rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C15.3. SOSD: Social Sustainability Index C15.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.92*** 0.97***<br>H<br>Kendall tau-b 0.83*** 0.78*** 0.89***<br>Spearman 0.92*** 0.85*** 0.78**<br>A<br>Kendall tau-b 0.78*** 0.72*** 0.67**<br>Spearman 0.98*** 0.92*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C16. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Equality before the law (Peru)/Satisfaction with the way corruption is combatted (South Africa) indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ <!-- Start of picture text --> Peru<br><!-- End of picture text --> A1.1. FOSD: Counting Vector A1.2. Dominance Analysis <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>A1.3. SOSD: Social Sustainability Index A1.4. Rank correlations<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> 66
# _4.2 Electricity Consumption Growth and Economic Indicators_

Despite obvious noise in the data, electricity consumption growth is strongly correlated with other measures of economic activity. Figure 3 shows quarterly changes in electricity consumption from the first quarter of FY2016 to the last quarter of FY2021 and quarterly changes in industrial production and exports. At 0.60 for industrial production and 0.66 for exports, both correlations are strong. The fourth quarter of FY2020, when economic activity was severely disrupted due to the COVID-19 pandemic, is a clear outlier with a strong decline in exports and electricity consumption.

**Figure 3:** Correlation between Electricity Consumption and Economic Indicators

a. Quarterly changes in electricity consumption b. Quarterly changes electricity and industrial production consumption and exports <!-- Start of picture text --> Industrial Production, Percent Exports, Percent<br>25 R=0.60 20 R=0.66<br>FY21 Q4<br>20<br>10 FY21 Q4<br>15<br>FY21 Q3<br>FY21 Q1<br>10 0<br>FY21 Q3<br>FY21 Q2<br>5<br>FY21 Q1 FY21 Q2<br>-10<br>0<br>FY20 Q4<br>FY20 Q4<br>-5 -20<br>-20 -10 0 10 20 30 -20 -10 0 10 20 30<br>Electricity consumption (% change) Electricity consumption (percent %)<br><!-- End of picture text --> Changes in electricity consumption can be used as a proxy to infer insights on the economic impacts of shocks, for example, the COVID-19 pandemic, associated lockdowns, or natural disasters. Figure 4 shows that electricity consumption declined sharply in the fourth quarter of FY2020, right after the first lockdown was imposed in Bangladesh at the end of March 2020. Electricity consumption recovered subsequently, but year-over-year growth remained minimal in the first quarter of FY2021. It took another two quarters before electricity growth was again close to the rates observed prior to the pandemic. By the fourth quarter of FY2021, electricity consumption growth reached the pre-pandemic level.

7
Anderson DM, Cronk R, Fejfar D, Pak E et al (2021). Safe healthcare facilities: a systematic review on the costs of establishing and maintaining environmental health in facilities in lowand middle-income countries. Int J Environ Res Public Health 18(2): 817.

Anguzu JR, Olila D (2007). Drug sensitivity patterns of bacterial isolates from septic postoperative wounds in a regional referral hospital in Uganda. Afr Health Sci. 7(3): 148-54.

Atif ML, Sadaoui F, Bezzaoucha A, et al (2008). Prolongation of hospital stay and additional costs due to nosocomial bloodstream infection in an Algerian neonatal care unit. Infect Control Hosp Epidemiol 29: 1066–1070.

Bailey PE, Andualem W, Brun M, Freedman L et al (2017). Institutional maternal and perinatal deaths: a review of 40 low- and middle-income countries. BMC Pregnancy Childbirth 17(1): 295.

Banzhaf H (2022). The value of statistical life: a meta-analysis of meta-analyses. Journal of Benefit-Cost Analysis 13(2): 182-197.

Barasa GB, Butt F, Onyango JF, Mutua FM, Dimba E (2015). Antibiotic sensitivity patterns of aerobic bacterial agents in post-surgical orofacial infections. The Annals of African Surgery 12(1).

Bayardorj D, Promsatit P, Chirangi BM, Mahmoud E (2023). Surgical site infections at Shirati KMT hospital in Northeastern Tanzania. Cureus 15(2): e34573.

Bebell LM, Ngonzi J, Bazira J, Fajardo Y (2017). Antimicrobial-resistant infections among postpartum women at a Ugandan referral hospital. PLoS One 12(4): e0175456.

Berhe DF, Beyene GT, Seyoum B et al (2021). Prevalence of antimicrobial resistance and its clinical implications in Ethiopia: a systematic review. Antimicrob Resist Infect Control 10: 168.

