rafmacalaba/datause-encoder-data huggingface dataset:
Recurrent costs include payments to facilities for prenatal care, delivery and hospital postnatal care services as well as other joint recurrent costs such as external auditing, IT services, and management personnel. These other joint recurrent costs are only available at the aggregate (national) level year. We estimate other joint recurrent costs for maternal services by multiplying total joint recurrent costs by maternal services share of total billings for services each year for the seven provinces.

Fixed costs include investment in durable goods such as medical equipment, information technology, vehicles, and office equipment. We calculate _E_ , the “annual use cost” of a durable, using the following formula: where _K_ is the purchase price, _n_ is the useful life of the durable, _S_ is the end of life resale value, _r_ is the discount rate, and _A(n, r)_ is the annuity factor and given by the <u>( )</u> equation: . We followed WHO guidelines to estimate the useful life and [ ] resale value of an asset ( _n_ and _S_ ). We classified durables into five groups: medical equipment (10 years), medical equipment of high complexity (15 years), ambulances (15 years), information system equipment (5 years), and others (small items/program credentials). We assume a zero-resale value at the end of the project and applied a 3 percent discount rate.

We were able classify medical equipment according to its medical use — maternal care, baby or children care, or all care. However, information technology, vehicles, and office equipment were only available at the aggregate level. Again, we estimated fixed costs from theses associated with maternal health services by multiplying total costs by maternal services share of total billings for services each year for the seven provinces.

# **8.2. Disability-Adjusted Life Years Averted**

DALYs are the sum of the present value of future years of lifetime lost through premature mortality, and the present value of future years of lifetime living with a disability caused by disease or injury adjusted for the average severity of the disability. DALYs allow us to sum years of life lost from neonatal mortality with the years living with disability associated with low birth weight.

We calculate DALYs averted due to Plan Nacer as: 29
small amount to be offset by cost savings. The total “burden reduction” to farmers was 39 percent (with tax payments and voluntary contributions totaling RMB 6.23 billion).<sup>46</sup> As it turned out, since farmers did not approve any contribution under the _yishi yiyi_ system, the actual burden reduction was 48 percent. Depending on how much village-level infrastructure was built and financed by the government, this left up to 17 percent less revenue available for spending.

**Table 22.  Rural Revenues Pre- and Post-RFR in Henan**

|**Revenue source** (RMB billion)|**2000**|**2002(actual)**|**change **|
|---|---|---|---|
|Agricultural tax|0.9|3.73|+2.83|
|Agricultural tax surcharge(20%)|-|0.75|+0.75|
|Agricultural specialproducts tax|0.66|0.19|-0.47|
|Slaughter tax|0.05|0.06|+0.01|
|Specialproducts tax on tobacco|0.2|0.2|0|
|**_Total agricultural taxes_**|**_1.81(18%)_**|**_4.93(51%)_**|**_+3.12_**|
|Townshipunified levy|2.6|0|-2.6|
|Village levy|2.35|0|-2.35|
|Other fees and levies|3.4|0|-3.4|
|**_Total non-tax revenues_**|**_8.35(82%)_**|**_0_**|**_-8.35_**|
|**_Centralgovernment subsidy_**|**_-_**|**_2.82(29%)_**|**_+2.82_**|
|**_Provincial subsidy_**|**_-_**|**_0.7(7%)_**|**_+0.7_**|
|**_Farmers voluntary contribution*_**|**_-_**|**_1.3(13%)_**|**_1.3_**|
|**Total available revenue**|**10.16**|**9.75**|**-0.41(4%)**|
|**_Totalpaid by farmers_**|**_10.16_**|**_6.23_**|**_-3.93(-39%)_**|

Note: Figures in parentheses refer to shares of total available revenues

* Projected, with a ceiling set at RMB 15 per year from farmers. Source: Fieldwork, and Henan Statistical Yearbook 2004.

