rafmacalaba/data-use-mentions-extended huggingface dataset:
the aggregate panel PMG in this study dispel implications on the relationship with the GDPPC indicator. What is more, the results of the aggregate panel PMG also discard the existence of a long-run relationship with the FX and IPI indicators, a contradiction with former findings (Binswanger, 2001; Domian and Louton, 1997; Ajayi & Mougoue, 1996; Lin, 2012; Chkili & Nguyen, 2014) that may be market specific because of the lesser relevance of U.S. dollars on the economies of Southeast Europe, the prevalence of U.S. dollar-sensitive investors, or the representative importance of the industrial production sector on the overall capital markets and economies.

The results of this empirical research uncover new information that improves the prospects of academics, investors, policymakers, and others to understand and use it to support diversity and sustainability in financial intermediation. The results of the sensitivity testing show that individual countries likely have spurious relationships, which points to a diffusing model robustness and consistencies in the environment that are underpinned by the fundamental structure of the Southeastern European capital markets: lower capitalization, liquidity, a lack of infrastructure, weak intermediation in market making, and higher inherent costs and risks when compared with the benchmarks in the capital markets of advanced countries. The weaknesses in the Southeastern European capital markets also are evident in the lack of investor confidence because of subpar investor protection and transparency.

Looking forward, it would be important in future studies to incorporate and endogenize additional qualitative factors such as event studies and governance indicators in the development nexus for the capital markets of Southeast Europe. It would also be important to repeat studies on the impact of structural reforms on other Southeastern European countries, do panel multivariate model testing of the relationships under the reverse direction order, incorporate a more extensive data set for longer periods of time, and update the tests on the chosen time intervals and statistical methodologies.

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The next step is to create a single time series of harmonized learning outcomes (HLOs) for each country, combining HLOs from different testing programs in a way that balances coverage and comparability over time. This is done in three main steps:

- <u>International Testing Programs:  Data from TIMSS and PIRLS are combined into a single testing</u> program, recognizing that both tests are carried out by the same parent organization, the International Association for the Evaluation of Educational Achievement (IEA), and use common units.  TIMSS assessments have been carried out in 2003, 2007, 2011 and 2015, and PIRLS in 2001, 2006, 2011, and 2016.  PIRLS assessments are shifted to the nearest TIMSS year, and then a combined TIMSS/PIRLS score is created by averaging the two (for countries participating in both), and otherwise using whichever of the two is available.  TIMSS/PIRLS and PISA scores are chosen by taking whichever of the two is available for each country‐year, and the average of the two in 2003 and 2015 (when the two testing programs were both carried out in the same year, for the set of common countries).  The end result is that the HLO data as used in the HCI has 175 country‐year observations between 2000 and 2017 based on TIMSS/PIRLS, 278 country‐year observations based on PISA, and 68 country‐year observations based on an average of PISA and TIMSS/PIRLS.

- <u>Regional Testing Programs (LLECE, SACMEQ, PASEC).  Scores from these programs are used in</u> the combined HLO measure only for those country‐year observations where no data from international testing programs is available.  This adds 25 country‐year observations from LLECE, 37 from SACMEQ, and 30 from PASEC.  Six country‐year observations from LLECE and one observation from PASEC are dropped because the occur in country‐years for which an international test is available.

- <u>Early Grade Reading Assessment
u> World Bank, 2012b, Data Indicators. Available at: http://data.worldbank.org/ World Bank. 2011, "Zambia: Country Economic Memorandum." Policies for Growth and Diversification. 2004. Report No. 28069-ZM.

World Bank, 2010. _Zambia, Impact Assessment of the Fertilizer Support Program: Analysis of_ - _effectiveness and efficiency_ , Washington DC. Available at: http://www <u>wds.worldbank.org</u> World Bank, 2008, Social Protection Atlas, Human Development Network, Washington DC.

