rafmacalaba/datause-encoder-data huggingface dataset:
13 growth. This was largely due to much faster rates population growth - especially in the western part of Indonesia.

Rapid and relatively uniform growth in real RGDP continued across provinces into the 1990s. Growth rates ranged across a relatively narrow band of 5-10 per cent in most provinces in the period 1987-92 (Table 6). It was most rapid in group 3 ('other land abundant' provinces) and slower on Java. But on a per capita basis rates were quite rapid on Java and also in the poorer, densely populated regions in eastern Indonesia.

The time period is too short to draw firm conclusions regarding differing rates of output growth across sectors. However, two patterns appear clear. First, in two of the slowest growing sectors on a national scale - agriculture and government administration - increases in growth were more rapid Outside Java than on Java. Second, manufacturing growth continued to be surprising rapid in most Outer Island groups of provinces during the deregulation period. It was slower in the resource abundant and southern Sumatran groups. But even in these groups, growth rates were still well above five per cent per annum. The Outer Islands did not lag as a group relative to Java in terms of growth in manufacturing output.

What was the impact of these changes on employment and wages? For the period 1987-94, the contrast in employment experience between Java and the Outer Islands was greater than was implied by their respective RGDP growth rates.<sup>2</sup> 1 The absolute decline in employment in the agricultural sector and the substantial rise in manufacturing employment on Java-Bali contrasts with continued labour absorption in agriculture in all of the Outer Island groups.

The share of new jobs created in manufacturing was much higher on Java-Bali than in all the Outer Island groups (Table 7). Whereas close to half all non-agricultural jobs went into manufacturing on Java, the corresponding share was around one-third to one-quarter outside Java.

This pattern is not unexpected. Much of the labour intensive-industry was concentrated on Java. Thus the employment elasticity - growth in employment per (percentage) unit growth in output - could also be expected to be considerably 21 The RGDP and empoyment data cannot be compared directly because they refer to different time periods. Also, as already noted, the growth rates implied by the employment data need to be interpreted with care, given quite large year to year fluctuations by sector - even on a national scale.
36 Bank, and of several household survey collections. We also searched for household surveys online. Through this process, we have so far identified, tried to access and (where possible) vetted 1,752 household survey data sets from 178 countries – see Table 5. We are in the process of identifying, trying to access and vetting other data sets, some of which will be added to the HEFPI data set in due course. Of the 1,752 surveys, 299 are currently inaccessible and 465 lack key variables. The remaining 988 data sets were accessed, many through the World Bank Development Data Hub (DDH).<sup>34</sup> They were then vetted and those that had the necessary information were analyzed; after a series of quality checks (see below for details), 570 data sets were kept.

Most of the surveys are HIES or HBS surveys, or multipurpose household surveys like the LSMS. Very few come from a highly standardized multi-country survey program like the LSMS. However, many of the datapoints come from versions of the microdata that have been harmonized ex post, such as the Luxembourg Income Study (LIS) and various World Bank ex post harmonized data set ‘collections’ (e.g. ECAPOV). Such ex-post harmonization exercises consist of applying a common set of standards and guidelines to the construction of specific variables such as total income, or a consumption aggregate across different data sets. Table 6 summarizes the key details of these survey ‘collections’. Sometimes we have produced and compared results from both the original ‘master’ data set and the ex-post harmonized ‘adaptation’. Indeed, sometimes we have produced and compared estimates from different adaptations. In addition, both masters and adaptations can be updated, so we have recorded the version of each master and adaptation used. All datapoints in the data set are labeled with their data source in the referenceid variable – see annex for naming conventions.

> 34 <u>https://datacatalog.worldbank.org/.</u>
deficits in the crisis countries. We refer the reader to Corsetti, Pesenti and Roubini (1998a), IMF (1998) and Lane et al (1999) for more comprehensive surveys of the pre crisis situation in Asia.

