rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.2 Household Survey Data**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) is a household survey program that provides financial and technical assistance to national statistical offices in Sub-Saharan Africa for the design and implementation of national, multi-topic longitudinal household surveys with a focus on agriculture. Our analysis uses data from multiple rounds of panel household surveys from Ethiopia, Malawi, Niger, Nigeria, Uganda and Tanzania. Table 2 provides a summary of the countries, years, and observations used in the analysis.

In Ethiopia, we use data from the 2011/12, 2013/14, 2015/16, 2018/19, and 2021/22 rounds of the Ethiopia Socioeconomic Survey (ESS), which has been conducted by the Central Statistical Agency of Ethiopia (CSA, 2014; CSA, 2015; CSA, 2017; CSA, 2020; CSA, 2024). The Wave 1 data is representative at the regional level for the most populous regions in the country while Waves 2 and 3 expanded to include 1,500 households in urban areas. The 2018/19 round represented a total refresh of the panel, making it nationally representative as well as representative of each of the 11 regions in Ethiopia. The 2020/21 round followed up with the households from 2018/19, creating a new panel distinct from the three-round panel that ran from 2011 to 2016. Because of COVID and civil conflict in Ethiopia during the 2021/22 data collection effort, this fifth wave contains no households from Tigray and is thus not nationally representative, though it remains representative of ten of the regions. After data cleaning to remove urban and non-agricultural rural households, we are left with 10,674 observations from 5,333 distinct household across five survey waves.

In Malawi, the LSMS-ISA data includes two separate surveys that form three different data sets: the cross-sectional Integrated Household Survey (IHS), and the longitudinal Integrated Household Panel Survey (IHPS) (NSO, 2012; NSO
production and exports, this study also presents evidence suggesting that coal production did not diminish and that coal exports may have accelerated due to the combined policies.

The paper is organized as follows. Section 2 presents a review of literature, followed by the description of data and methodology in Section 3. Results and sensitivity analyses are presented in Section 4. The mechanisms of the policies’ impacts are explored in Section 5. Section 6 investigates if the carbon tax policy caused changes in coal production and exports. Section 7 concludes.

# **2. Literature Review**

A number of studies investigate the effectiveness of carbon pricing and other pricing instruments to reduce GHG emissions. Timilsina (2022b) synthesizes results from existing empirical studies on the impacts of various pricing instruments on GHG emissions. Examples of such studies include Green (2021), Best et al. (2020), Dechezlepretre, et al. (2020), Andersson (2019), Jaraite ̇ and Di Maria (2016), Murray and Maniloff (2015), Aydin and O<sup> ̈</sup> mer (2018) and Martin et al. (2014). With only the exceptions of Green (2021) and Best et al. (2020), these studies are either for a single country or a relatively small group of countries, mainly focusing on the European Union.

Green (2021) reviews empirical studies published since 1990 on the impacts of carbon pricing on CO 2 emissions. It concludes that carbon pricing policies have caused limited reduction in CO 2 emissions, between 0% and 2% per year on average. One difficulty is that the review compares many studies with heterogeneous identification strategies estimating the effect of many heterogeneous policies (including sectoral and fuel-level policies).

Best et al. (2020) estimate the effectiveness of carbon pricing at reducing national CO 2 emissions using panel data from 142 countries from 1997-2017. They find that the average annual growth rate of CO 2 emissions has been around two percentage points lower in
the literature (Yeaple, 2005; Matsuyama, 2007; Verhoogen, 2008) the training category linked with exporting is technical upgrading, which is aimed at improving the skills of experienced workers for enhanced performance, rather than teaching the skills needed to enter a specific occupation or field of work (as in the other training categories). Drawing on detailed worker-firm records for exporting firms, we have also provided evidence that technical upgrading has positive returns to trainees within exporting firms. Our results therefore support the hypothesis that export participation requires skill upgrading, and suggest that this is partially achieved by training firms’ existing workforce.

