rafmacalaba/datause-encoder-data huggingface dataset:
> are related to a common principle: that water supply and sewerage services should be treated

> as economic goods, and water companies as commercial enterprises. The Bank's expertise and experience in this issue area is well-established; and, through its policy dialog with borrowers,

> it has promoted economic and commercial principles. Some of the conditions have to do with

> preparation of the bidding and selection process for operational and investment contracts. In this area too, the Bank has also been a positive force, although some innovation is needed to develop

> selection procedures which reduce the cost of competitive bidding for small operational contracts. The remaining conditions have to do with the broader policy and institutional environments that prevail in developing countries: the credibility of the judiciary, and its experience in regulatory matters; the stability of political decisions dealing with regulatory principles and processes; uncertainty about consumer reactions. These are areas in which the

> Bank needs to focus more attention to establish expertise and develop the country dialog.

# 2.0 BACKGROUND

> 2.1 For the last 30-40 years, water and sanitation services in Latin America (LA) have been provided by the public sector. Prior to 1950, however, it was not uncommon to see private

> provision of these services. Private sector participation thus has some precedent in the region.

2.2 In 1990, 87 percent of the urban population in Latin America had access to drinking water and 79 percent of city dwellers were provided with sanitation services. In rural areas, 62 percent of the population had access to drinking water and only 37 percent had access to sanitation facilities.' Moreover, water is not treated properly: 95 percent of domestic and industrial waste waters are discharged without treatment. 2 2.3 Even where water supply is available, systems have been severely degraded due to chronic under investment, resulting in extensive water loss through leakage, poor water quality, and unreliable flow. In some cases, withdrawals from aquifers in excess of recharge threatens the long term availability of fresh water.

2.4 Aside.from the public health risk that these conditions impose, lack of acceptable water and sanitation services will have direct impacts on a county's economic health. To meet water and sanitation needs alone, LA countries will need to invest $10 billion _per year_ until the end of the decade; without an alternative source of financing, LA governments may not be able to

> I _The Decade and Beyond. at a Glance,_ Joseph Christmas and Carel de Rooy, Water International, 16 (1991) p. 129.

> 2 _Infrastructure Maintenance in LAGC. The Costs of Neglect & Options for Improvement. Volume 2, Water Supply & Sanitation Sector,_ Guillermo Yepes, Report No. 17, Regional Studies Program, Infrastructure & Energy Division, LAC Technical Dept., June 1992, p. 1.

3
capital spending plans for 1988 would not be negatively affected by the stock market slump.<sup>8</sup> This indicates that the fundamentals did not change with the crash of the stock market, even though investor sentiments did. Likewise, Seyhun (1990) has shown that insiders who bought stocks aggressively following the 1987 crash benefitted significantly suggesting that insiders did not perceive a shift in fundamentals and viewed the market break as being driven by investor sentiments.

(v) As noted by Abel and Blanchard (1986), one reason for the poor empirical performance of Q investment equations could be aggregation, since most of the early studies used aggregate time-series data. One solution to this problem of aggregation is to use firm-level data, as done in this paper and a host of others previously. The other advantage of using firm-level data is that several variables used in the construction of Q can be measured more accurately; in particular, using share prices of individual firms provides a direct measure of market value. The use of panel data also helps in improving the precision of parameter estimates.

Another explanation for the empirical shortcomings of the standard Q model is to note that if managers react only to changes in fundamentals and if the stock market is not efficient, then the value of shares is a very imperfect proxy for fundamentals.

Additionally, the fact that firms make so little use of new equity issues and that there appears to be so much noise in stock prices seems to suggest that investment may not be driven solely by the Q ratio.

s It should however be noted that for the year, the S & P 500 changed from its closing value of 246 on January 1, 1987 to 247 on December 31, 1987. Since managers are likely to pay more attention to this long-term change in share prices than daily movements in the context of capital expenditure decisions, the absence of any significant spillover of the market crash of October 1987 into capital expenditures may not be surprising.

