rafmacalaba/datause-encoder-data huggingface dataset:
_Baltics_ and the _CIS,_ as compared to around 2 in OECD countries. By contrast, two _CEE_ countries - the _Czech Republic_ and _Hungary -_ had significantly higher shares of 4 to 5 percent.

**Table 17. Capital Adequacy Ratio**

(End of Period)

|**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|
|---|---|---|---|---|---|---|---|---|
|_Central and Eastern Europe_|||||||||
|**Bulgaria**||||8.6|4.6|17.6|26.7|37.3|
|**Czech Rep.**|||||10.2|9.8|9.5|12.0|
|**Hungary**|||||16.9|17.6|18.3||
|**Poland**|||16.5|20.8|18.5|17.5|16.9|15.0|
|**Average**|||||12.5|_15.6_|**17.9**||
|_Baltics_|||||||||
|**Estonia**||||13.4|13.7|12.1|13.5|16.9|
|**Latvia**||||14.0|20.0|23.0|21.0|17.4|
|**Lithuania**||||||**10.5**|10.8|23.8|
|**Average**||||||15.2|_15.1_|19.4|
|_CEE_|||||||||
|**Georgia**|||||4.0||||
|**Kazakhstan' **|||||3.0|19.0|23.0|30.0|
|**Kyrgyz Rep.**|||||5.0||||
|**Ukraine**||||**l.0**|**4.0**||||
|**Average**|||||**4.0**||||

_Source:_ EBRD (1998) "Transition Report", International Monetary Fund, Central Banks. 1/ Data for 1996-1998 are taken from IMF (1999) "IMF Staff Country Report, pp. 69-70." The IMF noted that these figures should be used with care.

# **d. Confidence in the Banking Sector**

The experience of the twelve countries was generally positive with respect to confidence in the banking sector in the aftermath of bank crisis resolution as measured by the currency to deposit ratio<sup>47</sup> and the ratio of MI to M2 (Chart 5)48 Virtually all twelve countries experienced a decline in the confidence in the banking sector during banking crises, as measured by the currency to deposit ratio. Confidence did not recover in all the countries after the crises. Among the _CEEs,_ there was recovery in confidence (that is, the ratio fell) only in _Macedonia_ and _Poland,_ while in the _CIS_ countries, there was recovery only in _Georgia._ On the other hand, in all three _Baltics_ states, and in particular in _Estonia,_ confidence in the banking sector rose after crisis resolution. In 1998, confidence in the banking sector as measured by the currency to deposit ratio was similar in the _CEEs_ and the _Baltics,_ where the ratio was between 90 to 95 percent in these countries, compared with around 30 percent in OECD countries. The confidence in the banking sector was much lower in the _CIS,_ where the ratio was over 400 percent.

Data on the ratio of MI to M2 shows a generally positive experience for all the countries. With the exception of _Macedonia,_ all the countries showed a decline in the ratio in the aftermath of bank crisis resolution, reflecting an increase in confidence in the banking sector.

> **47** See Appendix Table 11 for data on currency deposit ratios for the twelve countries and for OECD countries. 48 See Appendix Table 12 for data on MI/M2 for the twelve countries and for OECD countries.

46
**Figure 19. Effect of change in net migration on gross financial assets**

<!-- Start of picture text --> with respect to net migration: 15,000 and 5,000 (respectively from the top)<br>140 .<br>120<br>100<br>Bo<br>60<br>% of GDP<br>40<br>20<br>96197 00/01 10111 20121 30/31 40141 50151<br><!-- End of picture text -->

# **b) Fertility and mortality**

Rates of fertility and mortality negatively affect fiscal balances if they lead to higher proportions of children and the elderly (and a lower proportion of the working population).

The sensitivity tests indicate that fiscal balances are strongest when both fertility and mortality rates are high. Compared with the baseline, low fertility/low mortality and high fertility/low mortality worsen the future fiscal position. _2_ 1 Low mortality (high life expectancy) is the leading factor weakening the fiscal stance since spending on the elderly exceeds spending on children.