World Health Organization 2011 The Abuja Declaration: Ten years on. WHO, Geneva. Available at: <u>www.who.int/healthsystems/publications/abuja_declaration/en/index.html</u> 39
reliance on intermediate inputs in production. They find positive spillovers on firms in downstream industries both domestically and abroad. Furthermore, their results suggest that the impact of upstream reforms is more positive for downstream sectors that exhibit a high level of competition ex-ante. Compared to services, manufacturing firms showed greater increases in employment, value-added, and output after the reform. Intuitively, more competition is associated with a higher output elasticity with respect to prices, such that a reduction in prices of intermediate inputs will lead to larger effects on output and employment.

Focusing on 26 advanced economies, Duval and Furceri (2018) rely on a narrative database of major reform shocks in a wide range of product and labor market areas. On the product market side, the reforms take place in any of the following sectors: telecommunications; postal services; electricity; gas; and air, rail, and road transport. In one section of the paper, the authors examine the impacts of deregulation on a sectoral level to understand the indirect effects that deregulation of a sector can have on upstream sectors (through forward spillover via reduced prices and increased quantities) and downstream sectors (through increased demand for intermediate inputs following deregulation). They find a statistically significant impact on output in both downstream and upstream industries of about 0.3 percent on average 4 years after the reform. This is mainly driven by increased labor productivity (as opposed to increased employment levels).

30
welfare costs are 0.98 and 1.96 percent of lifetime consumption, respectively. Thus, our calculation suggest that both the short-run direct effects of tariff reforms and the indirect effects of the level of the tariff in amplifying the effects of macroeconomic shocks are economically significant.

As we have mentioned before, the limited time series dimension of our income data might lead us to over-estimate the amount of permanent labor income risk Mexican workers face.

> 34Although the welfare formula (15) is non-linear in _γ_ , this non-linearity is not very pronounced for moderate degrees of risk aversion. For example, if _γ_ = 4, then the welfare cost of this short-run change in _σε_<sup>2is4</sup><sup>_._16%oflifetimeconsumption.NoticealsothattheresultsreportedinTableIXassume</sup><sup>_n_=4since</sup> we use quarterly risk and preference parameters and the increase in income risk lasts for four quarters (one year).

32
4). The World Bank collects these sectoral value added data from national accounts and converts them from local currency units to constant prices in 2010 U.S. dollars. The disaggregation of the national value added into three sectors accords with the sectoral definition of the ISIC 4 by the United Nations as follows.<sup>7</sup> Agriculture value added contains ISIC 4 sectors 01-03: forestry and hunting, fishing, cultivation of crops and livestock production; manufacturing or industry value added consists of ISIC 4 sectors 05-43: mining, manufacturing, utilities, and construction; and services value added covers the remaining ISIC 4 sectors 45-99: wholesale and retail trade, transport, government, financial, professional, personal services, and miscellaneous value added, such as imputed bank services and import duties.

The IEA provides sector-level electricity consumption by using a slightly different composition for the manufacturing and service sectors, while applying the identical definition for the agriculture sector. The IEA compiles the data on the country-sector level by using national sources.<sup>8</sup> It defines the agriculture sector according to the ISIC 4 and includes sub-sectors 01-03, where the electricity consumption for the fishing industry also contains the provision of electricity to ships refueling in the country. Furthermore, the manufacturing sector comprises the ISIC 4 codes 07-18, 20-32, and 41-43, notably leaving out subsectors 33-40 and including most of them in the services sector. From IEA data, the services sector combines separately provided transport and service sectors with their respective ISIC 4 codes: 49-51 for transport and 33, 36-39, 45-47, 52, 53, 55-56, 58-66, 68-75, 77-82, 84 (excluding 8422), 85-88, 90-96, and 99 for commercial and public services. While discrepancies in the classifications of the manufacturing and service sectors in WDI and IEA are insignificant, they should be considered when interpreting the results.

