rafmacalaba/datause-encoder-data huggingface dataset:
# **V. THE BANKING SYSTEM: Vulnerabilities behind a strong faqade**<sup>**40**</sup>

## A. STRENGTHS

Hyperinflation and deposit confiscation at the end of the eighties<sup>41</sup> wiped out confidence in the peso and domestic financial intermediation. After Convertibility was enacted, a major effort was launched to recreate a solid financial sector mostly based in dollar-denominated deposits and loans. In 1995 Tequila contagion led to a run on 18% of total deposits and to systemic illiquidity which, in the absence of a domestic lender of last resort, required prompt support from the IFIs to avoid a collapse of the banking and payments systems. The authorities responded by "building" a large liquidity buffer and through otler ambitious reforms in order to consolidate a highly resilient financial system. Results were impressive. By 1998 Argentina ranked second (after Singapore, tied with Hong Kong, and ahead of Chile) in terms of the quality of its regulatory environment, according to the CAMELOT rating system developed by the World Bank<sup>42</sup> (Table 5.1).

**Table 5.1. Camelot Ratings for Banking System Regulation**

|**Country**|**TotalScore***|
|---|---|
|Singapore|16|
|Argentina|21|
|Hong Kong|21|
|Chile|25|
|Brazil|30|
|Peru|35|
|Malasya|41|
|<br>Colombia|44|
|Korea|45|
|Philippines|47|
|<br>Thailand|52|
|Indonesia|52|

### **Note:**

*Lower numbers indicate better ranking.

**Source: World** Bank. Argentina Financial Sector Review (1998).

**40** This section, and part of the next, is based on a technical note prepared for the first version of this paper by Augusto de la Torre and Sergio Schmukler, which later on evolved into a full-fledged paper (De la Toffe, Levy Yeyati and Schmukler 2003) in which the summary analysis presented here is developed in much more detail.

**41** The so-called Bonex plan instituted in 1989.

**42** The CAMELOT index combined separate rankings for capital requirements (C); loan loss provisioning requirements and definition of past-due loans (A); management (M), defined by the extent of high-quality foreign bank presence; liquidity requirements (L); operating environment (0) as measured by rankings with respect to property rights, creditor rights, and enforcement; and transparency (T), as measured by whether banks are rated by international risk rating agencies and by an index on corruption. Argentina ranked I for C (tied with Singapore), 4 for A, 3 for M, 4 for L, 7 for 0, and 2 for T. For further discussion see World Bank (1998).

42
# **Appendix B: Balance Tables**

**Table B1** Impacts of _Supply_ Treatment on Contraceptive Uptake

|||(1)|(2)|(3)|
|---|---|---|---|---|
|**Outcome**|**Time**<br>**C**|**ELA Only**<br>**ontrol Mean**|**Supply**|**DD**|
|**Panel A. Wh**|**ole Sample**||||
|Injectable|Baseline|0.021|0.026||
||Endline|0.024|0.021|-0.008|
|||||(0.008)|
|Implant|Baseline|0.007|0.011||
||Endline|0.008|0.022|0.010|
|||||(0.007)|
|IUD|Baseline|0.007|0.003||
||Endline|0.002|0.003|0.005|
|||||(0.004)|
|Observations|Baseline|1074|1012||
||Endline|1766|1746|5598|
|**Panel B. Bal**|**anced Panel **|**Sample**|||
|Injectable|Baseline|0.020|0.026||
||Endline|0.028|0.020|-0.014|
|||||(0.010)|
|Implant|Baseline|0.008|0.010||
||Endline|0.011|0.023|0.009|
|||||(0.009)|
|IUD|Baseline|0.007|0.004||
||Endline|0.000|0.005|0.008|
|||||(0.006)|
|Observations|Baseline|871|795||
||Endline|871|795|3332|

