rafmacalaba/data-use-mentions-extended huggingface dataset:
Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them<sup>1</sup>

# Aziz Atamanov

Sharad Tandon Gladys Lopez-Acevedo Mexico Alberto Vergara Bahena JEL: C81, I32, O10 Keywords: Household budget surveys, poverty, Middle East and North Africa

> 1 This paper is written by Aziz Atamanov, Sharad Tandon, Gladys Lopez-Acevedo and Mexico Alberto Vergara Bahena from the World Bank, Poverty and Equity global practice. The team is very thankful to peer reviewer German Daniel Caruso. Daniel Lederman and Asif Islam provided very useful comments to improve the paper. Benu Bidani and Johannes G. Hoogeveen provided overall guidance for this work.
. The 2017‐18 National Annual Rural Sanitation Survey (NARSS) reported 77% toilet penetration across India.

2 Estimates of regular toilet use in rural India vary. The NARSS 2017‐18 found that 93.4% of people who had access to toilets used them regularly. A recent survey of rural north Indian states (Bihar, Madhya Pradesh, Rajasthan, and UP), completed in late 2018, found that the rate of open defecation among people who owned a latrine was at 23% (the same as four years prior), and that the overall open defecation rate among rural people across these states was 44% (Gupta et al., 2019).

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# **1 Introduction**

The macro-financial shock caused by the COVID-19 pandemic precipitated a global economic recession and put severe pressure on financial markets and institutions around the world. Policy makers reacted by implementing an unprecedented array of public health, fiscal, monetary, macroprudential, and financial measures to contain the spread of the virus and support the real economy. For example, authorities introduced travel bans, mandated the closure of businesses, limited social gatherings, and scaled up unemployment and social protection programs. Financial sector authorities undertook a complementary, wide-ranging set of temporary measures to preserve the well-functioning of core markets and maintain the provision of critical financial services to the real economy, including credit and payments, while at the same time safeguarding prudent risk management standards, bank balance sheet transparency, financial resilience, and a globally coordinated response.

To date, financial sector policy measures have focused on providing liquidity to financial institutions and markets (e.g., lowering reserve requirements, purchasing financial assets); maintaining operational and business continuity (e.g. extensions of deadlines on supervisory reporting); facilitating the flow of credit and supporting borrowers that face short-term repayment difficulties either directly (e.g., lowering interest rates, introducing debt repayment moratoria, facilitating loan restructuring, offering government guarantees and loans to affected sectors) or by providing regulatory relief (e.g., encouraging banks to use available capital and liquidity buffers, allowing for the flexible treatment of non-performing loans and asset classification). Several standard-setting bodies for the financial sector have also issued guidance (e.g., encouraging and clarifying the use of the flexibility embedded in the global standards) to ensure a coordinated global response and some have deferred the implementation of components of certain global standards, notably Basel III.

This paper makes three contributions. First, to support policy formulation and evaluation by policy makers and researchers, the paper introduces a new global database and a simple policy classification framework. Containing over 3,100 individually classified measures, the database provides a
through information. _IGC Growth Brief Series_ , _12_ .

Jain, M., Rao, P., Srivastava, A. K., Poonia, S., Blesh, J., Azzari, G., ... & Lobell, D. B. (2019). The impact of agricultural interventions can be doubled by using satellite data. _Nature Sustainability_ , _2_ (10), 931-934.

Jayne, T. S., Mason, N. M., Burke, W. J., & Ariga, J. (2018). Taking stock of Africa’s second-generation agricultural input subsidy programs. _Food Policy_ , _75_ , 1-14.

Jayne, T. S., & Rashid, S. (2013). Input subsidy programs in sub-Saharan Africa: a synthesis of recent evidence. Agricultural economics, 44(6), 547-562.

Ji, Y., Liu, H., & Shi, Y. (2020). Will China's fertilizer use continue to decline? Evidence from LMDI analysis based on crops, regions and fertilizer types. _Plos one_ , _15_ (8), e0237234.

Jones, B. A. 2019. “Infant Health Impacts of Freshwater Algal Blooms: Evidence from an Invasive Species Natural Experiment.” _Journal of Environmental Economics and Management_ 96: 36–59.

Kanter, D. R. (2018). Nitrogen pollution: a key building block for addressing climate change. _Climatic change_ , _147_ (1), 11-21.

