rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4: Impacts on aggressive behavior and violence**

<!-- Start of picture text --> Panel A: Friends involved in fights or robbery, %<br>50.0%<br>38.6%<br>40.0% 35.2% 35.3%<br>30.0%<br>22.5%<br>20.0%<br>10.0%<br>3.2%<br>0.0%<br>‐10.0% Before After D<br>‐12.7%<br>‐20.0%<br>Treatments Controls<br>Panel B: Used threat or force with somebody, %<br>25.0%<br>20.0%<br>16.0% 15.3%<br>15.0%<br>10.8%<br>10.0%<br>7.7%<br>4.4%<br>5.0%<br>0.0%<br>Before After D<br>‐5.0%<br>‐10.0%<br>‐8.3%<br>Treatments Controls<br><!-- End of picture text --> Source: Authors’ calculations.

A third indicator related to violence is the reported incidence of being attacked and fighting back. In the treatment group, this indicator declined from 43.8% to 31.4%, which is statistically significant at the 1% level. In the control group, which had a similar baseline level, we find no statistically significant change. The DD value is thus 14.7 percentage points, or a decline of 33.9 percent, significant at a 1% level, relative to the baseline value for the treatment group (see Table 6).

Finally, we also look at the reported incidence of doing damage to somebody’s property for fun or as a joke. In the treatment group, the incidence declined from 8.6% to 5%, which is a decline of 3.6 percentage points, or 41.4 percent (significant at a 5% level). We find no statistically significant change among the controls. The DD value is 6 percentage points, which is a statistically significant (at a 5% level) decline (see Table 6). Overall, unlike the results on social behavior reported in the previous section, the program is associated with reductions in anti‐social behavior and violence both for participants and their friends.

25
# **<mark>Table 8: Plots owned by the respondent</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled<br>N<br>(1)|Mean<br>R3<br>(2)|Impa<br>R1<br>(3)|ct byRound<br>R2<br>(4)|(OLS)<br>R3<br>(5)|Pooled<br>Impact<br>(6)|
|---|---|---|---|---|---|---|
|**_Panel A: Plots Owned by Women_**|||||||
|Number of inherited or purchased plots|2,401|.524|.102*<br>(.054)|.030<br>(.053)|-.099*<br>(.051)|.007<br>(.031)|
|**_Panel B: Plots Owned by Men_**|||||||
|Number of male plots|1,847|1.01|.172*<br>(.102)|-.020<br>(.102)|-.010<br>(.101)|.047<br>(.060)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

30
**Table 4.4. MDG and education indicators in 2012 and by simulation in 2022**

||**2012**|**base**|**inv-og**|**inv-tx**|**inv-sb**|**trn-og **|**inv-og- i**|**nv-og+**|**inv-tx- i**|**nv-tx+ in**|**v-ogsb+**|**hi-grw**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**MDG 1: Headcount poverty (%)**|15.0|8.6|7.9|9.0|8.5|7.4|9.1|6.7|10.8|7.6|6.7|3.1|
|**MDG 2: Primary net completion rate (%)**|48.0|59.5|62.3|61.7|61.6|59.9|61.4|63.3|60.9|62.5|65.0|66.7|
|**MDG 4: Under-five mortality rate (**‰)|85.5|74.8|72.4|73.1|73.2|74.3|73.3|71.6|73.9|72.3|70.4|68.6|
|**MDG 7: Water access (%)**|93.2|96.9|97.5|97.4|97.3|97.0|97.3|97.7|97.2|97.5|98.0|98.3|
|**MDG 7: Sanitation access (%)**|49.5|57.4|59.2|58.7|58.7|57.7|58.6|59.8|58.2|59.3|60.9|62.2|
|**GER* -- Primary (%)**|90.9|94.0|94.8|94.7|94.6|94.1|94.6|95.1|94.4|94.9|95.6|96.0|
|**GER -- Secondary (%)**|41.2|54.6|55.6|55.3|55.3|54.7|55.2|55.9|55.0|55.6|56.6|57.1|
|**GER -- Tertiary (%)**|6.7|14.7|15.0|14.9|14.9|14.8|14.9|15.1|14.9|15.0|15.3|15.5|
|**GCR** -- Primary (%)**|81.9|82.4|83.7|83.4|83.4|82.6|83.3|84.1|83.1|83.8|84.9|85.5|
|**GCR -- Secondary (%)**|27.2|38.7|39.2|39.1|39.1|38.8|39.0|39.4|38.9|39.2|39.8|40.1|
|**GCR-- Tertiary (%)**|3.6|9.4|9.6|9.5|9.5|9.4|9.5|9.7|9.5|9.6|9.8|9.9|
|**Gini**|0.329|0.347|0.347|0.344|0.345|0.344|0.348|0.345|0.344|0.343|0.344|0.345|

