rafmacalaba/datause-encoder-data huggingface dataset:
# **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.
# Figure 2. Debt developments during the fourth wave

# A. Debt

# B. Change in debt

<!-- Start of picture text --> Percent of GDP Percentage points of GDP<br>320 Government debt Private debt 60<br>Government debt<br>240 40 Private debt<br>20<br>160<br>0<br>80<br>-20<br>0<br>World Advanced EMDEs EMDEs World Advanced EMDEs EMDEs<br>economies excl. China economies excl. China<br>C. Debt, by region D. Change in debt, by region<br>Percent of GDP Percentage points of GDP<br>270 Government debt 90 Government debt<br>Private debt 60 Private debt<br>180<br>30<br>90 0<br>-30<br>0<br>EAP ECA LAC MNA SAR SSA EAP ECA LAC MNA SAR SSA<br>2010 2019 2020 2010 2019 2020 2010 2019 2020 2010 2019 2020 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20<br>2010 2019 2020 2010 2019 2020 2010 2019 2020 2010 2019 2020 2010 2019 2020 2010 2019 2020 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20 2010-19 2019-20<br><!-- End of picture text --> Source: Kose et al. (2021).

Note: Data are available for up to 192 countries, including 39 advanced economies, 153 EMDEs, and 23, 24, 33 18, 8, and 47 countries in EAP, ECA, LAC, MNA, SAR, and SSA, respectively. Nominal GDP weighted averages.

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We exploit other data sources that are available in Nigeria to estimate a long-run poverty trend for the country. First, _sectoral_ real GDP data are available from NBS: this allows the backcasts to be done with more granularity than if only _total_ real GDP growth estimates were available.<sup>6,7</sup> Second, household survey data from the GHS panel are available for four waves: 2010/11, 2012/13, 2015/16 and 2018/19 (see Section 3 for more detail on the GHS data). These data lend themselves to survey-to-survey imputations as they collect household-level information on non-monetary indicators in the same way as in the 2018/19 NLSS. The two surveys not only had identical questionnaires for these non-monetary indicators, but they were also collected by the same team and followed the same methodology as part of an ongoing NBSWorld Bank collaboration, therefore minimizing discrepancies driven by survey methodology and implementation. Moreover, the timing of data collection for the 2018/19 NLSS and 2018/19 GHS overlapped, so it is possible to test whether the imputed consumption and poverty estimates are well aligned with _actual_ consumption and poverty estimates from a similar period.

While the GHS also collects data on household consumption, these data are subject to a series of limitations that do not allow them to be used directly for poverty measurement purposes.<sup>8</sup> In particular, the early rounds of the GHS imposed standard units (such as grams on kilograms) on quantities in the food consumption module when non-standard units may have been more appropriate: this was addressed in later rounds of the GHS. However, this issue does not affect the non-monetary indicators in the GHS, on which survey-to-survey imputation techniques rely.

# 3. Data

The analysis uses four data sources. Household-level data on consumption and non-monetary indicators come from the 2018/19 Nigerian Living Standards Survey (NLSS) and the General Household Survey (GHS) available over four waves in 2010/11, 2012/13, 2015/16, and 2018/19. National accounts and other macroeconomic data are taken from the World Bank MFM-MOD tool, which incorporates data from NBS, and from the World Development Indicators (WDIs).

> 6 The World Bank's macroeconomic and fiscal model (MFMod) consists of individual country models for 181 countries. The models are used by country economists within the World Bank's Macroeconomics, Trade and Investment Global Practice to (i) generate country forecasts and (ii) simulate various policies.

> 7 Backcasting poverty rates beyond 2009 proves challenging for two main reasons. First, the backcasting exercise relies on strong assumptions that are likely not to hold for a period longer than a decade. Second, sectoral GDP data for the early 2000s shows some inconsistencies and large year-on-year volatility, which could bias the backcasted poverty estimates.

> 8 Moreover, the GHS survey is not representative at the state level, which makes it unsuitable to be used for official poverty measurement purposes.

