rafmacalaba/datause-encoder-data huggingface dataset:
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

social impact indicators under Table IX, suc h as welfare effects among income groups,<sup>2</sup> may be extremely time consuming to collect on a consistent basis.

As in some other areas of social and economic studies, data collection work can be initiated by relevant international organisations and can then be carried forward through co-operation with other international and national bodies. The International Energy Agency (IEA), the US Department of Energy’s Energy Information Administration (EIA), the Latin American Energy Organisation (OLADE) and the African Energy Policy Research Network (AFREPREN) provide a good starting point for international energy data. Due to the dynamic nature of electricity reform it is important that data is collected on a consistent, ongoing basis.

Not all data will be available for all countries in all years but this does not necessarily mean that the process cannot begin. Existence of a shared framework and co-ordinated effort is a first step and an incentive to join such an effort or to integrate them into the existing data collection programs. Costs and required resources are justified by the social and economic importance of the electricity sector. Electricity reforms can have a significant effect on many development goals (such as contained in the Millennium Development Goals<sup>3</sup> ) that are directly or indirectly dependent on developing sustainable energy systems in developing countries.

We tentatively suggest the following strategy for World Bank data collection in order to build a comprehensive resource base on electricity sector reforms:

- i. Agree a list of desirable indicators and countries,

- ii. Begin a search and summarise the main published (international) data resources,

- iii. Conduct a systematic search of internal World Bank resources e.g. by collecting latest country mission reports on energy sector,

- iv. Make inventory of missing data and countries,

> 2 Although welfare impacts would seem to be extremely important we do not highlight these as core indicators because of the difficulty of collection.

> 3 See http://www.developmentgoals.org/

28
**Table 2: Summary statistics for control variables**

||All<br>mean|All SD|Control<br>mean|Control<br>SD|Treatment<br>mean|Treatment<br>SD|Diff.<br>(Control -<br>Treatment)|P-val|
|---|---|---|---|---|---|---|---|---|
|Age of respondent|24.3858|0.635|24.4419|0.634|24.3287|0.630|0.1132***|0.008|
|Female respondent|0.5017|0.500|0.5011|0.501|0.5023|0.501|-0.0012|0.972|
|Treatment LR assignment among women|0.2560|0.437|0.2437|0.430|0.2685|0.444|-0.0248|0.403|
|Years of schooling in 2008|6.6831|2.097|6.6902|2.082|6.6759|2.114|0.0143|0.920|
|Attending school in 2008|0.7486|0.434|0.7494|0.434|0.7477|0.435|0.0017|0.953|
|Muslim|0.0023|0.048|0.0023|0.048|0.0023|0.048|-0.0000|0.991|
|Protestant|0.0287|0.167|0.0319|0.176|0.0255|0.158|0.0064|0.570|
|Catholic|0.8117|0.391|0.7859|0.411|0.8380|0.369|-0.0521**|0.049|
|Iglesia|0.0080|0.089|0.0068|0.082|0.0093|0.096|-0.0024|0.689|
|Aglipay|0.0161|0.126|0.0296|0.170|0.0023|0.048|0.0273***|0.001|
|Other Christian|0.0631|0.243|0.0752|0.264|0.0509|0.220|0.0242|0.141|
|Adventist|0.0115|0.107|0.0046|0.067|0.0185|0.135|-0.0140*|0.054|
|Tagalog|0.0023|0.048|0.0023|0.048|0.0023|0.048|-0.0000|0.991|
|Cebuano|0.1917|0.394|0.2118|0.409|0.1713|0.377|0.0405|0.129|
|Ilonggo|0.0092|0.095|0.0091|0.095|0.0093|0.096|-0.0001|0.982|
|Bisaya|0.7853|0.411|0.7585|0.428|0.8125|0.391|-0.0540*|0.052|
|Hiligaynon|0.0011|0.034|0.0023|0.048|0.0000|0.000|0.0023|0.318|
|Number of observations|87|1|43|9|43|2|||

24
- 47 -

In choosing a destination based VAT to replace an odd assortment of taxes on imports and domestic inputs, many developing countries have significantly enhanced the coordination of their trade and domestic indirect taxes. Nevertheless, there are a few instances where commercial and tax policies were reformed simultaneously and the danger remains that efforts to reform tariff policies may inadvertently distort a country's indirect tax system, For example, tariffs on imported inputs may produce greater uniformity in effective rates of protection but, by making accurate border tax adjustments more difficult, at the cost of more distortion in the configuration of indirect tax burdene.

