rafmacalaba/datause-encoder-data huggingface dataset:
|Lebanon|Quarterly|Q4 2019|n/a|n/a|only census|2018-2019|
|---|---|---|---|---|---|---|
|Libya|Annually|2014|n/a|n/a|Labor force<br>survey|2013|
|Morocco|Quarterly|Q3 2021|Quarterly|Q3 2021|Quarterly|Q3 2021|
|Oman|Quarterly|Q3 2021|n/a|n/a|Annually|2020|
|Qatar|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q2 2021|
|Saudi Arabia|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Syrian Arab Republic|Annually|2019|n/a|n/a|Annually|2019|
|Tunisia|Quarterly|Q3 2021|Monthly|September 2021|Quarterly|Q3 2021|
|United Arab Emirates|Quarterly|Q2 2020|n/a|n/a|Annually|2020|
|West Bank and Gaza|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Yemen, Rep.|Annually|2017|n/a|n/a|only census|2013|
|**MENA Total: 19**|**Quarterly:**<br>**15/19**||**Monthly or**<br>**Quarterly:**<br>**10/19**||**Quarterly:**<br>**8/19**||
|**Mexico**|**Quarterly**|**Q3 2021**|**Monthly**|**October 2021**|**Monthly**|**November**<br>**2021**|

Source: Gatti et al., (2022). Information obtained from country statistical office websites; government data portal websites; central bank websites and Ministry of Planning/Economy/Finance websites Notes: N/a means information is not readily available. All data accessed as of January 2022. This table is not exhaustive, it has only surveyed country Statistical Office Websites, Government Data Portal Websites, Central Bank Websites and Ministry of Planning/Economy/Finance websites. Third-party websites were excluded from this survey. The table has been updated and verified by World Bank country economists. Mexico was selected as a comparator because it is an uppermiddle-income economy with a well-functioning data ecosystem.

On top of the lack of published monthly unemployment data in the MENA region, there are also challenges regarding the definitions employed. For examples, countries around the world usually follow the ILO’s definitions of employment and unemployment, which are consistent with definitions adopted by other developed countries, such as the United States (see Table 4).

**Table 4 Definitions of Employment and Unemployment from the U.S.  BLS, the French INSEE and the ILO**

|**_Definitions_**|**Employment**|**Unemployment**|**Source**|
|---|---|---|---|
|**United States**<br>**-Bureau of**<br>**Labor**<br>**Statistics**<br>**(BLS)**|Someone, aged 16 or over, who has either (1) worked at<br>least 1 hour as a paid employee or (2) in their own<br>business, profession, trade, or farm, or (3) was not<br>temporarily absent from their job, business, or farm,<br>whether or not they were paid for the time off, or (4)<br>worked without pay for a minimum of 15 hours in a<br>business or farmowned by amemberoftheir family.|Someone, aged 16 or over, who (1)<br>does not have a job, (2) has actively<br>looked for one in the past 4 weeks, and<br>(3) is available for work.|https://www.b<br>ls.gov/cps/def<br>initions.htm|

16
we evaluate the robustness of our results using days in countries with emergency meetings by the monetary authority or when the interest rate cut was larger than the anticipated by market analysts.<sup>6</sup> Our paper is related to a growing literature that studies the economic impact of the pandemic using stock market data (e.g., Gormsen & Koijen, 2020; Landier & Thesmar, 2020; Ramelli & Wagner, 2020). Given the extraordinary scale and unprecedented nature of the crisis, it is difficult to quantify the effect of the shock versus the impact of the ensuing economic policies. We add to this literature by examining the effects of the pandemic on stock returns of banks using an event study methodology. According to Borio (2020), banks entered this crisis with better capital buffers than in the GFC.<sup>7</sup> The documented increase in banks’ risk premium suggest that markets expect that the banking sector would endure higher profit losses than other industries; perhaps as they absorb a large portion of the shock. Using the idea that asset prices can help evaluate expected profitability of firms in real time, we also contribute to the literature by evaluating the impact of different financial policy measures. For example, our evidence suggests that prudential policies are not associated with a reduction in the risk premium in high income countries, while markets seem to be pricing the reduced risk from borrower assistance measures, particularly in developed countries.

