rafmacalaba/datause-encoder-data huggingface dataset:
# E OTHER INSURANCE PLANS

Some descriptive information was obtained on four other insurance plans. The findings are summarized in Table E.1 and described below.

Table E.1 Characteristics of Other insureace **Plns**

|,............................................<br>..............................................|..............................<br>REIEF<br>............................|NASIS|I<br>UPW|<br>SNHR|
|---|---|---|---|---|
|Region|**Kinfhasb**|Kivu|Notional|Rutshuru Hz<br>**(Shlaba?)**|
|Ruralt/Urban|Urban|**Rural**|**Painly**Urban|<br>**Rural**|
|**Mnagement**|**Zairian**PVO|**Health Zone**|<br>Zafrian**PVO**|<br>**SNHR**|
|Year Started|**19b6**|1988|1966|<br>?|
|Eligible<br>Population|**MM00**|214,240|<br>8**sillion**|**SNHR**.mbers<br>&fmilies|
|Nuwberof<br>Nf"rs|**roo**to**1000**|**7**|**few**|**20 fmilies**|
|Services<br>Covered|**All**hosp.&_ob._<br>incL.<br>referrels|Hospital<br>services|**Hospital**<br>& ambulatory|Hospital<br>services|
|**Premiu**|**500/mo.**sdult<br>_250/mo._<br>child<br>Discounted rates<br>for poorL**farmers**|150**Z/**year<br>|?|<br>**SDOZ/o.**|

**..** ,  ....... ........................ ..................................................

# 1. Reseau Medecins de Familles (REMEF)

REMEF is a direct insurance plan which functions like a ataff health maintenance otganization (HMO). Started in February 1986 by a Zairian doctor who had studied family medicine in the United States, the REMEF clinic is the base for medical staff who provide comprehensive health services and outreach activities to the families in the nearby communities. The REMEF has benefitted from some Canadian assistance and sponsors a private nursing school in addition to the polyclinic. The focus of the REMEF is maternal and child health services.

# Li. Terms of the Insurance Plan

The REMEF insurance plan covers all ambulatory and inpatient services, including drugs and laboratory, surgical interventions, and preventive care services. In May 1989 the polyclinic moved to its current location, where yive beds are available for hospitalizations. There is an additional maternity bed as well. If a patient requires referral to a secondary hospital or medical specialist, the cost of the referral and all subsequent services is assumed by the plan.

While no census exists, the size of the community served is assumed to be about 7,000, of whom 48
Table 3. Decomposition of Changes in Poverty Headcount

|||Bangladesh|, 2000 to 201|0||Peru, 2|004 to 2010||Thai|land, 200|0 to 2009|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Poverty Line**|US<br>$1.25|US<br>$2.50|National<br>Extreme|National<br>Moderate|US<br>$2.50|US<br>$4.00|National<br>Extreme|National<br>Moderate|US<br>$2.50|US<br>$4.00|National<br>Moderate|
|**Poverty rate**||||||||||||
|Initialperiod|57.7|89.2|34.5|49.1|22.9|45.8|17.4|49.1|7.9|31.4|23.9|
|Finalperiod|40.3|84.0|17.6|31.5|11.7|30.0|10.5|33.0|2.5|16.6|9.8|
|Change|-17.4|-5.2|-16.9|-17.6|-11.2|-15.8|-6.9|-16.2|-5.3|-14.8|-14.1|
|**Contributions to Poverty Reduction**||||||||||||
|**1. Shapely decomposition**||||||||||||
|Consumption-income ratio|-10|-14|-11|-12|15|10|23|11|-30|-27|-24|
|Adultpopulation|36|43|37|40|-5|1|-11|2|28|23|21|
|Income|61|119|53|53|88|90|137|111|126|117|107|
|Unexplained|13|-47|21|19|2|-1|-49|-23|-24|-13|-5|
|Total change|100|100|100|100|100|100|100<br>0|100|100|100|100|
|**2. Full Barros decomposition**||||||||||||
|Consumption-income ratio|-6|-23|-5|-6|0|0|-2|0|-31|-26|-24|
|Adultpopulation|36|46|35|38|-1|2|-4|2|30|23|21|
|Occupation share|8|12|8|9|22|17|34|18|-14|-10|-10|
|Labor Income|95|132|99|97|82|67|128|74|76|81|74|
|Capital|-7|-8|-7|-7|4|3|6|4|1|1|1|
|Transfers|6|23|4|7|19|16|30|17|52|37|34|
|Other Non-labor|-41|-47|-40|-44|-1|-1|-1|-1|4|2|3|
|Unexplained|8|-35|7|7|-24|-4|-91|-15|-18|-8|2|
|**Total change **|100|100|100|100|100|100|100|100|100|100|100|

<mark>Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh HIES 2000 -2010.</mark> Poverty rates are own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010. International poverty lines are in US$ PPP terms. National lines are those establishes by the respective national statistical offices.

