rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9A: The Effect of Payroll data releases on Emerging Economies’ Financial Variables**

||E<br>k=1|xchange rate<br>k=2|s<br>k=3|k=1|Equity prices<br>k=2|k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|Payroll surprise|0.001**|0.001|0.000|0.001***|0.001**|0.001|0.005***|0.004***|0.003***|
||[2.56]|[1.49]|[0.45]|[2.99]|[2.29]|[1.40]|[4.64]|[5.13]|[3.55]|
|Constant|0.051***|0.051***|0.038***|0.050***|0.008***|0.014***|0.040***|0.008***|-0.118***|
||[225.38]|[151.73]|[104.32]|[82.64]|[11.32]|[14.12]|[38.50]|[9.21]|[-140.30]|
|Observations|1,860|1,860|1,860|1,718|1,727|1,731|1,515|1,516|1,524|
|R-squared|0.002|0.001|0.000|0.002|0.001|0.001|0.024|0.015|0.006|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent changes in exchange rate, equity prices, or bond yields, between the day of a payroll data release and k days after that. The variable payroll surprise is the difference between the actual payroll data release and the consensus forecast (measured in thousands). Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

**Table 9B: The Effect of Inflation data releases in Emerging Economies’ Financial Variables**

||E<br>k=1|xchange rate<br>k=2|s<br>k=3|k=1|Equity prices<br>k=2|k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|CPI surprise|-0.694***|-0.392|-1.361***|1.720***|1.743***|2.833***|1.189*|1.229|0.859|
||[-3.23]|[-1.67]|[-4.85]|[4.75]|[4.10]|[6.79]|[1.81]|[1.56]|[1.07]|
|Constant|0.037***|0.062***|0.112***|-0.067***|0.031***|0.036***|-0.033***|-0.069***|-0.056***|
||[11.68]|[18.02]|[26.99]|[-13.20]|[4.75]|[5.90]|[-3.42]|[-5.93]|[-4.68]|
|Observations|1,900|1,900|1,900|1,778|1,777|1,785|1,558|1,560|1,567|
|R-squared|0.004|0.001|0.009|0.008|0.007|0.012|0.004|0.003|0.001|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent changes in exchange rate, equity prices, or bond yields, between the day of a CPI data release and k days after that. The variable CPI surprise is the difference between the actual CPI data release and the consensus forecast (measured in percentage points). Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

Obviously emerging economies’ asset prices may react to many factors other than the US monetary policy. As the objective of this paper is to analyze specifically the impact of FOMC announcements and not to model emerging economies’ asset prices per se, including other potential explanatory variables, especially the domestic ones, is not of paramount interest. Nevertheless, as a robustness check we expand the regression specification by including percent changes in VIX and 24
for attitudes towards migrants and migration policy:

- Migrant as Neighbors (Rounds 5 and 6):

For each of the following types of people, please tell me whether you would like having people from this group as neighbors, dislike it, or not care: Immigrants or foreign workers. Strongly Dislike (1)... Strongly Like (5).

- Migrants can Move Freely (Round 6):

Statement 1: People living in East Africa should be able to move freely across international borders in order to trade or work in other countries. Statement 2: Because foreign migrants take away jobs, and foreign traders sell their goods at very cheap prices, governments should protect their own citizens and limit the cross-border movement of people and goods. Strongly Agree with Statement 1 (1)... with Statement 2 (5).

We capture possible support for restrictive migration policies by exploring preferences for more expansive or limited naturalization criteria using a two-part question: _In your opinion, which of the following people have a right to be a citizen of Uganda? A citizen would have the right to get a Ugandan passport and to vote in Ugandan elections if they are at least 18 years old:_

- Born Non-Ugandan (Round 5):

A person born in Uganda with two non-Ugandan parents? Yes (1)/No (0).

- Able to Naturalize (Round 5):

A person who came from another country, but who has lived and worked in Uganda for many years, and wishes to make Uganda his or her home? Yes (1)/No (0).

To assess feelings of insecurity, we use the following two questions:

- Feel Unsafe in Community (Rounds 5 and 6):

Over the past year, how often, if ever, have you or anyone in your family: Felt unsafe walking in your neighbourhood? Never (0)... Always (4).

- Feared Crime (Rounds 3 to 6):

Over the past year, how often, if ever, have you or anyone in your family: Feared crime in your own home? Never (0)... Always (4).

Since the possible responses to all of these questions are on different scales, we standardize all Afrobarometer measures to have a mean of 0 and SD of 1 for ease of comparison and interpretation.

