rafmacalaba/datause-encoder-data huggingface dataset:
6 of assurances provided by one ethnic or other interest group to another (Weingast, 1998). The DPI has information bearing on both of these issues.

Electoral rules, such as voting thresholds and other choices regarding the aggregation of votes, influence whether minority interests are more or less likely to be influential in the formation and dissolution of governments ~ and hence, more or less likely to abandon formal, institutional channels for the resolution of their grievances. Similarly, the existence of multiple veto players - checks and balances - should make it more likely that any given ethnic or other interest group has the ability to block state actions that are to its detriment. This is the essence of institutional arrangements that enhance credibility.

|

Electoral competitiveness and policy outcomes. To the extent that the “losers” from economic reform are politically influential - are key constituents of political decision makers - reform is less likely. This is a truism of political economy, but it leads to a still unanswered question: are politicians most likely to favor constituents who were supporters in past elections but deserted them recently; those whose support is up for grabs and could swing the next election one way or the other; or core supporters who never abandon them under any circumstances? All of these possibilities receive some theoretical and empirical support (see Dixit and Londregan, 1996, and Schady, 1998).

A simpler question, which is similarly unanswered, is whether greater competition in contests for political office increases or reduces the willingness to reform. Faced with the prospect of more competitive elections, politictans might be more sensitive to redistributive concerns and less likely to reform. However, politicians who feel few competitive pressures to reform may be more likely to engage in policies that benefit them and their core supporters at the expense of the rest of society. In the latter case, greater electoral competitiveness might

|
- **Ruggedness:** This study implements the terrain ruggedness index of Riley, DeGloria and Elliot (1999) using the tri() function in R.<sup>40</sup> The algorithm uses five neighboring pixels to calculate each pixel’s index from the elevation data. Our study uses the average of the ruggedness index at the census tract level.

- **Rivers and lakes:** Information on surface water bodies comes from the Google Earth Engine Data Catalog and is available at https://developers.google.com/earth-engine/ datasets/catalog/MERIT_Hydro_v1_0_1. The data comes from the MERIT Hydro dataset with a 3 arc-seconds spatial resolution (90 _mts_<sup>2</sup> ). Our variables take the value of one if a river or lake passes by a census tract.

- **Precipitation:** Precipitation was obtained from the Global Climate Database created by Hijmans et al. (2005) that is available at http://www.worldclim.org/. This data provides a historic time series of rainfall in millimeters from 1960 to 2018 at the 2.5 minutes spatial resolution (21 _km_<sup>2</sup> ) with a monthly periodicity. This study standardizes the series from 1960 to 1979 and calculates the standardized average of rainfall for each census tract from 1975 to 1979.

- **Temperature:** Maximum temperature was obtained at the Global Climate Database created by Hijmans et al. (2005) and is available at http://www.worldclim.org/. This data provides a historic time series of temperature in Celsius from 1960 to 2018 with a monthly periodicity at the 2.5 minutes spatial resolution (21 _km_<sup>2</sup> ) with a monthly periodicity. This study standardizes the series from 1960 to 1979 and calculates the standardized average of temperature for each census tract from 1975 to 1979.

- **Historical crop yield:** Agro-climatic yield rasters were obtained from the Global AgroEcological Zones version 3.0 (GAEZ v 3.0) project and are available at https://www.gaez. iiasa.ac.at. The data has a spatial resolution of 5 arc-minutes (9 _km_<sup>2</sup> ) and a yearly periodicity. We used the 30-year average starting in 1961 of the most relevant crops in terms of consumption and exports for 1990 (i.e., coffee, cotton, rice, beans, and sugarcane).

- **Roads and railways in 1980:** the map outlining the road and railway network in 1980 for El Salvador was obtained from the United States Library of Congress and is available at https://www.loc.gov/resource/g4840.ct000627/. This map was made by the

> 40Documentation of the R tool can be found at https://www.rdocumentation.org/packages/spatialEco/ versions/1.3-7/topics/tri

54
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Several large mobile groups with a multicountry presence dominate the regional telecommunications market. Across the board, ECOWAS member states have been very open to foreign investment in mobile telecommunications, with most countries having two or three foreign operators (table 4.4). In particular, seven major mobile operators—Etisalat, France Telecom, Maroc Telecom, Millicom, MTC (Zain), MTN, and Comium—have established a significant presence in the region, each covering between two and seven countries. Both France Telecom and French Vivendi channel their West African investments through local subsidiaries (Sonatel of Senegal and Maroc Telecom respectively), with the public-service philosophy of the subsidiaries sometimes guiding strategy and policy. These multicountry networks underpin the regional roaming arrangements detailed above, which essentially collapse into three roaming areas: Orange Zone, Zain One, and One World (table 4.5).

