rafmacalaba/datause-encoder-data huggingface dataset:
# **A Mapping and Sampling of Providers**

# **A.1 Mapping of Providers: Representative Sample**

We first randomly selected five districts in the state of Madhya Pradesh, stratified by region and an index of health outcomes. In each district, we sampled 20 villages by probability proportional to size (PPS) sampling. Because of the rural focus of the study, we restricted the sampling frame to villages with population below 5,000. The sample of villages is thus representative of rural Madhya Pradesh.

In each sampled villages, we conducted at least three Participatory Resource Assessments in different locations within the village and obtained a list of all the health care providers that households’ sought primary care services from. These lists were used primarily to identify the geographical locations that households’ sought care from. For instance, households may seek care from providers within the village, but also on the nearest highway. If 5 percent or more households reported visiting a provider in an outside location, we identified that location as a “cluster village” and considered it a part of the “health care market” for the sampled village. Fifty-five sampled villages have one cluster village, 13 villages have two, and one village has three. The remaining 31 villages have no cluster villages (i.e. less than 5 percent of primary health care visits were to a location outside the village). For our sample as a whole, we identified 184 unique locations, including the 100 sampled villages.

Surveyors then visited each location and administered a _provider census_ to all health care providers in the location - regardless of whether they had been mentioned in the participatory assessments. The provider census details the provider’s demographic, practice and clinic characteristics.

Following the provider census in the villages, we administered a short _household census_ with information on household demographics and health care seeking behavior. For each household member, we asked about incidence of any illness (primary or otherwise) in the past one month, if they sought medical attention for that illness, and (if yes), the name and address of the provider they visited (regardless of the location of the provider). Surveyors mapped the household visits to the providers lists; this is the mapping we use to compute the fraction of visits to public and private providers and providers with different qualifications in the text. In instances where households reported visiting providers not already on the list, we probed for providers’ name, address and practice details and added the providers to our listing and census exercise. We verified through this exercise that we had covered at least 95 percent of all providers visited by households in each village. This exhaustive mapping process ensured that we mapped the complete “health market" where households in our sampled villages sought primary care services.

41
Figure E.5 <!-- Start of picture text --> Net Exports Per Worker Net Exports Per Worker<br>Panama- 1965 Panama- 1988<br>100- - TROP - TROP -<br>*0 - PETRO MAT FOR A CER LAB CAP MACH CHEM<br>50- O._ _ ___ __ _<br>0 PETRO MAT FOR CER LAB CAP MACH CHEM - -<br>o 0<br>.100 2<br>-150 - -4<br>Net Exports Per Worker Net Exports Per Worker<br>Mexico- 1965 Mexico - 1988<br>20 TROP 200<br>MAT ANL * PETRO<br>PETRO FOR J.AB CAP MACH CHEM 5<br>0 -. I 5<br>0.20- 00<br>35 -40 .- 40 o , 50 50 1~~~~TROP P MAT j -0 LAB<br>FOR | ANL CER CAP MACH CHEM<br>* tO .50<br><!-- End of picture text -->
years of education, matter for individual earnings and economic growth (e.g., Hanushek and Woessmann 2008). Therefore, even where the number of years of education completed is not adversely affected by civil conflict, such conflict may have deleterious effects on human capital if the quality of learning deteriorates.

From an international perspective, this paper contributes to unpacking the complexity that lies behind the generic term civil conflict. The idiosyncrasies of each conflict highlight the need for additional research on the impacts of different conflicts to shed light on the range of potential effects rather than a focus on extreme, but thankfully rare, instances.

From a policy perspective, the present findings call for measures that aim to protect school children and teachers from being directly targeted by combatants. As shown in this paper, even where primary education systems appear very resilient to surrounding violence, direct targeting of schools, however mild (e.g., brief abductions of pupils and teachers for indoctrination purposes), has adverse effects on schooling, especially for girls.

# _NOTES_

The author thanks INSEC for sharing their conflict data and Martha Ainsworth, Quy-Toan Do, Helge Holterman, Steve McIntosh, Gudrun Østby, Kati Schindler, Olga Shemyakina, Helen Simpson, Sarah Smith, Frank Windmeijer, Hassan Zaman, the World Bank’s Nepal Country Director's office, and participants at two Gender and Conflict Research Workshops at the World Bank (Washington) and Peace Research Institute Oslo for their useful comments. The author also thanks three anonymous referees for their valuable comments and suggestions. This work was 26
27 Figure 3: Trade and Welfare Reduction Indexes, all covered products, 19 African countries and regional average<sup>a</sup> , 2000–04 (percent) <!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>TRI WRI<br><!-- End of picture text --> Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008) a. To get the regional average the national indexes are weighted by the average of the gross value of production and consumption at undistorted prices.
# How do people receive government transfers?