***GER = Gross Enrollment Rate; **GCR = Gross Completion Rate**

Figure 4.1. GDP and private consumption: real growth deviations from base <!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>GDP at factor cost Private consumption<br>Figure 4.2. Household consumption per capita by: real growth gains from base by year<br>6.0<br>5.0<br>4.0<br>3.0 inv-og<br>inv-tx<br>2.0<br>trn-og<br>1.0<br>inv-og+<br>0.0 inv-tx+<br>-1.0<br>-2.0<br>%-age points<br>% difference from base by year<br><!-- End of picture text --> Figure 4.2. Household consumption per capita by: real growth gains from base by year 25
thus this can be taken as a lower bound of the extensive margin.

Panel B of Table 3 reports the extensive margin using U.S. data at the 10-digit level. The data have more than twice as many codes (over 14,000 for U.S. China trade), allowing the extensive margin to be larger. Using all of the 10-digit exports from China to the U.S., net variety growth is negative and the extensive margin share of trade growth is 17 percent. The smaller value for the extensive margin in the U.S. data, as compared with the China data, is likely a result of there being fewer reclassi fi cation in the United States (81% of codes are permanent as compared with 76% in the China data). Including only codes that exist between 1997 and 2005, the net variety growth and the extensive margin’s share of trade growth are similar, at around 3 percent, and larger than measured using permanent 8-digit codes from the China data. Note that there is still signi fi cant growth in the number of new export variety categories, which increased by more than 40 percent but these new varieties account for a small share of export growth.

Compared to other non-OECD countries, China’s growth in the extensive margin has been small. Based on the HS 10-digit export data to the U.S. with all codes included, China ranks 80th out of a total of 133 non-OECD countries using the Feenstra net index of variety measure and 100th using the extensive margin measure.

All of these measures of the extensive margin should be interpreted with caution given that the magnitudes vary considerably depending on whether all product codes are used and whether the base period is before or after the major reclassi fi cations that took place in 1996. The calculations with the more disaggregated U.S. data from 1997 onwards indicate that a large portion of China’s export growth took place along its intensive margin.

# 7. Export Prices

The large increase in export growth along the intensive margin suggests that China’s export growth is likely to put downward pressure on world prices of these goods. Taking the subset of HS 10-digit goods that China exported to the US between 1997 and 2005, we construct an 13
is likely to occur in different sectors, e.g. metals and metal products, motor vehicles, textiles and apparel in Sub-Saharan Africa, or other manufacturing, chemical products, motor vehicles, crops, and textiles in South Asia.

**Table 3.** Top 3 import sectors for the Reshoring leading economies scenario. Exports compared with COVID L-shape scenario

