rafmacalaba/datause-encoder-data 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 the governments they represent._ Produced by the Research Support Team
results indicate that the willingness of an investor to rate a country is not correlated with its perceived or experienced readiness.

# **4. Results**

This section provides the main results from our investor survey. This section is structured along the three main research questions set out in the introduction. Section 4.1 therefore sets out the importance of different risk factors when evaluating power sector investments, whereas Section 4.2 gives an overview of the readiness of different segments of the power sector for private solutions. Section 4.3 then reviews the results in terms of the readiness of different countries.

# **4.1. Ranking of Risk Factors When Evaluating Power Sector Investments**

Investors and financiers assign the greatest importance to policy and regulatory risks (average (avg.) 6.8), followed by country context risks (avg. 4.6) and risks related to the wider power sector context (avg. 4.0). Among power sector policy and regulatory risks, the certainty of cash flow (avg. 7.4) is the most important for investors, with ease of market entry (avg. 6.6) and clarity of investment priorities (avg. 6.5) following closely behind. In the category overall power sector context risks, investors see the sectoral growth potential as the most important (avg. 4.7), whereas neither the sectoral track record nor the investors’ own track record are as important (avg. 3.6 and 3.7, respectively). In terms of country context risks, governance and political risks are the most critical for investors (avg. 6.7), followed by the general business environment (avg. 5.6), and the macro-economic framework (avg. 4.3). Risks related to domestic banking and capital markets are not considered important (avg. 1.7).

These results underscore that even in difficult country and sector contexts, policy makers have the power to attract private solutions, by putting in place policy and regulatory frameworks that make projects attractive for private solutions. By far the most important risk category score is the certainty of cash flow, which relates to the investors’ ability to recover its costs and investment returns, enforce payment discipline of the off-taker or customer, and the kind of government and possibly multilateral support that is provided to secure investors’ cash flows. Yet, governance and political risk also feature prominently for investors, likely because changes in that domain can have substantial consequences for the certainty of cash flow. Least importance to investors are domestic banking and capital markets, which suggest that investors largely rely on external sources of capital and there is still a long way to go for the development of local markets for infrastructure financing, which will certainly be needed as the market matures and scales up.

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# **4 Data sources**

We conduct our analysis using district-level data. Nepal has 5 major regions and 75 districts. A district can be thought of as being comparable to a county in the US in the sense that it is administratively below the region level. The average area of a district was 1948 square kilometers in 2001, and the average population was 309,000. Our measures of conflict intensity are based on data provided in the annual Human Rights Yearbooks published by the Informal Sector Service Centre (INSEC), a Nepalese non-governmental organization. INSEC was able to provide us with data on the number of people killed both by the Maoists and by the state in this conflict, as well as measures of the number of people abducted by the Maoists and the number of people arrested by the state. It is difficult to construct a unique reliable measure of conflict intensity. Our main measure is the number of conflict-related deaths in the district normalized by the district population. We use population figures from the (pre-conflict) 1991 population census data for this purpose. We also check the robustness of our results to other measures of conflict intensity, such as a dummy which equals one if more than 100 people have been killed in the conflict so far.<sup>9</sup> Another imperfect measure indicating the intensity of conflict is the number of people abducted by the Maoists normalized by the 1991 district population.

We construct our explanatory variables using data from the 1991 and 2001 population censuses, the Nepal Living Standards Measurement Survey 1995-1996, and the Nepal District Profiles based on official data. Details of data sources and variable construction are provided in the Data Appendix. As far as possible, these measures have been constructed using data collected before the violent conflict began in 1996. This helps to partially mitigate the concerns regarding backward causality, such as the possibility that the conflict itself has made some areas poorer or reduced the availability of public goods.

As suggested by the opportunity cost hypothesis, we construct district-level measures of poverty and literacy to proxy for the opportunity cost of recruiting rebel forces. We also have 9This is similar to the measures of civil war used in cross-country studies such as Fearon and Laitin (2003) and Miguel et al. (2004).

