rafmacalaba/datause-encoder-data huggingface dataset:
Urata, Shujiro. "Emergence of an FDI-Trade Nexus and Economic Growth<sup>in East Asia."</sup> Rethinking the East Asia Miracle. Eds. J. E. Stiglitz and S. Yusuf. Washington,<sup>DC:</sup> IBRD/The World Bank and New York: Oxford University Press, 2001.<sup>409-459.</sup> Urata, Shujiro. "A Shift from Market-led to Institution-led Regional Economic Integration in East Asia." Prepared for the presentation at the Conference<sup>on Asian</sup> Economic Integration organized by the Research Institute of Economy, Trade<sup>and</sup> Industry at United Nations University, Tokyo, on April 22 and<sup>23,2002.</sup> US-ASEAN Business Council. The ASEAN Free Trade<sup>Areaand other Areasof ASEAN</sup> Economic Cooperation. 9 May 2002 <http://www.us-asean.org/afta.htm>.

U.S. Trade Representative (USTR). Agreement Between the United States of<sup>America</sup> and the Socialist Republic of Vietnam on Trade Relations.<sup>13</sup> July<sup>2000.27 Sept.2002</sup> <http://www.ustr.gov/regions/asia-pacific/regional.shtml>.

U.S. Trade Representative (USTR) (2002a). "NAFTA<sup>At Eight:A Foundationfor</sup> Economic Growth." May 2002. 19 Sept. 2002 <http://www.ustr.gov/naftareport/nafta8_brochure_eng.pdfP.

U.S. Trade Representative (USTR) (2002b). "United States and Thailand Sign<sup>Bilateral</sup> Trade and Investment Framework Agreement." USTR<sup>PressRelease.23Oct. 2002.</sup> Wain, Barry. "Free Trade: Nothing's Free." Far Eastern Economic<sup>Review31Oct.2002.</sup> 24 Oct. 2002 <http://www.feer.com>.

Wei, Shang-Jin and Yi Wu. "Negative Alchemy? Corruption, Composition of<sup>Capital</sup> Flows, and Currency Crises." NBER Working Paper 8187. Cambridge, MA:<sup>National</sup> Bureau of Economic Research, March 2001.

Wheatley, Alan. "Asia sets May goal to complete currency safety net." Reuters<sup>English</sup> News Service 27 Nov. 2002. <http://www.djinteractive.com> Williamson, John. The Crawling Band as an Exchange Rate Regime: Lessons from<sup>Chile.</sup> Colombia. and Israel. Washington: Institute for International<sup>Economics,1996.</sup> Williamson, John. "The case for a common basket peg for East Asian currencies." Exchange Rate Policies in Emerging Asian Countries. Eds. S. Collignon,<sup>J.Pisani-Ferry</sup> and Y.C. Park. London: Routledge, 1999. 327-43.

Williamson, John. "Exchange-rate Regimes for Emerging Markets: Reviving<sup>the</sup> Intermediate Option." Mimeo. Washington, DC: Institute for International Economics, 2000.

Williamson, John. "The Case for a Basket, Band and Crawl (BBC) Regime for<sup>East</sup> Asia." Future Directions for Monetary Policies in East Asia. Eds.<sup>D. Gruen and J. Simon.</sup> 147
the Basel II IRB might be recalibrated for countries that wish to adopt this approach but that have default probabilities significantly higher than the G10’s.

In the second part of the paper, we discussed issues regarding the implementation of Basel II’s IRB approach in emerging countries. In particular we suggested that many emerging countries may be many years away from IRB implementation. Clearly a first priority for many countries is to improve supervisory skills and enhance resources and work on legal issues to more fully comply with the Basel Core Principals for Effective Banking Supervision. Given that this process will take time, we suggest a simpler Centralized Rating Based approach as a way to transit toward the IRB.

