rafmacalaba/datause-encoder-data huggingface dataset:
18 PFiure ;

# Resource Use, Growth and Bead Count

<!-- Start of picture text --> Latin American Archetype<br><!-- End of picture text --> <!-- Start of picture text --> African Archetype Latin American Archetype<br>Unemployment, Capacity Use Unemployment, Capacity Use<br>2.4<br>0 a ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~2.2<br>0 6<br>0 3<br>0.2- ,.6 -<br>0.I 0.4<br>2 3~~~~~~~~~~~~~~~~0<br>I 2 3 6 1 2 *S 6 7<br>0 AFBU * AFSU o AFBC AFSC 0 LABU LASU LABC LASC<br>GDP Growth (Index) GDP Growth (Index)<br>11~0<br>'09<br>in4<br>*02 95<br>* 2 3 4 5 6 7 7 3f 4 5 6 7<br>nAFS *AFB o LwAS * LA8<br>30.¶ Head Count Head Count<br>306~~~~~~~~~~~~~~~~~~~~~~~~9<br>30.2 .<br>30.1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1<br>304-31 , * 4<br>3903<br>20'<br>a AFS *AFB L AS *LAB<br><!-- End of picture text -->

**Notes: Time period on horizontal sisL(** shock; + no shockc)
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 11: Projected average replacement rates* for the stock of retirees** **<u>(reduced pension only) are decreasing over time</u>**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>gov ernment teachers<br>Replacement rate<br><!-- End of picture text -->

**Notes:** Average pension as % of the group average wage.  These replacement rates relate to reduced pension only.  If CPG is included, replacement rates are higher than full pension replacement rates due to the fact that CPG calculations are more than actuarially fair.  A 40 year career individual would retire with almost 100% of his/her final salary as full pension.  Total replacement rates based on average salary exceed 100%.

**Source** : WB calculations based on PROST

# **_IV.C Projected system expenditure_**

For the projections, pension expenditure is estimated on the basis of the provisions contained in CAP 286, irrespectively of potential delays in payment due to administrative or budgetary issues.<sup>12</sup> The projected pension expenditure in the future (under the assumption of no reform) is illustrated in Figure 12.

Under the assumption that life expectancy for the covered group is not different from the general population (variant 1 of the baseline scenario), total pension expenditure<sup>13</sup> for both sub-schemes is expected to increase from the current 1% of GDP to 1.9% around the year 2050, and further to 4.5% by the end of the simulation period. The steady growth of system pension expenditure relative to GDP is driven by several factors including assumed coverage expansion, wages growing more quickly than GDP per capita, and growing dependency rates.

If mortality rates for civil servant retirees are substantially lower (variant 2), clearly pension expenditure increases more quickly as the number of old age pensioners receiving benefits in each year increases. Under variant 2, the total system expenditure is projected to grow to 2.1% by 2050 and further 4.8% by 2075.

> 12 Again, ignoring arrears does not have an impact on our projections.  These are based on the reduced pension in the base year that is fully paid.

> 13 Including pensions to all beneficiaries, lump-sum payments to all beneficiaries, gratuities, etc.

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on average pay greater attention to transaction costs, and as such hold illiquid stocks for a longer period of time than less sophisticated investors do. In Column (2), we control for household specific effects using stratification and find consistent results.

In order to better interpret the economic significance of the results shown in Table 8, we assign each stock into liquidity deciles based on the adjusted Amihud ratio. We construct a dummy variable and assign a value of one to the decile with the most illiquid securities. In unreported results, we repeat the analyses in Panel A of Table 8 using decile values of the adjusted Amihud ratio and find that a stock in the highest illiquidity group in Finland is 40% less likely to be sold compared to a more liquid stock with similar firm and investor characteristics, which is comparable to the findings in the US.

These results serve as an out-of-sample test for our main findings in the US, verifying that our findings are not confined to one market and are not unique to one specific time period. More importantly, since the data set from Finland includes the complete transactions of all Finnish households between 1995 and 2003, our findings are generalizable to the full cross-section of households in Finland.

