rafmacalaba/datause-encoder-data huggingface dataset:
-7-

Table 2.1t The Use of Fuel by lector ln thg O.F.C.D. in 1988 (Milllon tons of OiL EquLvalentt MTOE)

||Coal|Other<br>Fuel|OI|Gas|ElectrLcLty|Total|
|---|---|---|---|---|---|---|
|Industry<br>Transport<br>AgrLculture|168.85<br>0.13<br>0.21|69.86<br>0.00<br>0.44|297.73<br>861.13<br>40.72|234.56<br>0.40<br>3.48|195.42<br>6.05<br>3.76|949.38<br>867.71<br>48.61|
|Commerce/Publlc<br>Sector|4.18|0.17|86.24|87.72|121.42|302.06|
|ReusdentLal|17.48|43.56|117.34|198.56|150.49|529.58|

Source Energy Balances of the O.E.C.D. countries, 1987- 9P8, I.E.A. 1989.
Third, the predominantly macro-derived estimates of the housing deficit miss key dynamics within countries for different segments of the population. Only few studies provide disaggregated estimations of housing deficits for different segments of the population (e.g., income groups). As a result, the needs of these segments compared to other countries, and the relative size of the housing deficit is often not fully understood. Through its micro-foundation, the methodology we propose allows not only for the segmentation within countries, pointing towards often veiled variation of housing adequacy, but also enables comparisons of disaggregated estimations (such as housing adequacy deficits in the biggest business city or deficits among the poorest income groups) across countries.

Fourth, in estimating the _AHI_ using HH expenditure and consumption surveys for the respective countries, we are providing principles of global harmonization across seven dimensions of housing adequacy. Understanding HHs’ access to these adequacy dimensions, such as access to water and sanitation, are key for monitoring progress towards the Agenda 2030. Our proposed harmonization can be considered a micro-founded supplement to existing country level measures of these adequacy dimensions. This might be helpful in providing a more nuanced picture of underlying issues for countries that are lagging behind. Also, applying these globalized standards to harmonize housing variables across HCES, we therewith expand the scope of work pioneered by the World Bank Global Monitoring Database (GMD), which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity.

Lastly, within the existing literature, the two realms on housing deficit and adequate housing conditions remain relatively separate. By introducing the _AHI_ , we combine these two realms, outlining how inadequate housing conditions at the HH level contribute to the overall housing deficit. Hence, we move beyond narrow estimations of housing backlogs by only considering severely dilapidated or overcrowded houses and instead add other key dimensions of housing adequacy which have consistently been found to be a determinant of socio-economic development outcomes and which are a key driver to the realization of the right to housing reflected in SDG 11. As a result, estimates of housing backlog available in literature can be understood as a subset of our deficit projections reflected in the _AHI_ . To the best of our knowledge, a global study applying a uniform methodological framework on housing adequacy and related housing deficit estimations is hitherto absent.

# **3. Methodology and Data: Constructing the** **_Adequate Housing Index_**

We develop a global standard for estimating the adequate housing deficit based on seven adequacy criteria that we aggregate into the _Adequate Housing Index_ . In the following section, we define these seven adequacy dimensions in more detail and further describe the multi-purpose household
Table 10. The **Origin** and Destination of Middle Eastern Countries' 1990 **Intra-Regional** Trade (Values **In $000).**

