rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Urban CO2 Emissions: A Global Analysis with New Satellite Data**

## **Susmita Dasgupta* Somik Lall David Wheeler**

## **World Bank**

Keywords: CO2 emissions, Environment Kuznets Curve, City CO2 performance metrics JEL classification: Q54; Q58; R40; R11 Acknowledgments: The authors thank Richard Damania, Sam Fankhauser, Stephane Hallegatte, Matt Kahn, Joanna Masic, Rick Van der Ploeg, Tony Venables, Sameh Wahba, and seminar participants at the World Bank and FCDO for helpful discussions. The authors acknowledge the generous financial support from the UK FCDO. 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.

> * Authors’ names in alphabetical order.
balances. _Sources:_ World Bank database; Central Bank of Colombia, table 4.

As the system operated in the late 1990s, strengths emerged, such as increased political participation, and so did weaknesses, such as mismatched spending authority and excessive borrowing by some subnational units. Overall, however, the fiscal deterioration occurred mostly at the national level. See table 1.

# **II. Explanatory Variables**

Because the Colombian experience with political and fiscal decentralization is relatively short and an economic crisis has not yet tested the system, we evaluate the system on dimensions that have proven critical in Argentina and Brazil for determining whether subnational govermments contribute to unsustainable overall public sector deficits. The explanatory variables fall into two 7
# **2. Data**

The main data source for this study is the _Pesquisa Nacional por Amostra de Domicílios_ (PNAD), and the period of analysis is 1995-2012. The PNAD is an annual, nationally representative household survey, covering both rural and urban areas.<sup>8</sup> It is fielded by the Brazilian Census Bureau ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE) every year, except for census years. The descriptive analysis uses all available years in the period 1995-2012, but for the decompositions we use the PNAD datasets for six years: 1995, 1996, 2002, 2003, 2011 and 2012.

The following filters were applied to the data to generate our working sample. Our sample includes all workers aged 18-65 who reported positive earnings during the survey’s reference week. Our measure of earnings is total monthly earnings from all jobs, and it is expressed in real values using the CPI deflator with base-year 2005. Monthly earnings are trimmed at the 1<sup>st</sup> and 99<sup>th</sup> percentiles. Sample sizes vary somewhat over time, but about 130,000 individuals per year are included on average.

All variables used in the analysis are categorical, except for earnings, schooling and experience in the labor market. The last two are measured in years. Demographic worker characteristics include a gender dummy and a three-way categorical variable for race (white, black and other). The analysis also distinguishes between rural and urban workers. Rural and urban areas are classified in accordance with the Brazilian census definitions. Spatial measures also include dummies for the five main geographic regions of Brazil: North, Northeast, Center-West, Southeast and South. Finally, regarding sectoral distribution, workers are divided into 17 different sectors of economic activity (see Table 2 below).

In terms of institutional factors, we distinguish three characteristics of the job. Workers are classified as formal employees if they have a job that is properly registered in their
require it to be very severe indeed in order to overturn the conclusion that the multiplier is significantly less than one.

Second, as long as the measurement error in government spending is uncorrelated with changes in disbursements on World Bank loans, the IV estimates of the multiplier are still consistent even in the presence of measurement error in total spending. Moreover, if measurement error were important, then other things equal, we should expect to find IV estimates that are larger than the OLS estimates. Yet in nearly all the specifications we have seen so far, the opposite is true. Taken together these arguments suggest that while measurement error in government spending surely is nontrivial for these countries, it is unlikely that my estimates of the multiplier suffer from major attenuation bias as a result.

