rafmacalaba/datause-encoder-data huggingface dataset:
the probability that the counterparty<sup>will not fulfill the contrdct</sup> and the difficulty of findIng<sup>buyers at the appropriaLe time.</sup> Futures market overcome<sup>this credit risk by "marking to markeL" on</sup> a daily basie. In principle,<sup>futures markets are always available-</sup> -at least for the period<sup>in which there</sup><sup>**Is**sufficient</sup> liquidity. 2

**Table** 2: Prancophone **Africa: Coton Planting awd Harvesting Patterns**

|**CounUy**|**Planting**<br>**Dates**|**Harvesting**<br>**Dates**|
|---|---|---|
|**Benin**<br>DurlhiVA<br>Paso<br>Cameroon|June-July<br>June-July<br>mid-July<br>|Oct-Doc<br>**Nov-Dec**<br>Nov-Dec<br>|
|Cntal**Africa**Republic|Late**June-ealy**July<br>|Nov-Jan<br>|
|<br>Chad|June<br>|Nov-Dec<br>|
|**CMe**d'Ivoire|June-A**;g**<br>|Oct-Jan<br>|
|Mali<br>|June-July|Oct-Dec|
|**Senegal**<br>**Togo**|**June-July**|Nov-Dec|

Source: International Cotton Advisory Committee.

2The total annual cotton production of FPA countries is approximately 550,000 tons.<sup>In the New York cotton futt'res market</sup> 260,000 tons are traded daily. The majority of the trades<sup>is</sup> concentrated in the four nearby<sup>contracts, covering about 7-8</sup> months ahead. Options contracts are<sup>less liquid. There is daily</sup> trading of about 100,000 tons<sup>of cotton in options with liquidity</sup> concentrated in the two nearby<sup>contracts, covering 4-5 months</sup> ahead. The size of the N.Y.<sup>cotton futures contract is 50,000 lb</sup> (roughly 23 tons).
until 1990. Then stopped. M2 is the retail component of M4, The Bank of England suggested the omitting very large depositors who had some market power and who were unlikely to be using M4 for transactions purposes was the correct measure. 1987-1994 from Bank of England, Statistical Abstract, 1995 part 2 Detailed monetary statistics. 1995 values provided by the Bank of England. Colombia, Banco de la Republica.

_Money multipliers:_ Ratios of MI to Base money'. Chile, Boletin Mensual, Banco Central de Chile.

_Industrial Production:_ All from line 66 of IFC except: Portugal: June 1994- Boletim Mensal de Estadistica, Instituto Nacional de Estatistica, Bank of Portugal; Chile, Boletin Mensual, Banco Central de Chile; Colombia, Banco de la Repuibliica, Brazil, Banco Garantia; Argentina; _Real Exchange Rate:_ IFS line reu or rec. For Latin America, EP*/P where P* is weighted average of the WPI of the principal commercial partners of each country. US WPI.

_Interest rates:_ IFS line 601 or closest market determined rate. Argentina, Informe Econ6mico, Ministerio de Economia y Obras y Servicios Nblicos.

Our thanks for essential help collecting data to: I)avid Willoughby, Bank of England, Charles Goodhart, LSE for the UK; Hemando Vargas, Banco de la Republica, Colombia; Jose Guerra, Cental Bank of Venezuela; Rodrigo Azevedo, Banco Garantia, Brazil; Ricardo Bebzuk and Abel Viglione, Argentina.

# **Appendix II: Countries and Sample Periods**

_Asia:_ Israel 1987:1-95:12,Indonesia 1987:1-95:12, Korea 1988:12-1995:12; Malaysia 1987:11988:12.

_Europe:_ Austria, Denmark, France, Italy, Holland, Finland, Greece, Ireland, Portugal, Spain, Sweden, UK; all 1987: 1-1995:12 _Latin America:_ Argentina 1992:01-95:12; Brazil 1994:07-95:12; Chile 1987:1-95:12; Colombia 1987:1-95:12; Mexico 1991:11-95:12; Uruguay 1990:12-95:12.

