rafmacalaba/datause-encoder-data huggingface dataset:
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE anchor for the rest. Half a dozen of the SADC’s member states are large or potentially large economies— such as Angola, the Democratic Republic of Congo, Mozambique, Tanzania, Zambia, and Zimbabwe. Knitting these emerging economies more closely together and linking them to markets in South Africa would help to create a larger market and greater economic opportunities in the region.

**Figure 1.1b Infrastructure’s potential future contribution to economic growth (% GDP per year)**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>North Africa West Africa East Africa Southern Cen<br>Source: Calderón 2009.<br>Note: GDP = gross domestic product<br>per capita<br>pact of better infrastructure on growth<br><!-- End of picture text --> There are two bands of intense economic activity in the region. The first and more intense band runs from Durban in South Africa northward up through Lesotho, Gauteng, Zimbabwe, and into the Zambian copper belt. The second (less intense) band runs from northern Angola across the southern Democratic Republic of Congo and all the way through Tanzania. Outside these areas, economic density trails off steadily in the arid areas of Botswana, Namibia, southern Angola, and Western Zambia; the forests of northern Democratic Republic of Congo; and the more depressed agricultural areas of Malawi and northern Mozambique (figure 1.2b).

Regional integration is the only likely way to overcome existing handicaps and to allow the SADC member states to participate in the global economy. Sharing infrastructure addresses problems of small scale and adverse location. Joint provision increases the scale of infrastructure construction, operation, and maintenance. Economies of scale are particularly important in the power and ICT sectors. Big hydropower projects that would not be economically viable for a single country make sense when neighbors share their benefits. Connecting countries via undersea cable or satellite communications requires large up-front investments that require a regional approach. Integrating physical infrastructure is both a precursor to and enabler of deeper economic integration, thereby helping countries to gain scale economies and harness regional public goods.

3
# **Table A.8. The district fixed-effect RDD-DD regressions of employment of individuals using the sample without the program** **<u>135 communes</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Currently<br>working<br>(yes=1, no=0)|Self-<br>employed<br>farm work<br>(yes=1, no=0)|Self-<br>employed<br>non-farm<br>work (yes=1,<br>no=0)|Having wage<br>job (yes=1,<br>no=0)|Log of<br>monthly wage<br>(wage<br>workers)|Log of<br>monthly wage<br>(all workers)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|-0.0356|-0.1896***|0.0623***|0.0917*|0.1535|0.7408**|
||(0.0292)|(0.0623)|(0.0225)|(0.0501)|(0.2958)|(0.3287)|
|(Poverty rate – 50) * Post-program|0.0020|-0.0140*|0.0050|0.0109**|0.0276|0.0326|
|period|(0.0040)|(0.0084)|(0.0034)|(0.0047)|(0.0398)|(0.0337)|
|Program * (Poverty rate – 50) * Post-|0.0002|0.0184**|-0.0061*|-0.0121***|-0.0297|-0.0665**|
|<br>program period|(0.0040)|(0.0080)|(0.0033)|(0.0041)|(0.0388)|(0.0310)|
|Age|0.0378***|0.0229***|0.0042***|0.0107***|0.0422***|0.1122***|
||(0.0017)|(0.0014)|(0.0006)|(0.0008)|(0.0063)|(0.0077)|
|Age squared|-0.0005***|-0.0003***|-0.0000***|-0.0001***|-0.0006***|-0.0015***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|
|Male|0.0130**|-0.0656***|-0.0086**|0.0872***|0.0968***|1.0201***|
||(0.0058)|(0.0085)|(0.0034)|(0.0056)|(0.0248)|(0.0655)|
|Ethnic minorities (yes=1, Kinh=0)|0.0497***|0.1700***|-0.0961***|-0.0242|-0.3798***|-0.2234|
||(0.0129)|(0.0283)|(0.0121)|(0.0169)|(0.0595)|(0.1549)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1920***|0.2662***|0.0321**|-0.1063***|6.9410***|-0.7118***|
||(0.0379)|(0.0366)|(0.0127)|(0.0212)|(0.1305)|(0.2077)|
|Observations|91,916|91,916|91,916|91,916|19,209|91,916|
|R-squared|0.292|0.204|0.068|0.183|0.189|0.162|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

