rafmacalaba/datause-encoder-data huggingface dataset:
**Plot: Other**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>gasmillonurl_linkvia propanahorrbeneficiarientregmecanlicu nuevdollar_amountmillracionalizsobr sertiendminecmañan energdesdcomercipartllamañoministriniciviceministrduitravesgtagureporthaciendconsumidortrabajhablmllscilindrreducningunestimanalentrehastdosagostmantendrrecibhancobrcredit seaverdmivalalimenttengcuantform masofrecpopultortillhayeconomi sacgeneralpreciwtfvidbusercocin vangastdan nos vaplanformat car porqudondsicambigracileñtienquitgasolinqueluzsubgobierncuand me no com arenfmln<br><!-- End of picture text --> Figure 6: The top 100 coefficients in absolute value associated with the “other” category throughout the entire sample. The blue indicates positive coefficients. The red indicates negative coefficients. Source: Author’s calculations.

sentiment in Figure 9 such as _bien_ and _pos_ _~~e~~ moticon_ . The signal is just too low for them to carry enough weight to overwhelm the rest of the language used in the corpus.

# **Coefficient Plot: Negative Sentiment**

<!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>no quecuand yagasquitsi nos masdantien porquvagasolinsub mehayleñsalsin commal cardondpopulteneg_emoticonnivalgentsacgracitodgobiernvanesta vernadcocinentiendasiacab vergelestan apagesto esosfunaltosgusttengfocalizcomercirevisentrevviceministrofrecañolicu arenmill ser asegurcilindrracionalizhast susdesd empresduipodrinicifmlnreductem nuevestadrecibministrmañanpartentregtiendahorrlbs energmecansobrtransportdicviaminecpidtarjetbeneficiarimillon propandollar_amounturl_link<br><!-- End of picture text -->

Figure 7: The top 100 coefficients in absolute value associated with negative sentiment throughout the entire sample. The blue indicates positive coefficients. The red indicates negative coefficients. Source: Author’s calculations.

We now turn our attention to the change in these use of language over time and 30
# <u>4.4 Parenting Practices and Children’s Human Capital Outcomes</u>

The behavioral component seeks to induce behavioral changes among cash transfer beneficiaries in four main domains: nutrition, health, psycho-social stimulation and child protection practices. As such, we first test whether the interventions lead to changes in practices in these four domains (Section 5.1). They represent intermediary outcomes that capture behaviors directly promoted by the program. We then consider whether the interventions affect final (or downstream) outcomes capturing children’s human capital, including anthropometrics, cognitive development and socio-emotional development (Section 5.2).

To analyze whether the program triggered behavioral changes, we build indices by aggregating indicators for the practices it promoted in each domain. The consideration of these families of outcomes helps to address potential concerns about multiple hypothesis testing, besides providing exact p-values as discussed in Section 4.3. The indices are built as additive indices of z scores, standardized in the control group.<sup>22</sup> We provide disaggregated results for the individual components of the indices in the annex to facilitate the interpretation of the results.

_Nutrition practices (Intermediary outcome domain #1)_ . The BCC intervention promoted exclusive breastfeeding until 6 months of age and encouraged greater diversity in children’s diet after 6 months. Therefore, we construct an index of nutrition practices for children aged 12-23 months, which captures (i) whether infants were exclusively breastfed until 6 months, and (ii) the number of types of foods each child started to consume between 6 and 9 months old, which measures food supplementation. We then analyze the food consumption score for all children 6 -59 months old, which provides a measure of dietary diversity at the time of the follow-up survey (WFP, 2008).<sup>23</sup> _Health practices (Intermediary outcome domain #2)_ . The BCC intervention promoted preventive health behaviors to protect children against disease, as well as encouraged health service utilization at the first sign of illness. We build an index of preventive health behavior capturing whether children have (i) received all their vaccines, (ii) received vitamin A, (iii) received iron supplementation, (iv) received deworming, (v) slept under a treated mosquito net, as well as (vi) an index capturing the frequency at which the main caregiver washes hands. We then analyze impacts on children’s illness, as reported by the

> 22 Note that we standardize indicators with respect to the full control group. When estimations are based on part of the sample only (as outlined in Section 4.2), the mean in the reference group displayed in the result tables is not necessarily exactly 0. 23 The food consumption score captures dietary diversity based on a set of questions asking about the consumption frequency of different food groups during the past seven days. This indicator is the primary dietary diversity measure used by humanitarian actors working on food insecurity in the Sahel.