30
3 Empirical strategy Guided by the theoretical relation in equation (2.6), our empirical strategy is implemented using the local projections method á la Jordà (2005), which has been widely applied in the empirical literature that examines the impact of government spending shocks on macroeconomic outcomes. In panel form, the estimable equation is as given as: ll h h h (3.1) = � pp pp gg ii,tt+h ss ii,tt−ss ii,tt ii tt ii,tt+h ΔII ρρ ΔII + αα<sup>h</sup> ΔII + ββ + δδ + εε where ≡ <u>p,i</u> +h <u>p,i</u> −1 and ss=1 ≡ <u>g,i</u> +h <u>g,i</u> −1 ~~.~~ The subscripts represents the i −1 i −1 pp II −II gg II −II country, ΔII the year, and ii,tt+h YY h the horizon, respectively. ΔIIii,tt−ss YY is private investment and ii is public investment and both are measured in constant local currency. We include a one lag term of pp,iitt gg,iitt tt II II private investment in our specification and it is captured by the parameter for horizon h≥0. The parameter of interest is , which captures simultaneously the structural (ll= 1) ρρ<sup>h</sup> relationship between private and public investment and the crowding-in (out) of private investment (that is, the contemporaneous and cumulative change over horizon αα<sup>h</sup> h in private investment because of a change in government investment). Here, notice that the sign of the estimated value of is governed by as highlighted in our simple model in section 2. Hence, a positive (negative) value of kkpp,kk implies that public and private investment are gg αα FF Edgeworth complements (substitutes) and that public investment crowds-in (out) private h ααh investment. The parameters and capture country- and year-effect for different horizons, respectively. The term represents the error term, which capture all other ii ii sources of variation in private investment changes ββ δδ h periods into the future. Finally, notice tt+h that equation (3.1) nests several scenarios. Specifically, when εε h= 0 and , the specification in (3.1) reduces to our baseline model in equation (2.1), and captures the ll= 0 impact multiplier. αα

# 3.1 Data

We use the IMF’s Investment and Capital Stock dataset which provides investment data for the general government and private sector. Investment data is based on gross fixed capital formation. The data is available for 109 economies. In line with equation (3.1), we convert nominal public and private investment in local currency to real using the domestic real GDP deflator. We take the annual changes in public and private investments, and then divide them by lagged real GDP from the IMF’s World Economic Outlook (WEO). One limitation of this dataset is that when public investment data is not available, it is assumed to grow by the same rate as private investment. This affects observations further back in history where data is sparse. The instrumental variable should ameliorate any concerns about the correlation this assumption imposes.

