rafmacalaba/datause-encoder-data huggingface dataset:
TABLE 6. OLS AND IV ESTIMATES OF THE RELATIONSHIP BETWEEN QUALITY OF GOVERNANCE AND POLITICAL ALTERNATION

|||Rule of law||Co|ntrol of corrupti|on|
|---|---|---|---|---|---|---|
||Pooled OLS|Fixed effects|IV|Pooled OLS|Fixed effects|IV|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Alternation|0.222***<br>(0.002)|0.034<br>(0.311)|0.714***<br>(0)|0.203**<br>(0.007)|-0.001<br>(0.982)|0.633***<br>(0)|
|GDP per<br>capita in 1990<sup>a</sup>|0.591**<br>(0.007)||0.997***<br>(0)|0.637***<br>(0.003)||0.916***<br>(0)|
|GDP per capita||0.244**<br>(0.036)|||0.431**<br>(0.009)||
|War<br>(cumulative)|-0.111*<br>(0.071)|-0.128<br>(0.557)|-0.120**<br>(0.02)|-0.100**<br>(0.045)|-0.261<br>(0.396)|-0.098**<br>(0.03)|
|Fuel/GDP in<br>1996-97|-5.00*<br>(0.055)||-14.34***<br>(0.001)|-6.265**<br>(0.014)||-13.89***<br>(0)|
|Fuel/GDP||-0.378<br>(0.300)|||-1.317**<br>(0.012)||
|Communist years|-0.011<br>(0.247)||0.013<br>(0.389)|-0.010<br>(0.268)||0.011<br>(0.427)|
|Democracy<br>(_Polity2_)|-0.030<br>(0.159)|0.004<br>(0.623)|-0.118***<br>(0)|-0.035*<br>(0.088)|0.009<br>(0.396)|-0.109***<br>(0)|
|Constant<br>|-5.112**<br>(0.011)|-2.388**<br>(0.018)|-10.46***<br>(0)|-5.526**<br>(0.006)|-3.869**<br>(0.007)|-9.606***<br>(0)|
|R<sup>2</sup>adjusted<br>F|0.733<br>29.57|0.127<br>3.67|0.416<br>26.36|0.707<br>22.56|0.105<br>2.95|0.452<br>22.06|
|Number of<br>observations|183|156|183|183|156|183|
|F test of excluded<br>instruments|---|---|11.50|---|---|11.50|
|Hansen J statistic|||0.041<br>(0.84)|||0.27<br>(0.60)|

Note: *** Significant at 1 percent. ** Significant at 5 percent. * Significant at 10 percent. _p_ values shown between parentheses. “0” indicates p=0.000.

a. In natural logs.

Column 1 reports the results of pooled cross-sectional and time series regressions without country fixed effects.<sup>19</sup> One additional alternation is associated with a significant improvement in the rule of law score ( _p_ -value = 0.002). The estimated coefficient of 0.22 means that in our sample, an increase of one political alternation is associated with one-third of a standard deviation 19 When we regress residuals from the pooled regression against time, they display no time trend. This provides justification for treating the time-ordered cross section observations for 1996-2006 as independent.

28
height for age of children under five years old and find that these economic shocks have lasting impacts several years after the event. Using nationally representative household survey data collected in January 1992 in Rwanda (prior to the 1994 genocide), we exploit variation in the timing and location of localized crop failure and civil conflict across regions of the country and variation in the birth cohort of children who are exposed to the shock. This exogenous variation allows us to measure the causal impact of the shocks these children experience at birth on their height several years later. We find that rural households in areas affected by either civil conflict or crop failure are unable to protect their offspring from the impact of these shocks. All children exposed to the civil war shock are negatively impacted, although the estimates for rich children are not precisely measured. Girls are particularly vulnerable in regions that experience crop failure, with girls in poor households bearing the brunt of the impact. We find no evidence of a negative impact of crop failure on the health status of boys or children in rich households. Results are robust to using a sibling differences estimator to control for household level factors that might influence a child’s health status.