19
# **ANATOMY OF COPING: EVIDENCE FROM PEOPLE LIVING THROUGH THE CRISES OF 2008-11**<sup>**1**</sup>

**RASMUS HELTBERG*, NAOMI HOSSAIN**, ANNA REVA*, AND CAROLYN TURK***

***WORLD BANK, SOCIAL DEVELOPMENT**

****INSTITUTE OF DEVELOPMENT STUDIES, SUSSEX**

_JEL: O12, O15_ _Keywords: Financial crisis, food crisis, vulnerability, coping, qualitative research._

> 1 We are grateful to Veronica M. Joffre for helpful comments and to the local researchers who carried out the country studies surveyed here. More details and select country case studies from the work summarized in this paper is available in our forthcoming book, _Living through Crises_ (2012). The research was funded by DfID and the trust Fund for Environmentally and Socially Sustainable Development housed at the World Bank and funded by the governments of Norway and Finland.
# **1. Introduction**

International migration from a poor to a rich country is perhaps the single act most likely to succeed in dramatically increasing the income of an individual (e.g. Clemens et al. 2009; McKenzie et al. 2010) as well as that of family members remaining behind (e.g. Yang and Martinez 2005; Yang, 2008; Gibson et al. 2011). The most direct channel through which international migration can lower poverty for household members remaining in a developing country is through remittances. However, high costs of sending remittances limit the amount received by remaining household members from a given remittance transfer, as well as the incentives of migrants to send remittances if such transfers are effectively taxed by these high transactions costs. Lowering the cost of sending remittances has thus become one of the most discussed areas for policy intervention in recent years (see World Bank, 2006 and the United Nations Global Forums on Migration), in part because doing so is viewed as politically uncontroversial compared to efforts to increase the opportunities for migration.

Two of the main policies to lower the costs of remittances have been regulatory reform to allow the introduction of new financial products, and efforts to increase the disclosure of the costs of remitting via each product, pioneered by Mexico ( _www.remesamex.gob.mx_ ) and the United Kingdom ( _www.sendmoneyhome.org_<sup>1</sup> ). However the efficacy of policies to reduce the cost of remitting and spur competition by allowing new product entry and increasing disclosure of costs relies heavily on the abilities of migrants to understand how to use the different methods available for remitting and the costs implied by each method. While systematic evidence on the financial literacy of migrants is scarce, the data available suggest migrants often lack knowledge of the components of a remittance cost, the methods available, or how to compare methods (Gibson et al, 2006; 2007). There therefore seems to be promising scope for financial literacy training to change remitting behavior.

There is also growing interest from policymakers in providing financial literacy training in this area. Much of the focus on financial literacy training for migrants and their families has traditionally been on either integrating immigrants into the financial system in the destination

> 1 This website has since expanded and changed name to www.fxcompared.com. The World Bank has also launched a remittance prices database (http://remittanceprices.worldbank.org) covering costs of remittances in 165 corridors.