Benova L, Cumming O, Gordon B et al (2014). Where there is no toilet: water and sanitation environments of domestic and facility births in Tanzania. PLoS One: 9(9): e106738.

Bocoum A, Fané S, Traoré Y, Sanogo S, et al (2019). Bacteriology of Healthcare-Associated Infections in the Gynecology and Obstetrics Department of CHU Gabriel Touré. Open Journal of Obstetrics and Gynecology 9: 1336-1346.

Bouzid M, Cumming O, Hunter PR (2018). What is the impact of water sanitation and hygiene in healthcare facilities on care seeking behaviour and patient satisfaction? A systematic review of the evidence from low-income and middle-income countries. BMJ Glob Health 3: e000648.

Browne AJ, Kashef Hamadani BH, Kumaran EAP, Rao P et al (2020). Drug-resistant enteric fever worldwide, 1990 to 2018: a systematic review and meta-analysis. BMC Med. 18(1): 1.

Bukasa JC (2018) Consequences of nosocomial infections in maternity in the city of Mbujimayi, DR Congo. Environmental and Water Sciences, Public Health and Territorial Intelligence J. 2: 18-23.

Chaitkin M, McCormick S, Alvarez-Sala Torreano J, Amongin I et al (2022). Estimating the cost of achieving universal basic WASH standards in health care facilities in the 46 least developed countries. Lancet Global Health 10(6): e840-849.

22
# **2. Data and main variables**

Our main data source is firm-level surveys conducted by the World Bank’s Enterprise Surveys (ES) between 2006 and 2011. These surveys employ a common questionnaire and sampling methodology (stratified random sampling) and aim to be representative at the national level of the non-agricultural private sector of the countries. The sample we use includes 19,040 small and large manufacturing firms in 76 developing countries.

We use two dependent variables from ES data. First, a dummy variable equal to 1 if a firm uses foreign inputs in its production process and 0 otherwise ( _Import dummy_ ). Second, the percentage of firm’s material inputs that are of foreign origin ( _Imports_ ). In our sample, about 60 percent of the firms use imported inputs. The mean value of _Imports_ equals 35.9 (percent) and the standard deviation is 39. Across countries, _Imports_ is highest in Albania (69.9 percent) and lowest in Indonesia (2.8 percent).

Our main explanatory variable is country-size measured by the (log of) total population of the country in the year covered by the ES ( _Population_ ). In our sample, _Population_ varies between 13.1 (Cape Verde, 0.49 million) and 19.3 (Indonesia, 240 million). The mean value of _Population_ equals 16.3 (or 28.9 million people) and the standard deviation is 1.4.

3
**Figure 8 Impact of biofuels on consumer prices**

<!-- Start of picture text --> Agriculture and food Agriculture<br>Middle East and North Africa<br>Europe and Central Asia<br>East Asia and Pacific<br>Latin America and the Caribbean<br>Sub Saharan Africa<br>South Asia<br>Developing countries<br>High income countries<br>World total<br>0 2 4 6 8 10 12 14 16 18<br>Percent difference in CPI relative to baseline<br>Source : Simulations with World Bank’s ENVISAGE model.<br><!-- End of picture text --> As a result of these price shocks, the extreme and moderate poverty headcounts in developing countries increase by 0.6 and 0.9 percentage points, respectively (Table 7).<sup>13</sup> This increase is determined entirely by South Asia, where an additional 32.5 million people slip into extreme poverty due to higher food prices brought about by increased production of biofuels. South Asia followed by Sub-Saharan Africa, where extreme poverty rises by 1.8 million. On the other hand, the number of poor is reduced significantly in Latin America, where higher farm incomes contribute to an exit of 2.3 million people out of extreme poverty. Overall, extreme poverty rises by 32 million people; while a large number, this is only one-fifth of the near-term increase in the number of poor shown in the previous section.

