rafmacalaba/datause-encoder-data huggingface dataset:
is higher, 83 percent, if pre-assistance consumption is used. Poverty is also extremely elevated (around 70 percent) among host communities who live within 5km of a refugee camp (nearby hosts) respectively within a 15 km radius (distant hosts). It is much higher than poverty for rural Chadians (50 percent) or the average Chadian, 42 percent, though these numbers are still concerningly high. It illustrates two points: (i) Chad is a country with limited economic means and (ii) Sudanese refugees are hosted in areas with limited economic potential.

**Figure 7: Poverty and poverty gap for existing refugees from Sudan and host communities**

<!-- Start of picture text --> 100<br>88<br>90 80<br>80 71 70<br>70<br>60 50<br>46<br>50 42<br>40 32<br>27 27<br>30<br>20 15 13<br>10<br>0<br>Poverty Poverty gap<br>Sudanese refugees Sudanese refugees, net of assistance<br>Nearby hosts Distant hosts<br>Rural Chadians Average Chadian<br>Percent<br><!-- End of picture text --> Source: Staff calculations and World Bank 2021.

Consideration of the poverty gap, also shown in Figure 7, informs about the shortfall of consumption relative to the poverty line. Thus, a poverty gap of 0.32 suggests Sudanese refugees fall on average about a third of the poverty line short on consumption. Given that 80 percent of refugees are poor, poor refugees have consumption levels that are on average 40 percent (0.32/ 0.80) below the national poverty line of FCFA 684 per person per day. Without assistance, the depth of poverty would be even worse. It would be 46 percent on average and 52 percent for refugees who are poor (0.46/0.88).

In combination, poverty incidence and the poverty gap inform about the importance of aid relative to consumption financed by own resources. Some 12 percent of Sudanese refugees generate sufficient resources to have pre-assistance consumption levels which fall above the poverty line, while 88 percent do not. Refugees experience an average shortfall in consumption (relative to the poverty line) of 32 percent, receive 14 percent in assistance (46-32) and finance 54 percent from self-generated income.

This is reflected again in the last bar of Figure 8. Relative to the poverty line of FCFA 684 per day, the consumption of Sudanese refugees falls short some FCFA 220, almost a third. Refugees consume a total of FCFA 464 per day, of which FCFA 97 is obtained as aid, and FCFA 367 is from self-generated resources. Thus, consumption levels of Sudanese refugees are extremely inadequate, as further evidenced by the finding that 47 percent of Sudanese refugees are unable to meet their minimum caloric intake of 2,400 calories a day (World Bank 2021). Moreover, aid, while critical, is inadequate to bring refugees to a decent standard of living and would have to increase more than 6-fold to bring refugees to the poverty line. Finally, the importance of assistance is dwarfed by the importance of self-financed consumption, which, 10
which was considered backward. During the 1950s, the intellectual discussions opposed the partisans of balanced and unbalanced growth, but no-one questioned the primacy of industry.<sup>12</sup> Although the primacy of investment and industrialization was widely accepted, Prebisch (1962) and the Latin American CEPAL school opted for import substituting industrialization (ISI), behind high barriers to trade, because of their “export-elasticity pessimism,” which largely amounted to the view that, since terms of trade for primary products were declining, longer term growth could not be expected from the dependence on primary commodity exports.<sup>13</sup> Countries had to develop the manufacturing sector to meet domestic demand and, where possible, generate revenues from exports to earn enough foreign exchange.<sup>14</sup> By contrast, Asian economies, which developed a national vision of development in the 1950 (essentially, South Korea in the aftermath of its war with the communist North), championed the advantages of export-oriented industrialization. Both export oriented and import substituting types of countries, however, were strongly devoted to a _dirigiste_ approach to development complete with five-year plans and an array of tax incentives, subsidies, exchange rate policies, tariffs and directed credit to help new industries germinate and grow a generation of public and private entrepreneurs.

