rafmacalaba/data-use-mentions-extended huggingface dataset:
sub-option `userpath("` _string_ `")` ; _ii_ ) the name of the `.csv` file containing the table, via the sub-option <u>`tablename(`</u> _string_ `)` ; the name of the file containing the country list, via the sub-option `countrylistname(` _string_ `)` .

Notice that the table’s `csv` file must contain only one matrix of dimension ( _G × N_ ) _×_ ( _G × N_ + _G × U_ ), where _G_ is the number of countries, _N_ the number of sectors and _U_ the number of uses (i.e. consumption, investment, etc.). See the `icio` help file for more details.

# **3 Supply, demand and supply-demand linkages**

ICIO tables can be used in combination with long-established accounting relationships (Leontief, 1936) to measure the net value of production (GDP) of a country/sector, the value of final demand in a given country/sector and to pin down the links between the country-sector where the value-added originates and the market where it is absorbed in demand.

In Appendix B we show how to retrieve supply, demand and supply-demand linkages, i.e. GDP by country/sector of origin and/or destination, from ICIO tables while in Section 3.1 we describe how to compute these measures in `icio` , providing also some examples.

## **3.1 Implementation: Supply and demand with** `icio`

The `icio` command can be used to retrieve the GDP (i.e. value-added) produced by a given country or industry (origin of value-added) by specifying the option `origin()` , to measure the final demand in different countries and sectors (destination of the value-added) by specifying the option `destination()` , or a combination of the two (both `origin()` and `destination()
_</sup> _p <_ 0 _._ 01.

# **8 Conclusion**

Annual DMSP nighttime light data have already contributed greatly to deepening our economic understanding (Donaldson and Storeygard, 2016). With the availability of monthly VIIRS data at an even finer spatial grid and with greatly improved measurement of low-light areas, nighttime lights are becoming an even more important source of information.

In this paper, we initially provided some stylized facts about global VIIRS nighttime light data at quarterly frequency. We documented the need to consider a few technical details when aggregating the data at the country level, including its ge27
To assess the consequences for poor households, we overlay our results with an administrative map of Bangladesh and compute mean percent changes in fish species for 110 _upazila_ s that lie within the region. We construct an impact indicator that weights these results by _upazila_ poverty populations identified using two bounds: an upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics; and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line. Our calculations encompass 54 cases (27 scenarios, 2 poverty definitions). We find that potential impact rankings are highly correlated, so we use the mean rank across 54 cases as a robust general impact indicator. This enables us to produce rank-orderings for 76 _upazila_ s that lose fish species and 11 _upazila_ s that gain species (23 _upazila_ s exhibit no change). Among the 20 _upazila_ s with top-ten loss and gain indices, 19 are in Khulna and one (a species loss case) is in Barisal.

Our summary results provide striking evidence that projected aquatic salinization may have a strongly regressive impact on poor households in the Sundarbans region. For both poverty definitions, we find that poverty populations in _upazila_ s that lose and gain species have a ratio of approximately 6:1. Given that fish is the main source of protein in the diet of 43.2 million poor people, and the chronic as well as acute malnutrition levels, as indicated by statistics on wasting and stunting of children in Bangladesh, are higher than the WHO’s thresholds for public health emergencies,<sup>18</sup> our finding is serious and emphasizes the importance of mainstreaming climate change in relevant policies, action plans and programs in the country.

As we note in the paper, we must attach one strong caveat to our results. Our measure of potential risk is simply the change in species count because we do not have good evidence on other

> 18 Government of Bangladesh: Strategic
the Iran-Iraq war between 1980 and 1988, the 1986 South Yemen civil war, the Gulf war in 1990 and 1991, and the Iraq war in 2003 following the United States invasion. The identifying assumption is that conflict occurrence in these countries constitute a strong predictor of return migration. The number of active armed conflicts is both country and year specific and comes from the Monadic Conflict Onset and Incidence data set of the Uppsala Conflict Data Project (UCDP).<sup>19</sup> When estimating the reduced form equation (2), equation (3), and equation (4), our regressions include the same vector of individual controls ( _Xi_ ). In these equations, _Xi_ additionally includes decades of migration dummies. In our CMP model, the errors terms _v_ , _ε_ , _ω_ , and _τ_ are allowed to be correlated through a multidimensional distribution.

# **4.2 Discussion on the Exclusion Restrictions**

In this section, we discuss issues pertaining to the validity of our empirical model presented in section 4.1. First, in section 4.2.1, we discuss the exclusion restriction of oil prices and present evidence supporting the orthogonality of oil prices to both illegality and return decisions. Second, in section 4.2.2, we discuss the evolution of Egypt’s diplomatic relations with other Arab countries and provide evidence that migration is not influenced by the bilateral relationship of countries. Third, in section 4.2.3, we discuss concerns related to the exclusion restriction of conflict occurrence and how we address them empirically.

