rafmacalaba/datause-encoder-data huggingface dataset:
- 7-

Table 3: TRADE UDERALIZATION AND GROW1H: 1910-19g (Dekd vwrbblk: ra GDP groth)

||RER|ATR(t-l)|11<br>GDP'Ot-I)|Capacity<br>Wtilinti|T|C|PAM<br>of<br>abo.|R2|S.E.R|D.W|F-assstsd|
|---|---|---|---|---|---|---|---|---|---|---|---|
|(A)Insrsve<br>iadeadiusten|-D.03<br>(LOD),|-0.07<br>(2.9?)'|_0.05_<br>(1.22)|||0.07<br>(.64)|45|0.15|0.02|1.93|3.6-|
|(Ghan. Indies-.<br>Mexico.||||||||||||
|Macco<br>sa Turkey)|-0.2<br>(1.49)|4.06<br>(2.89?|0.04<br>(1.06)|(3L1<br>(1.71)||0.06<br>(2.07)|45|°2|0-c2|1.56|4.13|
||-0.04<br>Q.22T)|-(.LU<br> <br>0.M)4)|0.05<br>(1.27)||40.1<br>(0.92)|0.10<br>(55)|45|0.15|o.m|195|2.99|
|(13) les intemmwe adijsern<br>(Colombia.cGhed-lv_ire.|-0.04<br>(0.71)|-010<br>(1.25|-0.38<br>(1.79%|||0.12<br>(104)|36|0.9|o.m|2.27|2.19|
|in]ac<br>andPakista)o|-04.2<br>(0.58)|0.10<br>(1.7)|-0.44<br>l.85),|1.02<br>(4.0l||0.09<br>(1. is)|36|0.44|0.0|1.75|7.79|
||.0.03|-0.10|4.38||9.0|0.12|36|0.06|0.02|2.27|1.59|
||(0.45)|(1.42)|(1.W0-||_M.05_|(C61||||||
|_(C)_All aditnes|-0 36<br>(1.29)|0.07<br>(0.12)|0.93<br>(1a4)|||0.26<br>(0.53)|S3|-0.03|e.46|0. 14|0.93|
||-0.36|_0.07_|0.93|-023||01|Sl|1|0.46|OL13|0.69|
||(1.22)|QxlII)|(133)|(0.06)||_(OA9_||||||
||-0.36|0.07|0.93||042813|0.26|8|04M|0.46|OL14|0.69|
||(1.10)|(0.12)|(li)||OLm)|(037)||||||

Eq- ion: (ogGDP&-IoZgDPt-i1aG + at ogRERI **+** a2ATaz-1 + _.3_ hlODft-I + a4 CUt + aS T

1. S _fn:_ 19018: aa_tl dam

2. Absolute vale f t-sttistic in prremee ad - shows aigtficance at _S%_ kwl.

3. Edmat by OLS usiag pod dam: HCeekmdticicY-r-iSurt CnariaiG Matrix acne iaia 4. R2 adjwuted for dege sf fieedon. S. S.E.R: Staard Enr of Regssiu. Defrhuitons of variabks: 1. Redl GDP (in 1930 USS): BESD NPaioml ArrsnL 2. RER: Real ExchaWe RItes (1980-10): a fall of RER is deprccatic IMF. 3. ATR: AvengeTariffRafts(S).gireebytcl npondutiescoll-eedna raioof value of ip I--nstp dties: vriousCEMsadIFSdocunes: ce oriwona IFS.

