rafmacalaba/datause-encoder-data huggingface dataset:
While effects on health outcomes are large for both age groups, we see effects on weight for the younger group and on height for the older group. This difference can be explained by the role of breast-feeding on weight gain and height growth. Micronutrients that are not present in large amounts in breast milk are critical for improvements in height (Dewey and Adu-Afarwuah, 2008). Hence, during the period of exclusive breast-feeding children tend to gain weight rather than height. Reductions in illnesses that impede a child’s absorption of the nutrition in breast milk tend to lead to gains in weight. Once supplemental foods that contain more micronutrients are introduced into the child’s diet, reductions in illnesses that improve a child’s ability to absorb nutrition are manifest in height. According to the 2005 Demographic and Health Survey, 88% of children less than 6 months old are exclusively breastfed. And 31 percent of children age 6 to 9 months did not receive supplementary foods (Institut National de la Statistique du Rwanda and ORC Macro 2006).

# **b. Pathways**

We hypothesize that the observed impacts on child health are due to changes in their utilization and quality of prenatal and postnatal care induced by the introduction of P4P incentives. In this section, we examine the impact of P4P on utilization and quality of care and thereby document that the observed changes in health outcomes are plausibly caused by P4P.<sup>13</sup> In order to validate that the results on the effect of P4P on utilization and quality are plausible, we present and discuss the results in the context of our theoretical conceptual framework.

Our framework predicts that the introduction of P4P will lead to larger increases in services (the _U_ ’s) for which the relative price increase is highest and for those that have the highest relative marginal return to effort; conversely, there may be no effect on services for which payment rates and the marginal return to effort is low. Our theoretical discussion also predicted that the introduction of P4P would unambiguously increase quality _Q_ , the multiplicative factor in the payment formula. Our results are very consistent with this framework.

First, we find significant effects on the quality of prenatal care. Specifically, we estimate that P4P is associated with a 0.16 standard deviation increase in prenatal care quality (Table 7).

> 13 Many of the estimated impacts in Table 6 were previously reported Basinga et al. (2011). However, that paper used traditional asymptotic inference statistical tests based large samples. In this paper, we use randomization inference that is robust to small numbers of clusters and discuss the results in the context of the theory laid out in section 3.

20
Table 3. **Composition of municipal revenue, 2002** In per cent of GDP

||All<br>municipalities|Metropolitan<br>areas|Non-<br>metropolitan<br>areas|
|---|---|---|---|
|**Total revenue**|**7.9**|**4.0**|**4.0**|
|**Own sources**|**1.5**|**1.2**|**0.4**|
|Local property tax (IPTU)|0.5|0.4|0.1|
|Services tax (ISS)|0.6|0.5|0.1|
|Transfer tax (ITBI)|0.1|0.1|0.0|
|Other taxes|0.1|0.1|...|
|User charges and fees|0.2|0.1|0.1|
|**Shared revenue**|**3.2**|**1.3**|**1.9**|
|Municipal Participation Fund (FPM)|1.2|0.2|1.0|
|ICMS|1.5|0.7|0.8|
|Motor vehicle registration tax|0.2|0.1|0.1|
|Federal income tax (IRRF)|0.2|0.1|0.0|
|Other|0.1|0.0|0.1|
|**Specific grants and compensatory transfers**|**2.2**|**0.9**|**1.2**|
|Health care|0.5|0.2|0.2|
|FUNDEF|0.7|0.2|0.5|
|Other current transfers|0.7|0.4|0.3|
|Capital grants|0.3|0.1|0.2|
|Other Revenues|1.0|0.6|0.4|

Source: Rezende and Garson (2004).

# **Box 3. Municipal revenue**

# **Municipal taxes**

The main municipal taxes are those on services (ISS) and urban property (IPTU). In particular:

- ISS rates are set by the municipalities subject to the ceilings introduced by the federal government. Rates can vary considerably across sectors. Tax legislation is designed to avoid overlap with federal corporate income and state value-added taxes. In the banking sector, for example, ISS is levied on non-lending services such credit card and ATM fees but not on the interest charged on loans, which is taxed by the federal government. In the case of land transportation, ISS is levied on intra-municipal transport but not on intercity or interstate transport, which are subject to the state value added tax.

