rafmacalaba/datause-encoder-data huggingface dataset:
# **4.  Household characteristics and electricity adoption**

The first use of electricity in rural areas is household lighting. The reason is that electric light is much brighter than that provided by kerosene lamps, and the price per unit of light can be hundreds of time cheaper. Because of frequent power outages, people do not discard their kerosene lamps after they have grid connection, but relegate it to a standby energy source. Over time households diversify their electricity consumption by acquiring different electric appliances such television, radio, electric fans, refrigerator, electric pumps (for expanding crop production) and other tools and machinery (for home-based or outside enterprises). According to an earlier analysis of this survey data, the major uses of electricity in rural Bangladesh are reported to be for children’s education (83 percent), followed by entertainment (44 percent), information access (22 percent), and home-based businesses (9 percent) (BETS-BUP 2006).

Given these benefits, it is important to examine what determines electricity adoption at the household level. To accomplish this, we look at a number of factors at the household and village level, including household’s physical endowments (such as land) and human capital (such as adults’ education), price of different fuels in the village and others. Since household electrification can only be observed in villages that have electricity, the issue of sample selection needs to be addressed in estimating household’s access to electricity.<sup>8</sup> Under this condition, instead of a simple probit model, a maximum likelihood probit model with sample selection is implemented using a two-stage procedure that involves a latent equation and a selection equation (Van de Ven and Van Pragg 1981). The selection equation that determines village electrification is of following form: And household electrification (latent equation) is given by,

> 8 Of course village electrification is only a necessary but not a sufficient condition for household electrification, as there are many households without grid connection in electrified villages.

9
10ceiling of $5 milion was declared for auctions 1-19, raised to $9 million for auctions 20-52, and dropped to $5.5 million for auzcdons _53-68;_ but except for auctions 55-68, this bore little relation to actual supply (Table 1). Both Zambia and Nigeria were subject to sharp appreciations through periodic massive disqualifications of bids. Supply was not announced in Ghana, and was volatile (Table 2). Supply was naounced only just prior to opening the bids in Uganda, but while pre-announced supply was fairly constant, actual supply was fairy volatile (Table 2). It seems likely that an uncerain supply could foster uncertainty and place a premium on the amount bid, in the absence of a predictable reserve price- It would thus be interesting to test the impact of supply volatility on the behavior of the marginal auction rate.

The publication of bids occurred weekly in Zambia, listng each importer's bid, by quantity, price and use, for auctions 37-68. Uganda published the list of composite bids by price and quantity, idiucating disqualifications. Nigeria and Ghana did not publish details of the auctions. Whether the spectrum of bids should be published has no clearcut answer: it may crexte confidence thrugh trsparency. aad enhance learning, which were the intentions in Zambia. On the other hand, when the number and nature of bidders becomes known, this may foster collusion in repeated, sequentil auctions. Collusion is less likely to manter in an auction with a large number of bidders, however.

# -2.6 Tnsparency

An impornt lessonfrom the Zambian auction is ta if there isn't transparency inthe auction, it is probably poiless holding one. The degree of trmsparency of rules and management differed across the auctions. 'ransparency was poor in Zambia and Niria Ad hoc intrventions and frequent rule changes by auction managers were an important cause of instability, fosering uncertainty, incredibility aDd resulting in poor (non-market) allocation. The auction rules in Zambia actuly stated that the Central Bank had license to disqualify any bid without having to give any reasons: this was a serious design fault." Auctions in Ghana and Uganda were operted with tanarently, with clear ndes, and the public opening of the bids. The management committee in Ghana did not only consist of Central Bank personnel, but included representatives from the private sector. There was fairly broad consultation on changes in the Cental Bank's regulabons for the auction, which were published as amendments.

