rafmacalaba/datause-encoder-data huggingface dataset:
# **6.3 Global Economic Activity during the 2020 Pandemic**

During the early phases of the pandemic, it was widely reported in the media and noted in scientific studies that NO2 was falling globally, and that this was indicative of declines in economic activity (Carlton, 2020; Venter et al., 2020; Liu et al., 2020; Mesas-Carrascosa et al., 2020; Zhao et al., 2020). We now use the composite estimate to understand to what degree these measurements overturn officially reported GDP during this unprecedented year. This is done for the full subsample of 81 countries for which the World Bank had compiled 2020 GDP at the time of writing.

Table 8 presents the results, broken down into instances of understatement (Panel A) and overstatement (Panel B). In each panel, the countries are listed from largest absolute magnitude difference to smallest. For each country, we compute a percentile of how many other years in the 2005-2020 sample had larger absolute differences; thus, smaller percentiles indicate that the difference reported for this country is particularly large, and may be deemed an unusually sizable error.<sup>20</sup> Finally, we also compute a normalized difference as though every country were grade C (*). Here we show just the top 10 countries and selected others in each panel. Full expansions of both the understatement and overstatement lists to show all countries, as well as a visual depiction of the extent of normalizations versus the baseline are illustrated graphically in Online Appendix B.4.

**Understatement.** Table 8 Panel A lists the largest instances of understatement, in order. Beginning at the top of the list with #1, we find that grade C Belize’s reported -15.05% contraction in GDP was not a fully accurate depiction of the downturn, which was likely more muted at -10.55%. The percentile of 0.00 indicates that this was the greatest difference between reported GDP and the composite estimate for Belize over the whole 2005-2020 sample. Peru at #3 is also notable because it experienced one of the worst per capita death tolls from COVID19 worldwide. Notwithstanding, we find the formidable -11.81% figure reported in GDP is a significant understatement. Similar conclusions can be made for many other countries down the list. Further down, at #31, is the UK, previously depicted in Figure 12. While the absolute difference is smaller than for some other less highly-related countries, the -0.41% difference for the UK still is a 0.00 percentile figure, meaning it was unusually large. Overall, there is systematic evidence of underreporting.

**Overstatement.** Table 8 Panel B lists instances of overstatement, from largest to smallest. Notable instances include #4 China, previously depicted in Figure 7. While China experienced

> 20Percentile may not be directly interpreted as a _p_ -value, but does provide a qualitative sense of how much of an outlier a given difference is. It is important to consider percentile in the interpretation of under- or overstatement because except in knife’s edge cases where GDP happens to coincide with proxy, a composite estimate will always technically imply a difference.

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inantly in the manufacturing sector. Third, by their nature, domestic workers in the service sector cater mostly to domestic demands and are not easily replaced by workers abroad. Hence, we conjecture that the presence of the labor substitution channel should be consistent with pronounced negative employment effects in the manufacturing sector.

Figure 3: Event Study Analysis: All Sectors <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019<br>MNCs Non MNCs<br>.08<br>.06<br>.04<br>.02<br>0<br>Log Employment -.02<br>-.04<br>-.06<br>-.08<br><!-- End of picture text --> _Notes:_ The figure plots event study analysis results from equation (1) where the dependent variable is the log of employment. The results are illustrated separately for firms with foreign affiliates (blue circles) and firms with no foreign affiliates (red circles). Coefficients along with 95% confidence intervals are illustrated. Standard errors are clustered at the sector level.

Figure 4 repeats the event study regression in equation (1) by sector. Panel (a) reports the results in the manufacturing. As in the previous analysis, we do not find differential pretrends between the two groups. More importantly, for the MNCs, the employment levels dropped by 2.2% in 2017, 4.9% in 2018, and 5.1% in 2019, respectively.<sup>23</sup> In contrast, we do not find any downward trends in employment after 2017 for both groups in the non-manufacturing sector. Indeed, none of the coefficient estimates in panel (b) is Taken the event results to the statistically significant. together, study appear support presence of a labor substitution channel in the manufacturing sector. The rest of the paper corroborates these baseline results in various dimensions.

> 23Note further that when we restrict our sample to the manufacturing sector, the coefficient of the year indicator 2017 becomes significant, unlike when we use all sectors.

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children and 61% of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8% of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signi�icantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11% of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents' educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020).

_Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy)_ <!-- Start of picture text --> 68% 68%<br>61% 59% 59%<br>50%<br>35% 36%<br>31%<br>26% 26%<br>23%<br>Would like to continue living Would like to move back to Feel strongly connected to Feel strongly connected to<br>in Italy Ukraine Italy Ukraine<br>Caregivers (N=283) Children between 9 and 14 years old (N=141) Children between 15 and 20 years old (N=96)<br><!-- End of picture text --> Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25% of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. The lack of certainty means children are trying to prepare for further studies (e.g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children.

Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy.

