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Policy Research Working Paper 9845

# **Abstract**

This paper estimates an urban carbon dioxide emissions model using satellite-measured carbon dioxide concentrations from 2014 to 2020, for 1,236 cities in 138 countries. The model incorporates the global trend in carbon dioxide concentration, seasonal fluctuations by hemisphere, and a large set of georeferenced variables that incorporate carbon dioxide–intensive industry structure, emissions from agricultural and forest fires in neighboring areas, demography, the component of income that is uncorrelated with industry structure, and relevant geographic conditions. The income results provide the first test of an Environmental Kuznets Curve relationship for carbon dioxide based on actual observations. They suggest an environmental Kuznets curve that reaches a peak near or above $40,000 per capita, which is at the 90th percentile internationally. The research also finds that economic development has a significant effect on the direction of the relationship between population density and carbon dioxide emissions. The relationship is positive at very low incomes but becomes negative at higher incomes. The paper also uses cities’ mean regression residuals to index their carbon dioxide emissions performance within and across regions, decomposes model carbon dioxide predictions into broad source categories for each city, and uses the regression residuals to explore the impact of subway systems. The findings show significantly lower carbon dioxide emissions for subway cities.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. 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, slall1@worldbank.org, and wheelrdr@gmail.com.

_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 series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
# **Contents**

|I.IN<br>T<br>R<br>O<br>D<br>U<br>C<br>T<br>IO<br>N<br>.........................................<br>1|
|---|
|**II.**MOTIVATIONSFORFACILITYSURVEYS.........................................<br>**1**|
|Provider-householdlinkages........................................<br>2|
|Measuringandunderstanding providerperformance ........................................<br>3|
|**III.WHAT**DATATOCOLLECTANDHOWTO**Do**IT........................................<br>7|
|Inputs ........................................<br>7|
|Costs ........................................<br>9<br>|
|Outputs,activitiesandthroughputs .......................................<br>10<br>|
|Quality.......................................<br>13|
|Staffperceptionsandbehavior.......................................<br>18<br> <br>|
|Theinstitutionalandorganizational enviromnent .......................................<br>19|
|**IV.**HowFACILITYDATA HAVE**BEEN**USED**........................................**<br>20|
|Planningandbudgeting.......................................<br>20|
|Monitoring,evaluation,and accountability.......................................<br>21|
|Research...........<br>24|
|**V.CONCLUSIONS...........**<br>28|
|APPENDIX:**A**REVIEWOFHEALTH FACILITYSURVEYS........................................<br>30|
|DemographicandHealthSurveys(DHS andDHS+)............<br>...........................30|
|TheLivingStandardsMeasurement Study (LSMS).......................................31|
|Situation Analysis.......................................<br>33|
|RAND Surveys .......................................<br>34|
|MEASURE Evaluation.......................................<br>35|
|WHOMulti-CountryEvaluationofIMCI.......................................<br>37|
|Public ExpenditureTrackingSurveys.......................................<br>38|
> - 17 -

# _4._ A MACRO-ECONOMIC OVERVIEW OF THE SSA FOREIGN EXCHANGE AUCTIONS.

It is not our inteton to exami in any detail the macro-determinants of the aucton rate in this paper?' However, we present in this secton a brief overview of dte macro-econmic environment in dte four countries, with the objective of explaining the observed differentiated outcomes of foreign excdange auctions in the fur countries. The mac-economic sty is provided by Figures 1 and 4, fucusing on exchange rate prenia and monetary emission. The broad conclusion that can be derived from dise graphs points to the importance of a stable macro-economic envirnment for the achievement of a steady and susainable progress towards a unified exchange rate.

Ghana and Uganda seem Do be largely on arget regarding the above objective. Figures 1 b and I c show a steadiy depreciatng and sable auction rate over sime, while a significantly declinin bureaxlaucion pmium is observed. Thus, tese countries have been able to achieve a reasonable degree of unification and exchange rate stability. At the heart of this outcome, as shown by Figures 4 b and 4 % is a sutined decline in mo gow This is reflected in a decelemaion in inflatio and a stable buream rate in both Uganda and Ghana, where in Ghana this folowed an episode of deep depreciation towards what appears to be an eqauibrium path Th figues also show a clear leadllag. effect going fom monetay emsion to iatim and bureaux rat depreciato This indica that not only has macro-policy been consistent, but also a it has been credible in these two countrie In contrast, in Nigeia and Zambia Figures 1 a and 1 d also show a deprecating maction rate, but with far greater volatility. More importantly, unlike the othe two countries, the black/auction premium was steadily rising. Thi oumers both the objectives of stabiit and exchange rate unification. This could be a direct outcome of utaiable macro-economic policy and the collapse of policy credity. This is crborated by Fgures 4 a and 4 . Very volatie and significently rising rates of growth of money suppy over dme are observed in both coutis. This is reflected in high inflation and blacklauction premiavariability, with exteded episodes of sharply rising prmia. The relevance of macroeconomic poliy in - -if between acdon outcomes in Ghana and Uganda on one hand and Nigeria and Zambia on the othe, is fairly consistent and closely linked with the eviden on the microeconomic dermina of die auction rate (sections 2 and 3). For example, die reserve price policy in Ghana and Uganda has been credited with fostering auction rate stability. For tese two conries _to_ manage a scssful reser pruice policy, a susable secondary market signal is necsay. This was provided by a fairly stable bur rate, made possible%y a credible maco-enviroment These results are also corroboratd by the more fomal analrysis of Aron and Elbadawi (1994).