# 3. A STATISTICAL ANALYSIS OF MICRO-AUCTION DATA IN SSA

This section examines the evidence on the evolution of the auction rate and other auction variables for each of the four African countries. The objective of this section is to use h cs "This was exercised in auction 41, for instace, wher all bids largW than 8 kwachaiS were abhrarily declared to be d fied

> <sup>2</sup> ' n interiew at the I.M.F. in I993 revcaled thl foreign xchange auctns in Ronaniad Klaaktn hat time less thn anspar, and tbat this ws pving debitadng to the fuctioing of the aucions.

were at
**Figure 3. Changes in logs of Sol/USD and Net Interventions by BCRP:** **_∆Xt13-11_**

**Figure 4. Bid-Ask Spread in the Interbank Rate and Net Interventions by BCRP**

<!-- Start of picture text --> 2.5 Interbank Rates. 2004 - 2009 500 1.75 Interbank Rates. 2004 - 2009 500<br>1.50<br>2.0<br>250 1.25 250<br>1.00<br>1.5<br>0.75<br>0 0<br>0.50<br>1.0<br>0.25<br>0.5 -250 0.00 -250<br>-0.25<br>0.0 -500 -0.50 -500<br>3200 3400 3600 3800 4000 4200 4400 3200 3400 3600 3800 4000 4200 4400<br>StandDevSol/USD BCRP NetInterven bid-ask spread Net InterventUSD<br>% Change mill USD mill USD<br><!-- End of picture text --> Figures 1, 2, and 3 suggest that the BCRP decisions to intervene target both the levels and the changes in the sol/USD. Regarding spreads, it is not clear from Figure 4 whether the BCRP responds to large spreads and makes any attempt to avoid them, but we test for that possibility.

Notice that Figures 2 and 3 confirm the results of the sample statistics on the percentage changes in the exchange rate measured during times when the BCRP has announced that it can intervene and times when the BCRP has indicated it will not intervene in the forex. Notice that during the period between August 2008 (observation 4200) and December 2008 (observation 4300), the sol experienced a rapid depreciation and the highest volatility within the period of study. Nevertheless, the volatility was always smaller between 11.00 am and 1:00 pm on any specific day. We next need to determine whether such distinction is due to the BCRP’s intervention policy. How is the behavior of the participants in the exchange rate market affected by BCRP announcing that it will participate and by how much (at the end of its intervention activities)? How do market expectations regarding future 12
# **C. List of scenarios**

The list of scenarios below has been used for this paper. These have been run under two macroeconomic context: SSP4 and SSP5. Therefore, 36 scenarios were in total analysed.

_Table‐A 3. Full list of scenarios used in the paper_

|**ID**|**GCM**|**CLIMATE**<br>**CHANGE**|**CLIMATE**<br>**MITIGATION**|**COMMENT**|
|---|---|---|---|---|
|**B**|None|No CC|None|Baseline|
|**C1**|HadGEM2‐ES|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C2**|IPSL‐CM5A‐LR|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C3**|GFDL‐ESM2M|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C4**|MIROC‐ESM‐<br>CHEM|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C5**|NorESM1‐M|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C6**|HadGEM2‐ES|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C7**|IPSL‐CM5A‐LR|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C8**|GFDL‐ESM2M|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C9**|MIROC‐ESM‐<br>CHEM|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C10**|NorESM1‐M|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C11**|HadGEM2‐ES|RCP8.5*|None|Climate scenario RCP8.5withoutCO2effects|
|**CX1**|Avg|RCP2.6|None|Average over GCMs RCP2.6 with CO2effects|
|**CX2**|Avg|RCP8.5|None|Average over GCMs RCP8.5 with CO2effects|
|**CX3**|Avg|RCP8.5*|None|Average over GCMs RCP8.5withoutCO2effects<br>(equal to C11)|
|**M1**|None|No CC|FullTech|Mitigation policy 2 degrees ‐ high energy intensity<br>world ‐ high biomass use|
|**M2**|None|No CC|LowEI|Mitigation policy 2 degrees ‐ low energy intensity<br>world ‐ high biomass use|
|**M3**|None|No CC|LimBio|Mitigation policy 2 degrees ‐ limited biomass use|
|**M1A**|None|No CC|FullTechAgr|Mitigation policy 2 degrees ‐ M1 ‐ carbon tax on<br>agriculture only|
|**M1B**|None|No CC|FullTechLUC|Mitigation policy 2 degrees ‐ M1 ‐ carbon tax on<br>land use emissions only|
|**M1C**|None|No CC|FullTechBio|Mitigation policy 2 degrees ‐ M1 ‐ bioenergy<br>expansion only|

