rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6: China’s surge – and that of other developing countries – represents m ore opportunity than threat**

<!-- Start of picture text --> � Exporting requires im porting, and China’s im ports are rising rapidly<br>Im ports in USD billions<br>140<br>120 A fr ic a<br>S o u th A s ia<br>100 M id d le E a s t a n d<br>N o r th A fr ic a<br>L a tin A m .<br>80<br>E u r o p e a n d<br>60 C e n tr a l A s ia<br>40 E a s t-A s ia<br>P a c ific<br>20<br>0<br>1985 1990 1995 2000 2005<br>Source: W orld Bank G lobal Econom ic Prospects 2007<br><!-- End of picture text --> Nevertheless, at the beginning of the 21<sup>st</sup> century, African countries still ship nearly two thirds of their total exports to EU and the US. There has been, however, a recent shift in export patterns. Demand in Asia, and primarily in India and China, has been the main source of the accelerated growth of African exports since 1990 and the growth of such exports has intensified in the 2000s (Broadman (2007)).

**Table 1: Destination of Sub-Saharan (non-oil) Exports**

|**Destination**|**2004**|**2030**|
|---|---|---|
|_Asia_|18.3%|31.3%|
|_Eastern Europe and Central Asia_|8.2%|8.6%|
|_European Union_|53.3%|38.7%|
|_The Americas_|8.0%|5.7%|
|_o.w. USA_|6.2%|4.3%|
|_o.w. Brazil_|0.3%|0.2%|
|_Middle East, North Africa_|2.0%|2.5%|
|_Oceania_|0.3%|0.2%|
|_Sub-Saharan Africa_|9.9%|12.9%|
|**_Total_**|100.0%|100.0%|

_source: Linkage model, COMTRADE, own calculations; non-oil trade only_ In table 1 we show, using a very simple simulation based on the gravity model and scenarios of growth in the different regions drawn from _Global Economic Prospects 2007_ , that the importance of China and other East Asian countries as a destination for Africa’s 11
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# **Table 15-2. Infrastructure and services (cont’d)**

|||Nairo<br>|bi<br>|||Dak<br>|ar<br>||
|---|---|---|---|---|---|---|---|---|
||Po<br>|or<br>|Nonp<br>|oor<br>|Po<br>|or<br>|Non<br>|poor<br>|
|Indicator|N|Value|N|Value|N|Value|N|Value|
|Connections to electricity|||||||||
|% of households with their homes connected to electricity:_a, e_|1,282|18.2|473|30.5|1,615|80.9|345|86.4|
|Primary source of home lighting (% of households) :_a_|1,282||473||1,613||345||
|Electricity||19.1||29.6||81.2||86.1|
|Kerosene/paraffin||80.0||69.5||5.2||3.0|
|Bougie||||||13.3||10.9|
|Solar/other||0.9||0.9||0.3||0.0|
|Street lighting (%)|||||||||
|Households who say street light services exist in their settlements|1,282|14.4|473|13.9|1,608|57.9|345|62.7|
|Telephone access (%)|1,282||473||1,615||345||
|Land telephone with service d||0.2||0.2||26.9||38.0|
|Cell/mobile phone with service_a, d_||16.4||28.4||47.3||59.3|

_Note:_ Statistical significance for difference in the corresponding indicator value between poor and nonpoor households: (1) In Nairobi: “a”—1% level; “b”—5% level; “c”—10% level; (2) In Dakar: “d”—1% level; “e”—5% level; “f”—10% level.

39
|14<br>Masini and<br>Menichetti<br>(2013)|93 European from various<br>investor types (Venture<br>Capitalists, Investment<br>Funds etc.).|2009|Power|**Survey**|<br>Technological risk|
|---|---|---|---|---|---|
|15<br>Kruger,<br>Stritzke and<br>Trotter<br>(2019)|6 solar PV developers and<br>investors in South Africa<br>and Zambia|2017-2018|Power|**Survey**|<br>Site-specific risks|
|Mahbub and|25 private companies|||||
|16<br>Jongwanich|conducting FDI in|2015-2016|Power|**Survey**|<br>Regulatory quality|
|(2019)|Bangladesh|||||

7
In 2015, a National Water and Sanitation Survey (NWSS) of all water points and water schemes was conducted throughout Nigeria. This survey was commissioned by the Federal Ministry of Water Resources (FMWR), and data were collected across the country at the ward level. It consists of four surveys—of households, water points, water schemes, and public sanitation practices (conducted in schools and health centers). The household survey was of more than 202,000 households, while the water points and schemes surveys provided information on 90,508 “improved”<sup>7</sup> water points<sup>8</sup> and 5,100 water schemes across states and local government areas (LGAs).