At the higher (moderate) poverty line, an additional 15 million people slip into poverty due to higher prices of agriculture and food commodities. The regional incidence of moderate poverty changes is very different from changes in extreme poverty, with the differences determined by sources of income and density around each poverty line. In the case of East Asia, extreme poverty hardly changes because the 2.5 million persons increase in urban poverty is nearly offset by a compensating reduction in rural poverty. On the other hand, moderate poverty in East Asia rises by 29 million people (more than 60 percent of the total poverty increase) because there are many more urban households in the vicinity of the higher poverty line. In South Asia, where both farm and non-farm households experience welfare losses due to higher food prices, the density of the

> 13 This paper uses the new World Bank poverty line of $1.25 (2005 PPP) per day, and, in accordance with earlier practice, defines the moderate poverty line as twice the extreme poverty line ($2.50 per day, 2005 PPP). The poverty estimates presented in this paper do not line up to the official World Bank poverty estimates published in World Development Indicators or in Chen and Ravallion (2008) due to differences in country coverage. The extreme poverty statistics in this paper are fully consistent with Chen and Ravallion (2008) at the country level, and are reasonably close at the global and regional level.

20
**Table 2  Bangalore: Descriptive Statistics**

|_Variable Name_|_Mean_|_Std. Dev_|_Min._|_Max._|
|---|---|---|---|---|
|House Rent|3085.398|3254.841|99|45000|
|Size|1100.871|873.5702|100|18000|
|Number of Rooms|4.554208|2.75|1|25|
|Number of Bathrooms|1.2565|0.7743|0|25|
|Floors|.98787|0.1094|0|1|
|Walls|0.9629|0.1863|0|1|
|Roof|0.8144|0.3888|0|1|
|Kitchen|0.9941|0.0764|0|1|
|Electricity|0.9889|0.1044|0|1|
|Toilet-Sewer|0.3605|0.4802|0|1|
|Crime decr.|0.2179|0.4129|0|1|
|Water - DPW|3.3399|2.1079|0|7|
|Hand Pump|0.0077|0.0875|0|1|
|Tube Well|0.1345|0.3412|0|1|
|Fountain|0.1907|0.3929|0|1|
|Community Tube|0.0606|0.2387|0|1|
|Tanker|0.0040|0.0634|0|1|
|Other|0.0102|0.1009|0|1|
|Surface|0.0025|0.0506|0|1|

**Table 3  Bhopal: Descriptive Statistics**

|_Variable Name_|_Mean_|_Std. Dev_|_Min._|_Max._|
|---|---|---|---|---|
|House Rent|1704.315|2706.4|50|50000|
|Size|760.2766|772.0118|70|8000|
|Number of Rooms|3.5526|2.7912|1|35|
|Number of Bathrooms|1.1343|0.5522|0|5|
|Floors|0.8703|0.3360|0|1|
|Walls|0.8114|0.3912|0|1|
|Roof|0.5354|0.4988|0|1|
|Kitchen|0.9901|0.0990|0|1|
|Electricity|0.7505|0.4328|0|1|
|Toilet-Sewer|0.1750|0.3800|0|1|
|Women safe|0.8713|0.3348|0|1|
|Open Dump|0.4859|0.4999|0|1|
|Water - DPW|3.1593|3.4391|0|7|
|Else’s|0.0375|0.1900|0|1|
|Hand Pump|0.0151|0.1219|0|1|
|Tube Well|0.0666|0.2495|0|1|
|Community Tube|0.0552|0.2284|0|1|
|Community Tap|0.3234|0.4679|0|1|
|Community Hand Pump|0.1093|0.3121|0|1|
|Tanker|0.0270|0.1623|0|1|
|Other|0.0010|0.0322|0|1|
|Rain|0.0005|0.0228|0|1|
|Surface|0.0020|0.0456|0|1|
export values with _Explk_ denoting exports of country _l_ to country _k_ . If time does matter we expect that the coefficient estimate on _Export_ Time j_ / _Export_ Timeh_ to be negative, meaning that countries associated with higher relative time delays have lower relative exports. Errors are adjusted for clustering on exporter pairs, since each exporter pair will be associated with a numerous importers.

The estimating strategy depends on choosing exporters that are similar (in location and factor endowments) and face the same trade barriers in foreign markets, for example, comparing exports from Argentina to Brazil with exports from Uruguay to Brazil. Therefore we use 18 regional trade agreements among 98 exporter countries, and consider all cases where two countries in a trade agreement export to the same importer (Table A2). As a further robustness check, we eliminate country pairs that do not fall into the same of four World Bank income classifications.<sup>16</sup> This ensures that we are not comparing countries at different levels of development, such as Mexico and the United States or Singapore and Cambodia, but reduces the sample.

