rafmacalaba/datause-encoder-data huggingface dataset:
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_ base our weights, we calculated _dynamic weights_ which change over time as the proportion of trade with each partner within the representative set changes over time.

We built our BREER and MREER measures to be normalized at 1999 = 100 for all REER indices. That is the year following the CFA devaluation, when the CFA was thought by many to be more “fairly valued” that it had been for many years previously. And, 1995 is a common base year.<sup>2</sup>

**Figure 1: Indices of Cote d’Ivoire’s Bilateral and Multilateral Real  Exchange Rate, Based on Consumer Price Indices**

<!-- Start of picture text --> Bilateral Versus Multilateral Real Exchange Rate in Cote d'Ivoire<br>Computed Using Consumer Price Indices<br>Index Base Year 1995 = 100<br>130<br>Multi-RER-PPP w/12 Countries<br>Bi-RER-PPP w/France<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>Years - Annual Average Data<br>INDEX - 1995 = 100<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->

**The 1994 CFA devaluation improved competitiveness as measured by the REER** . Figure 1 presents our measures of the bilateral and multilateral REER indices based upon consumer price indices. Given the use of the Edwards, rather than the IMF definition, a high index number represents many CFA francs per unit of foreign currency, indicating relative competitiveness. The two measures tend to move together, although in the mid 1980s they diverge. Both measures moved upward by 40 index points in 1993-1994, at the time of the CFA devaluation. By our measure, the CFA may have been less overvalued against its _other_ trading partners in the years leading up to devaluation than it was against the French Franc.<sup>3</sup> Since the devaluation, the CPI BREER has declined by twenty index points, while the CPI MREER has not declined much at all.

> 2 These are relative and not absolute measures of competitiveness. They track relative competitiveness over time, but not across different sets of trade partners

> 3 In part, this depends upon how the two measurements were aligned in 1995, the base year for both. We do not believe that any statements can be made on “relative competitiveness” using these indices.

3
# **1.  Introduction**

This paper presents evidence on the trade-expansion potential of improvements in Sub-Saharan Africa’s road network. The impetus for our work is well-summarized by the following description of a trucker’s journey in Cameroon: _“The plan was to carry 1,600 crates of Guinness and other drinks from the factory in Douala where they were brewed to Bertoua, a small town in Cameroon's south-eastern rainforest. According to a rather optimistic schedule, it should have taken 20 hours, including an overnight rest. It took four days. When the truck arrived, it was carrying only two-thirds of its original load. ...we were stopped at road-blocks 47 times. ... Our road was rendered impassable by rain three times, causing delays of up to four hours. The Cameroonian government has tried to grapple with the problem of rain eroding roads by erecting a series of barriers ... that stop heavy trucks from passing while it is pouring. ... Early on the second evening ... we met a [locked] rain barrier in the middle of the forest. It was dark, and the man with the key was not there. ,,, he returned shortly before midnight. The hold-up was irritating, but in the end made no difference. Early the next morning, a driver coming in the opposite direction told us that the bridge ahead had collapsed, so we had to turn back._ (The Economist (2002))<sup>1</sup> In this case, the driver did not even face a long wait at a border station. According to the African Development Bank (ADB, 2003), roadblocks and border controls add four to seven days to the three-day trip from Bangui, Central African Republic, to Douala, Cameroon. The ADB also estimates that a loaded truck on the Bangui-Douala road pays about $580 at local barricades en route. A recent study by the Economic Community of West African States found up to seven check points per 100 km on parts of the region’s road network.

Similar conditions prevail in much of the subcontinent, and overland transport is so difficult and costly that Africa’s diverse regions remain largely isolated from one another. Overland trade between the large urban centers of West Africa and South Africa is basically nonexistent. Within many countries, abundant fertile soil lies fallow because

> 1  According to one critique of this story, some roads traveled were particularly bad because the trucker avoided parts of the main road where roadblock costs were even higher.  In any case, the critique reinforces the main point.  Our thanks to our colleague Elke Kreuzwieser for drawing our attention to this.

