rafmacalaba/data-use-mentions-extended huggingface dataset:
confidence interval on the value of a result from a competing model. The confidence intervals _cannot_ overlap.

Our approach builds on the extreme bounds approach to assessing difference in estimates from Levine and Renelt (1992) and the graphical methods to visualize these differences in Sala-i-Martin (1997b,a).

While the three metrics are formal statistics, our weak and strong tests are not and we do not treat them that way. Rather, we use the combination of metrics and informal tests as heuristics in evaluating what weather metrics matter, for what countries, from what remote sensing products, and from what extraction method. Again, since we lack data on the objective fact which each data source is trying to measure, all comparisons of one obfuscation/metric/source combination are made relative to a different obfuscation/metric/source combination. Our data and heuristics do not allow us to make claims regarding the accuracy of a remote sensing source. Rather, we quantify the significance and magnitude of measurement error in remote sensing data by comparing results from each product with results from the other sources always bearing in mind that, for a given metric and country, if there was no measurement error the results from our tens of thousands of regressions would be exactly the same regardless of the obfuscation/source combination.

An important caveat to bear in mind with respect to our results, and all results focused on _p_ -values, is that the significance of a point estimate does not imply that the model is correctly specified, that the point estimate is agronomically meaningful, or that the point estimate has the correct sign. It may very well be the case that the skew of temperature does not matter for agricultural production and that remote sensing products which result in a significant coefficient are therefore products with measurement error, while products which do not result in a significant coefficient are the more accurate products. These results and the associated figures simply allow us to visualize the variability in the number of significant coefficients across these specifications of interest. And any variability in
**Figure 4.6: Transmission lines classified according to forest cover in surrounding area in central Vietnam**

Source: World Bank, 2017 (transmission lines), Google Maps (background image).

# 4.5. Coastal erosion exposure

This analysis uses coastal erosion data introduced in section 2.3 to assess the overall exposure of coastal hotels in Vietnam to changes in coastal sediment. Coastal hotels are defined as lying within 5 kilometers of the shoreline. For these, the nature and state of the coastline are assumed to be significant factors in their attractiveness to tourists. In the 28 coastal provinces, the hotel dataset described in section 3.5 contains 3,309 hotels, of these 1,531 are located within 5 kilometers of the coast (figure 4.7).

The exposure of hotels to erosion is computed at the nearest point of the seashore for each coastal hotel. Almost a fifth experienced coastal erosion of more than 20m between 1988/1990<sup>7</sup> and 2015 on the nearest section of the coast. In contrast, accretion of more than 20m affected the coastline near more than a fifth of these hotels.

An analogous analysis is conducted to get an estimate of the exposure of built-up coastal areas. Based on the land use maps described in section 3.1, coastal settlements are defined as built-up areas within 250 meters of the coastline. Erosion is evaluated at the closest shoreline point for each of these built-up areas. The results show that only 19 percent of settlements in Vietnam’s northern and central shores are near stable coastlines, more than one-third are near moderately or severely eroding shores, and almost half experience moderate or severe accretion. In the Mekong Delta’s coastline is among the most dynamic, with more than two-thirds of built-up areas experiencing severe coastline changes — more than 20 meters of accretion or erosion — between 1988 and 2015.

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- Grubb, M., P. Drummond, A. Poncia, W. McDowall, D. Popp, S. Samadi, C. Penasco, K. T. Gillingham, S. Smulders, M. Glachant, G. Hassall, E. Mizuno, E. S. Rubin, A. Dechezleprˆetre, and G. Pavan (2021): “Induced innovation in energy technologies and systems: a
each quantile group is population-weighted, so every person is assigned the average per capita consumption expenditure of her quantile group in the country-year distribution.

Throughout, welfare is measured using per capita consumption expenditure, measured at the household level and expressed in 2011 PPP-adjusted USD.<sup>12</sup> This new set of PPP exchange rates was recently adopted by the World Bank in its global poverty measurement (Ferreira et al., 2015).<sup>13</sup> 7 Using a somewhat broader data set, Lakner and Milanovic (2015) covered only 29% of the population in Sub-Saharan Africa in 1988.

