rafmacalaba/datause-encoder-data huggingface dataset:
1981) the percentage was 52%. Ravallion and Chen find that with sustained economic growth, the incidence of relative poverty became less responsive to further growth. Thus the number of relatively poor rose in the developing world, just as the numbers of absolutely poor fell.

Equation (9) was calibrated to poverty lines for developing countries (Ravallion and Chen, 2009). If one extrapolates to rich countries it implies a poverty line with an elasticity close to unity; for example, for the richest country in Figure 1 (the US) the implied elasticity of the poverty line to mean consumption is 0.98.<sup>33</sup> Strongly relative lines only emerge as the limiting case for very rich countries.

# **5. Subjective poverty lines**

Just as richer countries tend to adopt more generous definitions of what it means to be “poor,” richer people within a given country tend to have higher personal “poverty lines.” Recognizing this fact explicitly leads to an interesting, and radically different, way of setting a national poverty line. This approach recognizes the inherent subjectivity and social specificity to any notion of “basic needs.” It also recognizes that there are many non-market goods that can influence individual welfare, but the value of which is not evident in demand behavior.

The essential idea is to ask individuals to rate their own welfare, or identify money metrics for pre-defined welfare levels. This provides new information for identifying a metric of welfare, including setting poverty lines. Typically, it is not one’s stated perception of ownwelfare that is taken to be the relevant metric, as this is bound to contain many idiosyncratic, psychological factors that one would not deem relevant. Rather, the subjective questions are used to calibrate an inter-personally comparable welfare function based on observed covariates deemed to be relevant on a priori grounds. In other words, subjective data are used to address the identification problem described in section 2. There is a large literature on subjective welfare. Here the focus is solely on their use in calibrating poverty lines.<sup>34</sup>

> 33 One could re-estimate the model using the full data set in Figure 1, but given that so many of the extra observations (for OECD countries) are strongly relative measures by design an elasticity close to one would no doubt re-surface mechanically.

> 34 Subjective data also offer a test of objective poverty lines, by regressing self-rated welfare on income normalized by the poverty line _plus_ the variables that went into the construction of the poverty line, which should be jointly insignificant if those lines accord with subjective welfare.  This approach is outlined in Ravallion and Lokshin (2002) and illustrated using Russia’s poverty lines.

20
to December) and then during the low malaria prevalence months. With the information available at hand, policymakers could do even better in targeting by using information from the previous year. They know which health facility areas/months had the highest level of malaria in the previous year and can target health facility areas in months according to their ordering the previous year.

The cell phone data allow for even more eective targeting. The piece of information currently not available to policymakers is the number of travelers entering an area from any given other district in the country. The cell phone data provide this information and combined with the incidence from 2012, it makes it possible to estimate which health facility areas are most at risk from travelers in certain months. This can be especially eective because travel choices are not random. There are often high movement corridors between certain communities in a country. Therefore, it is likely that there are pockets that will be more aected by travelers coming from high malaria areas than other parts of the district. By focusing on the pockets of areas most aected by travelers from high malaria settings, limited resources can be spent more eectively training health professionals and tracking travelers only in these areas.

Figure 6 demonstrates the full cost curves, from a strategy where only one health district area-month is treated, all the way up to all health district area-months being treated. The benet at each point is calculated based on the earlier model to calculate the total primary and secondary cases attributed to travelers. I assume that all travelers are targeted, but only 94% of them agree to take the test and contribute to the benet from targeting.<sup>27</sup> Depending on the resources available to the government, it is possible to determine for any given budget how many cases would be treated or averted depending on the targeting strategy chosen. Looking across the entire distribution in the top panel, the targeted strategy based on information from mobile phones is consistently the most cost- eective one. Across the whole distribution, the cell phone data based targeting policy performs 11.15% better on 27This is based on data from Richard Toll where 3,386 out of 3,609 targeted travelers agreed to be tested.

