rafmacalaba/data-use-mentions-extended huggingface dataset:
EAs to the original sample as part of an additional round of data collection. These were selected using a similar methodology with the objective to include more households from risk prone areas.

To capture enough households that had experienced floods, flood risk stratums were designed using the Ramani Huria community flood map. The community maps were created between 2015-2017 and used information from community members to identify flood prone areas. This generated a “flood prone tag,” which was used to categorize EAs into “no risk”, “low to medium risk” and “high risk” depending on how much of the area was covered by the tag. The city was divided into these three categories and this categorization was used to oversample in areas with flood risk. As a result, about 42.5% of households in the final sample are located in areas inside or adjacent to the flood prone tag (see Table 9 in Appendix 1 for sample selection by strata). The final sample includes a satisfactory number of households in flood prone areas, as well as low-risk areas to be able to draw statistically sound conclusions about the population. However, it is important to keep in mind that the uncertainty of being affected by a flood is higher in lowrisk areas, which represents a much larger section of the city.

In November and December 2017, data were collected from 1,058 selected households in Dar es Salaam, and in September 2018, 282 households were added to the original sample. Additional households were added to increase the number of data points located in high risk areas in order to improve precision in those areas and cover more affected households in the sample. All the selected households were randomly drawn within each EA using satellite imagery. Surveyors used GPS tags to locate the households and interviewed them using CAPI software on tablets. Participation in the survey was voluntary and not compensated.

## 2.2.1 Poverty assessment using Survey of Wellbeing via Instant and Frequent Tracking and Coping Strategy Index

The project assesses the relationship between poverty
which are developing), reveals that the H-statistic for Jordan is below the 0.52 average across all countries and the 0.55 average obtained across developing economies.

To identify the factors that explain differences in the H-statistic for Jordan vis-à-vis other countries, we estimate equation (3): where i is the country identifier, Z is a matrix of country characteristics discussed below, and u is the error term. We estimate equation (3) for 91 countries around the world using Bankscope data for the period 1994-2006 (see Table 7). The idea is to use the parameters from this estimation to determine the factors that explain the degree of competition in Jordan vis-à-vis other countries. In particular, we hope to compare Jordan to: (i) all other countries, (ii) all other developing countries, and (iii) a group of non-oil producing neighboring countries plus the Eastern European countries with similar GDP per capita.

Following Claessens and Laeven (2004), we include as regressors variables that capture countries’ market structure, contestability, macroeconomic conditions, and general level of development. Furthermore, we also include variables that capture the extent of transparency in the system. In particular, as indicators of market structure, we include the concentration ratio (defined as the share of assets held by the five largest banks), the number of banks per population, the share of assets owned by government banks, and the share of assets owned by foreign banks. To capture market contestability, we include the number of documents or procedures required to obtain a banking license, the fraction of applications for bank licenses denied, and an index of the extent of restrictions on bank activities. We also include the log of the minimum capital required to open a bank and the capital adequacy ratio, since high capital requirements are likely to deter entry. We include an index of bank auditing requirements and an index of bank disclosure requirements as measures of bank transparency. All the variables discussed so far come from the Barth, Caprio,
Policy Research Working Paper 9470

# **Abstract**

Leveraging data in 147 countries from the Johns Hopkins University, the World Bank, and other sources, we investigate how the COVID-19 pandemic spread and how the mortality rates are associated with preexisting vulnerabilities, the government’s mobility restriction policy, institutions (democracy), and culture (i.e., trust and individualistic culture). While delay in domestic mobility restrictions is not significantly associated with pandemic outcomes, increasing delay in restricting international mobility is associated with higher pandemic mortality rate. Some pre-existing vulnerabilities are positively associated with the spread of the pandemic but not the mortality rate. However, when vulnerabilities are accompanied with delay in domestic mobility restrictions, the pandemic has higher mortality. Democracy is associated with lower policy delay in restricting mobility, lower pandemic mortality rate, and features better protection of the vulnerable population from pandemic harms. However, delay in domestic mobility restrictions have more adverse effects in democratic countries. Trust is not associated with worse pandemic outcomes, but its combinations with policy delay and obesity are. More individualistic countries do not feature different pandemic outcomes. However, in such countries, government delay in domestic mobility restrictions is associated with significantly higher pandemic mortality rate, and the marginal effects of the share of elderly on pandemic spread is greater.

