rafmacalaba/datause-encoder-data huggingface dataset:
Figure 5 shows our estimates of the level of annuitization in different countries. There is 100 percent annuitization with universal coverage from the public pillars, including in the case of Denmark and Sweden the supplementary public ATP and PPM schemes and in the case of Chile the PBS. The level of public benefits is between 30 and 40 percent of average earnings in Denmark, Sweden and Switzerland. In Australia, it is lower because only half of pensioners receive the full age pension. And in Chile it is around 17 percent for recipients of the PBS.

**Figure 5: Level of Life Annuitization**

<!-- Start of picture text --> Public Pensions Private Pillar<br>120<br>100<br>80<br>60<br>100 100 100 100 100<br>40 80<br>70<br>50<br>20<br>30<br>10<br>0<br>Australia Chile Denmark Sweden Switzerland<br><!-- End of picture text --> Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Despite the imposition of mandatory or quasi-mandatory participation in funded second pillars, the level of annuitization of accumulated savings in the second pillar varies considerably across the five countries. This mostly reflects differences in the menu of permitted payout options, while the menu itself reflects the level of benefits from the public pillars (which take the form of pensions for life and are thus equivalent to full annuitization). In addition to the menu of permitted payouts, the level of annuitization also reflects other factors such as risk aversion, clawback provisions, tax incentives, and the terms and conditions of annuities.

The estimated level of annuitization from the second pillar is highest in Switzerland at 80 percent, followed by Chile at 70 percent. It is very low in Australia at less than 10 percent. It is probably 50 percent in Denmark and less than 30 percent in Sweden.

24
|_Vai_|: value added|
|---|---|
|_CIIi_|: Total intermediate consumption|
|_CIi_|: Total intermediate consumption excluding energy|
|_CIEi_|: Total energy intermediate consumption|
|_Fueli_|: Total fuel input|
|_Energi_|: Non fuel energy input|
|_FFueli_|: Fossil fuel input|
|_BFueli_|: Biofuel input|
|_Exe_|: exports|
|_Di_|: Interior supply for domestic production|
|_Dii,j_|: Intermediate consumption of non energy inputs|
|_Diti_|: Total demand of intermediate inputs|
|_Ldnqi _|: Non qualified labor demand|
|_Ldqi _|: Qualified labor demand|
|_Ldi_|: Composite labor demand|
|_Kdti_|: Composite capital of land and regular capital|
|_Landi_|: Demand for land|
|_Kdag_<br>|: Capital demand|
|_w_<sup>_1_</sup>|: qualified wage|
|_w_<sup>_2_</sup>|: non qualified wage|
|_rm_|: sectorial price of capital|
|_rr_|: price of agricultural capital|
|_ptag_|: price of land|
|_pkti_|: composite price of land and capital|
|_piii _|_:_ <sup>price of total intermediate consumption excluding energy</sup>|
|_pcii _|_:_ <sup>price of total intermediate consumption</sup>|
|_pcei _|_:_ <sup>price of total energy composite input</sup>|
|_pcai _|_:_ <sup>price of composite fuel input</sup>|
|_wi _|: sectorial composite wage|
|_Ymh_|: household income|
|_Smh_|: household savings|
|_Ydm_|: household disposable income|
|_Ye_|: revenu des entreprises|
|_Se_|: épargne des entreprises|
|_Tim_|: indirect taxes|
|_Timim_|: import duties|
|_Tiee_|: export taxes|
|_Tdh_|: household income tax|
|_Tde_|: firms income tax|
|_Yg_|: government revenues|
|_Sg_|: government savings|
|_Ch,i_|: household expenditure matrix|
|_Ctmh_|: discretionary expenditure of household|
|_Invi_|: investissement demand|
|_Cgi_|: sectoriel government consumption|
|_Pee_|: domestic price of exports|