In this paper, we focus on water schemes. Along with many other details, NWSS 2015 provides information on the location, type, promoters, population served, year of construction, maintenance and operation, and functionality of 5,100 water schemes, across states. The zonal distribution suggests that slightly less than 45% of all schemes are located in the northwest zone, followed by the south-central (20.2%), northeast (14.5%), and southeast (10.4%) zones. Around 5–6% of the total are in the southwest, and this share is similar in the north-central zone. Figure 1 maps the distribution of water schemes across the country. While it is evident that non-functional water schemes are distributed across the country, they are highly concentrated in the south. Just over half of all schemes are functional across Nigeria.

Table 1 provides information, by region, on the variables used in the analysis of water schemes between 1 and 15 years old. Over half of all schemes are promoted by the government at either the federal or state level. Their dominance varies by region. For example, the federal government is the primary promoter in the northeast and southeast, while state governments dominate the northwest and southcentral regions.

The number of schemes, considered alone, does not present a full picture of the overall situation in Nigeria (Table 1). We must also consider the size of each scheme and the number of beneficiaries it

> 7 For the purposes of this study, sources of improved water include tube wells and boreholes, infiltration galleries, protected springs and dug wells, rainwater harvesting sites, gravity flow systems, and pump-piped systems (using either ground or surface water).

> 8 In the functionality analysis of the water points we exclude rainwater harvesting and infiltration galleries due of their low incidence; these two sources, when combined, comprise a share of less than 1 percent. This reduces the number of observations used in the rest of the water points analysis to 89,871.

11
city, which implies that it cannot accommodate the population _N_ . A similar argument rules out the other cases where the inequality in the Proposition is violated, establishing the result (see the appendix).

# _2.3. A numerical simulation_

To further illustrate differences between the restricted and unrestricted cities, it is helpful to rely on a numerical simulation. The numerical example is based on U.S. parameter values, which can be chosen with some degree of confidence. However, the results apply qualitatively to any city, including the Bangalore case considered below.

The simulation is based on Cobb-Douglas forms for the utility and production functions. The number of households _N_ for the city is set at 800,000, implying an effective population of over 2 million in the case where a household realistically contains 2.6 persons. Income per household is set at the 2000 U.S. census value of $42,151. About two-thirds of the circular area of the city is available for housing, with θ in (6) and (9) set equal to 4 (as opposed to 2π) radians. Other parameter values are discussed in the appendix.

The unrestricted city has an<sup>_~~x~~_</sup> 0<sup>value of 21.4 miles. In the city,</sup><sup>_h_(</sup><sup>_S_) ranges from a value of</sup> around 17.5 at the CBD to 0.4 at<sup>_~~x~~_</sup> 0 . To interpret these values, note that FAR, as represented by _h_ ( _S_ ), is roughly equal to the number of floors in a building, in the case when the parcel contains no open space around the structure. On the other hand, if a building covers only half of a parcel's land area, then FAR is equal to roughly half of the number of floors. In the latter case, the above numbers suggest that buildings in the unrestricted city have more than 30 floors at the center and are single-storied at the edge of the city. This FAR pattern is shown in Figure 1.

10
that agricultural products are still very much protected through tariff and non-tariff barriers and for quality issues derived for Mexican taste for own corn. Furthermore, agricultural products usually face more competition from internal markets (especially in rural areas) and are characterized by higher transport costs. From this point of view, the change in border prices is more likely to be absorbed by changes in the profit margin of exporters rather than result in an adjustment in domestic prices. For rural areas poorly connected with the international markets, domestic supply is likely to set the price of certain agricultural products regardless of border measures. This is especially the case when the imports represent only a small part of domestic consumption, as with cereals, legumes and meat products in Mexico.