This paper is a product of the Development Research Group, Development Economics. 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 authors may be contacted at lxu1@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
|Global<br>C:ompetitiveness<br>S;urvev|Survey|54 developed<br>and developing<br>countries|0.42|
|GCSA|World Economic<br>Forum|Global<br>Competitiveness<br>Survey, Africa|Survey|23 African<br>countries|0.73|
|HFWSJ|Heritage<br>Foundation/Wall<br>Street Joumal|Economic Freedom<br>Index|Poll|154 developed<br>and developing<br>countries|0.06*|
|PERC|Political Economic<br>Risk Consultancy|Asia Intelligence|Survey|11 Asian<br>countries|0.83|
|PRS|Political Risk<br>Services|Intemational Country<br>Risk Guide|Poll|140 developed<br>and developing<br>countries|0.10*|
|WCY|Institute<br>Management<br>Development|World<br>Competitiveness<br>Yearbook|Survey|46 primarily<br>developed<br>countries|0.59|
|WDR|World Bank|World Development<br>Report|<br>Survey|74 developed<br>and developing<br>countries|0.25*|

Notes: Poll stands for "polls of experts" and Survey for "survey of entrepeneurs" (except gallup, which is a survey of citizens) (*) Representative source 21
# **Figure 19. MNA: Infrastructure, health, and education indicators**

_Infrastructure investment needs are high, especially in electricity and transport. While the Middle East and North Africa performs well relative to other EMDEs on basic health measures, it is at or below the EMDE average in terms of education indicators, despite considerable long-term gains._

# **A. Infrastructure investment needs**

<!-- Start of picture text --> US$, billions Oil importers<br>25 Non-GCC oil exporters<br>GCC<br>20<br>15<br>10<br>5<br>0<br>Electricity Transport ICT Water and<br>sanitation<br>C. Selected health care indicators<br>Per capita Percent of population<br>4500 120<br>4000<br>100<br>3500<br>3000 80<br>2500<br>60<br>2000<br>1500 40<br>1000 Range AE 20<br>500 EMDE MNA<br>0 0<br>Health Improved Improved<br>expenditure sanitation water<br>(LHS) (RHS) source<br>(RHS)<br><!-- End of picture text -->

# **B. Quality of infrastructure**

<!-- Start of picture text --> Index, 1-7 (7=best)<br>Oil importers Non-GCC oil exporters<br>6 GCC EMDE<br>5<br>4<br>3<br>2<br>D. Selected education indicators<br>Percent of per capita<br>income, ratio<br>45<br>40 Range AE<br>35 EMDEs MNA<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Government expenditure Pupil-teacher ratio
# **1. Introduction**

The COVID-19 pandemic is resulting in an unprecedented shock to the private sector, threatening the global progress in poverty reduction and shared prosperity made in recent years . The impact on firms and the destruction of existing productive capacities due to the pandemic could have large effects on the growth prospects of developed and developing economies, not only in the short-run but over the longer run; especially if this destruction also affects the more productive firms. Restrictions to mobility and economic activity, higher transaction costs and potential cross-country trade and currency disputes can limit the allocation of resources within countries and across sectors, worsening misallocation in the economy and lowering aggregate productivity growth.<sup>1</sup> Understanding the severity of the impact of the shock and its distribution, the adjustment mechanisms adopted by firms, and the uncertainty created, is critical to understand these reallocation channels and design better policies that can help smooth the impact of the shock and support recovery.