# **7. Conclusions**

This paper investigates how the trading decisions of 66,000 (303,000) households in the US (Finland) are influenced by transaction costs. It provides a direct link between investors’ holding periods and transaction costs and empirically verifies Amihud and Mendelson’s (1986) theory of liquidity clienteles. Three main conclusions follow from our analysis. First, we show that 35
showing, if anything, higher expected incomes for ballot losers living closer to those who didn’t take up their initial job offer.

Columns 3 and 4 of Table 7 then consider the effect on expectations about being employed if they migrate to New Zealand. Here we do find significant effects of distance. Ballot losers who live closer to any PAC migrants who didn’t take up their initial job offer expect lower likelihoods of being employed in New Zealand. Column 4 shows the size of the effect to be reasonably large. Individuals for which all PAC migrants within a 6 kilometer radius did not take up the initial job offer say they have a 19.6 percentage point lower likelihood of being employed. The constant term is 63%, and 65% if no controls other than the proportion of emigrants within 6km not taking up their initial job offer are used. Both of these are lower than the actual employment rate of 75% experienced by migrants in New Zealand, suggesting that feedback from migrants with bad experiences explains some, but not all of the low employment expectations. Overall, these results therefore suggest that proximity to the houses of migrants who did not take up their initial job offer lowers expectations about employment in New Zealand, but has no sizeable or significant effect on expected incomes conditional on working.

# **_Basing expectations on earlier cohorts_**

Potential migrants may also base their expectations on the experiences of earlier cohorts of Tongans migrating to New Zealand. Unemployment rates for Pacific Islanders in New Zealand have fallen sharply over the last decade, with the male unemployment rate falling from 15.2% in 1996 to 6.8% in 2005. Using the New Zealand Income Survey, we can look more closely at recent Tongan migrants in New Zealand (e.g. individuals who have lived in New Zealand for five or less years). Averaging over 1997-99, the percentage of 20-46 year olds employed was 44%, rising to 53% over 2001-03. While PAC migrants, as economic migrants coming with job offers, have higher employment rates than other Tongan migrants, who mostly come in through family reunification categories, the mean percent chance expected of employment in New Zealand of 57 percent is not inconsistent with employment expectations being based on the experiences of earlier migrants.

17
- The size of the vehicle fleet is important for the development of Motor and thus P&C insurance

- Life insurance development is held back by high inflation rates and low population densities and

- Government control of the insurance sector, limited legal rights, and certain cultural settings tend to be negatives for both Life and P&C.

**Table 2.1 Insurance Density: Additional Key Explanatory Variables**

|Variable|High<br>Inflation|Population<br>Size<sup>2</sup>|Population<br>Density|Legal<br>Rights|Private<br>Credit|Vehicle<br>Fleet<br>Size|Middle<br>Class<br>Size|Government<br>Control|
|---|---|---|---|---|---|---|---|---|
|Life|−ve|+ve|+ve|+ve|+ve|n.a.|+ve|−ve|
|Non-<br>Life|?|−ve|?|+ve|+ve|+ve|+ve|−ve|

_Source:_ Feyen, Lester, and de Rezende Rocha 20011. _Note:_ n.a. = not applicable.

# **The Link to Bancassurance**

One of the strongest positive predictive variables shown in table 2.1 is the development of private credit (that is, mortgage and personal loan markets). This is consistent with other studies that show a relationship between the development of the banking and insurance sectors, with causality possibly running in both directions (Arena 2008; Webb, Grace, and Skipper 2002). The exact nuance as to how and why Bancassurance is a strong driver in the effective development of insurance will become clearer as we conduct our analysis in more detail.

Before going further, it will be helpful to test whether the key effect of private credit development is driven by the role of the bank as an intermediator, or the broader role of institutional investors. Research shown in figure 2.2 shows the direct link with GDP per capita. Both bank intermediation (proxied by M2 [money and quasi money] as a ratio of GDP, shown by the red dots) and the role of institutional investors (assets under management as a ratio of GDP, shown by the blue dots) increase with income levels. However, institutional investors tend to become the major intermediaries between savings and investment only in the highest-income countries.