Other studies focused on the magnitude of the pass-through results have observed passthrough coefficients of around 60 percent for manufacturing products (Goldberg and Knetter 1997) and Yang (1997), which obtains similar results using United States manufacturing data.<sup>29</sup> The results of those studies are very similar to the ones of this paper with respect to the manufacturing sector. Pass-through coefficients for agricultural products have rarely been estimated, and in most cases have turned to be much lower than those for manufacturing products (Campa and Goldberg 2002). However, while most of the literature has explored only country average effect of pass-through, this paper is, to my knowledge, the first to explore the extent to which pass-through rates are different across regions. The findings indicate that for manufacturing products, passthrough coefficients decrease at a rate of about 10 percent per thousand kilometers from the main port of entry. Meanwhile, pass-through effects are more heterogeneous for agricultural products, with that of cereals decreasing at a rate of about 12 percent per thousand kilometers. The effect seems to be more heterogeneous for other agricultural products probably due to the more diverse origins of the imports. Further results on the observed change in prices and factor returns due to trade liberalization are presented in appendix B.

# **4.2 Results: Price-wage Elasticities**

> 29 Yang finds that the rate of pass-through varies substantially both across and within industries.

22
**Table 1:  Investment returns of pension institutions 1999-2004**

|€ billion or %|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|
|Return on investment|16.6|6.6|-1.4|1.5|8.8|14.2|
|Result after tax|3.2|0.6|-2.1|-1.4|1.9|2.2|
|Return on property|8.8%|8.1%|12.8%|12.1%|7.2%|12.0%|
|Return on equities|40.1%|4.3%|-15.6%|-27.4%|20.9%|13.3%|
|Return on bonds|1.7%|6.4%|6.9%|11.1%|5.3%|8.7%|
|Return on total assets|12.9%|5.7%|-1.6%|1.5%|7.8%|7.8%|

Source: Danish FSA, Market Development for Insurance Companies and Pension Funds Many of the larger pension institutions developed sophisticated internal asset management and risk control units. These set annual investment strategies and conduct detailed Asset Liability Management (ALM) analyses. A growing number of pension institutions outsource their portfolio management to external asset managers, while small pension funds often purchase group pension contracts. 14 percent of multi-employer pension funds and one-third of company pension funds are fully reinsured with life insurance companies.

Tax factors have played an important role in stimulating the growth of pension saving. Employee contributions to an occupational pension scheme are deductible from taxable income, with some limitations for lump sum contributions. Employer contributions are also tax deductible. The net tax subsidy in Denmark is around the average for OECD countries, but the high marginal income tax rate of up to 58 percent provides a strong incentive for tax-deductible contributions.

The taxation of investment income has changed substantially over the years. When introduced in 1984 taxation was imposed on real returns above 3.5 percent earned on investments in bonds. This provided incentives for allocating assets into (domestic) equities and real estate, the returns on which were left untaxed.

The taxation of investment income was changed in 1998 when a tax of 26 percent was applied on all income from bonds and real estate, while investment income from equities was subject to a lower tax of only 5 percent. The tax system was streamlined in 2001 when all investment income, irrespective of type of asset, was made subject to a 15 percent tax.

# **2. Risk Sharing**

As already noted, a distinguishing feature of Danish pension plans is the offer of guaranteed minimum investment returns for both the accumulation and payout phases.

Pension contracts are for the most part based on profit participation policies. The guaranteed minimum returns are low relative to prevailing returns at the time the contracts are created. Total returns and pension benefits depend on the realization and distribution of bonuses during the life of the contracts, which often also cover the payout phase and not just the accumulation phase.

8
# **Conclusion**

In sum, we have benchmarked South Africa’s infrastructure performance on all four major infrastructure sectors in terms of key indicators of access, affordability-pricing, and quality using the new World Bank database of infrastructure sector outcome indicators from 207 countries. The broad results suggest the following: access remains a major issue in electricity and, especially, water and sanitation (particularly in rural areas), and so does performance in local telecom services. Even transport performance appears comparatively less strong than would be expected, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport.