7
indebtedness occurred even though the average primary fiscal surplus (including grants) _fell_ by 1.6 percentage points of GDP; we will return to this point later when political risk is discussed. Third, big collapses of the real exchange rate (RER) and bank/other bailouts, which were salient features of most of the emerging market debt crises of the past decade, have not been major factors in Kenya.<sup>20</sup> 4.2 Primary Fiscal Balance and Interest Payments Figure 3 plots the primary balance and interest payments over the period 1995/962006/07. Two observations stand out: (i) the primary fiscal surplus including grants went up in the wake of the Goldenberg scandal and reached close to 6 percent of GDP by the mid-1990s but has been on a declining trend ever since, i.e., the primary deficit has been rising; and (ii) interest payments have fallen from 6 percent of GDP in 1995/96 to 2.8 percent in 2006/07.<sup>21</sup> Figure 3 Primary Balance and Interest Payment (% of GDP) <!-- Start of picture text --> 7<br>Primary balance Interest payment<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1995/96 1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07<br><!-- End of picture text --> The fall in interest payments (and interest rates, as we shall see later) even as the primary deficit was rising (primary surplus shrinking) is puzzling. A plausible explanation is that the

> 20 See the country case studies in Budina and Fiess (2005).  It also contains a derivation of the flow budget constraint of the government forming the basis for the decomposition shown in Table 1. 21 Unfortunately, we do not have a consistent GDP series for pre-1995.  Earlier numbers showed government interest payments to be as high as 10-12 percent of GDP in the aftermath of Goldenberg.

14
Field, E., & Vyborny, K. (2022). _Women’s mobility and labor supply. Experimental evidence from_ _Pakistan_ (No. 655; ADB Economics Working Paper Series).

https://www.adb.org/sites/default/files/publication/792221/ewp-655-womens-mobility-laborsupply-pakistan.pdf Gobillon, L., Selod, H., & Zenou, Y. (2007). The Mechanisms of Spatial Mismatch. _Urban Studies_ , _44_ (12), 2401–2427. https://doi.org/10.1080/00420980701540937 Guzman, L. A., & Hessel, P. (2022). The effects of public transport subsidies for lower-income users on public transport use: A quasi-experimental study. _Transport Policy_ , _126_ , 215–224. https://doi.org/10.1016/j.tranpol.2022.07.016 ILO. (2017). _World Employment and Social Outlook: Trends for women 2017_ . ILO.

https://www.ilo.org/global/research/global-reports/weso/trends-for-women2017/lang-en/index.htm ILO. (2020). _Labour force participation rate by sex and age (%)_ . ILOSTAT.

https://www.ilo.org/shinyapps/bulkexplorer33/?lang=en&segment=indicator&id=EAP_DWA P_SEX_AGE_RT_A&ref_area=JOR Kain, J. F. (1992). The Spatial Mismatch Hypothesis: Three Decades Later. _Housing Policy Debate_ , _3_ (2), 371–460. https://doi.org/10.1080/10511482.1992.9521100 Klasen, S., & Pieters, J. (2015). What Explains the Stagnation of Female Labor Force Participation in Urban India? _The World Bank Economic Review_ , _29_ (3), 449–478. https://doi.org/10.1093/wber/lhv003 Lucas, K., & Porter, G. (2016). Mobilities and livelihoods in urban development contexts: Introduction. _Journal of Transport Geography_ , _55_ , 129–131.

https://doi.org/10.1016/j.jtrangeo.2016.07.007

- Minster, C., Ait Bihi Ouali, L., DiDomenico, L., & Alam, M. M. (2022). _Gendered Economic Impacts of Mobility in the MENA Region: A Literature review._ World Bank.

Palacios Santana, M., & El-Geneidy, A. (2022). Cumulative versus Gravity-based Accessibility Measures: Which One to Use? _Findings_ . https://findingspress.org/article/32444-cumulativeversus-gravity-based-accessibility-measures-which-one-to-use 40
Policy Research Working Paper 10689

# **Abstract**

Air pollution is a major threat to health, and the dangers are particularly acute in low- and middle-income countries. However, little is known about how the burden of pollution is spread across the wealth distribution in these countries. This paper uses new data providing high-resolution wealth estimates for more than 100 low- and middle-income countries, combined with equally high-resolution estimates of air pollution, to estimate how wealth is correlated with ambient air pollution around the world. The findings show that on average air pollution is positively correlated with wealth, but the relationship is highly heterogeneous across countries. The fact that air pollution and wealth are both disproportionately high in urban areas, where economic activity is largely concentrated, appears to drive this relationship. When the analysis is limited to anthropogenic sources of pollution, the relationship becomes less heterogeneous and more systematically positive. The paper also examines the relationship between pollution exposure and wealth within large cities around the world. Again, the findings show substantial heterogeneity across cities. The paper explores several hypotheses for this heterogeneity but does not find a single explanation. Economic concentration within cities appears to explain some of the relationship. Cities with more concentrated economic opportunity tend to have more positive correlations between pollution and wealth.

