rafmacalaba/datause-encoder-data huggingface dataset:
The 1985-1994 Global Real Estate Cycle. Page 2 Figure 1: The European Office Property Boom European Property Index for Offices (1980=100) <!-- Start of picture text --> 4,<br>liii 1561 |@ 1502 le 1564 ¶56 iee l ie?| 1lee ieee gee Iiet lees tees<br><!-- End of picture text -->

Source: Jones Lang Wootton International.

From an analytical perspective, the global crash presents unusual research opportunities. More importantly, from a policy viewpoint, we need to understand much better how national and local market experiences differ to avoid other episodes of synchronous and extreme volatility. What has triggered this global cycle? Why is its volatility so much greater in some countries than others. The phenomenon hlas been global, yet knowledge about it is usually lirnited to one's own country, or remains fragmentary. Feor instance, the causes and consequences of regional and local boomns and busts in the US during the 1980 has been studied with a fair amnount of thoroughness. The existence of a Japanese asset "bubble" has .also been debated at some length. But the links between Japanese domestic asset inflation experience and the spread of the global boom should also be spelled out. Also, why have some groups of countries like the Nordic Countries - especially Finland - been hit so severely hits when other countries like Germany appeared rernarkably immune to the drama unfolding almost everywhere else?

This paper presents an exploratory survey of the global real estate cycle of 19P,5-1994.3 Prior to engaging in detailed quantitative analyses of the shape of individual cycles, for countries, cities and individual markets, it aims is to identify more clearly the international and domestic factors that triggered this new phenomenon of global real estate volatility. The intent is threefold. First, taking as a given that the globalization of financial mnarketi **s** most pm,bably an irreversible process, can we separate unique factors from recurring ones in this first global cycle? Second, is it possible to map the generic policy lessons and, by implication, identify more clearly policy priorities and research agendas? Can we identify more clearly the international and domestic faators at work which accentuate real estate price and investment volatility? Third, probing into the unappealing trilogy of "bad luck, bad policy, and bad 3 selecion of the years which bracket this first global cycle are partly a reporting convenience since the national real estate cycles surveyed have not been perfectly synchlronous. 1985 is the year of the G-5 Plaza agreement on currency realignment with implementation beginning in February 1996. The 1987 L,ouvre G-7 meeting agreed that new exchange levels were adequate and focused on the coordinated macroeconomic stimulation of the G-3 economies, particularly JapaI(See Volker-GyohIten [19921 pp. 274-S and 3S7-9]. 1994 usually mars the bottoming out of the real estate secor in most countries.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Cameroon's irrigation potential**

|||Large-scale||S|mall-scale|A||Total|A|
|---|---|---|---|---|---|---|---|---|---|
|Cutoff (%)|Investment|IRR|Area increase|Investment|IRR|rea<br>increase|Investment|IRR|rea<br>increase|
||$millions|%|hectares|$millions|%|hectares|$millions|%|hectares|
|0|986|5.4|505,124|1538|29.0|297,633|2524|14|802,757|
|6|557|6.9|285,461|1203|34.0|232,715|1760|19|518,176|
|12|0|0.0|0|881|42.0|170,463|881|42|170,463|
|24|0|0.0|0|553|59.0|106,978|553|59|106,978|

_Source_ : You and others 2009.

_Note_ : Water for irrigation can be collected in two ways: through large, dam-based schemes, or through small projects based on collection of run-off from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agro-ecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed. The unit cost for large scale projects is set at USD3000/ha and for small scale projects at USD2000/ha.

**Figure 14. Small-scale irrigation potential**

<!-- Start of picture text --> 45 250000<br>40<br>35 200000<br>30<br>150000<br>25<br>20<br>100000<br>15<br>10 50000<br>5<br>0 0<br>Burundi Congo, Rep. CAR Congo, Dem. Cameroon Chad<br>Rep<br>Area increase (thousand ha) Internal Rate of Return (%)<br>Internal Rate of return<br>Area Increase (thousand ha)<br><!-- End of picture text --> _Source_ : You and others 2009.

