rafmacalaba/data-use-mentions-extended huggingface dataset:
## _2. Y_<sup>_∗_</sup> _is complete for Z._

Completeness is common in the literature on nonparametric identification with instrumental variables (as in Newey and Powell, 2003), and is a weak regularity condition. Formally, completeness of _Y_<sup>_d_</sup> for _Y_<sup>_∗_</sup> is guaranteed if _E_ [ _h_ ( _Y_<sup>_d_</sup> ) _|Y_<sup>_∗_</sup> = _y_<sup>_∗_</sup> ] = 0 for all _y_<sup>_∗_</sup> only when _h_ ( _Y_<sup>_d_</sup> ) = 0. Intuitively, we require sufficient variability in the conditional distribution of _Y_<sup>_d_</sup> at different values of _Y_<sup>_∗_</sup> and, similarly, for the conditional distribution of _Y_<sup>_∗_</sup> given _Z_ . The latter requirement is a generalization of the rank condition for the instrument _Z_ to the non-parametric setting.

# **4.2 Empirical Specifications**

Assumptions 1-5 imply: and that there exists only one set of unobservable distributions on the right-hand side yielding the observable distribution on the left-hand side. In other words, under the assumptions stated, data on ( _Y_<sup>_d_</sup> _, Y_<sup>_r_</sup> _, Z_ ) are sufficient to retrieve the distribution of usual consumption: and the distributions of diary survey errors ( _fY d|Y ∗_ [ _y_<sup>_d_</sup> _|y_<sup>_∗_</sup> ]) and recall survey errors ( _fY r|Y ∗_ [ _y_<sup>_r_</sup> _|y_<sup>_∗_</sup> ]
and country factors. Firmspecific factors account for the largest share of variation of returns, far surpassing the influences of country and sectoral aspects. Indeed, the role of idiosyncratic firm factors is greater than in studies for publicly listed equities. Using data from 12 European countries, Heston and Rouwenhorst (1994) find that country and industry characteristics explain 24% and 5%, respectively, of the variance of returns.<sup>2</sup> Norges Bank (2019) built on the return decomposition framework by Heston and Rouwenhorst (1994) using an international dataset of firm-level public equity returns from developed and emerging markets, finding that country and industry factors explain about 30% of return variation, with firm-specific dynamics dominating. Using an ANOVA decomposition, our analysis extends this methodology to private equity in EMDEs. Our results reveal that sectoral effects account for 4.3% and country factors for 6.5%.

The importance of firm-specific factors is consistent with studies that have examined the underlying determinants of profitability and performance. For example, Esho and Verhoef (2021) 1Indeed, recent literature has emphasized the need for benchmarks with a closer risk profile to those in emerging markets (Jeffers et al., 2024).

> 2This observation aligns with the findings of the volatility decomposition of Campbell et al. (2001). The authors highlight that between 1962 and 1997, the proportion of firm-specific volatility in total common stock return volatility rose from 65% to 76%, while the contributions of market volatility and industry volatility declined from 20% to 14% and from 15% to 10%, respectively.

2
_Performance?_ Georgia Water Planning and Policy Center, Water Policy Working Paper No. 2005-014. Albany, GA.

Foulon, J., P. Lanoie and B. Laplante. 2002. Incentives for pollution control: regulation or information? _Journal of Environmental Economics and Management_ 44: 169-187.

- Garcia, J., Sterner, T., and S. Afsah. 2007. Public disclosure of industrial pollution: The PROPER approach for Indonesia? _Environment and Development Economics_ , 12, 739-756.

- Greenstone, M., 2003, Estimating Regulation-Induced Substitution: The Effect of the Clean Air Act on Water and Ground Pollution, _The American Economic Review_ , 93(2): pp. 442-448.

- Gupta, S. and B.N. Goldar. 2004. Do stock markets penalize environment-unfriendly behavior? Evidence from India. _Ecological Economics_ . 55, 2.

- Hamilton, J.T. 1995. Pollution as news: Media and stock market reactions to the toxics release data. _Journal of Environmental Economics and Management_ , 28, 1, 98-113.