YHowcvc. in a subsequ pmr we PIa to anays =ac-coooic nies in the coun of ueon md ccbmge rae aifimtios in SSA-
as the ratio of observed price per shipment to the price trigger, we have the following complementarity problem: Note that, unlike the quantity-based system, there is only one tier in the price-based safeguard. In addition, the safeguard tariff imposed can only amount to 85% of the difference between the shipment price and the baseline price.

There are two key differences between the price and quantity-triggered SSM regimes. The first has to do with bilateral price differences for wheat, and the second has to do with the focus of the P-SSM on shipments instead of annual-average imports. Both of these features are important to our findings, and so deserve special discussion at this point. Turning first to the bilateral price issue, we note that, because the price of each shipment of wheat is compared to an MFN average price in order to evaluate whether the SSM has been triggered, it is important to account for bilateral differences in commodity prices.

To better understand these bilateral price differences, we compute average unit values for wheat exports from each region in the model over the period 2000-2004. These are reported in the first column of Table 6, as the ratio (PR) of each region’s export unit value, relative to the global average export price. The entries in Table 6 show a general tendency for developing countries to have lower prices and developed countries to have higher ones, as shown by Schott (2004) for exports in general. But this is not always the case, with some high-income regions specializing in lower-priced varieties of wheat and some poorer countries having higher unit values. Regions with lower than average wheat export prices include: China, South Asia, Argentina, Rest of Latin America, Rest of Europe, Russia and the EU. Regions with relatively high unit values include: Australia, Canada, USA, Japan, Mexico, Brazil and Sub-Saharan Africa (largely South Africa).

The remainder of Table 6 uses these unit values and the bilateral trade pattern from 2001 to compute the ratio of a given bilateral exporter price to the average import price in each importing market (BIPR). Note that some exporters show significant 20
PRE Working Paper Series

||ita|Atnbor||Contact<br>for paper|
|---|---|---|---|---|
|WPS373|Are Better-off Households More<br>Unequal or Less Unequal?|Lawrence Haddad<br>Ravi Kanbur|March 1990|J. Sweeney<br>31021|
|WPS374|Two Sources of Bias in Standard<br>Partial Equilibrium Trade Models|Samuel Laird<br>Alexander J. Yeats|February 1990|J. Epps<br>33710|
|WPS375|Regional Disparities, Targeting, and<br>Poverty in India|Gaurav Datt<br>Martin Ravallion|March 1990|C. Spooner<br>30464|
|WPS376|The World Economy in the<br>Mid-I 990s: Altemative Patterns of<br>Trade and Growth|Colin I. Bradford, Jr.|March 1990|C. Evangelista<br>3264S|
|WPS?7-|Security for Development in a<br>Post-Bipolar World|John Stremlau|||
|WPS378|How Does the Deet Crisis Affect<br>Investment and Growth? A Neoclas<br>cal Growth Model Applied to Mexico|Patricio Arrau<br>si-<br>|March 1990|S. King -Watson<br>31047|
|WPS379|Some Implications of Policy Games<br>for High Inflation Economies|Miguel A. Kiguel<br>Nissan Liviatan|March 1990|R. Luz<br>39059|
|WPS380|Techniques for Railway<br>Restructuring|Lee W. Huff<br>Louis S. Thompson|March 1990|S. Shive<br>33761|
|WPS381|Trade in Banking Services:<br>Issues for Multilateral Negotiations|Alan Gelb<br>Silvia Sagari|March 1990|W. Pitayatona-<br>kam<br>37666|
|WPS382|The Indonesian Vegetable Oils<br>Sector: Modeling the Impact of<br>Policy Changes|Donald F. Larson|March 1990|D. Gustafson<br>33714|
|WPS383|On the Relevance of World<br>Agricultural Prices|Yair Mundlak<br>Donald F. Larson|March 1990|D. Gustafson<br>33714|
|WPS384|A Review of the Use of the Rational<br>Expectations: Hypothesis in Models<br>of Primary Commodity Prices|Christopher L. Gilbert<br>|||
|WPS385|The Principles of Targeting|Timothy Besley<br>RaviKanbur|March 1990|J. Sweeney<br>31021|
|WPS386|Argentina's Labor Markets in an Era<br>of Adjustment|Luis A. Riveros<br>Carlos E. Sanchez|March 1990|R. Luz<br>39059|
|WPS387|Productivity and ' xtGrnalities:<br>Models of Expoirt-Led Growth|Jaime de Melo<br>Sherman Robinson|March 1990|M. Ameal<br>37947|
<!-- Start of picture text --> Te)<br>ran<br>9<br>2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> <!-- Start of picture text --> year<br><!-- End of picture text --> <!-- Start of picture text --> 0OTFP_ACF_CD_r —e— 1TFP_ACF_CD_r<br><!-- End of picture text -->
**Table 6.10 Summary Health Benefits Due to Ozone and PM10 Air**<sup>**Pollution Reduction**</sup> (in million US$ per yeara 2010 values in 1999 prices, 3 percent discount rate)

