rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10391

# **Abstract**

This paper identifies high-priority areas for methane emissions reduction and estimates recent emissions changes in those areas using atmospheric concentration data from the European Space Agency’s Sentinel-5P satellite platform. The modeling approach is illustrated with three case studies: landfills in Spain (Madrid), irrigated rice production in India (Karnal district, Haryana state), and oil production in Iraq (Al Amarah district, Maysan governorate). For each case, the paper estimates two change models by fixed effects: the monthly trend in methane concentration from January 2019 to November 2022, and the difference between mean concentration in 2022 and the previous three years. The paper estimates the change models for 775 high-priority areas and finds that cases with decreasing methane emissions are outnumbered four to one by cases with increasing emissions. The paper also analyzes trends in high-priority areas for seven major methane source sectors (agricultural soils, livestock, gas, oil, coal, landfills, and wastewater) and finds only two where emissions decreases outnumber increases (gas and oil). Among World Bank income groups, decreases outnumber increases in high-income economies but increases are hugely dominant in the other three groups. The paper concludes with a presentation of summary emissions trend reports for all 775 high-priority areas, with accompanying maps and an Excel file. As satellite-based monitoring becomes more widely employed, such reports will provide a useful template for judging further progress toward fulfillment of the Global Methane Pledge.

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 sdasgupta@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
# **Figure 2: Cabinet size has a negative correlation with governance indicators (2005-14 averages)**

Notes: The variables on the Y-axes are from the September 2018 update of the Worldwide Governance Indicators (panels a-d), various editions of the Corruption Perceptions Index (panel e), and the July 2018 version of the V-Dem dataset (panel f). See data appendix for full variable definitions and sources. Country codes are taken from the IMF’s International Financial Statistics and included in appendix 2. The available data are averaged over the 200514 period.

To probe these relationships further, we run a set of regressions that exploit within-country variation and account for several potentially confounding variables discussed above. The basic specification is as follows: Governancei,t = β 1Cabineti,t + β kControlsi,t + Countryi + Yeart + ε i,t A governance indicator for country _i_ in year _t_ is regressed onto our measure of cabinet size and _k_ controls discussed in the previous section. Country fixed effects absorb unchanging country characteristics and much of the explanatory power of slowly or rarely changing variables, while year effects account for common shocks.

Table 1 displays the results. As in the cross-country averages, cabinet size has a consistently negative association with measures of governance in these regressions. The coefficients on this variable are significant in four of six cases, except for Regulatory Quality and the Executive Corruption Index, where they get close to statistical significance at conventional levels. Substantively, the size of the coefficients appears relatively modest but not negligible: Adding 10 9
standardized across all countries and set equal to an average household size of four with 1.5 full-time workers, following the extensive methodological review of Anker (2011).<sup>10</sup> This gives a wage threshold of US$5.07 (US$1.90 at 2011 PPP, multiplied by the ratio 4.0/1.5). If individuals are above this threshold, their labor earnings are sufficient to lift a family out of poverty, assuming other working household members contribute at least the same amounts.

Previous studies that measure job quality using microdata have proposed other income cutoff points. For example, Brummund, Mann, and Rodríguez-Castelán (2018) use as a threshold whether the job pays above the Latin America regional poverty line of US$2.50 a day (2005 PPP), while Ortega Díaz (2013) uses the minimum wage in Mexico, and neither considers any adjustment for dependents. Sehnbruch et al. (2020) choose a level of income that reflects minimum living standards defined by a vulnerability line of US$10 a day (2005 PPP) for a set of Latin American countries. They consider one dependent for each worker. Moreover, it has also been argued that a definition of a good job depends on a country’s level of development and that the income received should enable at least a middle-class lifestyle by the country’s standards (Rodrik 2019). Based on all these other options considered in the literature, this paper conducts sensitivity analysis with alternative income thresholds, dependency ratios, and other specification changes. The analysis shows that the main results are robust to these alternative specifications (see section 4).

Many surveys do not explicitly distinguish among income components, but instead report total labor income, including payments in kind. Given this and the fact that food payments in kind are the most common form of payment in kind, the income dimension includes not only wages, but also food payments in kind in the calculation of
2<br>6 0<br>-2<br>4<br>-4<br>2 -6<br>0 -8<br>-2<br>Agriculture Industry<br>Growth Rate of Services<br>Services<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Years refer to respective fiscal years. Agriculture includes crop, livestock, forestry and fisheries; industrial sector includes mining and quarrying, manufacturing, electricity, gas, water and other utilities, and construction; services include trade, hotels, transport, communication and services related to broadcasting, financial, real estate and professional services, and public administration, defense and other services.

