rafmacalaba/datause-encoder-data huggingface dataset:
PROVISION OF WATER TO THE POOR IN AFRICA Although numerous utilities and governments had made an effort to set a “retail” price for standpipe water, in general, the final “informal” price tends to be fixed by the manager of the standpipe. To better understand the underlying causes of the high standpipe prices, it is necessary to assess who acts as a price-setting agent (table 3.3). For standpipes and kiosks managed by private individuals or community groups, the price is primarily fixed by the kiosk manager or the community group respectively. In standpipes managed by utility staff, there is no principal actor setting the price. The number of standpipes managed directly by utility staff has been decreasing in all the cities studied and their relative weight is marginal.

**Table 3.3   Price setting in standpipes/kiosks**<sup>**20**</sup>

||||Price-setting age<br>|nt<br>|||
|---|---|---|---|---|---|---|
|Management (% of countries)|Kiosk manager|Utility|Government|Community|Other|TOTAL|
|Private individual|47|13|27|0|13|100|
|Utility|0|14|29|29|29|100|
|Community|0|11|22|67|0|100|

Source: AICD WSS database 2007.

The delegated management contract model for public standpipes, used by close to three quarters of utilities studied, falls short in terms of effective regulation of standpipe prices. Under this model, individual standpipes are directly regulated by local authorities or community groups and indirectly regulated by the utility or the water regulator agency. It is generally acknowledged that both the regulator and local officials lack the capacity, resources and incentives to regulate and monitor this activity. There is evidence of the lack of standpipe control in many cities, including the following: Dakar; Addis Ababa; Maputo; Kampala; Maseru; and Dar es Salaam.

# Household water resellers<sup>_21_</sup>

In general, household water resellers emerge largely as a result of malfunctioning or absent standpipes, or because of high connection costs. In two-thirds of the cities studied, the informal household reseller price is similar to the informal price for standpipes (figure 3.3). However, two cities are exceptions to this trend– Accra and Blantyre – because of supply-chain specificities. The vast majority of kiosks in Accra are supplied by water tankers; they have to pass-through the cost of the transport by water tankers. Discounting this cost, the water price of a household reseller is quite similar to the standpipe informal price. In Blantyre, certain parts of the city experience a huge increase in the price of water due to restrictions on the supply side. A couple of years ago, the price of water from the household resellers was equal to the informal price of the standpipes (Chriwa and Junge 2007).

> 20 Analysis based on data from 15 cities.

> 21 Analysis based on data from 9 cities.

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|Subcomponent|Able to do things as well as oth people<br>(Rosenberg)|I am able to do things as well as most<br>other people.|
|---|---|---|
|||Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Feel have not much to be proud of<br>(Rosenberg)|I feel I do not have much to be proud of.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Take a positive attitude towards self<br>(Rosenberg)|I take a positive attitude toward myself.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|I am satisfied with myself (Rosenberg)|On the whole, I am satisfied with myself.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Wish could have more respect for<br>myself (Rosenberg)|I wish I could have more respect for<br>myself.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Certainly feel useless at times<br>(Rosenberg)|I certainly feel useless at times.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|At times think I am no good at all<br>(Rosenberg)|At times I think I am no good at all.<br>To what extent do respondents: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement about them|
|**Secondary**<br>**Outcome**|**Psychological well-being index**|Summary index of 9 questions on well-<br>being in last 2 weeks and stress in last 4<br>weeks.<br>The questions used here belong to two<br>different sets of questions. The first set is<br>the 5 WHO questions on well-being, and<br>the second set is the Cohen stress scale<br>4 questions.|
21 Note that during periods in<sup>which DES were implemented inflation was,</sup> on average, growing. In Argentina<sup>and Brazil inflation rose in three out of</sup> the four quarters during which<sup>DES were implemented (until 1988). The</sup> positive correlation between<sup>inflation and the DES volume is even more obvious</sup> in the case of Mexico. It is<sup>clear, though, from the sheer size of DES vis-a-</sup> vis other factors of the money<sup>supply, e.g. fiscal deficits, that DES along</sup> cannot account for inflation.<sup>Yet, its impact on the margin could have been</sup> sizeable, and what is more important,<sup>easily managed by putting restrictions</sup> on DES programs, e.g., reportedly<sup>in Mexico. In some countries, such as Chile</sup> and Brazil, restrictions on<sup>debt-debt VDR were more directly targeted</sup><sup>**ct**</sup> colling off pressures in<sup>foreign exchange black markets, since foreign</sup> exchange is demanded by firms to<sup>repurchase their debt at a discount.</sup> Table 2 reports secondary market<sup>prices, deflated stock market price</sup> indices and actual volumes<sup>of offical debt reduction (DES, CDC and debt</sup> buybacks) in Chile and Mexico.<sup>These are the two Latin American countries</sup> with longer official programs<sup>of VDR.</sup> Over the same period in which<sup>the Chilean DES program was in force the</sup> Chilean stock market price index<sup>exhibited an upward trend. This is precisely</sup> what the short-term model<sup>predicted. However, other factors such as steady</sup> growth, structural reforms<sup>and economic and political stability have probably</sup> been more important determinants<sup>of the stock market trend. Although the</sup> linkage between the DES program<sup>and the stock market trend is consistent with</sup> the evidence, the significance, and even the direction, of<sup>causality are far</sup> from warranted.
# **Figure 2: Wealth composition by natural capital**