Table 5: Macroeconomic Fundamentals

||Real|GDP G|rowth|CPI I|nflation|Rates|Fisc<br>(%|al Sur<br>of GD|plus<br>P)|
|---|---|---|---|---|---|---|---|---|---|
||1995|1996|1997|1995|1996|1997|1995|1996|1997|
|Indonesia|8.2|8.0|5.0|9.0|6.6|11.6|0.8|1.4|1.9|
|Korea|8.9|7.1|5.9|4.7|4.9|6.6|0.4|0.3|-0.5|
|Malaysia|9.5|8.6|7.3|3.2|3.3|2.9|3.8|4.2|2.8|
|Philippines|4.8|5.7|5.1|11.0|5.2|6.1|-1.4|-0.4|-1.0|
|Thailand|8.7|6.4|0.0|7.5|4.8|7.7|2.6|1.6|-1.1|
|Hong Kong|3.9|4.9|5.3|7.0|6.6|5.2|-0.3|2.2|3.8|
|Singapore|8.7|7.0|7.6|0.9|2.0|2.0|2.7|2.8|1.8|
|Taiwan|6.0|5.7|6.9|4.6|2.5|-0.5|0.4|0.2|0.2|
|Japan|1.5|3.9|0.9|-0.1|0.1|1.7|-3.6|-1.1|-0.2|
|USA|2.0|2.8|3.8|2.8|2.9|2.3|-1.9|-1.1|-0.2|

_The Size of the Government's Implicit Liabilities_ We now discuss the impact of the banking crises on the governments' potential liabilities. We proceed in two steps. First, we provide evidence based on estimates of loan default rates prior to the crises. Second, we discuss the size of government outlays in Korea, Thailand, Indonesia and Malaysia associated with efforts to reorganize their financial sectors.

Corsetti, Pesenti and Roubini (1998a) provide estimates on loan default rates in Asia prior to the crises. These are summarized in the first columns of Tables 6 and 7. Note the sharp difference between default rates in the crisis and non-crisis countries. In the former they range from a low of 14 percent in the Philippines to a high of 19 percent in Thailand. In all of the non-crisis countries the ratio of non-performing loans to total loans is roughly 4 percent. Using these default rates 31
# **Implications for the Entry of Kazakhstan into the WTO**

As has been shown by both Shepotylo (2011) and Jandosov and Sabyrova (2011), Kazakhstan had to significantly increase its MFN tariffs in order to implement the common external tariff of the Belarus-Russia-Kazakhstan Customs Union. Importantly, Kazakhstan had already reportedly signed bilateral market access agreements with several WTO members during its accession negotiations in which it had committed to lower tariffs than the tariffs of the Customs Union. Since there is substantial water in the tariff of Russia’s commitments, however, Russia should be able to agree to lower the bound tariffs of the Customs Union to pay ―compensation‖ to the WTO members for Kazakhstan applying higher tariffs than its commitments, without actually having to lower any applied tariffs in Russia.

# **Comparison of Average Tariffs with Other Countries That Have Acceded to the WTO**

Compared with most countries that have acceded to the WTO since 1998, especially the Transition counties, the commitments of the Russian Federation on tariffs on goods are slightly higher. The average bound tariffs for other acceding countries that are not ―Least Developed Countries‖ are: Saudi Arabia, 10.5 percent; Former Yugoslavian Republic of Macedonia, 6.2 percent; Armenia, 7.5 percent; Chinese Taipei, 4.8 percent; China, 9.1 percent; Moldova, 6.0 percent; Croatia, 5.5 percent; Oman, 11.6 percent; Ukraine, 6.0 percent; Albania, 6.6 percent; Georgia, 6.5 percent; Jordan, 15.2 percent; Estonia, 7.3 percent; Latvia, 9.4 percent; Kyrgyz Republic, 6.7 percent; and Montenegro, 5.1 percent.<sup>19</sup> Thus, by the standards of countries that have acceded to the WTO since 1998 that are not ―Least Developing Countries,‖ the Russian Federation’s bound tariff commitment of 8.6 percent average appears to be slightly higher than average, especially in comparison to the other Transition Countries. Among the Transition Countries, only Latvia and China acceded with higher average bound tariff commitments. Thus, while the tariff commitments of the Russian Federation represent liberalization, they are not unusual compared with other acceding countries.

> 19 See World Trade Organization (2005).  The ―Least Developed Countries‖ acceded with relatively high bound tariffs: Cambodia, 17.7 percent; Nepal, 23.7 percent; Samoa, 21 percent; Vanuatu, 39.7%; and Cape Verde committed to average bound tariffs of 19 percent for agriculture and 15 percent for industrial goods.  The WTO accords a preferential status to the least developed countries. Although they are not officially designated as ―least developed,‖ Tonga committed to all tariffs being bound at no higher than 15 or 20 percent; and Viet Nam to a maximum tariff of 35 percent.