4. IIGDP1t-1): Real gres demnc _i-est.nt_ as a rtio of real GDP oF die previous yr: World Ba Datbase (atial Aceosa. 5. Capacity utilization: slc diltresn between nil lagGDP al Broed vaus of ogGDP. wher foetd vales acr estimatd by LtGiDP -c+Titme Trenax. 6. When oneyn lagged RER was added to sl equstn. slu ekeftim - it wa rna aignrua. (1) Inive wade adjourn cigGDF-ogCGDPZ-1) - 0.06+0.035 RERt-0.021 ATR(t-1)+0.004 lI(GDP(t-I))1O+O1 RER(tl) (1.350 (-1.903) (1.191) M.0M (2) Les ieve adjustrs (logGDPt4cgGDPt-1) - 0.075+0.09 RERt-0.021 ATR(t-l)-0.0l8 U (GDP(t-1).D.439 R30-1) (0.747) -1.094) (-2301) (.0.16%) 3) All adiers (lqgGDPx-tlaGDGtM-) - 0.082+0.37 RERt-0A002 AT14t-l)-.0.0 IflGDP(tO-0002 RtR(1t-1) (1-37) (4.191) (4137) (-0.D69
_Railway Reforn in the Central and Eastern European (CEE) Economies_ _3_ Figure 2 Figure 3 <!-- Start of picture text --> Tonnes of Cnude Steel - 1988<br>per $00,000 of GNP<br>cb- =13 9<br>FD-A M"",2 SI<br>FOU- 12<br>BEU- ILGARIA. ea asFtJ<br>HUNGARYP 3<br>AUBTRA CANADA , 6<br>N. GEFW 3e<br>SWE J.<br>IrALY IJ<br>GPMF<br>HOLUW0A2<br>UK 20<br>USA ula<br>FRANCE 1 a<br>YLOSLAVI 16 r ...<br>a 20 40 60 so IoW 120 1401C<br>198S GNP b isPxMg PcwaA4utd<br><!-- End of picture text --> <!-- Start of picture text --> Tonnes of Coal and Ugnite - 1988<br>per $000,000 of GNP<br>POA<br>CSF 1140<br>UGAN 907<br>- 9<br>YGL 4<br>W HUNAR .GEMN 301 1<br>USA 177W<br>CANADA 152<br>LUK<br>S PAIN- sa<br>AUSTR 151<br>BEEU1 14<br>WNE1<br>FRANC 1<br>rr AL 2 , ,<br>0 00 1000 1500 200 25sc<br>1388 GW b Purchasing PowerAdjustd<br><!-- End of picture text --> Figure 4 Figure S <!-- Start of picture text --> Tonnes of Cement- 1988 Millions of KWH of Electrcityy-1 988<br>per $000.000 of GNP per $000000 of GNP<br>POLAND 4 1272 137<br>BILGARIA 7. 77 PLD a7107<br>FU B :**J 105.9AAA 01<br>CSFR - _ .3 BULGAF11A Xeow lNNN<br>YUGOSLAVIA _V 88S<br>HLINGARY 572 cam~~~~___ 737<br>SPAN -Y 4SA -12<br>AUSt _s402ea US<br>LUM -E'LU3E<br>frALr 5 FRANCE 1347<br>CANADA - 25.7 ALIRIA 348<br>:PA m 23J N.ERAY 1 332<br>J.IERMANY -UK 325<br>IIK HOLL1ANFD Z<br>HOLAND 132 ffAY 1el B<br>a s0 40 eo ma 100 120 140 ISC 0 400 8a0 1200 IBOM<br>200 600 1000 1400<br>jq8GW1B~P PwhuriNgPawsrA*nWd 19888GNPisPurchasingPowaA4uMtd<br><!-- End of picture text -->
19 strengthening the capital base of<sup>the bankingsystem,upgradingtheaccountingframeworkand</sup> severing ownership links between<sup>banksand corporations.After underestimatingtheextentof</sup> the crisis initially, the Chilean<sup>governmentlaunched successiveandmassiveprogramsto</sup> recapitalize the banks and to<sup>bailoutborrowersinorder toresolve thecrisis. However,Chile's</sup> experience also showed that the<sup>government'slack of a realistic assessmentof corporate distress</sup> proved very costly since repeated<sup>recapitalizationand debt relief schemes served to reinforcethe</sup> perception by creditors and<sup>borrowers thatthegovernmentwould continueto provide additional</sup> assistance (Barandiaran and Luders<sup>2000).Theprogramsforbailingoutborrowerscreateda</sup> serious moral hazard problem<sup>that initially deterredrapidcorporaterestructuring.</sup> Following several across-the-board rescheduling<sup>programsin1984-85,the corporatesector startedto show</sup> signs of profitability and improved<sup>financialconditions (BarandiaranandHemandez1999).The</sup> quick pace of the economic<sup>recoverywas similarlyareinforcingfactor for corporate</sup> restructuring.