# **5.2.  Associated Reforms of the RFR Program**

From the outset, the architects of the RFR program had planned for restructuring rural governments and reducing staff to be core components of the RFR that would provide cost savings to offset revenue losses from the fee reductions.<sup>47</sup> In a speech to the National RFR

> 46 It should be noted that the calculations and reported figures for the impact of the RFR on the “burden reduction” should be used with care. While all calculations show substantial impacts, reported numbers are often not exact and not fully consistent.

> 47 These are included in the Implementation Document on the RFR issued in 2000: Chinese Communist Party Central Committee Document No.7, jointly issued by the CCPCC and State Council. Reprinted in State Council RFR Work Group (2001).

47
<!-- Start of picture text --> Figure 7f: Access to Adequate Health Services by Household Wealth<br>Prevalence of Adequacy of Health<br>By wealth<br>47<br>38 38<br>31 30<br>25<br>22 22<br>19<br>17<br>13 14 14<br>11<br>9<br>6 4<br>2<br>BGD, HK '10 BOL '08 IDN '10 NPL '11 ZWE '10<br>BGD, IFPRI '11 ETH '11 KHM '10 PER '12<br>Resource Poor Resource Rich<br>Source: Author estimates.<br>Note: (1) Bangladesh (IFPRI) has Vitamin A supplementation for 6 to 24 month olds only.<br>(2) Indonesia does not have information on prenatal checkups or Vitamin A supplementation.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

# **3.3 Additional Considerations: Weak Links in a Chain**

The preceding discussion suggests a correlation between the observed reductions in stunting and the increases in access to adequate food security, adequate care and adequate environment and health services over the same period in the countries studied. For the countries with two years of similar survey data, the prevalence of stunting fell (Figures 2 and 3). In the same time span there were marked improvements in the adequacy of care (Figure 6b), some improvement in adequate environment and health (Figures 7c and 7d) and a mixed story in adequate food with some countries experiencing a decrease in the access to adequate food (Figure 5b). Whether there is indeed a “causal” relationship as opposed to a simple correlation between improved access to adequate food, care and environment and health and the observed reduced stunting and increased average height‐for‐age of children, is a question that needs to be addressed with more complex models using detailed panel data on children through time.

As the first step towards exploring potential synergies in relation to child nutrition, the analysis below investigates in more detail the extent to which children have simultaneous access to adequate levels in any one, or two, or all three of the clusters of underlying determinants of nutrition. So far the access to adequate environment was treated separately from the access to adequate health services. In the remainder of this report environment and health are combined into one cluster mainly for the purpose of simplifying the presentation. One should bear in mind, that combining health and environment into one aggregate cluster creates a very strict set of standards 33
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE players. Zimbabwe‘s spending effort on power is substantially higher than the respective average for other low-income countries (figure 21). The largest share of infrastructure spending goes to power (48 percent), followed by ICT (24 percent), transport (17 percent), and water and sanitation (11 percent) (table 17).

**Table 17. Financial flows to Zimbabwe’s infrastructure, 2009***

<u>$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|Information and communication technology|175|0|1|0|14|15|190|
|Irrigation|1|1|0|0|0|1|2|
|Power|294|1|0|83|0|84|378|
|Transport|110|23|0|1|0|24|134|
|Water supply and sanitation|75|9|1|0|0|10|85|
|Total|654|34|2|84|14|134|788|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

* in few singular cases, where 2009 numbers were not available, 2004 spending was used instead.