**Table 4.4  Foreign investors in the ECOWAS telecom sector (percentage of shares held and numbers of operators)**

|Country|Etisalat<br>(UAE)|France<br>Telecom|Maroc<br>Telecom|Millicom<br>(Luxembourg)|MTC (Zain,)<br>( Kuwait)|MTN<br>(South Africa)|Comium<br>(Lebanon)|Other|TOTAL<br>Other note|
|---|---|---|---|---|---|---|---|---|---|
|Benin|51%|||||75%||1|3<br>Globacom (Nigeria)|
|Burkina Faso|51%||51%||100%||||3|
|Cape Verde||||||||2|2<br>Portugal Telecom (40%)<br>Teylium (Côte d'Ivoire) (70%)|
|Côte d'Ivoire|—|85%||||65%|—|1|5<br>Warid (UAE)|
|Gambia|||||||—||3<br>Other=Lintel (Lebanon, 100%)|
|Ghana||||100%|75%|98%||2|5<br>Globacom (Nigeria)<br>Vodafone (UK) (70%)|
|Guinea||38%||||75%||2|4<br>Teylium (Côte d'Ivoire)<br>Cellcom (U.S.)|
|Guinea<br>Bissau||42%||||100%|||2|
|Liberia||||||60%|—|1|3<br>Cellcom (U.S.)|
|Mali||30%|51%||||||2|
|Niger|57%|80%|||90%|||—|4<br>Other=ZTE (China) and LAP<br>(Libya)|
|Nigeria|40%||||66%|76%|||3|
|Senegal||42%||100%|||||3<br>Sudatel (Sudan) (100%)|
|Sierra Leone|||||100%||—|—|2<br>Other: Lintel (Lebanon, 100%)|
|Togo|—||||||||1|
|TOTAL|6|6|2|2|5|7|4|||

Source: Derived from Ampah and others 2009. — = Data not available.

60
|Couples without children|-0.173***|0.000|-0.011|-0.000|
|---|---|---|---|---|
||(0.049)|(0.011)|(0.011)|(0.011)|
|Single caregivers|-0.058***|0.054***|0.007|0.050***|
||(0.012)|(0.012)|(0.007)|(0.012)|
|Single person households|-0.130***|-0.011|-0.063***|-0.008|
||(0.020)|(0.015)|(0.011)|(0.014)|
|Unaccompanied children|-0.252***|-0.235***|-0.241***|-0.216***|
||(0.078)|(0.048)|(0.042)|(0.047)|
|Non-nuclear and other households with children|-0.004|-0.012|0.059***|-0.011|
||(0.012)|(0.013)|(0.008)|(0.012)|
|Non-nuclear and other households without children|0.060***|0.013|0.049***|0.008|
||(0.018)|(0.012)|(0.010)|(0.012)|
|Female PA|||||
|x family size||||0.030***<br>|
|||||(0.003)|
|x able bodied male adults, %||||-0.010<br>(0.031)|
|x number of children below 5||||-0.050***<br>(0.005)|
|x number of elderly in the hh||||-0.065***<br>(0.018)|
|x couples without children||||-0.172***<br>(0.050)|
|x single caregivers||||-0.108***|
|||||(0.017)|
|x single person households||||-0.114***|
|||||(0.023)|
|x unaccompanied children||||-0.072|
|||||(0.089)|
|x non-nuclear and other households with children||||0.010<br>(0.017)|
|x non-nuclear and other households without children||||0.059***<br>(0.021)|
|Constant|4.420***|4.504***|4.460***|4.481***|
||(0.029)|(0.015)|(0.013)|(0.014)|
|Observations|26,857|56,745|83,602|83,602|
|R-squared|0.075|0.095|0.094|0.100|

_Note_ : Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The dependent variable is the log of household expenditure per cápita.

With respect to household characteristics, model 4 shows that each additional household member is associated with a decrease in per capita spending of 4.4 percent. Interacting family size with female headship shows the decrease in expenditure associated with larger families is highest for male PA households.

