rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1. Summary statistics**

||Obs|Mean|SD|
|---|---|---|---|
|**Panel A: Individual-year means**<br>||||
|Age<br>|227,579|31.57|11.20|
|Have preschool-aged child|227,579|0.30|0.46|
|Work participation|227,559|0.52|0.50|
|Public preschool density (Inferred in-between)<br>|226,420<br>|0.16<br>|0.22<br>|
|Private preschool density (Inferred in-between)<br>|226,420<br>|4.60<br>|3.25<br>|
|Public preschool density (Linear projection)|227,579|0.15|0.21|
|Private preschool density (Linear projection)|227,579|4.45|3.16|
|Urban|227,579|0.51|0.50|
|**Panel B: Individual-year means (PODES years only)**<br>||||
|Age|77,318|31.98|11.22|
|Have preschool-aged child|77,318|0.30|0.46|
|Work participation<br>|77,312|0.53|0.50|
|Public preschool density|76,957|0.16|0.22|
|Private preschool density|76,957|4.52|3.22|
|Urban|77,318|0.51|0.50|
|**Panel C: Individual means**<br>||||
|Number of surveys|10,340|3.54|1.13|
|Number of years|10,340|22.01|5.00|
|Number of PODES years|10,340|7.48|1.46|
|Age of first marriage<br>|10,329|20.23|4.59|
|Age of first birth|10,337|22.13|4.52|
|Number of children|10,340|2.74|1.59|
|Years of education|10,140|7.75|4.39|
|**Panel D: District-year means (PODES years only)**||||
|Number of districts|290|||
|Public preschool density|2,559|0.24|0.35|
|Private preschool density|2,559|4.18|3.24|
|Public preschool count|2,592|10.27|14.11|
|Private preschool count|2,592|227.48|243.76|
|Child age 3-6 population|2,566|61,206|56,815|

Notes: We constructed individual-year panel of mothers aged 19-45 in at least two IFLS rounds. Panel A describes the pooled observations of mothers across all the observed years. Panel B describes the pooled observations of mothers in PODES years only. Panel C describes unique observations of individual mothers. Panel A, B, and C are constructed from IFLS 1-5. Panel D is constructed from multiple rounds of PODES and Susenas from 1990-2014, as described in Section 3. Panel D describes pooled observations of districts across PODES and Susenas years. Preschool counts are obtained from PODES and child age 3-6 population from Susenas; densities are defined as preschool count divided by 1,000 children in the district. Districts often split over time; by 2014, there were 511 districts. In our analyses, we maintain the 1993 district boundaries to allow comparisons over time.

46
# **When Face-to-Face Interactions Become an Occupational Hazard:**

# **Jobs in the Time of COVID-19**

# _Besart Avdiu and Gaurav Nayyar_

## **1. Introduction**

There is a crisis of demand brewing around the globe as social distancing becomes the norm to counter the COVID-19 outbreak. Examples abound of job cuts as authorities ask restaurants and bars to close shop. Manufacturing activity in global value chains is increasingly disrupted too. Ripple effects will likely be felt with a negative demand shock for consumer durables such as cars and the ability of factories to supply at capacity will be affected as lockdowns are imposed. So, which parts of the economy are most in the line of fire?

There is an emerging classification of “shutdown” sectors, which includes accommodation and food services, passenger transport, personal services (e.g. hairdressers), arts, recreation and entertainment, non-essential retail, and sensitive manufacturing (Vavra 2020). While transparent and quick, such measures are somewhat ad-hoc and not data driven. One way to assess vulnerability during the COVID-19 pandemic is to look at jobs that can be done at home. Dingel and Neiman (2020) use Occupational Information Network (O*NET) data from the United States to develop a home-based work (HBW) measure. The authors define a binary (dummy) variable for whether a job can be done from home or not based on whether it comprises tasks that relate to “daily work outdoors” or the “operation of vehicles, mechanized devices, or equipment”. An occupation may consist of many jobs, although the two are identical at the most granular level. Therefore, the HBW measure reflects the percentage of jobs that can be done from home by occupation. There is, however, also a different and equally relevant dimension of measuring face-to-face interactions with consumers, which we aim to capture through a face-to-face index based on Blinder (2009).

