rafmacalaba/datause-encoder-data huggingface dataset:
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" of the poor than the rich in both urban and rural areas had electricity, in-house taps, sewer connections and telephones.

Very few of the poorest rural households had in-house water taps (2 percent), sewers (1 percent), or telephones (2 percent). Rural coverage of these three services remains under 10 percent up to US$200 per household per month. Perhaps surprisingly, electricity is reaching a substantial number of the rural poor (27 percent in the poorest quantile).

By contrast, a significant number of the poorest urban households had in-house water taps (31 percent), sewers (28 percent), and telephones (14 percent). Coverage of these services rises steeply from each 5-percent quantile to the next. Electricity coverage in urban areas is surprisingly similar across income groups. Nearly 80 percent of the poorest urban households had electricity, and coverage rises further among higher income groups.

# 3.3. Coverage by country

Of the fifteen sample countries, those in Eastern Europe and Central Asia stand out for their high coverage rates in all sectors. Albania, Bulgaria, Kazakhstan, and the Kyrgyz Republic all have virtually universal coverage of electricity.'<sup>8</sup> Bulgaria, Kazakhstan, and Russia have the highest coverage rates for in-house water taps, sewers, and telephones as well.

Coverage rates among the poor are also higher in Eastern Europe and Central Asia than in the other countries in the sample (Table 3). Virtually all households in the poorest urban and rural deciles in Kyrgyz Republic, Albania, Bulgaria, and Kazakhstan had electricity. In contrast, in many other countries, fewer than half of the rural or urban poor had electricity. Bulgaria, Kazakhstan, and Russia are also the only countries in the sample where any significant number of the rural poor had inhouse water taps, sewer connections, and telephones in their homes.

**I8** Electricity data are not available for Russia. Albania survey does not include Tirana.

9
30

# EIIDNOTUS

1. Goto (1986) examined the impact of wage distortions in the auto industry. But he simulated a change from US autarky to integration into a single economy with Japan, rather than the effects of the VER in autos. De Melo and Tarr (1990b) examined separately effects (1), (2), (4), (5) and (6).

2. These restraints continued into 1990. See General Agreement on Tariffs and Trade (1990, p. 221).

3. Consumers Union, for example has reported that, when the quota was in effect, many dealers charged in excess of the sticker price, using such devices as charging high prices for decal stripes, rustproof ing and undercoating. Its readers indicate that this was especially prevalent among Toyota, Honda and Mazda dealers. Consumer Reportst August 1983, 391. See also the statement by Senator Chafee, Congressional Record, February 29, 1984, S. 1966; Fortune, "Can Detroit Live without Quotas," June 25, 1984, 20; and Washinaton Post, "Car Dealer Markups Raise Questions," Washington Business, November 19, 1984, 1, 34, 35.

4. See de Melo and Tarr (1990b) for the details of this calculation.

5. Using a different methodology, de Melo and Tarr (1990b), find a similar estimate of the premium earned by auto workers. On the other hand, the wage premium used by Dixit and by Krishna et al. is about double the actual wage premium because they did not adjust for the human capital differences between auto workers and the average manufacturing worker.

6. An alternate series for data on profits in motor vehicles and equipment is available from the Quarterly Financial Statistics, published by the US Federal Trade Commission until 1982 and the US Bureau of the Census for years after 1982. The Survey of Current Business and Quarterly Financial Statistics series differ mainly insofar as the former considers profits on US operations only, whereas the latter includes income from all sources including foreign operations. Both series would include profits of US subsidiaries of foreign corporations in their US operations, i.e., profits of companies like Honda USA earned on US sales are included in both; but only the Quarterly Financial Reports includes profits of Honda USA on its sales to Japan.

Between 1979 and 1984, both series are very close. From 1985 on, however, the Quarterly Financial Reports series shows much higher profits. This principally reflects the increase in FDI (shown in table 1) and the profits of US subsidiaries on their foreign sales. Since we are principally interested in the profits
operating companies, as well as rolling-stock leasing companies. The track and associated infrastructure, and responsibility for operating it, was placed in Railtrack. Railtrack was privatized in 1996.

# **3.5.2 Leverage and regulation**

The regulatory framework applying to Railtrack was based on the standard UK framework for a utility network company. Railtrack operated under a license that specified key regulatory obligations. The bulk of Railtrack’s revenue was derived from track access fees—the fees it charged train operators to use its track. These track access fees were controlled by the Office of the Rail Regulator (ORR). The ORR set maximum access fees for five years ahead, taking account of required outputs and forecasts of costs. In between five-yearly reviews, Railtrack bore the risk of any unanticipated costs. The regulatory regime approximated a revenue cap, rather than a price cap. That is, the total amount Railtrack could charge train operating companies did not increase in line with increases in the number of trains using the tracks. Railtrack was also accountable to Her Majesty’s Rail Inspectorate on safety issues.

