rafmacalaba/data-use-mentions-extended huggingface dataset:
(either collected or marketed) where no regular records are kept;

- because of this supply orientation, the aggregate figures do not reflect actual demand;

-information is presented only at the global level; no disaggregation is possible to pinpoint issues and problems that might affect specific re.gions, income classes or types of consumers; and

- consumption of biomass, often the most important household fuel, is usually a very rough estimate based on a set of untested assumptions.

Thus, energy balances are an insufficient, if not entirely inappropriate, means for focusing on the real issues and options in household energy.

# National Budget Surveys

These are large, statistically representative surveys which seek to develop data and trends on how families spend and save. They are usually conducted every 3 - 10 years, may invole several rounds with repeat visits in each round and focus on cash expenditure. These efforts have several advantages: (a) they are high-visibility efforts which receive substantial resources; (b) they are generally well-planned and statistically valid; and (c) they are nationwide. However, the energy analyst seeking to use results from these surveys faces several constraints:

- fuel consumption is rarely measured; it is usually based on iz-ollection and is thus unreliable;
total sample of 18344 households<sup>10</sup> . We drop these households from the current analysis since we are unable to map them with the remote sensing satellite data, and this exclusion may result in a selected sample. In table A.1, we present results from a series of weighted regressions of a dummy variable for bad geocodes (households dropped out of analysis) on demographic and location covariates. We find that households with bad geocodes were highly likely to be interviewed using paper surveys. While only 5 percent of households interviewed with tablets had bad geographical locations, 88 percent of households interviewed with paper questionnaires had incorrectly mapped locations. Households in rural areas, and Taliban-controlled and contested-regions were also more likely to have incorrect location data. Moreover, households with bad geocodes were also slightly more likely to be poor. With our sample skewed along the mentioned characteristics, our results should be interpreted as a lower bound on the impact of drought on welfare, as the analysis may be excluding poor households in the most remote areas.

With these caveats, Table 1 describes the household sample. Expectedly, households in our sample are predominantly rural, have male heads with less than 50 percent of the heads being literate. Half of the sample was collected after the impositions of COVID-19 restrictions. Over 50 percent of the sampled households resided in government-controlled districts, whereas 8 percent were in Taliban-controlled districts.

In Appendix B, we detail the methodology used to construct consumption aggregates and poverty rates based on cost of basic needs methods. The consumption expenditures per person per adult equivalent and poverty lines are estimated for each of the 14 regions.<sup>11</sup> Table 2 show the description of consumption aggregates and poverty line constructed with this socioeconomic data. Forty-seven percent of the Afghan people are poor, with urban poverty only slightly lower than rural poverty.

# **3.2 Weather Data**

As discussed earlier, we expect temperature and NDVI deviations to be better measures of weather shock in the
The second major reform is the introduction of the Goods and Services Tax (GST) which was passed by Bhutan’s National Assembly in 2020 and is planned to take effect in mid-2022. The GST will replace the existing sales tax and will be implemented at a single standard rate of 7 percent. The GST contains significantly fewer exempt goods and services than the existing sales tax which has a narrow base; it will apply to all services, including the economically significant tourism, transport, and consultancy sectors. Exports will be zero-rated and excise equalization rates of 20, 30, 40 and 100 percent will apply on top of the GST rate for select products whose use the government wants to discourage, such as environmentally harmful goods, alcohol and tobacco. The GST is widely expected to boost revenues by broadening the tax base and minimizing tax exemptions, curbing revenue leakages, and implementing a tax scheme that is simple, fair and equitable by design. Besides the sales tax, the existing green tax and custom duty will remain in place.<sup>27</sup> Excise taxes are expected to increase under the new regime because of new higher tax rates. Since one of the objectives of the GST is to minimize the cascading effects by allowing tax credits, we only model the direct effect for items that are subject to either the general GST tax rate or the zero rate. Indirect effects are modeled for exempt items since final producers or retailers are not allowed to claim credits for GST paid in earlier production stages. A caveat is that not all legally exempt items can be identified in the household survey. The list of exempt items that were found in the household survey is shown in appendix Table A.3. The new excise schedule is shown in appendix Table A.4.