||||||||D|estination of Exp|orts|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Exporter|Bahrain|Cyprus|Egypt|Iran|Israel|Jordan|Kuwait|Lebanon|Libya|Oman|Qatar|Saudi<br>Arabia|Syria|Turkey|UAE|
|Bahrain|--|254|138|108|0|11,088|9,792|12|0|5,995|6,530|107,036|2|4,932|65,857|
|Cyprus|2,645|--|19,113|188|4,011|4.447|4,865|13,050|6,861|2,598|2,764|22,315|1,158|9,186|10.715|
|<br>Egypt|1,520|11,160|--|4,139|167,652|15,728|11,396|9,228|43,550|14.521|4,192|76,579|11,682|16,069|0|
|Iran|7,316|2.571|0|--|0|2,508|10,937|na|2,778|55|14,235|178|32,579|492.399|169,073|
|Israel|0|33,286|6.366|0|-|0|0|0|0|0|0|0|0|88,438|0|
|Jordan|8,529|769|15.992|1,359|0|-|16,646|13,303|6,286|1,224|6,375|70.482|12,670|22.755|32,300|
|Kuwait|8,220|129|5459|3,133|0|10,984|--|2,574|--|4,082|1,520|74,142|426|1.145|28,062|
|Lebanon|11,311|9,725|14,563|na|0|16.619|17558|-|19,962|354|10.903|85.332|16,802|6,284|53,605|
|Libya|0|1,442|78,645|13,823|0|791|27|11,241|--|0|20|370|9,827|506.589|13,310|
|Oman|4,177|178|583|17,423|0|199|944|525|122|-|4,331|33,578|13|21|213,574|
|Qatar|6,664|0|49|40,414|0|5,454|19,258|259|0|12,972|-|61,842|99|445|122,308|
|Saudi Arabia|67,550|12,360|76,340|na|0|119,901|160,838|88,155|4,649|58,153|10,903|-|32,829|723,628|414,777|
|Syria|6,247|6,515|47,671|13,582|0|24,487|12,377|266,135|15.998|574|29,706|270,463|--|113,588|58,984|
|Turkey|3,532|154,841|160,104|495,483|45,504|80.870|92.208|50,666|220,541|4,545|6,115|338,427|194,494|--|75,426|
|UAE|50,561|793|0|na|0|4,531|43,549|na|5,117|580,291|73,522|161,266|4,261|192,511|--|
|MEMO ITEM||||||||||||||||
|Regional Trade||||||||||||||||
|Lxports||||||||||||||||
|Value ($mill.)|211.7|103.9|387.4|734.6|128.1|208.7|139.8|263.0|636.1|275.7|269.8|1,770.1|866.3|1,922.8|1,116.4|
|<br>Percent (%)|2 3|1.2|4.3|8.1|1.4|2.3|i.5|2.9|7.0|3.1|3 0|19.6|9.6|21.3|12.4|
|Value (Smill.)|178.3|234.0|424.0|589.7|217.2|297.5|400.4|455.1|325.9|685.4|171.1|1,302.0|316.8|2,178.0|1,258.0|
|Percent (%)|2.0|2.6|4.7|6.5|2.4|3.3|4.4|_5.0_|3.6|7.6|1.9|14.4|3.5|24.1|13.9|

Source: Computed from trade data extracted from United Nations Series D trade tapes.

Countrv Notes: Qatar - Regional exports as reported by Qatar for 1991 since no data are available for 1990. Bahrain - Regional exports reported for 1988 which is the last year available. Saudi Arabia, Lebanon,UAEand Iran - Reported 1990 imports by Cyprus, Egypt, Israel. Jordan, Libya, Oman, Saudi Arabia, Syria and Turkey, plus 1988 inpors by Bahrain and 1991 imports of Qatar. Exports of Cyprus to Turkey are based on Turkey's reported imports from Cyprus.
_... And volatile_ But there have also been very low real interest rate observations in developing countries, and, despite a low (negative) mean value, the mean of 25 developing country monthly standard deviations (excluding Argentina and Brazil) in our sample is 877 basis points, compared with just 18 7 basis points for the eight control industrial countries. And this is not just due to some outliers: the smallest of developing country standard deviations is 221 basis point fDr Singapore.

# 2.2 Negatively skewed distribution of $-adjusted rates

Volatility and extreme values are also evident in Table 3, which compares the 1980-97 average statistics of monthly wholesale returns for developing and industrial countries adjusted for actual change in exchange rates against the US dollar. Although the mean of developing country $-adjusted money market rates was on average much lower than for industrial countries, this mainly reflected the wider variation over time of exchange-rate adjusted interest rates for developing countries, and in particular the negative skewness (influence of extreme negative observations). In other words, occasional sharp devaluations were not fully compensated-for by a sufficient excess return or peso premia in developing country interest rates in normal times.

Table 3: _Ex post $-adjusted Money Market Rates: Industrial and Developing Countries, Monthly Data: 1980-97_

|_% per annum_|26 Developing Countries|12 Industrial Countries|
|---|---|---|
|_mean over countries of:_|||
|mean (standard error)|0.49 (9.0)|7.34 (1.1)|
|median|3.04|7.56|
|maximum|107.3|86.2|
|minimum|-490|-94.9|
|standard deviation|53.3|28.9|
|skewness|-3.95|-0.1|
|_median over countries of:_|||
|median|_6.95_|6.11|
|standard deviation|35.2|32.5|

Note: 26 large developing countries not including Argentina or Brazil 13 Unit root tests can reject non-stationarity of at least half of the country series at the 5 per cent level, suggesting that these apparent slow oscillations are not just an optical illusion.