## **Figure 2.2 Development versus Income (PPP, GDP per Capita): Bank Deposits and Institutional Investment, 23 OECD Countries**

30
percentage points in urban areas. For urban areas, there are probable signs of a turnaround with a marginal recovery; the FLFP rate increased from 19.4 percent in 2009-10 to 20.5 percent in 201112. Based on these participation rates calculated from NSSO data, Table 4 shows that the estimated total FLFP for the age group of 15 years and above had increased from 116.9 million in 1993-94 to 148.0 million in 2004-05. Thereafter, it dropped to 128.8 million in 2011-12.

# 2.2 Trend

Comparing the two periods between 1993-94 to 2004-05 and 2004-05 to 2011-12, most of the drop in FLFP is found to have occurred during the second period, and it was most pronounced in rural areas (Table 4). In terms of change, our estimation suggests that there were an additional 31 million females in the labor force during the eleven-year period between 1993-94 and 2004-05. In contrast, during the later seven-year period between 2004-05 and 2011-12, there was a significant drop in the female labor force by 19.2 million individuals (Panel II, Table 4). Of the 31 million additional female workers during 1993-94 to 2004-05, 25.9 million had joined the labor force during the period 1999-2000 to 2004-05 (Panel I, Table 4, second row, and third column). Of the 19.2 million drop, 19.13 million female workers had left the labor force during the short period of 2004-05 to 2009-10 (Panel I, Table 4, fourth row, and third column).

**Table 4: Change in Male and Female Labor Force Participation Across Rounds**

||Ru<br>|ral<br>|Urban<br>|<br>|Tota<br>|l<br>|
|---|---|---|---|---|---|---|
|Panel I|Male|Female|Male|Female|Male|Female|
|1993-94 to 1999-00|13,643,608|3,865,132|12,046,864|1,272,055|25,690,472|5,137,186|
|1999-00 to 2004-05|19,997,430|18,487,154|15,608,202|7,442,918|35,605,632|25,930,072|
|2004-05 to 2009-10|14,392,954|(18,148,143)|11,477,387|(982,351)|25,870,341|(19,130,494)|
|2009-10 t o2011-12|3,756,646|(2,780,432)|5,011,517|2,750,767|8,768,163|(29,665)|
|Panel II|||||||
|1993-94 to 2004-05|33,641,038|22,352,286|27,655,066|8,714,973|61,296,104|31,067,258|
|2004-05 to 2011-12|18,149,600|(20,928,575)|16,488,904|1,768,416|34,638,504|(1,916,0159)|

Source: Authors’ own estimate based on NSSO and census data. Numbers within parentheses indicate the drop in FLFP.

8
that the costs of doing so are covered, some fixed fraction of receipts might be retained by that administration before any is remitted for the purposes of climate finance. The only new institutional structure needed would thus be that needed to deal with the governance issues raised above.

97. **One interesting option for collection of a maritime charge (or auctioning of allowances under an ETS) is the establishment of a central global fund that companies (ships) remit to directly without intervention of the tax administration—but this raises new questions** . For this approach, the IOPC Funds could be something of a model for collection and administration of the central fund, while the implementation and enforcement regime would follow the well-established regulatory IMO framework for safety and environment standards in shipping through flag and port State controls. The purpose of a new MBI treaty would be similar to IOPC as it will provide economic compensation for environmental damage, but is not directly comparable, as participation in the IOPC regime brings some benefit in the form of additional insurance for all participating States, while an MBI may only provide such benefits for developing countries. Enforcement powers of the kind associated uniquely with sovereign tax-raising authority may not be sufficient to enforce collection of the charge from ships operating outside national waters if this is not associated with an international legal framework. At a minimum, controls such as those associated with conditions for port entry may be more necessary with such an approach in order to provide all participants with the assurance that the charge is being properly paid by all.

