rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9510

# **Abstract**

The economic crisis caused by the COVID-19 pandemic has sharply reduced mobility and economic activity, disrupting the lives of people around the globe. This paper presents estimates on the early impact of the crisis on labor markets in 39 countries based on high-frequency phone survey data collected between April and July 2020. Workers in these countries experienced severe labor market disruptions following the COVID-19 outbreak. Based on simple averages across countries, 34 percent of the respondents reported stopping work, 20 percent of wage workers reported lack of payment for work performed, 9 percent reported job changes due to the pandemic, and 62 percent reported income loss in their household. Stopping work was more prevalent in the industrial and service sectors than in agriculture. Measures of work stoppage and income loss in the high-frequency phone survey are generally consistent with gross domestic product growth projections in Latin America and the Caribbean but not in Sub-Saharan Africa. This suggests that the survey data contribute new and important information on economic impacts in low-income countries.

This paper is a joint product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. 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 dnewhouse@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
Figure 11. Trade and subsidies reforms

# A. Trade environment

<!-- Start of picture text --> Score<br>100 EMDEs Advanced economies<br>90<br>80<br>70<br>60<br>50<br>40<br>DB2006 DB2013 DB2020<br><!-- End of picture text -->

# C. Export concentration

<!-- Start of picture text --> Index<br>0.7 Interquartile range 2017 2007<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>exporting EMDEs<br>EMDEs Metal- exporting EMDEs Energy- EMDEs exporting importing EMDEs<br>Agriculture- Commodity-<br><!-- End of picture text -->

# B. Trade reforms

<!-- Start of picture text --> Number of reforms<br>Improving Deteriorating Difference<br>200<br>150<br>100<br>50<br>0<br>-50<br>DB2008-14 DB2014-20<br>D. Energy subsidies<br>Percent of GDP<br>8 Interquartile range Mean<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text -->

# D. Energy subsidies

Source: International Energy Agency, UNCTAD, World Bank.

A. Scores are unweighted averages of 39 advanced economies and 148 EMDEs. The Trade Across Borders indicator is spliced backwards where methodological changes affected the level. An economy's score is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier, which is constructed from the best performances across all economies and across time. “DB” before the year indicates the related Doing Business publication.

B. Trade reforms include those business reforms categorized under trade across borders in the Doing Business survey. The number of reforms is calculated using the business reforms by year and by country as listed in the World Bank’s Doing Business publications. These are codified from the text list of business reforms as reported by the Doing Business survey. C. Export concentration measured as the Herfindahl-Hirschmann Index (Product HHI). Observations for 2007 and 2017 are unweighted averages. EMDEs are based on data for 146 economies: 20 metal-exporting economies, 35 energy-exporting economies, 35 agriculture-exporting EMDEs, and 58 commodity-importing economies. Values closer to 1 indicate more concentration.

D. Based on data for 40 EMDEs.
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 15 Productivity efficiency, equitability and sustainability. It is also proven that the city’s annual costs, per household, of public infrastructure and services are lower for circular/compact cities compared to fragmented/irregular/sprawling cities. Thus, circular cities can reach efficiency at lower cost. Based on those premises, we hypothesize that cities whose shape approaches a perfect circle can reach economic efficiency cheaper and faster. It is important to make clear that measuring the geometric shape of the city using its boundary does not capture what is happening within the city in terms of accessibility or land use. (For example, a geometrically compact city may have an inefficient transport network that prevents taking full advantage of being a circular city.) These internal characteristics of the city are covered by the dimensions urban structure and land use.