## **4.2.1 The Exclusion Restriction of Oil Prices**

The exclusion restriction of oil prices is that oil prices affect wages in Egypt only through temporary migration decision. Since Egypt is a non-dependent oil economy, the exclusion restriction holds if oil prices at the age of 25 are not correlated with employment and wages in Egypt. To provide support to our identifying assumption, in Figure 1 in Panel A,
Bank in Vietnam. The expenditure poverty line is designed to measure the price of a consumption basket that meets pre-specified nutritional needs and essential non-food expenditures that include clothing and housing. For 2006, the expenditure poverty line was equal to 2,560,000 VND/person/year (in national real terms).

The income poverty line is set by MOLISA. It equals 2,400,000 VND/person/year (200,000 VND per month) for the year 2006 (in national real terms). When this poverty line is applied to household income data from the 2006 VHLSS, however, we obtain a 4
the year 2012, which is available from the BEA. From the latter we impute a _K × K_ matrix by using product-sector concordance tables from UNCTAD. The obtained matrix will serve to compute input-output-repercussion indirect effects of trade policies on sensitive products.

# **3 Effects of pandemic-related trade policy measures on trade costs and imports**

We are interested in distinguishing between direct and indirect effects (i.e. through imported inputs) of the trade policy measures of interest during the time period January 2018-December 2021. For this, let us use _gjk,t_ to denote the import-value equivalent for importing country _j_ , product _k_ , and month _t_ . We will establish later how to estimate it. We postulate that _gjk,t_ is linearly composed of the direct effects associated with the four import- and (partner-)export-related policy measures which are collected in ∆ _jk,t_ , and with as many corresponding indirect effects.

Suppose a _JK × JK_ input-output matrix _H_ exists. This matrix is obtained from _H_ = _O_<sup>_⋆_</sup> _◦ M_<sup>_⋆_</sup> , where _M_<sup>_⋆_</sup> is a _JK × JK_ (block-diagonal) expenditure-shares matrix consisting of _J × J_ blocks of expenditure shares for each product _k_ (Eaton and Kortum, 2002; Caliendo and Parro, 2014). For this, start with _M_ , a _JK × J_ imports matrix. The diagonal elements (expenditure on domestic produce) can be predicted from exporter and importer fixed effects in a gravity equation per product, assuming domestic sales frictions are zero. The matrix _M_ is then row-normalized (dividing by aggregate expenditure by product) to obtain _M_<sup>_⋆_</sup> . _O_<sup>_⋆_</sup> , a _JK × JK_ input-output matrix is obtained after row-normalizing _O_<sup>ˆ</sup> = _COC_<sup>_′_</sup>
develops a new toolkit of methodologies for value-added accounting of trade flows at the country-, bilateral- and sectoral-levels by using InterCountry Input-Output (ICIO) tables. The measures we propose can be used to properly address important empirical questions, such as those mentioned above, and to evaluate countries’ and sectors’ participation in GVCs. We argue that different empirical issues require distinct methodological approaches, and provide a mapping of the questions under investigation and the accounting methods needed to address them.

ICIO tables, like the WIOD, the OECD-WTO TiVA or the EORA, combine national input-output data with detailed trade statistics to trace cross-country and 2
and the number of beneficiaries is lagging by a million (also compared with 2001).

Important elements of the PET include extension to urban areas in response to job destruction in manufacturing and services, and support for the unemployed. The ability of PETS to reach urban — areas and the unemployed depends on Ministry of Labor ( _Secretaría del Trabajo y Previsión Social_ STPS), which has played a coordinating role among all the secretaries and agencies administering PETS since late 2008. Preliminary evidence seems to show that only one of the implementing agencies, the Ministry of Social Development ( _La Secretaría de Desarrollo Social_ —SEDESOL) has been able to reach the unemployed and the urban areas. Perhaps because of its experience in the expansion of other social programs in urban areas (such as _Oportunidades_ and _Estancias Infantiles_ ), SEDESOL has shown a pattern of allocation of PET resources that is correlated with the level of unemployment and the size of the urban population.

