rafmacalaba/datause-encoder-data huggingface dataset:
by the World Bank and the African Development Bank.

The program was launched in October 2014, and consisted of two main parts. One, the program aimed to sensitize communities living in the vicinity of each of the project forests to the detrimental effects of forest fires. Two, the program also aimed to actively engage community members in local conservation activities using a combination of technical support (by experts from the regional and national authorities) and improved coordination of management activities between adjacent communities. Communities were encouraged to participate in setting up fire barriers within forests to compartmentalize wildfires, in establishing forest management infrastructures and in protecting and monitoring the forests. Taken together, these efforts were expected to reduce both the frequency with which forest fires were started as well as their spread, and especially so at the end of the agricultural season (in November and December) when most of the forest fires take place. We use the Synthetic Control Method (Abadie and Gardeazabal, 2003; Abadie, 2021) to estimate the impact of the policy intervention over the period 2014-2019, the first five years after its inception, using remote sensing data.

Overall, we find that the project was not very effective in reducing fire-induced forest degradation. Forest fires were lower in the project forests, but only in the month of November (the month in which most of the post-harvest forest fires take place), and also in just the first four years after the start of the program. The program did not reduce forest fire occurrences in any of the other months in the dry season. And even though the project managed to decrease the November forest fires by on average 35% in the first four years, we fail to detect an increase in overall vegetation cover. This may be because the decrease in November forest fires was too small to be visible on the satellite images, or because the impact had dissipated too quickly for the forest vegetation to recover.

While the impact of the project was thus limited at best, we perform additional analyses to gain insight into the mechanisms via which the decrease in forest fire occur3
# **_Table 2: The Global Carbon Reduction Incentive, modifying for carbon stocks– payments and receipts for the top 10 countries valued at 0.3 USD per ton_**

|**Country**|**CO2 stock (mil**<br>**tons)**|**Population**<br>**2019(millions)**|**Per capital CO2**<br>**stock**|**Deviations from**<br>**global average (per**<br>**capita CO2 stock)**|**Tax or incentive**<br>**USD billions**<br>**(valued at 0.3 USD**<br>**per ton)**|
|---|---|---|---|---|---|
|United States|167927.36|329.06|510.32|400.17|39.50|
|Russian Federation|49909.67|145.87|342.15|232.00|10.15|
|China|184376.75|1433.78|128.59|18.45|7.94|
|Japan|36938.62|126.86|291.18|181.03|6.89|
|Germany|26251.71|83.52|314.33|204.18|5.12|
|Canada|16332.75|37.41|436.58|326.43|3.66|
|Korea, Rep.|14869.02|51.23|290.27|180.12|2.77|
|Saudi Arabia|12329.56|34.27|359.79|249.65|2.57|
|United Kingdom|15728.91|67.53|232.92|122.77|2.49|
|Australia|10982.38|25.20|435.75|325.61|2.46|
|Viet Nam|3004.90|96.46|31.15|-78.99|-2.29|
|Congo, Dem. Rep.|64.39|86.79|0.74|-109.40|-2.85|
|Philippines|2392.64|108.12|22.13|-88.01|-2.85|
|Brazil|11339.75|211.05|53.73|-56.41|-3.57|
|Ethiopia|198.21|112.08|1.77|-108.38|-3.64|
|Bangladesh|1273.65|163.05|7.81|-102.33|-5.01|
|Indonesia|11061.53|270.63|40.87|-69.27|-5.62|
|Nigeria|2530.88|200.96|12.59|-97.55|-5.88|
|Pakistan|4005.61|216.57|18.50|-91.65|-5.95|
|India|41536.37|1366.42|30.40|-79.75|-32.69|

Source: Author calculations based on ‘Our World in Data‘ (https://ourworldindata.org/).

The key finding of the GCRI based on production-related carbon stocks is that the rankings for both payers and recipients do not change much compared to the GCRI based on annual emissions. The United States and the Russian Federation are the top contributors based on stock values. China is the third largest contributor. Yet, the net contributions of China decrease drastically from $35 billion to $8 billion. The cumulative emissions since the 1990s have been low in China compared to the US and Russia. These numbers are computed using a production-based approach. With the consumption-based approach (reported in the Annex), however, China moves from paying into the fund to becoming a recipient.

10
- 13 -

literature supports a more modest range of elasticity, from -0.1 to -0.4 (e.g., Espey and Espey 2004; Bernstein and Griffin 2005; Reiss and White 2005). Our result seems reasonable because many households in Ethiopia are still using electricity for basic living needs. Therefore, the demand tends to be inelastic, even if the price is changed. Nonetheless, the price elasticity is statistically significant, indicating the importance of the price incentives to control for electricity demand.