# **References**

Andrews, Martyn, Thorsten Schank and Richard Upward (2006): ”Practical fixed-effects estimation methods for the three-way error components model,” _Stata Journal_ , 6: 461481. Bastos, Paulo and Joana Silva (2010): ”The quality of a firm’s exports: Where you export to matters,” _Journal of International Economics_ , 82(2): 99-111.

Bastos, Paulo and Joana Silva (2008): ”The wage and unemployment impacts of trade adjustment,”, in David Greenaway, Richard Upward and Peter W. Wright (eds.), _Globalisation and Labour Market Adjustment_ , 31-53, Palgrave Macmillan.

Bastos, Paulo, Joana Silva and Eric Verhoogen (2014): ”Export destinations and input prices,” NBER Working Papers 20143, National Bureau of Economic Research.

Bastos, Paulo, Nat ́alia P. Monteiro and Odd Rune Straume (2009): ”Firm heterogeneity and wages in unionised labour markets: Theory and evidence,” _Labour Economics_ , 16(4): 440-450.

Bastos, Paulo and Peter Wright (2012): ”Exchange rates and wages in unionized labor markets,” _Industrial and Labor Relations Review_ , 65(4), 975-999.

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all oil companies.|res1104.se.gov.ar<br>/consultaprecios.eess.php for<br>gasoline, diesel, and kerosene, and<br>government announcements for<br>LPG.|
|Bangladesh|95 RON gasoline.|www.bpc.gov.bd.<br>Visits to shops in Dhaka for LPG.|
|Bolivia|92 RON gasoline.|www.anh.gob.bo.|
|Brazil|87 octane index gasoline mixed with ethanol, LPG in<br>13-kg cylinders. Monthly average retail prices for the<br>entire country.|www.anp.gov.br.|
|Cambodia|92 RON gasoline, LPG in 15-kg cylinders. Prices in<br>Phenon Penh.|World Bank Phenon Penh office.|
|Cameroon|95 RON gasoline, LPG in 12.5-kg cylinders.|www.csph.cm.|
|Chile|93 RON gasoline, LPG in 15-kg cylinders. Monthly<br>average prices in Región Metropolitana.|www.cne.cl/estadisticas/energia/<br>hidrocarburos.|
|China|93 RON gasoline, LPG in 14.5-kg cylinders. Gasoline<br>and diesel prices in Beijing, LPG prices in Nanjing.|Local newspapers.|
|Colombia|81 octane index gasoline mixed with ethanol (reporting<br>of prices of 87 octane index gasoline stops in December<br>2011), diesel with 0.05 percent sulfur mixed with<br>biodiesel, LPG in 20-pound (9-kg) cylinders. Maximum<br>selling prices for gasoline and diesel in Bogotá.<br>Wholesale prices for LPG and kerosene for pass-through<br>calculations.|www.sipg.gov.co for gasoline and<br>diesel, www.ecopetrol.com.co for<br>kerosene wholesale prices, and<br>www.anciem.org for LPG prices.|
|Costa Rica
# **3.4 Post-experiment**

The interpretation of our estimates depends on what benefits children received between the experiment and the observed ENLACE tests. Self-reports from the follow-up survey show that households in treatment villages (both in-kind and cash) reported receiving on average 12 months of PAL aid, however we do not have household level information on the type of transfers received after the follow-up survey.

