rafmacalaba/datause-encoder-data huggingface dataset:
The experimental approach focuses on the households that are located between 5-16km of a 3G site. One can split this group into two categories – those relatively close to a site and those relatively far away. Figure A1 above shows that the majority of treated households were within 5km of site.<sup>39</sup> After this point, the likelihood of being covered for each incremental kilometer of distance is relatively stable, suggesting that these households may not have been the primary targets for operators to cover. Meanwhile, Figure A3 suggests that when looking at the untreated group, there is a degree of randomness in the likelihood of being treated when 5-16km from the nearest site (as the likelihood is not obviously declining as distance increases). We therefore use 5km as a threshold to determine the point at which households are unintentionally covered.

If the main reason why some households more than 5km from a site received coverage and some did not was driven by exogenous factors, then we can use this to isolate the impact of 3G coverage. In order to assess this, we used the Terrain Ruggedness Index (TRI) sourced from EarthEnv.<sup>40</sup> The TRI represents the mean of absolute differences in elevation between a focal cell and its eight surrounding cells – a higher value therefore represents more rugged terrain (for example mountain areas with steep ridges). The dataset we use is aggregated to pixels of 10km, which is merged with our survey dataset. Initial analysis we carried out shows that the average TRI is greater for untreated households than treated households that are between 5-16km from the nearest 3G site.<sup>41</sup> This suggests that exogenous factors such as terrain are indeed a relevant factor in determining whether households have coverage when more than 5km from the nearest 3G site.

# _Results_

As in our main specification, we estimate the following equation: (1) i 1D i 2 i i ii ii ii ii ii ii YY = αα + ββ + ββ XX + γγ + εε We apply the same fixed effect regression framework but we restrict the sample to households that were within 5-16km from the nearest site in Wave 3 of the survey. We impose the latter requirement so that the sample only captures households that operators did not intend to cover during the whole period of our analysis, but did so partly due to exogenous factors.

Tables A8-A10 present the results of our fixed effect regressions for household welfare, using indicators for consumption and poverty, when applying the above distance to site thresholds. They show that our main findings, that mobile broadband coverage increases overall consumption and reduces extreme poverty over time, still hold. We also carried out the analysis using first differences and the results remained robust. This initial analysis therefore provides further confidence that the link between mobile broadband coverage and household welfare is indeed causal.

> 39 For covered households, the median distance to a site is 1.9km and the 75th percentile is 6.8km. 40 See http://www.earthenv.org/topography and Amatulli et al (2018)

> 41 The average TRI is 2.1 for treated households and 2.3 for untreated households.

41
pendence cannot be avoided; for the EV corresponding to the change in the whole constellation is not, except by mere fluke, additively separable in the component parts. We therefore proceed as follows. Suppose, at one extreme, the road were to affect _only_ **p** _t_ . As before, we can calculate the value of _T_ 1<sup>2thatcorrespondstothishypothetical</sup> change in the original constellation, and then express it as a proportion of its counterpart under the complete change. The columns labelled _E_ 1 _U_ 1(Δ **p** ) and _T_ 1<sup>2(Δ</sup><sup>**p**)in</sup> Table 2 report the corresponding levels of expected utility and the said payments. In Variant 1, the value of the reduction in transport costs by itself makes up 68.1 percent of _T_ 1<sup>2,fallingto62.6percentinVariant2.</sup> At the other extreme, one can calculate the payment that corresponds to all the changes in the original constellation _except_ **p** _t_ , which is denoted by _T_ 1<sup>2(Δ</sup><sup>**p**</sup><sup>_c_).The</sup> residual left after subtracting this from the value of _T_ 1<sup>2correspondingtothecomplete</sup> change is then attributable to the change in **p** _t plus_ any interaction effects between changes in transport costs and all other changes. In Variant 1, the corresponding share is 65.2 per cent, just 2.9 percentage points smaller than that when the reduction in transport costs stands alone. The share in Variant 2 is likewise lower, at 60.7 percent. In period 2, these shares fall quite sharply, ranging between 41.4 and 44.4 percent.

