara Kolokani Douentza<br>Bamako<br>0 20 40 60 80 100<br>Poverty rate at the provincial level (%)<br>Source: ELIM 2010.<br>The red crosses are in Sikasso.<br>The dashed horizontal line represents the national aggregate for vulnerability incidence (38.6%),<br>and the dashed vertical line represents the national aggregate for the poverty rate (43.6%).<br>The solid line is a linear fit.<br>80<br>60<br>40<br>20<br>Expected vulnerability incidence (%)<br>0<br><!-- End of picture text --> In Table 11 we compare the poverty rate and the vulnerability incidence across regions. Overall, vulnerability among the poor is 3.5 times higher than among the non-poor. However, in Bamako, in other urban areas, and in the region of Sikasso vulnerability among the poor is between 5 and 6 times higher than among the non-poor.

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**<u>Table 11: Vulnerability and poverty across regions</u>**

|**P**|**overty Rate**||<br>**Vulnera**|<br>**bility Rate**||
|---|---|---|---|---|---|
|||**Overall (1)**|**Poor (2)**|**Non-poor (3)**|**(2) / (3)**|
|**Overall**|43.6%|39.2%|65.9%|18.6%|3.5|
|**Urban**|30.7%|15.7%|37.3%|6.1%|6.1|
|**Rural**|50.6%|48.1%|69.9%|25.9%|2.7|
|**Regions**||||||
|**_Kayes_**|26.5%|33.4%|56.5%|25.0%|2.3|
|**_Koulikoro_**|41.7%|41.2%|66.6%|23.0%|2.9|
|**_Sikasso_**|83.8%|73.0%|84.0%|16.5%|5.1|
|**_Segou_**|47.0%|51.0%|69.4%|34.7%|2.0|
|**_Mopti_**|46.6%|31.1%|47.9%|16.4%|2.9|
|**_Tombouctou_**|31.1%|19.9%|35.9%|12.7%|2.8|
|**_Gao_**|26.9%|19.9%|33.2%|15.0%|2.2|
|**_Kidal_**|14.3%|9.0%|16.8%|7.8%|2.2|
|**_Bamako_**|9.6%|1.4%|5.4%|1.0%|5.3|

Source: ELIM 2010 and authors' calculations.

In Table 12 we decompose vulnerability by distinguishing between poverty induced vulnerability (i.e low mean consumption) and risk induced vulnerability (i.e high consumption volatility). This distinction is important because the policies seeking to reduce households’ vulnerability would differ according to which source of volatility is most prevalent. If vulnerability is driven only by high volatility in consumption then designing policies to reduce exposure to risks or to strengthen households’ ability to cope with risks once they occur might be right strategy. If however vulnerability is also driven by low mean consumption, then policies aimed at improving livelihoods such as cash transfers might be necessary to complement risk reduction strategies.

If we focus on this dimension of vulnerability, we see from the table below that in Bamako, vulnerability is mostly driven by high volatility (risk induced). In other cities and also in rural areas however, vulnerability seems to be mostly driven by low mean consumption.

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**<u>Table 12: Vulnerability decomposition</u>**

||**_National_**|**Bamako**|**Urban**|**Rural**|
|---|---|---|---|---|
|**Poverty rate**|_43.6%_|9.6%|30.7%|50.6%|
|**Mean vulnerability**|_43.2%_|12.2%|26.7%|50.1%|
|**Vulnerability rate**|_39.2%_|1.4%|15.7%|48.1%|
|**Poverty induced vulnerability**|_28.7%_|0.5%|11.5%|35.3%|
|**Risk induced vulnerability**|_10.5%_|0.9%|4.3%|12.8%|
|**Poverty/risk**|_2.7_|0.6|2.7|2.8|
|**Idiosyncratic vulnerability**|_43.1%_|10.8%|25.0%|50.5%|
|**Covariate vulnerability**|_40.7%_|3.3%|19.6%|49.2%|
|**Idiosyncratic/covariate**<br>Source: ELIM 2010 and authors' calculations.|_1.1_|3.3|1.3|1.0|

The other dimension along which we can differentiate the sources of vulnerability is whether vulnerability is induced mostly by community level (covariate) or household level (idiosyncratic) variations in consumption. The study finds that community level and household level are about equally important drivers of vulnerability in Mali, both in rural and urban areas. Bamako is the exception to this finding: idiosyncratic risk is the dominant source of vulnerability in the capital.

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# IV. Analytical extensions

Based on the latest household expenditure survey available for Mali, we have analyzed the determinants of poverty, food poverty and of vulnerability to poverty in Mali up prior to the March 2012 coup.

Poverty, food poverty and vulnerability in Mali are mostly concentrated among the rural farmers. One region stands out with relatively high rates of poverty and vulnerability despite being a rich agricultural area where cotton represent the main cash cropping activity. This so-called “Sikasso-paradox” could be further analyzed either through qualitative methods, or through additional data collection combining agricultural information with household data. A first round of LSMS-ISA survey was scheduled to be conducted in Mali just before the coup of March 2012. This type of dataset could inform in more depth about the factors driving the Sikasso paradox.

Were additional and comparable household surveys be conducted in Mali after the situation is stabilized, small area based methods using repeated cross sections could extend the present analysis of vulnerability which is based on a single cross-section only.

Finally, poverty measurement can be improved in Mali through (1) strengthening the calculations of regional consumer price indices, (2) ensuring household survey comparability by keeping questionnaires and seasonal timing of surveys constant and (3) reviewing calculations of regional poverty lines.

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