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maps reveal two important patterns. First, all PRADAN offices in all blocks created SHGs both before and after the 2002 elections. Second, the villages in which SHGs were created earlier and later are neighboring one another. This is the result of PRADAN’s preference for having the same employee operate in the same villages over time: each employee is assigned a cluster of neighboring villages in which he creates SHGs following the pace and the direction he considers appropriate. This policy saves traveling time and limits transportation costs. We address potential employee biases by including ward fixed effects. As the villages in which PRADAN created SHGs before 2002, and those in which SHGs were created later are situated close together, it is likely that they have similar socioeconomic characteristics. We check whether this is indeed the case in the Tables 5 and 6.

Table 5 displays data from the 1991 Census of India. The village is the lowest level at which data is available, so we cannot do a ward-level comparison. We could match all but three villages (105 total). The first column provides details on villages in which the first SHG was created during the 1997-2001 mandate, the second column on villages that had a first SHG created during the 2002-2006 mandate, and the third column provides the difference. With the exception of power supply, there are no significant differences for a wide range of facilities and public infrastructures. Villages also have, on average, the same size, and similar rates of employment, and literacy.

We also used all the variables in Table 5 to predict SHG creation (a dummy taking value 1 (0) if the first SHG was created in 2002-2006 (1997-2001)). We cannot reject the joint null hypothesis ( _p_ -value = 0.64).<sup>23</sup> This result provides an argument in favor of SHG creation not being influenced by the elected WMs.

Next, Table 6 shows the outcome variables and controls of our main regressions for the period 1992-1996, i.e. before the first SHGs were created, at the ward level. Although the number of observations is limited, the difference is small for all variables, but for welfare schemes. Furthermore, the sign is not systematically positive or negative.<sup>24</sup>

> 23The regression is available upon request.

> 24We excluded the WMs who have been re-elected in 1997 and faced SHGs undertaking collective actions, since for them we do not have disaggregated information. This reduces the number of observations, but allows us to compare information on WMs before SHGs were active.

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getting to spatially separated parcels (Van Hung, MacAulay, and Marsh 2007; Tan et al. 2008). Finally smaller, more fragmented parcels hinder mechanization, increase fixed costs like fencing and increase the likelihood of land disputes (Foster and Rosenzweig 2011; Demetriou, Stillwell, and See 2013).

Shaw also noted that fragmentation had benefits – the risk of natural disasters was spread out spatially and diversification often meant that farmers had access to a greater variety of soils. This observation is also echoed in the literature. Using panel data from Rwanda, Blarel et al. (1992) show that increased fragmentation decreases the variance of total farm income per hectare over time. Veljanoska (2016) uses panel data from Uganda to show that land fragmentation mitigates the adverse impact of deviations in rainfall on yield.

While the basic economic issues of surrounding land fragmentation have been clear for decades, by the early 2010s empirical work on this topic had largely run its course without resolving three fundamental issues. First, land fragmentation reflects exogenous factors – such as legal and social norms regarding the acquisition and division of holdings – but also conscious decisions by farmers to buy, sell, rent in or rent out, plots of land. Where these decisions reflect unobservable characteristics such as farmer skill, the level of fragmentation becomes correlated with the disturbance term in regression analyses, rendering the coefficient on the land fragmentation term biased. Second, most papers measure fragmentation as the number of plots (or an index number based on count and area of plots), but fail to account for the geo-spatial dispersion of the land, which is likely the more relevant attribute for capturing benefits from fragmentation. Third, fragmentation remains pervasive throughout the developing world. While it reflects in part incomplete land markets or a lack of access to credit needed for land consolidation, the widespread existence of fragmentation despite its perceived drawbacks suggests that on balance, farming households 2
arbitrarily small. They apply this to the problem of surveying in China, where household registration lists are widely used as sample frames. Widespread migration from rural areas however means that many households are unlikely to be found on these registration lists. They use GPS to survey randomly chosen 54 × 54 meter squares (approximately one square second), and find that 45 percent of the households reached were not on household registration lists.