The worst fiscal imbalance would result under the low fertility/low mortality scenario, under which the system dependency ratio would approach 70 percent (nearly _21_ All of these alternatives assume the baseline immigration figure (15,000 until 1999 and 5,000 thereafter) and had been provided by NZ Department of Statistics. Mortality rates reflect mainly life expectancy at birth. Low mortality assumes an increase in life expectancy of about seven years; high mortality assumes an increase in life expenctancy of about 3 years by the year 2050.

27
background. It presents empirical evidence on the levels and trends in inequality in access to basic services among children and the resulting inequality in development outcomes early in life.

The paper is structured as follows. Section 2 presents a brief literature review of the early risk factors and associated health and education outcomes for children. Section 3 presents the data and empirical methodology. Sections 4 and 5 present the main results and discussions, while Section 6 concludes the paper.

# **2. Literature Review**

The literature on inequality of opportunity emphasizes the differences in outcomes that arise from differences due to predetermined circumstances over which an individual has no control. An increase in the coverage of a basic service increases equality of opportunity and an increase in coverage for the disadvantaged group of children carries with it further reduction in inequality of opportunities (Barros et al., 2009).

Numerous studies have been undertaken to examine the extent of inequality of opportunity on early childhood development. In a study investigating the correlation between socioeconomic status and malnutrition among children under the age of 5 in South Africa, Zere and McIntyre (2003) analyzed household income and expenditure survey data and found that stunting and wasting were most highly concentrated in the poorest regions of the country. The study found wasting and stunting to be phenomena the poor suffered from the most. It also highlighted that inequality was highest along racial lines: among the white population, no significant inequities were observed, while nonwhite children from metropolitan areas displayed the highest levels of stunting. A similar study undertaken in Brazil analyzed the prevalence of child malnutrition in relation with income and basic services redistribution policies (Monteiro et al. 2009). The study found that over the 33 years examined, the gap between children from poor and rich households in terms of stunting had 4
large and significant impact on local structural transformation. A one percentage point increase in FDI shock reduces change in agricultural employment share by 1.8 percentage points and increases changes in industrial and services employment share by 1.1 percentage points. Considering that average employment share in agriculture only declined by 9 percentage points from 1990 to 2005, a one percentage point increase in FDI shock accelerates a city’s pace of moving out of agriculture by around 3 years. The effect on industry employment share is insignificant, but manufacturing FDI significantly accelerates labor movement into services. Sectoral GDP shares reveal a similar impact. Cities that experienced larger FDI inflow shocks saw a faster decline in agricultural GDP share, and higher growth in industry and services GDP share.

Table 2. FDI inflows and local structural transformation

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Pan<br>|el A: Employm<br>|ent<br>||Panel B: GDP<br>|<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆agri_gdps|∆indu_gdps|∆serv_gdps|
|FDI_shock|-1.849**|1.098|1.050***|-2.379***|0.959***|0.863***|
||(0.826)|(0.675)|(0.334)|(0.390)|(0.329)|(0.239)|
|L.FDI_empshare|-0.156***|0.152***|0.0174|-0.0505|0.0322|0.00247|
||(0.0374)|(0.0341)|(0.0340)|(0.0328)|(0.0422)|(0.0336)|
|Observations|665|665|665|531|531|531|
|R-squared|0.463|0.504|0.368|0.772|0.659|0.720|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI inflows on city level structural transformation. Dependent variables are changes in city level agriculture, industry, and services employment share in Panel A and sectoral GDP share in Panel B. FDI_shock is FDI inflow shock calculated as the interaction of initial industry composition across cities and industry FDI growth explained by policy changes. Lagged city characteristics include average years of schooling, population, average age, share of Han ethnicity. The regressions also control lagged sectoral employment or GDP share. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 3.2 Local human capital accumulation

FDI inflows also induce positive effect on school enrollment. Column (1) of Table 3 reports the effect of FDI shock on change in high school enrollment, and column (2) reports the effect on change in college enrollment. A one percentage point increase in FDI shock raises a city’s change in high school enrollment by 1.2 percentage points, and the change in college enrollment by 1.5 percentage points. The coefficients on FDI shock are highly significant in both columns.