# **5.2  Special Features of World Bank‐Financed Spending**

The identification strategy in this paper hinges on the correlation between growth and changes in public spending that are associated with changes in disbursements on World Bank loans. This opens the possibility that there are unique features of World Bank‐financed spending that might reduce its short‐run stimulative effect on output relative to other forms of spending. And since the composition of total spending shifts towards World Bank‐financed spending when the latter increases, this may account for why the estimated overall government spending multiplier is so small. One such possibility is that the distribution of World Bank‐financed government spending between domestic production and imports is different from that of non‐World Bank‐financed spending. To take an extreme scenario, if a World Bank‐financed project consists exclusively of purchases of machinery or consultancy services imported from abroad, then one might expect the stimulative effects of this spending in the recipient country operating through expansions in domestic aggregate demand to be limited.

To investigate this issue further, I match up the project‐level data on disbursements with contract‐level data on individual procurement contracts awarded on World Bank projects. The World Bank’s procurement records include information on the country
<!-- Start of picture text --> Agr, Forestry, Fisheries<br>Beverages and Tobacco Products<br>Chemical, Rubber, Plastic Products<br>Construction<br>Dwellings<br>Electricity, Gas, Water<br>Energy Extraction<br>Ferrous Metals<br>Leather Products<br>Machinery and Equipment nec<br>Manufactures nec<br>Metal Products<br>Metals nec<br>Mineral Products nec<br>Minerals nec<br>Other Private Services<br>Paper Products, Publishing<br>Processed Foods<br>PubAdmin/Defence/Health/Educat<br>Textiles<br>Trade and Transport Services<br>Transport Equipment<br>Wearing Apparel<br>Wood Products<br>0 20 40 60 80<br>Total LVAX share (%)<br>ZAF COL CHL PER THA TUR<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

Minerals have a relatively high share of labor in exports even vis-à-vis other sectors. Exports of the minerals sector in South Africa are the most labor intensive among any of its comparators. In Figure 6 we observed that labor value added contained in South Africa’s minerals exports was primarily direct labor content with weak backward linkages, and in Figure 14 we assess whether this over performance is due to direct value added or indirect value added via backward linkages. Figure 14 repeats Figure 13 except for the direct LVAX shares across sectors and countries. The direct LVAX share continues to be above all other countries for minerals as well as for ferrous metals. In other sectors, however, the direct LVAX share is no longer higher than other non-BRIC comparators, implying that South Africa’s high LVAX share relative to other countries is due to backward linkages, rather than the direct content. Adding this result to the below finding that the number of jobs supported by exports of minerals is limited
data against those using the RLMS data in Figure 1.9, Panel A and Panel B (Appendix 1). The trends as shown by the HBS data are very similar to those based on the RLMS data.<sup>19</sup> Yet, while our results apply to the majority of the population, it is possible that we may not be able to capture well the super-rich in the population, such as the top 1 percent of the income distribution. Analyzing the latter group requires more fine tuned assumptions as well as better data on the top part of the income distribution such as tax information. As such, we refer interested readers to the comprehensive studies by Novokmet _et al._ (2018) for the top 1 percent of the income distribution, and Treisman (2016) for the number of billionaires in the country.

# _Attrition Bias_

Our estimation results are rather consistent for the different periods of varying lengths, which helps reduce concerns about potential attrition bias. But as discussed earlier, we offer further robustness checks using two popular inverse probability weighting methods that adjust for attrition: one by Fitzgerald _et al._ (1998) and the other by Wooldridge (2002). The intuition behind these methods is that, since households that drop out of the panel sample may have different characteristics from those that remain (e.g., higher education achievement or income level), we can reweight the latter by explicitly taking into account their characteristics. We apply these reweighting methods and show estimation results for the medium-term mobility and the long-term mobility in Table 1.4 (Appendix 1). Estimates using Fitzgerald _et al._ ’s method are qualitatively similar to the results shown in Table 1 and Table 3, while those using Wooldridge’s method display

