rafmacalaba/datause-encoder-data huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6 Benchmarking air transport indicators for Côte d’Ivoire and other West African countries**

|Country|Côte d‘Ivoire|Ghana|Nigeria|Senegal|Kenya|Tanzania|
|---|---|---|---|---|---|---|
|**TRAFFIC (2007)**|||||||
|Domestic Seats(millions per year)|**0+**|0.14|9.30|.13|2.09|1.87|
|Seats for international travel within<br>Africa(millions per year)|**0.85**|0.91|1.37|1.26|3.14|1.27|
|Seats for intercontinental travel (millions<br>per year)|**0.30**|0.83|2.44|1.23|2.76|0.59|
|Seats available percapita|**0.06**|0.08|0.09|0.23|0.28|0.12|
|Herfindahl index – domestic market (%)|**-**|100.0|18.0|100.0|60.5|31.0|
|Herfindahl index – international market|**9.8**|6.4|6.4|10.3|34.1|13.0|
|**QUALITY**|||||||
|Percent of seat km in medium or<br>smaller aircraft|**52.3**|15.7|29.6|39.3|23.3|48.6|
|Percent of seat km in newer aircraft|**90.8**|96.8|71.4|98.3|80.2|79.3|
|Registered carriers on EU blacklist|**0**|0|0|0|0|0|
|FAA/IASA Audit Status|**Fail**|Fail|No audit|No audit|No audit|No audit|
|Percent of carriers passing IATA/IOSA<br>Audit|**0**|0|28.6|50.0|11.1|33.3|

Source: Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

# Water supply and sanitation

## **Achievements**

According to the UNICEF-WHO Joint Monitoring Program (2010 report), access to improved water in Côte d’Ivoirestarted from a relatively high base of 76 percent. Nevertheless, progress since that date has been slow, with the access to improved water increasing only slightly to 80 percent by 2008. While the overall envelope of those with access to improved water has not moved significantly, the level of service received by those with access has improved substantially. In particular, the percentage of the population with piped water on premises almost doubled, from 22 percent in 1990 to 40 percent in 2008 (table 7).

A relatively small percentage of the population of Côte d’Ivoire relies on surface water—less than 10 percent, compared with 37 percent in the low-income benchmark group and 13 percent in the middleincome benchmark group (table 8). As of 2006, access to piped water was about twice as high in Côte d’Ivoire as the low-income benchmark, and access to stand posts was also somewhat higher. Around 50 percent of Côte d’Ivoire’s population relies on groundwater compared with 40 percent in the low-income benchmark group. Overall, the country has relatively well-developed utility water.

16
**Table 11: The LOGIT equations of the Decentralization Performance**

|Estimation procedure|LOGIT|LOGIT|
|---|---|---|
|Dependent Var.<br>Independent Var|InstCng|InstCng|
|Intercept|-1.375***<br>(-2.99)|-1.370*<br>(-1.27)|
|%Bgtspnt|1.400**<br>(1.91)|1.462**<br>(2.22)|
|InstDismntld|1.198***<br>(2.99)|1.266***<br>(4.10)|
|PltclCost|-0.753***<br>(-2.52)|-0.762***<br>(-2.35)|
|PrblmsBfr|0.198*<br>(1.32)|0.191*<br>(1.30)|
|Scarcity1||-0.005<br>(-0.86)|
|LR/Wald Chi-square|23.94***|22.71***|
|Pseudo R<sup>2</sup>|0.222|0.225|

Note: *** p<0.01; ** p<0.05; * p<0.10.

_The interaction between the decentralization process and its level of performance_ In order to assess the impact of the decentralization process on the level of success, we estimated several equation systems consisting of two equations. The first equation depicts the decentralization process, as was described in the previous section, and then the dependent process variable was included as an instrumental variable in a relationship that describes the performance of the decentralization process.

Table 12 presents the results of the estimation of system equations that include non OLS (GLM and Poisson) estimated decentralization process variables as instrumental variables in the decentralization performance equations (that are also GLM and Poisson estimates) in two stages. Because there is no procedure to run 2-stage models for GLM and poisson, it was performed manually by first estimating models (a), (b) and (c) and then using the predicted values to estimate Aa, Ab and Ac. This does produce the same coefficients but the standard errors are not the same as if we were to run a simultaneous 2 stage process.

