rafmacalaba/datause-encoder-data huggingface dataset:
(2009), and Forbes and Warnock (2012), we define a surge episode as an increase in inflows in a given year that is more than one standard deviation above the countryspecific (five-year rolling) average. The surge episode begins when the FDI-to-GDP ratio increases more than one standard deviation above its rolling mean and ends when the FDI-to-GDP ratio falls below one standard deviation above its rolling mean. In addition, we pose a restriction to the definition of an FDI surge in that the increase in the FDI-toGDP ratio should fall within the top 25<sup>th</sup> percentile of the entire sample’s FDI-to-GDP ratio growth. This not only ensures that the increase in FDI inflows is substantial, but also that only large surges by international standards are included in our definition of a surge (Ghosh et al., 2012).

This approach combines the two main empirical strategies present in the literature on surges and stops. One involves looking at deviations from the mean while the other requires factoring in minimum threshold values. Stops are defined in a symmetric way, with a stop episode defined as a decline in inflows in a given year that is more than one standard deviation below the rolling average. The stop episode starts when the ratio of FDI to GDP declines more than one standard deviation below its rolling mean and ends when the ratio increases above one standard deviation below its mean. We impose similar restrictions on stops as on surges.

To identify whether a FDI surge or a stop can be _mainly_ attributed to an increase in M&A activity or GF investments, we use Thompson ONE Source data on M&A inflows, available from 1990 onwards. Following Calderon et al. (2004), Wang and Wong (2009), and Bogach and Noy (2013), GF FDI is defined as the difference between gross FDI and M&A inflows. Using this information, we assess whether a surge in a given year is dominated by an increase in M&A activity or by an increase in GF investments. A surge is M&A-led when more than half of the increase in FDI can be attributed to an increase 7
Table 30

|Dependent variable: Fisca|l adjustme|nt when ne|eded based|on Escolan|o et al.(2|014)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.006|0.006|-0.004|-0.009|-0.002|0.069**|-0.063*|-0.001|
|structural as % GDP|(0.006)|(0.006)|(0.010)|(0.010)|(0.014)|(0.030)|(0.034)|(0.055)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.630||-1.331|-0.747|-1.437|-1.456|-1.335|
|||(1.036)||(1.348)|(1.248)|(1.256)|(1.334)|(1.345)|
|Inflation (t-1)||0.155||1.345|1.755|1.320|1.661|1.380|
|||(0.752)||(1.057)|(1.103)|(1.151)|(1.156)|(1.054)|
|U.S. Interest rate||-0.035||-0.051|-0.062*|-0.068**|-0.070*|-0.051|
|||(0.030)||(0.038)|(0.036)|(0.033)|(0.039)|(0.037)|
|**Political factors**|||||||||
|Gini|||0.005|0.006|0.007**|0.027***|0.005|0.006|
||||(0.004)|(0.004)|(0.003)|(0.007)|(0.004)|(0.004)|
|Rule of law|||0.096**|0.145***|0.094*|0.149***|0.022|0.147***|
||||(0.048)|(0.039)|(0.055)|(0.037)|(0.106)|(0.040)|
|Margin of Majority|||-0.067|-0.023|0.116|0.045|-0.054|0.122|
||||(0.225)|(0.327)|(0.359)|(0.321)|(0.318)|(0.847)|
|Election's year|||-0.099*|-0.112**|-0.095|-0.094*|-0.101*|-0.112**|
||||(0.054)|(0.054)|(0.059)|(0.052)|(0.055)|(0.054)|
|**Interactions**|||||||||
|EMEs|||||0.090<br>(0.275)||||
|EMEs * Deviation|||||-0.036<br>(0.023)||||
|Gini * Deviation||||||-0.002***<br>(0.001)|||
|Rule of law * Deviation|||||||0.012<br>(0.008)||
|Majority * Deviation||||||||-0.015<br>(0.096)|
|Observations|182|180|164|162|162|162|162|162|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p|0.00706|0.0236|0.0630|0.115|0.169|0.155|0.138|0.116|
|Robust standard errors cl<br>*** p<0.01, ** p<0.05, *|ustered by<br>p<0.1|country in|parenthese|s|||||

- 59 -
**Table S4.11.** ITT effects on number of adopted ISFM technologies, using an alternative measure.