30
important result from Kanbur (1987) is that if decision makers wish to transfer resources in such a way as to minimize poverty summarized by the Foster-Greer-Thorbecke (FGT) class of poverty measures with parameter value _α>1,_ then on the margin the group with the higher FGT<sup>_α-1_</sup> should be targeted. In other words, if the government wishes to minimize the squared poverty gap (equal to a poverty measure from the FGT class with _α_ =2), then geographic regions should be ranked by the poverty gap (FGT with _α_ =1) and lump-sum transfers made until the poverty gap of the poorest region becomes equal to that in the next poorest region, and then transfers to these two regions should be continued until their poverty gap is equal to the next poorest region, and so on, until the budget is exhausted.

Let _ych_ denote the per capita expenditure of household _h_ (with _m_ members) living in group _c_ . Assume that the government is able to provide lump-sum supports _ac_ that differ across groups _c_ . Thus, after-support expenditures are _ych_ + _ac_ . Suppose the government wishes to minimize expected FGT2 after transfers subject to the constraint that total transfers are limited by the budget _S_ : subject to ( _r_ ) The _r_<sup>_th_</sup> simulated expenditure of household _h_ in _c_ is indicated by _ych_ . The (linear) _Avg_ [ ] operator indicates taking the average over simulations r=1,....,R (see below). 1( _x_ ) is an indicator function, 1(TRUE)=1, 1(FALSE)=0. Standard applications of the Kuhn-Tucker conditions leads to first-order conditions for optimal _ac_ : 14
of the ELMPS is the panel component, which allows for a better modeling of transitions into different labor market states, as it follows the same workers across time.<sup>8</sup> Table 1.a: Labor states in FY18 (males)

||%|Obs.|
|---|---|---|
|Agriculture|13.2|4,764|
|Industry - Informal|14.3|5,161|
|Industry - Formal|5.8|2,081|
|Services - Informal|16.6|6,002|
|Services - Formal|18.0|6,515|
|Not working|32.1|11,603|
|N||36,126|

Table 1.b: Labor states in FY18 (females)

||%|Obs.|
|---|---|---|
|Informal|10|3,524|
|Formal|8|2,944|
|Not working|82|29,282|
|N||35,750|

Source : Authors’ calculations using HIECS 2017/18. Note : Unweighted percentages.

Using the panel data in ELMPS, a Mincer type multinomial logit regression for adults aged 15 and older was estimated to create a propensity score with the likelihood that an individual with a given set of observable characteristics (e.g., age, education, household size and demographics, location) would stay or transition to a different labor market state (Annex B). This propensity is then calculated for individuals in HIECS and used to remap them to match counterfactual FY20 states inferred from the LFS. This analysis was done separately by gender. Table 2 presents the two-year transition matrix used in the model, building on the LFS trends and ELMPS transition probabilities by gender to update the HIECS. About 18 percent of the sample of working-age males and 8 percent of the sample of workingage females are reallocated across states. Overall, the main transitions that needed to be modeled relate to stopping work and becoming informal. The adjustment also considers demographic changes by raking HIECS weights based on population estimates disaggregated by age categories and gender, as the working age population is growing.<sup>9</sup> Once the labor market states are updated, labor income is adjusted to reflect earnings growth over the period. Incomes are updated based on the nominal growth rate of their destination (FY20) income source over two years. The income growth rates are computed by labor market state, gender, and age group (ages 15-24, 25-34, 35 and above).

> 8 Attrition in the ELMS is about 20 percent over the period 2012-2018. 9 Population projections were obtained from the UN Population Prospects.

7
# **_2.2 Replacement Rates and Pension Eligibility_**

When it was introduced in 1908, the age pension was designed to ensure that an individual would live in modest comfort: this still remains the aim. However, as early as the mid-1970s, reform proposals began to emerge which sought to shift the emphasis for retirement income policy away from poverty alleviation towards income maintenance through compulsory superannuation. This shift towards greater financial independence has been given further impetus because of the projected fiscal costs of the ageing population which have seen a number of government initiatives to encourage independence through deferral of age pension take-up and higher superannuation contributions.