By way of an independent check on these findings, one can compare the above estimates with those yielded by a completely different approach. The one that comes immediately to mind involves the derivation of the so-called Value of a Statistical Life (VSL) from variation in wage rates across occupations or industries and the associated variation, if any, in fatality rates, an approach vigorously promoted by Viscusi (for a survey, see Viscusi [1993]). Pursuing this line, Simon _et al._ (1999) employ data from Indian manufacturing firms and arrive at the rather startling estimate that the VSL is 20 to 48 times larger than the present value of lifetime foregone earnings. As the authors note, this greatly exceeds the estimate of 7 to 8 obtained by Liu _et al._ (1997) for Taiwan, China in the 1980s.

Be that as it may, what is the ratio implied by the approach adopted here? Consider Variant 1 in period 1. The equivalent sum _T_ 1<sup>2(Δ</sup><sup>_q_),whichisdefinedinrelationto</sup> changes in mortality alone, stems from a reduction in _q_ 1 of 0 _._ 0125 (= 0 _._ 1 _×_ 0 _._ 125). Since the said sum is paid twice, the implied VSL is 19 _._ 264 (= 2 _×_ 0 _._ 1204 _/_ 0 _._ 0125), which is 2.5 times the value of output in old age of a surviving young adult, namely, 7 _._ 65 (= 5 _×_ 1 _._ 7 _×_ 0 _._ 9). The implied VSL in period 2 is 82 _._ 566 (= 2 _×_ 0 _._ 2188 _/_ 0 _._ 0053), as against lost output in the value of 9 _._ 656 (= 5 _×_ 2 _._ 1457 _×_ 0 _._ 9), a ratio of just over 8.5 to one. In the present framework, however, _Tt_<sup>2(Δ</sup><sup>_q_)relatesnottotheriskthatayoung</sup> 23
- 15 -

with the exclusion of Peru, the average discount on the debt of the other five Latin American debtors was nearly 35 percent in April 1987 and about 52.5 percent by March 1988. The table shows in the case of Peru how a strongly negative assessment of a country's debt policies will be reflected in the market value of its debt. It alsd shows, however, that the size of the discount will be influenced by factors which are external to the country concerned. Thus the discount on both Mexican and Venezuelan debt increased as oil prices fell.

The connection between the size of the discount on developing country debt and the amount of provisioning required is, to some extent, supported by admittedly sketchy information on individual country provisioning by the banks. In broad terms, discussions with bankers suggest that provisions are highest against those countries which have the greatest discos,nt on their debt in the secondary market. However, the relationship is by no means perfect. In 1986, for example, some banks were setting aside significantly greater provisions against Brazilian debt than against Venezuelan debt, even though there was a marginally greater discount on Venezuelan debt in the secondary market. At the same time, provisions against Argentine debt were significantly greater than those against Chilean debt even though there was very little difference in the secondary value of their debt.

If one takes the market valuation of developing country debt as a reasonably accurate indicator of risk and of its net present value, it would appear most banks in some countries are still under-provisioned. However, it is not entirely clear whether the market valuation of debt is an accurate measure of the extent to which the banks should provision against it. First, there is the argument that the market is too thin and vulnerable to
Policy Research Working Paper 7225

# **Abstract**

Shortly before the 2011 Libyan revolution, consumers’ subsidies were rapidly increased by the regime in an effort to reduce social discontent. In the aftermath of the revolution, these subsidies became important for people’s subsistence, but also a very heavy burden for the state budget. Since then, the Libyan government has been confronted with the necessity of reforming subsidies in a politically and socially complex environment. This paper uses household survey data to provide a distributional analysis of food and energy subsidies and simulate the impact of subsidy reforms on household wellbeing, poverty, and the government’s budget. Despite the focus on direct effects only, the results indicate that subsidy reforms would have a major impact on household welfare and government revenues.

