rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 6b: The Evolution of Access to Adequate Care<br>Prevalence of Adequacy in Care<br>84<br>61<br>59 59<br>55 55<br>52<br>50<br>37 36 38 36 39<br>28 28<br>24<br>BGD, HK BOL IDN NPL ZWE<br>BGD, IFPRI ETH KHM PER<br>Year 1 Year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010; Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010.<br>Note: (1) In Peru and Bolivia early initiation of breastfeeding is within 100 minutes, not within 60 minutes as in others.<br>(2) Bolivia 2003 does not include information on Vitamin A supplemenation.<br>(3) Bangladesh (HK, 2011) does not include information on vaccinations.<br>(4) Bangladesh (IFPRI) does not include information on early initiation of breastfeeding<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Children in resource‐poor households are more likely to have access to adequate care than children in resource‐rich households. Possibly contrary to expectation, in all countries except for Nepal, children in resource‐poor households were more likely to have access to adequate care than children in resource‐rich households (Figure 6c). That is, children from resource‐poor households did not have worse access to the components of adequate care that were measured. Although the differences in most cases were modest, in Peru 72% of the children in resource‐poor households were adequate in care whereas only 52% of children in resource‐rich households were adequate, a difference of twenty percentage points. In Bangladesh (IFPRI) and Bolivia the difference in adequate care was eight percentage points and for the rest the differences were six percentage points or less. Across the eight countries, children in resource poor households tended to be breastfed longer, but were less likely to receive complementary feeds during the ages of six to eight months (Annex 2). Overall, these findings further underscore the important role of nutrition knowledge regardless of socioeconomic status.

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**<mark>Table 24.  Debt and VF loans by household financial asset class, 2010</mark>**

|Value of household|Share of households|Household outstanding|% of households indicating|
|---|---|---|---|
|financial assets(‘000 baht)||debt(‘000 baht)|VF as source of first loan|
|None|<0.1|52|..|
|< 10|36.2|96|26|
|10 - 29.9|25.0|136|21|
|30 – 49.9|14.9|232|16|
|50 – 99.9|11.8|316|13|
|100 – 499.9|8.4|532|9|
|500 – 999.9|2.2|851|4|
|1,000 – 4,999.9|1.2|1,765|7|
|5,000 – 9,999.9|0.2|1,583|0|
|≥ 10 million baht|0.1|1,517|0|

_Source:_ Thailand Socio-Economic Survey 2010. Note: .. = sample size too small.

**<mark>Table 25.  Main sources of borrowing, 2010</mark>**

|Source of main loan|% breakdown<br>for all<br>households|Outstanding debt per<br>household (‘000 baht)|% breakdown for households<br>in the poorest expenditure<br>quintile|
|---|---|---|---|
|Commercial bank|3.5|839|0.6|
|BAAC|19.6|136|26.8|
|GSB|3.7|703|0.3|
|Other financial companies|11.7|185|4.6|
|Savings cooperatives / welfare orgs.|4.6|499|1.6|
|Village Fund|11.7|24|20.9|
|Persons outside the household|6.2|89|7.3|
|No loan|39.1||38.0|
|Memo: Overall(for those with debt)||221||

_Source:_ Thailand Socio-Economic Survey 2010.

proportion rises to 21% for households in the poorest quintile. This is despite the fact that VF loans are, on average, far smaller than loans from all the other main sources of credit, and make at best a relatively modest contribution to the overall availability of credit in Thailand (Menkhoff and Rungruxsirivorn 2011).

The data reject the use of the endogenous switching model for lending density, so the results in Table 26 are based on a linear model where non-significant variables have been removed; the estimated coefficients based on using all the variables are very similar (not reported here).

