rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10002

# **Abstract**

This paper investigates whether electricity consumption is a useful indicator for tracking economic fluctuations in Bangladesh. It presents monthly data on national electricity consumption since 1993 and daily consumption data since February 2010 for the country’s eight divisions. National electricity consumption is strongly correlated with other high-frequency indicators of economic activity, and it has declined during natural disasters and the COVID-19 lockdowns. The paper estimates an electricity consumption model that explains over 90 percent of the variation in daily consumption based on the trend, seasonality, within-week variation, holidays, Ramadan, and temperature. Deviations from the model prediction can act as in indicator of economic fluctuations. For example, during the first COVID-19 lockdown in April 2020, electricity consumption in Dhaka fell over 40 percent compared with normal and remained below the normal level until early 2021. The later lockdowns, in contrast, had only small additional impacts, in line with less stringent containment measures and more effective adaptation.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sarshad2@worldbank.org and rcmbeyer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or
|Nov 63|55670 F|Bank projection from<br>official data|Official est., based on<br>U.N. 1980 assessment|U.N. 1988 revision (prelim.)|
|Niue|Sep 76|4 F|UNPVSR 4/87 (official est.)|||
|Norway|Nov 80|4091 J|UNPVSR 4/87 (official est.)|Based on UNPVSR 4/87|Based on UNPVSR 4/87|
|Oman|--|--|U.N. 1988 revision (prelim.)|U.N. 1988 revision (pretim.)|<br>Based on UNPVSR 4/87|
|Pacific Islands<br>Trust Terr.|, Sep 80|133 J|Bank projection from official<br>1983 est., UNPVSR 4/87|Based on USBOC 1985|Bank est.|
|Pakistan|Mar 81|84254 F|U.N. 1984 assessment|Bank est.|Bank est.|
|Panama|May 80|1825 F|U.N. 1988 revision (prelim.)|U.N. 1988 revision (pretim.)|<br>1980-85, U.N. 1988 revision<br>(prelim.); 1985-90, Bank<br>projection|
|Papua New Guine|a Sep 80|3011 F|UNPVSR 4/87 (official est.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Paraguay|Jul 82|3030 F|U.N. 1988 revision (prelim.)|Celade printout 2/87|Celade printout 2/87|
|Peru|Jul 81|17005 F|Bank projection from census,|1980-85, Bank assessment;|1980-85, Bank assessment;|
||||adjusted<br>for 4.1X undercount|1985-90, Bank
**_<u>________________________________________________________</u>_**

under which urban mobility indicators have been gathered and analyzed in a sample of 50 cities worldwide for the year 2001.

This project follows in the steps of the Millennium Cities Database which made it possible to highlight the existing links between urban planning, use of the various transport modes, and performance of the transport system, by collecting reliable and comparable indicators of urban mobility economics. The comparison of indicators through time also allows a sophisticated analysis of the most recent evolutions in urban mobility, the identification of successful mobility policies, and the outline of future prospects. The database and the analysis report will be available via a CD-ROM enabling easy search and exploitation of data.

# **2.5 Services and Information Technology Indicators**

The role of Information and Communication Technologies (ICT) in economic growth and social change has received considerable attention in recent years. Information and communication technology in the form of state-of the art information management systems is an important factor for successful implementation of trade facilitation by reducing costs. Transmission delays, inefficient procedures etc., can be eliminated through automated technology. Computerized systems such as UNCTAD’s Automated System for Customs Data (ASYCUDA) and the Advance Cargo Information System (ACIS) (Whose is this system) can speed the flow of information of cargo.

# _<u>The WTO Services Database</u>_

The WTO Services Database contains the schedules of commitments and lists of Article II (MFN) exemptions of WTO Members. The database is maintained by the Trade in Services Division of the WTO. Only the commitments of countries which were members of WTO prior to 31 December 2004 are included in the database.

By using the predefined reports you can sort information:

- by country (select a country and retrieve details of all commitments of that country, listed by sector)

- by mode of supply (select from commercial presence, consumption abroad, cross-border supply or presence of natural persons to retrieve a
0662***|0.0661**|
|(0 to 100)|(1.44)|(2.64)|(2.27)|
|Time trend|-0.257|-0.377|-0.392*|
|(=0 in 2000)|(-1.00)|(-1.63)|(-1.93)|
|n|297|305|297|
|R-squared|0.564|0.811|0.749|
|RMSE|4.65|4.36|4.08|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. RMSE = root mean square error, or sample standard deviation.

