rafmacalaba/data-use-mentions-extended huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

## **Achievements**

Côte d’Ivoire has a relatively well-developed power sector (table 12). The national power grid relies on a balanced portfolio of hydropower and gas-fired plant based on domestic hydrocarbon resources, and 71 percent of the population lives in electrified areas. Yet access to power appears to be relatively low; according to the national utility only around 20 percent of households are connected to the electricity grid, although household surveys suggest that the real value may be significantly higher. Initial connection charges remain a barrier to access Basic indicators of installed capacity and power consumption compare favorably with the peer group for fragile states but are less than a tenth of the benchmarks for middleincome countries.

Côte d’Ivoire has been a pioneer in private sector participation in the power sector in the region. In 1990 the country awarded a concession contract for the national power utility Compagnie Ivoirienne d’Electricite, or CIE. The concession helped to improve performance and attract private investment in power generation (IPPs), and by 1999 distribution losses had been reduced to 14 percent and average outage duration to 13 hours compared to 32 hours in 1990. In 1994, the country awarded the first Independent Power Project (IPP) in Africa to CIPREL, and in 1999 it awarded the then largest IPP to Azito. Both contracts have withstood the crisis and continue to supply power effectively. Côte d’Ivoire has become a significant power exporter in the region, supplying neighboring Benin, Burkina Faso, Ghana, Mali, and Togo. That export contracts were honored during the recent period of crisis, earning the country a reputation as a reliable supplier of power. Côte d’Ivoire’s future role in regional power trade will depend on developments in neighboring Guinea and Ghana. If fully developed, Guinean hydropower would be more competitive for regional trade than Ivoirian gas. Yet due to the political and economic situation in Guinea, these hydro resources may not be developed for some
Implicitly, this simulation results in a cost saving of 50 percent (removing half the teachers). Our estimates suggest that learning outcomes could be maintained if a doubling of class sizes reduces learning by no more than 0.04 SD. A similar exercise suggests that even for the highest class-size effect considered, a cost saving of 12 percent could be achieved (by transferring the students of the 12 percent of teachers with the lowest content knowledge) without compromising learning.

_Long-run effects of increasing teacher knowledge and time spent teaching_ The last policy simulation we consider is the joint effect of increasing both teacher knowledge and effort. The provision of primary education has expanded greatly in low- and middle-income countries in the last two decades, including countries in Sub Saharan Africa. Among current teachers in the SDI data, we find that twice as many teachers have entered the profession in the last 10 years than in the decade before. This expansion of the teaching force will likely continue: According to recent population projections, close to half the world’s children will live in Africa by the end of the 21st century (UNICEF, 2014). The number of primary school age children in Sub-Saharan Africa is set to rise from about 170 million to 206 million between 2019 and 2029, reaching 270 million by 2050.<sup>50</sup> As illustrated in Figure 6, simply to keep pace with population growth—adjusting for teacher retirement—and to maintain pupil-teacher ratios at a rough benchmark of 40 students per teacher, would require the hiring of 1.6 million new teachers by 2030 and close to 5 million by 2050. As the large majority of teachers are employed on permanent and pensionable civil service contracts, and as upgrading current teachers’ knowledge is probably significantly more costly than raising the quality of new cohorts of teachers, an expansion of the teaching force at low knowledge represents a lost potential with durable impacts.<sup>51</sup> We use the estimate of the cumulative impact of content knowledge to
# suggest that large firms smooth the impact of variation in demand by contracting out to SMIs in

good times, but servicing all business internally in economic downturns.

**Table** 2. **Descriptive Statistics of Manufacturing Production (1992:01-1998:12)a**

||_Large Manufacturing Firms_|_SMIs_|
|---|---|---|
|Mean|0.71|0.51|
|Median|-0.18|-0.25|
|Maximum|16.75|18.76|
|Minimum|-14.83|-16.39|
|Standard Deviation|6.28|8.22|
|Skewness|0.19|0.21|
|Kurtosis|2.86|2.58|
|Coefficient Variation|8.85|16.10|
|Jarque-Bera|0.59|1.23|
|Observations|83.00|83.00|

a: Growth rate of the respective index.