- Healy, P. and K. Pelapu.  2001.  Information asymmetry, corporate disclosure, and the capital markets: a review of the empirical disclosure literature. _Journal of Accounting and Economics_ , 31: 405-440.

- Jin, Yanhong, Hua Wang and David Wheeler, 2010a, The Impact of Environmental Performance Rating and Disclosure: An Empirical Analysis of Perceptions by Polluting Firms’ Managers in China, World Bank Policy Research Working Paper #5419.

- Jin, Yanhong, Hua Wang and David Wheeler, 2010b, Environmental Performance Rating and Disclosure: An Empirical Investigation of China’s Green Watch Program World Bank Policy Research Working Paper #5420.

- Kathuria, V.  2006. _A Concept Note on Public Disclosures – Using Information to Reduce Pollution_ . Madras School of Economics, Chennai, India.

Kennedy, P. W., Laplante, B., and J. Maxwell. 1994. Pollution policy: The role for publicly provided information.
494,135|2,493,215|920|243|291|386|
|2020|2,464,407|2,463,514|893|233|290|370|

_Note:_ This table illustrates the total number of firms, including the distinction between private firms and BOS, in the sample. Data from RAIS, from 2016 to 2020.

Table 3: Sector taxonomy, 2020.

||Number of frms|(%)|Number of BOS|
|---|---|---|---|
|Competitive|2,234,377|90.67|521|
|Partially contestable|34,228|1.39|163|
|Natural monopoly|13,310|0.54|190|
|Excluded|182,492|7.41|19|

_Note:_ Data from RAIS, 2020.

7
|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|Walters et al.<br>(2019)|Global (130<br>countries)|Infants and<br>children <2 y|Not breastfeeding according to<br>recommendations|Child morbidity (diarrhea, pneumonia)<br>MaternalT2DM, breast & ovarian<br>cancer|Child and maternal<br>mortality, direct health<br>care costs, household<br>formula costs, future<br>economic cost of<br>mortality and cognitive<br>losses|

* diets low in fiber, fruits, legumes, nuts and seeds, polyunsaturated fatty acids, seafood omega-3 fatty acids, vegetables and whole grains, and diets high in processed meat, red meat, sodium, sugar-sweetened beverages and trans fatty acids

† India, Bangladesh, Bhutan, Pakistan, Sri Lanka, Maldives, and Afghanistan

> ‡ includes diets low in calcium and milk (in addition to the other 13 dietary risk factors included in GBD 2019)

22
economies. This could be in particular the case for less developed countries which suffer from weak reporting capacities. We check both approaches.<sup>15</sup>

# **3 Empirical Strategy**

This section sets out the empirical strategy in a two‐step approach. First a so‐called services linkage will be developed following previous works in that the policy variable indicators used for our regressions are “weighted” with the input‐output coefficients. Then, in a second step, our baseline specification for the regressions is presented.

# **3.1 Services Linkage**

The empirical estimation strategy follows the one which is pioneered by Arnold _et al_ . (2011; 2015) and which has been used in all subsequent papers with the purpose of creating a so‐called services reform index. This means that one needs to interact the input‐output coefficient of services input reliance for each manufacturing and services sector with the sector‐specific regulatory barrier index in services for each country. This is an identification strategy that relies on the assumption that manufacturing as well as services industries which are more reliant on services inputs would be more affected by changes in regulation within each input services sector over time. This “weighted” average of services policy regulation that relies on services intensities is a more just approach to measure the effect of services regulation on TFP in contrast to an unweighted one.

For this reason, each of the country‐sector specific reform indicators is multiplied with the proportion of services sourced as inputs in country _c_ , from service sector _s_ , for downstream services and manufacturing firm _j_ , i.e. _φcjs_ , which is the input‐output coefficients as explained in the previous section. As a consequence, we apply the next formula: where _φcjs_ comes from the input‐output tables from the year 2010 reported at 2‐digit level in which the input shares are computed based on the total value‐added of inputs used, and where the services reform index refers to the
online appendix

# **A.2 Customs data**

To compare the local and global mask markets we complement domestic EBM II data with similarly granular international trade data.