20
</sup>

# 3.2.  DATA

This study uses data from the Third Integrated Household Survey (IHS3), collected from March 2010 to March 2011 by the Malawi National Statistical Office, with support from the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS16 The estimate is based on the IHS3 data.

17 The difference between the IHS2 and the IHS3 national absolute poverty rates is not statistically significant. The IHS3 rural poverty rate is also statistically indistinguishable from its IHS2 counterpart.

18 The exchange rate for the IHS3 period is MK150 = US$1. During the 2009/10 agricultural season, each FISP beneficiary was entitled to vouchers that allowed them to purchase (i) two 50 kilogram bags of maize fertilizer at 500 Malawi Kwacha (MK) per bag, (ii) either 3 kilograms of hybrid maize seed or 10 kilograms of open-pollinated variety maize seed for the commercial market value net of the 1500 MK subsidy from the Government, (iii) 200 grams of storage pesticide for 100 MK, and (iv) 1 kilogram of legume seed (groundnuts, soybeans, beans and pigeon peas) for free. Upon the allocation of vouchers across the districts and the villages within each district, the program relies on community-based targeting to identify beneficiaries at the local-level, and is supposed to target households that are (i) resource poor, (ii) permanent village residents, and (iii) own and cultivate land, with preference given to heads of households that may be female, orphan, elderly, physically-challenged, or HIV-positive or individuals that look after the elderly and physically-challenged (MoAFS, 2009).

19 Concerns regarding the effectiveness of FISP in reducing poverty and achieving sustainable gains in maize production have been raised (Ricker-Gilbert and Jayne, 2011; Holden and Lunduka, 2010). More recently, RickerGilbert and Jayne (2012) focus on the question of whether FISP can simultaneously boost maize production and reduce poverty, and document that major returns from subsidized fertilizer accrue almost exclusively to households at
# **Section 9: Asset Classification, Provisioning, and Write-offs**

With 17 questions, Section 9 highlights the fact that, at least on paper, EMDEs tend to have a more comprehensive and stricter asset classification, provision, and write-off framework than advanced economies (Table A9). For example, while 96 percent of EMDEs have an asset classification system under which banks have to report the quality of their loans and advances, only 58 percent of advanced economies report having such a system in place. Similar differences between advanced and EMDEs exist when it comes to the coverage of asset the classification system, the minimum levels of specific provisions for loans and advances, the regulatory requirements for general provisions on loans and advances, and the requirement for banks to write off non-performing loans after a specific time period. Furthermore, the median for the maximum time that a loan or advance can be non-performing before it has to be written off is 12 months among EMDEs, while this figure is 18 months for advanced economies.

Most of the questions in Section 9 were new and, therefore, it is hard to document any changes relative to the 2007 survey. The only major measurable change that is observed between the two rounds is regarding the accrued, though unpaid, interest/principal entering banks’ income statement while the loan is classified as non-performing. The share of countries responding positively to this question increased by two-folds, from 14 percent in 2005 to 28 percent in 2010, with a larger increase happening among EMDEs.

**Table A9. Asset** **<u>Classification, Provisioning, and Write-off: Selected Responses</u>**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Asset Classification **|**% Yes**|**% Yes**|**% Yes**|
|
authorities, and complicated registration procedures. The CWG’s MPCA programs have been instrumental in reaching the vulnerable households in areas most affected by the conflict and where the SSN coverage remains limited. Over a million conflict-affected households have been reached with the MPCA since the start of the conflict with ISIS. The short-term MPCA aid programs, however, will require a more permanent solution and eventual integration in some form with the government’s SSN programs to continue reaching the vulnerable households affected by the conflict.

As an initial step, this paper proposes an analytical pseudo-PMT (PPMT) tool to bridge the targeting differences between the government SP program and the MPCA. The PPMT uses proxies that are common to both the humanitarian and the government CTP targeting formulas and provides each MCNA beneficiary’s probability of being eligible for the government’s CT program under different expansion scenarios. When applied to the existing humanitarian beneficiary database, the results suggest a strong source of referrals to the government’s SP system. Under a smaller program, targeting the poorest 15 or 18 percent nationally, between 24 to 54 percent of the existing humanitarian database would be available for referral to the government’s SSN programs with 90 to 50 percent (high and low) referral confidence levels. For a large program such as targeting the poorest 35 percent nationally, the referral rates increase to 40 to 73 percent for high and low referral confidence levels. Given a large proportion of the humanitarian database that could potentially be referred to MoLSA’s social protection programs, the results suggest a sequencing of smaller numbers with a high degree of likelihood to qualify could be referred first, with larger numbers being referred later when budgets expand.

