rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Determinants of Speculative Attacks on Target Zones, 1987-1995**

|________|||**~~~~**|**ALL**|||||**EUROPE**||||-|**IATIN**|**AME**|**RICA**|-|-|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**_la**|lb|2a|2b|3a|3b|**Ia**|lb|2a|2b|3a|3b|**la**|**lb**|2a|**2b**|<br>3a|3b|
|Index1**E-03**|-4.37<br>0.000||||||-0.302<br>0.014||||||-7.82<br>0.000||||||
|Index2 E-03||4.28<br>0.000||||||-0.243<br>0.047||||||-7.87<br>0.000|||||
|**R/M**I E-03|||4.26|-2.70|-4.56|4.23|||-0.788|-0.674|0.35|0.375|||-7.44|-5.38|-7.31|-3.75|
|**Ti**E-02|||0.000<br>-0.613<br>0.366|0.000<br>|0.000|0.000|||0.035<br>8.15<br>0.100|0.039|0.041|0.028|||0.000<br>-6.4<br>0.664|0.010|0.000|0.007|
|**-C2E-02**||||-8.61<br>0.082||||||4.48<br>0.697||||||-14.8<br>0.287|||
|**iti**E-02|||||1.41<br>0.070||||||3.85<br>0.066||10.218||||-0.63||
|12**13-02**||||||-3.56<br>0.010||||||0.73<br>0.438||||||-1.88<br>0.337|
|aI E-01|||||1.21<br>0.638||||||2.09<br>0.109||||||-5.22<br>0.840||
|02 L-Ui||||||-0.117||||||0.863||||||-5.75|
|||||||0.667||||||0.128||||||0.267|
|ijE-04|||||-1.2|-2.96|||||5.89|4.7|||||6.33|-7.840|
||||||0.915|0.785|||||0.420|0.5|||||0.144|0.874|
|constantE-03|2.78|2.770|2.87|3.37|2.71|2.97|0.721|0.691|0.076|0.322|-0.405|-0.326|9.310|9.310|11.2|11.90|10.90|10.50|
||0.007|0.007|0.008|0.002|0.066|0.027|0.498|0.512|0.951|0.813|0.723|0.762|0.088|0.090|0.068|, 0.040|0.144|**I**0.126|
|**R**|0.045|0.041|0.043|0.046|0.044|0.047|0.010|0.010|0.012|0.010|0.017|0.017|0.054|0.050|<br>0.048|<br>0.054|<br>0.072|0.059|
|**Obs**|1091|1091|1091|1091|<br>1091|1091|828|828|828|828|828|828|263|263|263|263|263|**_1_**<br>263|
|OverallSignif.|26.59|24.65|13.29|14.08|8.16|8.67|8.47|6.13|11.04|7.24|14.14|14.79|8.40|7.96|4.32|4.77|2.83|3.33|
|**P**value|0.000|0.000|0.000|0.000|0.000|0.000|0.015|0.047|0.026|0.124|0.048|0.039|0.000|0.00|0.002|0.001|0.007|0.002|

Notes: Results **are the** sunmation of **the** estimated **parameters** of the **first** two lags of the variables and beneath **them, the** P-value of tests under the null hypothesis that the coefficients are jointly equal to **zero.** Specification 'a' uses **the** GARCH estimates of **the vaiance** and **drift** while specification "b" employs a six month moving average. **Tau** is **the threshold** level of RA41 at **which** an attack **would** be expected. **The** sample size is 1987:1- 1995:12 for Europe while those for Latin Ametica depend on the individual country ( See appendix II). The complete sample and Latin American models are pooled regressions while the European specification employs a randorn Effects estimator as dictated by lausman and Breusch-Pagan tests. F tsts for the pooled regressions and Chi squared tests for the random effects estimators **were** used to evaluate the significance ofthe estimated parameters and of the averal rgession.
consistent with the same children being out of school. Districts with a higher prevalence of children attending private school show higher inequalities associated with household factors, but lower relative inequalities associated with schools. The latter provides further indicative evidence that (greater) competition across schools may help narrow the distribution of schooling effects. Furthermore, and as indicated by the interaction term, this effect seems most acute at lower grade levels – i.e., there is substantial homogeneity in the contribution of private primary schools to learning outcomes in early grades.