58
managers and production employees. Most of the interaction between GVC and such barriers are not statistically significant.

||Table 5|. Female L|abor Force|Participatio|n, GVCs an|d Barriers–|All region|s|
|---|---|---|---|---|---|---|---|---|
|||Finance|Tax.Rates|Pol. Inst.|Corrup.|Comp.|Elec.|Inad.Edu|
||||(a)|Female Own|<br>ership||||
||(1) GVC|-0.0154|0.0174|-0.0126|-0.0144|0.000437|0.00378|0.0281**|
|||(0.0121)|(0.0143)|(0.0123)|(0.0110)|(0.0128)|(0.0145)|(0.0124)|
||(2) Obstacle|-0.0788*|0.0114|-0.0160|-0.0617|-0.0276|0.0274|-0.0724*|
|GVC1||(0.0457)|(0.0423)|(0.0433)|(0.0523)|(0.0406)|(0.0385)|(0.0395)|
||(1)*(2)|0.160***|-0.0178|0.104***|0.125***|0.0681|0.0368|-0.0817|
|||(0.0543)|(0.0466)|(0.0394)|(0.0408)|(0.0543)|(0.0494)|(0.0514)|
||Observations|83,896|83,896|83,896|83,896|83,896|83,896|83,896|
||R-squared|0.115|0.115|0.115|0.115|0.115|0.115|0.115|
||(1) GVC|-0.145***|-0.0469|-0.111***|-0.117***|-0.136***|-0.0665|-0.0303|
|||(0.0311)|(0.0410)|(0.0330)|(0.0330)|(0.0302)|(0.0416)|(0.0426)|
||(2) Obstacle|-0.0529|0.00979|0.00591|-0.0399|-0.0202|0.0342|-0.081**|
|GVC4||(0.0449)|(0.0408)|(0.0420)|(0.0519)|(0.0393)|(0.0377)|(0.0382)|
||(1)*(2)|0.317**|-0.228*|0.0666|0.112|0.240|-0.130|-0.364**|
|||(0.139)|(0.124)|(0.0963)|(0.108)|(0.147)|(0.132)|(0.157)|
||Observations|83,896|83,896|83,896|83,896|83,896|83,896|83,896|
||R-squared|0.116|0.116|0.116|0.116|0.116|0.116|0.116|
||||(b)|Female Man|ager||||
||(1) GVC|-0.0192**|-0.00447|-0.0175*|-0.023***|-0.0123|-0.0170|-0.0122|
|||(0.00861)|(0.00944)|(0.00899)|(0.00843)|(0.00833)|(0.0111)|(0.0102)|
||(2) Obstacle|-0.0219|-0.0235|-0.0773**|-0.0896**|0.00312|-0.0228|-0.0704*|
|GVC1||(0.0393)|(0.0348)|(0.0373)|(0.0386)|(0.0427)|(0.0311)|(0.0394)|
||(1)*(2)|0.0490|-0.0249|0.0282|0.0559**|0.00969|0.0253|0.00989|
|||(0.0339)|(0.0285)|(0.0223)|(0.0275)|(0.0346)|(0.0337)|(0.0431)|
||Observations|84,287|84,287|84,287|84,287|84,287|84,287|84,287|
||R-squared|0.099|0.099|0.099|0.100|0.099|0.099|0.099|
||(1) GVC|0.00141|0.0161|0.00753|0.0106|0.00932|0.0186|-0.00947|
|||(0.0152)|(0.0182)|(0.0154)|(0.0158)|(0.0154)|(0.0223)|(0.0199)|
||(2) Obstacle|-0.00961|-0.0248|-0.0705*|-0.0767**|0.00867|-0.0138|-0.0699*|
|GVC4||(0.0395)|(0.0346)|(0.0374)|(0.0389)|(0.0411)|(0.0306)|(0.0401)|
||(1)*(2)|-0.0454|-0.0971*|-0.0629|-0.0924*|-0.0918|-0.102|0.0219|
|||(0.0679)|(0.0554)|(0.0403)|(0.0509)|(0.0702)|(0.0677)|(0.0776)|
||Observations|84,287|84,287|84,287|84,287|84,287|84,287|84,287|
||R-squared|0.099|0.099|0.099|0.099|0.099|0.099|0.099|
||||(c)|Female Emplo|yees||||
||(1) GVC|0.0913**|0.131***|0.0467|0.0448|0.0831**|0.0694*|0.0570|
|GVC1||(0.0376)|(0.0370)|(0.0352)|(0.0348)|(0.0378)|(0.0373)|(0.0386)|
||(2) Obstacle|-0.568**|-0.240|-0.0555|-0.241|-0.405|0.558***|-0.826***|
|||(0.243)|(0.228)|(0.205)|(0.233)|(0.245)|(0.213)|(0.271)|