> 19 We use the unweighted HBS data because population weights are not available. Official estimates by the Russian National Statistical Agency “Rosstat” also suggest that the Gini coefficient remains stable between 1994-2015 around 0.41 (Rosstat,
There is no robust evidence on the performance of merchant banks relative to privately owned commercial banks. While privately owned merchant banks have significantly lower NPL than commercial banks, the evidence on ROA and ROE is not robust across different estimation techniques and to the exclusion of foreign exchange profits. Similarly, merchant banks with minority government ownership enjoyed lower NPL than privately owned commercial banks, but the evidence on ROA and ROE is not consistent and robust. There is no significant difference between merchant banks that are completely privately owned and those with minority government ownership. Finally, the results indicate that older banks and smaller banks perform worse. This seems to indicate that larger banks did enjoy economies of scale and scope and that new entrants into the market were better able to pursue new profit opportunities. Given that age enters significantly even in the performance regressions where we exclude foreign exchange profits, this effect cannot be attributed completely to foreign exchange speculation.

The regressions in Table 4 confirm our previous finding of a positive link between privatization and performance, and give some additional insights into bank characteristics that explain variation in bank profitability. Here we include additional indicators of business and portfolio orientation and productivity. Columns one through five present results of the sample limited to the 1<sup>st</sup> through 99<sup>th</sup> percentile, while columns six through ten report the results of robust regressions.

While weak banks were chosen for privatization, privatization did close the performance gap relative to privately owned commercial banks. _Eventually Privatized_ enters significantly in all ten regressions, while _Privatized_ enters significantly in all but two regressions. As before, we cannot reject the hypothesis that the sum of both coefficients is not 19
# **2 Reserve requirement data**

Our starting point is the quarterly RR database put together by Federico _et al._ (2014), which identifies different types of RR in terms of maturity and currency of denomination.<sup>8</sup> Based on this, we construct a metric of effective RR that also takes into account the structure of deposits for each of the three countries included in this paper (Argentina, Brazil, and Uruguay).<sup>9</sup> As shown in Table 1, Panel A, we identify a total of 93 quarterly changes in RR. Specifically, Argentina, Brazil, and Uruguay changed RR on 49, 31, and 13 occasions, respectively. In other words, Argentina, Brazil, and Uruguay changed their RR, on average, about once every 7, 9, and 26 months, respectively.

To set the stage, we begin by briefly discussing some broad features of the data; in particular the varieties of RR (Section 2.1), long- and short-run properties (Section 2.2), and the variable effectively used to conduct the empirical analysis (Section 2.3).<sup>10</sup>

> 8The current version of this database comprises 66 countries, 15 industrial and 51 developing (see Federico _et al._ , 2014, for details) and is available at www.guillermovuletin.com.

> 9The analysis covers the period 1990:1-2018:1 for Argentina, 1994:3-2018:2 for Brazil, and 1990:1-2018:2 for Uruguay. This dataset is also available from www.guillermovuletin.com.

> 10Our study also uses other macroeconomic variables such as real GDP, inflation, central bank interest rates, and market interest rates, all at quarterly frequency. Most of these data were gathered from the IFS (IMF), the Global Economic Monitor (World Bank), and local sources. See Appendix 8 for a description of data and
per capita.

Furthermore, as explained above, the used of a single inflation rate to deflate the expenditures of poor and non-poor households may also bias the results slightly towards an adverse redistribution pattern. Another partial explanation may relate to the housing variable. The estimating equation for housing cost yields large negative dummies for the 1995 regional dummies. Since housing as a share of expenditure declines as expenditure increases, the use of a single dummy would result in a larger decrease, relative to total expenditure, of total per capita expenditure among poorer households. All other things being equal, this would tend to yield an adverse redistribution effect.

