rafmacalaba/datause-encoder-data huggingface dataset:
# **2 Data and Measures**

Measurement is very important for a proper diagnosis of the longer run constraints to reducing chronic malnutrition in any given context. Each of the three clusters of underlying causes of malnutrition is inherently multidimensional making measurement difficult and costly. As a consequence the main concepts underpinning the original UNICEF framework regarding the interrelationship between and synergies among food security, environment and health, and child care are usually taken for granted or mistakenly assumed to have been investigated by other earlier studies.

Table 1 lists the countries and sources of data used in this study. Two countries were purposefully chosen from each of the four regions where malnutrition is a problem, (i.e. South Asia (SAR), Latin America and the Caribbean (LAC), East Asia Pacific (EAP), and Sub‐Saharan Africa (SSA). Additional criteria applied included: (i) the survey contained reliable information of children’s height (and weight) which are the widely accepted measures of chronic and short‐term malnutrition. This criterion limited the analysis to the Demographic and Health Surveys (DHS) as these are the only surveys is most countries with child height (and weight) measures. (ii) Data were available for at least two years in the last decade; (iii) malnutrition rates were stable or slowly declining over time; (iv) there was parallel analytic work in the Bank on different dimensions of poverty and nutrition taking place in some of these countries (e.g. Ethiopia); and (v) there were more than one survey available in the same country (e.g., Bangladesh). The samples in most countries are mainly rural except for Bolivia, Peru, Indonesia, and the Helen Keller survey in Bangladesh where urban households make up between one‐third and one‐half of the samples. The IFPRI survey for Bangladesh includes only rural households.

**Table 1: Countries and Data Sources**

|**Region **|**Country ‐data source**|
|---|---|
|SAR|Bangladesh(Helen Keller 2010,2011andIFPRI 2011)|
||Nepal(DHS2001and2011)|
|LAC|Bolivia (DHS 2003 and 2008)|
||Peru (DHS2005 and2012)|
|EAP|Cambodia (DHS2005 and2010)|
||Indonesia (Riskesdas2010)|
|SSA|Ethiopia (DHS2000 and2011)|
||Zimbabwe (DHS2005 and2010)|

Given these criteria the sample of children used is between 0 and 24 months in Bangladesh (Helen Keller data), Cambodia, and Zimbabwe, between 0 and 25 months in Bangladesh (IFPRI data) and Indonesia, and between 0 and 36 months in Bolivia, Ethiopia, Nepal, and Peru.<sup>6</sup>

> 6 In spite of recent findings that catch‐up growth occurs without interventions (Prentice, et al., 2013), or as a result of interventions, much of stunting occurs before the age of 24 months (Victora, et al., 2010).  In addition recent research has found that catch up growth in school aged children is not associated with improvements in cognitive ability (Sokolovic, Selvam, Srinivasan, Thankachan, Kurpad and

7
income distribution for the sub-sample (not reported here) does not show any signs of truncation. Less than 3 percent of the observations are dropped which is sufficient to produce a sizable decline in inequality (see Table 8): the estimated Gini drops from 36 _._ 4 (full sample) to 31 _._ 9 (sub-sample).<sup>19</sup> Table 7 presents the estimates of _β_ 1, _α_ , _θ_ , _λ_ and _P_ . Note that _β_ 1 is estimated using the sub-sample of households, _α_ is estimated using the imputed rent data for the full sample (which now stand in for the house price database), while _θ_ , _λ_ and _P_ are estimated using a combination of the two data sources. Observe that the estimates of the Pareto tail index _α_ obtained using the imputed rent data from the survey are markedly higher than the estimates obtained using the house price database (see Table 3 in the main text). This is consistent with the hypothesis that top income households are under-represented in the original survey. The estimates for _β_ 1 are in the same range as those obtained using the full sample.

|sub-group|ˆ_β_1|ˆ_α_|ˆ_θmix_|ˆ_θsvy_|ˆ_λprop_7|ˆ_Pprop_7|
|---|---|---|---|---|---|---|
|Cairo|0.692|1.385|2.001|2.910|0.058|0.015|
|Alexandria|0.676|2.567|3.795|3.900|0.021|0.003|