The results are unsurprising. A higher proportion of households borrow from the VF if the fund received more initial capital, borrows to on-lend, or is well managed (as proxied by accounting experience on the committee). Lending density is lower when the village is larger, as one would expect, given that the initial injection of a million baht was given to each village and ward, irrespective of population. Demand plays a role too; when there are relatively more farmers, lending density is higher. Interestingly, in areas where 36
<!-- Start of picture text --> Figure 7. The effect of real vs. placebo discoveries<br>SIPRI<br>3 4 2.5<br>3 2<br>2<br>1.5<br>2 E ffect of real<br>discoveries1<br>1<br>1 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 .6<br>COMTRADE<br>2.5 2<br>2<br>2 1.5 1.5<br>1.5<br>1 1<br>1<br>.5 .5 .5<br>0 0 0<br>-.5 0 .5 -.6 -.4 -.2 0 .2 .4 -.5 0 .5<br>Import share<br>2.5 3 2<br>2 1.5<br>2<br>1.5<br>1<br>1 1<br>.5 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4<br>GDP share<br>3 2.5 2<br>2 1.52 1.5<br>1 1 1<br>.5 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 .6 -.4 -.2 0 .2 .4 -.6 -.4 -.2 0 .2 .4<br>Effect of placebo discoveries Effect of placebo discoveries Effect of placebo discoveries<br>200 shuffles across country-years 200 shuffles within year 200 shuffles within country<br>Density<br>Density<br>Density<br>Density<br><!-- End of picture text --> Notes: The figures show the distributions of the effects of 200 placebo discoveries on arms imports based on SIPRI (row 1), COMTRADE (row 2), on the arms share of imports (row 3), and on the arms share of GDP (row 4). The 200 placebo discoveries are generated by shuffling the discovery variable across country-years (column 1), across countries within years (column 2), and across years within country (column 3). These figures are generated by estimating 200 regressions akin to that of columns 1, 3, 5, and 7 in Table 2, using the sample of only discovery countries. The vertical line shows the effect of real discoveries as reported in columns 1, 3, 5, and 7 in Table 2.

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_Figure 2: Assumed Timing and Level of Ambition of Unilateral and Cooperative Domestic Climate Policies (Inputs to ENVISAGE)_ <!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>2016 2020 2025 2030 2035 2040 2045 2050<br>Unilateral carbon price by leading LCT pioneers<br>Unilateral shadow price of carbon by following LCT pioneers<br>Cooperative carbon prices<br>$/tCO2<br><!-- End of picture text --> _Source:_ World Bank and Purdue University (ENVISAGE).

_Note:_ $/tCO2 = U.S. dollars per ton of carbon dioxide; LCT = low-carbon transition. The level of ambition of climate policy in each club is represented in GTAPENVISAGE as an economy-wide carbon tax with revenues returned as lump-sum transfers to households. The red and orange lines show the domestic carbon prices that the modeling suggests the respective LCT pioneers would need to impose to stabilize global warming at 2 degrees Celsius (with respect to the preindustrial era), given lack of cooperation from fossil fuel–dependent countries. The green line is the uniform carbon price that would achieve the same purpose through harmonized cooperative climate policies.

In subsets of unilateral policy scenarios, LCT pioneers apply alternative types of border adjustment taxes against noncooperating FFDCs. All climate policies are calibrated to result in a similar 2015-2050 carbon budget within the IPCC AR5 and other IAMs range consistent with 66 percent probability of meeting the 2°C goal of the Paris Agreement (IPCC 2014).<sup>3</sup> The following three versions of the unilateral policies of LCT pioneers are simulated:

- _Unilateral carbon taxes without any trade measures_ . In all unilateral climate policy scenarios FFDCs apply only the carbon prices that were already in place in 2017.

- _Unilateral carbon taxes with traditional border carbon adjustment (BCA)_ in the form of an import tax based on the carbon content of goods and services imported from nonparticipating FFDCs. Border tax rates are aligned with domestic prices of carbon applied by the LCT Pioneers.

- _Unilateral carbon taxes with a 10 percent ad valorem import tariff on all goods and services from non-cooperating FFDCs_ , irrespective of their carbon content, including imports of manufactured products and services, as well as energy commodities. Such form of a border tax was proposed by Nordhaus (2015).

Two cooperative (internationally harmonized) carbon taxes are simulated:

- _Cooperative traditional emissions- or consumption-based domestic carbon taxes_ , covering

> 3 As with all IPCC AR5 emission pathways (IPCC 2014b), the consistency of our scenarios with the 2°C goal ultimately depends upon continued and amplified policies beyond 2050, including negative net emissions at the global scale. Like most policy simulation models, we do not run our policy scenarios beyond 2050, because it would be too speculative. We also have not calibrated out the scenario for consistency with the 1.5°C goal.

