rafmacalaba/data-use-mentions-extended huggingface dataset:
the ECU. Russian exports to Kazakhstan and Belarus

> 4 There have been concerns that introducing compound tariffs into the CET, adding a specific duty to ad valorem rates, may exceed Russia’s bound rates in products such as large-capacity refrigerators. The EU Market Access Database (MAD) also suggests that a total of 1,576 product lines have been “corrected” by Russia (abolished or introduced) relative to its 2007 tariff schedule, including tariff sub-codes (presumably national tariff lines below the HS-6 level). This could be characterized as “manipulation” to avoid implementing the tariff commitments. EU MAD also states that Russia’s import duties on coated paper and cardboard in the Single Customs Tariff were three times higher than its WTO commitment (15 percent instead of 5 percent).

> 5 Kazakhstan’s imports from Russia, as a share of its total imports, went up from 37.3 percent in 2007-08 to 42.2 percent in 2011-12. A portion of this increase is likely due to Kazakhstan’s adoption of the CET after Kazakhstan joined the ECU in 2010.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 4
. The magnitude of our findings appears to validate scenarios projected by the World Bank (Azevedo et al. 2021). This has consequences in terms of further education as the lost learning may impact opportunities for higher levels of schooling. There are also long-term future earnings losses associated with lost human capital, with students potentially losing trillions of dollars in future incomes.

# **Data**

The just-released PIRLS 2021 data include results for 65 countries and benchmarking participants. This is the first international picture of student achievement after the COVID-19 pandemic.

Our analysis uses data from 24 education systems of the European Union (22 countries and separate data for the Flemish and French parts of Belgium) and for an additional 7 countries when providing results for all ECA+ countries. We compare PIRLS results from assessment rounds in 2001, 2006, 2011, 2016 and 2021. Overall, our sample includes 582,112 students (459,039 in EU countries) participating in all rounds of PIRLS, representing data for nearly 30 million 4<sup>th</sup> grade students across 20 years. The sample from 2021 – the post-COVID-19 data – includes 157,167 students (120,895 in EU countries), representing the results of nearly 7 million 4<sup>th</sup> graders in ECA+ countries (4 million 4<sup>th</sup> graders in the European Union).

PIRLS data include the plausible values measuring reading achievement and information on student socio-demographic characteristics (e.g., age, grade, and gender). In addition, school principals surveyed by PIRLS provided details on how instruction was affected by COVID-193
# **1 Introduction**

The COVID-19 (Coronavirus) pandemic and the associated containment measures are causing far-reaching damage to firms around the world. Firms are suffering from reduced demand due to lockdowns and movement restrictions. Yet, the limited availability of firm censuses and survey data in many developing countries hampers policymakers’ ability to quantify the economic impact of the pandemic and the effect of policy measures to support firms. In this paper, we use a novel dataset of administrative corporate tax returns from 10 low- and middle-income countries to provide plausible estimates of the direct effects of the lockdown on firms’ profits, payrolls, and exit rates. We then derive the implications for tax revenues and government support policies. On average across countries, our lower-bound predictions (optimistic scenario) suggest that only half of all firms remain profitable, tax revenue remitted by corporations falls by 1.5% of GDP, and aggregate corporate losses increase by 2.9% of GDP.

Our analysis relies on a few simple and transparent assumptions about the shock firms face and the adjustments they make. We simulate scenarios in which a demand shock induces a drop in firms’ sales over either three or five months – which can be thought of as the length of the lockdown with perfect compliance to governments’ instructions, or of reduced activity for a longer period with imperfect compliance. The severity of the shock — the percentage drop in monthly sales — differs across economic sectors. We assume that firms produce a unit of output with a Leontieff production function which requires capital, labor and material inputs in fixed proportions, with the proportions estimated from each firm’s tax declaration. In our very stylized world, firms can reduce their material costs proportionally to the drop in demand; they reduce labor costs only when making losses because re-contracting workers is costly; and they cannot adjust their fixed costs. Implicitly, we assume that firms aim to weather the shock in a way that allows them to scale their production back up quickly at the end of the lockdown
10 1986 and then rise again until 1988. Like the inequality measures, they fluctuate without a trend between 1989 and 1993, and then begin a sustained decline which lasts for the next eleven years.