# **4.2. Responses to Policy Shocks by SMIs and Large Manufacturing Firms: Evidence from Vector Autoregressions**

Quantifying the effects of monetary shocks has been one of the most significant challenges of empirical monetary economics. Economic research has historically employed several competing approaches to identify policy effects from the observed data. On the one hand, there are techniques based on the Choleski decomposition of vector autoregression (VAR) residuals. Within this approach, the main debated issue pertains to whether an indicator of monetary policy exists which can justifiably be placed within a Wold-causal ordering. On the other hand, the approach proposed by Romer and Romer (1989, 1990) uses episodes viewed as monetary contractions as a means of identifying monetary shocks. Here the key issue causing 20
numbers decreasing sharply afterwards (7.9% at 15 and 2.85% at 16).<sup>2</sup> Therefore, an analysis of the effect of conflict on primary schooling attainment should consider children aged 14 or more at the time of the survey, and control cohorts should have been at least 15 at the beginning of the conflict, and preferably slightly older. I define the treated cohort as comprising individuals aged 5 to 9 years old at the start of the conflict in 1996, so that all treated cohorts are exposed to the conflict during most of their potential primary schooling career and the youngest exposed cohort is observed at age 14 in the 2006 DHS. The control cohorts include individuals aged 16 to 19 at the start of the conflict. I exclude cohorts aged 10 to 15 in 1996, as the treatment status of these cohorts is less clear-cut: many of these individuals could have been enrolled in primary schooling during the conflict, but they were not exposed to conflict during their whole primary schooling careers.

More specifically I estimate the following equation using the fixed effects estimator, and using data from the 2006 DHS: Where _Y ijrt_ is a dummy equal to one if the individual has completed primary education, and zero otherwise; _districtj_ is a set of district fixed effects, and _regionr*yeart_ are a set of interaction terms between binary indicators for each of Nepal’s five development regions and a set of birth year dummies _yeart_ (excluding the first year). _cohortt*casualtiesj_ is the interaction between a dummy equal to one when the individual belongs to the treatment cohort and the number of conflictrelated casualties (per 1000 inhabitants) in district _j_ during 1996-2006.

Under the assumption that there is no correlation between the number of district casualties and unobserved district-time-varying factors (after controlling for region-specific year fixed effects), _β3_ identifies the average treatment effect of exposure to conflict on those exposed. I also estimate variants of Equation (1) in which I add a regressor capturing specific aspects of
and post-pregnancy visits, the growth monitoring of preschool children, and regular attendance to the educational meetings. The required periodicity of health check-ups and attendance to educational meetings varied by household member.

Importantly, the roll-out of the program followed a very specific geographic targeting rule, reaching more disadvantaged communities earlier and gradually incorporating better-off communities over time. At first, only rural localities were eligible for the intervention. This restriction was later relaxed, with semi urban areas incorporated since 2001 and urban areas incorporated since 2002.

Among rural places, the selection of eligible localities was based on a marginality index computed from census data.<sup>4</sup> In contrast, due to the high degree of heterogeneity in socioeconomic status within urban localities, the geographic unit of interest to select urban places was the Primary Sampling Unit (PSU), where a PSU consisted of a group of 1 to 50 blocks. Only localities with at least one PSU with high concentration of poor households were selected.

After a community was targeted, eligible households were selected. In eligible rural localities the program carried out a census, computed a household-level poverty index, identified those households that would be eligible to enroll, and then informed them about their eligibility status. As a result, enrollment was almost automatic and administrative records suggest that 97 percent of eligible households were incorporated to the program. Facing a much higher cost-benefit ratio in conducting a census in urban localities, the program changed the enrollment process in 2002.<sup>5</sup> Instead of collecting information on every household, the program established registration offices

> 4 The program used information collected in the 1990 Mexican Population Census and the 1995 Population Count. 5 Semi urban areas incorporated in 2001 followed the same selection process as in rural areas. Similarly, the household selection process applied to rural areas incorporated to Oportunidades in 2002 or later was a variant of that applied in urban areas (Gutierrez et al., 2003).