The presence of able-bodied males increases spending and thus, reduces the risk of poverty. In contrast the presence of children and elderly in the household increase poverty risk, especially for 23
- 34 -

Graph 9 Pakistan -- Movements in Real Interest Rates <!-- Start of picture text --> 5 ....................... .............<br>RD<br>na<br>d .. .. . ... .. .. ... .. .. ... .. .. .. ... .. .. ... .. .. ... .. .. ..<br>RL<br>R _ ,\S/,............... ... ... ....... . . ...................................... ....<br>-25 ~ ~ I t I a I I I I I I I I I I I I I<br>72 78 74 75 76 77 78 79 80 81 82 88 84 86 86 87 88 89 90 91 92<br>- Real deposit rate + -Real lending rate<br><!-- End of picture text --> invest quite heavily in basic economic and social infrastructure to increase labor productivity and reduce the cost of production. So, another major challenge for fiscal policy is to ensure that the composition of the public investment program is consistent with Pakistan's development objectives and priorities. We now return to the role of public spending on human resource development in some greater detail in the next section.

# Human Capital Formation and Implications for Pakistan's Growth Performance

43. One of the key findings of the East Asian Miracle stmdy is that the high performing East Asian economies invested very heavily in human capital formation. Quantitative analysis suggest that human capital was a key factor explaining the better growth performance of these economies. There is similar evidence of a very strong role of human capital in explaining growth performance across
Summarizing, the bank-municipality regression results confirm the findings from the bank-level analysis that the probability of bank presence in a municipality increased after foreign acquisitions, while the number of deposit and loan accounts decreased. However, the positive effect of the geographic extension of foreign banks appears to be limited to urban areas, while the negative effect of foreign acquisitions on deposit and loan account penetration was stronger in rural and poorer areas. Finally, foreign acquisitions had a positive impact on branch penetration only in very urban and rich areas.

# **VI. Conclusions**

Foreign bank entry is a new and significant phenomenon that many developing countries are experiencing nowadays as part of a general trend toward financial liberalization and globalization. Though a literature has emerged analyzing the impact of foreign bank participation on bank efficiency, stability, and access to small business finance, to our knowledge, no study had thoroughly examined the implications for banking sector outreach.

Using country-, bank- and bank-municipality- level data, this paper analyzes how outreach changed in Mexico, during a period of rapidly rising foreign bank presence resulting from foreign acquisitions. Our estimations show a consistent decline in deposit and loan accounts following acquisitions. On the other hand, while country- and bank-level estimations indicate an increase in the share of municipalities with bank branches and in the likelihood of bank presence, bank-municipality- level regressions show that only rich and urban municipalities benefited. Overall, we interpret our results as suggestive of a decline in outreach following foreign bank acquisitions, since banks intermediated fewer deposits and loans and increasingly concentrated their operations in rich and urban areas.

26
**Table 1:  Data used for the analysis**

|**Region**|**Country**|**Slowdown in**<br>**GDPgrowth**|**Source**|**Period**|**Frequency**|**Note**|
|---|---|---|---|---|---|---|
||Argentina|-6.9|Household survey (EPH)|2006 Q4 -<br>2009 Q2|Quarterly|-Urban only<br>-rotational panel<br>-self employed and<br>family worker combined<br>|
||Brazil|-4.7|Monthly Survey of<br>Employment (PME)|1/2006 -<br>8/2009|Monthly|-Urban only<br>-Rotational panel<br>-No agricultural sector|
|**LAC**|Chile|-5.7|National survey of<br>Employment (ENE)|2006 Q1 -<br>2009 Q3|Quarterly|-Urban only<br>-No earnings info|
||Costa Rica|-5.4|Multi –purpose<br>Household survey (EHPM)|2006-2009|Annually||
||Mexico|-11.2|National Survey of<br>Occupation and<br>Employment (ENOE)|2007 Q1 -<br>2009 Q1|Quarterly|-Rotational panel|
|**EAP**|Indonesia|-1.7|Indonesian Labor force<br>Survey (Sakernas)|Feb 2006 -<br>Feb 2009|Biannually|-Earnings only for wage<br>and salary workers|
||Philippines|-4.5|Philippines Labor force<br>Survey|2006 -<br>2009|Biannually|-Daily wage rate for<br>earnings|
||Egypt|-2.5|Egyptian Labor Force<br>Survey (ELFS)|2006-2009|Quarterly|-rotational panel|
|**MENA**|Jordan|-5.6|Jordanian Employment<br>and Unemployment<br>Survey (JEUS)|2006-2009|Quarterly||
||Bulgaria<br>Latvia|-10.7<br>-20.7||||- Cell means by gender,<br>education, and age<br>reported by Eurostat|
||Lithuania|-21.3||2006 -||- Urban/rural|
|**ECA**|Macedonia|-6.1|Eurostat|2009|Annually|breakdown only<br>available for selected|
||Poland<br>Romania|-4.2<br>-12.2||||outcomes<br>- No earnings and hours<br>information|
||Turkey|-7.4||||-Earnings not available|
|**AFR**|South<br>Africa|-8.2|Labor force Survey and<br>Quarterly labor force<br>survey|2006 -<br>2009|Biannually|-urban/rural information<br>not available<br>-sector information is not<br>available|

