rafmacalaba/datause-encoder-data huggingface dataset:
We may be concerned that the counterintuitive health results could be driven by data quality issues. Here we explore these concerns in relation to diarrhea (7 and 14 day prevalence), hemoglobin level,<sup>33</sup> height-for-age, weight-for-age and head circumference z-scores.

# **_7.1._ Courtesy Bias**

While the nature of the intervention made it impossible to blind participants to their treatment status, we ensured that interviewers were blinded to the intervention status of each village in an attempt to reduce potential courtesy bias.

Objective biomarker measures help to overcome the potential of courtesy bias, which has been documented in previous literature related to WASH interventions (e.g. Schmidt, 2014; DFID, 2013). To test for potential courtesy bias in self-reported measures, we include a falsification question in the child health calendar survey instrument on whether the child has had abrasions, scrapes or bruising in the past 7 days, which we would expect to be uncorrelated with the handwashing or sanitation interventions. Results are presented in Table 15 and we find a small and borderline significant decrease in the combination ward, but no effect in the TSSM or HWWS wards suggesting potential but limited concern for reporting bias in the combined group.

# **_7.2._ Nonrandom Covariate Imbalances: Enumerator Bias and Differential Timing**

Biometric measures are able to remove any potential subjective reporting bias, but we may still be concerned about non-random measurement bias given the level of skill required to measure height, weight, head circumference and anemia levels. We may also be concerned that roll-out of the survey (nine months in total) may have resulted in control and treatment households being visited at different times in the year. This would be problematic in cases where outcomes are seasonal, such as for anemia, which manifests as a symptom of malaria. We run all of the regressions for health outcomes (Table 15), and include interviewer and interview month fixed effects to account for these potential biases. We find that results remain consistent and significant for diarrhea, weight-for-age and hemoglobin level, while remaining small and insignificant for height-for-age. In fact, impacts increase slightly in all cases, and the standard errors reduce, yielding more precise estimates. Head circumference results vary somewhat across specifications, bringing into question the accuracy of this measure.

# **_7.3._ Outliers**

> 33 Being anemic is based on the cutoff of having less than 110 g/L of hemoglobin in the blood. We find that, although significant, the result is highly influenced by the cutoff level chosen, and any cutoff between 73 and 103 g/L would fail to reject a difference in means between the control and combination group (Figure 9). In this case, we prefer to conduct robustness on the raw hemoglobin levels.

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**Figure 9. Fiscal Policy’s Impact on Inequality, 2015**

<!-- Start of picture text --> 0.33 0.325<br>0.322<br>0.32<br>0.307<br>0.31<br>0.299<br>0.30<br>0.29<br>0.281<br>0.28<br>0.27<br>0.26<br>0.25<br>Market (incl. Net market Disposable Consumable Final<br>pensions)<br>Gini coefficient<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget figures.

The redistributive effect<sup>42</sup> of fiscal policy in Egypt is about mid-range when compared to other countries in the region or those with similar levels of development (Figure 10).<sup>43</sup> This is due primarily to a relatively low impact of direct transfer spending on inequality. Egypt’s main cash transfer program, the food ration program, is approximately a universal transfer, so richer households receive the transfer approximately as frequently as poorer households; untargeted transfers often do not generate large impacts on inequality. Figure 10 also demonstrates that prefiscal inequality in Egypt is quite low.<sup>44</sup> Fiscal policy is also poverty reducing. As discussed in Lustig (2018), to measure the impact of fiscal policy on poverty, the indicator of choice—usually the poverty headcount rate or the poverty gap—is calculated for market income, disposable income, and consumable income. Poverty statistics are not typically measured for final income, which includes the government-cost value of health and education services (see Figure 7).

Figure 11 indicates that in the absence of the taxes and expenditures in this CEQ assessment, the poverty headcount rate at prefiscal income (market income including pensions) would be about 35 percent. At 42 The redistributive effect is measured as the absolute difference between the Gini for market income, including pensions and the Gini for consumable income. The CEQ Institute’s data archive, from which Figure 10 is drawn, uses the 2005 PPP conversion factor and the $1.25 PPP per‐capita, per‐day poverty expenditure level as those were conventional standards when earlier CEQ country case study results were generated. The Data Archive is currently being updated to reflect the newer 2011 PPP conversion factors and the $1.90 US$ per‐capita, per‐day poverty expenditure level.