**Figure 20. Zimbabwe’s existing infrastructure spending is one of the highest in the region**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>SADC<br>MIC<br>LIC-Fragile<br>Resource-Rich<br>Cape Verde<br>Zimbabwe<br>Ethiopia<br>Kenya<br>Uganda<br>Botswana Capex O&M<br>South Africa<br>Congo, Dem. Rep.<br>% of GDP 0 5 10 15 20 25<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income countries; MIC = middle-income countries; SADC = Southern African Development Community; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

45
# Appendix 1. Data and Assumptions

The assumptions about population-sizes, HIV prevalence, HIV incidence and sexual behavior are all contained in the table shown in Figure 1.

||||<br>|Figure1.Ta<br>|bleof**_Assu_**<br>|**_mptions_**<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|
|||South Africa|Namibla|Botswana|Lesotho|Swaziland|Zimbabwe|Zambia|Total|Assumptions|
|Total<br>Population|Population'000|43,421|1,771|1,695|2,143|1,083|11,343|9,582|**71,038**|SADCwebsitewww.sadc.int|
|Sex<br>|Population**'000**|434<br>|18|17|21|11|113|96|710|1%ofpopulation|
|Workers|HIVprevalence|61%|||||86%<br>____|69%<br>_______|66%|SAprevalencetheaverageof<br>~~~~~available<br>studies|
||PartnerHIVprevalence|38%<br>______________|_________|____________||||__________|38%<br>________|SA:averageadultpopulation<br>~~~~~~~~~~~and<br>high**risk**clients|
||5yrPeriodprobability|50%|||||14%|28%|40%|ASSAsimulation,female|
||STIprevalence|||||||19%|19%|Countryfactsheets|
||Partner**STi**prevalence|12%||||||14%|12%|DHSSouthAfrica|
||#regular partners|1||||||1|1|SAsplitasZambia|
||#regularcontacts|600||||||720|**622**||
||Regular condomuse|||||||44%|44%|Adetunjiandothers(2000,|
||#newcontacts|3,240||||||1,560|2,936|2contactsperclient|
||newcondomuse|||||||54%|54%|Adetunjiandothers(2000,|
|Males|Population'000|434|18|17|21|11|113|96|710|**1%**ofpopulation|
|attending<br>STDClinics|HIVprevalence||42%|54%|54%|50%|65%||60%|averageofavailableclinic<br>data|
||PartnerHIVprevalence|56%||64%|||86%|69%|63%|sexworkerprevalence|
||5yrPeriodprobability||41%|35%|35%|37%|29%<br>__________|26%|30%|ASSAsimulation,malePRO<br>~~~~~~~group|
||**STi**prevalence|100%|100%|100%|100%|100%|100%|100%|100%|by**definition**|
||PartnerSTiprevalence|12%||||||14%|12%|DHSSouthAfrica|
||#regularpartners|**1**|||||||**1**||
||**#**regularcontacts|600|||||||600||
||Regular condomuse|6%|||||||6%|DHSSouthAfrica|
||#newcontacts|||||||300|300||
||new condomuse||||||___________|26%|26%|Adetunjiandothers(2000.<br>~~~~~~~~~2001)|
|Military|Population'000|85|12|9|2|3|39|22|172<br>.________<br>_|TheStockholm International<br>PeaceResearchInstitute<br>_<br> _(SIRPRI):www.first_sipnorg|

_33_
a direct link the the tax form, and was personalized with the name of the addressee featuring in the address eld. The message also presented the social norm8 out of 10 [tax-registered] Costa Ricans have led their 2014 income tax returnand introduced the possibility of public shaming through the publication of names. The social norm and the deterrence content of the message are all fact-based and credible, given the usual enforcement activities the tax authority conducts through audits, rm closures, and the online publication of the list of late-lers (for the sales tax) and late-payers (for various taxes).<sup>21</sup> As the tax authority routinely uses third-party information in its enforcement activities and mentions this to taxpayers, the baseline treatment message (treatment 1) also stated that We have third-party information, conrming that you or your client performed activities in 2014 which require you to pay taxes.<sup>22</sup> Treatment 2 further strengthened this statement by providing rms with specic examples of third-party information held by the tax authority about their activities:

# Treatment 2 (T2):

We have third-party information conrming that you or your client performed activities in 2014 which require you to pay taxes. From third-party reports (D150, D151 and D153), we know about your operations, for example:

- Sales of at least XXX reported by COMPANY,

- Sales from credit/debit cards of at least ZZZ reported by BANK,

- Sales or contracts with state entities of at least WWW.

