rafmacalaba/datause-encoder-data huggingface dataset:
|P150938|2015|2016|6/27/2016|2 Active|Bangladesh|
|---|---|---|---|---|---|
|P150938|2015|2017|1/2/2017|3 Active|Bangladesh|
|P150938|2015|2018|7/19/2017|4 Active|Bangladesh|
|P150938|2015|2018|1/22/2018|5 Active|Bangladesh|
|P150938|2015|2019|7/19/2018|6 Active|Bangladesh|
|P150938|2015|2019|1/16/2019|7 Active|Bangladesh|
|P150938|2015|2020|7/19/2019|8 Active|Bangladesh|
|P150938|2015|2020|1/17/2020|9 Active|Bangladesh|
|P150938|2015|2021|7/14/2020|10 Active|Bangladesh|
|P150941|2015|2019|5/7/2019|1 Closed|Burundi|
|P150950|2016|2016|5/26/2016|1 Closed|Tunisia|
|P150950|2016|2017|12/6/2016|2 Closed|Tunisia|
|P150991|2017|2017|11/18/2016|1 Active|West Bank and Gaza|
|P150991|2017|2017|4/22/2017|2 Active|West Bank and Gaza|
|P150991|2017|2018|10/9/2017|3 Active|West Bank and Gaza|
|P150991|2017|2018|3/20/2018|4 Active|West Bank and Gaza|
|P150991|2017|2019|10/1/2018|5 Active|West Bank and Gaza|
|P150991|2017|2019|4/22/2019|6 Active|West Bank and Gaza|
|P150991|2017|2020|11/5/2019|7 Active|West Bank and Gaza|
|P150991|2017|2020|5/5/2020|8 Active|West Bank and Gaza|
|P150993|2015|2016|10/4/2015|1 Active|Egypt,Arab Republic o|
|P150993|2015|2016|6/6/2016<br>|2 Active|Egypt,Arab Republic o|
|P150993|2015|2017|1/13/2017|3 Active|Egypt,Arab Republic o|
|P150993|2015|2018|7/11/2017|4 Active|Egypt,Arab Republic o|
|P150993|2015|2018|5/29/2018|5 Active|Egypt,Arab Republic o|
|P150993|2015|2019|12/24/2018|6 Active|Egypt,Arab Republic o|
|P150993|2015|2019|6/28/2019|7 Active|Egypt,Arab Republic o|
|P150993|2015|2020|11/19/2019|8 Active|Egypt,Arab Republic o|
|P150993|2015|2020|2/28/2020|9 Active|Egypt,Arab Republic o|
|P150999|2017|2017|1/17/2017|1 Active|Cameroon|
|P150999|2017|2018|10/25/2017|2 Active|Cameroon|
|P150999|2017|2018|6/11/2018|3 Active|Cameroon|
|P150999|2017|2019|1/22/2019|4 Active|Cameroon|
|P150999|2017|2020|7/25/2019|5 Active|Cameroon|
|P150999|2017|2020|1/17/2020|6 Active|Cameroon|
|P150999|2017|2020|6/17/2020|7 Active|Cameroon|
|P151007|2015|2018|12/4/2017|1 Closed|Paraguay|
|P151026|2017|2018|8/28/2017|1 Active|Ghana|
|P151026|2017|2018|3/12/2018|2 Active|Ghana|
|P151026|2017|2019|7/18/2018|3 Active|Ghana|
|P151026|2017|2019|9/21/2018|4 Active|Ghana|
|P151026|2017|2019|1/15/2019|5 Active|Ghana|
|P151026|2017|2020|7/19/2019|6 Active|Ghana|
|P151026|2017|2020|1/21/2020|7 Active|Ghana|
|P151026|2017|2021|7/19/2020|8 Active|Ghana|
|P151030|2017|2017|6/12/2017|1 Active|Tunisia|
|P151030|2017|2018|12/15/2017|2 Active|Tunisia|
3 This paper investigates the effects on the scheme of the dramatic change in the EGS wage schedule in mid-1988. Three questions are addressed: i) What happened to EGS employment, wage rates, and the cost of the scheme to the government after the increase in the statutory minimum wage rate? Section 2 describes how the main variables of interest have evolved.

ii) What determines EGS employment, and did this change as a result of the wage increase? Section 3 examines this question using an econometric model of the monthly actendance count on EGS from 1975 to 1988. An estimatc is then made of the extent of work rationing on EGS during the 12 months after the wage increase.

ii) Does the EGS now "guarantee" employmant, in the sense that workars can find local employment when they want it, at the going EGS wages? We address this question using new data on the scheme for 1987/90 in Section 4.

