rafmacalaba/data-use-mentions-extended huggingface dataset:
of the study imply that green investments increase TFP at the aggregated level. Using a dataset of 5,498 manufacturing firms in Italy from 2000 to 2008, Leoncini et al. (2019) show the positive effect of green technologies on firm’s productivity growth is greater than that of non-green technologies.

There exists no consensus, however, on the relationship between the investment of green/clean technologies and productivity. Using the firm-level data from the Czech Republic, Horvathova (2012) finds a significant upfront innovation expenditure that led to firms’ lower productivity. However, this negative effect also tends to fade over time and transforms into a positive effect in the long run. Using cross-country sector-level panel data for 17 European countries between 1997 and 2009, Rubashkina et al. (2015) find no evidence of a positive relationship between pollution abatement and control expenditures, as a proxy for clean/green technology investment, and productivity. Analyzing the causal relationship between the growth of TFP and that of fossil and renewable energy consumption in the power sector in BRICS countries<sup>7</sup> during the 1992-2012 period, Tugcu and Tiwari (2016) find no remarkable causal link between renewable energy consumption and TFP growth. Instead, they find a positive relationship between non-renewable energy consumption and TFP in Brazil and South Africa.<sup>8</sup> Using firm-level data of selected firms collected through a survey in Austria, Germany, and Switzerland, Stucki (2019) finds significantly positive productivity effects of an investment in green energy technologies in firms with high energy costs but not in firms with medium energy costs. It finds significantly negative effects for firms with low energy costs.

# **3.2 Factors affecting the relationships between clean investments and productivity**

Several factors influence whether investments in green/clean technologies increase a firm’s productivity. These factors include the size of firms, their ownership, the presence of environmental regulation, and activities for investments (development vs adoption of technologies). Below
# **5.1 Descriptive Results**

These descriptive results provide insight by directly asking respondents if indicators for food insecurity are due “specifically to the COVID-19 crisis.” This allows for a self-reported causal statement, which is not necessarily equivalent to causal identification in the statistical use of the phrase. Nevertheless, these descriptive results highlight how respondents understand changes in their lives in the time of the coronavirus pandemic.

Figure 5 shows descriptive results using the May-June 2020 phone survey from Mali. The results suggest that the coronavirus pandemic has dramatically reduced the food security of households in Mali.<sup>13</sup> Overall, 50 percent of the full sample is worried that they will not have enough to eat. Of those households, 65 percent report that this worry is specifically due to COVID-19. This finding, that a substantial share of households identify COVID-19 as an important cause of food security challenges, is largely driven by households in urban areas. Specifically, we see an 11 percentage points difference, between urban and rural areas, in those who identify COVID-19 as the source of their worry that they will not have enough to eat. Similar findings persist for most of the remaining FIES questions. Across seven of eight food security indicators, the exception being FS8 which is only experienced by less than 4 percent of households, a larger share of households living in urban areas than in rural areas report food security challenges due to COVID-19. In five out of the eight food security indicators the difference between urban and rural areas in reporting of food security challenges due to COVID-19 is statistically significant at conventional levels.

These findings are striking for at least two reasons. First, specifically in the context of Mali, food security is on average more challenging in rural areas. Despite this, urban households tend to be more likely than rural households to identify COVID-19 as the most important cause of their food security challenges. In particular, 48 percent of rural households, compared to 42 percent of urban households, indicate being worried
11|0.04|0.23|0.01|0.28|0.42|0.42|3.05|19.54|24.20|
|Total|2.66|3.03|2.23|3.20|0.74|6.56|9.56|9.72|41.76|20.55|100.00|

_source: own calculations, using GTAP8 data._ motor vehicles and machinery account for 28.3 percent (last columns of rows S5 and S6). Like the results for total final demand in Table 2, in terms of exports, 25.5 percent of total value added costs for other machinery exports follow from inputs of market services (column S9 and column total for row S6). In terms of market access, there is a dual message here. Italy exports a substantial amount of services through the manufacturing sector. As such, the machinery sector has an interest in increased competitiveness and efficiency in the upstream services sectors. At the same time, the service sector clearly has an interest in improved market access for machinery, as this is an indirect channel for export. We return to these issues in Section 7.