In the five years following privatization, Railtrack took on increasing levels of debt. By October 2001, Railtrack was reported as having total borrowings of £3.3 billion. This increased to £6.4 billion by March 2002, against net total regulatory assets (equivalent to equity) of £1.9 billion—leverage of approximately 77 percent.<sup>17</sup>

# **3.5.3 Financial difficulties**

Immediately following privatization, Railtrack appeared to be performing well. However, demand for rail services increased more quickly than expected, especially in the South of England, where the network was already congested. The increase in traffic led to increased need for maintenance and new investment, as well as higher operating costs to co-ordinate trains. However, the regulatory regime did not allow Railtrack to recover these costs.

By 2000, a series of passenger train accidents raised questions about the condition of the network. Accidents at Southall in 1997, Ladbroke Grove near Paddington in 1999 and Hatfield in 2000 resulted in 42 deaths.<sup>18</sup> In addition to the loss of life, the Hatfield incident alone is estimated to have cost Railtrack around £600 million in repair costs, penalty payments, and the cost of a track repair program that was required following the crash.<sup>19</sup> These events gave rise to concerns about Railtrack’s ability to maintain the safety of the network. Factors cited as potential contributors to the perceived breakdown in safety include

> 17 Railtrack plc Regulatory financial statement s for the year ended 31 March 2002. Leverage calculated as debt / (debt + equity).

> 18 _Financial Times_ , 8 October 2001.

> 19 _The Independent_ , 16 January 2001.

30
# **1. Introduction**

We are still at an early stage of understanding the current and potential future health effects of climate change. In 2002 the World Health Organization (WHO) estimated that climate changes that had taken place over the previous 30 years (see IPCC, 2001a; Zwiers, 2002) were causing 154,000 deaths annually (WHO, 2002). Health impacts are likely to intensify given the anticipated speed of anthropogenic climate change (the scientific bases for which are discussed in detail in IPCC (2001b; 2007b)). Nordhaus (2007) comments that the Intergovernmental Panel on Climate Change’s (IPCC) best case (i.e. lowest) temperature increase estimate for the coming century in its Fourth Assessment Report is “much more rapid than any changes that have occurred for more than 10,000 years”.

As well as the often cited forecast increases in mean temperatures, the frequency of extreme weather events, such as heat waves, excess rainfall, droughts, and storms are likely to increase (McMichael et al., 2004; Karl et al., 1995; Easterling et al., 2000; Katz and Brown, 1992; IPCC 2001b; IPCC 2007b; Mason et al.,1999). Katz and Brown (1992) suggest that changes in variability are more important in their consequences than changes in mean climate variables. Extreme rainfall and temperature have serious implications for health outcomes. Crop yields may be reduced (see for example Ravallion, 1987), insurance markets disrupted, transportation systems damaged and sewerage and water systems overloaded. These factors can severely compromise people’s ongoing abilities to derive their livelihoods. Experience of repeated extreme events undermines the ability to cope and recover (Alderman, Hoddinott and Kinsey, 2006; Alderman, 1994) and if such events contribute to the burden of early childhood malnutrition, then this has long-term consequences for growth (Martorell, 1995; 1999), behavioral development (Lasky et al., 1981) and academic achievement (Glewwe, Jacoby and King, 2001).

There is little doubt that Sub-Saharan Africa is one of the regions most affected by climate change (IPCC, 2007a) and that the region will face an increasing burden of disease from ongoing climate change<sup>2</sup> . It is not only that changes in climate are likely to be experienced intensely in Sub-Saharan Africa, but also that the incidence of disease and rates of mortality are already significantly higher than in other regions. Black, Morris and Bryce (2003) estimate that 41 percent of global child deaths occur in Sub-Saharan Africa. In addition, Sub-Saharan Africa’s rapid population growth implies an increasing share of global child deaths would emanate from the region. UN population projections predict in the period from 2000 to 2020 the population of Sub-Saharan Africa will increase by around 400 million under its medium variant population projection with 41 million of this increase in the under-age-five population (UN, 2006).

> 2 Patz el al. (2005) define two regions of Africa, AFR D2 and AFR E2, where they estimate the effects of climate change are to increase mortality in 2000 by 66.83 and 109.4 deaths per million respectively.