This paper is a product of the Development Research Group, Development Economics. 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 abehrer@worldbank.org.

_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
Pension benefits used to depend on contributions made without taking their timing into account. The scheme was described as operating on an 'age-neutral basis', paying 'equal pensions for equal contributions, irrespective of the age of the member' (ATP 1999:11). Because contributions were mandatory and strictly regulated, this did not result in abuse of the system (as could happen if workers were allowed to make larger contributions near the end of their careers). But the system was cumbersome and was changed in 2002 when the guaranteed rate of interest was lowered from 4.5 percent to 2 percent. Benefits are now linked to the age of contributors. Early contributions are entitled to higher benefits.

The ATP law spells out the pension benefits for each type of contributions. For contributions made before 1972, the pension benefit was fixed at 60 DKK for each year of contribution, payable from age 67. It was raised to 100 DKK between 1972 and 1981. The calculation was then changed and set equal to 25.25 percent of total contributions made (without any allowance for investment income) for the period between 1982 and 2001 (a benefit of 100 DKK was paid for each contribution of 396 DKK). These calculations were based on a guaranteed rate of interest of 4.5 percent, during both the accumulation and payout phases.

**<u>Table 2: Deferred Annuity Conversion Factors by Age</u>**<sup>**6**</sup>

|**Age**|**Rate**|**Age**|**Rate**|**Age**|**Rate**|
|---|---|---|---|---|---|
|**16**|18.30|**33**|14.06|**50**|10.55|
|**17**|18.02|**34**|13.84|**51**|10.35|
|**18**|17.75|**35**|13.63|**52**|10.15|
|**19**|17.48|**36**|13.41|**53**|9.95|
|**20**|17.21|**37**|13.20|**54**|9.75|
|**21**|16.95|**38**|12.99|**55**|9.54|
|**22**|16.69|**39**|12.78|**56**|9.34|
|**23**|16.43|**40**|12.58|**57**|9.13|
|**24**|16.18|**41**|12.37|**58**|8.93|
|**25**|15.93|**42**|12.17|**59**|8.72|
|**26**|15.69|**43**|11.96|**60**|8.51|
|**27**|15.44|**44**|11.76|**61**|8.29|
|**28**|15.21|**45**|11.56|**62**|8.07|
|**29**|14.97|**46**|11.35|**63**|7.84|
|**30**|14.74|**47**|11.15|**64**|7.61|
|**31**|14.51|**48**|10.95|**65**|7.38|
|**32**|14.29|**49**|10.75|**66**|7.14|
|||||**67**|7.15|

Contributions made at the indicated age; pensions start at age 67; after deducting premium for term life insurance for a fixed benefit. Source: ATP For contributions made after 2002, the ATP law specifies the pension benefit, depending on the contribution amounts and the age of the contributors and using a 2 percent

> 6 This table will be removed in 2008 and will be replaced with annually calculated ratios (see brief reference in section 9 below).

7
are in a permanent contractual agreement with high dismissal costs (von Below and Thoursie, 2008; Mortensen and Pissarides, 1999).

Redundancy regulations have been shown to have two opposing effects on employment: on the one hand they increase employment by decreasing layoffs; on the other hand they decrease employment by decreasing turnover and the incentives of firms to hire new employees. Their impact on total employment is therefore ambiguous (Lazear, 1990; von Below and Thoursie, 2008). But there is strong evidence that overly restrictive redundancy rules distort the production choices of firms and reduce labor mobility and productivity (Kugler and Saint-Paul, 2004; Autor et al., 2007; Bassanini and Marianna, 2009). When redundancy is too costly, firms will retain unproductive workers at the expense of productive new hires (Blanchard and Portugal, 2001). And where the last-in-first-out rule applies, labor mobility will decline because the risk of changing jobs increases, since employees will lose the privileges of seniority in the new job (von Below and Thoursie, 2008).

