rafmacalaba/data-use-mentions-extended huggingface dataset:
Using historical and near real-time data from Google Trends, this paper provides fresh evidence on the immediate impacts of COVID-19 on selected sectors of economies. We quantify the impacts of the pandemic on consumer demand for services involving face-to-face interactions – such as travel and tourism, hotel and restaurant, retail trade – and services that require less physical interaction, such as ICT and delivery services. Google search data measure the search intensity or popularity of a search term submitted to the Google search engine. These search queries are submitted by billions of users around the world each day and capture consumer interests, perceptions, and demands for different services and products. With the increasing penetration of the internet across the world, the application of "big data" – such as search queries, social media, and web-scraping – to understand human behavior is becoming prominent in finance, economics, and many other disciplines.

Google search data accurately predict the present and future economic outlooks, including consumer demand (Vosen and Schmidt, 2011; Choi and Varian, 2012; Gilchrist and Sands, 2016). Google search data have been used to forecast macroeconomic indicators such as GDP (Narita and Yin, 2018); evaluate firm and stock market performances (Da et al., 2010; Bordino et al., 2012); predict demand for automobiles and consumer confidence (Choi and Varian, 2009); forecast house prices (Wu and Brynjolfsson, 2015); understand consumer behavior and quantify demand for information (Goel et al., 2010; Drake et al., 2012); predict unemployment rates (Pavlicek and Kristoufek, 2015); measure job search (Baker and Fradkin, 2017) and racial bias (StephensDavidowitz, 2014; Chetty et al., 2020). Google search data have also been used for predicting the outbreak and spread of epidemics (Ginsberg et al., 2009) and election outcomes (Metaxas and Mustafaraj, 2012). More recently, these data are used to assess the impact of COVID-19 on mental health (Brodeur
suitable jobseekers with firms they had business relations (with or without a specific request from the firm). Specific matching involved pre-selecting candidates and matching them with employers in response to specific needs of a firm. As part of the matching services, the private employment service provider also recorded interactions with employers including whether the employer requested CVs of certain jobseekers and whether they followed-up with the jobseeker based on submission of a CV. Table 1 provides an overview of the services provided under the program.

Payments to the private employment service provider were based on performance. The firm received a payment for every IAP developed based on the profiling interview. An additional payment was made for each beneficiary who secured a formal job, whether through their own search or through a match secured by the provider. To independently verify employment of the beneficiary, administrative data from the Federal Employment Institute (FEI) was used. For the purposes of payment, a jobseeker is considered formally employed if s/he appeared in the FEI employment database for at least three consecutive months (for details see section 4 describing the data sources below).

# **2.3 Impact Evaluation Design**

The impact evaluation focused on studying the relative effectiveness of the different services offered by the private employment service using a randomized design. A secondary objective was to open the “black box” to examine decisions of job counselors **.** While most of the existing literature focuses on comparing private providers to the PES, the present study evaluates the relative efficacy of different services provided by the same (private) provider, and to better understand the (typically unobservable) priorities and decisions made by the private provider. To do this, the research team first stratified program beneficiaries by gender and the length of unemployment.<sup>4</sup> Each stratum was then randomly divided into two arms. The private provider was given the names of jobseekers to allocate to a treatment arm which received more intensive services that included both job counseling (CV and interview
is one final point worth noting. The results obtained from the application of the CEQ methodology for Egypt in terms of inequality and poverty reduction follow closely the overall structure of expenditures and revenues of the government. In the Egyptian context, this means that the positive effects on inequality and poverty reduction of the fiscal policies are partly explained by the large deficit ran by the government. In fact, the overall budget deficit was 11.4 percent of GDP in FY15 (Figure 6). This unsustainable imbalance should be kept in mind as a frame to interpret the long-term ability of Egypt’s fiscal policies in addressing inequalities.

