rafmacalaba/datause-encoder-data huggingface dataset:
_The Data_ The data were collected from three of the former homelands, KaNgwane, Lebowa and Venda, all in the northern or eastern parts of Transvaal. Detail on the physical characteristics of the regions and the role of farmer support programs (FSPs) can be found in Kirsten (1994).

A total of 23 FSPs were established by the Development Bank of Southern Africa (DBSA) in 1987 to provide participating farmers with services and training programs, and to identify and address the major constraints to agricultural production (Van Zyl, Fenyes and Vink, 1992). All of the farmers in the sample used in this analysis benefited from the local FSP, either as current members or as previous members, and have benefited from some level of education and training. Many aspects of the programs are common to the three homelands, but one difference between the regions is the institutional arrangements for the provision of FSP services, since these are decided by the relevant homeland development corporation. However, the FSPs are similar to the extent that they all encourage the adoption of modern technology, including higher yielding varieties and chemical fertilizer (Singini, Sartorius von Bach and Kirsten, 1992). Extension and training relevant to new varieties and methods are also available.

The FSP data for KaNgwane, Lebowa and Venda cover the 1991 harvest, which is regarded as a normal year with respect to rainfall and yields. The original sample groups are shown in Table 2, but attempts to include numerous minor crops, that varied between regions, were hampered by the lack of price data, making comparisons difficult'.

**Table 2: Population of Farms in Sample**

|Area|Total Number of Farm<br>Households Surveyed|Households Producing only<br>Maize|Current Members of<br>FSP|
|---|---|---|---|
|Venda|60|30|27|
|Lebowa|84|64|56|
|KaNgwane|111|80|75|
|Total|255|174|158|

Thus, the only farms retained were those for which maize production accounted for all of land utilization, and inputs were maize-specific inputs. This is considered to be acceptable since maize is by far the most important crop in the regions studied. Not surprisingly, labor was not reported as a crop-specific These data include a range of socio-economic variables, since the original objective was to monitor farmer attitudes to the FSP initiatives. Only agricultural production is investigated in this paper, which is not intended, in any way, to be an evaluation of the FSPs. Special care was taken not to repeat the mistakes that abounds in the farm size efficiency literature (Binswanger, et. al, 1993), also in South Africa.

16
WPS4192 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 6</mark>

# **Ethnic Polarization and the Duration of Civil Wars**<sup>**1**</sup>

Jose G. Montalvo Universitat Pompeu Fabra and IVIE Marta Reynal-Querol Universitat Pompeu Fabra, CEPR and CESifo

## **Abstract:**

In this paper we analyze the relationship between ethnic polarization and the duration of civil wars. Several recent papers have argued that the uncertainty about the relative power of the contenders in a war will tend to increase its duration. In these models, uncertainty is directly related to the relative size of the contenders. We argue that the duration of civil wars increases the more polarized a society is. Uncertainty is not necessarily linked to the structure of the population but it could be traced back to the measurement of the size of the different groups in the society. Given a specific level of measurement error or uncertainty, more polarization implies lengthier wars. Our empirical results show that ethnically polarized countries have to endure longer civil wars than ethnically less polarized societies.

Keywords: ethnicity, discrete polarization, civil wars, duration.

World Bank Policy Research Working Paper 4192, April 2007 _The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at_ _<u>http://econ.worldbank.org/programs/conflict</u>_ <u>).</u> _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 these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._ 1 We thank the participants in several workshops. Montalvo acknowledges the financial support of the grant SEC2004-04429 from the Spanish National Science Foundation. Corresponding author: Marta Reynal-Querol, Universitat Pompeu Fabra, C/ Ramon Trias Fargas 25-27, Barcelona 08005, Spain. Office phone number: (34) 93 542 2509. Fax: (34) 93 542 1746. Email: marta.reynal@upf.edu.
particularly caring for the “destruction” half of creative destruction. Rajan and Zingales (2003) for instance emphasize that incumbent firms often favor a weak financial sector, unable to reallocate resources into new (potentially threatening) innovations. Hellman, Jones, and Kaufmann (2000) argue that in the transition to “better” policies in the transition countries the shift in the institutional environment create opportunities for massive “closed” deals in which forces “seized the state” and then used it to favor their economic interests in both policy implementation and policy formulation. And even worse than these problems with favored firms, even among the disfavored firms there is the problem of coalition formation to lobby to change a rule when in a deals environment one can buy/lobby/influence policy implementation<sup>35</sup> .