2
earner that may be related with household assets (like education and gender), but this may be insufficient. Second, Mexico offers several programs to support secondary school students and increase access to tertiary education. Besides its well-known conditional cash transfer _Prospera_ that includes families with children attending secondary school, there is a complex scheme of national scholarships designed to retain secondary students as well as to attract dropouts from households that are not beneficiaries of any other social program ( _Beca para la Continuación de Estudios, Beca de Excelencia, Becas de Reincersion_ among other). As a result, almost two out of five upper secondary students had public scholarships in 2016. _Prospera_ covered nearly 1.2 million students and another 628,000 were covered by different scholarships offered by the Ministry of Education (Secretaría de Educación Pública de Mexico, 2017). While Brazil offers financing and scholarship options for tertiary education at the university, technical and vocational level (for example, _Fundo de Financiamento Estudantil_ , _Programa Universidade para Todos_ , and _Programa Nacional de Acesso ao Ensino Técnico e Emprego_ ), there are few scholarships for secondary education beyond the conditional cash transfers from _Bolsa Familia._ Similarly, in Argentina, beside some provincial initiatives, secondary education scholarships are targeted at specific vulnerable groups (i.e. indigenous population, children of war veterans, and rural students).<sup>15</sup> The Kaplan-Meier survival estimates based on the specifications reported in Table 3 are graphed in Figure 7. It shows how survival rates of continued enrollment differ for secondary and postsecondary students in each of the three countries. In all cases, enrollment survival rates fall as students age, though the degree to which the survival changes across age groups differs across the six groups studied. The figures show a steeper drop off in enrollment survival rates in ages 18 and 19 in secondary school. Exit differences are less pronounced across years of age once enrolled in tertiary school. The figures also portray how suffering a household income shock shifts these survival rates. The most pronounced difference is for students in tertiary school in Brazil, though differences in the survival estimates among groups for secondary students in Brazil and tertiary students in Argentina are also statistically significant.

> 15 Beginning in 2014, young people between the ages of 18 and 24 who do not work, work informally, or earn less than minimum wage qualify for the PROGRESAR cash transfer to support their studies both at secondary and tertiary level.

19
**Annex Table A5-1: Person’s correlations between export share and determinants**

|Variables|1|2|3|4|5|6|7|8|
|---|---|---|---|---|---|---|---|---|
|1. export|1||||||||
|2. ln_hhcredit|0.1827*|1|||||||
|3. ln_nfccredit|0.3417*|0.6932*|1||||||
|4. fdi|0.6338*|0.1583*|0.3575*|1|||||
|5. ln_initialgdppc|0.2552*|0.6438*|0.5748*|0.2056*|1||||
|6. financialopenness|0.4917*|0.1016|0.3682*|0.6319*|0.2308*|1|||
|7. tot|-0.1777*|-0.2159*|-0.3196*|-0.1713*|-0.1380|-0.1314|1||
|8. financialcrisis|0.0141|0.1042|0.1928*|0.1746*|0.1535*|0.1309*|0.0038|1|
|9. commodityexporter|-0.1669*|-0.0416|-0.2165*|-0.1099*|-0.0383|-0.1039|0.2697*|-0.0128|

Person’s correlation coefficients are examined using five-year average data. * indicates significance at 5% level.

**Annex Table A5-2: Person’s correlations between trade balance and determinants**

|Variables|1|2|3|4|5|6|7|8|
|---|---|---|---|---|---|---|---|---|
|1. netexport|1||||||||
|2. ln_hhcredit|0.1262|1|||||||
|3. ln_nfccredit|0.2399*|0.6932*|1||||||
|4. fdi|0.3820*|0.1583*|0.3575*|1|||||
|5. ln_initialgdppc|0.2212*|0.6438*|0.5748*|0.2056*|1||||
|6. financialopenness|0.4373*|0.1016|0.3682*|0.6319*|0.2308*|1|||
|7. tot|0.0073|-0.2159*|-0.3196*|-0.1713*|-0.1380|-0.1314|1||
|8. financialcrisis|0.0908|0.1042|0.1928*|0.1746*|0.1535*|0.1309*|0.0038|1|
|9. commodityexporter|0.1811*|-0.0416|-0.2165*|-0.1099*|-0.0383|-0.1039|0.2697*|-0.0128|

Person’s correlation coefficients are examined using five-year average data. * indicates significance at 5% level.