Besides contributing to total revenue, luxury based excise taxes have the important role in most developing countries of securing a more equitable burden of in.direct taxation. In order to accomplisb this objective, developing countries need to produce reliable income and expenditure data since what is deemed to be a luxury in one country may be otherwise for other countries.

Given the relative importance of indirect taxes in the revenue systems of developing countries, the key equity concern under a tax reform is to design and apply both sales and excise taxes that impact as little as possible on low income groups in the economy. However, almost all of the
# **Figure 5: Higher South-North migration flows could lead to greater real returns to capital in migrant-receiving countries**

Impact on real returns to capital, percent difference relative to baseline Australia, New Zealand and Other Oceania 2020 Canada 2025 European Economic Area 2030 High-Income Asia High-Income Middle East Russian Federation USA Bangladesh Brazil Central Asia China India Indonesia Low-Income Sub-Saharan Africa Mexico Middle-Income Middle East and North Africa Other Former Soviet Union and Other Eastern Europe Other Latin America and Caribbean Other Low-Income South and East Asia Other Middle-Income Asia Other Middle-Income Sub-Saharan Africa Pakistan South Africa Turkey -10 -5 0 5 10 Source: Authors’ simulations 16
**<u>Table A.9: Relation between expenditure growth, poverty, and inequality with interactions</u>**

|Explanatory variables||Depen|dent variable is t|hegrowth ofpe|r capita expendit|ure(LogYt– L|ogYt-1)||
|---|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|
|Lagged log of per capita expenditure|-0.659***|-0.767***|-0.491***|-0.525***|-0.505***|-0.486***|-0.519***|-0.505***|
||(0.180)|(0.179)|(0.074)|(0.074)|(0.072)|(0.069)|(0.088)|(0.071)|
|Lagged log of per capita expenditure * Lagged log<br>of other State spending|0.011<br>(0.009)||||||||
|Lagged log of per capita expenditure * Lagged log<br>of investment spending||0.016*<br>(0.009)|||||||
|Lagged log of per capita expenditure * Lagged log<br>of population density|||0.001<br>(0.008)||||||
|Lagged log of per capita expenditure * Lagged||||0.000|||||
|share of urban population||||(0.000)|||||
|Lagged log of per capita expenditure * Lagged<br>share of population with high-school diploma|||||0.461*<br>(0.252)||||
|Lagged log of per capita expenditure * Lagged<br>share of ethnic minority population||||||0.001*<br>(0.000)|||
|Lagged log of per capita expenditure * Lagged Gini<br>index|||||||0.114<br>(0.261)||
|Lagged log of per capita expenditure * Lagged<br>poverty rate||||||||-0.188**<br>(0.077)|
|Lagged log of other State spending|-0.097|0.002|0.000|0.001|0.001|0.001|0.001|0.001|
||(0.083)|(0.002)|(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.002)|
|Lagged log of investment spending|0.003|-0.129|0.003|0.000|0.001|0.000|0.001|-0.002|
||(0.007)|(0.082)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Lagged log of population density|0.035***|0.035***|0.030|0.039***|0.034***|0.030***|0.033***|0.031***|