43 Based on the lower‐middle income economies of the World Bank. See World Bank, World Bank Country and Lending Groups, database, Washington, DC, accessed May 25, 2018. <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519‐world‐bank‐country‐and‐lending‐groups,</u> 44 The estimated low Gini in Egypt has been studied extensively. See, for example, Verme et al. (2014), Van Der Weiede and Ianchovichina (2016), Lara Ibarra et al. (2017), or Alvaredo et al. (2018).

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Free Trade Congress in Brussels by Engels. He defended his and Marx ́s pro-free trade stance as follows: Under freedom of trade the whole severity of the laws of political economy will be applied to the working classes. Is this to say that we are against Free Trade? No, we are for Free Trade, because by Free Trade all economical laws, with their most astounding contradictions will act upon a larger scale, upon a greater extent of territory, upon the territory of the whole earth; and because from the uniting of all these contradictions...will result the struggle which will itself eventuate in the emancipation of the proletariat.<sup>2</sup> Engels believed that by letting capitalist rules of the game embrace ever greater portions of the globe, the outcome of such a capitalist domination would be similarity of economic conditions among workers. The similarity in economic conditions would, in turn, lead to the concordance of economic interests and to the emergence of solidarity among workers of different countries. Ultimately, it would culminate in a worldwide revolution.

But did similarity of economic conditions among workers exist then? Did Marx and Engels depict the reality of that era correctly? Today we have more data than Marx and Engels possessed at the time. Yet, their insight is confirmed by what we know today, both as regards the ubiquitous split between the two major classes and similarity in the economic position of the laborers, or, more broadly, the poor across countries.

Consider class and income compositions of a couple of societies for which we have the data from the mid-19<sup>th</sup> century. In R.D. Baxter’s social table for England and Wales for 1867,<sup>3</sup> incomes of the wage-earning classes, representing 72 percent of the population and divided into eight subgroups, start at the bottom of the distribution, just above paupers, with an estimated per capita income equal to one-third of the national mean, and end at the richest point, with an income 10 percent below the mean. The propertied classes (a little over 6 percent of the population) covered the upper part of the income distribution. The income ratio between the two classes was greater than six to one.

> 2 See Karl  Marx and Frederick Engels, _Collected Works_ , vol. 6, New York, International Publishers, 1976, p. 290. Quoted from Moellendorf (2009, p. 94).

> 3 Available at Peter Lindert ́s Website ―Global Price and income History Group‖ , see  http://gpih.ucdavis.edu/.

3
**Table 7: Gains from Moving to Optimum Size of City for Chinese Cities**

||% c<br>|urrent size b|elow optim|um|
|---|---|---|---|---|
||50|40|30|20|
|% gain inVAperworker|35.0%|20.0%|9.5%|4.1%|
|Source: Henderson (2004)|||||

Livability depends on the quality of the physical infrastructure, social amenities, recreational facilities, and the environment. In many Chinese cities, these are barely adequate. In the face of continued migration and automobility, living condition could deteriorate in the absence of well planned investments and effective regulation.<sup>35</sup> Cities must also work to enhance the competitiveness of the local economic environment so as to facilitate the entry and exit of firms. In this regard, their policies towards the SOEs will be important in two respects. First, the treatment of the SOEs will influence the rules governing market competition more broadly. Second, it would determine how quickly and in what manner SOEs embark on a reform of their governance structure and operations. It is becoming clear that industrial policies that rely on directed credit, subsidies, trade barriers and government purchases of products to induce the development of targeted industries, are frequently a costly and ineffective approach to local industrialization. Moreover, many of the instruments used in support of such policies are now disallowed by the WTO. Government efforts to impose mergers and to create enterprise groups have not yielded robust results anywhere in East Asia. They can burden successful firms with unwanted baggage rather than creating larger and more dynamic entities. It is much better to let market forces do their job, and manage development with the help of a sound competition policy.

> 35 As migration increases, authorities will need to pay closer attention to the risks from infectious diseases such as HIV and sexually-transmitted disease (Smith and Hugo 2005).

