rafmacalaba/data-use-mentions-extended huggingface dataset:
households in flood-affected communes by 1.8 percentage points. Another study by Bui _et al._ (2014) for the same country estimate that poverty would have decreased by 2.7 percentage points if households had not been exposed to natural disasters. Analyzing data from Mexico during 2000-2005, Rodriguez-Oreggia _et al._ (2013) highlight significant adverse effects of natural disasters on both human development and poverty, with an increase in poverty of between 1.5% and 3.7%. Notably, floods and droughts are the most devastating. Similar results are found in Argentina by González _et al._ (2022) where they find that a variety of natural disasters from 1970 to 2010 contributed to more multi-dimensional poverty.

On a global scale, combining household surveys from 24 Sub-Saharan African countries, Azzarri and Signorelli (2020) find that flood shocks result in a 35% decrease in total and food per-capita consumption and a 17-percentage point increase in extreme poverty. Azzarri and Signorelli (2020) also predict that increases in rainfall and temperature, and consequently the incidence of disasters across Western and Central Africa, could result in dramatic increases in extreme poverty rates by up to 30 percentage points. Analyzing a new global, subnational database on poverty across 134 countries, Dang _et al._ (2023a) find strong and statistically significant global effects of both higher and lower temperature on poverty, with a one-degree Celsius (i.e., 1<sup>◦</sup> C) annual increase in temperature causing poverty increases of 0.9, 1.8, and 2.3 percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). But this study also finds smaller impacts in the longer term, suggesting household adaptation to gradual warmer temperature over time.

Other studies simulate the future effects of climate change and
low management public school (via teacher selection). In the numerical example shown in Figure 7, the vertical height of the blue point in the top panel is greater than the vertical height of the orange point in the bottom panel.

Column (2) of Table 4 shows that, consistent with the theory, the partial effect of operations score on the teacher motivation index is positive and significant at 1 percent; a one standard deviation increase in operation score is associated with a 0 _._ 238 standard deviation _increase_ in the teacher motivation index, significant at the 1 percent level.

**Teacher effort.** The expected effort level of a teacher hired to an intermediate management public school is higher than the expected effort level of a teacher hired to a low management public school (via teacher selection)

Column (3) of Table 4 shows that one standard deviation higher operations score is correlated with 0 _._ 076 standard deviations higher teacher effort, significant at the 5 percent level.

**Household effort** The expected level of household effort in an intermediate management public school is higher than the expected level of household effort in a low management public school (via augmentation of student intrinsic motivation): _a_<sup>_I_</sup> _> a_<sup>_L_</sup> .

We explore this prediction by using the school climate section of the school principal questionnaire to construct an index of household effort, combining student behavior questions (relating to how often students are truant, late, disrespectful and/or disruptive) and parental involvement questions (relating to the extent to which parents: are interested in, and discuss, their child’s progress and behaviour; volunteer for school activities; and participate in school governance or other forms of accountability). Column (4) of Table 4 shows that, consistent with the theory, one standard deviation higher operations management score is correlated with 0 _._ 160 higher household effort, significant at the 1 percent level.
# **10 Conclusion and next steps**

## **10.1 Conclusion**

**The fiscal incidence analysis for the West Bank and Gaza in 2017 shows that the combination of taxes and transfers modeled reduced inequality but increased poverty.** Inequality decreased by 6.5 Gini points, from 35.0 at Market Income to 28.5 at Final Income), while the national poverty headcount increased by 8.4 percentage points, from 30.6 percent at Market Income to 39.0 percent at Consumable Income. This pattern is common in developing countries where the tax collection is mostly comprised of indirect taxes and social protection coverage is limited.

**The large increase in poverty due to combined taxes and transfers suggests that fiscal redistribution in the West Bank and Gaza could be improved.** Only the poorest decile is a net-receiver of the combination of taxes and transfers modeled in this study, all other deciles were net payers. Almost 50 percent of the poor population at Consumable Income (roughly 19 percent of the total population) experienced some degree of fiscal impoverishment—that is, a reduction of their Market Income after they paid taxes and received transfers.

**Most fiscal interventions were progressive relative to Market Income.** We measured this using the Kakwani Index. The regressive interventions, looking at Market Income, were fuel subsidies, pension income, and property tax.

**The marginal benefit of including in-kind benefits from health and education into the system accounts for 40 percent of the reduction in inequality, while the marginal benefit of including the CTP into the system accounts for 18 percent of the inequality reduction.**<sup>80</sup> We measured the marginal contributions of each instrument to either reducing inequality or reducing poverty **.** The large marginal contributions to inequality reduction stemming from public basic education is expected since poor households have more children that access basic public schools. The model could, however, overestimate the inequality reduction stemming from
in Nigeria have tens of thousands of relay sites emitting in multiple frequencies that we overlaid to obtain the aggregated coverage.