2. Maharashtra's "Employment Guarantee 3cheme" Before and After May 1988.

We shall first look closely at the period April 1987 to February 1990, for which we have monthly data on a number of key variables.5 This will motivate our interest in the more analytical questions addressed in sections 3 and 4.

One can usefully divide the period into three sub-periods: Sub-period 1: Pre-May 1988, during which the average wage rate on EGS was approximately eaual to the agricultural wage rate. Sub-Deriod 2: June i988 to November 1988, during which there were some significant adjustments in the scheme, as will be discussed below. Sub-period 3: The period since December 1988 in which, we shall argue, the scheme appears to have settled into a new "equilibrium". The three sub-periods are identified in Figure 1, which plots
# **Table A15: Identifying the $1.90 poor based on covariates of poverty**

|||_Indonesia_<br>|_2017_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.002***|-0.003***|0|-0.006***|-0.008***|0|
|Asset index|-0.017***|-0.017***|0.8812|-0.046***|-0.047***|0.8812|
|Literacy|-0.027***|-0.063***|0|-0.089***|-0.125***|0|
|Not employed in the<br>agricultural sector|-0.012***|-0.016***|0.0057|-0.034***|-0.039***|0.0057|
|Access to electricity|-0.041***|-0.016***|0.0084|-0.094***|-0.069***|0.0084|
|Piped drinking water|-0.005**|-0.003***|0.4166|-0.011***|-0.009***|0.4166|
|Improved sanitation|-0.021***|-0.025***|0.1131|-0.063***|-0.067***|0.1131|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A16: Identifying the $3.20 poor based on covariates of poverty** _Indonesia 2017_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.003***|-0.005***|0|-0.025***|-0.027***|0|
|Asset index|-0.023***|-0.027***|0.0073|-0.178***|-0.182***|0.0073|
|Literacy|-0.021***|-0.053***|0|-0.230***|-0.263***|0|
|Not employed in the<br>agricultural sector|-0.016***|-0.025***|0.0015|-0.137***|-0.146***|0.0015|
|Access to electricity|-0.021***|-0.022***|0.9364|-0.231***|-0.232***|0.9364|
|Piped drinking water|-0.004|-0.002|0.5648|-0.031***|-0.029***|0.5648|
|Improved sanitation|-0.020***|-0.031***|0.0002|-0.211***|-0.222***|0.0002|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 55
95 years onwards, or may postpone retirement until age 70. The corresponding conversion rates are listed below (year 2005).

|**Table 40 PF ABB**<br>|**: Earl**<br>|**y Reti**<br>|**remen**<br>|**t Ages **<br>|**and C**<br>|**onver**<br>|**sion F**<br>|**actors, **<br>|**2005**<br>|
|---|---|---|---|---|---|---|---|---|---|
|Retirement Age|65|64|63|62|61|60|59|58|57|
|Conversion Factor|7.10|7.10|7.10|7.10|6.82|6.58|6.36|6.14|5.97|
|Retirement Age|66|67|68|69|70|||||
|Conversion Factor|7.24|7.39|7.54|7.69|7.84|||||

Source: ABB Pension Fund (2005), p. 28 The bridging pension corresponds to the maximum AHV/AVS retirement pension and is fully repayable.

Investment: The investment strategy of the ABB pension fund was tracking an index<sup>86</sup> , i.e., all purchases and sales aimed at reproducing a given market index. Thus, this strategy should achieve the market return. Consequently, participating in higher market returns is not possible. On the other side, the risk of achieving a lower return is mitigated. From the above strategy are deviating the investments in the Swiss money market, in domestic bonds, in emerging market shares, and in private equity. These securities are managed more actively. Generally, all the securities are managed by a specific and specialized institution that is supervised by the foundation board. The portfolio (as at 31.12.04) contained a high fraction of bonds issued in CHF (36.4 percent) and real estate (20 percent). Foreign bonds (14.7 percent), domestic and foreign shares (8 percent and 11 percent, respectively), money holdings (5 percent), private equity (1.6 percent), and hedge funds (3.1 percent) complete the portfolio. The return on investments was 4.67 percent in 2004.