|||Number of|ISFM tech|nologies ado|pted(0-4)||
|---|---|---|---|---|---|---|
||O|LS|Pois|son|Opr|obit|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T1 (extension)|0.541***|0.326***|0.543***|0.341***|0.446***|0.397***|
||(0.169)|-0.078|(0.165)|-0.087|(0.139)|-0.093|
|p-value|0.002|0.000|0.001|0.000|0.001|0.000|
|T2 (extension + video)|0.662***|0.418***|0.650***|0.406***|0.558***|0.527***|
||(0.157)|-0.079|(0.149)|-0.089|(0.135)|-0.094|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.241|0.141|0.241|0.234|0.231|0.109|
|Endline control mean|||2.1|99|||
|Additional controls|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.051|0.505|0.010|0.107|0.018|0.225|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson models show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 4 (excluding lime). Additional control variables identical to those listed in notes of Table 2. Onesided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

**Table S4.12.** ITT effects on integrated adoption of the full ISFM package, using alternative measures.

|||Integrat<br>|ed adoption<br>|of full ISFM<br>|package<br>||
|---|---|---|---|---|---|---|
||At least<br>|out of 5<br>|3 out<br>|of 3<br>|Region-<br>|specific<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T1 (extension)|0.152***|0.123***|0.113**|0.095***|0.062***|0.062***|
||(0.044)|(0.027)|(0.048)|(0.028)|(0.017)|(0.014)|
|p-value|0.001|0.000|0.017|0.001|0.000|0.000|
|T2 (extension + video)|0.188***|0.148***|0.156***|0.124***|0.075***|0.064***|
||(0.042)|(0.024)|(0.047)|(0.025)|(0.021)|(0.014)|
|p-value|0.000|0.000|0.001|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.233|0.189|0.215|0.176|0.241|0.457|
|Endline control mean|0.1|57|0.1|85|0.0|33|
|Additional controls|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.039|0.274|0.025|0.269|0.042|0.238|
|Observations|2,160|2,160|2,160|2,160|2,160|2,095|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of probit models. In columns (1) to (2), integrated adoption of full ISFM package is a dummy variable defined as adopting at least four out of five practices together on the same plot (including lime). In columns (3) to (4), full package is defined as adopting all three practices (compost, blended fertilizer, line seeding). In columns (5) to (6), full package is defined as adopting all five practices in Amhara and Oromia (including lime), but only four in Tigray, since lime is not relevant there. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

19
This is crudely shown in Table 1 which looks at recent trends in exports from key groups of AGOA beneficiaries to the United States and compares these recent changes with the increase in total exports. The choice of base year is fairly arbitrary although by going back to 1999 it does capture any changes immediately prior to the implementation of AGOA in anticipation of that policy change. A more detailed table with information for each country is provided in the Table in the Appendix. These tables show that, apart from the oil exporting countries, the United States is not currently a major destination for AGOA countries exports. For none of the non-oil exporting country groups does the share of the United States in total exports exceed 15 percent. The U.S. share of total exports exceeds 20 percent only for Madagascar and Swaziland together with the clear outlier, Lesotho, which sends 90 percent of total exports to the United States.

**Table 1: The Importance of the U.S. as a Destination for AGOA Countries Exports** **<u>and Trends in Exports over 1999-2002.</u>**

||Share of U.S. in Total<br>Exports:2002|Growth of Total<br>Exports:1999-2002|Growth of Exports<br>to U.S.:1999-2002|
|---|---|---|---|
|**AGOA Countries**||||
|_LDCs_||||
|Withoutapparelbenefits|6.4%|2.6%|-30.2%|
|Withapparelbenefits|13.7%|19.5%|80.1%|
|_Non-LDCs Oil Exporters_|33.1%|25.1%|30.9%|
|_Non-LDCs Non-Oil_||||
|Withoutapparelbenefits|8.2%|15.4%|-16.8%|
|Withapparelbenefits||||
|Liberal rules oforigin|6.6%|21.5%|38.0%|
|Restrictiverules oforigin|13.0%|11.1%|30.9%|
|**Other US GSP LDCs**<br>|33.5%|43.2%|41.4%|
|**Other UN LDCs**<sup>**6**</sup>|8.0%|86.6%|57.0%|

Source: WITS Within the group of LDCs, exports to the United States from countries that have not been eligible for AGOA benefits on clothing products have fallen by 30% over the past four years while total exports of these countries have increased slightly. On the other hand, exports to the United States from LDCs with clothing benefits have increased by over 80 percent, much faster than their exports to the world. Similarly, amongst the non-LDCs, exports to the United States from those countries without clothing benefits has fallen,

> 6 A number of UN classified LDCs are not eligible for US GSP preferences. These include Liberia and Sudan.