Figure 3 shows the aggregated 3G coverage in Nigeria in 2010, 2012, and 2015. During this period, 3G coverage evolved from the main cities only to a large number of intermediate cities and rural areas. This represented an increase in population coverage from 21% to 57%. This variability is not observed for 2G, which for the same period increased from 83% to 89%. The main operators did not start rolling out 4G networks until 2016, when it was limited to urban areas.

By contrast, during the same period, penetration of fixed broadband internet in the country averaged around 2%, reaching just over 5% in 2016.<sup>14</sup> Mobile has therefore been the primary platform for users in Nigeria to access broadband internet services.

# Linking coverage data to each household location

Figure 4 presents the location of the enumeration areas that were surveyed in the GHS and shows which had access to 3G or 4G coverage in 2010 and 2016. We consider a household to have mobile broadband coverage when it is covered by a medium or strong signal in 3G or 4G. The two data sets 10 P.L. Rice, A.G. Longley, “Prediction of Tropospheric Radio Transmission Loss Over Irregular Terrain – A computer method 1968,” Essa Technical Report ERL 79-IT S67. Available: <u>http://www.visuallmr.com/documentation/pathlossmodels/ntis.longleyrice.676874.pdf</u> 11 - We used the SRTM 90m Digital Elevation Database created by NASA. See: https://cgiarcsi.community/data/srtm <u>90m-digital-elevation-database-v4-1/</u> 12 The vegetation profile used for Nigeria was Maritime Subtropical (West coast of Africa).

13 These thresholds were chosen to fit industry standards. For reference see: BEREC, “Common Position on information to consumers on mobile coverage
More specifically, in the long-run a I percent increase in Ml will raise inflation by 0.41, while a I percent depreciation of the Lek will increase inflation by 0.17 percent On the other hand-- keeping with theory-- a 1 percent increase in real income will reduce inflation by 0.25 percent.

Second, in order to investigate the short-run dynamics, we turn to the estimates of the error-correction model, which is formulated as: where x, is the vector of regressors, EC is the error-correction term, and the lag length n is determined by Hendry's General-to-Specific-Modeling (HGSM) strategy. We include 11 lags on each term and eliminate the lags whose coefficients are not statistically significant.'<sup>4</sup> Table 7 summarizes the empirical results. Since the results of the _J-test_ introduced by Davidson and Mackinnon (1981) favor the model including Ml over both M2 and M3, we only report estimation results using MI definition of money stock."<sup>5</sup> The empirical evidence highlights that, as captured by the coefficient of the errorcorrection term (EC), inflation adjusts towards its equilibrium value fairly rapidly, that is

> (1995) argue that it is reasonable to assume that the energy intensity of real value added would not change significantly in transition economies. This is because, while a rise in overhead electricity use per unit of output would tend to increase absorption of electricity per unit of value added (due to declining capacity utilization and a fall in maintenance investment), higher electricity tariffs and shifts away from heavy industry would pull towards increased energy efficiency. As they suggest, until the quality of national income data improves, power consumption data can be used as a more reliable indicator of short-run economic activity.

14 Longer lag specification was not possible due to the relatively short length of our sample.

24
.00417|-0.000161|0.00170|0.000470|0.00283|
||(0.00280)|(0.00405)|(0.00260)|(0.00348)|(0.00307)|(0.00474)|
|Sector Shares<br>Spatial Fixed Effects||X||X||X|
|Observations|156|104|156|104|156|104|
|R-squared|0.068|0.217|0.108|0.356|0.084|0.285|

_Notes_ : R1=Rest of the country, R2=Anatolian Tigers, R3=developed west. Results from Equation 3 are shown. Regressions without sector controls are over the period 2007-2013. Regressions using sector controls are limited to 2007-2011. + p<0.10, * p<0.05, ** p<0.01 _Source_ : Ministry of Finance, LFS, SILC 2007-2011; authors’ calculations.

Initial conditions can also be a strong explanatory factor of the degree of poverty reduction. What are the demographic characteristics such as average educational levels and health indicators? These “initial conditions” may help explain the differences across regions as well. To take into account these additional variables of interest, equation 5 includes a vector of Demographic and Fiscal variables.

Educational PSIA score is increasing but there literacy disparities. Despite increased spending on education in recent years, educational outcomes have not improved in the East. Western regions are by far much more literate than eastern regions. Between 2001 and 2011, per capita government investment in education shifted largely to underserved Eastern regions but educational attainment in these regions are still lagging (World Bank, 2014, Fig 7.10).