17
# Table of Contents

# ACKNOWLEDGMENTS

# ACRONYMS

|I. INTROD<br>|UCTION ...............<br>I<br>|
|---|---|
|A.|OBJECTIVES<br>..<br>I|
|B.|HEALTH INSURANCE<br>.<br>I|
|C.<br>|HEALTH SECTOR ORGANIZATION<br>AND FINANCING IN ZAIRE .:<br>3<br> <br>|
|D.|PREVIOUS RELATED WORK<br>.<br>.<br>4<br> <br>|
||1.<br>Health Financing Research: User Fees vs. Insurance .4<br> <br> <br>|
||2.<br>GOZ Commission on the Organization of Insurance Systems<br>5<br> <br>|
|E.|SUMMARY OF RESEARCH METHODS<br>.<br>.<br>7|
|II.<br>CAS|E STUDIES: DESCRIPTIONS AND IMPACTS.<br>8|
|A.|CASE STUDY OF BWAMANDA .12<br> <br> <br>|
||1.<br>Terms of the insurance Plan<br>..<br>12<br>|
||2.<br>Organization and Management<br>..<br>17<br> <br> <br>|
||<br>3.<br>Resource Mobilization<br>..<br>20|
||4.<br>Utilizat-an and Access to Services<br>..<br>24<br>|
|B.|CASE STUDY OF BOKORO<br>..<br>28<br> <br> <br>|
||I.<br>Terms of the Jnsurance Plan<br>..<br>28<br> <br> <br>|
||2.<br>Organization and Man&gement<br>.<br>.30<br>|
||3.<br>Resource Mobilization<br>..<br>31<br>|
||4.<br>Utilization and Access<br>..<br>34|
|C.|CASE STUDY OF ST. ALPHONSE<br>.<br>.37|
||1.<br>Terms of the Insurance Plan .36|
||2.<br>Organization and Management<br>.<br>.38|
||<br>3.<br>Resource Mobilization: Financial Situation of Plan and Center . .<br>38<br> <br> <br>|
||4.<br>Utilization<br>..<br>40<br> <br>|
|D.|CASE STUDY OF CASOP ........................<br>...............<br>44<br> <br> <br>|
||1.<br>Terms of the Insurance Plan ............<br>44<br> <br> <br>|
||2<br>Oranization and Manaement<br>46|
||.<br>g g ....<br>..<br>......<br><br>|
||3.<br>Resource Mobilization: Financial Situation of Plan and Clinic<br>...<br>47<br>|
||4.<br>Utilization<br>............<br>47|
|E.|OTHER INSURANCE PLANS<br>.....<br>48|
||1.<br>Reseau Medecins d<br>e<br>Familles<br>i(REMEF)<br>48<br> <br>|
||1.1<br>Terms of the Insurance Plan.48<br> <br>|
||1.2<br>Resource Mobilization .49<br> <br>|
||1.3<br>Utilization .49|
||2.<br>Masisi Health Zone Insurance Plan .51|
||2.1<br>Terms of the Insurance Plan .50|
||2.2<br>Utilization and Success of the Plan .50|
||3.<br>Mutuelle Union et Prevoyance' (UP)<br>.<br>.51|
||<br>4.<br>SNHR Employees in Rutshuru<br>.<br>.51|
|F.|<br> <br>INFORMAL ASSOCIATIONS .52<br> <br>|
||1.<br>Size and Membership .52<br>|
||<br>2.<br>Contributions .52<br> <br>|
||3.<br>Reasons for Saving .53|
||4.<br>Emergency Loan Funds .53|
||<br>_5._<br>Use of Money Lenders for Medical Emergencies .53|
||<br>6.<br>Roles of Informal Associations .54|

i
Poli.cy Research Working Paper Serles

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS1911|The InternationalizationofFinancial<br>ServicesInAsia|StijnClaessen11<br>Tom Glaessner|April 1998|R.Vo<br>33722·|
|WPS1912|Pay and Grade Differentials at the<br>World Bank<br>..<br>•|Deon.Filmer<br>Margaret Grosh<br>Elizabeth King<br>Dominique van de Walle|April 1998|C.Bernardo<br>31148|
|.·<br>WPS1913|><br>The 1994Curren~yCrisisinTurkey|Oya Celasun|April 1998|KLabrie<br>31001|
|WPS1914|Distinguishing between Typesof<br>Data and Methods of Collecting Them|Jesko Hentschel<br>|April 1998|PREM Advisory<br>87736|
|WPS1915|Distortionary Effects. of State Trading<br>inAgriculture: Issues for the Next<br>RoundofMultilateral Trade<br>Negotiations|Merlinda lngco<br>FrancisNg|April 1998|M.Fernandez<br>33766|
|WPS1916|The Size, Origins, and Character<br>ofMongolia's Informal Sector<br>during the Transition|Jam~sH.Anderson ·|May 1998|P.Sintim-Aboagye<br>37656|
|WPS1917|Financial Liberalization and Financial<br>Fragility|Asli Demirguo-Kunt<br>Enrica Detragiache|May 1998|P.Slntim-Aboagye<br>37656 •|
conditions.”<sup>11</sup> Accordingly, we adopt the ecoregion as a general proxy for distinctive plant and inspect species, as well as animal species that are not represented in the range maps provided by IUCN and BirdLife International.