6
|**Interfaces and**|IFMIS Integration|
|---|---|
|**Data Exports**|World Bank/IMF MTDS<br>WB Debtor Reporting System (DRS)<br>IMF Quarterly External Debt Statistics (QEDS – GDDS, SDDS)<br>WB Quarterly Public Sector Debt Statistics (QPDS)|
|**Other Key**|Debt Re-organization (Restructuring, Refinancing, Write-Off, Debt Conversations)|
|**Functions**|Embedded Options (Interest Rate Cap, Interest Rate Collar, Currency Conversions,<br>Prepayment)|
||Non-Business Days|

# **<mark>Supporting Systems</mark>**

|**CS-SAS (front-**<br>**office**<br>**solution)**|CS-SAS supports the auctioning of government securities. Successful bids can be directly<br>uploaded to the Securities module in CS-DRMS.|
|---|---|
|**Horizon**|Decision support system enabling debt analysts to compare different borrowing strategies,|
|**(middle office**<br>**solution)**|implement the selected strategy through the development of an issuance calendar and<br>analyze the impact of liability management options on the debt portfolio. System can<br>analyze impact of various restructuring options (pre and post restructuring)|

# **Commonwealth Meridian**

Given the changes in public debt management coupled with significant advancement in technology, the Commonwealth Secretariat’s Debt Management Unit (DMU) is in the process of developing a new public debt management system, **‘Commonwealth Meridian’** , which incorporates advanced and improved functionalities to better address emerging debt management requirements while also taking advantage of the latest state-of-the-art technologies. Commonwealth Meridian will replace CS-DRMS and is scheduled for formal release to the general public in the first quarter of 2019. More information on Commonwealth Meridian can be found at (http://www.thecommonwealth.org/about-meridian).

37
11

# Tablek **I M Indis** (average per **perio)**

|||Argentin|a||Bolivia|||Cbile|||Mexico|||UmgU|uay|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Years|1980/<br>83|19W<br>87|1988<br>90|19801<br>83|<br>19W<br>87|1988<br>90|1980/<br>83|19841<br>87|<br>19881<br>90|<br>1980/<br>83|19W<br>87|1988/<br>90|19801<br>83|<br>1984t<br>87|1988<br>90|
|I Growth rateof GNP/<br>capita|- 4.0|- 0.02|3.8|* 5.8|-4.1|-0.S|-4.0|4.1|4.3|-0.1|-1.6|2.2|- 3.9|3.6|0.2|
|nGross domestic<br>investment/GDP<br>(%)|17.8|12.4|9.7|12.8|9.1|12.7|16.2|14.7|18.9|24.6|19.8|17.4|14.3|9.0|9.S|
|m Government deficite<br>(-) orrplus/GDP<br>_(5)_|- 5.9|-9.3|-8.9|-21.1|-10.3|- 1.12|2.9|-0.3|- 4.3|-11.6|-10.6|- 7.3|- 3.6|- 2.2|- 1.54|
|IV Unemployment<br>rate|4.0|4.7|6.9|10.1|18.4|19.0|14.4|12.1|6.9|5.1t|4.7|3.0|10.3|11.9|9**1b**|
|V Inflation (rate of<br>change in CPI)|178.3|380.1|1932.1|<br>119.6|3274.0|16.1|21.1|20.1|20.5|54.0|85.2|53.6|41.4|66.9|85.0|
|VI Total external debh/<br>GNP (%)<br>(Total debt service/<br>exports of goods and<br>services<br>(%)]|68.3<br>[50.71|74.7<br>(65.51|82.4<br> 138.31|113.8<br>(45.3)|160.4<br>[45.71|105.3<br>(40.01|67.7<br> (58.41|130.8<br> (47.41|82.5<br>(25.51|45.5<br>[52.41|69.3<br> [49.51|49.9<br>(37.9]|33.4<br>(23.3]|72.1<br> <br>137.1]|50.0<br> <br>[36.4]|

b. 1988.

c. 1981/83.

d. For Argentina and Mexico, figures are for the public sector borrowing requirement (PSBR).