Controlling for additional characteristics of the region does not change results. Expanding estimates from Table 7, the addition of controls were added such as schooling enrollment rates, higher education staff, physicians per capita, female labor
He or she is constrained by the institutions of society but does not have a strategic relation with powerful actors in the rest of society. In Nigeria this condition does not appear to hold. Nigeria has had its share of corrupt rulers, but its history suggests that removal of a head of state is not sufficient to generate meaningful reform (Herbst 1996). Powerful private actors and groups are an important constraint on government action. Embedded wealth-sharing practices resist reform.

_C Botswana_<sup>_44_</sup> At the other end of the spectrum is Botswana, which has not been plagued by the systemic corruption so prevalent elsewhere in Africa (Charlton 1990, p. 6). Botswana has enjoyed relatively good governance since independence (Raphaeli et al. 1984, Wescott 1994). Patronage is important, but graft is not. Private business people do not need to bribe government officials to carry on normal business activities; civil servants are paid sufficient (though by no means lavish) salaries so they do not need to solicit bribes in order to survive.

The lack of systemic corruption, along with the high levels of political and macroeconomic stability, has earned Botswana the confidence of foreign investors. Botswana has the lowest "political risk" factor of any nation in sub-Saharan Africa, including South Africa, as revealed in surveys of risk analysts, risk insurance brokers, and bank credit officers published _Euromoney._<sup>_4_</sup> _s_ The country has one of the highest rates of foreign direct investment per capita in Africa.

Although organized democratically, one party has held power for thirty years. The Botswana Democratic Party has delegated substantial power to the bureaucracy, and creation of a competent, efficient civil service was an important goal of the early independence period (Charlton, 1991). The country has abundant mineral resources with over 50% of its GDP 35
>past day|0.030|0.014<br>(0.013)|0.015<br>(0.016)|0.041|-0.002<br>(0.011)|
|Observations|1,074|3|,178|2,313|3,178|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

70
0061*|
|(0 to 100)|(3.58)|(-0.89)|(1.64)|
|Time trend|0.0316*|0.0007|0.0042|
|(=0 in 2000)|(1.69)|(0.07)|(0.60)|
|n|1,244|1,186|1,082|
|R-squared|0.915|0.812|0.903|
|RMSE|2.64|1.74|0.982|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

27
> 7 "Enterprise kindergartens were considered to be superior in quality to municipal facilities, and these differentials appear to have persisted: 1994 data for Kazakhstan show per child expenditures in enterprise kindergartens to be twice that in those run by the Ministry of Education." Klugman et al., "Enterprise Divestiture of Kindergartens and Childcare Arrangements in Central Asia," pp. 3-4. In another 1995 study for the Bank by ADB Associates, it was estimated that adoption of municipal standards would reduce total kindergarten costs in the Kyrgyz Republic by 20%. Cited in Freinkman and Starodubrovskaya, "Restructuring ofEnterprise Social Assets in Russia," p.<sup>24</sup> .
Optimal Pricing of a New Utility Service : The Case of Piped Water in Vietnam<sup>_∗_</sup> Quy-Toan Do<sup>_†_</sup> Hanan G. Jacoby<sup>_‡_</sup> Originally published in the Policy Research Working Paper Series on _April, 2020._ Previous versions were made on _May, 2021 and December 2021._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL codes_ : L95, D91, O18 _Keywords_ : Endogenous preferences, two-part tariffs, take-up, willingness-to-pay.

> _∗_ This study is registered in the AEA RCT Registry and the digital object identifier (DOI) is: “10.1257/rct.6589-1.0”. The authors gratefully acknowledge financial support from the World Bank’s Strategic Impact Evaluation Fund (SIEF), the Knowledge for Change Program (KCP), the Impact Evaluation to Development Impact (i2i) Fund, and the Research Support Budget. Tung Duc Phung and the Mekong Development Research Institute (MDRI) managed the price experiment and collected the baseline data. Trung Dang Le and RTAnalytics (RTA) collected quarterly monitoring data. Ir`ene Hu provided excellent research assistance. We gratefully acknowledge comments from Jishnu Das, Alejandro Molnar, Claudia Ruiz, Gil Shapira and seminar participants at the World Bank applied-micro seminar. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

> _†_ World Bank: qdo@worldbank.org

> _‡_ World Bank: hjacoby@worldbank.org
- 27 -

record" approach. Her', the local authority makes an informal determination of ownership in the course of its field survey. Compliance is encouraged by legislation which relieves the local authority of any obligation to prove legal ownership prior to imposing the tax (by specifying that the property itself is liable or by allowing the taxing authority itself to designate a presumptive owner).