To control for observed-country and sector-specific characteristics, the study includes several control variables from both the WDI and Penn World Tables Version 9 (
7 This trend-check exercise is detailed further in section 3.1 on dropped data points. Section 3.2 discussed data points which are included in both the 2018 and 2019 HEFPI versions but change in value due to changes in their underlying source or our indicator definitions, or because of coding corrections. Section 3.3 introduces the data points we newly added to the HEFPI database in its 2019 version. Finally, section 3.4 gives an overview of the subnational surveys included on the health equity side of HEFPI.

# 3.1. <u>Dropped data points</u>

For the 2019 HEFPI database, we quality-checked all points from the 2018 database and all newly obtained points for the following eight health equity indicators: Inpatient care use, 4+ antenatal care visits, skilled birth attendance, full child immunization, formal health care provider visits for children with acute respiratory infections, receipt of oral rehydration salts among children with diarrhea, adult inpatient care use, mammograms, and pap smears.

The quality check consisted in reviewing each point in a country’s time-series for the respective indicator via visual inspection of trend charts. For outlying points that we computed from microdata we reviewed, and if necessary corrected our code.<sup>3</sup> If the data came from publications or if we found our coding to be correct, we re-checked if the survey’s raw data indeed allowed us to construct the indicator in line with or sufficiently similarly to the HEFPI indicator definitions<sup>4</sup> – and dropped the point if we found this to not be possible. If definitions were sufficiently similar, we

> 3 Changes in indicator values between the 2018 and 2019 HEFPI versions due to such coding corrections or for other reasons are discussed further in section 3.2.3 below.

> 4 See Tables 1-3 in Wagstaff et al. (2018) for detailed HEFPI indicator definitions.
to value that consumption, irrespective of source, at market prices. Diaries instead collect information on food _acquired_ during the interview period regardless of when the food was consumed. There is little evidence on the best approach for managing the transition to recall interviews, with most countries accepting a break in the series on poverty, food security, and welfare aggregates, given that the survey mode affects indicators of poverty and inequality in the cross-section (Beegle et al., 2012) and over time (Battistin, 2003, Pudney, 2008, Attanasio and Pistaferri, 2016, and Coibion et al., 2020).

This paper studies how to obtain the most accurate measurements of population well-being in household expenditure surveys. Specifically, we define a household’s well-being as their _usual_ (or _average_ ) rate of consumption in a fixed period, which in our application is one week. Usual consumption is a longer-run average of weekly intakes and the quantity grounding welfare comparisons on the theoretical concept of consumption smoothing. The empirical challenge then is to use survey measurements collected as a snapshot, typically covering one week, to infer usual consumption. We develop a strategy to identify the distribution of usual consumption, which is _unobserved_ because of the short-term nature of interviews, without applying any corrections to raw data or assuming _ex ante_ which mode of collection – diaries or recall questions – yields the most accurate measurement.

We frame the identification of consumer welfare in a general setting, encompassing “survey errors” – defined here as differences between survey measurements and usual consumption – related to household preferences and misreporting behavior. Specifically, households with different consumption preferences in the interview week but planning for the same dollar amount of consumption over a longer period (e.g., a month) will have the same well-being. Such _irregularity of consumption_ has implications for welfare analysis even when survey measurements are not misreported (as discussed by Kay et al., 1984, and Deaton and Grosh, 2000, among others). In addition, our analysis allows for
living in a gas-flaring host community is associated with being hypertensive (Maduka and Tobin-West, 2017). Gobo et al. (2009) compared medical records in two communities with a long history of flaring with one with no flaring and found more frequent disease such as asthma, cough, breathing difficulty, eye/skin irritation in flaring communities. Sojinu et al. (2010) study distribution of polycyclic aromatic hydrocarbons (PAHs) in 44 soil sediment samples from rivers and canals adjoining oil and gas producing areas in parts of the Niger-Delta; PAHs from pyrogenic sources were present predominantly in surface soils. Yet while gas flaring associated with oil exploration work in the Delta affects surface soils, Sojinu and colleagues conclude that except for two locations, the observed contamination level from these sites was not a serious threat to the ecosystem.