19
Schultz, 1996). Empirical studies have consistently shown that lower child mortality is associated with lower fertility but have not been uniformly successful in addressing the endogeneity challenge. Convincingly addressing the joint determination of child mortality and fertility typically requires longitudinal data (see for instance Hossain et al., 2007). Since the goal of this paper is to examine to what extent reasonably exogenous transitions have affected fertility decline in Rwanda, we will not focus on the role of child mortality. We acknowledge that part of the impact of the exogenous factors on fertility runs through their effect on child mortality (in particular better female education and improved household living standards). The data at hand however do not allow for convincing identification of the exogenous effects of child mortality.

# **3. Data and Estimation Methods**

## **3.1 Data**

We use data from the 2005 and 2010 Rwanda Demographic and Health Survey (RDHS). Both surveys used a two-stage sampling design, first selecting enumeration areas (clusters) with probability proportional to size and then systematically selecting households within each cluster. The 2005 RDHS selected 462 clusters and 10,644 households, 10,272 of which were effectively interviewed. All women aged 15 to 49 years in the 10,272 interviewed households were eligible for the woman questionnaire. Of the 11,539 eligible women 11,321 women completed the survey. These 11,321 women form our 2005 sample. The 2010 RDHS sampled 492 clusters in the first stage and 12,792 households in the second stage. 12,540 households were effectively interviewed, resulting in a total of 13,790 eligible women. 13,691 women completed the survey and form our 2010 sample. In both the 2005 and 2010 RDHS every second household was eligible for the male interview. Overall, 4,820 men were surveyed in the 2005 RDHS and 6,329 in the 2010 RDHS.

7
In terms of change in the FLFP rate, however, there is a drop in FLFP in recent years across all educational categories, in both rural and urban areas. This indicates that irrespective of one’s educational attainment, the incentive for females to participate in the labor force has declined over the period. The FLFP rate had declined across all levels of education, from illiterates to college graduates, during 1993-94 to 2011-12. These two groups, illiterates and those with a college education, who initially held the highest participation rates, are also the groups that experienced the largest drops in FLFP rate during this period. In rural areas, there was an 11.5 percentage point decline in the FLFP rate among illiterate females, and for college educated females the decline was 8.0 percentage points (Table 9). In urban areas, there was a decline of 5.0 percentage points and 4.0 percentage points, respectively.

**Table 9: Change in FLFP Rate According to Level of Education**

||Rura <br>|l(%)<br>|Urba <br>|n(%)<br>|
|---|---|---|---|---|
||93-94 to 11-12|04-05 to 11-12|93-94 to 11-12|04-05 to 11-12|
|Illiterate|-11.5|-12.3|-5.0|-6.4|
|Below Secondary|-2.4|-8.3|1.1|-3.3|
|Secondary & HS|-4.8|-9.4|-4.1|-4.2|
|College &Higher|-8.0|-13.0|-4.0|-6.5|

Source: Authors’ own estimate based on NSSO data.

Here we have an intriguing situation, wherein both educational attendance and attainment are associated with declining FLFP. This apparent decline in the FLFP rate, which occurs in the presence of an increase in educational attainment up to a given level, calls for much more detailed analysis. The decline in the FLFP rate for all educational categories and the particularly poor performance of those with secondary (10) and higher secondary (10+2) levels of education need to be explored in detail. In understanding this phenomenon, one potential point of consideration is the link between the marriage market, social status and educational attainment. Another possible explanation could be poor demand side conditions for educated women.

17
Benassy-Quere, A. and B. Cceure.<sup>"Big and SmallCurrencies:the RegionalConnection."</sup> CEPI Working Paper No. 2000-10<sup>June.</sup> Bhagwati, Jagdish. The<sup>World TradingSystemat Risk.Princeton:PrincetonUniversity</sup> Press, 1991.

