rafmacalaba/datause-encoder-data huggingface dataset:
targeting 3,000 girls between the ages of 15‐24 in four states. Key interventions included the establishment of adolescent girls clubs to create safe spaces for social interaction and engagement, life skills and livelihoods training, financial literacy training, access to savings and credit facilities and community and parental sensitization efforts. To assess the effectiveness of the interventions in South Sudan, a rigorous impact evaluation was built into the project.

The baseline and endline surveys for this evaluation were conducted across the four target states of Juba, Rumbek, Torit and Yei in 2010 and 2015 respectively (Figure 1). In each state, respondents were drawn based on a two‐stage random selection using clusters as the primary sampling units. Given the high levels of mobility in South Sudan, these surveys were designed as repeat cross‐sections. Hence no efforts were made to re‐visit baseline respondents at endline.<sup>3</sup>

**Figure 1: Number of observations at baseline and endline.**

<!-- Start of picture text --> 1000<br>800<br>819 824<br>707 696<br>600<br>626<br>595<br>524 527<br>400<br>200<br>0<br>Juba Rumbek Torit Yei<br>Baseline Endline<br>Number of respondents<br><!-- End of picture text -->

# **3.1. Self‐reported conflict exposure**

The eruption of violence in 2013 impacted and delayed the implementation of the endline survey to early 2015. To measure the extent of this conflict, the endline survey incorporated an additional module on conflict exposure. Getting direct household conflict exposure measures is very meaningful. This module was developed based on similar conflict exposure questionnaires and adapted to the context in South Sudan with special consideration paid to the ethical administration of surveys in

> 3 Five clusters, which covered 173 households, were dropped from the analysis as these clusters were not revisited at endline.

9
# **5. Summary and Conclusion**

In this paper, we use a comprehensive data set from more than 70 developing countries over the period from 1986 to 2008 to identify the effect of different technologies of improved water and sanitation on child morbidity and mortality in rural and urban areas, with a particular focus on Sub-Saharan African countries. This is – to our knowledge – the first study that has attempted to combine a large number of population based surveys to estimate a global average treatment effect of water and sanitation infrastructure.

The results of this study show that the potential health benefits generated by improvement in water and sanitation infrastructure are considerable. On average, and for the total sample, we find that access to advanced water and sanitation technologies reduces the odds of suffering from diarrhea among children under five by 7.3 and 12.9 percent, respectively, and also find substantial and similar reductions in the under-5 mortality risk, which indicates the existence of the Mills-Reinke Multiplier. In addition to these reductions in health risk at the household level we also find strong evidence for positive health spillovers of access to improved sanitation at the village and urban district level, which we do not find for access to water infrastructure.

In the light of the results presented in this paper the current focus of international donors on water infrastructure appears slightly surprising. One reason for this preference is certainly the additional (and often large) time savings - and hence economic benefits - that come along with improved water infrastructure that are less clear for sanitation infrastructure. From an individual economic perspective, gaining access to improved water may seem preferable to gaining access to improved sanitation. From a public health perspective, however, sanitation infrastructure should in our opinion receive more attention, as the private health benefits to sanitation seem to be at least equal to the benefits of water infrastructure, while the social health spillovers are likely substantially larger.

