rafmacalaba/datause-encoder-data huggingface dataset:
Nearly 70 percent of these exports are composed of agricultural and natural resource-based industrial goods: crude oil (43.6 percent of total exports), wood processing (11.9 percent), cash crops (7.1 percent), and refined petroleum (5.8 percent). These sectors are, indeed, substantially export-oriented with export-orientation ratios (exports as a share of output) of 98.5 percent for crude oil, 43.0 percent for cash crops, 37.1 percent for wood processing, and 28.5 percent for refined petroleum. Cameroon's imports are predominantly composed of industrial goods (84.7 percent of imports). The highest import-penetration ratio (imports as a share of total domestic demand of a good) is recorded for crude oil (95.0 percent), followed by other manufacturing goods (29.1 percent), food processing (12.3 percent), and refined petroleum (10.7 percent).

We use this model first to examine the likely impacts of a successful conclusion to the Doha Round. In the subsequent section, we analyze outcomes from more ambitious world and Cameroon free trade scenarios. In a third section, we use the model to assess the poverty implications of fixed vs. endogenous terms of trade in the framework of Cameroon free trade ("own liberalization"). Finally, we compare the impacts of alternative tax mechanisms the government can adopt to compensate for losses in tariff revenues.

# **3. Impacts of the Doha Scenario**

The Doha scenario involves a reduction in world and domestic tariffs, exports subsidies and domestic support. Tables 2 – 7 report the impacts of the Doha scenario on Cameroon. These tables cover changes in macroeconomic variables (12.2), changes in household income and poverty (12.3 – 12.5) and the impacts on sectors and factor markets (12.6 – 12.7). For Cameroon, changes in tariff rates under the Doha scenario are minimal (Table 6, second group of columns) with the average tariff level falling from 11.79 percent to 11.66 percent. Results from global 9
Appendix Table A1. The resulting Chi-squared statistics indicate that these variables are jointly different from zero at the highest level of significance (the p-value is 0.000). This implies that these variables are significant predictors of attrition; that is, attrition may not be random.

We also perform the Becketti, Gould, Lillard and Welch test to determine whether attrition is random, which involves regressing the outcome variable on household and community level exogenous variables, the attrition dummy, and the interactions of the attrition dummy and the other explanatory variables.<sup>11</sup> Then a joint significance test of the attrition dummy and the interaction variables is performed to determine whether the coefficients of the explanatory variables vary significantly between the households that were lost and those that were resurveyed. If they do then we can reject the null hypotheses that attrition is random. From the results (Appendix Table A2) we see that at the 5 percent level, the randomness of attrition is rejected for all outcomes.

This non-randomness of attrition will introduce attrition bias in the estimated impacts if not corrected for. There are a number of ways attrition bias can be addressed, for example, estimating a selection model, which depends on finding suitable instruments (Heckman 1979); using inverse probability weights, which relies on auxiliary variables are related to both attrition and the outcome variables (Fitzgerald, Gottschalk and Moffitt 1998), using non-parametric techniques (Das, Toepoel, and van Soest 2011), and so on. We used the inverse probability weight since it is simple to implement and does not require strong conditions as required by the selection model. The rationale behind the calculation of inverse probability weights is that it gives more weight to households who were subsequently lost than to those with similar initial characteristics but are more likely to remain in the panel. Details on the implementation of this procedure can be found in Baluch and

> 11 See Becketti and others (1988).

10
- (4) We found that all findings using the share of exports in GDP could be obtained almost identically using the total trade or import share. Thus, cross-country growth studies that use export indicators should not be interpreted as studying the relationship between growth and exports per se but rather as between growth and trade defined more broadly.

- (8) We found that many fiscal indicators are not robustly correlated with growth. Total government expenditures as a fraction of GDP, government consumption expenditures as a share of GDP, government consumption expenditures less defense and education spending as a fraction of GDP, the average growth rate of government expenditures, the growth rate of the share of government expenditures in GDP, and deficits were found not to be robust in explaining growth. Also, disaggregated measures of fiscal expenditures - public investment expenditures, education expenditures, and defense expenditures as a shares of GDP - were not robustly correlated with growth. Similarly, disaggregated tax revenue data - the ratio of export taxes to exports, the ratio of import taxes to imports, the ratios of corporate, individual, and social security taxes in GDP - were not robustly correlated with growth.