This paper provides the first comprehensive assessment at the global level of the short-term impact of the pandemic across the world relying on a novel dataset collected during the months of April through August 2020.<sup>2</sup> The data collected cover more than 100,000 businesses across 51 countries, primarily low- and middle-income countries across the world.<sup>3</sup> Data collection aimed at covering almost all sectors of the economy (i.e. manufacturing, services and agriculture) and a large range of countries with different income levels (i.e. ranging from Afghanistan to Italy) as well as different levels of exposure to the COVID-19 shock.<sup>4</sup> To the best of our knowledge, this is the most comprehensive assessment of the short-term impact of COVID-19 on businesses in terms of Our paper contributes to the quickly expanding literature assessing the economic impact of COVID-19. Much of the early research attempts to draw parallels with previous crises or uses macro simulations in combination with some ex-ante
whether the growth acceleration is unique to India or if it has also been the experience of other emerging markets. For this we compare the linear trend in India with the trend in seven large emerging economies, Brazil, the Russian Federation, South Africa, Malaysia, Mexico, Turkey and Indonesia, which we refer to as EM7.<sup>9</sup> We estimate regressions of the following form: GDP Growth�� �β� �β�Trend� �β�India� x Trend� �ε�� �equation 1�

> 7 Arvind Panagariya has highlighted the importance of reviving bank credit to reach growth rates exceeding 8 percent: <u>https://blogs.timesofindia.indiatimes.com/toi-edit-page/how-to-revive-bank-credit-government-should-to-begin-withoffer-psbs-bonds-in-return-for-equivalent-equity/ . In a recent interview, Arvind Subramanian (Chief Economic</u> Advisor) indicated that reaching growth rates around 8.5 percent is conditional on a reform agenda that addresses banking sector and other issues: http://www.livemint.com/Politics/OUuLehx0uBAO32P1xhSYAN/India-can-return- <u>to-85-growth-rate-Arvind-Subramanian.html.</u>

> 8 The source of data is the Central Statistics Office. See Appendix 1 for details on the data used and on how we spliced the GDP series for different base years.

> 9 According to the World Development Indicators, these countries accounted for 12 percent of the world population (30 percent when India is included), 13 percent of world GDP (20 percent when India is included), and an average per capita income of $16,678 (in 2011 PPP $) in 2016.

4
poverty and achieving the Millennium Development Goals (MDGs). Compared to other CGE models, MAMS is distinguished by its coverage of human development, including MDGs, education and its link to the labor force and its educational make-up, as well as the interaction of human development with other aspects of economic policy and performance. In MAMS, government spending is disaggregated by function, singling out spending on education (disaggregated by level), health, and infrastructure, and other areas. Government financing is disaggregated into different types of taxes, domestic and foreign borrowing, and foreign grant aid. MAMS generates a wide range of measure of economic performance including the evolution of

- poverty and selected other MDG indicators, in this application also including undernourishment;

- macro indicators: private and government consumption and investment, exports, imports, GDP and absorption;<sup>3</sup> the government budget, the balance of payments; the savings-investment balance; total factor productivity; and domestic and foreign debt stocks;

- the sectoral structure of production, employment, incomes, and trade;

- the labor market: unemployment; and the educational composition of the labor force.

This section provides a non-technical summary of MAMS, split into two modules – core, and MDG and education – and its LIC database, developed for this application.<sup>4</sup>