7
We also use quarter-end outstanding aggregate credit for scheduled commercial banks from the Reserve Bank of India’s Quarterly Statistics on Deposits and Credit of Scheduled Commercial Banks (Database on Indian Economy). We use per-capita credit to measure access to finance.

Appendix Table 1 describes the summary statistics. In Panel A, the number of observations is 3,665, which reflects 733 districts across India each observed over five months (March 2020 to July 2020). Red zone districts have much higher levels of nighttime lights compared to Orange and Green districts. The levels of mobility are similar across zone types. Simultaneously, Red zone districts had 1,324 average infections, compared to 260 in Orange districts, and 89 in Green districts. These patterns are consistent with higher infection rates in large urban areas with greater international connectivity and lower distancing. Correspondingly, Red zone districts are more populous with greater density, have higher bank credit and a larger fraction of the population employed in the services sector. The mean age in Red, Orange and Green 7 CPHS does not cover Arunachal Pradesh, Nagaland, Manipur, Mizoram, Andaman and Nicobar Islands, Dadra & Nagar Haveli and Daman & Diu. 7
These results suggest that classroom observations that measure how teachers, students, and parents are interacting between and among each other (namely the ECERS-R) are capturing elements of playgroup quality beyond what can be predicted from student’s school readiness, teachers’ education and teaching experiences alone. Thus, observed playgroup quality is a strong predictor of whether project playgroups are sustained after the initial funding period.<sup>20</sup> Table 5 summarizes the relationship between sustainability and market conditions. First, we do not find support for the fact that distance to the village center or distance to neighboring playgroup services mattered in our setting. Second, project playgroups located in villages with a higher density of kindergartens during the project period were significantly more likely to be open (column 1) and remain open with two trained teachers (column 2) – holding constant all other variables in the model. As shown in Figure 4, below, the predicted probability that a project playgroup remains open increases from 82% to 97% as the number of kindergartens per 100 children increases from 0 to 1. Similarly, the predicted probability that a project playgroup is open with two trained teachers jumps from 29% to 41% as the number of kindergartens per 100 children increases from 0 to 1. We view kindergartens as services that complement playgroups given the age of students intended for each. Therefore, in villages where both kindergartens and playgroups are sought after, we would expect a positive relationship between the density of kindergartens and sustainability of project playgroups. Our results are consistent with this hypothesis.<sup>21</sup> <u>Table 5. Logistic regression of project playgroup sustainability on market conditions</u>

|<br> <br>|<br>(1)<br>Playgroup is open<br>in 2016 (1=Yes)|<br>(2)<br>Playgroup is open in 2016 with<br>two trained teachers (1=Yes)|
|
- National Statistical Office (NSO) (2017). Malawi Fourth Integrated Household Survey 2016-2017. Public Use Dataset. Ref: MWI 2016 ~~I~~ HS-IV ~~v~~ 04 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2936` on 6 September 2019.

- National Statistical Office (NSO) (2020a). Malawi Fifth Integrated Household Survey 2019-2020. Public Use Dataset. Ref: MWI ~~2~~ 019 ~~I~~ HS-V ~~v~~ 06 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/3818` on 9 February 2024.

- National Statistical Office (NSO) (2020b). Malawi Integrated Household Panel Survey 2010-20132016-2019 (long-term panel, 102 EAs). Public Use Dataset. Ref: MWI ~~2~~ 010-2019 ~~I~~ HPS ~~v~~ 06 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index.php/catalog/3819` on 21 May 2024.

- Novella, N. S. and W. M. Thiaw (2013). African rainfall climatology version 2 for famine early warning systems. _Journal of Applied Meteorology and Climatology 52_ (3), 588–606.

- Parker, W. S. (2016). Reanalyses and observations: What’s the difference? _Bulletin of the American Meteorological Society 97_ (9), 1565–72.