8
stock market accounting for just over half of the total value traded in both periods. Corporations, pension funds, mutual funds, and brokerage firms account for 98 percent of this volume over the entire sample, with banks, insurance companies and other institutions representing a small fraction of the trading activity of domestic institutions. Consistent with international trends, there is a growing presence of foreign investors over time. The number of foreign investors nearly doubled from 1,440 to 2,830,<sup>6</sup> and the share of total traded value by these group increased from 3.8% in the first half of the sample to 19.1% in the second half. Figures 1 and 2 plot the time series of the share of traded value and share of market capitalization respectively by investor type. These figures also highlight the increasing importance of foreign investors in recent years.

According to Table 1-Panel B, the average number of trades by domestic institutions and foreign investors are 317 and 316 respectively. The distribution of trades amongst different institutions, however, is quite heterogeneous, with brokerage firms and mutual funds performing the largest number of trades on average. We restrict our analysis by considering only those investors who traded at least a 100 times over the 10 year period.<sup>7</sup> We collect stock and market data from Bloomberg, Datastream and the _Superintendencia Financiera de Colombia_ (Colombian Financial Superintendency, SFC). We obtain stocks returns, share price, trading volume, and bid-ask spread from Bloomberg and book value of equity, shares outstanding, and aggregate institutional ownership from Datastream and the SFC. The number of traded stocks fluctuated between 62 and 71 during our sample period.<sup>8</sup> In Table 2, we present basic descriptive statistics relating to the stock market during our sample period. According to the table, the performance of the Colombian market closely mimicked that of the Latin American region. The table is also indicative of increased participation by foreign investors in the CSE. Despite the large amount of outflows by foreigners during 2014 and 2015, accumulated foreign net flows are positive over the entire sample period.

> 6The corresponding numbers for domestic institutions were 6,834 and 10,344 respectively.

> 7We are aware that this cut might bias the sample to those investors who have been active in the market for a longer period. We performed our analysis by alternatively reducing the threshold to 50 trades and also for a higher threshold of 200 trades. These changes do not affect any our findings.

> 8In calculating these numbers, we treat ordinary and preferred shares issued by the same firm as different stocks.

7
measure is the political competition index produced by the Polity5 project (Center for Systemic <u>Peace 2020). This categorical index measures the degree of political competition in a given</u> country, independently of the political regime, and ranges from a value of 1 (meaning that political competition is suppressed) to a value of 10 (meaning that political competition is institutionalized and electoral). We use this measure as an additional test of the Accountability Hypothesis under the assumption that regimes where political competition is higher tend to be more accountable and responsive to citizens’ demands.

In testing Governance 2, we again turn to the WGI, which also provides a measure of government effectiveness (GE) across countries. To quote the WGI website, “Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.” (World <u>Bank 2022b). GE is a composite index of indicators of the quality, coverage, and citizen</u> satisfaction with public goods and services (roads, public transport, electricity, health and education services, water and sanitation, bureaucratic quality). Similarly to VA, the GE index is normalized to have mean zero and a standard deviation of unity.

For the Technology Hypothesis, we draw on data from the International Telecommunications Union (ITU) that focuses on the use of ICT.<sup>26</sup> We focus on two technologies, namely mobile phones and the internet. For the former, we use the number of mobile phone subscriptions per capita. For the latter, we use the percentage of the population using the internet. Both these measures are only available from the year 2000 and onwards.

While we have about 2,500 observations of the expenditure on SP by country and year, the covariates used to test the hypotheses laid out in Section 2 sometimes have a shorter time period and more limited geographical coverage. The subsample for which all variables except relative prices are available includes about 1,500 observations covering 107 countries. Relative prices are only available for three years (2005, 2011 and 2017) and therefore the subsample including them (in addition to the remaining covariates) is limited to 220 observations from 95 countries.

> 26 One might prefer to test this hypothesis with information on the use of ICT services by each country’s government, but there is no data source that provides enough time and geographical coverage on that dimension.