The elimination of food subsidies would reduce household expenditure by about 10 percent and double the poverty rate while saving the equivalent of about 2 percent of the government budget. The elimination of energy subsidies would have a similar effect on household welfare, but a larger effect on poverty while government savings would be almost 4 percent of the budget. The size of these effects, the weakness of market institutions, and the current political instability make subsidy reforms extremely complex in Libya. It is also clear that subsidy reforms will call for some form of compensation for the poor, a gradual rather than a big bang approach, and a product-by-product sequence of reforms rather than an all-inclusive reform.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at araar.abdelkrim@ecn. ulaval.ca, pverme@worldbank.org, and nchoueiri@imf.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# 3.1 Improvements to the survey’s expenditure module

In lower income countries, a welfare metric based on consumption is generally preferred to income. As discussed in Mancini, Vecchi (2020), the choice between consumption and income would be inconsequential whenever all income is consumed, and all consumption is financed by income. However, in low-income economies and in contexts characterized by shocks, a metric of welfare based on consumption – which is smoother than income through households’ reliance on savings and borrowing – provides a better measure of living standards.

In order to make the Demographic and WASH survey better suited for poverty measurement, efforts were made to improve the design of the expenditure module of the survey questionnaire within the limits in terms of overall questionnaire length and complexity deemed feasible by the HNAP team.

In particular, efforts were made to improve the measurement of in-kind assistance and own-production (particularly for food); to use an annual recall period for items consumed infrequently; to expand (to the extent possible) the list of non-food items (Table 4).

_Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey_

|**SUMMER2021 EXPENDITURE MODULE**|**REVISEDSUMMER2022 EXPENDITURE MODULE**|
|---|---|
|_How much did your household spend on the following items_<br>_on a monthly basis?_**_(average of last 3 months)_**|_Please estimate the value of_**_monthly_**_household_<br>_consumptionfor thefollowing items._<br>|
|Food|Food and beverages (_bought or bartered_)<br>Food and beverages(_ownproduced or received_)|
|Electricity|Electricity|
|Cooking|Fuel for cooking|
|Heating/cooling|Heating/cooling|
|Safe water|Water|
|Hygiene items|Hygiene items|
|Transportation(public/private all costs)|Transportation(public/private all costs)|
|Communication|Communication|
|Covid related items|--|
||Personal care|
||Infant care(diapers,babyformula)|
||Childcare|
||Other|
||_Please estimate the value of_**_annual_**_household consumption_<br>_for thefollowing items._|
|Shelter maintenance|Regular shelter maintenance|
||Furnishings and household consumables(e.g. lightbulbs...)|
||Clothingand footwear(_bought or bartered or received_)|
|Education|Education|
|Healthcare (including medicines, visit to healthcare facilities,<br>medicalprocedures)|Healthcare (including medicines, visit to healthcare facilities,<br>medicalprocedures)|
|NFIs|NFIs (blankets, mats, kitchenware) (_bought or bartered or_<br>_received_)|
|Repair and maintenance of hh appliances and machines|Repair and maintenance of hh appliances and machines<br>Desludging|
||Other|
# **Children on the Move:**

# **Progressive Redistribution of Humanitarian Cash Transfers among Refugees**

Berk Özler ® Çiğdem Çelik ® Scott Cunningham ® P. Facundo Cuevas ® Luca Parisotto<sup>℘</sup> JEL classification codes: O15, I38, I32.

Keywords: cash transfers, humanitarian assistance, refugees, poverty, inequality.

> ℘ Özler (corresponding author): <u>bozler@worldbank.org, The World Bank. Çelik: ccelik@worldbank.org, The World</u> Bank. Cunningham: <u>Scott_Cunni</u> ~~<u>ngham@Baylor.edu, Ba</u>~~ ylor University. Cuevas: <u>fc</u> ~~<u>uevas@worldbank.org,</u>~~ The World Bank. Parisotto: lparisotto@worldbank.org, The World Bank. The authors would like to thank the Turkish Red Crescent Kızılaykart Cash-Based Assistance Programmes for sharing the data for this study and the World Food Programme for their overall collaboration. We are grateful for comments we received from Jeannie Annan, Andrew Foster, Ugo Gentilini, David McKenzie, Cyrus Samii, Alperen Açıkol, Bülent Öztürk, WFP staff, Turkey’s Ministry of Family, Labor and Social Policies, and participants at World Bank DECRG Seminar. Funding from the Swedish International Development Cooperation Agency (SIDA) is gratefully acknowledged. The study is registered at the <u>EGAP Registry (EGAP Registration ID 20190515AB). The names of the authors are in random order, using the author randomization tool of the American</u> Economic Association, indicated by ® in the author list and archived here.
- Fearon, J. D., and D. D. Laitin (2003): “Ethnicity, insurgency, and civil war,” _The American Political Science Review_ , 97, 75–90.