_Note_ : Based on 12% cutoff estimates, at which the estimated area increase for Central African countries not included in the figures is zero. Water for irrigation can be collected in two ways: through large, dam-based schemes, or through small projects based on collection of run-off from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for smallscale irrigation is assessed not only on the basis of agro-ecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed.

# Water supply and sanitation

## **Achievements**

Cameroon has reduced its reliance on surface water due to the rapid expansion of wells and boreholes. Reliance on surface water dropped from 37 percent of the population in 1991 to 32 percent in 1998 and to 9 percent in 2006 (table 9). Between 1998 and 2006 about 2.2 percent of the population shifted from using surface water to some other form of supply (figure 15a). In rural areas, 3.8 percent of the population moved up the water supply ladder from surface water, in particular by gaining access to wells and boreholes. At the national level, the use of wells and boreholes increased from 23 to 47 percent 25
**Table 4: Estimates of the Fiscal Impact of Aid** **<u>Sample</u>** <u>: 1981-2004</u>

|**Dependent**<br>**Variable**||**D(PUBINV**|**/TOTEXP**|**)**||**D(TAXRE**|**V/GDP)**||
|---|---|---|---|---|---|---|---|---|
|**Regression**<br>**Method**|**OLS**|**IV**|**OLS**|**IV**|**OLS**|**IV**|**OLS**|**IV**|
|**Equation No.**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|**Constant**|0.69<br>(0.29)|1.32<br>(0.53)|0.87<br>(0.76)|1.38<br>(0.99)|-0.1<br>(0.39)|-0.0911<br>(0.390)|-0.03<br>(0.13)|-0.03<br>(1.40)|
|**D(EXP/GDP)**|||1.16***<br>(2.54)|1.33***<br>(2.58)|||0.10<br>(1.25)|0.11<br>(1.40)|
|**D(LAB)**|-25.54<br>(1.62)|-31.45<br>(1.61)|||-0.85<br>(0.55)|-0.91<br>(0.60)|||
|**D(ODA/GDP)**|-0.32***<br>(3.56)|-0.53***<br>(6.05)|-0.31***<br>(3.15)|-0.6***<br>(13.19)|-0.06***<br>(26.78)|-0.07***<br>(7.78)|-0.06***<br>(18.54)|-0.07***<br>(6.50)|
|**Dummy(96+)**|3.50<br>(0.68)|3.99<br>(0.78)|-1.98<br>(0.89)|-2.56<br>(1.09)|-0.7<br>(1.38)|0.71<br>(1.37)|0.44<br>(1.07)|0.43<br>(1.14)|
|**D(ODA/GDP)***<br>**Dummy(96+)**|0.27<br>(1.40)|0.49***<br>(2.77)|0.32*<br>(1.82)|0.61***<br>(4.40)|0.0006<br>(0.03)|0.003<br>(0.13)|0.0008<br>(0.33)|0.001<br>(0.36)|
|**R-Squared**|0.41|0.24|0.46|0.16|0.72|0.72|0.73|0.73|
|**F-Statistics**|3.34|1.97|7.2|3.81|12.24|5.36|13.02|5.39|
|**D-W **|1.91|2.23|1.81|2.12|2.02|2.03|1.89|1.9|
|**S.E.**|7.76|8.84|5.77|7.17|0.88|0.88|0.86|0.86|

