rafmacalaba/datause-encoder-data huggingface dataset:
**Chart 2. Implications of ability-to-pay principle Change in emissions growth compared to business-as-usual (annual average % between 2010 and 2050)**

<!-- Start of picture text --> VNM MAR<br>NGAUZB PHL<br>BLR<br>PAK IRQ COL<br>ROMCHL<br>EGYDZA<br>KAZ SGP<br>IDN UKRTHA VEN CZEISRGRC AUT<br>ARG<br>ZAF TURMYS BELKWT<br>IND BRA POL AREHKG<br>IRN NLD<br>MEX<br>SAU<br>ESP<br>KORITAFRA<br>AUS<br>GBR<br>CAN<br>RUS<br>DEU<br>JPN<br>CHN USA<br>7 8 9 10 11<br>Log of GDP (PPP) per capita, 2008<br>5<br>0<br>-5<br>Percent<br>-10<br>-15<br>-20<br><!-- End of picture text --> Note that in the chart above (as in Chart 3B in the text), the relationship is downward sloping. But the relationship when allocations start with the country as unit (as in the chart above) is much less equitable because the correlation with income is -.54 compared with a correlation of - 0.95 when allocations start with the individual is the unit of analysis. The big difference is in the magnitudes of the reduction in emissions growth which is substantially greater for countries such as China and India. For example, under the individual level approach, China’s emissions growth declines relative to BAU by -3 percent but under the country level approach it declines by over 20 percent. The comparable numbers for India are 3.5 percent and -3.3 percent, respectively. The regressive nature of the country level approach is also revealed when we compute the weighted average decline in emissions (where the weights reflect both a country’s population share and GDP per capita share (inverse). This weighted average decline is -5.3 percent under the country level approach and 0.17 percent under the individual level approach. It must be noted that this 39
# Malaria: The Impact of Treated Bed-Nets on Childhood Mortality in the Gambia

# by

Pedro L. Alonso, Allan G. Hill, Patricia H. David, Greg Fegan, Joana R. M. Armstrong, Andreas Francisco, K. Cham, and Brian M. Greenwood

# Table of Contents

|The study|2|
|---|---|
|The interventions in the PHC villages<br>Mortality surveillance<br>Cause of death ascertainment|5<br>5<br>6|
|Results|7|
|Childhood mortality|7|
|The Impact of the bed-nets|10|
|General mortality|10|
|Cause-specific mortality|12|
|Theoretical explanation|14|
|The demographic impact of the bed-nets|15|
|Conclusion|17|
|Tables|20|
|Figures|26|
- Landler, M. (1999) “Year of Living Dangerously for A Tycoon in Indonesia” _New York Times,_ May 16, p.1.

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- Pangestu, M., Aswicahuyono H., Anas, T. and D. Ardyanto (2002). The evolution of competition policy in Indonesia. _Review of Industrial Organization_ , 21 205–224.

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- Transparency International (2004). Global corruption report: Political corruption.

27
Given (14), equations (12) and (13) incorporate three possibilities. Let η1 = ρμ and η2 = μ/(δ1-β3). If η1 and η2 are both less than one, the corridor with the longer road will have greater total profits and greater biodiversity loss. If η1 and η2 are both greater than one, the corridor with the longer road will have lower profits and lower biodiversity loss. In the third possible case -- η1 > 1 and η2 < 1 -- the corridor with the _shorter_ road will be favored unambiguously because it will have higher profits and lower biodiversity loss. It is important to note that this is a ceteris paribus result, since the corridor with the shorter road could have sufficiently greater biodiversity intensity to reverse the result. A complete calculation would also have to include consideration of trade-related regional income, which would favor the shorter road. Given the relatively complex set of factors that affect these results, the impacts of road improvement on income gains, forest clearing and biodiversity loss seem likely to vary significantly across countries, and across regions within countries. In this paper, we focus on the forest clearing and biodiversity components of the problem.