**Annex Table A5-3: Person’s correlations between manufacturing share and determinants**

|Variables|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|1. manuf._share|1||||||||||
|2. ln_hhcredit|-0.1755*|1|||||||||
|3. ln_nfccredit|-0.1717*|0.6932*|1||||||||
|4. fdi|-0.2204*|0.1583*|0.3575*|1|||||||
|5. enrolment|-0.2897*|0.5140*|0.4168*|0.1911*|1||||||
|6. ln_initialgdppc|-0.3214*|0.6438*|0.5748*|0.2056*|0.7740*|1|||||
|7. tradeopenness|-0.1253*|0.1819*|0.3397*|0.6238*|0.1371*|0.2458*|1||||
|8. financialcrisis|-0.1616*|0.1042|0.1928*|0.1746*|0.1619*|0.1535*|0.0060|1|||
|9. ln_population|0.2458*|-0.2539*|-0.4283*|-0.2860*|-0.3174*|-0.5547*|-0.5384*|0.0243|1||
|10. tot|-0.1276|-0.2159*|-0.3196*|-0.1713*|-0.0891|-0.1380|-0.1871*|0.0038|0.0288|1|
|11. exchangerate|0.0219|0.3859*|0.1168|-0.0592|-0.3239*|-0.2070*|-0.0238|-0.0083|0.0618|0.0922|

Person’s correlation coefficients are examined using five-year average data. * indicates significance at 5% level.

25
in real terms, this translated into an average annual decline of 3% over the same period. The lower growth rates observed for industrial tariffs relative to residential tariffs reflect a tendency to try and reduce the large divergence between tariffs paid by these two customer groups, given that industrial tariffs in Latin America tend to be two or three times as high as residential tariffs. Details of the Colombian experience with raising water tariffs are documented in Box 1.

**Figure 1: Average annual change in water tariffs 1997/03**

<!-- Start of picture text --> 30%<br>25%<br>20% Constant<br>LCU<br>15%<br>10%<br>5%<br>0% Current<br>LCU<br>-5%<br>-10%<br>Average annual growth rate<br>uipa Areq a Trujill o a Rica Lima llin Mede La Paz Ca li Average Bogota Managua Uruguay<br>Cochabamb Cost Santa Cruz Pernambuco<br><!-- End of picture text --> Source: Adapted from ADERASA, forthcoming 4
**Appendix 9.  Change in Reduced Coping Strategy Index Following the 2017 IPC Announcement**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|ln (rCSI)|ln (rCSI)|ln (rCSI)|ln (rCSI)|ln (rCSI)|
|Post Announcement Indicator|-0.014|-0.009|-|0.014|0.014|
||[0.019]|[0.021]||[0.030]|[0.030]|
|Quarter Indicators||||||
|____________________<br>Q2 2017 Indicator|-||0.057***|-|-|
||||[0.020]|||
|Q3 2017 Indicator|-||-0.003|-|-|
||||[0.015]|||
|Q4 2017 Indicator|-||-0.012|-|-|
||||[0.021]|||
|Q1 2018 Indicator|-||-0.012|-|-|
||||[0.033]|||
|Q2 2018 Indicator|-||-0.071**|-|-|
||||[0.030]|||
|Q3 2018 Indicator|-||-0.078*|-|-|
||||[0.038]|||
|Interactions Between IPC Status and the Post<br>Announcement Indicator||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-||-|-0.056|-|
|||||[0.033]||
|Interactions Between the Post Announcement<br>Indicator and Individual Governorates in IPC 4<br>Regions||||||
|____________________<br>Post Announcment Indicator*Abyan Indicator|-||-|-|-0.033|
||||||[0.030]|
|Post Announcment Indicator*Al Hudaydah Indicator|-||-|-|-0.044|
||||||[0.030]|
|Post Announcment Indicator*Hajjah Indicator|-||-|-|-0.039|
||||||[0.030]|
|Post Announcment Indicator*Lahj Indicator|-||-|-|-0.048|
||||||[0.030]|
|Post Announcment Indicator*Sa'ada Indicator|-||-|-|0.008|
||||||[0.029]|
|Post Announcment Indicator*Shabwah Indicator|-||-|-|-0.129***|
||||||[0.030]|
|Post Announcment Indicator*Taizz Indicator|-||-|-|-0.089***|
||||||[0.030]|
|Observations|357|357|357|357|357|
|Controls and Governorate Fixed Effects|N|Y|Y|Y|Y|