Figure 4. The **FinazcialW** Health<sup>of the Corporate</sup><sup>**Sector**</sup> <!-- Start of picture text --> 4.5 -<br>4<br>3.5<br>2.5 '- DAt io~~~~~~~~~~~~~~- the onset of the cisis<br>2.5 =Five - years after the start of the nsis<br>1.5 ' ^ _ _ * || ___ | _ |<br>°. F--5;1- 2 ll<br><!-- End of picture text -->

_Note:_ We rate the financial health<sup>of the corporatesectoraccordingtotwo factors.One,the sector weighted</sup> interest coverage ratio and second,<sup>the shareof companieswithaninterest coverage ratioof lessthan one.</sup> We assess the financial situation<sup>of the corporate sectoron a scalefromoneto four based on data availability</sup> for either of the two factors and<sup>therefore weclassifycorporationsintofourcategories.Aonedenotesa</sup> situation in which the weighted<sup>averageinterest coverageratio of all firmsisbelowoneand/or theshareof</sup> companies with an interest coverage<sup>ratioof oneor less ishigherthan30 percent.A two denotesasituation</sup> in which the weighted average<sup>interest coverageratioof allfirmsis betweenthree andoneand/orthe share</sup> of companies with an interest<sup>coverageratioof oneor lessis between21and30 percent.A threedenotesa</sup> situation in which the weighted<sup>averageinterest coverage ratioof allfirmsis betweenfiveandthreeand/or</sup> the share of cornpanies with an<sup>interestcoverageratioof oneorlessis between11and 20 percent.Afour</sup> denotes a situation in which<sup>the weighted averageinterest coverageratioof allfirms exceedsfiveand/or the</sup> share of cornpanies with an interest<sup>coverageratioofoneorlessis lessthantenpercent.</sup> _Sources:_ Argentina: World Bank 1985; Chile:<sup>World Bank1987; Hungary and Poland:Pohl, Anderson,Claessensand</sup> Djankov, 1997; Japan: Goldman<sup>Sachs2002a; Norway:staff calculationsusing Bloombergdata;Thailand:</sup> Claessens 2001.
Inspecting developments in the vanilla market since the reforms in 1995, three trends are apparent. First, recent years have been marked by a contraction of world exports matched by a spectacular rise in prices. Second, the decline in Madagascar’s market share has reversed itself during the 1990s. Third, new entrants are progressively overtaking Indonesia as the main competition facing Madagascar. This development mirrors the early days of phase 2 when high prices triggered Indonesia’s entry. As shown in figure 4, the evolution of prices is particularly striking as they slowly eroded during 1993-98 but literally exploded from a low of US$21.26 in 1997 to a high of US$251.17 in 2004 (some vanilla was reportedly sold at 600$ per kilo in 2004), a more than ten-fold increase, only to collapse again in 2005 much like they had before. During the same period, trade volumes contracted by about two thirds, from three thousand tons to little over one thousand tons, as several hurricanes in Madagascar and a large-scale fire destroyed a substantial chunk of the worldwide vanilla production capacity. While exogenous factors leading to supply shortages certainly contributed to this development, it is unlikely that demand was sufficiently inelastic to explain such a reaction, suggesting some market manipulation with supply withholding to fuel prices.

The erosion of Madagascar’s market share, which started in the 1970s as a result of the cartel’s high-price policy, strikingly reversed itself in the latter part of the 1990s. Some erosion is again apparent in later years but arguably due to adverse weather conditions rather than a policy shift. In a sharp reversal of fortunes, the Asian crisis and 1997 Indonesian fire precipitated a decline in Indonesia’s market share that started in the early to mid-1990s. Once again, following high prices, partly due to climatic conditions in Madagascar (cyclonic conditions in 2000 followed by heavy rains in 2002) competition from new entrants, which together account today for more than a third of world exports, is rising.<sup>16</sup> Undeniably, the free market has certainly not stabilized the price, and it is said that the price hike of 2004 which saw vanilla sold at 600$ per kilo, the food industry has substituted up to 30% of its purchases of natural vanilla with artificial vanillin (Ecott 2004, p. 18).