More specifically, compared to the benchmark and other country groupings, South Africa’s utilities provide solid service at reasonable quality and, in some cases, very competitive prices. But there remain significant shortfalls relative to benchmarks in all infrastructure sectors, largely related to limited access and less than expected quality, especially in rural areas, where large numbers of South Africa’s poor live with limited or no access to service. To _catch up_ with its income group in terms of these indicators, South Africa will need to improve access to and quality of electricity, and water and sanitation services in presently unserved areas. Indicators of transport performance, while less definitive, potentially also point in the same direction. Even in telecom, the most globalized and “modern” infrastructure sector, access in rural areas can be improved and pricing of local services remains uncompetitive. Moreover, to put it simply, if South Africa is to strengthen its overall infrastructure performance, it will need to improve these dimensions’ performance in the sectors _faster_ than the average country in the upper-middle-income group while maintaining or improving its position in the already strong areas of performance. This will not be easy in competition within a group of countries all aspiring to move to much higher levels of income and with ambitious poverty reduction targets. But catching up with these _relative_ shortfalls may also help South Africa reduce its economic inequities and poverty rates and, therefore, meet the infrastructure related Millennium Development Goals (MDGs). It could also help address some of the concerns about South Africa’s external and industrial competitiveness and service delivery that in good part depends on the efficiency and capacity of network utilities to deliver basic services to population and businesses alike.

31
<!-- Start of picture text --> Chart 3.4 lhe over-quota banana tadff Is clearly prohibve<br>EU dollar banana domand schodule post-July 199<br>;. Gnn ECU850/toinno= ECU 1025/tonno<br>EU 19tdl A .-. .j2_ .. Over-quot& b onano prico-<br>--. * price. .. . . . ;: * Iulilff o Growl ECUO50/1<br>"At 1at ECUS24/t ro: much -w s<br>..... ... no Import of ovr-quot bananas<br>At::. .: Only asp1cedeclines'below<br>r X 9t { s s s > i EcUl8a9/t will consumers:<br>. . . . .. :. . . S~~~*. Ed'emnnd oi,erquod abncrios..<br>s*n..er. ,essto w pay,.<br>.e':;: . e . . . :.':E.dbs..pk)l ':. ,i : . .. ., :x , ..<br>EUcrumton In'OOtrulbofdtaLmaxo<br>Dat sou M rMOd -fl<br><!-- End of picture text -->

# **The new polcy is a big step backwards**

In moving to adopt a new **unified** policy the European Union missed an opportnty to rationalize and improve **its** distortionary banana policies. Of options open to it, the European, Union chose one of the most discrimninatory and distortionary. There are two possible explanations for not choosing simpler and much more efficient policies. One is to Protect (anld exPand) the vested interests of EU based marketing companies. This group is clearly the **main** beneficres of the polcy. EU consumers, other markreters and Latin American suppliers are clearly big losers, If so, the main objective of the policy does not appear to be to provide aid to ACP **and** EC ternitorial producers. The ofthr possible explanation iss that policy makers still do not fully understand the effects of their polce.Either way, proper analysis and measurement appears to be essentiaL For EU territorial suppliers the policy seems to offer assured protection, but for ACP suppliers, the policy introduces considerable uncertaJinty and confusion about the level of support **they** will recive. Support is provided in a completely indirect way and is not well targeted, although the proposal to provide additional aid to assist in increasing productivity and. quality in ACP countries may be an acknowledgment by the European Union of a **need** for a diffeent form of aid. But as it stands now, in part the provision of support relies on margins coleted on the sale of Latin Ameeian fhuit to ber handed to ACP producers and in some cases t relies on one marketer earning the margin and then t femrng it to another before it is passed on. **The** mechanism is by no means transparent and there is, as of yet, no specific commiment to ACP suppliers as there is to EU territorial SUPPierS. The policy creates huge incentives for _rent_ 1 6
# **Table 1. Consumer Price Index Basket**

|||Coefficient of<br>Variationa|Coefficient of<br>Variation|
|---|---|---|---|
||Weight|1992.01-1997|.09<br>1993.01-1997.09|
|Total of all items|1.0000|<br>2.95|1.75|
|Food, beverages, and tobacco|0.7239|2.41|2.31|
|Bread and cereals|0.1547|5.16|3.98|
|Meat, poultry, and fish|0.1328|3.43|1.77|
|Dairy products and eggs|0.1299|3.72|2.98|
|Oils and fats|0.0715|4.61|2.51|
|Fruits and vegetables|0.1495|2.62|4.81|
|Sugar, coffee, and tea|0.0402|3.13|3.52|
|Beverages at home|0.0198|1.92|2.07|
|Food and beverages away from home|0.0088|1.50|1.08|
|<br>Tobacco|0.0167|1.93|2.44|
|Clothing and footwear|0.0282|1.41|1.04|
|Rent, water, fuel, and power|0.0641|3.06|3.63|
|Household goods|0.0827|2.18|2.70|
|<br>Medical care|0.0092|5.30|2.69|
|Transportation and communication|0.0516|3.02|3.43|
|Recreation, education, and culture|0.0357|1.69|1.98|
|Personal care|0.0046|1.39|1.35|

