rafmacalaba/datause-encoder-data huggingface dataset:
|**Authors**|**Year**|**Subjects**|**Criterion measure**|**Correlation**|
|---|---|---|---|---|
|||||.60 (3rd)<br>.39 (5th)|
|Jenkins & Jewell,<br>1993|1993|335<br>2nd to 6th grade with 15 LD,<br>MR, 1 ED|Gates-MacGinite<br>Reading<br>Test<br>(GMRT)<br>1|.86(2nd)<br>.82(3rd)<br>.86(4th)<br>.68(5th)<br> <br>.63(6th)|
||||Metropolitan Achievement Test<br>(MAT)|.84(2nd)<br>.67(3rd)<br>.82(4th)<br>.64(5th)<br> <br>.58(6th)|
|Hintze, Shapiro, Conte,<br>andBasile,1997|1997|57 2nd to 4th grade with<br>specialed.|8<br>Degrees of Reading Power Test<br>(DRP)|<br>.67 (Auth)<br>.64(Liter.)|
|Kranzler, Brownell,<br>and Miller, 1998|1998|57<br>4th grade general ed.|Kaufman Test of Educational<br>Achievement-Brief<br>(KTEA -B)|<br>0.41|
|Madelaine & Wheldall,<br>1998|1998|50<br>1st to 5thgrade generaled|Neale<br>Analysis<br>of<br>Reading-<br>Revised|0.71|
|Kranzler, Miller,<br>and Jordan, 1999|1999|326<br>2nd to 5th grade general ed.|California<br>Achievement<br>Test<br>(CAT)|.63 (2nd)<br>.52(3rd)<br>.54(4th)<br> <br>.51(5th)|
||||Mazes above gradelevel|0.87|
||||Questions below gradelevel|0.26|
||||Questions at gradelevel|0.5|
|Jenkins, Fuchs et al.,<br>2000|2000|113<br>4thgrade with7RD|Iowa Test of Basic Skills (ITBS)|0.83|

Source: Hosp, M. K. (2001). Variables that Affect the Correlation between Fluency and Accuracy with a Measure of Reading Comprehension. Nashville, TN: Peabody College of Vanderbilt University.

# **7.3. Random selection of schools, classrooms and students**

Within each group of schools (full-grade, multigrade, and single-teacher), schools were ordered by province and size, so that there would be proper representation of each province and smaller schools would also have a chance to be selected. However, schools registering less than 10 students or located more than two days away from the district capital (by the most appropriate form of transportation) were not included in the sample because of the imbalance between the high cost of reaching them and the limited benefits of inclusion to total the sample.<sup>50</sup> In each subgroup of schools, intervals of equal size were defined using the cumulative number of students. The starting point for the first interval was chosen randomly, and the schools that had the first student of each interval were selected. If any of the selected schools turned out to have too few students or to be located too far away (according to the aforementioned criteria), then the next school in the list was selected instead.

> 50 Schools in the districts of Pangoa and Mazamari were excluded from the sample for security reasons, as the area is currently under heavy military surveillance due to terrorist activity and drug trafficking. These districts account jointly for approximately 5.1 percent of the population of Junin (PNUD, 2006).

65
As HIV in Africa is spread primarily through heterosexual contact, lack of condom use and multiple partners should, all else equal, be associated with higher prevalence. Yet in Kenya HIV prevalence is higher among men who used a condom the last time they had paid sex (8.0 percent) than among men who did not (6.4 percent) (CBS, MOH, and ORC Macro 2004); it is much higher among men who had two partners in the past 12 months (9.7 percent) than among men who had three or more partners (3.3 percent). Among women in Cameroon who used a condom during their last sexual encounter in the past 12 months, HIV prevalence is slightly higher (7.5 percent) than among women who did not (7.0 percent) (INS and ORC Macro 2004).

These seemingly anomalous results, which are inconsistent with biological truths that condoms used throughout sexual history will lower prevalence, reflect the fact that risky behaviors are not undertaken in isolation.<sup>5</sup> Risky behaviors can reflect a person’s perception of risk; whether a man uses a condom when paying for sex may depend on his assessment of his partner’s risk of being infected with HIV. These apparent paradoxes may also reflect the difficulty of collecting accurate self-reported information on risky behaviors (Adams, Trinitapoli, and Poulin 2007; Gersovitz 2005) and measuring trends in these behaviors, especially when concepts, perceptions, and attitudes as well as the wording of survey questions change (Glick 2007).

