rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6: Regulation of Payout Options</u>**

||Free<br>Lump<br>Sums|Term<br>Annuities|Lifetime<br>PWs|Fixed<br>Nominal<br>Annuities|Fixed<br>Real<br>Annuities|Gtee &<br>Bonus<br>Annuities|Unit-<br>Linked<br>Annuities|
|---|---|---|---|---|---|---|---|
|Australia|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Chile|No*|No|Yes|No|Yes|No|Yes|
|Denmark:ATP|No|No|No|No|No|Yes|No|
|Denmark: Other|No*|Yes|No|Yes|No|Yes|Yes|
|Sweden:PPM|No|No|No|No|No|Yes|No|
|Sweden: Other|No|Yes|No|Yes|No|Yes|Yes|
|Switzerland|Yes|No|No|Yes|No|No|No|

* Restricted lump sums are permitted.

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).

Australia imposes no restrictions on payout options from the second pillar. Lump sums, term annuities, allocated annuities (which are similar to the Chilean phased withdrawals but can be based either on fixed terms or on remaining life expectancy) and various types of life annuities are all permitted and left to the choice of individual retirees. The public age pension is a life annuity that is indexed to wages.

In Chile, the basic choice until 2004 was among lifetime phased withdrawals (PWs), fixed real (inflation indexed) life annuities, and a combination of temporary lifetime PWs with deferred life annuities. Since 2004, retiring workers have been allowed to use a combination of a minimum pension fixed real annuity and either a phased withdrawal or a variable annuity. The PBS is a life annuity that is indexed to prices.

Lump sums are subject to tight restrictions. They are permitted for balances in excess of amounts required to provide specified pension benefits. Before 2004, the specified pension income was set at 50 percent of the retiree's average real earnings over the previous 10 years and 110 percent of the old minimum pension.<sup>6</sup> The 2004 amendments to the pension law raised these parameters to 70 and 150 percent respectively and also introduced a stricter definition of the average real wage, excluding periods of no contributions. The 2008 amendments to the pension law maintain the 70 percent of the real average wage requirement, but replaced the minimum pension requirement with an 80 percent of the PMAS requirement.<sup>7</sup> Workers meeting these conditions can opt for early retirement. This does not preclude them from continuing to work. Even workers retiring at the normal age of retirement are allowed to continue to work. This is therefore more a "withdrawal of AFP balances" rule than a "retirement" provision. However, the potential release of excess balances and access to two incomes explains the prevalence of early retirement and the close association between annuitization and early retirement. The tightening of retirement conditions in 2004 reduced the proportion of early retirees as a share of total retirees from 41 to 37 percent between 2004 and 2007.

> 6 The basic solidarity pension has replaced the minimum pension.

> 7 This 80 percent requirement becomes effective on 2012.

14
# **The Cost Structure of the Clean Development Mechanism**

## Shaikh M. Rahman, Donald F. Larson and Ariel Dinar

## **1 Introduction**

The Kyoto Protocol of the United Nations Framework Convention on Climate Change (UNFCCC) sets binding targets for the European Community and other industrialized countries (i.e., countries listed in Annex B of the Kyoto Protocol) for curbing anthropogenic greenhouse gas (GHG) emissions. While Annex B countries are committed to limit GHG emissions to pledged amounts primarily through national measures, the treaty offers three market-based mechanisms intended to lower the cost of mitigation: (1) Emissions Trading, (2) Joint Implementation (JI), and (3) the Clean Development Mechanism (CDM).<sup>1</sup> The JI and CDM are two project-based mechanisms that allow Annex B countries to meet their targets by sequestering GHGs or reducing GHGs emissions in other countries. While the JI mechanism enables the Annex B countries to carry out bilateral or multilateral emissions reduction projects among themselves, the CDM encourages investment in sustainable development projects that reduce emissions in developing countries.<sup>2</sup> The Emissions Trading (ET) allows Annex B countries to trade assigned amount units (AAUs) as well as credits generated by the project-based mechanisms among themselves.<sup>3</sup> In response to the CDM provision, a large number of emissions reduction projects have been initiated in different developing countries, which widely vary both in the type of abatement technology used and the size of operation. This paper examines the abatement cost structure of the CDM projects in the pipeline with the objective of assessing the cost-effectiveness of GHG reductions through the CDM

> 1 Annex B countries have accepted targets for limiting or reducing emissions. These targets are expressed as levels of allowed emissions, or “assigned amounts,” over the 2008-2012 commitment period. The allowed emissions are divided into “assigned amount units” (AAUs).

