rafmacalaba/data-use-mentions-extended huggingface dataset:
provided randomly by the service provider, but rather is typically assigned to declarations for which potential tax revenue losses are largest as is shown in Table 1, which presents descriptive statistics for declarations for which valuation advice was issued, as well as declarations for which it was not. Declarations for which valuation advice is issued have significantly higher risk scores, are subject to significantly higher taxes, are significantly more advice, the reported prices by other importers, the price included in the extensive comments, and a price obtained based on mirror export data from COMTRADE. The sample of declarations for which all these prices are comparable is small as it requires them to include a single HS 6-digit product, receive valuation advice and extensive comments in the RCT and have mirror export price for that HS 6-digit product available. The correlations among all these prices is shown in Appendix Table 2 and are all positive and significant at the 95 percent confidence level. But most of the correlations are less than 0.5.

17
)<sup>4</sup> . In equation 2, profit level increases in efficiency level. Thus efficient firms innovate at a faster rate. Lower cost of R&D decreases marginal cost and increases marginal

> benefit of innovation and also results in higher values of .

The model allows for entry. From a constant potential pool of entrants, successful ones enter the economy as a result of introducing a new variety. Firms discover their efficiency types immediately after they enter. Entrants face the same innovation cost function as incumbent firms. Although entering and exiting firms contribute to reallocation of resources and aggregate innovation in an economy, Prowess database does not allow us to analyze their dynamics. Firms can exit and re-enter the database. Since it is not possible to identify new entrants and actual exiting firms, the empirical analysis does not discuss the contribution of entrants and exiting firms to aggregate innovation. Goldberg et al. (2010b) also use the Prowess database and provide a similar discussion regarding to entering and exiting firms.

# **III. Data and Policy Reforms in India**

## **Data Description**

Firm level data used in the analysis is obtained from Prowess Database which is constructed by Centre for Monitoring the Indian Economy (CMIE) in India. This dataset has advantages over the Annual Survey of Industries (ASI) which is the India’s manufacturing census. ASI is constructed from a repeated cross section of firms whereas Prowess database includes a panel of firms. The panel feature allows us to tract firms over time. It is also among the few databases that records annual information on firms’ product

> 4 A formal proof of this relationship is presented in Klette and Kortum (2004).

5
statistically significant at the 10 percent or lower level in 7 out of 8 cases.<sup>23</sup> The estimates for the intercept shows a contrasting picture: the daughter dummy is _not_ statistically significant at the 10 percent level in 6 out of 8 cases. The evidence cannot reject the null hypothesis that _θ_<sup>ˆ</sup> 0<sup>_d_=</sup><sup>_θ_ˆ</sup> 0<sup>_s_.</sup>

> 22We also used other age groups for estimating the investment equation. The conclusions reached in this paper are robust to such alternative age samples.

> 23This is consistent with the existing evidence on gender bias against girls in educational expenditure in India (see Kingdon (2007), Azam and Kingdon (2013)). Azam and Kingdon (2013) find significant pro-male bias for the 10 years or older children, using the IHDS 2005 data. They point out that the focus on educational expenditure misses substantial gender bias against girls in terms of schooling continuation decisions. When gender bias manifests as early drop-out of daughters from school, this is captured by lower educational attainment in our data. Datta and Kingdon (2019) use 1995 and 2014 NSS data and find that the role played by gender bias in educational expenditure (the intensive margin) has increased substantially over time in India. _Note, however, that the existing evidence while suggestive does not provide estimates of the effects of father’s education on children’s educational expenditure, which is the focus of the investment equation in intergenerational mobility analysis._

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1

**How Valuable is the Reliability of Residential Electricity Supply in Low-Income Countries? Evidence from Nepal**

Anna Alberini, Jevgenijs Steinbuks and Govinda Timilsina<sup>1</sup> JEL Classification: O12, O13, O53, Q41 Keywords: Electricity Reliability, Nepal, Value of Lost Load, Willingness to Pay

> 1 Alberini is a professor at AREC, University of Maryland, College Park. Email: aalberin@umd.edu. Steinbuks and Timilsina are, respectively, Economist and Senior Research Economist at Development Research Group, The World Bank. Email: jsteinbuks@worldbank.org and gtimilsina@worldbank.org. The authors thank Elisa Portale, Bonsuk Koo, and Alisha Noella Pinto for their help with collecting the survey data; Gopal B. Bhattarai, Deb Chattopadhyay, Barsha Pandey, Rabin Shrestha, and Xiaoping Wang for advising on country context and sharing Nepal power sector data; and Shaun McRae, Michael Toman, and the participants of the AERE and the EMEE Annual conferences for helpful comments and suggestions. Nisan Gorgulu and Fatima Najeeb provided outstanding research assistance. The opinions reported in this paper are those of the authors and do not necessarily reflect the official views and positions of the University of Maryland or the World Bank, its Board of Directors, or member states.
and school administrative registers. Based on our discussions with these committees, it appears that conditionality was enforced. Cash transfer take-up rates (the fraction of eligible households receiving transfers for at least some children) in the CCT villages declined as the school year progressed, which is also consistent with conditionality being enforced.<sup>20</sup> In households randomly assigned to UCTs, the mother or father received a quarterly stipend for each child. There were no requirements or conditions linked to receiving the stipend.

