rafmacalaba/data-use-mentions-extended huggingface dataset:
using weighted averages from the World Bank’s Enterprise Surveys. Although Enterprise Surveys use stratified samples to depict an accurate portrait of the business environment in the host economies, they were conducted in different years in the ten countries of study<sup>7</sup> . Insofar as some conditions captured by the Enterprise Surveys are not expected to change drastically over short periods of time, comparisons with EPZ survey data should remain valid, especially when differences are relatively large. However, these comparisons should also be treated with caution.

Although it is possible to compare the business environment inside EPZs with exporting firms outside them, there are two problems with this approach. First, the Enterprise Surveys were not sampled to be representative of exporting companies, and any comparison would have to perform some sort of post‐ stratification with data not readily available. Second, in some countries, the sample size for exporters is small and particularly problematic for some questions in this study. For this reasons, the analysis presented here compares the business environment reported by companies inside the EPZs with Enterprise Survey results from both exporters and non‐exporters. For the issues compared in this analysis – utilities set‐up times, utilities outages, and customs clearance – we have no reason to believe there should be any systematic difference in response from exporters and non‐exporters.

> 7 Enterprise Surveys were conducted in 2009 in Lesotho, Nigeria and Vietnam; 2007 in Ghana, Kenya, Senegal, and Bangladesh; 2006 in Tanzania and Honduras; and 2005 in Dominican Republic.

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The regional and worldwide aggregates omit economies for which Gallup excludes more than 20 percent of the population in the sampling either because of security risks or because the population includes non-Arab expatriates. These excluded economies are Algeria, Bahrain, the Central African Republic, Madagascar, Qatar, Somalia, and the United Arab Emirates. The Islamic Republic of Iran is also excluded because the data were collected in that country using a methodology inconsistent with that used for other economies (the survey was carried out by phone from Turkey). The exclusion of the Islamic Republic of Iran has a nontrivial effect on regional aggregates because its population is larger and wealthier than those of other economies in the Middle East and North Africa. For example, account penetration in the region is estimated to be 18 percent when the Islamic Republic of Iran is excluded but 33 percent when it is included.

# **Survey methodology**

The survey methodology is that used for the Gallup World Poll. Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.<sup>5</sup> In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used
this progress has also raised questions about the survey methods by which child labor statistics are collected. While the agricultural sector accounts for by far the largest share of child labor, agricultural data is particularly vulnerable to recall errors due to the inherent seasonality of the activity, variable and irregular work schedules, multiple plots, and saliences of individuals and work activities (Beegle et al. 2012, Arthi et al. 2018).

This study addressed the measurement of agricultural child labor in rural Ethiopia with attention to the respondent type, seasonality, and gender-based information gaps. Our survey design intervention consisted of the random manipulation of the survey respondent in the application of the same survey instrument, which allows us to causally estimate survey design unbiased impacts of self/proxy reporting on child labor statistics. This design avoids potential strategic behavior where self- and proxy respondents within the same household might try to provide similar answers. To account for seasonality effects, we implemented the same survey design experiment in three different agricultural seasons of coffee production as the salience of farm activities, the complementarity of work across household members, and the overall demand for household labor vary across seasons.

The main findings show statistically significant child/proxy reporting gaps in child labor statistics for girls but not for boys. These results are prominent at the extensive margin of work but not at the intensive one. Although both self and proxy reports are measured with error, this result is in line with research that has shown that measurement in agricultural data is affected by the gender division of farm labor across agricultural seasons (e.g. Beegle et al. 2017), and by norms and beliefs about female employment (Reynolds and Wagner 2012, Lee and Lee 2012). We did not find support for recall decay bias as the mechanism that explains our results. Neither did we find support for cognitive burdens of reporting bias in child labor measurement.

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standard household-level data, although the “profiles”—the comparisons of these measures across subgroups such as urban and rural areas—were found to be quite robust. Using a survey for Senegal that (unusually) collected a relatively individualized measure of consumption, Lambert et al. (2014) find significant inequalities within the household and a sizeable gender gap in consumption. Using the same data, De Vreyer and Lambert (2016) estimate that about one in eight poor individuals live in non-poor households. Using anthropometric data, Sahn and Younger (2009) find that about half of country-level inequality in the Body-Mass Index is within households rather than between them.

Other work has emphasized the poverty of specific types of individuals. Recent research on Mali confirms that widows—most of whom are absorbed into male headed households and can be quite young—experience significantly lower levels of individual (non-income) welfare indicators than women 7
presence of vocational programs or the availability of higher human capital due to stronger schools and tertiary institutions.<sup>5</sup> District-time fixed effects should address this concern.