# **V.C)  Reducing policy uncertainty**

Given the variation across firms in the reported predictability of implementation and the associated spreads in actual policy outcomes across firms in the same location, it is not surprising any given firm’s outcome is uncorrelated with measures of the formal rules. Of interest is to know if the individual outcomes are correlated with other dimensions of regulations, namely assessments of the ability to implement rules. Figures 6a and 6b show the correlation of the share of firms reporting implementation is consistent and the standard deviation within the location-sector-size cells with measures of Government Effectiveness and Control of Corruption (Worldwide Governance

> are only a few examples of panel datasets that capture pre and post reform periods.  Looking at the Cameroon and Uganda, we find the dispersion actually increases post reform.

> 35 Moving beyond this naïve political economy there may be reasons for external actors (e.g. donors, foreign firms) to push for more favorable _de jure_ rules rather than focus on policy implementation.  Official external agencies are more comfortable dealing with rules as focusing on implementation would have to confront the massive deviation of _de jure_ and _de facto_ and its causes, which is a more awkward topic that technically neutral “policy reform.”  Foreign firms might prefer rules to deals if they have a competitive _dis_ advantage in local deals.  Administrative simplification of large enough magnitude to make compliance a feasible option might level the playing field for foreign firms.

53
<!-- Start of picture text --> oo.—@prey H:;:<br>=oe !<br>=& i<br>co 1<br>200we |<br>oa<br>Es u<br>ae ‘<br>oa '<br>a. 1<br>> be '<br>oc 1<br>23<br>€s i<br>oo. i<br>=aei<br>e'<br>©aH'<br>1<br>©H<br>-<br>201 imi Di2m1 aA 2013m1 2014m1<br><!-- End of picture text -->
# Figure 5. Poverty

# A. Global poor

<!-- Start of picture text --> Persons (millions)<br>2000 EAP ECA LAC MNA SAR SSA<br>1500<br>1000<br>500<br>0<br>1999 2008 2015<br>C. Shared prosperity<br>Percent<br>5<br>Shared prosperity<br>4 Shared prosperity premium<br>3<br>2<br>1<br>0<br>-1<br>MNA SSA ECA SAR LAC EAP<br>E. Projections of global extreme poverty<br>Percent<br>Historical growth 2 x historical growth<br>15<br>8 percent growth 3 percent target<br>12<br>9<br>6<br>3<br>0<br>2012 2018 2024 2030<br><!-- End of picture text -->

# B. Poverty rates

<!-- Start of picture text --> Percent of population<br>70<br>1999 2008 2015<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>ECA EAP LAC MNA SAR SSA World<br>D. Change in poverty rates, by GDP<br>growth<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points of population<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Fastest-growing Slowest-growing<br>F. Distribution of poverty<br>Percent<br>EAP ECA LAC MNA SAR SSA<br>100<br>80<br>60<br>40<br>20<br>0<br>1999 2008 2015<br><!-- End of picture text --> Source: World Bank PovcalNet.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A.B.F. Regional aggregation based on 2011 Purchasing Power Parity (PPP) and $1.90 per day poverty line. C. Shared prosperity is the average growth in household per capita income or consumption of the bottom 40 percent between 2010 and 2015. The shared prosperity premium measures the difference between income growth of the poorest 40 percent of households and the average household income growth.

D. Chart shows unweighted average of the average annual change in poverty headcount rates between two poverty estimates in each group of countries. “Fastest-growing” includes the quartile of EMDE country-year pairs with the highest average annual real GDP per capita growth between two poverty estimates; “slowest-growing” includes the quartile with the slowest average annual real GDP per capita growth. Based on data available from 1981.
_Figure 16: Factors behind well‐being improvement, vulnerable and non‐vulnerable groups, % of respondents_ <!-- Start of picture text --> 20%<br>18%<br>16% other<br>14% loan<br>12% change in hh composition<br>10%<br>additional income<br>8%<br>pension<br>6%<br>addittional work<br>4%<br>2% higher salary<br>0% employment<br>vulnerable average vulnerable average vulnerable average vulnerable average<br>2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

If we aggregate the responses on well‐being improvement and worsening related to employment and wages, we can see that the effect of employment during the crisis was quite small, while wage dynamics contributed much to the deterioration of well‐being of the population (Figure 17). Furthermore, it appears that those below the poverty threshold, as well as those who are vulnerable to poverty, suffered from falling nominal wages more than the population on average. Among the poor, according to the official definition, there was also a shift towards a more negative assessment of changes in the employment status.

