rafmacalaba/data-use-mentions-extended huggingface dataset:
However, Figure 10 shows that the number of people relying on social transfers to close their poverty gap increased steadily after 1998, approaching 20 percent in 2002. This means that headcount poverty would have been 20 percentage points higher if social assistance had not been available as a safety net.

Even though social transfers (excluding old age pensions) have a direct poverty reducing effect, the system is extremely costly and inefficient. Calculations based on Household Budget Surveys estimate the total amount of social transfers in 2002 at 6.3 percent of GDP, which is close to what government statistics report. However, 56 percent of this amount (or 3.6 percent of GDP) is not reaching the poor: 26 percent is in excess of the amount necessary to bring household consumption to the poverty line level (called “overflow”), while 30 percent (an equivalent of 2 percent of GDP) goes directly to non-poor households (see the left-hand panel of Figure 11). Although benefit tightening has been tried on several occasions, the amounts leaking were of a similar order of magnitude throughout the analyzed period.

**Figure 11:  Targeting of Social Transfers (Excluding Old Age Pensions)**

<!-- Start of picture text --> Who receives social transfers ? Social transfers per capita by<br>consumption deciles<br>% of resources going to:<br>d1 0<br>poor non-poor overflow<br>d9<br>d8<br>44% d7<br>26% d6<br>d5<br>d4<br>30% d3<br>d2<br>PLN /<br>d1 month /<br>eq. adult<br>0 50 100 150<br>Source: Authors’ calculations based on HBS<br>consumption deciles<br><!-- End of picture text --> The right-hand panel of Figure 11 illustrates the incidence of social transfers (excluding old age pensions) from
Multi-Agency Emergency Response Team and the Tanzania Red Cross estimate that 2,151 households in the city were displaced, 42 houses and 21 latrines completely collapsed and 342 houses severely damaged. The current report provides results suggesting the number of affected households is much larger than official numbers and that the impacts of the flood were significant – households lost on average 23% of annual income.

Recent flooding events affecting Tanzania in May 2019 are yet another reminder of the urgency to address the flood risk problem in the country. During May 5-7, Dar es Salaam received 144mm of rain, which is close to the rainfall average of the entire month of May, leading to flooding and closure of several roads. In mid-May, flooding killed 5 and left 2,500 people homeless in the district of Kyela in the country’s south after a week of torrential rain.

The Disaster-Poverty household survey analyzed the relationship between flood risk and poverty in Dar es Salaam. Two rounds of data collection were carried out between November 2017 and September 2018. The first round established an initial assessment and baseline of household exposure and behaviors in relation to flood events. The second round assessed the impact of the flood that happened in April 2018.

# 2 Methodology and Data

## 2.1 On project implementation

The study was conducted as part of the Tanzanian Urban Resilience Program (TURP), which is financed by the Government of the United Kingdom’s Department for International Development (DFID).

The Disaster-Poverty study has incorporated the work conducted for other TURP components. For example, the team used maps based on the Ramani Huria community mapping project to identify areas exposed to floods, and collaborates widely with other teams under TURP to align objectives and maximize synergies. For example, different teams involved with community work on flood mitigation under TURP were invited to add questions to the questionnaire, and results are shared with partners continuously.

> 4 <u>https://reliefweb.int/sites/reliefweb.int/files/resources/MDRTZ021do.
can pose a serious challenge for assessing and monitoring individuals’ welfare across the world. Dang et al. (2019) create a typology of consumption data, which classifies household survey data as either entirely missing individual consumption data; partially missing such data, as in a case in which the data is only available for certain administrative areas; or missing such data over time. The NNS is thus an advantageous data set as it provides a unique opportunity to investigate questions of individual consumption and expenditures within a household, which is not possible in most other contexts.

Our findings extend the existing literature on intra-household dynamics and the value of individual welfare estimates. Standard welfare measures rely on household per-capita consumption metrics, which assume equal allocations among family members (World Bank, 2015). This is the case, 4
|||||
|Percent of seat-km in newer aircraft|**93.4**|90.9|96.8|95.6|71.42||
|Percent of carriers passing IATA/IOSA audit|**0**|0|0|0|28.6|50.0|
|FAA/IASA audit status|**No audit**|Fail|**Fail**|No Audit|No audit|Audit<br>(as of<br>2007)|

_Source:_ Bofinger 2009. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly, while lower the HHI, the more diluted the market power exerted by one company/agent.

All data as of 2007 are based on estimations and computations of scheduled advertised seats, as published by the Seabury Aviation Data Group. This captures 98 percent of worldwide traffic, but a high percentage of African traffic is not captured by these data. FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit.

n.a. = Not applicable.