This graph shows the wealth composition of the 37 countries studied, ordered by the share of natural capital in the total wealth. The country names on the left (right) side belong to the A-rated (B-rated) category. We can see that countries with lower share of natural capital tend to belong to the A-rated category. Higher natural capital alone does not imply B-ratings or vice versa.

<!-- Start of picture text --> Natural capital Produced capital Human capital<br>United Kingdom 1.0%<br>Netherlands 1.2%<br>Japan 1.3%<br>Switzerland 1.4%<br>Germany 1.6%<br>Korea, South 1.7%<br>Denmark 2.2%<br> 2.3% Italy<br>France 2.4%<br> 3.3% Ireland<br> 3.5% Spain<br> 3.7% Portugal<br>United States 4.3%<br>China 4.4%<br>Austria 4.5%<br> 5.0% Hungary<br>Canada 6.3%<br>Slovakia 6.8%<br>Poland 7.4%<br>Finland 7.6%<br> 8.0% Philippines<br> 8.2% Thailand<br> 8.9% Mexico<br>Sweden 8.9%<br> 10.1% Romania<br> 10.8% Colombia<br>Malaysia 11.0%<br> 11.3% Turkey<br>Norway 11.6%<br> 12.1% South Africa<br> 12.3% India<br> 12.3% Indonesia<br>Australia 15.2%<br> 15.2% Peru<br> 15.6% Brazil<br> 15.9% Bulgaria<br> 20.8% Russia<br>0% 25% 50% 75% 100%<br>A-rated issuers (AAA to A-)<br>B-rated issuers (BBB+ to BB-)<br><!-- End of picture text --> capital is a function of (inflation-adjusted) unit rents, production quantities and the difference between deforestation and re-/afforestation (World Bank, 2018).

A main critique point of the wealth data is its low, annual frequency. While this characteristic may be undesirable for financial markets, where subsecond data can be used to arbitrage, the same property turns out to be an advantage in our case. Even if crop yield data would be available on a daily frequency, it would be conceptually meaningless, let alone the concerns about measurement errors and low signal-to-noise ratio.6 In this study, the low frequency alleviates an econometric concern. Suppose bond yields and natural resources are related, which way does the effect go? With monthly, stationary bond series on the one side and annual, persistent wealth data on the other side, the estimated effect is more likely to originate from the latter. Moreover, the discounted resource rents are calculated with lagged, 5-year moving average rents (World Bank, 2018). This effectively makes the wealth variables predetermined. These technical arguments, however, do not preclude the existence of long-term trends in the bond data or the presence of other confounding and possibly unobserved variables that influence bond prices as well as natural resources.

The wealth accounting indicators used in this study were compiled on an annual frequency and will be made available together with the accompanying report (Lange et al., 2021). The values are comparable and qualitatively consistent with the publicly available 5-year frequency data until

> 6Monthly or quarterly data, on the other hand, might be more informative as they capture seasonal variations and are aggregated enough for higher signal-to-noise ratios to emerge.

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technologies, like surveys, computerization, and color-coded cards.<sup>14</sup> This complicated process determines who gets counted as poor, how different degrees of deprivation are ranked, and who gets excluded from receiving government benefits. Public response to this process ranges from contesting the selection of certain beneficiaries, to demanding a finer calibration of the process, and even critiquing it for fundamental flaws. People often propose accounts of poverty for strategic purposes, revealing their covert desire to be counted among those in need of government assistance. In fact, when villagers talk about what poverty means, they are also talking about what they themselves want. The GSs therefore become sites for the joint production of an understanding of what it means to be officially classified as poor. Overall, these exchanges, although often initiated by political figures and GP officials, foster the future capability of the poor to engage in a critical dialogue with the state on definitional matters.