**Coverage** Customs data collected by the RRA contains the universe of tax-registered importing firms based in Rwanda. Importing firms are identified by the same Taxpayer Identification Number (TIN) which identifies a firm for domestic declarations (including VAT).

**Import data** The data includes the imported value, weight, product unit, Harmonized System (HS) product codes, an import origin and a timestamp at the point of entry. In addition, two free-text string fields are populated with product descriptions by customs officers. We identify masks using a combination of HS product codes, and product string descriptions. Just as in EBM data, we exclude items outside the scope of our study (e.g., oxygen masks).

## **A.2.1 Customs data construction**

**Product descriptions** At import, product descriptions are captured by RRA customs officers at the border. We identify imports exclusively containing masks using string descriptions. We subsequently remove products from customs data, which satisfy the matching algorithm, but are not face masks.

**Price** The most salient difference for our research, between customs and EBM data is that no natural price (per unit) variable exists in the international trade data. The data does include the total imported value, as measured by the Cost, Insurance, Freight (CIF), and several quantity variables including _product quantity_ and Net

A9
# **Provider Entry**

The ability of new providers to enter the market for health and education services can be an important determinant of outcomes in the sectors. Private non-profit and for-profit providers, as well as new public-sector entrants (such as charter schools), can affect the quality of service delivery in two ways: by providing care directly, and by exerting competitive pressures and spurring behavioral change in public providers. These effects can be either positive or negative and reflect a trade-off between quality control and competition. On the one hand, requirements for entry can ensure that only qualified service providers practice; on the other hand, requirements that are poorly designed or applied may prevent qualified schools or health clinics from practice.

Few studies have assessed the overall conditions for provider entry into the delivery of health and education services.<sup>16</sup> General indicators of ease of starting and running a business, such as those taken from the World Bank _Doing Business_ survey, may capture some of the general business climate variables that could affect whether it is feasible and potentially profitable to run a (for-profit) school. But they do not provide a solid basis to understand either governance policies or performance in health and education with the necessary granularity.

> 16 Harding and Preker (2003) discuss the core elements of such an assessment for health services. An example of the conditions for private-sector involvement in health delivery  can be found among the indicators for USAID’s PSPOne program in health (Private Sector Partnerships-One Project 2005). Similarly, African Private Schools Investment Index (School Ventures and Economist Intelligence Unit 2008) provides an assessment of the ease of starting up a school.

28
for location _c_ ; for example, for the Foster, Greer and Thorbecke (1984 - FGT) class of decomposable poverty measures, the function _f_ ( _yc_<sup>_∗_)isdefinedas</sup> where _z_ is the poverty line, _pch_ is the size of household _h_ from location _c_ in the census and _I_ ( _ych_<sup>_∗< z_) = 1if</sup><sup>_y_</sup> _ch_<sup>_∗< z_andisequalto0otherwise.</sup> 5. Repeat steps 2 to 4 _M_ times. Although traditionally _M_ = 100, a larger number of replicates _M_ is recommended. Let _τc_<sup>_∗_(</sup><sup>_m_)</sup> be the indicator of interest obtained in mth replicate of the bootstrap. The ELL estimator is then given by the average across the _M_ bootstrap replicates, and the ELL estimated variance of the ELL estimator is given by As opposed to the EB method of MR (2010), which uses a Monte Carlo simulation procedure and a separate bootstrap procedure for MSE (see Section 3), in the above ELL procedure, a single computational algorithm tries to capture the noise of the initial model parameter estimates in the ELL standard error by varying the model parameters across simulations, which is aligned to Rubin’s rules (Rubin, 2004). This means that, in each replicate of the bootstrap, different values of the model parameters _β_<sup>_∗_</sup> , _ση_<sup>2</sup><sup>_∗_and</sup> _σe_<sup>2</sup><sup>_∗_</sup> are used to generate the welfare data. However, this step might entail an increase of noise in the ELL estimators.