**Figure 2: Poverty indices over time in Brazil using the Administrative Poverty Line, 1981-2004**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1992 1993 1995 1996 1997 1998 1999 2001 2002 2003 2004<br>Years<br>Headcount Poverty Gap FGT(2)<br>FGT(a)<br><!-- End of picture text --> The poverty decline in 1986, which mirrors the enormous increase in mean and median incomes reported for that year in Table 1, deserves a word of explanation. These are the actual numbers from the PNAD survey, and they do not reflect any change in the questionnaire, reference period or survey design on that year. Despite considerable scrutiny from various authors, similar figures have been widely reported in the literature on Brazil, including Amadeo and Camargo (1997); Barros, Henriques and Mendonça (2000); and Ferreira and Litchfield (2000). The general view seems to be that this rise in mean incomes, and the corresponding decline in poverty, reflect the expansionary nature of the 1986 _Cruzado_ stabilization plan. GDP grew by 7.5%, and the FIESP industrial real wages by some 20% during that year, suggesting that _part_ of the increase in the survey mean and the decline in poverty are likely to have been real.

Nevertheless, the increase in the PNAD survey mean between 1985 and 1986 is of 46%, which is clearly inconsistent with the national accounts
with original weights (29.3%), they are still significantly lower than the national average poverty rate (38.2%). This finding suggests that reweighting alone does not fully address the bias in the phone owner sample.

Figure 2 (subfigure 2) shows that the SWIFT models trained with the full sample of EICV5 project the national poverty rate more accurately. Note that in the case of the Rwanda experiment, we develop urban and rural models separately. Applying the SWIFT models to the reference data set leads to a national projected poverty rate of 37.5 percent,<sup>9</sup> which is within the 95 percent confidence interval of the actual poverty rate of 38.2 percent. The details of the models are available in Appendix 1.

Applying the SWIFT models to the phone owner sample, even with original weights, greatly reduces the sampling bias in poverty estimates. The SWIFT models project a poverty headcount rate of 36.6 percent in the phone owner subsample, which is just 1.6 percentage points lower than the national average rate.

Additional reweighting procedures show slight improvements in poverty projections compared to the abovementioned SWIFT poverty projection with unadjusted weights. PSW and non-PSW adjustments produce a SWIFT-based poverty rate of 37.1 percent, which is around one percentage point smaller than the national average estimate (38.2 percent).<sup>10</sup> _Table 2_ displays the summary statistics of all variables involved in this process, namely, variables included in the reweighting procedure, variables included in the SWIFT projection model, and other untargeted indicators not included in either procedure. The weights calculated by PSW-type reweighting techniques produce close estimates for all the aforementioned indicators, and the combination of the PSW and non-PSW adjustments provides incremental improvements.

> 9 The SWIFT model for all countries can be seen in Appendix 1. The simulation results of the SWIFT model are available upon request.

> 10 Adding the non-PSW adjustment (maxentropy), compared to PSW (Lee 2006)
OR “adolescence,” which increased our total to 1182 records. Duplicates were removed, reducing our total to 614 records.

Titles were screened for topic relevance by a research assistant and one author of this review. We excluded studies focusing on samples of children with disabilities from birth, premature children, or teen mothers. We excluded outcomes on usage of health services (i.e., vaccinations, nutrition related behaviors, doctor visits). Similarly, we excluded outcomes that are considered mediators leading to the final outcomes in our list (e.g., dietary intake, sedentary behaviors, sleep, work-family conflict). We excluded employment relating to migration or sex work. This screening provided us with 313 records.

For the final decision regarding inclusion, the abstracts and/or methods sections of papers were examined by an author of this review, with the other authors consulted regarding the empirical quality of the studies. We limited the studies to those published between 2002 and 2021. Studies were included if they had quantitative analysis, a measure of maternal work as a causal variable in the paper, at least one child or adolescent development outcome (cognition, schooling, behavior, anthropometrics, and health). Moreover, we restrict the analysis to studies analyzing natural or quasi-natural experiments and using the following methodologies: individual fixed or random effects, lagged dependent variables,<sup>6</sup> sibling fixed

> 5 The search was restricted to the titles of articles, since when the entire content of the articles was searched, it returned tens of thousands of papers. Several keyword searches (in contrast to title searches) of the permutations that yielded the most results did not provide additional papers for the review, so we did not pursue a keyword search.

6 Even though many papers analyzed longitudinal data on maternal work, we excluded studies that only controlled for observables. One exception is the lagged dependent variable approach, which controls for the baseline value of the outcome variable. Although the lagged outcome variable is
used by scholars investigating inter-state cooperation over water (Brochmann and Hensel 2009; Tir and Ackerman 2009; Dinar et al. 2011). Most recently, scholars have also considered the role of water variability across time on hydro-political relations between states (e.g. Dinar et al. 2010; De Stefano et al. 2012). Given some of its advantages (e.g. ability to predict natural runoff with a higher accuracy and high significance levels than precipitation and measure the magnitude of extreme events particularly when precipitation and gauge data are limited), the BWI, therefore, provides another measure of water flows that scholars can utilize in their socio-political studies. In particular, the data provided by the BWI befit recent calls by scholars for better measures of ―dynamic scarcity‖ which make better use of satellite imagery and meteorological data to account for time-varying measures of water availability (Buhaug et al. 2008).