# **_A comparative overview of redundancy regulations_**

Sweden’s labor law stipulates a high level of protection for permanent employees compared with that in many other OECD countries. Among the 20 EU15, G7 and Nordic economies, Sweden provides the ninth highest level of protection for permanent employees as measured by the OECD employment protection legislation index – commonly referred to as EPL (Figure 8). Comparison of the level of employment protection for permanent employees in 2008 and 2013 reveals that six of the economies—France, Greece, Italy, Portugal, Spain and the United Kingdom—responded to the 2008–09 crisis by relaxing the redundancy rules for permanent employees. Eleven others, including Sweden, made no changes, while Denmark, Ireland and the Netherlands made the redundancy rules slightly stricter.

A distinguishing feature of Sweden’s employment protection legislation is that it provides high protection for permanent employees but very low protection for temporary employees. Indeed, among the EU15, G7 and Nordic economies, Sweden has the third largest difference in protection levels between permanent and temporary employees after the Netherlands and Germany (see Figure 8). While the majority of the countries have stricter redundancy rules for permanent employees than for temporary ones, the difference is less pronounced than in these three economies. Only four countries in the sample—France, Luxembourg, Norway and Spain—provide a higher level of protection for temporary employees than for permanent ones.

As will be analyzed in depth subsequently in this paper, a large disparity in protection levels between permanent and temporary employees can increase the share of temporary workers in total employment and create a dual labor market in which permanent employees enjoy secure and stable jobs with greater training opportunities and better prospects while temporary workers—who tend to be young, female and low skilled—bear the burden of economic adjustments and are largely 17
Revised Estimates of the Impact of Climate Change on Extreme Poverty by 2030<sup>_∗_</sup> Bramka Arga Jafino<sup>1</sup> , Brian Walsh<sup>2</sup> , Julie Rozenberg<sup>3</sup> , and Stephane Hallegatte<sup>3</sup>

> 1Delft University of Technology

> 2Global Facility for Disaster Risk and Recovery, The World Bank

> 3The World Bank

**Keywords** : development, poverty, climate change, shared socio-economic pathways, scenarios, uncertainty, inequalities **JEL** : I30, I32, Q54, Q56, O10, O40

> _∗_ The authors would like to thank Samuel Freije-Rodriguez, Michael Woolcock, Minh Nguyen, Alexander Cojocaru, Marta Schoch, Christoph Lachner, and the rest of the GMD and DEC teams for guidance and support on this work. Thanks as well to Emmanuel Skoufias for thoughtful contributions.
Table 6

# C6te d'Ivoire: Application **of** Household Size **Weights** for East Forest. 1985

|Size Class|Unweighted<br>Frequency<br>(%)|Ratio of Pool I &<br>Pool II Frequencies|Weighted<br>Frequency<br>(%)|
|---|---|---|---|
|1|3.3|1.71308|_5.6_|
|2|4.7|1.61806|7.4|
|3|7.7|1.30792|9.9|
|4|9.3|1.05894|9.8|
|5|10.4|1.39290|14.5|
|6|10.4|1.20289|12.5|
|_7_|7.4|0.93659|7.0|
|8|9.9|1.07337|10.5|
|9|6.6|0.88967|5.8|
|10|4.9|0.89273|4.4|
|11|3.6|0.79372|2.8|
|12|2.7|0.62512|1.8|
|13|3.0|0.56394|1.8|
|14|4.4|0.21430|1.1|
|15|1.1|0.80713|0.9|
|16|3.3|0.42977|1.4|
|17|1.9|0.30453|0.6|
|a 18|5.2|0.40731|2.1|
|Mean household size:|8.44||6.60|
|Standard Deviation:|6.99||5.15|

