rafmacalaba/datause-encoder-data huggingface dataset:
2000 is attributed to SWP.<sup>31</sup> However, this impact had dropped substantially by 2004/05; the corresponding _DD_ estimate fell to 0.032 (t=1.00). The transient schooling impact probably reflects the fact that the tuition subsidies ended with other SWP disbursements. Of course, even though the non-SWP village caught up substantially with the SWP villages in schooling by 2004/05. Thus there were children in SWP villages who entered school earlier than without the SWP and this will probably yield future income gains.

There was almost no sign of impacts on the prices of agricultural outputs and purchase prices for inputs for 13 items.<sup>32</sup> We found positive impacts during the disbursement period for a number of types of infrastructure, although they are generally not statistically significant. We found little sign of impacts in the 2004/05 data. The exception was TV reception, which showed significant impacts in the longer-term as well as during the disbursement period.

Table 5(a) gives the estimated impacts on the incidence of income poverty for various poverty lines; Table 5(b) gives the corresponding results for consumption poverty. Again we give estimates using the poverty line of 808 yuan per person per year as well as selected poverty lines above and below this figure.<sup>33</sup> The poverty impacts in the SWP disbursement period are broadly consistent with our findings for the impacts on the mean income and consumption in Table 3.<sup>34</sup> In Figure 1 we also give the results graphically, by plotting the _DD_ estimate of the impact on the headcount index of poverty (for income and consumption poverty in panels (a) and (b) respectively) against the poverty line, which we vary over virtually the whole distribution. Impacts on the income poverty rate are largest just below the 808 poverty line, for both end dates. The impacts on consumption poverty echo our results for mean consumption around the middle of the range of poverty lines, where 2004/05 consumption-poverty impacts exceed those for

> 31 The uncorrected _DD_ was 0.046 (t=1.41) and the kernel matched _DD_ was 0.072 (t=2.40).

> 32 The only exceptions were that diesel oil had a significantly higher price in the SWP villages by 2004/05 and edible oil crop had a slightly lower price.

> 33 The table only gives results for the trimmed sample, which is better balanced.  However, although the precise estimates differ between the two samples, the basic pattern was the same, and our main conclusions do not depend on this choice.

> 34 The results were also robust to deleting the county in which some SWP activity was recorded in non-SWP villages.  We found an impact on extreme consumption poverty in 2004

> after deleting the consumption outliers ( The weighted _DD_ at 500 consumption poverty line is -

> 8.06 with t-ratio of -1.72; the weighted _DD_ at 600 is -9.20 with t-ratio of -1.67.)

23
21 losses can be as much as 40% higher<sup>1</sup> llwhich would<sup>suggest that</sup> the welfare losses in Table 2 are underestimated.

Table 3 below assumes that all monopoly price distortions cause deadweight losses 40% larger than those in<sup>Table 2. It is</sup> important to note that because the effect is proportional,<sup>for</sup> most elasticity values and/or elasticity-efficiency improvement combinations, unregulated monopoly's effect on<sup>social welfare</sup> remains very small.

There is another factor that might seem to increase the social cost of monopoly pricing of utilities services. The elasticity of demand for electricity and other utility-like natural monopoly services is higher for industrial use<sup>than for</sup> consumer final demand. Therefore, because, as equation<sup>(1) above</sup> shows, welfare losses are proportional to the<sup>elasticity of</sup> demand, the total welfare losses might seem more appropriately located in the viLinity of the larger rather than<sup>smaller values</sup> in columns 3 to 6. This effect could appear<sup>to be significant in</sup> LDCs, where the proportion of electricity, gas<sup>or telephony</sup> services sold as an intermediate input is almost<sup>certainly higher</sup> than it is in industrialized countries.<sup>12</sup> We do not think this is a serious problem. Industrial<sup>use</sup> lScherer and Ross (1990, pp. 665-667) discuss<sup>this issue in</sup> some detail.

12 In 1972, in the United States, intermediate<sup>input use of</sup> electricity accounted for about 54% of total<sup>electricity demand.</sup> (See U.S. Department of Commerce, The Detailed<sup>InRut-Output</sup> Structure of the U.S. Economy: 1972. (Washington, U.S.G.P.Ot 1979): 173-178.
important role in determining the degree of insurability of such risks. On the other hand, currency crises dummies did not appear to be particularly relevant (actually, they never turned out significant, at least in our regressions, to explain the value of insurance coefficients).