|**Country/region**|**Sector**|**Reshoring**<br>**leading**<br>**economies**|**Reshoring**<br>**all**|**GVC friendly**<br>**liberalization**<br>**+ TF imports**|
|---|---|---|---|---|
||Electricity|5|-1|2|
|China|Natural resource products|1|-2|-1|
||Air transport|1|-5|-1|
|Rest East Asia &|Oil extraction|5|-25|-2|
|<br>Pacific|Metals|4|-12|-8|
||Computer, electronic and optical products|3|-25|8|
||Electricity|-4|-6|-6|
|Western Europe|Metals|-4|-3|-8|
||Non-metallic minerals|-6|-8|-8|
|E & Ctl|Metals|7|-6|-11|
|urope enra<br>Asia|Natural resource products|3|-10|-11|
||Non-metallic minerals|0|-11|-13|
|Ltin Amri nd|Oil extraction|5|-35|23|
|a eca a<br>Cibb|Metals|3|-10|-15|
|arean|Computer, electronic and optical products|3|-14|-9|
|Middle East &|Metals|3|-9|-14|
|<br>North Africa|Natural resource products|0|-6|-17|
||Water transport|-2|-11|-1|
|Rest of High-|Natural resource products|-3|-2|-7|
|<br>I Ci|Electricity|-4|-6|-7|
|ncome ountres|Livestock|-6|-9|-7|
||Gas extraction and distribution|3|-22|-7|
|South Asia|Oil extraction|1|-17|-15|
||Natural resource products|0|-7|-11|
||Metals|3|-10|-17|
|Sub-Saharan Africa|Gas extraction and distribution|3|-9|-15|
||Non-metallic minerals|1|-10|-18|
||Metals|1|-1|-2|
|United States|Electricity|-1|-4|-4|
||Non-metallic minerals|-3|-6|-5|
||Natural resource products|-1|-5|-6|
|World|Electricity|-3|-7|-6|
||Metals|-3|-9|-10|

Source: ENVISAGE simulations.

# Implications for GVC participation

With the reduction in the level of the economic activity due to the COVID-19 pandemic, overall trade participation rate by countries is also decreasing. While results suggest that due to the changes in trade 16
Figure 5.2Toxic Pollution Intensity by Medium for Selected Sectors

# Toxic Alr Pollution Intensity

<!-- Start of picture text --> 6000<br>5000 |<br>gE 4000|<br>ww” ~<br>a”<br>2 3000|.7<br>3<br>=<br>3<br>a2000;<br>1oco | ie .<br>-;<br>0,<br>=a mo xo 2 2o a= 2o Ss3 a2 x3 3 al”<br>ISIC Codes<br><!-- End of picture text -->

<!-- Start of picture text --> Toxic Land Pollution Intensity<br><!-- End of picture text --> <!-- Start of picture text --> 25000 _—_—__<br>| 20000<br>,|i & =E 45000<br>la<br>|3<br>H, a3=] 10000<br>o<br>: a 5000<br>0::<br>=® s 8 2 2: FE: FSF F HF KF GB B<br>| 8 5§ 8&8 5&5 ISIC§ Codes8 8 8 8 8 8<br><!-- End of picture text -->
|Biodiesel|10.0|9.8|26.5|26.3|55.9|55.8|116.3|116.7|277.8|280.5|
|---|---|---|---|---|---|---|---|---|---|---|
|Bioethanol|10.0|58.8|26.4|104.7|55.8|209.2|116.0|557.7|276.7|4600.0|

