rafmacalaba/datause-encoder-data huggingface dataset:
# **7. Reporting GGU Emissions Changes**

The models and results presented in this paper can offer potentially valuable information to participants in the Global Methane Pledge. Accordingly, we provide illustrative reports in tabular and graphical formats.

Table 13 reports results for EDGAR Decile 1 GGUs, which are dominated by Increasing in Coal (10 districts in China and Russia) and Livestock (4 districts in Pakistan and China). There are 2 other GGUs with Increasing (Soils in Chongqing, China; Landfills in Durango, Mexico). Only 2 GGUs have Decreasing, both in Gas (Aksu District, China and Al Gharbia District, UAE). Among the remaining GGUs in Table 13, 7 have Mixed results and 1 has No Trend. Complete results for GGUs in all five EDGAR deciles are provided in the accompanying Excel file<sup>19</sup> .

Figures 13 and 14 map our results for the top 4 EDGAR deciles.<sup>20</sup> Map icons are centered over their GGUs, color-coded by emissions change categories and bordered by color codes for dominant GGU sectors. To cite two examples: All Algerian icons are Decreasing with Gasdominated GGUs; all Sudanese icons are Increasing with Livestock-dominated GGUs.<sup>21</sup> Overall, the results can be roughly summarized as follows: Europe: Almost all Decreasing [Landfills, Wastewater] Russia: Rough parity in Increasing, Mixed, Decreasing [Oil, Gas, Coal] North Africa: Almost all Decreasing [Oil, Gas] Sahelian Africa: Rough parity in Increasing, Mixed [Livestock] Southern Africa: Mostly Mixed [Coal] Gulf Region: Decreasing or Mixed [Oil, Gas] Central Asia: Majority Decreasing [Gas] Pakistan: Majority Mixed [Livestock] India: Almost all Increasing [Livestock, Soils, Coal] Other South and Southeast Asia: Almost all Increasing [Soils, Livestock] China: Dominated by Increasing [Soils, Livestock, Coal, Wastewater] North America: Majority Increasing, along with Mixed [Oil, Gas, Landfills] Mexico: Increasing [Landfills] South America: Decreasing [Gas, Wastewater]<sup>22</sup>

> 19https://datacatalog.worldbank.org/int/search/dataset/0064309/methane_emissions_changes_by_edgar_decile

> 20 The maps are visually cluttered by spatial “overcrowding” when 5th-decile GGUs are also included.

> 21 To minimize clutter in the maps, we include only the names of the countries with EDGAR 1st-decile results.

> 22 One important caveat:  Our regional coverage is biased by the relative scarcity of clear imagery because of cloudiness in the West African coastal region, the Amazon and Central African rain forest regions, and large swathes of Southeast Asia.  Inspection of Figure 1 shows that numerous GGUs would be included for these areas if images were more plentiful.

