rafmacalaba/datause-encoder-data huggingface dataset:
For a few countries, final consumption expenditure had to be computed as a product of a variable expressing final consumption expenditure (including also statistical discrepancies) as a percentage of GDP, and GDP measured in constant 2000 US dollars. Household final consumption expenditure, on the other hand, did not need any additional manipulation.

The longest period covered by the data is 1965-2005, though regrettably this is not true for all countries. As a result, the longest estimation horizon has been that of 1971-2005. Due to the need to aggregate consumption and GDP across countries, data on consumption and GDP were measured in constant 2000 US dollars.

Consumption insurance tests were performed on a wide set of 93 countries from the following (geoeconomic) areas: East Asia and Pacific, Sub-Saharan Africa, Latin American and the Caribbean, and OECD. In particular, the following LAC countries have been covered (due to data availability): Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, El Salvador, Trinidad and Tobago, Uruguay and Venezuela.

The analysis on welfare gains from international risk diversification has been performed on a wider group of LAC countries, including also Barbados, Dominica, Dominican Republic, El Salvador, and Grenada.

To perform the welfare analysis, data on money market rates and treasury bills rates, and on CPI growth rates were obtained from the “International Financial Statistics” database, for the period 1994–2003, and cover all the countries in the larger group, except for Costa Rica, Ecuador, Honduras, Nicaragua, and Peru, for which only data on the official discount rate were available. Bahamas was also excluded from this analysis, as consumption data were not available for recent time periods.

# **4.2. A preliminary look at data and some empirical results**

To get a first grasp of the phenomenon under scrutiny, we may give a look at Graphs 1 and 2, containing scatter diagrams where the x-variable is represented by normalized per capita income (where the mean and standard deviation has been computed over the whole sample of countries) and the y-variable is represented by the values of coefficients β 1 − in equation (10).

11
Jaffe and Russell (2012) use a welfare economics approach to examine the relative roles of ex ante private insurance and ex post government aid to deal with catastrophic risks<sup>29</sup> . They find that individuals have heterogeneous perceptions of risk (over and above dealing with free rider and Samaritan’s dilemma issues) and that a welfare improvement could possibly be obtained by the imposition of actuarially priced mandatory catastrophe insurance.

# **Empirical evidence**

While there is a growing theoretical literature on exogenous shocks<sup>30</sup> , Cavalloy and Noy (2010) in a literature review on the economics of natural disasters highlight the lack of detailed public data sources<sup>31</sup> that can support empirical research work on the impacts of natural disasters. The authors specifically mention a lack of information about the channels through which natural disasters affect short and long term output, including at the household level. They point out that while insurance mechanisms can be useful<sup>32</sup> , politicians have very weak incentives to consider such expenditures. In addition if such mechanisms are to be effective they need strong institutional settings, including transparent asset management, good risk statistics and strong systems for loss valuation and funds disbursement (also see Petterson et al, 2005).

Toya and Skidmore (2005) examine direct losses and death rates after disasters, and control for income levels. Using a panel of 151 countries and an OLS methodology they find that socio/ economic infrastructure, including financial sector depth (using M3 as a proxy) is an important determinant of post disaster outcomes.

Beilharz et al (2013) summarize more recent literature on the impact of natural disasters on economies and fiscal space, and the potential role of the insurance function. The authors highlight the growing real direct global costs of natural disasters and point to the disproportionate direct losses (relative to GDP) suffered by emerging markets<sup>33</sup> . The average top decile of direct losses for emerging markets equates to 2.6% of GDP, compared to 1.9% in developing countries and 0.5% in industrial countries, implying that risk management (including building standards) lags the development of physical capital (Figure 4).

> 29 This is becoming a more important issue as governments face up to the fiscal implications of natural disasters in the face of increasingly severe events.

> 30 Some of which explore the exogenous/ endogenous growth debate – see Noy and Nuasri (2007) who argue that human capital loss is more important than loss of physical capital.

> 31 EM-Dat is seen to cover too many events and with insufficient granularity.

> 32 e.g.  Borensztein et al, 2009, show that Belize could significantly increase its debt sustainability through purchases of catastrophic risk insurance.

> 33 Emerging markets are defined as those with per capita incomes between US$1,036 and US$12,615. Developing economies have lower per capita incomes.