We also test the robustness of the crop failure results using two alternative measures. First, using alternative survey data on household level agricultural production for a sub-sample of these households, we confirm that positive crop production shocks improve a child’s height for age z-score. Second, we use the deviation of rainfall from the long-run province average to show that positive rainfall shocks are correlated with improved height for age z-scores. We also use an instrumental variables approach to demonstrate the causal mechanism running from rainfall shocks to crop failure to child health. The findings are robust to these alternative specifications.

quarter experience large negative or large positive rainfall shocks, respectively.

19
FEDERATED STATES OF MICRONESIA

- 2. Projections NRR-1 by 2020

|_Towi_<br>**Pi**|**puatimn**|.<br>ratio|**Period**|Growth<br>rate|<br>**CU**|CDt|TFR|**ales**|*0<br>**Fimta**|**in**|x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|105|82.7|1990-94|2.84|32.4|4.0|4.14|70.8|77.9|21|25|
|1995|121|74.9|1995-99|2.64|30.2|3.8|3.77|71.6|79.5|17|20|
|2000|138|73.8|2000-04|2.36|27.2|3.6|3.41|72.6|80.9|14|17|
|2005|156|69.0|2005-09|1.99|23.5|3.6|2.94|73.5|81.7|12|14|
|2010|172|61.4|2010-14|1.70|20.5|3.6|2.54|74.4|82.4|10|12|
|2015|187|54.3|2014-19|1.45|18.2|3.6|2.20|75.4|83.2|a|10|
|2020|201|49.4|2020-24|1.36|17.3|3.7|2.07|76.3|84.0|6|7|
|2025|216|47.4|2025-49|0.98|15.0|5.3|2.06|78.4|85.7|3|5|
|2050|275|54.4|2050-74|0.40|12.6|8.6|2.06|80.4|87.5|3|4|
|2075|305|69.9|2075-99|0.09|11.9|11.1|2.06|81.4|88.5|2|3|
|2100|311|73.1|2100-24|0.04|11.7|11.3|2.06|82.2|89.2|2|3|
|2125<br>2150|315<br>316|73.7<br>74.4|2125-49|0.02|11.7|11.5|2.06|82.5|89.5|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>70-74<br>65-89<br>80.64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>E>xxXx2Rxx ZUgUgggl10-14 |RR U<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR eo Growth rate<br>Country Rgion Incom group Country Region Income group Country Region Incom group<br><!-- End of picture text --> 47
18. CARE-Perú. 2008. “Cuarta Medición de Indicadores de los Objetivos 2-4 del Componente VIH”. http://www.bvcooperacion.pe/biblioteca/handle/123456789/6135 19. Bautista, C.T., Sánchez J.L., Montano S.M., Laguna-Torres V.A., Lama J.R., Kusunaki L., y Carr J.K. 2004. “Seroprevalence of and risk factors for HIV-1 Infection among South American men who have sex with men”. Sex Transm. Infect. 2004 80:498-504 20. Ministerio de Salud y Deportes, ONUSIDA, OPS/OMS, USAID, CDC, IBIS-HIVOS. 2008. ” Estudio de Sexualidad Masculina y VIH en Bolivia (SEMVBO) ”. La Paz, Bolivia 2010 21. Ministerio de Salud, Estrategia Sanitaria Nacional de ITS/VIH/SIDA, Coordinadora Nacional en Salud, ASAP, Perú. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

22. Programa Nacional de Prevención y Control del VIH/SIDA-ITS del Ministerio de Salud Pública, ASAP, Ecuador. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

23. Ministerio de Salud y Deportes Programa Nacional ITS/VIH/SIDA, ASAP, Bolivia. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

24. Maart S, Jelsma J. 2010. “The sexual behaviour of physically disabled adolescents”. Disabil Rehabil. 2010;32(6):438-43.

25. Heidari S, Kippax S. 2009. “Special theme on HIV and disability - time for closer bonds”. J Int AIDS Soc. 2009 Nov 9;12(1): 26. Rohleder P, Braathen SH, Swartz L, Eide AH. 2009. “HIV/AIDS and disability in Southern Africa: a review of relevant literature”. Disabil Rehabil. 2009;31(1):51-9. PubMed PMID: 19194810.