- IPTU is levied on the capital value of land and buildings in legally-designated urban areas. Property valuations are based on the physical characteristics of each property converted to an estimate of market value using construction cost parameters and surveys of land prices. These valuations are adjusted each year on the basis of an inflation index.

# **Revenue sharing**

Based on constitutionally-mandated revenue sharing (Box Table 1), the municipalities are entitled to: _i)_ 25 percent of revenue from the state value-added tax (ICMS), _ii)_ 50 percent of revenue from the state tax on motor vehicle registration (IPVA), _iii)_ 22.5 percent of revenue from the federal value added (IPI) and income (IR) taxes, _iv)_ all revenue from the income tax held at source (IRPF) and paid by the municipalities or by their decentralized agencies, _v)_ 70 <u>percent of revenue from the federal financial transactions tax levied on transactions with gold (IOF-Ouro), and</u> _vi)_ <u>50</u> 17
The TCS was prepared to collect the club “constitutions” and to provide tabulations of the more prevalent rules, regulations, and services of the clubs. The survey has four different modules: A) Regulations; B) Demographics; C) Services and Benefits; D) Other clubs (common practices of other tobacco clubs).

The Tobacco Clubs Survey is a small-scale survey with a design constrained mostly by budgetary reasons. It has a stratified sampling with strata based on the geography of Malawi. Using data on tobacco clubs by districts provided by the Tobacco Control Commissions, the sampling was designed in order to represent the actual distribution of tobacco clubs across the 3 regions: Northern, Central and Southern. This geographical division reflects disparities in terms of natural conditions to growth tobacco (i.e., soil quality, rains, etc.) as well as tribal representation (Tumbuka tribes dominate the northern region, Chewa tribes, the central and Yao tribes, the southern). Three districts were surveyed in the Northern region, seven districts in the Central region and two in the Southern region. The basic information is in Table A1.<sup>22</sup> In each district, twenty tobacco clubs were randomly selected. A total of 180 villages were visited. The weights used to compute descriptive statistics in tables 1 and 2 were constructed using the total number of tobacco clubs displayed in the fourth column of Table A1. This information was provided by the Tobacco Control Commission.

> 22The remaining districts, not included in the TCS, comprise only 8.7 percent of the tobacco clubs in Malawi.

38
IOp.<sup>20</sup> IOp and output per capita also has a stronger negative relationship than total inequality and output-per-capita, consistent with existing evidence that IOp is relatively more important for growth than total inequality (Marrero et al. 2013, Bradbury et al. 2016, Carranza 2020).

Finally, in Figure A3, we show the relationship between comparable IOp and intergenerational mobility (IGM) calculated for our countries of analysis by van der Weide et al. (2024). We measure intergenerational mobility as 1 minus the correlation coefficient between respondents’ and parents’ years of schooling. Globally there is a negative relationship between IGM and IOp, typically seen as driven by the fact that IOp decreases as parental socioeconomic characteristics become a less relevant predictor of income inequality (Brunori et al., 2013). This pattern persists as our estimates from SSA are included with global data.<sup>21</sup> However, the negative global correlation between IOp and IGM is relatively moderate (R<sup>2</sup> =.45), highlighting the value of IOp as a standalone measure of inherited inequality that carries additional information.

# Section 6: Conclusion

In this paper, we estimated IOp across 18 countries in SSA (the largest sample among existing studies on IOp in Africa) using the most recently available surveys, a large set of circumstances, and a machine-learning method that trades off upward and downward bias, reduces the need for arbitrary specification choices, and reveals the unique ways in which birth characteristics explain inequality across countries. The results show that more than half of inequality in SSA is explained by circumstances at birth. On average, IOp explains 54 percent of overall inequality in consumption, which is 15 percent higher than previous estimates in the region.

The outsized role of conditions at birth in determining lifelong income is concerning from a moral perspective, but also because existing evidence indicates that IOp is a more important determinant of growth than total inequality (Marrero et al. 2013, Bradbury et al. 2016, Carranza 2020). There is extensive variation across countries in the level of IOp and the role played by different circumstances, highlighting the unique challenges that countries face in addressing IOp. In general, birthplace, parental education, and ethnicity

> 20 It goes without saying that these are simply correlations and do not capture the causal relationship between IOp and income.

> 21 The relationship is similarly negative if we measure IGM as the share of people with a strictly higher educational category than their parents excluding those whose parents have tertiary education.

