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70
reducing child mortality: There is no country that has made major progress in reducing child mortality without considerably expanding vaccination coverage.

# _Evidence from Survival Analysis_

Despite these interesting insights and the illustration of some broad tendencies, these country comparisons based on aggregate data may hide more than they show. The determinants of child mortality however can only be understood by analyzing the relevant processes within households. In the following, we therefore use different types of microeconometric models estimated on the DHS datasets to uncover the relationship between socioeconomic and proximate determinants and how they affect child mortality outcomes. As pointed out above, we combine the three typical approaches to the study of child mortality determinants, i.e. the joint analysis of socio-economic and proximate determinants, the empirical assessment of ‘gross effects’ of socio-economic determinants, and the analysis of the relationship between socio-economic and proximate determinants. More specifically, we proceed by estimating the following models and specifications. In the first step, we estimate a discrete time survival model including both socio-economic – for example mother’s education and wealth – and proximate determinants – for example gender of the child and birth intervals (Table A4). In this specification we restrict the set of explanatory variables to those common to all surveys from all countries in all years. The estimates should inform us about major structural breaks over time, the robustness of the results across different datasets, and highlight possible differences in mortality patterns between the country cases. The second specification (Table A5) estimates the same survival model using only the latest survey for each country. This allows us to include information on a child’s vaccination status, which, unfortunately, is not available for earlier years. This model is also estimated with communitylevel fixed-effects (Table A6). These fixed-effect estimations should not only be seen as a robustness check. As supply-side factors, for example the presence of a health facility, can be assumed to be similar for all households within the same community, the coefficients, for example of education and wealth, can be interpreted as “pure” demand-side effects. Comparing the regular with the fixed-effects estimation results is of course a fairly indirect way to prove the importance of demand-side constraints. Yet, the interplay between demandand supply-side factors could only be examined with detailed supply-side information, which, as mentioned earlier, is available for only very few countries.

The next specification again uses the survival model but includes only child characteristics that cannot be influenced by the parents (gender, first born) plus one of two 25
7 and heat, on average, there is still significant underpricing, especially on the household side.

# _The rationale for keeping prices low_

There are several reasons for governments' reluctance to raise household energy prices. First is a concern over the impacts - direct and second round -- of higher energy prices on the overall price level, which will be a function of the structure of the economy and its energy intensity.<sup>6</sup> In addition, there is a reluctance to raise the _household_ prices for fear of contributing to wage pressures and thus to inflation -- the counterpart of raising industrial prices for fear of the impact on production costs and in turn inflation. Ironically, if such price subsidies are significant in budgetary cost and if the resulting deficits are financed by money creation, then keeping prices too low may in fact be inflationary. Sometimes the overall level of energy prices may be kept low in an attempt to keep down surplus revenues flowing to the utilities (Gray 1995). Since the utilities typically have very low debt/equity ratios (most investment was financed by the budget), higher prices quickly translate into larger corporate cash-flows, and, in the absence of strong corporate governance and financial controls, there is concern over how such additional resources would be used by energy enterprises, which may be powerful and have significant autonomy. Finally, since there is presently excess energy capacity in the transition economies, as pointed out by Gray, "In most countries there is a lack of serious supply crisis (for domestically produced power, heat, gas and coal) and a lack of long-term enterprise debt problem. Politicians are not motivated by a supply crisis (i.e., the lights aren't going out)." (Gray 1995, page 36). The excess capacity also means there is, apparently, little immediate need for foreign investment that might supply new capacity and thus results in little "demand" for typical western pricing policies and the regulatory framework that accompanies this. The exception is Hungary which has made the most progress on pricing and regulation. Its success is due in part to a 6/ NERA (1991) estimated that increasing domestic gas prices in Poland to border prices in 1990--an increase of more than 600% for households and 50% for industry--would result in an upper bound on the increase in inflation of about 4%. A lower bound on the inflationary impact on the CPI of total energy price increases is about 0.2 % for a I % increase in the overall price of energy (based on energy's share of household and industrial costs).
Table 4. Preference for Equality (Ordered Probit): 1999-2001 (Robust z-statistics in parentheses)