2
# **Figures**

|1.1|Innovative Mechanisms of Development Finance ..................................................................... 3|
|---|---|
|2.1|Innovative Fund-Raising and Financial Solutions as an Estimated Share of<br>Development Finance, 2000–7 ............................................................................................ 8|
|2.2|Average Annual Official Flows by Objective and Country Type, FY2000–7 ......................... 10|
|2.3a|-b Disbursements from World Bank–Administered Trust Funds Supporting Innovative<br>Finance Applications, 2002–8 ........................................................................................... 11|
|5.1|IFC Local Currency Lending By Sector and Region, FY1997–2009 ..................................... 32|
|**Tab**|**les**|
|1.1|A Snapshot of the International Innovative Finance Landscape ................................................ 4|
|3.1|Emerging Sovereign Donor Contributions through World Bank Group, FY2001–8 .............. 13|
|4.1|Contributions by Nonsovereign Donors through World Bank Group, FY2001–8 .................. 16|
|4.2|Funds Raised by Development Banks through Bonds Denominated in Developing Countries’<br>Currencies, 1995–2008 (US$ million) .............................................................................. 18|
|5.1|World Bank Share of ERPA Commitments and Payments, 2005–8 (US$ million) ................ 28|
|**Box**|**es**|
|1.1|The Role of Financial Engineering in Enhancing Value Added ................................................ 5|
|4.1|World Bank Targets Sustainable Investing with Climate-Related Themes ............................. 17|
|4.2|Local Currency Loans Using Derivatives: The Case of Hygeia Nigeria ................................. 23|
|4.3|World Bank Intermediation Services for Malawi Weather Indexed Insurance ....................... 26|
|5.1|Mongolia’s Livestock Insurance Indemnity Pool .................................................................... 33|
|6.1|A Country-Based Platform for Mainstreaming Innovative Finance ........................................ 37|
- Caicedo, Santiago, Miguel Espinosa, and Arthur Seibold. 2022. "Unwilling to Train?—Firm Responses to the Colombian Apprenticeship Regulation." _Econometrica_ 90 (2):507-550. doi: https://doi.org/10.3982/ECTA18911.

- Caria, S., K. Orkin, R. Garlick, N. Singh, R. Heath, and A. Andrews. 2024. "Barriers to Search and Hiring in Urban Labour Markets." _VoxDevLit_ 10 (1).

- Carranza, Eliana, Robert Garlick, Kate Orkin, and Neil Rankin. 2022. "Job Search and Hiring with Limited Information about Workseekers’ Skills." _American Economic Review_ 112 (11):3547-3583.

- Carranza, Eliana, and David McKenzie. 2024. "Job Training and Job Search Assistance Policies in Developing Countries." _Journal of Economic Perspectives_ 38 (1):221-44. doi: 10.1257/jep.38.1.221.

- Cho, Yoonyoung, Davie Kalomba, Ahmed Mushfiq Mobarak, and Victor Orozco. 2013. "Gender Differences in the Effects of Vocational Training: Constraints on Women and Drop-Out Behavior." _World Bank Policy Research Working Paper, (6545)._

- Crépon, Bruno, and Patrick Premand. 2019. "Direct and Indirect Effects of Subsidized Dual Apprenticeships." _IZA Working Paper_ .

- de Hoyos, Rafael, Orazio Attanasio, and Costas Meghir. 2023. "Targeting High School Scholarships to the Poor: The Impact of a Program in Mexico." _Economic Development and Cultural Change_ :000-000. doi: 10.1086/725232.

- Duflo, E., P. Dupas, and M. Kremer. 2021. "The Impact of Free Secondary Education: Experimental Evidence from Ghana." _(No. w28937). National Bureau of Economic Research_ .

- Duflo, E., P. Dupas, and M. Kremer. 2023. The Impact of Secondary School Subsidies on Career Trajectories in a Dual Labor Market: Experimental Evidence from Ghana. <u>https://www.dropbox.com/scl/fi/rtpe24jusfr64jadfsc6e/DDK_GhanaSchol arships.pdf?rlkey=j0swg4pzuxm8jl5ht8yn8u4a6&e=1&dl=0.</u>

- Dustan, Andrew. 2020. "Can large, untargeted conditional cash transfers increase urban high school graduation rates? Evidence from Mexico City's Prepa Sí." _Journal of Development Economics_ 143:102392. doi: <u>https://doi.org/10.1016/j.jdeveco.2019.102392.</u>

- Edmonds, Eric, and Caroline Theoharides. 2020. "The short term impact of a productive asset transfer in families with child labor: Experimental evidence from the Philippines." _Journal of Development Economics_ 146:102486. doi: https://doi.org/10.1016/j.jdeveco.2020.102486.

- Ferreira, Francisco, Deon Filmer, and Norbert Schady. 2017. "Own and Sibling Effects of Conditional Cash Transfer Programs: Theory and Evidence from

33
standard deviation) lower on average in LOW and 0.85 points lower in HIGH. Electricity is the top constraint in the SSA region, but is much lower down the list in all other regions except South Asia. Access to finance (col. 3) is 0.52 points lower in LOW and 0.28 points lower in HIGH. On the other hand, tax administration (col. 6) is reported as relatively more concerning for non-SSA entrepreneurs. It is more constraining in LOW countries and even more so in HIGH.