> 8 Chotikapanich et al. (2015) present annual estimates of the Gini and poverty headcounts for a number of African countries up to 2010. Similarly, Pinkovskiy and Sala-i-Martin (2014) calculate estimates for regional poverty and inequality up to 2011. However, their methods rely on using GDP per capita for the country mean (which is available for recent years), and extrapolating and interpolating distributional information (for which there is a longer time lag). 9 Violating the first rule, we use the 2005-2006 survey in South Africa for benchmark year 2003. This survey was largely conducted in 2006, so should be allocated to benchmark year 2008. We use four surveys (from Guinea-Bissau, Kenya, Nigeria and Tanzania) which are eight years apart from the survey in the previous benchmark year. We annualize growth rates when we estimate changes between benchmark years to account for growth spells of varying lengths.

> 10 To be precise, PovcalNet provides the overall mean and the consumption share of each decile group from which it is straightforward to construct the average income of the decile group.

11 We fit a lognormal Lorenz curve to the ten decile groups and use it to generate a distribution of 10,000 points. We use the ‘ungroup’ Stata routine (Abdelkrim and Duclos, 2007) which implements the Shorrocks and Wan (2008) method. As described in more detail in Lakner et
the last two columns of Table 6 we show the corresponding statistics for agricultural exports. The top two destinations are the same, and they even account for 80% of all exports (compared to two thirds in the case of manufacturing). For most export destinations, the relative importance of intermediate input exports is (relatively) higher for agriculture than for manufacturing output. The greater importance of intermediate goods exports, accounting for 74% in agriculture against only 70% in manufacturing, indicates that these products are still processed abroad before reaching final consumers. An often-suggested way to increase the manufacturing sector in sub-Saharan African region would be to expand processing activities domestically.

Finally, in Table 7 we report the export intensity for all broad industry groupings that we created.<sup>19</sup> Tables A.3 and A.4 in the Appendix contains the detailed country-destination breakdown for manufacturing, but it is useful to put those statistics in perspective. According to the Eora data, the average share of manufacturing in GDP is only 12% across the subSaharan countries (population weighted). The most important sector is business services, which also includes transportation, communication, and finance.

Naturally, the share of agriculture would be a lot higher if shares were calculated in terms of employment. The very low average share in GDP of 5.4% for agriculture suggests that it excludes agricultural output for own consumption in the household. We have not manipulated these shares, but report them as they appear in the Eora dataset. It is important to realize that all work with Eora assigns this low a percentage to the agriculture sector. The unweighted average across countries is slightly higher, but still only 6.4%.

The first four entries in Table 7 are the goods-producing industries, and they have the highest export shares. It is notable, however, that exports are not zero for the other sectors. Given the large overall size of the three service sectors listed in the bottom rows, their contribution to aggregate exports
cell, with a resolution of 3 arcseconds, thus specifying the distribution of population. This information is based on administrative or census-based population data, disaggregated to grid cells based on distribution and density of built-up area, which is derived from satellite imagery (Freire et al., 2016, 2020).

The choice of population density map is important for estimating people’s exposure to natural hazards. Smith et al. (2019) provide a sensitivity analysis for flood exposure assessments using different population density maps, including WorldPop (3 arcsecond). They show that high-resolution population density maps perform best in capturing local exposure distribution, particularly the High-Resolution Settlement Layer (HRSL) with 1-arcsecond, or ~30 m resolution, produced jointly by Facebook, Columbia University and the World Bank. While HRSL is only available for a limited number of countries, WorldPop is shown to perform better than alternatives with global coverage, such as LandScan data (30-arcsecond, ~900 m resolution) (Bright et al., 2015).

**Subnational poverty rates:** For 1,755 of the 2,227 subnational units, the World Bank’s Global Subnational Poverty Atlas offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country (World Bank, 2021). Areas where no poverty estimates are available tend to be high-income countries and small island states. For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

**Administrative boundaries:** The definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e
attempting to join efforts with each other. This effort translated into the creation of new, infornmal networks, which had a positive effect on firms' performance.