Second, the sector-specific proportion of women among employees may be correlated with other characteristics that matter for performance, both across plants and across sectors over time. For example, nearly everywhere in the world women tend to work in lower productivity sectors. Similarly, the evolution of performance over time may be different for female-dominated sectors, and this could also affect the demand for preschools. The inclusion of plant and sector-time fixed effects should absorb this unobserved heterogeneity across plants, sectors and time.

Thus, once we control for sector-time, district-time, and plant fixed effects, it is plausible that the observed differences in productivity associated with an additional preschool per 1,000 children between plants with higher and lower shares of female workers are due to the impact of preschools: plants with a higher share of female workers benefit from a higher “dose” of preschool treatment.

However, the possibility of endogenous placement of preschools remains a concern. For example, the government may incentivize preschool construction in districts with high levels of female-dominated industries with increasing productivity. Alternatively, the private sector may respond to demand for childcare in districts with large share of highly productive firms in female concentrated industries – as they expect women working in such firms to have higher demand for childcare, and be able to cover its costs. To address this concern, we carry out two separate checks for parallel trends. First, we carry out a placebo test using the data from 2000, 2001 and 2002, prior to extensive preschool expansion, and estimate: where Growthd captures change in preschool density between 2003 (when the NSEA was passed) and 2014. Posttp is a dummy equal to 1, if year _t_ is greater or equal to year _p._ We estimate

> 5 It could also be the case that the election of a particularly efficient district-level government would benefit firms, for
satellite data, numerical algorithms, computing

> 1 Historical flood records are extracted from the Emergency Events Database (EM-DAT22). Four flood hazard types are included: coastal flood, flash flood, ice jam flood and riverine flood. It should be noted that the number of deaths and the number of affected people are both conservative estimations. Three entry criteria need to be fulfilled to be entered into the database: ten or more deaths, 100 or more people affected/injured/homeless, and declaration by the country of a state of emergency and/or an appeal for international assistance. For more information refer to EM-DAT data entry guidelines at https://public.emdat.be/about. To calculate the percentage of people that were affected by floods over all weather related disasters from 1900 to 2021, we extracted the number of people affected by four types of floods: 3,879,727,358 and divide by the number of people that were affected by meteorological (extreme temperature, fog, storm), hydrological (wave action, landslide, flood), and climatological disasters (wildfire, glacial lake outburst, drought): 8,015,008,050.

2
Crucially, the paper shows that if the privatized bank received a capital injection before privatization its performance measures improve compared to the earlier period as well as to privatized banks without recapitalization. The results, using sample splits between developing and advanced economy banks, indicate that privatized banks in high-income countries perform better and with lower liquidity and lower costs. Developing country banks, on the other hand, seem to be increasing their credit growth (though still lower than their high-income country counterparts) as well as their number of employees post privatization.

The rest of this paper proceeds as follows. The next section presents the data set and some stylized facts about recent trends in bank privatization around the world. Section 3 presents the empirical analysis that estimates the relation between privatization and bank financial performance, strategy, as well as the distributional implications. Section 4 concludes.

# **2. Data and stylized facts**

Our data set covers 475 privatization events, that is equity sales of SOCBs in 70 advanced and emerging economies over a period ranging from 1995 to 2017.<sup>9</sup> In terms of country level of income, two-thirds of the transactions refer to banks in middle-income countries, while the rest is mostly composed of public banks in high-income countries. The data set is compiled by merging information from multiple different data sources. The main source is the World Bank Privatization Database, but given that this database was discontinued in 2008, further bank privatization events were incorporated from additional sources, including the Securities Data Company (SDC) Platinum New Issues database and the Privatization Barometer’s online database on European transactions as well as their annual reports, which included a section on both EU and non-EU privatization trends and major deals. Current commercial bank ownership information was obtained from Fitch Connect. The privatization events were individually examined to exclude noncommercial bank privatizations, and also to incorporate events announced in financial media outlets such as the Financial Times or The Economist. Non-commercial bank sales excluded from our database include development banks (agricultural banks
>_′, θ_</sup> _y_<sup>_′, θ_</sup> _p_<sup>_′}_.Thereasonisthat</sup> transfers to the Ricardian household do not alter the discounted flow of expected after-tax income of the Ricardian household when _θa_<sup>_′′_= 0.</sup>