19
security in healthcare and pensions (from the last change in status). This stage also includes a detailed module on past shocks (in the last 12 months) and the response to them. These include economic shocks (such as unemployment or the unexpected loss of assets); health problems (such as illness or grave injury), or of other types (for example, catastrophes or personal calamities such as marital separation or abandonment by an important household member). There is also a module on the perception of future risks (for the following 12 months) and the strategies, planned or executed, available to confront them. This round also includes a new module on valuations, knowledge, and participation in social programs, which is put to each household irrespective of the beneficiary’s social stratum.

Table A.1 presents the survey’s basic descriptors.

# **Table A.1 Selected Descriptors of the ELS 2008** .

|**Variable**|**Average **|**Standard**<br>**Deviation**|**Min**|**Max**|
|---|---|---|---|---|
|**Individual and household**|||||
|Age|33.4|21.3|0|99|
|Gender(% of males)|46.4|49.9|0|1|
|Years of schooling|9.0|5.2|1|26|
|Household size|4.8|1.9|1|12|
|**Labor**|||||
|Proportion of occupied workers(as % working-agepopulation,WAP)|49.8|50.0|0|1|
|Proportion of unemployedpeople(as % of WAP)|5.6|22.9|0|1|
|Proportion of inactivepeople(as % of WAP)|44.6|49.7|0|1|
|Proportion of salaried workers(as % of Occupied Individuals)|60.8|48.8|0|1|
|Proportion of self-employedpeople(as % of occupied workers)|37.7|48.5|0|1|
|Proportion of non-wagedperson(as % occupied workers)|1.5|12.2|0|1|
|Salaried workers with fullpackage of benefits(<sup>1</sup>)|5.4|22.6|0|1|
|Salaried workers with incompletepackage of benefits|60.2|49.0|0|1|
|Salaried workers with no benefits|34.5|47.5|0|1|
|“Obliged" salaried worker(as % of salaried workers)|55.6|49.7|0|1|
|“Voluntary” salaried worker(as % of salaried workers)|44.4|49.7|0|1|
|“Obliged” self-employedperson(as % of self-employed)|51.3|50.0|0|1|
|“Voluntary” self-employedperson(as % of self-employed)|48.7|50.0|0|1|
|Current occupied workers withpreviousjobs :|53.8|49.9|0|1|
|Transition from formal salaried to formal self-employed|2.6|16.0|0|1|
|Transition from formal self-employed to formal salaried|0.9|9.5|0|1|
|Transition from formal salaried to informal|27.0|44.4|0|1|
|Transition from formal self-employed to informal|0.6|7.5|0|1|

23
**Table 7: Breakdown of Population, GDP per Capita, and Zhongkao Scores by County, 2014**

||**Populatio**|**n**|**GDPper Ca**|**pita(Y)**|**Zhongkao**|**Score**|
|---|---|---|---|---|---|---|
||**Absolute**|**Rank**|**Absolute**|**Rank**|**Absolute**|**Rank**|
|**Dianbai**|1,460,000|5|28,969|3|398/780|7|
|**Lianjiang**|1,485,000|3|24,006|8|409/920|16|
|**Wuhua**|1,363,000|7|11,452|16|426/800|3|

Source: Data provided by Guangdong pilot counties, 2015.