Note: This table estimates how the average Reduced Coping Strategy Index changed following the announcement of the 2017 IPC in March 2017. Column (1) estimates a sparse specification regressing the average share of respondents in each governorate on an indicator equaling one if the survey was conducted after March 2017; column (2) adds governorate fixed effects and control variables; column (3) estimates an identical specification but replaces the post indicator with indicators equaling one if the survey happened during that quarter-year time period; column (4) interacts the post indicator with an indicator for the most severe IPC classification in the 2017 IPC announcement (IPC 4); and column (5) further interacts the post indicator with governorate indicators for each governorate that was classified as IPC 4 2017 IPC announcement. Control variables include the number of people killed in the month before the survey, and four lags of the same variable. Standard errors are clustered by governorate and reported in parentheses. * denotes significance at the 1 percent level; ** denotes <u>significance at the 5 percent level; and *** denotes significance at the 1 percent level.</u> 40
_Annex 1 Table_ _<u>5: Winsorizing ten percent of anthropometry data on 6-36-month-olds</u>_

||Height-for-<br>Age Z score|Stunting|Severe<br>stunting|Weight-for-<br>Age Z<br>score|Underweight|
|---|---|---|---|---|---|
|_Panel A: Program Impact_||||||
|Program village|-0.380*<br>(0.210)|0.129**<br>(0.059)|0.067*<br>(0.037)|-0.163<br>(0.169)|0.057<br>(0.043)|
|_Panel B: Interaction with ab_|_ove median satu_|_ration_||||
|Program village|-0.036|0.063|0.047|0.205|-0.046|
||(0.249)|(0.075)|(0.046)|(0.206)|(0.053)|
|Ab di tti|-0.189|0.054|0.020|0.087|-0.075|
|ove mean sauraon|(0.298)|(0.082)|(0.050)|(0.261)|(0.074)|
|Program village*Above|-0.771**|0.146|0.046|-0.812**|0.231***|
|median saturation|(0.382)|(0.110)|(0.075)|(0.349)|(0.087)|
|_Panel C: Interaction with to_|_pquartile of satu_|_ration_||||
|Program village|-0.191|0.058|0.036|0.066|0.018|
||(0.229)|(0.062)|(0.040)|(0.184)|(0.047)|
|Fourth quartile of|-0.000|-0.005|-0.033|0.518*|-0.125|
|saturation|(0.525)|(0.091)|(0.068)|(0.279)|(0.087)|
|Program village*Fourth|-0.883|0.329**|0.159|-1.322***|0.245**|
|quartile of saturation|(0.579)|(0.126)|(0.102)|(0.396)|(0.111)|
|Control Observations|151|151|151|156|156|
|Treated Observations|172|172|172|177|177|
|Control Mean|-1.588|0.402|0.106|-1.010|0.218|

*** p<0.01, ** p<0.05, * p<0.1

All specifications include linear controls for child age (in months). Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 69
welfare in rural Bangladesh,Policy Research Working Paper Series 6134, The World Bank.

Bhowmik, A, K, and Ana Cristina Costa (2012). A Geostatistical Approach to the Seasonal Precipitation Effect on Boro Rice Production in Bangladesh, _International Journal of Geosciences_ , 2012, 3, 443-462 Br ̈uckner, Markus, 2012. Economic growth, size of the agricultural sector, and urbanization in Africa, _Journal of Urban Economics_ , vol. 71(1), pages 26-36.

Bustos, Paula, Bruno Caprettini and Jacopo Ponticelli, 2016. ”Agricultural Productivity and Structural Transformation: Evidence from Brazil,” American Economic Review, American Economic Association, vol. 106(6), pages 1320-65, June.

Chernozhukov, V and C, Hansen (2008), “The Reduced Form: A Simple Approach to Inference with Weak Instruments”, Economics Letters, 2008, 100(1), 68-71.

Christiaensen, Luc & De Weerdt, Joachim & Todo, Yasuyuki, 2013.Urbanization and poverty reduction – the role of rural diversification and secondary towns, Policy Research Working Paper Series 6422, The World Bank.