The main difference with the IRB approach is that although banks would rate their clients, the regulator would set the rating methodology by defining the way in which the rating buckets would map to default probabilities. We suggest that initially this approach could be used to set forward-looking provisioning requirements. A country could then adopt Basel II’s standardized approach, set provisions using the CRB methodology to cover the value at risk minus the Basel II’s SA capital and in that way ensure that banks’ total reserves (provisions plus capital) covered the entire value at risk up to the desired level of protection.

21
importance.

The variety of health problems and priorities in developing countries suggests that there is little scope for surveys using standardised questionnaires and collecting a wide range of internationallycomparable information on patterns of disease and mortality. Instead the emphasis of the WHS should be placed on the development and dissemination of improved methodologies for conducting, processing and analysing health studies. Moreovex, it seems unlikely that detailed investigation of the informatio **n** eeds of a broad range of developing countries would justify a series of prospective surveys or surveys involving detailed medical examinations. We suggest that the focus of most country studies should consist of a single-round, retrospective health interview s. m. As we discuss in more detail in section 4, any core questionnaire should be relatively brief. It might include questions about mortality, sickness during the previous two weeks and actions taken as a result and perhaps about chronic ill-health among adults. Collection of anthropometric measures might also be considered a core activity. Questions about the demographic and socio-economic characteristics of respondents would also be collected in the core questionnaire. Conversely, the collection of information about specific health problems, about the coverage and use of particular health programmes and about living conditions should be oriented to the priorities of the country concerned. To some extent it might be possible to develop modules concerning particular topics. However, even these would probably need to be tailored to country circumstances. The structure of the health system and the living conditions of the population vary so much between developing countries that over-standardisati **o** fn questions about these subjects is likely to produce common-place or misleading findings. We would also emphasise the importance of in-depth studies of issues of importance in partirular countries. While we are sceptical about the scope for developing standardised protocols, more might be gained from devoting resources to such studies in certain countries than from conducting a larger number of more sup'erficial enquiries. Throughout the design of 12
The use committees of enquiry, of interpellations and of the ombudsman offices follows a different pattern. The survey data suggest that the use of interpellations as an oversight tool is very common in high-income countries, less common in low-income countries and least common in middle-income countries : they are used in about 81 % of the high-income cases, in about 77% of the low-income cases, and in less than 70% in the middle-income cases. Data are presented in Table 11.

**Table 11. Use of Interpellation by Income Level**

|Do the countries in these<br>|income level|use interpellati<br>|ons?<br>|
|---|---|---|---|
|Incomelevel<br>no|Yes|Total|% yes|
|Low<br>5|17|22|77.3|
|Middle<br>8|18|26|69.2|
|High<br>3|13|16|81.3|
|Total<br>16|48|64||

Finally, the presence of committees of enquiry and of Ombuds offices as oversight tools is most common in middle-income countries, less common in highincome countries and least common in low-income countries. Data concerning the presence of committees of enquiry and of the Ombuds offices by are presented in Table 12 and 13.

**Table 12. Committees of Enquiry by Income Level**

|Do the countries in these income lev|els have Com|mittees of En|quiry?|
|---|---|---|---|
|Incomelevel<br>no|Yes|Total|% yes|
|Low<br>2|20|22|90.9|
|Middle|30|30|100|
|High<br>1|19|20|95|
|Total<br>3|69|72||

16
# **III. The Rapid Growth of a Few Large Economies and the Rise of the New Growth Poles**

While there was progress in development during the twentieth century, the G7 countries still dominated the global economy in terms of size, contribution to economic growth and living standards. This dominance began to decline dramatically during the first decade of the twentyfirst century—in particular, due to the fast growth of large dynamic emerging economies –the socalled ―BRICs‖ (Brazil, Russia, India and China). As a result, the G7 share of global GDP declined rapidly from 45 to 37 percent in just 8 years (Figure 4). This is an unprecedented and historic shift. After a remarkable degree of G7 dominance for an entire century, one decade of extraordinary growth has caused a dramatic shift in the balance of economic power.