Home-based work (HBW) and face-to-face (F2F) interactions represent related, but complementary aspects of how social distancing is likely to affect jobs. They differ along three main dimensions, namely a) temporal (short run vs. medium run), b) the primary channel of effects (supply vs. demand) and c) the relevant margins (intensive vs. extensive).

2
in technology, product quality, transportation costs and other factors that affect demand. In addition, any misspecification error, especially due to heterogeneity in the elasticity of substitution, labor demand, and factor shares, will be reflected in the fixed effects and error term.<sup>19</sup> The inclusion of δrs means that the effect of immigration is identified from changes over time in the number of immigrants in each industry-region. The inclusion of δst and δrt implies that only deviations from the sector-specific and region-specific average in immigration flows to an industry-region are used for identification. The regression is estimated at the local labor market level (industry-region in a year).

Note that we are estimating the relationship between immigrant and native employment in levels,<sup>20</sup> as opposed to in natural logarithms or, similarly, these variables standardized by local labor market employment levels. The reason is that a log-log specification imposes the restriction that in percentage terms the effect of immigration on natives is always the same, independently of the fraction of immigrants.<sup>21</sup> However, as is evident from our discussion of equations (3), (4) and (5) in Section 2, the elasticity of wage and employment levels with respect to immigration will depend on the share of immigrants. A log-log specification is likely to fit the data well when directly estimating the elasticity of substitution, as is typical in the literature, and when factor shares do not vary too much, such as in the seminal work of Katz and Murphy (1992). In our case we are estimating the full derived-demand elasticity and the immigrant share varies enormously across local

> 19These fixed effects also control for the annual levies employers have to pay for employing foreign workers.

> 20Peri and Sparber (2010) describe this as the straightforward way of testing for displacement. We deal with their concern about differential sizes of local labor market through the inclusion of fixed effects. 21A very similar restriction is imposed when standardizing variables by the level of employment (or the population), such that the relationship between immigrant and native employment is constant in percent changes.

22
6 Following the comprehensive price liberalization of 1991, at a time when the economy was subjected to a series of severe external shocks<sup>3</sup> , the mirage. of "accounting profits" disappeared, leaving many state enterprises in the red. By 1993, the revenue significance of profits tax, levied at the reduced rate of 30%, fell behind social security **tax,** excises and customs, and non-tax revenues. After the turnover tax rate structure had bee.i considerably simplified and base broadened, it was replaced by a Value Added Tax with a single 18% rate in 1994. With the general shift in the emphasis from mandatory to voluntary compliance, a progressive income tax with marginal rates ranging from 20% to 52% gained more prominence. Revenues from income tax as a share of GDP rose by 25 percent between 1991 and 1993, compared with a sharp decline (about 70 percent) in revenues from profit tax.

Against this background, distributional effects of the emerging tax system in Bulgaria are far from clear and are not, to our knowledge, systematically analyzed. Yet, in the emerging market economy, understanding distributional effects of taxation may be of particular interest for policy. In this paper, we therefore attempt to provide a preliminary analysis of the effects of income tax on the distribution of income, using the household budget survey data. It is hoped that quantitative picture of the often poorly understood distributional effects of taxation will provide the basis for better tax policy analysis and tax design in Bulgaria as well as in other economies in transition.

3/ These include the loss of traditional external markets of the former CMEA countries, the impact of the Gulf War, the severed trade routes to the West due to the disintegration of the former Yugoslavia, and the subsequent U.N. sanctions against Federal Republic of Yugoslavia (Serbia and Montenegro).
**Table 8. Regional Trends in Self-Reported Food Insecurity (Percent Prevalence)**