The majority of the differences highlighted above are related to the specifics of regional economic development, and the way the crisis affected different sectors. Industrial and construction sectors suffered most from the crisis, leading to the fall in in economic activities in large cities. While labor market in Minsk is more flexible and reacted through employment reduction, in other cities employers, often state owned enterprises, tended to reduce wages more. In small cities and rural area labor demand was weak prior to crisis therefore it did not affect employment income in these areas that much (Figure 18).

_Figure 17: Balance of responses on the positive and negative role of employment and wages in well‐being changes_ <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>‐8<br>‐10<br>2014 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017<br>average vulnerable official poverty moderate poverty<br>employment wage dynamics<br><!-- End of picture text --> Note. Balance of responses is a difference between the share of population which marked change in wages (employment status) as a key factor of the well‐being improvement and the share of population that named change in wages (employment status) a factor of well‐being deterioration. Source: Authors’ estimates based on HSS data.

15
# **Table 14: Panel Regression of Remittances on Aspects of Well-Being**

||**Improving Life**<br>**Satisfaction**|**Worsening**<br>**Econ. Condition**|**Food Shock**<br>**Index**|**Services**<br>**Shock**<br>**Index**|**Health**<br>**Shock**<br>**Index**|**Coping**<br>**Shock**<br>**Index**|**All**<br>**Shocks**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Real Remittances (*10Mln)|-0.043***|0.023***|-0.004|0.022|0.011|0.006|0.035|
||(0.006)|(0.005)|(0.007)|(0.016)|(0.017)|(0.007)|(0.024)|
|Constant|3.285***|2.180***|0.298***|0.483***|0.498***|0.361***|1.641***|
||(0.039)|(0.033)|(0.029)|(0.038)|(0.056)|(0.037)|(0.095)|
|Number of observations|1,645|1,645|1,645|1,645|1,645|1,645|1,645|
|Adjusted R2|0.012|0.013|0.003|0.076|0.025|0.036|0.067|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_w|0.017|0.017|0.008|0.080|0.030|0.041|0.071|
|r2_o|0.004|0.008|0.006|0.052|0.023|0.039|0.048|
|r2_b|0.013|0.002|0.017|0.022|0.011|0.044|0.032|
|sigma_u|0.577|0.485|0.435|0.503|0.739|0.451|1.356|
|sigma_e|0.607|0.458|0.419|0.596|0.839|0.559|1.355|
|rho|0.474|0.529|0.519|0.416|0.437|0.394|0.500|

_note: .01 - ***; .05 - **; .1 - *; Panel regression; Standard errors clustered by household; Control for month of interview;_ 29
different from that between imported and domestic varieties, (ii) Chinese share in total imports of that good is strictly positive, and (iii) the exchange rate pass-through is non-zero.<sup>7</sup> Given our assumption regarding the symmetric elasticity of substitution between imported varieties, , the effect of a change in China’s exchange rate changes vis-à-vis country , , on country ’s imports of a good from country , , does not depend on any exporter attribute. This makes Equation (8) less amenable to empirical analysis. For example, if in order to test the prediction in Equation (8), we were to regress the import demand at the exporterimporter-product level on the Chinese exchange rate vis-à-vis the importing country, we would not be able to include destination country fixed effects. We could, of course, strive to include all the relevant destination country attributes explicitly, but the effect of the exchange rate would not be estimated precisely because we would inevitably fail to control for certain unobserved sources of variation at the destination country (or destination-year) level.

One way to have such more general controls is to introduce more variation—for example, across exporters—in the right hand side of Equation (8). This would allow importer demand at the exporter-importer-product level to be regressed on a term that had all three sources of variation, which in turn would allow the inclusion of general fixed effects in the regression. To find such variation across exporters, we consider country ’s imports, , from country of a particular bundle of goods , defined at a higher level of aggregation. In our empirical analysis, we use trade data at the Harmonized System (HS) 6-digit. Therefore is defined at the HS 6-digit level. Country ’s imports of (at say the HS 4-digit level) can be expressed as:

> 7 Note that in Broda and Weinstein (2006), , i.e. the elasticities of substitution between imported varieties equals the elasticity of substitution between home and foreign varieties.  In our framework, if , in response to a renminbi depreciation, consumers in country reduce their demand for varieties of good produced at home and hence there is no spillover effect.