Note that, if the model parameters were kept fixed to the initial survey estimates from step 1 (skipping step 2), the above
<br>notes) and one other disaggregation and (2.3) with one<br>disaggregation.<br>To receive half point:Must have one indicator with two<br>disaggregation or two indicators with one disaggregation<br>each.|
|3||Education Outcomes|(3.1) Enrollment rate;<br>(3.2) Completion or<br>graduation rate;<br>(3.3) Competency exam<br>results.|(3.1, 3.2) Sex; school stage;<br>age; school type.<br>(3.3) Sex.|To receive full point: Must have all indicators<br>disaggregated as follows: (3.1) and (3.2) by sex and one<br>other disaggregation, and (3.3) by sex.<br>To receive half point: Must have (3.1) or (3.2) with two<br>disaggregations, or (3.3) disaggregated by sex. Two indicators<br>with only one disaggregation each is not enough.|
|4||Health Facilities|(4.1) Number of health<br>facilities;<br>(4.2) Number of beds or data<br>on health care staff;<br>(4.3) Health budget data.|(4.1) Facility type;<br>(4.2) Facility type; department<br>type; staff type<br>(4.3) Functional categories|To receive full point: Must have two indicators with one<br>disaggregation each.<br>To receive half point:Must have one indicator with one<br>disaggregation.|

**<mark>Table A3 continued</mark>**

35
in underlying fundamentals to expect that large-scale increases in aid will be proportionately more effective than more moderate amounts of assistance. This does not mean that increasing aid to Africa (or other poor countries) is a bad idea. Rather, it suggests to us that we should not expect any disproportionate growth effects of large-scale aid.

We do not claim that this paper provides comprehensive coverage of the possible mechanisms of poverty traps, and the simple aggregate models we consider here are unable to capture some possible mechanisms mentioned in the literature. A significant fraction of this literature is based on highly stylized models, and on non-convexities at the micro-level, which are not easily amenable to calibration using aggregate data. Also, we face the standard trade-off of breadth versus depth. We have chosen to analyze a set of broad models that rely on aggregate mechanisms without delving deeply into the possible micro-mechanisms that lie behind our aggregate relations. A possible alternative approach would be to take seriously a specific micromechanism of poverty traps and make an effort to calibrate it using micro and macro data. Nevertheless, we consider that there are some important general lessons that can be derived from our analysis. The mechanisms that we analyze are usually mentioned in the discussion on poverty traps, and the models we use are standard in the growth literature. Also, under some conditions these models can be understood as reduced form representations of the underlying micromechanism.

It is important to notice that by using a representative-agent model in our analysis, we are implicitly assuming perfect factor markets, which leaves aside an important set of microeconomic models of poverty traps based on credit and labor market imperfections that result in apparently large TFP differences across countries.<sup>1</sup> However, despite their importance, these types of models do not speak directly to the issue of whether large across-the-board increases in aid are likely to move poor countries out of poverty. The reason is that the low aggregate productivity associated with these types of models is related to distributional problems. A redistribution of income from rich
2010|0.00|0.69||||
|2011|0.00|0.75||1.20|0.17|
|2012|0.01|0.62|||0.29|
|2013|0.00|0.45|0.15|1.44|0.26|
|Source: SEDLAC, microdata provid<br>calculations.|ed by regional focal points, memb|ers of the Global|Poverty Wo|rking Group. A|uthors’|

# **4. CONCLUSIONS**

Measuring the World Bank Group’s goal of shared prosperity is demanding in terms of access to and quality of microdata. Currently, corporate numbers on shared prosperity are estimated based on adjustments used for calculating international poverty headcount rates to ensure consistency for the two goals of the World Bank Group. This paper, for the first time, systematically explores how sensitive shared prosperity estimates are to the changes in these particular adjustments.

