rafmacalaba/datause-encoder-data huggingface dataset:
# **4.3 A commitment to transparency outside of the WTO**

Political realities may limit how much monitoring and cooperation/communication can actually take place within the WTO itself. Therefore, greater efforts to ensure transparency should go beyond the WTO. In this section of the paper we consider who else could be involved in this monitoring function before then turning to a discussion of what information is most useful for such organizations to provide.

Research institutes, think tanks, and public interest bodies should engage in monitoring and evaluation of policies at the country level. Such entities could also explore the economic and social aspects of particularly contentious issues or proposed areas for action at the WTO or in a preferential trade agreement context (Hoekman and Mavroidis, 2000), helping to generate information and build consensus on policy priorities. A regional entity that can coordinate with national think tanks and institutes may be the best model for this role. A combination of an independent regional “hub” institution that provides research support to a network of national think tanks and governments and works with them would help reduce the overhead costs of national entities by providing access to inputs such as databases and specialized information.

One of the lessons that can drawn from the World Bank report _Doing Business_ is that transparency can have a powerful effect in focusing the attention of policymakers on specific issues, especially if pursued in a way that generates data that allow cross-country comparability and monitoring of changes over time. _Doing Business_ has become an influential focal point for national policymakers, in part because it generates data on specific measures that resonate with firms and industries in the private sector, as well as with government officials. Examples are the number of days it takes a package to clear customs, or the time it takes for a standardized container to move from the factory floor to the nearest port.

When it comes to the issue of trade policy enforcement and surveillance of WTO-type commitments, it is important to recognize that such monitoring needs a substantive focus that goes beyond a technical analysis of legal compliance. Making trade agreements relevant for poor countries requires a convincing argument as to how WTO commitments can raise economic welfare. While it may be too difficult to accurately and expeditiously relate a particular policy change to changes in economic welfare, a first step would be calculation and regular reporting of simple measures of industrial structure and trade performance used to characterize the 20
**<u>Table 1. Status of Legislation on Women’s Rights to Land, Housing, and Other Property in Selected Countries, 2005.</u>**

|||**Latin America**||||**Africa**|||**Asia**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Chile**|**Ecuador**|**Guatemala**|**Ghana**|**Egypt**|**South Africa**|**Uganda**|**India**|**Sri Lanka**|**Vietnam**|
|**Gender-based**<br>**discrimination**<br>**is prohibited**|S: Yes|S: Yes|S: Yes|S: Yes, with<br>exceptions|S:<br>C: No|S: Yes|S: Yes|S: Yes|S: Yes|S: Yes|
|**Women’s equal**<br>**right to own**<br>**land and**<br>**housing**|S: Yes|S: Yes|S: Yes|S: Yes<br>C: No|S: No|S: Yes|C: Yes|S: Yes|S: Yes|S: Yes|
|**Gender equality**<br>**in household**<br>**representation**<br>**and**<br>**management**|S: No|S: 1989|S: 1998|S: No<br>C: No|S: No|S: No<br>C: No|S: No<br>C: No|S: No|S: Yes|S: Yes|
|**Land**<br>**Legislation:**<br>**Joint titling of**<br>**land required**|No|No<br>(Land titling<br>project only)|Yes|No|No|No|No|No|No|S: No|
|<br>**Land**<br>**Registration:**<br>**Priority to**<br>**female**<br>**household heads**|Yes<br>(Land titling<br>project only)|No|1999<br>(among<br>refugees<br>only)|No|No|No|No|No|No|Yes<br>(Land<br>titling<br>projects<br>only)|
|**Default marital**<br>**regime**|Partial<br>community<br>property|Partial<br>community<br>property|Partial<br>community<br>property|S: Separation<br>of property<br>C: Husband<br>owns property<br>acquired while<br>married|Separation<br>of<br>property|Full<br>community<br>property|Full<br>community<br>property|Depends<br>on<br>religious /<br>ethnic<br>group.|Depends on<br>religious /<br>ethnic group.<sup>a</sup>|Full<br>communi<br>ty<br>property|
|**Widow’s**<br>**inheritance**<br>**rights under**<br>**intestate**|Guaranteed<br>1/4 of<br>spouse’s<br>estate|Only a<br>marital share<br>in 1<sup>st</sup>order;<br>otherwise<br>2<sup>nd</sup>order|Only a<br>marital share<br>in 1<sup>st</sup>order;<br>otherwise<br>2<sup>nd</sup><br>Order|S: Yes|S: Yes|S: Yes in the<br>absence of<br>descendents<br>C: No|S: 1<sup>~~st~~</sup>order|S: 1<sup>~~st~~</sup><br>order|S: Yes in<br>the absence<br>of<br>descendents|S: 1<sup>~~st~~</sup><br>order,<br>half|
|**Daughters &**<br>**sons have equal**<br>**inheritance**<br>**rights**|Yes|Yes|Yes|S: Yes<br>C: lineage<br>property|S: Yes but<br>unequal<br>Shares|No:<br>Primogeniture|No:<br>Primogeniture|S: Yes|Depends on<br>religious /<br>ethnic group<sup>a</sup>|Yes|