23
Policy Research Working Paper 9676

# **Abstract**

Several developing countries are currently implementing phone surveys in response to immediate data needs to monitor the socioeconomic impact of COVID-19. However, phone surveys are often subject to coverage and non-response bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey. Using data from high-frequency phone surveys in Ethiopia, Malawi, Nigeria, and Uganda, this study investigates the magnitude and source of biases present in these four surveys and explores the effectiveness of techniques applied to reduce bias. Varying levels of coverage and non-response bias are found in all four countries. The successfully contacted samples in these four countries were biased toward wealthier households with higher living standards. Left unaddressed, this bias would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the country. However, phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative survey from which the sample is drawn. Applying these methods to the four surveys resulted in a substantial reduction in bias, although the bias was not fully eradicated. This highlights one of the potential advantages of drawing phone survey samples from existing face-to-face, representative surveys over random digit dialing or using lists from telecom providers where such adjustment methods can be more limited.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at kmcgee@worldbank.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
MiIF Imports for fixed capital formation MriC, MriG Imports for private and public consumption from region r MriIF Imports for fixed capital formation from region r K<sup>F</sup> Net payments on foreign capital holdings e Real exchange rate (price index for foreign exchange) B Current-account balance D Government budget deficit (held fixed) Si Supply on domestic market (Di + Mi) N pri Domestic price index for intermediate imports C G pri , pri Domestic price indexes for imports of private and public consumption F priI Domestic price index for imports for gross capital formation C F pi , piI Price index for private consumption/fixed capital of domestic goods pri Producer price index for goods exported to region r

# _LIST OF PARAMETERS_

σi Substitution elasticity between capital and labor ηa Substitution elasticity between intermediates and value added ηi Armington elasticity on imports between regions ηj Substitution elasticity between domestic and imported intermediates εi Transformation elasticity between domestic and exported output ei Transformation elasticity on exports between regions ψi Substitution elasticity between domestic and imported consumption tri Tariff rate on imports from region r (tri= 0 for service sectors) ui Resource-using services border barriers (ui=0 for non-service sectors) vi Service rents on output (vi=0 for non-service sectors) λi Service resource-using barriers on output (λi=0 for non-service sectors) EK , E1L Endowment of capital and labor m pri Price of imports from region r x pri Price of exports in region r r<sup>F</sup> Price of foreign capital payments 23
- [21] Choi, I. and H. Jeong (2017): " Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons" , Econometric Reviews

- [22] Choi, I., D. Kim, Y. Kim and N. Kwark (2017): "A multilevel factor model: identi...cation, asymptotic theory and applications", Journal of Applied Econometrics (forthcoming).

- [23] Davis, J. and E. van Wincoop (2017): "Globalization and the Increasing Correlation between Capital In‡ows and Out‡ows ", NBER Working Paper 23671.

- [24] Eichengreen, B., P. Gupta and O. Masetti (2017): "Are capital ‡ows ...ckle? Increasingly ? and does the answer still depend on type?", World Bank Policy Research Working Paper 7972.

- [25] Ergemen, Y. and C. Rodriguez-Caballero (2016): "A dynamic multi-level factor model with long-range dependence", CREATES Research Paper 2016-23.

- [26] Forbes, C. and F. Warnock (2012): "Capital ‡ow waves: surges, stops, ‡ight and retrenchment", Journal of International Economics 88, 235-251.

- [27] Forbes, K., D. Reinhardt and T. Wieladek (2017): "The spillovers, interactions, and (un)intended consequences of monetary and regulatory policies”, Journal of Monetary Economics 85, 1-22.

- [28] Fratzscher, M. (2012): "Capital ‡ows, push versus pull factors, and the global ...nancial crisis", Journal of International Economics 88, 341-356.

- [29] Ghosh, A., J. Ostry and M. Qureshi (2015): "Exchange Rate Management and Crisis Susceptibility: A Reassessment", IMF Economic Review 63, 238-276.

- [30] Jorda, O., M. Schularick, A. Taylor and F. Ward (2017): “Global ...nancial cycles and risk premiums”, unpublished manuscript.

- [31] Kose, A., C. Otrok and C. Whiteman (2003): "International Business Cycles: World, Region, and Country-Speci...c Factors", American Economic Review 93, 1216-1239.

- [32] Leamer, E. (1984): Sources of International Comparative Advantage: Theory and Evidence, Cambridge, MA: MIT Press.