|**Developing region**|**No. of**<br>**obs.**|**2005–06 surveys**<br>**(precrisis)**|**2008 surveys**<br>**(food crisis)**|**2009 surveys**<br>**(financial crisis)**|
|---|---|---|---|---|
|Eight most populous developing<br>countries*|8|32.7|28.0|30.6|
|sub-Saharan Africa|14|55.8|54.6|57.2|
|West Africa, coastal|4|48.5|51.3|58.0|
|West Africa, Sahel|5|59.6|49.2|55.2|
|Eastern & Southern Africa|5|57.8|62.8|58.6|
|Latin America & Caribbean|15|33.2|36.4|35.7|
|Central America, Caribbean|7|38.4|41.4|40.3|
|South America|8|28.6|32.0|31.6|
|Middle East (including Turkey)|3|19.7|26.0|21.3|
|Transition countries|13|31.9|30.2|34.6|
|Eastern Europe|6|21.8|19.7|25.8|
|Central Asia|7|40.6|39.1|42.1|
|Asia|12|28.8|29.0|30.8|
|East Asia|7|30.1|30.6|32.7|
|South Asia|5|26.8|26.8|28.6|

_Source_ : Author’s calculations from GWP (Gallup 2011) self-reported food insecurity prevalence rates. _Note_ : * “Large and fast growing” includes India, Indonesia, Brazil, Pakistan, Bangladesh, Nigeria, Mexico, and Vietnam but excludes China.

Although the results in table 8 cover the majority of the developing world’s population, there are still sizeable omissions. Although the GWP surveys cover China, we excluded the 2005–06 rounds due to specific concerns about biases in the responses to the food insecurity question. However, a number of other countries are lacking the requisite data for 2005–06 or 2008. China, of course, has a population of over a billion people, but 16 other omitted developing countries 28
Becker, M. (2015) Constructing SSLM: Insights from Struggles Over Women’s Rights in Nepal, _Asian Studies Review_ , 39 (2), pp.247-265.

Bedner, A. and J. Vel (2010) ‘An Analytical Framework for Empirical Research on Access to Justice,’ _Law, Social Justice and Global Development Journal_ , (1), <u>http://www.go.warwick.ac.uk/elj/lgd/20010_1/bedner_vel</u> Bekasinews.com (2007) ‘Wapres: Gaji Guru Seharusnya Masuk Komponen 20 Persen’, 21 August, available at http://bekasinews.com/berita/daerah/26-wapres-gaji-guru-seharusnyamasuk-komponen-20-persen.html, accessed February 2010.

Brinks, D. and V. Gauri (2014) ‘The Law’s Majestic Equality? The Distributive Impact of Judicializing Social and Economic Rights’, _Perspectives on Politics_ , 12 (2), pp.375-393.

Bergallo, P. (2011) ‘Argentina: Courts and the Right to Health: Achieving Fairness Despite “Routinization” in Individual Coverage Cases’ in A. Yamin and S. Gloppen (eds.) _Litigating Health Rights: Can Courts Bring More Justice to Health?_ , Cambridge: Harvard University Press, pp.43-75.

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Cassels J. (1989) ‘Judicial Activism and Public Interest Litigation in India: Attempting the Impossible?’ _The American Journal of Comparative Law_ , 37 (3), pp. 495-519.

Chang, M., S. Shaeffer, S. Al-Samarrai, A. Ragatz, J. de Ree, and R. Stevenson (2014) _Teacher Reform in Indonesia: The Role of Politics and Evidence in Policy Making_ , Washington DC: World Bank.

Cousins, B. (2009) ‘Capitalism Obscured: The Limits of Law and Rights-based Approaches to Poverty Reduction and Development,’ _Journal of Peasant Studies_ , 36 (4), pp.893-908.

Curnow, J. (2015) Legal Support Structures and the Realisation of Muslim Women’s Rights in Indonesia _Asian Studies Review_ , 39 (2), pp. 213-228.

Darmaningtyas, E. Subkhan and I. Fahmi-Panimbang (2009) _Tirani Kapital Dalam Pendidikan: Menolak UU BHP (Badan Hukum Pendidikan)_ , Yogyakarta: Pustaka Yashiba and Damar Press.