**The mental distress** resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16% of children and 24% of refugee caregivers reported experiencing psychological distress

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_ε_<sup>_hs_</sup> and _ε_<sup>_coll_</sup> are _iid_ and distributed Type I Extreme Value with a scaling factor of _σε_ . The individual chooses to attend college if As a result, the probability of college enrollment for an individual of type _j_ is Its complement, _P_<sup>_hs_</sup> ( _θ, y_ ) = 1 _− P_<sup>_coll_</sup> ( _θ, y_ ), is the probability of joining the labor force as a high school graduate.

# **6 Empirical implementation**

In this section we describe the parameterization and computational version of the model. We also describe the algorithm to compute model predicted values for a given parameter point.

## **6.1 Functional forms**

In the model, _t_ = 1 corresponds to age 18. Retirement age is 65, or _t_ = 48. Regardless of her educational attainment or when she joined the labor force, the individual accrues returns to experience (or becomes ”experienced”) from age 35 ( _t_ = 28) onward.

The utility of college students is given by where the need to meet the minimum consumption level, _<u>c,</u>_ might limit low-income students’ ability to enroll in college. To prevent this, we set _<u>c</u>_ equal to one million COP.<sup>12</sup> The utility of workers is given by We set _β_ = 0 _._ 04, which is consistent with an implicit discount rate of 4 percent, and assume _σε_ = 1.

The production function to complete classes has constant returns to scale in ability and where _α ∈_ (0 _,_ 1) is the elasticity of classes completed with respect to ability. Consistent with the model, we set _x_ ̄ = 20 classes. We set the minimum number of classes required to graduate,

> 12 Our chosen value for _<u>c</u>_ guarantees that, in our computational models, all students attain positive consumption if they enroll in college. We can think of _<u>c</u>_ as the minimum consumption guaranteed to college students through student subsidies, such as those for food and transportation in Colombia.

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Several studies directly examine whether an investment in green/clean technologies increase firms’ productivity or production costs (Hamamoto, 2006; Lanoie et al., 2008; Böhringer et al., 2012; Horvathova, 2012; Rubashkina et al., 2015; Tugcu and Tiwari, 2016; Palmer and Truong, 2017; Rath et al., 2019; Leoncini et al., 2019; Stucki, 2019; Sohag et al., 2021). Table 1 summarizes the methods used and the main findings and arguments of selected studies. Based on Japanese manufacturing industries, Hamamoto (2006) finds that the pollution control expenditures have a positive relationship with R&D expenditures and have a negative relationship with the average age of capital stock. It also shows that increases in R&D investment stimulated by the regulatory stringency have a significant positive effect on the TFP growth. Lanoie et al. (2008) find that investments in pollution-control technologies, as a proxy of stringent environmental regulations, resulted in modest, long-term gains in TFP in a sample of 17 Quebec manufacturing sectors. They show that this effect is more important in industries that are more exposed to international competition. Using a panel dataset of German manufacturing sectors for the 19962002 period, Böhringer et al. (2012) examine the relationship between environmental policies, including green/clean investment, and firms’ productivity growth. They do not find a positive impact of environmental investment on production growth. They, however, conclude that environmental policies or regulations should stimulate environmental investment to realize firms' productivity growth. Analyzing 79 global firms between 2007 and 2012, Palmer and Truong (2017) find a positive relationship between new green technologies use and firms’ profitability.

Using data from 25 OECD countries, Sohag et al. (2021) examine if the investment in renewable energy technologies promotes TFP. They find that a higher share of renewable energy in the production process significantly promotes TFP in the long run. This is not necessarily the case in the short run. The increased human capital, innovations, and trade openness augment the positive relationship between renewable energy investment and TFP. However, the paper does not explain the channels through which increased renewable energy improves TFP. Similar effects of renewable energy on TFP are also reported by Rath et al. (2019) when they examine the relationship between the growth of renewable energy and TFP using panel data of 36 countries for the 1981–2013 period. They show that an increased TFP is associated with an increased consumption of renewable energy, whereas the consumption of fossil fuels is negatively associated with TFP. The findings are, however, vary across different regions. Since increased consumption of renewable energy reflects increased investment in renewable energy technologies, the findings 11
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**Distribution of the income-poor population.** Figure 4.1 shows how the population in the lowest welfare quintile is distributed across settlement areas.  Two patterns should be noted.  First, the urban poor are overwhelmingly located in secondary cities in all countries, except for Armenia, Azerbaijan and Georgia where 20-30 percent of the poor are found in the capital city.<sup>20</sup> Second, the share of urban poor outnumbers the share of rural poor in six of the ECA countries for which we have data (Hungary, Armenia, Azerbaijan, Georgia, Belarus and Russia) and is equal in a seventh, Bulgaria.  In the case of Hungary, Belarus and Russia, the predominance of urban poverty results from the high level of urbanization since the incidence of urban poverty is lower than that of rural poverty.  However, in Armenia, Azerbaijan and Georgia both the incidence of urban poverty and the share of the urban poor exceed that in rural areas.