Bhagwati, J., D. Greenaway,<sup>and A.Panagariya. "TradingPreferentially:Theoryand</sup> Policy." Economic Journal<sup>108(1998):1128-48.</sup> Blomstrom, Magnus and<sup>Ari Kokko."RegionalIntegrationandForeign Direct</sup> Investment: A Conceptual<sup>Frameworkand Three Cases." Policy ResearchWorking Paper</sup> 1750. Washington, DC:<sup>WorldBank,April1997.</sup> Brainard, S. Lael. "An Empirical<sup>Assessmentof the Proximity-ConcentrationTrade-off</sup> Between Multinational Sales<sup>and Trade." The AmericanEconomicReview87.4 (Sept.</sup> 1997): 520-544.

Branson, William H. "Intermediate<sup>Exchange-RateRegimes for Groupsof Developing</sup> Countries." Don't Fix. Don't<sup>Float.Eds.J.Braga de Macedo, D. Cohenand H. Reisen.</sup> Paris: Development Centre of the<sup>OECD,2001.55-74.</sup> Brown, Gordon. "And<sup>Impose NewCodesof Conduct." WallStreetJournal6October</sup> 1998: A22.

Calvo, Guillermo A. and Carmen<sup>M.Reinhart."Fear of Floating."NBER Working Paper</sup> 7993. Cambridge: NIBER, Nov<sup>2000.</sup> Chanda, Areendam. "The Influence<sup>of Capital Controlson Long Run Growth: Whereand</sup> How Much?" Unpublished.<sup>Providence, RhodeIsland:Brown University,Departmentof</sup> Economics, 2000.

Chandavarkar, Anand. "Of<sup>FinanceandDevelopment:Neglected andUnsettled</sup> Questions." World Development<sup>10.1(Jan.1992):133-42.</sup> Cheng, Leonard, Larry Qiu<sup>and GuofuTan."ForeignDirect Investmentand International</sup> Fragmentation of Production."<sup>Fragrmentation:New Production Patternsin the World</sup> Economy. Eds. S.W. Arndt<sup>and H.Kierzkowski.Oxford:Oxford UniversityPress,2001:</sup> 165-186.

Claessens, Stijn and Daniela<sup>Klingebiel."CompetitionandScope of Activitiesin</sup> Financial Services." The World<sup>Bank ResearchObserver,16.1(Spring2001):19-40.</sup> Corden, W. Max. "Choosing<sup>Exchange Rate Regimes:Lessons from Europeand Asia."</sup> Second Randall Hinshaw Memorial<sup>Lectureat the BolognaCenter of The JohnsHopkins</sup> University Paul H. Nitze School<sup>of Advanced InternationalStudies(SAIS),on the</sup> 133
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31
13 differentiated product. Since tourists consume goods and services jointly with the unique natural amenities of a destination country, even small countries (e.g. the Caribbean islands) face inelastic demand curves and need not be price takers. α is a shift parameter that shifts out each component of the tourist demand vector: ∂ _Di_ /∂ α >0, _i=_ agriculture, manufacturing, services.

Theoretically, foreign lobbying might serve two purposes. It might work as the shift parameter α , which promotes tourism and thus increases tourism. Foreign lobbying (e.g. by Caribbean principals) might also “create” domestic amenities _a_ via lobbying of the government who, in turn, provide new information to potential tourists about existing and new amenities, that it is a destination that is “safe and free of diseases”, that it is “easier for foreigners due to a common language”, or that the destination “cares about security”. Such information serves two purposes: it makes demand less elastic and shifts tourism demand.

We hypothesize that foreign lobbying affects tourism through those two channels. Lobbying the U.S. government is used to create a differentiated product, one that is more priceinelastic. Second, lobbying is used to shift out the demand curve for tourism. By obtaining an implicit or explicit endorsement by the U.S. government, lobbying can spur tourism.<sup>14</sup> With a fixed or inelastic supply of services in the short-run, any increase in tourism has the salubrious effect of increasing nontradables prices far more than if the demand were elastic. As shown in Copeland (1991), the greater the increase in nontradables prices, the more welfare improving the increase in tourism. This motivates the following econometric model to estimate whether foreign lobbying (i) acts as a shift parameter, and (ii) decreases the price elasticity of tourism demand:

# LTOURISTS _it_ =

> 13  For the random effects model estimates to be consistent we must make the assumption that regardless of the time period, the observed regressors in (1) are strictly exogenous conditional on the unobserved effects _ci_ .