Our method for incorporating WWF ecoregions resembles our treatment of species endemicity. For this exercise, we identify all moist forest ecoregions in Bolivia, Cameroon and Myanmar (Figures 4-6). We compute the percent of total moist forest area in a country accounted for by each ecoregion. Then we compute its vulnerability index as the inverse of its area share and assign the appropriate index value to each cell. This accounting assigns high values to cells in smaller ecoregions, where clearing single cells may pose more significant threats to biome integrity.

> 11 Complete information about the WWF terrestrial ecoregions is available online at http://wwf.panda.org/about_our_earth/ecoregions/.

42
19 The concession was to be awarded to the bidder who offered the lowest average water tariff. The maximum allowable increase for the first year was 40 percent and afterwards tariffs would be increased according to a factor K plus inflation. K was set to zero for the first five years, after which K would vary depending on investment and efficiency improvements. For our base projections we assumed that all bidders would increase prices to cover marginal costs (using the World Bank estimate of US$0.45) or by about 38 percent, with no further real increases during the projection period. We also expected that a bidder who offered a lower retail price increase would be more likely to renegotiate the concession later in order to reduce the investment requirements; hence the net effect on our projected consumer surplus would be similar to our base projection.

We compared the concession projections with SEDAPAL's actual performance, projected to 2004 based on linear trends from 1988 to 1996. Residential and nonresidential demand was estimated for both the actual and counterfactual using the following methodology. We assume that total demand _(TQ)_ is a function of population _(N)_ times the individual demand curve (a _-b_ p), where a is the demand when the marginal price (p) is zero, such that: (1) _TQ, = N, (a - bp)_ The elasticity of demand is assumed to be -0.30 for metered residential customers and -0.20 for non-residential; unmetered customers are assumed to have an elasticity of zero since their volume of consumption is unaffected by price. 14 We assume that an individual's demand curve remains the same under the actual and the counterfactual and

> 14 To our knowledge, there is no study of the demand elasticity for Lima. We thus arbitrarily assigned a plausible elasticity based on the literature and the elasticity in other Latin American cities. Later we shall check the sensitivity of the welfare results to these assumptions.
- 4 -

Unfortunately, the RAI has not been updated since the initial work, for various reasons. For instance, the original work used household surveys, which are normally costly to carry out. In Roberts, Shyam, and Rastogi (2006), RAIs based on household surveys were reported only for a handful of countries. In addition, household surveys normally do not have sufficient spatial representativeness. In reality, rural accessibility differs significantly from one location to another even within a country. Thus, it is difficult to have spatial representativeness when the data are sourced from household surveys. Finally, because of the differences in spatial representativeness and method, the measured RAIs are not necessarily comparable across countries. As a result, there was little sustainability of regular data updates with the previous method.

Given the renewed interest in the RAI in the context of the Sustainable Development Goals (SDGs), a more sustainable and consistent method for measuring rural access has been proposed using new spatial data and techniques.<sup>2</sup> The SDGs aim to develop quality, reliable, sustainable, and resilient infrastructure to support economic development and human wellbeing, with a focus on affordable and equitable access for all (Target 9.1).

The new RAI method is conceptually the same as the previous work—that is, “share of the population who live within 2 km of the nearest road in good condition in rural areas”—but it uses newly emerging global data and advanced techniques. For instance, high-resolution population distribution data, such as WorldPop, developed by the international research community, can provide detailed information on where people live. Digitized road network data, including road conditions, are also often available at national road agencies. By virtually combining these data, the new method computes the RAI without counting households on the ground (figure 1).

The new method is expected to ensure the index’s sustainability as well as consistency across countries. Importantly, the new method allows the assessment of rural accessibility at any 2 See World Bank (2016) for a more detailed discussion.
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viewed 320 caregivers, 139 facilitators and 40 members of the Community Coordination Committee (CCC). We also collected classroom observation data from the 40 escolinhas located in these communities. A detailed description of this qualitative study is presented A. in Appendix

# **4.2 Survey Instruments and Outcomes**

In this section, we summarize the survey instruments used to study the first stage (compliance), primary, and secondary outcomes of the study. Table A2 summarizes the survey modules and the corresponding data collection stages.

## **Compliance**

_Preschool enrollment._ To measure preschool enrollment, we ask caregivers in the baseline and endline surveys if the target child formally enrolled in an _escolinha_ . This outcome is measured as a dummy indicator that takes the value 1 if the caregiver reported that the child is enrolled in a preschool center at the time of the survey.