There are also significant differences by exporting status and ownership. SSA exporters are particularly concerned about electricity and somewhat more concerned about labor and corruption, while less concerned about access to finance. Foreign owned, and to an even greater degree government owned enterprises, report fewer constraints overall. While foreign owned enterprises report being relatively less constrained in the other regions, this is not true for SSA. Foreign enterprises are particularly concerned with corruption and crime, the two areas that had received lower rating overall in SSA.

One of the benefits of the ES data is that these qualitative rankings can then be correlated with more objective measures of how these areas of the investment climate impact enterprises. To gain more insight into how these quantitative variables vary by size within regions, Table 4 presents results interacting region and size. The results demonstrate that there is a reason why infrastructure is the dimension with the biggest differences in severity of reported constraint. Enterprises in SSA experience more frequent interruptions in service than do enterprises in other regions. The frequency of outages falls with size, generally as larger enterprises are more likely to invest in generators. Clearly this is an area where SSA enterprises face greater challenges than their counterparts in most other regions, affecting their choices of technology, capital intensity and growth potential.

SSA entrepreneurs also face greater challenges in accessing finance. The share of working capital financed by banks is 2.2 percentage points lower in SSA than in LOW, which 19
where the matrix of Kalman gains K depends on the variance parameters _εt_ , _ηt_ and _vt_ . In particular, when the variances parameters are large, _st_ and _at_ are less precise signals of _xt_ and agents take longer to adjust their expectation _xt/t_ to the true value _xt_ (see Lorenzoni 2009 for more details).

Since _Xt_ = _AXt−_ 1 + _BVt_ and that _St_ = _CXt_ + _DVt_ , we can re-write the dynamics of _X_ as:<sup>2</sup> _t/t_

# **3 Results**

## **3.1 Maximum likelihood estimation**

After having solved in the previous section the consumers’ Kalman filter to obtain the dynamics of consumers’ expectations, we proceed to estimate the parameters of the model. The equations describing the equilibrium are the ones included in the vectors _Xt_ , _Xt/t_ , and the equation of the endogenous state (6). The variables observed are _ct_ and _at_ . We use _Dynare_ for maximum likelihood estimation.

The consumption series is constructed by dividing real consumption by population. The labor productivity series is constructed by dividing employment by real GDP. Both series are taken in logs. An important issue we have to confront is that, contradictory to our model, productivity and consumption have different growth rates over the sample. This difference reflects factors left out of our simple model such as changes in participation rate, or changes in saving rate (likely linked with the deterioration of the Irish current account during most of the studied period), or changes in the capital-output ratio. Consequently, we remove the drift in the consumption-to-productivity ratio applying the first-differencing filter to both series.

Our dataset spans the period 1997Q1 to 2011Q4. The series for Real GDP, Real Personal Consumption Expenditures, and Employment are from the OECD database. Population data comes from Eurostat and the Central Statistics Office (CSO). Figure 1 plots the data series.

## **3.2 Recovering shocks and states**

Figure 2 shows the recovered shocks from our structural estimation. We remark that permanent productivity shocks are much smaller compared to the size of noise and transitory productivity shocks.<sup>3</sup> This result highlights the major signal

> 3The high autocorrelation of the permanent shocks is a consequence of the difficulty to identify the precise quarter when the permanent change in productivity occurs.