To illustrate the utility of the BWI for the cooperation and conflict analysis, we show how actual past water related political events are distributed over time relative to the extreme weather events (particularly floods) which the BWI identifies in this study. To that extent, we utilize the Basins at Risk Data Base (TFDD website n.d.), which contains a database of historical water relations (such as events) documenting historical incidents of international water cooperation and conflict, and comb the databank for relevant flood-related events that transpire around the flood periods identified by the BWI. This exercise is not intended to confirm any type of causation between hydro-political events and extreme weather events as this will require a much more robust econometric approach utilizing control variables. We simply wish to demonstrate the value of the BWI (among the other measures of water variability and supply) for systematic and empirical research.

Various studies have already used the TFDD Basins at Risk database to make certain arguments about conflict and cooperation over water (Wolf, Stahl & Macomber 2003; Yoffe at al. 2004; Stahl 2005) and the application of the
8 where _PCC_ is the real quarterly CE cocoa price, P is the average observed real world price each quarter, _£_ is the iso-elastic short-run price elasticity of demand, a is the coefficient of variation of real quarterly world prices, and R is the relative risk aversion coefficient. The variable P is first calculated on a CIF basis from world market data (World Bank 1998) and then converted to a farm gate basis by subtracting maritime transport and direct domestic marketing costs. In terms of Table 1, P would then be 714 F/kg (i.e., the CIF price of 904 F/kg minus maritime transport of 75 F/kg minus direct domestic marketing costs of 115 F/kg). We refer henceforth to _P_ as the "farm gate FOB price".

We note, first, that output is affected by stabilization, even though stabilization is within seasons, given that there is some weak short-term supply response for tree crops. Second, it is assumed that R is unity, indicating that farmers are moderately risk averse (based on extensive empirical evidence launched by Binswanger, 1978). Third, in the absence of price stabilization, the volatility of domestic producer prices would be that of international cocoa prices. With those assumptions, we construct counter-factuals in which intra-annual producer price variability increases from zero (perfect stabilization) to world levels (perfect transmission of international prices) for each season from 1993/94 through 1997/98.

The within season coefficient of variation of monthly world prices had a maximum of 8.1 percent in 1993/94 and a minimum of 2.8 percent in 1994/95. The absolute value of the short-run price elasticity of demand is assumed to be 0.5. Counter-factual stabilization benefits expressed relative to the P from equation (1) would therefore have ranged from a maximum of 0.3 percent of P in 1993/94 to a minimum of zero in 1994/95. This range of benefits can be compared to the
Williamson and Mahar (1998) have found a similar progress in financial liberalization for these countries. In fact, if we define a countries financial system to be largely liberalized in the year when significant progress has been made with respect to five of our six measures of financial liberalization, that is when _FLI_ takes value 5, we find a similarity with the years in which Williamson and Mahar (1998) consider a country's financial system to be largely liberalized. Table 4 presents this comparison.

The period under consideration has not only been characterized by liberalization of the banking sectors. Developing countries have implemented many different types of reform programs during this period under changing political climates. In addition to liberalization of the banking sector, one key component of financial reform in most developing countries has been liberalizing of the stock market. Table 4 shows the dates on which IFC considers the stock markets of these countries to be open to foreigners. The table suggests that stock market liberalization has preceded liberalization of the banking sector in most countries, Chile being the only exception.

Furthermore, progress in financial liberalization seems to be strongly correlated with improvements in the political climate of a country. If we use the ICRG political risk index as a measure of political risk, we find a correlation as high as 66% between the political risk rating and our financial liberalization index (see Table 5). The ICRG political risk index is constructed by Political Risk Service, ranges between 0 and 100%, and is decreasing in the level of political risk. The result suggests that political stability is a pre-requisite for financial liberalization.

We collect firm-level panel data from World Scope on firms in developing countries for the years 1988-98. Using panel data has certain advantages. First, it allows to differentiate across firms. As explained before, it is likely that firms are treated differently in a regime of financial repression (for example, due to directed credit programs). It is also likely that the effects of liberalization differ across firms according to their size and other
# cheap.