**Figure 6. Total Revenues, Expenditures, and Overall Budget Deficit (percent of GDP)**

<!-- Start of picture text --> 35% 13.5%<br>12.90%<br>30% 13.0%<br>12.5%<br>12.5%<br>25%<br>12.0%<br>12.0%<br>20% 11.4%<br>11.5%<br>15% 10.9%<br>11.0%<br>10%<br>10.5%<br>5.0%<br>4.2%<br>5% 10.0%<br>0% 9.5%<br>FY13 FY14 FY15 FY16 FY17 Jul/Dec‐FY17 Jul/Dec‐FY18<br>Revenues (LHS) Expenditures (LHS) Overall Deficit (RHS) Jul/Dec Deficit (RHS)<br><!-- End of picture text --> _Source:_ Based on data from Ministry of Finance.

_Note:_ LHS = left-hand side; RHS = right-hand side 16
0.002|-0.021*|0.1702|-0.221***|-0.240***|0.1702|
|Improved sanitation|-0.015***|-0.063***|0.0008|-0.239***|-0.287***|0.0008|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A8: Identifying the $3.20 poor based on covariates of poverty** _Mali 2009_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Asset ownership|0.006|0.031|0.3504|-0.612***|-0.587***|0.3504|
|Literacy|0.007*|0.072***|0|-0.254***|-0.189***|0|
|Access to electricity|0.032*
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
|91.82|

Source: WBES, WDI, KOF data, PWT.

# 4. Empirical strategy

Using a firm-level treatment variable, we carry out an impact assessment method to understand how access to electricity leads manufacturing firms to become more export-oriented. We consider a binary variable that takes the value 1 if the company did not experience power outages in the last fiscal year, and 0 otherwise. To investigate the exposure of domestic firms to the treatment, we construct the interaction variable between the treatment and the dummy, which is 1 if the company is domestic and 0 otherwise (Treatment#Domestic_Firm). For foreign firms, we construct another dummy that is 1 if the company is foreign and 0 otherwise and we determine the interactive variable between the dummy and the treatment variable (Treatment#Foreign_Firm).

The initial idea of our impact assessment is to compare the export or domestic sales’ performance of manufacturing firms before ( ) and after ( ) receiving the treatment. Thus, the effect of the treatment would be determined by the difference between the outcomes and . Since we do not have YY<sup>0</sup> YY<sup>1</sup> information on the same firms before and after the treatment, we look for a counterfactual of our treated YY<sup>1</sup> YY<sup>0</sup> firms before the treatment. This counterfactual is therefore made up of firms that were not assigned to the treatment, i.e., that have experienced power outages during the last fiscal year. Hence, if the treatment was randomly assigned, then its effect would be determined by the difference between the outcomes of treated and untreated firms. Nevertheless, electricity access is not random, it is linked to certain characteristics and performances at the country and firm-level. The macroeconomic performances, the economic development, institutions, and the quality of electricity service provided by the country, can affect the occurrence of power outages. For instance, countries with good
this paper support the argument that the informal sector consists of different types of business owners. Some operate informally due to stringent regulation and simplifying regulation can entice them to register their business. Others run informal businesses while they are looking for a job and they switch to being wage earners when more job opportunities arise. This heterogeneity among informal firms may be one reason why a randomized control trial from Peru that tries to encourage informal firm registration through

> 13 Based on ten case studies of informal business owners conducted for the author in Puebla, Mexico, in 2009.

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and distribution of different migrant groups.

Also, spending on books and private tutoring decreased by 34% and 54%, respectively. A large share of spending on private tutoring is for pre-university students to increase their probability of success at the public universities entrance examination. Thus, children from treated households faced a decline in the financial resource available to enroll in private universities, as well as a reduction in receiving private tutoring, which may have increased their chance of getting placed in a public university.

An important concern is that the decline of fertility in Iran over the past decades could explain the reduction in household education spending. The average number of students in households decreased from 2.2 in 1995-2006 (pre-sanctions) to 1.5 in the 2007-2013 period (post-sanctions).<sup>54</sup> To check for this possibility, I evaluate the effect of the sanctions on education spending per child (Table 3, the last column). The results show reductions in education spending for each child, in particular per child spending on school and university tuition significantly decreased by 38% and 74%, respectively.