The MCNA households that are likely to be referred first, i.e., those with a high degree of likelihood (90 percent) under a smaller program expansion (targeting the poorest 18 percent nationally), are noticeable across several demographic and economic indicators. Majority of the early referrals are likely to come from urban centers such as
gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV/AIDS.

# _ReproSalud_

_(Peru):_ Manuela Ramos launched ReproSalud in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as important problems in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women. By 2002, ReproSalud had reached over 123,000 women and 66,000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project’s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman, " _Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more"_ (Rogow and Bruce, 2000, page 20).

# **Individual behavior change strategies**

Many other programs have attempted to produce individual (rather than community-level) behavior change by working
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# Note on the Adjustment for Unit Value Basis for Passenger Cars

The 1977 Census of Manufactures for the USA gives only a single<sup>entry</sup> for passenger cars, while Brazil and Mexico both<sup>provide simple, but</sup> different, breakdowr-z; the former according to horsepower and the latter according to engine type.

With limited census information (see table A.1) we would<sup>have to value</sup> passenger cars produced in Brazil and Mexico by the single average<sup>price for</sup> all passenger cars produced in the United States, i.e. US$ 5,200. But the USA produces more large (and expensive) cars than the other two countries, so the average US price is too high for revaluing car output in Brazil and Mexico.

TABLE A.1

|Industrial<br>Census Information<br>in the United<br>States<br>(1977).<br>B|on Passenger<br>Car O<br>razil<br>and Mexico (1|utput<br>975)|
|---|---|---|
|Quantity<br>(1000<br>units)|Value<br>(mill.<br>national<br>currencies)|Unit value<br>(innational<br>currencies)|
|United States (1977)<br>Complete passenger vehicles<br>9,192.2|<br>47,796.3|5,199.66|
|Brazil (1975)<br>Cars,assembled,less<br>than 75 hp.<br>132.2|<br>2,964.4|22,418.02|
|Cars n.e.s.<br>317.4|<br>6,322.1
Table 3: Descriptive Statistics for Unemployed

||All Unemployed|
|---|---|
|Sample Size|3,606|
|Population|1,810,468|
|P(_s ∈U_)|0.185|
|E(_tus_)|51.53|
|SD(_tus_)|53.15|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. Unemployment duration in weeks Table 4: Transitions across Labor Market States (Population Values)

|From _\_To|Unemployment|Formal Sector|Informal Sector|
|---|---|---|---|
|Unemployment|1,435,620|106,308|141,453|
|Inactive|124,238|196,693|261,717|
|Formal Sector|101,282|2,590,524|192,138|
|Informal Sector|149,328|191,894|4,294,197|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. Informality using Firm size and Occupation definition. By adding the number of rows in each column I recover the total number of individuals in each labor market state in the second quarter of 2013.

Table 5: Annual Discrete Transition Matrix

|From _\_To|Unemployment|Formal Sector|Informal Sector|Other|Sum|
|---|---|---|---|---|---|
|Unemployment|0.72|0.05|0.07|0.16|1.0|
||(0.004)|(0.0026)|(0.0020)|(0.0030)||
|Other|0.04|0.06|0.08|0.82|1.0|
|
quintile|20.00|20.00|20.00|20.00|20.00|100.00|

Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,078. Urban population is divided into quintile groups on the basis of total per capita fitted expenditures.

**a** In 1997,1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. **b** Includes expenditures on fully and partially subsidized sugar.

Difference between lowest and highest quintile groups is significant at the 0.05 level.
**Our review reveals that direct measures of energy poverty are less used in the European Union, although the recent introduction of smart meters could change that.** Given the limited availability of datasets on temperature or energy services, direct measures are more challenging to implement. Gathering this data involves visiting households and taking stock of indoor temperatures, for example, a procedure that might be burdensome. In addition, it is challenging to select appropriate standards, given that they might be subjective by nature. Also, standards can vary by context and individual factors (Palma and Gouveira, 2022). One potential solution to the lack of reliable data on energy consumption is the introduction of smart meters (European Commission, 2023b). Although smart meters potentially increase access to timely and more accurate information on energy usage, they are still subject to shortfalls, such as energy sources that are not metered (e.g., wood, heating oil, coal) (Frei et al., 2021). Research to date has mainly relied on temperature sensor deployment (ibid).