While all counties have roughly equal populations, they differ greatly in economic output and academic achievement. Wuhua, the poorest of the 16 project counties, is ranked an impressive third among the counties by average Zhongkao score. Lianjiang, with triple the GDP per capita of Wuhua, is ranked last of the project counties by this score. Dianbai, third richest among the project counties, is right in the middle of the group for this score. For the 16 project counties, the overall correlation between GDP per capita and Zhongkao score (expressed as a percentage) is only 0.15. This is worth further exploration, and raises questions over the best way to rank the counties when evaluating the data.

Despite these differences in GDP per capita and educational attainment among the three counties, the average CLASS scores were all relatively similar (figure 7). Wuhua, which scored highest among the three counties in its Zhongkao results, scored the lowest in CLASS scores for Analysis and Inquiry and Quality of Feedback, as well as Student Engagement, Regard for Student Perspectives, and Positive Climate. It had the highest Negative Climate scores.

**Figure 7: Average CLASS Scores by County**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>Dianbai<br>2<br>Lianjiang<br>1<br>Wuhua<br>0<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

16
|P058476|2001|2007|8/10/2006|16 Closed|Latvia|
|---|---|---|---|---|---|
|P058476|2001|2007|2/14/2007|17 Closed|Latvia|
|P058477|2001|2011|6/26/2011|1 Closed|Latvia|
|P058477|2001|2012|12/12/2011|2 Closed|Latvia|
|P058477|2001|2012|4/17/2012|3 Closed|Latvia|
|P058477|2001|2013|7/9/2012|4 Closed|Latvia|
|P058477|2001|2013|12/27/2012|5 Closed|Latvia|
|P058477|2001|2013|6/17/2013|6 Closed|Latvia|
|P058503|2003|2003|6/5/2003|3 Closed|Brazil|
|P058503|2003|2004|6/18/2004|5 Closed|Brazil|
|P058503|2003|2005|4/19/2005|8 Closed|Brazil|
|P058503|2003|2006|5/1/2006|9 Closed|Brazil|
|P058503|2003|2007|11/20/2006|10 Closed|Brazil|
|P058503|2003|2007|5/31/2007|11 Closed|Brazil|
|P058503|2003|2008|12/9/2007|12 Closed|Brazil|
|P058503|2003|2008|6/19/2008|13 Closed|Brazil|
|P058503|2003|2009|12/18/2008|14 Closed|Brazil|
|P058512|2000|2000|6/22/2000|1 Closed|Bosnia and Herzegovin|
|P058512|2000|2001|6/18/2001|3 Closed|Bosnia and Herzegovin|
|P058512|2000|2002|6/18/2002|5 Closed|Bosnia and Herzegovin|
|P058512|2000|2003|6/17/2003|7 Closed|Bosnia and Herzegovin|
|P058512|2000|2004|6/4/2004|9 Closed|Bosnia and Herzegovin|
|P058512|2000|2005|12/8/2004|10 Closed|Bosnia and Herzegovin|
|P058520|1999|1999||1 Closed|Latvia|
|P058520|1999|2000|6/26/2000|3 Closed|Latvia|
|P058520|1999|2001|6/18/2001|5 Closed|Latvia|
|P058520|1999|2002|4/5/2002|7 Closed|Latvia|
|P058520|1999|2003|5/22/2003|9 Closed|Latvia|
|P058520|1999|2004|12/17/2003|10 Closed|Latvia|
|P058521|2001|2001||0 Closed|Bosnia and Herzegovin|
|P058521|2001|2002|6/18/2002|2 Closed|Bosnia and Herzegovin|
|P058521|2001|2003|6/25/2003|4 Closed|Bosnia and Herzegovin|
|P058521|2001|2004|6/28/2004|6 Closed|Bosnia and Herzegovin|
|P058521|2001|2005|5/18/2005|8 Closed|Bosnia and Herzegovin|
|P058521|2001|2006|12/7/2005|9 Closed|Bosnia and Herzegovin|
|P058521|2001|2007|3/14/2007|10 Closed|Bosnia and Herzegovin|
|P058521|2001|2008|2/19/2008|11 Closed|Bosnia and Herzegovin|
|P058526|1999|1999||1 Closed|Argentina|
|P058526|1999|2000|6/30/2000|4 Closed|Argentina|
|P058526|1999|2001|11/30/2000|5 Closed|Argentina|
|P058536|1999|1999||1 Closed|Thailand|
|P058536|1999|2019|5/7/2019|2 Closed|Thailand|
|P058544|2000|2000|6/30/2000|1 Closed|Cambodia|
|P058544|2000|2001|6/20/2001|3 Closed|Cambodia|
|P058544|2000|2002|6/25/2002|5 Closed|Cambodia|
|P058544|2000|2003|5/13/2003|8 Closed|Cambodia|
|P058544|2000|2004|12/19/2003|10 Closed|Cambodia|
what he saw as the “silence about poverty” in the US. This is, however, hard to reconcile with Figure 1; the rising attention to poverty seen in the _Viewer_ after 1960 is hardly suggestive of a silence about poverty (though an even louder voice may have been needed).