**Table B3: Decrease of international prices of soy, soybean oil and other of agricultural products (20%)**

||**y1 **<br>|<br>|**y2 **<br>|<br>|**y3 **<br>|<br>|**y4 **<br>|<br>|**y **<br>|**5**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|4.5|8.1|8.5|12.3|12.7|16.7|16.9|20.9|20.1|
|Trade Balance|5.1|5.9|10.4|11.1|15.9|16.4|21.4|21.8|26.9|26.0|
|Fiscal Result|2.1|2.2|4.3|4.3|6.5|6.4|8.6|8.5|10.7|9.8|
|Rate of Unemployment|9.2|8.0|6.9|5.7|4.6|3.4|2.2|1.0|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|4.8|8.0|8.8|12.1|12.8|16.4|17.0|20.5|20.1|
|Richest Household|3.6|3.4|7.2|7.0|10.9|10.6|14.7|14.3|18.4|17.3|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|-8.0|8.4|-4.3|12.8|-0.4|17.3|3.6|21.8|7.2|
|Manufactures|4.4|7.7|8.8|12.2|13.4|16.8|18.1|21.5|22.7|25.1|
|Services|3.9|5.3|7.9|9.3|12.1|13.4|16.3|17.5|20.4|20.5|
|Soy|4.1|-13.4|8.3|-9.7|12.6|-6.0|17.0|-2.1|21.5|1.9|
|Maize|4.1|50.2|8.4|55.7|12.7|61.3|17.2|66.9|21.7|73.1|
|Sugar Cane|4.4|19.5|8.9|24.4|13.5|29.5|18.3|34.6|22.9|38.6|
|Rest of agriculture|4.2|-12.1|8.5|-8.4|12.9|-4.7|17.5|-1.0|22.1|2.0|
|Soybean oil|4.9|-82.8|9.9|-82.0|15.0|-80.9|20.2|-78.9|25.4|-70.9|
|Diesel|5.5|5.5|11.1|11.0|16.8|16.5|22.7|22.2|28.7|27.9|
|Gasoline|5.6|5.7|11.4|11.3|17.3|17.1|23.4|22.9|29.6|28.8|
|Biodiesel|10.0|54.5|26.5|95.8|55.9|184.9|116.3|445.3|277.8|2000.0|
|Bioethanol|10.0|12.6|26.4|29.5|55.8|59.5|116.0|120.1|276.7|276.4|

**Table B4: Mandatory substitution of 5% in diesel for biodiesel**

||**y1 **<br>||**y2 **<br>|<br>|**y3 **<br>||**y4 **<br>||**y5 **<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|3.9|8.1|8.0|12.3|12.2|16.7|16.6|20.9|20.8|
|Trade Balance|5.1|5.0|10.4|10.3|15.9|15.7|21.4|21.3|26.9|26.8|
|Fiscal Result|2.1|2.0|4.3|4.2|6.5|6.4|8.6|8.6|10.7|10.6|
|Rate of Unemployment|9.2|9.3|6.9|7.0|4.6|4.7|2.2|2.2|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|3.8|8.0|7.8|12.1|12.0|16.4|16.3|20.5|20.5|
|Richest Household|3.6|3.5|7.2|7.1|10.9|10.8|14.7|14.6|18.4|18.4|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|4.0|8.4|8.3|12.8|12.6|17.3|17.1|21.8|21.7|
|Manufactures|4.4|4.3|8.8|8.8|13.4|13.4|18.1|18.1|22.7|22.7|
|Services|3.9|3.8|7.9|7.8|12.1|11.9|16.3|16.2|20.4|20.3|
|Soy|4.1|3.9|8.3|8.1|12.6|12.4|17.0|16.8|21.5|21.3|
|Maize|4.1|3.9|8.4|8.2|12.7|12.5|17.2|17.0|21.7|21.6|
|Sugar Cane|4.4|4.2|8.9|8.7|13.5|13.4|18.3|18.1|22.9|22.8|
|Rest of agriculture|4.2|4.1|8.5|8.4|12.9|12.8|17.5|17.3|22.1|22.0|
|Soybean oil|4.9|4.7|9.9|9.7|15.0|14.8|20.2|20.1|25.4|25.3|
|Diesel|5.5|5.3|11.1|10.9|16.8|16.6|22.7|22.5|28.7|28.6|

34
falling there are relatively few children. And because mortality rates for the older generations are still high, there are relatively few elders to take care of. The result is a boom in working-age adults and a dramatic decline in the youth dependency ratio, what is called the “demographic dividend” (Bloom et al., 2001). While countries with a high youth dependency ratio are likely to devote substantial resources to children, depressing the pace of economic growth, countries with a boom generation of working-age adults can take advantage of an exceptionally large labor force to dramatically raise output levels. Lower youth dependency ratios are also linked to higher national savings rates, thereby enabling productive investment (Robalino et al., 2013 ).