5
- port). Retrieved from https://www.wateraid.org/in/sites/g/files/ jkxoof336/files/2022-04/Groundwater%20The%20world%E2%80%99s% 20neglected%20defence%20against%20climate%20change.pdf

- Xenarios, S., & Pavelic, P. (2013, 01). Assessing and forecasting groundwater development costs in Sub-Saharan Africa. _Water SA_ , _39_ , 00 - 00. Retrieved from http://www.scielo.org.za/scielo.php?script=sci ~~a~~ rttext&pid=S1816-79502013000400010&nrm=iso

- Xie, H., You, L., & Takeshima, H. (2017). Invest in small-scale irrigated agriculture: A national assessment on potential to expand small-scale irrigation in nigeria. _Agricultural Water Management_ , _193_ , 251-264. Retrieved from https://www.sciencedirect .com/science/article/pii/S0378377417302846 doi: https://doi.org/10 .1016/j.agwat.2017.08.020

- Xie, H., You, L., Wielgosz, B., & Ringler, C. (2014). Estimating the potential for expanding smallholder irrigation in Sub-Saharan Africa. _Agricultural Water Management_ , _131_ (C), 183-193. Retrieved from https://ideas.repec.org/a/eee/agiwat/ v131y2014icp183-193.html doi: 10.1016/j.agwat.2013.08.0

- You, L., Ringler, C., Wood-Sichra, U., Robertson, R., Wood, S., Zhu, T., ... Sun, Y. (2011). What is the irrigation potential for africa? a combined biophysical and socioeconomic approach. _Food Policy_ , _36_ (6), 770-782. Retrieved from https://ideas .repec.org/a/eee/jfpoli/v36y2011i6p770-782.html doi: 10.1016/j .foodpol.2011.09

- You, L., Xie, H., Wood-Sichra, U., Guo, Z., & Wang, L. (2014, 08). Irrigation potential and investment return in kenya. _Food Policy_ , _47_ , 34–45. doi: 10.1016/j.foodpol.2014.04 .006

- You, L. Z. (2008, June). _Africa : Irrigation investment needs in sub-saharan africa_ (World Bank Publications - Reports No. 7870). The World Bank Group. Retrieved from https://ideas.repec.org/p/wbk/wboper/7870.html

25
|Economic projections|Economic forecasts are from Global Economic<br>Prospects (GEP) June 2021 (for 2020) and January 2022<br>(for 2021).|Economic forecasts were from Macro Poverty Outlook<br>(MPO) October 2020.|
|---|---|---|

A key update in the latest simulations results is that with the school closure data from UNESCO, we no longer assume that closures are uniform across regions or countries, an assumption made in the previous simulation version due to limited data availability on school closures. As shown in Table 2, there are wide differences across regions in the share of the school system that was closed. Taking the intermediate scenario, in which partial closures are assumed to affect 85% of the student population, 70% of the school system was closed in South Asia, compared to 57% in Middle East and North Africa and 34% in Europe and Central Asia.

**Table 2. Share of the school system that is closed by scenario**

||Optimistic|Intermediate|Pessimistic|
|---|---|---|---|
|**Global**|42.9%|45.4%|49.2%|
|**Global(Part 2)**|45.1%|47.6%|51.4%|
|**By Region**||||
|East Asia and Pacific|33.8%|35.9%|39.1%|
|Europe and Central Asia|31.7%|33.7%|36.8%|
|Latin America and Caribbean|67.0%|71.0%|77.1%|
|Middle East and North Africa|53.6%|56.7%|61.2%|
|North America|56.5%|62.6%|71.9%|
|South Asia|66.0%|69.6%|75.0%|
|Sub-Saharan Africa|33.1%|34.7%|37.0%|
|**By Region (Part 2)**||||
|East Asia and Pacific|36.8%|38.9%|42.0%|
|Europe and Central Asia|36.0%|38.1%|41.3%|
|Latin America and Caribbean|72.0%|76.4%|82.9%|
|Middle East and North Africa|44.7%|46.8%|50.0%|
|North America||||
|South Asia|66.0%|69.6%|75.0%|
|Sub-Saharan Africa|33.1%|34.7%|37.0%|
|**By income level**||||
|High-Income|38.1%|40.8%|44.8%|
|Upper-middle-income|52.7%|55.9%|60.7%|
|Lower-middle-income|42.5%|44.7%|48.0%|
|Low-income|34.4%|36.0%|38.4%|
|**By Lending type **||||
|Part 1|37.7%|40.3%|44.2%|
|IBRD|54.7%|57.9%|62.8%|
|IDA/Blend|36.0%|37.7%|40.4%|
|Source: Authors’ calculations using the UNESC<br>learning data (LP, LAYS, or PISA) but no schoo<br>regional average byincome level. The estimates a|O School Closures database coverin<br>l closure data, we impute missing v<br>re countryaverages.|g February 2020-February 2<br>alues for share of school sys|022. For countries with<br>tem closed by using the|