This strategy eliminates the need to control for multilateral resistance on the importer side since we compare only imports to the same country. It also reduces the need to control for exporter remoteness because we are comparing proximate exporters. Endogeneity is reduced because effects of trade volumes on time are likely to be much smaller between similar countries in the same geographical region—for example, we are

> 15 Thus, it is one (negative one) if the associated dummy in the numerator country is one (zero) and the associated dummy in the denominator country is zero (one), and zero otherwise.  For example, if the dependent variable is Paraguay/Uruguay exports the variable landlocked is one, if it were instead Uruguay/Paraguay exports the variable landlocked would be negative one.  Similarly, if it were Argentine/Uruguay exports or the reverse, the variable would be zero.  Each country pair enters only once in the regression.

> 16 Classification by per capita income are as follows: Low-income, below $825; lower-middle income, $825-$3,255; upper-middle income, $3,255-$10,065; high income, above $10,065.

14
# **Table Sup 3: Admin 1 level robustness checks**

This table presents robustness checks for Table 1, column 2. Robustness checks are: Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Column (7): as in (1) but with a linear instead of quadratic country time trend. Columns 8 and 9: as in (1) but including one lag and three lags of growth using an Arellano Bond approach.

||(1)<br>Base|(2)<br>No Oil|(3)<br>No<br>US/China|(4)<br>ContYr<br>FE|(5)<br>ContYr FE<br>+no Trend|(6)<br>No YrFE|(7)<br>Linear<br>Time|(8)<br>LDV 1<br>Lag|(9)<br>LDV 3<br>Lags|
|---|---|---|---|---|---|---|---|---|---|
|Rainfall|1.075***|1.200***|1.090***|1.253***|1.847***|1.366***|1.626***|0.998***|0.758**|
||(0.244)|(0.243)|(0.250)|(0.249)|(0.284)|(0.244)|(0.255)|(0.245)|(0.251)|
|Rainfall<sup>�</sup>|-0.205***|-0.221***|-0.206***|-0.262***|-0.363***|-0.264***|-0.324***|-0.194***|-0.163**|
||(0.052)|(0.051)|(0.052)|(0.053)|(0.057)|(0.053)|(0.055)|(0.052)|(0.053)|
|Temperature|0.424*|0.491*|0.433+|0.296|0.838***|0.279|0.744***|0.180|-0.018|
||(0.216)|(0.198)|(0.225)|(0.226)|(0.218)|(0.216)|(0.221)|(0.222)|(0.235)|
|Temperature<sup>�</sup>|-0.016**|-0.018***|-0.016**|-0.012*|-0.028***|-0.015*|-0.025***|-0.013*|-0.004|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Observations|60,530|54,530|58,586|60,530|60,530|60,530|60,530|58,007|52,962|
|R-squared|0.251|0.272|0.246|0.288|0.185|0.226|0.195|0.261|0.220|

Clustered standard errors at the administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1.

17
Estimation of dynamically compensated demand requires estimates of price elasticities and income effects; in contrast to the static household model, rather than a single income effect, the full matrix of intertemporal income effects on demand is necessary. I take a sufficient statistics approach (Chetty, 2009) and show this matrix of intertemporal income effects is an inner product of two terms – the intertemporal matrix of marginal propensities to spend out of temporary income, and growth in demand. Restrictions on this matrix yield two common frameworks: with complete financial markets, the intertemporal matrix of marginal propensities to spend is rank 1, while absent financial markets and with separable utility (i.e., the static household model), it is diagonal. In applications, I use empirical estimates of growth in demand, and I suggest theory-motivated bounds on intertemporal marginal propensities to spend.

A number of strong assumptions are embedded in my modeling framework. Common to many frameworks used to analyze the impacts of financial technologies, I assume households have full information about the technology and are not behavioral. To focus on household welfare, I do not consider general equilibrium effects or producer surplus. However, by reducing the estimation of consumer surplus from financial technologies to the estimation of demand, I provide theoretical grounding for the application of the broad array of models and methods that build on demand in static models.

I next estimate consumer surplus from financial technologies in three randomized control trials (Duflo et al., 2006; Karlan & Zinman, 2018, 2019). Each experiment introduced variation in prices: for deposits into a retirement savings account, for deposits into a commitment savings account, and for microfinance loans, respectively. As in the stylized example, the experimental treatment either reduced savers’ deposits conditional on their end-of-period balance, or increased borrowers’ loan amount conditional on their repayment schedule; in turn, this increased deposits and loan amount. I then estimate price elasticities of deposits and loan amount ranging from 4-to-14 across the three experiments; these are simply appropriate rescalings of estimated impacts from each paper. I then construct bounds on compensated price elasticities of deposits and loan amount: in general, these bounds are narrow compared to statistical uncertainty. Across the three experiments, the compensated price elasticities of deposits and loan amount correspond to annual consumer surplus of $7-to-$27 per $100 of deposits or loan amount.