# **4.  Data**

# **4.1  Road Networks**

Table 1 and Figure 1 summarize 2014 road network information for Bolivia, Cameroon and Myanmar in digital maps (shapefiles) provided by Delorme, Inc. As Table 1 shows, the maps identify 4,607 road links in Bolivia, 2,951 in Cameroon and 6,980 in Myanmar. Network links are graded by status: secondary roads (orange in Figure 1), primary connector roads (red), and primary highways (purple).

**Table 1:  Road links by network status:  Bolivia, Cameroon and Myanmar**

|Road Links|Bolivia|Cameroon|Myanmar|
|---|---|---|---|
|SecondaryRoads|3,760|2,162|4,879|
|PrimaryRoads|847|789|2101|

10
# stocks in two tranches, the first in May and the second in July 1994.

The coffee market is always vulnerable to adverse weather conditions in the southern Brazilian states of Minas Gerais (now the coffee producing state), Parana and Sao Paulo. The severe frost in 1975 caused the boom prices of 1976-78, and drought conditions in 1985-86 resulted in the high prices of 1986. It is remarkable that the (southern hemisphere) winter of 1994 saw two severe frosts together with low rainfall in this same area which together generated a jump in the coffee price during June and July 1994 giving an overall fivefold increase over the year.

Evaluation of the impact of the coffee retention scheme is difficult since it is clear that the largest part of the increase in price was caused by the very tight market conditions resulting from the shortfall in the Brazilian crop against the background of stronger demand growth. Indeed, since the major effect of the retention scheme was simply to delay exports, it seems unlikely that prices could have been affected by the scheme after the mid-year when the retained stocks were released. In Figure 6, I plot the ICO CIP against the World Bank index of agricultural food prices with the ratio normalized at 100 in June 1993, the month before the initial Rio meeting. This plot suggests a significant effect from the announcement of the scheme, perhaps giving rise to a 20% rise in the price in the autumn of 1993,21 but with the ratio back to near its June level in the first quarter of 1994, as the prices of other agricultural food products benefited from tighter markets resulting from rapid demand growth. The largest claim that can be made is therefore that the coffee retention scheme resulted in the coffee price rising some months earlier than would otherwise have been the case.

21 This figure slightly underestimates the extent of the relative rise in coffee prices since the comparator index has a coffee content. There is however some suggestion of an element of options-related manipulation in the London September coffee contract which may make the peak price during that month unrepresentative of market conditions. Sources: agricultural foods index - Commodity Policy and Analysis Unit, International Trade Division, World Bank, Washington DC; ICO CIP - ICO, _Monthly Report on Prices,_ various issues.

32
# Table 3: Peer effects

||P|anel A||Panel B|P|anel C|
|---|---|---|---|---|---|---|
|||Over CES-|D cutoff (>10)||CE|S-D Score|
|||for depress<br>|ive symptoms<br>||<br>|(0-30)<br>|
||(1)|(2)|(1)|(2)|(1)|(2)|
|Asked about shocks first|-0.009|-0.009|-0.011|-0.009|-0.034|-0.030|
||(0.014)|(0.014)|(0.014)|(0.014)|(0.068)|(0.068)|
|Any shock|0.071***|0.072**|||||
||(0.014)|(0.028)|||||
|Covariate shock 2014-2016|||0.019|0.054*|0.069|0.215|
||||(0.021)|(0.031)|(0.103)|(0.157)|
|Idiosyncratic shock 2014-2016|||0.071***|0.050*|0.422***|0.379**|
||||(0.016)|(0.029)|(0.094)|(0.162)|
|Experienced HH death 2014-16|||0.033|0.079**|0.235**|0.386**|
||||(0.023)|(0.032)|(0.104)|(0.158)|
|Conflicts between 2014-2016|||0.139***|0.094**|0.606***|0.540**|
||||(0.037)|(0.045)|(0.196)|(0.236)|
|%HH with any adverse events|-0.010|-0.009|||||
||(0.039)|(0.050)|||||
|% HH with Covariate shock in EA (excluding self)|||-0.094**|-0.042|-0.695***|-0.503**|
||||(0.042)|(0.053)|(0.218)|(0.237)|
|% HH with Idiosyncratic shock in EA (excluding self)|||0.077|0.063|0.678**|0.630**|
||||(0.068)|(0.070)|(0.296)|(0.317)|
|% HH with Death experience in EA (excluding self)|||-0.027|0.026|0.190|0.345|
||||(0.090)|(0.087)|(0.324)|(0.357)|
|% HH with Conflicts experience in EA (excluding self)|||0.054|-0.031|0.587*|0.456|
||||(0.067)|(0.089)|(0.317)|(0.410)|
|Adverse events * %HH with adverse events||-0.003|||||
|||(0.065)|||||
|Covariate shock * %HH with any shock||||-0.116||-0.481|
|||||(0.079)||(0.392)|
|Idiosyncratic shock * %HH with any shock||||0.073||0.132|
|||||(0.081)||(0.483)|
|Death shock * %HH with any shock||||-0.326*||-0.950|
|||||(0.177)||(0.707)|
|Conflict * %HH with any shock||||<br>0.205||<br>0.310|
|||||(0.143)||(0.765)|
|N|4224|4224|4224|4224|4304|4304|
|Covariates|X|X|X|X|X|X|
|State fixed effects|X|X|X|X|X|X|