Table <u>3. FDI inflows and local school enrollment</u>

||(1)<br>Panel A: School enrollment<br>|(2)<br>|
|---|---|---|
|Dependent var|∆Highschool|∆College|
|FDI_shock|1.162***|1.465***|
||(0.187)|(0.222)|
|L.FDI_empshare|-0.0731***|0.0470*|
||(0.0279)|(0.0269)|

16
# **Table 5: Effect of Program Availability on Mother’s Time Spent on Housework**

# **Minutes per typical school day mother spent on housework**

||**Pooled OLS**||**Fixed**|**Effects**||
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Treatment District|-89.942***<br>(25.073)|||||
|Round 3|-133.901***|-133.743***|-157.467***|-176.368***|-197.911***|
||(13.073)|(13.166)|(18.160)|(33.311)|(35.644)|
|Round 4|-103.437***|-106.081***|-135.484***|-139.177***|-161.650***|
||(14.735)|(14.643)|(23.850)|(26.232)|(28.721)|
|Treatment District * Round 3����|111.537***|111.051***|106.291***|107.095***|101.943***|
||(27.268)|(27.024)|(27.668)|(29.028)|(29.912)|
|Treatment District * Round 4����|116.609***|117.421***|106.512***|98.441***|100.043***|
||(28.902)|(29.384)|(28.564)|(29.117)|(29.899)|
|Constant|606.215***|585.636***|672.595***|665.762***|700.157***|
||(11.083)|(7.038)|(103.884)|(140.299)|(137.522)|
|Number of observations|1,381|1,381|1,381|1,380|1,380|
|Number of mothers||482|482|482|482|
|R2 within||0.109|0.148|0.160|0.165|
|Controls|None|None|Basic|Extended|Full|
|p-value of F-test of��:�����|||0.9928|0.7210|0.9390|

note: *** p<0.01, ** p<0.05, * p<0.1.The mean of the dependent variable for columns 1 and 2 is 0.9688 at baseline. For columns 3 – 7 the mean of the dependent variable at baseline is 516 minutes/typical school day. Basic controls: a series of indicator variables that fully describe the composition of the household by age and gender. For example: an indicator for each number of adult men, women, teenage boys, teenage girls, pre-teen girls, pre-teen boys as well as infant boys and girls. Extended: In addition to basic specification includes age of mother and father as well as indicators for whether this information is missing. Includes an asset index measured as the first principal component of a list of 16 assets over all rounds of the data. Also includes a series of indicators for wealth status of household as measured by wealth relative to five years ago and harvest relative to last year. Full: In addition to extended controls a series of indicator variables for quarter in which household was interviewed. Round 3 is the first post-program year for which LEAPS data on this outcome is available. Heteroskedasticity-robust standard errors in parentheses. Standard errors adjust for clustering at the mother level.

35
<!-- Start of picture text --> N<br>2015<br>2 2017<br>iS<br>$ 2019<br>FS<br>a<br>He}<br>fo)<br>><br>o<br>a<br>3<br>)<br>£<br>a4<br>HH<br>2<br>I<br>aay<br>12<br>300,000 400,000 500,000 600,000<br>Income level (L)<br><!-- End of picture text -->
new, like South Sudan, and therefore are not included in older decades’ averages but could influence the estimated averages of recent periods.

Data sourced by surveys also present coverage and completeness issues. While this data is useful to understand better the reality the limited country-year information could result in a sample-selection bias. For example, the World Bank’s PovcalNet database (now PIP), that provides poverty and income data with an outstanding coverage, has not reported data in six Bottom Billion countries – such as Afghanistan or Myanmar. In nine other cases, household income data is only available for one year, which limits the possibility of estimating growth rates within a period. These include several African low-income countries – such as Chad, Sierra Leone, and Togo. Meanwhile, household income data has better temporal coverage in other Bottom Billion countries that have been more prosper, including Nigeria, Ghana, and Côte d’Ivoire. The same occurs with OECD countries, where household income data has full coverage and a consistent temporal availability. For instance, if we would use the dataset and the benchmark years used in Lakner and Milanovic (2015) we find that one-fourth of the countries have no data or just one year of income data. On the contrary, income data is available for all OECD countries and for 83 percent of the emerging markets.

