rafmacalaba/data-use-mentions-extended huggingface dataset:
level of carbon tax would reduce developed countries’ GDP by 2.4 percent and global trade volume by 2 percent. The economic costs vary significantly across countries, with heavy burden on fossil fuel intensive economies, such as Russia, Australia, United Kingdom and the United states. On the other hand, global emissions in 2030 would be still more than 15 percent higher from the current level.

The paper is organized as follows. Section 2 briefly describes the model following by discussions on the impacts on CO2 emissions and emission intensities of various countries and region due to the policy instrument implemented to reduce the intensity gaps in Section 3. Section 4 presents the impacts of the policy instrument on economic outputs and international trade. Finally, key conclusions are drawn in Section 5.

# **2. Methodology and Data**

We used a multi-regional, multi-sector, recursive dynamic CGE model for the purpose of this study<sup>1</sup> . The basic data needed for the calibration of the model is derived from the GTAP 7.0 database. The main reason for using the GTAP database is that no other comprehensive global database required by this study exists. Moreover, most CGE models simulating climate change policies use the GTAP database, and the use of this database in our study helps compare our results with those of others. The original GTAP database provides information for 113 countries and 57 commodities and production sectors. We further worked on the database to have only 28 sectors and 25 countries/regions as needed by our model.

Each of the 28 sectors is depicted by a set of nested constant elasticity of substitution (CES) production functions (see Figure 2). At the top tier of the production structure, firms in each country/region minimize their production costs by choosing an optimal combination of the aggregate non-energy intermediate input (ND) and the composite of value added and the aggregate energy input (VAE). On the left hand side of the second tier of the nested production structure, a
The welfare expressions (14) and (15) form the basis for our quantitative welfare analysis of trade reform. In order to conduct such an analysis, we need information about the income parameters _μ_ , _σε_<sup>2,and∆</sup><sup>_σ_andthepreferencesparameters</sup><sup>_β_and</sup><sup>_γ_.Ourempiricalanalysis</sup> provides estimates of the income parameters. We estimate an average value of a quarterly variance, _σε_<sup>2,ofof</sup><sup>_._008(averagedacrossindustriesandover time), andthisisalsothevalue</sup> we use in all welfare calculations reported below. Similarly, we choose a quarterly growth rate _μ_ = _._ 005 to match the average growth rate in aggregate real income in Mexico over the relevant sample period. For the preference parameters, we choose a quarterly discount factor of _β_ = _._ 99 and a degree of risk aversion of _γ_ = 1 (log-utility) for the baseline economy. These values for the preference parameters are in line with the values used in the macroeconomic literature (Cooley (1995)). However, we also report the welfare results for a higher degree of risk aversion ( _γ_ = 2).

We conduct the following exercises. Starting from a tariff level of _τ_ = _._ 10, which is roughly the average tariff level in our data set, we consider the welfare consequences of reducing the tariff level to _τ_<sup>_′_</sup> = _._ 05. Our empirical analysis in section III suggests that this tariff reduction has two effects for industries with high import penetration. First, there is a short-run effect that leads to an increase in income risk for one year (four quarters), and in this section we evaluate the welfare cost of this short-run effect. Second, there is an “interaction effect”, and we report the welfare cost corresponding to this effect as follows. We
a sample of countries that were attacked by removing countries representing the axis powers.<sup>9</sup> The results of this estimation are shown in row (3) of Table 3. The coefficient on the losses during WW2 is still significant and inversely related to the deaths from COVID-19, although the power of its significance declines.

We use the DFBETA influence statistics (Bollen and Jackman 1990) to identify the influential outliers in our regression. The method measures the difference, scaled by the estimated standard error of the coefficient, between the regression coefficient when the _i_<sup>th</sup> observation (country) is included and excluded.<sup>10</sup> (The Appendix provides the scatter plot of the values of DFBETA statistics.) Based on that criteria, Belarus (BRL) and Armenia (ARM) have a strong influence on the result of our estimations. The estimates of our main specification, excluding these countries from the sample, are shown in row (4) of Table 3.

We also conducted a falsification test to see if our results might be driven by unobservable confounding factors. The idea behind the falsification test is to demonstrate that the effect does not exist in the context where we expect no effect (see, for example, Rothstein 2010). We use the deaths from cardiovascular diseases, which predominantly affect the elderly population, as a placebo outcome. We assume that cardiovascular deaths are not affected by the levels of social capital and trust in society. Another measure of placebo outcomes is deaths from influenza and pneumonia during the season of 2017/2018. These diseases are similar to COVID19 in terms of contagion risks, but no social distancing and NPI were implemented to stop the infection despite the fact it killed more than 650,000 people worldwide in 2018 (Paget et al. 2019). We assume that influenza and pneumonia deaths are not associated with social capital and levels of trust in society that are required to internalize the negative externalities of
55