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data from between 1970 and 1980.9 Second, total public spending on health has had much less impact on average health status that one might have expected, and certainly less than hoped. Although the lack of data on public spending has, until recently, limited the direct examination of the issue, Musgrove (1996) summarizes studies of the impact of public spending on health on health status: "[m]ultivariate estimates of the determinants of child mortality give much the same answer [as his results on life expectancy or DALY burden of disease]: income is always significant, but the health share in GDP, the public share in health spending, and the share of public spending on health in GDP never are." Even using instrumental variables to account for data and endogeneity problems Filmer and Pritchett (1997) find that public expenditure on health as a share of GDP is a small, and statistically insignificant, determinant of child mortality. At the point estimates _doubling_ public spending from 3 to 6 percent of GDP would improve mortality by only between 9 to 13 percent (appendix Table A-I reproduces those regressions).<sup>1</sup> Other empirical studies that add measures of health resources, such as physicians, nurses, or hospital beds per capita to the basic socio-economic variables in this type of regression rarely find large and significant impacts for these variables (Kim and Moody, 1992).

9 Some have been confused about Anand and Ravallion (1993), claiming that they showed that income was unimportant, in spite of their explicit claim otherwise. What they showed is that they could not reject the restriction that average income affected health status only insofar as it affected the level of poverty, highlighting the fact that it is increased income among the poor that is most effective in improving health. However, since the correlation between average incomes and poverty is very high this still implies average income, through its poverty reducing effects, explains most health status variation.

10 Bidani and Ravallion (1997) show a large impact of public spending on the health status of the poor, but their estimated impact of public spending on aggregate health status (of the poor and non-poor taken together) was also quite small.

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**<u>Figure 11. Urban Housing Shortages by National Income</u>**

<!-- Start of picture text --> Shelter Shortage by Income<br>NERETHMWILBR RWAUGAMOZSLETZABFA STP NGA<br>KEN ZMB CIV<br>AGO<br>COM SEN<br>GHA<br>CMR<br>0 1000 2000 3000<br>GDP per capita (PPP)<br>shelter shortage Fitted values Fitted values<br>*fitted values including and excluding AGO<br>Infrastructure Shortage by Income<br>ZMB<br>SLE<br>BFA<br>NERETH TZA<br>NGA<br>LBR STP CIV<br>MWI AGO<br>KEN<br>MOZ CMR<br>COM<br>RWA<br>GHA<br>SEN<br>UGA<br>0 1000 2000 3000<br>GDP per capita (PPP)<br>infrastructure shortage Fitted values Fitted values<br>*fitted values including and excluding AGO<br>100<br>80<br>60<br>% of households facing shelter shortage<br>40<br>100<br>80<br>60<br>% of households facing infrastructure shortage<br>40<br><!-- End of picture text --> Examining housing shortages by levels of urbanization<sup>22</sup> offers another explanation of the degree of different types of housing shortages (Figure 12). Intuitively, the degree of urbanization is generally positively correlated to intensity of infrastructure provision, suggesting a negative relationship between urbanization and infrastructure shortages. In terms of shelter shortages, however, higher levels of urbanization may also give rise to higher costs of housing and land in cities leading to squatting and informal settlements. Urbanization levels vary considerably across the Sub-Saharan African countries studied, ranging from less than 16 percent in Malawi to more than 63 percent in São Tomé and Principe.

> 22 Levels of urbanization used here are from the World Development Indicators 2012 and the percent of the country’s population that resides in urban areas. Like the measure of GDP per capita used, the level of urbanization is taken for 2012 for all countries examined despite the fact that the years observed for housing shortages vary (see Annex 1).

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ˆ _C_ ˆ _P_ constituents. The single-party would spend more on the public good than a coalition, _g_ < _g_ , and ˆ _C_ ˆ _P_ would impose a higher tax rate τ < τ (Proof in Appendix 3). Spending on the specific transfers targeted to group members is ambiguous under this regime.

* _P_ ˆ _C_ ˆ _P_ * _C_ max ˆ _C_ * _P_ ˆ _P_ * _C_ max (5) _g_ < _g_ < _g_ ≤ _g_ < _g_ or _g_ ≤ _g_ < _g_ ≤ _g_ < _g_

The difference between this regime and regime (4) is that the constrained optimal policy of the single-party calls for less public good than the unconstrained policy of the coalition. The coalition will thus provide more public good, but the single-party will continue to levy higher taxes and provide more transfers to its constituencies. That is, while overall government size will remain higher under the singleparty, the distribution of public spending between the broad public good and targeted transfers will change (Proof in Appendix 4).