For each type of third-party information held by the tax authority (reports from other rms, state institutions, and credit/debit card companies), it listed the transaction amount and purchaser of the highest reported transaction. Providing evidence of third-party reported transactions and transaction partners clearly demonstrates the tax authority's possession of third-party reports and thus makes the message highly credible. In addition, providing examples rather than the total amount of third-party reported transactions, as in Carrillo, Pomeranz, and Singhal (2016), allows us to apply this treatment to small or marginally formal rms with very small third-party reports.

> 21As the message style and content used in past communication campaigns with non-lers varied from year to year, relied on a diverse set of communication methods (emails, SMS, phone calls, invitations for personal interview), and messages were not systematically sent to all late lers, we consider that a treatment eect compared to the control group cannot simply be due to sending a dierent message.

> 22This mention of your client is added to make the message suitable for being sent directly to the rm or the rm's legal representative.

12
study by Windsor and others (1988) which provided cash payments of $25 at 6 weeks and 6 months as a reward for smoking abstinence found no difference in quit rates between the control and treatment groups. Other early studies experimenting with prizes, vouchers, and in-kind gifts of free nicotine patches obtained mixed results and often the positive results disappeared after 6 months. In their systematic review, Cahill, Hartman-Boyce and Perera (2015) conclude that incentives appear to boost cessation rates while they are in place, but that few programs managed to yield sustained effects. However, Volpp and others (2009) found that while an incentive program’s effects on smoking cessation also diminished after withdrawal of the incentives, significant effects did remain 3-6 months later. Similarly, Etter and Schmid (2016) observed that financial incentives increased long-term smoking cessation rates among low-income smokers in Switzerland up to 12 months after the incentives were discontinued.

Giné, Karlan and Zinman (2010) evaluated a voluntary commitment contract to help smokers quit smoking in the Philippines. A smoker signing the contract pledged his own money that he would be negative in a urine test detecting nicotine and cotinine six months later. The participants who were randomly offered the commitment contract were 3.4 to 5.7 percentage points more likely to pass a urine test for short-term smoking cessation than the control group. This effect remained in surprise tests at 12 months, indicating that the commitment device led to lasting smoking cessation. White, Dow and Rungruanghiranya (2016) tested commitment contracts combined with team incentives in rural Thailand. The incentives increased tobacco abstinence, but their effects tapered off after they were discontinued.

Halpern and others (2015) compared four different types of smoking cessation incentives among employees of a large U.S. firm. Two incentive schemes used simple reward mechanisms while two combined commitment devices with personal deposits with rewards. In addition, two 13
# **Table S6: Effects of Parent Insurance on Out of Pocket Expenditures by Child Eligibility**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.048|-0.030|0.006|-0.012|-0.032|
||(0.079)|(0.081)|(0.004)|(0.009)|(0.073)|
|Parent Enrolled*Ineligible|-0.115|-0.132|-0.006|0.014|-0.298**|
||(0.148)|(0.149)|(0.004)|(0.010)|(0.117)|
|Ineligible|0.004|-0.003|0.001|-0.002|0.045|
||(0.048)|(0.049)|(0.001)|(0.001)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.07|0.066|0.009|0.001|0.074|
|P-value of Enrolled + Enrolled*Ineligible|0.242|0.244|0.812|0.332|0.001|
|Panel B: Inv. Hyperbolic Sine Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.401|-0.224|0.031|-0.024|-0.288|
||(0.532)|(0.521)|(0.022)|(0.034)|(0.439)|
|Parent Enrolled*Ineligible|-1.256|-1.139|-0.030|0.029|-1.858***|
||(0.969)|(0.931)|(0.021)|(0.037)|(0.711)|
|Ineligible|0.274|0.217|0.005|-0.008|0.295|
||(0.321)|(0.309)|(0.005)|(0.005)|(0.233)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.094|0.09|0.009|0.005|0.081|
|P-value of Enrolled + Enrolled*Ineligible|0.066|0.114|0.812|0.398|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