We also contribute to a longstanding literature that examines the role of policy initiatives during financial crisis (Claessens et al., 2005; Reinhart & Rogoff, 2009; Taylor & Williams, 2009). Ait-Shalia et al. (2012) examine the effects of policy announcements on liquidity risk premia and interbank credit during the GFC. While we also study the market response to policy initiatives, there are noticeable differences between the COVID-19 shock versus previous events of financial

> 6 Expectation data for other types of announcements were not available. 7 Although the sample only includes Basel Committee member countries.

7
_Source_ : Own calculations using WBSCPS, administrative data, and households’ surveys. The administrative data sources are the following: Annual Reports of the Social Security Institution ( _Relaҁão Anual de Informaҁões Sociais,_ RAIS) for Brazil for 2017, Social Security and Taxes database for 2019-2020 for Ecuador, _Observatorio Laboral para la Educacion (_ OLE) for Colombia for 2016, and _Ponte en Carrera_ for Peru for 2018. The household surveys are the original (unharmonized) surveys from these sources: Brazil: PNADC ( _Pesquisa Nacional por Amosta de Domicílios Contínua_ ) for 2018; Colombia: GEIH ( _Gran Encuesta Integrada de Hogares_ ) for 2018; Ecuador: ENEMDU ( _Encuesta Nacional Empleo, Desempleo y Subempleo_ ) for 2018; Peru: ENAHO ( _Encuesta Nacional de Hogares sobre Condiciones de Vida y Pobreza_ ) for 2017.

_Notes_ : This table presents a validation exercise for the outcomes reported in the survey data. Graduation, dropout, time to degree, and employment are expressed as percentages. * indicates that wages are expressed in annual USD PPP (2017) for Brazil when using administrative data (column 3). Column 1 presents outcome averages as reported by program directors in the WBSCPS; means are weighted by the WBSCPS sampling weights (see definitions of outcomes from survey data in Appendix 1). In column (2), we impute a formal employment rate based on the following survey questions: "Regarding the graduates of the program in recent years, how many were employed by a firm in the formal sector?" and "Regarding the graduates of the program in recent years, how many were self-employed in the formal sector?" Directors had to choose among three possible answers: almost all, some; almost none or none. We assume the following formal employment rates for those answers: 80, 40, and 10% respectively. Column (2) shows the resulting formal employment rate imputation. In column (3) we show average outcomes calculated using individual-level administrative data; wages refer to individuals working in the formal sector and the formal employment rate is the percentage of SCP graduates who work formally. Formal workers include the following: employed and self-employed in Colombia, Peru, and Ecuador; and employed in Brazil. Columns 4-6 report validation outcomes using household survey data for different age groups; wages refer both to formal and informal employees.

66
Table 2: Cross Sector Analysis

||Full Sample<br>(1)|Housekeeper<br>(2)|Nanny<br>(3)|General<br>(4)|
|---|---|---|---|---|
|South African|0.0335|0.0324|0.0688|0.00791|
||(0.0337)|(0.0411)|(0.0814)|(0.0830)|
|Foreign|-0.0753<sup>_∗∗∗_</sup>|-0.0670<sup>_∗∗∗_</sup>|-0.135<sup>_∗∗∗_</sup>|-0.0252|
||(0.0141)|(0.0180)|(0.0322)|(0.0397)|
|Male|-0.0120|-0.0648<sup>_∗∗∗_</sup>|0.0672|0.0415|
||(0.0163)|(0.0213)|(0.0446)|(0.0344)|
|Female|-0.00235|0.0224|-0.0682<sup>_∗∗_</sup>|-0.0126|
||(0.0143)|(0.0174)|(0.0318)|(0.0650)|
|Age: <25|0.0408|0.0374|-0.001000|0.0470|
||(0.0249)|(0.0332)|(0.0454)|(0.0745)|
|Age: 26-30|-0.0145|-0.0172|0.0190|-0.0384|
||(0.0195)|(0.0226)|(0.0423)|(0.0595)|
|Age: 31-35|-0.0155|-0.00951|0.0138|-0.118|
||(0.0282)|(0.0341)|(0.0556)|(0.103)|
|Age: 36-60|-0.0295|-0.0443|-0.0410|0.0960|
||(0.0307)|(0.0376)|(0.0522)|(0.147)|
|Experience (yr)|0.00138|0.0131<sup>_∗_</sup>|-0.00586|-0.0148|
||(0.00545)|(0.00716)|(0.00957)|(0.0144)|
|Live w Employer|0.0661<sup>_∗∗∗_</sup>|0.0848<sup>_∗∗∗_</sup>|0.0388|0.0303|
||(0.0185)|(0.0223)|(0.0351)|(0.0870)|
|Workpermit|-0.0714<sup>_∗∗_</sup>|-0.0537|-0.0720|-0.0323|
||(0.0295)|(0.0390)|(0.0615)|(0.0652)|
|Urgency|0.102<sup>_∗∗_</sup>|0.169<sup>_∗∗∗_</sup>|0.0919|0.0163|
||(0.0501)|(0.0607)|(0.0950)|(0.112)|
|Picture|0.427<sup>_∗∗∗_</sup>|0.442<sup>_∗∗∗_</sup>|0.478<sup>_∗∗∗_</sup>|0.341<sup>_∗∗∗_</sup>|
||(0.0258)|(0.0444)|(0.0405)|(0.0505)|
|_R_<sup>2</sup>|0.266|0.217|0.320|0.191|
|N|5338|3217|1133|988|
|p-value: For=SA|0.002|0.014|0.019|0.740|