- Fernandes, A. M., A. Mattoo, H. Nguyen, and M. Schiffbauer (2019): “The internet and Chinese exports in the pre-ali baba era,” _Journal of Development Economics_ , 138, 57–76.

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- Gebresilasse, M. (2023): “Rural roads, agricultural extension, and productivity,” _Journal of Development Economics_ , 103048.

- Gibbons, S., T. Lyytikainen, H. G. Overman, and R. Sanchis-Guarner (2016): “New road infrastructure: the effects on firms,” CEPR Discussion Papers 11239, C.E.P.R. Discussion Papers.

- Goldberg, P. (2019): “Moonshot Africa and jobs,” Mar.

- Goldfarb, A., and C. Tucker (2019): “Digital Economics,” _Journal of Economic Literature_ , 57, 3–43.

- Gordon, R. H., and W. Li (1995): “The change in productivity of Chinese state enterprises, 1983–1987,” _Journal of Productivity Analysis_ , 6, 5–26.

- Goyal, A. (2010): “Information, direct access to farmers, andrural market performance in central India,” Policy Research Working Paper Series 5315, The World Bank.

- Guerrero Barreto, M. E., and P. I. Ritter Burga (2014): “The effect of internet and cell phones on employment and agricultural production in rural villages in Peru.”

- Han, E., and C. Paik (2017): “Ethnic integration and development in China,” _World Development_ , 93, 31–42.

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- Herrera Dappe, M., and M. Lebrand (2021): “Infrastructure and Structural Change in the Horn of Africa.”

41
_Figure 9: Net interest rate sensitivity with derivatives, 2002 to mid 2006_ <!-- Start of picture text --> DKK bn<br>80<br>60<br>40<br>2006 (1st half)<br>20<br>2003<br>2005 2002<br>2004<br>0<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5<br>2002 2005<br>-20<br>2004<br>2003<br>2006 (1st half)<br>-40<br>-60<br>-80<br><!-- End of picture text --> _Table 14: Interest Rate Sensitivity With Derivatives_

|||InterestRa<br>|te Change<br>||10 Year government bond<br>|
|---|---|---|---|---|---|
||- 1%|-0.7%|+0.7%|+ 1%|yield(end ofperiod)|
|||_DK_|_Kbn_||Percent|
|2002|-14.8|-9.6|-3.5|10.9|4.46|
|2003|15.1|12.5|-18.8|-28.6|4.47|
|2004|8.4|7.7|-13.0|-20.9|3.85|
|2005<br>|10.0|9.7|-14.1|-20.4|3.28|
|2006(1<sup>st</sup>half)|25.8|17.6|-19.7|-28.3|4.12|

Source: Danish FSA. Bloomberg (yields) The net interest rate sensitivity of the industry in aggregate is illustrated in Figure 9 and Table 14 above. By using derivatives the interest rate risk of the industry has been greatly reduced. Caps have clearly been put in place for a large portion of the industry as there is no difference in the losses sustained in 2003, 2004 or 2005 by a 1 or 0.7 percentage point rise in interest rates. The “normal” relationship between interest rates and gains/losses that would be expected in the balance sheet of an institutional investor has been reestablished: when interest rates go up, capital losses are incurred, while when interest rates go down, capital gains ensue. In other words, the industry has succeeded in being net long duration.<sup>1</sup> This is the exact opposite of the situation without derivatives, where the effects of the liability side dominated those of the asset side, and the magnitude of change is significantly higher. In other words: derivatives have been used not only to change the risk profile in general but specifically to reduce overall interest rate risk.

> 1 It can be argued that if taking interest rate risk is not rewarded long term investors should not take any interest rate risk over and above what is needed to hedge their liabilities. If this holds true, the Danish industry has over-hedged, and would be better off not having a net long duration.