SEDESOL was able to provide us only with village-level administrative counts of the number of beneficiary households per year receiving PAL, _Oportunidades,_ or _Liconsa_ between 2005 to 2013. Figure 1 plots the average number of beneficiaries of PAL, _Oportunidades,_ and _Liconsa_ over time for the three experimental groups. While the average number of PAL beneficiaries fluctuated between 200 and 50 for in-kind villages and between 250 and 50 for cash villages, the control villages remained with an average number of beneficiaries below 50 during the entire period (left panel in Figure 1). The number of beneficiaries of _Oportunidades_ and _Liconsa_ steadily increased over time. Cash localities displayed on average a lower number of households that are beneficiary of the _Oportunidades_ transfer (middle panel in Figure 1). This difference, that is very small in size,<sup>15</sup> is likely to partly reflect differences in population. In fact, both in 2005 and 2010 - years for which the population census is available - we find that the number of households in in-kind and cash localities is lower than in control ones (see columns 1 and 4 in Appendix Table A1), possibly as a result of differential migration. The share of beneficiary households in neither of the treatment groups is statistically different from the one in the control group (columns 3 and 6 in Appendix Table A1).

Overall, the evidence presented in this section suggests that our estimates should be interpreted as the combined effect of differential exposure and differential take-up in treatment and control localities.

If the receipt of other programs after the experiment is correlated with the treatment assignment to the villages and those programs
a unit cost of less than USD 6 per parcel (Nkurunziza 2015), setting a new standard for first time registration of land rights that many African countries endeavor to emulate. Survey data collected soon after program completion in 2012 point towards impacts in three areas. First, tenure security increased markedly for males and females equally, including those not legally married. This suggests modifications to implementation modalities to include women in informal marriages were effective (Santos _et al._ 2014). Second, the program led to distinct improvements in land rental market functioning and efficiency-enhancing land transfers (Ali _et al._ 2015). Finally, although investment did not increase in the short term, the program provided a basis for higher levels of agricultural investment in the medium run.

# **3. Evidence from administrative data**

Registry data document the size and gender-friendly nature of Rwanda’s accomplishment but also point towards remaining challenges, in particular unclaimed parcels. They allow to document in real-time collateralized lending volumes, land prices for residential and agricultural land, and the extent to which LTR’s gender equality is maintained. We review registration procedures and use data on roll-out of sector land managers to estimate the impact of this intervention on levels of formal land transfers at sector level.

# **3.1 Level of registration and transaction frequency**

Official land administration information system (LAIS) data highlight Rwanda’s accomplishments. In the context of LTR, information was collected on 11.42 million parcels; 0.39 million in Kigali city, 1.99 million in Eastern, 2.67 million in Northern and 3.2 million each in Southern and Western Province (table 1). Of these, 64.3% are in agricultural (50% in Kigali to 73% in Northern Province), 11.7% in residential, 8% in forest, 1.5% in commercial, and 0.3% in administrative use. The share of parcels in residential use is,
to having timely and accurate data in other domains going forward. On the other hand, as we will argue in more detail later in the paper, the granularity and timeliness of the data have increased the risk of government surveillance for ends beyond just containing the virus.

# **Relation to other frameworks**

The 12 attributes relate closely to other frameworks that assess the quality of statistical data. Such frameworks have primarily been created by National Statistical Offices (see for example Statistics Canada (2017)), international organizations (see for example OECD (2011) and UN (2019)), and academia (see for example Biemer (2010)). Table 1 compares the framework derived in this paper with these other frameworks.

Unsurprisingly, there is a great overlap between the different frameworks, though the exact names and definitions differ from source to source. All 12 features we identify can be found in some shape or form in one of the other frameworks. Though many of the frameworks originate from work with survey data, they can be adapted to other data types. The framework of Biemer (2010), for example, which is based on the concept of a total survey error, has been adapted to big data (Amaya et al. 2020) and agricultural data (Carletto et al. 2021).