_Notes._ Column 1 shows control means for females in ELA only communites and column 2 shows means for females assigned to the _Supply_ treatment. Column 3 shows the simple difference-indifference estimate between the _Supply_ treatment and the ELA Only communities over time. Outcomes are indicators that the female used an injectable, implant, or intrautine device (IUD as contraception with her last partner. These are the primary methods that were promoted by the implementation partners in the _Supply_ treatment. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in column 3. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

69
- 46 -

with clear links to the central bank (Banco<sup>de la Republica).</sup> 39 It offers 33 distinct lines of credit for: pre- and<sup>post-shipment financing; long-</sup> term investment, working capital, promotion of exports, and technical assistance for quality control. A relatively minor<sup>share of PROEXPO's</sup> portfolio is in equity investments (about<sup>20%) and for the past two years</sup> policy has been to discourage such investments.<sup>PROEXPO does not raise</sup> money in capital markets to any extent but<sup>instead relies on rollover of</sup> existing loans (60-70%), interest earnings (7-10%)<sup>and earmarked taxes</sup> (15-20%) to provide funding for its loans (see<sup>Table All in statistical</sup> appendix). The earmarked tax is its share of<sup>a general 18% tax on c.i.f.</sup> imports which is divided in the following<sup>way: National Treasury-10.4%;</sup> PROEXPO - 6%; IFI - 0.8%; and Caja Agraria - 0.8%.

48. Overtime, PROEXPO has reduced the extent of the subsidy<sup>in its</sup> credit lines, but the subsidies still remain<sup>sizable. Most of its credit</sup> carried a 19% rate 1980-83 and 22% 1984-86,<sup>compared to commercial bank</sup> rates which averaged 44%.40 During the past<sup>two years, PROEXPO has sought</sup> to increase its financing of fixed investment<sup>by offering rates of 18% for</sup> terms up to 8 years while raising the rates on<sup>pre and post-shipment</sup> finance to about 35%. During the 1980s, PROEXPO<sup>credit has averaged the</sup> equivalent of 5.6% of domestic credit outstanding<sup>and the loans made each</sup>

- 39/ The General Manager of the Banco<sup>de la Republica is the Director of</sup> PROEXPO, which has two executive directors in charge<sup>of Export</sup> Promotion and Financial Operations. PROEXPO's employees<sup>are central</sup> bank officials but the fund has a separate salary structure.<sup>PROEXPO's</sup> Board of Directors consists of: Minister of Economic<sup>Development</sup> (President); Minister of Foreign Relations; Director of INCOMEX; Manager of Banco de la Republica; and three private<sup>sector</sup> representatives appointed by the President of Colombia. -

- 40/ See I.B.R.D. Colombia Trade Policy<sup>Survey (1988), pp. 42-43.</sup>
# **References**

- Basist, A., Williams Jr., C., Grody, N., Ross, T.E., Shen, S., Chang, A., Ferraro, R., Menne, M. J.: 2001. “Using the Special Sensor Microwave Imager to monitor surface wetness”. _J. of Hydrometeorology_ **2:** 297-308.

- Bardhan, P. and C. Udry, 1999. _Development Economics._ Oxford University Press, Oxford.

- Boer, G., G. Flato, and D. Ramsden (2000), “A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate for the 21<sup>st</sup> century”, _Climate Dynamics_ **16** , 427-450.

- Burton, I. 1997. “Vulnerability and adaptive response in the context of climate and climate change”, _Climatic Change 36_ : 185-196.

- Delgado, Christopher. Livestock to 2020: The Next Food Revolution. International Food Policy Research Institute. 1999. pp 79.

- Evenson, R., and D. Gollin. 2003. Assessing the impact of the Green Revolution, 19602000. Science 300 **:** 758-762 **.**

- De Janvry, A., M. Fafchamps, and E. Sadoulet, “ Peasant Household Behavior with Missi-Ng markets: Some Paradoxes Explained.” _Economic Journal_ 101, No.409 (1991), 1400-1417.

