rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10777

# **Abstract**

This paper studies how the 2022 energy crisis, which triggered the largest energy price shock since the 1970s, affected the diffusion of green technologies. It uses a dataset of online job postings in 16 countries to construct measures of green technology-related job openings, using these as a proxy for green technology adoption. It documents the significant growth in green-technology-related openings in 2022. It shows that establishments that had a higher exposure to the price shock, due to their pre-crisis energy intensity and location in countries with a greater dependence on natural gas imports, posted more green technology-related openings from March 2022.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pbastos@worldbank.org, Jacob.greenspon@economics.ox.ac.uk, kstapleton@worldbank.org, and dtaglioni@worldbank.org _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and water and sanitation—and various policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Burkina Faso, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Two sets of African benchmarks will be used to evaluate Burkina Faso’s situation: nonfragile LICs and MICs. Detailed comparisons will also be made with immediate regional neighbors in the West African Community.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In the years 1997–2007, Burkina Faso’s economy grew at an average annual rate of 6 percent, which (taking population growth into account) translates to roughly 2.5 percent growth per capita during the period. About half of this improved performance can be traced to improvements in the country’s infrastructure platform (Calderón, 2009), much of it in the ICT sector, which contributed 1.14 percent points alone. Meanwhile, deficient roads held growth back by at least 0.1 percent points (figure 1a).

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

a. Infrastructure’s contribution to annual per capita economic growth in West African countries, in percentage points, 2001-05 <!-- Start of picture text --> 5<
refer to them solely as “antidumping” in our comparison of the results for safeguards.

In addition to the 2002 steel safeguard (described below) that affected some 272 unique 10-digit HTS categories, in 2000 the US also imposed much smaller tariff-rate quota safeguards on five different 10-digit HTS product categories of steel wire rod and circular welded pipe.<sup>11</sup> 9 After 1998, only 1035 unique 10-digit HTS categories from chapter 72 or 73 are in our sample of trade data, which suggests that the ratio for the three safeguard actions (all implemented since 1998) is even higher if we factor into account administrative changes in the HTS schedule over time.

10 More generally and for the full sample of all US use of antidumping and countervailing duties over 1990-2009, Bown (2011) reports that it is relatively rare for a product to be subject to a CVD and not also be subject to antidumping. Nevertheless the converse is not true as most use of antidumping is not necessarily accompanied by a simultaneous CVD.

11 Note that while the United States excluded Canada and Mexico from application of these particular safeguards in 2000, it excluded no other developing countries. Furthermore, Korea filed a formal WTO trade dispute over the US safeguard on circular welded pipe; and, as part of the settlement, the US granted Korea additional access to the quota in September 2002. We examine the impact of this directly in the estimation results described below.

7
during 2009—an upward revision of salary scales could have a significant impact on private consumption demand in the short run.

# **Box 2 How Large Is the Fiscal Multiplier?**

The effect of fiscal expansion on output is referred to as the _fiscal multiplier_ . It is measured by the unit change in output or GDP per unit change in government expenditure, other determinants of output remaining unchanged: Where _F_ = fiscal multiplier, _Y_ = income or GDP, and _G_ = government expenditure. Large standard errors of estimation have prevented researchers from pinning down precise estimates of the value of _F_ . Recent estimates range from negative to more than one (Spilimbergo and others 2008). Calculating the fiscal multiplier is difficult because there is no simple way to control the “fiscal experiment” (Barro and Redlick 2011; Christiano, Eichenbaum, and Rebelo 2009)—that is, to separate the impact of changes in government spending from changes in other variables that simultaneously affect changes in income, through various channels.

Estimates of fiscal multipliers depend on the methodology, period, initial conditions, and controls applied. Study of the experience in the United States suggests that deficit-financed state government spending has a lower multiplier than windfall-financed spending, because deficit-financed state spending tends to crowd out private consumption and investment (referred to as _Ricardian equivalence_ ). However, in periods of stagnant or declining consumption and investment demand, deficit-financed government spending does not crowd out private demand. The multiplier is therefore larger than during boom times.