From a geometric perspective, a shape metric is usually focused in one of the three following geometric characteristics: degree of roundness, smoothness of its perimeter, and fullness (Angel et al., 2010). None of those geometric characteristics can be captured via the usual density-based measures of urban sprawl, but they affect aspects such as trip lengths and accessibility, and through that, have the potential of affecting productivity levels in a city. We used the Shape Metrics Toolbox<sup>4</sup> to calculate the shape metrics of the urban extent polygons. This tool is a Python script that runs out of ArcToolbox in ArcGIS 9.3, and we upgraded the script to properly run in ArcGIS 10.x. All the urban extent metrics calculated with the Shape Metrics Tool take values from 0 to 1, with 1 being a perfect circle: The _Exchange_index_ measures how much the urban extent has deviated from its compact shape towards irregular non-compact, and potentially inefficient, forms. Low values of the _Exchange_index_ tend to capture elongated urban extents. The _Perimeter_index_ measures how smooth is the perimeter of the urban extent. Low values of this index indicate that the urban perimeter is irregular, which can affect the free mobility of residents located near the periphery towards other periphery locations within the urban extent. It is expected that cities located in the rugged topography of Los Andes show growth patterns that follow ridges and valleys, which can create organic shapes that are associated to low values of the perimeter index. Note that these two measures, _Exchange_index_ and _Perimeter_index_ , are

> 4 This software is intellectual property of the Center for Land Use Education and Research (CLEAR) at the University of Connecticut (http://clear.uconn.edu/tools/Shape_Metrics/index.htm).
# **Will Elders Provide for Their Grandchildren?**

# **Unconditional Cash Transfers and Educational**

# **Expenditures in Bolivia**

Gustavo Canavire-Bacarreza<sup>∗</sup> Alberto Chong<sup>†</sup> Fernando Ríos-Avila<sup>‡</sup> Monica Yanez-Pagans<sup>§�</sup>

## **JEL Classification Codes:** H55, O15, I12, D12.

**Keywords:** unconditional cash transfers, education, expenditures, children, household, Bolivia.

> ∗Universidad EAFIT, Medellín, Colombia.

> †Georgia State University, Atlanta - USA and Universidad del Pacífico, Lima – Peru

> ‡Levy Economics Institute, New York – USA

> §The World Bank, Washington, DC - USA

- �The standard disclaimer applies.
||**in**<br>**2020**<br>**Euros**|
|---|---|
|Single Person (Average Monthly, Euros)|148.2|
|Single Parent and One child (Average Monthly, Euros)|294.0|
|Single parent and Two Children (Average Monthly, Euros)|439.9|
|Family of two adults and one child (Average Monthly, Euros)|442.2|
|Family of two adults and two children (Average Monthly, Euros)|577.7|
|Single Person (Average Monthly, Euros)- Including physical activity and other functions|180.6|
|Family of two adults and two children (Average Monthly, Euros)- Including physical activity and other<br>functions|669.1|

**Panel c. Average Monthly Cost per Capita, 2020 Euros**

|||**Food Basket (with non-food**|
|---|---|---|
|**Currency**|**Food Basket**|**components / other functions)**|
|Euros|126.0|180.6|
|RON|610|874|

_Source:_ Panel a. European Commission (2015). Panel b and c. Own estimates based on European Commission (2015).

**The second step involves defining the household type of the target interest group and obtaining EUSILC survey estimates necessary to estimate living wages.** One needs to define what a typical Romanian household living in relative poverty with positive labor income<sup>18</sup> looks like. Based on the 2021 EU-SILC, a household living below the relative poverty line has, on average, 2.51 household members. This allows us to get to the cost of the food basket for a family of this average size or how much is needed to afford a food basket for a typical poor household. Then, we calculate the amount of this household income that needs to come from labor income, given that about 82 percent of overall household income comes from labor income in these families (our estimate based on EU-SILC data). To get the wage per worker estimate, we estimate the number of full-time equivalent workers in these households. These families have, on average, one member working full-time and 0.13 members working part-time (our estimate is based on EU-SILC data) and convert

> 18 These statistics are for households living below the relative poverty line whose labor income is strictly positive, and therefore, are affected by changes in wages via their labor income channel. These exclude households with solo pensioners, or other households that receive other sources of income but not earnings. 67% of poor single-person households are households with pensioners, and 42% of poor households are single-person households.

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# **10. REFERENCES**

Balassa, Bela. 1982. The Structure of Protection in Developing Countries, Baltimore, MD, Johns Hopkins University Press.

- Blonigen, Bruce A. and Jee-Hyeong Park. 2004. ‘Dynamic Pricing in the Presence of Antidumping Policy: Theory and Evidence,’ American Economic Review, vol. 94, no 2, pp. 134-54.