- Ritchie, J. and D. NeSmith (1991). Temperature and crop development. In R. J. Hanks and J. T. Ritchie (Eds.), _Modeling Plant and Soil Systems_ , pp. 5–29. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America.

- Sarsons, H. (2015). Rainfall and conflict: A
guarantees to borrow USD 1.2 billion from international banks Credit Suisse and VTB without disclosing these additional liabilities to bond investors and the public.<sup>5</sup> Two years later, in 2015, Mozambique first started to experience debt servicing difficulties. The tuna fleet, funded with the USD 850 million bond issuance, pulled in less than 5% of the tuna that it had expected, and the government approached bondholders with a plan to restructure the bond. After most bondholders had agreed to the restructuring in early 2016, rumors about large hidden debts started to emerge. In April 2016, the Wall Street Journal first reported about the additional and previously undisclosed bank loans that Mozambican state-owned enterprises had borrowed and the state guarantees that they carried.<sup>6</sup> The rumors about hidden debt led to a surge in Mozambique’s bond spreads (see Figure 1), with bond investors expressing concerns that the existence of undisclosed, additional liabilities would dilute the recovery value of their bonds in the event of a default, which ultimately happened in January 2017. Audits of Mozambique’s debt through the IMF and international accounting firms followed.<sup>7</sup> They confirmed the existence of USD 1.2 billion in hidden liabilities and the country’s Constitutional Council ruled that the loans were illegally contracted (Cortez et al., 2021). Ultimately, it took 4 years to settle renegotiations with bondholders and the country has not accessed international bond market ever since.

Mozambique’s hidden debts and their revelation can be quantified by comparing the country’s debt reports across different vintages of the World Bank’s International Debt Statistics. When initially reported, the 2014 debt figure for Mozambique was USD 7.1 billion (see Figure 2). This figure was strongly upward revised in subsequent vintages as the previously undisclosed loans became public knowledge. The latest debt figure for year 2014 is USD 8.5 billion, an increase of USD 1.4 billion
).<sup>27</sup>

> 26 The percentage of baseline schoolgirls who reported having been ever pregnant was less than 2% at baseline. Hence, children directly exposed to the program in this stratum are almost exclusively born during the intervention. However, approximately 45% of baseline dropouts had already started childbearing at baseline. Therefore, our analysis includes children under two at the start of the program, who were at least partially exposed to cash transfers. 27 Of the two anthropometric measures that we collected for children aged 0-59 months – height and weight – stunting (height-for-age z-score<-2) is the key indicator of malnutrition in Malawi: almost half of the children under the age of 5 were categorized as stunted in 2010, while wasting (weight-for-height z-score<-2) rates are low at 4%

29
in STAN, which does not report a sufficiently disaggregated breakdown of employment for these 2 industries. As a result, the number of available 2-digit industries drops to twelve.

> 28As the change for Motor vehicles and Plastic and rubbers is much larger than for the other industries, the figure uses a log-scale to ease readability. The log-scale allows to include only nine of the twelve available industries as Textile and Paper did not experience any robots’ adoption in Indonesia and the average change in robots per thousand workers was negative in Other transport equipment for the OECD. Figure A1 in the Appendix shows that the positive relationship holds also when using a normal scale, which allows to include Textile, Paper and Other transport equipment while it excludes Motor vehicles and Plastic and rubbers.

26
For decomposition analysis, we obtain value added of manufacturing at the subsector level from United Nations Industrial Development Organization (UNIDO) Yearbook of Industrial Statistics (INDSTAT4) (2011). INDSTAT4 reports manufacturing value added at the three-digit level of ISIC Rev.3 for selected ECA countries. We match IEA (2012) with INDSTAT4 (2011) to calculate subsector energy intensity at the ISIC Rev.4 two-digit level. INDSTAT4 reports value added in current prices, denominated in US dollars. We use the GDP deflator of each country to convert values of different years into 2000 prices and then to constant international dollars using 2005 PPP rates.