|||||_Sce_|_nario_||||
|---|---|---|---|---|---|---|---|---|
||_1_|_0%_|_20_|_%_|_AQ_|_S1_|_A_|_QS2_|
|_Incomeelasticity_|_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|
|**Ozonebenefits**|||||||||
|Total- Morbidity(Prod.Loss+COI+WTP)<br>andWTPformortality|116|183|232|365|717|1129|794|1,250|
|Total- Morbidity(Prod. Loss+COI+WTP)<br>and humancapitallossesmortality|75|114|151|228|465|706|515|782|
|Total- Morbidity(Prod.Loss+COI)<br>andhumancapitallossesmortality|18|18|35|35|109|109|121|121|
|PM10 **benefits**|||||||||
|Total- Morbidity(Prod.Loss+COI+WTP)<br>andWTPformortality|1,451|2,549|2,903|5,098|3,186|5,595|6,793|11,931|
|Total- Morbidity(Prod.Loss+COI+WTP)<br>andhumancapitallossesmortality|644|1,184|1,289|2,367|1,414|2,598|3,016|5,540|
|Total- Morbidity(Prod.Loss+COI)<br>andhumancapital lossesmortality|96|96|191|191|210|210|448|448|

# **_Environmental Contingencies_**

The economic effects of ECs in the ZMVM industry sector have been explored through an analysis of value added losses in affected industries. Using 1994 data,<sup>value addedhas</sup> been estimated here to decrease 39 percent during one day of PM1o contingency and<sup>42 per-</sup> cent in an ozone contingency.<sup>22</sup> The costs of a one-day contingency for<sup>PMjoarelowerthan</sup> for ozone as fewer industries are involved in PMjo (the service sector is left out in PM1O, but included in ozone).

As mentioned in the earlier sections environmental contingencies (ECs) have two main cost components: production losses in industry and transportation. Productivity<sup>losses in the</sup> transport sector are much less straightforward to estimate, and the lack of data proved to be more severe in the transport sector. Given these constraints we focussed solely on<sup>production</sup> losses in industry. Knowing the value of production, the value added, and labor costs per day (percent participation in the total costs) allowed us to calculate the costs of<sup>productionina</sup> normal situation (without environmental contingency). From the normal situation scenario, the costs of ECs can be derived considering a decline of 33 percent of<sup>productionperday</sup> (see table 6.1 1).23

> **22** This is larger than the average decrease of production of 33 percent for all industries combined, as explained in detail in Cesar and others (2000). The discrepancy stems from the fact that<sup>mostworkersgo to work and get</sup> paid even on days when the production stops partially. The result is a higher<sup>costof productionper unitof prod-</sup> uct.

> For a more detailed analysis, see Cesar and others (2000).

_42_
Power How does this evidence contrast with the determinants of power winners (columns 5 to 8 in Table 8)? e Hypothesis 5: Returns to mother tongue Tetun rise strongly, while those to speaking Tetun decline. Power returns to education rise more than those for welfare returns, suggesting a greater importance of education for power than welfare. In contrast to welfare changes, the returns to initial economic status increase over time, suggesting that power gains are regressive. e Hypothesis 6: Negative household and village level shocks generally reduce power, although the coefficients are smaller in absolute terms than for welfare. °2 Resettlement has a larger impact on power than welfare changes, suggesting that the disruptive effect on social networks outweighs the loss of location-specific economic resources. Improvements in economic sectors leave power unchanged, while more political participation and higher community status raise power, as do less corruption and better own-health. °° Overall, the results suggest that power winners include those with strong social resources (Tetun as mother tongue rather than speaking Tetun and no displacement) (Hypothesis 5). Negative economic shocks diminish power but the impact is less than on welfare (Hypothesis 6).