> 14 In the context here, the extant Caribbean Basin Initiative can be modified to give preferential access (say, to U.S. tourists or tourism-related foreign direct investment by U.S. businesses) more easily than if there were no such agreement in place.
-2-

# **THE INTERNATIONAL OCEAN TRANSPORT INDUSTRY IN CRISIS** _Assessing the Reasons and Outlook_

# INDEX OF ILLUSTRATIONS

Page

# A. TABLES

|1.|The World Merchant Fleet and Newbuildings on Order ....<br>......|...<br>10|
|---|---|---|
|2.|The World Merchant Fleet - Ownership and Registration Pattern ....|...<br>12|
|3.|The Rise and Fall of Seatrade ............................|15|
|4.|Ocean Transport Rate Indicators ...........................|22|
|_5._|International Container Traffic - Vessel Capacity Utilization ....<br>.|.....<br>23|
|6.|Tonnage Price Fluctuations ..............................|24|
|7.|The International Commercially Operated Fleet ..................|33|
|8.|The World Merchant Fleet - Newbuilding Requirements ....<br>.....|...<br>34|
|9.|The World's Leading Shipbuilding Nations ....................|35|
|10.|Analysis of Vessels Broken up in 1992....................|...38|
|1.|B.**FIGURES**<br>World Seatrade - By Main Categories ........................|13|
|2.|Development of the World Merchant Fleet .....................|18|
|3.|Owners' Attitudes in Times of Uncertainty .....................|19|
|4.|The World Merchant Fleet - Newcomers and Departures ....<br>.....|...<br>20|
|5.|Volatile Freight Earnings ...............................|21|
|6.|The World Merchant Fleet - Age Distribution ..................|28|
**Table 3: Enrollment in Grades 6, 7 and 8**

||**School Fixed Ef**<br>|**fects Regression**<br>|
|---|---|---|
||**Girls**|**Boys **|
||**(1)**|**(2)**|
|Stipend District|||
|2005|7.398***|10.524***|
||(1.200)|(1.472)|
|Stipend District *2005|11.206***|9.777***|
||(1.810)|(2.047)|
|Constant|93.221***|162.161***|
||(0.459)|(0.540)|
|Number of observations|9,250|9,250|
|Number of schools|4,667|4,667|
|R2 within|0.041|0.040|

Note: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable in Column 1 is total girls’ enrollment in grades 6, 7 and 8. The dependent variable in Column 2 is total boys’ enrollment in grades 6, 7 and 8. Schools in this sample are restricted to High Schools and Higher Secondary Schools. Only school census data for 2003 and 2005 are used in this analysis. Point estimates are similar to those reported in Chaudhury and Parajuli (2006), Table 4. Standard errors are robust to heteroskedasticity and clustered at the school level.

37
8 propose a definition of UHC that captures these elements: UHC is a situation where _everyone – irrespective of their ability-to-pay – gets the health services they need in a timely fashion without suffering any undue financial hardship as a result of receiving the care._ This captures the idea that policy makers are concerned about _who_ pays out-of-pocket payments and _who_ receives care: many of the UHC-inspired reforms around the world have, as we will document below, been motivated by a concern to improve the coverage of the poor and the vulnerable. Moreover, the definition relates the two target variables – out-of-pocket payments and receipt of interventions – to a yardstick: the family’s ability-to-pay in the case of out-of-pocket spending; and the person’s medical needs in the case of receipt of health services. Both need to be defined, of course – an issue we deal with below.

There are strong echoes in this definition of UHC of earlier work in this journal: Wagstaff, van Doorslaer and Paci (1989) identified, and then operationalized, two widely-agreed principles of equity in the health field: that health services ought to be allocated on the basis of need and not ability-to-pay; and that payments for health care ought to be linked to ability-to-pay and not to receipt of services. As one of us has argued elsewhere (Wagstaff 2013), UHC is not, in fact, a new concept, and predates the 2010 World Health Report (World Health Organization 2010). A search for “universal health coverage” in Google Books Ngram viewer shows that the term has been in use in English-language books since 1945, with usage growing rapidly in the period 1990-2000 and more slowly over the period 2000-2008 (data thereafter are currently unavailable). Moreover, as Table 2.1 in Wagstaff and van Doorslaer 8