28
- 13

TABLE 3

# Lending for MSWM Projects in EMENA. FY74-88

|COUNTRY|PROJECT TITLE|LOAN<br>AMOUNT|TOTAL<br>PROJECT<br> <br>COST<br>(USS MILLI|<br>M1SW<br>COMPONE<br> <br>COST<br>ON)|M MSWM AS<br>NT PERCEN<br>OF TOTA<br>PROJEC<br>COST|T<br>L<br>T<br> <br>FY|STATUS AS<br> <br>OF 6/30/89|
|---|---|---|---|---|---|---|---|
|Afghanist|an Kabul Water/Sanitation<br>Project|9.0|11.7|0.2|2.0%|75|COMPLETED|
|Egypt|Urban Development Project|14.0|22.4|**_7.9_**|35.1%|78|COMPLETED|
|Egypt|Greater Cairo Urban Dev Project|59.0|116.2|0.6|0.5%|82|ONGOING|
|Jordan|Tourism Prciect|6.0|19.4|0.1|0.3%|76|COMPLETED|
|Jordan|Anean Transp&Municipal Dev Project|<br>30.0|65.6|9.2|14.0%|84|ONGOING|
|Lebanon|Reconstruction Project|50.0|83.6|3.0|3.6%|78|COMPLETED|
|Morocco|Rabat Urban DeveLopment Project|18.0|25.6|1.2|4.7%|78|COMPLETED|
|Morocco|Second Urban Development Project|36.0|81.0|3.1|3.8%|81|COMPLETED|
|Pakistan|<br>Lahore Urban Development Project|16.0|24.0|4.7|19.6%|83|ONGOING|
|Pakistan|<br>Karachi Special Development ProJect|70.0|148.6|8.9|6.0%|86|ONGOING|
|Tunisia|Second Urban Development Project|19.0|39.4|3.5|8.9%|79|COMPLETED|
|Turkey|Cukurova Region Urban Eng Project|9.2|10.7|0.2|1.6%|85|ONGOING|
|Turkey|Cukurova Urban Development Project|120.0|467.4|11.5|2.5%|87|ONGOING|
||TOTALS FOR EMENA REGION:|456.2|1115.7|54.0||||
||AVERAGE PER PROJECT:|35.1|85.8|4.2|4.8%|||
However, the tests reported so far are concerned with unconditional heterogeneity, and it might be possible to gain power by testing for more specific forms of parameter heterogeneity. For example, it could be argued that, owing to network effects, the elasticity of output with respect to infrastructure should be higher in countries with larger infrastructure endowments than the rest.<sup>28</sup> Alternatively, the elasticity could vary with the level of development – as captured for example by GDP per worker – reflecting the fact that poorer countries are less able to use infrastructure effectively. As another hypothesis, the output elasticity of infrastructure could depend negatively on the size of the overall population, owing to congestion effects.

To verify this, we re-estimate the model in column 1 of Table 3 without imposing homogeneity across countries of the long-run parameter of the infrastructure synthetic index, and then look for patterns of heterogeneity in the individual-country estimates of that parameter along the three dimensions just mentioned – GDP, infrastructure endowment (both in per worker terms), and population size .

Figure 1 plots the resulting country-specific estimates of the infrastructure long-run coefficient against each of the three variables just mentioned. While there are some obvious outliers, the conclusion from all three graphs is clear: there is no relationship between the country-specific coefficient estimates and the three variables considered. This points to the absence of cross-country heterogeneity of the output elasticity of infrastructure along any of these dimensions.

Table 5 presents the results of more formal tests of parameter heterogeneity along these dimensions, using the country-specific estimates of the output elasticity of infrastructure obtained above. The first two columns of the table test if the output contribution of infrastructure varies across countries with their respective level of income per worker. We divide the countryspecific estimates into two groups, one consisting of countries with high income and the other of countries with low income. In column 1, the groups are drawn using the World Bank‘s list of ̳high income‘ countries; the low-income group is made up by all other countries in the sample. In column 2, the grouping is based instead on the sample median income per worker in the year 2000. In each case, the table reports the simple average of the parameter estimates of each of the two groups, along with the _p_ -value of the test of difference in group means. In both columns, the

> 28 This is similar in spirit to Gregoriou and Ghosh (2009), who let the growth contribution of public expenditure vary with countries‘ average level of expenditure.

19
24 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

- Lo, C. P. (2001). Modeling the population of China using DMSP operational linescan system nighttime data. Photogrammetric engineering and remote sensing, 67(9), 1037-1047.

- Ma, L., Wu, J., Li, W., Peng, J., & Liu, H. (2014). Evaluating saturation correction methods for DMSP/OLS nighttime light data: A case study from China’s cities. Remote Sensing, 6(10), 98539872.

Morgan, D. R., & Mareschal, P. (1999). Central-city/suburban inequality and metropolitan political fragmentation. Urban Affairs Review, 34(4), 578-595.

Nelson, A. C., & Foster, K. A. (1999). Metropolitan governance structure and income growth. Journal of Urban Affairs, 21(3), 309-324.

Oakerson, R. J. (1987). Local public economies: Provision, production, and governance. Intergovernmental Perspective, 13(3/4), 20-25.

Oliver, J. E. (2001). Democracy in suburbia. Princeton University Press.

- Ostrom, E. (2010). Beyond markets and states: polycentric governance of complex economic systems. Transnational Corporations Review, 2(2), 1-12.