49
Table 2 – Meta-Regression for Primary Outcomes _Matching_ _<u>Grants (Exclude Outliers)</u>_

||Firm Performance|EmploymentCreation|Labor Productivity|
|---|---|---|---|
|RE Estimate -- No Controls|0.15**|0.13*|0.052|
|p-value|0.012|0.083|0.33|
|N|7|7|5|
|**_Moderator Variables_**<br>||||
|**_(Control Variables)_**||||
|Constant|0.11*|0.13|0.14|
|p-value|0.095|0.305|0.244|
|_LAC Fixed Effect (1 if LAC; 0_||||
|_otherwise)_|0.10|0.13|0.14|
|p-value|0.40|0.305|0.244|
|N|7|7|5|
|Constant|0.17***|0.17**|Na|
|p-value|0.000|0.029|Na|
|_Africa Fixed Effect (1 if_||||
|_Africa; 0 otherwise)_|-0.27**|0.17**|Na|
|p-value|0.03|0.029|Na|
|N|7|7|Na|
|Constant|0.17*|0.27*|0.24|
|p-value|0.084|0.053|0.113|
|_Firm Size (Continuous_||||
|_Variable)_|-0.001|0.27*|0.24|
|p-value|0.37|0.053|0.113|
|N|7|7|5|
|Constant|0.15|0.015|0.068|
|p-value|0.131|0.33|0.501|
|_Risk of Bias (1 for moderate_||||
|_and high risk of bias; 0 for_||||
|_low)_|-0.01|0.015|0.068|
|p-value|0.94|0.33|0.501|
|N|7|7|5|
|Constant|0.16***|0.20**|Na|
|p-value|0.002|0.018|Na|
|_Method (1 if RCTs; 0 if QE)_|-0.23|0.20**|Na|
|p-value|0.27|0.018|Na|
|N|7|7|Na|
|Constant|0.15**|0.16*|0.10*|
|p-value|0.012|0.074|0.047|
|_Export (Continuous Variable)_|2.23**|2.86|-2.85**|
|p-value|0.02|0.11|0.012|
|N|7|7|5|

26
<!-- Start of picture text --> Oo<br>=<br>ow<br>or<br>i=)<br>as)<br>o<br>&<br>a=<br>=<br>Pal<br>a<br>i)<br>LR<br>oS<br>15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Age<br>95% Cl mm— Survivor function<br><!-- End of picture text -->
<!-- Start of picture text --> A. Availability of Indicators by Country<br>Number of fields per availability status<br>9<br>i]<br>T<br>6<br>5<br>4<br>3<br>2<br>1<br>it) & g ; S @ & a & & a & S o s &. ‘<br>esx e &FsSe S & eC SPSF SF SSLF @& SF VYCeSK eeoe eseo gePop?SF<br>Ss ad se = a eS ere<br>Se<br>Collected and public WH Collected but mot public Wi Unknown or not collected<br>B. Availability across Countries for Each Indicator<br>ll<br>COVID-19 Hest Care Werke Itoa I<br>Average Hoe Coup Rts<br>Invoversthing Hest Sen<br>0 2 4 6 8 10 12 14 16 18<br>Number of countries per availability status<br>{HB Collected and public Wi Collected but not public Wi Unknown of not collected<br><!-- End of picture text -->
Table C.16: National Estimates of Effect of Years of Schooling on Number of Children Born - DHS

|**Y**<br>Add’l Years of Free|**ears of Schooling**<br>(First Stage)<br>(1)<br>0.115|||
|---|---|---|---|
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�|(0.053)<br>F-Test|F-Statistic|P-Value|
||[0.035]<br>_I_<sup>_F P E_</sup><br>_zy_<br>=_I_<sup>_MT I−T_</sup><br>_zy_|0.46|[0.501]|
|Add’l Year of MTI|0.175|||
|No Script Change|(0.087)|||
|�<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>�|[0.049]<br>_I_<sup>_F P E_</sup><br>_zy_<br>= (_−_1)_× I_<sup>_MT I_</sup><br>_zy_|0.01|[0.905]|
|Add’l Year of MTI|-0.128|||
|with Script Change<br>�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�|(0.073)<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>= (_−_1)_× I_<sup>_MT I_</sup><br>_zy_<br>[0.085]|0.12|[0.731]|
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_|-0.010<br>_I_<sup>_F P E_</sup><br>_zy_<br>=_I_<sup>_MT I−T_</sup><br>_zy_<br>(0.008)<br>= (_−_1)_× I_<sup>_MT I_</sup><br>_zy_<br>[0.239]|0.23|[0.794]|
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_|-0.001<br>(0.007)<br>[0.930]|||
|N|24,898|||