Key: S= Statutory Law, C=Customary Law Note: If a date is indicated, it reflects the year that legislation was passed changing the answer from no to yes. Sources: Deere and León (2001); Deere (2007); COHRE (2004 and 2006); Benschop (2002); Agarwal (2002b); Ramachandran (2006); Fujita (2006); Rugadya et al. (2005); Tripp (2004) a. Sri Lanka has four legal systems. The General Law is applicable to population unless covered by one of three personal laws: Kandyan which governs the Kandyan Sinhalese, Thesawalamai, which governs the Tamils, and Muslim, which mostly follow the Hanafi school.

12
Although the estimated population shares in the two most deprived categories (‘open defecation’ and ‘unimproved’) have decreased (statistically significantly) systematically between 2007 and 2014, the population shares in the other two deprivation categories have not. Table 4: Change in sanitation deprivation by ordinal poverty measures in Bangladesh and its divisions

|||_H_|||_PI_|||_PM_|||_PG_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2007|2011|2014|2007|2011|2014|2007|2011|2014|2007|2011|2014|
|Barisal|66.1|60.5|46.8|47.7|44.0|32.6|35.0|32.6|23.4|25.2|23.5|16.7|
||(2.7)|(2.1)|(3.3)|(1.9)|(1.7)|(2.9)|(1.4)|(1.4)|(2.4)|(1.1)|(1.1)|(1.8)|
|Chittagong|67.1|59.2|44.9|47.0|38.9|29.2|34.8|27.3|20.2|26.2|19.6|14.9|
||(2.9)|(2.1)|(3.1)|(2.8)|(1.7)|(2.9)|(2.7)|(1.5)|(2.7)|(2.6)|(1.2)|(2.5)|
|Dhaka|74.8|68.4|56.3|50.1|42.6|33.5|36.6|29.4|21.8|27.8|21.6|15.4|
||(1.9)|(1.9)|(2.5)|(1.7)|(1.3)|(1.8)|(1.6)|(1.1)|(1.6)|(1.4)|(1.0)|(1.3)|
|Khulna|69.0|61.4|50.3|48.9|41.8|33.0|35.7|29.4|22.7|25.9|20.9|16.1|
||(1.8)|(1.6)|(2.3)|(1.4)|(1.2)|(1.7)|(1.1)|(1.0)|(1.4)|(1.0)|(0.7)|(1.0)|
|Rajshahi|74.0|61.2|52.0|55.2|41.6|34.7|43.0|29.6|24.2|34.1|21.6|17.6|
||(1.9)|(2.3)|(2.3)|(1.8)|(1.9)|(1.8)|(1.9)|(1.6)|(1.5)|(1.9)|(1.3)|(1.3)|
|Sylhet|69.9|64.6|55.0|50.1|47.1|37.9|36.8|36.2|27.9|26.5|28.9|21.9|
||(2.3)|(1.9)|(1.7)|(1.9)|(1.6)|(1.7)|(1.5)|(1.4)|(1.6)|(1.1)|(1.4)|(1.4)|
|Bangladesh|71.5|63.4|52.2|50.4|42.3|33.5|37.5|30.1|23.1|28.5|22.2|16.8|
||(1.0)|(0.9)|(1.1)|(0.9)|(0.7)|(0.9)|(0.9)|(0.6)|(0.8)|(0.8)|(0.5)|(0.7)|

Sources: Authors’ own computations. Standard errors are reported in parentheses.