- Dinar, A., R. Hassan, R. Mendelsohn, and J. Benhin (eds), 2008. Climate Change and Agriculture in Africa: Impact Assessment and Adaptation Strategies, London: EarthScan (forthcoming).

Fafchamps, M., Udry, C., and Czukas, K., ‘‘Drought and Saving in West Africa: Are Livestock a Buffer Stock?,’’ _Journal of Development Economics_ , 55: 273-305, 1998.

- FAO. 1978. “Report on Agro-Ecological Zones; Volume 1: Methodology and Results for Africa” Rome.

- Fischer, G. and H. van Velthuizen 1996. “Climate Change and Global Agricultural Potential: A Case of Kenya” IIASA Working Paper WP-96-071.

- Ford, J. and K. Katondo, _The Distribution of Tsetse flies in Africa_ (Organization of African Unity: Nairobi, Kenya, 1997).

- IPCC 2007. (Intergovernmental Panel on Climate Change) _Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report_ [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom

- Kelly, D.L., Kolstad, C. D., and G.T. Mitchell, 2005. Adjustment costs from environmental change. _Journal of Environmental Economics and Management_ 50: 468-495.

Kurukulasuriya, P., R. Mendelsohn, R. Hassan, J. Benhin, M. Diop, H. M. Eid, K.Y. Fosu, 18
Figure 6: Distribution of poor population across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of poor population (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Each boxplot shows the distribution of the share of the poor population over different geographic areas in 16 SSA countries.

Figure 7: Comparison of urban shares of poor populations between official and DOU/DB urban definitions in 16 SSA countries <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>100 100 100<br>80 80 80<br>60 60 60<br>TCDETH TZA GAB TCD ETHGABTZA<br>UGA NERUGA NER UGA<br>40 TZA AGOGAB 40 MWI LSO AGO 40 LSOMWI AGO<br>ETH CIV BFA SENMRC TI V BFAMRTSENCIV<br>20 BFANERGINMWITCDGNBSENLSOMRTGHA 20 GINGNBGHA 20 GINGNBGHA<br>20 40 60 80 100 20 40 60 80 100 20 40 60 80 100<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

20
Figure 9: Marginal impact of the expected public debt-to-GDP ratio on the interest rate (Baseline and robustness check) <!-- Start of picture text --> IT PSTR GPSTR<br>10 10 10<br>Baseline Baseline Baseline<br>Robustness1 Robustness1 Robustness1<br>8 Robustness2 8 Robustness2 8 Robustness2<br>Robustness3 Robustness3 Robustness3<br>6 Robustness4 6 Robustness4 6 Robustness4<br>Robustness5 Robustness5 Robustness5<br>Robustness6 Robustness6 Robustness6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50<br>Share of the foreign private investors(%) Share of the foreign private investors(%) Share of the foreign private investors(%)<br>basis points basis points basis points<br><!-- End of picture text --> Notes: Basis points. The baseline and robustness1-5 employ real forward interest rates as a dependent variable. The baseline equation includes the two-year-ahead expected public debt to GDP ratio, the two-year-ahead expected growth rate, the lagged domestic official sector holdings ratio, the lagged foreign official sector holdings ratio, the lagged foreign private sector holdings ratio and the expected exchange rate depreciation in two-year horizon. Robustness1: Controlling for US monetary condition. Robustness2: Excluding the expected exchange rate depreciation. Robustness3: Controlling for the net foreign assets position. Robustness4: Controlling for the capital control. Robustness5: Controlling for the short-term interest rate. Robustness6: Dependent variable: nominal interest rates.
||€15,000 (nonresident consumers)||
|---|---|---|
|Greece|€1,500|January 1, 2011|
|Hungary|Ft 1.5 million (≈ €5,000) (legal persons)|January 1, 2013|
|Italy|€999.99|December 6, 2012|
|Portugal|€1,000|May 14, 2012|
|Slovak<br>Republic|€5,000<br>€15,000 (natural persons who are not entrepreneurs)|January 1, 2013|
|Spain|€2,500 (residents)<br>€15,000 (nonresidents)|November 19,<br>2012|

_Source:_ Beretta 2014.