38. The elasticity with respect to the Nordin’s _D_ is a mirror of income elasticity. Estimated elasticity is relatively high, at about 0.77, regardless of specifications. This must reflect the fact that the demand for electricity has been growing strongly in developing countries such as Ethiopia. As the economy grows, the electricity demand increases rapidly. This reconfirms the continuous need for electricity capacity expansion and the importance of demand-side management.

**<u>Table 3. Fixed-effects OLS and IV estimation results</u>**

||FE O|LS|FE O|LS|FE|IV|FE|IV|
|---|---|---|---|---|---|---|---|---|
|_CFL_|-35.30|(2.70) ***|-39.90|(2.34) ***|-45.54|(0.99) ***|-49.97|<br>(0.94) ***|
|_MP_|89.30|(28.82) ***|-123.89|(22.33) ***|-295.94|(13.17) ***|-269.83|(13.09) ***|
|_D_|-10.76|(0.23) ***|-11.46|(0.22) ***|-14.54|(0.06) ***|-14.37|<br>(0.06) ***|
|_Q_|||0.22|(0.01) ***|||0.16|<br>(0.01) ***|
|_T_|||3.28|(0.20) ***|||3.36|<br>(0.11) ***|
|_T_<sup>2</sup>|||-0.02|(0.003) ***|||-0.02|(0.002) ***|
|_SA_2|||-0.18|(0.99)|||-0.41|<br>(1.14)|
|_SA_3|||1.26|(1.23)|||0.81|<br>(1.15)|
|_SA_4|||-0.33|(1.12)|||-1.51|<br>(1.18)|
|_SA_5|||-5.75|(1.19) ***|||-7.48|<br>(1.20) ***|
|_SA_6|||14.65|(1.47) ***|||15.10|<br>(1.20) ***|
|_SA_7|||4.89|(1.27) ***|||5.70|<br>(1.26) ***|
|_SA_8|||-8.70|(1.12) ***|||-5.06|<br>(1.32) ***|
|_SA_9|||18.26|(1.29) ***|||14.96|<br>(1.22) ***|
|_SA_10|||17.49|(1.30) ***|||15.35|<br>(1.20) ***|
|_SA_11|||-3.46|(1.07) ***|||-1.72|<br>(1.21)|
|_SA_12|||-2.88|(1.05) ***|||-2.29|<br>(1.20) *|
|constant|-32.40|(10.37)***|-25.05|(9.26)***|47.99|(4.85)***|-10.41|<br>(6.32)*|
|Obs.|266836||262858||258747||254964||
|No. of groups|3998||3998||3993||3993||
|Fixed effects:|||||||||
|Individual|Yes||Yes||Yes||Yes||
|Time|Yes||No||Yes||No||
|F stat|168.31||555.11||||||
|Wald stat|||||1.2E+06||1.2E+06||
|Implied elasticity:|||||||||
|_MP_|0.09|(0.03) ***|-0.12|(0.02) ***|-0.29|(0.01) ***|-0.26|<br>(0.01) ***|
|_D_|0.57|(0.01)***|0.61|(0.01)***|0.77|(0.003)***|0.76|(0.003)***|
Learning from Developing Country Power Market Experiences: The Case of Peru <!-- Start of picture text --> 3,000 2,783 2,730<br>2,596<br> 2,500 2,348 2,330<br>2,108 2,072<br>1,914 1,872<br> 2,000<br>1,658<br>1,509<br> 1,500<br> 1,000<br> 500<br> -<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>HHI for energy generation<br><!-- End of picture text --> Figure 5-3 Evolution of the Herfindahl-Hirschman Index (HHI) for the Peruvian power market, in terms of energy output per company.

Source: Worldbank

# 5.2. Investment and security of supply

Over the past decade, installed generation capacity in Peru has grown from 4,790 MW in 2005 to 12,508 MW in 2017 (8.3% per annum), while peak demand has grown from 3,305 MW in 2005 to 6,596 MW in 2017 (5.9% per annum, see Figure 5-4). The reserve margin has therefore grown from 35% in 2005 to 81% in 2017. The reserve margin has steadily increased since 2011 due to the sustained growth in installed capacity, which outpaced the growth in peak demand.

19
Figure 6: Estimation with Propensity-Score Matched Sample <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019 2013 2014 2015 2016 2017 2018 2019<br>MNCs Non MNCs Dynamic Treatment Effects<br>(a) Event Study Analysis (b) Dynamic Treatment Effects<br>.08 .08<br>.06 .06<br>.04<br>.04<br>.02<br>.02<br>0<br>0 -.02<br>-.02<br>Log Employment Log Employment -.04<br>-.04 -.06<br>-.06 -.08<br>-.08 -.1<br><!-- End of picture text --> _Notes:_ Manufacturing only. Each subfigure is analogous to Figure 4-(a) and Figure 5, respectively, but now with the PS matched sample and frequency-weighted regressions.

sen multiple times with replacement. The average employment response of the matched non-MNCs in (a) does not look different from either the MNC group or the entire nonMNC group (see Figure 4-(a)) up to 2016; we do observe slight (statistically insignificant) increases in employment from 2017 through 2019. The gradual employment growth among the matched non-MNCs is reflected in panel (b), so the estimates over the postpolicy period are greater in absolute terms. The bottom line is, however, that our main findings remain robust.