Per capital GDP (PPP based), the capital-labor ratio of manufacturing and the share of exports in total manufacturing value added are all obtained from WDI (2012). Energy prices are obtained from Energy Regulators Regional Association’s tariff database and Eurostat.

# **IV. Empirical Strategy and Results**

In this section, we present three types of empirical analysis to identify the sources and mechanisms of energy intensity change in the manufacturing sector of ECA. First, we conduct a cross-country convergence analysis to test whether backward countries are catching up with efficient ones. Second, using an index decomposition method, we investigate the extent to which the change in energy intensity is induced by structural shifts vs. efficiency improvements. Finally we explore the determinants of energy intensity, the structural mix and the efficiency levels based on regression analyses.

# _A. Energy Intensity Convergence_

Convergence analysis is typically used in the context of economic growth and goes back to Barro and Sala-i-Martin (1992). There are different definitions of convergence in the literature. The reduction in cross-sectional variance in productivity is called σ-convergence. The tendency of less productive countries to grow faster is called β-convergence.

To measure σ-convergence in the context of the energy intensity analysis, we look at the trend of cross-country variance over time. The mean and standard deviation of energy intensity for an unbalanced panel of 28 countries in ECA during 1998-2008 are shown in Figure
African countries in the same way and making necessary adjustments to account for the commodity price crises. Lastly, we also adjust the estimated impacts for Sierra Leone according to the Epidemic Preparedness index of Chad and Sierra Leone.

Table 1: Estimated impact of the COVID-19 pandemic on main impact channels

|Impact channel|Shock Size (%)|
|---|---|
|Labor supply|-2.2|
|Labor productivity|-6.2|
|Capital productivity|-2.1|
|Trade and transport margins|2.7|
|F/x flows|-33.2|
|Commodity prices||
|Oil|-30.0|
|Minerals|-10.0|
|Tourism|-25.0|
|Source: Authors’ calculation followin<br>(2020)|g Djiofack et al.|

The model is set up for a short-run analysis by eliminating various substitution possibilities in production. The standard set of model parameters allows various production factors to be substituted with each other and adjust the export supply based only on the difference between domestic and international prices. In the version used for this paper, such substitutions are either not possible or very limited. These changes reflect the possible disruptions and difficulties many sectors are likely to face while adjusting to the pandemic conditions. In this short-term setting:

- Substitution between capital and skilled labor is not possible.

- Substitution between capital vintages (i.e., new and old capital) is not possible.

- Substitution between land, labor, and capital is not possible at the top production nest.

- Substitution of export and domestic supply is significantly reduced.

# 2.2. Econometric estimation using data from Chad’s household surveys

We leverage data from the Chad High-Frequency Phone Survey (HFPS)<sup>8</sup> conducted in May-June 2020 by INSEED (with support from the World Bank) to assess the actual impact on female-headed households in Chad. The
education to estimate access to human opportunities. Regarding the former, access to clean water, access to electricity and access to sewerage systems are consistent across time and countries. Regarding the latter, this analysis focuses on school enrollment and primary education completion.

# **2.2. Harmonized database on individual-level incomes**

This paper relies on the SEDLAC database as a primary source of comparable welfare aggregates among Latin American countries. SEDLAC is a database of harmonized household surveys of LAC countries compiled by the poverty group at the World Bank in partnership with the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina. This database provides the most comparable socioeconomic measures across LAC countries, including almost 400 household surveys of 18 countries from the 1970s to the present.

The SEDLAC project implements a few adjustments to the original microdata to enhance comparability across countries and over time. First, incoherent information is handled to avoid biases. An observation is considered incoherent when individuals first identify themselves as currently (at the moment of the interview) working in a paid job, but in subsequent questions regarding earnings from their main occupations they fail to declare any income or declare zero income. After identifying the household head, the project excludes those households where i) the labor income of the household’s head is individually incoherent; ii) households with a single employed member whose income is incoherent (whether he/she is the head of the household or not); and iii) households where the head is not employed and the income of the employed member with the highest educational attainment (as a proxy for income level) is individually incoherent.

6