Second, and relatedly, sorting effects appear be larger, in both absolute _and_ relative terms, in districts with a higher prevalence of disadvantaged households (i.e., those with lower average SES values and a higher proportion of mothers with no education). The data do not indicate what lies behind this – e.g., it may be due to greater clustering or residential segregation in these districts, or a less equal distribution of school quality. However, this result reinforces the idea that a deeper investigation of sorting processes may be helpful. Equally, and third, country differences remain persistent after controlling for other covariates. One interpretation is that such persistence can be explained by differences in the overall organization of schooling systems (macro-policies) and that these differences are material for educational inequalities. This would be in line with previous studies that find policy differences, such as ability-tracking and the allocation of teachers among schools, can contribute to national differences in educational inequalities (Hanushek and Wößmann, 2006; Van de Werfhorst and Mijs, 2010). And this agenda merits further attention in the context of East Africa.

# **5    Conclusion**

The purpose of this paper is to shed light on the sources of educational inequalities in East Africa. Starting from the proposition that household circumstances, school factors and assortative matching (sorting) are _all_ potentially important components of IOE, we seek to parse out their respective contributions to the observed variation in test scores. To do so, we review various variance decomposition procedures and suggest how a partitioned iterative algorithm can be used to estimate the relevant variance components, treating the two effects of interest as fixed latent variables. In order to address technical challenges of estimation, namely the problem of a mechanical negative bias in the correlation between the fixedeffects, we elaborate a unified procedure that controls how the fixed effects are estimated (initialized) and that maps to alternative assumptions about the between-factor covariance.

We apply the approach to rich test score data from East Africa from which three main findings stand out. First, we confirm that how the fixed effects are initialized under a partitioned iterative algorithm matters for subsequent estimates of the variance components.

24
probability distribution function for a standard normal random variable, and let zit denote the poverty line for household _i_ and time _t_ . The conditional probability of being poor is seen to solve: where σ<sup>2</sup> denotes the variance of _u_ . The unknown model parameters will in practice be replaced by estimates, so that the estimated poverty rate solves:

**Remark 2** : It is important to note that the imputation-based poverty estimate will be subject to both sampling error and model error. Standard errors can be estimated accurately by means of bootstrapping, which will be our method of choice in the empirical section of this paper. The HES will be bootstrapped to capture the model error, and the LFS will be bootstrapped to capture the sampling error, see Fujii and van der Weide (2012, 2013) for more details. For asymptotic standard errors that account for the dual error structure, also see Fujii and van der Weide (2012, 2013). Note that ignoring this error structure, for example by treating the imputed data as observed data, will lead to underestimation of the standard errors and thereby to overestimation of the poverty estimates.

**Remark 3** : The poverty line must be measured in the same time _t_ prices as the expenditure (or income) variable that was used as the dependent variable in the regression model. In other words, if a model is estimated on 2001 household expenditure data (measured in 2001 prices) and subsequently applied to a 2007 LFS, then the imputed data will represent 2007 expenditure measured in 2001 prices. As a result, this imputed expenditure would have to be compared to the 2001 poverty line in order to obtain an estimate of 2007 poverty (see also Remark 1).

**Remark 4** : The approach has the best chance of identifying trends in poverty if the changes over time can be traced back to changes in the observed independent variables (e.g. changes in education levels, employment characteristics, and housing conditions) as opposed to changes driven by exogenous shocks that are not well captured by the observed data.

**Remark 5:** The approach adopted is implicitly an evaluation of the between-survey comparability of expenditure data, comparability of HES and LFS, and of temporal price adjustments adopted.

**Remark 6:** Finally, while endogeneity (reverse causality) will generally bias estimates of the model parameters, it does not necessarily bias the imputed values. In fact, endogeneity may benefit the statistical precision of the imputed data since a non-zero correlation between the independent variables and the error term implies that the error term is now not entirely unpredictable.

10
# **3. Empirical Evaluation of Household WTP for Electricity Access**

To empirically test the model predictions, we use unique survey data from rural areas of three representative Sub‐Saharan African countries with deep pockets of extreme poverty: Burkina Faso, Rwanda, and Senegal. The data were collected between 2010 and 2013 for impact evaluations on electricity access interventions. All three countries are among the world’s poorest countries, classified as “low‐income” by the World Bank, and had very low rates of electricity access, particularly in ultra‐ poor rural areas at the time the survey.<sup>6</sup> The surveyed areas correspond to subcase b) shown in Figure 1 as very few (if any) households in the sample use diesel generation. The analysis thus focuses on the determinants of WTP by rural households for solar generation and grid electricity access.

# _3.1 Data and Quantification of WTP_

Identical WTP questions were asked in all three countries. Table 2 describes the basic information on the collected data sample.