Simulation models can be a powerful tool to analyze the ex-ante distributional impact of significant fiscal reforms such as these. This done by adjusting the CEQ income concepts in line with the changes in these tax policies. In the case of the
for 2000 were updated to 2005 using updated population and 2005PPP factor. Also, the mean income at the country level was adjusted to match the latest poverty rates reported in POVCAL<sup>3</sup> . Thus, the poverty rates at the country level are consistent with the 2005PPP poverty estimates in Chen and Ravallion (2008).

The GIDD database covers all regions in the world. Eastern Europe and Central Asia is 100 percent covered; Latin America 98 percent; South Asia 98 percent; East Asia and Pacific 96

> 3 POVCAL is a tool developed at the World Bank for calculating poverty and inequality indices.

12
of PMT schemes are widely used by practitioners and academics, ignoring geographical targeting and arbitrarily defining selection thresholds could potentially lead to large biases in the simulated targeting performance.

Our analyses also allow some conclusions on CBT, which are often ignored by studies based on simulated targeting mechanisms using existing household data sets, since CBT is not as easily simulated as PMT selection. We show that CBT selects households that are systematically different from PMT, and that the performance of CBT is lower than PMT’s when assessed based on performance measures related to percapita consumption.

Per-capita consumption is only one of the potential welfare metrics used to define targeting objectives. Reaching food-insecure households is actually a key objective of humanitarian interventions through which hundreds of millions of dollars are spent yearly in the Sahel. Nonetheless, little is known about ways in 23
0.15|0.14|-0.083|0.17|0.42|0.26|0.062|0.11|-0.099|-0.17|
|Robust p-value|0.053|0.453|0.997|0.079|0.046|0.587|0.235|0.407|0.534|0.696|0.957|
|<br>MSE-opt. bandwidth|18.7|22.5|21.9|16.6|20.8|23.1|18.6|20.2|18.5|15.3|25.0|
|Effective obs.|161|181|177|141|173|182|160|172|160|133|196|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). In column 1, the dependent variable is a standardized index of local service provision constructed using the 2021 _Podes_ survey. Remaining columns report RD estimates on the individual index components. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, paved roads, public transit, kindergartens, primary schools, village maternities ( _polindes_ ), and community health centers ( _puskesmas_ ). We first standardize each individual component before taking the village-level average of all components. The sample includes all villages in our sample that conducted their last election before 2021. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.
Restricted dimensions of quality of health care and infrastructure The analysis is restricted to the relationship between health services quality and infrastructure dimensions for which we have data. As health care quality is a multidimensional concept, we are not able to explain all its variability with the proposed approach and the available data sets. However, our approach allows us to explore the associations between the variables of interest and provide suggestive evidence on the relevance of the access to infrastructure to the quality of health services in Kenya. We based our discussion and recommendations acknowledging these limitations of the methodology.

Longitudinal and cross-sectoral dimensions of SDI Despite SDI surveys being a rich source of information, these data did not allow us to pursue a causal analysis due to lack of an exogeneity source. Therefore, this study only presents correlations between access to infrastructure and health service quality. These are still useful benchmarks for illustrating the potential that investments in infrastructure might have for improving the quality of health service delivery.<sup>17</sup> Infrastructure is a cross-sectoral and typically long-term investment that might influence services simultaneously in multiple sectors over many years. Ideally, the education SDI data would also have been exploited in this paper, but the 2012 education data was several years more outdated and done at a smaller scale (i.e., only representative at the country level, not at the county level). Moreover, it was impossible to perform an intertemporal analysis using the 2012 and 2018 health SDI data sets due to several methodological changes that made data not fully comparable over the years (e.g., different sample methodologies, different facilities, and different levels of representativeness).

Finally, as mentioned before, the last SDI data set for Kenya is from 2018. Although it is not completely outdated, in the past years, Kenya has made important improvements both in infrastructure and health service delivery, which might not be captured by the present analysis. Nevertheless, for the purpose of the analysis, we use the most up-to-date data available.

