rafmacalaba/datause-encoder-data huggingface dataset:
assessment of decarbonization of the power sector, but rather to showcase the strengths and weakness of using FTT-FLEX for such an exercise.

# **5.1 Background on the power sector in Guinea-Bissau**

Guinea-Bissau is a small coastal country, situated in West Africa. It is one of the world’s poorest and most fragile countries. Only 35% of its population has access to electricity (World Bank, 2021) and the country contributed to less than 0.01% of global emissions (UNDP, 2022). Given its modest size, limited energy consumption, and lack of data, it is not surprising that most global energy-economic models, including the main FTT model, do not include Guinea-Bissau.

Historically, Guinea-Bissau has depended heavily on fuel oil for electricity generation. However, since 2020, solar power has emerged as a significant player in the market, experiencing rapid growth from a 1% share in total power generation in 2020 to 7% in 2021.<sup>3</sup> The remaining oil-based electricity generation is supplied by a floating power station docked near its capital city, Bissau, and operated by the Turkish company Karpowership.<sup>4</sup> The country plans to build its first hydropower plant in 2024, which is expected to contribute approximately 9% to the country’s power mix. Currently, a 20MW solar PV power project is in the permitting stage and, if approved, is expected to come online in 2025,<sup>5</sup> nearly doubling its current total electric power generation capacity. Guinea-Bissau boasts significant solar potential, with the International Renewable Energy Agency (IRENA) indicating that 100% of the country's land area is suitable for an annual PV output of 1.6-1.8 (MWh/kWp<sup>6</sup> ), compared to a global average of 20%.

Unfortunately, the planned hydro plant as well as other energy infrastructure projects to enable power imports from its neighboring countries are facing severe delays,<sup>7</sup> suggesting that reliance on Karpowership will persist in the short to medium term.

# **5.2 The Guinea-Bissau 2020 least cost generation expansion plan**

The World Bank’s 2020 Power Sector Policy Note, using the least cost approach, recommended GuineaBissau move away from heavy-fuel oil for power generation towards low-cost electricity imports from the neighboring country of Guinea.

> 3 - - <u>https://www.irena.org/ /media/Files/IRENA/Agency/Statistics/Statistical_Profiles/Africa/Guinea Bissau_Africa_RE_SP.pdf</u>

> 4 <u>https://www.karpowership.com/</u>

> 5 <u>https://www.power-technology.com/data-insights/power-plant-profile-bissau-solar-pv-park-1-guinea-bissau/</u> 6 kWp = kilowatt peak power output.

> 7 Guinea-Bissau Public Policy Notes 2023:Energy, World Bank, forthcoming.

12
# Table B.3: **Effects of Inpres Schools on Intergenerational Educational Mobility: Comparison Cohorts: 1959-1962**

**Panel A: Dependent Variable: Primary Completion**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.210***<br>(0.041)|0.219***<br>(0.041)|0.199***<br>(0.043)|0.202***<br>(0.044)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***<br>(0.006)|-0.013**<br>(0.006)|-0.037***<br>(0.012)|-0.017*<br>(0.010)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001*<br>(0.001)|
|R2|0.103|0.166|0.114|0.181|
|Observations|1178986|822791|1178986|822791|
|**Panel B: Dependent**|**Variable: Ye**<br>**Linea**|**ars of School**<br>**r Model**|**ing**<br>**Quadra**|**tic Model**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|1.666***<br>(0.389)|0.656<br>(0.467)|1.570***<br>(0.396)|0.609<br>(0.494)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.131**|0.066|-0.208**|0.017|
||(0.051)|(0.061)|(0.097)|(0.091)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.010<br>(0.007)|0.006<br>(0.007)|
|R2|0.320|0.405|0.321|0.405|
|Observations|1178986|822791|1178986|822791|

Notes: Robust standard errors in parentheses clustered at the district of birth(*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1959 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

66
Lown, Cam S., 1988, "The Credit-Output Link vs. the Money-Output Link: New Evidence," Eoonomic Review: Federal Reserve Bank of Dallas, (November).

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-,, 1993a, 'Regional Fiscal Policy Differentials,' Mimeo, China and Mongolia Department, The World Bank.

- and David Walker, 1993b, "China's Financial Development in a Reform Era," Draft, Georgetown University.

-, 1993c, "Modelling Intergovernmental Fiscal Relations in China," Draft, Georgetown University Economics Department.