Source: World Tables, World Development Report, Internatonal Fdancial Statistics, World Debt Tables, Yearbook of Labor Statitics.
Policy Research Working Paper 5905

# **Abstract**

This paper provides a methodological tool to support the collection and preparation of standardized, comprehensive data regarding public spending on infrastructure services that can be rigorously compared across countries. Infrastructure is defined to cover six sectors: irrigation, energy (primarily power), transport, communication, wastewater management, and water supply.

The guide is designed to provide a much richer and more complete measurement of infrastructure spending than the limited highly aggregated data currently available through the IMF Government Financial Statistics. Originally developed for Africa, the methodology is relevant and readily applicable to any developing country.

With the aim of being as comprehensive as possible, the methodology covers central and sub-national government expenditures, non-budgetary vehicles (such as road funds), state-owned enterprises (SOEs), and publicprivate partnerships (PPPs). While the methodology focuses on collecting quantitative data on the level and composition of spending, this is complemented with qualitative data that provides the institutional context. Importantly, the methodology allows for crossclassification of infrastructure spending by purpose (power, roads, etc) and by function (operational versus capital spending).

This guide provides practical guidance—including concepts, definitions, and classifications—for each of the three stages of work, namely: (i) pre-field, (ii) field, and (iii) back office.

This paper is a product of the Sustainable Development Unit, the Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbricenogarmendi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. 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 this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
whose Brazilian imports accounted for more than 50 percent of production. Once this "exclusion provision" was applied, the remaining firms opposing the investigation accounted for only 38.64 percent of U.S. production and, accordingly, did not represent a majority of the industry.

In 1988, the question of majority support was brought up again by Citrosuco in a petition to the U.S. Court of International Trade." The argument was again rebuffed, with the court stating that the Commerce Department had the discretion to reject a petition for lack of industry support but was not "required to dismiss petitions that are not proven to be affirmatively supported by the domestic industry."26 Finger and Murray (1990) have pointed out that unfair trade procedures in the United States, once initiated, have a high probability of culminating in an affirmative determination. The broad interpretation of injury is a major factor behind this bias in favor of the petitioner. In the case of the frozen concentrated orange juice investigation, the final dumping margin was quite small and the penetration ratio for Brazilian frozen concentrate in the U.S. market had changed little in 1985/86 from the previous season.<sup>9</sup> Yet, the ITC reached a positive injury determination by a vote of 3 to 2. **LA** short, the history of this antidumping investigation highlights the flexibility of U.S. legislation in accommodating the protectionist interests of domestic producers under competitive pressure from foreign imports.

The Australian antidumping case. On July 25, 1986, the Australian Citrus Processors Association submitted a complaint on behalf of eight processors to the Australian Customs Service, claiming that Brazilian frozen concentrated -- orange juice was being exported to Australia at prices below "normal value" that is, at prices that did not recover the full cost to produce and sell the frozen concentrate. The gist of the complaint was that international prices had fallen from $1,800 to $800 per metric ton (65 degrees Brix) between February 1985 and March 1986, while the normal value for Brazilian concentrate was about $1,401 a ton (fob Santos). The normal-value estimate was based on cost of production data published in a U.S. periodical _(CItrograph,_ June

> - 24 -
percentile of its historical empirical distribution. This is a definition commonly used in the literature, such as Perotti(1999), and it can be thought of as a Reinhart and Rogoff(2008) type debt trigger. Again, when the economy is in high-debt regime, an additional expansionary effect is present. The additional effect of tax consolidations is driven entirely by changes in tax rate: the second column shows that a 1 percentage point increase in taxation by raising tax rates is associated with a 63 basis point decrease in output at all times, and a 57 basis point increase in output during “bad times”. Both of these effects are statistically significant. Although the net effect of a tax-hike by raising tax rate during bad times is not expansionary under this alternative definition of high-debt regime, it is hardly contractionary in terms of output losses.