# **4.3.2 Additional Robustness Checks**

We conduct additional robustness checks. First, we check the robustness of our results by adding time-varying firm attributes in the specification. Considering that the employment effects could be also affected by within-firm changes that are omitted in the baseline specification, we add essential firm-level variables—(log of) sales, (log of) purchase, exporter dummy, and importer dummy—into equation (2) to control for time-varying firm characteristics that may have affected employment.<sup>28</sup> Table 5 presents the estimation results. Column (1) is from column (4) in Table 4 and shown here as a benchmark. We add sales and purchase variables in column (2); and exporter and importer dummies in column (3). All the columns yield negative and statistically significant coefficient estimates on the interaction term, confirming the baseline result above. In addition, coefficient es28Note that time-invariant firm-level attributes are controlled for by firm fixed effects.

20
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Table 8: Poverty and Age

|_Age Bracket_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||Headcoun|t(Po,<br>in p|ercent)||
|0-4|29.0|30.0|35.3|33.3|46.4|47.9|
|5-9|28.2|26.0|31.6|32.0|46.0|42.9|
|10-14|24.2|20.9|27.6|34.1|41.1|40.5|
|15-24|24.1|19.7|23.6|26.4|41.8|36.6|
|25-34|23.5|21.7|26.2|27.6|43.3|41.6|
|35-44|18.8|17.1|21.3|28.6|38.2|34.7|
|_45-54_|20.2|13.7|16.0|24.1|35.2|29.7|
|55-64|27.6|15.6|14.5|31.6|42.6|41.7|
|65-74|35.0|23.6|18.3|37.0|47.6|45.0|
|75&Over|47.5|37.7|22.1|47.9|41.4|45.9|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||Poverty G|ap (in pe|rcent)j/||||
|0-4|21.2|14.8|14.2|20.4|24.9|29.6|
|5-9|18.7|13.6|12.9|17.6|24.8|27.5|
|10-14|19.5|13.2|12.4|18.6|23.6|25.9|
|15-24|18.7|14.1|12.8|18.5|24.1|28.4|
|25-34|20.1|13.6|13.5|20.3|25.8|28.0|
|35-44|17.7|13.5|12.6|18.7|24.1|27.6|
|45-54|17.0|12.9|13.6|20.8|24.8|30.0|
|55-64|19.2|13.9|14.5|21.4|26.1|32.5|
|65-74|20.6|14.4|14.2|22.0|28.5|35.2|
|75&Over|26.1|17.4|15.4|26.0|34.2|37.7|
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE).

_Notes:_ 1/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

38
**Table 1**

**Major Creditors and Debtors, 2004**

|Country<br>NF|A / World GDP|Country<br>N|FA / World GDP|
|---|---|---|---|
|1 Japan|4.34|1 United States|-6.49|
|2 Switzerland|1.25|2 Spain|-1.19|
|3 Taiwan (China)|1.06|3 Australia|-0.96|
|4 Hong Kong (China)|1.05|4 Italy|-0.75|
|5 United Arab Emirates|0.54|5 Brazil|-0.72|
|6 Germany|0.54|6 Mexico|-0.71|
|7 Singapore|0.46|7 United Kingdom|-0.67|
|8 Norway|0.40|8 Greece|-0.37|
|9 Saudi Arabia|0.39|9 Turkey|-0.33|
|**10 China**|**0.32**|10 Poland|-0.32|
|11 Kuwait|0.31|11 Canada|-0.30|
|12 France|0.27|12 Indonesia|-0.29|
|13Belgium|0.27|13 Portugal|-0.28|
|14 Libya|0.16|14 Hungary|-0.24|
|15 Qatar|0.15|15 New Zealand|-0.22|
|16 Iran, Islamic Republic|0.12|**16 India**|**-0.18**|
|17 Luxembourg|0.09|17 Argentina|-0.18|

Net foreign assets divided by world GDP. Numbers next to countries show position in world ranking. Source: Author's calculations based on dataset of Lane and Milesi-Ferretti (2006).
# 1 Introduction

The Africa Infrastructure Country Diagnostic (AICD) has conducted extensive data collection and analysis of the infrastructure situation in Africa, including in the countries of the Economic Community of Central African States (ECCAS) region. The results have been presented in a variety of continental reports covering different areas of infrastructure—information and communication technology (ICT), irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

The purpose of this regional economic community is to present the key AICD findings for ECCAS. The main value in doing so is that it allows ECCAS’s infrastructure situation to be benchmarked against that of other African peers, the main gaps in the regional infrastructure backbones to be identified, and the costs and benefits of regional integration (as well as their distribution across member states) to be quantified.