Has this estimated reduction pattern been replicated within all divisions? Table 3 also presents the changes in the discrete probability distributions of three divisions: Dhaka, Rajshahi, and Sylhet.<sup>16</sup> The estimated population shares in the ‘improved’ category have increased (statistically significantly) gradually in all three regions (Table 3), and so the shares

> 16A new division called Rangpur was formed in 2010, which was a part of the Rajshahi Division. The new division did not exist during the 2007 DHS, so we had to combine this division with the Rajshahi division in the 2011 and 2014 DHS in order to preserve comparability over time. Likewise, our selected surveys were not affected by the carving out of northern Dhaka to form the Mymesingh division in 2015.

29
# **Section1. Introduction**

There is a growing amount of work investigating the causes of forced displacement. In addition to the onset of conflict being associated with many forced displacement crises (e.g., UNHCR 2016), there is a large amount of empirical evidence demonstrating that displacement is strongly associated with households living in close proximity to violence (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.). However, displacement decisions are complex, and both the decisions of whether and when to become displaced are affected by the type, frequency, and timing of violence. Better investigating potential causes and possible heterogeneity can help to better contextualize the escalating forced displacement crisis across the world (e.g., World Bank 2017a).

This paper investigates the relationship between violence and forced displacement during the Republic of Yemen’s current conflict. Importantly, this is a setting where the onset of conflict led to an immediate and widespread forced displacement crisis, where displacement might have been forced by either violence or the humanitarian situation caused by the conflict (e.g., TFPM 2016; OCHA 2018; etc.). Additionally, one of the reasons there has been limited investigation into the events forcing displacement is the difficulty in collecting information from displaced households before, during, and immediately following displacement. However, the World Food Programme (WFP) has been implementing a monthly monitoring survey in the Republic of Yemen that includes both cross-sectional and panel components, and the survey reached a large number of households before they later became displaced. The survey is able to track the food security and displacement status of these households in the months leading up to and following displacement, which allows us to identify the violence occurring in the district from which households were forcibly displaced (origin district) at the time of displacement.<sup>1</sup><sup>_,_2</sup>

> 1Although the survey misses households without access to mobile phones, the relatively high penetration of mobile phones allows an analysis that is representative of the majority of the population. Furthermore, the survey reaches a large number of displaced households, where the share of displaced households in each round varied between 20 and 40 percent. See the data section for a description of mobile phone penetration in the Republic of Yemen.

> 2Even for households that were only interviewed once, a displacement module asks whether the household was displaced, and if so, the month, year, and governorate of displacement. However, only for households interviewed before and after displacement are we able to observe the actual district of

2
- 7 -

Table 2. Assistance Provided to Major Cotton Producers, 1999/00

||Production||Assi|stance|
|---|---|---|---|---|
|Country|Thousandtons|U.S.$Perkilogr|am|MillionU.S.dollars|
|**Total**|**9,942**|**0.57**|**1/**|4,764|
|UnitedStates|3,694|0.56||2,056|
|China,People'sRepublicof|3,829|0.34||1,534|
|EuropeanUnion|558|1.42||795|
|Greece|428|1.39||596|
|Spain|130|1.53||199|
|Turkey|791|0.36||287|
|Brazil|700|0.06||44|
|Mexico|135|0.21||28|
|Egypt|235|0.09||20|
|Memorandumitem:|||||
|World cottonprice||1.25|||

Source: Intemational Cotton Advisory Committee.

1/ Average.

Figure 1. Nominal Price, Real Price and World Stocks of Cotton, January 1985 - December 2002 I/ <!-- Start of picture text --> 90 . . --.. ... _ Projedlons 120<br>800 Real price ofcotton 110<br>(In C:A francs per kilo;<br>left alas) 100<br>790<br>90<br>600W<br>5W0<br>70<br>400<br>60<br>300 Norninal price ofcotton<br>i (Cotlook A) World Stocks 1/ 50<br>(InUScentperpound; (In tions of480 lb bales;<br>200 rigpht axns) c (rtght axis) 40<br>10 1, 1 1 1, 2, 0o, ,<br>Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Sources:<br>USDA; and IMF staff.<br><!-- End of picture text --> 1/ Stocks reported in August, the last month of the cotton marketing year. Real cotton price relative to average WAEMU CPI, based in 1990.
# 3 Data

