rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 3: Estimated changes in labor income

|Males|Workers reporti<br>decline in earnin<br>due to COVID<br>(percentage)|ng a<br>gs|Average income cha<br>Q4 versus Q3 FY20<br>(percent)|nge,|
|---|---|---|---|---|
|Agriculture||18||-33|
|Industry - Informal||46||-19|
|Industry - Formal||26||-4|
|Services - Informal||51||-28|
|Services - Formal||22||-5|
|Females|||||
|Informal||22||-19|
|Formal||8||0|

Source : Authors’ calculations using LFS data. Note: Income changes are based on a total monthly income variable in nominal terms.

As mentioned, an important objective of this analysis is to look at the role that cash transfers played in supporting households’ incomes. For that purpose, we model the emergency expansion of the main cash transfer programs (Takaful and Karama) described in the previous section. We identify recipients of these programs in HIECS using the targeting rules and adjust the number of households receiving and the size of the transfer as a response to COVID-19. As noted, Takaful targets households with children under 18, while Karama targets the elderly poor, orphans, widows, and people living with disabilities. The selection of beneficiaries is conducted through geographical and categorical criteria, with proxy means testing (PMT).<sup>12</sup> We also simulate the emergency cash transfer to informal workers that was paid starting in April 2020. The grant of EGP 500 per worker was paid monthly for three months to around 2 million informal workers registered in the workforce databases of the Ministry of Manpower across governorates.<sup>13</sup
file more quickly after suffering shocks to fundamentals (such as declines in sales and revenues). Second, labor intensive firms are more likely to be liquidated during the bankruptcy process.

# 2.3.3 <u>In Small and Medium- sized Enterprises (SMEs)</u>

Most literature considers the importance of bankruptcy codes to address the needs of creditors that lend to large, capital intensive firms. However, good bankruptcy systems can also be important for smaller and labor intensive service firms. For instance, 80 percent of U.S. firms that filed for bankruptcy reported assets under $1 million, and 88 percent reported having fewer than 20 employees (Warren and Westbrook, 1999). In additions, SME’s are especially vulnerable to macroeconomic and financial shocks; for example, SME insolvencies in Denmark, Italy, Spain, and Ireland exceeded 25 percent between 2007 and 2008 (OECD, 2009).

However, despite the importance and the complexity of small business as a contributor to the economy, there has been little academic research on the effect of bankruptcy legislation on SMEs. In many jurisdictions, different bankruptcy procedures are available for corporate and individual debtors, or distinguish between debtors who are ‘traders’ (individual or corporate) or consumers (Armour and Cumming, 2007).

Lenders to small businesses often require that the owner provide a personal guarantee to the loan, such as a second mortgage on his house (Berkowitz and White, 2004; Djankov et al., 2002). Guarantees of this sort put the personal assets of the firm’s owner on the line and blur the distinction between the assets of the firm and those of the owner; in other words, the limitedliability of the firm no longer applies to this particular loan. In addition, personal bankruptcy laws would apply to this firm in the case of default. A survey of a sample of individuals from the United States who filed for bankruptcy during the 1980s estimates that around 20% had debts 12
could actually rise, rather than fall.

# **3 Descriptive analysis using panel data**

In this section, we analyze survey data from eight different countries and approximately 140,000 respondents or about 190,000 respondent-waves. We provide descriptive evidence of two previously undocumented facts. First, for most countries, the majority of individuals who are self-employed at home are not self-employed after migrating, even though they are more likely to be self-employed after moving than wage workers or individuals who are not employed at home. Second, migration rates are lower for the than for workers or individuals with no self-employed wage job. This result holds for internal and international migration, for short- and long-distance migration, and conditioning on baseline covariates such as gender, age, years of education, and income.