The views about developing countries during the 1950s were optimistic—with a rather touching belief in the possibility to achieve rapid development, especially with outside intervention—but also patronizing. For instance the World Bank 1951 report on Guatemala mentioned above states that one obstacle to development is the “cultural 12 Rosenstein-Rodan (1943) argued for a ”Big Push” of investment led-growth which would enable an economy to loosen multiple constraints, realize scale economies and generate the needed demand. Gerschenkron (1962) showed that economic backwardness could be turned to the advantage of late starters by means of institutional innovations that enabled them to surmount barriers and exploit the potential inherent in catching-up. Nurkse (1959) visualized “balanced growth”, i.e., a mutually supporting advance across a broad range of sectors, through a coordinated investment strategy that would propel the economy out of poverty. Hirschman (1958) pleaded for “unbalanced growth”, maintaining that leading sectors should emerge which would stimulate the rest of the economy with the help of profitable forward and backward linkages (see Yusuf 2008).

13 CEPAL is the Spanish acronym for the United Nations Economic Commission for Latin America and the Caribbean.

14 Coatsworth and Williamson (2002) show that from the middle of the nineteenth century, Latin American governments had begun relying on tariffs to generate revenues and protect special interests. The import barriers erected after the First World War to develop industry were thus an outgrowth of past policies.

7
minimum performance that they might try to improve upon. In the World Bank publication<sup>33</sup> , Viceira notes that regulators could limit the level of ‘active bets’ that managers could take by defining (measuring and verifying) maximum tracking errors, just as institutional investors do with the active managers they hire. This would enable the pension system to remain within the overall risk level that is deemed appropriate.

39. For systems where the type of portfolio is left to individual choice, such outcome orientated portfolios could be set as default options (which most members are likely to end up with) and the supervisor could then monitor the suitability of the default fund.

# **IV. Examples of Use of Benchmark Portfolios**

## **Benchmarks used by Leading Pension Funds**

37. The world’s leading institutional investors are increasingly adopting this approach to asset allocation and are utilizing the advantage they have as long-term investors. It should be noted that many of these funds are non-profit organizations and their interests are consequently better aligned with the members of the funds they manage. As a consequence, they focus on output targets rather than maximizing short-term returns – i.e. what level of pension do they wish to pay people and how can they achieve this with the greatest certainty.

38. The examples given do not correspond precisely with individual account-based, DC systems - the Canadian and New Zealand funds, for example, manage national pension reserves and not individual accounts. It should also be noted that the CPPIB and NZSF reference portfolios are not DC funds but rather target (effectively) DB plans. They are not _directly_ driven by delivering a target income and do not necessarily use an optimization process. That said their approaches can provide interesting practical examples. In addition, the role of the independent commission outlined above is fulfilled by the trustee boards of these funds.

## **<u>Canada</u>**<sup>34</sup>

39. The Canada Pension Plan (CPP) is a contributory, earnings-related social insurance program. It forms one of the two major components of Canada's public retirement income system, the other component being Old Age Security (OAS). As of December 2013, the fund’s assets amounted to CAD $201.5 billion.

40. The fund is managed by the Canada Pension Plan Investment Board (CPPIB), established in 1997 as an organization independent of the government to monitor and invest the assets of the CPP. Independence is enshrined by any change to the CPP Act and the CPPIB Act having to be approved by two-thirds of the provinces with two-thirds of the population. Board members are chosen based on their financial experience and other criteria, and are appointed by the Finance Minister, from a list drawn by a nomination committee.

41. The CPPIB have used a Reference Portfolio since 2006. It serves as a performance benchmark against which the CPP Investment Board’s value-added activities are measured. The Reference

> 33 (Rudolph et al 2010).

> 34 Information taken from CPPIB website and Annual Report – www.cppib.com

12
case of Argentina. Credit collapses range from 10% in the case of Malaysia to 69% in the case of Indonesia. In the case of Thailand, GDP drops by 12% and credit drops by 38%. In the nonmiracles case, output drop ranges between 3% in Turkey to 7% in the case of South Korea and while Korea shows no drop in private sector credit, the two episodes in Turkey register credit drops of 17% and 33%, respectively.