- Pandey, B., Joshi, P. K., & Seto, K. C. (2013). Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 23, 49-61.

- Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., ... & Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publ. Off. Eur. Union.
Figure 3. Event Studies, Total Crime Reports <!-- Start of picture text --> All<br>Quarter<br>Male<br>Quarter<br>Female<br>Quarter<br>2017−Q1 2017−Q2 2017−Q3 2017−Q4 2018−Q1 2018−Q2 2018−Q3 2018−Q4 2019−Q1 2019−Q2 2019−Q3 2019−Q4<br>2017−Q1 2017−Q2 2017−Q3 2017−Q4 2018−Q1 2018−Q2 2018−Q3 2018−Q4 2019−Q1 2019−Q2 2019−Q3 2019−Q4<br>2017−Q1 2017−Q2 2017−Q3 2017−Q4 2018−Q1 2018−Q2 2018−Q3 2018−Q4 2019−Q1 2019−Q2 2019−Q3 2019−Q4<br>.015<br>.01<br>.005<br> (95% CI)<br>β<br>0<br>−.005<br>.015<br>.01<br>.005<br> (95% CI)<br>β<br>0<br>−.005<br>PEP−RAMV Roll−out<br>PEP−RAMV Roll−out<br>.015<br>.01<br>.005<br> (95% CI)<br>β PEP−RAMV Roll−out<br>0<br>−.005<br><!-- End of picture text --> _Notes:_ The figure presents the estimation of an event study based on specification (1). It estimates the effect of the treatment interacted with 12 quarter dummies from Q1 of 2017 to Q4 of 2019. The gray vertical line represents the beginning of the rollout of the PEP amnesty program in August 2018. The results are based on OLS estimates. The whiskers represent 95 percent confidence intervals based on standard errors clustered at the city level.

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# **D. C Non-Inferiority Test**

Table D.3. Impacts of PEP Program on Mayoral and Presidential Elections Non-Inferiority Test for Potential Violation of Parallel Trend Assumption

||Election|Shar|e of Vote|s for|Electoral|
|---|---|---|---|---|---|
||Turnout|Left|Center|Right|Competition|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Mayoral Elections - C_|_anonical 2x_|_2 variable_||||
|I(PEPm)_×_I(Post2018)t|-0.006|-0.008|0.015|-0.013|0.007|
||(0.003)|(0.019)|(0.025)|(0.020)|(0.014)|
|q-values|[0.653]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel B. Mayoral Elections - C_|_ontinuos va_|_riable_||||
|PEPm_×_I(Post2018)t|-0.003|0.001|0.010|-0.011|0.010|
||(0.002)|(0.007)|(0.009)|(0.008)|(0.007)|
|q-values|[0.316]|[0.530]|[0.383]|[0.316]|[0.316]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel C. Presidential Elections_|_- Canonical_|_2x2 varia_|_ble_|||
|I(PEPm)_×_I(Post2018)t|0.002|-0.009|0.004|-0.001|-0.004|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|_Panel B. Presidential Elections_|_- Continuos_|_variable_||||
|PEPm_×_I(Post2018)t|-0.019|-0.036|0.007|0.022|0.051|
||(0.014)|(0.016)|(0.016)|(0.021)|(0.026)|
|q-values|[0.237]|[0.124]|[0.467]|[0.276]|[0.124]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal Controls_×_Year|Yes|Yes|Yes|Yes|Yes|
|<br>Differential Trend|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. It shows the results of our main specification in equation 1, controlling for an indicator variable of differential linear pre-trends between the treatment and control groups. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

75
level outcomes and do not find any statistically significant effects of the two placebo cutoff points.

In the third test, we assess the program impact on exogenous variables including individuals' age, gender, ethnicity, and education completion. For the education variables, we restrict the sample to individuals ages 30 or above to ensure that their education was not affected by the program. We use the same model as described in equation (3), utilizing both the pre-program and post-program data from the VHLSSs from 2004 to 2020. We do not find significant impact of the program on these exogenous variables, suggesting that there is good balance in the exogenous variables around the cutoff point (Appendix A, Table 16).