6
# _Quality Issues of Clearing Agents_

In some cases, the freight forwarders’ behaviors also distort transit efficiency. In several countries, access to the Clearing and Forwarding (C&F) business is not difficult. One consequence of this easy access is “suitcase” companies with low capacity, low training, and low professionalism. These may bribe officials to stay afloat instead of following procedures properly. In this case, customs clearance, dwell time and uncertainty increase significantly and the responsibility for low performance is not due to public parastatals. Small traders are harmed because they are less likely to use large and expensive C&F agents. Statistical evidence from Cameroon suggest that more than 50% of dwell time in the Port of Douala is caused by the C&F low capacity to properly fulfill documentary requirements or inability to provide the necessary payments or securities<sup>42</sup> . When the Cameroon Facilitation Committee tried to set quality criteria for clearing agents in 2005, less than 10% managed to fulfill them even after one year. In Kazakhstan, small size operators are specialized according to the types of commodities. Competition between freight forwarders does not exist in practice, with predetermined market shares.

# _Low Quality of Some Transport Operators_

Quality of service providers and mismanagement of fleet is a key factor of uncertainty for traders to/from landlocked countries. Tanzania Railways Corporation has an error margin of 4 to 5 days to predict the arrival of any shipment due to locomotive shortages and wagon mismanagement. As a result, although the rail mode is cheaper, road transit traffic to Northern Tanzania from Kenya has increased by more than 20% per year in the last five years.<sup>43</sup> It also explains why today more than three quarters of the Rwandan trade is through Kenya, while three years before over 50% was through Tanzania.

> 42 Cameroon Guichet Unique du Commerce Extérieur, 2004.

> 43Source from NCTTCA, 2005 and the Tanzania DTIS Study, Transport report, 2004.

38
Table 9: Heterogeneous impacts of _Mais Educação_ by student coverage in the program

||Dropout rates|Test score<br>|Test score<br>|
|---|---|---|---|
|Dependent variable||mathematics|Portuguese|
||(1)|(2)|(3)|
|Panel A: Impacts|on fifth-grade variab|les||
|Dummy_Mais Educação_* year 2011|1.270|–6.231**|–5.135**|
||(1.482)|(2.651)|(2.317)|
|Dummy_Mais Educação_* year 2009|1.063|–7.433**|–6.195**|
||(1.391)|(3.003)|(2.654)|
|_Dummy Mais Educação_*_ME_student coverage|–0.00744|3.935|4.199|
||(0.930)|(3.021)|(2.701)|
|_R_ <sup>2</sup>|0.666|0.786|0.789|
|Observations|2,988|2,988|2,988|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Impacts|on ninth-grade varia|bles||
|Dummy_Mais Educação_* year 2011|0.948|–2.853|–1.009|
||(1.494)|(2.414)|(2.486)|
|Dummy_Mais Educação_* year 2009|1.275|–4.202*|–2.744|
||(1.474)|(2.510)|(2.682)|
|Dummy_Mais Educação_*_ME_student coverage|–1.855|–1.448|0.516|
||(1.495)|(2.589)|(2.903)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
58

# **Appendix Table. Total Administrative Costs as a Percent of Pension Plan Assets by Asset Size Class**

||Entire<br>Samnple (N)|No External<br>Managers (N)|With Extemal<br>Managers (N)|
|---|---|---|---|
|Asset Size Class ($Million)||||
|< 100|4.52% (40)|18.50 ( 6)|2.05 (34)|
|2100 to <1,000<br>|1.08% (50)|0.36 (12)|1.30 (38)|
|Ž1,000 to <10,000<br>|0.33% (48)|0.49 (17)|<br>0.25 (31)|
|210,000|0.10% (20)|0.06 ( 4)|<br>0.12 (16)|

Source: Computed from PENDAT data file.

Olivia S. Mitchell - Public Pension (Govemance and Perfornance