18
15 The maps in Figure 6 show the addition to the per capita poverty gap from out-ofpocket health expenses, again for the same four poverty lines. As the poverty line is raised, the addition to the poverty gap inevitably increases; the cutoffs are therefore held constant across the four maps and are chosen to divide countries at the $10.00a-day poverty line into five equal-sized groups. At the $10.00-a-day line, the countries with the largest additions to the poverty gap are Cambodia, Egypt, Moldova, Nepal and Nicaragua. The countries with the smallest additions are the Czech Republic, Germany, Luxembourg, Timor-Leste and the United Kingdom. Other low-income countries in the bottom fifth – apart from Timor-Leste – include Lesotho, Madagascar, Mozambique, Rwanda and Zimbabwe. Chile is the highincome country with the highest addition to the poverty gap at the $10.00-a-day poverty line. Other OECD countries with relatively high additions to the poverty gap include Israel, Korea, Poland, Switzerland and the United States. Table 9 shows the medians by income group and region for the $1.90-a-day line: only in the low-income group do we see any appreciable contribution to the poverty gap. The regression results in Table 4 suggest that the addition to the poverty gap due to outof-pocket expenditures (at the extreme poverty line) is lower in richer countries and in countries channeling larger shares of their total health spending through nonprofit schemes.

# **Conclusions**

We have tried to give a sense of the state of research on out-of-pocket health expenditures prior to the present paper, assembling and explaining the various summary measures used in national and international studies, and reviewing the results of multi-country and global studies that have used these measures. These studies have almost all used just one or two measures, and some measures have not been used at all in any multi-country study prior to the present paper. In our empirical analysis, we presented results for 146 countries from all World Bank income groups for all summary measures. We also presented correlations between the summary measures and macroeconomic and health system indicators.

Our results reveal large differences across countries in per capita out-of-pocket health expenditures in 2011 international dollars. These are driven in large part by differences in per capita income, with high-income countries tending to spend more out-of-pocket than poorer ones. But we find out-of-pocket expenditures varying sharply within income groups, ranging from $32 in Sweden to $1,200 in Switzerland in the high-income group, and from $6 in Madagascar to over $100 in Cambodia, Haiti and Nepal in the low-income group. Out-of-pocket expenditures are also positively correlated with the share of GDP spent on health.

We find the two measures of dispersion or risk – the coefficient of variation and Q90/Q50 – are only weakly correlated across countries. Dispersion in out-of-pocket expenditures is sometimes high in countries where the amount in dollar terms is low, such as Sri Lanka and the UK, and we find only one macroeconomic and health
# **Is Climate Change Slowing the Urban Escalator out of Poverty? Evidence from Chile, Colombia, and Indonesia**<sup>**1**</sup>

## **Shohei Nakamura** , World Bank

**Kseniya Abanokova** , World Bank **Hai-Anh Dang** , World Bank **Shinya Takamatsu** , World Bank **Chunchen Pei** , Beijing Normal University **Dilou Prospere** , Osaka University

## JEL Classification: R23; O18; I32

Keywords: Migration; Urban agglomeration; Poverty; Climatic Change; Flooding

> 1 Corresponding author: Shohei Nakamura (snakamura2@worldbank.org). This paper was prepared as a background paper for the World Bank report _Thriving: Making Cities Green, Resilient, and Inclusive in a Challenging Climate_ . An updated version of this paper will be forthcoming in the International Journal of Environmental Research and Public Health. We would like to thank Mark Roberts, Megha Mukim, Sailesh Tiwari, Leonardo Lucchetti, Somik Lall, Rinku Murgai, and Carlos Rodriguez Castelan for their valuable comments on an earlier draft. We are also grateful to Benny Istanto, Imam Setiawan, and Luis Quintero for sharing and processing datasets for us. 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.
# **Table 2. Percentage of Service Providers who Received ECD-Related Training by Type of** **<u>Training and ECD Program Area, Philippines ECD Study (Round 3)</u>**