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, 1993e, 'China: Interbank Market Activities," The World Bank, China and Mongolia Department, Mimeo.

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73
<u>Table 1: Secondary Data Indicators</u>

|**IDP Return**|**Social Cohesion**|**Security**|
|---|---|---|
|Number of returnees|Whether returnees feel welcome|Existence of blocked IDP returns|
||Threats between tribalgroups|Revenge attacks|
||Mistrust between returnees and host<br>community|Fear of revenge attacks|
|||Perceptions of ‘daily public life’<sup>71</sup>|

The purpose in presenting these indicators is to understand changes in the context rather than imply that the LPA was directly responsible for changes. This is an important limitation to recognize: due to the myriad of factors affecting IDP return, security, and social relations in each community between 2015 and 2021, it is impossible to isolate the impact of each LPA, and we do not intend to imply there is a causal link between the signing of an LPA and changes in the context. Rather, the secondary data offers a way to understand the context that existed in each community before and after each LPA was signed and therefore make sense of the way each LPA was perceived.

There were several limitations in comparing indicators across the eight LPAs. Each LPA was signed at a different time and there is no consistent (single or consolidated) dataset that can cover all eight agreements. This means that it is impossible to compare identical indicators across all LPAs; or even to compare the same indicator across all LPAs – to give one example, the returnee population prior to LPA signature was available days before signature for some LPAs, months before signature for others, and not available at all for yet others (since in some locations, international actors were not present when the LPA was signed).<sup>72</sup> The ILA and Return Index also utilize different methodologies, varied timing for data collection, different indicators for similar issues, and different questions for similar indicators. However, despite these limitations, the combined data sources still offer significant contextual information that enables us to identify key markers associated with IDP return, security, and social cohesion in the eight areas before and after each agreement.

Another limitation in comparing LPAs was the absence of data related to the return of IDPs with perceived ISIL-affiliation, since this information is not available in public datasets and is not recorded by humanitarian actors. In addition, data by ethnoreligious background is not systematically collected, so it is not possible to disaggregate population movements (or other indicators) by ethnoreligious group. Instead, the number of returned IDPs refers to the overall returns to the subdistrict,<sup>73</sup> while the reported return of ISIL-affiliated families pursuant to each agreement is captured via interview data and secondary sources.

> 71 This indicator measures how comfortable people feel in engaging in public spaces. 72 We rely on the ILA datasets for agreements signed prior to mid-2018 (since no other datasets are available), while indicators for more recent agreements are drawn from the Return Index. 73 Genat, M. (2020).

13
24 There are, of course, important factors<sup>in addition to host-country</sup> economic size that affect FDI flows. The<sup>balance of this chapter considers the</sup> role of additional economic factors and the general<sup>political environment of the</sup> host country as influences on FDI flows.<sup>The next chapter considers more</sup> specifically and narrowly the FDI policies<sup>of the host countries, and chapter 6</sup> considers the policies of home governments and international institutions as they bear directly on FDI in developing countries.

Table 4-2. Ratio of FDI Flows to GDP for Eleven<sup>Host Countries</sup> (percent)

|Country|1968-72|1I271ZZ|1928-82|1283-87|
|---|---|---|---|---|
|Argentina|0.1|0.1|1.0|0.8<br>|
|Brazil|1.0|1.3|1.0|0.4|
|Colombia|0.6|0.2|0.5|1.6|
|Mexico|0.8|0.7|0.9|0.5|
|India'|0.0|0.0|0.0|0.0|
|Indonesia|1.5|2.4|1.4|0.3|
|Korea|0.2|0.2|0.2|0.2|
|Malaysia|2.1|3.6|4.0|2.3|
|Thailand|0.9|0.7|0.5|0.6|
|Kenya|1.0|0.6|0.6|0.2|
|Nigeria|1.1|0.7|0.2|0.3|

a. India varied between 0.01 and 0.04 percent. Sources: See table 4-1.

