rafmacalaba/datause-encoder-data huggingface dataset:
# VI – References

<mark>Bardasi, Elena, et al. "Do labor statistics depend on how and to whom the questions are asked? Results from a survey experiment in Tanzania."</mark> _<mark>The World Bank Economic Review</mark>_ <mark>25.3 (2011): 418-447.</mark> <mark>Beegle, Kathleen, et al. "Methods of household consumption measurement through surveys: Experimental results from Tanzania."</mark> _<mark>Journal of Development Economics</mark>_ <mark>98.1 (2012): 3-18.</mark> <mark>Beenstock, Michael, Ephraim Goldin, and Yoel Haitovsky. "Response bias in a conjoint analysis of power outages."</mark> _<mark>Energy Economics</mark>_ <mark>20.2 (1998): 135-156.</mark> <mark>Watson, N. R., A. Miller, and M. Campbell. "Smart meters to monitor power quality at consumer premises." (2015).</mark> <mark>Fricker, Scott, et al. "A Review of Measurement Error Assessment in a US Household Consumer Expenditure Survey."</mark> _<mark>Journal of Survey Statistics and Methodology</mark>_ <mark>(2015)</mark> <mark>Hartman, Raymond S., Michael J. Doane, and Chi-Keung Woo. "Consumer rationality and the status quo."</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>(1991): 141-162.</mark> <mark>Heath, Rachel, et al. "Measuring Employment: Experimental Evidence from Urban Ghana."</mark> _<mark>The World Bank Economic Review</mark>_ <mark>35.3 (2021): 635-651.</mark> <mark>Jimenez, Raul, Tomás Serebrisky, and Jorge Enrique Mercado Díaz.</mark> _<mark>What does better mean? Perceptions of Electricity and Water Services in Santo Domingo</mark>_ <mark>. Inter-American Development Bank, 2015.</mark> <mark>Kahneman, Daniel, Jack L. Knetsch, and Richard H. Thaler. "Anomalies: The endowment effect, loss aversion, and status quo bias."</mark> _<mark>The journal of economic perspectives</mark>_ <mark>5.1 (1991): 193-206.</mark> MacMillan, Douglas <mark>"Actual and Hypothetical Willingness to Pay for Environmental Outputs: Why are they Different?” (2004).</mark> <mark>Woo, Chi-Keung, et al. "Residential outage cost estimation: Hong Kong.”</mark> _<mark>Energy policy</mark>_ <mark>72 (2014): 204-210.</mark> 17
# **2.4.2 Forward interest rates**

We use forward interest rates instead of current bond yields as the dependent variable. According to Shiller et al. (1983), the _푚_ -to- _푚_ + _푛_ -year forward nominal interest rate E _푡 퐿푖,푡_<sup>_푛_</sup> + _푚_<sup>is implied from the year-end</sup><sup>_푛_−and</sup> _푚_ + _푛_ −year zero-coupon interest rates as follows: We employ the 5-to-10-year forward real interest as the dependent variable.<sup>13</sup>

# **2.4.3 Expected public debt**

We employ projections of gross public debt to-GDP-ratio E _푡 퐷푒푏푡푖,푡_ + _푛_ by the IMF _World Economic Outlook_ , _Article IV Consultations_ , OECD _Economic Outlook_ , European Commission (EC) _European Economic Forecast_ , and Economist Intelligence Unit (EIU). Following Gruber and Kamin (2012) and Ichiue and Shimizu (2015), we use two-year-ahead projections of gross public debt-to-GDP-ratio as an independent variable.<sup>14</sup> Since the IMF _World Economic Outlook_ releases its public debt projections for most countries twice a year, we use biannual data. However, as OECD, EC and IMF _Article IV Consultations_ had made two-year-ahead projections once a year over the years, we interpolate the same projections to construct bi-annual data.

# **2.4.4 Foreign private holdings ratio**

We use the lagged share of private foreign investors _푓푝푖,푡_ −1 as the threshold variable and one independent variable. The composition of government bond holdings is drawn from Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b). As we use forward interest rates for government debt securities denominated in the local currency, the foreign private holdings ratio _푓푝푖,푡_ −1 is measured as the foreign private holdings share of government debt securities denominated in the local currency.<sup>15</sup>

> 13The zero-coupon interest rate can be obtained from Bloomberg. To deflate the nominal interest rate into the real interest rate, we use two-year-ahead projections of inflation E _푡 휋푖,푡_ +2 due to data availability. The robustness check examines the nominal interest rate as well.