Notes: Robust standard errors parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 , _p <_ 0 _._ 05 , _p <_ 0 _._ 01

# **4.1.3 Robustness Test: Controling for Unobservables**

While I observe the exact information as employers by the time they decide whether to click on a profile, one may be concerned that I fail to codify all relevant information as job seeker texts are multidimensional. To address this concern, I take advantatge of the fact that I observe _more_ information than the employer by the time she makes the screening decision. Specifically, I observe the residential location of 2,120 job seekers (which is revealed on the profile page). Given South Africa’s history of spatial segregation, residential locations are correlated with socio-economic variables. By observing 13
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

Balancing Act. 2008. African Telecoms and Internet Markets. Part 2: Central Africa. London, UK.

Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

- World Bank. 2009b. ―Note sur le Secteur des Télécomunicacions au Mali. Etude des situations de concurrence sur le marché des télécomunications (TA P107-128). » Washington, DC: World Bank.

- France Telecom. 2010. ―Registration Document 2009.‖

- www.orange.com/en_EN/press/press_releases/cp100608en3.jsp.

# Irrigation

FAO (Food and Agriculture Organization). 2004. ―Aquastata: Cameroon Country profile.‖ <u>www.fao.org/nr/water/aquastat/main/index.stm</u>

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

- Global Water Partnership. 2010. ―Partnerships for Africa’s Water Development: Planning for Integrated Water Resources Management and Development in Cameroon.‖ Yaoundé, Cameroon.

# Power

Briceño-Garmendia, Cecilia, and Maria Shkaratan. 2010. ―Power Tariffs: Caught between Cost Recovery and Affordability.‖ First draft, February 2010.

- GTZ (Deutsche Gesellschaft für Internationale Zusammenarbeit). 2009. ―International Fuel Prices 2009.‖ 6th ed. Eschborn, Germany.

49
This result shows that _C_<sup>∗</sup> is an increasing function of _ε_ , which itself depends on the combined volatility _σ_<sup>2</sup> and the growth rates _αv_ and _αf_ .

The option value _X_ ( _V ,F_ ) can be explicitly written as (McDonald & Siegel 1986): This expression demonstrates the dependence of the option value _X_ on the project’s value _V_ , the investment cost _F_ , the critical threshold _C_<sup>∗</sup> , and the elasticity _ε_ . The relationship between volatility and the critical threshold _C_<sup>∗</sup> can be further examined by differentiating _C_<sup>∗</sup> with respect to _σ_ : since _dσ_<sup>_<u>dε</u>>_0 and</sup><sup>_ε >_1.Therefore, both the value of the investment option</sup><sup>_X_and the critical threshold</sup> _C_<sup>∗</sup> are increasing functions of the variance _σ_<sup>2</sup> : This proportionality demonstrates that as uncertainty (volatility) increases, the value of the option to delay investment rises, similar to financial options where greater volatility expands the range of possible outcomes. This flexibility allows firms to respond more effectively to future changes, such as shifts in prices or market trends. Firms can capitalize on upside opportunities by exercising the option to invest or expand, while avoiding losses by deferring in unfavorable conditions. This asymmetry, which favors the investor, means that higher uncertainty or a broader range of potential outcomes benefits the firm by increasing the value of waiting.

The following sections outline the data sources and empirical strategies used to test the real options hypotheses, focusing on temperature uncertainty at the grid level to explore the potential negative relationship between temperature uncertainty and firms’ investment decisions.