With the knowledge that cyclonic conditions and the Indonesian fire in 1997 have contributed to the volatility of the vanilla market during the last decade, the challenge is to try and detect whether the policy reforms carried out since 1995

> `16` Entrants include Costa Rica, India (whose output, at 120-130 tons, is equivalent to the Comoros’), Mexico, and Papua-New Guinea (around 300 tons). Uganda is also reported to be raising output and quality (see e.g. www.cgiar.org/foodnet).

20
Table 2. Self-reported reasons for rainfall insurance non-purchase

|**Why did you not purchase insurance?**|2004|2006|
|---|---|---|
|Insufficient funds to buy insurance|27.1%|80.8%|
|It is not good value (low payout / high premiums)|16.4%|7.85%|
|Do not trust insurance provider|2.34%|5.23%|
|It does not pay out when I suffer a loss|17.8%|2.91%|
|Do not understand insurance|21.0%|2.33%|
|Do not need insurance|2.80%|0.58%|
|No castor, groundnut|6.07%|n.a.|
|Other|6.54%|0.29%|

Note: Non-purchasing households in the study areas Andhra Pradesh and Gujarat analyzed in Cole et al. (2009) are asked to explain why they did not buy insurance. In Andhra Pradesh, non-purchasing households were asked the top three reasons why they didn't buy insurance. Only the primary reason cited by the household for non-adoption of insurance is reported. This table is reproduced from Cole et al. (2009).

2
Ethnic polarization and the duration of civil wars Our empirical estimation shows that the hazard function for the event of the end of a civil war is much lower for countries with a level of ethnic polarization over the median than for the rest of the countries. In fact, an increase of ethnic polarization from 0 to 1 multiplies by 2 the median duration of a civil war. This result suggest that we should pay special attention to countries that are polarized, in order to implement appropriate institutional settings and policies that reduce the feeling of grievance, that seem to be higher in polarized countries.

# _2. A short guide to the recent literature_

Recent literature on the causes of civil wars has postulated several factors as robust determinants. Collier and Hoeffler (2004) use logit regression to estimate the onset of a war breaking the time dimension into five-year episodes. They find that higher primary commodity exports<sup>3</sup> , diaspora and higher and more dispersed population increase the risk of a conflict. By contrast, a higher per capita income, time since the last conflict, growth and secondary enrollment reduce the risk. Collier and Hoeffler (2004) find no effect of income inequality, political liberties or ethnic heterogeneity on the risk of a civil war. Fearon and Laitin (2003) consider annual data and a different specification that includes as a regressor the lag of the dummy for civil wars<sup>4</sup> . In their analysis mountainous countries, oil exporters, anocracies, and highly populated country face a larger risk of conflict. The only factor that decreases the probability of war is a high per capita income. Fearon and Laitin (2003) find no role for ethnic heterogeneity or democracy as a risk factor for civil wars.

Montalvo and Reynal-Querol (2005a) analyze the incidence instead of the onset of civil wars. They find that ethnic polarization is a very robust determinant of the incidence of civil wars, while the statistical significance of population and the initial level of per capita income seem to be dependent on a particular specification. In their analysis, neither primary exports, democracy nor ethnic fractionalization had any significant effect on the incidence of civil wars. A set of articles on the duration of civil wars are found in a special issue of _Journal of Peace Research_ published in 2004. Fearon (2004) classifies the wars into different categories (coups

> 3 Fearon (2005) disputes this fact and finds that the effect is weak and not robust even if one adopts the Collier and Hoeffler (2004) specification and uses their definition of civil wars.

> 4 Since the specification is not properly transformed to deal with the inclusion of a lagged dependent variable, the results are econometrically questionable.

4
competitiveness.<sup>14</sup> Table 1 presents 12 of the most economically competitive countries in 2006 as ranked by the Global Competitiveness Index and the World Competitiveness Scoreboard. Table 2 presents the top 12 countries ranked by surveys which measured “satisfaction with life”. The overlap is significant. Happy and healthy people are more likely to be productive, to pursue knowledge more avidly and put it to more ingenious uses. To express this in more conventional terms, it is intuitively plausible that creativity will be affected by the physical, emotional and intellectual quality of human capital. Thus, one of the uppermost objectives for a creative society is to invest in human capital so as to raise both volume and quality.