Table 7: Estimates of _β_ 1, _α_ , _θ_ , _λ_ and _P_ The corresponding estimates of total inequality for urban Egypt are shown in Table 8. While we do not perfectly reproduce the original estimates of inequality, we find the results encouraging. All measures of inequality considered show a clear upward adjustment. We slightly over-estimate the Gini, and slightly underestimate the MLD and Theil indices. This may be due to the fact that we are using an approximation to the sub-group decomposition for the Gini (see Alvaredo, 2011), while the sub-group decompositions for the MLD and Theil indices are exact.<sup>20</sup>

||Survey only (full-sample)|Survey only (sub-sample)|Survey + imputed rents|
|---|---|---|---|
|Gini|0.364|0.319|0.387|
|MLD|0.217|0.163|0.201|
|Theil|0.258|0.175|0.253|

Table 8: Estimates of inequality for (urban) Egypt in 2009/10: Survey-only versus Survey+imputed rents

> 19Note that we have not adjusted the sampling weights after dropping the selected households.

> 20There is no additive sub-group decomposition for the Gini that is exact.

29
The potential for extensions and applications of this work is large. Section 8 has discussed various avenues for future extensions and applications and suggested a number of options, including the use of the estimated quarterly poverty series for further cross-section and panel econometric work, forecasting and simulation of policy reforms and economic shocks. These are all possible extensions of the work presented in this paper that can substantially increase the toolkit of the welfare economist.

# **References**

Christiaensen, L, Lanjouw, P., Luoto, J and Stifel, D. (2012) Small area estimation-based prediction methods to track poverty: validation and applications, Journal of Economic Inequality, 10, 267-297 Deaton, A. (2008): Price trends in India and their implications for measuring poverty. Econ. Polit.Weekly, 7, 3729–3748 Deaton, A., Kozel, V. (2005) Data and dogma: the great Indian poverty debate. World Bank Research Observer, 20 (2), 177–199 Douidich, M. and Ezzrari, A. (2008) Dynamique de la pauvreté au Maroc 1985-2007, Haut Commissariat au Plan, Morocco Elbers, C., Lanjouw, J.O., Lanjouw, P. (2002) Micro-level estimation of welfare. Policy Research Working Paper 2911. World Bank, Washington DC (2002) Elbers, C., Lanjouw, J.O., Lanjouw, P. (2003) Micro-level estimation of poverty and inequality. Econometrica, 71(1), 355–364 Elbers, C., Lanjouw, J.O. and Lanjouw, P. (2005) Imputed Welfare Estimates in Regression Analysis, Journal of Economic geography, 5, pp.101-118 Fujii, T. and R. van der Weide (2012), Two-Sample Cross-Tabulation, mimeo, The World Bank Fujii, T. and R. van der Weide (2013), Cost-effective estimation of the population mean using prediction estimators, mimeo, The World Bank HCP (2000-2010) National Accounts, Haut Commissariat au Plan, Maroc Grosse, Melanie, Klasen, Stephan and Spatz, Julius, (2009), Matching Household Surveys with DHS Data to Create Nationally Representative Time Series of Poverty: An Application to Bolivia, No 21, Courant Research Centre: Poverty, Equity and Growth - Discussion Papers, Courant Research Centre PEG.

Kijima, Y., Lanjouw, P. (2003): Poverty in India during the 1990s: a regional perspective. Policy Research Department Working Paper 3141. World Bank, Washington DC 25
generality, substitute _τ_ from (b) to gravity equation (1): Estimating equation (2) identifies _δ_ , the product of two parameters _σ_ and _θ_ . To recover the parameter _θ_ , one has to assume a value of _σ_ to calculate _θ_ = _δ/σ_ . For aggregate trade flows, typical values from the literature range between 5 and 10 (Anderson and van Wincoop 2003, 2004). Note that the impact of varying travel distance or travel time on trade flows is still informed by the combined elasticity _δ_ . However, if one is seeking to quantify the elasticity of trade costs to travel distance or time, the correct value is _θ_ .