In Burkina Faso, the data come from an impact evaluation of a Solar Home System electrification intervention in the rural province of Kénédougou supported by the Netherlands’ Ministry of Foreign Affairs (see Bensch et al. 2013). For this purpose, a random sample in villages, drawn in 2010, was offered a solar home system (SHS) on a fee‐for‐service basis. The WTP survey was only elicited among households that did not have any electricity source at the time of the survey. Most of the remaining households had already had a private solar panel bought on local markets. Grid electricity was not available among the surveyed population.

In Rwanda, the 2011 data are a representative random sample of rural beneficiaries of the nationwide electrification project EARP (Electricity Access Rollout Programme, see Lenz et at., 2017 and Peters et al., 2014). All these households were hence located in rural areas where grid electricity had not yet been available. The

> 6 According to the World Bank Sustainable Energy for All (SE4ALL) database, rural electricity access in 2013 was 0.8% in Burkina-Faso, 7.7% in Rwanda, and 27.5% in Senegal.

14
# **2.2 Hurricane Damage**

## **Hurricane Windstorm Model**

To measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America. The main model output is predicted wind speed in kilometers per hour (km/h) at the height of 10 meters for each cell and tropical storm that has since 2000 affected the following countries: Costa Rica, El Salvador, Honduras, Guatemala, Nicaragua, and Panama. Further details of the model can be found in supporting information.

## **Hurricane Destruction Index**

To match the wind speed data set to our nightlight data, we construct a cell-year panel, that uses only observations from the hurricane with the highest recorded wind speed during a given year.<sup>3</sup> Approximately a quarter of the cells in our sample experience non-zero wind speed.

We convert wind speed into potential damage using the transformation and parameters proposed by Emanuel (2011). This transformation imposes a threshold below which damage is unlikely to occur, it guarantees that damage will approach unity for very high wind speeds, and it accounts for the physical property that wind power (the rate of increase of kinetic energy) from a hurricane is proportional to the third power of wind speed. The damage index _f_ is given by: where _Vit_ represents wind speed in cell _i_ and year _t_ , _VT_ is the threshold below which damage is unlikely to occur, it is set just below severe gale speeds 50 knots ( _≈_ 92 _._ 6 km/h), and _VH_ is the wind speed at which half of all structures are expected to be destroyed 150 knots ( _≈_ 277 _._ 8 km/h). Additionally, because physical damages may be higher non-linear

> 3We also calculate a cell-year panel using the highest recorded wind speed in each cell-year, without taking into account the overall strength of the hurricane.

6
# S. **ANNOTATED BIBLIOGRAPHY**

The following is a reference to some of the general works that pertain to costing issues in Safe Motherhood Programs. Annotations are made with reference to the work's pertinence to costing issues rather than a general discussion of its contents.

Assessment of Technology Needs at the First Level of Referral, Program for Appropriate Technology in Health final report to MotherCare Project, March 1990.

This very substantial report provides a large volume of medical information on requirements for essential obstetric care. It includes a literature survey, an MCH expert survey, case studies of referral centers, and other information. For costing issues, its relevance includes significant information on equipment and supplies, as well as some information on the prices of these materials.

Barnum, H., Evaluating Healthy Days of Life Gained from Health Projects, Social Science and Medicine, Vol. 24, No. 10, 1987.

This pape: highlights the importance of the assumptions made when evaluating the benefits of a health project. In particular, it emphasizes the importance of properly evaluating the future healthy days of life gained by a project, demonstrating that different discount rates will change the relative ranking of competing projects. The relative value of infant and adult lives saved by a health project is of particular importance to Safe Motherhood programs.

Cost Analysis in Primary Health Care: A Training Manual for Programme Managers, A. Creese and D. Parker eds., World Health Organization, WHO/SHS/NHP/90.5.

This volume presents a practical exercise for program managers in the collection and use of cost data. While not referring directly to Safe Motherhood programs, the discussion provides useful material on the general approach to working with cost information. The book is divided into three parts: A. Unit Financial Costs, B. Cost-effectiveness Analysis, and C. Using Cost Data in Planning. Issues considered include methods of disaggrQgating costs into components, measuring effectiveness, accounting vs. economic costs, and methods of estimating future costs.

Essential Obstetric Functions at First Referral Level, Report of a Technical Working Group, World Health Organization, 1986.

This is a medical text, discussing the content and implementation of obstetric care at the first referral level. There is some discussion of the cost of providing this care, including buildings, equipment, supplies, personnel, and other costs, and the report concludes that, "the cost of providing essential obstetric care at the first referral level may vary greatly even from province to province and country to country according to the administrative and managerial skills deployed and the community support enlisted." Annexes provide lists of the buildings, equipment, materials and drugs needed for this care.