# Valuation

The purpose of valuation for tax purposes is to provide the basis for distributing the burden of property taxes. It is important to distinguish this objective from the objectives of valuation where the government intends to purchase a private property outright. In the latter case, precision is criticai, and the valuation method should attempt to produce an accurate estimate of the current market value. In valuing property for tax purposes, only a measure of relative value at a common point in time is needed. Tax valuation can therefore use highly simplified valuation methods. What is essential in a tax valuation system is that it be objective, so as to produce legally defensible valuations, and that the methodology be appropriate to the skill levels of the local authority, and market information available in the local jurisdiction.

Value for tax purposes is generally denominated in one of two ways: as annual rental value or as capital (or sales), value. The definition used in a specific country generally reflects its colonial history: much of Africa and South Asia define value on the basis of rent; Latin America and East Asia define value on the basis of sales or market value.
|39.2|<br>11,930|4,317|36.2|<br>14,849|2,049|13.8|
|Metal Products|38,782|18,080|46.6|<br>31,280|13,535|43.3|<br>64,570|15,783|24.4|
|Electrical<br>Machinery|17,655|3,637|20.6|<br>13,430|2,566|19.1|<br>34,845|1,543|<br>4.4|
|Machinery & Trans-<br>port Equipment|<br>51,192|14,411|28.1|<br>26,743|14,542|54.4|<br>96,381|21,466|22.3|
|Other|10,005|----|----|3,241|----|----|<br>21,738|**----**|**----**|
|Total<br>|306,893|101,404|33.0|219,820|85,511|38.9|442,485|86,645|19.6|

- a) indirect taxes and subsidies are deducted (see table<sup>2.3).</sup>

- b) the footwear industry (3,188 million cruzeiros) was reallocated<sup>from wearing apparel</sup> to footwear and leather.

- c) Includes 7,148.0 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the Resumen General but taken from Sistema de Cuentas Nacionales de Mexico, 1981.

-
as different sets of variables generate different poverty figures, consequently determining the set that best predicts poverty. To ensure comparability across the three data sets, we restrict the analysis to 16 of the 19 refugee sites, because the PDM data cover only 16 sites. Consequently,</mark> this study covers only refugees from the Central African Republic and Sudan, and not the Nigerian refugees, and all the estimates provided in the paper are not representative of all the refugees living in the country.

> 5 The aim of the paper is not to measure consumption accurately or estimate nationally representative poverty figures for refugees in Chad. The purpose of the paper is only to test the cross-survey imputation methodology using a sample of refugee data. In this respect, our only concern is that the poverty predictions are close to the poverty rates calculated with the consumption data. Whether our consumption data are accurate or not, this is less relevant for us. We should expect the cross-survey methodology to produce even better results if the quality of the consumption aggregate improves.

9
measures may fall short in the face of multiple or severe disasters occurring within short timeframes. This indicates that the capacity to adapt and mitigate climate change effects is a critical factor in determining how these events influence poverty and inequality.

In summary, understanding the mechanisms that link climate change and disasters with poverty and inequality is a multifaceted endeavor. While we have gained substantial insights from the focus on agriculture, numerous mechanisms remain relatively unexplored. These might include conflict, migration, and changes in labor productivity, which are promising areas for future research.<sup>13</sup>

# **4. New Estimates for Chronic and Transient Poverty**

# **4.1. Data and Analytical Framework**

Our empirical analysis relies on data from the Subnational Poverty and Inequality Database (SPID), a recently introduced global database mapping poverty and inequality on a subnational scale (Dang _et al._ , 2023a; Nguyen _et al._ , 2023). SPID is compiled from the Global Monitoring Database (GMD), an extensive repository of household survey data managed by the World Bank. The database derives from official national household income and consumption surveys, incorporating over 1,594 subnational units from 134 countries and spanning the period from 2003 to 2019 with most observations starting from 2010. Typically, a subnational unit aligns with a state or province (that is, first-level administrative boundaries - ADM1), but can also represent a cluster of regions, as determined by the specific household surveys’ sampling approach.

> 13 For example, climate change and related disasters could lead to an increase in conflict over increasingly scarce resources (Burke _et al._ , 2009; Koubi, 2019), which in turn could exacerbate poverty and inequality. Migration, both within and across borders, is another possible response to climate change and disasters (Gröger and Zylberberg, 2016; Missirian and Schlenker, 2017), which could either mitigate or exacerbate inequality depending on the socio-economic characteristics of the migrants and the receptiveness of