98. **For shipping, the high concentration of emissions from a limited number of ships and current enforcement mechanisms (flag and port state controls) may be helpful for implementing carbon pricing on bunker fuels** . Around 80 percent of emissions from international shipping are produced by only around 17, 300 vessels (Table 5). Levying a tax only on fuel used by ships over some threshold tonnage would thus capture the bulk of environmental concerns while enabling control to be focused on only a portion of the fleet. While there could be some risk of distorting ship size, this would likely be modest if the threshold were set at a reasonable level. Countries concerned to assure themselves that tax has been collected elsewhere could expand their port entry requirements to include the provision of acceptable documentation, backed if need be by satellite monitoring of ships‘ prior movements.

**Table 5. Ship Sizes, Numbers, and Associated Emissions**

|**Ship size**<br>**threshold**<br>**(GT)**|**No. of ships**|**No. of ships as**<br>**percent of ships ≥**<br>**400 GT**|**Emissions (as percent**<br>**of emissions from ships**<br>**≥ 400 GT)**|
|---|---|---|---|
|≥ 400|42,697|100|100|
|≥ 500|39,180|92|99|
|≥ 1,000|34,866|82|98|
|≥ 2,000|30,138|71|96|
|≥ 4,000|24,267|57|91|
|≥ 5,000|22,311|52|89|
|≥ 10,000|17,346|41|81|

Source: IMO (2011).

48
tries that have volatile electricity demand and / or rely heavily on electricity imports (Albania, Benin, Botswana); or countries affected by major conflicts (Iraq, Libya, Syrian Arab Republic) or environmental disasters (Haiti).

Table 8 shows the average sMAPE and MdRAE measures of forecast accuracy across country income groups over the 5- and 10-year forecast horizons. It follows from Table 8 that electricity demand forecasts are less accurate for the lower income countries. For low income countries, the average sMAPE is 9 and 12 percent over the 5- and 10-year forecast horizons, respectively. These errors are twice as high compared to high-income countries. The accuracy of forecasting methods relative to the Näıve model is also considerably diminished for lower income countries. For low-income countries, the value of MdRAE is 0.79 over the 10-year forecast horizon, which indicates that the best performing method is just 21 percent more accurate than the Näıve model.

Table 8: Comparison of Forecast Errors across Income Groups

|I|5 year for|ecast horizon|10 year|forecast horizon|
|---|---|---|---|---|
|ncome|sMAPE|MdRAE|sMAPE|MdRAE|
|Low|0.09|0.49|0.12|0.79|
|Low-Middle|0.05|0.39|0.07|0.44|
|Upper-Middle|0.06|0.50|0.08|0.59|
|High|0.05|0.46|0.06|0.49|

Table 9 shows the average sMAPE and MdRAE measures of forecast accuracy across installed capacity categories over the 5 and 10 year forecast horizons. Forecast accuracy is the highest for the countries with large installed capacity and diminishes significantly as the size of the installed capacity falls. For countries with the largest installed capacity (over 100GW), the average sMAPE is 3 and 4 percent over the 5 and 10 year forecast horizons, respectively. These errors are twice as low as compared to countries with medium installed capacity size (1 to 10 GW). For countries with the smallest installed capacity (less than 1GW), electricity generation is particularly difficult to forecast, with the average sMAPE of 9 and 10 percent over the 5- and 10-year forecast horizons, respectively. The countries with large installed capacity also have higher accuracy of forecasting methods relative to the Näıve model. The value of MdRAE for countries with the largest installed capacity (over 100GW) is 0.34 and 0.23 over the 10-year forecast horizon, which is 1.5-2 times smaller as compared to 15
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While the stunting rate in Madagascar stands at 40%, our research has unveiled new policy relevant insights after appending the two surveys at the commune level. For instance, 17.9% of stunted children reside in non-poor households, suggesting that using household poverty levels as a targeting tool for identifying stunted children is ineffective. Similarly, 21.3% of non-stunted children live in impoverished households, underscoring that malnutrition is not solely a product of destitution, reinforcing Sen’s (1981) argument that famine or malnutrition is not the results of lack of food or poverty, but a result of entitlement failure.