CCT and UCT households were told they could use the funds at their convenience and no instructions were given as to how to spend the money. CCT and UCT cash distribution was done separately in each village to minimize any risks of cross-village information contamination of the randomization, since we did not want households in UCT villages to believe that health clinic or school attendance was going to be checked in their villages as well. In addition, each village had only one primary school, and no children attended a primary school that is not in their village.

Furthermore, our program design explicitly assumed each treatment group would receive equal amounts of resources per capita over the two-year pilot, if households randomly allocated to the CCTs fully satisfied conditionality. In practice, because there was not full compliance with conditionality, households receiving UCTs, on average, received more money per capita.

In the CCT and UCT programs, for each child under age seven, the mother or father would receive 4,000 FCFA per year distributed in quarterly payments (approximately $8.8 USD or 4.0 percent of household per capita expenditures). For each child ages 7 to 10 (or in grades 1 to 4 in the CCT villages), the mother or father would receive 8,000 FCFA per year in quarterly payments (approximately $17.6 USD or 8.0 percent of household per capita expenditures), while

> 20 The CCT take-up rates by quarter for school year 2008-2009 are 99.0, 91.0,
For educational outcomes, we focus on the impact of transfers on school enrollment, attendance, and achievement tests in math and French for children between the ages of 7 to 15. For enrollment and attendance, we can rely both on household self-reports and on administrative data collected from the school ledgers. For child health outcomes, we focus on routine preventive health clinic visits, anthropometrics (weight, arm circumference, and height), self-reported illnesses, and biomarker blood tests that measured C-reactive protein, which is a marker of inflammation and infections. For household welfare, we focus on livestock, agricultural production, demographics, assets, self-perceptions of social status, housing, and entrepreneurship.

The randomized experimental design provides a strong identification strategy and allows us to attribute any differences in outcome indicators between the treatment and control groups to

> 12 At baseline in 2008, the exchange rate was CFA 415 = $1 USD.

8
MEI) using the registry of firms from the Brazilian tax authority (SRF) for 2012 through 2020. MEI is a type of firm with a simplified registration process, created in 2008 by Complementary Law 128. The only tax MEIs need to pay is a flat monthly fee below USD 15, whose major component is a contribution to the public pension system. An MEI is subject to a revenue cap of BRL 81,000 (about USD 17,000) per year and can hire at most one employee. Registration as an MEI (as opposed to operating without registration) has the advantage that it allows entrepreneurs to provide receipts to their customers, increasing the pool of partners with whom they can conduct business. The name of an MEI is automatically generated as the name of its owner concatenated with the CPF, allowing us to match the MEI database with our sample via the CPF. At the end of 2019, the SRF registry of firms included 12.8 million MEIs, representing about 42 percent of all firms in the SRF registry.<sup>9</sup> We do not use data on other types of firms in our analysis since they have identification numbers that are not clearly linked to the CPF of the owner.

Fourth, we use data on formal employment from RAIS,<sup>10</sup> a database maintained by the Ministry of Labor. In RAIS, all employers in Brazil are required to report their employees who have a written contract. RAIS also includes information on wages, education, gender, sector, and type of occupation. However, RAIS is not designed to capture business owners or the self-employed. We

> 7 Available in Portuguese at <u>https://www.bcb.gov.br/content/cidadaniafinanceira/Documents/RIF/Relatorio%20Cidadania%20Financeira_BCB_ 16jan_2019.pdf</u>

> 8 Using the exchange rate of 4.3410 BRL/USD, of February 2020 (free exchange rate - period average).
from which we obtain: 3. Movements in and out of the NEET condition as well as immobility probabilities are calculated using (10) and the observed NEET/non-NEET condition of individuals in round 2 via Equations (4) to (7).

- via Equations (4) to (7).

- 4. Steps 1 to 3 are repeated times, and the average over all replications is taken. RR

Sensitivity analyses carried out using different numbers of replications suggested that precision gains beyond 50 replications are modest.<sup>8</sup> Therefore, for the following analysis, estimates are based on 50 replications.