Research shows that the economic impacts of the demographic dividend can be substantial: Estimates indicate that population dynamics related to the demographic dividend accounted for up to one-third of the “East Asian Miracle”<sup>6</sup> (Bloom and Willamson, 1998) and explain to a large extent Ireland’s economic success in the 1990s (Bloom and Canning, 2003). Focusing on India, Aiyar and Mody (2011) find that falling fertility has spurred the growth acceleration in India since the 1980s and estimate that the demographic dividend will add two percentage points per annum to India’s per capita GDP growth over the next two decades. Lower fertility does not only promote economic growth through changes in the age structure of the population. As women have fewer children they are more likely to enter the labor market, adding to the already increased labor supply resulting from the shifts in age structure and spurring income growth (Bloom et al, 2009). Since the opportunity cost of having extra children is higher when women participate in the labor market, employed women typically choose to have fewer children, leading to a virtuous circle of falling fertility and wealth creation. When families have fewer children, they also tend to invest more in the education their children (Becker, Murphy et al., 1990; Galor, 2006) and save more, pushing up the savings rate and facilitating productive investment (Bloom et al., 2003).

> 6 The “East Asian Miracle” refers to the extraordinary growth experienced by many East Asian countries between 1965 and 1990-see World Bank (1993).

3
# **Appendix** E: **Old system formula in Mexico**

> NOTE: OLD SYSTEM

The annual retirement pension is based on: a) base amount given by a percentage of the income earned during the last 5 years of contribution,

> b) an increase for each additional year contributed, and

c) the number of years of contribution in excess of the minimum 10 year requirement.

> The value of the annual pension is calculated according to the following expression:

## S * [(CB) + (Y)* (Al) ]

> S= base salary used for the last 5 years of contribution

> Base%= percentage of base salary in base amount

> Y= additional years contributed beyond the 10 year requirement

> Al= annual increment as % of S, for each additional year contributed beyond 10 years.

> In addition, the IMSS provides retirees with a yearly bonus equivalent to

> one month of the pension payment they were receiving.

Thus the total annual amount received would be

> 13/12 of the value obtained by the expression given above.

> Base% and Al are rates determined in a table (IMSS, 1993), which vary according to the amount S expressed in number of minimum wages. The Base% is inversely related to S,

> and ranges from 80% to 13%. The annual increment,

Al, is directly related to S, and ranges from 0.563% to 2.45%.

> Below we provide an example of the calculation for levels S of 1 and 6 minimum wages, assuming a total of 30 years of contribution to IMSS.

|S<br>|Base%|Al|Y|Estimated annualpension|
|---|---|---|---|---|
|1minimumwage<br>|80%|0.56%|20|13/12* S*(96.9%)<br>=105%|
|6minimumwages|13%|2.45%|20|13/12*S*(86.5%)<br>=93.7%|

Replacement rates of base salary range between 94% and 105% for high and low wage worker, respectively, who has worked for 40 years. For 10 years of work these replacement rates would be 14% and 87%, respectively.

64
- 13 -

more effective use. On the other hand, equation 2 in Table I (using 1970 education data) fails to confirm the interaction. What we can say definitely is that family planning does matter and may well interact with education, and the magnitude of the effect of female education remains large, even controlling for its interaction with family planning services. Controlling for education and family planning services, the GDP variable is significant in only one of the equations. In two equations the regional dummy for LAC is significant. On the whole, the results suggest that the total effect on fertility of female education combined with family planning services is substantial.

4.4 To get a better idea of the magnitude of the effects of female education and family planning services on total fertility, elasticities were computed. Figure 1 shows the values of elasticities of the TFR (1985) with respect to female secondary enrollments (1970) (controlling for male enrollments), and with respect to family planning services (1982) at different levels of female secondary enrollments and family planning service scores. These were computed from equation 1, Table 1. The elasticity of TFR with respect to female education has a "U" shape, suggesting that the effect of female secondary enrollment on fertility is high initially until it reaches a threshold level of and then tapers off. The elasticity of fertility with respect to female education is very high for female enrollments from 0 to 40 percent - a range in which all but 4 of the sample countries fall. Female secondary enrollment has the greatest negative effect on fertility at around 30 percent enrollment; the mean of female secondary enrollment in the 1970 sample is 13 percent. Family planning services undoubtedly reduce the fertility rate,9 but the magnitudes of the elasticities of the TFR with respect to family planning services are small.

