rafmacalaba/data-use-mentions-extended huggingface dataset:
Health Organization 2022). These data are not reporting direct COVID-19 deaths only but rather are an estimate of the combination of direct and indirect COVID-19 mortality, as measured by excess deaths.

Lacking actual measures of direct and indirect COVID-19 mortality by sex, we rely on estimates of excess deaths by sex. We check the reliability of these data by comparing sexmortality ratios from the estimate of excess deaths and those from reported COVID-19 deaths for a subset of countries for which this is available. This ratio of ratios is not always 1. However, there is no clear pattern suggesting that the ratio based on excess deaths is different from that constructed from reported COVID-19 mortality (not shown here but available upon request).

This dataset also contains population counts from the World Population Prospects by country, year, sex and age (UN 2022). The dataset covers 194 countries in 2020 and 194 in 2021. For this analysis, only countries with excess death estimates based on actual all-cause deaths (not predicted) by sex and age-group are included. This limits the sample to 75 countries in 2020 and 62 countries in 2021. Additionally, we exclude countries with total excess deaths (both sexes combined) below 2,000 in either 2020 or 2021. After applying these two criteria, we have 66 countries in this analysis: 54 in 2020 and 57 in 2021 (Table 1). Lastly, we analyze mortality data only for adults 45 years and older, since excess death rates are extremely low for younger ages.

[TABLE 1]

# _Estimating age-mortality patterns_

To characterize the age-mortality patterns drawing on the data from multiple countries, we estimate a model of the age-mortality curve with interactions by sex for each country. The advantages of working with a model are twofold. First, it produces a slope of the agemortality curve by sex. Second, it enables the use of predicted values for some estimated quantities which minimize the influence of outliers (discussed above) and, importantly, allows calculation of confidence intervals. The models are estimated separately
Policy Research Working Paper 7917

# **Abstract**

Public financing of child care can allow for more equitable access to these services in places where public provision and capacity are low. However, the mechanisms of subsidy delivery will affect who benefits, and the overall cost effectiveness of such subsidies. This paper sets out an ex ante simulation model for estimating the benefit incidence of expanded capacity and enrollments resulting from different child care subsidy mechanisms. It uses a supply-side provider level and a demand-side household model, and combines the two models. The paper considers investment grants to providers, operational monthly grants to child care providers, combinations of the investment and operational grants, and demand-side vouchers to households. The model is applied to empirical data from child care centers and households in Turkey. The results reveal that the choice of the subsidy delivery model has a strong bearing on the benefit incidence and cost effectiveness of the subsidy. In the case of Turkey, where significant supply-side constraints exist in the market, a demand-side voucher system is shown to be the least cost-effective subsidy delivery model. A targeted demand-side voucher does not necessarily deliver the most “pro-poor results,” and combinations show different benefits and costs. The proposed simulation model can be applied in other country contexts, with the only data requirements being micro data on the costs and pricing structure of child care providers, as well as household data with variables on household welfare and child care utilization.

This paper is a product of the Poverty and Equity 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 amunozboudet@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
COV-BPS survey in each country and the average mobility trend as a proxy for the severity of the COVID-19 shock over this period.

||Sample size|FAT|COV-BPS|
|---|---|---|---|
|Brazil-Cear ́a|124|08/2019-12/2019|05/26/20 - 07/24/20|
|Senegal|464|08/2019-02/2020|04/28/20 - 05/08/20|
|Vietnam|475|11/2019-02/2020|06/12/20 - 07/07/20|

Table A1: Sample size and interview dates for the FAT and BPS surveys.

26
<br>to 6 years. Some<br>job titles further<br>categorized<br>accordingtograde|58|
|Tailoring Trade|Manufacturing|5|Learner specified by<br>year;5years|9|
|Tea Industry|Agriculture,<br>forestry and fishing<br>Manufacturing|32|NA|32|
|Travel Agents and Tour<br>Operators|Finance and<br>professional<br>services (admin and<br>support)|17|All except Trainee<br>and Watchman<br>specified by year; 4<br>years|62|
|**_Total_**||**_446_**||**_2003_**|

# **2 Data and Legislated Wages’ Trends**

We use a series of repeated cross-section data from the Mauritian labor force survey, known as the Continuous Multi-Purpose Household Survey (CMPHS), conducted between 2004 and 2014. The CMPHS was launched by Statistics Mauritius in April 1999. Since then it has been conducted monthly with the exception of 2000 when the survey was suspended to avoid overlapping with the Housing and Population Census.