The relation between socioeconomic status and risky behaviors does not necessarily shed light on the relation between socioeconomic status and HIV status. Sexual behaviors, at least in isolation, are not necessarily substitutes for measuring HIV status. Thus, finding a correlation between a socioeconomic status (SES) indicator and a risky sexual behavior (e.g. lack of condom use at last sex) does not imply the same correlation between that SES indicator and HIV status. This also extends to knowledge. Knowledge about HIV does not necessarily translate into behaviors associated with prevention (see, for example, Booysen and Summerton 2002); knowledge about one’s own status may not affect the propensity to buy condoms (Thornton 2005).

> 5 They may also reflect the fact that lower condom use at last sex may say little about condom use throughout sexual history.

5
-8 -

service institutions, and norms and regulations which prevent political interference in the work of policy units.

The limitations of this paper should be addressed upfront. It does not analyze the nature of governments, regime types, or the nature of political institutions, i.e. a benevolent social guardian or predatory state or the hard and soft character of the state, or the protypes of autonomous and factional states of the Lal-MyintILl typology. In that typology, autonomou **s** tates are all characterized by the relative freedom from overniding influences of particular economic groups, while the factional state is characterized by a coalition of different interest groups. The governing coalition seeks to maximize the well-being of the members of the coalition, subject to attempting to stay in power and retain their governmental budget constraint. We concede that these influences affect the behavior of governments in maldng economic decisions and these decisions may, in turn, hamper or facilitate the politcal process itself. However, this kind of analysis, though useful and interesting, falls beyond the scope of the case studies upon which this paper is based.

As outlined above, we also do not examine the question of "conditionality" of the IFI's and the bilal donors or the enforcement of conditionalities and the bargaining power of the IF's vis-a-vis the recipient countries-important as they are. Official foreign capital plays a critical role in the initiation and sustainability of reform efforts, policy advice and dialogue with the IFI's and the extent of compliance with loan agreements do make a difference. We do, therefore, investigate whether the FI's financing was a motivating force behind the decisions by various countries to inifiate reforms. KahlerW has made an interesting analysis of the external influence and conditionality of the IFI's wiffi respect to economic policy reforns in developing 111 Lal, D. and Myint, IH. **_Poveny, Equity and_** _Ecoxonoc_ **_Growth._** World Bank Comparative Study, Mimeo 1990.

12/ Kahler, M., "External Influence, Conditionality and the Politics of Adjustment", in S. Haggard and R. F. Kaufman (eds), **_The Poliics QfJEconmic_** _Adjmaent,_ Princeton: Princeton University Press, 1992.
**Table   VI:   Test   Scores   -­‐   Impact   by   Provider   Quality   and   Type**

|||**Test Sco**<br>|**res(Year 2)**<br>||
|---|---|---|---|---|
||**BySchoolTypea **|**ndQuality**|**GovernmentSchoo**|**lsCombined**|
|||(1)||(2)|
|Report Card (RC)||0.315**||0.313**|
|||(0.122)<br>||(0.122)<br>|
|RC*Government (Gov)||-0.241**||-0.217*|
|||(0.120)<br>||(0.123)|
|RC* High||-0.389***<br>(0.133)|||
|RC*Gov*High||0.505**|||
|||(0.218)|||
|RC * High * Private||||-0.389***<br>(0.133)|
|High||0.211***<br>(0.0736)<br>|||
|Government||-0.208***||-0.225***|
|||(0.0516)||(0.0593)|
|High * Private||||0.218***<br>(0.0757)|
|Baseline||0.542***||0.534***|
|||(0.0608)||(0.0630)|
|Controls||Village||Village|
|Observations<br>||780||780|
|R-Squared||0.65||0.648|
|SUBGROUP POINT ESTIMATE,|F-TESTp-VALUES IN BRAC<br>|KETS|||
||Low Private School|0.315|Low Private School|0.313|
|||[0.011]||[0.011]|
||High Private School|-0.0738|High Private School|-0.0755|
||<br>|[0.194]|<br>|[0.181]|
||High Gov School|0.190|Gov School|0.0965|
|||[0.262]||[0.044]|
||Low Gov School|0.0742|||
|||[0.111]|||
|Baseline Test Score(mean)||-0.021||-0.021|