# **PF Oerlikon Contraves AG**<sup>**87**</sup> **:**

_The pension fund of Oerlikon Contraves provides old-age benefits to its employees and economically or financially associated companies. Old–age and risk insurance are funded by defined contributions, where both the employer and the employee pay the same amount. From the age of 60 onwards, members may at their request take early retirement. Additionally, the option of a full or partial capital payment exists. In 2004, this pension fund covered 1084 active contributors and 2120 retirees with old–age insurance. Despite the fact that the majority of the insured are retirees, the pension fund is quite healthy. Since 2002, the funding ratio has been increasing again, and reached 106.7 percent in 2004. Thereby, a technical interest rate of 3.5 percent was applied. As at 31.12.04, total assets amounted to 1.2 billions CHF._ Early Retirement: Employees of Oerlikon Contraves can chose the early retirement option at age 60. The corresponding conversion factors (year 2005) are:

> 86in 2001

> 87c.f. Pensionskasse der Oerlikon Contraves AG (2005)
# **Notes**

> 1 See, e.g., _Nudge_ by Thaler and Sunstein (2008); Bertrand et al. (2009) on advertising; Choi et al. (2012) on savings cues; Karlan et al. (2011) on reminders; Stango and Zinman (2011) on overdrafts; and Zwane et al. (2010) on survey measurement.

2 For broader reviews of evidence on the drivers of recycling behavior, see, e.g., Carlson (2001) and Chu and Chiu (2006).

3 The Burn and Oskamp message included information on sorting, information on benefits to the environment, the local average participation rate, and an endorsement from the municipality.

4 See Gerber, Green, and Larimer (2008) on voting.

5 See Lerner and Tetlock (1999) on accountability; Gerber, Green, and Larimer (2008) on voter turnout; Frey and Meier (2004); and Alpizar, Carlsson, and Johansson-Stenman (2008) on social signaling in charitable giving.

6 See Bryce, Day, and Olney (1997) for results on paid versus free bins.

7 As a result of its past experience, PRISMA does not enter the wealthiest zones, where families are reluctant to talk to the canvassers, or the poorest zones, which may be dangerous for the canvassers. 8 Supplemental tables S1.6a and S1.6b show the treatments and hypotheses for each section of the study. 9 The only difference significant at more than a 10 percent level was the number of persons in the household. Those households in which the flyer had to be left at the doorstep were slightly smaller on average. Because a larger household is presumably more likely to have someone present at any point in time, this issue does not seem overly concerning. Significant at the 10 percent level, households with flyers delivered in person were slightly more likely to use “advanced fuel” for cooking but slightly less likely to have a color TV.

10 The service provider that sent the SMS messages provided reports of whether the message reached the intended phone, from which we estimated the delivery success rate. Network failures, off phones, and incorrect phone numbers were the main factors for delivery failure.

11 These participation rates are accurate although they are based on back-of-the-envelope calculations.

12 PRISMA considers a household to be “participating” if it gave residuals to the collector at least once during the trial period.

13 Attrition in the treatment and control groups was 0.130 and 0.138, respectively. With a p-value of 0.449, a t-test failed to reject the null hypothesis that these are equal.

14 The service provider that sent the SMS messages provided reports of whether the message reached the intended phone, from which we estimated the delivery success rate. Network failures, off phones, and incorrect phone numbers were the main factors in delivery failure.

15 Scaling by the number of the people in the household yields no differences in results.

16 Figures showing these findings are available upon request. In addition, we performed a simple two-stage Heckman selection equation and found no difference between the groups.

17 Currency conversion is based on the average 2010 exchange rate.

30
countries combine low transaction costs with a high number of regulatory good practices. This points against the argument that improvements in regulatory quality involve high efficiency costs. Spain displays good practices across several regulatory areas. It enacts sound requirements on registration, labeling and monitoring of new fertilizers. Further, it ensures reliable pest management and control at the border through surveillance and pest reporting obligations, quarantine pest lists and risk-based border inspections, domestic containment and border quarantine procedures. At the same time, transaction costs that agribusinesses in Spain bear to register new fertilizer, commercialize new seed varieties and export agricultural goods are rather low. Conversely, other economies perform poorly in either dimension. Liberia, for example, lacks most mentioned good practices while imposing high transaction costs on agribusinesses. Finally, several countries display high scores in only one of the two dimensions while not performing as well on the other, showing potential for targeted regulatory improvement.

