rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 1980 1990 2000 2010<br>Year<br>IDA Bilateral Aid<br>.8<br>.6<br>.4<br>Policy-Selectivity<br>.2<br>0<br><!-- End of picture text --> Figure 3: Policy Selectivity of IDA vs. Bilateral Aid equity, and on public sector management), and calculate z-scores from the ratings for each year. The z-scores thus indicate each country’s distance from that year’s mean rating. By this procedure, we eliminate any effects of changes in the CPIA content and methodology over time, while minimizing any loss of information in the ratings relevant to how IDA was allocated in each year. Because IDA resources are essentially fixed for any given year, allocations can be viewed as a zero-sum contest among recipients in each individual year, so it is a country’s rating relative to others’ that matters, not its absolute score.

In testing the associations of poverty and policy with IDA disbursements, we control for population size, following the empirical literature on aid allocations (see Knack, Rogers, and Eubank, 2011). All three independent variables are lagged by one year. We obtain a measure of policy- and poverty selectivity by estimating the following model for every year between 1978 and 2012 Grossaid _i,t_ = _βo_ + _β_ 1Policy _i,t−_ 1 + _β_ 2GNIpc _i,t−_ 1 + _β_ 3Population _i,t−_ 1 + _εt,_ where _i_ indexes aid recipient countries and where per capita income and population are in log form so that the coefficients can be interpreted as elasticities. We run these regressions for IDA and for total bilateral aid separately. For an aid allocation to be policy- and poverty selective we expect _β_ 1 _>_ 0 and _β_ 2 _<_ 0 respectively. Figures 3 and 4 plot the estimation results. They show that IDAaid has been always more policy selective from 1984 onwards, and it has always been more poverty selective than the typical bilateral donor. The difference in 26
<!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>5 70% .<br>2 60% @ Rudimentary<br>S 50% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>5 70% .<br>2 60% @ Rudimentary<br>S 50% Informality<br>2 40% TM Modern<br>S 30% Informality<br>XS 20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>@ 80%<br>5 70% .<br>Sox @ Rudimentary<br>S so% Informality<br>B 40% ® Modern<br>—<br>S230% Informality<br>20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
The data on religious composition is taken from LLSV (1999) to create the dummy variable **Catholic Religion** which takes the value 1 if Catholics are the dominant religious group in the country, and 0 if the dominant religious group in the country is Protestants or Muslims or Other Religions. Stulz and Williamson (2003) show that Catholic countries are particularly weak in creditor rights protection as well as the enforcement of shareholder rights, creditor rights, and property rights. We also use data on **Ethnic Fractionalization** from Alesina, et al (2003), which measures the probability that two randomly selected individuals from a country are from different ethnic groups.

To measure geographical endowments, we use **Latitude** , which is the absolute value of the latitude of the country scaled between 0 and 1, from LLSV (1999). Countries closer to the equator tend to have a more tropical climate that was inhospitable to European settlers and therefore may have fostered “extractive” institutions. Table 1 shows that the variable Latitude varies from 0 for Congo Democratic Republic which is located on the Equator and 0.01 for Kenya and Uganda (close to the equator) to 0.72 for Iceland.

Following Ayyagari, Demirguc-Kunt and Maksimovic (2005a), we construct quintiles of the continuous variables, Ethnic Fractionalization and Latitude to overcome non-linearities in the construction of the variables and use the discrete versions of the variables for the rest of our analysis. The search algorithm also works best with only continuous or discrete variables rather than with a mix of both types of variables.

Table 2 presents the summary statistics and correlation matrix between the variables. Panel B shows that Property Rights is highly correlated with Latitude, 15
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Railway indicators for Senegal and select other countries, 2005–09**

||**TRANSRAIL**<br>**(Senegal–Mali)**|SITARAIL<br>(Côte d’Ivoire—<br>Burkina Faso)|Camrail<br>(Cameroon)|Madarail<br>(Madagascar)|
|---|---|---|---|---|
|Concessioned (1)/ state run (0)|**1**|1|1|1|
|Freight traffic volume (million tonne-km)|**393**|794|1,061|113|
|Passenger traffic volume (million passenger-km)|**91**|210|377|3|
|Total traffic volume (million TU)*|**429**|878|1,212|114|
|**Efficiency**|||||
|Staff: 1,000 TU per staff|**247**|558|547|118|
|Derailments per million TU|**0.45**|0.01|0.15|2.31|
|Mainline locomotive breakdowns per 100,000 km|**15**|6|9|6|
|**Tariffs**|||||
|Average unit tariff, freight, US cents/tonne-km|**7.0**|6.3|8.1|6.0|

_Source:_ Bullock 2009. Derived from the AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ *2.5 passenger-km equivalent to 1 TU, 1 tonne-km equivalent to 1 TU. TU = traffic unit.