However, one potential problem with this approach is that the sample size is not known until data collection has occurred, since the number of households within a spatial block is not known ex ante. Landry and Shen use existing population data to create a rough population model of Beijing, but found that the number of dwellings within their spatial units was four times as large as they had budgeted for, so they only administered their questionnaire to one-fourth of the units. It appears likely that aerial photography will alleviate such problems in the future. For example, Cowen and Jensen (1998) extracted individual dwelling unit information in a 32 census block area in South Carolina from aircraft multispectral data. They found the correlation of dwelling unit data derived from remote sensing with similar data derived from the census to have a correlation of 0.91. As the resolution of satellite imagery continues to improve and fall in price, it appears likely that the combination of remote sensing and spatial sampling will become the standard for constructing sample frames in situations where reliable census or registrar data are not available.

Another example of combining remote sensing and GPS for drawing samples is provided by Kumar (2007) in a survey of 1600 households spread across different air pollution zones in Delhi, India. The study area was partitioned into different strata characterized by air pollution levels (obtained by remote sensing) and proximity to main point sources of air pollution. Random points were then simulated using GIS techniques (weighting by the size of the residential area in each strata) and GPS was then used to navigate to the households located at each selected point. These households were then asked to participate in the survey. This method of creating a frame and drawing a sample should be more efficient than simply imposing a regular grid across the study area, since air pollution is irregularly distributed over space.

Visualization of the locations at which sampling has occurred can provide a useful form of quality control to ensure that interviewers conduct surveys where they are supposed to, and to check whether any dwellings are inadvertently missed. In 2004, Timor-Leste became the first country to use GPS units to record the locations of all households in their Census. USAID

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first point to note is that there is considerable worker churn: there are 23,156 distinct workers who work at least one month in one of our firms in the 2013 to 2017 period, but only 7,500 to 8,000 workers in any given month. On average firms have 3 percent of their workforce leave each month and 3 percent join. Most of this churn comes from outside of the study firms: only 272 workers (1.2%) worked for two or more firms in our sample during this five-year period, and only 32 workers worked for firms in more than one treatment group. The growth in the group treatment firms therefore did not come from them hiring away workers already working in the other treatment groups. Table A8 then examines the impact of treatment on the composition of workers, finding no changes in the gender or age of workers with treatment, and no significant changes in worker retention or worker compensation, but point estimates suggesting that the group treatment firms retained more of their workers and slightly increased salaries.

# **6.2 Impact on Sales**

Monthly sales data were collected directly from firm record books, and are converted into millions of real (December 2017) Colombian pesos using the Producer Price Index. We have some months of post-baseline sales data for 145 firms, and data on 99 firms for the balanced panel of all 60 months between January 2013 and December 2017.

Figure 5 uses the balanced panel and plots the trajectory of mean real sales by treatment group, demeaned by the 2013 treatment group means (left panel). We see the means of the three treatment groups track each other closely until the group intervention starts. Firms in the group treatment then see mean sales increase relative to the other two groups, with this gap widest in the first six months after treatment, and then closing. The right panel shows the distribution of changes in annual sales between the year 2013 and year 2017. We see the control and individual treatment groups have similar distributions of change (Kolmogorov-Smirnov test of equality p-value 0.855), while we can reject equality of the individual and group distributions (p-value 0.032). The group intervention has more variation in the change of sales, with a few firms experiencing a drop in sales, and more firms also experiencing growth in sales than occurs in the other two groups.

Table 6 estimates equation (3) for the level and the inverse hyperbolic sine of sales, using firm fixed effects to account for potential baseline differences across treatments that can arise from sample attrition, firm heterogeneity, and the sample size. Columns 1 and 4 use the unbalanced panel, and columns 2, 3, and 5 the balanced panel. The group treatment has positive treatment 22
# **I.  Introduction**

The economic performance of a firm is influenced by two types of factors. The first type comprises internal factors such as the technology embodied in the firm’s capital stock, its management practices and its marketing strategies. The second factor type may be referred to collectively as the investment climate: the policy and institutional environment in which the firm functions. Even the best-managed firms have difficulty flourishing in a poor investment climate.

In this paper, we conduct a two-step analysis of manufacturing firms surveyed in China and Brazil.<sup>1</sup> In the first step, we estimate a measure of total factor productivity (TFP) for each firm in the survey. In the second step, we test for a statistical relationship between the productivity measure and indicators of both types of factors: those internal to the firm and those related to the investment climate.