# _Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level The   outcome   variable   is   Year   2   school   average   (across   all   three   subjects)   test   score.   Column   1   separates   the   effect   by school   type   and   by   school   performance.   Column   2   combines   government   school   and   focuses   on   private   school   type. Regressions   are   weighted   by   number   of   children   tested   in   the   school,   and   include   district   fixed   effects   and   cluster standard   errors   at   the   village   level.   The   same   village   controls   as   in   Table   II   are   included   in   both   regressions.

Regressions  include  interaction  terms  with  NGO,  as  well  as  other  interactions  and  level  terms  that  are  necessary  given the  interaction  terms  included.  The  lower  panel  displays  the  estimated  coefficients  and  p-­‐values  [in  square  brackets] for  relevant  subgroups  obtained  from  the  coefficients  estimated  in  the  top  panel.  Baseline  Test  Score  (Mean)  displays the  baseline  school  test  score  mean  for  the  sample,  weighted  by  number  of  children  tested.

39
**Policy, Research, and External Affairs**

**Population, Health, and Nutrition**

Thispaper-- a product of the Population, iealth and Nutrition Division, Population and luman Resources Dcpaiiment -- is pail of a larger effort in PRE to providc informationi on1 activities in population, health, and niutrition assisted by **the** Bank under its Special Grants Program. Special Grants Programs support multi-countr., activitics, complementing the Batik's direct lending operations. Copies arc available frcc from thc World Bank. 1818 H Street NW, W'ashinrg ton DC 2('433. Plcase contact Otilia Nadora, room S6065, extension 31019 (13 pages). October 1991.

rhe Special Programmin of Rescarcihl Dccvlopment and Research Training in Hluman Reproduction (HRP) - which began as a special prog ram ol thc World Hcalth Organization -- is onc o' the **few** organizations engageds in contraceptive research and development that Iocu;cs on **thc** needs of thei developing world.

Nassim reviews the factors that lcd **thc** World Bank to begin providing the organization with fi nmcial supporl **iT)** FY88 and examines the program's accomplishil, .nts and presenl direcion.

MNajor evaluations ot HiRP strongl1 cndorse the program, concluding that thc program has - h3ad a major impact thi principal. it not the onl) impact, in many areas (such as coordinating world rcsearch efforts and developing **new** metihods for regulating fertility).

> **TIhc** Bank's cosponsorshiip of HRP has strengthened the program's; links with governments and facilitated its access to ministries outside the health field. The Bank's rolc is important to policy coordinationi and to sustaining donor commitinent to the program.

The Bank's commitment of $2 million a year represents 10 perc,cnt of HRP's tota1 fundinig. IIRP has gain' inticniational rcspect, and its iolid record o achievemtii justify the Banik's conitn1iued - anid expanided - support.

111i I'R L rkOnTIt011;' **SCII'I** JK;i.1 -OnnIIcs die **Fi. r:i'** - * ii \ **utr \\j\ ji** .Im Oin l'Oi **P\.** RcLarTl **i. and** F \Etr,lma Aff,.irsCo'nplkcx jd eo! IL Rcric **i I . C1 r'l > .UI** 4uIk. . *\ cn **I** nIdtIt; 1 ' **I** pnihch xl. X',l . **Ilk'** ]nn.lTsK. ¶nIcrrJ.IcI'mnK, and,.1 'JSIMIJ in I hII \ O1 **I&v** nD i 7ce(*w aTio \ r.rr.cnt 'f'k **_l:fl_** Rini l

> llYxi,i ,,' hx !h3.' I RI I )I **\.T]InJlTln ( .'nl.'r**
7 We include two additional variables. The log of real per capita GDP (LPCGDP) is motivated by gravity considerations (see below). This variable is also an adequate proxy for amenities that are correlated with the island’s economic development, for example, security, transportation, and infrastructure, which are hard to measure directly. The log of the real exchange rate (LRER) captures the price sensitivity of the tourism demand. Considering the proximity of one destination to another, variation in a destination’s price should play a role in determining variation in the amount of tourist arrivals at that destination. Below (see Section 5) this variable is also interacted with LLOB in order to capture the effect of foreign lobbying on the tourism demand elasticity. Finally _uit_ encompasses unexplained tourism shocks. We presume these shocks to have constant variance and to be cross-sectionally and serially uncorrelated.