Kanter, D. R., Bell, A. R., & McDermid, S. S. (2019). Precision agriculture for smallholder nitrogen management. _One Earth_ , _1_ (3), 281-284.

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demonstrate how changing the value of h alters commonly held views of larger households being poorer on average.

For the analysis in this paper, where we aim to understand how the allocation of resources affects the global profile of poverty, we only have data on household size and not the age composition of each household. The implication of this is that we can only consider changing the single parameter, h, which describes the elasticity of needs to household size. The per-capita allocation sets h=1 which would be defensible if households only consumed non-shareable goods, such as food. When the initial “dollar-a-day” IPL was set (World Bank, 1990), and the decision to use per capita consumption as the measure of wellbeing was made, food budget shares were very high in most poor countries. Yet, it has been the case for some time now that budget shares allocated to food have been declining across most all countries of the world (Traub, 1992, Table 2), and the assumption of setting h=1 has become less tenable. As related evidence, the share of food and nonalcoholic beverages in gross domestic product (GDP) has dropped from 49 to 37 percent between 2005 (Muhammad et al., 2011, Table 2) and 2017 (World Bank, 2020b, Figure 1.4). While there is variation in values used for h, Johnson and Torrey (2004) note that adjusting needs by the square root of household size (i.e., p=1, h=0.5) is becoming common in international poverty comparisons.<sup>6</sup> Buhmann et al. (1988), Ruggles (1990) and Vleminckx and Smeeding (2001) provide examples of this, and Dudel et al. (2021) suggest that the square-root adjustment performs well, particularly for larger households.

> 6 The square-root adjustment is often applied in research work (Johnson et al., 2005; Ravallion, 2016; Smeeding, 2016; Taylor
on firms due to disrupted supply chains, underutilization of production capacity, and costly adaptation measures.<sup>9</sup> In the transport sector, assets are typically exposed to multiple hazards that compound the risks of asset failure and service disruption. In a global study of road and rail networks, Koks et al (2019) found that 27% of road and railway assets are exposed to at least one natural hazard and that 7.5% are exposed to a major hundred-year flood event. Expected annual damages to roads and rail due to natural hazards range from US$3.1 billion to US$22 billion, with most damages (73%) due to flooding. While the global annual expected damages are minimal with respect to annual maintenance costs, for countries with higher exposure levels to major floods – including Small Island Developing States – damages can reach catastrophic levels. In these countries, increased flood protection would have positive returns on about 60% of exposed roads and improved flood resistance is likely to be a low-regret investment in the context of climate change.<sup>10</sup> Limao and Venebles (2001) estimate that poor maintenance and unreliable transport service accounts for 40% of transport cost for coastal countries and up to 60% for landlocked countries. Increased costs have significant impacts on the real costs of trade, often limiting a country's ability to participate fully in the global economy.<sup>11</sup> The monetary costs of infrastructure disruptions results in estimated utilization losses

> 6 Obolensky, M., Erman, A., Rozenberg, J., Rentschler, J., Avner, P., & Hallegatte, S. (2019). Infrastructure disruptions: how instability breeds household vulnerability. Global Facility for Disaster Reduction and Recovery, World Bank, Washington DC.

> 7 Hallegatte, S., Rentschler, J., & Rozenberg, J. (2019). Lifelines: The Resilient Infrastructure Opportunity. Washington, DC:
based on a set of items for which both the quantity and the expenditure information is available in the HIES.

<mark>Households of different sizes and age composition have differing consumption needs and patterns. In order to reflect these differences, adjustments need to be made to the residual non-housing expenditure aggregate to reflect the age, and sometimes the gender distribution, of the household members. This is also done to account for the fact that larger households may be able to purchase goods in bulk at cheaper rates and may be able to economize on the purchase of certain items. Adjusting the non-housing expenditure aggregate using equivalence scales can address these concerns. The welfare aggregate used for poverty measurements in Pakistan is based on an adult equivalence scale which assigns a weight of 0.8 to each individual below the age of 18, and a weight of 1 to each individual age 18 and above.</mark><sup>23</sup>

**<mark>Estimating the housing affordability threshold: minimum standard for non-housing expenditures.</mark>** <mark>In the absence of readily available budget standards, poverty estimation techniques can be adapted to estimate a minimum standard for non-housing (food and non-food) expenditure. In particular, the process followed for determining the poverty line can also be used to determine a non-housing poverty line – or a threshold for spending on non-housing residual needs. This consists of basic food needs to which a provision is added for basic non-housing and non-food needs.</mark>