> 2 The JI and CDM are also intended to attract the private sector to contribute to mitigation efforts. According to the JI and CDM pipeline database, most of the projects are private initiatives (UNEP Risoe CDM/JI Pipeline Analysis and Database, 01 November 2008).

> 3 As set out in Article 17 of the Kyoto Protocol, Annex B countries with fewer emissions than permitted are allowed to sell the excess AAUs to the countries with more emissions than permitted.

2
in variable _x_<sup>26</sup> . The change in welfare or real income as a percentage of initial income in response to a change in the nominal effective exchange rate, can be divided into income and consumption effects. On the income side, household’s income will be affected by how wages respond to movements in the NEER. On the consumption side, the household’s budget exposure to goods that are tradable will determine the cost of devaluation<sup>27</sup> .

To compute the welfare measure, income and budget shares can be obtained from the household survey. What remains are to determine how wages and consumer prices respond to changes in the NEER. Assume that the pass-through coefficient is τ ∈ []1,0 and _E_<sup>~</sup> is the percentage increase in the nominal exchange rate (a devaluation), then the percentage change in price,<sup>~</sup> _p_ in response to a devaluation is ~ ~ _p_ = τ _E_ . If there exist some degree of public wage indexation captured by the parameter, β∈ []1,0 , then the percentage change in public sector wages, _w_<sup>~</sup> is given by, ~ ~ _w pub_ = β _p_

# **_5.2. Main Assumptions_**

For illustrative purposes, we present results for a nominal exchange rate devaluation of 30 percent and with no wage indexation. Subsequently, we will determine the expected real income loss to poor households in the population<sup>28</sup> under various exchange rate and wage indexation scenarios. To simulate the effect of a 30 percent nominal Djibouti franc devaluation, the following assumptions are made:

- (i) Tradable goods consist of food (excluding bread and crepe), tobacco, qhat, school supplies, clothing and medical drugs while non-tradable  goods and services are water and electric expenses, rent, health services and transportation;

- (ii) Assuming a long-run elasticity of 0.6 for tradable goods, a 30 percent nominal devaluation translates into an 18 percent increase for tradable prices. For sensitivity analysis, this exercise is repeated assuming 0.5 and 0.7 long-run NEER elasticity for tradable goods;

- (iii) Holding nominal wages fixed, the impact of a real devaluation is felt in terms of a decline in purchasing power or real income across households.

Three scenarios are considered, using a pass-through elasticity of 0.5, 0.6 and 0.7 for tradable goods which correspond to scenario one, two and three respectively. The higher the degree of exchange rate pass-through to domestic prices of tradable goods, the larger the resulting real income loss for the population of Djibouti.

own price elasticity for good i.

> 27 All services are assumed to be non-tradable.

> 28 The household survey used for the analysis is EDAM 1996, which surveys 2380 households from the sedentary population in 1996. The survey consists of a household and individual questionnaire. Questions are asked on household expenditures and migration and individual level income, education attainment and health status.

17
discrepancies between the treated and the control persisted. This imbalance on household characteristics may derive from the fact that randomization is at the mandal level and the small number of mandals in treatment and control areas (41 and 10, respectively).

# **2.3 SHG level evidence**

In each village, the 2004 survey also canvassed six randomly selected SHGs with a group level instrument that included retrospective information going back to 2001. The 2<sup>nd</sup> round revisited all SHGs included in the original sample, plus up to two newly formed groups per village.<sup>10</sup> Table 3 presents participation rates of program SHGs based on household-level data and summarizes SHG activities based on SHG-level data in treatment and control areas in 2001, 2003 and 2006, respectively. Note that 2001 is the start year of the program, 2003 is when the program had been active for three years in treated but had just started in control areas, and 2006 is when both had been exposed to the program for three or six years, respectively. The SHG survey includes program and non-program SHGs. However, most nonprogram SHGs converted to program SHGs by 2006. Overall participation rates point towards increased program coverage over time in both treatment and control areas, with a clear lag in the latter. In 2006, about 46% of households in the treated areas and 33% households in the control areas participated in program SHGs. Participation by the poorest and poor households is considerably larger than that of more affluent ones, in line with the program’s intention to target the poor.