33
- 26 -

Figure 4. Liquidity Ratio of the DMBs (Reserves Plus Net Foreign Assets)/ Total Deposits TRESRAT <!-- Start of picture text --> 0.45<br>0.4 /<br>0.35 -<br>0.3i<br>0.25<br>0.2__<br>0.1s ,__<br>0..05 _<br>76 77 78 79 so 8 82 83 84 as 86 87<br><!-- End of picture text --> <!-- Start of picture text --> Source: IFS.<br><!-- End of picture text -->
Table 2. The price schedule of the gasoline in the Islamic Republic of Iran, November 2019

|||Initialprice|Initial subsidy|Finalprice|Final subsidy|
|---|---|---|---|---|---|
|only car|below 60 liters per month<br>more than 60 liters|10,000|56,301<br>56,301|15,000<br>30,000|51,301<br>36,301|
|onl motorccle|below 25 liters per month|10000|56,301|15,000|51,301|
|y y|more than 25 liters|,|56,301|30,000|36,301|
|both car and|below 85 liters per month|10000|56,301|15,000|51,301|
|motorcycle|more than 85 liters|,|56,301|30,000|36,301|
|Source: Authors’ co|mpilation.|||||

|Table 3. Inputs fo|r calculatingcost recovery price|forgasoline|||
|---|---|---|---|---|
|unit|U.S. Regular All Formulations<br>Retail Gasoline Prices|Federal and state<br>taxes|Cost recovery<br>price,USD|Cost recovery price,<br>IRR|
|pergallon|2.5980|0.481|2.11740|250,975|
|per liter|0.6863|0.127|0.55936|66,301|
|Source: Authors’ calcula<br>(https://www.bonbast.c|tions using information from U.S. Energy In<br>om/historical)|formation Administration|(https://www.eia.gov/|) and Bonbast website|

3. Distribution of subsidies and gasoline expenditures More than 40 percent of households in the Islamic Republic of Iran do not report expenditure on gasoline and as a result have zero consumption (Figure 2). A further 40 percent consume 60 or more liters per month. The remaining 19 percent consume 1-60 liters per month. There are more households with zero consumption of gasoline in rural than in urban areas.

Consumption of gasoline is directly linked to the ownership of personal transport. About 41 percent of all households in the Islamic Republic of Iran have neither car nor motorcycle, consistent with the number reporting zero consumption of gasoline. Forty-one percent have only cars, 11 percent only motorcycles and 7 percent both cars and motorcycles. Figure 3 shows the structure of population deciles by ownership of cars and motorcycles. Richer households are more likely to own private transportation, in particular cars, while poorer households are more likely to own motorcycles.

<!-- Start of picture text --> Figure 2. Distribution of households by quantity of Figure 3. Distribution of population by ownership<br>gasoline consumed, % of car and motorcycle by deciles, %<br>100%<br>80%<br>40 41 60%<br>40%<br>20%<br>5 4<br>10<br>0%<br>0 liters less than 20 between 20-39<br>40-59 more than 60 car and motorcycle only car only motorcycle<br>%<br><!-- End of picture text --> Source: Authors’ calculation using forecasted HEIS 2018/2019.

Note: Quantity consumed is calculated by using current price of gasoline and reported expenditures. Given that we do not know the quality of gasoline the price of 15,000 rials is used which may overestimate consumption among the richest households who use more expensive high-quality gasoline. We also do not how may cars and motorcycles households have. We have to assume ownership of one car or/and one motorcycle per household.

5
export growth whereas the intensive margin on average represents the larger share in the increase. The first figure in Appendix 5 provides the breakdown country and by country. It is worth noting that the amounts vary widely across nations.

**Table 1: Growth in exports in new and old products by areas**

||**Intensive (1)**<br>|Geographic(<br><br>**Exte**|2.1) Product(2<br><br>**nsive (2)**|**Overall (3**<br>.2)<br>|**) % Ext (2) /**<br>|**(3) % Geo. Ext (2.**<br>|**1) / (3) % Prod. Ext (2.2) / (3)**<br>|
|---|---|---|---|---|---|---|---|
|**LAC**|94.35|23.29|6.97|118.06|0.22|0.20|0.06|
|**DEV**|55.60|3.94|0.16|59.59|0.05|0.07|0.00|
|**SSA**|28.81|17.00|2.69|45.73|0.16|0.37|0.06|
|**ASIA**|133.54|15.59|3.84|150.53|0.14|0.10|0.03|
|**ECA+MENA**|104.02|9.36|3.67|114.94|0.12|0.08|0.03|

_Note: Author's calculations using Comtrade data._ Columns (2.1) and (2.2) decompose the extensive margin into the product and the geographic dimensions as explained in Figure 1.<sup>13</sup> As we can see, geographic diversification is more important than product diversification for all regions.