17
# **Appendix Table 2: Data Sources**

**Data Sources** _Aid variables_ Net and gross aid disbursements DAC databases; OECD (2009) Net aid transfers Roodman (2009)

_Banking crisis variables_ Banking crises with beginning and end dates Banking crisis with beginning dates Caprio, Klingebiel, Laeven and Noguera (2005) Laeven and Valencia (2008) _Other variables_ GDP per capita (constant 2007 USD) Total population Government expenditure as % of GDP Trade as % of GDP Budget deficit as % of GDP Unemployment rate Inflation rate Real exchange rate Inequality (Gini coefficients) Corruption index (0‐6) Right‐wing party Left‐wing <u>party</u> World Development Indicators; World Bank (2009) World Development Indicators; World Bank (2009) World Development Indicators; World Bank (2009) World Development Indicators; World Bank (2009) World Economic Outlook, IMF (2009) World Economic Outlook, IMF (2009) World Development Indicators; World Bank (2009) World Development Indicators; World Bank (2009) Luxembourg Income Study project, (2009); World Development Report, various years International Country Risk Guide (ICRG 2009) Database of Political Institutions (Beck, Clarke, Groff, Keefer and Walsh 2001; updated 2008) Database of Political Institutions (Beck, Clarke, Groff, Keefer and Walsh 2001; updated 2008) 24
|**INTRODUCTION................................................................................................................... 3**|
|---|
|**1 THE WB-WTO SERVICES TRADE POLICY DATABASE ..................................... 5**|
|**2 MEASURING SERVICES TRADE RESTRICTIVENESS ....................................... 7**|
|2.1 Selection................................................................................................................................... 7|
|2.2 Determination of level of restrictiveness...................................................................... 7|
|2.3 Aggregation............................................................................................................................. 8|
|**3 PATTERNS OF SERVICES TRADE POLICY ......................................................... 12**|
|3.1 Services trade policy in 2016 (68 economies)......................................................... 12|
|3.2 Evolution of services trade policy since 2008-11 (55 economies)................... 16|
|3.2.1 Financial services............................................................................................................. 18|
|3.2.2 Telecommunications....................................................................................................... 21|
|3.2.3 Transport services........................................................................................................... 24|
|3.2.4 Professional services...................................................................................................... 26|
|**CONCLUSION ...................................................................................................................... 30**|
|**ANNEX 1: LIST OF ECONOMIES COVERED, AND SOURCE ............................................................. 32**|
|**ANNEX 2: SUMMARY OF THE COVERAGE OF SECTOR AND MODES ............................................. 34**|
|**ANNEX 3: COMPARING THE 2016 STRI FOR ALL THE SECTORS WITH THE STRI FOR THE SUBSET OF**<br>**SECTORS FOR WHICH DATA WERE COLLECTED IN 2008-11 ........................................................... 35**|
|**ANNEX 4: CHANGES IN SUBSECTOR STRI BETWEEN 2008-11 AND 2016, BY ECONOMY ................ 36**|
|**REFERENCES ....................................................................................................................... 39**|

2
primary school in the locality and whether there is a secondary school in the locality. The locality level characteristics are established using November 1999 follow-up data. We found that there is one statistically significant difference between the treatment and control group: treatment localities are about 4 percentage points more likely to have a primary school than control localities. We do not present these balance tests here, as numerous other studies have investigated the balance of the Mexican data. The most notable of these is Behrman and Todd (1999), who find minor but statistically significant imbalances when using the household level (instead of locality level) data.