27. Cáceres CF, Mendoza W. 2004. “Monitoring Trends in Sexual Behaviour and HIV/STIs in Peru: Are Available Data Sufficient?”. Sex Transm Infect 2004; Suppl 2:ii80-4.

41
# 4. Results

In this section, we briefly describe the data from the 2024 update of the WGI, emphasizing how to interpret the units of the governance indicator, as well as how to interpret cross-country and over-time differences in estimates of governance given the associated margins of error. For reasons of space, we focus only on a few graphs and indicators here. Users can visit <u>www.govindicators.org</u> for full access to the aggregate and individual indicators for all countries. In addition, more technically oriented users of the WGI can find the full set of parameter estimates for the UCM, a tool to reproduce the WGI calculations in Excel, and a full reproducibility package at www.govindicators.org.

## 4.1 Cross Country Comparisons

The WGI aggregate measures are reported in two ways: (a) in their natural underlying standard normal units, ranging from approximately -2.5 to 2.5, and (b) in percentile rank terms ranging from 0 to 100.<sup>15</sup> Figure 1 reports the data in these two ways, for two of the six aggregate indicators, Government Effectiveness and Control of Corruption. In each panel of the graph, countries are ordered in ascending order of their estimates of governance, and the figures plot the percentile rank (on the horizontal axis) against the natural units (on the vertical axis). The point estimate of governance is shown as a solid dot, and the thin vertical lines show the associated 90 percent confidence interval for governance, i.e. the estimate of governance (Equation (2)) plus or minus 1.64 times the standard deviation of governance (Equation (3)). In the WGI data available online, we also report the 90 percent confidence interval in percentile rank terms, i.e. we report the percentile rank (among all country estimates of governance) of the upper and lower bounds of the 90 percent confidence interval for each country. For illustrative purposes, we have labeled a selection of countries equally spaced throughout the distribution of governance scores for both indicators.

The length of the confidence intervals varies across countries, for two reasons: (a) the intervals are smaller for countries where the estimate of governance is based on more data sources, and (b) the intervals are smaller if the data sources for the country are more precise, in the sense of having a smaller estimated variance of measurement error, σ<sup>2</sup> (k) . For many countries, these confidence intervals are substantial. This means that in many cases, pairwise comparisons of governance estimates between

> 15 The units of the estimates of governance are those of a standard normal distribution.  This means that roughly 99 percent of estimates will fall within the range -2.5 to 2.5.  It is however possible to see more extreme values in rare cases.  Looking across all six aggregate indicators for all 25 years covered in the WGI, just 77 out of 30,974, or 0.2 percent of observations, fall outside this range.

16
Our second set of analyses focuses on the effects of household participation within the context of established conservancies. Our multivariate, instrumental variable and propensity score analysis do not indicate that participation has a noteworthy effect on household welfare within established conservancies.

# **6. Conclusions**

This paper fills an important gap in improving our understanding of households residing in conservancy areas in Namibia. By obtaining information on households in seven different conservancies, it allows us to gain useful insights into awareness of and perceptions about conservation, household participation in conservancy activities and the direct and indirect benefits that accrue as a result. However, because households that reside outside conservancies are excluded from this survey, analysis of the impact of conservancies is somewhat difficult.

Survey data reveal that a majority of households have limited knowledge about conservancies and their activities. Only about 23% of surveyed households knew about conservancy plans and some 26% of households had some knowledge about conservancies and their constitutions. Thus, there appears to be is a need for awareness creation and education about the role of conservancies and their potential benefits.

Approximately 34% of households report that they are conservancy participants. Our analysis of the determinants of participation indicates that participation is mainly influenced by peer-pressure. Over time, with increased awareness and development of the conservancies, participation may become more widespread. This could potentially lead to greater ownership and control over conservancy activities among members.

Households gain from conservancies either through cash income, non-cash rewards and community level benefits. Our survey data show that only a small number of households obtain cash income -- some 12% of the surveyed households report conservancy related income. While there is likely to be underreporting of income, it is also clear that conservancies have not been a source of cash revenues for most households. It should be noted that conservancies also provide non-cash income. For example, some 21% of all survey households viewed meat distribution as a benefit of conservancies. We are unable to capture the monetary value of such food subsidies.