7
<!-- Start of picture text --> 101 schools. n= 13527<br>MALARIA CONTROL GROUP MALARIA INTERVENTION GROUP<br>Randomly selected: N=3487 Jan Randomly selected: N=3850<br>Refused/non response: N=76/643 2010 Refused/non response: N=65/781<br>Consented: N=2768 Consented: N=3004<br>SUBST<br>EPS NG ay RETA TET<br>5050.5 Clusters(6.9) [unavailableBaselinei education for survey: assessments N =0=0 (0(0 . 0%)] Feb [iunavailableBaseline educationfefor survey‘ yassessments : N=34 (1(1 .3% ,)] S4152.5Clusters(3.6)<br>(18, 58] N= 2523 (100%) 2010 N= 2676 (98.7%) (41, 58]<br>Class 1: N= 1222; Class 5: N=1301 Class 1: N= 1294; Class 5: N=1382<br>a eT<br>| Withdrawn: N=21 (0.8%); Deceased: N=0 (0.0%) |} " Withdrawn: N=36 (1.3%); Deceased: N=0 (0.0%) |<br>ee ee<br>50 Clusters [unavailable for survey: N=129 (5.1%)] Mar [unavailable for survey: N=274 (10.1%)] || <1 clusters<br>47.5 (6.9) Baseline health assessments 2010 Baseline health assessments 47.1 (6.1)<br>(16, 55] N= 2373 (94.1%) N= 2400 (88.6%) (26, 60]<br>Class 1: N= 1140 Class 5: N=1233 Class 1: N= 1160; Class 5: N=1240<br>Withdrawn: N=8 (0.3%); Deceased: N=1 (0.0%) Withdrawn: N=28 (1.0%); Deceased: N=0 (0.0%)<br>50 Clusters funavailable fefor survey:. 4 N=235 ((9.3%,M Oct [unavailableilabhfor survey N=248 ((9.2%, 51 Clusters<br>45.2 (7.0) FU1 educational assessments 2010 FU1 educational assessments 47.0 (4.1)<br>(16, 54] N= 2258 (89.5%) N= 2398 (88.5%) (36, 55]<br>Class 1: N= 1073; Class 5: N=1185 Class 1: N=1164 ; Class 5: N=1234<br>| Withdrawn: N=12 (0.5%); Deceased: N=3 (0.1%) i i Withdrawn: N=12 (0.4%); Deceased: N=3 (0.1%) i<br>50 Clusters [unavailable for survey: N=330 (13.1%)] Feb/ [unavailable for survey: N=333 (12.3%)] 51 Clusters<br>43.0 (6.6) FU1 health assessments Mar FU1 health assessments 45.1 (4.4)<br>(15, 53] N = 2148 (85.1%) 2011 N = 2298 (84.8%) [34, 55]<br>’ Class 1: N= 1039; Class 5: N=1109 Class 1: N= 1126; Class 5: N=1172<br>| — Withdrawn: N= 8 (0.3%); Deceased: N=2 (0.1%) + { Withdrawn: N=10 (0.4%); Deceased: N=2 (0.1%)<br>[ee |<br>50 Clusters [unavailable for survey: N=488 (17.8%)] | cope [unavailable for survey:. N=493 (18.2%)] 51 Clusters<br>39.6 (6.8) FU2 education assessments 2012 FU2 education assessments 41.7 (4.6)<br>(16, " 49] N= 1980 (78.5%) N= 2126 (78.5%) (35 55]<br>Class 1: N= 963; Class 5: N=1017 Class 1: N=1064 ; Class 5: N=1062 ’<br>Withdrawn: N=0 (0.0%); Deceased: N=0 (0.0%) | +Withdrawn:N=0 (0.0%);Deceased:N=0 (0.0%) |<br>50 Clusters funavailablefo for survey:y. N=441 ((17.5%,)] Mar | [unavailablejlablfor survey1 N=445 ((16.4%1] 51 Custer<br>40.5 (6.3) FU2 health assessments 2012 FU2 health assessments 42.6 (4.9)<br>Class 1: N=992 ; Class 5: N=1035 Class 1: N= 1091; Class 5: N=1083 :<br><!-- End of picture text -->
38 countries, however, the Japanese NTB coverage ratios are zero, or very low.

Table 9 provides a further perspective on OECD protection facing African exports by showing NTB trade coverage ratios for major product groups, both in total and for individual countries. These tabulations indicate NTBs on most African countries' food exports are considerably more important than those on manufactures (i.e., the coverage ratio for food is 23 percent versus 5.7 percent for manufactures). For some countries the food coverage ratios exceed 80 percent (Djibouti, Mauritius and Reunion) and reach 94 percent for the Seychelles. The importance of this point is highlighted by the fact that numerous studies have estimated that European Union and Japanese NTBs on many food products have ad valorem equivalents of 50 to 200 percent or more (see Laird and Yeats 1991 for a survey of these studies' findings or Table 12 in this report for pre- and post-Uruguay Round estimates).

In contrast to foods and feeds, only about three-tenths of one percent of African agricultural raw material exports encounter NTBs, while the ratio for the ores, minerals and metals group is under 6 percent -- about the same for manufactures. Mauritius and Malawi's coverage ratios for manufactures are exceptions, being 8 or more times the sub-Saharan African average. As previously noted, US textile quotas are a factor responsible for the Mauritius' results -- see Figure 3 for estimated ad valorem equivalents of these MFA restrictions. Import certification regulations are applied to ivory and reptile skin products exported from Malawi. Between 17 to 24 percent of Cameroon, Cote d'lvoire and Madagascar's manufactures exports encounter NTBs, but aside from these countries the coverage ratios are low. In fact, manufactures exports from 11 of the 44 countries encounter no NTBs, and 56 percent (25 out of 44) of the African countries have less than one percent of their manufactured exports facing nontariff measures.