- ***   Significant at 99% confidence level

- **    Significant at 95% confidence level

- Significant at 90% confidence level

Instruments in the IV method are: Constant, D(ODA/GDP)(-1), Dummy(96+), D(ODA/GDP)* Dummy(96+), and either D(LAB) or D(EXP/GDP). Notations: PUBINV_TOTEXP is Public investment to total government expenditure (recurrent public expenditure plus public investment) ratio (multiplied by 100). TAXREV is tax revenue to GDP ratio (multiplied by 100). D(.) denotes first difference. ODA_GDP is ODA to GDP ratio (multiplied by 100). ODA_GDP_Dummy is in fact D(ODA_GDP) times the dummy variable for 1996-2005. PUBINV_T is PUBINV/(100-PUNINV). TAXREV_T is TAXREV(100-TAXREV) 28
12

# **4.2 Data Description**

# **A. Profile of Firms**

For this study, we use firm-level data obtained from Malaysia’s manufacturing sector census in 2000, 2005, and 2010.<sup>16</sup> The data encompass individual firms belonging to 59 industries classified according to the 3-digit Malaysia Standard Industrial Classification (MSIC) 2000. To standardize the classification of firms by industries across the three census periods, the census data in 2010, which are based on MSIC 2008 codes, are converted into MSIC 2000.

The firms in the census frame report data on gross output, percentages of export sales, material expenditures, energy expenditures, salaries, employees’ benefits, number of employees, investments, book values, and ownership structure.

When examining the three periods of the census data, we noticed that there were significant shifts in the distribution of firms. In particular, there was a sharp increase in the number of small firms entering the second and third periods of the census. In our analysis, firm size is defined by the number of employees, where small firms are defined as ones that have 1–19 workers, medium-sized firms have 20–99 workers, and big firms have more than 99 workers. The distribution of log of employees over the three census periods shows that it has become increasingly skewed toward small firms (skewness in 2000, 12.9; in 2005, 16.9; in 2010, 26.1). Figure 4 shows the number of small firms increased by a compound annual growth rate (CAGR) of 11% during the 10-year period.

**Figure 3:** Distributions of Log of Employment **Figure 4:** Firms Classified by Size Entering in the Three Census Periods                                   the Three Census Periods

<!-- Start of picture text --> .4 30,000<br>25,292<br>25,000<br>.3<br>20,000 17,879<br>.2<br>15,000<br>.1 10,000 8,884<br>0 5,000<br>0 2 4 6 8 10<br>Log employment distribution<br>0<br>2010 2005 2000 2005 2010<br>2000<br>>100 workers 20–99 workers 1–19 workers<br>Number of firms<br>Density<br><!-- End of picture text --> Source: Department of Statistics Malaysia (DOSM) 16 The Department of Statistics Malaysia (DOSM) conducts a census on the manufacturing sector every five years.
**Table 5. Determinants of Household Level Agricultural Investment**