Limitations of
# **Variables and Indices Composition**

Table A4: Indices composition

|Index|Variables|
|---|---|
|SRH behavior (15 yo+)|Ever had sex [yes = 1] (15 yo+) (Reserve)<br>Had transactional sex in the last 6 months [yes = 1] (15 yo+) (Reserve)<br>Was sexually active in the last 12 months [yes = 1] (15 yo+) (Reserve)<br>Used a condom in last sexual intercourse [yes = 1] (15 yo+)<br>Ever used a contraception to avoid pregnancy [yes = 1] (15 yo+)<br>|
||Engaged in any IGA (last 6 months) [yes = 1]<br>Total profts cash (last 30 days) (IHS)|
|IGA|Received income from IGA (last 30 days) [yes = 1]<br>Number of IGA adolescent has been engaged in (last 6 months)<br>Number of IGA adolescent took the initiative (last 6 months)<br>Worked in a nonagricultural IGA (last 6 months)[yes = 1]|
|SRH knowledge & attitude|Knows of at least one modern contraception method [yes = 1]<br>Has ever heard of HIV/AIDS [yes = 1] (15 yo+)<br>Agrees using a condom can reduce HIV transmission risk [yes = 1] (15 yo+)<br>Attitude index on maternal health care (15 yo+)<br>|
||Index gender equality (std)<br>Index gender equality based on conformity to traditional gender role beliefs (std)<br>Ideal age for a woman to marry|
|Gender attitudes|Ideal age for a man to marry<br>Finds at least one disadvantage for a girl to marry before 18
- 15 -

agricultural census. Nevertheless. they once again point to service, commerce and restaurants as the fastest growing nonfarm sectors. While overall manufacturing grew more slowly than services and commerce, the Rwanda data, like those from Sierra Leone, do point to woodworking, metal work and textiles as among most buoyant rural industrial activities.

E. Correlates of Growth in the Rural Nonfarm EcJjw We hypothesize that several factors affect the growth of the rural nonfarm economy.

1. DeveloDMent of rural towns. In part this may be due to the benefits of economies of scale as well as the prospects for centralized, cost-effective provision of key infrastructure and services.

2. Leyel of infrastucture. Decreases !n the cost of information and transport flows improve the efficiency with which rural labor and financial markets channel inputs into activities yielding the highest returns. Noreover, decreased transport costs open up rural resources and markets to viable exploitation, facilitating movement to a more specialized, productive rural economy.

3. Per capita income. Engel's Law mandates this tandem movement for the overall economy, and if definitions of "rural' reoain liberal enough to encompass the nonfarm growth in rural towns, increases in income will lead to an increase in the nonfarm share of total rural income.
by decree has reproduced the old state-centered system based on interventionism and paternalism in MENA countries, thus moving them away from the prospect of establishing economic and political institutions to sustain a market economy. Most importantly, this topdown approach meant that the prospect for establishing a consensus around a renewed SC became more remote. In the 1990s, government transparency, rule of law, and voice and accountability all suffered in MENA (Yousef, 2004).

# **_Potential for the advent of a new participation-strong social contract_**

What was the potential for reform at the turn of the 21<sup>st</sup> century in MENA? On the economic front, the global situation was such that most countries in the MENA region could no longer afford to undertake partial and often reversible reforms while relying on oil revenues and foreign aid, which have declined significantly. Governments in the region have become increasingly dependent on national resources to finance economic and social programs, and the need for an institutional and regulatory framework to foster growth has remained a high priority. On the political front, the public opinion showed significant support for an electoral reform, good governance, and an integration of opposition parties into the political game. For example, respondents to a 2002 survey have rated civil and individual rights as the highest priority in eight MENA countries (Zogby, 2002). Moreover, a Gallup survey undertaken that same year showed that individual attitudes in MENA are more driven by political and economic considerations than religion and family factors (Tessler, 2003).<sup>21</sup> Unfortunately, this potential has not materialized and around the middle of the last two decades, the MENA region has been the scene of events that have left their mark on people's minds, both in their sheer scale and in the suddenness with which they have occurred. In the early 2010s, a series of anti-government protests, uprisings, and armed rebellions spread across many countries of the region, more specifically, the Arab world. This
# **Bibliography**

Arnold, J., B. Javorcik and A. Mattoo (2011) “The Productivity Effects of Services Liberalization: Evidence from the Czech Republic”, _Journal of International Economics_ , Vol. 85, No. 1, pages 136‐ 146.