In addition, preliminary evidence from administrative data shows some association between PET budget allocations and poverty, unemployment, and urbanization. However, these variables are highly correlated so that conditional associations are needed to ascertain if PET is reaching the 35
# **4 Data**

We employ an unbalanced panel of 35 European countries where annual wind installation and incentive policies for wind investment are observed during 1991-2010.<sup>18</sup> The information on renewable policy design was obtained mostly from IEA’s renewable policy database, and cross-checked with government websites, legislative texts and other related publications. Our review indicates a great deal of variation and complexity over the design of FiT policies<sup>19</sup> . First, FiT rates can be defined very specifically according to the installation location and size. For instance, Greece offers separate rates for mainland vs.island wind installations. Many countries restrict FiTs to installations below a certain size (such as below 20MW), or have size-specific rates. Given the nonuniformity of FiT rates, we collect generator-level wind installation data from a global wind farm dataset<sup>20</sup> to match wind installation with specific policies.

Second, policies sometimes have built-in digression rates so that FiT rates for existing contracts decrease over time. Four countries in our sample, i.e. Austria, France, Germany and Switzerland, offer stepped reductions in tariff for existing contracts but not until at least after 10 years in the contract. For instance, in France, wind tariffs will be reduced by 2 percent annually starting from year 11. We did not specifically estimate the effect of rate digression on current investment, recognizing that ignoring future rate reduction may inflate actual FiT remuneration. However, since price adjustment occurs far into the future, the present value of the price reduction is likely to be small. We also exclude these four countries from the sample for robustness check. The main results do not change.

Third, although in most cases FiT rates are fixed over the contract length (standard FiT), rates can also vary annually according to electricity market price changes or other factors (variable FiT). For example, the first type of FiT enacted in Germany in 1991 involves an energy
)_|_M-F_|
|**Gd 4**|Mathematics|385 (108)|412 (91)|_-26_|496 (103)|504 (97)|_-8_|
|**rae**|Science|373 (116)|434 (97)|_-60_|483 (101)|518 (96)|_-35_|
|**Grade 8**|Mathematics|385 (80)|403 (74)|_-17_|493 (101)|507 (99)|_-14_|
||Science|408(91)|455(79)|_-47_|480(100)|521(96)|_-42_|

_Source:_ TIMSS 2019 data reported by Mullis et al. (2020). NALO data are authors’ own calculations. All differences between genders have been rounded.

### _4.1.2 Descriptive statistics of predictor variables (TIMSS 2019)_

Table 3 shows the percentages of boys and girls in grades 4 and 8 in Saudi Arabia across the TIMSS 2019 contextual categorical variables of interest to this paper (i.e., categorical variables that were included in the models). Table 4 shows the means ( _M_ ), standard deviations ( _SD_ ), minimum ( _min_ ), and maximum scores ( _max)_ for boys and girls in grades 4 and 8 in Saudi Arabia across the TIMSS contextual continuous variables of interest to this study (i.e., continuous variables that were included in the models). The tables also include the effect sizes ( _φ_ / _φc_ and _d_ ) for the gender differences in each of the contextual variables (see section 3.2 for further information). Given that some of the estimates presented in table 4 are not intrinsically interpretable (i.e., their values cannot be interpreted as being large or small per se), cut-scores and their corresponding interpretation along the continuum of each of these variables as set by TIMSS are provided in
2022).

A second avenue for research is in further developing an integrated product and activity data set. Our data tracks exports at a rather aggregate industry level compared to the detailed product level export data that is available. Suppose that production technologies for a particular detailed product are the same around the world. In that case, cross-country differences in the occupational composition of the exporting industry (as documented in this study) are due to a different set of products being exported. In that case, activity specialization across the extensive margin can also 28
household surveys and censuses between 2009 and 2018 have included disability questions. It analyzes the types of disability questions implemented during this period, with the particular aim to identify surveys and censuses that can potentially be used to produce SDG or CRPD related indicators disaggregated based on disability status using internationally tested and comparable disability questions.

Disability has been a drop in the ocean of international development work. This lack of attention to disability is perhaps not surprising. There are confusions and misconceptions on what disability is and the scale of the phenomenon. The term “disability” itself is elusive. One cannot simply ask in a survey if anyone in the household has a disability as individuals and cultures may have different understandings of disability. In general, respondents tend to report only extreme cases of impairments (e.g. blindness), leaving out severe or moderate cases. They may also not report a disability due to stigma and older people may also not think of themselves as having a disability, but as being old. Disability is therefore not a simple characteristic. It can be defined and measured in different ways, for instance in medical terms or as resulting solely from barriers in society (Goodley 2016, Mitra 2018).

While the lack of data on disability has long been noted (e.g. WHO-World Bank 2011), the Disability and Development Report (United Nations 2019) notes that “National disability 2
In this section we will present an econometric analysis on the impact of the loan on student behavior, using both the SOFES database and the survey data. It should be noted that in the latter case the treatment effect refers to an effect along, as we call it, the “external” margin, i.e. being a SOFES recipient or not. In contrast, the impact of SOFES using the RD-design should be interpreted as an effect along the “internal” margin, i.e. between SOFES-recipients with different credit levels. Econometric results may differ due to unobserved heterogeneity and non-linearity.