The fact that growth has not just accelerated, but has also become more stable over time is reflected in its steadily declining standard deviation, and a declining coefficient of variation (Figure 3).<sup>10</sup> Particularly remarkable is the sharp increase in the stability of GDP growth in the post-reform period since 1991.<sup>11</sup> Even if growth accelerated episodically in the decades prior to 1991, it was

> 10 While the figure only shows the coefficient of variation, the results are very similar for standard deviation.

> 11 The figure also documents a decline in the coefficient of variation in the 1980s, coinciding with some acceleration in growth in the 1980s.

7
# **Near-Real-Time Welfare and Livelihood Impacts of an Active Civil War:  Evidence from Ethiopia**

Kibrom A. Abay<sup>†*</sup> , Kibrom Tafere<sup>‡</sup> , Guush Berhane<sup>†</sup> , Jordan Chamberlin<sup>⁑</sup> , and Mehari H. Abay<sup>¥</sup> _Key words_ : Ethiopia, civil war, conflict, food security, livelihoods, phone surveys _JEL Codes_ : D74, I31, I32, J21, O13, Q12, Q13.

______________________________________________________________________________ † International Food Policy Research Institute (IFPRI), ‡ World Bank,<sup>⁑</sup> Food and Agriculture Organization (FAO) of<sup>the United Nations,¥Independent Consultant.*Corresponding author, email:</sup><sup><u>k.abay@cgiar.org.The views</u></sup> expressed in this paper are those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development /World Bank Group, FAO and IFPRI.
# **References**

<mark>Abdallah Ali, M., M. Mughal, and D. Chhorn. (2021). Empowering Women through Microfinance in Djibouti. Mimeo.</mark> <mark>Afridi, F., A. Mukhopadhyay, and S. Sahoo. (2016) “Female labor force participation and child education in India: Evidence from the National Rural Employment Guarantee Scheme.” IZA Journal of Labor & Development 5(7).</mark> <mark>Ajayi, K. F., A. Dao, and E. Koussoubé (2022). “The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation in Burkina Faso.” World Bank Policy Research Working Paper 10239.</mark> <mark>Alaref, J., A. Patil, T. Rahman, A.M. Munoz Boudet, and J. Rajbhandary. (2024). “Women’s Labor Force Participation in Nepal An Exploration of The Role of Social Norms.” World Bank Policy Research Working Paper 10810.</mark> <mark>Alik-Lagrange, A., N. Buehren, M. Goldstein, and J. Hoogeveen (2023). “Welfare impacts of public works in fragile and conflict affected economies: The Londö public works in the Central African Republic.”</mark> _<mark>Labour Economics</mark>_ <mark>81.</mark> <mark>Anderson, S. and M. Eswaran (2009). “What determines female autonomy? Evidence from Bangladesh in Bangladesh.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>90(2): 179-191.</mark> <mark>Bagga, A., M. Holmlund, N. Khan, S. Mani, E. Mvukiyehe, and P. Premand (2023). “Do Public Works Programs have Sustained
6es-

# TABLE 5

# Self-financing Ratios for Comoleted Prolects. 1966-83

<!-- Start of picture text --> Projects (with<br>Self-financing Ratio Estimated Ratios<br>at Prolect Comoletlon In Sam le Survey<br>Numb er %__<br>0 10 21<br>1 tolO 9 19<br>11 to 20 8 17<br>21 to30 10 21<br>31 to40 6 13<br>41 to60 3 7<br>over 60 1 2<br>Average Ratio 18 Total 47 100<br><!-- End of picture text --> One of the weakest areas of forecasting appears to be for selffinancing ratios, for they are not only consistently cverestimated but also the magnitude of error Is one ot the highest. In about 65% of cases, the actual self-financing ratio was more than five percentage points below that for=ast Of particular concem Is the 31% of Instances where the actual ratlo was more than 20 percentage polnts below that forecast and the 7% of projects where It was more than 50% below that forecast. The actual ratio was higher than forecast In only 14% of cases. The magnitude of the forecasting error on average has Increased since the early 19708 and appears to have continued to deteriorate In the 1980s.