Firm-level survey data has also proven useful in our understanding of the recent East Asian crisis. Detailed information collected on the crisis through surveys administered in 1997 and again in early 1998 offer good examples of how firm-level data can help studying responses to shocks in a way that complements the more traditional macro/aggregate approach. Dollar and Hallward-Driemeier (1998) focus on the adjustment process in the Thai industry following the crisis. Their work highlights the link existing between firms' behavior and (poor) government policies in the year preceding the crisis. As the crisis developed, firms struggled to cope with the existing constraints and to adjust to the new environment.

# **IV. QUESTIONING QUESTIONS**

Having discussed the type of data that would be most useful for firm surveys to gather, we now turn to the subject of questionnaire design itself. Surveys generally ask respondents both about their attitudes towards various issues and their recollection of past factual events. Moreover, in-person surveys pose these questions in a social setting, albeit one with a script and a purpose. Survey designers must realize both that the survey asks the respondent to perform a series of cognitive tasks and that it is an interaction governed by governed by social norms (Sudman, Bradburn, and Schwarz 1996). It is therefore important to consider how the questions themselves can influence responses. This section reviews the literature on questionnaire design. In particular, it seeks to learn how the survey design itself affects responses to questions, and therefore conclusions the researcher may draw from those responses.

# **General Issues**

Rea and Parker (1997) note six general problems to consider when preparing questions: (1) an inappropriate level of wording; (2) ambiguous words and phrases; (3) rnultipurpose questions (i.e., questions that ask two or more opinions simultaneously); (4)
.2)<br>nterval or one<br>it into two sub<br>ed in Appendi<br>tion error are<br>. We use five|standard err<br>sets: 50% u<br>x A, Table A<br>used to deter<br>nearest neig|or of the true<br>sed for trainin<br>.21 for Lasso,<br>mine the best<br>hbors with the|poverty rate.<br>g and 50% us<br>and Table A.<br>possible mod<br>predictive m|Lasso,Elastic<br>ed for testing<br>22 for Elastic<br>el. The list of<br>ean matching|

Note: Estimates shown in boldface or with a “<sup>**a**</sup> ” respectively fall within the 95% confidence interval or one standard error of the true poverty rate. Lasso,Elastic net linear models and Random Forest models are trained in the first round when the data is split into two subsets: 50% used for training and 50% used for testing (validation). The list of selected variables with penalized unstandardized coefficients is provided in Appendix A, Table A.21 for Lasso, and Table A.22 for Elastic net. The number of sub-trees is set at 1000 in Random Forest. Both out-of-bag error and validation error are used to determine the best possible model. The list of selected variables with the variable importance scores is provided in Appendix A, Table A.23. We use five nearest neighbors with the predictive mean matching method. 50 simulations are implemented. True poverty rate is the estimate directly obtained from the survey data.

62
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# **APPENDIX A: GENERAL FIGURES**

<!-- Start of picture text --> FIGURE A 1. PREDICTED GLOBAL POVERTY HEADCOUNT AND GINI COEFFICIENT<br>Panel A. Poverty headcount Panel B. Gini coefficient<br>(Base period: 1980–2014) (Base period: 1980–2014)<br>75 70<br>60<br>57.5<br>45<br>45<br>30<br>15 32.5<br>0<br>20<br>1980 1990 2000 2010 2020 2030 2040<br>1980 1990 2000 2010 2020 2030 2040<br>Years<br>Years<br>Poverty headcount o utcomes derived from 500 random draws per year in each of the 150 Aggregated Gini index stats der ived from 500 random draws pe r year in each of the 150<br>selected countries. The 2015 and 2030 corresponding results are: i) median = 11.42 and 8.84
respective distributions of the variables are statistically indistinguishable, that is, the sample mean is statistically equal to the population mean. The variables that satisfy this criterion are then candidate regressors in the modeling of household per capita income in the survey dataset.