## _Choice of panel surveys_

Our descriptive analysis uses panel data from seven developing countries: two waves of the China Family Panel Studies (CFPS), the three main waves of the Egypt Labor Market Panel Survey (ELMPS), baseline and follow-up of the India Human Development Survey (IHDS), the latest three waves of the Indonesia Family Life Survey (IFLS), three rounds of the Mexican Family Life Study (MxFLS), three waves of the Nigeria Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), and all three waves of the Tanzanian Kagera Health and Development Survey (KHDS). We benchmark our developing country patterns against those from the United States, using three rounds of the Panel Survey of Income Dynamics (PSID). Collectively, these eight countries contain one-half of the world’s population, and provide large samples from a range of different regions of the world.

In each sample, we focus on individuals aged 18 to 65, for whom we have information on occupational status and migration rates. Our primary focus is on whether or not individuals are self-employed at baseline, which is typically based on the primary job during the past week, month, or quarter. Online appendix B describes how this
predictive power of the auxiliary geospatial indicators. Additional research, drawing on data from different contexts, will help inform the choice between using more recent estimates obtained by combining survey and geospatial data, and older small area estimates derived from a census.

# 2. Small area estimation

Small area estimation deals with methods to reliably estimate parameters of interest in areas without adequate sample sizes (Molina and Rao, 2015). Here, we discuss the basics of small area estimation, with a particular focus on the Mexican context we study in this paper. Interested readers can find more details in Rao (2003), Molina and Rao (2010, 2015), and Jiang and Rao (2020). Ghosh (2020) details the evolution of small area estimation over the last five decades.

In Mexico, it is worth differentiating four separate levels in the data: households, AGEBs, municipalities, and states. Households are nested within AGEBs, AGEBs are nested within municipalities, which are nested within states. We are interested in estimating poverty rates for municipalities – the target areas – but the only national surveys available are representative at urban-rural areas within states. Therefore, although these estimates are unbiased over repeated samples, a single municipality-level sample is generally too small to provide a reliable estimate of poverty rates.

To estimate these poverty rates, we need to build a model at the level of the municipality or lower. In theory, we could directly estimate poverty rates at the municipality level – that is, taking poverty rates at the municipality level and estimating the model using municipalities as the unit of analysis – which is referred to as an area-level model. We could also estimate poverty in AGEBs and then aggregate AGEB-level poverty rates up to the municipality based on AGEB populations, referred to as a sub-area model. Finally, we could estimate poverty at the household level and then aggregate household-level poverty rates up to the municipality, using a household-level (or unitlevel) model.

These methods share some characteristics but also offer different advantages. One similarity
The village and teacher surveys included questions about the ECD services available in the sample village. This allowed us to monitor implementation of program interventions and precisely know when each preschool service was available. Parts of the teacher survey and the classroom observation tool administered at endline are based on the Measuring Early Learning Environments (MELE) module of MELQO. MELE includes key domains for quality in early learning environments and the sample items used to measure them. During pretests preceding the endline data collection, constructs to be measured were selected and specific items were adapted to take into account culture-specific views on what defines a high-quality learning environment. We divided the final module into five domains: teacher characteristics, equipment, classroom setting, curriculum content and pedagogy, and teacher-child interactions.

# **3.2.2 Balance and Attrition**

The main baseline characteristics of villages, households, children, and their caregivers are summarized in Table 3. To test for statistically significant differences, we regress each variable on binary indicators for the treatment groups and a set of province dummies to account for stratified randomization. Overall, the sample is balanced in child, household, and caregiver characteristics.