Returning to Table 1, note that values are influenced by net export positions. For example, the United States runs a net trade surplus in services. As such, while the share of services in total exports on a value added basis is roughly 30 percent, most of this is because of direct exports rather than linkages to (and exports contained in) manufacturing. Thus, 23.9 percent of value added in exports is in services and follows from direct services exports, while just 5.2 percent is linked to indirect exports and downstream linkages. In contrast, for both Korea and Brazil, though commercial services are a relatively low share of exports on a gross value or direct value added basis, they are clearly a major factor
**Figure 5 Operating subsidies of infrastructure SOEs as a percent of operating expenses, by sector**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Power Airlines and airports Roads Railways Average<br>Operating subsidies<br>as percent of operating expenses<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18.

# **_4.4 Costs_**

Employment costs represent a very large share of revenues (figure 6). They exceed revenues in the railways sector, and they represent about 91 percent and 70 percent of revenues in the airlines and airports and roads sectors, respectively. Employment costs are lower in the power sector, where they account for 23 percent of revenues. Maintenance costs are high for roads and low for the other sectors. Fuel is the main expense item for the power sector. The government take (taxes plus dividends) is low across all sectors.

# **Figure 6 Costs of infrastructure SOE as a share of revenues, by sector**

<!-- Start of picture text --> a. Employment costs b. Fuel costs<br><!-- End of picture text --> <!-- Start of picture text --> 200 200<br>188<br>150 150<br>100 100<br>91<br>62<br>50 50<br>40<br>17 14 20<br>8<br>0 0<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Percent of revenues Percent of revenues<br><!-- End of picture text --> 12
>142.7<br>80.0 140.0<br>76.5<br>75.0 71.0 130.0<br>125.9<br>70.0 73.5<br>120.0<br>118.0<br>65.0<br>110.0 113.4<br>60.0<br>100.0<br>Backcast, pass-through=0.42<br>Backcast, pass-through=0.42<br>Backcast, pass-through=0.75<br>Backcast, pass-through=0.75<br>Backcast, pass-through=0.87<br>Backcast, pass-through=0.87<br>Backcast, pass-through=1<br>Backcast, pass-through=1<br>PovcalNet<br>PovcalNet<br><!-- End of picture text --> Note: the figure shows backcasted series using different values of the pass-through rate used to account for the difference between growth in national accounts and in household consumption as measured in household surveys. Household consumption data from the 2018/19 NLSS is matched to sectoral GDP growth rates (MFM-Tool World Bank) based on 22 It should be noted that the purpose of this exercise is to check whether different assumptions of the pass-through rate can help explain the difference between the backcasted estimate and the 2009/10 HNLSS estimate, rather than trying to estimate a specific value of the pass-through rate for Nigeria.

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# **Figure 3: Quantile Treatment Effects on Sales After Three Years**

<!-- Start of picture text --> Treatment Effect Spillover Effect<br>5 10 20 30 40 50 60 70 80 9095 5 10 20 30 40 50 60 70 80 9095<br>Percentile Percentile<br>6000 6000<br>4000 4000<br>2000 2000<br>Weekly Sales Weekly Sales<br>0 0<br>-2000 -2000<br><!-- End of picture text --> Notes: Intent-to-treat effects from quantile regressions controlling for randomization strata and baseline sales, combining round 4 and round 5 survey data. Straight dashed line shows OLS estimate. 95 percent confidence intervals displayed allowing for clustering at market level.