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (DHS) in years 2005, 2011, and 2016. _Note_ : MTI = mother tongue instruction. Column 1 is reproduced from Table C.9 in this supplementary online appendix. The dependent variable is years of schooling. The sample includes women in birth cohorts from 1970 to 1988. The regression includes birth year and zone fixed effects, zonespecific linear trends, and a cubic for age when multiple survey waves are included. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.35
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 2. Infrastructure’s low contribution to growth in Sierra Leone could be much greater**

a. Infrastructure’s contribution to annual per capita economic growth in selected countries, 2003–07, in percentage points <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>Telecom Electricity Roads<br>b. Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points<br>5<br>Main Telephone Lines<br>4 Electricity Generating Capacity<br>Length of Road Network<br>3<br>2<br>1<br>0<br>-1<br>-2<br>(2001-5 vs. 1991-5)<br>% changes in growth per capita due to changes in infrastructure<br>leader (Mauritius)<br>capita from improving<br>Potential changes in growth per infrastructure to level of African<br><!-- End of picture text --> b. Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points Source: Calderon 2009.

# The state of Sierra Leone’s infrastructure

Sierra Leone‘s population of 5.7 million is sparsely distributed across the country, with more densely populated urban areas surrounding the major cities, and very lightly populated areas in the north. More than 60 percent of the population lives in rural areas. Poverty is high at around 70 percent of the population (figure 3a, b). Sierra Leone has mountains in the east, while the rest of the country is divided between plateau, hills, and coastal mangrove zones (figure 3c).

Sierra Leone enjoys an advantageous geographical location and is endowed with a wealth of natural resources including minerals—, such as diamonds, bauxite and rutile—forests, fresh water, fertile lands, a natural harbor, and potential off-shore oil reserves. The favorable climate and land resources provide for 6
transformed quakes in a country year.<sup>8</sup> The earthquake data have been taken from the United States Geological Survey Advanced National Seismic System (ANSS) Composite Earthquake Catalogue. Population density data are sourced from Gridded Population of the World, Version 3.

Earthquake propensity, the first variable of main interest, though not directly observable, can be indirectly inferred from historical earthquake records. Earthquakes are unevenly distributed around the world and are far more likely to occur in regions where different tectonic plates border each other. Figure 1 shows the global distribution of earthquakes and the borders of tectonic plates. Most earthquakes occur at the various borders of the Pacific plate, the Western border of the Latin American plate, and the boundaries between the African, the Arabic and the Indian plates and the Eurasian plate. It is in countries located in these regions that the opportunity costs of earthquake safety regulation are lowest.

> 8 Minor quakes are unlikely to cause major damage, let alone kill people, so we drop all quakes with a

magnitude below five on the Richter scale for constructing this variable.

21
this case is the share of household income generated by cotton production. To a firstorder approximation, the real income impact of a change in the price of cotton may be obtained by multiplying this income share by the percentage change in cotton price. This leads them to focus on the evolution of cotton income shares amongst the poor in Zambia. Since cotton is grown in significant quantities only in three provinces, this is where they focus attention.

One of the striking things about world cotton markets in the late 1990s was the collapse in world prices. Between 1996 and 1998, cotton prices in Zambia fell by 20 percent. Therefore, it is surprising that cotton’s share in income among the poor rose sharply in the Eastern and Southern Provinces over this same period. Indeed, amongst the poorest households in the Eastern Province, the increase was nearly five-fold – even as the income share fell for wealthier households. While there are many factors that may bear on this change, the authors argue that the most likely reason was the reform of the cotton marketing board system and the implementation of an out-grower scheme which proved effective in getting seed and fertilizer into the hands of credit-constrained, small scale producers. This increase in the cotton share boosts the potential benefits from multilateral agricultural reforms, since one of the main consequences of such reform would be to raise cotton prices.

Despite the increase in cotton income shares over this period, the income impact on the poor of higher cotton prices – the authors assume a 12% price rise, based on several independent studies of world cotton markets – is still relatively modest (of the order of one percent of real income, on average) because the average income share is about 8%. This brings them to a discussion of complementary domestic reforms. In 16
24 Where ep<sup>8</sup> is the earnings level of a poor pensioner of age a. epa is obtained from the age-earnings profile for the middle income class through the formulae: epa mepH l *1 + a - I(ema/1emfA-I -l)] epl a z -eml For the base case we discuss in detail, a = 0.8 and n= 2%. For the age-earmings profile used in our simulations, we find that this second ratio has a value of 0.14997.