(6) A variety of measures of trade - average growth rates, shares of GDP, and average growth rates of shares - were found to not be robustly correlated with growth when the investment share is included. (7) Finally, a large assortment of other economic and political variables were found not to be robustly correlated with growth including measures of investment in human capital, the average black market exchange rate premium, the standard deviation of the black market premium, the average inflation rate, the standard deviation of the inflation rate, the average 27
|P064926|2001|2001|4/19/2001|3 Closed|Samoa|
|---|---|---|---|---|---|
|P064926|2001|2002|5/29/2002|5 Closed|Samoa|
|P064926|2001|2003|6/16/2003|7 Closed|Samoa|
|P064926|2001|2004|6/23/2004|9 Closed|Samoa|
|P064926|2001|2005|5/19/2005|11 Closed|Samoa|
|P064926|2001|2006|1/26/2006|12 Closed|Samoa|
|P064926|2001|2007|9/20/2006|13 Closed|Samoa|
|P064926|2001|2007|12/27/2006|14 Closed|Samoa|
|P064961|2001|2001|6/8/2001|1 Closed|Burundi|
|P064961|2001|2002|4/30/2002|5 Closed|Burundi|
|P064961|2001|2003|1/22/2003|9 Closed|Burundi|
|P064961|2001|2004|5/25/2004|12 Closed|Burundi|
|P064961|2001|2005|4/15/2005|14 Closed|Burundi|
|P064961|2001|2006|11/23/2005|15 Closed|Burundi|
|P064961|2001|2006|5/30/2006|16 Closed|Burundi|
|P064961|2001|2007|11/9/2006|17 Closed|Burundi|
|P064961|2001|2007|2/28/2007|18 Closed|Burundi|
|P064961|2001|2008|10/13/2007|19 Closed|Burundi|
|P064961|2001|2021|7/20/2020|20 Closed|Burundi|
|P064965|2001|2001|6/29/2001|1 Closed|Rwanda|
|P064965|2001|2002|6/7/2002|3 Closed|Rwanda|
|P064965|2001|2003|3/28/2003|6 Closed|Rwanda|
|P064965|2001|2004|6/28/2004|9 Closed|Rwanda|
|P064965|2001|2005|5/26/2005|11 Closed|Rwanda|
|P064965|2001|2006|9/21/2005|12 Closed|Rwanda|
|P064965|2001|2006|3/27/2006|13 Closed|Rwanda|
|P064965|2001|2007|9/29/2006|14 Closed|Rwanda|
|P064965|2001|2007|4/13/2007|15 Closed|Rwanda|
|P064965|2001|2008|10/7/2007|16 Closed|Rwanda|
|P064965|2001|2008|3/17/2008|17 Closed|Rwanda|
|P064965|2001|2008|6/26/2008|18 Closed|Rwanda|
|P064981|2003|2003|6/26/2003|1 Closed|Yemen,Republic of|
|P064981|2003|2004|4/26/2004|3 Closed|Yemen,Republic of|
|P064981|2003|2005|3/11/2005|5 Closed|Yemen,Republic of|
|P064981|2003|2006|7/28/2005|6 Closed|Yemen,Republic of|
|P064981|2003|2006|12/5/2005|7 Closed|Yemen,Republic of|
|P064981|2003|2006|6/22/2006|8 Closed|Yemen,Republic of|
|P064981|2003|2007|12/20/2006|9 Closed|Yemen,Republic of|
|P064981|2003|2007|4/18/2007|10 Closed|Yemen,Republic of|
|P064981|2003|2008|11/4/2007|11 Closed|Yemen,Republic of|
|P064981|2003|2008|4/1/2008|12 Closed|Yemen,Republic of|
|P064981|2003|2009|7/31/2008|13 Closed|Yemen,Republic of|
|P064981|2003|2009|3/14/2009|14 Closed|Yemen,Republic of|
|P064981|2003|2010|7/8/2009|15 Closed|Yemen,Republic of|
|P064981|2003|2010|2/24/2010|16 Closed|Yemen,Republic of|
|P064981|2003|2011|11/2/2010|17 Closed|Yemen,Republic of|
|P064981|2003|2020|9/13/2019|18 Closed|Yemen,Republic of|
# **1.  Introduction**

Bahia has transformed itself from a dozy rural economy to the leading manufacturing state of the Northeast during the last three decades. In the process, the state government has developed a reputation as a modernizing, fiscally responsible administration. However, problems remain. Growth is lacking and poverty remains widespread (World Bank 2001b).