A number of methodological issues need to be borne in mind.

First, owing to the cross-country nature of the data collection, there is inevitably a time lag involved in the data. The period covered by AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Given the fast pace of regional integration, the snapshot presented here does not necessarily correspond to today’s situation but rather represents the 2006 baseline against which subsequent progress can be measured.

Second, given the need to make comparisons across countries, indicators and analysis had to be standardized and made consistent. That means that some of the indicators may be slightly different from those routinely reported and discussed at the country level.

Third, the database on which the analysis was based was designed to give a national and continental picture of infrastructure, as opposed to an explicitly regional picture. But national infrastructure provides the basic building blocks for regional integration, and hence can be used to build up a picture of the regional situation. Nevertheless, some specifically regional issues—particularly of the regulatory and institutional variety—may not have been explicitly addressed in the national data collection effort.

Fourth, while water resource management is an important aspect of regional integration in Africa, this report does not explore water resource issues. This is because the AICD project did not cover water resources per se, but rather the specific water resource needs associated with the power, irrigation, and water supply sectors.

## Why infrastructure matters

ECCAS has 10 member states: Angola, Burundi, Cameroon, Chad, the Democratic Republic of Congo, the Republic of Congo, the Central African Republic, Equatorial Guinea, Gabon, and São Tomé and Príncipe. A subgroup of these countries—Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon—forms the Economic and Monetary Community of
# **Table A.12. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 2 using 2019/20 Poverty Line, Tanzania** **<u>(percentage)</u>**

|<br>**Method**|||**T**|**A 2**|||
|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 8**|**Model 9**|
|1 Nl li i dl|**19.3**<sup>**a**</sup>|**19.2**<sup>**a**</sup>|28.8|**17.7**<sup>**a**</sup>|**19.7**|**20.6**|
|) orma near regresson moe|(2.5)|(2.6)|(3.1)|(2.0)|(2.7)|(2.7)|
|2 iil diibi f h|**18.6**<sup>**a**</sup>|**18.8**<sup>**a**</sup>|28.7|**17.0**<sup>**a**</sup>|**19.4**<sup>**a**</sup>|**20.2**|
|) Emprca struton o te error terms|(2.5)|(2.6)|(3.1)|(1.9)|(2.7)|(2.7)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.34|0.46|0.65|0.85|0.46|0.37|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|
|N2 (target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjusted for c<br>linear regression model with the theoretical distribution o<br>cluster random effects. Model 3.1 employs non-standar<br>expenditures, household size and age in both surveys are<br>TA 2 use the estimated arameters based on the 2019/20|omplex survey design<br>f the error terms and<br>dized variables. Mode<br>transformed to norma<br>data 1000 simulations|. All estimates a<br>Method 2 uses th<br>l 3.2 employs<br>lity using the B<br>are imlemente|17<br>(1<br>re obtained with<br>e empirical distr<br>variables in TA<br>ox-Cox method<br>d The true ove|.7<br>.9)<br>the population we<br>ibution of the err<br>2 standardized b<br>before standardiz<br>rt rate for TA 1|ights. Method<br>or terms. Both<br>y those in NP<br>ation. Imputed<br>is the estimate|1 uses the normal<br>methods employ<br>S 2020/21. Food<br>poverty rates for<br>directl obtained|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2020/21. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

56
# ISS9iS **In** ADJUSTNT LUEDING

## 1. An Overview

A combination of unfavorable external circumstances and inadequate domestic policies led to t.4 e economic and debt problems of the developing countries in the first half of the 1980s. As the decade progressed, it became clear that for many countries a return to previous long-term growth would require significant adjustments: this was true even for some that had avoided a debt crisis. At the same time, repaying debt and implementing structural adjustments called for increased financing from domestic and external sources. Such financing hinged, however, on changes in the status quo -- not only in domestic policies but also in external financing provided by multilateral, co=mercial, and government sources.

A major tomponent in the evolution of the World BankWs role in this connection was the expansion of adjustment lending. The development of the Structural Adjustment Loan (SAL) and the Sectoral Adjustment Loan (SECAL) since 1979-80 may also be representative of recent trends in conditionality of adjustment lending in general. 1/ As a percentage of total lending, adjustment lending has grown from an average of 10 percent in FY8O-85 to 23 percent in FY87, over four-fifths of the latter constituted by SECALs. Some fifty countries, many of them 1/ Discussions of the Bank's adjustment lending can be found in World Bank (1088), (1987b), (1987c), (1987d), (1986a), (1986b), Baneth (1983), Please (1984), Stern (1983).