# 3 Data

## 3.1 Data source

Firm-level data. We employ Orbis as our principal firm-level data source, which includes comprehensive financial statements for organizations on a global scale. Administered by Bureau van Dijk, the Orbis database integrates information from a variety of origins, including official company registries and regulatory filings. Its extensive coverage, particularly concerning balance sheet items and profit 6
# X. FUTURE ASSISTANCE TO HOST GOVERNMENTS

The foregoing discussion should make clear that there are a number of areas in which the Bank and IFC, if they wish to encourage BOT projects in the developing world, can provide significant assistance to host governments. The World Bank's newest affiliate, the Multilateral Investment Guarantee Agency ("MIGA") may also play a role in this area.

10.1 Education. First, given the complexity of most BOT projects and the sophisticated legal and financial concepts involved, the Bank or IFC could help to explain to host governments what a BOr project is, its possible advantages and disadvantages and the kinds of government support that may be needed.

10.2 Advisers. If a host government decides to pursue one or more BOT projects actively, we believe it is essential that the government hire early in the process a qualified financial adviser and experienced legal counsel to advise and represent the government in both the selection process and in negotiations with potential sponsors. An outside technical adviser, with experience in the sector involved (e.g., power plants, roads, airports, subways, etc.) should also be added to the government team. The Bank or IFC should encourage host governments to make use of such advisers, could provide lists of recommended advisers and may be able to provide funding to pay for them.

10.3 Identification of Prolects. The Bank can be of significant help to developing countries in analyzing their needs for infrastructure projects and sorting out priorities. The Bank can perform (or finance) sector studies and feasibility studies and help host governments identify specific projects to meet their general needs. If the decision is made to attempt to do a 47 -

-
# Table **2:** The effect of student background characteristics on Grade 8 protest **and** posttest mathematics achievement Thailand, 1981-82

|Independent<br>variables|Protest|Poattest|<br>Poattest|Posttest|
|---|---|---|---|---|
|Pretest score|--|--|.82***<br>(59.04)|80***<br>(54.05)|
|Sex (2-<br>boy)|-.17<br>(.68)|.18<br>(.62)||.32<br>(1.45)|
|Age<br>Age squared<br>Father's occupation<br>Mothers education|.26<br>(1.01)<br>.00<br>(1.38)<br>.69***<br>(4.82)<br>.89***<br>(5.30)|.36<br>(1.14)<br>-.00<br>(1.59)<br>.59***<br>(3.41)<br>*97***<br>(4.79)|<br>|.14<br>(.61)<br>.00<br>(.89)<br>.03<br>(.24)<br>.26<br>(1.70)|
|Calculator**at**home|.46<br>(1.63)|.62<br>(1.84)||.25<br>(1.00)|
|District per capita ircome (1000|bh) .28***|*35***||12**|
||(7.11)|(7.36)||(3.55)|
|Lang. of inst. at home|1.57*<br>(2.18)|2.33**<br>(2.70)||1.08<br>(1.67)|
|Dist. PCI x Lang of inst.|-.13*<br>(2.48)|-.00**<br>(3.10)||-.09*<br>(2.01)|
|Constant|-12.57|-14.87|4.71|4.05|
|Adjusted R-squared|.07|.07|.48|.49|
|_N_|3642|3627|3801|3625|

***p < .001, **p < .01, * p < .05.

Note: Unstandardized regression coefficients; t-statistics in parentheses.

- 25 -
Incidence of COVID-19 and Connections with Air Pollution Exposure

# 6.2.6 Re-estimation using satellite-derived PM2 _._ 5

This section evaluates whether the relationship with PM2 _._ 5 generalizes to measurements from a different source. Table 9 compares the municipality-level data. Both measurements trend in the same direction but the levels according to the RIVM are roughly one-third below those of van Donkelaar.

Table 9: Comparison of PM2 _<u>.</u>_ <u>5</u> statistics: RIVM 2017 vs van Donkelaar 2016.

|**Variable**|**Mean**|**Min**|**q.25**|**q.50**|**q.75**|**Max**|
|---|---|---|---|---|---|---|
|RIVM 2017|10.22|6.95|9.43|10.69|11.23|12.04|
|van Donkelaar 2016|14.60|10.15|13.44|15.01|16.01|18.05|

Correlation: 0.70 Model 5g in table 10, estimates step-wise AIC with the new pollution data and uses the selected covariates in the specification of Model 5. Model 5h uses the same controls as Model 5, and Model 5i applies the Yeo-Johnson power transform. The analysis makes use of the confirmed cases from March 22. It is clear from the estimation results that the use of satellite-derived PM2 _._ 5 results in similar conclusions. The parameter estimate for PM2 _._ 5 remains significant in the presence of controls and is significant at the highest level when the dependent variable is first normalized.