> 14Moreover, real-time vintages of data could affect long-term interest rates because fiscal data have been revised largely (De Castro et al. (2013)).

> 15While Arslanalp and Tsuda (2014b) provide foreign holdings share of central government debt securities denominated in local currency for emerging markets,Arslanalp and Tsuda (2014a) do not have the corresponding ratio in advanced economies but provide total general government debt securities, including debt denominated in both local and foreign currency. Hence, assuming that only foreign investors hold government debt securities denominated in foreign currency (data that can be obtained from the Bank

11
TABLE 2: GDP GROWTH AND CRIME AGAINST FIRMS – SME, POLICE, AND FEMALE MANAGER INTERACTIONS

||% of Losses Due|% of Losses Due|% of Losses Due|% of Losses|
|---|---|---|---|---|
|Dependent variable|to Crime Over<br>Sales|to Crime Over<br>Sales|to Crime Over<br>Sales|Due to Crime<br>Over Sales|
||Full Sample|Small a|nd Medium Firms sa|mple|
||1|2|3|4|
|Real GDP per Capita Growth|-0.0563**|-0.1699***|-0.0713***|-0.0699***|
||[0.0258]|[0.0479]|[0.0200]|[0.0215]|
|Real GDP per Capita Growth x Small and|-0.0319*||||
|Medium Firms|[0.0166]||||
|Real GDP per Capita Growth x lagged Police||0.0003**<br>[0.0001]|||
|Real GDP per Capita Growth x Female|||-0.0280*||
|<br>Owner|||[0.0145]||
|Real GDP per Capita Growth x firm with||||-0.0591*|
|<br>female owner and manager||||[0.0293]|
|Firm with female owner and manager||||0.4792**<br>[0.1940]|
|Police per 100,000 persons|-0.0011**|-0.0020**|-0.0010*|-0.0009*|
||[0.0005]|[0.0009]|[0.0006]|[0.0005]|
|Firm with Female Owner|0.1161*|0.1674*|0.2575**||
||[0.0617]|[0.0837]|[0.1055]||
|Real GNI per capita (in 100s)|-0.0057***|-0.0065***|-0.0063***|-0.0060***|
||[0.0012]|[0.0015]|[0.0014]|[0.0015]|
|GINI|-0.0064|-0.0019|-0.0017|0.0007|
||[0.0114]|[0.0115]|[0.0112]|[0.0108]|
|Population in Millions, Total|0.0034*|0.0029|0.0039*|0.0040**|
||[0.0017]|[0.0021]|[0.0020]|[0.0019]|
|Small and Medium firm dummy variable|0.3622***<br>[0.0871]||||
|Small firm dummy variable||0.2409***|0.2422***|0.2240***|
|||[0.0850]|[0.0851]|[0.0774]|
|Large City or Capital dummy variable|0.0255|0.0944|0.0811|0.0828|
||[0.0934]|[0.0957]|[0.0972]|[0.0966]|
|Manufacturing firm dummy variable|-0.2414***|-0.1922*|-0.1838*|-0.1750*|
||[0.0815]|[0.1032]|[0.1029]|[0.1015]|
|Number of Countries|27|27|27|27|
|Number of Observations|12274|9160|9160|9237|

* significant at 10%; ** significant at 5%; *** significant at 1%, Standard errors in brackets clustered at the country level

24
sector targeting strategy (and thus could not be evaluated within a difference-in-differences empirical strategy). In such cases, we were unable to gauge the relative effectiveness of these IPAs and as such they were excluded from the IPA characteristics analysis.

**Table 6: Results from sectoral gravity model with country interaction effects**

|**Country interaction results**|**Count**|**Defining IPA type **|
|---|---|---|
|Positive and statistically significant results|10|_High performing IPAs_|
|Statistically insignificant results|13|_Other IPAs_|
|Negative and statistically significant results|3|_Other IPAs_|
|No countryresults(no sector targetingor time variation)|10|_Not included in analysis of IPA characteristics_|
|**Total**|**36**||

Note: Author’s calculations summarizing the results from Table 7. Significance is defined at the 10 percent level or higher.