# Ports

## **Achievements**

The overall safety of maritime traffic improved significantly in the early 2000s, with the adoption of an updated maritime code and improvements in navigation and signaling equipment at the ports of Dakar, Kaolack, and Ziguinchor. The Port of Dakar is by far the most important port in Senegal and one of the most important ports in West Africa. Its traffic increased by over 300 percent between 1995 and 2005, going from 87,000 twenty-foot equivalent units (TEUs) in 1995 to over 363,000 TEUs in 2006.

The Dakar port has transit traffic arrangements with Mauritania; in fact 25 percent of that country’s import volume is moved by road from Dakar, in large part because Mauritania’s port at Nouakchott cannot meet the sizable demand of the country’s potential container transshipment traffic. In recent years, traffic volumes at the Port of Dakar have increased as the unfortunate crisis in Côte d’Ivoire has forced Mali and Burkina Faso to shift trade traffic from Abidjan to Dakar, now emerging as a key alternative corridor in West Africa. To put things in perspective, until 2000 Abidjan captured about 80 percent of all transit traffic to and from Mali. By 2003 that figure had dropped to just 14 percent, as traffic was diverted to Dakar in Senegal, Lomé in Togo, and Tema in Ghana. Dakar soon became the most important port for Mali, capturing one-third of its traffic.

The Dakar port is predominately a hybrid tool port: its infrastructure and most of its operations— including some frontline operations—remain in public hands. The container port was given in concession to Bollore; when this concession expired in 2008, a new 25-year concession contract was granted to Dubai Ports World. This concession includes the development and operation of the existing container terminal as well as the building and operation of a new container terminal at Port du Futur. The new operator has already started operations and has launched a significant investment program. The continuous involvement of the private sector in frontline activities is a step forward vis-à-vis the service ports model that predominates in SSA.

15
I

# **1. Introduction**

As a society, we seem to have made a generalized commitmen **t** o conserving biodiversity. But how do we spend our limited resources on this commitment? What _should_ we be doing, what do we _say_ we are doing, and what _are_ we doing? Such questions are easy to pose but difficult to answer. In this paper, we set a more modest goal than providing a single comprehensive answer. Our hope is to tease out hints of answers by studying actual decisions made by the U.S. govemment about which species to protect and how much to spend on them.

Narrowly, this paper is about determining what explains the species-by-species protection and spending decisions of certain relevant U.S. federal and state government organizations.' To perform this analysis, we have combined several distinct datasets from different government and scientific sources. We think that the resulting combination offers a rare opportunity for empirically-based insights into preferences about biodiversity conservation. Decisions about endangered species reflect the values, pecptions, and contradictions of the society that makes them. Thus, more broadly, this paper addresse some very general issues about man's relation to nature and about human choice when confronted by competing and often unquandfiable objectives.

The Endangered Species Act of 1973 gave the federal governmen **t** he power to protect U.S. species from extinction. Simply by listing a species as endangered, the govemment can cause many development projects to be delayed or canceled, and millions of dollars in opportunity costs to be incurred. Indeed, once a species is placed on the endangered species list, cost-benefit analysis is practically precluded. Additionally, all listed species are eligible to have funds spent directly on their recovery, with the eventual goal of

**'Readers interested in other** studies of revealed preference of government decision-making **are** referred to McFadden (1975) and (1976), Weingast and Moran (1983), Thomas (1988) and Cropper **etal.** (1992). The most closely related work to our own is Manm **and** Plummer (1993).
collected from the previous studies. The paper shows how different AEZs would be affected by future climate change. Based on the AEZ classification, we were able to extrapolate impact estimates to the whole Africa. The paper also combines crop and livestock income into a single net revenue measure in contrast to earlier studies that primarily focused on crop income alone.