|**Country**|**Types of fuel andprices**|**Source**|
|---|---|---|
|Malawi|Maximum prices set by government.|meramalawi.mw|
|Malaysia|95 RON gasoline in Jan 2012 (introduced in May 2009),<br>92 RON gasoline in Jan 2009 (discontinued in May<br>2009), diesel sulfur limited to 0.3 percent in Jan 2009<br>and to 0.05 percent in Jan 2012. LPG sold in 10-, 12-,<br>and 14-kg cylinders has the same unit price.|www.caltex.com/my/|
|Mexico|87 octane index gasoline. LPG prices in Districto<br>Federal.|sie.energia.gob.mx.|
|Mongolia|92 RON gasoline.|News reports|
|Morocco|95 RON gasoline, diesel with 0.005 percent sulfur. LPG<br>sold in 3-, 6-, and 12-kg cylinders has the same unit<br>price.|News reports of government price<br>announcements.|
|Mozambique<br>Namibia|Maximum prices set by government.<br>93 RON gasoline. Prices in Walvis Bay averaged for the<br>month of Jan.|www.portaldogoverno.gov.mz.<br>www.mme.gov.na|
|Nepal|88 RON gasoline and LPG in 14.2-kg cylinders. Prices<br>in Kathmandu.|www.nepaloil.com.np|
|Nicaragua|87/88 RON gasoline, LPG in 25-pound (11.4-kg)<br>cylinders. Monthly averages of weekly price surveys<br>conducted at 74 service stations in Managua for<br>gasoline, diesel, and kerosene, and monthly averages of<br>weekly official price ceilings in Managua for LPG.|www.ine.gob
Asia, and Pacific_|||
|Europe and Central Asia|0.339***|0.070***|
||(0.017)|(0.020)|
|Latin America and the Caribbean|0.237***|-0.011|
||(0.025)|(0.026)|
|Middle East and North Africa|0.426***|0.146***|
||(0.034)|(0.033)|
|North America|0.142***|0.140***|
||(0.044)|(0.040)|
|Sub Saharan Africa|0.040|0.070|
||(0.048)|(0.057)|
|Observations|7,427|7,427|
||0.1227|0.0368|

Note: *** 1%, ** 5%, and * 10% level of significance.

Lastly, the cutback in consumption and feelings of insecurity and loneliness are likely correlated with the heightened anxiety and depression experienced by individuals. The analysis of the full PHQ-4 scale shows that compared to cismen, the other groups experienced a higher rate of moderate to severe psychological distress during the pandemic. More than half of the transgender people reported feeling moderate to severe psychological distress over the last 2 weeks from their survey interviews (Figure 6). For non-binary people, an equally high percentage, 45 percent, experienced moderate to severe 22
While it is impossible to blind youth to their interventions, we attempted to minimize the likelihood of Hawthorne/John Henry/experimenter demand effects by not telling subjects that this was an experiment, or that alternative interventions were being offered in other settlements. The baseline survey was described as a research study aimed at understanding the desires and needs of Gambian youth. We asked both the Senegal and vocational training group participants why they thought the intervention was being offered. The two main reasons seen for the interventions were to provide them with more job opportunities, and secondly to reduce backway migration.

# **3.2 Ethical considerations**

Although deterring irregular migration is desired by European countries, revealed preference would suggest that the migrants themselves believe they are improving their lives by making this journey, raising the question of whether policy efforts to deter migration are desirable from a development perspective. There is indeed a vast literature showing that international migration in other settings has been more effective at increasing the incomes of poor people than almost any other development policy (e.g. McKenzie et al, 2010, Clemens et al, 2019). However, there are reasons to believe that revealed preference choices may still be suboptimal if potential migrants are misinformed about the severe risks involved in the migration journey. Indeed, a competing ethical consideration is the risk of accidentally encouraging more youth to attempt this journey and exposing them to these dangers. Finally, an additional ethical consideration arose due to the COVID-19 pandemic, which affected when it was safe to conduct surveys and offer in-person vocational training.

Our general approach to dealing with these ethical issues was to consider interventions that provided youth with more accurate information with which they retained their agency to make informed decisions, and to expand the set of potential opportunities they had available. We sought considerable local feedback on the design of the interventions to ensure they were accurate and appropriate for the context. Consultations with local organizations were done by the Gambian coauthor of this paper, and we worked with Gambian youth from the
<!-- Start of picture text -->
1.00%<br>Coefficient Confidence interval (90%)<br>0.50%<br>0.00%<br>-0.50%<br>-1.00%<br>-1.50%<br>-2.00%<br>-2.50%<br>All workers Males Females Low-skilled High-skilled<br>Effect on informality (percent)<br>2005-2010 2005-2013 2005-2016 2005-2010 2005-2013 2005-2016 2005-2010 2005-2013 2005-2016 2005-2010 2005-2013 2005-2016 2005-2010 2005-2013 2005-2016<br><!-- End of picture text -->

**Notes:** Author's estimates using data from Bangladesh LFS and UN COMTRADE. The confidence intervals are set at the 90% level. These graphs are based on 2SLS regression computed to estimate effect of an increase in exports on real wages and informality. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).