_Correlations with other measures of institutional quality_ The EBA overall score provides a synthetic measure of the quality of countries’ regulatory environment for agriculture. To be effective, however, regulations need to be enforced. One potential critique to EBA indicators could be that, being largely based on law on the books, the good practices that they reward may not be observed in practice. In this case one would expect a negative association between our scores and measures of law enforcement. Conversely, EBA scores could be perfectly correlated with other measures of business regulation making them redundant.

To address these points, we look at the correlations between EBA scores and the following indexes: Rule of Law (RoL) from the Worldwide Governance Indicators database and the Ease of Doing Business (DB) from the World Bank’s Doing Business data set. As expected, both correlations are positive and significant. The RoL index proxies the level of enforcement of laws and other societal rules. Its positive correlation ( _r_ =0.63) with the EBA score implies that where good regulatory practices are in place, laws also tend to be better enforced. On the other hand, the DB index measures the quality of a country’s business regulations for the manufacturing and services sectors. EBA scores do not correlate perfectly with the DB index ( _r_ =0.74). This implies that our measure captures different and more agriculture-specific dimensions of overall regulatory quality.

14
**<u>Table A.38. The spillover program effects on households’ income</u>**

||||D<br>|ependent variable<br>|s<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Districts in provinces with the|0.0496|-0.2784|0.0602|0.0823|0.3874|0.0189|0.5494**|
|program|(0.0764)|(0.3376)|(0.2964)|(0.1448)|(0.2804)|(0.1566)|(0.2394)|
|Age of household heads|0.0378***|0.0738***|0.0587***|0.1936***|-0.0313***|-0.1124***|-0.0666***|
||(0.0020)|(0.0143)|(0.0088)|(0.0127)|(0.0091)|(0.0104)|(0.0090)|
|Age squared of household heads|-0.0003***|-0.0011***|-0.0008***|-0.0019***|0.0006***|0.0014***|0.0009***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head|0.0236*|0.0657|0.1049*|0.8948***|-0.4331***|-0.2015***|-0.3998***|
|(male=1, female=0)|(0.0128)|(0.1114)|(0.0610)|(0.0602)|(0.0610)|(0.0545)|(0.0525)|
|Ethnic minorities (yes=1, Kinh=0)|-0.4812***|0.1094|-1.5872***|0.5220***|-0.4287***|0.3832***|-0.5147***|
||(0.0327)|(0.1153)|(0.1204)|(0.0956)|(0.0812)|(0.0836)|(0.1099)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.2226***|3.0168***|1.6135***|2.3383***|4.5253***|2.5174***|2.8994***|
||(0.0626)|(0.3755)|(0.2625)|(0.3108)|(0.3011)|(0.2823)|(0.2736)|
|Observations|54,870|54,870|54,870|54,870|54,870|54,870|54,870|
|R-squared|0.352|0.118|0.081|0.155|0.246|0.132|0.098|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