On the official side, GP representatives spend much of their time trying to convey to the public the ever-changing parameters of poverty as determined by the federal Government. The following example comes from Dakshin Kannada (KN): _Ward Member (male):_ How to check their [BPL households’] financial status? We cannot check this as accurately as doing a mathematics sum. Now, what is the definition of a family? Generally, it includes a husband, wife, and two children. If the family does not eat posh food everyday, but has “Ganji” [rice gruel] for breakfast, then they have to spend Rs. 25-30. Some people have unnecessary habits, like drinking tea. Taking all of this together, a family of four needs at least Rs. 60-70 [per day]. If they spend only Rs. 50 per day, even then it comes to more than Rs. 12,000. 15 This is the guideline that we’ve been given to decide the poverty status. We also have the details of households having telephone connections or mobile phones. Those who have these cannot be considered as BPL.

GP representatives also use discussions in the GS to help the public negotiate the labyrinth of government scores attached to each disadvantaging characteristic, such as

> 14 There are typically two types of cards, yellow and red.

> 15 This amount is the annual income cut-off below which households qualify as BPL.

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to consider the benefits of spending higher amounts, both upfront and over the lifetime of the road, given the long-term benefits it has to the people and the minimization of regular maintenance and repair, especially in the context of climate change. This is also important for low-volume rural roads, such as this one in Malaita, which is often the only way for the people living around it to remain connected to essential services.

<mark>It is also important to note that while this study limited itself to the impact of structural/technical road improvements on resilience, there are several other aspects that can influence the ALF. Resiliency benefits can be enhanced by improving contractual methods for road maintenance and repair that can lower the time for recovery. Similarly, the lack of funds for timely maintenance or operating expenditures can cause greater damage than anticipated to any type of road because of the lower ability of a poor-quality road to withstand natural events. Hence, a narrow focus on just technical improvement may not be sufficient to</mark> realize the benefits estimated in this paper.

<mark>Secondly, secondary effects of road improvement need to be carefully considered and managed in any road intervention project. For instance, Malaita has been heavily logged, with forest losses being as recent as 2019. Therefore, it is important to consider whether a better road network would accelerate the rate of deforestation, which is already ongoing at unsustainable rates, because large vehicles would be able to move faster across the island. Such impacts need to be considered and management plans developed for the same.</mark> This study relied on domain knowledge as a way to compensate for the lack of reliable measurements. This will remain as an area for future improvement. Many of the cost input assumptions are based on expert opinion and data from similar contexts (such as other Pacific Islands), but not the Solomon Islands itself. In addition, the selection of the rainfall event (in the lack of real rainfall data) as well impact of disruption on particular gradients is based on expert opinion. As such, the current analysis serves as an input to decision making that should be considered alongside expert, experience-based guidance. Another limitation of the study is that it is based on elevation data – the results could be further improved with knowledge on drainage and locations of bridges, which are more vulnerable to flooding events.

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# **1. Introduction**

Yemen is one of the poorest countries in the Arab region with a per capita GDP of US$1,160 for 2008 (WDI, 2010) and faces a wide range of developmental challenges, amplified in 2011 by deepened domestic conflict. In 2007, the country was ranked 140 out of 182 according to the HDI (Human Development Index) (UNDP, 2009). Ever since reunification in 1990, Yemen’s relative position on the HDI index has remained more or less unchanged, with very slow progress towards attaining the Millennium Development Goals (MDGs). At 3 percent, the country has one of the highest population growth rates globally, with the population expected to double in 23 years to around 40 million. This increases the demand for educational and health services, drinking water and employment opportunities. Yemen faces a severe water shortage, with available ground water being depleted at an alarming rate. Its oil production and reserves are declining with severe budgetary consequences. The Yemeni economy is caught in a jobless slow growth cycle leading to stagnant per capita incomes and rising levels of unemployment, particularly amongst the youth. Unless resolved promptly, the political crisis that erupted in 2011 threatens to make Yemen’s prospects for rapid growth and progress on MDGs even bleaker.

Social development indicators, such as child malnutrition, maternal mortality, and educational attainment remain discouraging. The Household Budget Survey of 2005/06 indicates that about 35 percent of the population lives below the national poverty line, with poverty more widespread and persistent in rural areas. However, given persistent increases in consumer prices, notably food prices, the rate of poverty had increased to 42.4 percent in 2010 (MOPIC, 2011).<sup>1</sup> There are large gender disparities, with significant gaps in women’s access to economic, social and political opportunities. As indicated by its first and second MDG Reports, Yemen was off track with respect to meeting the MDG

> 1 The Fourth Five-Year Socio-Economic Development Plan for Poverty Reduction 2011-2015 is an official document that, among other things, provides projections for several variables useful for generating the MAMS reference scenario as further explained below.