An alternative model to (1) is the gravity model (Leamer and Stern (1970), Anderson (1979) and Bergstrand (1985)). The main idea behind the gravity equation is that bilateral distances and incomes are the primary determinants of trade. That the gravity equation can be derived from different models in fact makes it appealing for studying tourism. Surprisingly, neither the empirical literature in economic journals nor in hospitality-and-tourism-industry journals contains application of the gravity equation. For example, the survey of empirical models of tourism in Lim (1997) and Sinclair (1998) has no mention of the gravity model. In the presence of fixed effects, time-invariant variables that are considered important to gravity models (e.g. dummy for common language, COMLANG; the log of land areas, LAREA; and the log of distance, LDIST) become redundant. Therefore, in order to investigate the gravity model, we also estimate a random effects model with gravity variables. The log of exports of goods (LRX) is also included as a measure of the importance of traditional trade in the country’s economy. We find the gravity model to not only be empirically well-suited to study the number of tourist visits, but it also affirms the fixed effects results.<sup>10</sup>

# **3.2 Data**

The Caribbean Tourism Organization tabulates time series on tourist flows by destination and origin from the statistical and tourism offices of its member states in the annual _Caribbean_

> 10 Gravity model variables (described below) were taken from the data set at the site <u>http://faculty.haas.berkeley.edu/arose/RecRes.htm#Software</u> made available by Andrew Rose, and used in Rose and Spiegel (2003).
dropped in the first two months of 1989, then increased again in March). It is also possible that the diligence with which records are kept at the health center improved somewhat in 1989.

To better understand the characteristics of insured and uninsured patients, a random sample of consecutive ambulatory patients was surveyed in October 1989. The questionnaire was written in French and posed in Lingala by bilingual Zairois interviewers (see Annex V). Table C.4 presents the characteristics of respondents by insurance status for this plan and the CASOP, the other Kinshasa case study, described below. Table V.2 in Annex V documents the decision rules for resolving missing and ambiguous data on membership status, which arose in 9% of responses.At the St. Alphonse center, 79 patients responded of whom 13% were insured. At the CASOP, 126 patients responded of whom 51% were insured.

Table CA4 Kinshass Survey: Population Characteristics

|Insured<br>Characteristic<br>(HW74)<br>..<br>.................<br>...................................................|Uninsur<br>(Hu131)|ed Valid<br>Responses|
|---|---|---|
|Center:|||
|St. Alphonse<br>14X|53X|205|
|CASOP<br>86X|47X||
|Gerder:<br>|||
|Female<br>44X<br>|63X|205|
|Male<br>55X|34X||
|Age group:|||
|<br>0-14<br>10X|11X|205|
|15-44<br>79X<br>|76X||
|**45 and**above<br>11|13X||
|Average<br>+S.D.<br>29 *14|<br>27 +13||
|Receives<br>a salary<br>each month<br>42K|33X||
|Occupation:<br>|||
|Professional, "cadre"<br>7X<br>|10X|205|
|Skfiled worker<br>13X|8X||
|student<br>24X|16X||
|Unskilled<br>worker<br>24X|16X||
|**Former**<br>**1i**<br>|2X<br>||
|Other<br>11X|13K||
|None<br>20K|35K||
|Eqployer:<br> <br>|||
|State<br>**21K**<br>|6X|205|
|Pare-statal enterprise<br>3X|2X||
|Private<br>enterprise,<br>for profit<br>11K|14X||
|Private<br>enterprise,<br>not for profit 6K|3X||
|Independ"nt<br>1K|7X||
|Household<br>1X|2Z||
|Other<br>Os|1X||
|Hone<br>or no response<br>56X|66X||
|Coqenpoys formdical care.:|||
|In full<br>35X|19X|205|
|**Some**<br>**6X**<br> <br>|**3X**||
|**Hon**<br>**3K**|13X||
|**Hot eployed**<br>**56X**|66X||
|**Is**nother**ftily**<br>**meber**<br> <br>|||
|_ployed<br>**nd**covered for|||
|medical<br>exenses?<br>44X|27X|202|
|<br>p<br> <br>.....................<br>...................................|<br>........||

