rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1A. Methodological Classification of the Studies Included in the Database. Country “X”, 2009**

||||||**ective**|**usion**<br>**on**<br>**n**|**T**<br>**es**<br>**ize**|**echnical**<br>**g**<br>**ue**|**Content**<br>**s**<br>**rrors**|<br>**al**<br>**ue**|**es**<br>**ns**|**al**<br>**ce**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||**j**|**cl**<br>**i**<br>**o**|**t**<br>**s**|**in**<br>|**e**<br>**e**|**ic**<br>|**b**<br>**o**|**c**<br>**n**||
|**N**<sup>**o**</sup>|**Type of**<br>**Document**|**Publication status**|**Type of study and**<br>**design**|**Population**|**fines ob**|**fines in**<br>**/ exclus**<br>**criteri**|**Calcula**<br>**sample**|**Sampl**<br>**techniq**|**Reduc**<br>**llection**|**Analyt**<br>**techniq**|**Descri**<br>**limitati**|**Statisti**<br>**significa**|**Maximum**<br>**score: 11**|
||||||**De**<br>|**De**<br>||**Weig**<br>|**co**<br>**hting**<br>|||||
||||||**1**|**1**|**2**|**2**|**2**|**1**|**1**|**1**||
|**1**|Monograph|Unpublished|Cross-sectional|Women who have<br>had a hysterectomy|1|1|0|0|0|0|0|1|3|
|**2**|Scientific<br>article|Journal with peer<br>review|Experimental|Students|1|1|0|1|1|1|1|1|9|
|**3**|Scientific<br>article|Journal with peer<br>review|Cross-sectional:<br>serological<br>survey<br>and KAP|MSM|1|1|1|0|1|1|1|1|9|
|**4**|Scientific<br>article|Journal with peer<br>review|Cross-sectional:<br>serological survey|Female sex workers|1|1|1|0|1|1|1|1|9|
|**5**|Scientific<br>article|Official bulletin|Cross-sectional:<br>serological survey|Oil workers|1|0|0|0|0|1|0|0|2|
|**6**|Scientific<br>article|Official bulletin|Cross-sectional:<br>serological<br>survey<br>and KAP|Female sex workers|1|0|0|0|1|1|0|1|5|
|**7**|Scientific<br>article|No<br>information<br>available|Cross-sectional:<br>KAP survey|Adolescent females|1|1|0|0|0|1|0|0|3|
|**8**|Technical<br>report|Technical<br>publication|Cross-sectional:<br>demographic<br>and<br>reproductive health<br>surve|General population|1|1|1|1|1|1|1|1|11|
||||y|||||||||||
|**9**|Technical<br>report|Unpublished|Cross-sectional:<br>KAP survey|MSM|1|0|1|0|1|1|0|0|6|
|**10**|Technical<br>|Technical<br>|Cross-sectional:<br>KAP survey|Youth|1|1|1|1|1|1|0|0|9|
||report|publication|Case series|Female sex workers|1|0|0|0|1|1|0|0|4|

KAP = Knowledge, attitudes and practices 47
Table 2: SECU[AR CHANGES IN DEVELOPING COUNTRIES' EXPORTS IN INDIVIDUAL COMMODITY PROCESSING CHAINS, 1965-1987 (Cont'd)