Deichmann, U., F. Shilpi, and R. Vakis. 2009. “Urban Proximity, Agricultural Potential and Rural Non-farm Employment: Evidence from Bangladesh,” World Development, 37(3), p. 645-660.

Emran, M. Shahe and Forhad Shilpi, 2012.”The extent of the market and stages of agricultural specialization,” Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 1125-1153, August.

Fafchamps, M. and F. Shilpi, 2003, “Spatial Division of Labor in Nepal, _Journal of Development Studies_ , Vol. 39, No. 3, pp. 23-66.

Fafchamps, Marcel, and Forhad Shilpi, 2005. “Cities and Specialization: Evidence from South Asia.” _Economic Journal_ ,Vol. 115, April.

Ferre, Celine, Francisco H.G. Ferreira and Peter Lanjouw, 2010.”Is there a metropolitan bias? The inverse relationship between poverty and city size in selected developing countries” Policy Research Working 30
Samuelson, William and Richard Zeckhauser (1988) “Status quo bias in decision-making”, _Journal of Risk and Uncertainty_ 1: 7-59.

Schewel, Kerilyn (2020) “Understanding immobility: Moving beyond the mobility bias in migration studies”, _International Migration Review_ 54(2): 328-355.

Shrestha, Maheshwor (2020) “Get Rich or Die Tryin’: Perceived Earnings, Perceived Mortality Rates, and Migration Decisions of Potential Work Migrants from Nepal”, _World Bank Economic Review_ 34(1): 1-27.

Sjaastad, Larry A (1962) “The Costs and Returns of Human Migration.” _Journal of Political Economy_ , 70(5): 80–93.

Stark, Oded and David Bloom (1985) “The new economics of labor migration”, _American Economic Review Papers and Proceedings_ 75(2): 173-78.

United Nations (2013) “Cross-national comparisons of internal migration: An update on global patterns and trends”, _Population Division Technical Paper 2013/1._ World Bank (2016) _Migration and Remittances Factbook 2016: Third Edition._ World Bank, Washington D.C.

Yasenov, Vasil (2020) “Who can work from home?”, _IZA Discussion Paper no. 13197._ Young, Alwyn (2013) “Inequality, the urban-rural gap, and migration”, _Quarterly Journal of Economics_ 128: 1727-85.

26
<mark>2.4% lower than the counterfactual. Even after ten years, these cases on average do not reach their pre-crisis GDP per capita levels, with a national per capita output level more than 24 percent below that of their synthetic control.</mark><sup>23</sup>

# **<mark>Social costs: Poverty and its facets</mark>**

<mark>Our results make the case that default leads to severe growth declines. We now take on the task of measuring the direct impact on poverty headcount and the ways in which living standards may decline in the face of a default.</mark><sup>24</sup> <mark>We start by estimating the effects of default on poverty headcounts, which our results suggest are large. Poverty ratios peak in the fourth year after default at 30 percent more individuals living on less than USD 1.9 a day than pre-crisis levels, which amounts to 70 percent more than in the synthetic counterfactual scenario. The gap in poverty headcounts between the defaulting country and synthetic controls remains large with poverty headcounts still 70 percent above the counterfactual seven years after default. Unfortunately, much of the data on poverty headcount ratios is only available starting in 1981 and many of the observations come from imputations performed by the World Bank’s Poverty and Inequality Platform (PIP) as opposed to survey observations.</mark><sup>25</sup> <mark>Yet, the findings are significant, particularly during the first two years after the default and their magnitude suggests that they are relevant to both the question we set out to explore and policy making.</mark>

> 23 <mark>Of course, this selection suffers from endogeneity. In what has been referred to as</mark> _<mark>gambling for resurrection</mark>_ <mark>creditors that observe sovereigns with steep declines in economic activity seem to portray a tendency to hold up with restructuring talks (Uribe, 2006; Benjamin and Wright, 2009; Kaminsky and Vega-Garcia, 2016). Possibly knowing that a restructuring at the depth of a recession will likely mean greater creditor losses. Not solving the debt crisis in turn prolongs the economic downturn.</mark>