40
**Table 5: Impacts on imports and exports of goods and services in 2030 (% change from the baseline)**

|**Sector**|**S1**|**S2**|**S3**|**S4**||**S5**||**S**|**6**||
|---|---|---|---|---|---|---|---|---|---|---|
|||||S4a|S4b||S6a|S6b|S6c|S6d|
|||||<br>Imp|<br>orts||||||
|Agriculture|-2.1|-1.7|-1.4|-1.4|-0.3|-1.2|2.2|2.3|2.1|1.4|
|Coal|-2.3|-2.6|-3.4|-1.4|-1.4|-2.4|-2.0|-2.3|-2.8|-1.5|
|Oil and gas|-3.9|-4.0|-3.3|-3.7|-4.1|-3.8|-3.2|-4.6|-4.6|-2.7|
|Other mining|0.8|0.2|-1.0|0.5|0.7|0.3|1.1|0.3|0.9|2.4|
|Food & tobacco|-1.6|-1.3|-0.9|-0.8|-0.4|-0.6|1.1|0.9|0.9|0.6|
|Textile|-1.0|-0.8|-0.7|-0.4|-0.1|-0.3|1.2|0.8|0.9|0.3|
|Wood & Furn.|-0.1|-0.7|-1.3|-0.2|-0.1|-0.4|1.6|1.3|1.0|0.8|
|Pulp & paper|0.7|0.9|0.6|1.0|1.0|1.0|2.0|1.0|1.8|0.9|
|Petroleum|3.4|3.1|2.7|3.6|3.6|3.4|3.9|3.6|3.4|4.0|
|Chemicals|2.5|2.4|1.5|2.8|2.5|2.6|3.5|3.4|3.7|4.2|
|Non-metals|13.0|11.0|7.7|12.0|11.1|11.3|14.4|13.5|13.5|14.2|
|Primary metals|4.4|3.6|1.8|4.0|3.8|3.8|5.0|4.4|4.6|7.8|
|Fab. metals|2.1|1.4|-0.5|1.8|1.6|1.5|2.9|1.7|1.4|3.4|
|Machinery|0.5|0.0|-0.4|0.3|0.2|0.1|0.8|0.9|0.8|-0.4|
|Other manuf.|0.9|1.1|0.6|1.2|1.3|1.2|0.7|-4.3|-4.1|-0.1|
|Construction|2.3|1.0|-0.5|1.5|1.3|1.1|2.2|2.1|2.0|1.1|
|Trade|-1.4|-1.4|-1.0|-1.1|-1.2|-1.1|-1.0|-0.6|-0.8|-0.5|
|Transport|-0.4|-0.5|-0.1|-0.2|-0.6|-0.3|0.9|1.0|1.1|1.2|
|Other services|-1.0|-0.3|-0.1|-0.8|-0.9|-0.9|-2.0|-1.9|-1.8|-1.8|
|Electricity|16.1|15.4|6.7|16.6|16.8|16.0|17.2|15.4|11.8|18.2|
|||||Exp|orts||||||
|Agriculture|2.5|2.1|1.1|3.0|0.1|2.5|-3.7|-3.5|-3.4|-3.8|
|Coal|-26.8|-26.4|-25.4|-28.1|-28.2|-26.9|-27.9|-27.5|-26.8|-28.2|
|Oil and gas|1.1|1.4|0.9|1.1|2.8|1.4|0.7|5.3|5.7|-1.4|
|Other mining|-5.1|-5.2|-3.0|-5.3|-5.1|-5.3|-6.9|-4.3|-5.8|-7.9|
|Food & tobacco|1.6|1.4|0.4|2.0|0.6|1.8|-0.8|-0.6|-0.6|-1.9|
|Textile|0.3|0.2|-0.3|0.5|-0.5|0.3|-3.3|-1.5|-2.0|-2.0|
|Wood & Furn.|0.4|0.0|-0.4|0.3|-0.3|0.1|-3.5|-2.4|-2.1|-3.4|
|Pulp & paper|-2.9|-2.7|-2.7|-2.9|-2.6|-2.8|-4.5|-2.2|-4.2|-3.3|
|Oil refinery|-13.1|-12.7|-12.2|-13.2|-13.0|-12.9|-13.4|-12.8|-12.4|-14.1|
|Chemicals|-5.7|-5.5|-4.7|-5.7|-5.2|-5.6|-6.8|-6.6|-7.1|-7.4|
|Non-metals|-12.0|-11.9|-10.5|-12.1|-11.5|-12.1|-13.7|-12.9|-13.2|-14.8|
|Primary metals|-8.5|-8.3|-6.4|-8.6|-7.6|-8.4|-10.0|-9.3|-9.3|-10.3|
|Fab. metals|-3.8|-3.8|-2.2|-4.0|-3.4|-3.9|-5.5|-3.6|-3.1|-4.7|
|Machinery|-1.5|-1.3|-1.4|-1.5|-0.5|-1.3|-1.8|-2.4|-1.7|5.6|
|Other manuf.|-3.0|-2.7|-1.8|-2.9|-3.0|-2.8|-1.5|8.3|8.0|-0.4|
|Construction|-0.9|-1.8|-2.6|-1.5|-2.0|-1.9|-1.9|-1.5|-2.0|-4.2|
|Trade|1.6|1.6|0.3|1.7|2.0|1.8|2.6|1.7|2.1|1.3|