We restrict our sample to include only employed individuals and those with non-zero reported earnings. Self-employed, own account and unemployed workers who have worked before are included in our analysis as the uncovered sector in our estimation of minimum wage effects. For this estimation, only workers who are in the covered RO sectors and the self-employed, employers and unemployed in or last employed in the RO covered sectors are considered.

We assign minimum wages to workers covered by 30 categories of remuneration orders and estimate employment and hours of work effects through the use of data related to two groups 7
the number of diseases associated with tobacco smoking, the generally accepted indicator in available literature, indicator of population attributable risk (PAR), was used. PAR is calculated based on the data on smoking prevalence and on the indicator of the relative risk of disease in smokers vs. nonsmokers (RR).

Calculation of the indicator of the relative risk of disease in smokers vs. non-smokers was not done, and values of the RR were taken from the open sources:

- Diabetes: during long-term follow-up: the Malmö Preventive Project. J Intern Med. 2006 Aug;260(2):134-41; http://ex-diabetic.com/vashe-zdorove/caharnyj-diabet-i-kurenie.html

- Cardio-vascular diseases:  How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. https://www.ncbi.nlm.nih.gov/books/NBK53012/

- Respiratory diseases:  Краснова Ю.Н. Влияние табачного дыма на органы дыхания//Сибирский медицинский журнал, Том 137, No6, стр.11-16.2015 г.

- Cancer:  Ying-Ying Wang, Wei Zhang, et.al. Population attributable risks of cigarette smoking for deaths of all causes, all cancers and other chronic diseases among adults aged 40-74 years in urban Shanghai, China. Chin J Cancer Res. 2015 Feb; 27(1): 59–65.; Белялова Н.С., Белялов Ф.И. Факторы риска и профилактика рака. Часть 1. Клиническая медицина 2005;11:17-21.

- TB:  http://www.who.int/tobacco/resources/publications/ru_tfi_tb_tobacco_factsheet.pdf

Population risk of smoking (RAR) was calculated by the formula:

# RAR = ((p0+p1*RR)-1)/(p0+p1*RR)

> 15 Global Adult Tobacco Survey: Overview, 2016. https://static1.rosminzdrav.ru/system/attachments/attaches/000/036/485/original
Figure 5: Contemporaneous secondary schools and births (percent change) by age <!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>15 17 19 21 23 25 27 29 31 33 35 37 39<br>Age at conception<br>Percent change in municiple births<br><!-- End of picture text --> Notes: This figure plots the coefficients from a regression of the number of live births for each age group on contemporanious secondary schools. The solid line plots these coefficients divided agegroup specific means for the outcome (× 100 ). Dashed lines show the corresponding 95% confidence interval. All regressions condition on lagged primary school enrolment, preschool classrooms, male and female age-specific population size, municipality expenditures, municipality and year fixed effects.

Source: School data come from the 1997–2009 waves of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age of conception from Brazilian Vital Statistics.