- Blonigen, Bruce A. and Chad Bown. 2003. ‘Antidumping and retaliation threats,’ <u>Journal of International Economics,  vol. 60, no. 2, pp. 249-73.</u>

- BRIDGES Weekly Trade News Digest - Vol. 12, Number 31 24<sup>th</sup> September 2008, ‘G-7 FAILS TO FIND CONSENSUS ON SSM,’ electronic edition.

- Bridges Weekly Trade News Digest. 2008. ‘Revised Ag Text Reflects Progress, But Final Deal Still Elusive.’ Volume 12, Number 42,  10 December 2008,

- de Gorter, Harry, Erika Kliauga and Andre Nassar. 2009. How Current  Proposals on the SSM in the Doha Impasse Matter for Developing Country Exporters, ICONE Working Paper 03/02/09 (January 2009) Accessed 1 April 2009 at http://www.iconebrasil.org.br/arquivos/noticia/1741.pdf

- Egger, Peter and Douglas Nelson. 2007. ‘How Bad is Antidumping?: Evidence from Panel Data,’ Research Paper 2007/17, Leverhulme Center, University of Nottingham.

- Eichengreen, Barry  and Hans Van der Ven. 1985. U.S. Antidumping Policies: the Case of Steel, NBER Working Paper No. W1098, February 1985

- Food and Agricultural Organization (FAO). 2003. Some Trade Policy Issues Relating to Trends in Agricultural Imports in the Context of Food Security, CCP 03/10, Sixty fourth Session of the Committee on Commodity Problems, 18-21 March 2003.

- Finger, J. Michael and Julio J. Nogués .2008. Safeguards and Antidumping in <u>Latin American Trade Liberalization, World Bank Policy Research Working</u> Paper 4680, July.

- Finger, J. Michael and Julio J. Nogués .2006. Safeguards and Antidumping in <u>Latin American Trade Liberalization: Fighting Fire with Fire, Washington,</u> DC, The World Bank and Palgrave Macmillan.

Finger, J. Michael. 2002. Institutions and Trade Policy, Edward Elgar.

- Finger, J. Michael and Ludger Schuknecht. 2001. ‘Market Access Advancers and Retreats: The Uruguay Round and Beyond,’ ch. 15, pp. 251-308 in Bernard Hoekman and Will Martin, eds., Developing Countries and the -

- <u>WTO: a Pro Active Agenda, Malden, MA. USA, Blackwell.</u>

- Finger, J. Michael and Ann Harrison. 1996. “The MFA Paradox: More Protection and More Trade?” ch. 4, pp. 197-259 in Anne O. Krueger (ed.) The

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Table 4: Primary Outcomes - Different Treatment Arms

||(1)<br>Filed declaration|(2)<br>Gross Revenue|(3)<br>Deductions|(4)<br>Taxable Income|
|---|---|---|---|---|
|**ITT estimates**|||||
|Sanctions treatment|0.004<br>[-0.009,0.018]|24.804<br>[-56.611,106.218]|8.857<br>[-70.397,88.112]|4.078<br>[-10.504,18.659]|
|Tax procedures treatment|-0.005<br>[-0.019,0.008]|23.813<br>[-54.347,101.974]|27.387<br>[-50.125,104.899]|-1.905<br>[-15.422,11.611]|
|Moral duty treatment|-0.002<br>[-0.015,0.012]|-26.499<br>[-96.645,43.646]|-27.805<br>[-97.125,41.515]|-0.996<br>[-11.780,9.788]|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Control mean|0.65|1,267.88|1,207.31|90.61|
|_β_1 =_β_2|0.247|0.984|0.704|0.454|
|_β_1 =_β_3|0.475|0.266|0.421|0.454|
|_β_2 =_β_3|0.652|0.264|0.221|0.890|
|**LATE estimates**|||||
|Opened (Sanctions)|0.013<br>[-0.026,0.052]|71.777<br>[-163.608,307.162]|25.612<br>[-203.561,254.786]|11.803<br>[-30.362,53.968]|
|Opened (Procedures)|-0.015<br>[-0.054,0.023]|67.637<br>[-154.072,289.347]|77.789<br>[-142.066,297.643]|-5.413<br>[-43.774,32.948]|
|Opened (Tax morale)|-0.005<br>[-0.046,0.036]|-81.077<br>[-295.647,133.492]|-85.083<br>[-297.145,126.979]|-3.046<br>[-36.022,29.930]|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.185|0.493|0.492|0.052|