Data used in this study come from UN-Comtrade for HS 6 digit-level import and export data, and UNCTAD TRAINS for tari¤ and NTM data. Most of the NTM data were collected around 2015/2016, so that determines the year coverage of the sample used in the analysis. Figure 1 presents the time series plot of the UK’s exports by destination country from 2000 to 2015. The EU is by far the top destination market for the UK exports, even though its importance has steadily declined over time, from about 60 percent in 2000 to 44 percent in 2015. The US has absorbed about 15 percent of the UK’s exports throughout this period, while China is gaining from 2 percent to close to 10 percent. Figure 2 presents the export pro...le of the UK by sector. Machinery, Chemical Products and Transportation jointly accounted for more than half of the UK’s export in 2015. Table 1 presents the some summary statistics that capture the trade policies of the UK’s main partners. With the exception of the EU, both the US and China do not currently give tari¤ preferences to the UK’s exports. Products from the UK enter the US and China facing the Most Favored Nations (MFN) tari¤ with no preferences. The EU on the other hand gives full preference to the UK’s exports under the EU Single Market arrangement, so products from the UK enter the EU with no tari¤s.

# 4 Overall Trade Restrictiveness Index of UK’s Main Partners

Table 2 presents the OTRI of the UK’s main trading partners, based on trade elasticity and AVE estimates from Bown, Kee and Nicita (2016). For details about the estimations of trade elasticity and AVEs, please refer to that paper. For the EU, the import-weighted average 5
**Table 4.2  Data Reconciliation and the Welfare Impacts of WTO Accession** -- with a decomposition of the impacts

|||Percentage<br>|change in E<br>or tran<br>|V due to cha<br>sfers,byagg<br>|nges in facto<br>regated deci<br>|r prices, go<br>les<sup>s</sup><br>|ods prices<br>|
|---|---|---|---|---|---|---|---|
|||Skilled<br>Wages|Unskilled<br>Wages|Return to<br>Capital|Taxes and<br>Transfers|Goods<br>Prices|Aggre-<br>gate EV|
|Decile 1 (0-10%) –<br>overall|Unreconciled Sequential approach|<br>5.3|2.2|0.2|-0.5|0.0|<br>7.2|
||Integrated Model|5.4|2.8|0.1|-0.3|0.3|<br>7.6|
|Decile 2 (11-20%) –<br>overall|Unreconciled Sequential approach|<br>5.3|2.0|0.3|-0.4|0.0|<br>7.3|
||Integrated Model|5.5|2.5|0.1|-0.3|0.3|<br>7.6|
|Decile 3 (21-30%) –<br>overall|Unreconciled Sequential approach|<br>5.8|1.7|0.5|-0.5|-0.1|<br>7.6|
||Integrated Model|5.9|2.2|0.2|-0.3|0.2|<br>7.7|
|Decile 4 (31-40%) –<br>overall|Unreconciled Sequential approach|<br>5.8|1.6|0.6|-0.4|-0.1|<br>7.6|
||Integrated Model|5.9|2.0|0.2|-0.3|0.2|<br>7.7|
|Decile 5 (41-50%) –<br>overall|Unreconciled Sequential approach|<br>5.7|1.5|0.9|-0.4|-0.1|<br>7.8|
||Integrated Model|5.9|1.8|0.3|-0.3|0.1|<br>7.7|
|Decile 6 (51-60%) –<br>overall|Unreconciled Sequential approach|<br>5.6|1.4|1.2|-0.5|-0.1|<br>7.9|
||Integrated Model|5.8|1.7|0.5|-0.3|0.1|<br>7.5|
|Decile 7 (61-70%) –<br>overall|Unreconciled Sequential approach|<br>5.7|1.1|1.6|-0.5|-0.1|<br>8.1|
||Integrated Model|5.8|1.4|0.6|-0.3|0.0|<br>7.5|
|Decile 8 (71-80%) –<br>overall|Unreconciled Sequential approach|<br>5.9|0.9|1.9|-0.5|-0.1|<br>8.4|
||Integrated Model|6.0|1.2|0.7|-0.4|0.1|<br>7.5|
|Decile 9 (81-90%) –<br>overall|Unreconciled Sequential approach|<br>5.1|0.9|2.6|-0.5|-0.2|<br>8.3|
||Integrated Model|5.3|1.1|1.0|-0.3|-0.1|<br>7.2|
|Decile 10 (91-100%)<br>– overall|Unreconciled Sequential approach<br>Integrated Model|<br>4.4<br>4.5|0.6<br>0.8|3.8<br>1.4|-0.5<br>-0.3|0.0<br>-0.3|<br>8.3<br> <br>6.8|