||Imports<br>f<br>Developing<br> <br>(njllions<br>o|rom<br>Countries<br>f USS)|Share<br>All|of developing<br>c<br>stages|ountries<br>Primary|in total<br>develop<br>stage<br>items<br>Pr|ed<br>countr<br>ocessed<br>|y<br>inports<br>(X)<br>stage<br>products|Share of p<br>all<br>developing|rimary<br>stage<br>in<br>country<br>exports|
|---|---|---|---|---|---|---|---|---|---|---|
|Processing<br>Chain|1965|1987|1965|1965-87 chg|1965|1965-87 chg|1965|1965-87 chg|1965|1965-87<br>chg|
|ORES, MINERALS AND METALS|||||||||||
|Phosphate|210|1,002|51|2|64|-4|4|33|98|-35|
|SuLphur|35|117|22|-5|26|-3|0|1|100|-1|
|Asbestos|27|60|8|-1|11|2|3|1|89|-35|
|Glass|8|574|2|6|3|1|2|6|11|-9|
|Iron|876|5,065|18|-1|53|0|2|6|93|-33|
|Manganese|160|155|55|-29|74|-24|12|-|93|-5|
|Copper|1,131|4,297|45|-9|65|-7|43|-11|13|15|
|NickeL|2|255|0|10|1|19|0|6|100|-44|
|Bauxite|319|3,560|23|-4|86|-11|8|8|72|-53|
|Lead|_127_|392|31|-3|41|6|27|-14|41|33|
|Zinc|127|416|27|-10|45|-20|13|-2|75|-13|
|Tin|426|752|72|4|94|-24|64|12|34|-26|
|Tungsten|25|38|47|-23|57|8|8|-1|96|-19|
|Si l ver|68|794|23|11|2|28|28|6|3|14|
A two-stage least squares regression (2SLS) additionally allows for an estimate of the local average treatment effect (LATE), that is the effect of opportunity cost “treatment” on bribe likelihood for compliers: that is, the effect of a higher opportunity cost for the group D�1. However, 2SLS is generally regarded as inappropriate for binary outcomes with binary, endogenous regressors. Termed the “forbidden regression”, this estimate will produce inconsistent and biased estimates, as the linear projection of the regressor from the first stage will not align with the form of the regressor itself (Wooldridge, 2002: 267–8). However, 2SLS has the advantage of being straightforward, without requiring a further transformation. The procedure also yields substantial information about the appropriateness of the instrument, which is unavailable for procedures for endogenous, binary regressors (Nichols, 2011). Thus, it is often good enough to estimate the average treatment effect by simply using 2SLS, conditional on controls, this is the local average treatment or LATE (Angrist and Pischke, 2009). 2SLS estimates are presented, therefore, alongside probit and biprobit estimates.

# <u>Choice of instrument</u>

As noted in Section III, pre-2009 was a particularly tumultuous time for Zimbabwe’s economy, including crippling hyperinflation. Though the country’s economy had been in freefall for several years, rates of inflation peaked in the later half of 2008. In fact, though inflation had reached a monthly rate of 50% in March 2007, more than a year later, in July 2008, this monthly rate was 2,600%.<sup>35</sup> The abandonment of the Zimbabwean dollar by the first months 2009 stabilized prices, and the economy showed some signs of recovery, with real GDP per capita even increasing by almost 5%. A cursory look at the movement of vulnerable employment before and after dollarization suggests that employment opportunities outside the informal sector stabilized (at least relatively) after 2009. Under these conditions, then, it can be presumed that a selection effect occurred after dollarization, when opportunities outside the informal sector were more available and viable. Those taking up an activity with the purpose of earning income post-2009, then, would be assumed to do so because they could not find formal-sector opportunities, even in a more stable environment. Put

> 35 Federal Reserve Bank of Dallas, 2011.

(24)
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<!-- Start of picture text --> 1<br>I<br>!<br>* i<br>2<br>4<br>ia] ||<br>wo l<br>I<br>Il<br>|<br>|<br>° 1<br>45 0 5 10<br>as<br>social inclusion t-statistics<br><!-- End of picture text -->

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|<br>8 I1<br>|<br>1<br>0° \<br>2<br>o ]I<br>AS |<br>QTM |<br>|<br>I<br>5 I<br>2 I<br>i]<br>s |<br>?<br>-40 -30 -20 -10 0 10<br>environment t-statistics<br><!-- End of picture text -->
# **16b. Formal minimum qualification criteria and impact on performance (no/ yes) Median ROE Median NPL**

<!-- Start of picture text --> No Yes No Yes<br>Median CAR<br>No Yes<br>5 5<br>4 4<br>ROE 2015 3 NPL 2015 3<br>2 2<br>1 1<br>0 0<br>5<br>4<br>CAR 2015 3<br>2<br>1<br>0<br><!-- End of picture text -->

# **Median CAR**

Just over 50 percent of Commercial SOFIs report having compliance systems in place (with the highest prevalence in Benchmark Europe), but still double the percent for Development SOFIs (Figure 17a). Only 65 percent of Development SOFIs report having the internal control function, and just 50 percent of all SOFIs have risk committees established (mostly absent in East ECA, Figure 17b).