# _Core module_

Figure 2.1 summarizes the payment flows that are captured by MAMS for the model country, here referred to as the LIC, in any single simulation year. In any application, including the current one, most of the payments flows and boxes shown in Figure 2.1 are further disaggregated; we present the current disaggregation later in this section. <u>Activities produce,</u> selling their output at home or abroad, and use their revenues to cover their costs (of intermediate inputs, factors, and taxes). Their decisions to pursue particular activities and employ factors are driven by profit maximization. The shares exported
qualify for<br>assessment:<br>**Tender phase**<br>•<br>Tenders per government office<br>•<br>Tender name<br>•<br>Tender description<br>•<br>Tender status<br>**Award phase**<br>•<br>Awards per government office<br>•<br>Award title<br>•<br>Award description<br>•<br>Value of the award<br>•<br>Supplier's name|
|Election<br>results|This data category looks at results for<br>the latest national electoral contest.<br>Election data informs about voting<br>outcomes and voting process. What are<br>electoral majorities and minorities?<br>How many votes are registered, invalid,<br>or spoilt? The Index consulted the<br>National Democratic Institute (NDI) to<br>develop this data category, but did not<br>take their latest recommendation which<br>will be considered for the next edition.<br>For more information, see the<br>NDI’sOpen Elections Data Initiative.|To enable the highest level of<br>transparency, the Index<br>assesses polling station-level<br>data. Polling stations are the<br>locations at which voters cast<br>their vote. Having this data<br>allows for independent<br>scrutiny of each stage of the<br>voting and counting process.<br>It also helps electoral<br>stakeholders better target<br>their voter education and<br>mobilization efforts for the<br>next elections.|Following data must be online to qualify for<br>assessment:<br>•<br>Results for major national<br>electoral contests (such as general<br>elections
Most of the work improving measurement of the top tail of the income or wealth distributions uses administrative data. But there is very little work on how one might improve estimates of incomes or wealth (and income from that wealth) that are outside the tax system. This is very important in LMICs where the informal sector is a large share of economic activity (Ravallion, 2022). Ravallion (2022) notes that if economic growth improves state capacity and this shifts more activity into the formal sector then this could generate rising measured top income shares, even when there is no actual change. This does not seem to have been investigated in the research discussed in this paper. Ravallion (2022) also highlights that there is little evidence on how large the informal or hidden sector share of top incomes or wealth is in any LMICs. This also seems an important area for further work. Alstadsæter et al. (2022) used leaked data on Dubai real estate ownership to show that the value of property ownership in Dubai by LMIC residents is a large share of GDP for several of these LMICs. Such data would be useful to improve the measurement of the top of the wealth distribution in these countries and serve as estimates for how much administrative tax data misses important sources of wealth at the top of the distribution.

# Conclusion

There has been a resurgence in research on the measurement of the top tail of the income and wealth distribution in the last 20 years. Kuznets and Jenks (1953) were the key authors that began this research agenda, but it declined in importance as household surveys became more widely available and researchers turned their attention to microdata. Piketty (2001, 2003) put the measurement of the top tail back onto the research agenda with his long run measurement of top incomes in France, and subsequently this area of research has exploded.

In this paper we have surveyed this literature, focusing on the challenges that have been identified when trying to measure the top of the income and wealth distributions, as well as
|223||410||
|of survey|||||||
|**_Scenario Comparison_**(annualized)|||||||
|Shared Cost/Individualized Diets Cost|1.34|(0.02)|1.31|(0.02)|1.33|(0.02)|
|N Households with solution under both scenarios<br>in month of survey|727||897||1,624||
|Households (total N)|1,424||1,693||3,117||

Population statistics corrected using sampling weights, standard errors clustered at the enumeration area level.

If we consider that households for whom the diet is infeasible as also not being able to afford the diet, then we can estimate the share of the population for whom the adequate diet (in the market) is out of reach. Since the lower bound cost is the least costly way for a household to meet all members’ nutrient needs, those for whom it is infeasible, or costs more than their total expenditure, do not have access to an adequate diet at all. Our results show 44% of rural Malawian households face this situation. For an additional 18%, the lower bound diet is feasible but unaffordable without increasing current food expenditure (though technically affordable within total expenditure). In total, 62% of rural Malawians cannot access any adequate diet in the
agencies on the ground.<sup>19</sup> Second, the

> 15Prior to the escalation of the conflict, 85 percent of the total population lived in a household that owned at least one mobile phone and would be represented by the mVAM. Furthermore, this figure remained high for the rural population (81 percent), the population living below the poverty line (77 percent), and the population of all governorates (over 60 percent for each). Authors’ calculations using the 2014 Household Budget Survey.