About 42% of projects actually had lower self-financing ratios at completion of the project than at appralsal and one half of these projects had ratlos tha. .#"re more than 20% lower. Of the one half of projects where utilities had poor ratlos at appraisal (I.e., 20% and lower), nearly 50% of utlittls had managed to Increase self-financing by more than five percentage polnts by the end of the project. However, 25% of utilitles actually had ratlos that had deteriorated even further.
generates a similarly signed coefficient, as does the policy index which remains negatively signed and highly significant. These estimations with identical country coverage but using different deflators yield qualitatively comparable estimates, which suggests that the relationships we have pinned down in the earlier part of the paper are **_50_** indeed robust Finally, we have included in each regression a set of regional dummy variables following previous researchers 5 Like these studies, we find that some dummy variables are highly significant, confirming the prior expectation that some regions have registered surprisingly low or high values of the dependent variables (e.g., government size, growth, infant mortality and life expectancy). However, as Barro and Sala-i-Martin (1995) observe, this interpretation of the estimated coefficients on dummy variables is problematic since the choice of regional dummy variables is endogenous. Nevertheless, the presence of significant regional dummy variables poses a challenge; we need to explain, for example, why Sub-Saharan Africa has a below average growth rate or East Asia has a below average government size without resorting to the inclusion of dummy variables for these regions. We have taken care of this criticism in two ways. First, we re-estimated each regression without regional dummy variables and the results we get are essentially the same, albeit with a lower R-squared. Second, we re-estimated each regression using the random effect technique with and without regional dummy variables.

> **50** Of course, the growth decomposition can differ across the two data sets and depend as usual on point estimates and average of each variable. For example, for low income countries, World Bank data tend to overestimate investmentlGDP, and underestimate per capita GDP and government consumption.

> _5_ See, for example, Barro and Sala-i-Martin (1995) and Easterly and Levine (1996).

54
# **Literature**

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- Duan, J.-C. (1994), "Maximum Likelihood Estimation Using Price Data of the Derivative Contract", _Mathematical Finance,_ 4(2): 155-167.

- Duan, J.-C. and M.-T. Yu (1994), "Assessing the Cost of Taiwan's Deposit Insurance", _Pacific-Basin Finance Journal_ 2: 73-90.

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22
|(121.838)|(140.327)|(120.366)|(82.424)|(132.392)|
|_Observations_|116|61|87|59|131|62|
|_R-squared_|0.255|0.600|0.374|0.567|0.203|0.450|
|_Number of countries_|55|31|39|29|64|30|
|_Country FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Time FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Standard errors in pare_|_ntheses_||||||
|_*** p<0.01, ** p<0.05,_|_* p<0.1_||||||

# **3. Concluding Remarks**

We examined the determinants of public employment based on the ILO data set, which is the most complete source for analysis of public employment across countries, We found a negative association between the size of public employment and country size by population, and a positive association with 25
25,000 businesses. In this paper we restrict the sample to the 50 countries that have sufficient sample size to conduct gender-disaggregated analysis to form a total sample size of 154,458 businesses. The target population of the Future of Business Survey are Facebook Business Page administrators that identify themselves as business owners and managers who take the survey on their Facebook mobile application.<sup>16</sup> In order to look at the impact of school closure, we use school closure policies related to COVID-19 extracted from data collated by UNESCO that indicates whether a country was: fully open, partially open, on an academic break, or fully closed due to COVID-19 across time measured in daily increments (UNESCO, 2020 and 2021).<sup>17</sup> In this paper we create a dummy variable to indicate whether schools were partially or fully closed in a country at the time of each survey wave (School Closure = 1 if schools fully or partially closed due to COVID-19, and 0 otherwise). Appendix B provides a table of the COVID-19 related school closure policies in place for all countries included in the analysis at the time of each survey wave throughout 2020 and in September 2021.<sup>18</sup> Fears about virus contagion that were often coupled with lockdown measures to contain the COVID-19 virus reduced mobility and social interactions leading to a large shock to business demand, while also restricting the ability of firms to perform key business activities such as in-person sales. To account for this in our analysis we measure mobility in a country at the time of each survey wave by utilizing Google’s Community Mobility Reports (Google, 2020 and 2021). The Google mobility data are created with aggregated, anonymized sets of data from users who have turned on the ‘Location History’ setting on their phone and show how visits and length of stay at different types of places change compared to the median value, for the corresponding day of the week, during the 5-week period from January 3
**Table 1 – Green occupation categories**