88
|(7)<br>(8)<br>Pre-trial<br>likelihood<br>of being<br>heard<br>Judge<br>more strict<br>(share)<br>0.027<br>0.058***<br>(0.022)<br>(0.015)<br>0.415<br>0.051<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>0.854<br>0.123<br>0.149<br>0.151<br>0.212<br>0.081<br>3533<br>3537<br> decree application is a<br> the decree. All models<br>hamber FEs, trend, and<br>ated by OLS. Standard<br> cases entering between<br>vations, except for col 1<br>ave any adjournments).|
|---|
|(6)<br>Number of<br>pretrial<br>hearings<br><br>-1.994***<br>(0.493)<br>0.041<br>Yes<br>Yes<br>Yes<br>8.259<br>6.468<br>0.146<br>4795<br> Entered after<br> application of <br>nteraction of c<br>ll models estim<br>ndow includes <br>n. 4795 obser<br>or cases that h|
|(5)<br>Fast-<br>tracked<br>0.092**<br>(0.040)<br>0.158<br>Yes<br>Yes<br>Yes<br>0.140<br>0.347<br>0.078<br>4795<br>ceedings. <br> after the <br>ree (full i<br>nstant. A<br>level. Wi<br>applicatio<br>l 8 (only f<br>_<_0.01.|
|(4)<br>Desk<br>rejection<br>0.169***<br>(0.020)<br>0.019<br>Yes<br>Yes<br>Yes<br>0.000<br>0.000<br>0.084<br>4795<br>pre-trial pro<br>ed chamber <br>fter the dec<br>cts and a co<br>g of entry) <br>fter decree <br>hearing), co<br>0.05, *** p|
|(1)<br>(2)<br>(3)<br>Duration<br>of pre-trial<br>hearings<br>(in days)<br>Hazard<br>ratio -<br>fnishing<br>pre-trial<br>Likelihood<br>of pre-trial<br>completion<br>in 4<br>months<br>Entered after decree<br>-46.091***<br>1.338***<br>0.239***<br>application (_β_)<br>(11.179)<br>(0.127)<br>(0.043)<br>P-value 6pWCBoot<br>0.072<br>0.034<br>Chamber FE x Trend<br>Yes<br>Yes<br>Yes<br>Calendar month FEs<br>Yes<br>Yes<br>Yes<br>Without adj. period<br>Yes<br>Yes<br>Yes<br>Pre-mean<br>156.941<br>0.487<br>Pre-sd<br>146.025<br>0.500<br>R-Squared<br>0.216<br>0.138<br>Observations<br>4568<br>4795<br>4795<br>Notes<br>: Estimates of case-level impacts of the reform on <br>dummy that takes value 1 when a case entered its assign<br>include chamber-specifc linear trends allowed to change a<br>Entered after decree application), calendar month fxed efe<br>errors in parentheses, clustered at the (chamber x hearin<br>38 and 4 hearings before and between 4 and 21 hearings a<br>(censoring), col 7 (only for cases that have more than one <br>Signifcance levels are denoted as follows: * p_<_0.10, ** p_<_|

42
**Table 1. Hotspots in SSA: Major source countries**

|**Top 10**|**by origin, b**|**ased on 19**|**90**||
|---|---|---|---|---|
|**Origin**|**1990**|**2000**|**2010**|**2013**|
|Mozambique|1,247,992|30|131|56|
|Liberia|735,689|266,930|70,129|17,576|
|Sudan|523,998|494,363|387,288|649,331|
|Somalia|470,174|475,655|770,154|1,121,738|
|Angola|407,760|433,760|134,858|10,297|
|Rwanda|361,322|119,056|115,524|83,937|
|Burundi|191,622|568,084|84,064|72,652|
|Chad|184,806|54,962|53,733|48,644|
|Dem. Rep. of the Congo|67,423|371,713|476,693|499,541|
|Senegal|60,006|11,088|16,267|19,884|
|**Top 10**|**by origin, b**|**ased on 20**|**13**||
|**Origin**|**1990**|**2000**|**2010**|**2013**|
|Somalia|470,174|475,655|770,154|1,121,738|
|Sudan|523,998|494,363|387,288|649,331|
|Dem. Rep. of the Congo|67,423|371,713|476,693|499,541|
|Eritrea|43|376,851|222,460|308,022|
|Central African Rep.|100|139|164,905|252,865|
|Mali|1|364|3,663|152,864|
|South Sudan||||114,467|
|Côte d'Ivoire|2|773|41,758|85,729|
|Rwanda|361,322|119,056|115,524|83,937|
|Burundi|191,622|568,084|84,064|72,652|

Note: Authors’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.