Finally, before turning to the formal empirical analysis, we explore the sectoral distribution of the extensive margin. With this aim, we have converted the HS-6 categories to the two-digit NACE classification system.<sup>14</sup> After the conversion we are left with roughly thirty categories which we have aggregated for expositional simplicity. We turn now to show what the most important sectors at the extensive margin are. The results are summarized in the following pie-graphs:

**Figure 5: New products by sector in developed and developing countries**

<!-- Start of picture text --> Sectoral distribution of the extensive margin<br>DEVELOPED DEVELOPING<br>Agriculture Chemicals<br>Food Machinery<br>Machinery(P&C) Mining<br>Services Textiles<br>Wood<br>Source: Author's calculations using Comtrade data.<br><!-- End of picture text --> Figure 5 shows the distribution of sectors at the extensive margin in developed versus developing countries. Around half of the new products that the developed countries export are concentrated on chemicals and machinery alone. The figures for mining

> 13 Both columns include the category of products defined as NPND, which constitutes the overlap between both definitions. If the category NPND is excluded, the difference between the geographic (OPND) and product (NPOD) dimensions is even larger.

> 14 See Appendix 4

9
The results emphasize the need for delinking malnutrition from poverty and designing nutrition policies according to the specific nutritional challenges faced by communes. For instance, in communes where stunting is prevalent, but poverty is not a significant issue, practitioners should incorporate elements that address behavioral changes in their interventions to help households overcome non-financial barriers preventing them from adopting nutritious diets. This involved identifying non-monetary barriers and challenges and designing interventions that “nudge” individuals toward healthier choices through repetitive messaging integrated in community based nutritional programs (Galasso and Umapathi, 2009). In communes where both poverty and stunting rates are high, policy makers can employ traditional stunting reduction interventions as stunting there may be highly linked to poverty. Facilitating access to lipid-based nutrient supplement to parents of stunted children can increase their children’s micronutrient status (Steward et al, 2020). For the remaining communes where stunting rates are low, and poverty levels vary from low to high, policy makers should prioritize policies that equipe commune level health facilities and those that reduce poverty, respectively (Razakamanana et al. 2023).

Sen (1981) argued in his influential book "Poverty and Famines" that food scarcity _due to poverty_ is not the only cause of famines and malnutrition. He emphasized the importance of examining entitlements and other social and economic factors that affect people's ability to access food. Sen's work has had a profound impact on how policymakers and researchers approach hunger and poverty, emphasizing the need to address not just food production, but also distribution, access, and social safety nets. Our findings add to this work by highlighting the importance of behaviors and norms, which can help to explain why malnutrition is so prevalent in many non-poor communes.

# References

Adeyeye SAO, Adebayo-Oyetoro AO, Tiamiyu HK. Poverty and malnutrition in Africa: a conceptual analysis. Nutr Food Sci. (2017) 47:754–64. 10.1108/NFS-02-2017-0027

- Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83(401), 28-36.

Brown, C; van de Walle, D, and M. Ravallion. 2017. Are Poor Individuals Mainly Found in Poor Households? Evidence using Nutrition Data for Africa. NBER Working Paper No. 24047

- <mark>Delisle HF. Poverty: the double burden of malnutrition in mothers and the intergenerational impact. Ann N Y Acad Sci. 2008;1136:172-84. doi: 10.1196/annals.1425.026. PMID: 18579881.</mark>

Edochie, I., D. Newhouse, T. Schmid, and N. Wurz, “Povmap: Extension to the emdi package for small area estimation”, available at Comprehensive R Archive Network Galasso, E & N. Umapathi (2009) Improving nutritional status through behavioural change: lessons from Madagascar, Journal of Development Effectiveness, 1:1, 60-85, DOI: 10.1080/19439340902727669 16 | P a g e
<!-- Start of picture text --> Entering poverty<br>Peninsular Malaysia Rural Peninsular Peninsular Burmiputera<br>ae)<br>=<br>z oe es ——_— a SSSSSSSSSSSSSSSSFSsssSF Seen<br>a<br>Fs East Malaysia Rural East Malaysia East M. Bumiputera<br>zo<br>=<br>=<br>2<br>Qo a ee —<br>fo a<br>gsty geeBha Be &Ay or FSau SKBha a oF st BhVv He a oF<br>k FS CCS SKS SS<br>Shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>Based on a poverty line of RM 527.<br><!-- End of picture text -->
period when licenses are obtained and exploration and extraction rights negotiated—accelerates development. For example, in LICs since 2000, rising copper prices at the time of discovery may have shaved off about two to three years from lead times. For the largest quartile of copper discoveries in LICs since 2000, the price boom may have reduced lead times by 21⁄2 years (Figure 4). Sound macroeconomic policies also appear to be important: lowering government debt below 40 percent of GDP accelerates development times by about 10 percent. These variables may proxy for generally sounder and more predictable macroeconomic policies.