_Note_ : This table presents the estimated impact of each of the treatment arms on primary outcomes. The first panel presents Intention-to-Treat (ITT) estimates using the specification in Equation 4. The second panel presents Local Average Treatment Effect (LATE) estimates. The dependent variables are an indicator equal to 1 if the taxpayer filed a declaration (Column (1)), the amount of gross revenue declared (Column (2)), the amount of deductions declared (Column (3)) and the amount of taxable income declared (Column (4)). 95% confidence-intervals using robust standard errors are presented in brackets. (* p<0.1, ** p<0.05, *** p <0.01) 27
factors of atmosphere's chemistry are involved. Such effects are not considered here, again giving conservative estimates.

The three basic procedures in quantifying the health effects of a pollutaat are: (1) to determine the ambient concentrations; (2) given the concentration levels and the population age distribution, to use a specified dose-response curve te, letermine the (incremental5) incidence of diseases in the population; and (3) based on the costs associated with each disease (treatment, lost wages or life), to determine the overall costs.

# Two brief qualifications

Data on air pollution in MCMA. All data on air quality was provided by the Departamento del Distrito Federal and by the air pollution unit of Mexico's environmental agency SEDUE. Since October 1989 SEDUE has been monitoring every major pollutant daily in different areas of the city, providing results grouped into five regions. For most stations, there is available the annual number of readings, the daily average, and the daily maximum. Scattered more specific data are also available for specific regions and periods (maximum-maximorum readings, number of days above standard, and so on).

Dose-response curves. Relations between concentration of pollutants and effects on health are based on both laboratory experiments and epidemiological studies. The references used here usually balanee the results obtained by the two approaches.

Two aspects are more difficult to address, however. One aspect is the possibility of threshold levels below which some health effects cease to occur; this would directly affect the benefits (or costs) associated with a change in pollution. There is insufficient evidence for the pollutants analyzed here, except for lead. Thus, the procedure adopted was to consider the legislation standard as the level of air quality associated to 'no" deleterious health effects, and zezo concentration level in the case of lead. The second aspect relates to potential differences in pollution-related health effects due to the high altitude of Mexico City, in contrast to the sea-level cities where dose-response studies are typically made. Again, there is little evidence on such differences. Similar estimates for _5. The benefits (or costs) associated with different levels of pollution-typically, the incidence of diseases-are a function of the desired change in the level of pollution. To estimate the costs stemming from a certain level of pollution, it is th;erefore necessary to consider the difference between that level and the one that causes no health effects. Such level of air quality may be zer vollutant concentration level tin the case of lead) or the legislation environmental standard (all other pollutants)._ 10
compared to 60 percent for male owners (p-value of 0.027 for testing equality). Moreover, 48 percent of female firms have _all_ their customers within 1km, compared to only 30 percent of men (the difference is significant with p<0.001). However, the propensity to sell locally, measured at the time of the baseline survey, is not significantly associated with returns to the treatments for either males or females. Thus it appears unlikely that the main explanation for the gender difference in returns across sectors is due to the geographic limitation of the market.

A second potential sector-related explanation is that female-owned businesses are more likely to be operated out of the home, where owners may be carrying out other tasks such as caring for family members or doing housework at the same time as operating their business. 74 percent of female-owned businesses in our sample operate out of the home, compared to 52 percent of male-owned businesses. However, Table 8 shows no significant interaction between treatment amount and dummy variables for operating a business out of the home, or for going into business to care for family members. The female-interaction coefficient does not significantly change when we control for either explanation. Thus industry differences also appear unlikely to be due to some firms being operated out of the home.