31
# Table 2 (cont’d): Recent <u>CGE Models related to water issues</u>

|**Model**|**Investment**|**Factors of Production**|**Assumptions about Factors**|**Sectoral Detail**|**Household Detail**|**Coverage**<br>**and**<br>**Regional**<br>**Detail**|
|---|---|---|---|---|---|---|
|**Seung****_et. al.,_2000**|Endogenously<br>determined<br>dynamically by capital stock,<br>sectoralprofits and return rate|Labor,Capital,Land.|Labor:<br>perfectly<br>across<br>regions;<br>partially<br>mobile<br>across sectors<br>Capital: immobile<br>Investable Funds: Perfect|8 Sectors, 3 agricultural, 5<br>Other includingfinance,|3 Households: Low, middle,<br>high income|<br>1 Region, North Nevada,<br>USA|
|**Goodman, 2000**|Determined by capital growth<br>and depreciation|Land,Labor,Capital,Water|Labor:<br>perfectly<br>mobile,<br>capital is sector specific land<br>and water is usage specific,|4 Sectors, irrigated and non-<br>irrigated agriculture, services<br>and manufacturing|2 Rural and Urban|South-Eastern<br>Colorado,<br>USA|
|**Peterson****_et. al.,_ 2004**|Investment<br>demand<br>is<br>determined exogenously|Land,Labor,Capital,Water|Labor is fully mobile within<br>regions,<br>partially<br>mobile<br>among regions, Land and<br>capital are mobile at varying<br>degrees|48 Sectors|1 Type|20<br>Regions<br>in<br>Sothern<br>Murray Darling basin of<br>Australia|
|**Dwyer****_et. al.,_2005**||net gains are greatest, and the<br>costs to industries and regions<br>are generally more dissipated,<br>when trade is unconstrained.<br>When regions with relatively<br>low<br>levels<br>of<br>water<br>consumption face shortfalls in<br>water availability and trade<br>with regions that use large<br>volumes of water they have<br>little effect on traded prices<br>andquantities.|||||
|**Tirado****_et. al.,_2004**<br>**Tirado ****_et. al., _2006**|Exogenous|Labor, capital, land, water and<br>seawater|Labor is perfectly mobile<br>Land and capital is mobile<br>amongagriculture sectors.|10 Sectors, 3 agricultural<br>(irrigated and non-irrigated,<br>livestock), 2 water sectors:<br>traditional and desalinization|1 Type|Balearic Islands,Spain<br>water efficiency measures do<br>not<br>reduce<br>economic<br>pressures<br>on<br>water<br>ecosystems.|

42
products. Fast track goods were to have their tariffs reduced to the same level by 2000. By 2003, the average CEPT tariff rate for products in the IL will be about 2.7 percent, compared to 12.76 percent in 1993 when the reduction program was initiated. Average CEPT tariff rates for the years from 1998 to 2003 are shown in Table 3.3.

Table 3.3

|||**AverageC **|**EPTTariff**|**Rates**|||
|---|---|---|---|---|---|---|
||1998|1999|2000|2001|2002|2003|
|Bruneil|1.35|1.29|**1.00**|0.97|0.94|0.87|
|Indonesia|7.04|5.85|4.97|4.63|4.20|3.71|
|Lao PDR|**5.00**|5.00|**5.00**|5.00|**5.00**|**5.00**|
|**Malaysia**|**3.58**|**3.17**|**2.73**|**2.54**|**2.38**|**2.06**|
|**Myanmar**|**4.47**|**4.45**|**4.38**|**3.32**|**3.31**|**3.19**|
|**Philippines**|**7.96**|**7.00**|**5.59**|**5.07**|**4.80**|**3.75**|
|**Singapore**|**0.00**|**0.00**|**0.00**|**0.00**|**0.00**|**0.00**|
|**Thailand**|**10.56**|**9.75**|**7.40**|**7.36**|**6.02**|**4.64**|
|**Vietnam**|**6.06**|**3.78**|**3.30**|**2.90**|**2.89**|**2.02**|
|**ASEAN**|**_**<br>**5.37**|**4.77**|**3.87**|**3.65**|**3.25**|**2.68**|
|**NOTE:Camb**|<br>**odianotinclu**|**dedinorigina**|**ltable.**||||
|Source:ASEA|NSecretariat|as**cited**at|<http:llwww.u|s-asean.org/|afta.htm>||