Optimal policy clearly does not only depend on the marginal health benefit, but also on the respective policy cost. This aspect will be the focus of a second part of this research project, 30/36
|**Table 1: Access to Remote Learning Technol**|**ogies by Stud**<br>|**ent Character**<br> <br>|**istics**|||Computer<br>||
|---|---|---|---|---|---|---|---|
||ame|<br>omuer||||||
||p<br>Size|<br>p<br>or Tablet|Internet|TV|Radio|<br>Internet|p-value|
|**Full Sample**|1552|**0.65**|**0.74**|**0.92**|**0.59**|**0.59**||
|Males|710|0.65|0.75|0.93|0.62|0.60|0.568|
|Females|842|0.65|0.73|0.91|0.56|0.58|0.568|
|Grade 10|545|0.66|0.77|0.93|0.61|0.62|0.072|
|Grade 11|595|0.60|0.70|0.91|0.56|0.54|0.002|
|Grade 12|412|0.71|0.77|0.92|0.60|0.62|0.127|
|Lowest Wealth Quartile|375|0.49|0.57|0.85|0.50|0.39|0.000|
|Highest Wealth Quartile|405|0.78|0.87|0.97|0.69|0.75|0.000|
|Mother's Education:high school or less|693|0.53|0.63|0.90|0.57|0.47|0.000|
|Mother's education:beyond high school|859|0.75|0.82|0.94|0.60|0.69|0.000|
|Mestizo or White|1306|0.67|0.76|0.93|0.60|0.62|0.000|
|Indigneous or Other|246|0.52|0.61|0.85|0.53|0.44|0.000|
|Pichincha|1066|0.71|0.79|0.95|0.67|0.66|0.000|
|Napo or Orellana|486|0.51|0.62|0.86|0.41|0.44|0.000|
|Notes:<br>Columns 2-6 show the proportion of student<br>Column 7 shows the p-value for testing comp<br>group.|s in this catego<br>uter & intern|ry who have<br>et access is e|the respect<br>qual for this|ive remot<br>group co|e learning t<br>mpared to e|echnology.<br>veryone not i|n this|

19
**Table 6. Interacting** **_Doing Business_ indicators with the Contract Enforcement indicator**

||Lgrow<br>(1)|ln(LP)<br>(2)|
|---|---|---|
|DB Get Electricity|-0.156|0.104**|
||(0.405)|(0.044)|
|DB Get Electricity * Contract Enforcement|0.990**|0.012|
||(0.387)|(0.052)|
|DB Get Credit|0.097|-0.031|
||(0.327)|(0.055)|
|DB Get Credit * Contract Enforcement|-0.486|-0.073|
||(0.373)|(0.070)|
|DB Construction Permit|-0.172|-0.057|
||(0.419)|(0.078)|
|DB Construction Permit * Contract Enforcement|-0.025|0.205*|
||(0.508)|(0.117)|
|Contract Enforcement|0.773|-0.137|
||(0.499)|(0.119)|
|Year, Industry FE|Yes|Yes|
|Firm, Country Ctrl|Yes|Yes|
|Observations|57124|51710|
|Adjusted_R_<sup>2</sup>|0.054|0.339|

SEs are clustered at country level. All BE indicators are standardized with their sample mean and standard deviation. Firm level control variables include biggest owner share, small/mediuam/ large scale firm, age 6-10, age 11+, exporter, foreign firm, foreign share, city population category dummies, and number of competitor dummies. Country level variables include ln(GDP pc), ln(population) and ln(population density). Contract Enforcement in this table is standardized with their sample mean (i.e., their value is their original value subtracting their mean value so that the standing alone BE indicator captures the mean effect of the BE variable at the mean Contract Enforcement).

BE = business environment; DB = Doing Business; FE = fixed effects; Lgrow = one-year employment growth rate in percentage points (growth); ln(LP)=ln(sales/number of employees) (labor productivity).

> * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01

33
to export. We are interested in the relative effects on time-sensitive industries and estimate a probit function to investigate this hypothesis:  _i_   ( _Dct_  _Ds_   _X i_   1 _tssEXP_ Tc_   2 _tssRMc_   3 _ksKc_   4 _hsHc_   5 _fdsCRc_ ) , (4) where<sup></sup> _i_<sup>is a measure of the probability that firm</sup><sup>_i_will export to foreign markets. The</sup> coefficient on time interaction,  1 , again implies the differentials between the effects of time on the probabilities of exporting across industries. A group of firm-level variables are included in the model to take into account that firms’ characteristics affect their ability to export, denoted by _X i_ . It consists of employment level, value added per worker, and capital stock per worker which are entered as logarithm and lagged by one year. Firms’ value-added per worker and capital stock per worker are used to control for differences in productivity<sup>12</sup> . Firm age and foreign ownership shares are shown to be correlated with export performance as well.