**Table 7: Sectoral gravity model with country interaction effects**

|VARIABLES|(1)<br>FDI Stock(Logged)|
|---|---|
|IPA Sector Targeting|-0.000775<br>(0.0681)|
|IPA Sector Targeting * Country 1|0.852***|
||-0.198|
|IPA Sector Targeting * Country 2|0.274***|
||-0.0913|
|IPA Sector Targeting * Country 3|0.262**|
||-0.129|
|IPA Sector Targeting * Country 4|0.249*|
||-0.149|
|IPA Sector Targeting * Country 5|0.230**|
||-0.0905|
|IPA Sector Targeting * Country 6|0.207*|
||-0.118|
|IPA Sector Targeting * Country 7|0.199*|
||-0.111|
|IPA Sector Targeting * Country 8|0.192**|
||-0.0888|
|IPA Sector Targeting * Country 9|0.190*|
||-0.113|
|IPA Sector Targeting * Country 10|0.133*|
||-0.0796|
|IPA Sector Targeting * Country 11|0.195|
||-0.22|
|IPA Sector Targeting * Country 12|0.134|
||-0.105|
|IPA Sector Targeting * Country 13|0.125|
||-0.08|
|IPA Sector Targeting * Country 14|0.121|
||-0.1|
|IPA Sector Targeting * Country 15|0.0386|
||-0.116|
|IPA Sector Targeting * Country 16|0.0389|
||-0.109|
|IPA Sector Targeting * Country 17|0.0309|
||-0.0913|
|IPA Sector Targeting * Country 18|0.108|
||-0.0907|
|IPA Sector Targeting * Country 19|0.00666|
||-0.0868|
|IPA Sector Targeting * Country 20|0.00257|
||-0.112|
|IPA Sector Targeting * Country 21|-0.0137|
||-0.134|
|IPA Sector Targeting * Country 22|<br>-0.0668|

16
with foreign ownership have also been found to enjoy numerous benefits such as better access to modern technology, greater access to international markets, among others. These features could affect the incentives to conduct R&D activity. Thus, we control for the proportion of firms’ ownership that is with foreign individuals and companies ( _Foreign Ownership_ ). Large cities are typically among countries’ largest economic centers and, therefore, can offer agglomeration benefits: larger markets, better infrastructure to access markets and operate, a larger pool of workers, and greater technology spillovers (Rosenthal and Strange 2004). This could impact firmperformance including R&D activity. To guard against the implied omitted variable bias problem, we control for a set of dummy variables for city-size category based on total population: 1 million or more, between 1 million and 250,000, between 250,000 and 50,000, and less than 50,000. Data source for the variable is ES.

We argued above that firms’ past performance and scope for future growth (demand pull factors) may impact the likelihood of R&D activity. We proxy for these factors using the annual growth rate of employment (firm size) over the last 3 years ( _Employment Growth_ ) are reported in the ES<sup>10</sup> and the annual growth rate of real GDP per capita ( _Growth rate of GDP per capita_ , lagged by two years). Data source for the variable is World Development Indicators, World Bank.

The next set of robustness controls include several variables for the quality of the business environment. The rationale for this has already been discussed above. The controls include measures of the regulatory burden on the private firms, tax rates, corruption and the quality of enforcement of the rules and regulations. For the regulatory burden, we use two proxy measures. These are the country-level average of the firms’ senior management’s time that is spent in dealing

> 10 Employment growth is computed using the Haltiwanger method to avoid extremely large or small values of the variable (see Table A1 in online Appendix for more details). Thus, the growth rate is bounded between plus/minus 100.

21
The best performing method is a conditional inference random forest which just predicts growth in the mean, giving an error of 3.65 percentage points (panel 4).

# **4.2 Comparison to models just using GDP growth**

It is to see how the methods above with the method that interesting compare simply scales the distribution up according to growth in GDP per capita. Such a model is simpler to understand, simpler to implement, and has the additional advantage that it can be used for nowcasting as well, given that GDP growth rates themselves are nowcasted by various institutions.<sup>3</sup> The models presented in the last subsection relied heavily on variables that are not available for nowcasting. on how well the models when values are Depending predict missing present, this will make them perform worse for nowcasting. In Figure 5 we compare the method of only using GDP growth with the best performing machine learning method, the best performing machine learning method that predicts poverty rates directly, as well as three other scenarios. These other three scenarios are as follows. First, a method which only allows a fraction of GDP per capita growth to be passed through to the distribution and where this fraction is estimated through a simple linear regression. The fraction passed through turns out to be 79%. This follows empirical evidence showing that on average, only a fraction of growth in national accounts trickles down to household surveys (Deaton, 2005; Lakner _et al._ , 2020; Pinkovskiy and Sala-i Martin, 2016; Ravallion, 2003). Second, we estimate this passthrough rate separately by consumption and income welfare aggregates. This is motivated by the fact that this interaction was the first variable to enter in the lasso predicting growth in the mean. With this method, 71% of growth is estimated to pass through to consumption aggregates while 97% is estimated to pass through to income aggregates. Third, we compare the predictions with the predictions we would get if we were perfectly able to predict the mean. This is done by shifting a distribution from the beginning of a spell such that it matches the mean of the distribution at the end of the spell. This is what would happen if our predictions of the mean or growth in the mean were error free. As such, it represents the lowest possible prediction error of using method (3) or (4).