37
village names for a block were written in pieces of paper and the block TSC coordinator blindly selected half of the papers as intervention villages. Overall, we assigned a total of 20 villages in each district to the TSC intervention and 20 to control (40 villages per arm).

# **3.3  Outcome Measurement**

The evaluation measured outcomes using a combination of survey questionnaires, field staff observations, environmental samples, child measurements, and specimen collection. Questionnaires used in the follow-up survey were the same as those used in the baseline survey with some additional questions to measure program exposure and program outcomes. The study collected the following sources of information.

**Household Questionnaire:** The household questionnaire collected information about household membership and demographics, income, assets, dwelling characteristics, TSC program exposure, water and sanitation infrastructure, sanitation- and hygiene-related behaviors, mortality, and exposure to health interventions.  Interviewers conducted standardized observations of dwelling hygienic conditions and sanitation and handwashing facilities when visiting households.

**Caregiver reported diarrhea** : For children < 5 years old, field staff administered a health calendar to each child’s primary caregiver, which was modeled on a previously published design [23]. The calendar measured daily symptoms back from the morning of the interview for 14 days (baseline survey) and 7 days (follow-up survey); the recall period was shortened for the second survey after we determined that recall errors were unacceptably large beyond 7 days [24]. We defined diarrhea as ≥3 loose or water stools in 24 hours, or a single stool with blood/mucus [25]. We additionally measured respiratory symptoms (cough, congestion, difficulty breathing) and child health symptoms not plausibly influenced by the TSC program (bruising/abrasions, itchy skin/scalp) to serve as negative control outcomes [26] because we were concerned that caregiverreported symptoms could be subject to differential reporting bias in this unblinded trial [27,28].

**Anthropometry:** We measured eligible children for height, weight, and mid-upper arm circumference (MUAC) using a standardized anthropometry protocols [29,30]. Pairs of anthropometrists measured child length/height to the nearest 0.1 cm using a portable stadiometer (Seca); children < 24 months were measured in the recumbent (lying) position and older children were measured standing. Weight was measured to the nearest 0.1 kg using an electronic scale (Tanita); children unable to stand were weighed in their caregiver’s arms and the caregiver’s weight measured separately. MUAC was measured to the nearest 0.1 cm using a pediatric measuring tape. All measurements were collected in duplicate and we used the average of the two measurements in the analysis. We excluded observations if the two measurements differed by > 10% (N=21 for height, N=85 for weight, N=23 for MUAC). We translated the anthropometric measurements into Z-scores using the WHO 2006 growth standards and a publicly available Stata algorithm [31].

8 / 41
Table 5: Impacts on children’s educational inputs: study time <u>(OLS)</u>