I conclude by discussing the interpretation of my estimates of consumer surplus. I compare my estimates to existing experimental and quasi-experimental studies of the impacts of access to savings and credit technologies on household consumption, a common proxy for welfare; standard errors are large relative to my estimates, suggesting these studies are ex-post underpowered to detect impacts equal to my estimates of consumer surplus. De4
# **Figure 1.3 EViews "Run" command window**

<!-- Start of picture text --> WORKFILE a:\LrrCYCI rS M 1993:1 1999:12<br>'LEADING INDICATOR PROJECT: LITHUANIA<br>READ(E,a2) a:\LITUDATAXLS 10<br>genr ir6inv = 1 /ir6<br>genr finanl inv = 1/finanl<br>THE SEASONAL ADJUSTED VERS W p ble finan25<br>'is not seasonally adjusted Ebecause c I<br>uip. cl i (m.i.o.) iipsa<br>ernp1.x11(rn,i,q) emplsa<br>finan20.x11(m.i,q) finan20sa<br>finanSl. seas(m , q) finan5l sa<br>reer.x 1(m,i,q) reersa<br>ir6inv.x11(mJ,q) ir6invsa~'...<br>finan55.seas(rmni,q) fina55sa .<br>finanli mw. seas(m ,iq) finanl inysa<br>finan19.x11(m i,q) finan19sa 9<br>'A HODRICK-PRESCOTT FILTER IS. I<br>iipsa.hpf(14400) iiphp<br>empi sa.hpf(14400) empl hp<br>finan20sa.hpf(14400) finan20hp<br>finan25.hpf(14400) fhnan2Shp<br>finanSl sa.hpf(14400) finan5l hp<br>reersa.hpf(14400) reerhp<br>ir6invsa.hpf(14400) ir6invhp<br><!-- End of picture text --> When the errors option has been set, press "OK" and the Leading Indicator is calculated and plotted.

The variable _cliamp_ contains the CLI, while _iipcyc_ contains the reference series. Both variables are generated in the file LITCYCLES.WK1.

**Step 5 (optional).** The program estimates the composite index using only five out of the nine series previously mentioned. In order to add (or subtract) any series to the CLI the program must be modified. Go to the 'Aggregation of the series' section at the end of the program, where the averaging commands are included:

smpl @first @first genr agrl = ((finan20norm+emp1 norm)/2)/100 smpl @first+1 @first+1 genr agrl = ((ir6invnorm+finan20norm+emp1 norm)/3)I1 00 10
- 77 -

Figure S CHILI <!-- Start of picture text --> N SW<br>23<br>II~~~<br>~IN .<br>1905 1961 162 I1983 1964 155 1906<br><!-- End of picture text -->

**3.5i1** VIVS Devaluetimi Rate, wwtUrtly _tW._ Inflatim **Rate.** OUWtU?lY **SAul,**
31 tries, **extensive** farm or enterprise **management surveys** are needed to measure current incomo comprehensively, along with changes in **net** worth.

Beyond these limits, economists strive to document the market value of goods produced and conumed **at** home, such **as** food, fuel, and housing, for which there **are** market priced equivalents. But nobody, to my knowledge, ascribes **a** market value to **the** fetching of water for family use, though conceptually it is no different from the collection of fuel, or the home production of food, all of which tend to be women's work. In high income countries, **as** such forms of income in-kind have become an progressively smaller share of income they have been neglected. When this omission is replicated in low income countries, where income in-kind can be a large share of personal income, a serious distortion is introduced. This systemat!cally affects one's perception of woman's contribution to the economy. It distorts the entire quantitative record of modern economic growth. Economists understate sources of income that are proportionately larger for poor families, and within families for women's earnings relative to men's (Kusnic and DaVanzo, 1980; Evenson, 1983). This partial accounting of income reinforces the view that women who engage in traditional household activities are less useful than they would be if they worked in the market labor .force. From another perspective, per capita national income will appear to grow more tapidly than it should, if women allocate **more** of their time to the market labor force, because we **make** no offsetting debit to national income for the time women retract from nonmarket production that would otherwise benefit then and their families. Thus, statistics interject a subtle but pervasive slant into our study of modern economic growth and affect the choice of development policies. Assessments of technical change can be distorted. If it boosts the pro-