8
INTRODUCinON lVii Table 14 Annual Population Increase and Growth Rate by World Bank Region and Country Department. 1985-2100

|Region<br>and<br>country<br>department|Annual<br>p<br>1985-90<br>2|<br>opulati<br> <br>000-05<br>2|on<br>incre<br> <br>025-30<br>2|ase<br>(mill<br> <br>050-75<br>2|ions)<br> <br>100 25|<br>An<br>1985-90<br>2|nual<br>gro<br> <br>000-05<br>2|wth<br>rate<br> <br>025-30<br>|(perce<br> <br>2050-75<br>|nt)<br> <br>2100-25|
|---|---|---|---|---|---|---|---|---|---|---|
|World|88.2|91.7|78.5|37.2|4.9|<br>1.74|1.43|0.91|0.35|0.04|
|Borrowers and non-borrowers|||||||||||
|Sub-Saharan Africa|15.4|22.6|27.2|18.2|2.2|3.11|2.92|1.91|0.82|0.08|
|Asia and Oceania|46.7|42.4|28.7|8.5|1.5|1.74|1.26|0.68|0.18|0.03|
|Europe, Middle East,|||||||||||
|and North Africa|15.2|17.3|16.6|9.1|1.0|1.29|1.21|0.88|0.40|0.04|
|America|10.9|9.4|<br>6.0|1.4|0.2|1.57|1.10|0.57|0.12|0.02|
|Borrowers only|||||||||||
|Africa Region|14.2|21.2|25.8|17.4|2.1|3.17|2.98|1.94|0.84|0.08|
|<br>1 Occidental and Central|1.4|2.1|2.6|1.8|0.2|3.32|3.03|2.03|0.89|0.08|
|2Eastern|3.6|5.5|<br>6.9|4.7|0.5|3.18|3.00|2.01|0.86|0.08|
|3South-Central and|||||||||||
|Indian Ocean|1.8|2.8|3.5|<br>2.4|0.3|3.13|3.06|2.04|0.89|0.08|
|4Western|4.1|5.8|5.8|3.5|0.4|3.15|2.86|1.63|0.69|0.07|
|5Sahelian|1.1|1.8|2.7|2.0|0.3|2.79|3.00|2.27|1.02|0.12|
|6Southern|2.2|3.3|<br>4.3|<br>2.9|0.3|3.33|3.05|2.06|0.88|0.08|
|Asia Region|45.0|41.1|28.6|8.6|1.5|1.80|1.30|0.71|0.19|0.03|
|<br>1Other South|3.5|<br>4.0|3.1|1.5|0.3|2.44|2.01|1.07|0.41|0.06|
|2 Southeast|6.0|<br>5.5|<br>4.1|1.3|0.2|2.08|1.46|0.83|0.22|0.03|
|3China|15.4|12.5|7.1|1.3|0.2|1.43|0.96|0.46|0.08|0.01|
|4India|16.5|16.1|12.0|3.9|0.7|2.05|1.53|0.86|0.24|0.04|
|5Indonesia and Pacific|3.5|<br>3.0|2.3|0.6|0.1|1.98|1.32|0.76|0.18|0.03|
|EMN Region|12.3|15.6|16.2|9.3|0.9|2.44|2.17|1.43|0.59|0.05|
|IPakistan and Turkey|*4.8|5.7|<br>5.8|3.2|0.3|3.01|2.40|1.50|0.59|0.05|
|2 North<br>Africa|1.6|<br>2.0|<br>1.8|<br>0.8|0.1|<br>2.76|2.29|1.31|0.46|0.04|
|3 Middle<br>East|5.4|<br>7.4|<br>8.4|<br>5.2|0.5|<br>2.99|2.68|<br>1.74|0.73|0.06|
|4 Europe|0.5|<br>0.4|<br>0.2|<br>0.0|0.0|<br>0.44|0.37|0.14|0.03|0.01|
|LAC Region|8.4|<br>7.5|<br>5.7|<br>1.6|0.2|<br>2.02|<br>1.40|0.80|0.19|0.02|
|1 Brazil|2.9|<br>2.4|<br>1.7|<br>0.4|0.1|<br>2.05|<br>1.28|0.71|0.14|0.02|
|2 Central<br>America|2.5|<br>2.4|<br>1.9|<br>0.6|0.1|<br>2.25|<br>1.61|0.96|0.23|0.02|
|3 Caribbean<br>and Other|1.5|<br>1.5|<br>1.1|<br>0.4|0.1|<br>2.08|<br>1.49|0.87|0.23|0.03|
|4 Teperate and Tropical|<br>1.4|<br>1.3|<br>1.0|<br>0.3|0.0|<br>1.77|1.28|0.73|0.17|0.02|

Note: World Bank regions and country departments are defined in Exhibit 2.