The last part of the analysis has been devoted to an assessment of welfare gains from risk pooling, based on the work by van Wincoop (1999), showing that potentially large benefits could be obtained by pooling idiosyncratic risk both within and outside the region.

# **7. References**

Artis, M.J. and M. Hoffmann, 2004, Financial Globalization, International Business Cycles and Consumption Risk Sharing, _CEPR Discussion Paper_ n. 4697.

Auffret, P., 2001, An Alternative Unifying Measure of Welfare Gains from Risk-Sharing, _Policy Research Working Paper_ n. 2676, The World Bank.

Banerjee, Abhijit V & Newman, Andrew F, 1993, Occupational Choice and the Process of Development, _Journal of Political Economy_ , 101,2.

Cochrane, J. H., 1991, A simple test of full consumption insurance, _Journal of Political Economy_ , 99, 5.

Crucini, M., 1999, On international and national dimensions of risk sharing, _Review of Economics and Statistics_ , 81,1.

Crucini, M. and G.D. Hess, 2000, International and Intranational Risk Sharing, in _Intranational Macroeconomics_ , edited by G.D. Hess and E.v. Wincoop, Cambridge University Press, Cambridge.

Devereux, M.B. and G.W. Smith, 1994, International Risk Sharing and Economic Growth, _International Economic Review_ , 35-3.

Greenwood, Jeremy and B. Jovanovic, 1990, Financial Development, Growth, and the Distribution of Income, _Journal of Political Economy_ , 98(5).

Grimard, F., 1997, Household Consumption Smoothing through Ethnic Ties, _Journal of Development Economics_ , 53, 2, pp. 391-420.

Imbs, J., 2006, The real effects of financial integration, _Journal of International Economics_ , 68, 296-324.

Jalan, J. and M. Ravallion, 1999, Are the Poor Less Well Insured? Evidence on Vulnerability to Income Risk in Rural China, _Journal of Development Economics,_ 58, 1, pp. 61-81.

Kim, S., Kim, S.H. and Y. Wang, 2005, Regional versus global risk sharing in East Asia, _Asian Economic Papers, 3,3._ 22
also find that high income households in developing countries did not abandon biomass use for many reasons including cost, lack of supply reliability and availability of modern fuels, and cooking practices and cultural preferences. There are also the cases where households switch back to traditional biomass even after adopting modern energy sources due to changes in several factors including price of fuels, reliability and availability of fuel supply, lifestyles and tastes. Using data from the Ghana LSMS survey, Akpalu et al. (2011) find strong evidence that the most preferred fuel is LPG, followed by charcoal, with kerosene the least preferred. Moreover, the study finds spatial differentiation in the type of fuel use with LPG primarily in the coastal zone, fuelwood in the savannah zone and kerosene in the savannah and forest zones. As part of the CSI study, ASTAE (2013a) finds significant progress in incentivizing Indonesian households to switch from kerosene to LPG for cooking needs through the government's inter-fuel substitution program.

Gender could be another factor. For example, Narasimha Rao and Reddy (2007) show that households headed by women generally opt for modern fuels than those headed by men. Women generally play a major role in household cooking decision-making activities. Based on the household survey of access and transitions to cleaner cooking fuels in Sri Lanka, Wickramasinghe (2011) finds that women are more likely to switch to cleaner fuels if they are employed in activities outside of the home. Miller and Mobarak (2013) find that women in rural Bangladesh, who bear disproportionate cooking costs, have stronger preference for ICS but they lack the authority to make the purchase.

In Nepal, Amacher et al. (1993, 1996) find that economic or organizational changes away from subsistence agriculture lead to adoption of ICS and its use reduced the household fuelwood consumption. Based on the survey of 2% households that use ICS in Nepal, Nepal et al. (2011) find that these households are more likely to use same amount or even more fuelwood than the households that use traditional mud or open-fire stoves. Although further investigation is suggested, the study finds that the rebound effect (lower shadow price), keeping their stove running for longer times to warm the house and cooking more frequently might be the main reason for more fuelwood consumption with ICS using households. A review study by Rehfuess et al. (2014) finds that many ICS programs had specific design problems that led to stove 17
- Financial aspects also play a role: households and company may delay (or give up) reconstruction because of insufficient financial resources, households may reduce final demand because of lost income and assets, company may go bankrupt even if their production capital is still partly opera‐ tional (e.g., Benson and Clay, 2004; Tierney, 1997). These problems are especially important in developing countries. In general, this research line should also touch upon the role of the insur‐ ance industry (and of its regulation) and its capacity to help fund reconstruction and reduce natu‐ ral disaster indirect cost.