a/ In the **integrated** (sequential) approach, the percentage change in skilled wages, unskilled wages and return on capital is **5.3%** (5.1%), **3.7%** (3.0%) and **1.8%** (4.9%), respectively. All households face the same factor prices, but depending on the factor endowments of the different households, this will impact the EV of the households differently. Similarly, for goods price changes. We have estimated the decomposed impacts as shown in the table. Aggregate EV for each decile is approximately the horizontal sum of the elements in the same row.

100
Surakhmad, W. (2007) ‘Kriminalisasi Pendidikan’, _Kompas_ , 2 May, available at http://educationwatchunj.blogspot.com.au/2012/12/kriminalisasi-pendidikan.html, accessed 28 January 2013.

Susanti, B. (2008) ‘The Implementation of the Rights to Health Care and Education in Indonesia’ in Varun Gauri and Daniel Brinks (eds.) _Courting Social Justice: Judicial Enforcement of Social and Economic Rights in the Developing World_ , New York: Cambridge University Press, pp.224-267.

Tahyar, B. (2012) _Patrimonialism, Power and the Politics of Judicial Reform in Post_ - _Soeharto Indonesia: An Institutional Analysis_ , unpublished PhD dissertation, School of Oriental and African Studies.

Talbot, S. and S. Sacco (2013) ‘Litigating the Right to Education in Africa: Editorial’, _Interights Bulletin_ , 17 (2), pp.50-51.

_The Economic Times_ (2012a) ‘Will Not Admit Poor Children – Mayo College Tells Rajasthan High Court’, available at: <u>http://articles.economictimes.indiatimes.com/2012-0503/news/31558926_1_mayocollege-free-education-weaker-section, accessed 21 April 2017.</u> _The Economic Times_ (2012b) ‘76% Cases of RTE Violations Remain Unresolved’, available at: <u>http://articles.economictimes.indiatimes.com/2012-0331/news/31266620_1_ncpcrrte-act-protection-of-child-rights, accessed 21 April 2017.</u> _The Hindu_ (2012a) ‘As RTE Turns Two, Monitoring Division Sans Staff’, available at: <u>http://www.thehindu.com/news/article3273840.ece, accessed 21 April 2017.</u> The _Hindu_ , 2012b. ‘Children From Weaker Sections Can Now Have Access to Private Schools’ _,_ available at: <u>http://www.thehindu.com/news/national/article3308323.ece, accessed</u> 21 April 2017.

_The Times of India_ (2012a) ‘Right to Education Impact: 66,000 Students Get Admission in Private Schools’, available at: <u>http://articles.timesofindia.indiatimes.com/2012-0801/pune/32979947_1_privateschools-socially-weaker-sections-unaided-minority-schools,</u> accessed 21 April 2017.

_The Times of India_ (2012b) ‘Supreme Court Upholds Law to Reserve 25% Seats in Private Schools for Poor - A Debate’ _,_ available at: <u>http://articles.timesofindia.indiatimes.com/201204-14/editpage/31338047_1_private-schools-education-law-primary-education, accessed 21</u> April 2017.

United Nations Committee on Economic, Social and Cultural Rights (1999) _General Comment No 13_ , available at: <u>https://documents-ddsny.un.org/doc/UNDOC/GEN/G99/462/16/PDF/G9946216.pdf?OpenElement, accessed 15</u> March 2017.

Van de Meene, I. and B. van Rooij (2008) _Access to Justice and Legal Empowerment: Making the Poor Central in Legal Development Cooperation_ , Leiden: Leiden University Press.