Few of these anecdotal observations from the historical literature are borne out by Figure 1. While there is no doubt that the late 19th century was significant in the emergence of scholarly research on poverty, there is little convincing sign of heightened public awareness around that time. (There is at most a small positive blip around 1900 that might reflect, but it can hardly be deemed significant, as similar blips occurred at other times).

However, the changes in public awareness of poverty in both Poverty Enlightenments were undoubtedly grounded on less popular, more scholarly, writings over many decades. Other factors were no doubt also at work. One can speculate that fears of revolution may have led to efforts in popular writings to justify poverty and inequality in the late 18th and early 19th centuries—efforts that Landauer (1959) argued helped stimulate the development of socialism. The spread of literacy may well have helped in generating a more empathic literature by the time of the Second Poverty Enlightenment.

It must also be realized that the time of the First Poverty Enlightenment lacked the body of theories and data that we take for granted today in trying to understand poverty and link that understanding to public action. Economics was still in its infancy. Systematic sample surveys of household livings standards were unknown. Nor was there much sign yet of the theories and movements that could claim to represent the interests of poor people. All that had changed by the Second Poverty Enlightenment, such that authors like Harrington and Galbraith could formulate accessible arguments grounded on a body of theory and data, the latter stemming from sample surveys and much analytic work in measuring living standards and setting poverty lines.

While tracking words over time cannot resolve all these historical issues, some further clues can be obtained by using the _Viewer_ to track other n-grams related to poverty (including “inequality”) and policies for fighting it.

17
23

# **h. The Bank u** Involvement in **Yopulation**

61. The World Bank entered the population field in 1969 out of growing concern that rapid population growth was a serious impediment to economic and social development in many countries. The Bank has maintained this position although it recognizes the complexity of the relationship. Indeed the 1984 World Development Report -- focused on population and timed to coincide with the International Conference on Population in Mexico -- was a major contribution in the field.

62. At that Corference, Bank President A.W. Clausen gave fresh impetus to Bank population activities. He pledged a doubling of Bank population and related health lending, to $500 million by 1990. Bank President Barber Conable endorsed this commitment in his speech to the Safe Motherhood Conference in Nairobi in 1986: "By 1990 we expect to have projects in about 50 countries with approximately 12 to 14 new operations a year. Lending for population, health and nutrition could reach $500 million a year, about twice our level in 1984-5." 63. In FY86, the total amount of PHN lending was $420 million. In terms of volume, lending in the sector was therefore already close to the target. An estimated $129 million (31%) of this was committed to population, according to an external review<sup>6</sup> of the Bank's lending in population. The review noted that this compared favorably to other donor funding -- about the same as the UNFPA budget and about half the size of the USAID budget for population activities at that time.

64. As the largest economic development agency, the Bank has the status and opportunities to discuss development policies across the board, and has often used its position to draw attention to population issues. "Perhaps the single most effective element in the Bank's work on population is the policy dialogue that links population issues with other aspects of development," says one review.<sup>7</sup> This policy dialogue has firm foundations in the Bank's economic and sector work which also paves the way for project identification and development. Since 1979, the Bank's ability to lend directly for health projects has greatly increased its opportunities to lend for population. And the Bank can complement assistance to family planning programs with activities in fields known to increase the demand for such services -- for example, female education.