Second, we examine the sensitivity of the estimated program effects when using different bandwidths. Initially, we expand the bandwidth to include districts with the 2006 poverty rate above 35%. Subsequently, we use narrower bandwidths by limiting the sample to households in districts where the poverty rate ranged from 40% to 60% in 2006. There are no available estimators for combining nonparametric RDD with fixed effects. However, we can apply nonparametric RDD to the post-treatment data to find the optimal bandwidth. We select optimal bandwidths using data-driven mean-squared errors (Calonico _et al._ , 2017), which yields an average optimal bandwidth of approximately 7%.<sup>8</sup> Using this bandwidth, we estimate the program impact and present the results in Tables A.17 to A.19 in the Appendix. Due to space constraints, we only provide robustness analysis for several key variables of individual and household outcomes. The estimated program effects on individual outcomes, using different bandwidths, closely align with the main results presented in Tables 2 and 3. As another check, Table A.20 in the Appendix illustrates the 'donut' RDD, wherein we exclude districts that are very close to the cutoff point (2006 poverty rate of 50%) to assess

> 8 To find these optimal bandwidths, we use the 'rdrobust' command developed by Calonico _et al._ (2017). 21
> influence over other enterprises such as PCL.<sup>38</sup> Consequently, enterprises in the private sector did not end up being an effective voice in support of reform.

_Concern about the fiscal cost of supporig postal service._ As discussed in greater detail above, although telecommunications were not a 'cash cow' in Malawi, MPTC did not appear to be a major drain upon the budget either. Furthermore, although the poor quality of MPTC's accounts meant that it was impossible to draw any conclusions about its profitability, it was widely believed that profits from MPTC's telecommunications services financed large losses associated with the provision of postal services. One fear, therefore, was that if postal and telecommunications services were separated and then MTL privatized, that Malawi Posts Corporation (MPC) would become a major burden on the Government's already troubled finances.

_Opposition from Management and Workers of MPTC._ Although the weak support from the private sector, the lack of a macroeconomic crisis, and concerns about the cost of supporting MPC tended to discourage reform, the main sources of opposition were the management and workers of MPTC. Although overstaffing was less pronounced in 1998 than it had been in 1995, when Pyramid Research ranked MPTC as the most inefficient telephone company in SubSaharan Africa (Economist Intelligence Unit, 1999b), it remained a problem. One concern was that privatization might lead to a significant number of layoffs and, consequently, workers were opposed to reform. The most visible sign of opposition was vandalism of MIPTC's lines and equipment. For example, in August 1999, MPTC lines in Blantyre were vandalized resulting in 10,000 customers, including the city's banks and airport, being temporarily being cutoff (Economist Intelligence Unit, 1999b). Although vandalism might have been the result of the

**38** Although in 1997 President Banda lost control of the Press Trust Corporation after a lengthy legal battle, control was awarded to a trust established to benefit all Malawians. In practice, this appears to have resulted in the government having a great deal of influence over the company. For example, following the appointment of a former finance minister to chair of the company over the objections of the PCL board, the Economist Intelligence Unit (2002, p. 28) wrote: "In business circles, it has been tacitly acknowledged that this exercise shows that PCL is not after all a company in the private sector, but remains a parastatal, despite being quoted on the London and Malawi Stock Exchanges."

32
in the 1980s, we also estimated the same equations for the shorter sub-period of 1980-87. Though the choice of the terminal year is constrained by the availability of data, it nevertheless covers a longer period, and a substantial part of the 1980s, than the earlir studies.

Data on capital stock, labor, GDP, and different measures of openness was compiled for as many Sub-Saharan African countries as possible for the 1967-87 period. Tbis was obtained from the Supplemental data base' of World Development Report 1991. Data on real gross domestic product (GDP) is based on national accounts data in constant 1980 US dollars, with rate of growth of GDP calculated as log differences of real GDP.