24
**Table 1. Infrastructure spending on capital and maintenance needs in low‐ and middle‐income countries between 2015 and 2030, by sector**

||_%_<br>|_of GDP_<br>|_US$_<br>|_, billions_<br>|
|---|---|---|---|---|
|_Sector_|_Capital_|_Maintenance_|_Capital_|_Maintenance_|
|Electricity|2.2|0.6|780|210|
|Transport|1.3|1.3|420|460|
|Water and sanitation|0.55|0.75|200|70|
|Flood protection|0.32|0.07|100|20|
|Irrigation|0.13|—|50|—|
|Total|4.5|2.7|1,550|760|

_Source:_ Rozenberg and Fay 2019 **.** _Note:_ — = maintenance costs of irrigation infrastructure are included in the capital costs.

The ambitious goals and high efficiency of Rozenberg and Fay’s “preferred scenario” depend on smart policies and good planning. Under this assumption, countries take long‐term climate goals into account now to avoid expensive stranded assets later; they invest now in renewable energy; they combine transport planning with land use planning, resulting in denser cities and cheaper and more reliable public transport; and they develop reliable railway systems that freight haulers find attractive. Decentralized technologies, such as minigrids for electricity and water purification systems powered by renewable energy, would be deployed in rural areas.

However, improving services requires much more than capital expenditures. Success will depend on ensuring a steady flow of resources for operations and maintenance. In the preferred scenario, low‐ and middle‐income countries would need to spend 2.7 percent of GDP a year to maintain their existing and new infrastructure, in addition to the 4.5 percent of GDP in new capital (table 1).

These estimates do not consider the need to build infrastructure systems that are resilient to natural hazards and adapted to climate change. The topic of this paper is to assess how these estimates change if infrastructure assets and systems are built using stronger and more resilient design, and to determine whether doing so is cost‐efficient.

# Engineering options to make assets more resilient

Interventions to make assets more resilient include using alternative materials, digging deeper foundations, elevating assets, building flood protection around assets, or adding redundancy into designs. Deeper foundations for power plants, windmills, or water treatment plants are often needed to protect them against liquefaction caused by earthquakes. Better materials for wind turbines, cell towers, and transmission and distribution systems can increase their resistance to strong winds and lengthen their lifetime. Increasing the redundancy of components of water and wastewater treatment plants by adding backup components can improve the performance of plants during earthquakes. Building higher dikes around water treatment plants and nuclear plants is the best option for protecting them against floods.

Miyamoto International (2019) presents a high‐level assessment of the costs and benefits of these various technical and engineering options. The additional cost of making assets stronger in the face of natural hazards depends on the hazard and the type of asset. Increasing the flood resilience of a road 3