65. On the other hand, the Bank faces certain difficulties in doing population work. For one thing, the political sensitivity of population issues limits governments' willingness to borrow for population. For another, the Bank provides assistance through loans, not grants as other population donors do, and some governments are reluctant to borrow for the social sectors (see para. 73).

6 Simmons and Maru, 'Population Lending and Sector Review'.

7 Ibid., p. 73.
|Advanced|Emerging|Developing|All|Advanced&Emerging|
|---|---|---|---|---|
|0.98(0.12)|0.92(0.10)|0.77(0.06)|0.89(0.08)|0.94(010)|

Table A.3: Exponent of cross-sectional dependence ( _α_ ), from Bailey et al. (2016b), of the flows of each country group shown. Two-standard errors shown in brackets.

||Advanced|Emerging|All|
|---|---|---|---|
|Infows|0.35 (0.06)|0.39 (0.05)|0.40 (0.05)|
|Outfows|0.45 (0.17)|0.69 (0.07)|0.45 (0.08)|
|Both|0.29(0.04)|0.60(0.07)|0.60(0.06)|

Table A.4: Exponent of cross-sectional dependence ( _α_ ), from Bailey et al. (2016b) of the residuals of the estimated model, by country group. Twostandard errors shown in brackets.

|Group||All|Adv|anced|Eme|rging|
|---|---|---|---|---|---|---|
|Flow|Infows|Outfows|Infows|Outfows|Infows|Outfows|
|IPS|0.|0.|0.|0.|0.|0.|
|Hadri|.81|.88|.86|.70|.63|.81|

Table A.5: Estimation residuals. Top row: p-values for the Im-PesaranShin (IPS) panel test of the null that all series have a unit root, implemented with 2 lags. Bottom row: p-values for the Hadri panel test of the null that no series has a unit root, implemented with a Bartlett kernel and 2 lags.

52
40 publicly provided services and, hence, about how the benefits of social expenditures are distributed across socio-economic groups. The paper has characterized the profile of education and health facilities utilization and the incidence of social sector subsidies using household level data for 1987. It has also examined how the utilization and subsidy incidence profiles have altered since the late 1970s, a period which has seen a steady fall in absolute poverty in Indonesia.

In the education sector, the paper finds that the proportion of children attending school in each age group is correlated with living standards as measured by consumption expenditures, and that this correlation is increasing in education level, from negligible divergence across consumption groups at the primary school age level to considerable divergence at the senior secondary and university age group levels. Enrollments are higher in urban than in rural areas, for male than for female children, and in the Outer Islands relative to Java.

The observed patterns are qualitatively similar to those indicated for 1978. But there are some quantitative differences. Level improvements have occurred for all groups at all schooling levels. Although disparities similar to those found in 1978 are still evident, they are much less pronounced. Thus, rates of improvement have been higher for rural children, female children, and poorer children -- precisely the groups which had been lagging most in the late 1970s. In education at least, there is distinct evidence of catching up by the sector's historically disadvantaged groups.

This result cannot be attributed to higher overall living standards alone. A decomposition of the percentage change in aggregate enrollments between 1978 and 1987 has been used here to investigate what contribution changes .n living standards have made relative to other factors such as government policies and taste changes. The results indicate that rising living standards played a part (particularly at the highest education levels and for male enrollments), but that other factors (identified by holding the 1978 distribution of consumption levels constant) contributed
# **8 Appendix B: Figures**

<!-- Start of picture text --> 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Foreign Domestic Institutions<br>Domestic Individuals<br>.8<br>.6<br>.4<br>.2<br><!-- End of picture text -->

**Figure 1.** Percentage of monthly traded value by investor type relative to the total traded value of the month.

<!-- Start of picture text --> 2008q3 2010q1 2011q3 2013q1 2014q3<br>Foreign Domestic Individuals<br>Domestic Institutions<br>.12 .9<br>.1 .88<br>.08 .86<br>.06 .84<br>.04 .82<br>.02 .8<br><!-- End of picture text -->

**Figure 2.** Percentage of stock ownership by investor type. Foreign (solid line) and domestic individual (light solid) ownership in the left axis. Domestic institutions in the right axis (dashed line).

40