17
**Table 4: Percentage of valid submissions for urban and rural areas**

**Table 5: Percentage of missing values for food items in urban and rural areas**

|**Region**|**%**|**Region**|**Percentage **|
|---|---|---|---|
|Mogadishu (Urban)|99.9|Mogadishu (Urban)|54.8|
|North‐east Urban|99.6|North‐east Urban|58.4|
|North‐east Rural|100.0|North‐east Rural|61.2|
|North‐west Urban|99.2|North‐west Urban|58.2|
|North‐west Rural|100.0|North‐west Rural|61.2|
|Central regions Urban|99.0|Central regions Urban|57.9|
|Central regions Rural|97.0|Central regions Rural|58.3|
|Jubbaland Urban|99.3|Jubbaland Urban|49.8|
|Jubbaland Rural|94.6|Jubbaland Rural|49.1|
|South West Urban|98.6|South West Urban|57.5|
|South West Rural|98.1|South West Rural|56.5|

Source: Authors' calculations.

Source: Authors' calculations.

**Table 6: Number of flags in the cleaning of food items for urban and rural areas**

**Table 7: Items consumed by 10% more/less households in each region relative to the urban/rural average**

|**Region**|**Average number**<br>**per household**|**Region**|**Number of core**<br>**food items**|
|---|---|---|---|
|Mogadishu (Urban)|1.0|Mogadishu (Urban)|5|
|North‐east Urban|0.8|North‐east Urban|6|
|North‐east Rural|0.8|North‐east Rural|20|
|North‐west Urban|0.9|North‐west Urban|7|
|North‐west Rural|0.8|North‐west Rural|17|
|Central regions Urban|1.8|Central regions Urban|1|
|Central regions Rural|1.1|Central regions Rural|13|
|Jubbaland Urban|2.1|Jubbaland Urban|20|
|Jubbaland Rural|2.6|Jubbaland Rural|20|
|South West Urban|1.0|South West Urban|10|
|South West Rural|0.9|South West Rural|8|

Source: Authors' calculations.

Source: Authors' calculations.

While the validity rate of submissions was in line with other regions (Table 4), the consumption data were flagged as outliers more often than in other regions during the review and cleaning process (Table 6). Further, the profile of food consumption for households in urban Jubbaland was different than in other urban areas for 20 of 38 core food items (Table 7). These issues in the consumption modules led to inconsistent poverty rates. Therefore, the information on the consumption modules (food, non‐food and assets) was discarded and poverty estimated based on sociodemographic and other household characteristics in a multiple imputation process routine.

# Rural North‐East

The implementation of the survey also experienced some constraints in the recruitment of field teams in the rural North‐East regions. The access of some areas in this region is possible only for team members from certain clans. Thus, enumerators had to be selected and replaced based on this criterion. Some of these candidates might not otherwise have been selected given their performance during training, the pilot, and data collection. This was judged to have affected the quality especially of the consumption data collected.

23
is unresponsive to wage levels relative to other occupations and to wage growth prospects within teaching.

# **II. Indonesian context**

The overall teacher supply seems sufficient in Indonesia, with the pupil-teacher ratio declining steadily in recent years (Figure 1). The current pupil-teacher ratio is estimated at 19:1 for the primary level and 13:1 for the secondary level. These ratios clearly indicate that at an aggregated level, Indonesia does not have teacher shortage issues, unlike some other countries in the region, such as the Philippines or Cambodia. As a matter of fact, its pupil-teacher ratios are close to those of some developed countries such as Japan (20:1 at primary level, 13:1 at secondary level), or the US (15:1 at primary level and secondary level)<sup>2</sup> . Based on a school survey, the World Bank (2007) reports that a large proportion of Indonesian schools has an over-supply of teachers even according to the existing generous staffing norm. The report further points out that there is some space to improve the efficiency of the staffing norm. Based on the recommended formula, nearly 85 percent of the schools are over-staffed.

> 2 Edstats online database.

5
_Private Sector Approaches to Effective Family_<sup>_Planning_</sup> _48_

- Janowitz, B. S., J. H. Bratt, and D. B. Fried.<sup>1990. Investing in the Future: A Report on the Cost</sup> of Family Planning in the Year 2,000.<sup>Family Health International.</sup>

- Janowitz, B. S., M. Suazo, D. Fried, J. Bratt,<sup>and P. Bailey. 1991. Social Marketing in Honduras:</sup> Impact on Contraceptive Prevalence and Costs.<sup>Unpublished report, Family Health International.</sup>

- JSA Healthcare Corporation. 1991. TIPPS<sup>final r.</sup> .t. Draft report to A.I.D., March 1991.