The probability of exporting captures how the time needed to export affects extensive margins. But how about intensive margins? One would expect that exporters, on average, send more products abroad when there are fewer delays domestically. Still following the insights of Melitz (2003), falling trade costs have two effects: first, exporting becomes more profitable; second, the competition of domestic market becomes fiercer. As a result, all exporters will find it appealing to increase the proportion of its foreign sales, i.e. export intensity. Clarke (2005) and Yoshino (2008), for instance, show that in Africa, exporters have higher export intensities in countries where customs administration is more efficient. In order to investigate this idea, we estimate a tobit model:

* _yi_  _Dct_  _Ds_  _X i_  1 _tss EXP_  Tc_  2 _tss RM_  3 _k s K c_  4 _hs H c_  5 _fd sCRc_  _i where_  _i_ ~ _N_ ()1,0

> <sup>_y_</sup> _i_<sup>represents the export intensity of firm</sup><sup>_i_, which is measured as the share of exports in total</sup> * sales. The observed firm export intensity equals the latent export intensity, denoted by<sup>_y_</sup> _i_<sup>, if</sup> * and only if<sup>_y_</sup> _i_<sup>is within zero and one. All other variables retain their meanings in equation (4).</sup> The coefficient on time interaction,  1 , represents the relative effects of time on export intensities across industries. A negative coefficient will suggest that exporters of time-sensitive industries send disproportionately fewer products abroad in countries with longer time required for exports to happen.

# **IV. Data**

> 12 We use the World Bank Enterprise Surveys for firm-level information. The data does not have panel structure so we are unable to compute more sophisticated measures on productivity, for instance, following Olley and Pake (), or A (). The quality of surveys also varies considerably over countries. In order to  include as many observations as possible, we use a simple measures to control for productivity.

9
# **<u>Table A.4. Poverty Lines, Iraq, in LCU, Per Capita Annual Consumption in Survey Year Prices</u>**

||2007|2012|
|---|---|---|
|**Kurdistan**|1212|1709|
|**Baghdad**|987|1391|
|**Rest of Iraq**|865|1220|
|**Total**|1073|1266|

Source: World Bank “Poverty Estimates and Trends in Iraq” <u>https://microdata.worldbank.org/index.php/catalog/2334/download/34771</u> 47
**Table 11 Norms for Determining Current Expenditures for Standardized** Items **in Provinces 1994**

|Expenditure Itcms|Unit|Cides|Plains|Midland<br>andCoas-<br>talAreas|Low Moun-<br>ta nous and<br>Remote Areas|High Moun-<br>tainsand<br>Islands|
|---|---|---|---|---|---|---|
|1. Norms Based tn C|atcgorization of Regions||||||
|1.Education|Dongs per capita|37.500|26.700<br>J9.400||33.500|42.800|
|2. Culture and<br>information|Dongs per capita|3.750|2.500|2,940|3.350|4.Z80|
|3. Sports|Dongspercapia|_1.540_|1.250|1.150|700|960|
|4.Health:<br>i) Prcventive<br>ii) Curative|Dongs per capita|4.750<br>10.520|3.340<br>7.520|3.700<br>8.270|4.170<br>9.400|5.340<br>12.030|
|5. Administration<br>(i) Provincial lev-el<br>(ii) District levcl|Mn dongs/per<br>employte in paymil|9-5<br>7.5|6.7<br>6.7|8.2<br>5.5|13.5<br>11.0|-<br>12.0|
|11.Norms Uniformf|orall Regions||||||
|Normsfor<br>Teehnical Education<br>- Mn dongs per<br>sLudent|Science and<br>Technotogy|Pailgogy<br>- Genral|Pedagogy<br>- Higher|Agri..<br>Formsty.<br>Fisheries<br>and Health|Economics<br>and Lcgal<br>Affairs|Culture and<br>Arts|
|_.tiCnilkcc_and<br>Universi1t|3.8|4.0|2.7|3.9|3.3|4.2|
|(iil Technical (3 yrs<br>aftergencral<br>eduatkion)<br>(iii) Vocational|2.4<br>2.9|2.5|-|2.1<br>2.4|1.9<br>-|3.0<br>2.9|

Source: Ministsy of Finance. Govcrnmnmt or Vic Nanm.