52
# **7 (b) Bhutan**

<!-- Start of picture text --> 33000<br>30000<br>Technical potential (30,000 MW)<br>27000<br>24000 Economic potential (25,500 MW) Timilsina &<br>Toman<br>21000<br>18000<br>BPC<br>15000<br>12000<br>9000<br>SARI<br>6000<br>3000<br>0<br>2020 2025 2030 2035 2040<br><!-- End of picture text --> _Note and source: ‘BPC’ refers to the capacity requirement provided, through personal communication, by Mr. Gem Tshering, Managing Director and Mr. Gorab Dorji, General Manager, R&D, Bhutan Power Corporation (BPC). This forecast provides data up to 2030. Data for 2040 was extrapolated using the same annual average load growth for 2025-2030 period and assuming the same capacity to load ratio as in year 2030. ‘SARI’ refers to the forecast available in SARI/EI (2016). We assumed the same capacity factor for power generation for meeting domestic and export demands. ‘Timilsina and Toman’ refers to capacity requirement corresponding to load projections used in Timilsina and Toman (2016) study. The ‘Timilsina & Toman’ projection applies the same ratio of domestic firm power capacity requirement to load used by ‘BPC’ forecast to calculate domestic capacity requirement._ 15
Development planning and capacity-building programs can take these results into consideration – along with many other factors – as integration into the European Union continues. In general, improvements in port facilities and IT infrastructure are likely to be more costly than the administrative reforms at the center of customs regimes and regulatory policy -- but they can have correspondingly high payoffs. The eligibility for additional EU financing with accession should provide more scope for improvements in these areas. For the candidate member countries, such as Bulgaria and Romania that are currently negotiating the formal accession with the EU, it may be of particular interest to explore acceleration of telecommunication liberalization and investment, for example.

# **References:**

Overman H.G., Redding S. and Venables A.J. (2001), “The Economic Geography of Trade, Production, and Income: A Survey of Empirics”, _website:_ _<u>http://econ.lse.ac.uk/staff/ajv/hosrtv.pdf</u>_ Diamond J. (1997), “Guns, Germs, and Steel: The fates of Human Societies”, New York: W.W. Norton.

- Limao N. and Venables A.J. (2001), “Infrastructure, Geographical Disadvantages, Transport Costs and Trade”, _World Bank Economic Review_ , 15, pp.451-479.

Redding S. and Venables A.J. (2003), “Geography and Export Performance: External Market Access and Internal Supply Capacity”, _website:_ _<u>http://econ.lse.ac.uk/staff/ajv/Isit22.pdf</u>_ Hummels D. (1999), “Have International Transportation Costs Declined”, _mimeo, website:_ _<u>http://www.mgmt.purdue.edu/faculty/hummelsd/</u>_ IMD (2000), _World Competitiveness Yearbook._ IMD: Lausanne.

Kaufmann D, Kraay A. and Zoido-Lobaton P.(2002), _“_ Governance Matters II: Updated Indicators for 2000–01” _World Bank Working Paper #2772,_ The World Bank, Washington, D.C..

McCallum J (1995), "National borders matters: Canada-U.S. regional trade patterns", _American Economic Review_ , Vol. 85, No.3, pp.615-623.

Wilson J.S., Mann C.L., and Otsuki T. (2005), “Assessing the Benefits of Trade Facilitation: A Global Perspective", _The World Economy_ , Vol.28 Issue 6, pp. 841-871.

- Wilson J.S., Mann C.L., Woo Y., Assanie N., and Choi I. (2002). _Trade Facilitation: A Development Perspective in the Asia-Pacific Region._ Asia Pacific Economic Cooperation: Singapore

26
often cheap (e.g. Hanna et al., 2016) and generally require minor changes in cooking habits (Jeuland and Pattanayak, 2012).

Many ICS were introduced in the 1980s and generally accepted into peoples’ homes, but often it was found that cooks did not regularly use these stoves (e.g., Gil 1987 and Barnes et al. 1993). It soon came to be recognized that ICS adoption really has two parts. The first is acceptance to try stoves, which implies distribution to households. The second and more ambitious aspect of adoption is regular use, which has come to be seen as the main litmus test of true adoption (Beyene, et al., 2015; Bensch and Peters, 2015; Hanna et al. 2016; Johnson et al. 2009). For this reason, in this paper we focus on adoption measured as regular use.