Products can be excluded from the CEPT by placing them on one of the other three lists. The TEL is for items for which tariffs will eventually be reduced to 0-5 percent but are temporarily protected by delaying the reduction. Items on the SL are unprocessed agricultural products, which were added to the CEPT in 1994 and are due for tariff reduction by 2010. The addition of these products was considered a very important step, as it was an indication of liberalization in the area of agricultural trade, nearly always difficult to accomplish. Only the GEL items (which satisfy Article XX of the GATT) may be permanently excluded from tariff reductions for very specific reasons. The 2001 package of tariff cuts under the CEPT includes 55,680 tariff lines in the IL (84.7 percent of total tariff lines), 8,660 lines in the TEL (13.4 percent of total), 829 lines in the GEL (1.3 percent of total), and 360 lines in the SL (0.6 percent of total). The long-term goal of ASEAN is to have zero tariff rates on nearly all imports by 2010 for the original signatories, and by 2015 for the four newer members.<sup>74</sup> AFTA's CEPT scheme is considered a significant improvement over ASEAN's earlier PTAs. For example, in the CEPT, concessions are granted on a reciprocal product-by-product basis, meaning that a member country can receive concessions from partners only for items the country itself is liberalizing. Members are thus encouraged to maximize the number of tariff lines in their liberalization schemes. 1<sup>75</sup> The PTAs were open-ended (no timeframe), the choice of preference items was voluntary with the designation of "sensitive" items left up to individual countries, and the reduction of non-tariff barriers (NTBs), although included, was not rigorously pursued. Contrarily, the CEPT has a definite timeframe for completion, SL and GEL items are subject to specific definitions and restrictions, and countries must eliminate all 52
related in addition to a control set, to the lagged endogenous variable so that one can capture mean reverting forces. The need to work with the first difference of the real exchange rate in turn implies that it is probably more appropriate to work with transformations of the original explanatory variable set. For example, rather than working with the levels of the terms of trade we work with their changes. Similarly, rather than working with GDP per capita we work with GDP growth rates.

A third difference with respect to Amuedo-Dorantes and Pozo (2004) is that we explore whether the results are robust to excluding GDP growth from the specification. The reason for this is that if remittances affect per capita growth and growth in turn affects the real exchange rate either _à la_ Balassa-Samuelson or as in portfolio models, then econometric models that control for the evolution of income levels will not capture the full impact of remittances on the exchange rate (i.e. these models will produce results that are biased).

Fourth, our variable of interest is the changes in the remittances to GDP ratio whereas Amuedo-Dorantes and Pozo (2004) use remittances per capita. We believe that our choice is more appropriate in this context since it is likely to better capture the importance of remittances flows relative to the size of the economy (see also the annex). Finally, we also explore the extent to which the results for the real exchange rate are driven by changes in fundamentals and changes in the disequilibrium level. In this regard, we also present the results of a regression model that has as dependent variable an index of overvaluation based on Dollar (1992). To better understand the idea here, consider the following decomposition of the real exchange rate: where the bar on top of the variable indicates that an equilibrium value and the hat indicates a disequilibrium value. Then our strategy is based on the following regression: Note that same simple manipulations of (1), (3) and (4) yield so that from those regressions it is possible to recover the impact of remittances on the real equilibrium exchange rate.

# _Econometric issues_

One problem that has to be faced before proceeding with the results of the estimation is the potential reverse causality from the real exchange rate to the remittances. Rajan and Subramanian (2005) note that countries that had overvalued exchange rates in the early 1990s received significantly lower remittances during the rest of the decade, and argue that it is plausible that if emigrants perceive an overvalued exchange rate, they may switch to sending goods directly. Similarly, emigrants can find more attractive sending remittances home following devaluation. In this regard, the expected positive causal relationship from remittances 14
_Fig. 2_ Development indicators: Africa compared<sup>with all developing countries</sup>

|_Country group_|_Per capita_<br>_income_<br>_1982_<br>_(dollars)_|_Adult_<br>_literacy_<br>_1980_<br>_(percent)_|_Life_<br>_expectancy_<br>_1982_<br>_(years)_|_Primary-school_<br>_enrollment ratio, female_<br>_1981_<br>_(percent)_|
|---|---|---|---|---|
|Sub-Saharan Africa|||||
|Low-income|249|38|49|57<br>|
|Middle-income|777|35|50|70|
|All low-income countries|280|52|59|81<br>|
|All middle-income countries|1,520|65|60|95|