8
Figure 4: Sources of pension expenditure <!-- Start of picture text --> 14<br>12 -<br>10 _<br>4-<br>~~~ 6<br>2 -<br>0~~~ , ..... ...._,_ ............ ..<br>89 90 91 92 93 94<br>Lin-- NPS: net actual pensions Series2 -o-- NPS89: share of 1989 pensioners - GNEW: generosity effect]<br><!-- End of picture text -->
# <u>Table A18. Schooling, skills, migration, and earnings for men in Cambodia</u>

||<br>|<br>**Median re**<br>|<br> **gressions**<br>|||**Mean re**<br>|**gressions**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|Monthly<br>earnings<br>(TC)|
|Years of schooling (a1)|-3.74|-1.24|-1.05|-2.33|-13.0|-15.5|-13.8|-15.5|
||(2.82)|(4.52)|(4.68)|(4.59)|(10.9)|(12.3)|(12.5)|(12.7)|
|Cognitive skills (a2)|||-0.62|1.35|||-22.5|-14.3|
||||(5.56)|(6.24)|||(17.4)|(17.1)|
|SEM skills (a3)|||4.03|2.03|||34.3**|38.1**|
||||(4.35)|(4.69)|||(16.8)|(17.7)|
|Out Village (a4)||||-22.2||||-50.1|
|||||(63.4)||||(452.3)|
|Interaction YrsSchooling and Out Village (b1)||||1.79||||16.1|
|||||(11.0)||||(67.6)|
|Interaction SEM and Out Village (b2)||||1.36||||-50.4|
|||||(32.6)||||(56.3)|
|Interaction Cog and Out Village (b3)||||-17.4<br>(21.1)||||-87.3<br>(71.9)|
|Constant|44.4|72.2|31.9|38.7|100.7|115.6|187.1|219.1*|
||(670756.7)|(658360.5)|(169809.0)|(167865.0)|(87.7)|(98.6)|(127.5)|(127.6)|
|Observations|1451|1173|1173|1173|1451|1173|1173|1173|
|R-squared|0|0|0|0|0.0090|-0.022|-0.017|-0.016|
|<br>Median/Mean dependent|160.9|160.9|160.9|160.9|296.3|292.1|292.1|292.1|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|a1+b1=0||||-0.54||||0.61|
|||||(11.18)||||(65.33)|
|a2+b2=0||||<br>-16.03||||<br>-101.61|
|||||(19.24)||||(71.30)|
|a3+b3=0||||3.39||||-12.31|
|||||(32.22)||||(52.84)|
|* p<.10 ** p<.05 *** p<.01|||||||||

_Notes_ . This table reports estimates of the relationships between schooling, skills, migration, and earnings for men in Cambodia. The dependent variable “monthly earnings” in columns (1) to (4) is the raw monthly earnings; the dependent variable “monthly earnings (TC)” in columns (5) to (8) is top-coded at 2,000 USD per month. The cognitive index is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computer-adaptive test. The socioemotional (SEM) skills index is computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The “out of village” variable is a dummy variable that indicates whether the respondent lives outside the village where we originally surveyed their household. The sample for columns (1) and (5) are all men in the sample who are working 102
<!-- Start of picture text --> Traffic (% of Total Traffic of all Telephone subscription<br>Scheduled Indian Airlines) 100<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>2000 2005 2010 2015 2001 2005 2010 2015<br>Private Private<br><!-- End of picture text --> 22 B: But remains low and sticky in the Banking space....

<!-- Start of picture text --> Assets (% of Total Assets of SCBs) Advances (% of Total Advances of<br>100 SCBs)<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>2000 2005 2010 2015 2000 2005 2010 2015<br>Private Private<br><!-- End of picture text --> Note: Data is from the RBI (banking), Directorate General of Civil Aviation (Aviation), data.gov.in, and TRAI (Telecom). Years refer to respective fiscal year.

There has been a distinct difference in the trends for credit growth and asset quality for public sector banks and private banks. Credit growth has been slower and the pace of bad assets higher for public sector banks. In the last few years, the pace of growth of public sector bank credit (outstanding) has continued to decline, growing at 1.8 percent in 2017, the lowest in the last two decades. On the other hand, credit by private banks grew at double digit rates.