- [33] Leamer, E. (1995): The Heckscher-Ohlin Model in Theory and Practice, Princeton Studies in International Finance.

- [34] Miranda-Agrippino, S. and H. Rey (2015): "World asset markets and the global ...nancial cycle", NBER Working Paper 21722.

- [35] Obstfeld, M., J. Ostry and M. Qureshi (2017): "A Tie That Binds: Revisiting the Trilemma in Emerging Market Economies", IMF Working Paper 17/130.

- [36] Onatski, A. (2012): " Asymptotics of the principal components estimator of large factor models with weakly in‡uential factors", Journal of Econometrics 168, 244-258.

29
Presbitero, A., and U. Wiriadinata. 2020. “The Risks of High Public Debt Despite a Low Interest Rate Environment.” VoxEu.org, August 5.

Reinhart, C., and C. Trebesch. 2016. “Sovereign Debt Relief and its Aftermath.” Journal of the European Economic Association 14 (1): 215-251.

Reis, R. 2021. “The Constraint on Public Debt When r< g but g< m.” Working Paper 939, Bank for International Settlements, Basel.

Rogoff, K. 2021. “Fiscal Sustainability in the Aftermath of the Great Pause.” Journal of Policy Modeling 43 (4): 783-793.

Stiglitz, J., and H. Rashid. 2020. “Averting Catastrophic Debt Crises in Developing Countries.” CEPR Policy Insight 104, Centre for Economic Policy Research, London.

Truman, E. M. 2020. “Sovereign Debt Relief in the Global Pandemic: Lessons from the 1980s.” PIIE Policy Brief 20-13, Peterson Institute for International Economics, Washington, DC.

World Bank. 2017. Global Economic Prospects: A Fragile Recovery. June. Washington, DC: World Bank.

World Bank. 2019. Global Economic Prospects: Darkening Skies. January. Washington, DC: World Bank.

World Bank. 2020a. Africa’s Pulse: An Analysis of Issues Shaping Africa’s Economic Future. October. Washington, DC: World Bank.

World Bank. 2020b. Global Economic Prospects. June. Washington, DC: World Bank.

World Bank. 2020c. Ensuring Integrity in Governments' Response to COVID-19. World Bank, Washington, DC.

World Bank. 2020d. COVID-19 Role of Supreme Audit Institutions (SAIs) in Governments' Response to COVID-19: Emergency and Post Emergency Phases. World Bank, Washington, DC World Bank. 2020e. Agile Treasury Operations During COVID-19. World Bank, Washington, DC World Bank. 2021a. Renewing with Growth. LAC Semiannual Report: April 2021. Washington, DC: World Bank.

World Bank. 2021b. Uneven Recovery: East Asia and Pacific Economic Update, April 2021. Washington, DC: World Bank.

World Bank. 2021c. The Fiscal Impact and Policy Response to COVID-19. Equitable Growth, Finance and Institutions Insight. World Bank, Washington, DC World Bank. 2021d. Dashboard on Prudential Policy Responses (live site). EFI COVID-19 Response Resource, Washington, DC: World Bank. Accessed on 09/24/2021.

World Bank. 2021e. “Impact of COVID-19 on Business.” EFI Policy Note, January. Washington, DC: World Bank.

World Bank. 2021f. “The Calm Before the Storm: Early Evidence on Business Insolvency Filings After the Onset of COVID-19.” EFI COVID-19 Notes, Finance Series. Washington, DC: World Bank.

World Bank. 2021g. Global Economic Prospects. January. Washington, DC: World Bank.

World Bank. 2021h. South Asia Economic Focus, Spring 2021: South Asia Vaccinates. Washington, DC: World Bank.

19
Figure 1.5 offers another view of the state of infrastructure in Africa. The World Bank’s ACR (2007) evaluates a wide range of factors related to economic activity, infrastructure among them. Once again there are clearly different performance levels across the two blocks of countries. While in Namibia, South Africa, Botswana, Egypt, and Morocco the quality of infrastructure exceeds the approval level; in the remaining countries this quality is rated low in most cases. The same holds for the disaggregated results, including the number of telephone lines and the quality of ports, air transport, and electricity supply.