||Pan|el-2||Panel-3||
|---|---|---|---|---|---|
||1995-97|1999-01|1990-93|1995-97|1999-01|
|Age<sup>1</sup>|-16.493|-4.084|-23.786|-22.045|-7.964|
||(4.64)**|(1.550)|(2.66)**|(2.81)**|(1.670)|
|Age-squared<sup>1</sup>|126.392|21.078|237.163|202.542|61.385|
||(3.32)**|(0.730)|(2.42)*|(2.28)*|(1.170)|
|Female|-0.083|-0.019|-0.079|-0.013|-0.002|
||(5.00)**|(1.450)|(2.00)*|(0.380)|(0.100)|
|No. of Children|0.042|0.006|0.072|0.023|0.014|
||(5.87)**|(1.250)|(4.22)**|(1.700)|(1.680)|
|Married|0.001|-0.046|-0.056|-0.021|-0.139|
||(0.060)|(3.05)**|(1.150)|(0.540)|(5.16)**|
|_Education_||||||
|Incomplete Technical/Vocation Education|0.026|0.216|0.053|0.014|0.066|
||(0.910)|(7.82)**|(0.840)|(0.280)|(1.400)|
|Complete Technical/Vocation Education|0.219|0.317|0.215|0.182|0.317|
||(9.17)**|(15.05)**|(4.90)**|(3.74)**|(7.99)**|
|Incomplete Secondary Education|0.049|0.18|0.385|0.015|0.013|
||(1.410)|(6.56)**|(6.63)**|(0.230)|(0.250)|
|Complete Secondary Education|0.173|0.264|0.365|0.086|0.101|
||(5.90)**|(12.47)**|(4.21)**|(1.380)|(2.74)**|
|Incomplete University Education|0.375|0.416|-0.23|0.269|0.442|
||(9.07)**|(14.96)**|(1.680)|(3.39)**|(8.25)**|
|Complete University Education|0.348|0.417|9.514|0.307|0.296|
||(12.68)**|(18.73)**|(70.97)**|(5.69)**|(7.43)**|
|_Income_||||||
|Lower Income|-0.023|0.000|-0.051|-0.127|-0.050|
||(1.180)|(0.010)|(1.150)|(3.33)**|(1.750)|
|Higher Income|0.126|0.084|0.16|0.124|0.03|
||(6.20)**|(5.49)**|(3.41)**|(3.27)**|(1.030)|
|_Labor Force Status_||||||
|Part-Time Employed|-0.049|-0.039|-0.151|-0.088|-0.078|
||(1.420)|(1.540)|(2.67)**|(1.300)|(1.540)|
|Self-Employed|0.159|0.145|0.301|0.160|0.193|
||(5.51)**|(6.30)**|(4.01)**|(3.17)**|(5.05)**|
|Unemployed|-0.085|-0.035|0.038|-0.102|-0.071|
||(2.56)*|(1.440)|(0.310)|(1.450)|(1.540)|
|Retired|-0.048|-0.061|-0.145|-0.114|-0.147|
||(1.540)|(2.20)*|(2.07)*|(1.620)|(2.95)**|
|Other|0.104|0.012|(0.057)|(0.020)|0.027|
||(4.13)**|(0.660)|(0.880)|(0.380)|(0.750)|
|_Country Group Dummies _<sup>_2_</sup>||||||
|Developing countries|0.312|-0.082|-0.087|0.126|-0.084|
||(1236)**|(444)**|(1250)|(251)*|(231)*|
|Central and Eastern Europe|.<br>0.077|.<br>0.024|.<br>0.824|.<br>0.733|.<br>0.500|
||(2.91)**|(1.080)|(11.77)**|(11.05)**|(11.11)**|
|Baltics|0.062|0.098|0.562|0.246|0.344|
||(2.01)*|(3.10)**|(9.37)**|(4.88)**|(8.11)**|
|Other Former Soviet Union (FSU)|0.519|0.406||||
||(20.23)**|(19.00)**||||
|Observations|23,571|31,491|5,164|6,084|10,509|

- significant at 5 percent; ** significant at 1 percent

Note: Dependent variable is a 10-point scale from 1 (Preference for equality) to 10 (Preference for inequality).

- 1Age and per capita income divided by 1,000, to produce sufficently large coefficients for display.

- 2Benchmark group are the advanced economies.