The paper examines four different specifications of the Ricardian regression of farm net revenues on climates: a two season model, a temperature and precipitation interaction model, a country fixed effects model, and a four season model. The results indicate that climate variables are important determinants of farm net revenues in Africa. Summer and winter temperature and precipitation are all significant. A small increase in temperature would harm agricultural net revenues in Africa across all the models. A small increase in precipitation would harm farmers according to the country fixed effects model but help them according to the OLS model.

The estimated coefficients from the models with and without country fixed effects were then used to predict climate change impacts for the coming century for Africa as well as for each AEZ. Two AOGCM scenarios were used to reflect a range of climate predictions. With country fixed effects included in the model, farms are expected to lose 10% of their income under CCC scenario, but gain 24% under PCM by 2020. Over time, the impacts become slightly more harmful. Without country fixed effects, farmers are increasingly vulnerable over time to climate change under the CCC scenario. Damage estimates increase from 16% in 2020 to 27% in 2100. With the mild PCM scenario, African agriculture is predicted to benefit on average.

The predicted outcomes are surprising in contrast to earlier studies. This study is suggesting that farm incomes will be threatened only if the harshest climate scenarios come to pass. Farmers will be able to tolerate and even take advantage of climate change. The reason for this new result is that the study takes into account both crop and livestock income whereas earlier research focused primarily on just crop income. Warming is likely to increase livestock income which will offset losses in crop income.

The study also suggests that impacts will vary across Africa. Farms in some AEZs will benefit while farms in other AEZs lose. For example, farms in moist savannah and dry savannah are the most vulnerable to higher temperature and reduced precipitation. On the other hand, the farms in sub-humid or humid forest gain even from a severe climate change. This indicates that the impacts of climate change will not be evenly distributed across Africa.

13
2003 and 2015. A subset of countries have multiple surveys over time, with an average of 1.9 surveys per country. The World Bank outsources data collection to third-party agencies in order to remove the official affiliation of the surveyors and not contaminate responses. The survey agencies draw upon the list of registered establishments provided by the national statistics office.<sup>11</sup> The random stratified sampling is done at the industrylevel, corresponding to the 2-digit ISIC level, and over-samples from large firms to capture a large share of economic activity. Given the industry stratification, over-sampling of large firms does not impact the relative size of ISIC-2 industries. However it could impact the relative size of ISIC3 industries, which we use in some specifications, and where we have to assume that within ISIC3, firm sizes follow similar distribution shapes such that over-sampling at the top does not impact relative rankings. Firms with fewer than five employees, government-owned establishments and co-operatives are dropped from the sampling frame. The surveyors contact firms from this stratified-random sample and conduct the surveys with the person who most often deals with banks or government agencies.

We measure size as the average of the log of number of employees per firm in a three digit ISIC sector,<sup>12</sup> excluding part-time and temporary workers. We calculate size for all ISIC3-country-year cells in the WBES. The size distribution in the US is drawn from the 2002 Census of Employment and Wages. To be consistent with sampling in the WBES, we exclude firms with five employees or less from the Census.<sup>13</sup> One caveat to is that we solely use industry level variation in average firm size. In the data, the intraclass correlation between log employee size and industries is 15%, and hence a large share of We construct extensive and intensive margin measures of tax inspection within an

> 11Sometimes supplemented with the list of firms registered with the chamber of commerce. 12Sectors follow version 3.1 of the ISIC international industry classification.

> 13In robustness tests we use an alternative size measure which places additional weight on the firms which have a higher proportion of the total sectoral employment as suggested by Davis and Henrekson [1999] and Kumar et al. [1999].

8
- 14 -

# Box 4: The Efficient Component Pricing Rule

[Town R oute AB_ Town B _]_ Route BC J Town **Marginal Cost Marginal Cost Joint Cost Access Price* Price (AB) (BC) (Average Cost over AC**)** Incumbent 5 5 10 20 Efficient 4 15 **19** Entrant Inefficient 6 15 21 Entrant This is a simple example of the Efficient Component Pricing Rule developed by Baumol as a principle for setting access prices. In this example, due to Baumol, a vertically integrated incumbent offers a rail service between towns A, B and C. An entrant wants to develop a rival rail service between towns A and C but has to pay for access to the vertically integrated incumbent for its bottleneck service between towns A and B (route AB) and will provide the service itself between towns B and C (route BC).