A survey of empirical evidence suggests that for the 2008–09 stimuli by the G20, the low set of multipliers was 0.3 for revenue, 0.5 for capital spending, and 0.3 for other spending; the high set of multipliers was 0.6 for revenue, 1.8 for capital spending, and 1.0 for other spending (Spilimbergo, Symansky, and Schindler 2009). For a sample of 102 developing countries, the one-year government spending multiplier resulting from borrowing from official creditors
Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility ~~JEL Classification:~~ Q57, C8 Conservation planning, global biodiversity, species’ occurrence region, endemic and smallBank program managed by the authors and Dr. Nagaraja Rao Harshadeep, Global Lead, Disruptive Technology. We are also thankful to the participants of the webinar conducted during Biodiversity Week, including our colleagues from the World Bank Sustainable Development Global Practice and the Environment, Natural Resources and Blue Economy Global Practice.
them to communicate more and transfer information faster compared to the other country with slower fixed line connection. We control for the quality of Internet access in a country in the regression by using data on download speeds. The data is obtained from the Net Index dataset based on the Ookla Speedtest and provides information about the download speed measured in kilobytes per second for a wide set of countries starting from 2008.<sup>16</sup> The logs of the download speed in the importing and exporting countries is used an alternative measure of the Internet in our baseline regressions. In order to facilitate comparison of estimates, given the different time period available in the Net Index dataset, we also report the OLS estimate of our baseline regressions using Internet adoption for the same time period 2008-13 where data are available for the download speed. The results are reported in Table 7.

The results are similar to the baseline regressions with Internet adoption as the measure of the Internet. Faster Internet in the exporting country increases bilateral exports. A 10 percent increase in download

> 16 The dataset provides daily measures of the speed of download and upload. The daily value is calculated as the average of the last available 30 days of test. For our purpose, we construct a yearly variable by averaging the speeds of the last day of each month. See http://www.netindex.com/ for additional information about the available indexes.

13
A set of “systemic early warning indicators” would allow supervisors to detect, monitor and address the buildup of vulnerabilities that may otherwise become a source of financial and economic distress in the future. Examples of these are the extent to which households are overstretching their repayment capacity, or exposed to interest or currency risk. This is especially relevant for banks actively involved in cross-border lending.

- **Systemically important borrowers** . PCRs should be used to detecting and monitoring systemically important borrowers (e.g. the largest borrowers of the financial system, typically accounting for a large share of total credit in the economy). Much of the traditional toolkit used in the off-site supervision, such as early warning indicators and stress tests, is being revisited and adapted to take into consideration the potential spillover effects imposed by these obligors.

# **VI. Optimal PCR architecture**

In the preceding sections we explored how PCR data can be best used to strengthen banking sector regulation and supervision and to monitor the global performance of the credit market. It is therefore important that PCRs contain accurate, timely and sufficient data to those purposes. They should be able to provide timely information of each borrower (i.e. full name and ID number, address or location, type of obligor - household, SME or corporate obligor) and for each of their corresponding credits (i.e. type of credit, outstanding debt, days past due, date of origination and contractual maturity, type of interest rate and currency of the credit). Information of any risk mitigation measures securing the credits (e.g. credit derivatives, guarantors, financial collateral) is useful to estimate the severity of losses in the event of default and is of paramount importance to support supervisors in jurisdictions admitting the use of IRB models, particularly to estimate or revise banks’ Loss Given Default (LGD) estimates. **Figure 1** depicts in more detail the basic set of information that PCRs should contain.

16
Estimates from the RLMS show that informal employment broadly increased through the first half of the 2000s (2001-2004), levelled off in the period 2005-2010, and had a subsequent upward trend since 2010. Estimates show that the second half of the 2000s was marked by a temporary stagnation of informal employment, possibly as a consequence of the financial crisis. Informality started to increase again after 2010. The latest estimate for 2016 puts informal employment at 16.9 percent, up from 14.5 percent in 2010, and from 10.7 percent in 2000 (Figure 2).<sup>11</sup> The RLMS trend is broadly consistent with the official trend published by Rosstat in that informal employment increased substantially in the early 2000s, came slightly down around the financial crisis period, and then continued to increase again. Rosstat estimates show that informal employment increased from 12.5 percent in 2001 to 17.6 percent in 2005, then slightly decreased to 16.4 percent in 2010 but increased significantly to 21.2 percent in 2016.

LFS microdata are only available for a shorter period of 2010-2015, but the share of informally employed is invariably increasing, which is consistent with the trend from RLMS as well as Rosstat. Our estimates show that informal employment was at 15.1 percent in 2010 and 19.3 percent in 2015 (Figure 3), which again, is a significant increase over a relatively short period of time. Our definition is focused on measuring informality in workers’ primary jobs which yields a slightly lower estimate compared to the official Rosstat measure which is also based on LFS.