||Probit|To|bit|
|---|---|---|---|
||AgncInvestmentdummy|AmountofAgnc.|Investment(log)|
|Villageinvestmentdummy|0.939<br>0.963*|4.676|_5_237|
||(1.87)*<br>(1.91)|(1.35)|(1.49)|
|Lagged agric. invest.(dummy/amount)|-0.237<br>-0.258***|-o607***|-o609***|
||(-2.37)<br>(-2.58)|(-4.24)|(428)|
|VillageadoptedTwoNos|0.349**||1979*|
||(2.26)||(180)|
|Householdswithland certificate|0.302<br>0.238|1.549|1.304)|
||(1.41)<br>1.160|(1.03)|(091)|
|Householdwithlandtransferright|0.884***<br>0.746**|-5212**|4.473**|
||(-2.75)<br>(-2.33)|(-2.30)|(-199)|
|Head'smaxeducation|0.014<br>0.013|0.199|0.1910|
||(0.90)<br>(0.79)|(1.76)*|(1.69)|
|Pastmigrationbyhead|<br>-0.193<br>-0.196|-1.196|-1216|
||(-I28)<br>(-1.29)|(-1.13)|(-I15)|
|Migrationbychildren|-0.246**<br>-0.246**|-I461*|-1.481*|
||(-2.19)<br>(-2.19)|(-1.86)|(-I88)|
|Ageofhouseholdhead|0004<br>0.003|0.034|0029|
||0.73<br>(0.56)|(0.89)|(075)|
|No.ofhouseholdmembers>60|0.199**<br>0.194**|1.492**|1490**|
||(2.17)<br>(2.14)|(237)|(238)|
|No.ofhouseholdmembers<14|0.087<br>0081|0703|0683|
||(I38)<br>(1.28)|(I57)|(1.54)|
|No.ofhouseholdmembers14-60|0149***<br>0.147***|1189***|1180**|
||(3.34)<br>(331)|(379)|(377)|
|Percapita paddyland|0.220**<br>0.249**|1.705**|1877***|
||(2.06)<br>(2.29)|(240)|(260)|
|Percapita upland|-0.095<br>-0.105|-0.388|-0.451|
||(-1.10)<br>(-1.21)|(-063)|(-073)|
|Per capita otherland|0.101***<br>0.121***|0695***|0.830***|
||(3.53)<br>(3.61)|(3.49)|(3_50)_|
|Guizhou dummy|0.433**<br>0.380*|2.812*|2657*|
||(2.08)<br>(1.91)|(1.92)|(1.88)|
|Yunnan dummy|-0.076<br>-0.023|-0783|-O443|
||(-0.48)<br>(-0.14)|(-0.69)|(-0.38)|
|Constant|-2.238***<br>-2.388***|-15.837***|-17.129***|
||(-3.57)<br>(-3.60)|(-352)|(-358)|
|Observations|944<br>922|944|922|
|Log-liklihood|-558.20<br>-557.22|-1280.93|-1280.11|
|Absolute valueofz-statisticsinparentheses||||

* significant at 10%; ** significant at 5%; *** significant at 1%

29
Table 3: EFFECTS ON INFORMAL TRANSFERS OF ULTRA-POOR HOUSEHOLDS

||Transfers<br>in past 1|received<br>2 months|Transfe<br>in past 1|rs given<br>2 months|Net trans<br>in past|fers received<br>12 months|
|---|---|---|---|---|---|---|
||(Yes=1)|(BDT)|(Yes=1)|(BDT)|(Yes=1)|(BDT)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|-0.129***|-104.664|0.012|40.877|-0.131***|-151.721**|
||(0.020)|(78.648)|(0.007)|(45.704)|(0.019)|(74.478)|
|Mean in control|0.497|783.055|0.059|143.978|0.484|639.076|
|Adjusted R-squared|0.126|0.027|0.030|0.001|0.121|0.019|
|Observations|13464|13464|13464|13464|13464|13464|
|_Panel B:_|||_Within-vil_|_lage Transf_|_ers_||
|Treatment|-0.037***|41.246*|-0.000|-4.120|-0.036***|45.558*|
||(0.013)|(23.040)|(0.002)|(4.078)|(0.013)|(23.656)|
|Mean in control|0.148|142.403|0.015|11.865|0.147|130.538|
|Adjusted R-squared|0.041|0.004|0.013|0.001|0.041|0.003|
|Observations|13464|13464|13464|13464|13464|13464|
|_Panel C:_|||_Outside-vi_|_llage Transf_|_ers_||
|Treatment|-0.123***|-144.864*|0.011*|45.021|-0.123***|-197.782***|
||(0.019)|(72.655)|(0.006)|(43.650)|(0.018)|(65.197)|
|Mean in control|0.430|640.652|0.048|132.113|0.420|508.539|
|Adjusted R-squared|0.117|0.027|0.023|0.001|0.112|0.018|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any transfers (in cash or in kind) from another household during the 12 months preceding the survey; in column 2 it is the monetary value of transfers received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any transfers to another household during the past 12 months; in column 4 it is the monetary value of all transfers given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net transfer receiver in the past 12 months; in column 6 it is the monetary value of transfers received minus transfers made by the respondent’s household in the past 12 months. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village transfers.