# _Conclusion_

In the short run, the devaluation accompanied by the increased in export taxation did not appear to alter the pattern of poverty that took shape prior to the devaluation, assuming that the results of the 1993 and 1995 surveys are roughly accurate, about which there is considerable cause for doubt. Abidjan experienced the largest increase in poverty between 1993 and 1995. Food crop farmers in rural areas got absolutely poorer. And depending on whether or not one accepts the 1995 West Forest results, export crop farmers got slightly worse off or remained about the same. The increase in poverty among food crop farmers is likely linked to the downward trend in real food crop prices evident in the pre-devaluation period 1987-1993 , which continued after the devaluation. As producers of nontradables, it would be expected that food crop farmer would not have benefited from the devaluation in the short term unless a substantial part of their income comes from wage labor or off-farm sources. Unfortunately we know very little about the sources of income of food crop farmers. The failure of export crop farmers to show a more important decline in poverty is due to several things: first the survey took place relatively soon after the devaluation, so that coffee and cocoa farmers had little time to respond to higher crop prices. A second reason, particularly relevant for cocoa, is that the government taxed away much of the benefit of the devaluation and the price increase.
# **S8 Additional IV Results Analyzing the Impact of Areas Incidentally Connected to the RSDP**

Our main specification examining the impact of the RSDP on areas incidentally connected to RSDP roads uses Kebeles as the unit of analysis and uses a minimum spanning tree (MST) constructed from regional least cost distance MSTs appended together as an instrument. In this section, we test the sensitivity of results to using 1x1km pixels as the unit of analysis and using different versions of the MSTs. Section S8.1 shows sample sizes and the number of units near the RSDP and MSTs. Section S8.2 shows OLS results, section S8.3 shows first-stage results from IV models, section S8.4 shows second-stage results, and section S8.5 shows second-stage results when using two groups of baseline nighttime lights as opposed to four.

## **S8.1 Treated and Control Summary Stats**

For instrumental variable models, units that are near targeted areas are removed. Table S3 shows the number of units after removing targeted areas. In addition, S3 shows the number of units treated (near an RSDP road) and near the MST networks.

**Table S3:** Number of Treated and Control Units for IV Analysis Across Different Units

||Sam|ple Size||N Units|Near RSDP and D|iferent MSTs||
|---|---|---|---|---|---|---|---|
|Unit|N Total|N, Targeted|RSDP|MST Least|MST Least|MST Least|MST Least|
|||Areas Removed||Dist|Dist: Regional|Cost|Cost: Regional|
|1x1km Grid|1,337,567|113,908|1,271,094|95,603|106,534|124,674|136,769|
|Kebeles|15,714|3,157|10,220
# **References**

- Angelini, Paolo, and Andrea Generale. 2008. “On the Evolution of Firm Size Distributions.” _American Economic Review_ 98 (1): 426–38.

- Aterido, Reyes, and Mary Hallward-Driemeier. 2010. “The Impact of the Investment Climate on Employment Growth: Does Sub-Saharan Africa Mirror Other Low-Income Regions?” Policy Research Working Paper 5218, World Bank, Washington, DC.

- Aterido, Reyes, Mary Hallward-Driemeier, and Carmen Pagés. 2007. “Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms.” RES Working Paper 4559, Research Department, Inter-American Development Bank, Washington, DC.

- ———. 2009. “Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms.” Policy Research Working Paper 5032, World Bank, Washington, DC.

- Ayyagari, Meghana, Asli Demirgüç-Kunt, and Vojislav Maksimovic. 2006. “How Important Are Financing Constraints? The Role of Finance in the Business Environment.” Policy Research Working Paper 3820, World Bank, Washington, DC.

- Batra, Geeta, Daniel Kaufmann, and Andrew H. W. Stone. 2003. _Investment Climate around the World: Voices of the Firms from the World Business Environment Survey._ Washington, DC: World Bank.

- Beck, Thorsten, Asli Demirgüç-Kunt, and Vojislav Maksimovic. 2005. “Financial and Legal Constraints to Growth: Does Firm Size Matter?” _Journal of Finance_ 60 (1): 131–77.

- Bigsten, Arne, and Mans Söderbom. 2006. “What Have We Learned from a Decade of Manufacturing Enterprise Surveys in Africa?” _World Bank Research Observer_ 21 (2): 241–65.

- Cabral, Luis M. B., and Jose Mata. 2003. “On the Evolution of the Firm Size Distribution: Facts and Theory.” _American Economic Review_ 93 (4): 1075–90.