# **_3.2 Micro Evidence_**

# _3.2.1 Sales versus GDP Recovery_

In this sub-section, we first examine whether corporate sales tracks recovery in GDP. Figure 3A shows the behavior of corporate sales (dotted line) along with GDP (bold line) over the period t- 2 to t+2. Interestingly we find that while GDP recovers at t and returns to pre-crisis levels by t+2, we find that recovery in sales begins a whole period later at t+1 and does not return to pre-crisis levels. The drop in average corporate sales across the nine 3S episodes is a 43% compared to the 8% drop in GDP across the same set of crisis episodes.<sup>12</sup> Figure 3B shows the relation between sales and GDP from 6 periods before the crisis to four periods after the crisis. The figure reveals that across the countries aggregate firm sales tracks GDP closely except during the crisis period.<sup>13</sup> In Figure 3C we explore other measures of corporate output including Sales-COGs, profitability as measured by EBITDA, and profitability adjusted for wages or EBITDA + Wages<sup>14</sup> and find a similar one period lag between GDP recovery and corporate output recovery. As in other cross-country micro studies, we are restricted to examining the micro data for public firms since detailed balance sheet data on private firms across countries is unavailable. However, we were able to obtain operating revenue on a sample of private firms (156 firms with nonmissing data over 10 years) in Argentina from the ORBIS database.<sup>15</sup> In Figure 3D, we examine

> 12 It may be noted that our micro level analysis is focused only on the corporate sector (largely manufacturing firms) while the GDP numbers reflect other sectors of the economy and not just the corporate sector.

> 13 Figure 3B does not include Russia and Turkey because the firm-level data do not allow us to go back six years before the crisis.

> 14 A description of the construction of national income statistics from the International Financial Statistics reveals that sales from commercial income statements are adjusted for changes in output inventories and work in progress to measure production. The components of inventory are not well populated in the Bloomberg database and so we use other approximations of production.

> 15We used the 2009 version of ORBIS data which goes back ten years to 1999. Hence we are unable to replicate the private firm analysis for the other 3S countries since the crises periods are before 1999.

11
**Table 9: Real Hourly Labor Earnings in Urban Areas (Constant 1994 pesos)**

|||**1988**|||**1994 **|||**1999**||
|---|---|---|---|---|---|---|---|---|---|
|**Type of Occupation**|**Mean**|**Median**|**S.D.**|**Mean**|**Median**|**S.D.**|**Mean**|**Median **|**S.D.**|
|Primary Teacher in Public School|9.9|8.2|7.2|15.1|14.9|5.8|11.7|11.1|8.0|
|Primary Teacher in Private School|9.8|8.7|6.9|15.9|12.9|12.7|7.8|6.8|4.6|
|Lower-Secondary Teacher in Pub. School|12.3|8.8|12.4|15.4|14.8|7.2|11.9|11.4|4.9|
|Lower-Secondary Teacher in Priv. School|9.2|8.3|4.7|16.7|13.7|12.5|11.7|10.2|6.7|
|Upper-Secondary Teacher in Pub. School|14.6|10.8|11.5|16.4|15.1|10.9|12.0|11.4|6.3|
|Upper-Secondary Teacher in Priv. School|18.4|7.2|35.3|15.6|13.4|8.1|13.2|11.5|9.8|
|University Teacher in Public School|16.6|15.2|9.2|20.1|18.5|12.4|19.2|14.7|24.5|
|University Teacher in Private School|11.4|10.5|7.6|20.1|18.2|13.2|16.2|12.0|11.5|
|**Professionals with Upper-Secondary in**<br>**Education not teaching**|**6.7**|**5.5**|**4.2**|**13.7**|**10.5**|**11.0**|**10.6**|**7.6**|**11.7**|
|**Professionals with a University degree in**<br>**Education not teaching**|**15.1**|**14.7**|**6.2**|**19.4**|**15.5**|**16.3**|**13.3**|**10.7**|**16.4**|
|**The mixed-skilled group**|**7.4**|**4.9**|**15.4**|**9.8**|**5.5**|**98.1**|**6.1**|**3.9**|**7.9**|
|The agricultural group|5.1|2.7|9.0|6.4|3.6|15.2|5.0|2.3|15.9|
|The low-skilled group|5.2|3.9|11.7|5.6|4.0|75.9|3.8|2.8|4.7|