The minimal data needed for this estimation is trade flows between locations within a country. Ideally, these locations should be spatially disaggregated so that most flows are across regions for which one can construct fine measures of distance or travel time. Otherwise if data is spatially not disaggregated enough and locations are fairly large, one has to take a stance on distance for within-regional shipments. One common approach is to use a proxy for internal distance (Head and Mayer, 2010). However, this ignores the distribution of economic activity within the region, and would be biased if it is concentrated in a few locations.

High quality data on domestic trade flows typically exists in developed or middle income countries. These are compiled from various sources. Some are due to specialized surveys, such as the _Commodity Flow Surveys_ in the U.S. or in Colombia ( _Encuesta Origen - Destino a Vehiculos de Carga_ ). While there is a voluminous literature making use of the former, few studies analyzed road transport costs using the Colombian data. Estimating a gravity framework, Duranton (2015) finds that a 10% increase in the travel time between Colombian cities reduces the value of trade between them by about 7%. To gauge whether within-city roads affect trade, he constructs a municipality-level road index summing up the log of the mileage of principal roads and the log of the number of exists from these roads. A 10% increase in a city’s road index increases the value of its exports by about 4%, suggesting that congestion may be an important factor for transport costs.<sup>7</sup> In some developing countries, comprehensive domestic trade flow data may not be available. All countries, however, collect customs level international trade data. In many cases, these records contain information about exporting/importing firms, their locations in the country, and the port of export/import. Linking these two provides researchers with a snapshot of trade flows within the country, despite the restricted and

> 7A similar data source from India— _Inter-State Movement/Flows of Goods by Rail, River and Air_ —does not contain information about motorized shipments on roads.

13
# **3 Data and sample**

# **3.1 Data**

Our main data comes from seven waves of the Future of Business (FoB) survey that collected data from business owners and managers from over 84 countries.<sup>12</sup> The FoB survey is a collaboration between Facebook, the Organisation for Economic Co-operation and Development (OECD), and the World Bank to survey micro, small and medium (MSME) sized businesses on Facebook.<sup>13</sup> In May 2020, Facebook adapted the standard bi-annual approach of the FoB survey to instead run monthly waves of the survey to provide timely insights on the impact of the COVID-19 pandemic on small businesses. The partners developed a short enterprise survey instrument to collect data from business owners and managers who have a Facebook Business Page. The questionnaire is designed to measure the impact of COVID-19 on some key dimensions: operation of the business, revenues, employee adjustments, expectations and uncertainty about the future, and preferred methods of policy support during COVID-19. A number of demographic questions (for example, gender, age, and marital status) as well as time spent on work and domestic and care responsibilities for the respondent and their spouse, and breadwinner status within the household were also included.

The COVID-19 monthly waves sampled from over 84 countries around the world where the Facebook small business penetration is sufficient to achieve a meaningful sample.<sup>14</sup> The first of these monthly surveys Wave I from 28-31 May 2020 surveyed around 26,000 mostly small businesses.<sup>15</sup> The monthly survey waves are a repeated cross-section of approximately

> 12The sample for this paper consists of approximately 150,000 business owners from 50 countries as we restrict the sample to those countries with sufficient sample size to conduct gender-disaggregated analysis in each survey wave.

> 13The country level aggregated data set from the Future of Business Survey is provided for free through a Creative Commons license and is available on the World Bank Data Catalog as well as on Humanitarian Data Exchange.

> 14The bi-annual FoB surveys typically are done June and December of every year and cover a larger set of over 100 countries. While 84 countries were surveyed for the COVID-19 high-frequency waves in 2020, only 50 countries had sufficient sample size to conduct gender-disaggregated analysis.

> 15Each survey wave was targeted in the last week of every month over a six-month period in 2020. The first wave (Wave 1) of the FoB COVID-19 survey was conducted in May 28th to 31st 2020, Wave 2 June 24-30, Wave 3 in July 23-31, Wave 4 in Aug 24-31, Wave 5 September 23-30, and Wave 6 October 24-31.

10
Region, Poverty Reduction and Economic Management Unit, World Bank, Washington, D.C. Processed.

_______ 1997. “Testing Capital Account Liberalization without Forward Rates: Another look at Chile 1979-1982.” Journal of Development Economics, 52:139-168.