Although this effect is still significant for the time-span 2005 to 2013, the magnitude of the effect decreases substantially to 658 _takas_ . In turn, in the most prolonged period of analysis, 2005 to 2016, impacts dissipate, not only diminishing in magnitude of the coefficient but also becoming not statistically significant. Looking at manufacturing workers exclusively, we observe the same effects: a positive and decreasing effect on wages over time, notwithstanding that the only statistically significant results are those for the 2005 to 2013 period.

We also analyzed the effects of an increase in trade exposure on textiles and apparel workers’ outcomes, since the Bangladeshi export basket is highly concentrated in the garments industry. We found that the trend of dissipating effects over time holds, but no effect is statistically significant. A plausible explanation is that dissipation occurs very quickly; it may take less than five years for the expanding apparel industry to attract workers from other industries. In this case
.004)|(0.004)|(0.004)|(0.032)|(0.030)|(0.032)|
|US Real Interest Rate|0.017|0.015|0.124|1.806*|1.924**|0.698|
||(0.110)|(0.112)|(0.225)|(0.960)|(0.931)|(1.855)|
|Initial log GDP|0.146|0.270|0.407|1.650|1.668|2.296|
||(0.218)|(0.268)|(0.247)|(1.303)|(1.531)|(1.591)|
|Region FE|NO|YES|YES|NO|YES|YES|
|Initial Decade FE|NO|NO|YES|NO|NO|YES|
|Spells|74|74|74|74|74|74|
|Log-_L_|-189.696|-186.990|-176.881|-331.143|-328.817|-324.852|
|Adjusted R<sup>2</sup>|0.001|0.029|0.200|0.222|0.235|0.256|

_Notes_ : All models include a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables
incidence of these cases is believed to be relatively rare, any resulting bias should be negligible.

The second necessary simplifying assumption is that the arriving members have the same probability of selection, on average, as those original members living in the household to which they come. This would not be true on a case-by-case basis but would be true in the aggregate. With these simplifying assumptions, the household probability of selection is increased by a factor of 2 for all households that have new members arriving from other households, whether the receiving household is a parent or splitoff, and regardless of if they have been selected for tracking. (Note that children born since the previous round of data collection was completed are not taken as new members from this perspective because they could not have been selected in round 1.) This takes into account the fact that they could have been selected in two ways, and assumes that the probability of selection is equal.

A limitation of the panel methodology used in the LSMS-ISA surveys is that the represented round 2 population is not completely representative, as it does not include those solely from outside the original universe of selection who formed households. In this case, it would not include households completely formed by those leaving an institutionalized population, or immigrants from outside the study country. Inclusion of such households would necessitate refreshing the sample with new households. However, in cases where this type of in-migration into the household population is relatively rare, the represented population is close enough to the actual population to permit the desired estimates.

# _5) Pooling_

> 8 One example of a rare circumstance where the exact calculation would be possible would be if two (or more) eligible members of existing separate panel households merged into a single household, for example if the children of two panel households in the same village married and established a new household.  In this case the probability of selection for both the husband and wife would be known, and the exact probability could be calculated.

6
The CNRS data make use of annual retrospective employment histories dating to 2000, when the first round of the survey was implemented and of detailed monthly employment histories for the 24 months preceding 2009.<sup>9</sup> Information on employment history was used to calculate the counterfactual level of employment for 2009. The research team used estimates of off-farm employment trends (based on data from 2005 through the second quarter of 2008) and monthly employment data from the 12 months before the crisis and annual 2005–07 growth rates. The results of the extrapolation exercise and actual off-farm employment rates are shown in Figure 6.<sup>10</sup> Absent the financial crisis, Huang et al. (2011) argue that 57.8 percent of the rural labor force would have been working off-farm but that instead only 51 percent had off-farm employment. By April 2009, a gap of 6.8 percent had opened between the counterfactual and the actual share of the rural labor force working off-farm. At the national level, that percentage would imply 279 million rural residents were actually working in nonagricultural activities in September 2008, whereas, under the existing trends and seasonal adjustments, 301 million might have been expected to be working off-farm in April 2009. In fact, the analysis estimates that only 265 million rural individuals were working off-farm in April 2009, implying that the net effect was a loss of 36 million jobs. This number is consistent with a drop in nonagricultural employment of 12 percent or, in terms of the entire rural registered workforce, a decline of 6.8 percent in the ratio of business-as-usual to actual share of those working outside of agriculture **.** The net impact, however, differs from the number of rural workers who were actually laid off. That number cannot be deduced from this net gap. A number of factors affect the gap between the business-as usual scenario in April 2009 and the actual level of employment at that time. First, the gap includes those who were laid
FDI is taking place, especially among the OECD countries (Reinicke 1996).