33
Table 3: Spatial Disparity and Access to Infrastructure and Services

|||2000|||2010||
|---|---|---|---|---|---|---|
||All|Urban|Rural|All|Urban|Rural|
||||Panel A (b|ase case)|||
|Neighborhood Correlation (r)|0.363***|0.385***|0.218***|0.266***|0.298***|0.199***|
||(19.929)|(13.271)|(11.820)|(20.405)|(12.998)|(14.158)|
|||Pan|el B ( access|to electricit|y)||
|Neighborhood Correlation (r*)|0.231***|0.277***|0.154***|0.199***|0.223***|0.161***|
||(14.937)|(10.355)|(10.100)|(17.676)|(11.138)|(12.858)|
|Contribution of electricity (r-r*)|0.131|0.108|0.064|0.068|0.076|0.038|
|% Decrease[((r-r*)/r)*100]|36.2|28.0|29.2|25.4|25.3|19.2|
|Controlling for access to phone||P|anel C ( acce|ss to phone)|||
|Neighborhood Correlation (r*)|0.246***|0.246***|0.196***|0.175***|0.164***|0.153***|
||(15.631)|(9.702)|(11.229)|(16.673)|(9.704)|(12.581)|
|Contribution of phone (r-r*)|0.117|0.139|0.022|0.092|0.134|0.046|
|% Decrease[((r-r*)/r)*100]|32.1|36.1|10.1|34.5|44.9|23.1|
|||Pane|l D ( access t|o large mark|ets)||
|Neighborhood Correlation (r*)|0.318***|0.369***|0.202***|0.237***|0.266***|0.187***|
||(18.170)|(12.785)|(11.399)|(19.190)|(12.161)|(13.735)|
|Contribution of access to market (r-r*)|0.045|0.016|0.015|0.030|0.033|0.012|
|% Decrease[((r-r*)/r)*100]|12.4|4.2|7.1|11.2|11.0|6.2|
|Controlling for access to santitation||Pa|nel E ( access|to sanitatio|n)||
|Neighborhood Correlation (r*)|0.235***|0.269***|0.184***|0.208***|0.205***|0.182***|
||(15.183)|(10.159)|(10.860)|(18.011)|(10.632)|(13.531)|
|Contribution of sanitation (r-r*)|0.128|0.116|0.034|0.058|0.094|0.017|
|% Decrease[((r-r*)/r)*100]|35.2|30.1|15.4|21.9|31.4|8.6|
|||Panel F|( access to cl|ean drinking|water)||
|Neighborhood Correlation (r*)|0.260***|0.277***|0.218***|0.227***|0.255***|0.193***|
||(16.263)|(10.500)|(11.777)|(18.790)|(11.879)|(13.932)|
|Contribution of drink. water (r-r*)|0.102|0.108|0.000|0.040|0.043|0.006|
|% Decrease[((r-r*)/r)*100]|28.2|28.1|0.0|14.9|14.5|2.9|
||||Panel G ( Inf|rastructure)|||
|Neighborhood Correlation (r*)|0.150***|0.111***|0.144***|0.156***|0.130***|0.142***|
||(12.289)|(6.128)|(9.664)|(15.760)|(8.608)|(12.055)|
|Contribution of infrastructure (r-r*)|0.213|0.273|0.073|0.111|0.168|0.057|
|% Decrease[((r-r*)/r)*100]|58.6|71.0|33.7|41.5|56.3|28.7|
|||Panel H|( Infrastructu|re and geo-c|limate)||
|Neighborhood Correlation (r*)|0.141***|0.100***|0.130***|0.133***|0.118***|0.108***|
||(11.663)|(5.493)|(8.871)|(14.500)|(7.863)|(10.421)|
|Cont. of infrastructure & geo-climate (r-r*)|0.222|0.285|0.088|0.133|0.181|0.091|
|% Decrease [((r-r*)/r)*100]|61.1|74.0|40.4|50.0|60.6|45.8|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
Compared to Japan, the European Community, and the United States, they have a lower capital intensity and a higher relative capital-labor price.