We examine adoption of the Mirt ICS, which has been promoted in Ethiopia since 1998, and is used primarily to cook injera. Mirt has been estimated to use 50% less wood in laboratory tests (GIZ, 2011), 40% to 50% based on surveys (Megen Power, 2008; Dresen et al., 2014) and 20% to 30% under field-based controlled cooking tests (Gebreegziabher et al., 2018). Injera is a type of pancake that is widely eaten in Ethiopia and requires hot, even energy delivery to be properly baked. For this reason, injera is baked on a clay pan called a mitad, using very high heat. The average household uses about 20 kg of fuelwood per week to make injera and injera baking is estimated to use about half of the primary energy of the country (Bizzari, 2010; Tesfay, 2014; DFID, undated). Very few rural households in Ethiopia use ICS (Mondal et al., 2018), with most households relying on three-stone tripods that waste approximately 90% of the energy input (DFID, undated; Alem et al., 2013).

We extend our previous work on this topic, which examined use of Mirt over a period of about a year (Beyene et al. 2015) for three monetary treatments which were randomly applied at baseline. Injera is not cooked every day, but regular baking would take place at least twice per week 3
The coefficient for capital expenditure, on the other hand, is 2.74 and significant at the 1 percent level. The correlation of capital expenditure with GDP growth is much stronger than that of current expenditure especially in Pakistan, where periodical macroeconomic adjustments are usually characterized by a simultaneous slowdown in GDP growth and reductions of capital expenditure.

**Table 4** Short-run coefficient for capital and current expenditure

|**Dependent variable**|**μ**|**θ**|**γ**|**β**|**Observations**|
|---|---|---|---|---|---|
|Total expenditure|-0.014|1.28**|-0.21*<br>|0.94*|162|
|Current expenditure|0.022|0.69|-0.06<br>|0.92|161|
|Capital expenditure|-0.09**|2.74***|-0.31***<br>|0.88***|161|

Note: ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

Next, we compare the spending multipliers for current and capital expenditure. The former is only 0.12 on impact, never surpasses 0.15, and after four years the cumulative effect is basically 0. Given the small effect, it is little surprising that it is not statistically significant in any year. The expenditure multiplier of capital expenditure, on the other hand, is very large and always statistically significant. On impact, an additional USD of public capital expenditure results in an increase of GDP by 0.62 USD. Over time, additional benefits drive up the cumulative effect close to 1 USD. Our results hence suggest that positive impact of public spending on economic activity in South Asia comes entirely from capital expenditure. These results are very much in line with the findings of Ilzetzki et al. (2012) for developing countries in general. They find an insignificant multiplier for government consumption and a significant impact of public investment. The latter is 0.6 on impact and 1.6 in the long run. While we find nearly the same initial impact, the long-run effect seems lower in South Asia compared to other developing countries.

**Table 5** Multipliers of current and capital expenditures

|**Multiplier **|**Current**<br>**expenditure**|**Capital**<br>**expenditure**|
|---|---|---|
|Impact|0.12|0.62**|
|Peak|0.15|0.96*|
|Cumulative (4 years)|0.02|0.96*|

**Note:** Both models are balanced panel VAR models with variables current and capital expenditure respectively, instead of the total expenditure. The model with 3 lags is estimated using the annual data from 1990 to 2017. ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

**Sources:** ADB, IMF, World Bank, and own calculations.

# **7. Robustness checks**

In the following, we present some robustness checks that provide additional support for the findings discussed above. We present an alternative measure of cyclical components for the correlation exercise, a different estimation procedure for the error-correction model, and a couple of alternative PVAR specifications.

12
evaluate the extent to which the comovement in stock market returns was driven by real linkages between economies, financial linkages across markets, or was the consequence of a “wake-up call” (see Goldstein, 1998) or “demonstration effect” (see Masson, 1998) where investors became aware that certain vulnerabilities present in the US context could put other economies at risk.