Conclusion This paper evaluated changes in well-being in Timor-Leste between 1999 and 2001, covering the period from prior to the referendum on independence from Indonesia to the eve of independence at the end of 2001. It demonstrated how subjective, objective and recall data from a single cross-sectional survey can be combined to provide a rich characterization of the changes in economic and power status during a time of systemic economic and political transformations.

The mobility analysis established that overall self-rated welfare improved little, while self-rated power increased dramatically. However, the changes were not 52 The coefficient on household livestock loss is insignificant and close to zero. However, power changes are negatively correlated to livestock loss in the bivariate setting. Dropping regional controls (area dummies and language variables) from the regression, the coefficient on livestock loss is negative and significant at the 2-percent level. 53 The null hypothesis of equality of coefficients is rejected for demand for products, employment, housing, health care, political participation, status in community and housing destruction at the 10-percent significance level (Column 14 of Table 10).
Poland to 57 percent in Burundi, with a standard deviation of almost 15. While most developed countries have very low values of undernourishment ⎯ it is zero in almost all developed countries, 25 percent of our sample has an undernourishment rate higher than 30 percent.

Following the literature, we proxy financial development by private credit to GDP, the value of credit extended by financial intermediaries to the private sector as a percentage of GDP. This measure excludes credits issued by the central bank and development banks, and credit to the public sector, credit to state-owned enterprises, and cross claims of one group of intermediaries on another. This comprehensive proxy is widely used and has been shown to be a driver of GDP per capita growth. Data on private credit is for most countries available for each year in the period 1980-2003: for 99 countries we have observations for each year. In the basic sample, private credit has an average value of 26 percent. However, there is wide variation: the minimum value is 2.9 percent and the maximum is 112 percent.

Overall agricultural productivity is defined as the yearly value added per agricultural worker, expressed in constant 2000 US Dollars. We have on average 20 observations per country for the period 1980-2003. The average level of agricultural productivity is about $1,800. However, there is wide dispersion, with a standard deviation of about $2,650. Rich countries have an average productivity of over $20,000, whereas poor countries may have productivity as low as $100.

Finally, to take a first step in assessing the importance of access to finance, we use the number of banking branches per 1,000 square kilometer from Beck, Demirgüç-Kunt, and Martinez Peria (forthcoming). These data are only available for 2003-2004. For about 100 countries, the average density of banking branches is 30 per 1,000 square kilometer, but this differs vastly between countries. Not surprisingly, large and developing countries tend to have a lower density. For example, the number of banking branches per 1,000 square kilometer in Ethiopia is less than 1. In contrast, Singapore has over 600 branches per 1,000 square kilometer.

10
cooperatives and credit unions. In New York City, one of the more highly concentrated urban markets in the U.S, the five largest banks account for 54 percent of all deposits. Competition for deposits is also more varied in the United States. Large commercial banks compete with community banks, thrifts, and credit unions, all of which are regulated and offer deposit insurance. In Mexico, non-bank savings institutions are much smaller and many have traditionally been largely unregulated. Second, computerized credit histories for consumers and automated credit risk assessments are highly developed in the U.S. and cover nearly all working adults. In Mexico, such institutions are far less comprehensive and sufficiently less developed that there can be little meaningfully predictive automated credit risk assessments for most lower-income Mexican households. Third, labor costs are significantly less in Mexico compared to the U.S. This means that labor-displacing financial service technologies employed in the U.S. may not be appropriate in the Mexican context. Despite these differences, however, this paper identifies many parallels in the issues that the two countries face in trying to improve financial services for lower-income households, even if the scale and context of the problem differs.

# Who are the unbanked?

In both countries, the unbanked are characterized by lower incomes and lower education levels than the population at large, and they also tend to be marginalized in socio economic terms. In Mexico this is inferred by the high percentage of unbanked dependent on the informal sector, while in the U.S. it is defined by the disproportionate representation of immigrant and minority groups. (According to US Federal Reserve Bank reports based on surveys of consumer finance from 1997, 54% of the unbanked are Latin American migrants, compared with 15.1% over the population as a whole<sup>3</sup> .) However, although in line with income and education levels overall, the percentage of unbanked is markedly greater in Mexico than in the U.S. Surprisingly, home ownership among the unbanked is also greater in Mexico.

## **The Unbanked in Mexico and the U.S have similarities and differences**

## **Similarities**

|% of Unbanked Households below Median Income|90%|79%|
|---|---|---|
|% of Unbanked with household head less|||
|than High School Education|51%|56%|
|% of Unbanked Socio-Economically Marginalized<br>-<br>Immigrant or Minority Group in US<br>**-**<br>Informal economic sector in Mexico|60|90%|
|**Differences**|**Mexico**|**U.S.**|
|Overall Percentage of Unbanked Households|75%|15.1%|
|Home ownership among Unbanked Households|63%|7.8%|

> 3 Kennickkell et. al, 1997

7