rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3:** Comparison of Levelized Costs of Wind Power with Other Technologies, 2003 $US/MWh

|Plant|At|5% discou|ntrate||At|10% discou|ntrate||
|---|---|---|---|---|---|---|---|---|
|Type|Levelized<br>cost|Share in l|evelized co|st (%)|Levelized<br>cost|Share in l|evelized co|st (%)|
||US$/MWh|Capital|O&M|Fuel|US$/MWh|Capital|O&M|Fuel|
|Wind (onshore)|31.1-92.3|79|21|0|46.1-144.2|85|15|0|
|Wind (offshore)|50.5-94.3|67|33|0|66.0-123.4|75|25|0|
|Solar thermal|165.5|77|23|0|269.4|86|14|0|
|Solar PV|120.6-484.8|96|5|0|209-1876|97|3|0|
|Small hydro|39.7-142.9|78|22|0|63.5-241.9|87|13|0|
|Large hydro|45.4|96|4|0|84.9|98|2|0|
|Nuclear|20.8-48.0|51|30|18|31.7-68.6|67|21|13|
|Lignite)|29.4-56.9|33|17|50|37.1-64.4|48|14|38|
|Coal|17.9-47.8|33|21|47|25.9-69.1|57|14|29|
|Coal (IGCC)|27.3-48.2|34|26|40|38.2-59.1|50|20|30|
|Gas (CCGT)|38.2-60.4|13|7|80|40.9-62.6|19|7|74|
|Gas (open)|46.70|8|4|88|49.0|13|4|83|
|CHP (Gas)|28.3-62.3|21|21|58|31.9-80.9|30|19|51|
|CHP (Coal)|25.0-36.8|41|34|25|34.8-46.9|56|26|18|
|CHP (other)|29.4-96.3|22|12|67|33.5-99.5|31|11|59|
|Biomass|37.3-85.2|36|24|40|50.3-100.5|52|18|30|

_Source_ : NEA & IEA (2005) Fuel costs of CHP plants are net of heat benefits. Weighted average costs of various plant sizes were considered while calculating the shares of capital, O&M and fuel costs. Estimates do not account for tax preferences, subsidies or environmental externalities. More detailed discussion of methods is found in NEA & IEA (2005).

Due to decreasing capital costs of wind power (see Neij, 2008), potentially increasing fossil fuel prices in the long term, and an increasing desire to account for environmental costs and benefits in electrical generation (e.g., via carbon credits), wind power is becoming more competitive with conventional resources for electricity generation. However, the direct cost of construction and operation is not the only factor to determine penetration of wind power into electricity grids. Other factors such as transmission access, intermittency, system reliability and grid characteristics significantly influence the contribution of wind power to a country’s electricity supply system (see Sovacool, 2007).

11
would only likely to be the case for countries that are small and poor and thus have low GDPs and are very open to migration and recipients of large amounts of remittances.

In summary, the cyclicality of remittances may indicate whether altruism or self-interest is the dominant motive in a particular country. It will also indicate whether remittances are stabilizing or not in most cases, though certainly not in all of them. Counter-cyclical remittances (or other sources of capital inflows) may be destabilizing, and vice versa, though the latter would seem to be less likely prevalent. As shown in Section 4.3, these types of results actually do occur for some countries.

# 3. Data

We employ the following indicators: Remittances, Foreign Direct Investment or FDI (net inflows), Official Development Aid (ODA), and GDP. Remittances are defined as the sum of three series from the IMF Balance of Payments: workers’ remittances, migrants’ transfers and compensation of employees. All the other data come from the World Bank’s World Development Indicators (WDI). The FDI, originally reported by the IMF Balance of Payments Statistics as “Direct Investment in reporting country”, is comprised of equity capital, reinvested earnings and other claims/liabilities on/to direct investors. Variables are expressed in US million dollars. Remittances, ODA and FDI are converted from current US dollars into constant 2000 US dollars by using the US GDP deflator.

Our sample includes 116 developing countries of which 36 are low income, 45 lower middle income and 35 upper middle income. By geographical criterion, 15 countries are from East Asia and Pacific (EAP), 20 from Europe and Central Asia (ECA), 28 from Latin America and the Caribbean (LAC), 10 from Middle East and North Africa (MENA), 6 from South Asia (SA), and 37 from Sub-Saharan Africa (SSA). Appendix Table A1 lists the names of the countries and the classifications by income group and by regions.