- Kenney, G. 1990. Legal and Regulatory<sup>Reform. Prepared for the Options II Project. Washington,</sup> D.C.: Urban Institute.

- Kenney, G. and M. Lewis. 1989. Contraceptive<sup>Users' Sources of Supply. Unpublished paper.</sup> Washington, D.C.: Urban Institute.

- Korten, D. C. 1987. Third Generation<sup>NGO Strategies: A Key to People-Centred Development.</sup> _World Development,_ 15 (Autumn Supplement),<sup>145-159.</sup>

- Lande, C. 1991. Paying for Family Planning.<sup>_Population Reports,_Series J, Number 39.</sup>

- Lewis, M. 1991. The Role of the Private<sup>Sector in Providing Social Services in Brazil. Preliminary</sup> Draft. World Bank.

- Lewis, M., and 0. Kenney. 1988. The<sup>Private Sector and Family Planning in Developing</sup> Countries. Working Paper 96. Washington,<sup>D.C.: World Bank.</sup>

- Maher, S. M., C. R. Cisek, and V. S. Baird.<sup>1990. Lessons Learned in Contraceptive Social</sup> Marketing. Background paper for the World<sup>Bank. Washington, D.C.: The Futures Group.</sup>

- Martine, G. 1990. Population in Brazil: Agenda<sup>for a Country Strategy. Report prepared for the</sup> John D. and CatLarine T. MacArthur Foundation.

- Mwabu, G. 1986. Health Care Decisions<sup>at the Household Level: Results of a Rural Health Survey</sup> in Kenya. _Social Science and Medicine,_<sup>22(3), 315-319.</sup>

Nassim, J. 1991. NGOs in Family Planning.<sup>Draft report prepared for the World Bank.</sup>

- PATH. 1991. Current Trends in the<sup>Local Production of Contraceptives: Toward Meeting the</sup> Commodity Requirements of the Year<sup>2000. Seattle, Washington.</sup>

- PATH. 1990. Ptivate Sector Local<sup>Production of Contraceptives: Current Options for A.I.D.</sup> Seattle, Washington.

- Piotrow, P. T., K. A. Treiman, J. G. Rimon,<sup>S. H. Yun, B. V. Lozare, and R. C. Meyer. 1990.</sup> Strategies for Family Planning Information,<sup>Education, and Communication. Background paper</sup> for the World Bank. Baltimore, Md.:<sup>Johns Hopkins University.</sup>

- Rinehart, W., R. Blackburn, and S. H. Moore. 1987. Employment-Based Family Planning Programs. _Population Reports, 15,_ J921-J951.
<!-- Start of picture text --> Amazon<br><!-- End of picture text -->

# Transition

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# **Challenges**

_Connectivity with Rwanda and to some extent Burundi, however, remains limited._ Not only does Uganda have fewer flights to Rwanda and Burundi than to any other destination in East Africa, but the convenience and speed of air travel on these flights is significantly worse than on other routes due in part to long layovers on multi-stop flights (figure 14).

**Figure 14. Slow speed of service from Uganda to Rwanda and Burundi (kilometers per hour), 2007**

||Burundi|Ethiopia|D<br>Kenya|estinatio<br>Rwanda|n<br>Sudan|Tanzania|Uganda|
|---|---|---|---|---|---|---|---|
|Burundi||460|410|296|||512|
|Ethiopia|460||582|620|497|557|610|
|<br>Kenya|350|582||363|298|275|436|
|Origin<br>Rwanda|_16_|620|305|||590|**_73_**|
|Sudan||572|330||||513|
|Tanzania||662|_193_||||661|
|Uganda|256|610|440|_176_|332|307||

_Source:_ Bofinger 2009.

_Resource constraints in Uganda pose a threat to air safety_ . Challenges are not due to the lack of professionalism in the Ugandan Civil Aviation Authority, which is dedicated to oversight, correct policy, and a high standard of service, but rather due to the size of the country and the limited resources available. This has led to a number of safety deficiencies that have been highlighted in the 2009 International Civil Aviation Organization (ICAO) audit. Some of the issues found, such as poor search and rescue capabilities, as well as weak accident investigations and resources for flight checks and ramp inspections, are regional in nature, and will be addressed in due course with the recent formation of the East African Civil Aviation Authority, an entity set up in the EAC with support from, among others, the U.S. Safe Skies Over Africa Program.

24