# _Revenue projections_

The expenditure budgets of provinces are determined by the revenue projection for domestic resources (plus planned domestic borrowing and external assistance). Analysis of 29
# **Table A28:  Reporters Without Borders Press Freedom Index (RSF)**

<!-- Start of picture text --> Data Provider Reporters Without Borders<br>Description International nongovernmental organization headquartered in Paris, France, devoted to the<br>protection of reporters and respect of press freedom.<br>Website www.rsf.org<br>Data Source Worldwide Press Freedom Index<br>Type Expert assessment<br>Respondents Reporters, researchers, legal experts and press freedom advocates in assessed countries<br>Frequency Annual since 2002<br>Coverage Global sample of countries<br>Public Access Yes<br>Description The press freedom index is based on a 50-question checklist on the incidence and severity of<br>restrictions on reporters and the media<br>2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>Press Freedom Index X X X X X .. .. ..<br>Political Stability and Lack of Violence<br>NA .. .. .. .. .. .. .. ..<br>Government Effectiveness<br>NA .. .. .. .. .. .. .. ..<br>Regulatory Quality<br>NA .. .. .. .. .. .. .. ..<br>Rule of Law<br>NA .. .. .. .. .. .. .. ..<br>Control of Corruption<br>NA .. .. .. .. .. .. .. ..<br>Country Coverage 166 165 165 164 138 .. .. ..<br>Year of Publication 2006 2005 2004 2003 2002 .. .. ..<br><!-- End of picture text --> 66
Note also that in the extreme case of _q2 ≥ q1_ (the cost of the option to sell ITMOs is at least as high as the ITMO price), no put options are purchased, since there are no net gains from ITMO sales.

Whenever _q2 < q1_ , some put options are purchased. Typically, _q2_ is only a fraction of _q1_ , implying that a host’s optimal options purchases could be large relative to the maximal number.<sup>15</sup> Optimized welfare for the host from having access to put options ex post (given no other access to ex-post ITMO sales) is: _A1_ is a constant representing welfare without put option access. _EW(p)_ implies a non-trivial value of put options access, easily (much) larger than the standard deadweight cost of imposing a moderate carbon tax _t_ (which is _t_<sup>_2_</sup> _/2γ_ ).<sup>16</sup> Inefficiency of this solution occurs as the number of ITMOs sold in the most favorable ex post states, with values of _z_ below _z1_ , is capped by the number of put options purchased, from (4.3).

Arguably, put options for future ITMO delivery will be demanded mainly by higher-income or emerging countries and not by low-income hosts. A cost to cover the option price must be incurred early; the same must any investments required to harness the later mitigation benefits; while the payments for exercising the put options come at the end point. This skew cost-benefit profile might however be alleviated by climate finance payments when such financing is required to make the market operative and available to lower-income hosts.

Finding countries willing to supply such put options may be challenging. They must expect to be future ITMO buyers, thus typically higher-income countries. Expected ITMO buyers also face uncertainty, which may make them unwilling to take ITMOs when these countries happen to be in actual compliance, as ITMOs are not bankable beyond 2030 under current Article 6 rules.

Several questions arise with this analysis, one is whether such an options market will necessarily exist; and if they can be purchased, whether they will always be honored ex post.

> 15 For example, in PAF pilot auctions carried out so far, the transacted put options costs have been between 15% and 40% of the transacted strike price for certified emissions reduction sales.

> 16 In particular, note that 2z0 represents the range of values of the uncertain variable (with same dimension as t).