Source: Own calculations based on ENEU survey In 1999, teachers in the public education system earned higher mean hourly labor earnings than their counterparts in the private sector and in other occupations. This pattern changes at the tertiary level, where private school teachers earned twice the labor earnings of public school teachers. There is not a significant difference in basic public teachers’ mean hourly labor earnings in urban and rural areas. Basic public teachers’ hourly labor earnings are significantly above those earned by _the mixed-skilled_ group, _the agricultural_ group or _the low-skilled_ group. Teachers in the basic public school level earned three times more than the earnings of other workers. Alternately, adjusted real hourly salaries of primary teachers in public schools were higher than the adjusted real hourly salaries of those in the private schools.

16
**Changes in tariff structures and the consumer’s surplus**

Let us revert to theory and consider some implications of the nonlinear price schedule in terms of consumer surplus and deadweight loss.

As shown in Figure 7, in block 1, the provider can offer to each household any quantity until the maximum of block 1 with the first applied marginal price. In the case where all households buy the maximum quantity, the maximum supplied quantity can be denoted by . However, since poor households consume less than the maximum of block 1, the demand of quantity within the first block is . With increasing marginal price in consumption, the elasticity of demand within the first block is expected to be low. This is because the marginal utility of the first electricity units is higher than the marginal utility of subsequent units. In other words, the elasticity of demand with respect to the marginal price of the first block must be lower than the elasticity of subsequent blocks. And the partial demand curve must be steeper in the first block.

In block 2, all those who consume in block 3 will buy the maximum quantity allowed in block 2. Other consumers can buy only a given quantity in block 2, and this is depending on the applied marginal price. The partial demand curve is more flat compared to that of the first block. This is because of the declining marginal utility of the units in block 2 compared to those bought in the first block. Finally, in block 3 and analogously to the discussion for block 2, we find that the demand curve is more flat than in the previous two blocks.

**Figure 7: Demand and supply of electricity with the increasing marginal price**

<!-- Start of picture text --> Marginal<br>price Block 1 Block 2 Block 3<br>Supply<br>Cons<br>umer<br>surpl<br>us<br>Quantity of Electricity (Good 1)<br><!-- End of picture text --> Based on this schedule of prices, the total exchanged quantity in this market will equal to .

32
**16**

(i) The growth elasticity of poverty is likely **to chiange** over time; thln elasticity is a function of **the** mean (as well as the Lorenz curve parameters) aiid its derivative wvith respect to the mean can be readily calculated. Tab.1 3 gives the log derivatives in the mean for both elasticities (the formulae are given in Ravallion and Huppi, 1989). The absolute elasticity of the h1adcouiit index to distributionally neutral growth in Bangladesh is an

**i:'*)** 'Sing functionl of the mean. Thus, with growth in the mean, the ninill!uil! gr-owth rate needed to alleviate poverty (in terms of absolute numbers of poor) will start to fall. However, this effect turns out to be quantitatively Smil' **J OIle** pe:.ce:.t increase in the mean will increase the absolute growth elasticity of the headcount index by only about .0001 (Table 3). Note also th it growth will lead to an increase in the elasticity of poverty with resi, to the overall Giri (Table 3); the proportionate poverty alleviation effects ,f reduction-s in overall inequality will increase with growth in irean coa'sul;unn tion, rhough, arain the quantiltative effect is not found to be 1 a .Xable 3%