8
on the process and potential benefits of registration. In practice the staff members presented a set of potential legal and economic benefits of the registration and the step-by-step registration process. They were equipped with two booklets containing all the information in easy to understand (Bangla) language. The benefits booklet also contained virtual stories of different entrepreneurs, so that the treatment group could relate their personal situations to these fictitious characters. The potential legal and economic benefits of the registration are as follows: protection of business name and goodwill, greater access to bank loans, limited liabilities, continuity of businesses, better business confidence, raising finance, greater ownership rights, and enhancement of social status. The visits were conducted in two waves, according to the original spacing of the BCS and informality surveys. The first set of firms were visited in March-June of 2010, while the remaining firms were visited during January-February of 2011. We then conducted follow-ups (phone) interviews in April-July 2011 for the first batch of firms

> 2Details of the surveys are available from the authors.
# **b. 50% price increase, medium-bound elasticity**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>c. 100% price increase, medium-bound elasticity<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Chile Ukraine Moldova<br>South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br>Income Gains (%)<br>Income Gains (%)<br><!-- End of picture text -->

# **c. 100% price increase, medium-bound elasticity**

_Source:_ Based on national household budget surveys; Goodchild, Nargis, and Tursan d’Espaignet 2018; GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, <u>http://ghdx.healthdata.org/gbd-results-tool.</u> _Note:_ Deciles calculated from household per capita consumption (excluding identifiable rent and lumpy expenses). Years of life lost calculated form death events related to the risk of smoking tobacco.

Page 21
On the opposite direction, the most important contributor are the indirect taxes, which increase poverty by 6.1 percentage points (Figure 12).

**Figure 12. Marginal contributions to poverty reduction (percentage points)**

|Indirect Taxes|**-6.1**<br>_-1.9_<br>Extreme Poverty|
|---|---|
|Other (DT)<br>Direct Taxes<br>IPTU/ITR (DT)|**0.1**<br>**0.0**<br>**-0.1**<br>_0.0_<br>_0.0_<br>_0.0_<br>Moderate Poverty|
|Unemployment Benefits (DT)|**0.2**<br>_0.1_|
|Salario Familia (DT)|**0.2**<br>_0.1_|
|Bolsa Familia (DT)|**0.4**<br>_0.1_|
|Abono Salarial (DT)|**0.5**<br>_1.9_|
|BPC (DT)|**1.9**<br>_2.0_|
|Rural Pension (DT)|**4.0**<br>_4.2_|
|-8|.0<br>-6.0<br>-4.0<br>-2.0<br>0.0<br>2.0<br>4.0<br>6.0|
||**Marginal contribution at Consumable Income (percentage points)**|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a
5|3.8|12.5|3.1|0.8|6.8|5.1|
|Germany|22.4|34.3|38.5|3.0|12.8|-1.3|4.3|10.6|2.1|
|Japan|4.0|8.9|13.0|6.7|9.1|-1.3|8.1|2.4|6.7|
|United States|12.6|20.9|23.6|9.1|7.6|3.7|5.4|5.4|2.2|
|Total: above<br>industrial co.|13,5|21.5|26.6|6.4|9.6|1.6|4.8|5.8|3.8|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Source: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

There is one important difference between the first period 1991/92- 2001/02 and the second one 2001/02-2007/08. Industrial countries’ demand growth almost quadrupled from 1.6 percent p.a. to 5.8 percent p.a. Market share changes moved in the opposite direction but only declined to 3.8 percent p.a. from 4.8 percent p.a. Average import growth, which is the sum of demand and market share changes, accelerated from 6.4 percent to 9.6 percent per annum.