_Note:_ Averages are weighted by 1982 population. a. Number of females enrolled in primary school<sup>as a percentage of all females of primary-school age.</sup> _Source._ The World Bank (1984). World Development<sup>Report1984:Ref-p.</sup> 162.
The next step is to create a single time series of harmonized learning outcomes (HLOs) for each country, combining HLOs from different testing programs in a way that balances coverage and comparability over time. This is done in three main steps:

- <u>International Testing Programs:  Data from TIMSS and PIRLS are combined into a single testing</u> program, recognizing that both tests are carried out by the same parent organization, the International Association for the Evaluation of Educational Achievement (IEA), and use common units.  TIMSS assessments have been carried out in 2003, 2007, 2011 and 2015, and PIRLS in 2001, 2006, 2011, and 2016.  PIRLS assessments are shifted to the nearest TIMSS year, and then a combined TIMSS/PIRLS score is created by averaging the two (for countries participating in both), and otherwise using whichever of the two is available.  TIMSS/PIRLS and PISA scores are chosen by taking whichever of the two is available for each country‐year, and the average of the two in 2003 and 2015 (when the two testing programs were both carried out in the same year, for the set of common countries).  The end result is that the HLO data as used in the HCI has 175 country‐year observations between 2000 and 2017 based on TIMSS/PIRLS, 278 country‐year observations based on PISA, and 68 country‐year observations based on an average of PISA and TIMSS/PIRLS.

- <u>Regional Testing Programs (LLECE, SACMEQ, PASEC).  Scores from these programs are used in</u> the combined HLO measure only for those country‐year observations where no data from international testing programs is available.  This adds 25 country‐year observations from LLECE, 37 from SACMEQ, and 30 from PASEC.  Six country‐year observations from LLECE and one observation from PASEC are dropped because the occur in country‐years for which an international test is available.

- <u>Early Grade Reading Assessment (EGRA).  The HLO database contains data from 75 EGRA</u> assessments carried out in 48 low‐ and middle‐income countries.   There are 27 countries where EGRA is the only available international test, and so for these countries the HLO simply is based on the EGRA assessment.  The remaining 22 countries also have data from one of the major international and/or regional testing programs.  For these countries, EGRA data are included in the time series of test scores for the country only if (a) the additional test substantially expanded the time series coverage of the combined HLO, and (b) the additional test did not

30
Table (G8) Impacts of the Program on Food Security

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|N meals yesterday|0.08|(0.10)|[0.71]|2.03|(0.71)|1,101|
|Food diversity|0.65**|(0.31)|[0.30]|8.12|(1.17)|296|
|Days last week FR went hungry|-0.13|(0.14)|[0.71]|0.28|(0.90)|1,102|
|Days last week adults went hungry|0.03|(0.11)|[1.00]|0.18|(0.70)|1,018|
|Days last week children went hungry|-0.16|(0.14)|[0.68]|0.22|(0.75)|707|
|women go hungry more often than men (SD)|0.09|(0.13)|[0.72]|0.04|(1.02)|962|
|girls go hungry more often than boys (SD)|-0.37*|(0.21)|[0.30]|0.04|(1.02)|425|
|elderly go hungry more often than young (SD)|-0.02|(0.28)|[1.00]|0.09|(1.06)|135|
|Reduced coping|-1.68*|(0.86)|[0.30]|3.10|(6.91)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: N means yesterday reports the number of meals the respondent ate yesterday. “How many meals did you eat yesterday? Tea alone is not to be considered as a meal.” Food diversity reports the number of food categories out of 9 the respondent ate from in the past 12 months. Days last week FR/adults/children went hungry reports the number of nights in the past 7 days the respondent/adults in household/children in household went to bed hungry. Women/girls/elderly go hungry more often than men/boys/young reports agreement with the statement as a standardized 5-point likert scale. Reduced coping is the reduced Coping Strategies Index, where larger numbers correspond to more food insecurity.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 8 in the pre-analysis plan.