Replacement rates in Australia are very much at the low end of the OECD average. But the system was never designed as a comprehensive social income system. The OECD estimates that, when combined with other benefits and particular taxation arrangements in place for age pensioners then the gross replacement rate provided by the first pillar in Australia is 40 percent and the net replacement rate is 52 percent for a pensioner whose pre-retirement income was at the average level. As a result of the means test, the replacement rate for a pensioner earning half the average income before retirement was estimated to be 65 percent on a gross basis and 77 percent on a net basis. For a pensioner earning twice the average before retirement, the estimate is 26 percent gross and 37 percent net.<sup>6</sup> The Australian government has not set an explicit replacement rate target for Australia’s retirement income system. However, the former Senate Committee on Superannuation noted that there was a strong consensus among superannuation industry representatives that an adequate retirement income was between 60 and 65 percent of pre-retirement gross income.

Analysis undertaken by the Treasury’s Retirement Income Modeling Unit (RIM) indicates that current policy based on the basic age pension and mandatory and voluntary private savings will deliver substantially higher replacement rates in Australia over the longer term (Figure 2). RIM calculates replacement rates based on a comparison of potential net expenditure before and after retirement.<sup>7</sup> This includes income from all investments, all private pension payments and the age pension, and drawdowns from capital less any tax payments. As shown in Figure 2 RIM projects that aggregate adequacy rises from around 50 percent at present to around 80 percent and to higher levels for people from higher income deciles. The latter reflects contributions above the SG and additional private savings made by higher income groups.

The effect of the means testing is to limit the eligibility of those of pensionable age to the full pension providing instead either a partial pension or no pension at all. Figure 3 shows that the trends over the past decade have seen the number of retirees who are eligible to receive the age pension rising, although those receiving the full age pension have been falling as a proportion of the total.

> 6 Source: OECD (2005).

> 7 Rothman, G. 2007.

10
are significantly different from zero. Reaching Completion Point is however significantly related to an increase in infant mortality among urban mothers and an insignificant decrease among rural mothers.

The Decision Point results indicate that the positive effects from debt relief come when countries enter the program and receives interim debt relief (usually in the form of partly or fully canceled debtservice payments). At Decision Point, the country’s commitment to reform and pro-development policies in exchange for promised assistance out of a heavy debt burden from the IMF, the World Bank, and other multi- and bilateral creditors is finalized. However, there are no positive effects from reaching Completion Point where creditors agree to deliver full, irrevocable debt relief. This may suggest that the external scrutiny and focus on pro-development reforms play an important role for child health in HIPCs.

These heterogeneous effects indicate that policies introduced in relation to Decision Point are effective in improving infant health among poor and rural mothers. The results are in accordance with intentions of the health and rural development policies introduced in the country-specific PRSPs which to various degrees emphasize these groups as beneficiaries of pro-development policies in connection with debt relief under the HIPC Initiative.

My findings are in line with the few earlier results on debt relief at Decision Point under the HIPC Initiative and infant mortality (see Schmid, 2009). The negative relationship between debt relief at Decision Point and infant mortality from the within-mother estimations (and to some extent also the within-country with mother-specific characteristics) suggests that problems with confounding factors, demographic composition, and unobservables are less likely to be driving the results in this analysis in contrast to earlier findings. I look further into problems with potential confounding factors in the sensitivity analyses in the next section (Section 4.2).

# **4.2 Sensitivity Analyses**

The DHS data and the information on HIPC status (which month debt relief at Decision Point and Completion Point is granted, see Table 1) allow for a more detailed analysis of the relationship between debt relief and infant mortality. In a sensitivity test, I thus exploit this monthly variation and the results that are presented in column (2) in Table 9 show that the month of Decision Point is associated with a 0.57 percentage point reduction in infant mortality. This is in line with the baseline findings in Section 4.

Columns (3), (4), and (5) in Table 9 show that the findings on debt relief at Decision Point are stable to including the two main debt relief variables in the same regression, a variable for the MDRI<sup>7</sup> , and

> 7The MDRI dummy equals one in the year 2006 for non-HIPCs Cambodia and Tajikistan and HIPCs which reached

12
(Haushofer et al. [2021]). On the curative care side, the subsidy significantly increases the use of oral rehydration treatment for children acutely ill with diarrhea.