In summary, there has been no large-scale examination of the impact of gas flaring on human health in Nigeria even with a 60-year history of routine gas flaring. Prior evidence is limited to the type of studies discussed above which focused on a handful of communities. Our study overcomes some of these data limitations by examining the association between flaring and health outcomes using linked georeferenced data on all flaring locations from satellite observations (including offshore flare sites) and pooled cross-sectional child health information from Demographic and Health Surveys (DHS).

# 3. Data and Methods

## Data

We use data from two sources: the first is satellite observation on gas flaring locations and estimates of flare volumes. These come from the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the _Suomi_ satellite. Launched in 2012, the VIIRS sensors can detect heat emitted by gas flares through the collection of shortwave and near-infrared data at night, recording peak radiant emissions from flares. Elvidge, et al. (2016) present a methodology for estimating the volume of gas flared at each site with the accuracy of the flared gas volume estimates rated at ±9.5%. These data and methods have been used to identify global flaring locations and
Our preferred model of only using growth in real GDP per capita does not suffer from the first issue but it does suffer from the last issue. The nowcasted growth rates have not yet been realized and are likely to deviate from the growth rates that eventually will be estimated by national authorities. Hence, they will likely be less predictive of welfare changes observed in household surveys. Since our preferred method for nearcasting, in that sense, only suffers from one of the two extra challenges that the other models suffer from when used for nowcasting, we think its advantages stand out even more when nowcasting. In fact, when running all our methods earlier in the calendar year (recall that earlier in the calendar year the informational space available for nearcasting approximates that of nowcasting), just using GDP growth becomes relatively more advantageous.

Using growth in real GDP per capita also comes with risks. If it is the case that nowcasted estimates of GDP are much more off target than nowcasted estimates of other variables, then it is possible that using only GDP growth will be relatively less attractive for nowcasting than nearcasting. Some evidence suggests that nowcasted growth rates by IMF might be too optimistic (Sandefur and Subramanian, 2020).

Another concern arises if GDP numbers are subject to quality concerns and are not deemed credible. If this is the case, then they may likewise be less relevant for both nearcasting and nowcasting. The fact that using growth rates in real GDP per capita worked well when trained on historical data suggests that historically this has not been frequently the case (or at least not more so than quality concerns with other indicators). Nonetheless, Figure 8 did show a large discrepancy for India when using growth in real GDP per capita and using more explanatory variables. Though this may simply be because true growth in real GDP per capita is less connected with household consumption in India than in other countries, evidence suggests that GDP growth rates in India in recent years are non-credibly high (Subramanian, 2019). If this is indeed the
I will now discuss three selected examples of the use of household surveys, namely (i) those carried out under the LSMS program; (ii) other general household surveys and (iii) specialized financial services surveys.<sup>13</sup>

# _LSMS surveys_

The World Bank’s LSMS was a pioneering effort to extend the use of best practice in national living standards surveys worldwide. The LSMS website reports on 51 surveys covering 30 countries at various dates since 1985. Of these, 39 surveys, covering 25 countries, included a module on “savings and borrowing” and a handful of others also include some savings and borrowing questions in other modules. (The Annex Table summarizes the main questions asked in this module in 27 of these surveys). The content of this module varies from survey to survey – indeed a degree of variation is inherent if the questionnaire is to reflect local conditions and as such resonate with the respondent – but recent surveys have contained useful though limited information mainly about credit. The sample sizes typically range between 1500 and 7500 which is sufficient for low sampling errors on nationwide totals, but small if subgroups are to be examined.

For instance, the 2001 survey for Bosnia interviewed 5400 households and included a credit module<sup>14</sup> ) containing 13 questions. These ask (i) how often the respondent borrowed from identified sources in the previous 12 months, together with flow and stock amounts; (ii) from whom, how much and why the latest loan was obtained (and if none why not); (iii) whether and by whom loan was refused. Other financial service questions are also embedded elsewhere in the questionnaire. For example, items 42 and 43 of the 48-item expenditure survey are spending on insurance and other financial services including bank charges.