46
Policy ReseaRch WoRking PaPeR 4462

# **Abstract**

This paper investigates individuals’ bypassing behavior in the health sector in Chad and the determinants of individuals’ facility choice. The authors introduce a new way to measure bypassing using the patients’ own knowledge of alternative health providers available to them instead of assuming that information as previously done. The authors analyze how perceived health care quality and prices impact patients’ bypassing decisions. The analysis uses data from a Quantitative Service Delivery Survey in Chad’s health sector carried out in 2004. The survey covers 281 primary health care centers and 1,801 patients. The matching of facility data and patient data allows the analysis to control for a wide range of important patient and facility characteristics, such as income, severity of illness, quality of health care, or price of services. The findings show that income inequalities translate into health service inequalities. There is evidence of two distinct types of bypassing activities in Chad: (1) patients from low-income households bypass high-quality facilities they cannot afford to go to low-quality facilities, and (2) rich individuals bypass low-quality facilities and aim for more expensive facilities that also offer a higher quality of care. These significant differences in patients’ facility choices are observed across income groups as well as between rural and urban areas.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand how health care services price and quality interact with care seekers' characteristics and knowledge of the local health care market to determine patients' choice of health provider and bypassing behavior. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wwane@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
**Causes and consequences of civil strife:**

# **The case of Uganda**

# **1. Introduction**

Ethnic diversity, war, and civil conflict have recently been "re-discovered" by economists as important determinants of economic outcomes and overall development (Stewart 2000). One of the key reasons is that it is now realized that "state failure" or weakness, often brought about by civil unrest, can be an important impediment to social and economic development. However, empirical support for the relevance of these factors largely rests on cross-country regressions which generally provide insufficient detail on the factors at work in order to support policy conclusions. Limitations of data-availability and -quality are of particular relevance for crisis-tom states in Africa where dealing with this phenomenon is of particular urgency. In-depth analysis of the factors causing civil strife, the scope for policies to prevent it, and the potential economic consequences of this phenomenon in Africa would be of great importance.

In this paper we use micro-data for Uganda to explore causes of civil conflict in an attempt to gain insight into both causes and economic consequences of civil strife. We use a stylized model that assumes that rebels decide whether or not to initiate activity in a given community based on the potential tax revenue, the pre-existing level of "resistance", the presence of economic infrastructure, and the wage rate they need to offer to potential recruits. Based on these parameters, individuals decide on whether or not to join the rebels and, subsequent to receiving their income for the period, make decisions on investment. Even though the model requires an abstraction from the complex history of armed struggle in Uganda and the intricacies of changing coalitions, it provides policy-relevant predictions that can be tested empirically.
occupy around 84 percent of the nurse and about half of the administrative positions. The attendants who are at the bottom of the ladder are almost exclusively women.

Table 2: Prevalence <u>of Informal Payments (%)</u>

||**Doctor**|**Nurse/Feldsher**|**Administrator**|**Hosp. Att**|**All**|
|---|---|---|---|---|---|
|**Dushanbe**|77.1|75.0|50.0|25.0|71.6|
|**Sogd**|56.7|52.5|40.4|16.7|48.8|
|**Khatlon**|72.2|74.7|46.4|23.8|60.2|
|**RRS**|71.0|64.7|37.5|19.0|53.8|
|**GBAO**|4.8|4.3|0.0|0.0|2.8|
|**Men**|67.8|63.0|40.0|75.0|60.4|
|**Women**|55.0|59.0|37.1|17.2|46.8|
|**Tajikistan**|63.5|59.6|38.5|18.5|51.2|

**Source** : Authors’ calculations from Tajikistan Health PETS 2006 data **Note** : Gorno-Badakhshan Autonomous Oblast (GBAO) and Rayon under Republican Subordination (RRS) are regions

Despite female domination, Tajikistan’s health care system is characterized by a high level of informal charges as shown by table 2. Indeed, more than half the health workers admit to informally charging patients. The raw averages show a clear gender gap with 60.4 percent of men reporting informally charging patients compared to 46.8 percent for female workers. Health workers in Dushanbe, the capital city, are more likely to resort to informal charges than elsewhere in the country followed by the Khatlon region.