Empirical research reveals that the impact of applying grouped versus microdata has a minimal effect on estimates of shared prosperity. The means for the bottom 40 percent of the population obtained from grouped and microdata can differ substantially. However, given that the shared prosperity indicator is calculated for two points in time, the impact on the consumption per capita growth rate is much smaller if the differences in means are similar across two points in time. There is tentative evidence that the difference between grouped and microdata does not depend on the source of welfare aggregate ‐ income or consumption‐ but this requires more rigorous testing. Overall, the issue of using grouped versus microdata is less of a concern because the World Bank Group has started using microdata for corporate poverty and shared prosperity estimates since 2015.

Using a spatially adjusted or nominal welfare aggregate for measuring shared prosperity does not appear to have a substantial impact on the indicator itself, but can change the distribution. We did not discover significant differences in the shared prosperity
# **Table 10:  Determinants of Indoor Air Pollution Six Rural Areas of Bangladesh**

||Sam<br>|ple Incidenc<br>|e of Pollution F<br>|actors<br>|
|---|---|---|---|---|
|Rural Area<br>of:|Mud<br>Walls<br>%|Thatch<br>Roof<br>%|Detached<br>Kitchen<br>%|Open-Air<br>Kitchen<br>%|
|Cox's Bazar|71|51|14|0|
|Faridpur|0|8|60|36|
|Jessore|64|16|66|12|
|Rajshahi|89|0|70|7|
|Rangpur|0|20|72|20|
|Sylhet|20|18|12|35|

Our survey results indicate the potential importance of regional customs and differential availability of building materials and fuels. Mud walls, the most important ventilation characteristic, are extremely common in Cox’s Bazar, Jessore and Rajshahi, but extremely scarce in Faridpur and Rangpur. Significant variation is also apparent for thatch roofs. Detached or open-air kitchens are present in 14% of households in Cox’s Bazar, 47% in Syhlet, and 96% in Faridpur.

To capture the simultaneous effect of ventilation factors and fuel use in each surveyed household, we use the PM10 model in Table 7 to estimate PM10 concentrations in kitchens and living areas. We drop ventilation after midday meals because this was not recorded in the country-wide survey, and use of jute fuel because its incidence in our sample is negligible (3 users in 599 households). We tabulate the results by geographic area in Table 11, which highlights the effect of local variations. For kitchens in rural areas, mean estimated PM10 concentrations range from 410 in Cox’s Bazar to 202 in Faridpur. Similar variation
level, they take on an exporter-specific dimension via country-level exclusions.

> 50Broda, Lim ̃ao and Weinstein (2008) find that US NTBs are higher in sectors with high inverse export supply elasticities. Bown and Crowley (2013) find that United States’ use of antidumping and safeguards is consistent with the Bagwell and Staiger (1990) model of self-enforcing trade agreements and cooperative tariffs. Trefler (1993) also used US NTB data in studying endogenous trade policy, and Goldberg and Maggi (1999) and Gawande and Bandyopadhyay (2000) used US NTB data in their empirical examination of the protection-for-sale model [Grossman and Helpman (1994)].

> 51TTB coverage ratios have a mass point at zero. While we could use limited dependent variable methods to take this into account, we focus on OLS results here for several reasons. First, TTBs are a rare event in the data, occurring in only 6 percent of our importer-exporter-industry-year observations. Standard binary outcome models (e.g., Probit and Logit) are biased in this context [King and Zeng (2001)]. Further, for Tobit models, the distribution of the rare positive outcomes is constrained to follow the extreme upper tail of the normal distribution, which is an untenable assumption in our context. Second, as a practical matter, presuming that zero TTB coverage ratios conform to our basic theoretical predictions, OLS would then understate the true role of value-added content in shaping TTBs (coefficients of interest would be biased toward zero). Thus, OLS is a robust and likely conservative approach to characterizing our data.

> 52One minor point is that we cluster in this table on importer-exporter-industry, in contrast to previous tables. The reason is that TTB policy decisions are independent across industries. This contrasts with tariff policy, where tariffs may be correlated across sectors for institutional reasons – e.g., due to signing bilateral trade agreements that cover multiple sectors, or due to the application of exporter-specific exemptions in the GSP program. The significance levels of our