Progress in reducing poverty and improving social indicators has taken place in the last decade in Bahia. Despite this progress, poverty is among the highest, and social indicators are among the lowest, in Brazil. The prospects for Bahia’s poor are improving, but more needs to be done to ensure a better life for those who are vulnerable. With decisive action, Bahia could continue to build upon its recent achievements.

The recent growth of the economy, demographic changes, and macroeconomic stabilization have reduced poverty in Bahia. Comparing household survey data from 1993 with the recent available data (2001) shows that the number of people living in poverty has declined. More than half of Bahia’s population lived in households below the poverty line in 1993; this proportion fell to 41 percent in 2001, a drop in absolute terms of 14 percentage points over the eight-year period. Nevertheless, as of 2001<sup>2</sup> , Bahia was one of the poorest states in Brazil. In 2001, median per-capita income in Bahia was only R$100, 55 percent of the national average. The fall in Bahia’s social indicators, such as infant mortality and adult illiteracy, corroborates the improvement in measured income poverty.

Reducing poverty is one of the central challenges in Bahia. This poverty assessment attempts to answer several basic questions about poverty in Bahia. It is a quick assessment and is therefore not able to focus on all areas of poverty. The paper addresses poverty in broad sense, that is, both monetary and non-monetary poverty. A broad view of poverty was wished for when producing the paper, defining poverty as unacceptable deprivation in human well being, including not only insufficient income and consumption, but also voicelessness, powerlessness, and the lack of basic needs such as education, health, nutrition, and security. However, data are not available for all these measures of poverty in Bahia, so for quantitative analysis this paper accepts a much narrower definition of poverty as insufficient income, education, employment, access to public services, social assistance, and poor health.

Given the heterogeneity of the poor in terms of income sources, human resources, and physical endowments, there is no single or simple solution. The paper concludes that a poverty reduction strategy must include different exit paths for different households, via a set of integrated policies tailored to capitalize on 2 2001 is the most recent year for which data from the national household survey are available.

3
- [11] Combes, P.P., Duranton, G., Gobillon, L., Roux, S., (2010). “Estimating Agglomeration Economies with History, Geology, and Worker Effects", _Chapter in_ nber _book Agglomeration Economics (2010), Edward L. Glaeser, editor_ , 15 - 66.

- [12] Combes, P.P., Duranton, G., Gobillon, L., Roux, S., (2008). “Spatial Wage Disparities: Sorting Matters!", _Journal of Urban Economic_ , 63(2): 723-742.

- [13] Combes, P.P., Duranton, G., Gobillon, L., Roux, S., (2012). “Sorting and local wage and skill distributions in France", _Regional Science and Urban Economics_ , 42(6): 913930.

- [14] da Mata, U., Deichmann, U., Henderson, J.V., Lall, S.V., H.G. Wang, (2005). “Determinants of city growth in Brazil”, _Journal of Urban Economics_ , 62(2): 252-272.

- [15] De Mel, S., McKenzie, D., and , C., (2008). “Returns to Capital in microenterprises: Evidence from a Field Experiment", _Quarterly Journal of Economics_ , 123(4): 1329-1372.

- [16] de Paula, A. and J. Scheinkman, (2011)“The Informal Sector: An Equilibrium Model and Some Empirical Evidence from Brazil”, _Review of Income and Wealth_ , Series 57, Special Issue.

- [17] Djankov, S., R. LaPorta, F. Lopez-De Silanes, and A. Shleifer, (2002). “The Regulation of Entry”, _Quarterly Journal of Economics_ , 117: 1-37.

- [18] Duranton, G., Puega, D., (2004). “Micro-foundations of urban agglomeration economies", In _Handbook of Regional and Urban Economics_ , vol. 4, edited by J. Vernon Henderson and Jacques-Francois Thisse, 2063-2117. Amsterdam: NorthHolland.

- [19] Ellison, G. and E. Glaeser, (1997). “Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach,’ _Journal of Political Economy_ , 105(5): 889-927.

- [20] Ellison, Glenn D., and Edward L. Glaeser, (1999). “The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?? _American Economic Review, Papers and Proc_ , 89(2): 311-316.

- [21] Ellison, G., Glaeser, E. and W. Kerr, (2010). What Causes Industry Agglomeration? Evidence from coagglomeration patterns, _American Economic Review_ , 100(3): 11951213.