Note: Slowdown in GDP growth rate due to the recession is calculated as GDP growth rate in 2009 compared from the average GDP growth rates in 2007-2008. For detailed numbers, see Appendix Table A1.

26
**Annex I: Cameroon Key Economic and Social Indicators (1981 – 2001)**

||**1981**|**1982**|**1983**|**1984**|**1985**|**1986**|**19**|**87**|**1988**|**1989**|**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999 **|**2000**|**2001**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Real Setor**|||||||||||||||||||||||
|Real GDP growth (%)|17.1|7.6|6.8|7.5|8.1|6.8|-2|.2|-7.9|-1.8|-6.2|-3.8|-3.1|-3.2|-2.5|3.3|5.0|5.1|5.0|4.4|4.2|5.3|
|Real GDP per capita growth (%)|13.7|4.6|4.0|4.7|5.3|3.9|-4|.9|-10.5|-4.7|-9.0|-6.6|-5.9|-6.0|-5.3|0.5|2.1|2.2|2.2|1.6|1.3|2.4|
|RGDP per capita in 1990US$|1,051|1,100|1,144|1,197|1,260|1,310|1,2|46|1,115|1,063|968|903|850|799|757|760|777|794|811|824|835|855|
|CPI (% change), (%)|7.5|15.3|20.5|12.1|4.2|4.3|2|.8|1.7|1.6|1.5|-0.6|1.9|-3.7|12.7|25.8|6.6|5.1|0.0|2.9|0.8|2.8|
|Total investment in % of GDP|27.2|24.8|26.0|25.9|24.9|25.5|24|.7|20.9|17.1|17.8|16.7|13.5|16.6|15.3|14.5|15.4|16.2|17.5|18.7|16.4|17.8|
|Gross public invest. as share of GDP (%)|4.5|4.2|4.9|6.6|7.7|10.4|11|.4|9.2|6.1|5.5|4.0|2.7|1.8|1.3|1.2|0.5|1.0|2.0|2.3|1.4|2.1|
|Population growth (%)|2.9|2.8|2.7|2.7|2.7|2.8|2|.8|2.9|3.0|3.1|3.1|3.0|3.0|3.0|2.8|2.8|2.8|2.8|2.8|2.8|2.8|
|**Fiscal**(in percentage of GDP)|||||||||||||||||||||||
|Overall surplus/deficit, excl. all grants|-0.2|0.2|4.1|-0.3|-1.6|-1.2|-13|.0|-5.9|-4.5|-7.6|-7.0|-7.9|-6.3|-9.1|-3.2|-1.5|-1.4|-1.7|-3.4|1.4|2.0|
|Overall surplus/deficit, incl. all grants (%)|-0.2|0.2|4.1|-0.3|-1.6|-1.2|-13|.0|-5.9|-4.5|-7.6|-7.0|-6.6|-6.3|-9.1|-3.1|-1.3|-1.0|-1.4|-3.2|1.4|2.4|
|Govern. revenue, incl. all grants|20.9|24.7|26.5|22.6|21.0|21.4|18|.4|16.4|16.0|14.3|16.4|15.7|13.7|10.2|13.1|14.8|15.1|16.5|15.7|18.8|21.0|
|Govern. revenues, excl. all grants (%)|20.9|24.7|26.5|22.6|21.0|21.4|18|.4|16.4|16.0|14.3|16.4|14.4|13.6|10.2|13.0|14.6|14.7|16.2|15.5|18.8|20.6|
|Gov. Expenditure|21.1|24.5|22.4|23.0|22.6|22.6|31|.3|22.3|20.6|21.8|23.4|22.3|20.0|19.3|16.2|16.1|16.1|17.9|18.9|17.4|18.6|
|**External**(in % of GDP)|||||||||||||||||||||||
|External current account|-6.8|5.0|2.3|4.3|4.2|-3.3|-6|.3|-5.1|-2.1|-4.4|-2.2|-2.5|-5.4|-4.4|-0.9|-4.1|-2.8|-2.5|-4.3|-1.7|-2.2|
|External current account excluding grants|-7.2|4.6|1.9|3.9|3.8|-3.7|-6|.5|-5.3|-2.6|-4.6|-2.9|-4.1|-5.9|-4.9|-1.9|-4.4|-2.8|-2.8|-4.3|-1.7|-2.2|
|Exports of goods and services|25.3|36.0|32.7|33.0|33.4|23.3|16|.7|16.0|20.7|20.4|20.8|20.5|17.1|22.1|25.7|22.5|25.3|26.5|24.4|30.7|31.8|
|Imports of goods and services|29.0|27.7|26.5|24.1|21.9|20.7|18|.2|16.0|18.0|20.1|17.0|18.3|16.0|19.5|20.7|20.5|22.4|24.8|24.7|26.8|29.2|
|External public debt|15.8|16.7|16.3|15.4|26.4|26.5|27|.8|26.5|30.7|38.0|49.0|54.2|63.6|107.2|85.3|77.6|73.8|77.0|76.7|69.6|67.1|