# Economic Policies

A broad spectrum of host-country economic policies<sup>is inevitably of concern</sup> to foreign direct investors. All of the usual<sup>domestic economic policies that</sup> affect businesspeople's calculations of expected<sup>returns and risk pertain, but</sup> foreign direct investors are also concerned<sup>about host countries' international</sup> economic policies. Perhaps above all<sup>else, investors desire as much stability</sup> in economic policy as is practicable,<sup>but they also want economic adjustment</sup> processes to be allowed to operate with<sup>minimal government intervention.</sup> Thus, for instance, they want exchange<sup>rates and interest rates to be</sup> allowed to adjust to changing inflation rates.<sup>If exchange rates, in particular,</sup> are not allowed to reflect cross-national differences in inflation rates, foreign direct investors (as well as other businesses)<sup>are exposed to price distortions</sup> in their costs and revenues that can<sup>threaten their long-run competitive</sup> position. Althcough investors prefer stable exchange rates in the short<sup>run, then'</sup> do not want exchange rates to be inflexible<sup>and insensitive to basic price and</sup> income forces in the long run.
benefited larger firms more (Table 18). The finding that, in Georgia, smaller firms benefited more, at least in terms of one outcome variable, increases our confidence in the overall analysis. This is because, in the context of panel regressions, we expect selection bias to be negative. Since smaller firms are likely to be needier, the negative selection bias would work against finding the pattern that we do find. While we find similar size dependency in El Salvador, the coefficients are mostly not statistically different from zero there (Table 15 and Table 17). We think this may be due to the size information being more outdated for El Salvador, where the baseline ES was collected in 2016, compared with Georgia’s baseline ES of 2019.

The breakdown of the effectiveness of government support by sector confirms some of our expectations but is mixed otherwise. The baseline ES in El Salvador had manufacturers of textiles and garments stratified separately, while the ES in Georgia had hotels and restaurants stratified separately. Both ES had food manufacturers as separate stratification sectors, as well as retail, which in our analysis we broke down into food retailers or pharmacies and the rest of the retailers. As expected, manufacturers of textiles and garments in El Salvador show signs of benefitting from government support in terms of permanent and temporary workers (Table 19), while hotels and restaurants in Georgia seem to have benefitted in terms of permanent workers. Effects on food manufacturers are very different in the two countries, perhaps related to the structure of the supply chain leading towards hotels or restaurants as opposed to grocery stores (this information is not available in the ES or COV-ES). Effects on subgroups of retailers are also mixed, including several counter-productive effects of government support, indirectly suggesting that perhaps reduction of total hours of operation was perceived desirable from the health and safety perspective.

In terms of the outcome variables of interest, we find most effects on the percentage change in the total number of permanent full-time workers since December 2019, with the dummy variables on temporary workers and hours of operations also showing important reactions to the government support. However, the variable capturing firms’ expectations about falling in arrears on outstanding liabilities in the six months following the interview appears the hardest to predict or make sense of, perhaps pointing to considerable uncertainties that firms faced during the pandemic, complicating the formation of consistent expectations.

30
The eastern Himalaya (Nepal, Bhutan, Sikkim, and sub-basin of Ganges in China) has a large number of glacier lakes compared to the western Indian Himalaya (Bajracharaya and Mool, 2010). Komori (2008) studied more than 50 moraine-dammed ice-contact or ice-proximal lakes in the Bhutan-China border region using satellite imagery and concluded 14 lakes were growing over the analysis period (late 1960s-2001). They report higher growth rates on the southern side (35-70m/year; <0.04 km<sup>2</sup> /year) compared to the northern side (10-40m/year; < 0.03 km<sup>2</sup> /year) of the Bhutan-China border region. Bajracharya and Mool (2010) found that from 1960-2000, 245 small (<50x50 m<sup>2</sup> ) lakes disappeared from Nepal’s Dudh Koshi Basin, while 24 new lakes were formed. Additionally, 11 supraglacial lakes (lakes formed within the glacier mass) have converted into moraine-dammed lakes and another 34 glacial lakes have grown in size (Bajracharya and Mool, 2010). Gardelle et al. (2010) suggest a 25-45 ha/year rate of growth for glacial lakes in the Nepalese and Bhutanese Himalayas between 1990-2009, contrasting with a relatively stable growth average growth rate of 4 ha/year in the western Indian Himalayas. The study found higher growth rate of glacial lakes in the Everest region during 2000-2009 than 1990-2000 periods, contrasting with Bhutan and Western Nepal Himalayas. In the eastern Indian Himalaya, Kulkarni et al. (2011) report about five-fold increase in Lonak Lake from 23 to 110 ha between 1976 and 2007. In Mt. Qomolangma Nature Preserve in China, Yong et al. (2010) found 64.7% increase in glacial lakes (at a rate of 1.23 km<sup>2</sup> a<sup>-1</sup> ) between 1976 and 2006, with a higher rate of change during the 1976-1988 period (2.9 km<sup>2</sup> a<sup>-1</sup> ) over the recent 1988-2006 period.( 0.95 km<sup>2</sup> a<sup>-1</sup> ). While, local topography, geological, and glaciological processes determine the biophysical risk due to GLOFs (Watanabe et al., 2009; Reynolds and Taylor, 2004), climate warming can create favorable environment for lake expansion.