9 In this equation, since it does not have an intemcetion **term,** we can estinate the independent **effect** of family planning **services.**
# **Appendix Figure C. WTO Membership Use of Global Safeguards (SG), 1995-1Q 2009**

# **SG Historically, by year: 1995-2008**

<!-- Start of picture text --> 40<br>35<br>30<br>25 SG Initiations<br>20<br>SG Measures<br>15<br>10<br>5<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

**SG During the Crisis, by quarter: 1Q 2007- 1Q 2009**

<!-- Start of picture text --> 8<br>7<br>6<br>SG Initiations<br>5<br>4<br>SG Measures<br>3<br>2<br>1<br>0<br>1Q- 2Q- 3Q- 4Q- 1Q- 2Q- 3Q- 4Q- 1Q-<br>2007 2007 2007 2007 2008 2008 2008 2008 2009<br><!-- End of picture text --> Sources: data in both panels taken from the WTO member reports to the Committee on Safeguards and comprehensively covers the full WTO membership.

27
**Table 15: Social and human rights indicators, regressions, Probit model**

|**GRI G4**<br>**Metric**|**GRI Indicator**|**_MNE_**|**p‐value**|<br>**_Large_**|**p‐value**|
|---|---|---|---|---|---|
|**G4‐EC5**|**Ratios of standard entry level wage by gender compared to local minimum wage at**<br>**significant locations of operation**|**‐0.231***|**0.052**|**0.461*****|<br>**0.007**|
|G4‐EC6|Proportion of senior management hired from the local community at significant locations of<br>operation|<br>‐0.069|0.541|**0.352****|**0.038**|
|G4‐HR1|Total number and percentage of significant investment agreements and contracts that<br>include human rights clauses or that underwent human rights screening|0.026|0.834|**0.707*****|<br>**0.000**|
|G4‐HR3|Total number of incidents of discrimination and corrective actions taken|0.021|0.840|**0.388****|**0.015**|
|**G4‐HR9**|**Total number and percentage of operations that have been subject to human rights**<br>**reviews or impact assessments**|**0.270****|**0.023**|‐0.012|0.948|
|G4‐HR10|Percentage of new suppliers that were screened using human rights criteria|0.067|0.557|**0.397****|**0.023**|
|G4‐HR11|Significant actual and potential negative human rights impacts in the supply chain and<br>actions taken|0.175|0.159|**0.454****|**0.026**|
|G4‐HR12|Number of grievances about human rights impacts filed, addressed, and resolved through<br>formalgrievance mechanisms|0.127|0.283|**0.366****|**0.035**|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01. Note: Variables refer to 2015 GRI data (see section 2.2). All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

# **4.3 ENVIRONMENTAL INDICATORS**

Regarding the disclosure of environmental indicators, a higher percentage of firms across the board reports on issues related to energy usage, and the average disclosure of MNEs is even higher than for other firms (Figure 7). The fraction of MNEs disclosing information regarding energy consumption within the organization (EN3) is very high (83.4%), while rates regarding energy intensity (EN5) (56.2%) and reduction of energy consumption (EN6) (65.7%) are somewhat lower.

Similarly, more firms tend to disclose information related to greenhouse gas emissions. Indicators related to direct and energy indirect greenhouse gas emissions (EN15 and EN16) show a disclosure coverage of 78% for MNEs, while disclosures related to other indirect greenhouse gas emissions (EN17), their intensity and reduction (EN18 and EN19) show somewhat lower disclosure shares of 48.9%, 55.6% and 61.7%, respectively. However, the difference between MNEs and large companies is very small.

The regression analysis in Table 16 shows that MNEs underreport with regard to reductions in energy requirements of products and services (EN7). Only 39.6% of MNEs disclose information, compared to 36.4% for large firms and 18.3% for SMEs (Figure 7). Firm size (large), by contrast, explains reporting differences across all energy‐related environmental indicators (Table 16).

23