21
# **Appendix D: Partial Residual Plots, Bottom 40, Linear Regressions**

# **a. Without controlling for population**

<!-- Start of picture text -->
-6 -4 -2 0 2 4<br>e ( ln B40 | X )<br>coef = .0117319, (robust) se = .00277914, t = 4.22<br>b. Controlling for population<br>-3 -2 -1 0 1<br>e ( ln B40 | X )<br>coef = -.01771341, (robust) se = .00527589, t = -3.36<br>.6<br>.4<br>.2<br>0<br>-.2<br>e ( share of World Bank funding | X )<br>-.4<br>.6<br>.4<br>.2<br>0<br>e ( share of World Bank funding | X )<br>-.2<br><!-- End of picture text -->

# **b. Controlling for population**

_Source:_ Estimates based on Global Monitoring Database (internal database), Poverty and Equity Global Practice, World Bank, Washington, DC; World Bank Geocoded Research Release, (database), AidData, College of William and Mary, Williamsburg, VA, http://aiddata.org/data/world-bank-geocoded-research-release-level-1-v1-4-2.

19
<!-- Start of picture text --> 400 Total loss<br>Capital stock loss<br>Gross savings<br>Gross disasters savings<br>200 change in consumption less aid<br> change in consumption incl. aid<br>0<br>1998-2005<br>-200 1990-2005<br>1982-2005<br>-400<br>1974-2005<br>-600<br>-800<br>Constant USD 2000<br>1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> Fig. 5: Disaster losses and changes in consumption in Honduras with and without aid Note: Values in 2000 constant USD per capita.

In Honduras, we observe multiple events leading to total and capital stock losses; beyond the ones we look at in our sample with the 1% threshold, there are another 35 events of smaller magnitude over the time horizon studied listed in the EMDAT database. Disasters seem to have led to decreases in consumption spending in the year of and following events (also depending on whether catastrophes happened early or late in a given year). Aid seemingly has had a smoothing effect, and consumption generally exhibited some volatility due to other reasons (for example, the hyperinflation in the 1990s). The chart also shows the varying time horizons adopted in this study for examining effects on consumption changes over time.

# **3 FINDINGS**

For our sample of 99 events, we start out with assessing whether asset losses can be said to affect the present value of post-disaster consumption changes. As shown on Table 5 for the whole sample and for two further samples for which we report results further below, all hydrometeorological events and hydrometeorological events in the low-income group of countries, the loss is highly insignificant (this is also the case for all further regressions undertaken), and the (nonstandardized) coefficient is positive, which is 14
# **Section II: Risk Measurement and Analysis**

The first step in developing a catastrophe risk management strategy is to perform a loss risk assessment. The result of this analysis provides an objective estimate of potential losses and helps to focus the global risk management effort. It will also facilitate the calculation of the pure risk premium and market clearing premium for risks covered by a catastrophe insurance pool.

A loss risk assessment has four stages:

- **Hazard module.** First, the natural disasters and geographic areas to be studied must be selected.  The characteristics of historical events and physical models are utilized to generate stochastic events using simulation techniques.  The hazard module then analyzes the intensity of the event (e.g. ground shaking in an earthquake; peak winds in a typhoon) at specific locations when a stochastic event has occurred.

- **Exposure module.** Next, the exposed asset base is estimated for each location using available property data or is derived from population distribution data.  The exposure model calculates the value exposed by multiplying the building stock by the average building replacement cost.

- **Vulnerability module.** Third, the vulnerability model is used to calculate the damage to each type of building from a given intensity event at a specific site. Building classifications are based on factors such as construction material and type, usage, number of stories and age.  A damage ratio is calculated relating the repair cost to replacement cost for each peril at various intensities and locations.

- **Loss analysis module.** Fourth, the damage ratio from the vulnerability module is multiplied by the value of the exposed risk at a location to calculate an estimated dollar loss.  Results are calculated for each type of property at each location and then locations are aggregated as required to arrive at the estimated loss.

There are five key measures of loss risk that can be derived from the data produced through the loss risk assessment.

- **Average annual loss** is the expected loss per year when averaged over a very long period.  This is the amount that governments ideally would budget for in planning disaster response strategies under Arrow Lind assumptions.<sup>29</sup> .

> 29 Arrow, 1970

13