21
**21**

# TABLE I

# HOUSEHOLD FIWANCIAL INDICATORS

|..............................|||||||||
|---|---|---|---|---|---|---|---|---|
||1981|1982|1983|1984|1985|<br>1986|1987|1988|
|............................................|...................|..............................|...............................<br>In b|........................<br>illion|s of Fo|rints|||
|1. Currency|76.0|80.0|88.2|98.3|110.0|122.7|144.3|152.0|
|2. Demand and Time Deposits|'53.7|167.6|187.0|206.6|229.0|257.7|268.2|286.7|
|3. SavingNotes|6.4|<br>_8.2_|10.1|12.8|15.1|17.8|18.4|19.5|
|4.gonds|0.0|<br>0.0|<br>0.1|<br>0.9|<br>2.7|<br>6.8|<br>12.0|<br>0.2|
|5. COs|0.o|<br>0.0|<br>0.0|0.0|0.0|<br>0.0|<br>0.0|<br>6.6|
|6. Total Househotd Financial Assets|236.1|255.8|285.4|318.6|356.8|405.0|442.9|465.0|
|7. Total FinanciaL Assets|378.4|406.7|426.4|479.8|520.9|572.0|474.7|'64.'|
|GOP|780.0|847.9|896.4|978.5|1033.6|1087.8|1226.9|1400.6|
|||||As X|of GOP||||
|1. Currency|9.7|<br>9.4|<br>9.8|<br>10.0|10.6|<br>11.3|11.8|10.9|
|_2._DemandandTime Deposits|19.7|<br>19.8|20.9|21.1|<br>22.2|23.7|21.9|<br>20.5|
|( 1_+_2 )|29.4|29.2|30.7|<br>31.2|32.8|35.0|33.6|<br>31.3|
|3. Saving Notes|0.8|<br>1.0|<br>1.1|<br>1.3|<br>1.5|<br>1.6|<br>1.5|<br>1.4|
|4.Bonds|0.0|0.0|0.0|0.1|0.3|<br>0.6|<br>1.0|<br>0.0|
|5. Cos|0.0|<br>0.0|0.0|0.0|0.0|0.0|0.0|0.5|
|( 3+4+5|0.!|1.0|1.1|1.4|1.7|2.3|<br>2.5|<br>1.9|
|6. TotaL Housefotd F;nancial<br>Assets|30.3|30.2|31.8|32.6|<br>34.5|37.2|36.1|<br>33.2|

.... . . . . . . . ...............................................................................

Source: National Bank of Hungary, CentraL Statistical Office pubLic4tions, and own estimates.
Table 3: Summary Statistics

|Conflict Casualties Tercile|Low In|tensity|Medium|Intensity|High I|ntensity|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Male|Female|Male|Female|Male|Female|
|Panel A – Sample used to estimate E|q. (1) and Eq.|(2) in theprima|ryattainment a|nalysis|||
|=1 if primary education|0.769|0.562|0.743|0.545|0.742|0.511|
|District casualties per 1000|0.265|0.270|0.647|0.642|1.564|1.549|
||(0.098)|(0.101)|(0.160)|(0.161)|(0.958)|(0.972)|
|School destructions per 1000|0.003|0.003|0.005|0.005|0.006|0.006|
||(0.005)|(0.005)|(0.010)|(0.010)|(0.008)|(0.008)|
|Maoist abductions per 1000|1.135|1.301|11.890|11.411|5.131|4.904|
||(2.515)|(2.454)|(27.159)|(27.138)|(5.482)|(5.264)|
|=1 if district controlled by<br>Maoists (Definition 1)|0.042|0.058|0.123|0.129|0.304|0.321|
|=1 if district controlled by<br>Maoists (Definition 2)|0.102|0.122|0.357|0.337|0.541|0.554|
|Age at interview|19.983|20.376|19.020|19.659|18.873|19.928|
||(5.286)|(5.327)|(5.018)|(5.098)|(5.032)|(5.220)|
|=1 if DHS 2006|1.000|1.000|1.000|1.000|1.000|1.000|
|Observations|1264|1454|904|1122|887|1247|
|Panel B - Sample used to estimate E|q. (3) in theye|ars of educatio|n and enrollmen|t analysis (all ch|ildren aged 5 t|o 18)|
|Years of education|2.887|2.405|2.841|2.372|2.763|2.338|
||(3.060)|(3.002)|(2.900)|(2.787)|(2.856)|(2.744)|
|=1 if enrolled in schooling|0.758|0.631|0.806|0.678|0.798|0.664|
|District casualties before survey|0.132|0.134|0.316|0.321|0.680|0.699|
|per 1000|(0.150)|(0.152)|(0.329)|(0.328)|(0.714)|(0.700)|
|Age at interview|10.922|11.048|10.854|11.124|10.929|11.226|
||(3.932)|(3.991)|(3.846)|(3.969)|(3.880)|(3.966)|
|=1 if DHS 2006|0.463|0.464|0.469|0.476|0.461|0.479|
|=1 if rural|0.741|0.756|0.857|0.867|0.839|0.834|
|Education of the household head:|||||||
|=1 if no education|0.430|0.447|0.416|0.455|0.433|0.464|
|=1 if primary education|0.358|0.351|0.399|0.384|0.401|0.380|
|=1 if secondary education|0.181|0.176|0.170|0.147|0.151|0.136|
|=1 if higher education|0.031|0.026|0.015|0.013|0.015|0.021|
|Observations|6088|5889|4842|5114|4607|4707|
|Panel C - Sample used to estimate E|q. (1) in the ea|rlymarriage an|alysis||||
|=1 if married by 15 years old||0.207||0.167||0.190|
|District casualties per 1000||0.270||0.654||1.569|
|||(0.103)||(0.163)||(1.010)|
|Maoist abductions per 1000||1.348||12.072||5.002|
|||(2.641)||(27.335)||(5.397)|
|Age at interview||21.394||20.307||20.976|
|||(5.150)||(4.959)||(5.108)|
|Observations||1239||928||1050|