34
_Africa’s Macroeconomic Story_

**Figure 19: Policy Interest Rates in Selected Countries, 2005Q1-2013Q2**

_Percent_ <!-- Start of picture text --> 25 20<br>20<br>15<br>15<br>10<br>10<br>5<br>5<br>0 0<br>2005Q1 2007Q1 2009Q1 2011Q1 2013Q1 2005Q1 2007Q1 2009Q1 2011Q1 2013Q1<br>Uganda Kenya Malawi Nigeria Burundi Zambia Tanzania<br><!-- End of picture text --> Source: IMF _International Financial Statistics_ database.

In Uganda, inflation increased from under 3 percent in late 2010 to 15 percent in July 2011 (12-month change in core CPI) at which point the Bank of Uganda announced its intention to bring inflation down to 5 percent over the medium term and proceeded to raise the Central Bank Rate by over 20 percentage points over the following 18 months. Inflation peaked at 30 percent in late 2011 and subsequently declined to 5.6 percent in May 2013. Kenya’s experience was similar. Inflation increased from under 1 percent in late 2010 to over 10 percent in late 2011 (12-month change in core CPI). In response, the Central Bank of Kenya raised the Central Bank Rate by 12 percentage points. Inflation peaked at 18 percent in January 2012 before declining to 4.3 percent in April 2013, a level considered to be broadly consistent with its medium-term objective of 5 percent.

In Malawi the aggressive monetary policy response was spurred by the collapse of the fixed exchange rate regime. Inflation increased from 7 percent in early 2011 to 12 percent in April 2012 (12-month change in total CPI) when the Kwacha depreciated by 65 percent against the $US. In response, the government announced the adoption of a flexible exchange rate regime, abandoning the _de facto_ exchange rate peg to the $US. The Reserve Bank of Malawi raised the Bank Rate by 12 percentage points over the balance of the year 2012 in an attempt to quell inflationary pressures, 28
# 1. **Introduction**

Theoretical and empirical research on the relationship between trade and knowledge accumulation suggests at least two causal links: one stresses the role of knowledge accumulation in determining trade performance and competitiveness', the other looks at the role of trade in enhancing 2 knowledge accumulation through imports . This paper tries to capture both elements in a unified perspective.