> 16Registration for the World Bank’s cash transfers program being implemented by UNICEF, which covers approximately one-quarter of the total population and is aimed at relatively poorer households, demonstrates the vast majority of households can be reached via phone (e.g., World Bank 2018c); evidence from different WFP surveys of food aid beneficiaries suggests that the share of food aid recipients that can be reached via mobile phone has remained very high (e.g., WFP 2018b); and evidence from the WFP mobile phone survey itself suggests that particularly struggling households are well represented in the sample with approximately one-third of the sample being IDP’s and over one-third receiving food assistance.

> 17See Appendix 4 for a map presenting the number of respondents by district over the entire course of the WFP survey, where there are only two districts out of 333 that do not have any respondents. Alternatively, the vast majority of districts are represented in any single month of the survey. For example, in the November 2017 survey, the survey reached respondents in 264 of the 333 districts in the country- nearly 80 percent of the total. It is important to note that the districts for which there are zero respondents tend to be less populated and in the east of the country. Only 9.2 percent of the country’s 2017 population resided in the 69 districts for which there are zero respondents; and the districts overwhelmingly came from the governorates of Hadramaut and Al Mahrah, where only 39
Policy Research Working Paper 6267

# **Abstract**

This paper analyzes real estate market dynamics over the past decade in the city of Ahmedabad, India, with a view to improving the living conditions of the large population living in slums. The paper combines census data, the National Sample Survey, and slum household surveys to review the demand side of the market. Satellite photography was used to estimate the production of both formal and informal housing over the past ten years. Analysis of the execution of the development plan for the Ahmedabad region and town planning schemes shows how the system of housing supply has evolved. These analyses are used to assess the feasibility of various approaches to achieving “slum free” cities, the goal of the Government of India’s planned assistance program Rajiv Awas Yojana. The paper concludes that notwithstanding a substantial increase in public housing production in recent years, providing subsidized formal homes from government or through reservations for lower income groups in private developments would take more than a generation just to handle the current slum population—representing one-third of households. Providing basic environmental infrastructure services in existing underserved neighborhoods—a proven approach under the Slum Networking Program—and bolstering infrastructure networks for the city to accommodate increased demand are affordable and feasible. Addressing issues such as rural-urban land conversion and ambiguous land tenure, and allowing flexibility for realistic building standards and increasing maximum floor space standards in certain neighborhoods can help to ensure a growing supply of housing that is affordable for moderate and low-income households.

This paper is a product of the Finance Economics and Urban Department, Sustainable Development Network. 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 author may be contacted at pannez@worldbank.org or pcannez.clarke.work@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work
the estimation of price elasticity, described in section 5.1. The demographic statistics of POF are aligned with other household surveys, such as the Continuous National Household Sample Survey ( _PNAD Contínua_ ), showing that the higher the consumption decile, the higher the incidence of educated white men as heads of household.

Table 1 - Demographic statistics

|Consumption<br>Decile|Woman<br>head<br>of household|White head of<br>household|Literate head of<br>household|Urban household|Years of schooling of<br>head of household|
|---|---|---|---|---|---|
|1|49%|26%|81%|76%|6.25|
|2|47%|32%|85%|80%|6.78|
|3|43%|34%|86%|81%|7.31|
|4|42%|37%|88%|84%|7.69|
|5|41%|41%|92%|87%|8.29|
|6|42%|44%|92%|88%|8.78|
|7|39%|48%|94%|90%|9.37|
|8|39%|53%|96%|91%|10.1|
|9|38%|60%|97%|93%|11.4|
|10|37%|69%|99%|96%|13.3|
|Total|42%|44%|91%|86%|8.93|

Source: POF 2017/2018 9