Globally, 13 percent of adults reported having received government transfers in the past 12 months (figure 2.5). Government transfers include any kind of social benefit payments—such as subsidies, unemployment benefits, or payments for educational or medical expenses—but do not include wages or other payments related to work. Not surprisingly, the share of adults who reported receiving government transfer payments was almost twice as high in highincome OECD economies (21 percent) as in developing economies (12 percent).

FIGURE 2.5 Government transfer recipients and how they receive payments Adults receiving transfers in the past year, by method (as % of all adults), 2014 <!-- Start of picture text --> Using other method<br>In cash only<br>21 Into an account<br>15 15<br>13<br>8 7<br>3<br>East Asia Europe High-income Latin Middle South Sub-Saharan<br>& Pacific & Central OECD America & East Asia Africa<br>Asia economies Caribbean<br>Source: Global Findex database.<br><!-- End of picture text --> Just as for wage payments, in high-income OECD economies the overwhelming majority of those receiving transfer payments—83 percent—reported receiving them into an account. Another 13 percent of recipients (3 percent of all adults) reported receiving government transfers in some way other than through an account or in cash, likely in the form of vouchers such as food stamps. In developing economies, by contrast, only about half those receiving government transfer payments reported receiving them into an account. Globally, 130 million adults without an account receive government transfer payments only in cash.

Like wage employment, however, government transfers can be an important reason why people open accounts. Far fewer adults receive government transfers than receive wage payments. But among those receiving government transfers into an account in developing economies, about a quarter reported that this account was their first one and was opened specifically so that they could receive government transfers.

GO TO BOX 2.1 Indeed, many developing economies have used government transfer payments to increase financial inclusion (box 2.1). These include countries in Latin America and the Caribbean, where on average 68 percent of transfer recipients receive the payments into an account. The share is especially high in Brazil: among the 15 percent of adults receiving government transfers, 88 percent receive them directly into an account. Another notable example is South Africa, where a third of adults receive government transfers—and 82 percent of them receive the payments into an account.

Most adults receiving government transfers into an account reported using their account for cash management purposes—with 61 percent doing so in developing economies and 87 percent in high-income OECD economies. But there are exceptions. In Brazil, for example, only 12 percent of adults receiving government transfers into an account reported withdrawing the money over time as needed; 88 percent withdraw all the money right away.

> THE GLOBAL FINDEX DATABASE 32
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Table 5: Benefit Types under CAP 286**

|**Type of Benefit**<br>**`(a) Old age pensions`**|**Requirement/s**|**Formula**|
|---|---|---|
|**`Full monthly pension`**<br>**`(if no CPG)`**<br>**`Commuted Pension`**<br>**`Gratuity (CPG)`**<br>**`Reduced Monthly Pension`**<br>**`(if CPG)`**|<br> <br>`Length of service 20 years,`<br>`or 45 years of age and 10`<br>`years of service. Mandatory`<br>`retirement age: 60`<br> <br> <br>`Length of service 20 years,`<br>`or 45 years of age and 10`<br>`years of service. Mandatory`<br>`retirement age: 60`<br> <br> <br>`Length of service 20 years,`<br>`or 45 years of age and 10`<br>`years of service. Mandatory`<br>`retirement age: 60`|`((1/500th of the annual`<br>`salary before retirement *`<br>`total number of months of`<br>`service) /12`<br>`(1/500th of the annual`<br>`salary before retirement *`<br>`total number of months of`<br>`service) *(1/3) *15`<br>`(1/500th of the annual`<br>`salary before retirement *`<br>`total number of months of`<br>`service) *(2/3) /12`|
|**`(b) Survivors' Benefits`**|`Paid to spouse and children`<br>`when pensioner/active member`<br>`dies before the expiry of`<br>`fifteen years after the date`<br>`of his/her retirement (for`<br>`full 15 years in case of`<br>`active member`|`Pensioner/active member`<br>`monthly pension paid for up`<br>`to 15 years`|
|**`(c) Pension on abolition of`**<br>**`office`**|`Abolition of office`|`Normal pension + 25 % of the`<br>`pension + severance pay +`<br>`repatriation expenses + 6`<br>`months pay in lieu of notice`<br>`+ payment in lieu of`<br>`approved earned leave`|
|**`(d) Contract Gratuity for`**<br>**`Public Officers serving on`**<br>**`contract`**|`Contract agreement`|`Gratuity computed according`<br>`to the terms of the contract`<br>`agreement. Usually a`<br>`prescribed percentage of the`<br>`annualgross salary`|
|**`(e) Death Gratuity`**|`Representative of a deceased`<br>`public officer`|`Deceased public officers'`<br>`annual salary at the time of`<br>`death by three, or the`<br>`deceased officer's would-`<br>`have-been CPG, which ever is`<br>`greater`|
|**`(f) Short Service Gratuity`**|`Public officers retired in`<br>`the public interest or on`<br>`medical grounds provided`<br>`they had completed 10 years`<br>`ofpensionable service`|`(1/500th of the annual`<br>`salary before retirement *`<br>`total number of months of`<br>`service) *10`|
|**`(g) Marriage Gratuity`**|`Payable to women public`<br>`officers who retire on`<br>`marriage grounds`|`(Total number of months *`<br>`annual salary) *5/ 500`|
|**`(h) Gratuity for Police and`**<br>**`Prisons officers`**|`Police office who retires`<br>`below the rank of assistant`<br>`inspector, or in case the`<br>`prisons service below the`<br>`rank of principal officer`<br>`and has served for 12 years,`<br>`or more`|`25 % of total emoluments or`<br>`less`|