**Based on our review, energy poverty indicators are mainly only available at aggregated levels; generating them at disaggregated levels would be useful as tackling energy poverty requires local policy engagement.** The European Commission recommends a local approach to tackle energy poverty, meaning that solutions must be adapted to national and local conditions. Therefore, it is worth generating more data and information on local (disaggregated) energy poverty. The geographical distribution and identification of energy poverty are critical for tackling energy poverty. The Energy Poverty Advisory Hub (2022) advises tackling energy poverty via a three-step process at the local level.<sup>21</sup> Given the local approach towards energy poverty, there is a need for indicators at the disaggregated geographic level. Palma and Gouveira (2022) review the literature on subnational analyses around energy poverty. They found that studies mainly focused on Southern Europe. While they do not mention any studies on Bulgaria,
Unionstats). This database provides annual industry estimates at the 4-digit level. Results, however, do not substantially differ from the reported results employing firm-level unionization rates. Moreover, there is a 58% correlation between firm- and industrylevel unionization rates.

87 As a robustness check, we also employed alternative methodologies: (i) we used different percentages (20% and 50%) to define a unionized company; and (ii) we sorted companies into, alternatively, three buckets and five buckets, based upon unionization rates. Results, however, do not substantially differ from the reported results employing the 30% unionization definition. 88 O-score is defined as: _o_ -score = - 1.32 - 0.407 log( _SIZE_ ) + 6.03 _T LT A_ - 1.43 _W CT A_ + 0.076 _CLCA_ - 1.72 _OENEG_ - 2.37 _NIT A_ - 1.83 _FUT L_ + 0.285 _INT W O_ - 0.521 _CHIN_ where _SIZE_ is total assets divided by the consumer price index, _TLTA_ is the ratio of total liabilities to total assets, _CLCA_ is the ratio of current liabilities to current assets, _OENEG_ is a dummy variable set equal to one if total liabilities exceeds total assets and zero otherwise, _NITA_ is the ratio of net income to total assets, _FUTL_ is the ratio of funds from operations to total liabilities, _INTWO_ is a dummy variable equal to one if net income was negative for the past two years and zero otherwise, and _CHIN_ is change in net income over the last quarter: ( _NIt-NIt−1_ )/(| _NIt_ | + | _NIt−1_ |).

89 We regress daily stock returns from the previous calendar year on the Fama-French 3 factors: r _ti_ = _a i_ + _b MKTi MKT t_ + _bSMBi SMB t_ + _bHMLi HMLt_ + _eti_ . Idiosyncratic volatility is then calculated as the standard deviation of the residuals: var(<sup>_e_</sup> _ti_ )<sup>.
# **3.3 Comparing spending across sectors**

In addition to compiling access and quality data, the household survey collected valuable information on household spending patterns across infrastructure services. Typically, this type of data is collected as part of sectoral studies that focus exclusively on household expenditures for a particular service. By offering comparison across sectors the data set provides a rare comparative insight into household spending by sector. It reports household expenditures on different services and consumption items simultaneously, allowing us to examine absolute expenditures – and their share of household budgets – in relative terms.

Table 3.2 provides data on monthly infrastructure spending for four sectors - electricity, water, garbage collection, and phone. For comparative purposes, data on cooking fuel costs and rents were added to the compilation. As before, this section will focus on data for all cities. Comparative analysis of spending by settlement type and household poverty is discussed in Annex 4.

**TABLE 3.2:  Monthly household spending on infrastructure services**

|**Infrastructure**<br>|**N**|**ALL**<br>|**By**<br>|**Settlement**<br>|**Type**<br>|**By**<br>|**Poverty L**<br>|**evel**<br>|
|---|---|---|---|---|---|---|---|---|
|**Monthly**<br>**Expenditure**<br>(Ksh/month)||**Urban**<br>**KSH**|**N**|**Formal**<br>**KSH**|**Informal**<br>**KSH**|**