Remittance data for some individual countries are incomplete. Sixty-four of the 116 countries taken into consideration have values for 20 or more years (with an average of 910 million 9
∆���� are the log changes in lagged asset prices and oil prices (they are control variables).

We present regular standard errors in our main results section, and bootstrap standard errors as robustness checks in Appendix. The two methods yield similar results.

# **Identifying oil-supply events**

Identifying oil-supply events is challenging. There is not a fixed calendar for oilsupply events, so one has to screen these days from financial news. Since there are multiple events that could happen in those days, it is not certain that oil supply news drives oil prices.

We employ several rounds of screening to identify oil-supply events. In the first round, we use the Seeking Alpha news portal (www.seekingalpha.com).<sup>7</sup> Seeking Alpha records all surprising events and announcements that arguably affect the oil supply. They range from surprising announcements by OPEC officials and OPEC member countries to unexpected developments in key oil exporters. From 1/1/2014 to 10/15/2016, we record 29 events. The window for our event study is one day. For announcements that happen after trading hours, we examine the change in financial markets on the following trading day. These dates are shown in Table A1 in the Appendix, along with links to in-depth financial news discussing the events.

There could be concerns with this list. The first potential problem is that recorded events could reflect ad-hoc ex-post explanations of the analysts. For example, an analyst could see oil prices drop during the day and look for news about oil supply

> 7 <mark>Seeking Alpha is a community-based platform for investment research, with broad coverage of stocks, asset classes, ETFs and investment strategy. In contrast to other equity research platforms, insight is provided by investors and industry experts rather than sell-side analysts. S</mark> eeking Alpha has 4M registered users (48% YOY growth). Over 18.5% of the audience are financial professionals.

9
– bootstrapped standard errors from maximum likelihood they are 27% smaller for UCT in Panel A, and 14% smaller for TUP in Panel B. We interpret this as evidence that the bias in inference from unmodeled within-RCT correlation between estimates mentioned in Section 3.1 is relatively small, and focus our discussion in the remainder of the paper on our Bayesian estimates.

The estimated average impacts on consumption per unit of transfer is meaningfully smaller in UCT than in TUP studies; we explore this dimension of heterogeneity further in Section 6.

Leveraging our estimates of the average impact and the standard deviation of impacts of UCT and TUP per unit of transfer, we plot the full set of posterior estimates and 95% credible intervals for each of the estimates in our metaänalaysis in Figure 1. The posterior estimates are equal to the reported estimates, but shrunken towards our estimates of average impacts, as described in Section 3.1. As we estimate a relatively tight standard deviation of impacts of UCT, most estimates are shrunken quite close to the posterior mean (and particularly those estimates with wide standard errors). As more precise estimates receive higher weight when estimating the average impacts of UCT and TUP, Figure 1 visualizes which estimates drive our result.

With the average impacts of cash transfers on household consumption per unit of transfer analyzed, we now return to our motivating question: how do the effects of large and small cash transfers differ over time? In particular, are the impacts of large cash transfers more persistent per dollar transferred? In the next section, we model consumption responses to cash transfers to identify a theory consistent test of the relative persistence of larger cash transfers and implement this test.