# 3.3. **Calibration. General Parameters**

Many of the most important parameters are not country specific, and so we calibrate these to international data or take them from the literature. The most important are the persistence of commodity price shocks, which we calibrate to a weighted average of oil prices and gas prices taken from BJS2013. The overall persistence ( _ρ_ = 0 _._ 93) is a weighted average of oil prices ( _ρ_ = 0 _._ 94) and gas prices ( _ρ_ = 0 _._ 89) as many oil exporters also produce gas.<sup>15</sup> We set _β_ = 0 _._ 96 so that long-run annual real rate of interest is 4%. The coefficient of risk aversion _σ_ = 2, which is a standard parameter in the literature. We follow Galí _et al_ (2007) to set _ω_ = _ωy_ = _ωU_ = 0 _._ 5 (50% of the population is HtM). For simplicity we set _Bss_ = 0 so that steady-state consumption is equal across households.

We set the benchmark debt-elastic interest spread to be _ψ_ = 0 _._ 01, which implies that a 100% of GDP increase in debt (or reduction in assets) increases interest rates by 1%. Schmitt-Grohe and Uribe (2003) set _ψ_ = 0 _._ 001 to match volatility of the observed current-account-to-GDP ratio for Canada. Schmitt-Grohe and Uribe (2016) argue that _ψ_ should be set to match the autocorrelation of the trade-balance-to-GDP ratio, and they estimate _ψ_ = 1 for Argentina. We take _ψ_ = 0 _._ 01
insignificance. Among the districts, misperception appears to be most prevalent in _Comilla_ , and significantly lower in _Munshiganj_ .

The third column of Table 2 presents the determinants of the farmers’ _overuse_ of pesticides. Unfortunately, misperception did not yield the expected sign, however, it was not a significant determinant of overuse.<sup>11</sup> Among the individual characteristics, _income_ and _ownership_ influence the propensity to overuse. For pesticide toxicity class, farmers with higher proportions of _highly hazardous_ (WHO Ia & Ib) pesticides are less likely to overuse, and farmers with higher proportions of _moderately hazardous_ (WHO II) _pesticides_ are more likely to overuse. Neither training in safe-handling nor use of IPM techniques has the expected effect. However, the application of pesticides revealed strong crop patterns. The incidence of overuse is less likely in the case of _potato_ , _rice_ , _cabbage_ , _sugarcane_ and _mango_ as compared to _eggplant_ and _beans_ . In addition, even after controlling for individual circumstances and crops, we find a strong locational pattern. Overuse is more prevalent in the districts of _Chapainawabganj, Chittagong, Comilla, Jessore, Narshingdi, Rajshahi_ and _Rangpur_ than in _Bogra_ , _Mymensingh_ , _Munshiganj_ and _Kishoreganj_ (excluded).

# **6.  Summary and Conclusions**

Empirical evidence on pesticide application in developing countries is rather thin. Drawing on a new survey conducted in Bangladesh, this paper is an attempt to address this gap. In our survey, 47% of the farmers were found to have overused pesticides, with an average overuse rate of 3.4 kg per growing season. To model pesticide overuse, we used a trivariate probit framework with health effects and misperception of pesticide risk as endogenous dummy variables.

The results, to some extent, are consistent with our expectations. The variable found to be significantly associated with the probability of a farmers’ health impairment from pesticides is: _amount of pesticide applied_ . Among the determinants of probability of

> 11 One possible explanation could be that with prolonged use over time, pesticide
20 percentages are highest at the lowest levels of income and among the heaviest gas users. What is striking is that in half of the income-usage cells virtually everyone would be classified as fuel poor. Panel B presents the same shares once the HUS is received. In the first quartile of the distribution of income, for example, the HUS reduced the rate of fuel poverty by half. Massive improvements are also observed in the top income quartile, especially at high consumption levels.

Table 14 displays fuel poverty over time—without and with the HUS. Clearly, the HUS reduces fuel poverty by half. Yet, a good one-third of the households in Ukraine meets the definition of fuel poor, even when the HUS is in place.

# _5.B. Estimating Demand_

What would be the welfare effects of removing the HUS completely? We answer this question using two possible approaches. The first fits a gas demand function to households that use gas for space heating, assuming that the HUS serves as a subsidy on the price of gas per cubic meter. As shown in table 10 and figure 3, treating the HUS as a price subsidy increases substantially the variation in the effective price of gas, making it possible to fit a proper demand function.