**Table 2: Sectoral Export and Import Shares in World Trade**

(Percent)

||USA|Japan|China|ASEAN4|Asian<br>NIEs|EU|ROW|Toral|
|---|---|---|---|---|---|---|---|---|
|**Shares**<br>in<br>**World**<br>**Exports:**|||||||||
|G.rains|54.8|G.0|7.5|5.2|0.2|11.0|21.3|100.0|
|Other<br>Agriculture|21.4|0.6|6.0|10.6|3.7|9.6|48.1|100.Q|
|Forestry<br>& Fishery|16.9|1.9|4.7|16.6|8.7|5.4|45.7|100.0|
|Energy<br>& Minerals|4.0|0.4|2.4|7.6|0.3|6.2|79.2|100.0'|
|Food<br>Processing|19.7|1.4|4.0|9.0|4.7|29.6|31.7|100.0|
|Textile<br>& Apparel|a.2|0.7|19.2|8.1|17.0|17.7|20.2|100.0|
|Wood<br>& Paper|16 .|3.0|2.4|8.0|5.1|17.4|45.2|100.0|
|Basic<br>Intermediates|17.3|10.2|2.6|2.4|8.6|27.9|31.0|100.0|
|Machinery<br>& Equipment|22.6|23.7|2.6|2.9|9.4|23.4|15.2|100.0|
|Services|17.9|8.8|2.8|2.5|5.9|27.0|34.6|100.0|
|_Total_|17.9|12.7|4.4|4.3|7.9|22.7|30.1|100.0|
||USA|Japan|China|ASEAN4|Asian<br>NIEs|EU|ROW|Total|
|**Shares**<br>in**World**<br>**Imports:**|||||||||
|Grains|2.6|19.2|10.2|6.0|11.0|5.Y|45.1|100.0|
|other<br>Agriculture|14.3|13.7|4.8|3.9|9.4|37.1|16.8|100.0|
|Forestry<br>& Fishery|14.9|42.9|4.0|3.7|6.4|22.1|6.0|100.0|
|Energy<br>& Minerals|22.1|23.8|2.3|2.7|9.2|36.2|6.8|100.0|
|Food<br>Processing|15.1|16.5|5.2|3.3|6.2|22.4|31.3|100.0|
|Textile<br>& Apparel|26.7|7.5|7.1|2.7|5.1|26.4|24.5|100.0|
|Wood<br>& Paper|20.6|9.6|4.1|2.3|5.3|33.2|25.0|100.0|
|<br>Basic<br>Intermediates|17.1|8.5|6.9|6.7|10.0|20.7|30.1|100.0|
|Machinery<br>& Equipment|23.6|4.3|6.5|5.4|6.3|19.9|33.0|100.0|
|Services|12.5|12.9|3.0|1.9|5.4|24.8|39.5|100.0|
|Total|19.2|0.9|4.9|3.9|7.4|23.9|30.8|100.0|
|_Note:_<br>Calculated|<br>from<br>APE|C' model<br>data|<br>base.||||||

Since one focus of the model is on international trade flows, it is useful to present the trade structure in some detail. International trade theory generally identifies two different types of international trade. Trade among developed industrial countries with similar endowments and technology has been increasingly characterized as "intra-industry,"7 whereas trade between high and low-income economies that have different factor endowments and technological processes is still on an inter-industry basis. The tremendous range in factor endowments and level of economic development among our model economies suggest that perhaps the traditional Heckscher-Ohlin arguments (based on different factor endowments) may explain trade among them to a large extent.

"Intra-industry" in this context refers to the two-way trade between industries which produce commodities that are similar in input requirements and highly substitutable in use, such as similar televisions manufactured by different producers.

_Lewis, Robinson, and Wang_ 7
Policy ReseaRch WoRking PaPeR 4406

# **Abstract**

Studies of risk and its consequences tend to focus on one risk factor, such as a drought or an economic crisis. Yet 2003 household surveys in rural Kilimanjaro and Ruvuma, two cash-crop-growing regions in Tanzania that experienced a precipitous coffee price decline around the turn of the millennium, identified health and drought shocks as well as commodity price declines as major risk factors, suggesting the need for a comprehensive approach to analyzing household vulnerability. In fact, most coffee growers, except the smaller ones in Kilimanjaro, weathered the coffee price declines rather well, at least to the point of not being worse off than non-coffee growers. Conversely, improving health conditions and reducing the effect of droughts emerge as critical to reduce vulnerability. One-third of the rural households in Kilimanjaro experienced a drought or health shocks, resulting in an estimated 8 percent welfare loss on average, after using savings and aid. Rainfall is more reliable in Ruvuma, and drought there did not affect welfare. Surprisingly, neither did health shocks, plausibly because of lower medical expenditures given limited health care provisions.

This paper—a product of the Africa Technical Families (AFTP2) Division in the Africa Region—is part of a larger effort in the department to document and analyze the determinants of household vulnerability in Tanzania. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lchristiaensen@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team