Limited temporal availability of data in Bottom Billion countries also reduces the possibility of including the full sample for all benchmark years. For instance, the Bottom Billion have on average 2.6 years of benchmark data, compared to 3.3 in the Emerging Markets group and 4.7 in the OECD group. (Figure 23). Therefore, if we reproduce Figures 11 with a sample that excludes countries where income data is not available in the Lakner an Milanovic (2015) dataset, we observe that average GDP growth of the bottom billion group during the New Normal is revised upwards by 0.1 (Figure 24).

The implication of this is that limitations on country coverage may _bias upwards_ the result for the world’s poorest countries – and therefore lead us to be more sanguine, and less concerned about their economic plight. By using wider country coverage, we revise downwards the estimates. If such data limitations systematically bias availability away from the poorest and slowest growing countries, we are likely over-estimating convergence and overlooking the important policy questions posed by continued divergence.

# **Figure 23. Cross-country and temporal data availability**

Panel a. Share of countries with less than 2 benchmark years of data Panel b. Average benchmark years with nonmissing data 30
# **D. Assets and welfare**<sup>**ix**</sup>

Food-poor households appear to have few assets (worth 10.107 FCFA per capita). This implies that in addition to being extremely poor they also tend to be very vulnerable. In case of weather or health shocks they have limited assets that they can sell to meet emergency needs. The food-poor and the malnourished are over-represented among livestock owners: 35 percent of the poor and 36 percent of the food-poor own livestock while 27 percent of all Malian households do (Table 5). However in case of shocks such as droughts, livestock prices often tend to drop dramatically reducing the ability of these households to turn them into cash to purchase emergency needs.

**Table** **<u>5:  Assets of the poor the food poor and the chronically malnourished</u>**

||**Whole**<br>**population **|**Poor**|**Food poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Mean consumption expenditure per<br>capita (FCFA)|206,901|121,025|95,092|177,942|
|Value of owned durable goods|18,571|11,062|10,107|15,408|
|(median per capita, FCFA)|||||
|Owns livestock (cows, sheep, goat,<br>camels) (% of column total that|27%|35%|36%|32%|
|does)|||||
|Value of livestock(median,FCFA)|393,519|450,640|477,897|433,088|

_Source: authors’ calculations using ELIM –MICS 2010 household survey data_ We analyze the characteristics of the food poor and malnourished households with low levels of assets (Table A 2 in Annex). Results suggest that the group with low assets tend to have smaller households and a higher ratio of dependent/ active household members compared to those with high assets. Malnourished households with low assets are more likely to be headed by a member with less than primary education. Furthermore, the amount of assets held does not reveal any geographical pattern, except for Sikasso. While this region houses only 16% of the Malian population, it has 36% of the food-poor with low-asset holdings. Malnourished households with low levels of assets are only overrepresented in farming and primary sector (respectively 84% and 85%).

# **E. Transfers**

Transfers such as remittances from household members or support from the state or NGOs can improve household resilience. We find that the poor food poor and malnourished households are as likely as other households to receive domestic transfers (21-22%) (Table 6). The median value of these transfers (in per capita terms) is however about 2500, 1875 and 3333 FCFA per capita for the poor, the food poor and the malnourished respectively, less than other households , who receive 4166 FCFA per capita. Fewer poor and food-poor households receive foreign remittances compared to all households (12-14% against 19%) but the median value of the transfers received is about the same (about 5000 FCFA). The 8
where young people congregate, and where they can sense what attracts consumers. These "antenna districts" are places where they can see, hear, touch, experiment with, purchase and reengineer the most advanced and potentially trend setting product designs, materials and technologies. [<sup>16</sup> ] Spatial proximity among functions enhances the coordination required for simultaneous development and reduces the time it takes to upgrade existing products and commercialize new ones. Take cell phones, for example. Efforts to upgrade cell phones in response to consumer demand for miniaturization, portability, increased functions, and lower cost require reduced part sizes and increased integration among technologies. Reduced part sizes require advances in packaging techniques, like surface mounting; integration of technologies requires hybrid assemblies; and both require forward and backward coordination among firms.