|Dependent variable:|Per-da|y minutes (|average from|Sep 2013 to|Apr 2014) spe|nt on:|
|---|---|---|---|---|---|---|
||Study||Study|at home||Other|
||at school|total|day|night|under|activities|
||||||kerosene<br>lamps||
||(a)|(b)|(c)|(d)|(e)|(f)|
|**With no baseline con**|**trols**||||||
|All products|1.263|11.527**|-7.996***|19.523***|-105.801***|-4.531|
||(3.049)|(5.439)|(2.807)|(3.512)|(3.247)|(10.997)|
|One product|2.913|18.865***|-5.522*|24.387***|-104.483***|-5.565|
||(3.354)|(6.059)|(2.812)|(3.718)|(3.265)|(9.971)|
|All = one (p-val.)|0.668|0.160|0.436|0.084|0.523|0.906|
|All + one = 0 (p-val.)|0.422|0.005|0.006|0.000|0.000|0.600|
|R-squared|0.122|0.146|0.183|0.198|0.821|0.445|
|No. of obs.|620|620|620|620|620|620|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**With baseline contro**|**ls**||||||
|All products|1.643|12.950**|-7.029**|19.979***|-105.381***|-4.968|
||(3.148)|(5.600)|(2.825)|(3.544)|(3.263)|(10.826)|
|One product|2.903|19.025***|-5.097*|24.122***|-104.529***|-7.378|
||(3.449)|(5.979)|(2.749)|(3.653)|(3.348)|(9.332)|
|All = one (p-val.)|0.739|0.263|0.537|0.162|0.666|0.804|
|All + one = 0 (p-val.)|0.407|0.003|0.012|0.000|0.000|0.490|
|R-squared|0.137|0.173|0.212|0.221|0.825|0.451|
|No. of obs.|620|620|620|620|620|620|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean _y_ (control)|197.72|229.54|110.75|118.78|111.03|847.61|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number). (4) Other activities include sports, cooking and bathing, household chores, and some other activities, including sleeping and rest.

29
**Table 5: Impact of Internet job search among the unemployed on employability within 5 years, heterogeneous effects**

|Dependent variable: Employed<br>**By g**|**ender**|**By residen**|**ce area**|**By **|**age **|**By education**|
|---|---|---|---|---|---|---|
|||||||Secondary or|
|Men<br>(1)|Women<br>(2)|Urban<br>(3)|Rural<br>(4)|15-35<br>(5)|36-64<br>(6)|<br>above<br>(7)|
|Internet job search<br>0.146*|0.075|0.118**|0.072|0.103*|-0.011|0.105*|
|[0.086]|[0.068]|[0.060]|[0.108]|[0.056]|[0.018]|[0.055]|
|Internet access at home<br>-0.014|0.006|-0.016|0.035|0.000|0.007|0.000|
|[0.058]|[0.034]|[0.033]|[0.056]|[0.032]|[0.013]|[0.030]|
|Male||0.023|0.060*|0.040*|0.036|0.035|
|||[0.028]|[0.031]|[0.024]|[0.033]|[0.021]|
|Married<br>0.054|-0.007|0.01|0.008|0.005|0.021|0.008|
|[0.045]|[0.016]|[0.021]|[0.025]|[0.018]|[0.023]|[0.017]|
|Age<br>-0.003|-0.001*|-0.003***|-0.001|-0.002|-0.003|-0.002**|
|[0.002]|[0.001]|[0.001]|[0.001]|[0.002]|[0.003]|[0.001]|
|Primary or preparatory education<br>-0.133*|-0.000|-0.021|-0.118|-0.061|-0.048|-0.090*|
|[0.074]|[0.015]|[0.019]|[0.071]|[0.048]|[0.049]|[0.049]|
|Secondary education<br>-0.048|0.021|0.045*|-0.067|0.004|-0.041|-0.027|
|[0.076]|[0.013]|[0.025]|[0.068]|[0.048]|[0.048]|[0.048]|
|Above secondary education<br>-0.07|0.031**|0.031|-0.048|0.009|-0.054|-0.026|
|[0.078]|[0.013]|[0.026]|[0.069]|[0.049]|[0.056]|[0.049]|
|Rural<br>-0.013|-0.051|||-0.06|-0.006|-0.05|
|[0.054]|[0.047]|||[0.042]|[0.008]|[0.038]|
|Observations<br>269|659|455|473|826|102|811|
|R-squared<br>0.061|0.048|0.074|0.045|0.052|0.203|0.050|
|Region dummies<br>YES|YES|YES|YES|YES|YES|YES|
|Dependent variable mean<br>0.071|0.024|0.042|0.034|0.041|0.010|0.043|
|Robust standard errors in brackets. *** p<0.01, ** p<0.05,|* p<0.1||||||