# **6. Acknowledgment**

The authors want to thank Celine Guivarch, Julie Rozenberg, and partners from the ConHaz project for their useful comments and suggestions on an earlier version of the paper. This work benefited from fi‐ nancial support from the ConHaz project, a 7th Framework research project, funded by the European Union. This work benefited from many exchanges during an author’s visit at, and funded by, the World Bank in February 2010.

# **7. References**

Albala‐Bertrand, J. M. (1993), The Political Economy of Large Natural Disasters with Special Reference to Developing Countries, Oxford: Clarendon Press.

Alderman, H., J. Hodditnott and B. Kinsey (2006), Long‐term Consequences of Early Childhood Malnutri‐ tion, Oxford Economic Papers 58, 450–474.

Boarnet, M. G., 1998. Business losses, transportation damages, and the Northridge earthquake. Journal of Transportation and Statistics 1, 49–63.

Booker, J.F. (1995). Hydrologic and economic impacts of drought under alternative policy responses. Wa‐ ter Resources Bulletin, 31: 889‐906.

Benson, C., & Clay, E. (2004). Understanding the economic and financial impacts of natural disasters. Dis‐ aster risk management series no. 4.

Bowles S. et Gintis H., L’idéal d’égalité appartient‐il au passé ?. Homo reciprocans versus Homo oecono‐ micus, Revue du Mauss 2008/1, n° 31, p. 209‐241.

Brown Gaddis, E., Miles B., Morse S., Lewis D.. (2006). Full‐cost accounting of coastal disasters in the United States: Implications for planning and preparedness. Ecological Economics 63 (2007) 307‐318.

Cavallo, E., & Noy, I. (2009). The economics of natural disasters – a survey. Inter‐American Development Bank Working Paper #124.

Cochrane, H. (2004), “Economic Loss: Myth and Measurement”, Disaster Prevention and Management 13, 290–296.

22
58 Ferracane (2017). 59 Meltzer and Lovelock (2018). 60 World Bank (2016). 61 CSIS (2014). 62 OECD (2013b); UNCTAD (2016). 63 UNCTAD (2016). 64 Czernich et al. (2011) 65 Grimes, Ren, and Stevens (2012); World Bank (2016). 66 Manyika et al. (2016); UNIDO (2016). 67 Economides and Jeziorski (2017). 68 Klapper (2017). 69 Klapper, Miller, and Hess (2019). 70 Muralidharan, Niehaus, and Sukhtankar (2016). 71 Merchants often also request payments for sales in weekly or monthly batches, which makes it even more difficult to measure. 72 Peake (2012). 73 Suri (2017). 74 Surane and Cannon (2018). 75 Economides and Jeziorski (2017).

76 Global Partnership for Financial Inclusion and International Finance Corporation (2012).

77 “User fund” refers to the net value of unutilized funds held in an account of the customer by the payment service provider. 78 Grandon and Pearson (2004). 79 <mark>De Koster, Le-Duc, and Roodbergen (2007); Lam et al. (2015).</mark> 80 Ramanathan, Ramanathan, and George (2014). 81 Universal Postal Union (2019). 82 Pitney Bowes (2018). 83 World Bank (2017). 84 DHL (2017). 85 See International Chamber of Commerce (ICC) Guideline 11, http://tfig.unece.org/contents/icc-customs-guidelines.htm. 86 Hufbauer, Jung, and Lu (2018).

87 Cost, weight, or quantitative norms within which goods for personal use are imported into the customs territory of the Eurasian Economic Union without paying customs duties and taxes. See http://fl.customs.ru/index.php?option=com_content&view=article&id=95:2015-12-09-11-56-14&catid=5:2008-10-20-15-3816&Itemid=1795.