This implies that the upper bound for the fiscal cost /GNP of maintaining the transfers associated to the initial social security is BzspO.14997 .Using the value of B used in our simullations, of 0.65, this upper bound is O.O975zsp %of GNP.

This means that if the initial income inequality is larger (z is smaller) then the fiscal cost of compensating the poor falls, because the basic pension that must be replaced is smaller. On the other hand, if the relative size of the poor population is larger, then the fiscal cost of compensation grows proportionately.

The final element for an assessment of the total volume of resources required for compensation is an estimate of the duration of the transition between steady states. The simulations of transitions into PAYG-financed social security by Auerbach and ICotlikoff (1987, p. 152) suggests that most of the ciange associated to that pension reform occurs within 20 years. The transitions from PAYG to full funding studied by Cifuentes (1993, p. 163 and 183) take at most 50 years. We will use the second estimate - 50 years - to be on the safe side.

The fact that real wages grow when PAYG financing is abandoned implies that the poor are gradually made better off by capital accumulation. In both cases the path of the real wage rate is above a linear interpolation between the extreme years. Therefore, an upper bound for the volume of compensation required is a linear function that starts at the initial value, and falls to zero by year 50.

With this assumption, we find tat the required compensation is 0.0975zsp in year one of the transition, which falls linearly to zero in year 50 of the transition. The sum of all these annual compensations, measured as fractions of GDP, is (1/2)- 50 -0.0975zsp =<sup>2</sup> .<sup>4375</sup> zsp.

As 40 percentage points of GDP of resources are available for distribution and consumption during the transition, we conclude that this will be enough to compensate the poor in the short run as long as: Z-Sp 5 0.1641
and λ _t_ +1 is given by the upper branch of (3). Observe that κ _t_ +1 = 1 if and only if there is no premature adult mortality ( π _t_ +1(1) = 1), and that κ _t_ +1 _<_ 1 otherwise. A reduction in κ _t_ +1, therefore, effectively entails a weaker taste for the children’s education. By way of illustration, let premature mortality among adults be independently and identically distributed, and denote the probability that an adult will survive to old age by _pt_ . Then, It will be convenient in what follows to rewrite (7) as A family in state _st_ (= 1 _,_ 2 _,_ 3) in period _t_ solves the following problem: Let [ _ct_<sup>0(</sup><sup>_st_)</sup><sup>_, e_</sup> _t_<sup>0(</sup><sup>_st_)]solveproblem(10),whoseparametersare(κ</sup><sup>_t_+1</sup><sup>_,_λ</sup><sup>_t, nt, st,_α</sup><sup>_,_β</sup><sup>_,_γ).By</sup> the envelope theorem, we have Since current consumption is maximized by choosing _et_ = 0, it follows that the parents’ altruism towards their children must be sufficiently strong if they are to chose _et >_ 0 _._ _Assumption 6._ Both goods are non-inferior.<sup>3</sup> It follows at once that: Inspection of _EUt_ ( _st_ ) reveals that an increase in κ _t_ +1 induces an increase in _et_<sup>0(</sup><sup>_st_)if</sup> 0 _< et_<sup>0(</sup><sup>_st_)</sup><sup>_<_1andpreserves</sup><sup>_e_</sup> _t_<sup>0(</sup><sup>_st_) = 1;foritincreasestheweightonν(λ</sup><sup>_t_+1)relativeto</sup> 3Note that Λ _t_ enters both the budget constraint and the utility that adults derive from λ _t_ +1. Therefore, the definition of inferior goods is not the same as the textbook description in this particular set-up.

16
- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), Economic Implications for Turkey of a Customs Union with the European Union, _European Economic Review_ , 41(3-5), 861-870.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (2002), ―Trade Policy Options for Chile: The Importance of Market Access,‖ _World Bank Economic Review_ , Vol. 16, Number 1.

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35