Source: INSTAT Note: (a) Coefficient of variation is defined accordingly - the ratio of standard deviation of the monthly inflation of **the** each item to its respective mean.

As can be seen in Table 1, bread and cereals, medical care, and oils and fats were the most volatile items throughout the entire sample period -- due mainly to liberalization of many administered prices in 1992.2 However, as one would expect owing to seasonal patterns, during 1993.01 - 1997.09, fruits and vegetables had been the most volatile item followed by: bread and cereals; rent, water, fuel, and power; sugar, coffee and tea; and transportation and communication.

> 2 In particular, the price of bread, which had been unchanged for more than 40 years, was increased by 385 percent.

6
The last part of the paper discusses the road ahead given the magnitude of the challenge. Based on the observed performance of countries in mobilizing investment in infrastructure, it argues that in a context in which public budgets face strong constraints and large increases in private investment are unlikely to be forthcoming, the solution may lie in other parts of the delivery equation. Specifically, we discuss three promising margins: refocused investment goals, demand management, and interventions to improve public spending efficiency.

The paper is organized as follows. Section 2 reviews the related literature and shows how this paper contributes to it. Section 3 presents the data used in the analysis. Section 4 documents the evolution in infrastructure demand throughout the period in the different countries, and the implications for infrastructure delivery looking at the future. Section 5 discusses the policy challenges and concludes.

# **2. Literature Review**

For decades, the economic literature on infrastructure has mostly focused on aggregate data at the regional or more often the national level. Traditionally, infrastructure has either been measured through supply-side physical indicators, such as electricity generation capacity or kilometers of roads, or demand-side ones such as aggregate electricity or water connection rates. Given the fact that such data ignore both the spatial nature of and the distributional disparities in infrastructure access, the policy relevance of the conclusions have been limited (see Straub 2011 for a critical review).

There were of course micro-econometric studies using household-level data to look at specific local outcomes, such as Gibson and Rozelle (2003), Galiani, Gertler, and Schargrodsky (2005), or Dinkelman (2011), to cite only a few. More recently, the growing use of spatial data has been instrumental in generating a renewal of the field, allowing for example for local (often municipal) level infrastructure usage indicators.<sup>2</sup> However, there have been surprisingly few papers using micro-data to analyze systematically how infrastructure demand at the household level varies along the income distribution. As a result, little is known about the way demand for infrastructure varies with income at any point in time, and how it evolves over time as household income grows.

> 2 See Straub (2008, 2015) for reviews.

3
- _Level of Risk_ :  a worker who receives a fixed salary from the “client” and is not subject to the risk associated with price or demand fluctuations (Prugl 1997);

- _Nature of Output_ :  workers in industrial home-work, craft production, making and selling of foods, and telecommunications-based services (Prugl 1997);

- _Ownership of the Production Process_ :  assembly where the inputs and production tools may be provided by the employer or by the employee (Pollack 1998, Arriagada 1998);

- _Skill level_ :  those who work out of their homes but are not professionals (the latter are classified as entrepreneurs); and

- _Type of Client_ :  those whose client is an intermediary (i.e. not the public) as opposed to those who sell directly to the public (ILO 1995).

The wide variation in definition leads to two problems in analysis. First, many are difficult to quantify. For example, “degree of subordination” or “level of risk” can only be proxied by a number of other characteristics about autonomy in decision-making. Also, the workers themselves may not have the necessary information for classification; she/he may not know if the output is sold to an individual for own use or if that buyer will turn around and re-sell it to another client. Only firm surveys that follow a product through the global value chain can provide these type of information.

Second, studies of home-based workers often use different definitions, thus hindering the construction of a single, coherent picture of the sector. Seemingly contradictory findings from qualitative data may in fact be a result of different methods of measurement rather than true differences in the realities studied. The absence of single 5