26
**Table 12b:** Regression of log per capita total expenditure - main respondent characteristics

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|Model 9|
|Recall short-list|0.020|0.003|-0.092|-0.049|-0.001|-0.115**|0.019|-0.021|-0.289**|
||(0.038)|(0.039)|(0.094)|(0.084)|(0.050)|(0.058)|(0.059)|(0.067)|(0.133)|
|Recall long-list|0.095**|0.078**|0.065|-0.083|0.080*|0.037|0.124*|0.071|-0.088|
||(0.038)|(0.038)|(0.072)|(0.088)|(0.045)|(0.055)|(0.063)|(0.066)|(0.121)|
|Recall short-list*female|||0.137||||||0.146|
||||(0.115)||||||(0.107)|
|Recall long-list*female|||0.019||||||0.021|
||||(0.089)||||||(0.085)|
|Recall short-list*employed||||0.118|||||0.120|
|||||(0.098)|||||(0.093)|
|Recall long-list*employed||||0.261**|||||0.135|
|||||(0.115)|||||(0.102)|
|Recall short-list*non-wage|||||0.045||||-0.047|
||||||(0.130)||||(0.119)|
|Recall long-list*non-wage|||||-0.004||||-0.096|
||||||(0.110)||||(0.105)|
|Recall short-list*floor||||||||||
|material||||||0.217***|||0.216***|
|||||||(0.075)|||(0.076)|
|Recall long-list*floor||||||||||
|material||||||0.088|||0.110|
|||||||(0.073)|||(0.071)|
|Recall short-list*water||||||||||
|(rainy)|||||||0.075||-0.054|
||||||||(0.094)||(0.118)|
|Recall long-list*water||||||||||
|<br>(rainy)|||||||-0.000||-0.039|
||||||||(0.097)||(0.133)|
|Recall short-list*water||||||||||
|(dry)||||||||0.116|0.042|
|||||||||(0.088)|(0.112)|
|Recall long-list*water||||||||||
|<br>(dry)||||||||0.098|0.069|
|||||||||(0.089)|(0.131)|
||11.334**|11.156**|11.560**|11.460**|11.290**|11.066**|11.145**|<br>11.112**|<br>11.302**|
|Constant|*|*|*|*|*|*|*|*|*|
||(0.040)|(0.061)|(0.084)|(0.075)|(0.045)|(0.039)|(0.048)|(0.058)|(0.094)|
|Observations|2,118|2,100|2,114|2,110|2,114|2,118|2,118|2,108|2,100|
|R-squared|0.003|0.278|0.026|0.009|0.025|0.215|0.084|0.096|0.284|

Note: Dependent variable is the log of per capita total consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes head of household and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

44
reservoirs and irrigation systems, as hydroelectric facilities and water supplies are affected by changes in the water flow from melting glaciers.

Finally, it should be warned that the impacts found for Peru cannot be generalized to other countries. The impacts of climate change differ from country to country depending on the spatial distribution of the population, the types of activities they are engaged in, and the particular patterns of climate change.

# **References**

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- Horowitz, J. K. (2006) “The Income-Temperature Relationship in a Cross-Section of Countries and its Implications for Global Warming.” Department of Agricultural and Resource Economics, University of Maryland, Submitted manuscript, July. - -

- <u>http://faculty.arec.umd.edu/jhorowitz/Income Temp i.pdf</u>

- Magrin, G., C. Gay García, D. Cruz Choque, J.C. Giménez, A.R. Moreno, G.J. Nagy, C. Nobre and A. Villamizar (2007) “Latin America. Climate Change 2007: Impacts, Adaptation and Vulnerability.” Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 581-615.

- Masters, W. A. & M. S. McMillan (2001) “Climate and Scale in Economic Growth,” _Journal of Economic Growth_ , **6** (3): 167-186.

- Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J. Weaver and Z.-C. Zhao (2007) “Global Climate Projections.” In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- Meerhoff, E. (2008) “Análisis de los Impactos Causados por el Fenómeno Meteorológico El Niño 1997-1998 a Escala Regional y por Países.” Informe de Pasantía PHI-LAC UNESCO.

- Mendelsohn, R., W. Nordhaus & D. Shaw (1994) “The Impact of Global Warming on Agriculture: A Ricardian Analysis,” _American Economic Review_ , **84** (4): 753-71.