9
Policy ReseaRch WoRking PaPeR 4770

# **Abstract**

This paper analyzes the effects of capital controls and crises on international financial integration, using data on stocks from emerging economies that trade in domestic and international markets. The cross-market premium (the ratio between the domestic and international market price of cross-listed stocks) provides a valuable measure of how capital controls and crises affect integration. The paper shows that, contrary to the common perception that capital controls can be easily evaded, they do affect the cross-market premium. Controls on capital inflows put downward pressure on domestic markets relative to international ones, generating a negative premium. The opposite happens with controls on capital outflows. This signals the inability of market participants to engage in perfect arbitrage, due to the segmentation of domestic markets from international ones induced by capital controls. Crises affect financial integration by generating more volatility in the cross-market premium and putting more downward pressure on domestic prices.

This paper—a product of the Development Economics Research Group—is part of a larger effort in the department to understand financial integration and financial crises. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at sschmukler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
||:Nmber|of:||:|<br>:||X|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||:countrle|s:Constant|<br>y|N<br>:|<br>-<br>|i|||2|F|RSS|
||S. PAM|SP||||Y|Y|||||
|5.Regressions::::::::::||||||||||||
|cmperlson||||||||||||
|by perlod.||||||||||||
|GOPgrowth rate.||||||||||||
|Source wrld<br>Bank||||||||||||
|1970-61:<br>cf.eq.1|69|||||||||||
|(27) 1974-41|69|2,663|:<br>0,216|0,473<br>:|<br>-0,032<br>:|<br>-0,016|0,4||0,32|5,942|376,01|
||||;(O,S49)|<br>(2,009)*s|(2,461)|(0,467)|(4,n221)ss|||||
|6.<br>Cross-sectIonal<br>analysis/||||||||||||
|pooling<br>comparison||||||||||||
|(28)<br>Cross-sectional||||||||||||
|regression:||||||||||||
|averae<br>GOPgrowth|||||||||||I|
|rate<br>(1974-81).|:|||||||||||
|Source:<br>World Bonk|40|3,623|:<br>0,110|:<br>0,456<br>:|<br>-0,026<br>:|<br>-0,034|:<br>0,4|:|0,40|4,600|190,37<br>0|
||||1<br>(0,210)|: (1,634)*<br>:|<br>(1,029)|(0,639)|: (3,478)"|:|||I|
|(29)Pooling||||||||||||
|regression:|:|:|:|:||||||||
|annualGOPgrowth||||||||||||
|rate (197541).||||||||||||
|Source: World Benk|40|2,729|:<br>0,192|:0,547<br>:|<br>-0,043<br>:|<br>-0,016|0,473||0,27|20,790|5345,35|
|(280 observations)|||: (0,599)|: (2,907)*|(4,665)ss :|(0,470)|<br>(7,627)*"|||||

Meaning nd sourc of the Independent variables:

- y: Per capita 6NP In USS In Initial **year** ) Source:

- N: Number of Inhabitants In initial year ) World Bank

- _Y:_ Gross doestic product In national currency ) World Tables, 1964

- P: Consume price Index (sourco: _IW,_ IFS)

- 6: Total public expenditure of central government (source: IF, _Government_ Finance Statistics Yearbook - 1906

# Betwoen parenthebss student T:

* Significant at 5S a: Significant at 10%.
<!-- Start of picture text --> Figure 5: Growth in Share of Services and Goods Exports From South Asia and East Asia<br>700<br>South Asia<br>600 service exports<br>500<br>East Asia goods<br>exports<br>400<br>300<br>South Asia<br>goods exports<br>200<br>East Asia service<br>exports<br>100<br>1996 values are indexed to 100<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>a n d se rv ic e s<br>S e rv ic e a n d G o o d E x p o rts/W o rld E x p o rt o f g o o d s<br><!-- End of picture text --> _Source_ : Balance of Payments, IMF. 2008.

_Note_ : Growth in share of export in world export is calculated by taking the respective region’s export as a share of the world’s export of goods and services.

Is South Asia an outlier in terms of service exports? Which kind of services export has grown faster – modern or tradition? What is the level of sophistication of service exports from South Asia?

13
program and fathers are less likely to have completed at least some secondary education, suggesting that they are from relatively more disadvantaged households. Drop-out rates are also higher in schools that held a parent workshop. These differences imply that the parent workshop results are not representative of the full sample, although they are valid for the group of parents who attended a workshop (and the corresponding students).

As shown in Table 9, we find no significant improvements in parents’ financial behavior as a result of watching the financial literacy video – parents who watched this video were no more likely to make a budget or improve savings behavior compared to those who watched the health video. The lack of significant impact of the parents’ workshop may be due to the relatively low intensity of the treatment or other constraints parents face in responding to the information. The exposure to financial education material through the DVD was relatively short and the workshops included no supplementary discussions. This finding is consistent with the existing literature on financial education (see for example, Bruhn et al., 2013, Cole et al., 2011, and Fernandes et al., 2013).