|Provider Type:|Rural<br>Mid|Health<br>wife|Baranga<br>Wor|y Health<br>ker|Day<br>Wor|Care<br>ker|Child<br>Development<br>Worker<sup>a</sup>|
|---|---|---|---|---|---|---|---|
|Program (P) or non-|P|NP|P|NP|P|NP|P|
|program (NP) region:||||||||
|Parent education|36.3|1.0|4.9|0|83.7|0|61.5|
|Management of Child|52.5|4.2|4.9|1.7|n.a.|n.a.|1.8|
|illness||||||||
|Growth monitoring|45.3|10.4|16.7|0|3.4|0|0.9|
|Health Care Service|13.4|3.1|||||1.8|
|Delivery|||6.5|1.7|1.0|1.1||
|N|179|96|306|174|208|92|109|

a. Child development workers are found in ECD program areas only (see text). n.a. not applicable to this provider type. _Source_ : OPS (2005).

The program has also significantly increased several primary health care and other services designed to promote ECD. Table 3 shows the percentage of municipalities that had functioning elements of various ECD-related services in the first round and the latest available round depending on whether they are in program or non-program regions. This table omits many of the programs listed in Appendix A, because these services were present in all municipalities in Round 1 and Round 3, but received important upgrades of equipment, training and supplies in program areas. Between the baseline and latest round, there is a clear positive trend in the spread of functioning feeding programs, parent education programs, and home-based day care in program areas that is either not observed or not as strong in non-program areas.

**Table 3. Percentage of Municipalities with ECD-related Programs by Program Regions and** **<u>Survey Round, Philippines ECD Study</u>**

|Type of Program<br>Program Areas|Non-Program Areas|
|---|---|
|_Feeding Programs_||
|Round 1<br>32.3|28.1|
|Round 3<br>69.4|38.6|
|_Parent Education/Effectiveness Seminars_||
|Round 1<br>77.4|68.4|
|Round 3<br>100.0|63.2|
|_Home-Based Day Care_||
|Round 1<br>16.1|8.8|
|Round 3<br>44.4|0|
|_Number of Municipalities_||
|Round 1<br>31|57|
|Round 3<br>36|57|

_Source_ : Calculated based on data shown in OPS (2005).

7
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE speed of much more than 60 kmph. It does, however, require that infrastructure and rolling stock be maintained ―fit for purpose,‖ that en-route stops do not disrupt schedules, and that commercial arrangements ensure agreed rolling-stock turnarounds (often a limiting factor for small railways).

**Figure 2.8. Traffic densities on African railways**

<!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Million Traffic Units per route km<br>Congo, Dem Rep, SNCC Congo, Rep, CFCO Angola, CFB Angola, CFL Angola, CFM Gabon, SETRAG Central Africa Cameroon, CAMRAIL West Africa Southern Africa North Africa<br><!-- End of picture text --> _Source:_ Bullock 2009.

_Note:_ Density is normally expressed as traffic units per route-km. The traffic units carried by a railway are the sum of the passenger-km and the net tonne-km.

The railways in Cameroon and Gabon, as well as the CFM line in Angola, perform relatively well and have good productivity indicators. Cameroon implemented one of the earliest rail concessions in the region—Camrail—followed by a concession of railways in Gabon. The concession arrangements for these two railways have helped to boost operational efficiency and thus traffic, so that labor and rollingstock productivity measures are better than for the region’s major publicly owned railways and compare favorably with other rail concessions in the region (table 2.4). Camrail is carrying about 60 percent of nonmineral traffic from Douala toward the borders with Cameroon and Chad, and it also compares favorably with competing bus services on the route from Yaoundé to Ngaoundere, for which travel by unpaved road becomes difficult in the rainy season.

By contrast, the railways in the Democratic Republic of Congo and the Republic of Congo have very poor performance indicators. Angola’s northern Benguela railway has fallen completely into disuse, though it is now in the process of being rehabilitated. These rail lines’ poor quality of service makes it increasingly difficult for them to compete with roads. Poor maintenance over extended periods of time has caused the deterioration of many sections of the track beyond repair and has resulted in a loss of competitiveness and rolling-stock productivity. While such inefficiencies can be tolerated on low-volume feeder lines, and may be the only way some can be viably operated, they are a major handicap when competing against the modern roads being constructed along major corridors.

20
- 31 -

# NOTES

1. For recent discussions see Mishra (1985), Bhattacharya et al (1987), Sun-rum (1987), Dev (1988), Jain et al. (1988), Sundaram and Tendulkar (1988), and Prasad (1988). On sectoral policies and their implications for the poor see Lipton (1977).