|**Green job category**|**Definition**|**Examples**|
|---|---|---|
|**Increased Demand**<br>**Occupations**|Occupations that already exist. They may<br>face increased demand with an expanding<br>green economy. While their work context<br>may change, the tasks and skills they use will<br>not.|Carpenters and welders that will<br>be required in the construction of<br>new energy-efficient buildings or<br>agricultural workers needed for<br>more organic farming.|
|**Enhanced Skills**<br>**Occupations**|Occupations that already exist. A growing<br>green economy may or may not increase the<br>demand for these occupations, but it would<br>affect the type of tasks or skills required.|Construction<br>inspectors,<br>agricultural<br>technicians,<br>and<br>architects. Workers in these jobs<br>may need to learn how to use new<br>materials or adhere to new<br>energy-efficient building codes.|
|**New and Emerging**<br>**Occupations**|The “purest” forms of green jobs. These<br>occupations emerge because of the green<br>economy creating the need for new and<br>unique work and worker requirements. As<br>such, these occupations emerged more<br>recently.|Wind energy managers, climate<br>change<br>analysts,<br>or<br>water<br>resource specialists.|

Source: Own elaboration based on O*NET database and Dierdorff et al. (2009, 2011).

This paper uses the International Standard Classification of Occupations
# **D.2 Alternative Samples and Specifications**

The results shown throughout Appendix Section D.2 re-estimate the output shown in the main body of the paper using a number of alternative cohort ranges, specifications, and samples. Each panel is described below, and the results in the following tables display the panels in a consistent order.

## **Panel A – Baseline**

For reference, Panel A reproduces the results used in the paper. These estimates use a sample of all Ethiopian women born between 1970 and 1988. All estimates in this section include a cubic in age, and birth year and district fixed effects. The baseline estimates use the FPE instrument, _Izy_<sup>_F P E_</sup> , starting age data from the 2007 census, and a district specific linear time trend.

## **Panels B to E – Alternative Cohort Ranges**

These panels include two expanded samples, 1968 to 1992 (Panel B) and 1969 to 1989 (Panel C), and two more restrictive ranges. In Panel D, one cohort from each end of the baseline sample is removed, yielding a range from 1971 to 1987. This sample no longer includes any fully post-reform cohorts. The data are restricted to 1972 in Panel E, the final fully pre-FPE cohort. Removing additional cohorts on the later end of the range would remove significant and necessary identifying variation; therefore, the 1987 cohort remains the cutoff on the upper end of the range in Panel E.

## **Panel F – Matched 1984 Start Ages**

Intensity measures are constructed using starting age information from the 1984 census. While the prereform timing of these data are ideal, the administrative boundaries are not consistent between the 1984 and post-1991 periods. Therefore, while there is starting age information contained in the 1984 census, the level two administrative information does not match with the zones used in the study. To adjust the 1984 data to the 1994 geographical boundaries, shapefiles from each time period
household is visited for 5 consecutive quarters, with 20 percent of the sample being replaced each quarter. This allows us to track each individual for a period of 1 year and 3 months.<sup>8</sup> The survey consists of two primary instruments: (i) a socio-demographic survey which collects information from all selected household members on characteristics such as age, gender, and education level at the time of visit, and (ii) an occupation and employment survey which collects information on the labor market status of household members aged 12 years and above, using the week prior to the survey visit as the period of reference. We focus on education and labor variables as our outcome variables, looking specifically at whether individuals work and/or study in a given period, as well as their labor income. The survey also collects information on a rich set of socioeconomic characteristics such as gender, marital status, demographic characteristics, and whether respondents live in rural/urban areas, among others.

Our analysis focuses on the data for individuals aged 15 to 24 years old who completed all 5 interviews in the period between 2005:Q1 and 2013:Q4. This means we have information for 36 cohorts, around 650,000 individuals represented by nearly 3,260,000 observations.

Additionally, we use quarterly state level inflation and industrial production indicators from the Mexican Statistics Office (INEGI). We merge this information with individual information at the state-quarter level to capture the macroeconomic conditions of areas where youths live.

# **2.2 Descriptive Statistics of Nini Incidence**

An individual is generally classified as being a Nini if s/he has not worked for pay _in the labor market_ for at least one hour during the week prior to the survey, and is not in formal education at the time of the survey. According to this definition and using the sample described above, during the period from 2005 to 2013 approximately 21 percent of young people aged 15 to 24 years were Ninis. Of