7
Source: Tingvall, 2008

# _United Kingdom_

General purpose transfers to local governments in the UK are distributed through the Local Government Finance Settlement comprising (a) revenue support grant; (b) redistributed business rates; and (c) Police Grant. The overall system is termed as the Formula Grant (FG). The FG is calculated as follows: FG= Relative Needs (RN) _minus_ Relative Resources (RR) _plus_ central per head allocation (CA) and _plus/minus_ stabilization (floor damping) adjustment (SA) RN is determined by classifying local expenditures into 6 major service groups: children‘s services, adults‘ personal social services, police, fire, highway maintenance, environmental, protective and cultural services. Population, social, economic and physical characteristics of each local authority are used as indicators of need. An attempt is made to keep these indicators to a manageable level. Fixed costs of all services are also taken into consideration. RR is calculated separately for four separate groups (upper tier services, lower tier services, fire authorities, police authorities) of authorities by examining a local authority‘s (LA) tax base per capita against minimum LA tax base per capita. CA is determined residually as the balance of central grant after compensating for needs. SA adjustment is made to ensure that all LAs receive a guaranteed minimum increase in grant over the previous year (see Ponsford, 2007 for further details).

Overall, the UK uses an objective method to assess fiscal capacities and somewhat subjective methods to assess expenditure needs.

# **5. Practical Difficulties in Implementing Expenditure Needs Equalization with Special Emphasis on the Australian Experience**

The previous section highlighted international practices in determining expenditure needs. We observed that these approaches over time have grown in their rigor and complexity in their attempts to achieve a better handle on differential expenditure needs attributable to exogenous factors. Is all this effort worthwhile? We draw upon a half a century of the Australian experience to reflect on this question as most of these approaches have replicated a variant of various models developed initially by the Commonwealth Grant Commission (CGC) for Australia. In view of the demonstration effect of the work by the CGC worldwide, it is useful to review the Australian approach to draw lessons from the Australian experience.

The overall approach in assessing expenditure needs used by the CGC is highly data and subjective judgment intensive. Continuous refinements over time to accommodate opposing points of view have led to super complexity and non-transparency. Further, the approach assumes that costs are independent of management paradigm and resource use is independent of incentives. As an example, expenditure need for government secondary 18
**Table 2. Average prices of key commodities**

||**National**|**Agricultural**<br>**Households**<sup>a</sup>|**Nonagricultural**<br>**Households**<sup>a</sup>|
|---|---|---|---|
|Price of wheat flour|28.45|29.01|28.45|
||(8.15)|(8.35)|(8.15)|
|Price of vegetable oil|81.16|82.39|81.16|
||(6.88)|(6.97)|(6.88)|
|Price of rice|42.77|43.25|42.77|
||(14.93)|(15.07)|(14.93)|
|Price of lamb|184.44|183.01|184.44|
||(11.97)|(12.38)|(11.97)|
|Price of milk|26.94|26.57|26.94|
||(23.91)|(24.08)|(23.91)|
|Price of kerosene|48.12|48.80|48.12|
||(6.25)|(6.05)|(6.25)|

Note: Estimated population weighted means. Standard deviations in parentheses. Prices in Afghani per kilogram or liter (kerosene).<sup>a</sup> Statistical tests of differences in means (corrected for complex sample design) between prices faced by Agricultural and Nonagricultural Households (based on location) show significant differences at a 5% level of significance for all estimated means; the price of lamb is the exception. _Source:_ NRVA 2007/08 27