Table 3 also highlights some significant baseline differences in preschool enrollment and take-up of demand-side interventions. These differences are due to the aforementioned early roll-out of some preschool interventions. Children in program villages are about 8 pp more likely to have enrolled in preschool at baseline (driven by community preschools which, by definition, were newly constructed). Similarly, the HBP intervention started in some treatment villages before baseline. These baseline imbalances at the individual level are confirmed by imbalance in the availability of preschools and HBP at the village level, shown in the baseline panel of Table 5. At baseline, some villages in the treatment group had access to CPS and many treatment groups’ villages had access to HBP. There are reasons to believe that these early interventions are unlikely to affect our estimation. baseline results do not pick up any significant cognitive differences between the treatment groups and the
9.43|8.47|11.3|9.32|8.91|9.10|
||_0.20_|_0.19_||_0.20_|_0.19_|_0.20_|
|||||**[11.5]**|**[54.1]**|**(34.4]**|
|Squared poverty gap index**(%)**|3.93|3.48|13.0|3.89|3.67|3.78|
||_0.11_|_0.Io_||_0.11_|_0.11_|_0.11_|
|||||**[9.51**|**[57.01**|**(33.5]**|
|Theil's T-index|0.513|0.512|0.1|0.513|0.513|0.511|
||_0.057_|_0,056_||_0.057_|_0.058_|_0.056_|
|Variance of logs|0.628|0.633|-0.9|0.635|0.624|0.631|
||_0.011_|_0.011_||_0.011_|_0.011_|_0.0o_|
|Generalized entropy measure (e=2)|2.02|1.97|2.4|1.99|2.05|1.98|
||_0.68_|_0.67_||_0.67_|_0.71_|_0.66_|
|Generalized entropy measure (e=3)|75.0|69.7|7.5|72.1|76.4|71.0|
||_48.4_|_44.4_||_46
with certainty. To overcome this challenge, Chainalysis combines proprietary knowledge of crypto-assets wallets ownership with web traffic data provided by SimilarWeb, a website analytics & traffic intelligence platform, to provide estimates of the total on-chain value sent to countries (in US dollar terms). Figure 2 provides an overview of the methodology. More specifically, Chainalysis maps known on-chain addresses to services such as crypto-exchanges which can be associated with many on-chain addresses. Next, Chainalysis allocates transaction volumes of a service to a country in proportion to the web traffic that originates from that country to each service’s website. To further improve classification, Chainalysis also accounts for time zones, fiat currency pairs offered, website language options, and the location of the service’s headquarters.

These volume estimates come with important caveats. First, they do not capture “off-chain” transactions which are recorded on the private order books of intermediaries such as cryptoexchanges or financial institutions (with the exception of peer-to-peer exchanges such as Paxful). As such, our data do not capture activities that are facilitated by such intermediaries which include purchases of crypto-assets with fiat currency, sales of crypto-assets for fiat currency and swaps between crypto-assets. Total off-chain volumes appear significantly larger than on-chain transactions with some industry estimates suggesting the approximate ratio of off-chain to onchain volume being roughly 6:1.<sup>13</sup> Total off-chain volume in the first half of 2021 was approximately US$16 trillion (CryptoCompare, 2021), compared to the on-chain volume of US$2.8 trillion. Second, the web traffic data does not account for virtual private network (VPN) activity which obscures the true destination of web traffic. Third, the transaction value associated with a known crypto-exchange wallet is assumed to be proportionate the volume of web traffic, an important limiting assumption.

> 13 Chainalysis estimate based on trade volume data from Kaiko and on-chain transaction data from Chainalysis. https://blog.chainalysis.com/reports/fake-trade-volume-cryptocurrency-exchanges

14
outflows of refugees who mostly settled in selected European countries. It is uncertain how many refugees the Balkan wars generated but using census data from seven European countries Borjas and Monras (2017) find about 259,000 people who were born in former Yugoslavia and moved to Europe during the decade. All considered, this is not a massive inflow of people but certain municipalities had a sizable increase in labor supply. The refugees had similar levels of education compared to the native population, but were faced with a lack of language skills.<sup>28</sup> They were offered different types of residence status by the different host countries but most of them were allowed to work.

Angrist and Kugler (2003) studied this caseload and, measuring aggregate differences across 18 European countries, find a negative and significant effect on employment with both an OLS and an IV approach. An increase in the share of immigrants by 10 percent reduces native employment rates by 0.2-0.7 of a percentage point with men and younger workers being the most affected groups.<sup>29</sup> These effects tend to be accentuated in localities with more rigid labor and product markets, weak institutions and stagnant labor markets. Borjas and Monras (2017) also cover refugees from Former Yugoslavia in seven of the 18 European countries studied by Angrist and Kugler (2003) and, differentating by education level and withincountry regions, find a significant positive effect on the unemployment rate of the locals with an OLS estimation and no significant effect with an IV estimation and a non significant effect on the employment rate. The results of these two papers are not comparable because of the models used, categorizations of variables and sample covered, and they also provide different results on employment. Clemens and Hunt (2017), carrying out a Kronmal correction on the instrument used by Borjas and Monras (2017), find a statistically insignificant effect on unemployment.