20
**Table 28: IV Estimation (3)**

||No. of trips to:<br>|||||||
|---|---|---|---|---|---|---|---|
||Buy food|Buy goods|Go to|work|Do business<br>|Visit friend|s/relatives|
|D|-1.239 (0.472) ***|0.105 (0.167)|0.785|(0.602)|-<br>0.295 (0.469)|-0.156|(0.383)|
|lnHH size|0.238 (0.147) *|0.068 (0.052)|0.129<br>-|(0.188)|0.184 (0.146)|-0.112|(0.119)|
|Adult share|0.243 (0.309)|0.147 (0.110)|0.492<br>-|(0.394)|0.265 (0.307)<br>-|-0.214|(0.250)|
|HH head sex: Male|0.060 (0.178)|0.038 (0.063)|0.378|(0.226) *|0.198 (0.177)|-0.074|(0.144)|
|HH head education: Elementary|-0.107 (0.127)|0.065 (0.045)|0.215|(0.162)|0.241 (0.126) *|0.123|(0.103)|
|HH head education: Secondary|0.038 (0.208)|0.063 (0.074)|0.920|(0.265) ***|0.591 (0.206) ***|0.179|(0.168)|
|HH head education: University|0.112 (0.421)|-0.022 (0.149)|0.771<br>-|(0.536)|0.265 (0.418)|0.331|(0.341)|
|Appliance ownership: Refrigerator|0.252 (0.193)|0.043 (0.068)|0.069|(0.245)|0.101 (0.191)|0.015|(0.156)|
|Appliance ownership: Color TV|-0.312 (0.191) *|0.055 (0.068)|0.478|(0.244) **|0.110 (0.190)<br>-|-0.223|(0.155)|
|Appliance ownership: Radio|-0.320 (0.119) ***|0.028 (0.042)|0.125|(0.151)|0.143 (0.118)|-0.123|(0.096)|
|Appliance ownership: Gas stove<br>Appliance ownership: Washing|-0.394 (0.199) **|-0.061 (0.071)|0.208|(0.254)|0.211 (0.198)<br>-|0.026|(0.161)|
|machine|-0.024 (0.139)|-0.009 (0.049)|0.107|(0.177)|0.267 (0.138) **<br>-|0.063|(0.113)|
|Gas use for cooking|0.028 (0.134)|0.025 (0.048)|0.136|(0.171)|0.002 (0.133)<br>-|-0.119|(0.109)|
|Power use for lighting|0.345 (0.203) *|0.010 (0.072)|0.089|(0.259)|0.139 (0.202)|0.075|(0.165)|
|Tap water for cooking|-0.112 (0.129)|0.022 (0.046)|0.167|(0.164)|0.242 (0.128) *|0.080|(0.104)|
|Constant|3.346 (0.706) ***|4.926 (0.250) ***|4.551|(0.898) ***|4.394 (0.701) ***|3.733|().572) ***|
|Obs.|911|911|911||911|911||
|R-squared||0.003|0.026||0.028|0.005||
|Wald-stat|2.26|0.54|3.12||1.94|0.65||
|Sargan overidentifying restriction test|<br>1.87|0.25|4.60|**|0.13|2.51 *||
|Hausmanexogeneity test|8.56***|10.16***|28.99|***|5.35**|45.70*|**|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

47
**Figure 2.10. Employment increased driven by urban areas.** Employment rate (Percent of LFP), 2009– 2019

<!-- Start of picture text --> 100<br>2009 2014 2019<br>80<br>60<br>40<br>20<br>0<br>Source: Authors’ elaboration using data<br>the CSES household survey.<br>Employment rate<br>Urban Male Rural Male Female Urban Female rural Urban Rural Male Female Cambodia<br><!-- End of picture text -->

**Figure 2.11. The unemployment rate halved since 2009.**

Unemployment rate (Percent of LFP), 2009–2029 <!-- Start of picture text -->
1.4<br>2009 2014 2019<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Source:  Authors’ elaboration using data<br>the CSES household survey.<br>Unemployment rate<br>Urban Male Rural Male Female Urban Female rural Urban Rural Male Female Cambodia<br><!-- End of picture text -->

Structural transformation and economic growth have translated into wage gains. Over the past decade, real wages for employees in Cambodia nearly tripled (Figure 2.13). These wage increases have been greater for women than for men, contributing to a reduction in the gender pay gap. Over the last ten years, the wage gender gap decreased by 10 percentage points from 27 percent in 2009 to 18 percent in 2019 (Figure 2.12).

Between 2009 and 2014, the gender wage gap narrowed by 6 percentage points, primarily driven by substantial wage growth in sectors where women had relatively higher employment rates, such as mining and apparel. In the second period from 2014 to 2019, reduction in the wage gap slowed, decreasing by only 3 percentage points, mainly due to slowing wage growth in sectors where women were relatively more employed (Savchenko et al, 2016).

8 | P a g e
## _Gender_

Another council-level variable that may affect public goods provisions relates to the number of women elected to the group. Research on the political economy of gender suggests that women’s presence in political bodies translates into substantive policy decisions in at least three ways. First, female representatives may prefer a different bundle of public goods than male representatives (Chattopadhyay and Duflo 2004). In particular, evidence from the United States (Miller 2008; Rehavi 2008) and India (Bhalotra, and Clots-Figueras 2010; Clots-Figuerasa 2012) suggests female elected officials prioritize health and education spending. Second, when governing by consensus, female constituents may be more likely to express their opinions in councils that are led by women (Ban and Rao 2008). Finally, in the Indian context, Munshi and Rosenzweig (2008) find that female council leaders are more competent than their male counterparts in delivering public good investments to their constituencies. From this we expect that local councils with more women may have higher rates of public investments in health and education services than those with fewer women, all else equal.