2. See GOI (1988). The specific formula used by the Finance Commission is somewhat contentious; see, for example, Arun (1989).

3. This has been a theme of recent analytical work or. poverty alleviation policies; for further discussion and empirical examples see Kanbur (1987), Besley and Kanbur (1988), Ravallion and Chao (1989), Ravallion (1989a,b) and Glewwe (1989). Ravallion (1989b) has examined the potential for poverty alleviation through regional targeting in Indonesia, recognizing explicitly that the policy maker is constrained by (often highly) imperfect information on individual incomes.

4. Ravallion (1989b) discusses how the present methodology can be adapted to incorporate effects on pre-transfer incomes, though empirical implementation looks difficult. The possibilities for adverse genera'l equilibrium effects on incomes of the rural poor arising from attempts to redistribute incomes from the urban to the rural sector are discussed in Ravallion (1984). For an interesting approach to this problem using social accounting matrices see Thorbecke and Berrian (1989).

5. The results for a 2 1 used in Propositions 1 and 2 can be found in recent literature, particularly following Kanbur (1987). The appendix summarizes relevant analytical results from which these propositions can be readily proved, including the properties claimed here for a=0.

6. The approach follows Ravallion and Chao (1989). Also see Ravallion (1989b) for further discussion..

7. Sub-state level date beyond the urban/rural split is not available (the most recent available sub-state level distributional data from the NSS appears to be 1973-74). But state level analysis is probably of greater interest in this context, since state level disbursements are the centre's main policy instrument for regional redistribution. A few states and union territories were excluded on account of data gaps or in view of their extremely small share in national population.

8. Using the ICRISAT panel data for three villages in India's semi-arid tropics, Ravallion (1988) finds that variability over time (particularly in crop and labor incomes) is an important contributing factor to poverty in the long-run, as measured by the expected value of distributionally sensitive FGT measures (though the contribution to the expected value of the headcount index is small). Inter-sectoral transfers may thus be one way to alleviate aggregate poverty by reducing income variability in the rural sector. Indeed, this is arguably an important function of Maharashtra's famous "Employment Guarantee Scheme", which finances agricultural work in lean seasons by taxes on that state's urban sector.
thors have subsequently extended this approach to a number of advanced economies and EMDEs. The popularity and simplicity in implementing this approach has led to similar measures for several other EMDEs.<sup>3</sup> This work was also extended to specific policies such as monetary policy in Husted et al. (2020) for the United States and Arbatli et al. (2017) for Japan. In Hołda et al. (2019), fiscal and monetary policy uncertainty is identified separately. A strand of textual analysis has also focused specifically on text released by central banks (see, for example, Ehrmann and Fratzscher, 2007).

Another method to measure policy uncertainty is to use statistical models with heteroskedastic errors where the second moments of policy variables vary over time. Fern ́andezVillaverde et al. (2015) use this approach to define tax and fiscal spending uncertainty. Mumtaz and Surico (2018) use this approach to identify both fiscal and monetary policy uncertainty in the United States employing a methodology that can solve the problem of the causal link between measures of uncertainty and other macroeconomic variables reported in Baker et al. (2016), Caggiano et al. (2014), and Stock and Watson (2012).

A third approach uses market data such as options and futures. Monetary policy uncertainty can be measured from the implied volatility of option prices on interest rates and realized volatility from futures (see, for example, Chang and Feunou, 2013; Bauer et al., 2012; and Carlson et al., 2005).

Finally, uncertainty measures can be constructed through survey data by asking participants about their perceptions of uncertainty. In the context of policy uncertainty, the Federal Reserve Bank of New York’s Survey of Primary Dealers (which started in 2004) asks primary dealers to provide their forecast of policy rates and their forecast uncertainty. Husted et al. (2020) use these survey responses to compare their news-based measure of monetary policy uncertainty. Dahlhaus and Sekhposyan (2018) use survey-based expectations of the policy rate to define a monetary policy uncertainty measure based on the underlying distribution of expectations.

> 3These include South Africa (Redl, 2018), Poland (Hołda et al., 2019), China (Davis et al., 2019), Turkey (Jirasavetakul and Spilimbergo, 2018), and Pakistan (Choudhary et al., 2020).

5