The remainder of this paper is structured as follows: In the next section, we delve into the nutritional landscape of Madagascar, presenting key statistics on macronutrient and micronutrient consumption, as well as the distribution of caloric intake across the country. Section 3 provides a literature review to contextualize the paper's contributions. Section 4 outlines the data and methodology employed to implement the small area estimation approach for deriving nutrition and poverty indicators at the commune level. Our findings are presented in Section 5, and we conclude the paper in Section 6, offering policy recommendations.

# 2. Nutrition in Madagascar

## Caloric intake

Table 1 provides information on the distribution of calorie intake in rural and urban areas of Madagascar for the year 2021-22, as well as the overall figures for the country. The data is categorized based on per capita real consumption (PCER) deciles, which divide the population into 10 equal groups based on their calorie intake and poverty status. In rural areas, the lowest PCER decile (1st) has an average calorie intake of 707 kcal per person per day, while the highest decile (10th) has a significantly higher intake of 3,827 kcal. This suggests a substantial disparity in calorie consumption between the most impoverished and least impoverished individuals in rural settings. The trend shows a gradual increase in calorie intake across the deciles, with each decile having a higher average intake than the previous one.

**Table 1: Distribution of calorie intake (kcal/person/day) by per capita consumption deciles and urban/rural location, 2021-22**

|**PCER deciles**|**Rural**|**Urban**|**Madagascar**|
|---|---|---|---|
|1|707|669|696|
|2|1,120|1,051|1,098|
|3|1,380|1,211|1,330|
|4|1,596|1,415|1,535|
|5|1,793|1,540|1,700|
|6|1,983|1,709|1,868|
|7|2,203|1,776|2,010|
|8|2,487|2,046|2,253|
|9|2,859|2,309|2,543|
|10|3,827|3,451|3,572|
|Madagascar|2,112|2,227|2,167|

Source: Staff calculations based on Enquête Permanente Aupres des Menages 2021-2022.

3 | P a g e
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may consider in seeking to stimulate and sustain effective evaluation practice.

Much of this insight arises from the fact that the theory and practice of educational evaluation in the U.S. have changed considerably over the past twenty-five years, both in response to changes in the policy context for evaluation and as a result of dissatisfaction among the evaluation community with the approaches and methodologies with which the field began. The changes that have taken place have led to a clear focus on the generation of information useful for decision-making at the school and district level, and consequently, on achievement testing and on pragmatic and responsive evaluation designs. The evaluation practice that has emerged is thus oriented toward incremental improvements in education quality; large-scale researchevaluation studies seeking generalizable truths about education have largely disappeared. These changes bring current practice in the U.S. close to the kinds of evaluation that will be needed as developing countries turn their efforts tce:ards quality and efficiency through programs of educational change and improvement.

The role of the federal government in stimulating and developing evaluation practice has been crucial over the past twenty- five years. The structure of much of this support has been analogous to World Bank investment projects in that funds are supplied to States and localities to support interventions aimed at raising the quality of schooling. The history of this involvement yields insight into the possible effects of policy and financial incer. ives for evaluation. Importantly, as developing countries consider and in some cases initiate the decentralization of education management, lessons from the highly decentralized U.S. system become more relevant.

The discussion begins with an historical overview of the origins of educational evaluation in the U.S. Three major forces for changed practice are then discussed: the problem of utilization, the problem of methodologies, and the effects of changes in legislation and funding, including the rising importance of the accountability of schools for student performance.

# Origins of Educational Evaluation in the United States

Educational evaluation in the United States developed in response to the social and political circumstances that defined the arena within which it matured. As one writer recently noted, evaluation, "unlike many other sciences.. makes no distinction between applied and basic research. The real world of social programs is its only home; there is no laboratory for evaluation practice." (Cook, 1986; p. 194).

Evaluation was an accepted part of the American educational scene before the turn of the century. Horace Mann directed an evaluation of the Boston schools in 1845 and Joseph Rice, between 1887 and 1898, conducted a study involving 33,000 students on the effects of the teaching of spelling. Standardized testing became popular in the early 1900s in response to a greater emphasis on the efficient use of educational resources and increasing interest in the accreditation of educational institutions.