- [22] Evans, David S. and Boyan Jovanovic, (1989.) “An Estimated Model of Entrepreneurial Choice under Liquidity Constraints.” _Journal of Political Economy_ , 97: 808- 827.

47
# **<u>TABLES</u>**

**Table 1 ‐ SET OF FAST GROWING COUNTRIES**

|||GDPPCGR|Pub<br>spending<br>/GDP|Fiscal<br>balance/<br>GDP|Pro/TEXP|Soc/TEXP|Eco/TEXP|Edu/TEXP|Hea/TEXP|TRA/TEXP|CORE/TEXP|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Botswana|1970‐79|11.93|28.37|‐5.03|76.61|35.67|31.37|16.54|5.66|13.71|75.39|
|Botswana|1980‐89|8.00|32.53|9.65|68.87|35.59|26.32|19.23|5.52|9.92|63.39|
|Botswana|1990‐99|3.74|36.91|7.60|74.11|44.31|15.46|23.01|5.22|6.76|53.05|
|Botswana|2000‐05|5.60|38.83|0.53||||23.52|8.48||32.00|
|Indonesia|1970‐79|5.31|18.36|‐3.35|62.79||34.55|8.42|2.03|12.24|45.27|
|Indonesia|1980‐89|4.41|21.17|‐3.59|63.20||31.49|9.15|2.18|9.83|58.05|
|Indonesia|1990‐99|3.26|17.23|‐0.85|53.02|35.68|22.70|8.72|2.54|7.35|49.42|
|Indonesia|2000‐05|3.37|18.33|‐1.31||||||||
|Korea, Rep.|1970‐79|6.32|15.48|‐0.79|68.33|24.67|21.28|16.21|1.29|5.47|39.52|
|Korea, Rep.|1980‐89|6.38|15.91|0.19|65.52|28.83|16.05|18.66|1.65|3.77|36.09|
|Korea, Rep.|1990‐99|5.24|16.39|1.21|55.99|33.12|20.44|18.90|1.04|5.94|37.51|
|Korea, Rep.|2000‐05|4.60|20.71|1.83|59.87|35.01||15.38|0.45|6.67|47.51|
|Malaysia|1970‐79|5.18|23.70|‐6.33|58.93|34.74|15.96|22.45|6.71|5.87|42.44|
|Malaysia|1980‐89|3.18|30.58|‐9.55|57.90|31.49|29.49|18.80|4.85|8.36|48.00|
|Malaysia|1990‐99|4.52|24.23||60.92|||20.95|5.85|8.02|50.36|
|Malaysia|2000‐05|3.15|26.99|‐6.48|61.09|||24.60|7.06|8.03|52.58|
|Mauritius|1970‐79||30.16|‐11.28|51.42|47.68|16.46|14.10|8.50|4.30|52.54|
|Mauritius|1980‐89|4.90|26.81|‐6.62|46.29|43.36|14.96|14.86|7.78|4.50|45.50|
|Mauritius|1990‐99|4.24|23.59|‐3.26|45.85|48.97|14.43|15.95|8.56|4.37|44.62|
|Mauritius|2000‐05|3.15|24.36|‐3.32|52.89|52.34|15.07|15.74|8.70|3.40|52.21|
|Singapore|1970‐79|7.55|18.07|0.87|79.37|33.78|10.62|16.74|7.89|5.31|49.91|
|Singapore|1980‐89|5.32|24.99|2.68|71.59|34.96|15.34|18.62|5.72|8.01|50.83|
|Singapore|1990‐99|4.39|15.60|7.80|90.26|46.13||24.24|7.67|6.02|59.09|
|Singapore|2000‐05|3.44|16.58|4.73|91.49|51.17||23.70|6.65|7.82|56.58|
|Thailand|1970‐79|4.83|15.49|‐2.71|62.21|32.15|23.25|20.70|4.11|10.29|43.76|
|Thailand|1980‐89|5.48|18.39|‐3.53|57.40|30.41|22.24|19.64|5.41|7.02|40.28|
|Thailand|1990‐99|3.99|17.61|‐0.25|61.35|36.12|30.92|20.38|7.51|11.28|48.94|
|Thailand|2000‐05|4.08|19.59|‐3.41|59.96|43.09|24.03|21.30|8.97|7.11|55.74|
|**AVERAGE**||**5.02**|**22.75**|**‐1.28**|**63.74**|**38.15**|**21.54**|**18.17**|**5.48**|**7.36**|**49.28**|

Note: GDPPCGR is the growth rate of GDP per capita in real terms. Pub spending/GDP is the ratio of total public spending in %. Fiscal balance is the ratio of fiscal balance to GDP in %. Pro/TEXP is public productive expenditure in total public expenditure in%. Soc/TEXP is public social expenditure in total public expenditure in %. Eco/TEXP is public economic expenditure in total public expenditure in %. Edu/TEXP is public education expenditure in total public expenditure in %. Hea/TEXP is public health expenditure in total public expenditure in %. Tra/TEXP is public transportation expenditure in total public expenditure in %. CORE/EXP is the core spending in percent of total expenditure.