These findings are important for three reasons. First, they help understand TPOs’ structure, organization and objectives, and how those differ across income groups. It is, for example, surprising that TPOs focused mainly on established exporters. The existing literature has shown that returns to export promotion are higher along the extensive margins, rather than the intensive margin (Volpe Martincus and Carballo, 2008). In principle, established exporters already know how to penetrate foreign markets. Non-exporters face higher information barriers, and the literature has shown that this is an important margin on which TPO programs can exhibit high returns (Lederman et al., 2016). Arguably, the global disruptions in supply chains due to the pandemic have increased the information needs for established exporters. In contrast, the focus on medium rather than small firms is less surprising.<sup>1</sup> To be able to penetrate foreign markets, scale is important and small firms may fall short by definition in this aspect. Second, they help us understand the responses of TPOs during the pandemic and how differences in budget can help explain how TPOs adapted. For example, the fall in marketing activities expenditures in low-income countries is probably linked to the slow adoption of virtual tools and the absence of a recovery plan being put in place at the beginning of the pandemic. This in turn may be explained by the much smaller budgets of TPOs in low-income countries. Finally, they provide a first look at what the post-COVID TPO world may look like, with virtual tools being more prominent. This combined with the large demand for training on e-commerce could potentially lead to a more inclusive export composition with smaller firms being able to participate more easily not only in export promotion programs, but also in world markets through electronic platforms.

The remainder of this paper is organized as follows. Section 2 describes the sample that responded to the TPO survey. We divide our sample into four groups: high-income countries (17 TPOs), upper-middle-income countries (20 TPOs
Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Displaced persons after the earthquake.

Table 8 presents the characteristics of host households in the non-directly affected areas. After the earthquake, the percentage of households who hosted displaced people is 24.5% among the poorer and 63.8% among the richer. The average number of people hosted is 3.2 on average and does not vary much according to household wealth. Most of them are adults (2.3 out of 3.2). In June, about half of the hosted people were still living in host households.

**Table 8. Host households, by pre-earthquake quintile of wealth**

||||||Amonghost|households<br>|<br>||
|---|---|---|---|---|---|---|---|---|
|Pre-<br>earthquake<br>wealth<br>quintile|Number of<br>households|% Host<br>households|Number<br>of<br>persons<br>hosted*<br>(mean)|Number<br>of<br>children<br>hosted<br>(mean)|Number<br>of adults<br>hosted<br>(mean)|Number<br>of elderly<br>people<br>hosted<br>(mean)|Some<br>hosted<br>people<br>still<br>remain|All<br>hosted<br>people<br>still<br>remain|
|Poorest|151|24.5|3.3|1.3|2.0|0.0|8.1|
to understand how FSW were affected economically.

# **Study setting**

# _COVID-19 in Tanzania_

The Director-General of the World Health Organization (WHO) declared COVID-19 a global pandemic on March, 11 2020.<sup>14</sup> The first case of COVID-19 was reported in Tanzania five days later,<sup>15</sup> and was followed by WHO-recommended public health restrictions,<sup>16</sup> including government closures of schools and universities. Public gatherings were banned with the exception of churches and mosques, many businesses were temporarily or permanently closed, and travelers were required to quarantine for a 14-day period.

Government reporting of COVID-19 data ended in April 2020 after 509 cumulative cases and 21 deaths.<sup>17</sup> The late president, John Magufuli, declared in June 2020 that COVID-19 had been 2
may also pick up the variation in social acceptability of divorce. The study cross-checked this using data from the World Values Survey (WVS), available for 19 countries in the sample. In the sample with equal access, the average score for social acceptability is 4.28, while in the sample where women have weaker access to divorce, the social acceptability score is 3.53. Note that social acceptability is scored 1 to 10, where 1 indicates that divorce is never justifiable, 10 indicates it is always justifiable and the average across the (19) DHS countries for whom WVS data is available is 3.68 while the average over the UK and the US is 6.03.

11
# **Figure 10.  Part-Time and Temporary Employment in the EU Accession and Candidate Countries, 2002**

(as percent of total employment and of all employees respectively) <!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>part-time<br>10<br>fixed-term<br>8<br>6<br>4<br>2<br>0<br>percent<br>RO PL LT LV CY EE SI CZ HU BG SK ACC EU-15<br><!-- End of picture text --> Notes: ACC – EU accession and candidate countries. _Source_ : _Eurostat_ (based on 2<sup>nd</sup> quarter LFS data).

# **Box 2.  Self Employment in the Latvian Economy**

The share of self-employed among the employed the extent to which employment is based on one’s own capital resources and carried at one’s own risk. As self-employment was often restricted in socialist countries, the share of self-employment can be seen as a gauge of progress towards a market-oriented economy. Furthermore, the establishment of additional enterprises in expanding sectors of the economy is very important for job creation. Still, it is apparent that some of the self-employment in transition economies is a low productivity, semi-legal alternative to joining the ranks of the unemployed. This may be a rational response on the part of those who lose, or are in danger of losing, their attachment to the formal labor market.

The data in the table below confirm the relation between self-employment and the agricultural character of the regions. In Romania, more than 20% of employed workers are self-employed in agriculture. In Poland this fraction is 12.7 %, but in three regions of the country the share also lies at around 25 percent. In contrast, in Latvia the share of self-employment is only 5.5 percent.

The industrial sectors of