<u>Table 3: Magnitude of Informal Payments [Full Official Salary] (in Somonis per month)</u>

||**Doctor**|**Nurse/Feldsher**|**Administrator**|**Hosp. Att**|**All**|
|---|---|---|---|---|---|
|**Dushanbe**|131.6 [73.9]|59.2 [58.7]|42.5 [73.3]|54.0 [58.3]|95.7 [67.5]|
|**Sogd**|32.8 [56.7]|16.1 [39.5]|25.9 [49.1]|16.2 [28.0]|22.4 [44.8]|
|**Khatlon**|64.0 [58.1]|28.9 [43.4]|32.4 [46.3]|8.1 [29.1]|34.1 [44.9]|
|**RRS**|31.6 [67.0]|22.1 [41.8]|18.0 [52.9]|4.2 [27.8]|20.8 [47.8]|
|**GBAO**|3.8 [58.7]|1.7 [43.8]|0.0 [43.4]|0.0 [33.0]|1.5 [44.4]|
|**Men**|64.4 [60.9]|28.0 [43.8]|24.7 [50.2]|122.5 [53.5]|48.2 [54.8]|
|**Women**|27.0 [61.7]|21.6 [42.4]|25.4 [46.7]|6.0 [29.2]|19.4 [42.7]|
|**Tajikistan**|51.8[61.20|22.7[42.7]|25.1[48.4]|8.6[29.7]|28.8[46.6]|

**Source** : Authors’ calculations from Tajikistan Health PETS 2006 data **Note** : Gorno-Badakhshan Autonomous Oblast (GBAO) and Rayon under Republican Subordination (RRS) are regions

One would naturally expect the frequency of contact with the patients and the level of responsibilities in the health facility to be positively correlated with both the prevalence and intensity of informal charges. For instance, hospital attendants who do not provide care are probably the least able to extract money from patients, whereas doctors who can refuse to see patients or oblige them to sustain long waiting times wield more power and therefore can 12
- EMIS 2018 (collected in October-November 2017, and reflecting the dropout and repetition status at the end of the 2016/17 school year, prior to the allocation of teachers) with

- EMIS 2019 (collected in October-November 2018, and reflecting the dropout and repetition status at the end of the 2017/18 school year, following the first full year of school with the increased level of staffing and reduced PqTR).

The COVID-19 pandemic led to the closure of all schools in Malawi for seven months during 2020 and appears to have led to significant dropout.<sup>9</sup> As the EMIS data collected in October-November 2019 is the most recent available prior to the onset of the pandemic, we restrict our analysis to the 2017 and 2018 allocations of teachers for which lagged information is available prior to the pandemic.

Tables 5.3-5.6 show DiD results. In each table:

- The first column defines treated schools as those which received at least one additional teacher in 2017 and as a result whose PqTR was brought below 90 in that year.<sup>10</sup> The regression compares the repetition or dropout rate in schools at the end of the 2016/17 year to those at the end of the 2017/18 school year, reflecting a full year of the increased staffing.

- The second column defines treated schools as those which received at least one additional teacher in 2018 and as a result whose PqTR was brought below 90 in that year.<sup>11</sup> The regression compares the repetition or dropout rate in schools at the end of the 2017/18 year to those at the end of the 2018/19 school year, reflecting a full year of the increased staffing.

# **Repetition**

Tables 5.3 and 5.4 show DiD results for repetition rates. **Table 5.3** focuses on overall repetition across grades. We find that in schools treated in 2017, repetition rates were significantly reduced by 0.28 percentage points. We do not observe significant impacts on overall repetition rates from treatment in 2018.

> 9 The total enrollment in Malawi’s primary schools fell for the first time in over a decade following the closure of schools, with 4,815,286 students enrolled in public primary schools in 2020/21 versus 5,274,819 in 2019/20.