32
<!-- Start of picture text --> 0.5 .<br>68% confidence interval<br>o —Median<br>0.3<br>0.2<br><!-- End of picture text --> <!-- Start of picture text -->
-0.1 -<br>vay \O | ad 10,4) ON (on) —_ N foe) wT ~y \O<br>oS (an) S oS S al _ —_— — _ —_ =<br>(o>) Oo =} oO =} oO (=p) So So oO So So<br>N N N N N N N N N N N N<br><!-- End of picture text -->
Table 8 Percentage of Providers Observed during Consultation from Voucher and Nonvoucher Facilities That Performed Various Clinical Examinations in 2010

|Clinical examination|A|NC|P|NC|
|---|---|---|---|---|
||Voucher<br>facilities<br>(_N_= 95)|Comparison<br>facilities<br>(_N_= 51)|Voucher<br>facilities<br>(_N_= 59)|Comparison<br>facilities<br>(_N_= 24)|
|Weight|70|55|n.a.|n.a.|
|Height|21|4**|n.a.|n.a.|
|Temperature|19|6*|10|4|
|Blood pressure|80|51**|34|4**|
|Anemia check|66|59|39|4**|
|Breasts/nipples exam|n.a.|n.a.|20|0*|

_Source:_ Population Council, Health Facility Survey 2010–11.

_Note:_ Differences between voucher and nonvoucher facilities are significant at * _P_ <0.05, ** _P_ <0.01. Abbreviation n.a. = not applicable.

Among women who participated in the 2010–11 household survey, nearly all (98 percent) of those who had ever used the HealthyBaby voucher indicated they would recommend its use to a friend. Their most commonly cited reasons included the fact that (1) it offers free, cheap, or affordable services, (2) it is good for or helps poor pregnant women access relevant services, and (3) it enables women to receive good quality or prompt service. Among the 2 percent who indicated they would not recommend the use of a voucher to a friend, the major reasons were (1) poor quality services, including rude, untrained, or uncaring SPs; (2) lack of drugs and unavailability of some services; (3) double payment for services; and (4) distance to accredited facility.

In terms of workload, an independent study using semistructured interviews with SPs showed that nearly all (98 percent) of those from voucher facilities reported an increase in the number of clients during the year preceding the interview date, as compared with 30 percent of SPs from nonvoucher facilities (Brody et al. 2015). This, however, had varied consequences. On the one hand, some SPs reported increased revenue as a result of increased workload, which, in turn, enabled them to retain or hire more staff and improve facility infrastructure. Other SPs 32
Table 11: Leadership Competition

||Mean Control|Effect of VH<br>Training|Effect of VH<br>+ CL Training|CL Effect|N|
|---|---|---|---|---|---|
|Whether community leaders involved in|0.242|-0.008|-0.124|-0.116|195|
|distributing food aid (0/1) (CL survey)|(0.431)|(0.074)|(0.076)|(0.086)||
|Whether HHs would take dispute (land/livestock)|0.025|-0.002|0.003|0.005|2154|
|to CL for resolution (0/1) (HH survey)|(0.156)|(0.007)|(0.009)|(0.008)||
|Whether HHs ask CLs for assistance|0.255|0.006|-0.033|-0.039|2151|
|(0/1) (HH survey)|(0.436)|(0.026)|(0.029)|(0.030)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

regulations and should interact more with village heads as a result of the intervention, in the first instance in order to help them implement reforms and in the second instance to monitor their behavior. Because our data measures effects across civil society leaders as a whole in the study communities, not on the trained civil society leaders specifically, we are underpowered with respect to identifying these effects. Even so, table 12 shows that civil society leaders as a whole appear more informed of village regulations and more likely to interact with the village head in villages subject to the civil society leader intervention.