**Table 1 – 12 of the Most Economically Competitive Countries in 2006**

|GlobalCompetitivenessIndex|World Competitiveness Scoreboard|
|---|---|
|Switzerland<br>Finland|Iceland<br>Denmark|
|Sweden|Australia|
|Denmark|Canada|
|Singapore|Switzerland|
|United States|Luxembourg|
|Japan|Finland|
|Germany|Ireland|
|Netherlands|Norway|
|United Kingdom|Austria|
|Norway|Sweden|
|Iceland|Netherlands|

Source: IMD World Competitiveness Yearbook 2006; World Economic Forum.

**Table 2 – 12 of the Top Ranked Countries by Surveys which Measured "Satisfaction with Life"**

|Satisfactionwith Life|
|---|
|Denmark<br>Switzerland<br>Austria|
|Iceland|
|Australia<br>Finland|
|Sweden|
|Canada|
|Ireland<br>Luxembourg|
|Norway<br>Netherlands|

Source: Veenhoven, R., Average happiness in 95 nations 1995-2005, World Database of happiness.

> 14 There is also some weak evidence of a casual relationship running from happiness to economic growth (Kenny 1999). Di Tella and MacCulloch (2006) find that both unemployment and inflation reduce happiness with unemployment having a greater effect.

5
# **Notes**

1 Saunders 2010, 1. 2 China Daily 2012. 3 Ruhs 2013. 4 A study in the _Financial Times_ found that “foreign buyers spent £5.2 billion on property in central London in 2011, £1.5 billion more than in 2010, with most of the cash going to a small number of exclusive postcodes” (Collinson 2012). 5 Daily Motion 2011. 6 Unruh 2004. 7 Unruh 2004. 8 Fitzpatrick 2005; Fernandes and Varley 1998; Durand Lasserve 1998. 9 Cities Alliance 2011. 10 Roy 2005. 11 Ostrom 1990. 12 Unruh 2004. 13 Unruh 2004. 14 Ward 1999. 15 Assies 1994.

Box 3 1. Ward 1999. 2. Ward 1999. 3. Roy 2003. 4. Assies 1994. 5. Nogueira Budny 2007.

6. Nogueira Budny 2007.

Box 4

1. Satterthwaite 2005. 2. Jamdar 2003 in Roy 2005. 3. Tran 2011. 4. Satterthwaite 2005.
Moreover, the region exhibits the world’s highest loan concentration ratio, reflecting a bias towards lending to large firms and excluding young and small firms (World Bank, 2011). Thirtyseven percent of MENA firms report that access to finance is a major constraint to doing business, higher than all regions except for Sub-Saharan Africa (Figure 1).

Stimulating trade in the MENA region is not only an economic challenge, but also a governance challenge. Many empirical works find a strong relationship between poor governance and stunted growth in MENA.<sup>10</sup> World Governance Indicator (WGI) scores for countries in the MENA region are much lower relative to other countries, and along certain dimensions of governance, MENA scores are lower than even lower-middle-income economies. Firms in MENA report being significantly more constrained along regulatory factors than in any other region. For example, 25.9 percent of MENA firms reported that business licensing and permits are a major constraint to their business operations, which is nearly double the rate in any other region (Figure 1). In addition to low governance ratings, political issues impede the movement of goods in the region. For example, Jordan neighbors the West Bank, which is largely excluded from the global market.