Real linkages refer to trade effects of which there are two main kinds: competitiveness effect - when changes in relative prices affect a country’s ability to compete abroad – and income effect - when the crisis reduces income and consequently import demand. Financial linkages across markets operate primarily through the financial (or capital flows) account among countries that are connected to the international financial system. Such linkages can be direct or indirect. Direct financial linkages arise due to direct financial exposures between the crisis country and other countries. For example, when foreign investors own assets from the crisis country or vice versa. Indirect financial linkages involve the actions of international investors (“common creditors”), who transmit crises across the various countries where they hold assets either because of margin calls, changes in risk aversion, or herding (due to asymmetric information). Finally, comovement across markets might not be related to any sort of linkages across markets, but might happen as a result of a new interpretation of existing information, which stimulates learning and awareness. In particular, after investors see a certain economy collapse (e.g., U.S.), they might start to question the safety of investments in countries with similar economic vulnerabilities.

Using stock market data for the US and 83 other countries between July 2007 and April 2009, we evaluate the significance of the transmission channels discussed. In order to determine how important these different factors are in explaining cross-country linkages during the 20072008 financial crisis, we follow a one-step approach in which each markets’ correlation vis-a-vis the US market is interacted with country-level characteristics representing the channels mentioned above. This methodology allows us to identify and compare the extent to which different channels have an impact on the sensitivity of domestic stock market returns to US market returns.

3
78

||**Table 343a Capital managed by Banks**|||
|---|---|---|---|
|Total 3a (m<br>CHF)|Big Banks Kantonalban<br>ken<br>Regional<br>Banks<br>Raiffeisnenb<br>anken|Other<br>Banks|All Banks|
|# of banks|3<br>24<br>83<br>1|227|338|
|Year–end values||||
|1997|7,929<br>5,614<br>1,812<br>1,460|866|17,681|
|1997 in %|45%<br>32%<br>10%<br>8%|5%|100%|
|1998|7,452<br>5,836<br>2,402<br>1,715|834|18,239|
|1999|7,122<br>5,822<br>2,018<br>1,758|836|17,556|
|2000|6,321<br>5,475<br>1,946<br>1,777|877|16,396|
|2001|6,323<br>5,916<br>2,137<br>2,082|1,041|17,499|
|2002|6,692<br>6,638<br>2,360<br>2,506|1,239|19,435|
|2003|7,108<br>7,422<br>2,595<br>3,026|1,553|21,703|
|2004|7,357<br>8,116<br>2,830<br>3,563|2,110|23,976|
|2004 in %|31%<br>34%<br>12%<br>15%|9%|100%|
|Total increase|-7.21%<br>44.56%<br>56.16%<br>144.06%|143.64%|35.60%|
|S<br>**Fi**<br>150<br>170<br>190<br>210<br>230<br>250|ource: Schweizerische Nationalbank (2005), p.A<br>**gure 27 Growth of the 3a Capital at Different**|72 sqq.<br>**Banks**||
|110<br>130<br>||||
|90||||
|70<br>||||
|1997|1998<br>1999<br>2000<br>2001<br>2002|2003|2004|
||Big Banks<br>Kantonal|banken||
||<br>Regional Banks<br>Raiffeisn|enbanke|n|
||All Banks<br>All IC|||

Source: Schweizerische Nationalbank (2005), p.A72 sqq.

Table 34 also presents the allocation of the 3a–capital relative to the different types of banks in Switzerland. In 2003, the big banks (UBS, Credit Suisse and Credit Suisse First Boston) and the different “Kantonalbanken<sup>73</sup> ” each controlled one third of the total 3a– capital. The “Raiffeisenbanken” with a 15 percent market share and the different regional banks with a 12 percent market share are the other important players in this business. The remaining fraction of 9 percent is allocated to all other banks (as e.g. commercial banks, private banks, and foreign banks) operating in Switzerland. In 1997, the market of conditional precautionary capital was more concentrated, i.e., the big banks controlled 45 percent of the market and the “Kantonalbanken” 32 percent. The “winner” over the last 7 73In Switzerland, each canton (except Solothurn) has its own Kantonalbank (cantonal bank). Most of them are administrated under public law and enjoy public warranty.