Along with the acceleration of demand growth during the second period, relative contribution of demand and market share changes to import growth got reversed. During the first period, except for France, the contribution of market share changes to import growth is much larger
Iran, Rep. of Mauritania TajikistanAzerbaijanSouth Sudan QatarFijiEquatorial GuineaLiberia Bolivia KenyaTimor-Leste NicaraguaNigerCongo, Rep.Ghana<br>Brazil<br>Kuwait Mali TogoIran, Rep. of Tajikistan Guinea-Bissau Iraq Chile Costa Rica NorwayTanzania Zambia Guyana South Africa MadagascarKyrgyz RepublicGuinea-Bissau Cameroon<br>Papua New Guinea Kuwait Armenia Botswana New Zealand Kazakhstan<br>Guatemala Cabo Verde Kyrgyz Republic Peru Nigeria Bahrain Saudi Arabia São Tomé and Príncipe<br>Canada Malawi Indonesia Solomon Islands United Arab Emirates Guinea Australia Sierra Leone<br>Norway<br>Correlation between cyclical components of real government spending and real GDP 1980-2020 Correlation between cyclical components of real government spending and real GDP 1980-2020 Canada Russian Federation<br>Sierra Leone São Tomé and PríncipeBrunei Darussalam<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for each country in good and bad times. Good (bad) times are defined as a positive (negative) HP-cycle. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on WEO (IMF) data.
Policy Research Working Paper 6120

# **Abstract**

This paper presents new global evidence on the key determinants of public-private partnership investment in electricity generated by renewable energy based on a panel data analysis for 105 developing countries over a period of 16 years from 1993 to 2008. It aims to identify the key factors affecting the private investor’s decision to enter renewable-based energy generation, through a probit analysis and the amount of investment sunk in this market segment, based on Heckman’s sample selection analysis. One of the key results of the paper is that the market for renewable-based energy is strongly driven by supportive policies. Support policies serve not only to attract the entry of private investors, but also to determine the level of investment. In the latter case, its impact is less significant, suggesting the need over time to revisit the power of the incentive schemes, as well as the implied allocation of risks between the public and private sector to ensure that feed-in tariffs produce the desired amount of investment. In contrast, broader economy-wide governance factors, including control for corruption and degree of political competition, are considered by private investors mainly for taking the decision to enter into renewable-based generation. This reinforces the expectation that private investors seem to be adequately protected against their risks, so that once they have entered the market, they can accommodate the governance environment. Private investors in renewablebased energy also require technical and regulatory certainty about the availability of renewable-ready transmission resources, if they are to finance investments. Private investors entering the market look more at the size of the market rather than the income level, whereas when determining the level of investment they assess both the size and “affordability” level. This raises some concerns on the sustainability of support mechanisms and their financing, particularly when the incremental costs implied by renewable-based generation are passed through to consumers.

This paper is a product of the Energy Unit, Sustainable Energy Department. It is part of a larger effort by the World Bank to provide open access to its research and make a
UN Guiding Principles) (Wyndham 2006).<sup>26</sup> The UN Guiding Principles define IDPs as “persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or humanmade disasters, and who have not crossed an internationally recognized State border.” The 2006 Great Lakes Protocol on the Protection and Assistance of IDPs (Great Lakes Protocol) and the 2009 African Union Convention for the Protection and Assistance of Internally Displaced Persons In Africa (Kampala Convention) explicitly extend the definition of IDPs to include those displaced by development projects.<sup>27</sup> The UN Guiding Principles and the IASC Framework on Durable Solutions for IDPs identify three ways in which internal displacement can end: (a) voluntary and sustainable reintegration at the place of origin (return); (b) sustainable local integration in areas where IDPs take refuge (local integration); and (c) voluntary and sustainable integration in another part of the country (settlement elsewhere in the country) (IASC 2010).

This stocktaking exercise focuses on data on conflict-induced internal displacement. While the UN Guiding Principles includes displacement due to natural or human-made disasters, and regional instruments such as the Great Lakes Protocol and the Kampala Convention include displacement due to development projects, this stocktaking report focuses on conflict-induced internal displacement only. Statistics on IDPs protected or assisted by UNHCR only include IDPs (and people in IDP-like situations) who have been

> 25 In 1967, the UN General Assembly adopted a Declaration on Territorial Asylum which reasserts the right of everyone “to seek and to enjoy in other countries asylum from persecution” (Article 14 of the Universal Declaration of Human Rights) while avoiding reference to the right to be granted asylum. Rather, the Declaration highlights the responsibility of the country of asylum to evaluate an individual’