A 1998 survey for Ghana interviewed about 6000 households included as the last section a “credit, assets and savings” module (Section 12). Here credit respondents were asked (i)
36

|**Secondary**<br>**Outcome**|**Handwashing with soap/ash**|Share of adult household members who<br>were observed washing their hands with<br>soap/ash at any juncture, measured via<br>structured observations.<br>The considered junctures are the<br>following:<br>Before obtaining water from a wide-<br>mouthed storage container;<br>Before cutting or preparing food;<br>Before serving food;<br>Before eating;<br>Before feeding child under 5;<br>Before breastfeeding child;<br>After defecation;<br>After toileting;<br>After cleaningchildpost- toileting|
|---|---|---|
|**Secondary**<br>**Outcome**|**School attendance**|Number of days child aged 6 to 18 years<br>old attended school in past week, based<br>on responses to “How many days has this<br>child attended school in the past week?”<br>Children who were not enrolled in school<br>were coded as zero.|
|**Secondary**<br>**Outcome**|**Water point has water**|1 if water point provides water<br>0 Otherwise<br>(Locked water points during survey are<br>not considered)|
|**Secondary**<br>**Outcome**|**WP Coliforms MPN/100mL**|Water quality at point of collection (village<br>water source) is Most Probable Number<br>(MPN) in 100 mL as defined for<br>Aquagenx CBT EC+TC MPN water<br>quality testing kits and follow WHO<br>standards. For nondetects, we substitute<br>
38|
||Import value in 2008|9,447.67<br>870.24|
||% Change in import value|1.35<br>-3.15|
|800700<br>Other articles of tin.<br>177.96|China|Russian Federation|
||Change in import value|178.62<br>-0.96|
||Import value in 2008|8,731.13<br>15.96|
||% Change in import value|2.05<br>-5.99|
|180631<br>Chocolate & other food preparations containing cocoa, in blocks/slabs/bars, weighing 2kg/less, filled.<br>177.39|Germany|Russian Federation|
||Change in import value|576.50<br>-516.66|
||Import value in 2008|3,509.00<br>41,083.14|
||% Change in import value|16.43<br>-1.26|
|640399<br>Other footwear without outer soles of leather, not covering the ankle.<br>160.12|China|Russian Federation|
||Change in import value|81.80<br>-8.65|
||Import value in 2008|16,228.41<br>667.85|
||% Change in import value|0.50<br>-1.29|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 51
# **1. Introduction**

Governments around the world provided unprecedented support to firms when the COVID-19 pandemic started. For example, the World Bank’s SME-Support Measure Dashboard tracked 1,500 government measures to support small and medium-size enterprises (SMEs) in 132 countries.<sup>1</sup> According to policy makers, these initial support measures aimed to prevent mass insolvency of viable firms and related knock-on effects for the financial sector, to preserve jobs and firm-specific intangible capital, and to reduce the friction costs of firms temporarily exiting the market (World Bank 2021).

Evidence from several countries suggests that government support measures were successful in helping firms weather the crisis and preserving jobs in the short run (see Chen et al. 2022, on China; GuerreroAmezaga et al. 2022, on Latin America, and Turkson et al. 2021, on Italy), although at a high cost (Autor et al. 2022, on the US). However, longer-run effects of COVID-19 support to firms are uncertain (Staples and Krumel 2023), in part since these measures may simply have delayed insolvency for some firms (Dörr, Licht, and Murmann 2022).

We contribute to the literature by assessing the medium-run effects of government support to firms during the COVID-19 crisis. We use data from three rounds of the World Bank’s Enterprise Surveys (ES) COVID-19 Follow-up Surveys, allowing us to look at firm performance in mid-2021, over a year after support measures were first enacted.

Another contribution of our paper is that we test whether the effectiveness of government support varied with its timing. The data allow us to examine support received at the onset of the crisis (during the first half of 2020, as reported in Round 1 of the ES COVID-19 Follow-up Surveys), as well as support received later (during the second half of 2020 or early 2021, as reported in Round 2 of the surveys). While initial support may be effective, if it continues over an extended period