- Cooley,
at 55 percent. In Malawi, Mozambique and Tanzania, about 40 percent of adults reported agriculture to be their household’s main source of income, followed by about a third to a quarter of adults in most other economies. The Democratic Republic of Congo has the lowest share of adults reporting relying on crops and livestock as their household’s main source of income, at 17 percent. See detailed country-level data on adults who rely on crops and livestock as their household’s main source of income in Annex 1.

> 2 The Global Findex database measures how adults around the world save, borrow, and make payments. For the complete methodology, see <u>https://globalfindex.worldbank.org/.</u>

> 3 Data were also collected in three additional upper-middle-income economies in Sub-Saharan Africa: Botswana, Gabon, and South Africa. However, the low rate of adults who reported that their household relied on agriculture by growing crops or by raising livestock as the main source of their household’s income does not allow for meaningful analysis because of the small number of relevant observations.

> 4 The module on agricultural risk management was also added to the 2017 Global Findex questionnaire in select non-Sub-Saharan African economies. Key summary statistics are reported in Annex 2.

2
_H_<sup>_G_</sup> = _G_<sup>_H_</sup> _._ (20)

# **6.  Market-Clearing Condition and Population Growth**

The asset-market clearing condition requires equality between savings and investment, or equivalently, that tomorrow's private capital stock be equal to today’s savings by adult workers. Given that _s_ is savings per family, and that the number of families is ( _N_<sup>_m_</sup> + _N_<sup>_f_</sup> )/2, we have The number of adults alive in period _t_ is itself given by that is, the number of children born in period _t_ -1, _n_ ( _t_ -1) _N_ ( _t_ -1), who survived to period _t_ .

Thus, there are two concepts of gender inequality in the model: a _time allocation gap_ to market work between adult men and women, measured by the ratio 1/ε<sup>_f,W_</sup> ; and a _core wage gap_ in the market place, as measured by the parameter _b_ . The first gap is endogenous and corresponds to the _(market) participation gap_ in de la Croix and Vander Donckt (2010), for instance. However, because ε<sup>_f,W_</sup> is determined residually in our setting, it also reflects gender bias in the allocation of time to _home activities_ —household chores and child rearing. Both measures can be viewed as pertaining to the concept of “economic participation and opportunity” for women incorporated in the Global Gender Gap Indicator proposed by the World Economic Forum (2010).<sup>31</sup> Most importantly, in our analysis, the time allocation gap is influenced by access to infrastructure, which affects the efficiency of time allocated to home production and child rearing.

# **IV. WOMEN’S TIME ALLOCATION AND GROWTH: EQUILIBRIUM**

An equilibrium of the economy requires
Figure 7: Change in sales: Relative Shapley decomposition <!-- Start of picture text --> 60 58.6 59.7<br>40<br>32.8 32.9<br>30<br>21.6<br>20 17.2<br>13.3 12.8<br>7.6<br>.2 1 2.6 0 .9 1.4 1.5 .1 1.3 2.2 2.4<br>0<br>Round 1 Round 2 Round 3<br>Informality Size<br>Age Sector<br>Female-led Severity of the crisis (weighted Google mobility index)<br>Initial prod.<br>R2 explained by group<br>Relative Shapley Value (%) - Change in sales<br><!-- End of picture text --> Notes: The regressions also control for the country, but only the relative contribution of firm-level characteristics, including informality, are presented here. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

Table 3: Oaxaca Blinder decomposition

|Dependent variable: Change in sales|(1)|(2)|
|---|---|---|
||Conditional|Cont. initialproductivity|
|Diferential|||
|Prediction formal|-32.80***|-31.27***|
||(0.404)|(0.446)|
|Prediction informal|-44.81***|-44.94***|
||(0.600)|(0.747)|
|Diference|12.01***<br>(0.724)|13.67***<br>(0.870)|
|Decomposition|||
|Endowments|7.625*