In addition, the gender composition of local councils may mediate the potential for different types of councilor/chief relations. Recent work from a policy experiment with reserved electoral districts for female councilors in Lesotho presents evidence that traditional leaders loose authority in districts reserved for women because the presence of female councilors implicitly challenges the patrilineal tradition on which chiefs base their authority (Clayton 2014). Again, however, how this dynamic affects local development outcomes relates to our main empirical question.

In addition to these council-level compositional variables, we discuss the EA-level controls we include in our models in Section 5.3.

# **_Section 5: Data, Measures, and Methods_**

Our analysis draws on data from two types of surveys in Sierra Leone. First, we create the local council variables from the Decentralization Stakeholders Survey (DSS), which was given to all local authorities affected by the decentralization process. We use data from the survey conducted immediately after the 2008 local council elections that surveys councilors from 12
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EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE growth forecasts prior to the onset of the global financial crisis of 2008. On the assumption that the economic crisis could halve anticipated economic growth rates over this region, the estimate of demand for 2015 would fall by 2,101 megawatt-hours

**Table 3.2 . Demand and suppressed demand in the East Africa Power Pool–Nile Basin countries**

All figures are in TWh unless noted otherwise

||**Total net demand**<br>**in 2005**|**% suppressed**<br>**demand as a share of**<br>**net demand (2005)**|**Market demand**<br>**2015**|**Social demand**<br>**with national**<br>**targets 2015**|**Total net demand**<br>**2015**|
|---|---|---|---|---|---|
|Burundi|0.2|13|0.3|0.5|0.7|
|Djibouti|0.2|6|0.3|0.1|0.4|
|Egypt|84.4|0|119.9|3.4|123.3|
|Ethiopia|2.1|5|3.4|7.4|10.7|
|Kenya|4.6|8|6.8|5.2|12|
|Rwanda|0.1|5|0.2|0.3|0.5|
|Sudan|3.2|5|5.2|3.9|9.2|
|Tanzania|4.2|5|6.2|1.7|7.9|
|Uganda|1.6|5|2.5|1.7|4.2|
|**EAPP**|**100.6**|**1**|**144.8**|**24.2**|**169**|
|SAPP|258.8|1|383|14|396.9|
|WAPP|31.3|30|69.6|24.8|94.3|
|CAPP|10.7|9|17.1|3.1|20.2|
|Island States|1.1|5|1.6|1.5|3|

Source: Rosnes and Vennemo 2009.

Note: The East African Power Pool is expanded to include key Nile Basin trading partners Egypt, Ethiopia, and Sudan. CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool; WAPP = West African Power Pool.

Future power demand can either be met by expanding national production or by expanding crossborder power trade within EAPP. Two alternative scenarios will be considered in this report. The _trade stagnation_ scenario assumes that no additional cross-border interconnectors will be built. In this scenario, trade is constrained at the levels observed today, and countries are thus obliged to meet incremental power demands solely through the development of their own domestic power sectors. For many EAPP countries that lack significant energy resources of their own, this entails increased reliance on thermal generation fueled by oil imports.

Alternatively, under the _trade expansion_ scenario, future regional power demand is met by the most cost-effective energy resources available to the region as a whole, and additional cross-border transmission capacity is added wherever required to allow power to flow from production to consumption locations. Essentially, this scenario takes regional power trade to its fullest economic potential, assuming that there are no restrictions to cross-border exchange and that the necessary infrastructure can be built wherever it is required. Reality is likely to lie somewhere in between the trade stagnation and trade expansion scenarios, and in this sense the two scenarios serve to frame the range of possible outcomes.