A lower bound of mobility is provided by assuming perfect correlation between the error terms in the two rounds (Dang et al., 2014). Precisely, lower-bound estimates for transitions in and out of NEET condition are obtained as follows:

- condition are obtained as follows: 1. Using data from round 1, we estimate Equation (8) to obtain the predicted coefficients 1′ 2. Using data from round 2, we estimate the logit model: ββ̂

′ .

> 8 The results of these analyses are not shown in the paper but are available upon request from the authors.

16
As of 2016, informal employment is estimated to be between 15.1 and 21.2 percent. The variation in estimates is largely determined by the different definitions used<sup>12</sup> (Figure 6). While these rates are not particularly high levels by international comparison, the continued increase over the past two decades warrants further attention.

For the rest of the paper, we maintain the definitions of informal employment described in this section. For the analysis on well-being, we rely on three surveys (RLMS, LiTS and ESS) as the LFS has very limited information on welfare outcomes. We focus on the latest year for which data are available, which for all surveys is 2016.

# **_Does de-industrialization explain the trends in informal employment?_**

Some have hypothesized that rising informal employment could be related to de-industrialization. As the sectoral structure of the economy shifts from manufacturing to services, and because the latter has been observed to have higher informality rates than the former in some countries (see, for instance (Vanek, Chen, Carré, Heintz, & Hausmanns, 2014) ), deindustrialization could bring higher incidence of informal employment. While a deeper analysis into the drivers of the increase in informal employment is beyond the scope of this paper, we first test whether changes in the shares of employment across different sectors is a plausible explanation of the increase in informality in Russia.

To investigate this, we modify the original Huppi-Ravallion decomposition to examine whether changes in the rate of informal employment are due to increases in the average informality in specific sectors (“intra-sectoral shift effect”) or whether employment in high-incidence sectors is increasing (“population shift effect”).<sup>13</sup> We would expect the latter effect to dominate if the increase in informal employment was associated with de-industrialization trends and the relative share of employment shifting away from manufacturing and towards services sectors that have a higher incidence of informality.

Specifically, this decomposition is an accounting exercise that breaks
. Other countries such as India, Pakistan, and South Africa have rapidly introduced virtual courts to resolve urgent matters as a result of the pandemic. These technologies filled in a crucial gap in access to justice as courts closed, yet they may also bring risks to effective dispute resolution going forward. For example, a study in the United States finds that the switch to video in bail hearings in Cook County led to an average increase of 51% in overall bail amounts (Diamond et al., 2010). A rigorous evaluation of the trade-offs between faster and–potentially–more accessible justice, on the one hand, and the risks associated with online communication, cyber-security, and disparities in access, on the other, should be undertaken before adopting these technologies going forward.

The data resulting from this switch to digital systems may also open new opportunities. For instance, recordings of online hearings represent another method to evaluate and understand biases in judicial decisions. Using court recordings and 3D Virtual Reality technology to alter the defendants’ race, Bielen, Marneffe, and N. Mocan (2018) conducted a study to understand courtroom biases. The authors found that while evaluators are harsher towards defendants of their own race during the guiltinnocence decision, in the sentencing phase they tend to be more lenient towards defendants of their own race. In terms of overall bias, it was found that minorities are more likely to get convicted and are given stiffer punishments on conviction. Thus, recordings of online hearings can be leveraged to understand aspects such as racial or gender bias in hearings, and to explore potential solutions. These data may also be used to develop ways of improving the effectiveness and quality of hearings, as well as to address deficits in communication and equal representation.

One of the most salient problems facing legal systems around the world is still the lack of access to formal justice mechanisms. During the pandemic, those without legal representation remain defenseless against criminal activities. In a recent briefing, the New York University Center on International Cooperation
their employment contracts were short-term. Chacaltana (2005) uses a Peruvian panel from 1998 to 2001 to show that 60 percent of young workers changed their work status while only 40 percent of adults did.

> 3 Strictly speaking, the available data do not allow us to follow the same people for the entire time that it is necessary to build a complete life cycle pattern. However, separating transitional matrices into different age ranges permits us to estimate age-specific transition probabilities of moving between employment states to approximate such pattern.

> 4 As Topel and Ward (1992) say, “job changing is a critical component of workers' movement toward the stable employment relations of mature careers”.