Across the region, there has been a tendency for aircraft fleets to be renewed and scaled down in size to facilitate the consolidation of routes toward a hub-and spoke-system. The aircraft fleet serving Burkina Faso has been renewed rapidly in recent years, with the share of recently manufactured aircraft rising from 73 percent in 2004 to 93 percent in 2007, positioning Burkina Faso in the average range for the region (table 7).

Burkina Faso has a very active airline. Air Burkina was found in 1960s and later
into poverty and below the level that makes it affluent.<sup>3</sup> Using data from four-year panels of the EU Statistics on Income and Living Conditions (EU-SILC), an annual harmonized income and living standards survey conducted in EU countries, we can observe transitions into and outside poverty and thus specify a low probability of falling into poverty. Clearly, the vulnerability to falling into poverty is negatively correlated with the lifetime or permanent income. This, in turn, is a function of household and individual assets and characteristics. Focusing on income generation characteristics, such as the assets and skills of household members, we link vulnerability to poverty to permanent income rather than to the more transient ups and downs of annual income flows.

# **_Data_**

The EU-SILC survey is based on a rotating panel that allows for cross-sectional and longitudinal estimations for most EU and three other countries—Iceland, Norway, and Switzerland. Our analysis focuses on the European Union (excluding Germany<sup>4</sup> ), Iceland, and Norway<sup>5</sup> (Table 1). Panels in two- to four-year increments are defined from 2005 through 2021. We use the four-year panels corresponding to reference years 2005–08 (just before the global financial crisis) and 2015–18 (the post–global financial crisis period).<sup>6</sup> We exclude the panels that span the COVID-19 pandemic (2020 and 2021).<sup>7</sup> The choice of the two periods—the first characterized by inclusive growth throughout Europe, the second characterized by recovery but still plagued, in many cases, by scarring from the global financial crisis—was motivated by our desire to assess changes in vulnerability.

income). We therefore use the high-income poverty lines of $24.35 per day. Note that this poverty line is the poverty line for high-income countries and that that all countries in the European Union except Bulgaria (
year , on the number of births in period (Panel A), and the growth rate in teen births over the previous 4 years (Panel B). Coefficients are multiplied by 100 for display purposes. Growth in teen births calculated as the average annual percent change in cohort births between tt tt−1 _t_ and _t-4_ . Estimates condition on lagged primary school enrolment, nursery and preschool classrooms, municipality expenditures, municipality and year fixed effects. Municipality-clustered standard errors are reported in parenthesis.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

34
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In the next section, the model is generalized to include trade taxes, personal taxes and sales or excise taxes, and the derivation of appropriate tax bases is discussed. Additional factors which may have affected output growth during the period are also explored.

# III. Emuirical Implementation of the Theoretical Model

The assumption of a two sector model is an obvious simplification, and it is shown in the appendix that the results derived above carry over to many sectors. Corporate and personal taxes will likely affect the manufacturing and mining sectors, while the import tax is expected to provide protection for import-substitution industries. The export tax will affect export industries, while the sales/excise tax may distort the use of market goods versus home production. Each tax is entered separately in the regressions to reflect potentially different effects on output growth. The tax rates required for the empirical estimation are discussed as follows.

ImRort Tax: The most straightforward tax to calculate is the import tax, defined to be the ratio of import tax revenue to total imports. Error may be involved measuring this tax, since imports for government or foreign aid use may not be taxed, while non-tax exchange constraints could lead to unmeasured "shadow" tax rates. The import tax, and the variables that follow (unless otherwise noted), come from the IMF's Government Financial Statistics and the World Bank World Tables, collected in an integrated computerized data set in 1986 at the World Bank.

Exnlort Tax: The export tax is measured as export tax revenue divided by the export tax base. It is therefore an output tax on the export sector. The measured tax will likely be biased downward, since marketing boards often collect an implicit tax on exported agriculture.
For example, by the year 2013 cash transfers are used in practically all developed, developing and emerging countries around the world and close to 1 billion people are covered by this type of protection.<sup>27</sup> School feeding programs, recently reviewed by the World Food Program and the World Bank, exist in 78 developing countries in the world and cover 270 million children.<sup>28</sup> Public works at scale exist in over 50 countries.

But how do these programs compare to the size of population? Data from household surveys assembled by the World Bank in the resource called ASPIRE give a sense of coverage across countries.<sup>29</sup> Based on responses from households in nationally representative surveys, one can estimate what percent of a population is covered by different forms of social protection programs by regions (Figure 1). We see that globally even though all developing countries seem to have some form of social protection, less than one half of the population benefits from it. Social protection of any sort reaches less than a quarter of the population in Sub-Saharan Africa. Despite South Asia’s longer history of locally owned safety net programs, just over one third of population (and slightly more- 40 percent of the poorest quintile as shown in Annex 3) is reached by any sort of social protection. Many countries have incomplete social protection systems with important instruments missing. But in some countries, such as Romania, Mongolia, Chile or Thailand, social protection covers close to 100 percent of their poorest and most of the population (see Annex 3).