2
# <u>Table 5:</u> Electricity Distribution

||(1)<br>Number of<br>Connections|(2)<br>Energy Sold<br>per year|(3)<br>Number of<br>Employees|(4)<br>Connections<br>per employee|(5)<br>Energy per<br>employee|(6)<br>Distributional<br>Losses|(7)<br>Duration of<br>interruptions|(8)<br>Frequency of<br>interruptions|(9)<br>Coverage|(10)<br>Avg price per<br>MWH<br>(in dollars)|(11)<br> <br>Avg price per<br>MWH (in real<br>local currency)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Model 1: Log le**|**vels without fir**|**m-specific time**|**trend**|||||||||
|Transition|0.150***|0.201***|-0.307***|0.442***|0.474***|-0.031**|-0.144***|-0.107***|0.053***|-0.013|0.105***|
|(-2<=t<=1)|(0.005)|(0.007)|(0.016)|(0.019)|(0.021)|(0.013)|(0.028)|(0.025)|(0.004)|(0.018)|(0.008)|
|<br>Post Transition|0.326***|0.370***|-0.500***|0.810***|0.819***|-0.172***|-0.488***|-0.415***|0.130***|-0.041**|0.177***|
|(t>=2)|(0.006)|(0.008)|(0.018)|(0.021)|(0.023)|(0.014)|(0.031)|(0.028)|(0.004)|(0.019)|(0.009)|
|Observations|823|808|586|575|570|614|376|377|698|687|685|
|Log Likelihood|1082.0|839.9|217.8|180.7|149.4|407.9|29.0|83.0|1185.8|315.2|677.4|
|**Model 2: Log le**|**vels with firm-s**|**pecific time tre**|**nd**|||||||||
|Transition|-0.002|0.040***|-0.054***|0.049***|0.086***|0.021|0.068**|0.076***|-0.007***|0.078***|0.034***|
|(-2<=t<=1)|(0.002)|(0.005)|(0.013)|(0.012)|(0.017)|(0.013)|(0.033)|(0.029)|(0.002)|(0.012)|(0.008)|
|Post Transition|0.007**|0.026***|-0.007|0.013|0.006|-0.018|-0.047|-0.043|0.002|0.114***|0.041***|
|(t>=2)|(0.003)|(0.009)|(0.022)|(0.022)|(0.030)|(0.023)|(0.055)|(0.047)|(0.003)|(0.018)|(0.012)|
|Observations|823|808|586|575|570|614|376|377|698|687|685|
|Log Likelihood|2214.9|1415.6|723.3|623.4|541.9|736.9|138.8|230.7|1898.1|659.0|1046.0|
|**Model 3: Growt**|**h**|||||||||||
|Transition|0.001|-0.002|-0.050***|0.048***|0.046***|-0.042***|-0.063***|-0.050**|-0.000|-0.117***|-0.082***|
|(-2<=t<=1)|(0.001)|(0.003)|(0.008)|(0.008)|(0.010)|(0.010)|(0.023)|(0.024)|(0.001)|(0.011)|(0.007)|
|Post Transition|-0.003***|-0.027***|0.064***|-0.065***|-0.092***|0.015|0.001|-0.048**|-0.000|0.023***|0.009|
|(t>=2)|(0.001)|(0.003)|(0.008)|(0.008)|(0.010)|(0.010)|(0.021)|(0.021)|(0.000)|(0.008)|(0.006)|
|Observations|803|783|566|557|554|592|339|341|669|633|631|
|LogLikelihood|1999.6|1265.7|575.0|486.7|401.5|486.6|16.6|56.1|1667.1|379.3|750.2|

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% _20 of 28_
||(0.81)|(2.16)|(1.33)|(0.64)|(1.03)|(1.88)|
|---|---|---|---|---|---|---|
|Services|0.0597|0.128**|0.0133|0.362**|0.0427|0.0478|
||(0.73)|(2.66)|(0.31)|(3.05)|(1.11)|(1.71)|
|Agriculture|-0.0476|0.0584|-0.0361|0.0687|-0.00842|0.0126|
||(-0.69)|(1.70)|(-1.06)|(0.73)|(-0.24)|(0.42)|
|Households received wages|||||||
|(dummy)|-0.0653|-0.0120|-0.00795|-0.0669|-0.000448|-0.0268|
||(-1.81)|(-0.65)|(-0.41)|(-1.40)|(-0.03)|(-1.77)|
|Household receives pensions|||||||
|(dummy)|0.117|-0.0174|0.0365|0.162|0.188|0.241**|
||(0.69)|(-0.37)|(0.45)|(0.75)|(1.64)|(2.64)|
|Household receives remittances|||||||
|(dummy)|0.0441|0.0799***|-0.00653|0.104|0.0759***|0.0304|
||(0.97)|(3.73)|(-0.31)|(1.57)|(3.73)|(1.86)|
|Household receives social benefits|||||||
|(dummy)|-0.0198|-0.0147|-0.0372|-0.0123|-0.0262|-0.0479*|
||(-0.36)|(-0.65)|(-1.41)|(-0.19)|(-1.29)|(-2.53)|
|Constant|0.937***|0.367***|0.529***|0.0439|0.00360|-0.0526|
||(9.65)|(6.09)|(9.14)|(0.32)|(0.08)|(-1.56)|
|Observations|631|1982|2856|365|1572|2641|