# Refugees in Denmark

Foged and Peri (2015) studied the inflow of refugees from conflict areas
the effects suggest that the selected women are not just able to appropriate any additional income they may earn thanks to the promotion income, but also that a reallocation of the income they would earn in the absence of the program is taking place. A back-of-the-envelope calculation shows that the intent-to-treat impact on female expenditures and remittances combined adds up to about 1,543 Taka.<sup>51</sup> This is far greater than the intent-to-treat increase in personal income of about 689 Taka in Table 3. This suggests that the promotion program leads to renewed bargaining over more than just the newly earned income.

The effect is positive for all components of women’s assignable expenditures, though the effects are largest on expenditures on women’s clothing and footwear as well as accessories and

> 48See Figure B.4 in the Online Appendix. Note that the outcome variables for the separate decisions again are indices, since I dichotomize the four different categories of involvement explained in Section 3 into dummy variables, and then create a standardized Anderson index.

> 49See Figure B.5 in the Online Appendix

> 50Table C.12 in the Online Appendix investigates whether other household expenditures change, such as on food, different type of durables, housing, savings and recreation. None of these expenditures is significantly affected, though the point estimates are sizeable at -0.8 to -0.9 percentage points for kitchen durables, furnishing durables and savings, and +0.8 percentage points for food.

> 51See Table C.11 in the Online Appendix.

21
majority-owned, more than 50 percent; blocking minority, between 25 and 50 percent; and minority participation, between 10 and 25 percent), while at the same time is not as low as to a large number of companies in which the government might not have any level of control.<sup>16</sup> This expands the scope of previous work on SOE analysis to trace state presence in the corporate sector, which has often been limited to majority-owned enterprises.

In line with the existing literature and practice, the Global BOS database only includes entities that are legally independent. That is, an entity is not considered an SOE if it is a branch of another company that operates as a single legal entity or if it is a part of a public authority without any juridical status. The rationale for the definition is that a branch of another company without autonomous legal status would not have an independent balance sheet, hence would be “consolidated” by the company which it belongs to while a branch of a public authority would be consolidated within the public sector. It is worth to note, that under this definition, we exclude those non-legally independent entities such as parastatals or dependencies under Ministries that which could operate and potentially influence markets (e.g., fiber backbone networks managed by government ministries). The database also distinguishes between corporatized or non-corporatized SOE. An SOE is corporatized if it operates under the company law as any other private sector company, with the only difference being that the government is a direct or indirect

> 14 It excludes non-profit institutions and government units (see paras 2.30-31 of the GFSM).

> 15 As established by the IMF (2020), although there are different national legal definitions of SOEs, there are three common elements to identify an SOE: it is controlled by the government, it is a legally separate entity from its owners, and it engages in market production and commercial activities.

> 16 The 10% threshold is proposed as a proxy of control.
# **Annex 6: Dispatch, sales, and T&D loss data**

**Table A6: Dispatch, sales, and T&D loss data for the reference utility in the reference year**

|**Country**|**Total dispatched or**<br>**purchased(GWh)**|**Sales (GWh)**|**Sales (kWh per**<br>**person)**|**T&D losses**<br>**(GWh)**|**T&D losses (% of GWh**<br>**dispatched orpurchased)**|
|---|---|---|---|---|---|
|Angola_2011|—|2.394,14|n/a|—|—|
|Benin_2013|1,102|875|85|227|21%|
|Botswana_2013|3,706|3,449|1,706|257|7%|
|Burkina Faso_2014|1,359|1,125|65|234|17%|
|Burundi_2014|258|209|20|49|19%|
|Cameroon_2014|6,080|4,360|191|1,720|28%|
|Cape Verde_2012|300|205|415|95|32%|
|Central African||||||
|Republic_2014|137|71|15|66|48%|
|Chad_2012|—|n/a|n/a|—|—|
|Comoros_2012|82|49|69|33|40%|
|Congo,Dem. Rep._2013|8,349|7,479|111|870|10%|
|Congo,Rep._2012|1,725|940|217|786|46%|
Table 2. Summary Statistics: Migration for Marriage Purposes