## **Academic Outcomes**

_Primary School Enrollment and Academic Achievement._ We use our endline survey data to measure if children are currently enrolled at primary school, whether they repeated a grade, and if they were at an appropriate grade for their age. Primary school enrollment is an indicator equal to 1 if the child is formally enrolled in school. “Repeated grade” is an indicator equal to 1 if the child had repeated a grade at least once during the past three years, i.e., since the project started. The variable “appropriate grade for age” is an indicator that takes the value 1 if the child is at a grade that corresponds to their age, while allowing children at the age limit to be in a lower grade.

_Child Cognitive and Social-Emotional Skills._ At baseline, we measured pre-intervention child development in domains that are considered to play a key role in early development (Sabol and Pianta, 2012; Piek et al., 2008), can stimulate future learning abilities (Cunha and Heckman, 2007), and are potential predictors for cognitive outcomes later in life.

istrative and project monitoring purposes.

12
**<u>Table A2: Data Sources</u>**

|**Variable**|**Definition**|**Frequency**|**Source**|
|---|---|---|---|
|Output|Real gross domestic product (GDP)|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private consumption|Real personal consumption expenditure|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private investment|Real private gross fixed capital formation|Quarterly||
|Government consumption|Real government consumption expenditure<sup>a</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Government investment|Real government gross fixed capital formation<sup>a</sup>|Quarterly|OECD, Haver Analytics, Eurostat|
|Real effective exchange rate|Real effective exchange rate<sup>b</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), BIS|
|Current account|Current account as percent of GDP|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), WEO|
|Government debt|General government debt as percent of GDP|Annual|WEO|
|Fiscal balance|Overall fiscal balance as percent of GDP|Annual|WEO|
|Government consumption-to-GDP ratio|Government consumption as percent of GDP|Annual|WDI|
|Government investment-to-GDP ratio|Government investment as percent of GDP|Annual|WDI|
|Interest rate|Short term nominal interest rate|Quarterly|Ilzetzki,Mendoza,and Vegh(2013)|

_Note:_ The main source for the quarterly series is Ilzetzki, Mendoza, and Vegh (2013). This database which ends around 2008 is extended by splicing from different sources as mentioned in the table.

a This refers to general government for most countries while for a few countries central government is taken. See Ilzetzki, Mendoza, and Vegh (2013). b The narrow index wherever available is taken while the remainder uses the broad index. Details are available upon request.

28
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2006 to 2016.<sup>12</sup> In 2012, the registry adopted a panel structure with unique firm and worker identifiers that allow both firms and workers to be followed over time.

The registry includes very detailed information at the firm level. It has rich information on financial statements—net sales, asset and liability statements, debt, and profits—among firms that pay institutional and corporate taxes. The database also contains records on capital and machinery stocks, capital expenditures, labor costs, exports and imports, and output and income. The data also include standard information on age, the economic sector at the four-digit level, and the geographical location of firms up to the district level.<sup>13</sup> At the worker level, the registry includes individual-level information on workers who are registered with the Turkish Social Security Institution and employed in those firms. As of 2016, the EIS database included about 12.6 million workers, representing the universe of formal wage workers in nonfinancial sectors, and about 80 percent of all wage workers.<sup>14</sup> Information on the age, gender, occupation, days worked, and wages is available for each registered worker. This allows us to calculate total employment and the total wage bill for each registered firm by quarter.

A first major advantage of using the EIS database to study the impacts of the minimum wage is that it has no minimum threshold for firm size. This is a critical feature for analyzing firm responses to the minimum wage, especially in the context of a developing economy in which small firms predominate. In Turkey, 50 percent of all registered firms have at most two employees, and 75 percent have at most five employees. These firms typically pay low wages, and, as a result they are more exposed to increases in the minimum wage. A second attractive feature of the EIS is the broad coverage of sectors of activity: it includes registered private firms in all sectors of activity, apart from the financial sector. We can thus examine the effects of the minimum wage on the formal economy as a whole, and we can examine heterogenous effects across sectors.

> 12 The EIS was constructed by merging various administrative datasets across government entities in an effort led by the Ministry of Industry and Technology. The original sources of the administrative datasets included the Ministry of Industry and Technology, the Ministry of Trade, the Revenue Administration, the Social Security Institution, the Small and Medium Business Development and Support Administration, the Turkish Statistical Institute, the Turkish Patent and Trademark Office, and the Scientific and Technological Research Council of Turkey.

> 13 Turkey has 923 districts.

> 14 Those in the remaining 20 percent are largely agricultural workers who are rarely registered with social security institutions.

9
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