38
Figure A.5: Distribution of payment date for treated, untreated, and pure control (October 2020 billing period) (a) Treated vs. Pure Control <!-- Start of picture text --> .15<br>Treated<br>Due date of<br>October bill Pure Control<br>.1<br>.05<br>0<br>30sep2020 14oct2020 28oct2020 11nov2020 25nov2020 09dec2020 23dec2020<br>(b) Untreated vs. Pure Control<br>.15<br>Untreated<br>Due date of<br>October bill Pure Control<br>.1<br>.05<br>0<br>30sep2020 14oct2020 28oct2020 11nov2020 25nov2020 09dec2020 23dec2020<br>Fraction<br>Fraction<br><!-- End of picture text --> _Notes_ : These figures show the fraction of individuals paying the October 2020 bill before and after the due date (October 9th, 2020). Panel (a) shows the distribution of payments for treated units (in blue) relative to pure control units (in red). We pool together treated units from _Tg_ = 1 _,_ 2 _,_ 3. Panel (b) shows the distribution of payments for untreated units (in blue) relative to pure control units (in red). We pool together untreated units from _Tg_ = 1 _,_ 2 _,_ 3. The area of each histogram integrates to one. A larger bar on a particular date means that the payment frequency of the corresponding group is higher than the other group.

A-6
ity is thus driven by variation across the 75 different equilibria of district governments. We argue and provide supporting evidence to interpret the correlation as follows: in districts where higher integrity politicians are endogenously selected, these politicians exert greater effort towards public goods than rent-extraction; as a result, the local implementation of national health programs improves. Even if omitted variables that capture citizens’ political preferences or prevailing social norms are the underlying driver of variation in political selection, the proximate channel of influence of political integrity on service delivery is through the effort exerted by selected politicians. We show that the correlation with political integrity holds even after accounting for plausible sources of variation in voter demands (such as, partisan attachment to national leaders, and the presence of local radio), thus suggesting that local politicians have a direct or proximate channel of influence on service delivery, apart from other channels through which citizens might influence service delivery without going through politicians.

We further provide qualitative evidence and support from other studies in Uganda of the role of local councilors in monitoring local health facilities, and how they can play these roles in obstructionist, rent-seeking ways (such as shielding absent health workers from disciplinary action, or pilfering funds at the health clinic) versus facilitating better delivery (such as encouraging health workers to show-up, and monitoring that clinic funds are appropriately used). We suggest that the measure of integrity is capturing the proclivity of politicians to exert effort in one type of action compared to another–whether in the extraction of private rents versus the promotion of broader service delivery. Higher integrity politicians are more motivated to pursue public goods and therefore more effective in improving public health service delivery.

Nevertheless, omitted variables that explain both integrity and the ability of politicians to influence service delivery could be biasing the coefficient on integrity that we estimate. Random assignment of higher integrity politicians to districts may yield different estimates of impact on service delivery. However, random assignment of integrity may not be the appropriate way to test theories where political selection arises endogenously. Learning from correlations revealed by the data is valuable to test theories of endogenous relation5
the analysis in World Bank (1998), Burnside and Dollar (2000, 2004), Collier and Dollar (2002), Collier and Dehn (2001), and Guillamont and Chauvet (2001).

The academic discussion above is truly vigorous and useful, but while producing interesting results and raising fascinating questions, cross-sectional macroeconomic studies cannot be the last word on the topic of aid effectiveness. Small changes in specification and country coverage lead to qualitative changes in results in all of these studies, reflecting the fact that there is not enough variation across countries and too much collinearity among determinants of growth. This paper attempts to go beyond the existing rhetoric and introduce microeconomic evidence on factors conducive to project success in developing countries. If aid works the same everywhere, then one would expect to find clear evidence of this in the data on the outcomes of projects funded by development assistance. The finding of a strong relationship between institutional quality and project outcomes, on the other hand, would serve to provide further support to the validity of the third hypothesis of aid effectiveness, with the effect of aid conditional on institutions and policies of the recipient country.

In focusing on project-level evidence, we follow studies performed by World Bank researchers, such as Isham and Kaufmann (1999), Isham et al. (1997), and Kaufmann and Wang (1995). Instead of growth, these three studies use project-level indicators – economic rate of return and project success / failure, provided by World Bank’s Operations and Evaluations department (OED) – as the dependent variable.

Putting different recipient country-specific institutional and policy indicators on the righthand side of their models, the three studies find that the ERRs are higher and probability of project failure lower in countries with better policies and institutions. In the present paper, we extend this analysis and introduce other possible explanations of the rate of project success across countries.

2