**Source** : CAP 286.

- 27 -
The table shows that enrollment of boys is higher than that of girls: in this sample of applicants who were not offered a scholarship 63 percent of boys and 54 percent of girls are enrolled. Among their siblings, who are on average younger, overall school enrollment is higher—86 percent for boys and 80 percent for girls. Applicants who are enrolled in school attend, on average, for about 26 hours per week, and their siblings attend for approximately 21 hours.

About 31 percent of applicant boys in the control group worked for pay, compared to 37 percent of girls: Among those who work for pay, average hours are 24 for boys and 28 for girls. Siblings are much less likely to work for pay—9 percent of boys and 17 percent work of girls work. Work for pay among children in this age group is concentrated in the farm sector and construction for boys, and in the farm sector and garment industry for girls.<sup>13</sup> Work without pay is much more widespread among applicants and their siblings: 64 percent of applicant boys and 51 percent of applicant girls work without pay. On average, these children work for about 19 hours. Among siblings, the incidence of work for pay is once again lower—52 percent among boys, and 47 percent among girls.

The last two columns of the table focus on patterns of work among applicants’ parents. Many more adults work in the no-pay sector than in the for-pay sector, a pattern that is apparent for both men and women. Work hours are approximately 30 hours per week in the for-pay sector, and 32 to 36 hours in the no-pay sector.

# **4. Identification strategy**

The basic identification strategy we use in this paper is based on regression discontinuity (RD). The regressions we estimate take the following form: (14) Yihs = αs + f(Sh) + **X** iβ + (R*Male)δ1 + (R*Female)δ2 + (R*S*Male)δ3 + (R*S*Female)δ4 + εihs

> 13 Our survey did not collect information on what work for pay children are engaged in, so we make use of a recent nationwide household survey, the 2004 Cambodia Socio-Economic Survey (CSES). We limit the sample in the CSES to rural areas, which most closely corresponds to the catchment areas of the CSP schools. In rural areas, 35 percent of boys age 10-18 who work for pay are farm workers, and another 26 percent work in construction; among girls, 35 percent of those who work for pay are farm workers, and 27 percent work in the garment industry.

16
# **5. Robustness analysis**

This section discusses the sensitivity of the baseline results to a number of robustness checks with respect to the country-sample composition, the type of estimator, and the issue of income endogeneity.

# **5.1 Region-level estimates**

It is reasonable to expect that the factors behind extreme attitudes vary across countries and geographic regions. For instance, the factors that motivate Islamist terrorist attacks in the Middle East may be very different from those motivating inter-ethnic attacks in Sub-Saharan Africa or inter-caste radical conflicts in Asia. Although the inclusion of country-fixed effects allows us to capture some aspects of these regional specifics, it appears prudent to replicate our baseline regression by region.