Such limits force people to make electronic payments, leaving a trail tax authorities can trace. However, for tax authorities to access information on financial transactions, relevant legislation enabling such access must be prepared, which has not happened in all EU member countries (Jensen and Wőhlbier 2012). In addition, the European Central Bank (ECB) warns that although such limitations on cash payments are compliant with EU law, they should be carefully weighed to achieve the desired balance between the policy’s objectives and its possible negative effects on public benefits (European Central Bank 2014a, 2014b).<sup>2</sup> Although cash payment limitations are intended to have a direct impact on cash transactions, a core aspect of the shadow economy, they might overly restrict individuals’ freedom of economic activity. Another question regarding this approach is how cash limits can be enforced. As noted earlier, cash transactions mostly occur in collusive situations. If buyers and sellers agree to pay and receive cash without leaving a financial footprint, the relevant authorities’ ability to detect those transactions and enforce cash limits remains questionable.

# Incentive Policy Measures on Undeclared Work

Measures to induce voluntary compliance by providing monetary or nonmonetary incentives have been introduced in several EU countries. One prominent incentive scheme addresses household services, which tend to remain undeclared work in the European Union. In recent

> 2. “In any case, limitations on cash payments should be proportionate to the objectives pursued and should not go beyond what is necessary to achieve such objectives. . . . Any negative impact of the proposed limitations should be carefully weighed against the public benefits expected to be derived from them” (European Central Bank 2014a, 2014b).

10
# developing countries are due to perinatal conditions. Since these are the largest causes of death,

# a substantial reduction in overall mortality must involve a fall in mortality from these causes.

Table 6: Percent of deaths by cause for population subgroups and cost per death averted

||Perce<br>(Es|nt of deaths by c<br>timates for 198|ause<br>5)|Estimates of cost per death<br>averted of treatment|
|---|---|---|---|---|
||Indust. mkt.<br>economies|Developin|g countries|Developing countries|
||All ages|All Ages|Under-5||
|Infectious and parasitic|4.6|44.9|27.7||
|Diarrheal Diseases|<br>0.0|13.2|10.6|$ 1000 - $ 10,000'|
|Tuberculosis|<br>0.2|7.9|0.8|$ 20-$ 76b|
|ARI|3.6|16.6|11.3|$379-$ 1,610'|
|Non-ARI measlesand<br>whooping cough|0.0<br>_<br>l_|1.8<br>l|1.8|$ 10- $ 561l|
|Malaria|0.0|2.6|2.0|$ 78 - $ 9904|
|Other|0.0|2.6|1.2||
|Compl. of pregnancy|0.0|1.3|0.0|$836-$3,967'|
|Perinatal conditions|0.6|8.4|8.4||
|Non-communicable|78.6|29.8|0.0||
|External (incl. injuries)|17.0|15.6|2.4||
|Total|100|100|38.5||

Source: Causes of death calculated from Lopez (1993), sources for costs are Diarrheal-Martines and others (1993), TB-Murray and others (1993), ARI-Stansfield and Shepard (1993), Measles-Foster and others (1993), Malaria-Najera and others (1993). Notes: a) Current US dollars, from model estimates, b) 1989 US dollars, from 3 Sub-Saharan African countries (Malawi, Mozarnbique, and Tanzania), c) Current US dollars, from model estimates, measles only, d) Malaria control, 1987 US dollars, from 2 studies, I in Sri Lanka ($78) using insecticides, one for Developing countries ($990) using vector control, e) Current US dollars, from model estimates, matemal and no-natal deaths are not distinguished in this study.

27
of earnings in the adjustment process may not be precise. It is areassuring, however, that data on earnings from seven middle income countries also tend to show moderate to large earnings adjustments (Cho and Newhouse, 2011).