# **6. External Validity**

The experimental results in this paper clearly demonstrate a lower return to capital in female-owned enterprises in Sri Lanka than in male-owned enterprises, with the sector of work appearing to explain some of this gender difference. As with any empirical result based on data from a single country, a question which then arises is whether the results of the experiment apply to other settings. In this section we provide suggestive (nonexperimental) evidence that the pattern of returns by gender is not limited to Sri Lanka.

We use microenterprise data from detailed microenterprise surveys in Brazil and Mexico. The Brazilian data come from the Economia Informal Urbano survey of 2003 (see Fajnzylber et al. 2006) and Mexican data from pooling the 1992, 1994, 1996 and 1998 National Survey of Microenterprises (ENAMIN) surveys (see McKenzie and Woodruff, 2006). For each survey we restrict our analysis to firm owners aged 18 to 65, working more than 30 hours per week, with less than $US500 of capital stock, exclusive 25

-  -
||**Online experiment**<br>**(unweighted)**|**Phone survey**<br>**(unweighted)**|**National**<br>**population***|
|---|---|---|---|
|Male|73.2%|73.3%|51%|
|Adultpopulation aged 40years or older|25.9%|36.1%|37.0%|
|Highlands Region|25.5%|33.2%|39.2%|
|Islands Region|21.6%|21.2%|15.1%|
|Momase Region|41.7%|29.8%|25.7%|
|Southern Region|11.2%|15.8%|20.0%|

*Based on the 2011 Census as it provides data at the individual level.

To make comparisons between the insights that the phone survey and online experiment reveal about the general population in PNG it is necessary to apply a common approach to the survey weights. As mentioned in the preceding subsections, the phone survey was designed with the intention of household weights being used based on the 2016–18 DHS, which would allow for representative analysis at the household level about a range of COVID-19 related topics. In contrast, the online experiment, as is standard practice for this type of work, was designed for data analysis to be primarily conducted at the individual level without applying any survey weights (Haaland et al., forthcoming). However, it is common practice for survey weights to be applied to online experiments to provide an estimate of how generalizable the findings are likely to be.

Inverse probability weights at the individual level based on the 2011 population census were calculated for both the phone survey and online experiment so as to be able to compare representative findings across the two surveys. Respondents to the phone survey and online experiment were disproportionally more likely to be male and younger than the national population and their geographic location also differed (see Table 2). Inverse probability weights were used to adjust for these differences. For example, weights greater than one were applied to female, older respondents in the Southern region and weights of less than one were applied to younger men in the Momase region (see balance table once weights are applied for the online survey experiment as well as a full list of weights for the two surveys in Appendix A, Tables A1-A3). The weights were calculated at the individual level (as opposed to household) because the questions about vaccine hesitancy focused on an individual’s willingness to get vaccinated and their reasons behind this.

# **5. Findings**

This section presents the descriptive results from the phone survey and the control group in the online experiment. It also highlights what the phone survey reveals in terms of the drivers of vaccine hesitancy. In addition, this section details the efficacy of the treatments used in the online randomized survey experiment.

# <u>5.1 Descriptive results from the phone survey and online experiment</u>

This subsection presents broadly representative, descriptive findings from the phone survey and the control group in the online experiment, using the same approach to weighting (that is, inverse probability weighting at the individual level as discussed in Section 4.3). Both the phone survey and online experiment show there were high levels of vaccine hesitancy in PNG, which respondents stated 14
pattern of trade in goods differently in the U.S. and in India. In the United States, the liberalization of services appears to lead to a slight contraction in goods exports, with resources reallocated toward now more profitable service sectors. In contrast, the rise in exports of services from the United States to India appears to contribute to a rise in India’s exports of “goods”. This is perhaps because imported services provide important inputs into the production of goods, and the increased availability of efficient services may also reduce transaction/transportation costs. Several papers find that service imports may cause developing countries to increase their international competitiveness and facilitate exports (e.g., Cebula et al. 2011; Hoekman and Braga 1997). Analyzing U.S. trade data, Cebula et al. (2011) find that service imports from the U.S. have a significant and positive impact on goods exports to the U.S. in the case of low-income countries but not in the case of high-income countries.

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