We conduct region-specific regression analysis pulling the observations by region, following the World Bank’s geographic regional classification for developing countries. We omit LAC as we have information on very few LAC countries in our sample and present the results for EAP, ECA, MENA, SA, and SSA in Tables A8 to A12, respectively. As in the baseline results, radicalization falls with income, notably in SA (Table A11). In all regions, income improves significantly the likelihood of experiencing a high quality life, although the size of the coefficient on income varies across regions, ranging from 0.197 (s.e.=0.034) in SSA to 0.556 (0.037) in SA.

The inverted U-shaped relationship between the respondent’s age and radicalization is significant only in SSA, where the turning points is 32 years of age. The association between age and radicalization has the expected inverted U-shape but is not significant in the other regions. The 27
also see Romer (1989)): researchers regress the average growth rate for a samle of count tes on a g oup of explanatory variables and focus on the coefficients of one or two variables of interest. This paper studies whether we should have much confidence in the conclusions that have been drawn from these regressions.

Since past cross-country studies do not provide much evidence regarding the sensitivity of their findings to small alterations in the list of explanatory variables, this paper uses a variant of Leamer's extreme bounds analysis to examine the robustness of a variety of "fiudings* described in the empirical growth literature (See Leamer (1983, 1985) and Leamer and Leonard (1983)}). We first describe the extreme bounds procedure and then return to discuss and study the empirical growth literature.

As in the studies we are considering, our extreme bounds test is based on linear equations explaining output growth and investment: where Y is either per capita GDP growth or the share of investment in GDP, I is a set of variables thas are always included in the regression, M is the variable of interest, and 2 is a subset of variables chosen from a large pool of variables identified by past studies as important explanatory variables of growth. Our extreme bounds procedure involves varying the subset of Z-variables included in the regression to find the widest range of coefficient estimates on the variable of interest, M, that is accepted by standard hypothesis tests. In particular, we first choose a variable that has been the focus of past empirical studies, M, and vericy the bi-variate sign and significance of these past 4
# **Table 16: DID regression (2)**

||Travel tim<br>Populated|e to:<br>area|Municipal|capital|Hospital||School||
|---|---|---|---|---|---|---|---|---|
|D|0.496|(0.066) ***|0.426|(0.050) ***|0.362|(0.053) ***|0.003|(0.072)|
|T|-0.204|(0.069) ***|0.020|(0.055)|0.088|(0.056)|0.194|(0.080) **|
|D*T|-0.009|(0.100)|-0.081|(0.071)|-0.109|(0.076)|0.416|(0.102) ***|
|lnHH size|0.060|(0.065)|0.079|(0.049) *|0.050|(0.053)|0.002|(0.070)|
|Adult share|0.099|(0.139)|-0.017|(0.100)|-0.056|(0.111)|0.307|(0.146) **|
|HH head sex: Male|0.222|(0.070) ***|0.066|(0.056)|0.028|(0.061)|-0.046|(0.083)|
|HH head education: Elementary|0.000|(0.061)|0.082|(0.042) **|0.001|(0.043)|-0.134|(0.060) **|
|HH head education: Secondary|-0.284|(0.105) ***|-0.077|(0.074)|-0.212|(0.087) **|-0.231|(0.097) **|
|HH head education: University|-0.417|(0.226) *|-0.113|(0.173)|-0.375|(0.196) *|-0.333|(0.228)|
|Appliance ownership: Refrigerator|-0.108|(0.085)|-0.061|(0.061)|-0.006|(0.067)|0.026|(0.089)|
|Appliance ownership: Color TV|-0.088|(0.078)|-0.120|(0.055) **|-0.203|(0.062) ***|-0.212|(0.077) ***|
|Appliance ownership: Radio|0.100|(0.053) **|-0.034|(0.037)|0.011|(0.042)|0.074|(0.053)|
|Appliance ownership: Gas stove|-0.154|(0.075) **|-0.167|(0.051) ***|-0.177|(0.055) ***|-0.140|(0.077) *|
|Appliance ownership: Washing|||||||||
|machine|-0.062|(0.067)|-0.242|(0.052) ***|-0.178|(0.057) ***|-0.081|(0.069)|
|Gas use for cooking|-0.078|(0.062)|-0.011|(0.047)|0.027|(0.053)|-0.110|(0.063) *|
|Power use for lighting|-0.302|(0.080) ***|-0.287|(0.060) ***|-0.281|(0.067) ***|-0.372|(0.084) ***|
|Tap water for cooking|0.003|(0.052)|-0.099|(0.038) ***|-0.181|(0.043) ***|-0.439|(0.054) ***|
|Constant|3.384|(0.209)***|4.068|(0.161)***|4.171|(0.173)***|2.861|(0.225)***|
|Obs.|1,375||1,976||1,908||1,946||
|R-squared|0.216||0.208||0.091||0.159||
|F-stat|23.48||30.05||10.25||22.03||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