Source: Calculations based on HBS data. Note: Regression model includes region- fixed effects and accounts for weights.

37
Given most small businesses in developing economies do not keep formal (or any) financial records and administrative data are predominantly unavailable or expensive to collect, it is difficult to determine with certainty what the true value is for sales and profits in the previous month. In this subsection, we explicitly seek out administrative data on businesses across two samples for which we contemporaneously collect AAA data. In this manner, we can directly compare the magnitude and CV of AAA measures against administrative records.

The two sources of administrate data are loan officer evaluation files in Ghana and professional business coach assessments in Uganda. In Ghana, we identify 50 small firms from a partner bank’s database that had financial statements prepared within the last month by the bank’s loan officers. We complete the AAA performance tool with these 50 firms for the same reporting period and reference month as the financial statements in order to directly compare our performance measures to the administrative data. In Uganda, we use data from an intervention in which professional coaches mentored businesses in our study for 12 comprehensive sessions over a six-month period. In the final session, the consultants were asked to assess the financial performance of the businesses and generate estimates for monthly sales and profits. We compare the coaches’ estimates to the estimates generated from our survey tool for the same reporting period and reference month for 31 firms for which we have data. Firms were not privy to either the loan officer evaluations or the professional coach assessments, so their responses to the AAA survey instrument were not influenced by prior knowledge from these sources.<sup>9</sup> Tables 5A and 5B presents the results of this analysis and show very similar findings in Ghana and Uganda, respectively. For sales, the AAA estimates and administrative estimates are statistically indistinguishable in both settings, although the magnitude of administrative sales is larger. In fact, the CV is lower for the AAA measure, with a value of 0.86 compared to 1.07 in Ghana; and 1.58 compared to 2.01 in Uganda. Hence, AAA sales match administrative sales very well across two sources of administrative data.

> 9 Note that both sample sizes are small because we were limited in matching the same reference month across administrative data and the AAA survey.

23
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40
That is one reason why Strand and Toman (2010) express some skepticism about proposals for a green fiscal stimulus, particularly in developing countries. Road-building, for example, is relatively labor-intensive and can help to provide valuable infrastructure, but it is not particularly green. They also note that many of the studies for developed countries do not emphasize short-run employment creation, because of the lead times necessary for replacing capital in the energy sector. In their view, “these studies strongly indicate that government support to producing and developing renewable energies is not a very efficient way of creating additional short-run employment in high-income countries; at least, not when appropriately accounting for the opportunity cost of public funds going into such support.” Overall, Strand and Toman conclude that there are likely to be trade-offs for employment generation, in the sense that programs that yield larger employment effects tend to lead to more employment gains for largely lower-skilled workers, so that the long-term growth effects are relatively small. Long-term development, including sustainable development, requires more of a focus for public investment on growth-enhancing infrastructure, as well as private sector investment, which is not necessarily labor-intensive. The argument for investing in initially labor-intensive low-carbon technology in order to benefit from experience, scale economies and learning by doing, thus driving costs down, is weaker for developing countries because they are less likely to have a comparative advantage in lowcarbon innovation, at least in the energy sector.

Strand and Toman (2010) also review some of the small literature on green fiscal stimuli and job creation specifically in developing countries, noting that the evidence is scant and spotty. Studies largely focus on the direct effects of particular activities on employment. Barbier (2009) investigates the South Korean green stimulus while the UNEP/ILO/IOE/ITUC (2008) study looks at China’s experience. What is most striking is the large variation in employment creation in jobs created per US$ billion spent. In South Korea, forest restoration is estimated to be highly labor-intensive, generating nearly eight times as many jobs per dollar as the least labor-intensive green objective, ‘vehicles and clean energy.’ In China, biomass spending is thought to be nearly 30 times more effective in generating jobs per dollar spent than wind power. That suggests that the focus on renewable energy and low-carbon manufacturing prevalent in studies for Europe and the USA may miss the opportunities for 16