Figure 1. Share of Firms Identifying Business Enviroment Factor to be a Major Constraint <!-- Start of picture text --> 50<br>43.0<br>40 36.6<br>33.4<br>30.8<br>30 26.6 25.9<br>22.3<br>20 19.7 17.8 17.1 19.1 15.9 15.1 18.3<br>11.8<br>8.4 7.6 9.2<br>10<br>0<br>Customs and Trade Regulations Access to Finance Business Licensing and Permits<br>East Asia & Pacific Eastern Europe & Central Asia Latin America & Caribbean<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>Percent of Firms<br><!-- End of picture text --> _Notes: Surveys were implemented following the GLOBAL sampling methodology. However, the MENA average includes non-GLOBAL surveys since region has not had a complete survey roll-out. MENA average includes Algeria 2007, Egypt 2008, Iraq 2011, Jordan 2006, Lebanon 2009, Morocco 2007, Syria 2009, West Bank and Gaza 2013, and Yemen 2010._ _Source: Enterprise Surveys_ High-value added sectors tend to have higher shares of intangible assets and create innovative products requiring substantial R&D or investments, making these sectors more dependent on external financing. The composition of MENA exports is currently skewed towards low valueadded goods. Investment in intangible-asset-intensive goods relies on a strong legal framework where assets are protected, the legal system functions efficiently and individuals or firms do not face risk of expropriation of their assets. To promote more rapid export driven inclusive growth,

> 10 See Elbadawi, 2005; World Bank, 2003; Aysan, Nabli, and Veganzones-Varoudakis, 2007

4
20 <!-- Start of picture text --> COt4VENTOIAL AMD PROPOSED PRIVATE<br>SAVING bWASURES (1980 PESOS).<br>0.35<br>0.25 A<br>K 0.2 i<br>0.1<br>19801981 1982 198419841985 19561987 1988 1,89 1990<br>C-o nanmd Prqposd<br><!-- End of picture text --> Figure 10

# 3.3 Disacqrecgation of Consumption Series

A detailed disaggregation of consumption, including series with quarterly and monthly data frequencies, is provided in Annex C. Our analysis is limited to data with annual frequency and only considers disaggregation<sup>of</sup> consumption into durable consumption, non-durable consumption and services.

In 1989-90 private consumption grew at an average annual real rate of 5.4%; as a consequence, in 1990 the private consumption-GDP ratio got close to the record level for the decade attained in 1980. It is useful to check whether any particular component of consumption explains this trend. As Figure 11 shows the shares of the three consumption components out of total consumption were relatively stable over the 1980-90 period. However,<sup>durable</sup> consumption increased 28% between 1988 and 1990 and represents about<sup>one-</sup> quarter of the increase in total consumption over the same period. Durable consumption cannot explain an important part of total consumption growth because durable consumption only represented about 11% of total consumption during the period: the average growth of non-durable and services over 1988-90 is still a high 4.9%, or about 2 percentage points faster than output growth.
28 (see also U.S. Congress, 1986); the market becomes more competitive, not perfectly competitive.

# 2.2 The cross-country evidence

Prices of pharmaceuticals also differ significantly among countries. Table 4 shows a cross-country comparison of an index of 1975 prices of identical

# Table 4 : CROSS-COUNTRY PRICES INDEX OF PHARMACEUTICALS - 1975

|Country|Price Index<br>Pharmaceuticals|Country|Price Index<br>Pharmaceuticals|
|---|---|---|---|
|Malawi|60.83+|Ireland+|73.58+|
|Kenya|50.63+|Hungary|57.25|
|India|31.71|Poland|53.98|
|Pakistan|38.76|Italy|69.01|
|Sri Lanka|15.22+|Spain|69.68|
|Zambia|96.58+|United Kingdom+|<br>71.19+|
|Thailand|48.01|Japan|81.88|
|Philippines|51.14+|Austria|139.53|
|South Korea|35.10+|Netherlands+|137.29+|
|Malaysia|70.74|Belgium+|101.73+|
|Colombia|48.07|France+|91.56+|
|Jamaica|46.13+|Luxembourg+|100.27+|
|Brazil|63.83|Denmark+|157.56+|
|Mexico|69.68|Germany (F.R.)+|<br>152.52+|
|Yugoslavia|48.24|United States+|100.00+|
|Iran|70.42|Uruguay|65.95|

+ Listed in Schut and Van Bergeijk (1986) as providing patent protection.

Source: Schut and Van Bergeijk (1986, Table 2). Patent information for 1988 from WIPO (1988).

packages of pharmaceutical products. After fitting a simple econometric model Schut and Van Bergeijk concluded that a "...strong positive relationship between price level and per capita GDP is found, a lOX increase in per capita income