35
several service sectors, such as marketing/advertising, consultant, legal services, IT services, accounting, etc., has increased quickly in recent years. However, the rates on quality are comparatively lower. Many firms were reluctant to outsource critical services, such as R&D and accounting, because they are not satisfied with the services provided outside, and afraid that the service providers cannot guarantee to protect their valuable commercial secrets. Still, quite a lot companies tried to outsource more services, currently provided in-house to reduce cost and improve business efficiency. Most of firms acknowledged that the quality of services is getting better.

**Table 28: Assessment of these business services: availability and quality**

||Beiji<br>|ng<br>|Shang<br>|hai<br>|
|---|---|---|---|---|
||Availability|Quality|Availability|Quality|
|R&D|3.9|3.7|3.4|3.4|
|Marketing|3.6|3.3|3.4|2.9|
|Financial/accounting|4.2|3.5|-|-|
|Legal|4.1|3.4|-|-|
|Human resources management|3.5|3.8|-|-|
|Consulting|3.5|3.1|3.5|3.0|
|Logistics|3.3|2.8|3.1|3.0|
|Education/training|3.0|3.0|-|-|
|IT services|3.9|3.1|-|-|

_Note_ : Measured on a Likert scale of 1 (very difficult/very poor) to 5 (very good/very poor)

# _Cluster Economies_

The cluster economies refer to the geographical concentration of firms in the same and related industries that can generate spillover benefits.

We asked two questions to identify whether cluster economies exist in each category of innovative industries (see Table 29 **)** . The cluster economy is most significant in media and content industries in both cities, and most insignificant in knowledge intensive business services.

As for high-tech manufacturing companies, we also asked whether their suppliers in this city are part of industrial clusters of their own (see Table 30). The majority answered “no” (Beijing 67%, Shanghai 43%). Most of IT firms in Beijing have their suppliers in southern China. And firms in Shanghai often have their manufacturing suppliers in the suburbs of Shanghai or other cities in the Yangtze River Delta.

39
News" by Dow Jones to limit the potential noise stemming from the large pool of articles used initially (Appendix Table A17). The results remain robust and highly significant in this subsample, further supporting the results on the dynamics of reform attitudes presented in this section.

Next, we explore whether reform chatter is associated with reforms. Actual reforms are difficult to measure. Data on structural reforms for a large sample of countries are drawn from Ostry et al. (2009) and the World Bank’s Doing Business project. The results presented in this subsection are mere associations. However, the strong relationship between reform chatter and actual reform suggests that reform chatter is not “cheap talk”.

The main specification for testing the relationship between reform chatter and actual reforms is: R R R R R R R ii,tt SSaaRRcca ccRRa ss ii tt ii,tt ii,tt ii ii,tt R R = ααR + ββ captures the changes in the principal component of domestic, international + λλR RRha aaR + γγR RRha aaR × λλR RRaaRRa cc + εε and aggregate indices. and are country and time fixed effects. The main coefficients of ii,tt interest are SSaaRRcca ccRRa and , which highlight the potential heterogeneity in the impact of reform chatter on ss ii tt actual reforms in the data. αα ββ λλ γγ Using data on structural reform (Ostry et al. 2009), Appendix Table A20 Panel A shows that reform chatter at time _t_ is associated significantly with more domestic reforms (Column 2) but neither international reforms (Column 3) nor composite reforms (Column 1). However, Table A20 Panel B show that for more democratic countries, reform mentions are significantly associated with the composite index of actual reforms (see Columns 1-3). Columns 4-6 in Panel B show that the results are not significant for international reforms. Columns 8-9 in Panel show that domestic reforms are associated with reform chatter in democracies, though the result in Column 7 is not significant when using the democracy index.

The attitudes towards reform presented in Table A21 are associated with actual reform. In Panel B, columns 1-3 reveal that there is no heterogeneity alongside polity. These results suggest that reform attitudes are associated with actual reforms—when considering intensity of reform chatter the association with actual reform holds more in democracies.

15