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place. They also caution (p. 2 and p. 14) that the politics of the SSG presumed shorter departures from trend than world markets tend to produce.

# **Differences by provenance of unit values: another source of mistaken signals**

(This section was prepared by J. Michael Finger and Francis Ng.) The SSM price trigger takes unit values aggregated over imports from all sources as the trigger or reference price, though in real-world commerce, unit values differ considerably from one source to another. Thus, even though the price from a source may be constant, the price trigger will allow additional duties on imports from that source.<sup>52</sup> Because unit values of imports from developing countries are often lower than those of imports from developed countries, using as triggers unit values calculated from imports from all sources will introduce a bias against developing country exporters. To develop an indication of how large this bias might be, we compared for a sample of developing country importers (Brazil, China, India, Indonesia, Malaysia and Mexico) the unit values of imports from different countries. The experiment covers these six importing countries and twenty-five 6-digit product lines. All of the data were for calendar year 2006 – we were comparing unit values from one source with unit values from other sources, not unit values in one year with unit values in previous years. The ‘restrictions’ that this experiment will trigger result entirely from differences in prices from one source to another, not from declines of prices over time.

The exercise then is about identifying unintended consequences of the proposed trigger mechanism, particularly that:

- Even if prices are unchanging over time, the price trigger as proposed will authorize a considerable number of restrictions.

- Most of these restrictions will be on imports from developing countries.

For each of these countries as importers, we calculated for each HS 6-digit line in agriculture the following:

- The unit value of imports from all sources combined

52 This point is easier to explain with an example. Suppose imports in the base year include <u>shipments from two different sources, A and B, as follows:</u>

|Source|Volume|Value|Unit value|
|---|---|---|---|
|A|100|$1000|$10.00|
|B|100|$1500|$15.00|
|Aggregate|200|$2500|$12.50|

The aggregate unit value, the reference price is based is $12.50.

Suppose in the next year, shipments are identical. The unit value from A is 20 percent below the reference price, hence an additional duty would be triggered.

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# **II. The Customs Administration in Madagascar: Context**

Customs plays a very important role in revenue collection in Madagascar. The size of the customs administration is limited in terms of the number of customs officers (in comparison to other countries with similar characteristics). Customs controls are still largely ineffective.

In 2014, imports accounted for 31 percent of GDP (US$3.29 billion).<sup>8</sup> Accounting for 22 percent of imports, the European Union is the biggest supplier of non-oil goods (cf. Table 11, appendix A). Due to large tax rates, taxes on petroleum products are the main source of revenues collected by customs (26 percent).

# **<u>A. The Importance of Revenues Collected by Customs</u>**

As for many other African countries, revenues collected at the border from trade remain significant in Madagascar (see _i.e.,_ Keen (2003), Zake (2011), Montagnat-Rentier and Parent (2012)). Customs collected 45 percent of total tax revenues in 2014 (see Figure 1). Due to the decline in tariff protection and the rise of indirect taxation, value-added tax now represents 72 percent of revenues collected by customs (see Figure 2). As duties on petroleum products account for 30 percent of revenues collected at the border, revenues from customs are consequently heavily dependent on oil prices.

A comparison of the data on revenue collected by Madagascar customs with that of other African countries (especially low-income countries), yields the following results (see Table 1):

- There is potential to improve the level of effective taxation.

- The customs administration of Madagascar is plagued by a lack of human resources, with around 15 percent lower resources than the administrations of countries in a similar category. This explains why the level of revenues collected by each customs officer is higher than the median.

**Table 1: International Comparison with Madagascar Customs**

||**Customs revenue/per**<br>**capita (US$)**|**Customs revenue/per**<br>**customs agent (US$)**|**Number of**<br>**employees**|
|---|---|---|---|
|**Madagascar**|22.7|416,600|1,182|
|**Median for Africa**|28.5|282,900|1,393|

_Note:_ Data for 31 African countries provided by World Development Indicators. Data on number of agents provided by the World Customs Organization, 2012 figures.

**Figure 1: Structure of Tax Revenues Figure 2: Structure of Customs Revenues by Major Type of Taxes**

> 8 The structure of Madagascar’s economy leads to a sizeable balance of trade deficit. In 2014, the trade deficit was 10.5 percent of GDP. Unless otherwise indicated, all statistics in the rest of this document pertain to the year 2014.

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