_Notes._ Each cell represents a coefficient estimate using OLS regression. The analysis is restricted to unemployed individuals in 2012 that were also interviewed in 2018, aged 15 to 64 years old in 2012. Unemployed individuals are defined according to the market definition of labor force participation. The dependent variable is a dummy variable indicator equal to one if the individual is employed within 5 years after this unemployment spell in 2012 (between the years 2013 and 2017, included). The control variables refer to the year 2012 and they include: a dummy variable equal one for individuals who report that finding a job is one of the purposes of Internet use, a dummy variable for having Internet access at home, age, a dummy variable for being male, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions (Greater Cairo, Alexandria and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt and Rural Upper Egypt). The dependent variable mean is reported in the last row.

30
In addition to funding, external actors provide a range of other support for community responses. These include capacity building and organizational development, technical assistance, and support for networking and coordination. (See box 8.) A UNAIDS (1999) best practice document also highlights the role of lessons learned from experience elsewhere and of international guidance in driving community responses. For example, Cambodia’s home care program, a collaboration involving community‐focused NGOs and the Ministry of Health, drew on the experience of TASO in Uganda, NGOs in Thailand, and WHO guidance.

## **Box 8: Range of external support and support mechanisms for community responses**

A study in South Africa (Birdsall et al. 2007) documented seven models for funding and coordinating community responses: (1) a private grant‐making institution; (2) a CBO mentoring organization; (3) a membership network of groups working with affected children; (4) a small‐grants scheme; (5) a provincial health department collaborating with NGOs; (6) an umbrella network for ASOs; and (7) a community clearinghouse for AIDS activity. All provide support to community organizations through funding, capacity building, or networking in order to foster and promote community‐level action. Two of these models—the Children in Distress Network and the AIDS Foundation—have their origins within the communities they serve. The other five were initiated by external actors, including government departments.

# **_3.4. Contextual factors_**

The country context influences the landscape of community responses. For example, differences in the ways that TASO in Uganda and the Treatment Action Campaign in South Africa developed and expanded are attributed to differences in the political context: the government response to HIV and to civil society and the priorities of the donor community, among other issues.

Countries and regions have different historical trajectories which have resulted in the development of particular **civil society contexts** and different manifestations of civil society. For example, broad regional similarities have been noted between community‐based response to HIV in Anglophone African countries (with greater numbers of associations organized in a decentralized manner, and faith‐based groups playing a key role in health care). This can be compared with Francophone (West African) countries which tend to have more compact community‐based groups based on standard service delivery models. North African countries in turn tend to have fewer community‐based organizations involved in HIV‐related activities, likely as a result of a lack of overall community and civil society development (UNAIDS 2005, Schmid et al 2008).

19
the 10% level in the fixed effects estimations. Openness, investment, and the terms of trade are significant regardless of the estimation method. We also perform the Hausman test to determine whether the random effects or the fixed effects specification is more appropriate for each measure of natural resources. In both cases we reject the null hypothesis that there are no systematic differences in the coefficients estimated by the two methods.

Table 3: Determinants of Economic Growth (3.1-3.5). Instrumental Variables Estimations. Fixed (F.E.) and Random Effects (R.E.)