The above results suggest a regional effect of technology diffusion linked to trade flows. The question then arises as to whether these effects are determined by the same factors which shape regional trade patterns. Here the evidence is still very limited. Sjoholm (1996) looks at the role of geographical proximity and international trade in the international transfer of knowledge in the case of Sweden. Geographical proximity may be important to the extent that knowledge spreads through informal contacts and these may be limited by distance, as Breschi and Malerba (1996) also find. Distance is found to be related to technology transfers measured by the number of patent citations. However, an extreme bound analysis finds that only trade is a robust explanation of technology transfers.

Baldwin and Seghezza (1996) consider the effect of regional agreements on growth induced by technology accumulation: i.e. they look at the reverse causality between regional patterns and technology with respect to the literature reviewed above. They find "weak evidence that EU membership allowed member states to enjoy a higher level of (TFP) ... than they would have otherwise. In other words EU membership led to knowledge-led growth effects" (p.<sup>1</sup> 8) In the above studies technology is measured directly through cumulated R&D expenditure or indirectly through TFP. Archibugi and Pianta (1992, 1994) look at technological convergence using indicators of technological activity such as R&D/GDP and patents/exports. They find that production in advanced countries has become more technology intensive over the last two decades while the group of countries capable of innovating at the frontier of technological specialization has widened. However, they also report increasing divergence among the group of top R&D investors. Finally, they find that size is important in defining the direction of 5
6 households, offer them cheaper financial instruments to manage their risk, or finance the expansion of more firms that would be using their skills. The more a financial structure would be bank-oriented the narrower income inequalities and the lower poverty would be.

Empirically, most studies looking at the structure of the financial system tend to suggest, however, that it is irrelevant. Beck and Levine (2004), Levine and Zevros (1998), and Rousseau and Wachtel (2000) use indices of stock market development and bank credit to the private sector to measure the finance-growth link. They show that that both banking sector and stock market developments positively influence economic growth. Looking at firm level data, Beck and Levine (2002) and Levine (2002) come to the conclusion that financial structure per se does not importantly explain industrial performance or the creation of new firms.

Arestis, Demetriades, and Luintel (2000), nevertheless, suggest that banks could play a more important role. Using quarterly data and applying time series methods to five developed economies, they show that, while both banking sector and stock market development explain subsequent growth, the effects of banking sector development is substantially larger than that of stock market development. Honohan (2004) shows that bank credit reduces poverty while market indicators (capitalization and turnover) do not seem to have any significant effect.

# **III.   EMPIRICAL ANALYSIS**

# **A.   Sample**

We compiled data for a sample of 47 developing economies over the period 1984-2008. Developing countries are defined as countries classified by the World Bank as low- or middle-income countries. Excluding developed countries from the sample reduces sample heterogeneity. The sample size and the period of study are limited by the availability of data on poverty and stock market indicators. To smooth short-term fluctuations of macroeconomic variables and take into account the fact that yearly poverty indicators may not be available for many countries, we averaged the data over a 5-year period, which gives us up to five data points per country
cities, 41.90 for other urban areas, and 32.98 for rural areas. For level 2 eligibility, which we use to determine Sisben eligibility, the respective scores are 39.32, 43.63, and 35.26.

The third criterion revolves around income. It establishes that, in the case of one-member households, a beneficiary’s income may not exceed half of a minimum wage. For beneficiaries living with other individuals, the entire household income may not exceed one minimum wage. In the data described below, we are able to observe how all three criteria would function; yet, we consider a potential beneficiary eligible for the program if the age and Sisben score requirements are met. We have decided not to impose the income criterion because it likely suffers from substantial measurement error in our data. The income criterion is also subject to considerable changes over time. We believe, moreover, that, given the difficulty of sorting out this criterion within the Sisben score, it is also the criterion that is most problematic to apply in practice. In filling out the Sisben questionnaire, respondents have perverse incentives to underreport income to qualify for various government assistance initiatives. Adding support to these concerns, our own data show only a limited overlap between the Sisben scores and households that are eligible based on income. The survey data employed and described in more detail below is designed to allow construction of Sisben scores.

In addition to eligibility, the program also employs a prioritization strategy, mostly based on age, plus other criteria, such as disability status, number of dependents, and so on. The strategy represents an attempt to focus the program on the population 65 years of age and older. Every year, each municipality is given a certain number of slots for new beneficiaries. If the number of eligible petitioners exceeds the number of available slots, the prioritization strategy is intended to determine who receives the benefit.

# **4. Program Targeting**

According to the country’s Ministry of