31
73

# **Table A2a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Innovation Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A2a.1 **|**Reg A2a.2 **|**Reg A2a.3 **|**Reg A2a.4 **|**Reg A2a.5 **|**Reg A2a.6 **|**Reg A2a.7 **|**Reg A2a.8 **|**Reg A2a.9 **|**Reg A2a.10 **|**Reg A2a.11 **|**Reg A2a.12 **|**Reg A2a.13**|**Reg A2a.14**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**Per Worker**|**0.2117*****|**0.2119*****|**0.2084*****|**0.1973*****|**0.2147*****|**0.2126*****|**0.2133*****|**0.2251*****|**0.2031*****|**0.2111*****|**0.2118*****|**0.2020*****|**0.2027*****|**0.2088*****|
|**Initial GDP (1960)**|**-3.5651*****|**-3.7103*****|**-3.5568*****|**-3.8061*****|**-3.7533*****|**-3.5929*****|**-3.6067*****|**-3.5395*****|**-3.6165*****|**-3.4645*****|**-3.4598*****|**-3.6375*****|**-3.7376*****|**-3.0540*****|
|**HC15**|**4.4076****|**4.4661****|**2.2944**|**2.4694**|**4.6428****|**2.5014**|**2.3396**|**4.0179****|**2.3258**|**2.8198***|**2.9314***|**2.2785**|**2.6361**|**4.0967****|
|**Log (Educational**<br>**Quality)**|**0.3702**|**0.3719**|**0.8628**|**0.8223**|**0.4121**|**0.9246***|**0.9059**|**0.5337**|**0.5712**|**0.3736**|**0.3454**|**0.5610**|**0.2392**|**0.4817**|
|**Sq (HC15)**|**-0.9730****|**-1.0965****|**-0.6301***|**-0.6259**|**-1.1303****|**-0.6686***|**-0.6203**|**-0.9878****|**-0.5221**|**-0.6735***|**-0.7006***|**-0.5215**|**-0.5778**|**-0.9107****|
|**Log (Patents)**||**0.1956*****|**0.2130*****|**0.2178*****|||||||||||
|**Patents * Citations**||||**-0.000001**|||||||||||
|**Log (Utility Patents)**|||||**0.1839****|**0.1977*****|**0.2048*****||||||||
|**Utility Patents ***<br>**Citations**|||||||**-0.000001**||||||||
|**Log (Citations)**|||**-0.3042**|**-0.4284**||**-0.2836**|**-0.2813**||||||||
|**Log (Journals)**||||||||**0.2295*****|||||||
|**Log (Royalty**<br>**Payments)**|||||||||**0.0265****||**-0.0047**||||
|**Log (Royalty**<br>**Receipts)**||||||||||**0.0338*****|**0.0369****||||
|**Log (Royalties)**||||||||||||**0.0268****|||
|**Log (Gross FDI /**<br>**GDP)**|||||||||||||**0.1609**||
|**Log (Manufactures**||||||||||||||**0.2213**|
|**Trade Share)**|||||||||||||||
|**Phones**|**0.0052*****|**0.0040*****|**0.0037*****|**0.0037*****|**0.0041*****|**0.0039*****|**0.0039*****|**0.0037*****|**0.0051*****|**0.0051*****|**0.0051*****|**0.0052*****|**0.0054*****|**0.0045****|
|**Constant**|**-4.6058***|**-4.5304****|**-3.7439****|**-3.6273***|**-4.8463****|**-4.1920****|**-4.0014***|**-5.5338****|**-3.4742***|**-3.1395**|**-3.1130**|**-3.3607**|**-2.3097**|**-5.4836****|
|**R squared**<br>|**0.5596**<br>|**0.5912**<br>|**0.7221**<br>|**0.7329**<br>|**0.5854**<br>|**0.7124**<br>|**0.7127**<br>|**0.6248**<br>|**0.5990**<br>|**0.6124**<br>|**0.6126**<br>|**0.5999**<br>|**0.5982**<br>|**0.6349**<br>|
|**Number of Countries**<br>*, **, *** denotes signifi<br>Robust standard errors|**85**<br>cance at the<br>obtained us|**85**<br>90, 95 and<br>ing White's|**75**<br>99 percent c<br>correction for|**72**<br>onfidence lev<br>heterosced|**85**<br>el respective<br>asticity.|**75**<br>ly|**75**|**85**|**84**|**84**|**84**|**84**|**84**|**83**|