# **FIGURE 1: PERCENT COVERED BY MAIN TYPES SOCIAL PROTECTION PROGRAMS BY REGIONS**

_(Percent of population receiving transfers from social protection program)_

> 27 Barrientos (2012). Discussing the choice of specific instruments and their relative role in each country is beyond the scope of this paper; see World Bank (2012b).

> 28 World Food Program school feeding global survey
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planned Investment program, and the ability to forecast peak demand. It **Is** not surprising, therefore, that no trend Is evident In the peak demand/installed capacity ratios.

Using overall losses, then, as the sole Indicator of quality of service, no significant trend over time can be ;n7frred with regard to the quality of service. For the period 1965-1986, It Is concluded that there was no overall Improvement In service quality during the project Implementation perlod while for about 30% some Improvement occurred and for the remaining 20% of projects an overall deterioration Is assumed to have occurred.

# =:ost of SUDDIy and Price

Various approaches were used In the course of this study to letermine the trend of the cost per kWh of electricity over time. Data llmitatlons In terms of the number of valid observations that were avallable were such that no firm conclusions could be drawn from the cost figures themselves. The wide fluctuations In Inflatlon rates and Inconsistencies In exchange rates (which complicated the conversion of costs to a base of constant US dollars) made a comparison among countries and over time unrealistic.

Further attempts were made using national consumer and wholesale price Indlces to obtain a ratio of costkWh at completion to the cost/kWh at appraisal In local currency for each country. Since economic costs were not generally available, financial operating costs were used In thelr place. From the very limited data available for operating costs at the beginning and end of projects, i would appear that nineteen utilities had lower cost/Wh at project completion than at appraisal In real terms and eight had hlgher costs (using the wholesale price Index as a deflator). These very limited results suggest that over the project Implementatlon period (6.5 years on average) the financial cost of power declined by about 14% In real terms. Several points would modify such a conclusion, however:
# **Impact of Fiscal Policy on Inequality and Poverty in the Arab Republic of Egypt**

Gabriel Lara Ibarra,<sup>1</sup> Nistha Sinha,<sup>2</sup> Rana Fayez,<sup>3</sup> and Jon Jellema<sup>4</sup>

**JEL Codes:** D31, H22, I14, I24, I32

**Keywords:** Fiscal Policy and Inequality, Income Inequality, Poverty, Social Assistance, Taxation, Egypt, CEQ

> 1 2 Senior Economist, Poverty and Equity Global Practice, World Bank, glaraibarra@worldbank.org Senior Economist, Poverty and Equity Global Practice, nsinha@worldbank.org<sup>3</sup> Consultant, Poverty and Equity Global Practice.<sup>4</sup> Deputy Director for the CEQ Project at GDN. Email: jon.jellema@ceqinstitute.org. The authors would like to thank the UK-WB Trust Fund in Egypt for financial support, the Government of Egypt for sharing indicators on spending and revenue without which the exercise could not be carried out, and to CAPMAS for their support and access to a representative sample of the HIECS 2015. We thank Amr Elshawarby for his data support, the Egypt MTI and Energy teams at the World Bank, Sherine Al-Shawarby for their comments, and Nora Lustig, Director of the Commitment to Equity (CEQ) Institute, and the rest of the CEQ team, for helpful advice, and verification of findings.
_Social Capital, Household Welfare and Poverty in Indonesia_ 10 specific attention to the differential impact between voluntary associations and those with mandatory membership (section 7).

Lastly, we revisit the question of causality: does social capital cause higher incomes or do households with high incomes have better access to associational life? We use instrumental variable methods using independent village data to address this question (section 8).

Before turning to the empirical results, we discuss in the next section the data set, and the comparative study of which it is part.
firms still report according to the old system. For those firms, we have used the conversion accounting tables for each country as produced by PriceWaterhouse. The analysis is then based on IAS across all firms. Sales and inventory changes are reported in all cases. This allows us to adjust the revenue numbers to account for sold (rather than produced) output during the period when countries still used old accounting conventions. Firm-specific output prices are not available. Instead, we use output price indices at the industry level, as reported by the respective statistical offices. All nominal values are deflated using these price indices. This limits the comparisons between firms within the same sector and country, but would not appear to introduce a significant bias in comparisons across sectors or countries. We recognize, however, that the use of separate inflation corrections across firms may be beneficial, given the likely variation across firms. The last year of the sample (1995) is taken as a base year on the assumption that relative prices are closer to relative market prices than in earlier years.