37
Gregorio Impavido

**Table 3: Transitional and New Generations in the Pension System (2004)**

|**Workers**|**Number**|**Distribution**|
|---|---|---|
|Transitional generation|7,743,057|63 percent|
|Actual generation|4,604,993|37 percent|
|Total|12,348,050|100percent|

**Source** : IMSS

Due to the difference in generosity between the old and new old age benefits, it is expected that all retirees in the transition cohort will elect the on average more generous old age benefits provided for under the LSS-73. In other words, the short-to-medium term prospects for development of the annuity market in Mexico very much depends on the accumulated funds in the IV and RT lines of benefits only.

The next section will analyze the evolution of the pension annuity market as derived from the SAR-97 reform between 1998 and end of 2005. While the term “pension annuities” is used to indicate all annuities derived from the SAR-97 reform, it is often used to indicate the only _de facto_ existing market of IV and RT annuities. The market for old age (retirement) annuities is currently non-existing due to the fact that workers at the time of the reform have the option to elect the (on average) more generous old age benefits under the pre-reform provisions (see more on this issue in section III). Therefore, in the rest of this paper the term “annuity” will be used for IV and RT annuities, unless otherwise indicated.

# **II THE MARKET FOR PENSION ANNUITIES**

## **_II.A Market Structure and Growth_**

With the SAR-97 reform 13 insurance companies were licensed to provide pension annuity benefits in 1997. At the end of 2005, 11 specialized pension annuity companies were active in the market. Seven of these companies were subsidiaries of foreign companies (F) whilst four companies were part of financial groups (GF). The following table lists the companies operating in the market as of end of 2005 and their market shares in term of written premiums.

- 4 -
# **2. DSI PERSPECTIVE**

In this section, we present the perspective guiding the construction of the DSI index. The categories of indicators are presented as well as the suggested indicators in each sector for measuring citizens’ experience with service delivery.

# **2.1 Service Delivery Outcomes and DSI Perspective**

Service delivery outcomes are determined by the interplay of government, providers and citizens. In particular, health and education outcomes are the results of the interaction between various actors involved in the multi-step service delivery systems and depend on characteristics and behavior of individuals and households. While delivery of quality health care and education is contingent foremost on what happens in consultation rooms and in classrooms, a combination of various basic elements have to be present in order for quality services to be accessible and produced by health personnel and teachers at the frontline, which all depend on the overall service delivery system and supply chain. Adequate financing, infrastructure, human resources, material and equipment have to be available for service delivery at the frontline, as well as proper institutions and governance structure to provide adequate incentives to the service providers. The availability of these essential elements and institutions are a function of the efficiency of the entire service delivery system.

Government at the upstream level sets overall policies, allocates resources and designs rules and service providers’ incentive systems; service providers’ behavior downstream is conditioned by these sets of constraints and incentives as well as rules determined locally; while citizens’ decisions are influenced by the choices offered and the services’ characteristics. Together, the choices and behavior of these various actors determine outcomes (Figure 1).

The DSI index seeks to measure key performance processes as experienced by the average citizen. Thus, the DSI index can be viewed as a Service Delivery Report Card on education and health. While emphasizing the experience of citizens at the service provider level, the index accounts for the various components of the supply side of service delivery and the multiple actors and multiple steps involved in service delivery systems. It builds on the Citizen Report Card approach but assembles information using one (composite) survey instrument, combining modules from PETS, QSDS, SAS, and observational surveys. Because health and education services are largely a government responsibility, and quite a lot of public resources have gone into these sectors, the DSI will at the initial stage focus on public providers.
_Schooling Levels.—_ We construct a score for each grade and pool across grades within each schooling level to produce primary and secondary school scores. We distinguish primary from secondary schooling since enrollment rates drop off between levels in many developing countries. This introduces a potential selection term in secondary school scores, with the highest performing students progressing in the system, biasing scores up due to selection rather than actual learning.