One unique observable implication of the human resource scarcity mechanism is that the effect of the civil society leader intervention should have been particularly large in places that initially had particularly low human resources. In table 13, we re-run the regressions in table 6 splitting the sample by villages with high and low initial human resources as measured in turn by the education level of the village head, the age of the village head and whether the village head’s dare consisted of more than four men. Interestingly, the evidence suggests that, if anything, the effect of the intervention was higher in high human resource communities. This is inconsistent with the human resource mechanism.

The monitoring mechanism is distinguished from the human resource mechanism in that it involves civil society leaders changing not just the capacity but the incentives of village heads to implement reform. The ability of civil society leaders to affect change via monitoring depends on two things: (i) their ability to observe and recognize transgressions by the village head, and (ii) their ability to levy sanctions on them. The 31
|P118109|2011|2019|12/27/2018|15 Closed|Mongolia|
|---|---|---|---|---|---|
|P118109|2011|2019|6/20/2019|16 Closed|Mongolia|
|P118112|2011|2011||0 Closed|Ghana|
|P118112|2011|2012|9/21/2011|1 Closed|Ghana|
|P118112|2011|2012|6/12/2012|2 Closed|Ghana|
|P118112|2011|2013|1/8/2013|3 Closed|Ghana|
|P118112|2011|2014|10/13/2013|4 Closed|Ghana|
|P118112|2011|2014|4/29/2014|5 Closed|Ghana|
|P118112|2011|2015|12/22/2014|6 Closed|Ghana|
|P118112|2011|2015|6/29/2015|7 Closed|Ghana|
|P118112|2011|2016|12/31/2015|8 Closed|Ghana|
|P118112|2011|2016|6/22/2016|9 Closed|Ghana|
|P118112|2011|2017|12/30/2016|10 Closed|Ghana|
|P118113|2010|2010||0 Closed|Indonesia|
|P118113|2010|2011|6/17/2011|1 Closed|Indonesia|
|P118113|2010|2012|5/3/2012|2 Closed|Indonesia|
|P118113|2010|2013|2/16/2013|3 Closed|Indonesia|
|P118113|2010|2014|10/25/2013|4 Closed|Indonesia|
|P118113|2010|2021|7/21/2020|5 Closed|Indonesia|
|P118131|2010|2010||0 Closed|Tunisia|
|P118131|2010|2011|7/30/2010|1 Closed|Tunisia|
|P118131|2010|2011|5/11/2011|2 Closed|Tunisia|
|P118131|2010|2012|1/3/2012|3 Closed|Tunisia|
|P118131|2010|2013|8/22/2012|4 Closed|Tunisia|
|P118131|2010|2013|4/14/2013|5 Closed|Tunisia|
|P118131|2010|2014|8/9/2013|6 Closed|Tunisia|
|P118131|2010|2014|10/16/2013|7 Closed|Tunisia|
|P118131|2010|2014|4/15/2014|8 Closed|Tunisia|
|P118131|2010|2014|6/8/2014<br>|9 Closed|Tunisia|
|P118131|2010|2015|12/8/2014|10 Closed|Tunisia|
|P118131|2010|2015|5/18/2015|11 Closed|Tunisia|
|P118131|2010|2016|11/18/2015|12 Closed|Tunisia|
|P118131|2010|2016|4/15/2016|13 Closed|Tunisia|
|P118131|2010|2017|10/13/2016|14 Closed|Tunisia|
|P118131|2010|2017|4/20/2017|15 Closed|Tunisia|
|P118131|2010|2018|11/9/2017|16 Closed|Tunisia|
|P118131|2010|2018|5/18/2018|17 Closed|Tunisia|
|P118131|2010|2019|12/18/2018|18 Closed|Tunisia|
|P118131|2010|2019|6/12/2019|19 Closed|Tunisia|
|P118131|2010|2020|2/19/2020|20 Closed|Tunisia|
|P118145|2012|2012|6/13/2012|1 Closed|Middle East and North|
|P118145|2012|2013|12/22/2012|2 Closed|Middle East and North|
|P118145|2012|2014|12/10/2013|3 Closed|Middle East and North|
|P118145|2012|2015|11/25/2014|4 Closed|Middle East and North|
|P118145|2012|2015|5/23/2015|5 Closed|Middle East and North|
|P118145|2012|2016|11/2/2015|6 Closed|Middle East and North|
|P118150|2011|2011||0 Closed|Indonesia|