88 Malaysian Ministry of International Trade and Industry Fact Sheet 2018. See https://www.miti.gov.my/miti/resources/Media%20Release/Fact_Sheet_DFTZ_at_Malaysia_Digital_Economy_2018_SME_Fact _Sheet.pdf.

26
produced audited financial statements.<sup>10</sup> In 1997/98, Price Waterhouse produced<sup>aseparation</sup> balance sheet to help divide assets<sup>andliabilities betweenMalawiTelecomLimited (MTL) and</sup> Malawi Postal Corporation (MPC). However, even with regards<sup>tothese figures, Price</sup> Waterhouse noted that these figures<sup>(see Table5) should notbe consideredaudited or auditable,</sup> and that no audit opinion other<sup>thana total disclaimercouldbe expressedwith regardsto the</sup> data.

This said, however, several items on<sup>theseparationbalancesheetareinteresting. First,</sup> liabilities are greater than assets, suggesting<sup>thatlossesover timehadleftthecompanywith</sup> negative equity. Most of the losses<sup>wereconcentratedinthepostalsector.Second,debtors</sup> owed the company a large amount,<sup>net of provisions.Based upon1997revenue estimatesfrom</sup> the ITU, net of provisions, debtors<sup>owedthetelecommunicationspart of the companyabout8</sup> months of telecommunications revenues. This is consistent with information<sup>frombefore</sup> comnmercialization, when collection rates<sup>alsoappear to have been very low.WhenMPTC was</sup> first commercialized in 1995, defaulters<sup>owedtheincumbentoperatormorethanMalawi</sup> Kwacha 63 million (equivalent of<sup>US$7.21million),of which thegovernment owedalmost 70</sup> percent. As soon as MPTC was commercialized,<sup>itissued ultimatumstonon-payingcustomers</sup> and disconnected those who did not<sup>respond favorably asa first moveto streamline its activities.</sup> This enabled the company to recover<sup>MK17million (equivalentof US$1.95million)fromlong</sup> overdue accounts in just few months."'

> 10 According to revenue and expense<sup>data reportedtoITU,MPTC'sreturnonsales oscillatedbetween negative2</sup> percent in 1995 and 60 percent in<sup>1997.However,given the absenceof auditedaccounts,thesenumbersshould be</sup> treated with considerable caution.

South African Business Intelligence,<sup>#59, February1995.</sup> 15
26 <!-- Start of picture text --> 3: Feed demand Maize<br><!-- End of picture text --> Trode liberaitzation vs Price Guarontee <!-- Start of picture text --> 10 .<br>9<br>8<br>lo/T<br>6<br>c 5<br>0<br>1974 75 76 7, 78 79 80 81 82 83 84 8S<br>fears<br>a price guarontee + = trode lib<br><!-- End of picture text -->

# 4: Per Capita consuumption Maize

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Learning from Self and Learning from Others: Experimental Evidence from Bangladesh<sup>∗</sup> Florence Kondylis John Loeser Mushfiq Mobarak Maria Jones Daniel Stein JEL Classification Codes: D83, O12, Q12, Q16 Keywords: Agricultural Extension, Demonstration Plot, Technology Adoption, Learning

> ∗Kondylis: Development Impact Evaluation, World Bank, fkondylis@worldbank.org; Loeser: Development Impact Evaluation, World Bank, jloeser@worldbank.org; Mobarak: Yale University, NBER, ahmed.mobarak@yale.edu; Jones: Development Impact Evaluation, World Bank, mjones5@worldbank.org; Stein: IDInsight, daniel.stein@idinsight.org. We thank Paul Christian, Kyle Emerick, Andrew Foster, Erin Kelley, Sylvie Lambert, Arianna Legovini, Karen Macours, Jeremy Magruder, and seminar audiences at IFPRI, Naples, Paris-Dauphine, Paris School of Economics, UC Berkeley, University of Virginia, and Wageningen University. We thank the Global Agriculture and Food Security Program, the World Bank Bangladesh Country Management Unit, the World Bank i2i, RSB, and SAFANSI Trust Funds for generous research funding. Kieran Byrne and Marcela Mello provided fabulous research assistance; Kristoffer Bjarkefur, Cynthia Sobieski, Christina Seybolt, Md. Munstasir, Mehrab Ali, and Ahmad Wahdat managed impressive field work. We thank Innovations for Poverty Action in Bangladesh for data collection work. Finally, we thank the staff at the IAPP project implementation unit for being outstanding research partners. The study protocols were approved by Yale University IRB (#1208010629) and IPA IRB (#00006083), and is registered in the AEA RCT Registry (AEARCTR-0010751). Computational reproducibility verified by DIME Analytics. The views expressed in this manuscript do not reflect the views of the World Bank. All errors are our own.
households. There are variations based on gender as well: Young women are more likely than young men to be unemployed and to stay out of the labor force entirely.