Table 3: Expansionary Austerity: Narrative shocks, alternative regime

||GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|
|---|---|---|---|
|Tax Shock|-0.7321***<br>(0.1972)|||
|Tax Shock*Dummy|0.4207*<br>(0.2740)|||
|Tax Rate Shock||-0.6310***<br>(0.2240)||
|Tax Rate Shock*Dummy||0.5742**<br>(0.3230)||
|Tax Base Shock|||-0.6051**<br>(0.3176)|
|Tax Base Shock*Dummy|||0.2930<br>(0.3806)|
|Sample Size|1215|1215|1215|

* ** *** _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a constant and a full set of country and year dummies. Standard errors are cluster-robust.

In sum, using quarterly narrative data on fiscal consolidations, I find that austerity programs which increase tax rates during bad times do not necessarily cause output losses, and can be potentially expansionary. This finding is robust to how one defines the critical debt level beyond which an anti-Keynesian effect is expected to kick in.

15
# **Overview of demographic changes in Mexico**

In 2000, 24.5 million of Mexican’s total of 97.5 million people lived in dispersed rural areas, defined as localities with less than 2,500 inhabitants (Table 2.1 and Appendix A). Expanding the definition of rural to locations with less than 15,000 inhabitants, or semi-urban, increases the rural population to 38.1 million. In Mexico, the population is slowly moving to urban areas. In 2000, 25 percent of the Mexicans lived in rural areas, down from 29 percent one decade earlier. The rural population is not distributed equally across regions. For example, in the South region, nearly 50 percent of the population lives in rural areas, a total of more than 6.8 million people (Table 2.1).

|**Table 2.1: Rural - Urban Population and Gro**<br>**by Region, Selected Years 1990-**<br>|**wth rates in**<br>**2000**<br>|**Mexico**<br>**Growth**<br>**1990-2000**|
|---|---|---|
|**Region**<sup>**3**</sup><br>**Zone**<br>**1990**<br>**1995**|**2000**|**(%)**|
|**México**<br>Total<br>81,249,64591,158,290|97,483,412|1.8|
|Urbana (%)<br>71.3<br>73.5|74.6|2.3|
|Rural (%)<br>28.7<br>26.5|25.4|0.6|
|**North**<br>Total<br>13,246,99115,242,430|16,642,676|2.3|
|Urbana (%)<br>84.7<br>86.5|87.9|2.8|
|Rural (%)<br>15.3<br>13.5|12.1|-0.1|
|**Capital**<br>Total<br>18,051,53920,196,971|21,701,925|1.8|
|Urbana (%)<br>91.4<br>91.5|91.6|1.9|
|Rural (%)<br>8.6<br>8.5|8.4|1.5|
|**Gulf**<br>Total<br>10,121,38511,388,767|12,024,666|1.7|
|Urbana (%)<br>59.9<br>62.5|63.7|2.4|
|Rural (%)<br>40.1<br>37.5|36.3|0.7|
|**Pacific**<br>Total<br>9,077,660 10,177,075|10,745,699|1.7|
|Urbana (%)<br>75.7<br>77.4|78.7|2.1|
|Rural (%)<br>24.3<br>22.6|21.3|0.3|
|**South**<br>Total<br>12,398,89213,600,852|14,424,973|1.5|
|Urbana (%)<br>48.7<br>52.0|52.9|2.4|
|Rural (%)<br>51.3<br>48.0|47.1|0.6|
|**Center-North**Total<br>10,382,37511,488,771|12,113,254|1.5|
|Urbana (%)<br>59.4<br>62.9|64.8|2.5|
|Rural (%)<br>40.6<br>37.1|35.2|0.1|
|**Center**<br>Total<br>7,970,803 9,063,424|9,830,219|2.1|
|Urbana (%)<br>64.0<br>66.5|67.7|2.8|
|Rural(%)<br>36.0<br>33.5|32.3|1.0|
|Source:INEGI.|||

> 3 The regions correspond to the following states: **North** : Baja California, Coahuila, Chihuahua, Nuevo Leon, Sonora, Tamaulipas; **Capital** : Distrito Federal, México; **Gulf** : Campeche, Quintana Roo, Tabasco, Veracruz, Yucatán; **Pacific** : BC South, Colima, Jalisco, Nayarit, Sinaloa; **South** : Chiapas, Guerrero, Michoacan, Oaxaca; Center-North: Aguascalentes, Durango, Guanajuato, Queretaro, San Luis Potosi, Zacatecas; **Centro** : Hidalgo, Morelos, Puebla, and Tlaxcala.