_Headquarters._ Over a third of Japan's 2.7 million firms headquarter in greater Tokyo, and 60 percent of Japanese companies with capital of more than 100 billion yen (TMG 2002b: 14). (See Table 1) Tokyo's central business districts (CBD) consisting of three central wards— Chiyoda-ku, Chuo-ku and Minato-ku—have the highest _overall_ number of head offices. [<sup>17</sup> Ota-ku, south of the CBD, has the highest concentration of _manufacturing_ head offices, followed by the three central wards. In the electrical machinery industry, Toshiba, Sony, Hitachi, NEC, Canon, Fujitsu, and Oki are all headquartered in Ota-ku, or in adjacent Minato-ku, Meguro-ku, and Shinagawa-ku. [<sup>18</sup> ] Small but globally competitive firms also headquarter in Tokyo, as do many suppliers to the large technology firms.

_Research & development._ Pressures to co-locate functions so as to flexibly respond to fast changing niche markets exist in a state of tension with national land policies and high central city land prices. The central government has prohibited construction of new manufacturing plants and university campuses in Tokyo's urban core to alleviate congestion. Steep land prices also make building large-scale R&D centers in central Tokyo all but prohibitive. Tokyo firms have tried to optimize the conflicting pressures, first, by locating new R&D centers outside but close to the central city, and second, by turning their existing high volume manufacturing plants in Tokyo into R&D linked mother plants performing pilot production of prototypes, and relocating their mass production facilities further out in greater Tokyo and elsewhere in Japan. A recent government survey of location preferences among Japanese firms demonstrates their 18
# **Box  4.1: Survey on Duration of Corporate Bond Issuance Processes**

A survey was conducted on the duration of corporate bond issuance processes in emerging markets by e-mailing a response format to 70 regulators and approximately 30 intermediaries in emerging markets from October 2007 to March 2008. The regulators and intermediaries were asked the number of weeks that corporate issuers realistically take for each of the following: bond issuance authorization by the issuer, documentation, prospectus review by the regulator, offering, and closing of stand-alone public offerings (see Figure 3.3) in their markets.

Useful responses were collected from the regulators and intermediaries of the following countries: Bangladesh, Brazil, Bulgaria, Chile, China, Colombia, the Czech Republic, the Arab Republic of Egypt, Indonesia, Israel, Kazakhstan, Malta, Morocco, Oman, Poland, Romania, Sri Lanka, Thailand, Trinidad and Tobago, Turkey, Uganda, the United Arab Emirates, and Uzbekistan. The responses are not necessarily consistent, and they are small in number. Therefore, the results are not suitable for a rigorous analysis. Nonetheless, they provide a general view of the duration of the corporate bond issuance processes (see Figure 4.1).

# **Figure  4.1: Total Number of Weeks for Issuance, by Country and Region**

<!-- Start of picture text --> (Number of w eeks)<br>0 10 20 30 40 50 60<br>COUNTRIES<br>SSA-1 (R) 32<br>MENA-1 (I) 51<br>MENA-2 (R) 33<br>MENA-3 (R) 24<br>MENA-4 (I) 21 Avg.=23 w eeks<br>MENA-5 (I) 16<br>MENA-6 (R) 14<br>MENA-7 (R) 13<br>MENA-8 (R) 10<br>LAC-1 (R) 34<br>LAC-2 (R)LAC-3 (R) 1818 Avg.=19 w eeks<br>LAC-4 (I) 15<br>LAC-5 (R) 12<br>ECA-1 (R) 36<br>ECA-2 (I) 19 Avg.=15 w eeks<br>ECA-3 (I) 15<br>ECA-4 (R) 14<br>ECA-5 (I) 12<br>ECA-6 (I) 12<br>ECA-7 (I) 12<br>ECA-8 (R) 10<br>ECA-9 (R) 8 Avg.=14 w eeks<br>SAR-1 (R) 17<br>SAR-2 (I) 12<br>EAP-1 (I) 12 Avg.=10 w eeks<br>EAP-2 (I) 8 10<br>EAP-3 (R)<br>SSA<br>MENA<br>LAC<br>ECA<br>SAR<br>EAP<br><!-- End of picture text --> _Note:_ SSA = Sub-Saharan Africa, MENA = Middle East and North Africa, LAC = Latin America and the Caribbean, ECA = Eastern Europe and Central Asia, SA = South Asia, and EAP = East Asia and Pacific Region; R = regulator and I = intermediary. Some responses did not provide the number of weeks for bond issuance authorization by the issuer, offering, or closing; in such cases, the author assigned one week to each of the processes.

18