Arnold, J., B. Javorcik, M. Lipscomb and A. Mattoo (2015) “Services Reform and Manufacturing Performance: Evidence from India”, _The Economic Journal_ , Vol. 126, Issue 590, pages 1‐39.

Bauer, M., F. Erixon, H. Lee‐Makiyama, M. Krol (2013) “The Economic Importance of Getting Data Protection Right: Protecting Privacy, Transmitting Data, Moving Commerce”, Washington DC: US Chamber of Commerce.

Bourlès, R., G. Cette, J. Lopez, J. Mairesse and N. Nicoletti (2013) “Do Product Market Regulations in Upstream Sectors Curb Productivity Growth? Panel Data Evidence for OECD Countries”, _The Review of Economics and Statistics_ , Vol. 95, No. 5, pages 1750‐1768.

Christensen, L., A. Colciago, F. Etro and G. Rafert (2013) “The Impact of the Data Protection Regulation in the EU”. Intertic Policy Paper, Intertic.

Ferracane, M.F. (2017), “Restrictions on Cross‐Border data flows: a taxonomy”, ECIPE Working Paper No. 1/2018, European Center for International Political Economy, Brussels: ECIPE.

Ferracane, M.F. and E. van der Marel (2018) “Do Data Flows Restrictions Inhibit Trade in Services?”, ECIPE DTE Working Paper Series No. 2, Brussels: ECIPE.

Ferracane, M.F., H. Lee‐Makiyama and E. van der Marel (2018b) “Digital Trade Restrictiveness Index”, European Centre for International Political Economy, Brussels: ECIPE.

Ferracane, M.F., J. Kren and
describes both the research design as well as key variables. Section 3 reports results and section 4 concludes with a discussion of the implications for policy and programming.

# **2. Data and Methods**

## Research Design and Data Collection Strategy

Gallup Pakistan was contracted by the World Bank to conduct a nationally representative survey. As pandemic health protocols precluded in-person interviews, interviews were conducted by telephone. Calls were made from December 2021 to February 2022 using random digit dialing (RDD) of mobile phones using all four telecom providers with active numbers across the country.

8
or unable, to subsidize the banks for political reasons. Table 2 shows that banks that were privatized earlier (banks who started the privatization process before the Tequila Crisis of December 1994) tended to be worse performers in 1992 (prior to 16 We used 1992 fiscal data because we want to compare deficits prior to any privatizations. This is important because the act of privatization might affect the provinces' deficits (for example by ending the need to finance recapitalization of the provincial banks).