The costs of the service are as follows. There is a marginal cost (assumed constant) of service for each leg of the route AB and BC of 5. In addition, there is ajoint cost of service of 10 (an average fixed cost incurred by the incumbent for operation of the entire rail network) so that the average cost of the service AC is the sum of the marginal costs and the joint cost, ie 20. The incumbent charges the average cost of the service (20) and the entrant charges a price equal to its marginal cost over BC and the access price to AB.

As illustrated in the table above, the Efficient Component Pricing Rule states that the correct access price to charge the entrant for the bottleneck service (route AB) is the sum of the marginal cost of access to the bottleneck AB which equals 5 and the joint costs of service 10 (the opportunity cost of entry to the incumbent). The efficient access price is therefore _15._ This example is illustrated by two entrants. The first, the efficient entrant, has marginal costs of 4 over the route BC. It therefore can profitably enter at the ECPR access price of 1 5 and undercut the incumbent with an average cost of 19, which is less than the incumbent's average cost of 20. If an inefficient entrant has marginal costs for the route BC of 6, then it will have average costs of 21, ie more than the incumbent and hence will not enter. In other words, the correct access price induces efficient entry. An access price less than the ECPR (in this simple example) will induce inefficient entry.

- Access Price (under ECPR) = MC(BC) + JC

- ** Average Cost (to incumbent) = MC(AB) + MC(BC) + JC Average Cost (to entrant) MC(BC) + AP

The efficient component pricing rule defines in effect an upper limit for access prices, because it still allows the incumbent vertically integrated firm to make excessive profits - as in the past. A lower bound is set by the marginal cost of granting access to the network. The marginal cost
**Table 3.4. Balance of payments in 2022 and by scenario in 2030 (% of nominal GDP)**

|**Indicator**||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta r**|**ec+ta+**|
|---|---|---|---|---|---|---|---|---|---|
|**Outflows **|**Imports**|44.2|45.7|47.0|47.6|47.6|47.9|47.9|49.6|
||**Factor payments**|0.1|0.1|0.1|2.4|2.4|4.3|4.2|4.4|
||**Change in foreign reserves**|0.2|0.2|0.2|0.2|0.2|0.2|0.2|0.2|
||**Total**|44.4|46.0|47.3|50.2|50.2|52.3|52.3|54.2|
|**Inflows**|**Exports**|7.5|7.2|8.1|8.9|8.9|10.1|10.1|11.9|
||**Transfers to households**|22.7|23.8|24.1|23.7|23.7|22.9|22.8|22.9|
||**Transfers to government**|14.0|14.8|14.9|14.1|14.1|13.1|13.0|13.1|
||**Private borrowing**|0.2|0.2|0.2|0.2|0.2|0.2|0.2|0.2|
||**Government borrowing**|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
||**FDI**|0.0|0.0|0.0|3.3|3.3|6.1|6.1|6.1|
||**Total**|44.4|46.0|47.3|50.2|50.2|52.3|52.3|54.2|

**Table 3.5. GDP at factor cost by sector -- level in 2022 (bn 2020 YER) and annual growth by scenario**

||**2022**|**base**|**limbo**|**rec-t**|**rec-ta**|**rec+t**|**rec+ta**|**rec+ta+**|
|---|---|---|---|---|---|---|---|---|
|**Agriculture**|245.4|1.9|2.9|3.1|3.1|4.0|4.0|5.0|
|**Industry**|255.8|1.8|2.9|4.7|4.8|7.2|7.3|8.8|
|**Mining**|72.1|0.1|0.3|2.1|2.1|3.9|3.9|5.0|
|**Manufacturing**|128.5|2.4|4.1|5.3|5.4|7.8|7.8|9.7|
|**Other**|55.2|2.1|3.0|6.0|6.1|9.4|9.4|10.4|
|**Services**|797.9|2.1|3.2|4.1|4.1|6.2|6.2|7.2|
|**Private**|593.9|2.1|3.2|4.9|4.9|7.3|7.3|8.5|
|**Government**|204.0|2.0|3.1|1.3|1.3|2.3|2.3|2.3|
|**Total**|1,299.1|2.0|3.1|4.0|4.0|6.0|6.0|7.1|

19
were reported to engage in 0.19 more learning activities (q-value = 0.08) but attend 0.43 fewer days of school on average, at a time when most in-person schooling was suspended due to the COVID19 pandemic. Children in treated households also experienced a decrease in reported alertness of 0.73 standard deviations (q-value _<_ 0.01). Beyond child outcomes, other significant effects at midline include the probability that the respondent is living with a disability that complicates self-care (0.38 SD higher difficulties with self-care, q-value _<_ 0.08). Yet only the child alertness reductions and days of school attendance are robust to accounting for non-random attrition (Table A.2).