||3_._1|3_._2|3_._3|3_._4|3_._5|
|---|---|---|---|---|---|
||_Growth_|_Growth_|_Growth_|_Growth_|_Growth_|
||R.E.|F.E.|R.E.|F.E.|F.E|
|log(Income)|-0.023|-0.092|-0.027|-0.090|-0.090|
||(0_._004)_∗∗∗_|(0_._013)_∗∗∗_|(0_._004)_∗∗∗_|(0_._012)_∗∗∗_|(0_._012)_∗∗∗_|
|Openness|0.019|0.043|0.008|0.043|0.043|
||(0_._006)_∗∗∗_|(0_._017)_∗∗∗_|(0.005)|(0_._015)_∗∗∗_|(0_._015)_∗∗∗_|
|Investment|0.063|0.105|0.064|0.088|0.088|
||(0_._027)_∗∗_|(0_._044)_∗∗_|(0_._027)_∗∗_|(0_._043)_∗∗_|(0_._043)_∗∗_|
|Government Exp.|-0.100|-0.024|-0.106|-0.012||
||(0_._025)_∗∗∗_|(0.048)|(0_._025)_∗∗∗_|(0.047)||
|Human|0.004|0.023|0.005|0.021|0.021|
||(0_._001)_∗∗∗_|(0_._012)_∗_|(0_._001)_∗∗∗_|(0_._012)_∗_|(0_._012)_∗_|
|Natural Resources (<sup>_XNR_</sup><br>_Y_<br>)(|-0.057<br>(0_._016)_∗∗∗_|0.026<br>(0.035)||||
|Natural Resources II (<sup>_XNR_</sup><br>_TX_ <sup>)</sup>|||-0.029|-0.031|-0.032|
||||(0_._007)_∗∗∗_|(0_._014)_∗∗_|(0_._014)_∗∗_|
|Terms of Trade|0.195|0.299|0.206|0.285|0.287|
||(0_._049)_∗∗∗_|(0_._063)_∗∗∗_|(0_._048)_∗∗∗_|(0_._060)_∗∗∗_|(0_._059)_∗∗∗_|
|R2 within|0.29|0.34|0.33|0.37|0.37|
|Observations|326|326|326|326|326|

Standard errors in parentheses; _∗_ significant at 10 percent ; _∗∗_ significant at 5 percent; _∗∗∗_<sup>significantat1percent.Alltherandomeffectsregressionsareestimatedwithregional</sup> dummies for African and Latin American countries.

Thus the results in Table 3 show an elasticity of the growth rate with respect to the relative abundance of natural resources (measured as a share of total exports) of around _−_ 0 _._ 03. The estimations largely support the hypothesis that natural resources 19
We implement these calculations using the set of globally representative data sources in the WGI, reporting the resulting estimates of the global mean and standard deviation of governance in Figure 5. The six panels of the graph correspond to the six dimensions of governance measured in the WGI. Each panel reports the estimated global mean and standard deviation (on the vertical axis) plotted against time (on the horizontal axis). For ease of visual reference, the graphs also show horizontal lines at zero and at one. Figure 5 shows little evidence of trends over time in the global mean and standard deviation of governance. While the graphs show some small fluctuations from year to year, the overall pattern suggests that our assumption in the baseline UCM estimates of the WGI that the global mean and standard deviation of governance are constant over time is not unreasonable. This in turn means that the changes over time in countries’ positions in the WGI relative to the global mean can reasonably also be interpreted as absolute changes.

We conclude this section with one caveat. These estimates of trends in global averages of governance are based on the information in the cross-country averages of the globally representative data sources that feed into the WGI. If some of these underlying data sources themselves measure governance in relative rather than absolute terms, it is possible that that global averages of governance could be changing even though the data sources themselves do not register any changes in their global averages. However, we can conclude that, conditional on the information contained in the underlying data sources, the normalization of the global mean and standard deviation of governance to be the same in every period in the baseline WGI estimates is a reasonable approximation.

# 6. Conclusions

This paper has provided a summary of the WGI data sources and aggregation methodology as of the time of the 2024 WGI annual update. While the core methodology of the WGI has not changed over the lifetime of the project, the set of underlying data sources continues to expand and evolve as new data become available, and in some cases as data sources cease to exist. The resulting aggregate indicators cover six dimensions of governance for over 200 countries since 1996, synthesizing the wealth of information in the component data that feed into the WGI. The WGI dataset also includes margins of error that clearly indicate the unavoidable imprecision in measures of governance, reflecting differences in views across the multiple data sources on which the WGI are based.

that it creates a very transparent link between the parameter estimates of the baseline UCM and this alternative model, as given in Equations (8) and (9).

29