Additional estimates of informal employment from the LiTS and ESS show a slight decrease between 2006 and 2010, followed by a marked increase through 2016 (Figure 4 and Figure 5). Using the LiTS, the share of informal workers decreased from 16.1 percent in 2006 to 11.7 percent in 2010, then rose to 15.1 percent in 2016. Using the ESS, that same share started
020)|(0.029)|(0.279)|
|_NO_MINWAGE_: the country<br>|-0.165|-1.817***|-2.557|-2.764***|-1.353|-3.790*|
|does not have a minimum wage|(0.452)|(0.343)|(2.353)|(1.059)|(1.759)|(2.295)|
|_lnGDP__ _per_capita_, PPP USD|-4.959|-3.085**|-1.254|4.108***|3.419***|5.169***|
|(annual average, lagged 1 year)<br>_IMMIGR* log_GDP_ _per capita_|(3.275)|(1.473)|(6.094)|(1.058)|(1.293)|(1.382)<br>-0.219**<br>(0.093)|
|_IMMIGR:_Population with|||||||
|<br>immigrant background, %|-0.310***|0.124***|-0.103|0.081*|0.060|1.950**|
||(0.077)|(0.046)|(0.132)|(0.046)|(0.072)|(0.859)|
|Real GDP growth, y-o-y,|<br>0.279***|<br>0.286**|<br>0.185**|<br>0.092|<br>0.131***|<br>0.132***|
|quarterly data lagged 1 quarter
# **10.TABLES**

## **i. BANK ACCOUNTS**

Figure 20. EACR Decomposition of Individual Bank Account Ownership <!-- Start of picture text --> FINDEX 2011 FINDEX 2014<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>Brazil Bosnia and Belarus United Arab Brazil Bosnia and Belarus United Arab<br>Herzegovina Emirates Herzegovina Emirates<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base. In FINDEX, 141 countries are sorted on Coverage in 2011 and 143 countries are sorted in 2014, and only nearest neighbors in coverage rates are shown. _Sources_ : FINDEX Figure 21. EACR Decomposition of Household Bank Account Ownership <!-- Start of picture text --> Household (LITS)<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>Russian Bulgaria Albania Montenegro<br>Federation<br>Composition Equalization Scale<br><!-- End of picture text --> _Notes:._ LITS is household level dataset. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : LITS 30
Our comparative ambient monitoring in peri-urban Dhaka has been undertaken in areas with rural characteristics, well-removed from major transport arteries and industrial sites. MiniVol monitoring at three locations yields a mean 24-hour concentration of 48 ug/m<sup>3</sup> , which compares favorably with the Indian safe standard of 100 for rural areas (World Bank, 2002). Nevertheless, our results suggest that ambient pollution from biomass burning is substantial in rural villages.

# **Figure 4:  PM10 Concentrations: Outdoor vs. Indoor for Piped Natural Gas**

# Ambient Overlay

<!-- Start of picture text --> 0 5 10 15 20 25<br>24-Hour Time<br>Piped Nat. Gas<br>0 5 10 15 20 25<br>24-Hour Time<br>400<br>300<br>200<br>Ambient Concentration<br>100<br>0<br>250<br>200<br>150<br>Kitchen Concentration<br>100<br>50<br><!-- End of picture text --> <!-- Start of picture text --> 0 5 10 15 20 25<br>24-Hour Time<br>Kitchen Concentration Ambient Concentration<br>400<br>300<br>200<br>100<br>Kitchen Concentration/Ambient Concentration 0<br><!-- End of picture text --> 16
1.6 results 37

R-squared is much higher in the ad hoc regression, the results show that the coefficients vary and inferences would be very different depending on whether the model ignores the zeros (as in column 1), corrects for them (as in columns 2-4), or makes an atheoretical ad hoc adjustment (column 5).