> 10 To identify treated schools, we compare data from EMIS 2016 and EMIS 2017. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2017 and 2018.

> 11 To identify treated schools, we compare data from EMIS 2017 and EMIS 2018. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2018 and 2019.

18
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

The multi-faceted nature of electricity reform results in a variety of outcomes so effectiveness and impact can be measured through different performance indicators, e.g. operating efficiency or increased investment. There can also be linkages between sectoral performance and the driving forces that trigger reform. These forces can be internal (e.g. poor sector performance) or external (e.g. foreign debt).

<u>Figure 1</u> illustrates a conceptual model of the main aspects of a reform and their interrelationships. The model is broadly along the lines of the structure-conduct-performance paradigm of industrial organisation. The model outlines the most important issues and causal effects that are generally involved in the reform. It also helps to place individual studies reviewed in this study in the wider context of reform to facilitate identification of research areas and questions that have received less attention. It is noteworthy that each component of this sector-level model can be thought of as having a counterpart in a higher or country-level framework of an economy. The remainder of this section outlines these aspects of reform in some detail.

<!-- Start of picture text --> Country/macro-level equivalents of the sector-level components<br>Institutions<br>(e.g. regulation)<br>Performance Structure Sector<br>(e.g. private (e.g. market endowment<br>investment) concentration) (e.g. resource<br>Reform measures<br>(e.g. privatisation)<br>Motivation for reform<br>(e.g. private capital)<br>Figure 1: Anatomy of electricity sector reform<br><!-- End of picture text -->

# **2.1.1 Motivation for reform**

Actual reform steps, their sequence, and ultimately the reform performance, may be influenced by the motives (e.g. privatisation proceeds) behind the decision to reform. High electricity price levels may be an important driving force. Joskow (1998) points out that, in the US, states with the highest electricity prices were most likely to implement reform. The main differences in reform issues, in both developing and developed countries, are often rooted in the determinants and driving forces behind reform.

6
**Table 11: Number of enterprises and sampling weights in UBI 2002 and UBI 2009 by single digit ISIC industry**

|**One Digit Industry (ISIC Rev. 3.1)**|**Number of e**|**nterprises**|**% of shar**|**e of total**|**Sampling **|**weights**|
|---|---|---|---|---|---|---|
||**2002**|**2009**|**2002**|**2009**|2002|2009|
|**Traded sector:**|||||||
|A ‐ Agriculture, hunting and forestry|152|94|3.2|2.0|28|420|
|B ‐ Fishing|96|7|2.0|0.1|5|449|
|C ‐ Mining and quarrying|9|37|0.2|0.8|66|79|
|D ‐ Manufacturing|956|1,080|20.2|22.9|537|910|
|_Traded sector total_|1,213|1,218|25.7|25.8|635|1,857|
|**Non‐traded sector:**|||||||
|**_Other industry:_**|||||||
|E ‐ Electricity, gas and water supply|3|17|0.1|0.4|22|15|
|F ‐ Construction|97|91|2.1|1.9|15|27|
|**Services:**|||||||
|G ‐ Wholesale/retail trade and services|1,117|1,347|23.7|28.5|2,646|6,211|
|H ‐ Hotels and restaurants|626|416|13.3|8.8|51|278|
|I ‐ Transport/storage/communications|492|161|10.4|3.4|165|234|
|J ‐ Financial intermediation|95|120|2.0|2.5|73|342|
|K ‐ Real estate, renting business|323|265|6.8|5.6|146|679|
|L ‐ Public administration and defense|14|48|0.3|1.0|72|8|
|M ‐ Education|132|603|2.8|12.8|115|53|
|N ‐ Health and social work|257|207|5.4|4.4|65|154|
|O ‐ Other social and personal|347|155|7.3|4.5|308|2,271|
|Q‐ Extraterritorial organizations|6||0.1||8|1|
|_Non‐traded sector_<br>_total_|3,509|3,430|74.3|73.9|3,649|10,232|
|**Grand total**|4,722|4,648|100.0|99.7|4,323|12,135|

45