<mark>In the case of Pakistan, this threshold is estimated by adopting a modified CBN method. This methodology is implemented for the REM approach in two stages. In the first stage, the food poverty line was estimated, or the minimum expenditure required to fulfil basic food requirements for a particular reference group. This reflects the cost of a basic food basket that meets the minimum daily caloric requirement, using food items, quantities and prices faced by a specific reference group. In the case of
Nevertheless, rigorous validation strategies must be developed. At a minimum, in-sample validation techniques should be used.<sup>18</sup> The potential for out-of-sample validation remains unknown, but access to longer CDR data series, which include more than one household survey, could help address this concern.

## **_Privacy_**

Data privacy is a sensitive and often controversial issue, and certain precautions must be taken before analyzing CDR data. Identifiers should be obscured before records are exported from MNO systems, so that cell phone numbers or similar fields do not remain in the output data. This process is referred to as “pseudonymization.” In general, pseudonymization should ensure that the same pseudonym is applied to a given user’s entire call history.

Research suggests that simple pseudonymization can be reversed by a determined individual armed with relatively easy-to-obtain auxiliary information, such as a person’s home and work addresses and one or two other locations that they are known to visit at particular times.<sup>19</sup> Therefore, additional technical and institutional measures are necessary to restrict access to the data. This has generally entailed nondisclosure agreements between MNOs and researchers, and the research, development and MNO communities are striving to streamline this process.

In addition, any final data produced as a result of the CDR analysis should not compromise individuals’ privacy. Similar precautions to those used when releasing tabulations from traditional survey data can reinforce the privacy of CDR data. These measures may include grouping data where cell sizes fall below some fixed number of people (e.g. 5 or 10), or top- and bottom-coding variables in cases where extreme values could be revealing.

# **Other Promising Applications of CDR Analytics**

CDRs offer a rich dataset for studying populations, and numerous applications are emerging in areas beyond poverty measurement. Two applications of particular relevance to the World Bank’s work in Guatemala and the Latin America region are described below.<sup>20</sup
employers in the Tunisian labor market and to quantify its magnitude, we conducted a field experiment based on the correspondence method used by Bertrand and Mullainathan (2004) in their groundbreaking study. The experimental design consisted of sending fictitious and substantially identical male and female resumes in response to job advertisements. Any differences in callback rates could then be attributed to gender, and thus could be interpreted as gender discrimination. To the best of our knowledge, this is the first study of its kind conducted in the MENA region.

> 9 In industries that may require a higher degree of physical endurance, male employment is indeed higher than female employment. For example, in construction and mining, the share of male employees is over 95 percent (Tunisia Labor Market Panel Survey, 2014).

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# Robustness check: Expanding the set of countries

Lopez-Calva and Ortiz-Juarez (2014) and Ferreira et al. (2013) only use a particular set of high and uppermiddle-income countries in LAC to define the regional middle-class bounds. The limitation of the selection of countries depends on the availability of longitudinal data. Given that this is not a restriction in the proposed approach, expanding the number of countries, including high, upper-middle, and lowmiddle income, enhances the estimation results by increasing regional representativeness. Following Jolliffe et al. (2022) to check the robustness of results, both lower and upper middle-class bounds were calculated cumulatively by ranking countries from lowest to highest GDP per capita. Figure 2 shows that both vulnerability and middle-class lines estimates are robust to using fewer countries. Each point in the figure corresponds to the line estimated using the synthetic panels available for that country and all those to the left.

**Figure 2** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 38 synthetic panels built with available surveys within +/- 2 years circa 2017 and 100 bootstrap repetitions.

# Robustness check: Increasing the time span

When estimating the
some countries to close to 40 percent in Mongolia.