|State|No. of migrants|Percentage|Out of district, same state|Percentage|Out of state|Percentage|
|---|---|---|---|---|---|---|
|Gujarat|1,937,395|4|1,560,660|81|376,735|19|
|Neighbors of Gujarat|10,416,132|24|8,062,937|77|2,353,195|23|
|Rest of India|31,629,202|72|23,066,868|73|8,562,334|27|
|Total|43,982,728|100|32,690,404|74|11,292,264|26|

_Note:_ This table reports the number and percentages of women migrating out of state and out of the district (same state) in Gujarat, its neighboring states, and the rest of India. Data source is the 2001 Indian Census.

Table 3. Women’s Marriage Outcomes Post the Gujarat Riots of 2002

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|(3)<br>Age at<br>Cohabitation|(4)<br>Age at<br>First Sex|(5)<br>Age at<br>First Birth|
|---|---|---|---|---|---|
|Post X Riot|-0.330<sup>_∗∗∗_</sup>|0.044<sup>_∗∗∗_</sup>|-0.271<sup>_∗∗∗_</sup>|
effects of consensus among multiple information sources.

Given the consistently significant impacts of dynamic social norms messages across countries, public health authorities in low- and middle-income countries could benefit from leveraging these social norms messages. Consistent with this, previous studies have shown that social norms messages focusing on descriptive norms (e.g., widespread vaccine acceptance) can effectively increase vaccination intentions (Ryoo & Kim, 2021; Sinclair & Agerström, 2021). Future research could explore strategies for creating injunctive social norms (Martinez et al., 2023) to complement dynamic norms in contexts where norms are not yet prominent to identify the best methods of promoting vaccination without triggering unintended consequences such as boomerang effects.

Our study also adds to the literature on message fatigue and health risk communication in dynamically evolving situations in that using diverse messages that shift contexts, such as endemic and variant framings, may help mitigate the fatigue and keep messaging relevant. These messages acknowledging the ongoing risks of COVID and highlighting that vaccination is protective even in contexts where disease is endemic were effective in increasing intentions, suggesting that nuanced information reflecting the experience of the public can be effective. This finding underscores the need for ongoing message adaptation and evolution to maintain engagement and effectiveness over time, especially in prolonged public health crises like the COVID-19 pandemic and slow-burning crises.

# **4.3. Study-Level and Individual-Level Moderators**

While the message framing focusing on endemic, safety, experts, efficacy, and prosocial, and variants showed moderate-to-high heterogeneity in message impacts across studies and countries, we found that country-level vaccination rates, intervention start dates, and GDP per capita PPP do not 25
weaker negative impact on its export growth. The other findings are comparable to the baseline effects reported in Table 2 in terms of sign, size and significance.

**Table 7: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, mirror data, OLS regression results**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply shock***|**_work mobilityit*remoteik_**|**0.074**|||**0.044**|
|**sector characteristic**||(0.336)|||(0.353)|
||**_work mobilityit*gvcil_**|**1.398*****|||**1.647*****|
|||(0.510)|||(0.517)|
|**Partner demand**|**_retail mobilityjt*durablek_**||**0.414*****||**0.425*****|
|**sector characteristic**|||(0.099)||(0.103)|
||**_retail mobilityjt*gvc_**||**-0.186****||**-0.214*****|
||||(0.072)||(0.074)|
|**Third country**|**_competition shockijkt_*
9|14.1|12.0|
|Eighth 10|13.1|8.2|11.2|13.7|
|Ninth 10|13.0|11.3|19.8|17.2|
|Top10|8.2|18.8|17.3|25.6|
||100.0||100.0||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Ghana), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from African or other countries), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances. See equation (7) and text for predicted income equation.

Source: Calculated from 1998/1999 Ghana Living Standards Survey (GLSS 4).