In both areas, SHG numbers increased by about 60% from 2001 to 2003, pointing to possible spillovers. One notes clear improvements in the level of group activity and adherence to rules over time for SHGs in both control and treatment areas, with some lag in control mandals. In treatment areas, the share of groups that met at least monthly rose from 48% in 2001 to 71% in 2003, where it more or less leveled off. By comparison, there is very little change in meeting frequency for the control before 2003 (from 0.46 to 0.50) but a marked increase, to 79%, thereafter. Virtually all SHGs indicated that members made savings in meetings throughout the 2001 to 2006 period. The data also suggest that the program helped increase activity in areas such as insurance, nutrition, marketing, and training which are the responsibility of VOs and MSs. In 2003, interventions to reduce vulnerability (i.e. food credit, insurance and disability programs), to provide RCL, and to help access markets were implemented by 49%, 40%, and 10% of treated groups, with increases to 71%, 54%, and 22%, respectively, in 2006. Groups in the control areas lagged in such activities in both 2003 and 2006 although about 17% (vs. 22% in treated areas) implemented job training programs for SHG members.

> 10 Attrition was higher at the group than at the household level, with about 10% of program SHGs and 22% of non-program SHGs having disbanded between the two surveys. In SHGs that existed in both periods, 6% of households included in 2003 could no longer be found in 2006.

8
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Table 6 presents results from estimating full-sample models that address these two problems. The sample for these models included only WTP predictions from Round 2, which are assumed to be more accurate than predictions from Round 1. It included WTP predictions for both Plan A and Plan B, with a dummy variable used to control for the larger area protected under Plan A. PPP-adjusted GDP per capita was used as the measure of income in all the models; other measures, including regular GDP and GNI per capita and PPP-adjusted GNI per capita, yielded virtually identical results. The models also include dummy variables to control for regional differences in the timing of the surveys, the wording of the questionnaires, and the distances of the regions from the Amazon; Europe was the excluded region.<sup>21</sup>

**Table 6. Effect of income on mean and median WTP, with regional differences controlled by dummies.** Dependent variables: ln(1 + mean WTP) and ln(1 + median WTP). Observations: individual respondents in the models in the first two columns; countries in the models in the last two columns.

||Individual<br>|predictions<br>|Countr<br>|y means<br>|
|---|---|---|---|---|
|Variables|Mean|Median|Mean|Median|
|ln(GDP per capita, PPP)|0.707***|0.735***|0.905***|0.932***|
||(0.210)|(0.154)|(0.195)|(0.157)|
|Dummy: plan A|0.285***|0.293***|0.301***|0.317***|
||(0.0347)|(0.0367)|(0.0299)|(0.0315)|
|Dummy: Asia|0.0304|0.369|0.608|0.894**|
||(0.405)|(0.322)|(0.403)|(0.368)|
|Dummy: North America|0.575**|0.757**|0.535**|0.661***|
||(0.284)|(0.314)|(0.216)|(0.253)|
|Dummy: Oceania|-0.234|-0.298|-0.137|-0.141|
||(0.243)|(0.476)|(0.169)|(0.251)|
|Country FEs?|No|No|No|No|
|Weighted regression?|Yes|Yes|No|No|
|Income elasticity|0.729|0.775|0.940|0.996|
|P-value for H0: elasticity = 1|0.211|0.167|0.767|0.982|
|_R_<sup>2</sup>|0.359|0.283|0.612|0.611|
|_F_statistic|47.4|33.3|21.2|21.1|
|Observations|429|429|73|73|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors  are in parentheses and were estimated jointly for the pair of equations (Mean, Median), with clustering by country.

Weights: inverse to number of respondents from a given country.

21 A weaker control would be to include a regional dummy only for Europe, where the survey was conducted a year earlier than in the other regions. One benefit of doing this is to increase the income variation in the sample (e.g., Canada and the U.S. have similar income, whose effect is largely absorbed by the North America dummy). The income elasticity estimates change little, however, if we do this.