25
**Inflation.** A surge in food prices increases consumer price inflation. For example, the 200708 and 2010-11, LIC inflation more than doubled, from 7 to 15 percent during 2007-2008 and from 5 to 11 percent during 2010-2011. The increase in EMDE inflation was less pronounced, from 7 to 11 percent during 2007-2008 and from 5 to 6 percent during 20102011. Food prices accounted disproportionately for these increases in inflation—for about two-thirds in LICs and more than half in EMDEs. In vulnerable LICs such as Benin and Niger, where net food imports amount to 15 and 7 percent of household consumption, respectively, inflation surged from 1 percent to 8 percent and 0.2 percent to 11 percent, respectively, during the 2007-08 food price spike.

**Terms of trade.** Sharp increases in food prices can constitute significant adverse terms of trade shocks that lower growth, especially in countries that are large net importers of food. More than three-quarters of LICs are net food importers. The median LIC’s terms of trade declined by 2 percent and 4 percent during the 2007-08 and 2010-11 food price spikes, respectively. In some, the deterioration was much steeper. For example, the terms of trade of Sierra Leone, a LIC highly reliant on food imports, weakened by 10 percent during each of these food price spike episodes.<sup>5</sup> In heavy food importers, the exchange rate depreciation typically associated with adverse terms of trade shocks can compel central banks to tighten monetary policy and further lower growth. Indeed, during the 2007-08 food price spike, close to half of EMDE central banks responded to rising inflation and depreciation by tightening monetary policy.<sup>6</sup>

**Fiscal policy constraints.** Absent stabilizing fiscal arrangements, heavy reliance on food and agricultural trade can contribute to volatility in public finances and erode fiscal sustainability: rising food prices may increase tax revenues from the agricultural sector and encourage governments to spend. Conversely, when food prices fall, evenue losses in the agricultural sector are exacerbated by political pressures to subsidize food production. During the sharp rise in food prices in 2007-08, LICs’ fiscal balances deteriorated, on average, by close to 1 percentage point of GDP, in part due to higher food import bills. Food price spikes may also cause sociopolitical instability, including political unrest and food riots (Barrett 2013).

**Monetary policy constraints.** In countries where inflation expectations are not wellanchored and monetary policy frameworks are weak, the increase in inflation caused by 5Severe terms of trade shocks are considerably more common in LICs than in advanced economies and, of all possible external shocks, tend to have the most severe output cost in LICs (IMF 2011; Becker and Mauro 2006).

> 6Based on a sample of 54 EMDEs.

7
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unnecessary complexity and detail from the very start. In addition, given the limited capacity of debt management o¢ ces in developing countries we do not see the numerical procedures as the mainstream approach. Instead, we would suggest that forecasts of the expected paths for economic fundamentals, such as output growth, interest rates, in‡ation, exchange rates etc., including the associated con...dence intervals are taken from the national central banks which have usually much higher capacity in this respect and put a considerable e¤ort into forecasting of such variables. This can enable the debt managers to construct some baseline scenarios and perform a sensible scenario analysis. The use of macroeconomic predictions from the national central banks is also advisable in order to ensure consistency of macroeconomic policies and the assumptions on which they are based.

# 3 Issues with Current Approaches

Typical economic problems dealing with optimal allocation of sovereign debt portfolios imply that the optimal currency structure of foreign debt is to a large extent determined by magnitudes of covariances between domestic fundamentals and the considered exchange rates, and variances of the considered exchange rates - see section 2. In this section we aim to further discuss (i) the economic concept underlying the variances and covariances in the aforementioned economic problems, (ii) their empirical measures, and (iii) their role in risk analyses of sovereign debt portfolios.

## 3.1 Covariances of Exchange Rates and Domestic Fundamentals

Following the outlined structure we (i) discuss the economic concept underlying the role of the covariances in problems dealing with optimal currency denomination of foreign debt. The covariances appear in the solution results since the variables characterizing servicing costs of the debt and performance of the domestic economy are uncertain (random variables) and so is the economic structure relating all the economic variables in question. The main purpose of the covariances is thus to capture 18