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_The Cultural Trade Index_ Page | **5** of **14**

# **III. Method**

The CTI would initially be web-based, and the website could be maintained by an international organization or an academic institution. As the first index of its kind, the data could be fed annually or biennially. Nonetheless, improvements in data collection could mean that it might be possible to consider shorter time periods in the future.

Working with statistical agencies, trade ministries, and cultural and media organizations, the web-based system could allow self-reporting data where necessary. Some data may come from UNESCO’s Institute for Statistics, the United Nations Conference on Trade and Development, the World Trade Organization, the International Monetary Fund, the World Integrated Trade Solution, the United Nations Comtrade Database, Fair Trade Organizations, and other sources.

With respect to ranks, in addition to the global rankings, ranking could also be regional and economic group classifications (for example low income, mid income and upper income countries). The CTI could look like this:

## Table 1 **<u>Version 1</u>**<sup>**13**</sup>

|**Country**|Exports of<br>cultural<br>services -<br>contribution<br>to GDP<sup>a</sup>|Exports of<br>cultural<br>goods -<br>contribution<br>to GDP<sup>a</sup>|Percentage<br>of people<br>employed in<br>the cultural<br>sector|Cultural<br>contribution<br>and related<br>sectors to<br>GDP (%)|Government<br>Expenditure<br>on cultural<br>trade<br>promotion<sup>b</sup>|Population<br>Expenditure<br>on imports<br>of cultural<
_Energy Policy_ , 35(6): 3194– 3202.

- UNEP. 2021. _Regulating Air Quality: The First Global Assessment of Air Pollution Legislation_ . United Nations Environment Programme.

- van Donkelaar, A, Martin, R V and Park, R J. 2006. “Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing.” _Journal of Geophysical Research: Atmospheres_ 111(D21): 201.

- van Donkelaar, A, Hammer, MS, Bindle, L, Brauer, M, Brook, JR, Garay, MJ, Hsu, NC, Kalashnikova, OV, Kahn, RA, Lee, C, Levy, RC, Lyapustin, A, Sayer, AM and Martin, RV. 2021. “Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty.” _Environmental Science & Technology_ . DOI: 10.1021/acs.est.1c05309

- Westerlund, J. 2007. “Testing for error correction in panel data.” _Oxford Bulletin of Economics and Statistics_ 69: 709–748.

- WHO. 2018. World health statistics 2018: monitoring health for the SDGs, sustainable development goals. World Health Organization. https://apps.who.int/iris/handle/10665/272596. WHO. 2021. _WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide._ World Health Organization. https://apps.who.int/iris/handle/10665/345329.

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<br>2<br>0<br>2000-10 2011-21 2000-10 2011-21<br>China EAP excl. China<br><!-- End of picture text --> F. China’s potential growth by different estimates <!-- Start of picture text --> Percent<br>2000-10 2011-21<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>PF MVF UVF Exp.<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; UN Population Prospects; World Bank, World Development Indicators database; Note: EAP = East Asia and Pacific. Shaded areas indicate forecast. GDP weighted averages (using average real U.S. dollar GDP at average 2010-19 prices and market exchange rates). Period averages. A. Markers show median GDP-weighted averages of the six EMDE regions; orange whiskers show minimummaximum range.

B.C. Potential growth estimates based on production function approach. Sample includes six EAP economies (China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand).

C. Blue bars denote average actual growth over each ten-year period. Red bars denote contribution of potential growth to change in actual growth between the two five-year periods; orange bars denote contribution of cyclical growth.

D. Orange whiskers show min-max range of potential growth estimates in the four sources listed above. “EAP ex. China” includes Indonesia, Mongolia, Philippines, and Thailand.
620|0.168|***|1.315|0.047|***|
|Time dummy τt+1|1.103|0.164|***|1.559|0.046|***|
|Public stayersm11|0.309|0.197||0.231|0.079|***|
|Private stayersm00|−0.256|0.164||−0.158|0.046|***|
|Switchers topublicm01|−0.524|0.199|***|−0.108|0.103||
|Switchers toprivatem10|−0.021|0.186||0.061|0.046||
|Sample size|1750|||7786|||

_Source:_ Author's calculations based on VLSS and VHLSS data.