**Figure 1.5 The state of infrastructure in Africa, at first glance**

<!-- Start of picture text --> A. Quality of overall infrastructure B. Quality of railroad infrastructure<br>Namibia Namibia<br>South Africa South Africa<br>Mauritius Egypt<br>Botswana Botswana<br>Egypt Morocco<br>Morocco Tanzania<br>Algeria Burkina<br>Tanzania Algeria<br>Uganda Lesotho<br>Mali Kenya<br>Malawi Mauritania<br>Kenya Malawi<br>Madagasca Cameroon<br>Lesotho Mauritius<br>Ethiopia Mali<br>Benin Uganda<br>Burkina Madagasca<br>Burundi Ethiopia<br>Cameroon Benin<br>Mauritania Burundi<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Score (1-7) Score (1-7)<br>C. Quality of port infrastructure D. Quality of air transport infrastructure<br>Namibia South Africa<br>Mauritius Mauritius<br>South Africa Kenya<br>Morocco Namibia<br>Egypt Egypt<br>Tanzania Morocco<br>Kenya Ethiopia<br>Algeria Botswana<br>Botswana Tanzania<br>Benin Algeria<br>Mauritania Uganda<br>Madagascar Madagascar<br>Cameroon Burkina<br>Uganda Malawi<br>Lesotho Mali<br>Burundi Benin<br>Burkina Mauritania<br>Malawi Burundi<br>Mali Lesotho<br>Ethiopia Cameroon<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Score (1-7) Score (1-7)<br>E. Quality of electricity supply F. Telephone lines<br>Mauritius Mauritius<br>Namibia Egypt<br>Morocco South Africa<br>Egypt Algeria<br>Botswana Botswana<br>Algeria Namibia<br>South Africa Morocco<br>Ethiopia Lesotho<br>Mali Mauritania<br>Lesotho Benin<br>Kenya Malawi<br>Burkina Fasso Kenya<br>Mauritania Ethiopia<br>Benin Mali<br>Malawi Burkina<br>Cameroon Cameroon<br>Tanzania Tanzania<br>Madagascar Madagascar<br>Burundi Burundi<br>Uganda Uganda<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30<br>Score (1-7) Total number of telephone lines per 100 inabitants<br><!-- End of picture text --> _Source:_ Africa Competitiveness Report (2007), World Bank, Washington, DC. _Note:_ No data are available for Cape Verde, Eritrea, Niger, Senegal, Swaziland, or Zambia .

9
communication. Global poverty lines (both absolute and relative) are statistical products, but their acceptance by policy makers and advocates is affected by how easy they are for nontechnicians to understand and communicate. Stating that the SPL is a dollar a day plus 50 percent of median daily consumption is a relatively simple construct.

In addition to simplicity, the intercept and slope also have links to the poverty and inequality literature and to measures used by many countries. The slope coefficient of half the median is widely used by many countries as measures of relative poverty and inclusion. The Organization for Economic Co-operation and Development (OECD) uses 50 percent of median household income as their headline poverty indicator for country poverty rates.<sup>24</sup> Similarly, Vecchi (2015, pp. 4-5) notes that European countries typically set their national poverty thresholds at 50 or 60 percent of median disposable household income. For rich countries, the intercept of $1 proposed by our line will make relatively small difference. And, 50 percent of household median income or consumption is being used as an indicator of SDG 10.2 on inclusion. The intercept of one US dollar (per person, per day in 2011 PPP terms) has a couple of interesting links with existing research on consumption floors and measures of the cost of survival. Ravallion (2016) estimates an empirical lower bound on consumption, a consumption floor as he refers to it, as essentially a measure of the lower bound of support for a distribution of consumption. In one specification from his analysis, he identifies the value of this floor at $0.67 in 2005 PPP terms. This value expressed in 2011 PPP terms is approximately one dollar.<sup>25</sup>

> 24 OECD (2017), Poverty rate (indicator). doi: 10.1787/0fe1315d-en (Accessed on 26 January 2017). 25 See Ferreira et al. (2016) for discussion on inflating 2005 PPP values into 2011 PPP values. Ferreira et al. assert that, on average, $1.90 in 2011 PPPs maintains the same purchasing power as $1.25 in 2005 PPPs for the set of 15 poor countries that determine the international poverty line. They also demonstrate that this inflation rate of about 52 percent maintains on average purchasing power for essentially all countries in the PovcalNet database for which they estimate poverty (and have measures of PPP in both