Table 10: Dependent Variable: Confirmed COVID-19 cases <u>per</u> 100,000 inhabitants.

|**Variable**|**Model 5g**|**Model 5h**|**Model 5i**|
|---|---|---|---|
|(Intercept)|-127.26*|-213.60***|-11.17***|
||(74.02)|(72.86)|(2.46)|
|Population density||0.90|-0.04|
|||(1.93)|(0.07)|
|Share from 25 to 44||-0.50|0.026|
|||(1.07)|(0.04)|
|Share above 65|2.38***|2.21**|0.10***|
||(0.91)|(1.06)|(0.04)|
|Share of unmarried|3.50***|4.14***|0.14***|
||(1.02)|(0.96)|(0.03)|
|Share of single households|-1.76***|-1.78***|-0.07***|
||(0.46)|(0.50)|(0.02)|
|Share of non-western immigrants|-0.53|-0.71*|-0.02|
||(0.34)|(0.41)|(0.01)|
|Share of water surface|2.47|2.22|-0.79***|
||(8.71)|(8.75)|(0.28)|
|Share of overweight|-1.32<br>(0.91)|||
|Share with obesity|1.60<br>(1.53)|||
|Share with long-term illness||0.68|0.01|
|||(0.78)|(0.02)|
|Mean PM2_._5|2.75**|3.13**|0.28***|
||(1.28)|(1.29)|(0.03)|
|_λ_|0.41*|0.40*|0.10|
||(0.24)|(0.24)|(0.19)|
|_ρ_|0.45**|0.46*|-0.16|
||(0.22)|(0.22)|(0.19)|
|R2|0.50|0.50|0.30|
|AICc|3265.34|3268.53|920.81|
|N|355|355|355|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 23
- Transparency (ie regulator has statutory obligation to explain basis for decisions)

- Redress (ie appeal rights)

This list includes a key aspect of regulatory practice – transparency. However, the study only looks at whether and how this is covered in the law. Indeed, it seems that the study is only a study of Electricity/Energy _Laws_<sup>28</sup> . In consequence, there is no indication of whether, how and how far the letter of the law is reflected in regulatory practice nor how the spirit of the law is interpreted.

The Prayas Report and, to a lesser extent, Stern and Holder (1999) show how regulatory _practice_ can and is typically significantly different from what legal provisions would lead one to expect. This is confirmed in numerous one-off case studies as discussed in Kessides (2004). Similarly, Stern (2000) and (2002) – as well as the EBRD 2001 Transition Report on Energy - would suggest that the actual performance of regulatory frameworks for electricity in transition economies is significantly different from the relatively optimistic picture set out in the Kennedy tables.

In fairness, Kennedy’s paper supplements the tables with brief descriptions of the electricity regulatory experience in Hungary, Kazakhstan and Moldova. These descriptions do bring out clearly the gap between de jure provisions and de facto experience. Nevertheless, counting the positive entries as a proportion of the total, Kennedy’s Table 2 provides a picture for Central Europe and the Baltic States of a glass that is over 80% full. This is not what one finds in the case studies, nor what one finds in the papers referred to above.

The importance of this example is not to criticize the Kennedy paper. Rather it clearly demonstrates that data based on analysis of laws without collection of information on practice are, at best, seriously problematic; and, at worst, may be biased and misleading.

# **3.3.2 The 2002 Indian Ministry of Power Study**

There are other studies (including the econometric studies of regulatory effectiveness that we will discuss below) which rely largely or wholly on studies of laws and formal regulatory governance properties but which provide little or no detailed examination of regulatory practice. We will not discuss these here as they add little additional information. There is, however, a study for Indian electricity regulation, which contrasts interestingly with the Prayas study and which shows the limitations of primarily paper-based studies (as well as the issue of the relative assessment/marking standard).

The Ministry of Power in India commissioned (apparently in 2002) an appraisal of power sector reform and a performance rating for 26 Indian states by CRISIL/ICRA. The study was primarily from paper records, but supplemented by face-to-face meetings with SERC members, heads of utilities, etc.

> 28 Unfortunately, the paper does not explicitly record the data collection methods for the EBRD/WB study of regulators.

21