16
Bangladesh, Ecuador, and the Islamic Republic of Iran, reported very high agreement that vaccines are important.

Despite this very positive outlook towards vaccines in general, existing cross-country research suggests slightly less accepting views towards COVID-19 vaccines. A multi-country study of 20 LICs and LMICs (sample of 10,477 participants) examining perceptions of COVID-19 vaccine effectiveness showed moderate to high levels of acceptance ranging from 69 percent in Indonesia to 94 percent in Bangladesh (Marzo et al., 2022). This betweencountry variation in attitudes towards COVID-19 vaccines is similar to a large-scale study focusing on 15 countries in Africa (Africa CDC, 2021) where the variation ranged from 59 percent in the Democratic Republic of Congo to 94 percent in Ethiopia. Results from a World Bank study on COVID-19 vaccines in six Sub-Saharan African countries by Kanyanda et al (2021) conducted between December 2020 and June 2021 showed acceptance rates ranging from 64.5 percent in Mali to 97.9 percent in Ethiopia. These studies also showed that trust in the effectiveness and safety of the vaccine was the most common reason for acceptance (Marzo et al., 2022; Arce et al., 2021).

Existing publicly available research on ways communication campaigns<sup>2</sup> can increase COVID-19 vaccine acceptance comes almost exclusively from high-income countries and has returned mixed results. Some experiments have successfully changed views, while others have not, or have only found certain treatments to be effective (Ashworth et al., 2021; Chang et al., 2021; Pink et al., 2021; Kaplan and Milstein, 2021; Loomba et al., 2021). One of the few published experimental papers on attitudes about COVID-19 vaccines in the United States found that norm-based appeals emphasizing vaccination uptake among others increase people’s propensity to state they will be vaccinated (Palm et al., 2021). To the best of our knowledge, the only publicly available experimental research on this in a low- or middleincome country comes from Papua New Guinea. This online experiment demonstrated that a message emphasizing the safety of the vaccine relative to getting COVID-19 increased

> 2Other approaches that have been shown to increase vaccine uptake, such as mandates and incentive schemes (Dubé et al., 2015; Eskola et al., 2014), are beyond the scope of this research.

7
9 to do sensitivity tests using alternative measures to test the robustness of the poverty line. This paper refers to two alternative measures which have been used in other poverty analyses. The first is the minimum wage of the target group converted appropriately to account for per capita level. The second is the concept of "generalized poverty line" proposed in World Development Report (1990), which are set at US$370 and US$275 per person per year in 1985 prices. These are converted to comparable indicators using currency conversion factors developed by the World Bank. The latter have the attractiveness of being comparable across countries and useful for relative welfare analysis across countries.

In inter-temporal poverty analysis, even if a relative poverty line is chosen for the initial period, by keeping it constant when looking at other periods of time the line becomes absolute. Also, this paper is more interested in the distribution of poverty within a country than its level. The former is more useful for orienting poverty alleviation measures towards those groups most affected by poverty. Sensitivity analysis is also used to make sure that the distribution of poverty is not highly sensitive to the exact choice of the poverty line.

# **3.6 Poverty Indices**

It has become customary to use the so called P-alpha measures in analyzing poverty. The measures relate to different dimensions of the incidence of poverty. P0 Pi, and P2 and are used for headcount, depth and severity of poverty respectively.

The mathematical formulation and poverty measures described below, are drawn from Foster, Greer and Thorbecke (1984). The three measure are all based on a single formula, but each index puts different weights on the degree to which a household or individual falls below the poverty line. To see how the measures are defined, let consumption or household expenditures be arranged in ascending order, from the poorest **Y 1 ,** the next poorest Y2...... with the least poor Yq. The poverty gap index is defined as follows: where Z = the poverty line; q = the number of individuals below the poverty line; N = the total number of individuals in the reference population; Y1 = the expenditure of the household in which individual i lives; x = the Forster-Greer-Thorbecke (FGT) index and takes on the values 0, 1, 2. The quantity in parentheses is the proportionate shortfall of income below the poverty line. This quantity is raised to a power a. By increasing the value of a, the "aversion" to poverty as measured by the index is also increased (See Boateng _et al,_ 1992).

Suppose o _=_ 0, so that the index measures no aversion to poverty, then P0 = H = q/n is the proportion of the population that falls below the poverty line. This is the "headcount Ratio" defined as the proportion of the population for whom consumption falls
Panel A focuses on debt stocks, while Panel B focuses on debt flows. Revisions are scaled by debtor country GDP, which is taken from the latest World Bank data release and not subject to revisions.