While lower commodity prices could lengthen lead times for copper mines, their effects can be mitigated by strengthened policies. Had the average LIC had the same quality of government index or the same control of corruption index as Chile or Namibia, the lead times for the development of copper discoveries since 2000 might have been shortened by as much as two years (Figure 7).

# **Figure 7. Lead times between resource discovery and extraction**

_Lead times between discovery and production are considerably longer for copper deposits than gold deposits, especially when commodity prices are low. However, they can be shortened by improving business environments._

## **A. Scenarios: Reductions in lead times**

## **for copper mines**

<!-- Start of picture text --> Years<br>0<br>-1<br>-2<br>-3<br>Price Control of Quality of<br>upswing Corruption governance<br>Namibia Chile Namibia Chile<br><!-- End of picture text --> Source: World Bank staff calculations, MinEx Consulting.

Note: Reduction in average lead times for average LIC mine if price downturn shifts to price upswing, if control of corruption is improved to the level of Chile or Namibia, or if quality of governance was improved to the level of Chile or Namibia. Derived from differences in predicted values predicted by a duration model described in Section V. “Price upswings” denotes reductions in lead times for the largest quartile of copper discoveries in LIC since 2000 as a result of switching from a commodity price downturn to an upswing. Reductions in other variables for the same mines as a result of raising control of corruption and quality of governance to average levels prevailing in Namibia and Chile.

17
12 value-added tax rate, the pollution tax rate, the mc.nitoring costs for the SEPA, the inspection costs for the FEPA, the federal penalty level and a coefficient reflecting the relative level of the federal government aversion to pollution.

The weakness of the data base implies that the specific numerical results obtained here are not robust enough to be able to make specific recommendations on policy decisions such as the optimal federal fine or inspection rate or on the optimal pollution tax or monitoring rate. The numerical results are however robust enough to illustrate the importance of some specific aspects of the design of pollution control policies such as the size of fines or of the pollution tax, the differences in monitoring and inspection requirements of dirty and clean industries or the potential role of subsidies to the firms or to the states.

The illustration focuses on the Printing and Publishing sector as a representative dirty industry and on the Food sector as a representative cleaner industry as shown by Table 1. For these two sectors, the coefficients of the production functions were statistically significant. They are likely to be somewhat outdated however as they are based on data for the seventies. For pollution intensity, we relied on series prepared by David Wheeler and his team in the Environmental Department of the World Bank. The series provide measures of toxicity per sector in the U.S. When applied to Brazilian production data, they provide an indicator of the absolute level of pollution generated by each sector in the sample.1<sup>0</sup> The estimates suggest that Printing and Publishing is a much more polluting industry than the food industry per unit of capital. However while it is also much more polluting per unit of output, it is actually somewhat less polluting that the Food sector when production levels are accounted for. The Food industry is indeed much larger than the printing and publishing industry in the country. In absolute termsg both sectors are in the top 5 polluting sectors for the sample selected.

This section is organized as follows. It first presents a detailed discussion of the optimal inspection and monitoring policies for the two case studies. Next, focusing on the dirty industry, Printing and Publishing, it provides simulations of the importance of fines, VAT rates, pollution tax rates and abatement efficiency levels for the success of pollution control.

10. **The** selection of **the** sample **was** based on the infonnation available to estimate the capital **stock.** Only those sectors in the SffC categories for which capital stock could be estimated were rtined.
# **Table 18: Prediction model for the number of loans per thousand adults**

This table shows the estimates of OLS regressions of the number of loans per thousand adults on a quadratic function of the natural logarithm of GDP per capita and the 2008 Credit Information Index of _Doing Business_ . Our preferred specification is presented in column (1).

|(1)|(2)|
|---|---|
|ln(GPD per capita)<br>3.18***<br>(0.65)||
|ln(GPD per capita)<sup>2</sup><br>-0.14***<br>(0.04)||
|ln(Value of Loans per 1000 adults)|2.31***<br>(0.29)|
|ln(Value of Loans per 1000 adults)<sup>2</sup>|--0.10***<br>(0.02)|
|Credit Information Index<br>0.09**|0.07*|
|(0.04)|(0.04)|
|Constant<br>-11.57***|-6.04***|
|(2.50)|(1.04)|
|Observations<br>69|58|
|AdjustedR-squared<br>0.817|0.867|
|Note: Standard errors inparentheses;***p<0.01,**p<0.05,*p<|0.1|