17
driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **7 Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme, which is 15.7% of the control mean. We also present the corresponding event study graph in Figure C3, which shows that this marginal increase persisted in the post-implementation periods. The estimated coefficients for time periods 1 and 2 after the policy are statistically significant at 10% and 15% levels, respectively. In period 3, the magnitude remains unchanged, though it is imprecise. These findings indicate that more women are moving from being out of the labor market to actively looking for jobs, although they are not more likely to find employment in this short-run period.

18
The Alkire-Santos MIP is a special case of the theoretical measure proposed in an elegant formulation of the problem by Alkire and Foster (2007). This fact helps in understanding the Alkire-Santos MIP, but does not really get us very far since the theoretical formulation in Alkire and Foster (in common with other papers in this literature) takes virtually all the elements of the measure as given (determined outside the measure), notably the dimensions of poverty, the dimension-specific cutoffs, the weights on deprivations and the minimum number of deprivations needed to be deemed “poor.” As we will see, the devil is in these details.

What dimensions? A key step in implementing any multidimensional measure is to select a set of dimensions. There is, of course, ample scope for debate here. There is a (rather poorlyunderstood) issue about what dimensions are intrinsically, versus instrumentally, important. For example, we can probably all agree that “health” is valued intrinsically, independently of command over commodities. However, it is more contentious that education has such an intrinsic value—as implicitly assumed by the Alkire-Santos MIP—rather than being (very) important to income and (hence) command over commodities (and health too). And even if we agree that education is to be valued intrinsically, it is far from clear that “education poverty” should have the same weight as “health poverty.” The data requirements of the Alkire-Foster index entail that relevant dimensions of poverty must invariably be left out in practice. Consider first the material goods entering the Alkire-Santos MIP. This is a rather narrow set of goods, leaving out a great many things that people do in fact consume. The consumption measure formed from a modern household budget or living-standards survey will aggregate (actual or imputed) expenditures on literarily 100’s of consumption items (1,000 or more items in some surveys). Yet the Alkire-Santos MIP only identifies six factors for “living standards” (as described above). So their measure leaves out a great many of the multiple dimensions poverty—indeed, their MIP has far fewer dimensions of living standards than those included in a standard (“unidimensional”) consumption-based measure.

Nor does the index appear to span the relevant “non-income” dimensions. In a blog comment, Duncan Green criticized the Alkire-Santos MIP for leaving out “conflict, personal security, domestic and social violence, issues of power/empowerment” and “intra-household dynamics.” 5
**Figure 2: World export share, BRICS vs. low-income countries**

<!-- Start of picture text --> 1991 1996 2001 2006 2011<br>Year<br>BRICS Low income<br>20<br>15<br>10<br>World export share (%) 5<br>0<br><!-- End of picture text --> _Source: World Development Indicators_ Note: Low-income are a sample of 28 countries for which data on exports are available between 1991 and 2011.

As economic growth is the main driver of poverty reduction (Dollar and Kraay 2002; Ravallion, 2001; Dollar et al., 2013), increased trade also tends to be associated with poverty reduction in the long run (Dollar and Kraay 2004). Increased trade should also boost shared prosperity, given that the elasticity of income to GDP for the poor is similar to that for the non-poor (Dollar et al., 2013). Several recent examples demonstrate the role of export growth in ‘poverty reduction miracles’ observed in East Asia, South East Asia, Botswana and Mauritius, inter alia.

These observations are consistent with the correlations summarized in Figure 3 that suggest that even among developing countries poverty levels are statistically higher in more closed economies (as measured by the trade-to-GDP ratio (regression A in Figure 3)).<sup>4</sup> This relation holds true also when relating changes in poverty to changes in trade openness within countries; that is, within a specific country (controlling for fixed effects), as trade openness rises, poverty declines (Figure 3, regression B). And it also applies to the level (regression D) and growth in income of the bottom 40 percent of the income distribution. However the correlation ceases to be statistically significant once we control for the general increase over time in both poverty reduction and trade openness (regressions C and E).While these results are not based on causal inference, they do support the idea that the direct relationship between trade, poverty reduction and shared prosperity in the short run is more complex than the long-run positive association between trade openness and poverty reduction identified in the literature.

> 4 These regressions utilize poverty data from the World Bank povcalnet (measuring poverty as the number of people below US$1.25 at international PPP 2005 prices) and from the World Bank World Development Indicators for exports and imports as a share of GDP.

4