Notes: Ordered logit with clustered standard error (EA), *** p<0.01, ** p<0.05, * p<0.1. Covariates include urban, household head, female, age, age squared, years of education, marital status, Muslim, “other or unknown religion" and "recently moved or missing EA".

22
through, for examnple, the establishment of a customs union among Belarus, Kazakstan, the Kyrgyz Republic and Russia (BKKR) in 1996. Finally, as countries applied for WTO membership, reforms started to be introduced to their trade and related regimes to bring their policies and institutions in line with WTO requirements and obligations.

The very sharp declines in trade among the FSU countries in the early part of the period, appear to have been partly reversed later on, even in the Baltics-as trade channels and some financing were reestablished. But some of the increases in dollar values of trade in 1994-1996 resulted from real appreciation of the new currencies vis-a -vis the US$.

Table 2

|Trade with the Wor<br> <br>|ld, exce<br>|pt FSU|, as a P<br>in/%<br>|roport<br>|ion of<br>|Total T|rade,<br>|1991-1998|
|---|---|---|---|---|---|---|---|---|
|**_Former_**_Sovied_<br>**_Unio_**<br>**_Countries_**|**_n 1991_**||_1993_||_1996_||_1998*_||
||**_Exports_**|**_Imports_**|**_Exports_**|**_Imports_**|**_Exports_**|**_Imports_**|**_Exports_**|**_Imports_**|
|**_Armenia_**|_1.8_|_15.0_|_19.0_|_54.2_|_36.9_|_74.9_|_56.5_|_76.1_|
|**_Azerbaijan_**|**_5.1_**|_15.1_|_40.5_|_27.9_|_49.4_|_73.6_|**_473_**|_69.6_|
|**_Belarus_**|_6.7_|_8.8_|_19.2_|**_18.8_**|_26.4_|_29.2_|_11.9_|**_18.1_**|
|**_Estonia_**|_1.3_|_6.4_|**_57.4_**|_72.8_|_61.0_|_79.7_|_73.0_|**_7Z0_**|
|**_Georgia_**|**_0.5_**|_9.1_|**_42.9_**|**_515._**|**_379_**|_73.7_|_51.2_|**_70.8_**|
|**_Kazakstan_**|**_Z76_**|_13.1_|**_32.8_**|_26.2_|_42.4_|_36.2_|_48.6_|**_38.5_**|
|**_Kyrgyz Republic_**|**_0.4_**|**_15.5_**|_28.4_|_22.9_|_21.9_|_37.4_|_44.5_|**_55.9_**|
|**_Latvia_**|**_2.1_**|_9.9_|**_48.2_**|_49.2_|**_52.5_**|**_62.4_**|_62.3_|_65.1_|
|**_Lithuania_**|_3.6_|**_7.1_**|_42.8_|_30.4_|_43.2_|_61.7_|_65.5_|_64.9_|
|**_Moldova_**|_2.8_|_10.6_|**_374_**|_28.4_|_23.7_|_41.8_|_33.8_|**_52.9_**|
|_Russia_|_32.8_|_35.1_|_73.6_|_75.8_|**_77.4_**|_76.4_|_74.7_|_76.0_|
|**_Tajikistan_**|_10.9_|_13.9_|_69.0_|_65.4_|_54.9_|_41.6_|_34.5_|_32.2_|
|_Turkmenistan_|**_2.3_**|_14.4_|_40.0_|_46.1_|_32.2_|_68.9_|_72.2_|_36.5_|
|**_Ukraine_**|**_14.6_**|_15.6_|_52.6_|_33.8_|_48.7_|_4Z2_|_63.1_|_56.0_|
|**_Uzbekistan_**|**_8.4_**|_12.7_|_41.3_|_36.5_|_54.5_|_54.7_|_43.7_|_62.6_|
|**_Former Soviet Union_**|_20.1_|_22.2_|**_62.4_**|_56.9_|_66.9_|_64.5_|_64.9_|**_63.8_**|