30
# Figure 4. Demographics

A. Impact of 1 percentage point higher B. Working-age population working- age population share on per capita GDP growth <!-- Start of picture text --> Percent<br>3<br>2<br>1<br>0<br>Aiyar and Bloom and Bloom and Bloom et Ahmed<br>Mody Williamson Canning al. (2000) and Cruz<br>(2011) (1998) (2004) (2016)<br>C. Working-age population<br>Percent of total population<br>75 EAP ECA LAC<br>MNA SAR SSA<br>70<br>65<br>60<br>55<br>50<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total population<br>70 AEs World EMDEs<br>65<br>60<br>55<br>D. Potential growth<br>Percent Percent<br>Baseline Demographic trends Other factors<br>2.8 5.6<br>2.6 5.2<br>2.4 4.8<br>2.2 4.4<br>2.0 4.0<br>1.8 3.6<br>World EMDEs (RHS)<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br><!-- End of picture text --> Sources: United Nations World Population Prospects: The 2022 Revision. World Bank.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and
|
|_Panel C: Per_|_u 2008_|||||||
|PovertyRate<br>|35.16|34.22|33.57|33.19<br>33.35<br>36.52<br>(30.44,36.25)|34.88|34.81|34.97|
|Obs. Panel A|823|823|823|.<br>823<br>823|823|823|.|
|Obs. Panel B|684|684|684|.<br>684<br>684|684|684|.|
|Obs. Panel C|2,368|2,368|2,368|2,368<br>2,368<br>2,368|2,368|2,368|2,368|
|Data source:<br>ENAHO 200<br>Note: R-squa<br>constrained t<br>Results are<br>show a simpl<br>sub-national<br>interactions<br>Columns [4]<br>poverty. 95<br>based on 50 r|Chile C<br>8 and 2009<br>red is cal<br>o the pane<br>weighted<br>e model<br>controls fr<br>between<br>and [9] ad<br>percent co<br>epetitions.|ASEN 1<br>.<br>culated<br>l sampl<br>using ho<br>with hou<br>om cen<br>househo<br>d retrosp<br>nfidenc<br>|996 and<br>for opp<br>e of hous<br>usehold-<br>sehold ti<br>sus and r<br>ld time<br>ective as<br>e interva
# **Table 3: Progress on Malaysia’s National Key Result Areas**

|**2010 Target**|**2012 Target**|**Reported Progress in 2010**<sup>**ix**</sup>|
|---|---|---|
|**REDUCING CRIME**|||
|20% reduction of street crimes|5% annual reduction in overall<br>reported crime|street crime down 37%<br>overall reported crime down 16%|
|**CORRUPTION**|||
|Malaysia’s CPI score at 4.9<br>(from 4.5)||CPI 2010 score 4.4|
|37% approving Government<br>efforts on corruption in TI<br>Global survey (from 28%)<br>Average audit findings per<br>ministry at 10.6 annually<br>(from 11.2)||48% approving government efforts in<br>TI Global survey|
|**IMPROVING STUDENT OU**|**TCOMES**||
|Pre-school enrolments at 72%<br>(from 67%)|Pre-school enrolments at 87%<br>All children having basic literacy and<br>numeracyskills at end ofyear three|72.42% enrolment|
|**POVERTY**|||
|Poverty incidence at 2.8%||(13,471 households removed from|
|(from 3.6%)||poverty)|
|Hardcore poverty at 0% (from<br>0.72%)||Hardcore poverty at 0%|
|**RURAL INFRASTRUCTURE**|||
|Peninsula: 91.4% living within<br>5km of a paved road (from<br>91%)|close to 100% ditto||
|16,000 new/
# EMDEs.

# Data

The estimation relies on bilateral trade costs from the UNESCAP-World Bank Trade Costs Database. Following Novy (2013) and Arvis et al. (2013), bilateral trade costs are obtained as geometric averages of flows between countries i and j. They are computed according to the formula below:

# _(Xii Xjj)/(Xij Xji)_<sup>1/2 (</sup><sup>_σ_-1)</sup> ,

where _Xij_ represents trade flows between countries _i_ and _j_ (goods produced in _i_ and sold in _j_ ) and _σ_ refers to the elasticity of substitution. This measure captures international trade costs relative to domestic trade costs. Intuitively, trade costs are higher when countries trade more domestically than they trade with each other, i.e., as the ratio ( _Xii Xjj_ )/( _Xij Xji_ ) increases. Intra-national (that is, domestic) trade is proxied by the difference of gross output and total exports.

Trade costs thus computed implicitly account for a wide range of frictions associated with international trade, including transport costs, tariffs, and nontariff measures, and costs associated with differences in languages, currencies and import or export procedures. Trade costs are expressed as ad valorem (tariff) equivalents of the value of traded goods and can be computed as an aggregate referring to all sectors of the economy, but also specifically for the manufacturing and agriculture sectors.