38
- 23 -

issues. The paper takes several commonly used definitions of poverty in examines whether they select the same group as poor as does an equivalence scale adjusted per capita consumption measure. In both urban and rural C6te d'Ivoire, the answer is often "no." As seer in Table 4, definitions of poverty based on income data, agricultural land, and adult schooling are somewhat questionnable, and definitions based anthropometric measures (weight for height or height for age), the fraction of total expenditures devoted to food or the floor area of the dwelling can be completely urcorrelated or only weakly correlated with a (equivalence scale adjusted! consumption-based definition of poverty. Whether this is true in other countries is a topic for future research.

The paper by Glewwe and van der Gaag also points out that different definitions of poverty have different biases. For example, per capita income and per capita expenditure tend to select large households, while total family expenditure selects small households. Using agricultural land as a definition in rural areas often selects relatively well to do households whose income derives from sources other than agriculture. Finally, some definitions are more likely to classify urban residents as poor than others (such as ones that tend to pick smaller households as poor).

# C. Str ctural Adjustment and the Poor

Two recent papers by Clewwe and de Tray (1988, 1989) focus on issues of structural adjustment and the poor in two countries with LSMS data, Cote d'Ivoire and Peru. Both papers discuss many aspects of structural adjustment, but here the discussion will be limited to the effect of structural adjustment
With respect to gender and geographic area the results show that rates of return to tertiary education are higher for both urban and rural men compared to women, particularly in the upper tail of the conditional earnings distribution.

In sum, the returns to education increased in Mexico from 1988 to 1997, especially for higher levels of education and in the upper tail of the conditional earnings distribution. However, there was a reversal to this trend after 1997, especially for higher levels of education and in the middle and lower tail of the conditional earnings distribution. This may reflect a structural development, if expanding relative supplies of school-leavers are offsetting the secular tendency for rising relative demand for skills — especially at the tertiary level (see de Ferranti et al., 2004). Alternatively, it may reflect a cyclical fall in education premia in times of recession, which has also been observed in the data for Latin America (ibid). But for the present, the labor force patterns by labor force status and education are fully consistent with the equalizing patterns of income growth.

# 5. CONCLUSION

Even though the levels of educational attainment expanded very rapidly, Mexico experienced a pronounced increase in the degree of inequality over the 1980s and mid1990s. Most of the deterioration in the distribution of income happened in the middle to late 1980s (1984–89). The early 1990s displayed little change in total current income inequality except for a slight trend toward deterioration. The trends in the distribution of earnings differ from the trends in the distribution of current income in two ways. First, the gains are not limited to the richest 10 percent, as those in the seven-, eight-, and ninetenths of the distribution improved their relative earnings over the period by almost 2 percentage points. Second, the distribution of earnings clearly worsened in the 1990s until 1996, although the inequality associated with total current income was moderately stable in the 1990s, displaying an improvement after 1996. Differences in the behavior of total current income and labor earnings inequalities from 1994 to 1996 support the idea that the poor, who rely the most on labor as a source of income, are the least able to protect themselves during a recession.

30
Council (GCC) countries, while equally rich, exhibit some of the most restrictive policies observed in the sample, thus placing Qatar, Kuwait, Bahrain, Oman and Saudi Arabia in the top-right corner of Figure 2.

**Figure 2: Country-level services trade restrictions index (STRI) plotted against the per capita income of the country**