*Estimate based on data for first three quarters

Source: See Table 1 9
**4.1 Focusing on Latin America: Countries with the largest expansion of the middle class** More than 135 million of LAC residents, almost a quarter (24 percent) of the population, belong to the middle class in 2005. By 2030, 310 million will be in the middle class and will account for 43 percent of the population. This overall regional improvement conceals noticeable variations across countries and sub-regions. Even in terms of starting points, LAC countries show a high degree of heterogeneity. In 2005, members of the middle class in Argentina and Chile represented more than 35% of their respective populations; in Mexico and Brazil these proportions were at about 25 percent; and, finally in Peru and Venezuela the middle class accounted for less than 15% of total population. In the coming decades, the middle class is expected to expand the most in Peru, Chile, Venezuela and Argentina and the least in Mexico, Brazil and Colombia. In Peru and Venezuela the middle class will triplicate, while it will increase only by a little more than 50% in Mexico and Brazil. As a result, by 2030, the size of the Peruvian middle class relative to its population (50%) will surpass the relative size of the Mexican (43%) and the Brazilian (41%) ones.

**Figure 10: Middle Class as a percentage of population in 2005 and its expansion (in percentage points) over the 2005-2030 period, by country**

<!-- Start of picture text --> 40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Argentina Chile Mexico Brazil Colombia Rest South Caribbean Peru Venezuela<br>America and Central<br>America<br>2005 Middle class incidence 2005-2030 Expansion (in percentage points)<br>Mean LAC 2005 Mean LAC difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

The country composition of the LAC middle class will have also changed quite significantly. As Figure 11 shows, out of 100 Latinos in the middle class in 2005, 37 were Brazilians, 22 were Mexicans, 7 were from Caribbean and Central America, 3 were Peruvians and 3 were Venezuelans. By 2030, still out of 100 middle class members, only 31 will be Brazilians and 19 Mexicans and it will be at least 10 people from Caribbean and Central America, 6 Peruvians and 5 Venezuelans.

24
_i,+ o-m-L'S i_ (1) _E[R]=(1-o-).P_ (2) _E[C]=Ls id +q.w.p.L'+a-c(l-m)-(Ji -AL)_ where _i,, id,_ and _pE(-oo,+oo)_ are the loan interest rate, the deposit interest rate and the return on the bank's equity (or opportunity cost of capital), respectively.

Equation (1) states that the expected revenue of the bank is not only a function of the risk of default by the sub-national borrower, but also of the portion, m, of the loan that the federal government is expected to bailout if default takes place. As m approaches 1, the moral hazard inherent in the market increases, and thus it is more likely that the lenders will receive their due revenue in full, or simply E[R] = _il_ . Similarly, Equation (2) argues that the expected cost of the loan depends on the cost of funds, the cost of fulfilling capital risk weighting regulations (given the quality of enforcement)<sup>5</sup> and, in the event of default, the portion of the unpaid balance (that is, the loan's principal minus the low asset value, _AL) not_ bailed out by the federal government.<sup>6</sup> If there is close to perfect moral hazard, _m-)l,_ and no capital risk weighting requirements exist, w **_-O,_** or the quality of their enforcement is poor, _q->O,_ the expected per unit cost of the loan approaches the deposit interest rate, E[C] _- 'd._ Assuming that the market for loans is competitive, i.e., equilibrium is characterized by no supra-nornal profits and E[JR] = E[Cj, the following implicit function for the supply of loans obtains from equations (1) and (2): _5_ We adap **t** he middle tenn of Equation (2) from LUvy-Garboua (1993).