The data on factor inputs include detailed information on firm expenditures, employment, and average hours worked. Expenditures on energy are available separately from other material inputs' expenditures. We use industry-level input and energy price indices reported by the statistical offices to deflate nominal values. Following Gordon and Li (1995), we use purchased inputs rather than used inputs in constructing our material input variable. Under this definition, output produced using materials drawn from inventory results in increased productivity.<sup>2</sup> Data on firm-specific fixed assets are also available but not used in the analysis. Table 2 lists for each country the number of firms in the dataset, aggregate employment in 1992, and its share of total manufacturing employment, and the sectoral distribution of employment, as well as descriptive statistics on employment (mean and median number of employees).

The data cover 6,354 firms with over 6.5 million employees. Polish and Romanian firms are the
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**Table 3: Sources of rich country advantage in resource availability for education**

|Indicator|Item in<br>equation (1)|Country an<br>G<br>A: $200|d per capita<br>NP<br>B: $3,000|Country B's<br>advantage<br>over A**d'**|% contribution<br>to country B's<br>advantage|
|---|---|---|---|---|---|
|Budget allocations||||||
|Overall govt. budget as % of GNP'|G,GNP|26.8|31.7|1.18|12.1|
|Education as % of overall govt. budget|G,/Gt|13.4|16.7|1.25|16.3|
|Student aid as % of education budget||9.8|5.4|1.05|1.8|
|Education budget as % of GNP||3.2|_5.0_|_1.55_|30.2|
|Demographic burden a|P.a/Pt|23.0|17.5|1.31|18.9|
|Teacher pay b'|TS/(GNP/P)|6.3|3.0|2.08|50.9|
|Spending per school-aged population c/|-|-|-|4.23|100|

Source: columns 3 and 4 are simulated from regression estimates of the relation between the corresponding indicator and per capita GNP, based on the data for some 125 countries in 1993; the last two columns are authors' computation, following procedures explained in the text.

a/ Population aged 6-1 1 as percent of total population
Financial Regulation and Development billion birr, and the seventh bank, Construction and Business Bank of Ethiopia (CBB), with assets below four billion birr.

Therefore, we set the hypothesis that large banks are also endowed with lower unit cost (a better technology) than smaller banks: thus, larger banks match the _ηL_ case and smaller banks match the _ηH_ case. For this reason, given that the largest six banks are more than twice as large as the remaining eight, we classify these banks as “more efficient” (hence presenting a lower cost of branch opening, _ηL_ ) and we define a dummy variable “Big Bank” taking unit value for all of these. The remaining are categorized as “less efficient” (embedding the parameter _ηH_ ).<sup>10</sup> Once both the time-series and cross-sectional variation is clear, we present the following tests of our proposition.

A. Main Results. In this section we verify the predictions of Proposition 1 and 2 on the following databases.

1. Balance sheet data. Using monthly data, we verify that safe asset purchases increase after the policy, that new deposits are collected in old branches, and loan volume also increases (Section 2.2.1).

2. Branch map. Using monthly data, we give evidence that branch installation increases more markedly after the policy and more cities see their first branch installed after the policy (Section 2.2.2).

3. Annual reports. Using yearly data, we show that bank profitability does not decline in absolute value and actually increases for larger banks. We also show that the number of employees grows much more after the policy, as banks start to expand further (Section 2.2.3).

B. Robustness Checks. In Section 2.3 we explore a variety of factors which might confound our estimates and verify the soundness of our results.

# **2.2 Main Results**

## **2.2.
7 and Grosse, Klasen and Spatz (2009). Models based both on consumption subcomponents and on different combinations of non-consumption assets are explored. This provides a test of the predictive power of the most commonly used poverty prediction models, including the validity of the parameter stability assumption.

In this study, we use repeated cross-sections with highly comparable survey and questionnaire design from Vietnam (Vietnam Living Standards Surveys (VLSS) of 1992/1993 and 1997/1998) and rural household panel data (2000-2004) from two western provinces in China—Gansu and Inner Mongolia—to assess the validity of the SAE-based poverty prediction methodology. These two settings span periods of deep structural change, accompanied by marked reductions in poverty. At first glance one would not expect model stability in such settings, and so these applications put the prediction techniques to a demanding test. In addition, the paper presents two applications, for Russia and Kenya respectively. Rather than providing a validation exercise, the two applications demonstrate how the poverty prediction methods considered here can help to confront comparability issues arising from problematic temporal cost-of-living indices.

The results are quite encouraging. In Vietnam the poverty prediction method works quite well both with models using certain expenditure components (particularly non-rice food spending and non-food spending) and with comprehensive models specified on the basis of non-consumption assets. Similarly, in rural Gansu and Inner Mongolia, models based on non-expenditure assets work fairly consistently, while models using certain expenditure subcomponents also work satisfactorily in some instances. The broad conclusion is that the general approach appears to work, and its underlying assumptions of stable parameters seem to hold in these two ―test‖ settings of