Conceptually, the broader categories of ‘primary’ and ‘secondary’ scores enable us to categorize learning at schooling levels across assessments which span multiple grades and age groups. If the test is designed for an age group (for example, PISA) we code it at the relevant schooling level (for example, secondary for PISA). We specify an approach to including specific grade levels to ensure we have a tight grade interval within one to two years to minimize scope for grade-fixed effects. While the interval is relatively small, it still leaves room for grade-fixed effects rather than test-fixed effects when linking tests. For example, linking PIRLS 2001 grade 4 with SACMEQ 2000 grade 6 might capture a grade difference in PIRLS in addition to difficulty. However, to enable greater country coverage, we put up with the need to expand beyond single grade level intervals. Moreover, these differences are often small and since linking functions are applied to all tests being linked, original ranks will be preserved. An analysis of EGRA in Supplement Figure VIII demonstrates sensitivity to grade. We run a regression with and without grade-fixed effects comparing mean scores relative to a country which participates across all three grade levels 2-4. We find small differences, with near complete overlap in the confidence intervals on the grade and non-grade-fixed estimates. This sensitivity analysis increases our confidence that the EGRA data, and other regional assessment data, is robust to data availability by grade. We also include a variable with grade information in the database to make this transparent.

<!-- Start of picture text -->
-100 -50 0 50 100<br>Coefficients<br>Effects, All Grades<br>Effects, Grade-Fixed<br>Country-Year Dummies<br><!-- End of picture text -->

<!-- Start of picture text --> SUPPLEMENT FIGURE VIII<br>EGRA, Grade-Fixed Effects<br><!-- End of picture text --> _Notes:_ We run a regression with and without grade-fixed effects comparing mean scores relative to a given country which participates across all three grade levels 2-4 using EGRA data. We only include nationally representative data. The darker “All Grades” bar represents the confidence interval without grade-fixed effects. The lighter “grade-fixed effects” represents the confidence interval with grade-fixed effects.

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Promoting Democracy in Fragile States or third-party security programs. We used a combination of surveys, behavioral activities, and electoral returns to measure the effects of these programs. We found that the civic education and town halls program increased enthusiasm for electoral participation and generated a coordinated shift from parochial to national candidates, while possibly increasing levels of contestation within communities and heightening citizens’ willingness to report on intimidation. The security committee program had only a weak effect on preferences for national versus parochial candidates. We found no evidence of synergy between the programs.

Relating these results to our theoretical discussion, the experiment provides insights on barriers to citizens’ ability to express policy preferences with their votes, and therefore the quality of elections, in fragile states. The substantial effects of the civic education and town hall program is evidence in itself of barriers to information and opportunities to coordinate for citizens in rural Liberia. This raises questions as to why such information and coordination opportunities are lacking. One reason for the lack of information is of course material: census data show that only about five percent of households possess a radio, indicative of the extent to which individuals rely on word-of-mouth, and therefore locally mediated, transmission of political information. But this lack of media access likely interacts with strategic factors to leave voters with access only to biased and limited information. Parochial patrons have incentive to monopolize what constituents hear to secure their positions. Similarly, parochial patrons have every incentive to limit opportunities for citizens to discuss and coordinate politically. In the absence of third parties that might intervene, these strategies allow patron’s to sustain their parochial advantage, potentially preventing citizens from expressing their policy preferences through the elections. By intervening in such settings, third party actors such as civil society organizations or even foreign donors could help to remove such barriers to political expression.

35
Information", _American Economic Review,_ Vol. 71, pp. 393-410.

- Sydney, Ludvigson (1998), "The Channel of Monetary Transmission to Demand: Evidence from the Market for Automobile Credit", _Journal of Money, Credit, and Banking,Vol.30,_ No.3 (August), pp.<sup>3 6 5</sup> -<sup>8 3</sup> .