The effects on preventive and curative care behaviors by parents are equal or greater in the full program group in almost every aspect of care seeking. In families receiving both interventions, caretakers are more knowledgeable about ORS and self-report higher water disinfectant use. Moreover, formal care is increased significantly in the 0-2 age group, while ORT use is significantly higher among 2-4 year olds. Overall, the findings suggest better targeting and use of care in the group receiving both interventions, although with the caveat that the sample size of 102 children with diarrhea is small. This is consistent with the results on formal care seeking in the 2013 survey, which show that the CHW complement the subsidy in increasing medically needed care for the youngest children.

The two interventions seem to work particularly well together for the use of ORT, likely due to the fact that ORT use is promoted by both CHW and formal health care providers. We saw that the subsidy-only group significantly more often receive ORT than the control. They are also 9.7% more likely than the control to know the recipe for ORS (Table 3, column (4)). For both receipt of ORT and ORS knowledge, effects are largest for households that received both CHW visits and the subsidy.

Overall, the interventions are successful at changing parents’ care giving behavior, and we do not find any evidence of moral hazard on the part of the parents, in the sense that households for example reduce prevention in response to subsidized care. If anything, both preventive knowledge and behaviors and curative care are most improved in the full-program group.

Likely as a result, we find that the full program group has significantly lower diarrhea incidence than the other groups, driven by the youngest children, who are also the most vulnerable group, with the highest incidence in the control. The number of days with diarrhea is reduced by over 70% of the control group incidence for the sample as a whole, and by over 90% in the 0-2 age group. While the reduced incidence implies gains in children’s 27
<!-- Start of picture text --> N<br>Nae<br>2<br>oO<br>®<br>><br>£2<br>2<br>5S<br>c<br>L<br>Bm<br>&<br>$<br><<br>== All countries<br>foe) —————— Central Europe and Baltic Countries<br>————— Northern Europe<br>—————__ Southern Europe<br>~M —————_ Western Europe<br>1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->
-21-

TABLE 1 Demands for Finanial Aa&egs

# eel uai

||Somalia|Zaire|
|---|---|---|
|a|-.52|-.53|
||(1.85)|(1.68)|

# Adjustment Eauation

||Somalia|Zaire|
|---|---|---|
|a|.66<br>(2.09)||
|bO|-.47<br>(2.34)|-.46<br>(2.6)|
|bl|.54<br>(2.28)||
|c|.88<br>(3.2)|.23<br>(.83)|
|6|-.49<br>(2.6)|-.35<br>(1.84)|
|R2|.62|.58|

III.3.c. Moderate Inflatin in Sub-SahAr The case of country that has experienced episodes of moderately high inflation is found in Nigeria. The time series data is represented in Figure
improvement as was the case in education. Information on the distribution of performance based on all available past changes and based on the information conditioned on characteristics relevant for the different police districts can help inform those negotiations.

For example, homicide data may be available for all police districts in a state (as was the case for Minas Gerais). The target that may be negotiated between the principal and the agent may be for improvement in homicides at the state level, but if the target is to be met, it will be up to individual police districts to improve the outcomes in each one of their districts. It is often the case that a sector will set targets not only at a high administrative level but also for sub state administrative units. Setting the same state-level target for each sub state administrative unit implies greater or less demands on performance improvement, given that the police districts are starting from different initial conditions. Comparing the targets to some of the changes that have taken place in the recent past may help provide some check on the reasonableness of the performance improvements which are being sought.