Continued on next page 38
Table 3. Level of wage-earner occupation of native Danes and immigrants by job skill level, <u>1995-2002, percent</u>

||Elementary<br>qualificatio<br>n|Lower level<br>qualificatio<br>n|Med. level<br>qualificatio<br>n|High level<br>qualificatio<br>n|All|N|
|---|---|---|---|---|---|---|
|NativeDanes|13.6|49.9|17.6|18.9|100|520,677|
|Immigrants,<br>all non-Western|22.0|51.3|10.9|15.8|100|65,909|
|Turkey|32.1|54.2|5.5|8.3|100|9,841|
|Pakistan|31.6|52.1|6.4|9.8|100|3,774|
|Vietnam|10.9|66.3|11.5|11.3|100|4,791|
|Iran|7.9|32.9|25.4|33.8|100|7,357|
|Iraq|14.9|43.8|10.5|30.8|100|2,413|
|Ex-Yugoslavia|24.8|63.0|5.7|6.5|100|12,347|
|Somalia|17.5|51.0|9.3|22.2|100|594|
|Stateless|15.7|48.5|12.7|23.1|100|1,819|
|OtherNon-Western|22.8|47.5|11.9|17.8|100|22,973|

Note: See note for Table 1.

Table 4. Level of wage-earner occupation of native Danes and immigrants by job skill level, <u>1995-2002, percent</u>

||Elementary<br>qualificatio<br>n|Lower level<br>qualificatio<br>n|Med. level<br>qualificatio<br>n|High level<br>qualificatio<br>n|All|N|
|---|---|---|---|---|---|---|
|NativeDanes|13.6|49.9|17.6|18.9|100|520,677|
|Immigrants,<br>All non-Western|22.0|51.3|10.9|15.8|100|65,909|
|– Immigrants with<br>Danisheducation|12.7|45.4|18.5|23.4|100|30,740|
|– Immigrants with<br>foreigneducation|30.1|56.5|4.2|9.2|100|35,169|

Note: See note for Table 1.

Table 5. Educational attainment and wage-earner job skill level of native Danes, 1995-2002, <u>percent</u>

||Elementary<br>qualification|Lower level<br>qualification|Med. level<br>qualification|High level<br>qualification|All|N|
|---|---|---|---|---|---|---|
|Primary|31.4|57.9|8.0|2.7|100|120,055|
|Secondary|10.1|35.3|30.9|23.7|100|19,679|
|Vocational|11.5|66.5|16.5|5.4|100|247,539|
|Higher education,<br>short|3.6|44.0|40.6|11.9|100|27,436|
|Higher education,<br>medium|1.5|8.0|30.1|60.4|100|64,485|
|Higher education,<br>long|1.0|3.4|11.4|84.2|100|41,483|
|All nativeDanes|13.6|49.9|17.6|18.9|100|520,677|

Note: See note for Table 1.

36