ˆ _C_ ˆ _S_ * _S_ * _C_ max (6) _g_ < _g_ ≤ _g_ < _g_ < _g_ This regime is the polar opposite of regime (1)—swing voters are not demanding and sufficient swing votes can be won for single-party and coalition incumbents to win re-election while implementing their preferred policies ( _g_ * _P_ , τ * _P_ , _f_ * _P_ ) and ( _g_ * _C_ , τ * _C_ , _f_ * _C_ ) , respectively. The reelection constraint imposed by the swing voters is not binding. Following Proposition 1, taxes and total government spending are higher under single-party governments.

Summarizing the arguments above, we have:

**Proposition 2** : _Taxes and total government spending are always higher when incumbent governments consist of a single political party versus a coalition of parties._

The model therefore makes a strong and robust prediction about the impact of party composition of governments on taxes and total spending, which is contrary to the prediction thus far modeled in the literature. Predictions on _composition_ of spending are ambiguous—while broad public goods provision by single-party governments is higher for a middle range of reservation utilities of swing voters, it is lower than provision by coalition governments for some polar values. Furthermore, we have largely ambiguous results with regard to the comparative size of spending on targeted transfers by the two types of government.

# **3. Empirical specification and data**

To test the model’s prediction about the correlation between type of government and fiscal policy, we begin with a specification drawn from Persson and Tabellini (2003, 2004) and Persson, Roland and Tabellini (2006): _Yit_ is a fiscal variable (spending, tax, or deficit, as defined further below) for country _i_ , measured over time _t_ , expressed as a percentage of GDP; economic characteristics relevant for 14
Table 4: Consumption, Income and Wealth by Income Partition (Malawi ISA 2010)

||Bot|tom (|%)||Q|uintile|s|||Top (%)||All|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0-1|1-5|5-10|1st|2nd|3rd|4th|5th|10-5|5-1|1|0-100|
||||||(A) R|ural Re|sidency||||||
|Consumption|911|738|796|832|1002|1269|1498|2218|2140|2596|4501|1364|
|”<br>(p.c.)|297|236|258|267|312|336|367|466|473|510|905|350|
|Income|-40|90|165|183|416|679|1091|3182|2397|4092|15983|1110|
|Wealth|419|632|592|642|854|971|1396|2521|2331|3544|5243|1277|
|_▷_Land|125|241|291|320|396|475|723|919|955|1069|1118|567|

||||||(B) U|rban R|esidenc|y|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Consumption<br>”<br>(p.c.)|1990<br>623|1903<br>550|1708<br>453|1761<br>524|1771<br>581|2182<br>662|2677<br>720|5877<br>1381|5309<br>1066|8300<br>2028|15902<br>2939|2851<br>773|
|Income|-121|62|169|201|577|979|1674|8979|5220|12881|61655|2478|
|Wealth|1567|800|747|910|1930|1527|1888|9134|6154|16954|33881|3074|
|_▷_Land|17|67|66|77|445|564|520|490|415|521|2287|419|

_Notes:_ All variables are averages in current USD. The construction of the measures of household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix. Per capita variables are computed dividing by total household size; alternative adult-equivalent measures are discussed in Section 5.1.
to 74 percent). This means that a lot of the difference in teacher practices and behaviors is within schools (consistent with what Bruns and Luque 2015 document for Latin America).

# **5. Properties of the SDI, Stallings, CLASS, and** **_Teach_ instruments**

We turn now to some of the properties of the various instruments. We first investigate how stable the instruments were across enumerators to assess whether some of the instruments produce more consistent results than others. Next, we analyze how the various elements of each instrument relate to each other to assess the extent to which the instruments are able to isolate independent dimensions of quality. In a last step we investigate how the elements of each instrument relate to the elements of the other instruments to assess the extent to which the instruments are identifying similar dimensions of quality.

# **_5.1 How stable are the scores across different enumerators/raters?_**

All four approaches have clear guidelines for how to record information, and CLASS and _Teach_ have detailed instructions on what scores and scales mean, and rater’s certification requires that they rate consistently with expert-rated videos. In these systems, raters nevertheless have to process what they observe and use their judgement in assigning a rating. We would perhaps therefore expect that scores assigned by different enumerators to be more similar using the low-inference approaches than the highinference ones. In this analysis, it is important to keep in mind that the findings are not necessarily attributes of the assessment systems themselves, but rather of this implementation of them. While every effort was made to train enumerators and raters according to high standards (and all of them met the requirements associated with the observation system they were tasked with using) the findings are ultimately a reflection of both the observation system and its implementation in this context.