ii) It should also be recalled that this rule -of-thumlb assu:lmes thit 'ro:h is dal:rI})`utionaillv neutral; literally speaking, that is quite rarti x'.er _.cc_ it is instructive to note that the Loreniz cur-ve in Bangladesh has : ,. dli--it overt ime (FiMo,ures 1 and 2), and thlw h.oao an i . ;e,s Vi rlnv unrc;-ponsive to at least 'proportionail' shitts . lhus, th above no. - eo - nallb maN still give quite a good approx lllationi, given the type of growt' observed over recent times. (iii) The above calculation inay be rather sensitive to the choice of ' poverty line, and that choice is always likely to be somewhat arbitrary. A-t three-qua:ters of the BBS poverty line, the ahsolute elasticity to growth is
# **5.2 Planning and Management**

We next turn to the second component that we exogenously varied in the couples’ training: the planning and management portion.

## **5.2.1 Planning for co-management**

One possible explanation for the improved productivity of the couples’ group could be that their farms are better managed. To start examining this, we focus on the action plans that they developed as part of the intervention. The first column of Table 10 shows that lead farmers who completed the action plan with their wives were 19 percent, or 7 percentage points, more likely to still have an action plan at endline compared to the individual training group.<sup>9</sup> This suggests that the action plans remained more salient for this group over time.

Recall from Section 2, that we digitized farmers’ action plans and conducted text-based analysis to examine mechanisms. In the remainder of the table, we examine differences in how farmers assigned to the couples’ training filled out their action plans. In column 3, we can see a substantial increase in the number of tasks that farmers filled out in the action plan, indicating a more detailed planning process. When we turn to who was responsible for managing the listed tasks, we notice a more than three-fold increase in tasks managed by the wife, and a more than seven-fold increase in tasks managed _solely_ by the wife (i.e., when we do not allow for joint management). We also see significant increases in tasks managed by other family members beside the spouse or planter, and by hired labor (more than two-fold increase). We detect no significant change in the number of tasks managed by the planter: while planters assign themselves 3.3 times as many tasks to manage as their spouse in the individual training’s action plans, they are only assigned 22% more tasks to manage in the couples’ action plans. Finally, the last column indicates that a significantly higher percent of tasks in the couple’s training action plans have anyone assigned to them, indicating a higher level of accountability or responsibility. These are very substantial changes in the planned management of rubber cultivation that are in line with our results on labor hours on rubber plots (Table 7).

> 9 We note in the second column that some pure control individuals also report having an action plan, meaning that there was either some non-compliance or spillover in the use of such plans within the community.

2
# **S3 DATA SOURCES**

# **S3.1 The Design of the Young Lives Study**

The Young Lives study was designed to analyze the changing nature of childhood poverty in four developing countries (Ethiopia, India, Peru, and Vietnam) over 15 years. This is a long-term research project that has tracked the same children since 2000. In Peru the survey consists of a stratified (urban and rural) nationwide sample of over 2,000 children, in 20 geographical sites, ages 6 months to 17 months in 2002<sup>38</sup> .

The Young Lives Sampling strategy selected approximately 100 children in 20 different clusters. These clusters were located in 14 different regions. In the first round about 60% of these regions had both urban and rural clusters. However, over 4% of the original sample migrated to a different region during the second round, which increased the number of regions in the sample to 23. In the second round, about 70% of the regions had both urban and rural geographical sites. I tested the existence of an association between unanticipated mining revenues and the classification of areas between urban and rural to rule out any confounding effects. I confirmed that unanticipated mining revenues are not related to the urban–rural divide either in levels or in changes by running a regression of unanticipated mining revenues on a dummy for being in an urban area and controlling for regional and time-fixed effects. I also looked into differences between urban and rural areas for both rounds of the survey and found that the differential access to public health facilities and programs narrows in the second round, with significant increases in rural areas. I tested if mining unanticipated mining revenues explains the narrowing of the differential access to public health centers in urban and rural areas between rounds by running a first difference regression of the change in access to public health centers on the change in 13