We find that the best performing machine learning method only reduces the

> wealthy countries. Note also that if the distributional assumptions or growth incidence curves we impose are not accurate, then even if we predict the Gini and mean well, it does not mean that we predict the poverty rate well.

> 3A similar method would be to rely on growth in Household Final Consumption Expenditure (HFCE), often the largest component of GDP. HFCE is rarely nowcasted and not available for all countries for nearcasting, making it slightly less attractive from a practical perspective.

17
# **4 The Kyoto Protocol and Stabilization Scenarios**

# **4.1 The Kyoto Protocol**

The Kyoto Protocol to the UNFCCC mandated Annex I countries to limit their GHG emissions over the 2008-2012 period 5.2 percent, in average, below their 1990 level; the Protocol does not set any targets for the rest of the countries. Australia and United States have not ratified the Protocol. Table 3 translates global CO2 emissions in year 2010 under three different conditions: (a) No- Kyoto obligations (i.e., BAU); (b) full Kyoto obligation including the United States and Australia’s participation and (c) Kyoto obligation without the United States and Australia. The table also presents percentage changes in CO2 emissions in 2010 from 1990.

**Table 3: CO2 Emissions Change as Specified in Kyoto Protocol**

||No Kyoto Obligation<br>(BAU)|Full Kyoto<br>Obligation|Kyoto Obligation<br>without US and<br>Australia|
|---|---|---|---|
|1990 CO2Emissio|ns (Million MetricTons of|CO2)||
|Annex I||15,091||
|Non-Annex I||6,335||
|Global||21,425||
|2010 CO2Emissio|ns (Million MetricTons of|CO2)||
|Annex I|16,168|14,366 (12,359)|16,323 (14,117)|
|Non-Annex I|14,690|14,690|14,690|
|Global|30,858|29,056 (27,049)|31,013 (28,807)|
|Percentage Chang|einCO2Emissions betwee|n 1990 and2010 ( perce|nt)|
|Annex I|7|-5 (-18)|7 (-6)|
|Non-Annex I|132|132|132|
|Global|44|36 (26)|44(34)|

_The numbers in parenthesis refer to the case where Russia and Ukraine do not sell their surplus allowances_

-18-
4 1987 and **several** intermediate years.4 In addition, imports from developed and c''veloping c iritries w. e tabulated separately in order to determine **how** the latcer's competitive position (market shares) was changing. Finally, both import value and quantity statistics were drawn for each processing chain stage so unit values could be computed. In general, these unit values were calculated for the most highly aggregate country group possible (i.e., data for the EC(10) would have preference over (say) statistics for France and Germany combined) with tne selection determined by the availab:lity of consistent value and quantity data. Appendix Table 1 lis:s the specific countries that were used for computation of each processing chain's unit values.<sup>5</sup> Once unit values were derived for each processing chain stage two measures **of** price instability were computed. The first instability index was based on the average absolute percentage change in prices from one year to the next while the second was based on the variation that could not be accounted for by a

> 4 The end years 1965 and 1987 were chosen since UN records were incomplete before and after these dates. Also, one would ideally want to analyze trends in commodity exports to all destinaticns but the UN Series D Tapes--the sole source of consistent reccrcds n developing countries' impcrts and exports in term.s of the SITC--have .azcr gacs for South-Scuth and South-East trade. (See Yeats (1990) for an evalua-.:-n of Sob-Saharan African trade data). Due to these limitations the present study had to focus on developing countries' exports to develcped .ountries USing data cc-p;'ee fron the latter's impcrt statistics. Full trade data .eLe rct a.a-lable fcr so,me of the relatlvely unimportant processing chaLns (i.e. , ha r, horn and whalebone, etc.) and these were dropped from the analysis.