The remarkable result from this intervention, however, is the impact of the parents’ workshop on student behavior. The parent workshops significantly improved the percentage of disposable money saved among students by 2.5 percentage points. Specifically, students whose parents participated in the health literacy workshops saved on average 13.5 percent of their disposable money. In comparison, students whose parent participated in the financial literacy workshops saved 16 percent of their money. This result suggests parents were able to use their improved awareness of financial issues to reinforce the school messages with their children.

# **7.6. Student Graduation**

An important corollary effect of the financial education program was on student graduation rates. One hypothesis on student graduation suggests a positive impact especially if the financial education material covered was engaging and interesting, with the new textbooks providing both teachers and students more motivation and interest in teaching and learning. At the same time, however, a greater emphasis on financial education topics and exercises may have come at the expense of neglecting aspects of the regular curriculum. Hence, it is important to empirically investigate impacts on graduation rates.

The data on graduation comes from administrative records of the Brazilian Ministry of Education for 2011 – the last year of the financial education program when the students in our sample where in the third and final year of high school. While we were not allowed access to individual student-level or class-level data, we were provided information at the aggregate level for final grade classrooms. Table 10 shows the impact of the financial education program on 2011 gradelevel graduation/passing, failure, and dropout rates. Column (1) presents the treatment coefficient on passing rates and finds a 1 percent improvement over the control group, a statistically 22
access to the solar lamp at higher income levels.

These results suggest that the best strategy for improving household electricity access in low‐income countries requires combining short‐term rural electrification efforts using ultra‐low‐cost off‐grid technologies with a range of efforts for longer‐term poverty reduction including investments in human capital and productive infrastructure. As households escape extreme poverty, income growth increases their WTP for electricity services, and on‐grid electrification allows for better development potential in the longer run.

# **References**

Atkeson, A., and P. J. Kehoe, (1999). Models of Energy use: Putty‐putty versus Putty‐ Clay. _American Economic Review_ , _89_ (4), 1028‐1043.

Assunção, J., Lipscomb, M., Mobarak, A. M., and Szerman, D. (2018). Agricultural Productivity and Deforestation in Brazil. Unpublished manuscript.

Bensch, G., J. Peters, and M. Sievert (2011). “Report on ERSEN Baseline Study”. Baseline report on behalf of the German Technical Cooperation (GTZ).

Bensch, G., M. Grimm, J. Langbein, and J. Peters (2013). “The provision of solar energy to rural households through a fee‐for service system”. Final Report on behalf of the Policy and Operations Evaluation Department (IOB) of the Netherlands Ministry of Foreign Affairs.

Bernard, T. (2012). “Impact Analysis of Rural Electrification Projects in Sub‐Saharan Africa.” _World Bank Research Observer_ , 27(1): 33–51 Blimpo, M. P., and Cosgrove‐Davies, M. (2019). _Electricity Access in Sub‐Saharan Africa: Uptake, Reliability, and Complementary Factors for Economic Impact_ . World Bank, Washington, D.C.

27
69

||**Table 26 **|**Money Val**<br>|**ue of Shor**|**tage**<sup>**63**</sup>|||
|---|---|---|---|---|---|---|
|Pension Funds||2003|||2002||
||Under-<br>funding<br>m CHF|% of total<br>assets of PF<br>with DC<sup>64</sup>|% of tot.<br>assets of<br>all PF|Under-<br>funding<br>m CHF|% of total<br>assets of PF<br>with DC|% of tot.<br>assets of<br>all PF|
|Registered PF:|||||||
|PF, no public warranty|8,960|12.00%|2.80%|13,278|11.90%|4.40%|
|PF with public warranty|16,343|31.00%|23.30%|29,253|40.30%|36.10%|
|Total registered|25,303|19.80%|6.50%|42,531|23.00%|11.10%|
|Total all PF|25,376|19.60%|6.30%|42,709|22.90%|9.90%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.5, T3 2: Bundesamt für Sozialversicherung (2003), p.6, T3 Panel A of Table 27 shows the evolution of pension funds with underfunding in recent years. For institutions under private law, this number fell form 583 to 331 cases. The situation of pension funds under public law with a public warranty, however, is different. The number of underfunded funds within this group increased from 38 cases in 2002 to 45 cases in 2003.