10
gender equality based on conformity to traditional gender role beliefs (std)<br>Ideal age for a woman to marry|
|Gender attitudes|Ideal age for a man to marry<br>Finds at least one disadvantage for a girl to marry before 18 [yes=1]<br>Expected education for adolescent’s female children<br>Gap between the ideal level of education for her daughter(s) and son(s)|
|Aspirations|Adolescent’s ideal number of children<br>Occupation desired by adolescent is high or intermediate status [yes = 1]<br>Revenue adolescent wishes to make from desired occupation (IHS)|
|SES|Self esteem index<br>General efcacy index<br>Self-awareness index<br>Problem-solving index<br>Perseverance index<br>Index on ability to infuence others<br>Creativity index|
|Marriage and pregnancy|Adolescent ever been married [yes = 1] (Reserve)<br>Adolescent currently pregnant [yes = 1] (Reserve)<br>Adolescent ever been pregnant [yes = 1] (Reserve)|
||Experienced at least one type of emotional violence [yes = 1] (Reserve)|
|Violence (15 yo+)|Experienced at least one type of physical violence [yes = 1] (15 yo+) (Reserve)<br>Experienced at least one type of sexual violence [yes = 1] (15 yo+) (Reserve)|
|Decision-making individual|Decision-making index on individual issues (std)|
|Decision-making couples (married)|Decision-making index on couples issues (if married at baseline and midline) (std)|
|Finances|Borrowed money in the last 12 months [yes = 1]<br>Saved money in the last 12 months [yes = 1]|
|Nutrition|Adolescent’s dietary diversity score [0–1]<br>|
|Mental health|Overall,
∆_Izy_)|[0.657]|[0.380]|[0.458]||[0.226]|[0.208]|
|N|14,833|14,833|14,833||14,833|14,833|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : MTI = mother tongue instruction; FPE = free primary education. The dependent variable is years of schooling in panels A.i and B.i, and is number of births in panels A.ii and B.ii. All samples include women in birth cohorts from 1963 to 1979, the same number of cohorts as the baseline estimates but moved nine years back. In the baseline sample the first post-reform cohort is 1988, also at least a nine-cohort difference. This timing matches the period in which the FPE intensity measure ( _Izy_<sup>FPE</sup> ) predicts little impact of the reform, as seen in fig. 2 of the main article. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.43
adding the functions corresponding to each characteristic _i_ in that country and year. Finally, these yearly returns are averaged for each country. The result is provided in uu table 4. ii bb To estimate the standard errors the study uses a wild bootstrap version introduced by Mammen (1992) for nonlinear cross-sectional regression models, and studied in Franke, Kreiss, and Mammen (2002) for nonlinear time series data. A main advantage in this case is that it automatically accounts for the presence of unknown heteroscedasticity and potential non-normality. The procedure is as follows; for a sample {( _Yc , t , Xc , t_ )} _tT , c ,_ = _nt_ 1 with _nt_ countries in year _t_ , and estimates [ _Y , ct_ | _Xc , t_ ] bootstrap samples are generated: EE<sup>�</sup> _Yc_ ∗ _, t_<sup>=EE�[YYoo</sup> ,<sup>oo|XXoo</sup> ,<sup>oo] +�YYoo</sup> ,<sup>oo−EE�[YYoo</sup> ,<sup>oo|XXoo</sup> ,<sup>oo]�</sup> · _v , v_ ∼ _N_ (0 _,_ 1) _._
|(0.077)|(0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|0.000|0.024|0.031|0.018|4993|
||(1.000)|(0.102)|(0.115)|(0.116)||
|||[1.000]|[0.513]|[1.000]||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.Maternal death is defined as death relating to either pregnancy complications or childbirth. The Pentavalent Vaccination targets diphtheria, tetanus, whooping cough, hepatitis B, and haemophilus influenza type B. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A23
yield losses, ranging from -13% for spring wheat to -22% for soybeans and -27% for maize. From the point of view of poverty, this is not a good outcome. It suggests lower farm incomes in the poorest countries (due to the sharper productivity reductions there) where extreme poverty tends to be predominantly rural, with somewhat higher world prices overall, thereby hurting low income consumers worldwide. While Agro-PEGASUS is not yet available to the broader research community, it is a tool worth keeping an eye on for future research.

# _Statistical Approaches_

An alternative to the crop simulation approach is to estimate statistical relationships between crop yields on the one hand, and temperature and precipitation on the other. The advantages of this approach are that it requires relatively less data and it can be readily implemented for large geographic areas (e.g., nations or continents). However it is not process-based, and so it relies on predicting future responses based on past relationships. Thus changes in varieties grown, and other agro-ecological choices, such as planting and harvesting dates, etc. are not taken into account. Recent examples of this approach are offered by Schlenker and Roberts (2006), Lobell et al. (2008) and Schlenker and Lobell (2010).

Statistical studies can be based on cross-sectional data or time series data. The former naturally correspond to longer run adaptations to climate. The problem with cross-section analysis, however, is the potential for omitted variables to bias parameter estimates. An excellent example of this is offered by Schlenker, Hanemann and Fisher (2005) who show that, if irrigation is not explicitly taken into account and irrigation tends to take place in hotter regions, then yields (economic returns) might appear to be positively related to temperature when in fact this is not the case.

9