> 5 In Appendix Table 1 several S'TC products may be shown within a primary, intermediate, or final stage item. In these -ases, the unit value was an aggregate for all S1TC products listed in the specific stage. For example, the intermediate stege in the cocoa prccessing chain consists of cocoa powder (SITC 072.2) and cocoa bu;ter (072.3). The price for the intermediate stage is a trade weight-ed unit value fcr these compone=nts.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- Teravaninthorn, Supee, and Gael Raballand. 2009. ―Transport Prices and Costs in Africa: A Review of the International Corridors.‖ Africa Region, World Bank, Washington, DC.

- World Bank. 2009d. ―CEMAC Transport Transit Facilitation—Additional financing Central African Republic and Republic of Cameroun. Project Information Document (PID), Concept Stage.‖ Report AB4892, August 13.

- ———. 2010a. ―International LPI: Ranking.‖

- www1.worldbank.org/PREM/LPI/tradesurvey/mode1b.asp#ranking.

- ———. 2011c. ―Project Paper on a Proposed Second Additional Financing and Restructuring Credit to the Republic of Cameroon for the CEMAC Transport and Transit Facilitation Project.‖ Report 59869-CM, Africa Region, World Bank, Washington, DC. March 31.

# Water supply and sanitation

- AMCOW (African Ministers’ Council on Water). 2010a. ―Country Status Overviews on Water Supply and Sanitation 2010. Regional Synthesis Report.‖ August 2010.

- ———. 2010b. ―Water Supply and Sanitation in Cameroon: Turning Finance into Services for 2015 and Beyond.‖ August 2010.

- Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008a. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008b. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- CDE (Camerounaise des Eaux).2011. ―Actors of the Water Sector.‖ www.la-cde.com/en/sectorsactors.html.

- Ministère de L’Energie et de l’Eau. 2011. « Audit des contrats de délégation de service public du secteur de l’hydraulique urbaine au Cameroun. Crédit N° 4313-CM, Project P084002. » Draft audit report, 2d semester 2010. March 30.

51
issuances of corporate debt and equity would annually amount to about LE 200 billion (in FY09 terms) in new corporate bonds, IPOs, and SPOs.

**Exhibit 3.10 – Raising capital on exchanges using IPOs, SPOs, and domestic private bonds** _Total capital raised as a % of gross fixed private capital formation, 2005 (upper left graph) Total capital raised as a % of gross fixed private capital formation, 2006 (upper right graph) Private capital raised as a % of gross fixed private capital formation, 2004-06 (lower graph)_

<!-- Start of picture text --> Capital raised on stock exchanges as a percentage of private Capital raised on stock exchanges as a percentage of private<br>sector gross fixed capital formation, 2005 sector gross fixed capital formation, 2006<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Sri Lanka Turkey Poland India Peru Argentina Thailand Egypt, Arab Sri Lanka Turkey Poland Peru India Argentina Thailand Egypt, Arab<br>SPO/private sector GFCF (%) Rep. SPO/private sector GFCF (%) Rep.<br>IPO/private sector GFCF (%) IPO/private sector GFCF (%)<br>Domestic private sector bonds/private sector GFCF (%) Domestic private sector bonds/private sector GFCF (%)<br>Private capital raised as % of private sector gross fixed<br>capital formation<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2004 2005 2006<br>SPO/private sector GFCF (%)<br>IPO/private sector GFCF (%)<br>Domestic private sector bonds/private sector GFCF (%)<br><!-- End of picture text --> _Source: EGX, World Federation of Exchanges, World Development Indicators; World Bank staff analysis_

# _3.3.2 Non-privatization public offering activity: SPOs activity is high but not substantial; IPO activity is both low and not substantial_

Having peaked at ten in 1998, non-privatization IPO activity has been low since, both in terms of numbers and relative to market capitalization (Exhibit 3.11). In fact, IPO activity was completely dormant during 2002-04. However, since 2005 there has been some activity, albeit most of it has been largely privately placed. For example, the 2005 IPO of Raya Holding for Technology and Communications was 100 percent privately placed. 2006 only had two IPOs in the form of El Swedy Cables and Al Arafa Investment and Consulting, with a private placement average of 93 percent. In addition, as a reaction to increasing share prices, Egypt also has been seeing an SPO increase: 2007 had 37 rights issues with a value of almost one percent of market capitalization.

31