> 3These indexes, computed across villages, municipalities, or states, summarize the degree of deprivation in 12 and 9 indicators, respectively, and focus on the dimensions of education, access to basic services in the dwelling, the quality and size of dwellings, health (the social backwardness index), and labor income (the marginalization index). The two indexes are computed through the principal component analysis technique and are then stratified into five groups according to the degree of backwardness or marginalization: very low, low, medium, high, and very high.

7
Policy Research Working Paper 9893

# **Abstract**

This paper presents a novel framework to estimate the elasticity between nighttime lights and quarterly economic activity. The relationship is identified by accounting for varying degrees of measurement errors in nighttime light data across countries. The elasticity is 1.55 for emerging markets and developing economies, with only small deviations across country groups and different model specifications. The paper uses a light-adjusted measure of quarterly economic activity to show that higher levels of development, statistical capacity, and voice and accountability are associated with more precise national accounts data. The elasticity allows quantification of subnational economic impacts. During the COVID-19 pandemic, regions with higher levels of development and population density experienced larger declines in economic activity.

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 rcmbeyer@worldbank.org, yhu@jhu.edu, and jyao@imf.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
in relaxing credit constraints. In fact, findings from the Gallup survey confirm this reasoning (Figure 4). Asked about the most important reason why people save money, 29 percent provide a precautionary motive behind their most important reason to save, stating saving for either “a rainy day” or “in case we get sick”. The second most important reason that people report saving is “to start a business” (almost 20%). These numbers suggest that almost half of the people surveyed are actually using savings for purposes that credit was either supposed to, or is billed to, serve. What is more, enabling savings neither creates the burden of debt, nor the resource diversion that credit does.

# **Figure 4: Reasons for Saving (average 2009-10 over SSA countries)**

<!-- Start of picture text --> What is the most important reason people, like you, save money?<br><!-- End of picture text --> <!-- Start of picture text --> Just in case I might need money / for a rainy day<br>To start a business<br>To send children/grand-children to schoool<br>To buy a house / to build a house<br>In case we get sick / for medical treatments<br>To buy livestock<br>To buy a plot<br>To get interest<br>To pay for my own education<br>To immigrate<br>For special events (burial/parties/holidays)<br>Other<br>To get married<br>For when I'll retire<br>To buy a car<br>To secure a loan / as guarantee for loan<br>0% 5% 10% 15% 20% 25%<br><!-- End of picture text --> <u>Source: Gallup World Poll 2009 and 2010.<
# Figure 7.  Country correlations and exchange rate regime

<!-- Start of picture text --> CE with Fixed ER CE with floating ER<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>CE with fixed ER regimes = 0.46***<br>-0.2<br>CE with floating ER regimes = 0.36***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemenCôte d'IvoireUkraine<br>Rwanda Oman Congo, Rep.Trinidad and TobagoNicaraguaEcuador UruguayGuyanaLiberiaArgentina<br>BrazilNamibiaEquatorial GuineaNigerBolivia KenyaZimbabweFijiAngolaQatar<br>SudanSenegalMaliBurkina Faso<br>GhanaBurundiSaudi ArabiaBeninTurkmenistanMozambiqueChad ComorosThe GambiaColombiaTanzaniaLibyaCentral African RepublicGabonIcelandEthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Rep. of Madagascar<br>Kyrgyz RepublicRussian FederationPapua New GuineaLao P.D.R.Cabo VerdeUgandaCameroonIndonesiaHondurasSuriname TogoSeychelles<br>Bahrain Congo, Dem. Rep.Belize<br>Solomon Islands Iraq<br>NigeriaPeru<br>Armenia<br>Sierra LeoneKazakhstanGuinea-BissauMauritaniaCosta Rica Botswana<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>AustraliaGuatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>Canada<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries operating under fixed exchange rates or floating exchange rate regimes during the sample period, according to the classification in Ilzetski _et al_ (2021). The difference between the two averages is not significant (p-value is 0.17). *, **, and ***
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