# Estimation

Gravity equations are widely used as a workhorse to analyze the determinants of bilateral trade flows. Chen and Novy (2012) and Arvis et al. (2013) also employ a gravity specification to analyze the determinants of bilateral trade costs in a cross-sectional dataset. In line also with Moïsé, Orliac, and Minor (2011), this study estimates determinants of trade costs in a panel specification.

The regression equation takes the following form: _TCijt_ = β1 _RTAijt_ + β2 _tariffijt_ + β3 _LSCIijt_ + β4 _LPIijt_ where for any given country pair _ij_ , bilateral trade costs _TC_
specific to the Belt and Road Initiative. The initial vision for a Green Belt and Road was released in 2015, followed in 2017 by a guidance document and cooperation plan. All three documents are ambitious and paint a picture of the BRI being able to, “...promote ecological progress in conducting investment and trade, increase cooperation in conserving eco environment, protecting biodiversity, and tackling climate change, and join hands to make the Silk Road an environment friendly one.” (Chinese National Development and Reform Commission, Chinese Ministry of Foreign Affairs, and Chinese Ministry of Commerce, 2015) All three documents are still high‐level and lack critical details on implementation, monitoring, and enforcement (Chun 2017). These policies do depict, however, how BRI projects with appropriate environmental mitigation mechanisms and enforcement could serve as a model of good development practices throughout the region. On the other hand, it is quite clear that positive statements without enforced implementation achieve little. Again, the question is raised concerning how compliance with related promises should be enforced.

# D. Industry and Nonprofit Standards

Firms engaged in BRI projects may voluntarily adhere to best practice environmental standards – typically stricter than those prescribed by lenders or governments – from industry associations or nonprofit organizations. Firms might elect to follow these guidelines because they calculate that it is in their financial interest to invest sustainably or because they receive external pressure – from their customers, governments, or the general public – and reputational benefit from adopting these criteria.

One useful reference concerning such voluntary standards, codes of conduct, and audit protocols relating to sustainability in supply chains is the Standards Map database (www.standardsmap.org), created by the International Trade Centre (Booth 2017). Again, their impact is another question in light of the fact that many positive and well‐intentioned statements are made that do not shift outcomes.

39
of enumeration areas was based on the Population Census 2011, and a fresh household listing was conducted in each of the selected EAs to attain a current household sampling frame from which to randomly select 12 households in each EA.

Randomization of the treatment arm assignment was conducted within each EA, to account for unobserved differences at the village level that could potentially affect responses to the land tenure questions as well as to avoid confounding enumerator effects. From each EA, three households were assigned to each of the treatment arms identified in Table 1. Treatment arm assignment was completed at the headquarters level and preloaded in the computer assisted personal interviewing (CAPI) program developed in Survey Solutions,<sup>5</sup> and therefore could not be altered by enumerators. In Arms 1 and 2, up to three adult household members were randomly selected by the Survey Solutions software for individual interviews, again limiting the scope of bias in the ALTA sample.

_Table 1. ALTA Treatment Arms_

||Respondent|Level of Land|
|---|---|---|
||Approach|Data Collection|
|Arm 1|Self-Respondent|Parcel|
|Arm 2|Self-Respondent|Aggregate|
|Arm 3|Proxy|Parcel|
|Arm 4|Proxy|Aggregate|

The versions of the land tenure module, and therefore the treatment arms, differ on the level of respondent approach, either self- or proxy respondent, and in the level of land reporting, either parcel or aggregate level. Individuals selected for the self-respondent questionnaire report the land tenure status on the land they directly own or use, while proxy respondents answer the same set of questions for themselves as well as for other individuals in their household. In parcel-level treatment arms, questions on land tenure are asked for each reported parcel separately, while aggregate-level treatment arms ask about the land tenure status for the overall land owned or used by specific household members, with the only disaggregation being by agricultural and non-agricultural land use (as this is a
time periods, in this paper, we primarily focus on the weekly sub-sample in order to identify severe illness shocks that lead to an entire week of missed work. Note also that the daily data we collected allow us to confirm that week-long illness shocks are more empirically relevant for labor supply than shorter illness shocks. Indeed, table A1 shows that there is no evidence of labor supply responses to another worker’s absence from work in a given day. When we examine the time path of response to a daily illness shock using an event study framework in figure A1, we find some evidence of lagged responses among men, but these responses (a greater probability of working the day after illness, but a lower probability two days later) still lead to a net zero response during the period surrounding a daily illness shock.