_The analysis compares growth rates during the crisis year with averages over the prior two years._ Focusing on the change in the growth rate, rather than post-crisis declines, is important to accurately measure the effect of the crisis relative to pre-crisis trends.<sup>8</sup> Extending back two years allows a comparison with changes immediately preceding the crisis, while mitigating the influence of the rapid price increases in food and fuel in 2008. GDP growth in crisis-affected countries fell sharply in the fourth quarter of 2008. Therefore, the post-crisis period includes the four quarters from Q4 2008 to Q3 2009, while the pre-crisis period includes the eight quarters from Q1 2006 to Q3 2008.<sup>9</sup>

# **The Extent and Nature of the Labor Market Adjustment**

_The_ **cost of the crisis to** _workers was considerable but delayed and gradual._ As shown in Figure 1 the average wage bill, which had been growing by nearly 10 percent over the pre-crisis years of 2007 and 2008, came to a near standstill in 2009, with the growth dropping to less that 1 percent.<sup>10</sup> The drop was only slightly less than the fall in GDP growth over this period (9.4 percentage points) but the decline in wage bill growth began later than that of GDP and continued longer. This is in line with findings from previous crisis that suggest long term impacts on workers may be deep and protracted.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>GDP growth Employment growth<br>Real earnings growth Wagebill growth<br>Percent<br><!-- End of picture text -->

**Figure 1: The employment adjustment was delayed and gradual**

> 8 . A simple example makes this clear. China was far more affected by the financial crisis in 2009 than Morocco. But a simple comparison of post-crisis rates of growth would have given the false impression that Morocco suffered more. This is because Morocco’s GDP grew only half as fast China’s, even after the crisis, and employment growth in the two countries was nearly the same. However, in the pre-crisis period the Chinese economy had been growing more rapidly, so GDP growth slowed by 6 percentage points more in China than it did in Morocco. Employment growth also slowed by 3.5 percentage points more.

> 9 Using eight quarters of pre-crisis data rather than four may mitigate the impact of the food and fuel crisis of 2007. 10 The sample used to construct Figure 1 consists of the 24 countries that reported GDP growth, employment growth and real earnings growth in each quarter

4
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **6. Wages, Growth and Employment**

**In 1996-2000, nominal value added grew only slightly faster than the wage bill or work force** . Table 2 reports statistics<sup>18</sup> on employment, the nominal wage bill, nominal value added (total and per worker) and a rough estimate on unit labor cost for major sectors, with compound average annual growth rates to measure trends, for 1996-2000. Nominal value added grew very slightly faster than either the wage bill or work force. However, value added per worker grew more slowly than inflation, signaling continued declines in real output per worker. The rough estimate of unit labor costs is approximately flat for the period<sup>19</sup> . According to these figures, the traditional pattern is continuing: most of the macro-economy’s growth is fueled by labor force growth.

**Table 2: Employment, Wages, Value Added, Value Added Per Worker, and Rough Estimate of Unit Labor Cost. All value data in current CFA.  Compound Average Annual Growth Rates**