3
Given the wide range of instruments to send money to developing countries, what types of transactions are recorded as remittances by central banks? According to this survey, in most countries (33 out of 40 countries, or 83% of countries) remittance transactions that occur via electronic wires transmitted by commercial banks are captured in official statistics. Remittances that occur via money orders and drafts – regardless of which type of financial or non-financial institution pays the beneficiary -- are documented as remittances in 63% and 45% of countries in this survey, respectively. Not surprisingly, remittances that are collected by beneficiaries through cash withdrawals at ATMs (using dual cards) are hardly reported, only 23% of central banks cover them in their remittances statistics.

<!-- Start of picture text --> Chart 2. Types of Transactions Recorded as Remittances.<br>Percent of Central Banks that Record the following<br>Transactions<br>Withdrawals at ATMs<br>Drafts<br>Money orders<br>Electronic transfers<br>0% 20% 40% 60% 80% 100%<br>Percent of central banks that collect data<br>Percent of central banks that do NOT collect data<br><!-- End of picture text --> With the increasing number of money transfer products available in the marketplace, it is becoming more difficult for central banks to cover remittance transactions, especially when the transactions occur through new products, such as dual debit cards or smart cards. Moreover, it is also becoming more difficult to distinguish which transactions are actually remittances from those that are not. For instance, a cash withdrawal at an ATM using a card issued by a bank in a foreign jurisdiction may not necessarily be a remittance transaction. It may just be a cash withdrawal by a foreign tourist.

It has also become difficult for central banks to identify the source country of remittances, as well as the number of transactions that occur through the different financial instruments used by
<mark>The pre-COVID-19 LSMS-ISA-supported surveys that serve as sampling frames for the phone surveys were designed to be representative at the national, regional and urban/rural levels. These surveys include the Ethiopia Socio-economic Survey (ESS) 2018/19, Malawi Integrated Household Panel Survey (IHPS) 2019, Nigeria General Household Survey (GHS) - Panel 2018/19, and Uganda National Panel Survey (UNPS) 2019/20. In Ethiopia, Malawi, and Uganda, the phone survey attempted to call all LSMS-ISA households for whom at least one phone number was available either for a household member or for a reference individual. In Nigeria, a national subsample was drawn from the universe of LSMS-ISA households with phone numbers.</mark> The anonymized survey data and documentation are accessible through the World Bank Microdata Library<sup>_12-15_</sup> and are <mark>comparable across countries, based on the template questionnaire modules and the phone survey sampling guidelines that were made publicly available by the World Bank prior to the start of the phone survey activities</mark> (see Supplementary Materials – Methods).

We directly measure the effects of the pandemic on 8,603 households across the four countries, as well as how households attempt to cope with these effects. We use well-established sampling techniques<sup>_7-9_</sup> to correct for potential selection bias associated with not interviewing households that do not own mobile phones or that cannot be reached despite repeated call attempts (see Supplementary Materials – Methods for more details on the calculation of phone survey sampling weights). The correction for selection bias allows us to provide estimates of the total number of households, individuals, and children associated with any of the reported outcomes.

We then use reduced-form econometric techniques,<sup>_10_</sup> which are common to the field, in order to estimate heterogeneity in effects across 1) countries, 2) rural and urban sectors, 3) pre-COVID-19 wealth, 4) gender, and 5) time
across countries, which undermines the quality of investment data.<sup>10</sup> Also investment data are rarely available at the bilateral sectoral level. To circumvent these limitations, we use investments from the fDi Markets database collected by fDi Intelligence, a division of Financial Times Ltd. The data is available at the firm-project level and corresponds to announcements of “Greenfield FDI in a new physical project or expansion of an existing investment, which creates new jobs, and capital investment.” The announcements are collected from publicly available sources, such as media sources, industry and investment promotion agencies, or market search and publication companies. Each project identifies is cross-referenced against multiple sources. One concern is that recorded announcements differ in their advancement. The data set includes (but does not clearly distinguish) FDI projects that have been announced or opened by a company, which means that firms have made their final decision and projects are moving towards implementation in the former case or are already fully operational in the latter.<sup>11</sup> Yet, to the extent possible, project status is updated if a company makes further announcements. Large, announced projects above $1 billion, are researched on a quarterly basis to verify their progress. If the project is cancelled, it is removed from the database. Working with announcements represents an important advantage for our identification strategy in that it reduces the scope for endogeneity caused by simultaneity.<sup>12</sup> fDi Intelligence mentions that it takes on average two years for a project to materialize after an announcement is made in the media. Hence, regressing announcements on PTA variables at time _t_ is

> 10 Despite the international reporting practices set by the IMF, countries tend to i) deviate from the 10 percent ownership threshold, ii) not use uniform industrial classification, or iii) not report all types of investment properly (short-term intra-company loans, re-invested earnings are often missing).

11 The data does not cover merge and acquisition or other equity