52
Table A4: Balance check: Follow-up 3

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Con|trol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Socio-Demographic**||||||
|Female|0.49|0.50|0.49|0.50|0.32|
|Married|0.11|0.32|0.14|0.34|0.34|
|Has Children|0.13|0.33|0.12|0.32|0.70|
|Age|27.56|2.84|27.57|3.10|0.75|
|Province: East|0.08|0.27|0.08|0.28|0.69|
|Province: Kigali|0.48|0.50|0.43|0.50|0.14|
|Province: North|0.14|0.35|0.17|0.37|0.36|
|Province South|0.17|0.37|0.21|0.41|0.06|
|Province West|0.13|0.34|0.11|0.31|0.26|
|Mother is employee|0.04|0.21|0.05|0.22|0.68|
|Father is employee|0.06|0.24|0.06|0.24|0.93|
|Wealth Index|-0.06|1.44|0.03|1.42|0.33|
|**Panel B. Education**||||||
|Years since graduation|1.93|0.92|1.84|0.96|0.19|
|High Education Diploma|0.22|0.41|0.21|0.41|0.62|
|Bachelor Degree|0.76|0.43|0.77|0.42|0.69|
|Masters Graduate|0.02|0.14|0.02|0.15|0.81|
|Public School|0.74|0.44|0.75|0.43|0.72|
|Good English Language|0.97|0.17|0.97|0.16|0.83|
|Good French Language|0.44|0.50|0.43|0.50|0.84|
|Good Swahili Language|0.14|0.35|0.13|0.33|0.56|
|**Panel C. Employment**||||||
|Has ever worked|0.84|0.37|0.85|0.35|0.65|
|Not paid job last month|0.59|0.49|0.61|0.49|0.62|
|Works as employee|0.11|0.31|0.08|0.28|0.24|
|<br>Works as self-employed|0.04|0.20|0.06|0.25|0.15|
|<br>Works as family employee|0.08|0.28|0.08|0.28|0.95|
|Works as casual employee|0.18|0.38|0.16|0.37|0.58|
|Has formal job|0.03|0.18|0.02|0.14|0.26|
|Job accordance to degree|0.32|0.47|0.28|0.45|0.11|
|Labor earnings (monthly USD)|43.41|82.61|33.98|75.02|0.07|
|<br>**Panel D. Training**||||||
|<br>Willingness to pay for course (USD)|177.53|996.74|142.08|338.15|0.50|
|<br>Training: get job faster|0.58|0.49|0.57|0.50|0.74|
|Training: get higher pay job|0.30|0.46|0.28|0.45|0.49|
|Training: get better job|0.35|0.48|0.39|0.49|0.26|
|<br>Prefers to attend Kigali|0.66|0.47|0.66|0.47|0.45|
|Prefers to attend North|0.19|0.39|0.18|0.38|0.16|
|Prefers to attend South|0.15|0.36|0.16|0.37|0.75|
|**Panel D. Joint test for panels A-D**<br>||||||
|p-value (joint F-test)|||||0.50|
|Observation|427||436||863|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. We do not test for differences in means for females since the lottery was randomized for each gender separately. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

29
Tables

|Table 1:|Regression-Based Estimates of Child Mortality . . . . . . . .<br>10<br>Decline in the 1980s for Ten Sub-Saharan African<br>Countries with Demographic and Health Surveys|
|---|---|
|Table 2:|Regression Coefficients for Probability of Dying . . . . . . .<br>14<br>by Age Five on Time Period Dummy Variables and<br>Socioeconomic Categories for Ten Countries of<br>Sub-Saharan Africa|
|Table 3:|External Checks of 1985/86 LSMS Mortality Data<br>. . . . . . .<br>32<br>for Cote d'Ivoire|
|Table 4:|Infant and Child Mortality Rates in Cote d'Ivoire . . . . . .<br>34<br>1967-86 LSMS 1985-86|
|Table 5:|Definition and Labels of Variables Included . . . . . . . . .<br>48<br>inLogistic Models|
|Table 6:|Logistic<br>Regression Results for Infant Mortality<br>. . . . . .<br>50<br>Components for Birth Cohorts 1970-86 in Cote<br>d'Ivoire (LSMS 1985/86)|