13

1..
- 55 -

**<u>Table 12. OLS estimation of average traffic speed</u>**

||Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|
|---|---|---|---|---|---|---|---|---|---|---|
|ln_Age_|0.007|(0.042)|||||||||
|ln_Traffic_|||0.044|(0.017) **|||||0.207|(0.042) ***|
|ln_securityafter_|||||0.0002|(0.037)|||-0.271|(0.070) ***|
|ln_rainafter_|||||||-0.005|(0.032)|-0.044|(0.023) *|
|ln_cost_|0.082|(0.057)|0.068|(0.058)|0.081|(0.057)|0.082|(0.057)|0.042|(0.055)|
|ln_length_|0.120|(0.038) ***|0.160|(0.039) ***|0.114|(0.037) ***|0.115|(0.037) ***|0.181|(0.032) ***|
|ln_lane_|0.244|(0.267)|0.286|(0.248)|0.224|(0.270)|0.248|(0.269)|0.333|(0.164) **|
|ln_thickness_|0.010|(0.032)|0.053|(0.029) *|0.008|(0.032)|0.009|(0.031)|0.051|(0.029) *|
|ln_gravel_|0.014|(0.015)|0.010|(0.014)|0.015|(0.016)|0.014|(0.015)|0.003|(0.016)|
|ln_bitumen_|-0.031|(0.013) **|-0.037|(0.013) ***|-0.030|(0.013) **|-0.030|(0.013) **|-0.032|(0.012) ***|
|ln_earth_|-0.019|(0.017)|-0.022|(0.017)|-0.019|(0.020)|-0.018|(0.017)|-0.018|(0.017)|
|ln_brick_|-0.014|(0.018)|0.004|(0.018)|-0.019|(0.018)|-0.014|(0.017)|0.015|(0.016)|
|ln_gabion_|0.001|(0.014)|-0.003|(0.014)|0.002|(0.013)|0.002|(0.013)|0.006|(0.013)|
|ln_excavate_|-0.004|(0.010)|-0.008|(0.009)|-0.004|(0.010)|-0.004|(0.010)|-0.008|(0.009)|
|ln_cement_|0.015|(0.014)|0.016|(0.014)|0.016|(0.014)|0.015|(0.014)|0.011|(0.014)|
|D(_postqualify_)|-0.081|(0.104)|-0.028|(0.109)|-0.100|(0.107)|-0.085|(0.104)|0.004|(0.094)|
|Constant|1.754|(0.738)**|1.128|(0.747)|1.850|(0.699)***|1.849|(0.734)**|0.400|(0.812)|
|Obs.|148||141||145||148||138||
|R-squared|0.478||0.714||0.675||0.678||0.759||
|_F_-statistics|12.86||13.48||13.33||12.97||19.41||
|No. of dummy variables|:||||||||||
|Project districts|18||18||18||18||18||

Note: The dependent variable is the average speed in logarithm. Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively. Due to multicollinearity, ln _Age_ is omitted from the last column model.
|Cohort 2: 7-12 yrs in 1974<br>**_Pre-programme share of children in the population in region of_**<br>|1.0381<br>(0.7728)<br>**_birth x birth co_**<br>|0.9746<br>(0.7757)<br>**_hort_**<br>|
|---|---|---|
|Cohort 1: 1-6yrs in 1974|0.2220|0.2175|
||(2.6827)|(2.7392)|
|Cohort 2: 7-12 yrs in 1974|-4.7321<br>(3.0088)|-5.1736<br>(2.8828)|
|**_Pre-programme enrolment rate in region of birth x birth cohort_**|||
|Cohort 1: 1-6yrs in 1974|0.5200<br>(1.3412)|1.4728<br>(0.6916)*|
|Cohort 2: 7-12 yrs in 1974|1.0195<br>(1.7073)|2.2119<br>(0.8579)**|
|Constant|-2.7583|-2.3658|
||(0.4857)**|(0.3961)**|
|Observations<br>|3648|3648|
|Model Wald chi-squared<sup>(a)</sup>|629.55|517.23|
|Prob > chi-squared<br>|0.0000|0.0000|
|Wald test of exogeneity<sup>(b)</sup>|||
|chi-squared|0.65||
|Prob > chi-squared|0.4214||
|**_Hausman specification test for endogeneity_**<sup>**_(b)_**</sup>|||
|chi-squared|4.5||
|Prob > chi-squared|1.0000||

Notes: (a) Wald statistics and p value for the test of the hypothesis that all of the slope coefficients are jointly zero (b) Haussman test of the null hypothesis that both the standard probit and IV-probit estimators are consistent.

Robust standard errors in parentheses

* significant at 5%; ** significant at 1%

# **8. Discussion**

## **The schooling effect**

To summarize, the IV method identifies the effect of giving one more year of education to a randomly selected woman before she started having children. Table 9 provides a summary of the marginal effects of an additional year of schooling on the fertility and maternal care outcomes examined in this paper. It is clear that schooling increases the age at which women begin childbearing. The estimated effects of schooling on fertility 51