# **5.2.3 Income Elasticity of Education Spending**

To compare these negative effects on education spending to the current literature, I calculate the income elasticities of education spending. Following Grimm (2011), I use a 2SLS estimator and instrument income with the interaction effect of being a child in a treated household after the sanctions conditional on being in a treated household and the time effects. As explained above, since HIES is a cross-sectional survey, I observe single-year measures of 52For school tuition, the sample consists of all children aged 6-24 who have not graduated from high school. For university tuition, the sample consists of high school graduates aged below 24 years. For spending on books and private tutoring, I consider all children aged 6-24.

> 53Also, as Panel B of Table **??** shows, treated households with more girls are less likely to
business growth after having implemented some of the best practices included in the handbook. This treatment (hereafter _Documentary_ ) targets the psychological and emotional influences that are triggered through observing the successful experience of similar others (see La Ferrara, 2016, for a review). A second sub-set of handbook recipients is offered light implementation assistance in the form of two 30-minute shop visits by trained facilitators (hereafter _Assistance_ ). These helpers provide one-on-one implementation guidance and troubleshooting for the business practices highlighted in the handbook, hence facilitating learning through own experience (Kolb, 1984). Finally, to account for complementarities we offer a third sub-set of handbook recipients both _Documentary_ and _Assistance_ (hereafter _All Three_ ). Overall, this study design allows us to test simple channels of disseminating relevant local business information, as well as test the subsequent impacts of adopting these business practices on performance outcomes.

The analysis presented in this paper is based on pooled estimates from two follow-up surveys, six and eighteen months after the intervention. While attrition in either survey is low (less than 20% in second endline) and uncorrelated with treatment status, the take-up rates especially for the _Documentary_ and _All Three_ groups are also low at 52% and 47%, respectively. Therefore, we present both intent-to-treat as well as treatment-on-treated results in the analysis. First, we find underwhelming evidence for the effectiveness of the _Handbook_ alone, with this pure information shock resulting in no significant improvements in the adoption of business practices. By contrast, we find strong and statistically significant impacts when pure information is complemented with experiential learning. After correcting for multiple-hypothesis testing, we find that adding either emotional learning, simple hands-on assistance, or both results in up to a 6.5 percentage point improvement in relevant record-keeping practices covered in the handbook – a 33% improvement over the control group. In fact, these coefficients reflect statistically significant improvements over providing the _Handbook_ alone. For the _All Three_ group, we further find statistically significant improvements in the adoption of relevant practices related to planning (
7 Latin American) and 8 formerly Communist countries where household surveys provide information on household head’s parents, but do not include the nonresident children.<sup>14</sup> When non-resident children are excluded from the survey, it results in truncation of the

> 13The data on the income of parental generation is especially difficult to find. Preponderance of home based economic activities including own-farming in parental generation makes it challenging to estimate income in many developing countries.

> 14Hertz et al (2007) are careful about sample selection bias, and they do not focus on the household head’s children as has been the case in many recent studies that rely on data without non-resident children. To the best of our knowledge, the only survey in Hertz et al. list of countries that cover all of the non-resident children in the survey is that for Bangladesh.

6
# Is Informality Good for Business? The Impacts of IDP Inflows on Formal Firms

Sandra Rozo<sup>_∗_</sup> Hernan Winkler<sup>_†_</sup>

## **JEL Classification:** D22, J61, O17.

**Keywords:** Forced Migration, Firms, Informality.

> _∗_ USC Marshall School of Business. Corresponding author. Email: sandra.rozo@marshall.usc.edu

> _†_ The World Bank. We would like to thank the Colombian Statistics Department [ _Departamento Nacional de Estad ́ıstica_ ] for providing the data for this study.

We thank Stephen Trejo and two anonymous referees for their suggestions. We are also grateful to Maria Jose Urbina for her excellent work as a research assistant. We also thank the participants of the World Bank Policy Forum on Impacts of Refugees and IDP on Host Communities for their comments and suggestions. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent. This paper uses confidential data from the Colombian statistics Agency (DANE). To obtain access to the micro data authors should send a formal letter to: contacto@dane.gov.co. Upon receipt, DANE will request the signature of an information agreement. DANE will allow the researchers to keep the results of their estimates, but not the micro data. The agreement does not pose any other restrictions on the investigation. There are no conflicts of interest or financial support to disclose.
)|(0.0242)|(0.2245)|(0.0455)|(0.1173)|(0.1150)|(0.1686)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1410***|0.0497***|-0.0162|-0.7904***|6.7972***|0.7029***|0.6362***|-1.2519***|
||(0.0428)|(0.0140)|(0.0205)|(0.2558)|(0.1504)|(0.0908)|(0.0885)|(0.3508)|
|Observations|41,780|41,780|41,780|41,780|7,773|62,232|62,232|19,133|
|R-squared|0.220|0.083|0.102|0.161|0.201|0.099|0.094|0.417|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. The sample used in these regressions includes VHLSS 2004, 2006 and 2008. The VHLSS 2008 is used as the survey after the placebo program. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