Table 10 addresses the question of what types of nontariff measures do African exports most frequently encounter. Shown here are total 1992 OECD imports (both including and excluding fuels) from individual African countries and summary statistics showing African group totals. The table also indicates the share of this exchange that face six categories of nontariff measures: (i) all NTBs; (ii) directly price raising measures; (iii) quotas and prohibitions; (iv) "voluntary" export restraints; (v) non-automatic licensing requirements; and (vi) other restrictions. The notes to Table 10 indicate the specific types of measures included in each nontariff barrier group.
consumers pay in net consumer departments.<sup>16</sup> Transportation costs as a driver of price differences matters more for less processed products (e.g.: bananas, onions, potatoes) than for more processed ones (e.g.: wheat flour, cooking oil), as they explain a smaller share of the price variations of the latter group.

These findings can be visualized in figures (5), (6), (7), (8), (9), (10), (11), (12), and (13). For each product, the map on the left hand side (LHS) shows the geographical distribution of output of the commodity divided by the population. Each color corresponds to a given amount of output per capita in kilograms, being produced in the department, as indicated in the legend next to the map. The intensity of the color increases with the amount of output per capita produced in that department. The map on the right hand side (RHS) shows the distribution of price differences (in percentage points) with respect to the price in La Paz, which has been normalized to one. The legend shows the correspondence of the price difference with the color in the map. Darker colors are consistent with prices higher than in La Paz, and the converse is also true.<sup>17</sup> Take for example the map on the RHS of figure (5). The price of rice is about 15% lower in the net producer area of Santa Cruz than it is in the net consumer area of La Paz, and about 9% lower in Cochabamba than in La Paz. Santa Cruz accounts for 75% of Bolivian rice output (output of rice per capita is 115 kilograms), while only 5.2 and 6% of national output is produced in Cochabamba (11 kilograms per capita) and La Paz (7.7 kilograms per capita) respectively. Population differences between Cochabamba and La Paz (see table (14)), together with the fact that Cochabamba is about 400km closer to Santa Cruz than La Paz (see table (15)) explain that rice is cheaper in 16 Because we do not have data on consumption, we can only approximate whether a region is a surplus or deficit area by calculating the ratio of output to population, and taking the average as a benchmark.

17 Data on output are available for all Bolivian departments. Data on prices are only available for La Paz/El Alto, Cochabamba and Santa Cruz, therefore, all other regions are in grey in the RHS maps, indicating no data are available.

14
resulted in a total of 10,449 observations. The two stage sampling procedure adopted ensures that statistically reliable estimates of key demographic and health variables are available for all regions of Ethiopia.<sup>3</sup> The Ethiopia DHS used three questionnaires for the survey. The household questionnaire collects information on basic socioeconomic characteristics of all household members, such as the age, sex, marital status, education, and so on. It also includes information on the dwelling, such as the floor, ceiling, wall materials, and the number of rooms; the source of drinking water and the type of toilet facilities used by the household; as well as ownership of a variety of durable assets. The women’s questionnaire collects information on reproductive history; contraceptive use and knowledge; fertility preferences; antenatal, delivery, and postnatal care; infant feeding practices, child immunization and health; among other things. All eligible women (15-49 years of age) and children born after September 1994 were weighed and measured. Finally, the males’ questionnaire interviews eligible males (15-59 years of age) in every fifth household to collect information on the respondent’s background; contraceptive knowledge and use; marriage; fertility preferences; attitudes about family planning; and knowledge of HID/AIDS and STDs. Interviews were completed for 99 percent of occupied households, with no difference in the overall response rate for urban and rural eligible women. Rural men were slightly more likely than urban men to have completed an interview.

# **3. Malnutrition in Ethiopia**

The Ethiopia DHS data add to the vast and growing international database on demographic and health variables collected as a part of the MEASURE DHS+ project. These surveys have been carried out in 32 countries in Sub-Saharan Africa, facilitating the comparison of malnutrition indicators among low income countries in the region. In a wellnourished population, the distribution of children’s height and weight at a given age approximates a normal distribution. For comparison purposes, children’s height and weight

> 3 The sample population in Affar and Somali excluded the nomadic population and therefore some bias in the representativeness of the regional estimates for these two regions may occur. However, the population in these two regions relative to the country as a whole is small and it is unlikely the sampling strategy affects the reliability of the national and urban-rural estimates.