<!-- Start of picture text --> ETH<br>IND<br>ZWE IRN<br>QAT<br>PHL<br>DRC IDN EGY BHR KWT<br>JOR THA PANMYS OMN<br>NPLBGD VNM TUNLBNBWA SAU<br>LKANAMCHN DZA<br>MWIUGA ZAFBLRVEN<br>BDI MOZRWAMDGMLITZAGHAZMBLSOKENSENCIVKHMNGAKGZCMRYEMUZBPAKNICMNGHNDMARBOLPRYGTMGEOARMDOMUKRALBPERCOLBRACRIKAZBGRURYMUSROMTURARGCHLMEXRUSPOLLTUHUNPRTTTOCZEKORNZLGRCESPITAFRAJPNAUSDEUFINBELGBRSWEDNKAUTCANNLDIRLUSA<br>ECU<br>5 6 7 8 9 10 11 12<br>Log of GDP per capita<br>Note: GDP per capita 2007, PPP (constant 2005 internat US$)<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Note: Banking (2 subsectors; mode 1 and 3), insurance (3 subsectors; mode 1 and 3), telecommunications (2 subsectors; mode 3), retailing (mode 3), transportation (maritime and air: mode 1 and 3; maritime auxiliary services, rail and road: mode 3), and professional services (4 subsectors; mode 1, 3 and 4).

Among developing countries the restrictiveness of applied policies varies widely. Interestingly, some of the most restrictive policies today are visible in the fast-growing economies of Asia, including China, India, Indonesia, Malaysia, Philippines and Thailand, as well as in the Middle East, including Egypt, Iran, Saudi Arabia and Tunisia. Some of Africa‘s poorest nations also have rather restrictive services policies. In particular, Ethiopia, Zimbabwe and DR Congo turn out to be amongst the most restrictive countries in the sample (top-left corner). At the same time, another group of developing countries like Rwanda, Madagascar, Senegal and Mongolia are remarkably open (bottom-left corner). It is also true, 21
<!-- Start of picture text --> Portugal Turisia<br>Morocco<br>> 2<br>° sgh° 4 cee erm en Libya<br>Algeria<br>omyer Spanish Sahara<br>Mauritania<br>aoe 4 : S<br>Ee) } wie<br>5<br>4<br>5 _‘ 2Sit Re<br>Economic .<br>GDP per 100km2(in million) 100km2(in million)(in million) million) « Eq gbrial G inea<br>= 10 So Tomé ahd Princip<br>(> 40-400 y Gabon Congo<br>@ 100-1000<br>@ 1000- 500- 500 500 ‘<br>@ = 5000<br><!-- End of picture text --> <!-- Start of picture text --> Economic<br>GDP per 100km2(in million) 100km2(in million)(in million) million)<br>= 10<br>(> 40-400<br>@ 100-1000<br>@ 1000- 500- 500 500<br>@ = 5000<br><!-- End of picture text -->
# Contribution calculations

The accumulation of individual pension contributions is modelled in LIFEMOD, but in order to set some reasonable parameters to the modelling process, and to get some intuitive idea of the sensitivity of results to key assumptions, we have first estimated contribution rates for the average LIFEMOD fully-employed male worker. Again it is necessary to base the modelling on a range of assumptions about real rates of return, real earnings growth, and the age at which contributions commence. We have produced estimates on the basis of contributions beginning at 18, 21 and 25, to take account of alternative assumptions about age of entry into the permanent workforce. Real earnings growth rates of 0%, 1% 1.5% and 2% per annum have been used to establish the lifetime earnii.gs stream. For all four retirement ages used, retirement is assumed to occur in 1985, so the age-specific level ot real earnings will vary with the age of retirement as well as the rate of real earnings growth. In Table 7 we report the annual contribution rate that would produce a pension replacement rate of 15%, 33%, 50% and 70% for the average fully-employed LIFEMOD male worker, given alternative assumptions about contribution age, retirement age, age at death, real earnings growth and real rate of return as applied to both contribution and annuity streams. Since the model is of a funded scheme, we have incorporated some plausible adminstration costs which might be levied on contributors to such a pension scheme. We have assumed that the whole of the first year's contribution, and 5% of each subsequent year's contribution, will be devoted to costs of administration. This is equivalent to a reduction in yield of about 1.3%, which is in the mid-range of administration charges currently levied by the British personal pensions industry.

31