**Figure 6  – Unconditional relative performance on homicide reduction from 2000-2007, by Police Regions**

<!-- Start of picture text --> Trend 2000/2008<br>Barbacena<br>Trend 2004/2008 100<br>Vespasiano Belo Horizonte<br>Target 2008/2009<br>80<br>Unaí Contagem<br>60<br>Uberlândia 40 Curvelo<br>20<br>Uberaba 0 Divinópolis<br>Teófilo Otoni Governador Valadares<br>Patos de Minas Ipatinga<br>Montes Claros Juiz de Fora<br>Lavras<br><!-- End of picture text --> Source: Author’s calculations using data from Peixoto et al (2010) Figure 6 represents a simple way to present comparison for many police districts simultaneously. This type of figure is useful when the number of districts is limited, but would (obviously) not be useful for 15
Social intermediation services can help surmount some of these barriers – though by far not all – as demonstrated by several examples across the world. Two such interventions have been in implementation in Chile and Colombia for many years, and provide lessons on the experience of social intermediation in reaching and benefitting the extreme poor, design elements for program success, and challenges in implementation.

# **3. Design and implementation of** **_Chile Solidario_**

The 1990s was a good decade for poverty reduction in Chile, thanks to a combination of growth-oriented economic policies, improvements in the quality of education and health, and a set of new social policies focused on the poor. The faster pace of poverty reduction occurred between 1990 and 1996, where poverty fell from 38.6% to 23.2%.

Thereafter, however, the rate of poverty reduction slowed down, declining 1.5 percentage points every two years, and extreme poverty stagnating in the 2000s at around 5.6%. Policy makers began questioning why policies had been working for the poor, but not for the extreme poor. An assessment (MIDEPLAN 2000) was therefore conducted to better understand the situation and recommend improvements for existing social policies aimed to better serve the extreme poor population. The main findings of this assessment were the following:

- a. _Social policies were successful in targeting poor families, but not the poorest._ Extreme poor families had proportionately fewer benefits than poor families – the benefits were concentrated in the upper side of the poverty distribution.

- b. _Social programs were based on explicit demand._ People had to apply for social benefits, by going to a government office and formally requesting enrollment.

- c. _The information available about social benefits did not reach the poorest population._ In general, processes for applying to social benefits were complex and required the applicant to manage a lot of information, not always in a language or in a format that facilitated proper understanding.

- d. _Extreme poor families were excluded from social and community networks,_ and were isolated even within their own communities. This was the case particularly of extremely poor women, who had low self-esteem and exhibited in many cases symptoms of depression.

9
Table 3. Summary Statistics of the Monte Carlo Analysis testing the Importance of Measurement Error (based on 100 iterations)

||Mean|Std. Dev.|Mininum|Maximum|
|---|---|---|---|---|
|Model 1|||||
|quake propensity|-0.00365|0.00011|-0.00397|-0.00339|
||0.00104|0.00002|0.00096|0.00109|
|Model 2|||||
|propensity in developing countries|-0.00302|0.00010|-0.00330|-0.00268|
||0.00105|0.00003|0.00098|0.00116|
|propensity in developed countries|-0.00886|0.00015|-0.00938|-0.00853|
||0.00143|0.00002|0.00139|0.00149|
|Model 3|||||
|propensity in autocracies|-0.00293|0.00015|-0.00351|-0.00257|
||0.00165|0.00010|0.00132|0.00205|
|propensity in democracies|-0.00445|0.00014|-0.00490|-0.00384|
||0.00152|0.00005|0.00135|0.00177|
|Model 4|||||
|propensity in autocracies|0.00699|0.00055|0.00512|0.00887|
|w. low institutionalization|0.00669|0.00037|0.00492|0.00748|
|propensity in autocracies|-0.00353|0.00011|-0.00380|-0.00322|
|w. high institutionalization|0.00116|0.00004|0.00102|0.00125|
|propensity in young democracies|-0.00630|0.00018|-0.00697|-0.00561|
||0.00171|0.00003|0.00161|0.00177|
|propensity in old democracies|-0.00905|0.00011|-0.00933|-0.00878|
||0.00097|0.00002|0.00093|0.00102|
|Model 5|||||
|propensity in corrupt countries|-0.00143|0.00025|-0.00214|-0.00082|
||0.00290|0.00010|0.00261|0.00317|
|propensity in non-corrupt countries|-0.00675|0.00023|-0.00747|-0.00603|
||0.00216|0.00006|0.00200|0.00230|

35