**Figure 5:** The distribution of debt stock and flow revisions

_Panel A:_ Revisions to debt stocks in percent of GDP <!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>-5% 0% 5% 10%<br>Ex-post PPG debt stock revision in % of GDP<br>median mean<br>Percent<br><!-- End of picture text --> _Panel B:_ Revisions to debt flows in percent of GDP <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-5% 0% 5% 10%<br>Ex-post PPG debt commitment revision in % of GDP<br>median mean<br>Percent<br><!-- End of picture text --> _Notes:_ The figure shows the percentage distribution of data revisions to debt stocks and debt flows (i.e. commitments) as defined in equation (1), in percent of GDP. Solid lines show the median, dashed lines show the mean. GDP data is taken from World Bank (2022) and not subject to revisions.

14
# (2) the potential tax benefits of write-offs,

(3) the extend of exposure relative to bank market capitalization, (4) the operation of deposit insurance and other regulatory safeguards. Information on bank exposure (to Brazil and Mexico) relative to market capitalization is easily computed. Table 19 gives this information for the top British, Canadian and Japanese banks for which data is available. If we are willing to assume that implicit LDC discounts and tax regulations aren't too different across countries, then we know from our previous discussion that a debt swap's impact on bank stock returns, dMV/MV, is proportional to the ratio of LDC exposure to market capitalization, Exp/MV . Table 19 shows that exposure-to-market value ratio's tend to be lower for the British, Canadian and especially the Japanese banks than for the top U.S. banks. In fact LDC exposures relative to market capitalizations are virtually nil for the top Japanese banks. Thus it follows that cash sales of LDC debt affect bank stock prices of British, Canadian and Japanese banks less than the stock prices of U.S. banks. Only the stock price of Midland Bank in the United Kingdom would be affected to about the same extent as those of the top U.S. banks. For the Japanese banks, the debt crisis can at best produce a small ripple in bank stock returns prices. If the many short-cuts that underly this reasoning are accepted, then we can conclude that the major non-U.S. banking systems are even less jeopardized by the debt crisis than the U.S. banking system.

As in the United, deposit insurance in the other major creditor nations may mitigate the link between stock returrs and the value of LDC debt. A 1977 European Community directive requires the Community's member countries to establish deposit protection schemes.2 1 As a response, the 1979 Banking 24
# Table A.2: Election and District Characteristics

||N|Mean|S.D.|
|---|---|---|---|
|**Panel A: Election data**||||
|Share of constituencies won by women in a district (F)|628|0.09|0.13|
|Share of male-female close elections won by women in a district (FC)|628|0.01|0.05|
|Share of close male-female elections in a district|628|0.03|0.08|
|Whether the district had at least one seat won by a woman|628|0.40|0.49|
|Whether the district had at least one male-female election|628|0.57|0.49|
|Whether the district had at least one male-female close election|628|0.13|0.34|
|Whether the district had at least one male-female close election won by a woman|628|0.07|0.25|
|**Panel B: Census data**||||
|Fraction of female population|628|0.49|0.02|
|Fraction of urban population|628|0.26|0.20|
|Fraction of SC/ST population|628|0.33|0.22|
|Female literacy rate|628|0.55|0.12|
|Male literacy rate|628|0.69|0.09|

_Notes_ : The table presents the number of observations, the means, and standard deviations (S.D.) for district-level characteristics of the election data and the Census data. Panel A presents these summary statistics for the electoral data from the Election Commission of India, which was obtained at the constituency level and aggregated to the district level. Panel B presents them for the district characteristics obtained from the SHRUG dataset as per the 2011 Census of India (Asher et al. 2021).

29
Rate of Return Regulation and Emission Permits Trading under

# Uncertainty

Fan Zhang<sup>1</sup> , Tao Huang<sup>2</sup>

**Key Words:** Rate of return regulation, Emission permits trading, Uncertainty **JEL Classification** : D21, D81, L11, Q48, Q52, Q58

> 1 Corresponding author. The World Bank. 1818 H ST NW, Washington DC, USA 20433. fzhang1@worldbank.org.

> 2 Guanghua School of Management, Peking University, Beijing, China 10081.
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