8
Ranger, N., Hallegatte, S., Bhattacharya, S., Bachu, M., Priya, S., Dhore, K., Rafique, F., Mathur, P.,

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# Appendix: Resilience and risk for all 90 countries

||**GDP**<br>**per**<br>**capita**<br> USD<br>PPP|**Population**<br>|**Protection**<br>**level**<br>return<br>period<br>in years|**Asset**<br>**exposure**<br>% of<br>GDP|**Asset**<br>**vulnerability**<br> % asset<br>value|**Resilience**<br>%|**Total**<br>**risk**<br>% of<br>GDP|
|---|---|---|---|---|---|---|---|
|Albania|9,961|2,897,366|15.5|38.2|33.8|71.0|1.19|
|Argentina|8,087|41,446,246|64.4|27.3|32.3|52.9|0.21|
|Armenia|7,527|2,976,566|11.5|9.5|12.4|69.1|0.13|
|Australia|42,834|23,129,300|100.0|15.6|44.7|71.3|0.08|
|Austria|4,056|8,479,823|600.6|37.6|16.0|69.2|0.01|
|Azerbaijan|16,593|9,416,801|14.0|8.2|12.4|58.7|0.12|
|Bangladesh|2,853|56,594,962|50.0|76.7|38.0|64.2|0.76|
|Belarus|17,055|9,466,000|14.5|8.8|26.2|68.3|0.19|
|Belgium|0,609|11,182,817|213.0|51.0|14.7|76.5|0.04|
|Benin|1,733|10,323,474|6.4|10.5|60.3|41.7|2.04|
|Bolivia|5,934|10,671,200|6.7|10.3|25.4|48.2|0.73|
|Bosnia and|9,387|3,829,307|18.8|40.9|38.4|54.3|1.50|
|Herzegovina||||||||
|Botswana|15,247|2,021,144|16.7|10.8|55.8|55.4|0.50|
|Brazil|14,555|00,361,925|22.2|18.4|57.0|58.1|0.76|
|Bulgaria|5,695|7,265,115|28.3|12.4|39.3|67.2|0.21|
|Burkina Faso|1,630|16,934,839|1.9|2.0|60.0|70.8|0.82|
|Cambodia|2,944|15,135,169|5.3|24.5|24.5|48.1|2.44|

25
# **5. Interaction Results**

The damaging impact of low levels of competition on access could be either mitigated or exacerbated by certain features of the environment in which banks operate. Here we investigate three such factors. First, we consider the extent of financial development in a country, measured by the ratio of private credit to GDP. This measure implicitly captures the institutional factors that determine the level of financial development, such as property rights protection or contract enforcement, as well as the actual use of finance by the private sector. We investigate whether the impact of competition varies depending on countries’ levels of financial development.

Our hypothesis is that financial development will mitigate the damaging impact of lack of competition. As we discussed above, the _market power hypothesis_ argues that competition in the banking market reduces the cost of finance and increases the availability of credit. Financial development is commonly associated with reduced cost of finance and wider availability of finance (see King and Levine, 1993 and Love, 2003). Therefore, in an environment with relatively low cost of finance, the marginal negative impact of low competition on costs is likely to be less damaging than in an environment where the costs are high to start with. Similarly, in an environment with wider availability of finance, the reduction in credit availability that is due to lack of competition is likely to be less damaging than in an environment with more scarcely available finance. Therefore, we anticipate that financial development will lessen the damaging impact of low competition on access.

Our second variable of interest is credit information. Availability of credit information is directly linked to the impact of competition through the information hypothesis discussed in the introduction. The hypothesis states that in the presence of information asymmetries and agency costs, competition can reduce access to finance by making it more difficult for banks to internalize the benefits of investing in building lending relationships, in particular, with opaque clients (Petersen and Rajan, 1995; Marquez, 2002). Therefore, with improvement in the availability of information through public or private credit registries, the information asymmetries are reduced and, thus, the impact of low competition on access to finance through its impact on information production is likely to be reduced.

11