> 6 Note that _E[ A]_ = _a - AL + (1 -_ a-) _AH ._ However, since the bank would never lend more thanAH (net of interest), it will, in the event of a high outcome, recover its principal and make its return on the loan, L _i_ l, in full. Thus only AL factors into the expected cost function.

11
Policy Research Working Paper 4951

# **Abstract**

Successful export growth and diversification require not only entry into new export products and markets, but also the survival and growth of export flows. This paper uses a detailed, cross-country dataset of product level bilateral export flows to illustrate that exporting is an extremely perilous activity and especially so in lowincome countries. The authors find that unobserved individual heterogeneity in product-level export flow data prevails despite controlling for a wide range of observed country and product characteristics. This questions previous studies that have used the Cox proportional hazards model to model export survival. The authors estimate a Prentice-Gloeckler model, amended with a gamma mixture distribution summarizing unobserved individual heterogeneity. The empirical results confirm the significance of a range of products as well as countryspecific factors in determining the survival of export flows. From a policy perspective, an interesting finding is the importance of learning-by-doing for export survival: experience with exporting the same product to other markets or different products to the same market are found to strongly increase the chance of export survival. A better understanding of such learning effects could substantially improve the effectiveness of export promotion strategies.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to understand the constraints to export diversification and growth in low income countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbrenton@worldbank.org and csaborowski@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
interviewers posing as customers and by contacting individual firms. Within each corridor, the data were gathered on the same day to control for exchange rate fluctuations and other changes in fee structures. In general, cost data were collected for 8 to 10 major service providers in each corridor, including both the main money transfer operators (MTO) and banks active in the market.<sup>9</sup> Companies surveyed within each segment were selected to cover the maximum remittance market share possible.<sup>10</sup> Costs based on two amounts were surveyed per corridor: the local equivalent of US$200, and the local equivalent of US$500. Because previous studies have found that a typical remittance transaction involves sending close to US$200, we conduct our analysis based on the costs associated with this amount.<sup>11</sup> Furthermore, the costs of sending US$200 and US$500 (expressed as a percentage of the amount sent) are highly correlated (the correlation is 0.91), so we do not expect results to vary significantly if we use costs based on the higher amount.

Table 1 shows the average and median costs (based on transferring US$200) in each of the 119 corridors, calculated across surveyed remittance service providers in each corridor.<sup>12</sup> Average and median costs are highly correlated (96 percent). The average remittance costs are lowest in the Saudi Arabia-Pakistan corridor (2.5 percent) and highest in the Germany-Croatia (25.8 percent) corridor. Across all corridors the average mean cost is 10.2 percent. The median costs are lowest in the Singapore-Bangladesh corridor (2.3 percent), highest in the GermanyCroatia corridor (25.9 percent), and average 9.8 percent across all countries. Averaging costs for each sending country, we observe that costs are lowest for transfers initiated from Saudi Arabia (3.9 percent) and highest for transactions originating in Japan (17.8 percent).

There is significant heterogeneity in costs even when we consider the same sending or the same remittance-receiving country. For example, Figure 1 shows the costs associated with sending remittances from the US to 22 receiving countries, while Figure 2 shows the costs associated with remittances received by India from 8 sending countries. Figure 1 shows that the

> 9 The actual number of respondents by corridors varies depending on the number of firms active in the corridor. In some cases (like the Spain-China corridor) only 2 firms are included, while in others, like the US-Mexico corridor, the number of respondents climbs to 18.

> 10 Unfortunately, information on the market share covered by each provider is not available.

> 11 Freund and Spatafora (2008) use the same amount in their study.

> 12 Note that the averages reported are not weighed. That is, the costs from each remittance provider are averaged without taking into account their relative market shares, which we do have not available.

5