# _6.2 Impact on student performance_

Does the level of student support impact on the academic performance of students? On the one hand, students receiving a large loan might be enticed to put in less effort. Universities carry the default risk, and this could lead to moral hazard among students (see Section 2). On the other hand, peer pressure to show good performance on students eligible for a SOFES credit might lead to an opposite effect: students receiving a large loan perform better than students receiving a smaller credit, everything else being equal. In addition, and perhaps more importantly, the loan program is contingent on student performance: a student will receive renewal of the loan only after passing the previous term of the program. To investigate the relationship between loans and student performance, we estimate a model of the following form, where GPA is the grade point average achieved in the previous term of the program.

Results using the SOFES database are presented in Table 3A (Bachelor-students) and 3B (Masterstudents). The table should be read as follows: The cut-off at ENF=0.08 is denoted by DS=0.08 (DS stands for discontinuity sample). We want to compare the change in the outcome variable close to the point of discontinuity. To that end, four subsamples are defined within a narrow range around the cut-off point, but with an increasing width, e.g
weather variables, which we match with survey data based on the date of the interview, cover different parts of the year, and in the case of households involved in agriculture, the agricultural calendar. We control for seasonal variation by including dummies based on the month of data collection, but there may be unobserved variation we fail to capture. Against this backdrop, we expect the true impacts of extreme weather events to be larger in magnitude than what we can estimate with our data, given that the impacts will be most harshly felt only after the summer, or most severely if weather extremes occur during crop growing cycles. Furthermore, temporal fluctuation in our main variable of interest may persist, especially in rural settings where growing and harvest seasons add to seasonal variation.

There are a few limitations to our study. First, since we only have cross-sectional data, we are unable to track households over time and estimate relationships between weather shocks and poverty over time. Furthermore, these results may underestimate the true impact of weather 26
# **Figure 2.2: Employment Distribution in Urban Areas, 2006**

<!-- Start of picture text --> 6%<br>33% 20% Public wage non-agr.<br>Private wage non-agr.<br>Household enterprise non agr.<br>Wage agriculture<br>Family farming<br>1%<br>40%<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data._

**Figure 2.3: Growth of Employment by Type of Job, National, 2000/01 – 2006 (percent)**

<!-- Start of picture text --> Total employment 4.0<br>Family farming 2.1<br>Wage agriculture 4.7<br>Household enterprise non agr. 12.9<br>Private wage non-agr. 11.2<br>Public wage non-agr. 3.2<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br><!-- End of picture text --> _Note: Type of jobs refers to primary employment only. Source: Calculations based on the ILFS 2000/01 and 2006 data._ 8
classification of sectors follows Malaysia’s national accounts; all output figures have been adjusted to reflect 2015 prices.

As compared to output, the consistent classification of sectoral employment data requires slightly more effort. The relevant classification has undergone several revisions during the observation period. Employment data from 1987 to 2000 published by DOSM are based on the (updated) Malaysia Standard Industrial Classification (MSIC) 1972, data from 2001 to 2009 are based on MSIC 2000, and data from 2010 to 2018 are based on MSIC 2008. In general, MSIC very closely follows the United Nation’s International Standard Industrial Classification of All Economic Activities (ISIC) and its various revisions. For the purposes of this study, all relevant data are standardized to match the national accounts sectoral classification, as shown in Table 2. This standardization is straightforward and uncontroversial on the one-digit sectoral level but more complicated for a more granular disaggregation of the employment data. Therefore, this study focuses on the one-digit level and distinguishes between eight sectors: agriculture; mining; manufacturing; construction; trade; transport; business; and other (which includes utilities, government services and other services).

**Table 1: Description of sectors based on Malaysia’s national accounts**

|**Sector**|**Full description of sector in national accounts**|
|---|---|
|Agriculture|Agriculture, livestock, forestry and fishing|
|Mining|Mining and quarrying|
|Manufacturing|Manufacturing|
|Construction|Construction|
|Trade|Wholesale and retail trade, accommodation, food and beverage|
|Transport|Transport, storage, information and communication|
|Business|Finance, insurance, real estate and business services|
|Other|Utilities; government services; other services|

**Table 2: Categorization of sectors based on MSIC (Updated) 1972, MSIC 2000 and MSIC 2008**

|**Sector**|**MSIC (Updated) 1972**<br>*
from 296 Trade Agreements. World Econ, 39: 97-131. doi:10.1111/twec.12272

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