Compared with the other models presented above, our model accounts for any characteristic that contributes to workers' comparative advantage from being in the public sector rather than in the private sector. In this sense, our model allows for a greater effect from workers' selection into one sector or the other. However, we find significant differences in the relative returns to workers' comparative advantage between the public 21
.3%_|_0.9%_|_13.5%_|
|**ICT Hardware Exports**|69|2,487|2,690|_16.8%_|_2.7%_|_15.1%_|
|_% of World Merchandise Exports_|_11%_|_15%_|_16%_||||
|Final Goods|41|1,291|1,410|_16.2%_|_3.0%_|_14.6%_|
|_% of ICT Hardware_|_59%_|_52%_|_52%_||||
|Intermediates|28|1,196|1,280|_17.7%_|_2.3%_|_15.8%_|
|_% of ICT Hardware_|41%|48%|48%||||

Note: all values in millions of US dollars at current prices and exchange rates. Sources: Merchandise Exports: UNCTADstat, http://unctadstat.unctad.org/wds. World GDP: World Bank World Development Indicators, <u>http://data.worldbank.org/data-catalog/world-development-indicators</u> ICT Hardware trade: World Bank MC-GVC Database using a consistent 175 country panel that accounts for 95-98% of world trade, see: http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator 5
24

- qualitative learning about the determinants of behavior of clients and service providers is used in project design (ready models can be found in the existing experience with beneficiary assessment, focus group interviewing and functional task analysis, and funding could often be arranged through the Project Preparation Facility);

- quantitative output and process targets are developed;

- particularly disadvantaged groups are identified and targeted where feasible (in this connection, special consideration should be given to the wider use of growth monitoring along the lines practiced in the Tamil Nadu nutrition project, not solely as a way of targeting supplementary feeding, but as a way of identifying the particularly disadvantaged for targeting of the whole available range of PHN and other services); and

- MIS systems are developed which feed back performance data to field workers as well as upwards to managers.

In addition, it is recommended that PHRHN, the TDs and the CDs should jointly

- identify several countries and projects for systematic experimentation with outreach systems which promote learning from clients and the adaptation of project interventions during implementation on a decentralized basis (ready models are available in the existing experience with social marketing and T&V); and

- identify one or two countries and projects where a special effort would be made to develop private sector provision of services and/or IEC.

Supervision. The appropriateness of learning processes as opposed to blueprints in PHN service development at the periphery suggests a more important role for supervision, as cGmpared to preparation and appraisal, than has been the de facto case in the Bank in the past. Yet, in a period of budgetary stringency and reduced supervision coefficients, it is clear that the time staff can put into field supervision will be strictly limited, and will, in many countries, decline. Under these circumstances, it is recommended that

- project staff encourage more emphasis in project designs on strengthening the Borrower's own supervision process, with technical assistance as necessary, thus minimizing Bank staff's own technical assistance role during implementation;

- project staff work with Borrowers
**2 Data**

We combine information from three sources. Data on NTPs come from the World Bank Deep Trade Agreement Database (Hofmann _et al._ , 2019; Mattoo _et al._ , 2020). This includes information on all non-trade related provisions contained in 279 agreements signed between 1958 and 2015. The database distinguishes 14 “core” provisions that reconfirm existing WTO disciplines or impose additional (WTO-plus) obligations in policy areas that are covered by the WTO, as well as 38 provisions in areas that go beyond extant multilateral commitments (WTO-extra provisions). The dataset documents the growth in the inclusion of provisions on civil rights, environmental protection, and labor rights in trade agreements (Figure 1). In addition to reporting on the existence of provisions on a given subject, the database also provides information on their legal nature, including their enforceability.<sup>6</sup> We focus on three types of WTO-extra provisions in PTAs; those related to civil and human rights promotion, labor rights protection, and environmental protection. We reduce the original bilateral dataset to a panel defined at _country ∗ year_ level. For each country, we consider the year it signs a PTA containing the provisions of interest, the partner country (whether the PTA includes the EU, the US, or other countries), and whether the obligations arising from the agreement can be considered as binding (i.e., if they can be enforced through a dispute settlement mechanism). In case a country signs more than one agreement, we consider the first one in which a provision of interest is signed and assume that it stays in force even when additional agreements (possibly with different sets of partners and different sets of obligations) are signed. We allow for changes in legal enforceability over time with subsequent agreements.