The estimation results are displayed in col. (1) of table A.1. in the Appendix.<sup>21</sup> They show that the price elasticity of residential gas demand is -0.17, a figure that is very close to that (approximately -0.16) in Alberini et al. (2020) based on a sample from Transcarpathia. The

> 21 We remind the reader that the sample used for the regression of Appendix A includes only the HBS households that use natural gas for space heating, and only the 2017 and 2018 HBS data. This is because the one-part tariff with stable, uniform prices make it possible for us to compute the quantity consumed from the expenditure and HUS information. The tariff structure remained in
funds|2,838,361|415,487|95,995|51,362|249,391|337,903|1,521,374|

Source: Indicators of Science (2009) – MoES, ROSSTAT, and the Higher School of Economics.

Strategic sectors accounted for around 44 percent of all R&D expenditures. Among the strategic sectors, the majority of resources – 52 percent – were employed by the transport, aviation, and space systems sector, followed by ICT at 16 percent, in 2007 (Table 1). The heavier expenditures in transport may guarantee Russia’s place at the technological frontier in the field, especially in space and aircraft technology. Additionally, the relatively large resources devoted to ICT may partly explain the emergence and growth of software start-ups and software exports in recent years. Formal investments in R&D in ICT 11
in their migration journey.<sup>2</sup> While migrants who do and do not use coyotes differ in systematic ways, such as indigeneity and age (see Online Appendix Table A3), we focus our analysis exclusively focuses on the majority of respondents who did use a coyote, of whom 17% experienced extortion. Over half of these people (10%) were extorted en route to the U.S., while the remaining 7% were extorted after crossing the border.

To measure perceptions of economic difficulty, we primarily use a 5-point scale where respondents rated their current economic situation (“Bad Econ”), collected in the 1- and 6-month post-arrival survey waves. For this measure, higher values indicate more negative assessments of one’s economic situation. We use two other variables as robustness checks in the main tables detailing our results. First, in the initial survey wave, respondents reported the degree to which needing to pay outstanding debts poses a challenge to their reintegration in Guatemala on a 10-point scale (“Debt Barrier”).<sup>3</sup> In addition, respondents in waves 2 and 3 rated their level of economic hardship as low, moderate, or high (“Econ Hardship”). We also examine more objective indicators of economic hardship, monthly income and unemployment (see Table A6).

To conceptualize fear, we use a set of questions asked in the second and third survey waves about actions taken by respondents since their deportation out of fear of being a crime victim. The behaviors include: avoiding leaving their homes by themselves, avoiding using public transit, preventing children from leaving the house, feeling the need to move to a different neighborhood, changing their job or place of study, or obtaining a weapon for personal security. For this analysis, “Fear Any” is a binary variable which indicates whether the respondents answered any of these six questions affirmatively. “Fear Count” is a 0-6 count variable summing how many of
1 -2<br>6<br>4<br>2<br>0<br>Inconme Gains (%)<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. Data on smoking-attributable death events is taken from the GBD (2017), and tobacco-related out-of-pocket medical expenses (adapted from Goodchild et al. 2018 and WDI). _Notes:_ Deciles are created based on per capita household total consumption.

> 19 The effect for the third decile is also positive, though close to zero.

21
sup> Although news outlets sometimes focus on scandalous or titillating events, rather than day-to-day disrespect and unfairness, if the denials are egregious or frequent, a news outlet might run a story on them. This can then lead to a response on the part of officials who staff a redress process. And if redress is provided and subsequently publicized, the process may raise the hopes of other citizens, who might be more inclined to demand redress for themselves in the future.

At the same time, tropes in the news media tend to follow patterns available in the popular imagination. For example, when designing social policy, Brazilian policy designers typically anticipate efforts by unqualified individuals to fraudulently attain benefits, but are less likely to anticipate the bureaucratic processes that will lead to the denial of benefits to qualified individuals and families. Both the law establishing and the regulation implementing the Bolsa Família program included detailed sections on procedures to identify and penalties to punish fraud, but do not describe standard redress procedures to give unjustly excluded individuals and

> 80 - DWP, February 2009, ―If you think our decision is wrong,‖ available at: http://www.dwp.gov.uk/contact <u>us/complaints-and-appeals/#ac</u>

> 81 - Their database of recent reports (1990-2010) can be searched at http://research.dwp.gov.uk/asd/asd5/rrs <u>index.asp</u>

> 82 <u>http://esicvadodara.org/index_com.html</u>

> 83 Pippa Norris and Sina Odugbemi, The ideal roles of the news media in the public sphere, Harvard-World Bank Workshop, May 29-31, 2008. The paper is available at: <u>http://www.hks.harvard.edu/fs/pnorris/Conference/Conference%20papers/Norris_Odugbemi%20The%20roles%20o f%20the%20news%20media.pdf</u>

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