# **Figure 23: Growth of outstanding credit has declined in the last few years; decline is sharper for public sector banks**

39
Table 3: The Sources of Misallocation

|||||In Isolation|Other|Factors<br>W|ith Interactio|ns|
|---|---|---|---|---|---|---|---|---|
||Adjustment Costs|Uncertainty|Correlated|Permanent|Transitory|Correlated|Permanent|Transitory|
|**ARG**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._00|0_._03|0_._16|0_._36|0_._00|0_._14|0_._36|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(6%)|(29%)|(67%)|(0%)|(25%)|(67%)|(0%)|
|Effect on TFP|0_._00|0_._01|0_._07|0_._16|0_._00|0_._06|0_._16|0_._00|
|**BRA**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._00|0_._05|0_._19|0_._42|0_._00|0_._17|0_._42|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(8%)|(29%)|(64%)|(0%)|(26%)|(64%)|(0%)|
|Effect on TFP|0_._00|0_._02|0_._08|0_._18|0_._00|0_._07|0_._18|0_._00|
|**CHN**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._01|0_._09|0_._33|0_._51|0_._00|0_._31|0_._51|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(9%)|(36%)|(55%)|(0%)|(33%)|(55%)|(0%)|
|Effect on TFP|0_._00|0_._04|0_._14|0_._22|0_._00|0_._13|0_._22|0_._00|
|**COL**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._02|0_._05|0_._30|0_._60|0_._01|0_._30|0_._60|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(3%)|(6%)|(31%)|(61%)|(1%)|(31%)|(61%)|(0%)|
|Effect on TFP|0_._01|0_._02|0_._13|0_._26|0_._00|0_._13|0_._26|0_._00|
|**MEX**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._00|0_._04|0_._36|0_._42|0_._00|0_._33|0_._43|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(5%)|(45%)|(53%)|(0%)|(41%)|(54%)|(0%)|
|Effect on TFP|0_._00|0_._02|0_._16|0_._18|0_._00|0_._14|0_._19|0_._00|
|**MYS**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._02|0_._03|0_._54|0_._18|0_._00|0_._50|0_._18|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(3%)|(4%)|(73%)|(25%)|(0%)|(68%)|(25%)|(0%)|
|Effect on TFP|0_._01|0_._01|0_._23|0_._08|0_._00|0_._22|0_._08|0_._00|

Continued on next page
> where C, and so are uncorrelated white noise processes with variances d. and o2. respectively (V' appears by construcdon). Here X can be thought of as the frequency of the cycle and p as the damping factor of the amplitude. Although this formulation appears rather peculiar, it allows a great variety of processes. The cycle can be rewritten as

_(5)_ (l-2pcosAL+02V)V, = (1-pcosAL), +(p sin _A)<_ which is an ARMA (2,1) (L is the lag operator). If . = 0, it reduces to an AR (2) with complex roots, whereas if either A = 0 or A=a, then iv, -AR(1). Also we assume that a. = cr.

The basic stutura model (1), (2), (3), (5) can be written in _state space form._ The _state_ or _transition_ equation is:

# (6)

rt. 1 I 0 o ][ j r[i, 1 Pt =0 1 0 0 1 +4 0 0 pcos psin x iv. + . y. 0 0 -psinX pcos)jr or, more compactly _(7)_ at =S _+;._

> where a, _=[ ,. V, V],_ and so on. The _measurement_ equation is

## (8)

Y=[I 0 1 0{ Pj+et = ZA +et The parameters A,p,0,2,02 can be estimated by maximizing the likelihood of the observed sample with respect to these parameters, through the Kalman filter. Maximum likelihood esdmators can be obtained either in the dme do.iain or in the 6
Table 4: List of donor countries’ total contributions and Number of donations (cont.)

|**Donor**|**Total contribution**|**Events**|
|---|---|---|
||**(inUSD)**||
|Portugal|10,127,312|30|
|Qatar|22,350,468|13|
|Romania|2,639,255|8|
|Russian Federation|6,615,748|27|
|Rwanda|10,000|2|
|San Marino|19,807|1|
|Saudi Arabia (Kingdom of)|83,804,806|63|
|Seychelles|n.a.|1|
|Singapore|4,850,500|23|
|Slovakia|2,705,516|21|
|Slovenia|709,334|17|
|South Africa|3,852,500|10|
|Spain|73,199,347|78|
|Sri Lanka|n.a.|1|
|Sudan|10,000|1|
|Swaziland|15,000|1|
|Sweden|107,626,853|210|
|Switzerland|19,264,147|113|
|Syrian Arab Republic|n.a.|5|
|Tajikistan|n.a.|1|
|Thailand|1,085,202|13|
|Trinidad and Tobago|2,625,000|5|
|Tunisia|n.a.|3|
|Turkey|40,724,138|58|
|Ukraine|n.a.|2|
|United Arab Emirates|34,668,256|35|
|United Kingdom|306,310,134|343|
|United States of America|460,435,164|495|
|Venezuela|1,800,000|11|
|Vietnam|n.a.|1|
|Zambia|20,000|3|

17