> 24 <mark>The results of this section appear robust when using a fixed-effects event study methodology (Gourinchas and Obstfeld 2012). The results of that exercise broadly confirm our findings on poverty headcounts, infant mortality, calorie and energy supply; which can be found with a more detailed discussion in the appendix. Overall, the direction and timing of the effects confirm the findings presented here using SCM.</mark>

> 25 <mark>Data starting in 1981 implies we can only consider defaults after1988, given the pre-treatment matching requirement, which we reduce to seven years for the poverty headcount analysis, given the data constraints.</mark>

20
Table 1: Multiple Foreign Exchange Markets in Sub-Saharan Africa.

|_COUmN7Y_|_PA YMEN7 RESI7_<br>_ON:_|_UCn7ONS_|_NATURE OF_<br>_DUAL_<br>_MARKET_|_FREE RAE _|_MANMEMEP_<br>_OFFICIALR _|_r OF_<br>_ATE_|_ADDI77OA?L_<br>_EXCHANGE_<br>_RtATE_|
|---|---|---|---|---|---|---|---|
||_Capital Accouns_|_Curmrnt_<br>_Accomnt_|||_WJIEN_<br>_MFRS_|_POST_<br>_MERS_||
|Angola|yes75-90|yoe75-90|lllogaltolerated|black 75.90|fixod|not unifid||
|Burundi|_yos_70-90|yes 70.90|illegal 70.90|black 70.90|fixed 70-86<br>peg_16-90_|notIfnid<br>ed||
|Ethhopia|yes70-90|yes70-90|illegal tolerated|black 70-90|peg|not unified|no|
|Ghana|ye. 70.90|yes 70-90|illcgal tolerated|black 70488<br>legal 85-90|fixed70-13<br>peg 13-88|r.<br>unirted 4.88|yeau 73.90|
|_Kenya_||||black 70.90|peg|not unified|yes 79-90|
|Malawi|ye. 70.90|ye, 70-90|illegal tolerated|black 70-90|peg|not unified|no|
|Nigeria|yea 70-90|yes 70-85<br>no86-90|illegal tol. 70-87<br>legal 87-90|black 70-87<br>lgal 87-90|peg 70-90|free<br>unified 8.90-|yes 86-89|
|Rwanda|ye 70-90|yes 76-90|||fixed|||
|S. Lcono|ye. 70-5.86<br>no 6.1C4.87<br>yea 587-90||illegal 70-12.12<br>leg. 12.82-7.83<br>leg. 1.84-9.84<br>leg. 6.86-4.87|legal 12.82-7.83<br>legal 1.84-9.84|fixed|unified83<br>unified8S|yes 70-86|
|Sudan|yea 70-90|yes 70-90|illegaltol.7-87|black 70-90|fixed|nolunified|yes|
|Tan-ani|yes 70-90|yes 70-90|illegal tolerated|black 70-90|peg|not uified|yes 82-85|
|Thc Gambia|yea 70-86|yes 7175|illegal 70-86|black 70-86|fixed 70-86|unified||
||no 87-90|no 77-S0<br>yes 81-86<br>no 87-90|legalS6-90|legal 87-90||||
|Uganda|yes 70-86<br>yes 88-90|yea 70-90|legal 70-82<br>illegal 83-84<br>legal85-90|black 70-90|peg|not unified|yes|
|Zaire|yes 71-90|yes 71-90|illegal tol. 70-83<br>legal 13-84<br>illegal tol. 84-90|black 70-13<br>legal 83-84<br>black 84-90|peg|free<br>unified 8.19|yes 9.78-83|
|Zambia|yea 70-90|yes 70-90|illegal tol. 70-85<br>legal 85-87|blick 70-85<br>legal 85-87|fixed 70-76<br>peg 76-87|not unified|yea 2.80-87|
||||illegal 87-90|black 87-89|fixed 87-90|||
|Zimnbabwe|yes 80-90|yes 80-90|illegal 80-90|bliek 1090|fixed|not unified|yes 84-90|

MERS : Multiple Exchange Rate Systems.

ADDITIONAL ER: Special exchange rates for cenain transactions. In some Cases, there was no dual market, even though the official exchange rate was fixed or pegged.

Source: Kiguel and O'ConneL (1992); IMF and World Currency Yearbook (various issues).