|EMPLOYMENT|1996|1997|1998|1|999<br>2000|CAAGR|
|---|---|---|---|---|---|---|
|Agriculture<br>Industry|2,444,297<br>601,870|2,459,78<br>637,31|1 2,627<br>3 675|,078<br>,635|2,714,788 2,824,679<br>717,154 770,710|3.68%<br>6.38%|
|Services|2,178,418|2,254,52|5 2,407|,793|2,496,090 2,673,677|5.25%|
|C ote d'Ivoire|5,224,585|5,351,61|9 5,710|,506|5,928,0326,269,066|4.66%|
|WAGE BILL<br>Agriculture|1996<br>103,857|1997<br>109,996|1998<br>124,085|1999<br>152,2|2000<br>CAAGR<br>44<br>187,750<br>15.95%|<br>|
|Industry<br>Services<br>C ote d’Ivoire|305,093<br>962,337<br>1,371,287|341,462<br>1,060,918<br>1,512,376|378,548<br>995,422<br>1,498,055|449,7<br>961,0<br> 1,563|92<br>434,369<br>9.23%<br>63<br>1,080,615 2.94%<br>,099<br>1,702,7345.56%||
|VALUE ADDED<br>Agriculture|<br>1996<br>1,527.8|1997<br>1,595.5|1998<br>1,798.9|1999<br>1,768.|2000<br>CAAGR<br>1<br>1,906.7<br>5.70%||
|Industry|1,268.0|1,618.1|1,649.3|1,742.|3<br>1,640.8<br>6.66%||
|Services|2,734.5|2,952.9|3,278.3|3,525.|9<br>3,474.1<br>6.17%||
|C ote d'Ivoire|5,530.3|6,166.5|6,726.5|7,036.|3<br>7,021.7<br>6.15%||
|Value Added/Wo|rker(000) 1996|<br>1997|1998|1999|2000<br>CAAGR||
|Agriculture|62|5 649|685|651|675 1.94%||
|Industry|2,10|7 2,539|2,441|2,430|2,129 0.26%||
|Services|1,25|5 1,310|1,362|1,413|1,299 0.87%||
|C ote d'Ivoire|1,05|91,152|1,178|1,187|1,1201.42%||
|Unit Labor Cost<br>Agriculture|1996<br>0.067979|1997<br>0.0689422|1998<br> 0.068979|1999<br>0.086|2000<br>CAAGR<br>1045 0.098467 9.71%||
|Industry|0.240619|0.2110264|0.229518|0.258|1547 0.264722 2.42%||
|Services|0.351922|0.3592833|0.303637|0.272|5745 0.311051 -3.04%||
|C ote d'Ivoire|0.247961|0.2452585|0.222708|0.222|14670.242497 -0.56%||

18 This data was supplied by Richard Doffonsou of the World Bank field office in Abidjan. 19 We are warned that data consistency problems make this estimate problematic.

15
**Table 2: Land access and production structure** by **group in** 2000

||TotalSample|Established<br>Producers|Landscarce<br>households|Titling prog.<br>Participants|Landbuyers|
|---|---|---|---|---|---|
|**Landaccess,productionstructure,an**|**dproductivity**|||||
|Operatedarea (Mzs.)|47.98|58.14|3.17|27.03|92.33|
|Ownedarea(Mzs.)|46.65|57.07|2.20|26.90|87.32|
|ofwhich cultivated(Mzs.)|13.45|16.26|1.43|15.10|13.02|
|ofwhich pasture(Mzs.)|27.24|33.95|0.52|7.16|62.97|
|ofwhichotheruses(Mzs.)|5.97|6.86|0.26|4.64|11.34|
|Producersrentingin|19.1%|14.4%|39.2%|14.8%|22.3%|
|Producersrentingout|7.9%|9.9%|0.8%|7.6%|8.5%|
|Profitpermz.(median)C$|310.49|357.11|522.75|190.50|282.47|
|**Titling**status**andtypeofdocument**||||||
|Nodocument|3.4%|2.4%|5.4%|4.4%|5.5%|
|Informaldocument|11.1%|_10.5%_|10.8%|7.9%|20.0%|
|Registered document|63.0%|78.3%|70.3%|16.2%|64.3%|
|Agrarianreform title|33.7%|17.8%|14.9%|87.7%|26.7%|
|Documentindividual|95.0%|98.3%|96.2%|83.6%|98.5%|
|Documentmancomunado|6.0%|1.8%|3.8%|18.6%|5.1%|
|Titlejoint|4.9%|1.7%|2.7%|15.1%|3.9%|

Source: Own computation from 2000 World Bank/University of Wisconsin survey. Notes: The types of document may sum up to more than 100% due to ownership of multiple plots within the same household. IUS$=12.3 C$ in 2000 I mz. = 0.7 ha 24