5
countries with the smallest installed capacity size (less than 1GW).

Table 9: Comparison of Forecast Errors across Installed Capacity Categories

|ItlldCit|5 year fo|recast horizon|10 year|forecast horizon|
|---|---|---|---|---|
|nsae apacy|sMAPE|MdRAE|sMAPE|MdRAE|
|less than 1GW|0.09|0.50|0.10|0.57|
|1GW-10GW|0.06|0.47|0.08|0.59|
|10GW-100GW|0.02|0.41|0.04|0.47|
|more than 100GW|0.03|0.34|0.04|0.23|

Table 10 shows the average sMAPE and MdRAE measures of forecast accuracy across energy intensity categories over the 5 and 10 year forecast horizons. Forecast accuracy is the highest for the most energy intensive countries (more than 12$/kgoe) with average sMAPE of 2 and 4 percent over the 5 and 10 year forecast horizons, respectively. Compared to other countries these errors are 2 to 3 times smaller over the 5 year forecast horizon and 1.5 to 2 times smaller over the 10 year forecast horizon. However, the more energy intensive countries also have the lower accuracy of forecasting methods relative to the Näıve model, at least for the shorter term forecast horizon. The value of MdRAE for the most energy intensive countries is 0.75 over the 5 year forecast horizon, which is twice as high as compared to the most energy efficient countries. To the contrary, the most energy efficient countries have the lowest forecast accuracy relative to the Näıve model over the 10 year forecast horizon, with MdRAE of 0.68.

Table 10: Comparison of Forecast Errors across Energy Intensity Categories

|EnerIntensit|5 year for|ecast horizon|10 year|forecast horizon|
|---|---|---|---|---|
|gy y|sMAPE|MdRAE|sMAPE|MdRAE|
|<3$/kgoe|0.04|0.34|0.09|0.68|
|3$/kg-6$/kgoe|0.06|0.41|0.08|0.47|
|6$/kg-9$/kgoe|0.07|0.43|0.09|0.56|
|9$/kg-12$/kgoe|0.06|0.50|0.06|0.46|
|>12$/kgoe|0.02|0.75|0.04|0.52|

16
**13**

Establishing the empirical validity of these linkages has been a ‘cottage industry since the early 1970s (Timmer, 2005). While the models adopted in this literature typically embrace both production and consumption linkages,<sup>10</sup> it is the latter that have been found to be more important. Delgado et al. (1998) concluded that for both Africa and Asia, consumption-based agricultural growth linkages are four to five times more important to growth than production-based linkages. For the linkage effects to be significant, four conditions must apply (Delgado, et al., 1998). First, agriculture must be a sufficiently large sector in employment terms for the income generating effects to be significant. Second, the income gains from agricultural growth must be reasonably widespread, so that effective demand for locally produced goods and services is raised. Third, the consumption patterns of people in agriculture must favor locally produced non-tradable goods. And finally, the non-agricultural (non-traded) sector must have to hand underutilized resources and appropriate institutional arrangements to be able to respond to the raised levels of demand coming from agricultural growth.

Using micro data on consumption patterns in five African countries, Delgado et al (1998) concluded that the farm sector in Africa is better able to propagate income growth than previously thought. They estimated (p. xii) that ‘adding US$1.00 of new farm income potentially increases total income in the local economy by an additional $1.88 in Burkina Faso, $1.48 in Zambia, $1.24 to $1.48 in two locations in Senegal, and $0.96 in Niger.’ Further corrected estimates to account for potential inelastic supply response of the non-tradable non-agricultural sector by the same authors, situate the agricultural multiplier effects around 1.6 for Asian countries and 1.1 for the African cases. This difference is ascribed to the labor-abundant nature of the Asian economies, facilitating a larger supply response of the Asian non-tradable sector. Similarly, using computable general equilibrium (CGE) models applied to archetype economies for Africa, Asia and Latin America de Janvry and Sadoulet (2002) find the employment and overall linkage effects from

> 10  Production linkages refer to purchases and sales of intermediate goods between the sectors.  Consumption linkages occur when the incomes generated by growth in one sector lead to increases in final demands for the good of other sectors and as a result also increase employment in that sector.