Mendoza, Enrique G. and Martin Uribe. 1996. “The Syndrome of Exchange-Rate-Based Stabilizations and the Uncertain Duration of Currency Pegs.” Board of Governors of the Federal Reserve System, International Finance Discussion Papers: 548 April 1996 Working Paper.

Obstfeld, Maurice and Kenneth Rogoff. 1996. “Foundations of International Macroeconomics.” Massachusetts Institute of Technology, Boston.

Rebelo, Sergio and Carlos A. Vegh. 1995. “Real Effects of Exchange-Rate-Based Stabilization: An Analysis of Competing Theories.” National Bureau of Economic Research macroeconomics annual: 125-74. Cambridge and London: MIT Press.

Rodriguez, Carlos Alfredo, 1982 “The Aregentine Stabilization Program of December 20<sup>th</sup> , World development 10:801-811.

Roldos, Jorge E. 1997. “On Gradual Disinflation, the Real Exchange Rate, and the Current Account.” Journal of International Money and Finance v16, n1 (February 1997): 37-54.

_______ 1994. “Supply-Side Effects of Disinflation Programs.” International Monetary Fund Working Paper: WP/94/84 July 1994.

Schmidt-Hebbel, K. 1988. “Consumo e inversion en Chile (1974-1983): Una interpretacion ‘real’ del boom” in F. Morande and K. Schmidt-Hebbel, eds., “Del Auge a la Crisis de 1982.” Instituto Interamericano de Mercado de Capital, Santiago.

Taylor, Lance. 1979. “Macro Models for Developing Countries.” New York.

Uribe, Martin. 1997. “Exchange-Rate-Based Inflation Stabilization: The Initial Real Effects of Credible Plans.” Journal of Monetary Economics v39, n2, July 1997: 197-221.

Talvi, Ernesto. 1997. “Exchange Rate-Based Stabilization with Endogenous Fiscal Response.” Journal of Development Economics v54, n1 (October 1997): 59-75.

28
# **List of Acronyms**

|AIDS|Acquired Immune Deficiency Syndrome|
|---|---|
|AGOA|African Growth and Opportunity Act|
|ALAFA|Apparel Lesotho Alliance to Fight AIDS|
|BWL|Better Work Lesotho|
|CCMA|Commission for Conciliation, Mediation, and Arbitration|
|CHAL|Christian Health Associations of Lesotho|
|COSATU|Congress of South African Trade Unions|
|CPF|Country Partnership Framework|
|DHS|Demographic and Health Survey|
|FAWU|Factory Workers’ Union|
|FGD|Focus Group Discussion|
|HBS|Household Budget Survey|
|HIV|Human Immunodeficiency Virus|
|IFC|International Finance Corporation|
|ILO|International Labor Organization|
|ISGA|Integrated Social and Gender Assessment|
|LCMPA|Legal Capacity of Married Persons Act|
|LECAWU|Lesotho Clothing and Allied Workers’ Union|
|LNDC|Lesotho National Development Corporation|
|LPPA|Lesotho Planned Parenthood Association|
|M&E|Monitoring and Evaluation|
|MCC|Millennium Challenge Corporation|
|NGO|Non‐governmental Organization|
|NUTEX|National Union of Textile|
|RSA|Republic of South Africa|
|SADC|Southern African Development Community|
|SADSAWU|South African Domestic Service and Allied Workers’ Union|
|UNCTAD|United Nations Conference on Trade and Development|
|UNITE|United Textile Employees|

2
# **B SAM accounts**

<u>Table 13: Microeconomic SAM Accounts</u>

|Type|Abbreviation|Full name|
|---|---|---|
|Activities/|ban|Bananas|
|Commodities|cof|Cofee|
||sug|Sugar|
||min|Mining|
||liv|Livestock|
||woo|Wood|
||nta|Non-traditional crops|
||sub|Domestic crops|
||oil|Oil|
||ali|Food, beverages, tobacco|
||tex|Textiles|
||pap|Paper|
||che|Chemicals|
||met|Metals, minerals, machinery|
||oma|Other manufacturing|
||ewg|Electicity, water, gas|
||con|Construction|
||srv|Other services|
||gov|Public sector|
|Factors of|lab-n|Unskilled labor|
|production|lab-s|Skilled labor|
||lab-t|Tertiary-skilled labor|
||kaptl|Capital and land|
|Institutions|hhld|Households|
||entr|Enterprises|
||govnt|Government|
|Current and|xca|Rest of CACM and Dominican Republic|
|capital|eun|European Union (EU-15)|
|accounts|usa|United States|
||xrw|Rest of the World|
||bop|Balance of payments|
|Taxes|dirtx|Direct taxes|
||imptx|Import taxes (tarifs)|
||indtx|Indirect taxes|
||prdtx|Production taxes|
||vattx|Value-added taxes|
|Investment|invst|Private investment|
||ginvst|Public investment|
||delst|Change in stocks|