> 6Information on legal enforceability is reported for 52 selected policy areas in total. An extended version of the dataset provides more detailed indicators for a subset (18) of these policy areas.
_

The above results help shape the policy debate regarding the characteristics of country pairs that benefit most from trade agreements: they show that the benefits of extra-regional EIAs are likely smaller than for intra-regional EIAs in terms of spurring trade flows of intermediate goods. Yet, we also acknowledge that this effect may not be driven by distance alone. Our results might be biased by the omission of other factors which have been shown to influence trade agreements’ impact and are correlated with distance. Two such factors are the depth of trade agreements<sup>15</sup> and how similar countries are in terms of their income levels.

To make this point more explicit, Figures 1 and 2 below show the relationship between geographic proximity and trade agreement depth and “economic proximity”<sup>16</sup> and trade agreement depth. Here, trade agreement depth is measured in two separate ways (per Baier and Bergstrand and DESTA). In Figure 1 (which relies upon the Baier and Bergstrand dataset), we observe that the log of bilateral geographic and economic distance decreases as trade agreements become deeper. Otherwise stated, closer and more similar countries have the deepest agreements. In this figure one-way preferential agreements are the shallowest agreement and economic unions the deepest.

Figure 2 (which relies upon the DESTA dataset) shows that the log of bilateral distance decreases until four levels of provisions are included, after which it increases to roughly the same level as agreements with no provisions at all. Economic distance follows a similar trend, decreasing until five provisions are included and then increasing. Although the DESTA depth index rates a 7 as the deepest form of agreement, one possible explanation for the U shape observed in Figure 2 is that certain categories of provisions may divert rather than encourage trade. Two examples are provisions relating to intellectual property rights or competition policy.

> 15 In their 2014 paper, BBF convincingly argue that the marginal impact of EIAs on bilateral gross exports is the highest for deepest agreements, both at intensive and extensive
*|0.0651*|0.1954**|0.6479**|1|
|Number of observations|193|964|964|964|964|962|964|
|||**EU**|**28(except for**|**Czech Republ**|**ic and Lithua**|**nia)**||
|Support per hectare UAA|0.1061+|-0.0401|0.0353|0.0733+|-0.0125|1|0.5392**|
|Number of observations|250|1165|1165|1165|1165|1165|1164|
|Support per AWU|0.0814|0.002|0.1195**|0.1098**|0.0628*|0.5392**|1|
|Number of observations|250|1166|1166|1166|1166|1164|1166|

Note: (1) Symbol + indicates correlation statistically significant at 0.1 level; * indicates correlation statistically significant at 0.05 level; ** indicates correlation statistically significant at 0.01 level; (2) CAP support are the sum of payments for the period 2008-2013; (3) Unemployment rate data is at NUTS2 level; (4) EU15 are Belgium, Denmark, Greece, Finland, France, Germany, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Spain, Sweden and the UK

**Table 2** . Pearson correlation coefficients between the level of market price support and direct income payments accruing to NUTS3 regions (2008 - 2013) and socio-economic indicators (2011)

|Variables|Unemploy<br>ment rate|Povertyrate|Share of the<br>country<br>poor|GDP per<br>inhabitant|Population<br>change<br>(2008-2013)|Support per<br>hectare<br>