The largest percentage of households reporting conservancy-related cash income was from the Torra conservancy (27%). Torra was the first conservancy to be established and conservancy income is the major source of income for reporting households. Thus, as conservancies grow into maturity, it is possible that more households will directly benefit.

Living within conservancies and close to wildlife also comes with its costs. Over 50% of the households surveyed reported that they suffered crop or livestock damage from wildlife. Thus, conservation of wildlife can also result in signficant expenses to households. This makes it all the more important for households to gain direct income 15
<!-- Start of picture text --> Figure 11 - Trend of the Marginal Productivity of Capital* in Egypt (1975-2008)<br>24<br>20<br>0<br>12<br>2.8<br>3.4<br>7.0<br>2.4 :<br>2.8 : , : .<br>12<br>3<br>1976 1990 1986 1990 19o5 2000 2005<br>Approximated as the ratio of growth to the investment output<br>ratio. Source: Adapted from Favero et al, 2009. Normalized<br>data to make the average of the entire period equal to zero<br><!-- End of picture text -->
## **C. Matching with Administrative Data and Attrition**

The average matching rate of administrative data of enrolled children was 94% at baseline and 97% at follow-up. We present these estimations in Table A2 in the Appendix section. All the matching rates were balanced between T and C groups, except for the matching rate of science scores before the intervention. To account for this difference, we first impute the course average at baseline for those missing observations. Then, we include this variable with imputed values and a dummy of missing values in estimations of academic outcomes. Further details of these estimations are explained below.

As we show in Table A2 in the Appendix section, 968 of 1,056 baseline enrolled children (92%) were surveyed at endline. In the T group, 731 of 798 students (91%) were surveyed at endline, and in the C group, 237 of 258 (92%). This is a low attrition rate compared to those levels found in similar studies of at-risk youth. We regress the attrition dummy on the T dummy and the result shows no difference in the likelihood of attrition between the T and C groups. Therefore, results will not be driven by the absence of follow-up survey data for any group.

Finally, the average matching rate of administrative data of nonenrolled students was 85% at baseline and 98% at follow-up. In estimations using the nonenrolled sample, we follow the same imputation procedure for baseline variables as in the sample of enrolled children.

# **2.5 Summary Statistics**

Descriptive statistics of the full sample and T and C groups are shown in Table 1. Column (1) exhibits statistics for the total sample and columns (2) and (3) are for C and T groups, respectively. Column (4) shows unadjusted _p_ -values for tests of differences in means between T and C means, and column (5) presents adjusted _p_ -values for multiple hypothesis testing of means and FWER following Jones et al. (2018). Each group of variables per panel constitutes a family for FWER control. All tests of differences in means control for randomization blocks and standard errors are clustered at the course-by-school level.

Panel A presents the summary statistics of the violence determinants. Participants are on average 12 years old, 49% are male, and 73% live in an urban area. Regarding family composition, 91% of the students live with at least one parent, and 9% live with a relative or an unrelated adult. On average, 62% of students’ mothers have an intermediate education level (7–12 years), and 31% have less than six years of schooling. Regarding risk exposure, only 5% of students reported being alone at home when they are not at school. However, on average they travel around 18 minutes to school. Additionally, 29% of students are enrolled in the afternoon shift, increasing the probability 15
<!-- Start of picture text --> 2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>Number of Attacks Success Rate<br>250 .35<br>200 .3<br>.25<br>150<br>.2<br>Success Rate<br>Number of Attacks 100<br>.15<br>50<br>.1<br><!-- End of picture text --> Figure 3: Number of attacks and success rate, Somalia Note: The figure plots the number of attacks and the average success rate of attacks, defined as the number of hijacks over the number of attacks in a given year. Data source: International Maritime Bureau.

that Somali pirates extracted around US$338 million in ransoms over the 2005-2015 period (see Section 4 for a description of the data). The data also shows that the amount of ransoms extracted and the duration of captivity increased over the years (see Figure 4). In 2012, the data is however censored as some vessels were still being held hostage by 2015.