49
with the gender difference in time spent on household tasks, this may indicate a higher risk of fatigue resulting from home schooling for female students.

A potential concern is that students may start off attempting to carry out educational activities, but that their enthusiasm and usage may wane over time, especially once it becomes clear that they will not be heading back into classrooms this school year. Since we randomly ordered students, the groups of students called in each week are similar, and so we can compare time-use of students interviewed in different weeks. Figure 5 shows that this fear does not appear to have been realized during April (covering weeks 3, 4, 5 and 6 since schools closed). Exactly the same proportions of students are engaged in education at each hour in the morning for each of the four weeks of April, and although there is more variation in the afternoon, it is not the case that the proportion doing schooling falls as the month progresses.

# **5. Mental Health**

The COVID-19 pandemic is likely to both directly cause anxiety to students as they worry about the potential health consequences of its spread, as well as indirectly affect their mental well-being through concerns about family finances, their ability to continue their education, and through social isolation. Giannini (2020) reports that a survey conducted by the United Nations in Thailand found more than 7 in 10 children and young people said the pandemic is causing stress, worry, and anxiety.

Our phone surveys measured mental well-being using the 5-item MHI-5 index of Veit and Ware (1983). This asks students to answer on a five-point scale how often in the past month they have been happy, felt calm and peaceful, been very nervous, felt downhearted and blue, and felt so down in the dumps that nothing could cheer them up. Scores range from 5 to 25, where higher scores indicate better mental health. We follow several studies (Urban Institute, 1999; Yamazaki et al. 2005) in using a cutoff of 17 as an indicator of major depression. We also use the first question from this scale to code a binary indicator of happiness, which takes value one for students who say they were happy all of the time or most of the time.

Table 4 summarizes the levels of indicative depression and happiness overall, and by different student characteristics. We see that 16 percent of students have mental health scores that indicate 9
entry. Reduction in the constraints on foreign direct investment will induce foreign entry that will typically lead to productivity gains because when more varieties of service providers are available, buyers can obtain varieties that more closely fit their demands and needs (the DixitStiglitz variety effect).

# **VII.  Data of the Model and Evidence for Key Elasticities**

# **Social Accounting Matrix**

The core of the model data is the Social Accounting Matrix (SAM) for 2001 developed by Thurlow and Wobst (2003). The SAM contains 43 sectors, most of which are agricultural and food-producing sectors and only a few are service sectors.

Two sectors in the SAM, “Transport and communication” and "Business and other service activities", account for most business services in Tanzania. Given our focus on services, these sectors are disaggregated into 10 sectors using unpublished national accounts data for the year 2006 from the National Bureau of Statistics of Tanzania.

Data for gross domestic product for the sectors “Transport”, “Communications”, “Financial intermediation”, and “Real estate and business services” is officially published (National Bureau of Statistics (2007)). Upon specific request, we received a breakdown of these data for the main types of services within each sector. Specifically, the data allows us to disaggregate “Transport and communication” into road transport, railway transport, water transport, air transport, postal services and telecommunication. "Business and other service activities" is disaggregated into insurance, banking, business services and other services.

Within each of the two aggregate sectors, the share of gross domestic product by disaggregate sector is used to decompose the corresponding aggregate sector. It is furthermore assumed that the input output structure for all the disaggregate sectors is identical to the input output structure of the corresponding aggregate sector. Along with the addition of the tourism sector, the resulting table has 52 sectors as shown in table 1.

The Thurlow and Wobst SAM allocate international travel expenditure (mainly international tourism) to the “Transport and communication” sector. According to a survey 30