Table 5 reports the intraclass correlation coefficient (ICC) for Level 1 (left column) and Level 2 (right column) variables.<sup>26</sup> The columns also report the mean of the absolute value of the differences in the scores across the raters for the variables. The ICC values can be interpreted similarly to a standard correlation coefficient—with values closer to 1 suggesting closer agreement between the two enumerators or raters.<sup>27</sup> The values for SDI and Stallings are derived from the classrooms in which two enumerators observed the same teacher during the same lesson and recorded their observations in parallel (this occurred in 27 for SDI

> 26 These are calculated using a one-way random effects model (implemented using the “icc” command in the Stata software package).

> 27 In this and subsequent sections we describe individual correlations as being, for example, “low” and “high.” There is, of course, no absolute standard for describing a correlation as such.  We have used a rule of thumb that above 0.3 is notable, above 0.5 is generally high, and above 0.8 is very high.

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<!-- Start of picture text --> Figure 1. Trend in fiscal deficits Figure 2. Public debt and interest payments<br>70 4<br>60 3.5<br>0.0 % 3<br>50<br>-0.5 %<br>2.5<br>-1.0 % 40<br>-1.5 % 2<br>-2.0 % 30<br>1.5<br>-2.5 %<br>20<br>-3.0 % 1<br>-3.5 % 10 0.5<br>-4.0 %<br>-4.5 % 0 0<br>Overall fiscal deficit (%GDP)<br>Fiscal deficit excl. grants and privat. (%GDP) Interest payments (%GDP)-RH axis<br>Linear (Overall fiscal deficit (%GDP)) Outstanding public debt (%GDP)-LH axis<br>199719981999200020012002200320042005200620072008<br><!-- End of picture text --> Source: Ministry of Finance, Tunisia

# **3. Dynamic effects of fiscal policy on output**

Our objective is to determine the extent to which increasing public expenditures can help offset the deceleration in growth driven by the decline in external demand. To that effect, we use a model to simulate the effect of 1 dinar of increase in public spending (public investment and recurrent expenditure) on Tunisia’s GDP in the short and medium-term. In other words, how much additional GDP can be obtained from 1 dinar additional public spending?

# **_3.1. The impact of fiscal stimulus: empirical strategy_**

 The system to be estimated consists of four variables: _t_ , , , . _G Inv_ is public investment, _G R_ current expenditure, _I_ Pr _iv_ private investment and _Y_ output. The extent to which an increase in _G Inv_ and _G R_ affect _Y_ , as well as the temporal profile of the impact (lag and persistence of the shock) is the main focus of this paper.

Most recent studies of the impact of fiscal shocks on macroeconomic variables use the structural vector autoregressive regression (VAR) approach proposed by Blanchard and Perotti (2002) and Perotti (2002). This approach consists of isolating the structural shocks in a VAR system by imposing restrictions based on economic theory and institutional features that constrain the behavior of policy makers. For instance, government spending categories are generally contemporaneously unaffected by GDP. Therefore, reactions of fiscal policy to output changes only result from so called “automatic” responses, which are defined by existing laws and regulations. All fiscal policy developments in a given quarter or a year in some instances, which 5
Table 17: Medium-term impacts of _Mais Educação_ on 2007 Portuguese test scores

|High mathematics<br>test scores in 2007|Medium<br>mathematics test<br>scores in 2007|Low mathematics<br>test scores in 2007|
|---|---|---|
|Dependent variable|||
|(1)|(2)|(3)|
|Panel A: Impacts on fifth-grade|variables||
|Dummy_Mais Educação_* year 2011<br>–2.062|–4.549*|–3.951*|
|(2.938)|(2.337)|(2.280)|
|_R_ <sup>2</sup><br>0.784|0.788|0.800|
|Observations<br>612|740|766|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|
|Panel B: Impacts on ninth-grad|e variables||
|Dummy_Mais Educação_* year 2011<br>–1.819|–2.921|–4.629*|
|(3.957)|(2.909)|(2.460)|
|_R_ <sup>2</sup><br>0.625|0.734|0.791|
|Observations<br>584|656|670|
|Dummy year 2011<br>YES|YES|YES|
|School fixed effects<br>YES|YES|YES|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their mathematics achievement in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1) in the paper, where the dependent variable in all columns is the mathematics test scores. In column (1), we show results for treated schools with high mathematics test scores in 2007, in column (2) schools with medium mathematics test scores, and in column (3) schools with low mathematics test scores. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.