62
in transport, where the reported income was 32% lower than the true income. Similar low compliance rates were seen in construction (26% gap) and accommodation and food services (18% gap), which is unsurprising since these sectors are known for having a high degree of informal employment. To reduce the informality in the construction sector, the government has introduced a differentiated/higher minimum wage as of 2019 and, at the same time, had granted very generous fiscal facilities (PIT break) exception from the 10% health insurance contribution, and suspension of the transfer to the second pension pillar (3.75%), while for employers the contribution for work insurance was reduced from 2.25% to 0.33%. However, the public administration sector showed relatively low compliance (18% gap). The public administration sector is typically associated with formal employment and compliance with tax regulations; this finding of relatively low compliance in this sector is puzzling. This could be due to the relatively small sample size of employment in this sector in the EU-SILC data, or the performance of the imputation among subgroups, which may affect the reliability of the estimates. It is also possible that specific factors unique to the public administration sector, such as complex tax rules or administrative difficulties, could make it challenging for public servants to recall the exact amount earned, as in the public sector, several bonuses/allowances are granted depending on the number of days worked in a month (which represents approximately 20% of the total income earned). In 2020, due to the pandemic, a large part of public servants worked remotely, reducing their wages, as these bonuses were contingent on physical presence.

25
: Procedures Update,” Human Resource Research Organization, Alexandria.

- Frey, Carl Benedikt and Michael A. Osborne. 2017. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” _Technological Forecasting & Social Change_ 114:254-80.

<mark>GSO (General Statistics Office). 2008.</mark> _<mark>List of Occupations 2008</mark>_ <mark>. Hanoi: GSO.</mark>

- Handel, Michael J. 2016. “The O*NET content model: strengths and limitations,” _Journal of Labour Market Research_ 49(2):157-76.

- Harvard’s Growth Lab’s. _Atlas of Economic Complexity_ . Available at <u>https://atlas.cid.harvard.edu/countries/239/export-basket</u>

- ILO (International Labour Organization). 2012. _International Standard Classification of Occupations: ISCO-08. Volume I_ . Geneva: ILO.

- Lewandowski, P., A. Park, W. Hardy, and Y. Du. 2021. “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data. _World Bank Economic Review (forthcoming)_ .

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Dublin Core metadata standard (IHSN, 2022). We considered all of these for the schema and adopted many usage recommendations from Dupriez et al. (2021).

Most existing data catalogs do not cover fields related specifically to the design of RCTs. For these, we turned to two important trial registries. The **AEA RCT Registry** of the American Economic Association is likely the most complete record of past and ongoing RCTs in economics, including unpublished studies (AEA RCT Registry, 2022). The registry metadata contains information specific to RCTs that is not typically included in survey schemata, such as the intervention, randomization method, outcome measures, or the reviewing IRB, although these contents are mostly stored in free text fields and there is no cross walk with DDI or other metadata standards. The US National Library of Medicine and the National Institutes of Health maintain the trial registry and results database **ClinicalTrials.gov** for clinical trials in the United States (McCray and Ide, 2000). All clinical studies of drugs and devices controlled by the Food and Drug Administration (FDA) must be registered here. The registry has detailed metadata field definitions and maintains an API feed (ClinicalTrials.gov, 2022). Many repositories draw from or link to this registry (e.g. Vivli, 2022; ISRCTN, 2022). The fields on interventions, study arms, and outcome measures provided a

> 6Impact evaluation often means RCT in this context but can also mean other rigorous analysis methods aimed at estimating causal impacts.

6