# _Youth unemployment rates_

Youth currently constitute a quarter of the working-age population worldwide, of which some 47 percent are unemployed. According to estimates drawn from the ILO, the global unemployment rate for youth has increased steadily over the last 10 years, from 11.7 percent in 1993 to 14.4 percent in 2003.<sup>2</sup> Youth unemployment rates vary widely across regions, from a low of 7 percent in East Asia to 13.4 percent in industrial economies to a high of 25 percent in the Middle East and North Africa.<sup>3</sup> Furthermore, there is considerable cross-country variation, not only in the levels of youth unemployment but also in the ratio of the youth to adult unemployment rate. A very high ratio in some countries is usually taken as a signal of atypical youth difficulties in the transition to work, a clear source of concern for policymakers.

Table 1. Average Unemployment and Jobless Rates for Youth: Selected Countries in Europe & Central Asia

||Unemployment|Jobless|
|---|---|---|
|Adult|11|21|
|Youth|21|24|
|_of which:_|||
|Male|22|22|
|Female|21|26|
|Urban|22|22|
|Rural|23|24|

_Source_ : Authors calculations based on available household survey data.

Across all markets, youth unemployment rate is typically 2 to 3 times higher than the adult unemployment rate, regardless of the aggregate level of unemployment. In the Europe and Central Asia (ECA) region, the average unemployment rate for youth is 21 percent, about twice as high the unemployment rate of adults. The jobless rate, which includes the unemployed as well as the discouraged youth, is estimated to be as high as 24 percent. The youth jobless rate is higher among females (26 percent) and in rural areas (24 percent). O’Higgins (2004) shows that transition countries in Europe, nowhere is the youth unemployment rate lower than 10 percent. In fact, in Bulgaria, Croatia, Poland and Slovakia it is over 35 percent. In 2002, the ratio of youth to adult unemployment rate was found to be just under 2-to-1 in EU countries, while in the EU

> 2 See ILO (2004). The ILO produces global and regional estimates based on several national surveys and the ILO Global Employment Trends model. 3 See ILO (2004).

3
19 Data on informal employment or the size of the informal economy are not readily available for transition countries. Although there are rough estimates provided by ILO and some national statistical offices, their reliability and comparability across countries is not guaranteed and they are available for only a few countries in our sample. However, there have been some attempts in the literature to estimate at least the size of the informal economy using indirect approaches, focusing on various macroeconomic and institutional indicators which are argued to be linked with the evolution of the informal sector. We follow the approach proposed by Kaufmann and Kaliberda (1996) and Johnson et al. (1997) and measure the size of the informal economy by comparing official measures of annual GDP growth with total electricity consumption growth. The basic assumption is that electricity consumption is closely related to overall economic activity, with a short-run electricity-GDP usually close to one; hence, the annual growth in electricity consumption represents a good proxy for the total (formal plus informal) GDP growth in a given year. If this is the case, rough estimates of the informal GDP can be obtained simply by calculating the difference between the estimated total GDP and the official GDP measure. This simple calculation method avoids important the endogeneity problems likely to be found in estimates coming from modeling approaches which derive the size of the informal economy from economic and institutional variables.

The method is not free from criticisms, of course. The assumption of unitary electricityGDP elasticity has been criticized mainly on the grounds that (i) technical progress over time has made electricity use more efficient than in the past, (ii) higher electricity prices reduce electricity consumption per unit of output, and (iii) many informal activities are not electricity-intensive, such as in the services sector. For these reasons, we also follow Kaufmann and Kaliberda and Johnson et al. and depart from the unitary elasticity assumption, considering three different
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# _% Patients _w/_ Tests

1 % Norm Costs Expend Source: The Urban Institute