3
# **Table 5: Change of imports and exports relative to BAU in the year 2020 (%)**

|Commodity|||Impo|rts|||||Expo|rts|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Subsidy|||Mandate|||Subsidy|||Mandate||
||H|HW|HWS|H|HW|HWS|H|HW|HWS|H|HW|HWS|
|Agriculture|0.2|0.5|0.7|0.1|0.0|-0.4|-0.47|-1.10|-1.83|-0.26|-0.31|-0.12|
|Forestry|0.4|0.7|0.3|0.2|0.3|-0.3|-0.48|-0.71|-0.65|-0.33|-0.11|0.52|
|Other Mining|-0.3|-0.8|-1.6|-0.3|-0.7|-1.2|-0.94|-2.78|-5.72|-0.75|-1.87|-3.28|
|FoodandTobacco|0.2|0.5|0.8|0.1|0.0|-0.3|-0.31|-0.80|-1.48|-0.20|-0.34|-0.42|
|TextileandLeather|0.7|2.2|3.9|-0.1|-0.6|-1.9|1.24|3.82|6.86|-0.09|-0.92|-3.14|
|Chemical|0.0|0.1|-0.1|-0.1|-0.2|-0.5|-0.91|-2.58|-5.24|-0.46|-0.85|-1.23|
|Machinery|0.3|0.8|1.1|0.2|0.7|0.9|0.06|0.24|-0.05|0.04|0.24|0.12|
|Other manufacturing|0.3|1.1|1.9|0.2|0.8|1.2|0.06|-0.04|-0.94|0.04|-0.01|-0.55|
|Electricity|-1.2|-2.8|-4.2|0.0|1.2|3.6|2.10|5.31|8.68|-0.80|-4.52|-10.69|
|Transport|0.2|0.5|1.0|-0.1|-0.2|-0.5|-0.66|-2.11|-4.54|-0.14|-0.21|-0.25|
|Services|0.1|0.2|0.1|0.0|0.0|-0.2|-0.11|-0.18|-0.35|-0.02|0.14|0.29|
|Coal|-5.9|-16.5|-28.5|-6.9|-19.3|-32.7|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Crude oil|-1.0|-3.1|-6.3|-0.4|-0.9|-1.4|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Naturalgas|-6.4|-17.7|-30.7|-7.3|-20.3|-34.6|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Gasoline|-0.4|-1.1|-2.0|-0.4|-1.0|-1.6|-1.38|-4.42|-9.06|-0.36|-0.76|-1.16|
|Diesel|-0.2|-0.5|-1.3|0.1|0.6|1.1|-1.41|-4.34|-8.60|-0.64|-1.55|-2.49|
|LPG|-0.3|-0.8|-1.9|0.2|0.8|1.5|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|

Note: Morocco does not produce fossil fuels (coal, crude oil and natural gas) and therefore does not export these commodities; hence corresponding cells in the table are designated “n.a.” 22
**Graph 4**

Monthly Closing Values **of** the Istanbul Stock Exchange Index, **1986:100** <!-- Start of picture text --> 4,000 :<br>3,500 . ... , . . ..<br>3,000 ........ , , , , ,.....,. ,i t . ..<br>2,500- ' ' ''', ''' '' -- ,--.v,,,<br>2,000 . ........ ......... . ....<br>1,5 0 0 . .... ......... .....- . ..... ...... ......-.-- ........... , -- - -,eX ----- ............ ......<br>1,000 . .... ' ' ' , , . .........<br>Nominal<br>500 I....... .......... 11; ...... .<br>eT ~~~~Real *e__ .*.*_r<br>1 5 9 1 5 9 ¶ 5 9 1 5 9 1 5<br>i 86 I I7 i 88 I 89 1 90 i<br><!-- End of picture text --> SOtRCE: Istanbul Stock Exchange.

**34**