Panel B presents the corresponding data for the size of the funding deficit. The number of pension funds under private law with a funding gap improved, but the percentage of the funding shortage with respect to the total assets of these pension funds remained the same. This implies that the average funding ratio increased for those pension funds affected by a shortage in 2002 (assuming that the funds that were close to a funding ratio of 100 percent could recover). For the pension funds under public law with a public warranty the number of underfunded pension funds increased, while the relative deficit with respect to total assets decreased. Nevertheless, the average situation improved considerably, as the total funding gap decreased from 42,531 million CHF to 25,303 million CHF.

**Table 27  Underfunding** **<u>and Legal From</u>**<sup>**65**</sup>

|||P|anel A||Pa|nel B||
|---|---|---|---|---|---|---|---|
||PF wit|h DC|In % of t|otal PF|Underfunding<br>(m CHF)|% of tota<br>of PF wi|l assets<br>th DC|
||2003|2002|2003|2002|2003<br>2002|2003|2002|
|PF under private law|331|583|16.02%|28.02%|7,406 11,213|11.63%|11.89%|
|PF under public law<br>(with no public warranty)|8|21|(34.80%)|(84.00%)|1,554<br>2,065|13.80%|11.70%|
|PF under public law<br>(withpublic warranty)|45|38|54.90%|49.40%|16,343 29,253|31.00%|40.30%|
|Total|384|642|17.80%|29.40%|25,303 42,531|19.80%|23.00%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.7, T6 2: Bundesamt für Sozialversicherung (2003), p.9, T7

> 63without “Collective-Institutions” 64DC = deficient cover

> 65without “Collective-Institutions”
# **References**

Ahn, J., A. Khandelwal, S.J. Wei (2011) "The Role of Intermediaries in Facilitating Trade," December 2010, Journal of International Economics, 84(1), 73-85.

**Antras, P. and D. Chor (2012) "Organizing the Global Value Chain,'' Harvard University Working Papers.**

**Antras, P., Costinot, A. (2011) ‘‘Intermediated Trade", Quarterly Journal of Economics, 2011, vol. 126, issue 3, pp. 1319-1374.**

Antras, P., D. Chor, T. Fally and R. Hillberry (2012) “Measuring the Upstreamness of Production and Trade Flows" forthcoming, American Economic Review Papers and Proceedings, <mark>Vol. 102, No. 3, pp. 412-416.</mark>

**Asiedu, E. and J. Freeman (2007) ‘‘The Effect of Globalization on the Performance of Small-and Medium-Sized Enterprises in the United States: Does Owners' Race/Ethnicity Matter?’’ American Economic Review, Vol. 97, No. 2 (May, 2007), pp. 368-372.**

**Bernard, A., Jensen, J. Bradford, S. Redding, and P.K. Schott (2007) ‘‘Firms in International Trade,’’ Journal of Economic Perspectives, 21(3), pp. 105-130.**

Bernard, A., B. Jensen, S. Redding, and P.K. Schott (2010) "Wholesalers and Retailers in U.S. Trade,” American Economic Review, 100(2), 408-413.

# **Branstetter, L. and Nicolas L. (2006) ‘‘China's embrace of globalization,’’ NBER Working Paper.**

Brandt, L., Van Biesebroeck, J., and Zhang, Y. (2012). “Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing,” Journal of Development Economic, 97(2), 339-351.

**Brooke, K., Meeraus, and Raman (2005) GAMS - User's Guide, Washington, DC: GAMS Development Cooperation.**

Chen, X., Cheng, L. K., Fung, K. C., Lau, L. J., Sung, Y. W., Zhu, K., and Duan, Y. (2012) “Domestic value added and employment generated by Chinese exports: A quantitative estimation,” China Economic Review.

**Costinot, A., J. Vogel and S. Wang, ‘‘Global Supply Chains and Wage Inequality", American Economic Review Papers and Proceedings, forthcoming.**

**Costinot, A., J. Vogel, and S. Wang (2012) ‘‘An Elementary Theory of Global Supply Chains,’’ Review of Economic Studies, forthcoming.**

**De La Cruz, J., R. Koopman, Z. Wang, and S.J. Wei (2010) ‘‘Estimating Foreign Value-added in Mexico’s Manufacturing Exports.’’ USITC Working Paper.**

**Hummels, D., Ishii, J., and Yi, K-M. (2001). ‘‘The nature and growth of vertical specialization in world trade.’’ Journal of International Economics, 54(1), 75-96.**

**Koopman, R., Z. Wang, and S.J. Wei (2008) ‘‘How Much of Chinese Exports is Really Made in**

31