17
_The Cultural Trade Index_ Page | **6** of **14**

# **Version 2**

> Table 2 <u>|</u> **Art** **<u>Crafts Trade,</u>** 2011 **<u>|</u>** USD at current prices and exchange rates in millions

|**Art Crafts**<sup>a</sup><br>Values and share<br>goods exports an|s of creativ<br>d imports, (|e industries<br>annual, 20|related<br>11)|**Total T**<br>**Creativ**|**rade in A**<br>**e Goods**<sup>b</sup>|**ll**<br>(2011)|**Total Trade in All**<br>**Goods and Services**<br>(BPM6)<sup>c</sup>(2011)|
|---|---|---|---|---|---|---|---|
|**Country**|Exports<br>(E)|Imports<br>(I)|Total E + I|E|I|E + I||
|**Egypt, Arab**<br>**Rep.**|$628|$131|**$759**|$1 153|$641|**$1 794**|**$47 053**|
|**South Africa**|$20|$155|**$175**|$364|$2 170|**$2 534**|**$126 798**|
|**Ghana**|$116|$10|**$126**|$152|$155|**$307**|**$14 596**|

aIncludes Carpets, Celebration, Other, Paperware, Wickerware, and Yarn. bIncludes Art Crafts, Audio Visuals, Design, New Media, Performing Arts, Publishing, and Visual Arts. So ‘goods’ here probably implies goods and services given the composition of the items included.<sup>c</sup> BPM6 = Balance of Payments and International Investment Position Manual Sixth Edition.

Source: UNCTADstat Table 2.1 <u>|</u> **Art** **<u>Crafts</u>** <u>, 2011 | Total Trade analysis in millions (USD)</u>

|**Country**|**Egypt, Arab**<br>**Rep.**|**South Africa**|**Ghana**|
|---|---|---|---|
|Art Crafts Trade/Trade in all Creative Goods|759÷1794 =<br>~0.423077|175÷2 534 =<br>~0.069061|126÷307 =<br>~0.4104235|
|Percentage of Art Crafts Trade in Total Trade<br>inCreative Goods|~ 42.3077%|~ 6.9061%|~ 41.04235%|
|Art Crafts Trade/Total Trade in all Goods<br>and Services|759÷47 053 =<br>~0.0161307|175÷126 798 =<br>~0.00138015|126÷14 596 =<br>~0.0086325|
|**Percentage of Crafts Trade in Total Trade**|~**1.61307%**|~**0.138015%**|~**0.8633%**|
|Trade in all Creative Goods/Trade in<br>allGoods and Services|1 794÷47 053 =<br>~0.03812722|2 534÷126 798 =<br>~0.0199846|307÷14 596 =<br>~0.0210332|
|Percentage ofCreative Goodsin Total Trade|~3.8123%|~ 1.99846%|~ 2.10332%|

Table 2.2 | **Example of Index Computation** | Art Crafts Total Trade, 2011 Figures in millions <u>(USD) Total Trade Base Country: Ghana</u>

|**Country**|**Egypt, Arab**<br>**Rep.**|**South Africa**|**Ghana**|**Rank**(Ba<br>Result in|sed on<br>Step 3)|
|---|---|---|---|---|---|
|**Step 1**: Art Crafts||||||
|Trade/Total Trade between<br>the 3 countries (Egypt, South<br>Africa, and Ghana i.e. 47<br>053+126 798+14 596) = 188<br>447|759÷188 447<br>= 0.0040276576|175÷188 447<br>= 0.0009286431|$126 ÷$188 447<br>= 0.000668623|**Egypt,**<br>**Arab**<br>**Rep.**<br>**Ghana**|**#1**<br>**#2**|
|**Step 2**: Base Country||||||
|Chosen: Ghana∴Country’s|47 053÷14 596|126 798÷14 596|14 596÷14 596|**South**|**#3**|
|TotalTrade/Ghana’s Total<br>Trade|= 3.2240339819|= 8.6871745684|= 1|**Africa**||
|**Step 3**:Outcome in Step 1|0.0040276576÷|0.0009286431÷|0.000668623÷|||
|divided by outcome in|3.2240339819**=**|8.6871745684 =|1**=**|||
|Step2.|**0.0012492603**|**0.0001068982**|**0.000668623**|||