Participants interviewed by phone were given a phone at the beginning of the survey to avoid the possibility of selection into the survey based on phone ownership. Respondents were paid 3 cedis ($1.36 US) for each completed phone survey. Together with the fact that surveys were quite short (4 minutes on average), 89 percent of scheduled weekly phone surveys and 94 percent of weekly in-person interviews were successfully conducted.

The illness shocks we can detect in this high-frequency data occur when a working-age adult did not work in a particular week, but had planned to work. These respondents were then asked to give the reason for missing work. In coding these free responses, we consider as an illness shock any response that mentioned the illness or injury of the respondent or another person. (See the online appendix for a list of the exact responses included as illness shocks.<sup>5</sup> ) That is, we do not separately examine the effects of absence due to own illness and caretaking both for reasons of power and because we suspect that the modal response of “I was sick” – given in 74 percent of the instances of illness shocks that we categorize as illness
only “comparable surveys” as defined in PovcalNet’s comparability data set.<sup>21</sup> Results are reported in Figure 4 for pass-through rates between 0.42 (pass-through estimate for countries in fragile-conflict affected situations, see Corral et al. 2020) and 0.87. Poverty estimates for 2009 at the US$1.90 poverty line range between 42.2 (assuming a pass-through of 0.42) and 45.3 percent (assuming a pass-through of 0.87), and between 73.5 and 75.8 percent at the US$3.20 poverty line (assuming a passthrough of 0.42 and 0.87 respectively). These result show that changing the assumption about the passthrough rates has little effect on the backcasted series. Indeed, if anything, using lower pass-through rates

> 20  We focus on GDP and GNI pass-through rates as HFCE is not currently used in PovcalNet’s lining-up exercise to extrapolate household consumption estimates from surveys collected in Sub-Saharan African countries (see Prydz et al. 2019). Pass-through rates estimates using HFCE remain within the range reported in this note, for comparable consumption-based surveys we estimate a pass-through of 0.66 in line with what reported in World Bank (2020, box 2).

> 21 <u>https://development-data-hub-s3-public.s3.amazonaws.com/ddhfiles/506801/povcalnet_comparability.csv</u>

16
mitigation and deferral of social insurance contributions in the context of the current crisis bolstered the ability of firms to weather the economic downturn during COVID-19 (Cui et al., 2020; Chen et al., 2020).

By contrast, some policies such as the United States $2.2 trillion economic stimulus package called the Coronavirus Aid, Relief, and Economic Security (CARES) Act, in response to the economic fallout of the COVID-19 pandemic has been less effective in alleviating the cash constraints or improving outcomes of those they intend to assist.<sup>9</sup> What makes some programs effective while others less so? This section uses the BPS data to draw insights on the design of firm support programs, by alluding to the results on access to public programs and the potential mismatch between preferred instruments of support relative to that actually received by firms.

# **4.1 Rationale for supporting firms**

Apart from the obvious rationale of saving millions of jobs that are at the brink of being permanently lost, supporting firms during the pandemic is essential for several reasons (Jones et al., 2020). _First_ , there are efficiency gains via network externality from preserving existing relationships among numerous inter-connected entities in the economy. Preserving such organizational capital can also indirectly benefit upstream suppliers and downstream firms. _Second_ , the severity of the shock can endanger otherwise productive and viable firms. These firms deserve to be protected for both efficiency and fairness reasons. _Third_ , given the changes in demand and supply structure, adjusting to the "new normal" will necessitate public intervention to ensure coordinated reorganization and transformation of key industries. _Finally_ , in the absence of government intervention, financial frictions will limit the amount of credit available to the small but viable borrowers and likely limit their adjustment in response to the crisis.

Nevertheless, caution in providing government support is warranted because unanticipated shocks "cleanse" the pool of inefficient firms. The objective of preserving existing economic relationships may impede the creation of new, potentially superior