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<!-- Start of picture text -->
# — Goodsupstream Sectorstariff ranked(as % total by largestinput reductioncost) in BaselineUpstream(%input — SimulatedUpstream(% input Changeinp.p. | |# Service Sectorsin upstream tariff ranked(as % bytotal largestinput reductioncost) BaselineUpstream[as % total SimulatedUpstream(as % total Changep.p. in<br>720 PCR-RMK-Raw Processed Milk rice 24%97% 17%93% 05%OI% 12 OBS:WTR-Other Water: business collection,servicespurification and distribution ai%0.9% 01%10%<br>34 CTL-BovineWOL - Wool, silk-wormcattle, sheep cocoons and goats, horses 30%63% 86%61% 04%0.2% 3.4 ROSDWE - Recreation- Dwellings & Dither Services 19%1% 2%20%<br>56 QAP COA-CoalAnimal products n.e.c. 57%30% 55%29% aye01% 56 — CMN-WTP - Communications Water transport 27%28% 3.0%30%<br>7 GAS-Gas 28% 27% 01% 7 OF! - Other Financial intermediation 29% 34%<br>8 OIL-Gil 28% 28% 0.0% 8 ISR-Insurance 29% 35%<br>370.1112 NFM-Metalsne.cNMM-S-FerrousPC-Petroleum,Mineral metalsproductscoal productsne.c. 268%30%23%17% 263023%17% % ae0.03%001%00 910112 OSGATPTRD-OTP- -- Trade Other Transport OtherAir transport Services (Government) 4.0%68%72%7.0% 47%8%81%79%<br>13. GDT-Gas manufacture, distribution 07% 07% | ]13 CNS- Construction: building houses Factories offices a 13.1% 65%<br>14 CMT- Bovine meat prods 07% 07% 4<br>15 OMT -Meat products ne.c. 07% 07% 6<br>16 ELY -Electricity 38% 38% 6<br>1? MIL- Dairy products 03% 09% v<br>38 LUM- Wood products 5.3% 53% 8<br>39 FMP - Metal products 09% 10% 9<br>20 ELE - Electronic equipment 03% 10% 20<br>21 SGR- Sugar 07% 08% 21<br>22. OMN-Mineralsnec. 14% 15% 2<br>23 OME -Machinery and equipment ne.c. 12% 13% 2B<br>24 OTN- Transport equipment n.e.c. 1% 12% 24<br>25 PPP - Paper products, publishing 13% 15% 5<br>26 MVH-Mator vehicules and parts 18% 18% %<br>27 B_T- Beverages and tobacco products 29% 3% 2<br>28 FSH-Fishing 28% 3% 2<br>29 TEX- Textiles 32% 38% 23<br>30 OFD - Food products ne.c. 27% 7.9% 30<br>31 OMF - Manufactures ne.c. 28% 31% 3<br>32 V_F - Vegetables, fruit, nuts 42% 4.8% 32<br>33 O8D-Oil seeds 49% 54% 3<br>34 OCR-Crops ne. 50% 55% 4<br>35 GRO-Cereal grains nec. 51% 55% 5<br>36 FRS-Forestry 47% 5.2% 8<br>37 CRP- Chemical, rubber, plastic products 34% 39% 7<br>38 C_B- Sugar cane, sugar beet 84% ai% 8<br>39 WAP - Wearing apparel 37% 44% 39<br>40 WHT - Wheat 85% 93% 40<br>41 PDR- Paddy rice 87% 9.4% 41<br>4243.44 PFB-Plant-basedVOL-LEA-Leather Vegetableproductsoilsfibers and Fats 85%8.0%20.9% 93%93%23.9% 42844<br><!-- End of picture text -->
# **Table 2 (continued)**

||**Mean**|**Std. Dev.**|**Min**|**Max**|**Label**|
|---|---|---|---|---|---|
|Local GDP|0.8355642|23.57658|0|3058.596|Total incomeper cell,millions USD(2006 PPP)|
|Transportation cost to market|45.10281|28.44443|0.3138928|165.4458|Transportation cost in US dollars|
|No. of fatalities within cell|0.0063334|0.0268568|0|0.3062945||
|Population|2517.983|28082.6|0|3,327,414.00|Populationper cell(thousands),Landscan 2009|
|Cassava Potential Yield|6960.317|2120.109|0|11161|Yield Kg/ha,GAEZ FAO|
|Groundnut Potential Yield|184.5166|124.6515|0|945|Yield Kg/ha,GAEZ FAO|
|Maize Potential Yield|949.325|362.9007|0|3380|Yield Kg/ha,GAEZ FAO|
|Distance to nearest mine|337.3761|147.4657|1.270597|500|Distance in km|
|No. of Observations: 26,|330|||||

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