Figures 4 and 5 highlight two stylized facts about ransoms and durations of captivity. First, payments increased over time and so did hold-out periods for hijacked vessels.<sup>3</sup> Second, overall, ransom amounts are positively correlated with durations of captivity (Figure 5 panel a) and the size of the vessel hijacked (Figure 5 panel b).

3The longest reported case is the crew of the MV Albedo, hijacked in 2010 and released only in June 2014 after 1,288 days of captivity; the vessel had sunk in 2013 and among the crew, 5 members had died or were reported missing.

9
47

# 12. AGGREGATION OVER COUNTRIES

So far, this paper has been focused<sup>on the aggregationofyearly growth</sup> rates of a country over a period<sup>of time. If we wish to compare the</sup> performance of countries grouped according<sup>to some socio-economic</sup> and demographic criteria, it will be necessary<sup>to aggregate growth rates over a</sup> set of countries. For instance,<sup>if we are interested in judging the growth</sup> performance of Africa, then we need<sup>to aggregate the growth rates of each</sup> country in Africa. The World Development<sup>Report (WDR), every year, presents</sup> such information for a wide range<sup>of country classifications. Difficulties</sup> associated with the procedure followed<sup>by the WDR are discussed in this</sup> section.

Let Xit and Pit be the real output<sup>(GDP or GNP in local currency but</sup> adjusted for inflation) and the population<sup>of the ith country in the tth year,</sup> respectively. And suppose we wish<sup>to aggregate the growth rates of a</sup> countries so that i varies from<sup>1 to m. Since the output is measured in</sup> (constant) national currency units<sup>of each country it, as such, cannot be</sup> aggregated over countries. So one<sup>needs to convert the series in constant</sup> national currency to one in US dollars<sup>by dividing it by some fixed exchange</sup> rate. Let e1 be the exchange rate,<sup>then the total output of a countries in</sup> year t will be

**_fi4_** _el_

and Pt. the total population<sup>of m countries in the tth year, will be</sup>
than individual predictions. For example, Dellavigna and Pope (2018) find that taking the average of even five experts leads to a large improvement in accuracy over individual forecasts. Otis (2022) uses data from seven randomized experiments and finds that the average of groups of expert predictions does better than individuals at predicting which of two treatments will have larger effects, with only 10 experts needed to produce a 18 percentage point improvement compared to individual-level predictions. Aggregating across respondents then makes the results less sensitive to the idiosyncratic beliefs of any one individual, and can also provide a smoother distribution that is easier to fit a parametric model to (see below). Second, we are primarily interested in obtaining priors for types of users (policymakers, academics, program respondents), since there are typically multiple decision-makers involved in using knowledge.

# **3.4 Aggregation and fitting of elicited priors**

Since respondents all used the same grid of bins to place their stones, we can easily aggregate responses by determining the proportion of all stones that get allocated to different intervals. This gives us an empirical CDF prior for each of the three groups of respondents. However, because we use Markov-Chain Monte-Carlo (MCMC) methods to simulate draws from unknown distributions for our Bayesian analysis, we require PDFs rather than CDFs in order to use these priors in our impact evaluation. We fit prior distributions to the elicited priors using two distinct types of parametric models: skewnormal distributions and finite mixtures of Gaussians with up to 5 components. We then judge the fit of these models by checking the fitted quantiles are close to their empirical counterparts. Full details of this procedure and our registered priors can be found as part of the study’s registration in the AEA registry.

# **3.5 Literature-informed priors**

We complement and compare our elicited priors to priors that are informed by the literature. Since there were no previous studies in the literature that conduct management improvement experiments aimed at improving exports, we can not use a meta-analysis of existing treatment effects as a prior. Instead, we use the result from McKenzie and Woodruff (2017) that an approximate estimate of the treatment effect of a business training intervention on firm outcomes is equal to the treatment effect on a business practices index, multiplied by the correlation between business practices and this firm outcome in the cross-section. Based on the existing literature on business training programs and management consulting, we took a literature-informed prior that the impact of the program on export practices would be a 0.10 increase, with a standard deviation of 0.03. We then multiplied this by the estimated coefficients in a baseline regression of our treatment 13