# **5.3 Impacts in the endline survey round**

The endline survey was carried out in-person in 2021 as pandemic conditions eased, shortly after program assistance had ended for most households. As noted above, we find that the program significantly reduced housing expenditures by 82.05 USD PPP at endline relative to the control group (q-value=0.07). Effects on measured housing quality, on the other hand, largely dissipate by this point, with no significant differences between the treatment and control groups in any dimension of the housing quality index. Some of this could have been due to catch-up investments in the control group, or the possibility that housing improvements like window repairs and mold removal depreciate relatively quickly. Likewise the endline survey data reveals no statistically significant effects on any of the other pre-specified primary outcomes: program impacts on total household consumption, respondent depression levels, and child well-being are all negative, in fact, although relatively small in magnitude and none are significant at traditional confidence levels.

By the endline survey, most impacts observed at midline – including on food security, household composition, child learning outcomes, and COVID-19 – were no longer statistically significant. Four statistically significant effects survive multiple hypothesis testing adjustments (Table 6). First, there was a notable reduction in the amount of time respondents devoted to chores and childcare, with a decrease of 6.91 hours in these activities over the past week and corresponding 26
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

# **Table 7c Cost-benefit analysis of  own generation, by sector**

|Percent<br>Industry|Investment<br>costs|Own-<br>generation<br>costs|Total<br>costs|Reduced<br>sales<br>losses|Cost-<br>benefit<br>ratio|
|---|---|---|---|---|---|
|Textiles|0.42|0.29|0.71|0.23|–0.48<br>**|
|Food and beverages|0.79|0.77|1.56|0.53|–1.03<br>***|
|Metals and machinery|1.08|0.13|1.21|0.26|–0.95<br>*|
|Chemicals|2.92|0.14|3.06|0.43|–2.63|
|Construction|2.08|0.46|2.54|0.57|–1.97|
|Wood and furniture|0.42|0.37|0.79|0.20|–0.59|
|Nonmetallic and plastic materials|0.91|2.30|3.20|0.36|–2.84<br>*|
|Mining and quarrying|0.11|3.10|3.22|0.62|–2.60|

_Source_ : World Bank, Enterprise Survey Database. *** statistically significant at 1 percent level. ** statistically significant at 5 percent level.

* statistically significant at 10 percent level.

# **With improvements in quality of public power supply**

The results of the probit model discussed earlier can be used to evaluate the extent to which an improvement in the reliability of power supply will affect generator ownership. The marginal effects of the probit model (equation 1) suggest that the probability of a firm’s owning a generator is not very sensitive to power supply reliability. Reducing power outages by half the mean outage reduces generator ownership by less than 2 percent (table 8). It appears that thoroughly reliable power would reduce generator ownership by no more than 12 percent.

# **Table 8 Simulated change in generator ownership**

|||Change in probability that firm will own|a generator|
|---|---|---|---|
|Variable|Mean|Min>Max<br>1⁄2 mean change|1⁄2 std change|
|Days of power outages|37.9|0.12<br>0.02|0.03|
|Age|18.3|0.16<br>0.02|0.02|
|Employment|111.7|0.87<br>0.12|0.16|
|Exporter|0.19|0.10<br>n.a.|n.a.|

_Source_ : World Bank, Enterprise Survey Database. _Note._ Exporter is a binary variable, therefore 1⁄2 mean change and 1⁄2 std change statistics are not reported. n.a. = not applicable.

The predictions of the probit regression are extended to individual countries in appendix 1, table 6. Raising the reliability of power supply to the level of South Africa results in a mere 3–5 percent reduction in generator ownership.

Although the regression results suggest that improving the reliability of power supply would have a relatively small effect on generator ownership, there are several reasons to expect that the effect would be greater. First, the regression analysis does not account for unobserved explanatory variables, such as firms’ access to finance and productivity, which may bias the regression results downward. Second, because investment in in-house generation is irreversible, reductions in generator ownership will occur with a lag as public power supply is improved. The cross-sectional data analysis conducted in this study 24