46
Women and children in low income countries are the most vulnerable to HAP related health impacts as 60% of all premature deaths attributed to HAP occur in women and children (WHO, 2016). This is because, women and children get higher level of exposure to HAP due to their greater involvement in daily cooking and other domestic activities. In terms of geographic distribution of HAP mortality, regions with higher poverty incidence where traditional biomass (wood fuel, animal waste and agriculture residues) are the primary fuels for cooking and home heating, suffer the most (Figure 7). South-east Asia and the Western Pacific account for almost 80% of the global mortality due to HAP. India is the single country that suffers the highest from HAP with 1.3 million premature deaths each year from HAP.

# **Figure 7: Geographical Distribution of Mortality from Household Indoor Air Pollution in 2012**

<!-- Start of picture text --> 1,690<br>1,620 Total deaths due to HAP in 012 in the world<br>4.3 Million<br>581.3<br>200.8<br>117.2<br>81.3<br>South & East Western Pacific Africa Eastern Europe The Americas<br>Asia Mediterranean<br><!-- End of picture text --> Source: WHO (2016) Besides air pollution, activities in energy supply chains impact human health through water and soil contamination by release of the toxic substances. Coal mines pose risks to human health even after the mining operation is over as rainwater in abandoned mines reacts with exposed rock and oxidizes metal sulfide minerals, thereby releasing iron, aluminum, cadmium, and copper into the surrounding water system which could be a source of drinking water (Lashof et al. 2007). Coal washing forms slurry of polymer chemicals, which is injected into old mine shafts that can release 22
<!-- Start of picture text --> Figure 4.4. Sector export growth by scenario<br>(% pt. deviation from base)<br>13<br>12<br>11<br>10<br>9<br>8 Agriculture<br>7<br>6 Mining<br>5<br>Manufacturing<br>4<br>3 Private services<br>2<br>1<br>0<br>-1<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text -->

**Figure 4.5. Headcount poverty rate in 2022 and by scenario in 2030 (%)**

<!-- Start of picture text --> 80<br>74.4 74.4<br>75<br>72.0<br>70.0 69.8<br>70<br>66.0 65.8<br>65 62.4<br>60<br>55<br>50<br>2022 base limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text --> 30
# Contents

|1|Intr|oduction|3|
|---|---|---|---|
|2|Ma|cro-RHG Models and Poverty Analysis|5|
|3|Lin|king Income Survey Data with Macro-RHG Models|6|
||3.1|A Simple Micro-Accounting Method . . . . . . . . . . . . . . .|7|
||3.2|An Extension with Reweighting Techniques . . . . . . . . . . .|10|
||3.3|The Use of Distribution Functions . . . . . . . . . . . . . . . .|12|
|||3.3.1<br>Methodology<br>. . . . . . . . . . . . . . . . . . . . . . .|12|
|||3.3.2<br>Estimation Results and Goodness of Fit<br>. . . . . . . .|14|
|4|The|Macro-RHG Framework|15|
||4.1|Production and the Labor Market . . . . . . . . . . . . . . . .|16|
||4.2|Composition of Demand and Prices . . . . . . . . . . . . . . .|18|
||4.3|Profts and Income . . . . . . . . . . . . . . . . . . . . . . . .|19|
||4.4|Investment-Savings Balance . . . . . . . . . . . . . . . . . . .|19|
||4.5|Public Sector and the Balance of Payments . . . . . . . . . . .|20|
|5|Co|mparing Policy Shocks with Alternative Linkages|20|
||5.1|Reduction in the Minimum Wage . . . . . . . . . . . . . . . .|21|
||5.2|Increase in Employment Subsidies . . . . . . . . . . . . . . . .|26|
|6|Con|clusions|30|
|Ap|pen|dix A: Reweighting when using Real Survey Data|36|
|Ap|pen<br>Me|dix B: Estimated Shape Parameters and Fitted Poverty<br>asures with Beta Distribution Functions|38|

2
Figure A9: Specification Charts for Deviations in Days with No Rain <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.02 .04<br>.01 .02<br>0 0<br>* Weather Product * -.01 * Weather Product * -.02<br>TAMSAT -.02 TAMSAT -.04<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.06 .02<br>.04 .01<br>.02 0<br>0 -.01<br>* Weather Product * -.02-.04 * Weather Product * -.02-.03<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.005 .04<br>0 .02<br>-.005 0<br>-.01<br>* Weather Product TAMSAT* -.015 * Weather Product TAMSAT* -.02-.04<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

57