**Figure 4.1. Share of poor in capital cities, other urban and rural areas in ECA countries**

<!-- Start of picture text --> % rural other urban capital<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>Hungary Lithuania Poland Albania Bulgaria Kosovo* Moldova Romania Serbia Armenia Azerbaijan Georgia Kazakhstan Kyrgyz Republic Tajikistan Turkmenistan Uzbekistan Belarus Russian Federation<br><!-- End of picture text -->

* In Kosovo, households were classified as urban or rural.

Source: See Table 2.1

**Characteristics of the poor.** Two features which the household surveys in ECA find almost invariably to be associated with poverty are low education of the household head (less than secondary school completion) and large family size.  These characteristics can also be examined separately by settlement area to separate the effects of location.

The incidence of **low education among household heads** is found to be significantly greater in rural areas than in urban areas in all the countries with available data, as is the pattern worldwide. However, the rate of poverty among uneducated household heads is often greater in the urban settlements than rural, as seen in Figure 4.2. A lack of education is most associated with poverty in the secondary cities of Moldova, Azerbaijan, Georgia, Tajikistan and Belarus, the mixed areas of Bosnia, and in the capitals of Serbia and Armenia. This outcome presumably reflects the fact that uneducated household heads have less capability of competing for well-paying urban employment, in addition to lacking access to farm

> 20 And possibly in Bosnia as well, however, since the stratification used in the survey (urban, rural and mixed) was not comparable with that used in other ECA countries (capital, other urban and rural) so Bosnia was excluded from this discussion.

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<!-- Start of picture text --> Dependent Variable: Nighttime Lights<br>0.2; dil<br>0.0<br>-0.1<br>Dependent Variable: Urban<br>0.15<br>O<br>in 0.10 Road Type<br>$ $ All<br>(0)<br>‘O 50 km/h<br>= 0.05 | | | | $ >=50 km/h<br>4 0.00<br>O<br>-0.05<br>Dependent Variable: Cropland<br>0.000 1<br>-0.050<br>-0.075<br>-10 -5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
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factors including state regulations governing budget and funding practices, controls for plan size, type and in some cases portfolio composition. In this section the disturbance terms are assumed to be distributed normally with zero mean; in Section IV this assumption and others, are discussed in more detail.

Evidence on the Investment Performance of State and Local Pension Systems In 1990, the annual investment yield reported by the 168 PERS systems was 7.74% (Table 6). This compares favorably with market data showing a +6.8% return for securities that year, and with the 1990 inflation rate of 6.1%. However, not all plans performed this well -- one plan reported a -5.5% return that year, while at the other extreme a plan reported a yield of +24.5%. **12** This range (see Figure 1) is almost certainly due to different portfolio composition patterns across the plans; in the market as a whole, the +6.8% return for securities that year was offset by an average -3.2% yield on bonds, which suggests the importance of holding constant the portfolio composition of pension plans when comparing their investment yields. A narrower frequency distribution of pension yields characterizes YR5ROR, returns averaged ove; the period 1986-1990 (see Figure 2). Across the 128 plans reporting the figure averaged 11.58%, with the lowest return reported of -2.54% and the highest being 31%.'3 The overall mean was lower than the annualized return on bonds for the same period of 13.5%, but exceeded the average stock return of 10.1% as well as the annualized inflation rate of 3.3%. The pattern of RETURNSD is sketched in Figure 3: this variable increases when there was a larger gap between the 1990 one-year return and C!ivia S. Mitchell - Public Pension Govemance and Performance
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``` ``` In the short-term, too, Kenya seemed to illustrate the potency of ‘just enough governance’. In 2001, the country witnessed the electoral defeat of a corrupt long-term incumbent, Daniel Arap Moi, and the promise of far-reaching constitutional reform. Instead, constitutional reform stalled – and, within three years of the exit of Moi, the country was subjected to a new round of high-profile allegations of corruption against the successor government. Nonetheless, the ouster of Moi proved sufficient to trigger improved economic performance, with growth -- which had stubbornly remained below 3% prior to 2003 -accelerating to 6% by 2006. Yet the turnaround seemingly was shortlived, with renewed political turbulence overtaking the country in the aftermath of bitter disputes over who won a 2008 presidential election.
``` ``` The Bangladesh, Kenya and Thailand examples point to a puzzle vis-àvis a ‘just enough governance’ development strategy: what is the longrun trend – and what is the short-run ‘noise’? Is the trend sustainable development -- with governance weakness the ‘noise’ in the system? Or is the seemingly strong growth simply a short-term bubble – with governance turbulence signaling the hazards ahead? Certainly, HPRs empirical analysis confirms that many ‘growth accelerations’ are followed by subsequent reversals. Indeed, natural resource driven boomlets in particular are notorious for the capacity destruction they can inflict on fragile public sectors – as the prospect of huge rents induces a country’s elites to undermine already fragile institutions of accountability.
``` ``` Yet for all the caveats, growth in each of the three examples signals that, at least in some circumstances – those where the underlying growth drivers are strong and/or capacity and institutional constraints are not overwhelmingly constraining -- ‘just enough governance’ would appear to be a viable short-term development ```