FDI might affect female entrepreneurship via several channels. Via FDI, there can be social norms transmitted from investing countries to host countries. There can be knowledge spillover, both across and within industries, spurred by the new knowledge stock brought by FDI firms (Acs _et al_ ., 2009, 2011). There can be crowding out effects on female entrepreneurship due to women’s lack of access to key resources to compete with FDI firms within FDI-intensive industries. It is thus an empirical question as to whether and how FDI affects female entrepreneurship. With a data set of firms in around 130 3
shares include Mozambique and Central African Republic, both with 31% of income flowing to the top 1% – but in both countries these measurements are obtained from adjusted survey data, so are arguably less precise. It is crucial to note, nonetheless, that this type of detailed inequality accounting is not available for the majority of countries, so it is not clear how many of them would fare in comparison with Honduras. Its regional peers like Guatemala, El Salvador and Nicaragua do not have similar estimates – the only country in the region with similar estimates is Costa Rica which registers a much lower 21% share flowing to the top 1%. We also include in the table the estimated top shares for Honduras currently available on the WID platform – the share of the top 10%, at 52%, is not too dissimilar to our results; the share of the top 1%, however, is only 18% or 12 p.p. below our estimates. This further suggests that inequality in other countries in the region, for which detailed accounting exercises have not been performed, is likely much higher than current estimates.

To illustrate the importance of the series of adjustments that leads us to our main estimates using the DINA guidelines, in Figure 5 we present the share of income flowing to the top 1% highest earners measured using three different distributions. First, we show the share of the top 1% highest earners when we use only survey data, without any further adjustments. In that series, the share fell from approximately 15% in the early 2000s to less than 10% by 2019, showing a steady decline in the period and a level of top share three times smaller than our estimates using national account adjustments. We also present the top 1% share using the microdata complementing survey data with tax records, but before any adjustments to national accounts. For recent years, this series roughly doubles the top 1% share from 10% to 20%, but still falls 5 - 10
total assets. We again truncate the top and bottom 5 percentile of the distribution of these two variables so as to diminish the influence of outliers.

> 12 Employment growth rates calculated using the base year or the average value over the periods as the denominator can yield substantially different growth rates, especially for micro and small firms. We are using the latter method because, like log growth rates, it naturally bounds the employment growth rates so that the regression analysis is not skewed by big jumps from micro and small firms. For more discussion on implications of the two definitions, see Haltiwanger et. al. (2013).

> 13 We calculate the growth rate as before as the difference divided the average value over the two years.

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Policy Research Working Paper 10847

# **Abstract**

Good road infrastructure decreases travel time and improves accessibility to urban areas. Improved rural-urban linkages could also affect rural employment through decreased time and travel costs. To study this link, the paper analyzes the impact of good quality roads on agricultural and non-agricultural jobs in Armenia, using different sets of data and different methodological approaches. To address endogeneity and reverse causality issues of road quality, the paper uses a historical instrumental variable obtained by digitizing historical roads which were mainly used for military purposes—from a military-topographic map of the Caucasus from 1903. The results show that a shorter distance to a good quality road has a statistically significant positive impact on overall non-agricultural employment for men and women, increasing the likelihood of cash-earning jobs for rural women and skilled manual and non-seasonal employment for rural men. People are more likely to work outside their villages and work for more hours if they have access to good quality roads. The results are robust from the analysis of Demographic and Health Survey as well as the Integrated Living Conditions Survey of Armenia.

This paper is a product of the Transport Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at npkhikidze@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
does not vary substantially across countries (i.e., that _β_ = 0), and conclude that “there is no prima facie support for the view that international credit frictions play a major role in preventing capital flows from rich to poor countries.” Chari and Rhee (2020) use Worldscope data to construct a sample of publicly traded companies in 44 emerging stock markets between 1997 and 2004. They find financial returns for publicly traded firms to be equalized across countries and draw a similar conclusion. With 58 years of data, and coverage of many more countries than most data sets, the IFC data provide a stronger test of the capital market integration hypothesis than all of these datasets would.

Studying returns to private equity specifically, rather than returns in the national accounts or public indices, also provides a more demanding test of the market integration hypothesis. As fund managers have argued, it is not the listed or largest firms whose capital shows up in national accounts that are most likely to have high-return projects that go unfunded, but rather firms on the margins, precisely those targeted by private equity investors.

**Measurement of Risk.** Investors care about risk-adjusted returns, and risk may vary across countries. The cash flow data enables us to account for some risk in two ways. First, we measure returns _ri_ using the public market equivalent (PME). This measure is described in detail in Section 3.2, and expresses investment performance relative to a benchmark index such as the S&P 500. As a result, our measure of returns is risk-adjusted for correlation with the benchmark index of

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