The approach followed is "sectoral" in the sense that it considers an economy where more than one good is produced. Two reasons can be suggested in favor of such a choice. A first one (Pasinetti 1981) stresses the role of growth in different sectors in affecting aggregate performance, because, inter alia, different rates of growth of sectoral demand will lead to growth through structural change. A second one stems from the opportunity of adopting a sectoral perspective in the investigation of the relationship between knowledge accumulation (and diffusion), dynamic comparative advantages and trade (and growth) performance. This second point is also not new. Grossman and Helpman (1989, 1990) and Romer (1990) consider an economy with three sectors (R&D activity, intermediate goods, consumer goods) to study the role of knowledge accumulation and diffusion through the introduction of intermediate goods in the production of final goods. More generally, the role of intermediate goods in the knowledge diffusion process is well recognized in the literature on new growth theory<sup>3</sup> . Other more institutionally oriented approaches<sup>4</sup> stress the role of knowledge spillovers between sectors and the consequences on trade and growth of the performance (and relative weight) of knowledge producing sectors..

- Theoretical contributions include Grossman and Helpman (1989), (1990), Romer (1990), Verspagen (1993). Empirical contributions include Fagerberg (1988) Amable and Verspagen (1995), Amendola, Guerrieri, Padoan (1992) Magnier and Toujas-Bemate (1994)

- 2 Theoretical contributions include Ben David and Loewy (1995), empirical analyses include Coe and Helpman (1993), Coe, Helpman and Hoffmeister (1994), Keller (1995)

- 3 A recent survey is offered in Fagerberg (1994) Ben David and Loewy (1995) develop a model of trade and growth where aggregate results depend on structural characteristics. While the model shows that openness enhances growth thanks to knowledge diffusion through trade it remains unclear where, in a n-country world, knowledge is produced in the first place.

See Pavitt (1984), Dosi, Pavitt and Soete (1990), Bell, Pavitt (1995) 3
The 1985-1994 Global Real Estate Cycle. Page 1

# THE 1985-1994 GLOBAL REAL ESTATE CYCLE:

# ITS CAUSES AND CONSEQUENCES.

" II y a quelque chose de commum entre l'art du puzzle et l'art du go; seules les pieces rassemblees prendront in caractere lisible, prendront un sens: consideree isolement une piece d'un puzzle ne veut rien dire, elle est seulement question impossible, defi opaque..." (Georges Perec, _La vie mode d 'emploi)_<sup>_1_</sup>

## 1. THE GLOBAL REAL ESTATE CRASH, A NEW PHENOMENON

The rapid emergence of a global financial system durng the late 1980s has been signaled in the real estate sector of most OECD industrial countries -and NIE middle-income countries as well-by coincident cycles with unusually strong booms followed by exceptionally sharp and protracted real estate busts. The peak of the boom was reached in 1990 in most OECD countries. The massive asset inflation which tool place can be illustrated by the performance of _office_ markets acrtss Europe as monitored by the Jones-Lang-Wootton European Property Index. _Figure I_ shows that capital values rose 400% between 1980 and 1990 with an acceleration after 1986. During that period, average consumer price inflation went up only 150%. Since 1990, the decline in property values has been sharper, most property markets losing at least 20% in nominal terms the first year of the onset of the crash. Cumulative falls in capital value have often reached 50% by the end of 1993. This pattern of a long sustained buildup in real estate asset prices usually peaking in 1990 followed graphically by a very sharp fall in nominal values thereafter has been encountered in most OECD markets and in several NIE countries. 2 Real estate booms and busts are recurring events. However, real estate volatility on the scale and intensity just experienced is costly and destructive. Real resources are misallocated, the impact on the barldng system can be lasting and reach far beyond the real estate sector itself. The widespread appearance of negative housing equity can have a catastrophic impact on large numbers of households. In addition to their unusual intensity, the coincidence of so many real' estate crashes on a global scale is also a new event. The effects of this global crash on the real estate industry, the financial system, the household sector and the overall economy are still being felt in most countries today.

I "There is something in common between the art of puzzle assembly and the game of _go._ Only when the pieces are fitted together do they acquire a legible caracter, gain a meaning. Examined in isolation a piece of a puzzle means nothing; it is just an impossible question, an opaque challenge". Georges Perec, _Life, A User's ManuaL_ 2 'Me JLW office index is weighted by market size and compiled across 14 of the largest metropolitan markets in Europe. Price pattems similar to Europe's real estate cycle are fbund in East and South East Asian countries like Taiwan (Taipei), Hong-Kong, Korea (Seoul), Thailand (Bangkok), Malaysia (Kuala Lumpur).