Finally, Silva & Tenreyro (2006) point out that the presence of heteroskedasticity in trade data actually requires the use of _non_ log-linearized models. Their method allows the inclusion of zero flows in the sample by estimating the gravity model with original (non-log) FDI as the dependent variable. The results from their Poisson Pseudo Maximum Likelihood (PPML) method in column 6 do not show coefficients on the FDI-related policy variables to be statistically significant, but some variables have surprising coefficients. The elasticity of distance, for example, drops by about two-third as in the main bias-corrected model. An issue with the method proposed by Silva & Tenreyro (2006) in the context of FDI flows is that it does not seek to explain zeros as anything special. In my model, zeros are intimately linked to heterogeneous productivity of firms. A zero bilateral flow indicates that firms are not finding it profitable to invest abroad because, for a given level of foreign demand, either the firm productivity is low, or the fixed and variable costs induced by distance and policy are prohibitive.

Addressing the zero observation problem (regardless of whether the selection bias is serious or not) still does not give us consistent estimates when firms are heterogeneous. In earlier trade models that follow Krugman (1979), firms are symmetric and all firms export. Only their volume is constrained by trade costs, not the decision of firms of whether to export. When trade barriers are infinite, foreign varieties are still consumed, but zero quantity of each. The effect of trade costs is only on the intensive margin of trade. Helpman et al. (2008) show that the correction of selection bias is inadequate when the assumption
worth mentioning that the commodity price indices from Gruss and Kebhaj’s (2019) are countryspecific. Specifically, for each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level data for the period 1962-2018.

> 40If we use the Arellano-Bond estimator for dynamic linear panel methods to deal with lagged-dependent variables, the same results hold for regressions (3) and (6). Also, using as controls world trade (as percentage of GDP) instead of terms of trade and public debt as percentage of GDP does not change the results.

> 41Notice that regressions (4)-(6) control for the EMDE response by introducing an interactive term.

> 42Since all variables are log-differences, the estimated coeffi cients are elasticities.

43 Comparing our quantitative estimates to those of other studies is not easy since empirical specifications vary considerably. With that caveat in mind, our estimates are very much comparable to those of, for example, Richaud et al. (2019). Based on a sample of 87 EMDE commodity exporters, Richaud et al. report a range of 0.34 to 1.25 percent for the cumulative impulse response of GDP to a one standard deviation shock in commodity prices. In our case, the sample is 11 EMDE with a coeffi cient range in the panel regressions in Table 3 of 0.063-0.085. Since the standard deviation of changes in commodity prices for EMDE is 14.4 percent, the range for the impact on GDP is 0.92 to 1.22 percent. Notice that the comparison is valid because changes in commodity prices tend to be very persistent (see Cashin et al., 2000).

> 44Again, if we use the Arellano-Bond estimator for dynamic linear panel methods to deal with laggeddependent variables, the same results hold for regressions (3) and (6). Further, if we use as control variables (i) volume of world trade as percentage of GDP (in lieu of terms of trade)
In recent decades, PTAs have changed considerably in focus and content. Older agreements aimed largely to reduce formal barriers to trade and expand market access between member countries. Beginning in the 1990s they expanded considerably in ambition and coverage. This process began with the North American Free Trade Agreement (NAFTA) and numerous bilateral treaties between the European Free Trade Association and individual countries, such as Estonia, Latvia, and Mexico. This round of trade agreements expanded beyond tariffs to rules covering certain regulatory regimes, including intellectual property rights. A decade later, the EU announced its “new trade policy,” shifting its negotiating priorities toward rigorous protection of patents, copyrights, geographical indications and other IPRs in its many new PTAs in Eastern Europe and the Middle East, Latin America, Canada, and Japan.

NAFTA was among the first multi-country and economically large PTAs to set minimum standards for, and move toward partial harmonization of, nearly every aspect of IPRs as they existed then. Regarding patents, NAFTA required minimum patent duration, confidentiality for pharmaceutical clinical trials data, and extensions in patent length to compensate for administrative delays in granting protection, among other elements. These provisions became the foundation for expanding TRIPS-Plus patent standards in later PTAs. It also stipulated a minimum copyright length, the types of works that must be protected, and various neighboring rights. NAFTA required automatic recognition of internationally well-known trademarks, another TRIPS-Plus standard. And the accord called for protection of geographical names through systems effectively the same as those covering trademarks and collective marks. PTAs reached by the EU and EFTA in this era had similar requirements, though differing in particular areas of concern, most prominently in setting up specific systems for geographical indications (GIs).

NAFTA was preceded by the formation of the European Economic Area (EEA) in 1994, linking three members of EFTA to the EU’s single-market program. Central to the EEA is the adoption of largely harmonized intellectual property standards throughout the region.

The expanded coverage of PTAs sharply increased after 2000, with the