An economy may grow in the short run for many reasons – widespread exploitation of cheap labor, massive public expenditure, protectionist policies – but it will need its industries to increase productivity in order to sustain its growth. The choice of China and Brazil for our analysis is based in part on the relatively rich databases that the Investment Climate Surveys have produced for those two countries. But more importantly, they, along with Russia and India, make up a bloc of large developing countries (known recently as the BRICs) that by some estimates could come to dominate the world economy by the middle of the 21<sup>st</sup> century (Wilson and Purushothaman, 2003). As these economies grow, we expect to see significant variation in economic performance across firms and significant variation in investment climate across regions and sectors.

The remainder of the paper is organized as follows. Sections II and III explain how TFP is measured and suggest links between TFP and the investment climate. Next, Section IV reviews basic investment climate indicators for both countries. Sections V and VI report on the two stages of the analysis: measuring TFP and defining statistical links between TFP and a variety of indicators. Finally, Sections VII and VIII provide an interpretation of results followed by a set of general conclusions from the analysis.

> 1 World Bank Investment Climate Surveys.

2
Thus, when analyzing determinants of informal finance, we find that gender, education and formal employment play a significant role. Given the widespread reach of self-help groups, particularly at the grassroot levels through agencies like cooperatives, where women are the main participants, our results confirm the popularity of informal finance in this demographic (of women, those informally employed, and those with lower levels of education).

# **5. Conclusions**

This paper analyzes the status and determinants of financial inclusion in Sri Lanka, with the objective of understanding its implications for inclusive growth.

We present data both from a comparative peer analysis using the Global Findex 2017, as well as country data from the 2018 National Financial Inclusion Survey. It is seen that gaps remain, particularly in the ‘use’ of formal financial services rather than just having access to the same. While Sri Lanka is ahead of its regional peers in terms of basic financial inclusion measured via access to a formal account and having formal savings, it lags its more aspirational upper middleincome peers in East Asia. Similarly, for credit and usage of digital finance, there is much room for the country to improve and thereby match its East Asian peers.

Given the implications of financial inclusion as an enabler for inclusive growth, understanding the determinants of financial inclusion for Sri Lanka is important. Our results indicate that being a male, being more educated and (possibly therefore) being formally employed increase a person’s access to, and usage of, formal finance. Thus, the gender gap is evident in financial inclusion. In a country where 52 percent of the population comprise women, combined with the demographic transition towards an increasing elderly population with higher longevity for women, the findings imply that urgent and targeted action is necessary for the financial inclusion of women and making them part of the economy.

Our analysis indicates the crucial role played by education in determining formal financial inclusion. While Sri Lanka has a high literacy rate with gender parity at all levels of education, the 19
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# **1I. Inflation Before and After the Heterodox Shocks**

# Inflation Before

The inflation process in these two high chronic inflation countries was ery differenit prior to the heterodox shocks (HSs) of the eighties. As can e noticed from figures 2.a and 2.b, which show the evolution of annual nflation and seigniorage2 for the two countries since the early 1970s, nflation was more unstable in Argentina than in Brazil during this period, while its fiscal nature was more apparent in the former than in the latter. Argentina Changes in inflation in Argentina were closely related to changes in the budgat deficit and seigniorage in the same direction. As can be noticed from figure 2.a, the 1973-75 and 1983-85 explosions in inflation occurred in periods of extremely high budget deficits in which seignic %ge had reached unsustainaLle levels (exceeding 7 percent of GDP). Although the fiscal view explains a significant part of the behavior of inflation, non-fiscal factors also had a bearing on the inflation process. The long tradition of failed stabilization attempts, for example, implied that each new anti-inflation program had to confront severe credibility problems that added to the downward rigidities of the inflation process. In practice this meant tha.

2 Seigniorage is measured as the change in central bank non-interest bearing liabilities (typically the monetary base) as a percentage of GDP. Given the type of remuneration on bank reserves in Argentina, Ml **is the** basis of seignorage and inflation tax. The level of seigniorage is **an** indicator of the amount of resources the government extracts from the rivate sector thanks to its monopoly in printing money. The figures of udget deficit and inflation, available from the authors upon request, present a similar pattern.