Though we have used the largest sample of Sub-Saharan African countries for which all necessary data is available (i.e. twenty-seven, see Table 6) questions may be raised about the homogeneity of this sample of Sub-Saharan African countries For example, it has been argued that countries like Ethiopia, iAberia, Sudan, Rwanda, Uganda, and Zaire have been characterized by protracted civil wars and internal disturbances and thus their economic performance may not be related to economic policy. Similarly, CFA countries' performance is affected by the fact that they have not had the use of a key policy instrument for improving incentives for tradables (i.e. nominal exchange rate adjustment) which was available to other countries.' Of ou sample, thirteen countries belong to these two categories.2 "The data on physical capital stock is due to BhaDa and Lau (1991) and is calculated from data on annual fuxed investment for 1960-87 in constant 1980 US dollars. Capital was accumulated using the perpetual inventory method and a 5% depreciation was assumed to derive the capital stock series in the data base.

24It has been argued that their growth performance over the 1980s, has been more dismal than others in SSA (Devarajan and de Melo 1990) and worse than even CFA performance in the 1970s, in part because of their inability to adjust the nominal exchange rate in the face of large external shocks.

'These exclusions can reduce the sample of 27 to only 12 Sub-Saharan African countries, a set which is surprisingly similar to the group of SSA countries deemed to be in the 'adjustment phase" by Hussain (1992).

18
using labor market outcomes alone failed during a particular period in Sri Lanka. This emphasizes the importance of verifying that a particular survey-to-survey imputation model performs well, using as many past periods of consumption data as possible, before using it to generate new poverty projections.

We propose four practical steps to assess whether imputing from one survey to another across different time periods is feasible. These are: Closely examining the surveys to ensure that the questions are compatible, examining whether the sampling strategy of the two surveys is comparable, verifying that the means of the proxy poverty measures are balanced between the two surveys, and ensuring that common variables explain a large portion of changes in household expenditure per capita. For survey-to-survey imputation to succeed, both surveys must contain a set of common variables based on substantively identical questions that track changes in household expenditure per capita closely. Therefore, it is important to better understand which variables are strongly correlated with changes in welfare and poverty in a variety of contexts. For example, in urban Sri Lanka between 2006 and 2009, a small set of labor outcomes captures changes in household welfare quite well, whereas in rural and estate sectors, information on assets are necessary to adequately capture changes in poverty.

Looking forward, additional empirical analysis can examine which types of variables best track changes in poverty in a variety of developing country contexts, to better inform future survey design. Future work can also further explore the extent to which ex-post adjustments to survey weights can make imputations across surveys more accurate. Finally, efforts could be made to develop more sophisticated diagnostic techniques to better identify which set of existing common variables can best track changes in poverty.

25
subsidies and tax benefits (Wu et al. 2012), and are more likely to be bailed out when facing financial stress (Faccio et al. 2006). Moreover, there is evidence that politically connected firms tend to be less efficient than non-connected firms (Leuz and Oberholzer-Gee 2006, Fan et al. 2007; Boubakri et al. 2008, Faccio 2010).<sup>6</sup> Relative to politically connected firms in China, politically neutral start-ups experience faster productivity improvement, conditional on survival (Du and Girma 2010). The quality of earnings reports from politically connected firms is significantly poorer than that of similar non-connected companies, and lower quality in earnings reports is associated with a higher cost of debt for the non-politically connected firms (Chaney et al. 2011). Politically-connected firms are also more immune to government regulations (Berkman et al. 2010). Our findings add to the literature in that we find non-politically connected firms in China face stronger financial constraints, which may stem from scarce financial resources flowing to inefficient, but connected firms and therefore reflect resource misallocation.

We provide evidence that non-connected, large, non-state firms are especially financially constrained. We also provide evidence of weaker CEO incentives and lower investment intensity for politically-connected firms.

Our paper also fits into a large microeconometric literature that investigates the role of financial factors in corporate investment decisions. The earliest contributions demonstrate that in well developed economies such as the U.S. (e.g. Fazzari, Hubbard, and Petersen 1988; Almeida, Campello and Weisbach 2004), U.K. (e.g. Bond, Elston, Mairesse, and Mulkay 2003), and Japan (e.g. Hoshi, Kashyap, and Scharfstein 1991), firms face substantial financial constraints. In those studies, a significant correlation between investment and measures of internal funds are attributed to capital market imperfections that give rise to financial constraints. There is also an

> 6 But political connections have returns, as shown by Cooper et al. (2010) and Calomiris et al. (2010).

7
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