It should be noted that this data uses Maddison‘s PPP adjusted figures, which increase the developing country‘s share of GDP, as compared to GDP measured at market exchange rates. On the other hand, even using market exchange rates, there was a dramatic shift during the 2000s, as the changing shares were driven by real growth. Data on the BRICs are illustrative, as we see that, even using market exchange rates, the BRICs‘ share nearly doubled in a decade (Figure 9). These recent developments are unlike the pre-industrial period of Asian dominance of the world economy (in particular China and India) when economic importance was driven by sheer population size in a context of stagnant global growth. In the recent decade, the rapid growth of the BRICs was driven by technological adaptation and integration into the modern global economy and in the context of an expanding global economy where countries need to ―run‖ just to keep their existing relative ranking in income per capita.

**Figure 8:  G7 and BRIC Shares of World GNI, 2000-2009**

<!-- Start of picture text --> 70%<br>60%<br>50% G7 Share (Market E-rates)<br>40%<br>G7 Share (PPP$)<br>30%<br>BRIC Share (PPP$)<br>20%<br>10% BRIC Share (Market E-Rates)<br>0%<br>2000 2009<br><!-- End of picture text --> Source: World Development Indicators.

In terms of their contribution to economic growth—the driving force behind these changing shares—we also see that large emerging market economies have now moved ―up the 13
Table A.5: OLS analysis, 1995‐2015, Log of ERR Difference as dependent variable, High‐CBA investment <u>projects only (Model 2)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ERR dif|ERR dif|ERR dif|ERR dif|ERR dif|ERR dif|
|Over estimation of ERR in PAD ( 1 if PAD ERR>ICR ERR|0.146*|0.172**|0.147*|0.154*|0.180*|0.176**|
||(1.80)|(2.09)|(1.82)|(1.69)|(1.95)|(2.15)|
|CPIA|‐0.105<br>|‐0.0487<br>|‐0.0753<br>|‐0.189*<br>|‐0.106<br>|‐0.00143<br>|
||(‐1.11)|(‐0.46)|(‐0.79)|(‐1.76)|(‐0.88)|(‐0.01)|
|CPIA scale dummy|0.128|0.0702|0.0848|0.161|0.0485|0.0143|
||(1.03)|(0.55)|(0.68)|(1.15)|(0.34)|(0.11)|
|Approval fiscal year sequence|0.00399|0.00537|0.000877|0.0103|0.00949|0.00217|
||(0.47)|(0.62)|(0.10)|(1.02)|(0.93)|(0.25)|
|East Asia and Pacific|‐0.408***|‐0.397***|‐0.414***|‐0.333**|‐0.332**|‐0.403***|
||(‐3.09)|(‐3.00)|(‐3.15)|(‐2.17)|(‐2.17)|(‐3.05)|
|Africa|0.0670|0.0693|‐0.0182|0.0732|‐0.0170|‐0.0271|
||(0.48)|(0.49)|(‐0.13)|(0.45)|(‐0.10)|(‐0.19)|
|East Europe and Central Asia|0.0755|0.0170|‐0.0178|0.172|0.00592|‐0.0992|
||(0.52)|(0.11)|(‐0.12)|(1.03)|(0.03)|(‐0.63)|
|Latin America & Caribbean|0.0410|‐0.00983|0.00385|0.189|0.0878|‐0.0621|
||(0.29)|(‐0.07)|(0.03)|(1.16)|(0.52)|(‐0.42)|
|Middle East & North Africa|‐0.119|‐0.139|‐0.194|‐0.109|‐0.239|‐0.222|
||(‐0.68)|(‐0.78)|(‐1.10)|(‐0.54)|(‐1.14)|(‐1.24)|
|Agriculture|0.00326|0.00239|‐0.0520|‐0.0153|‐0.0823|‐0.0611|
||(0.03)|(0.02)|(‐0.50)|(‐0.13)|(‐0.69)|(‐0.58)|
|Energy & mining|‐0.0285|‐0.0180|‐0.0256|0.0192|0.0376|‐0.0155|
||(‐0.25)|(‐0.16)|(‐0.23)|(0.15)|(0.29)|(‐0.14)|
|Water|0.0666|0.0791|0.0269|0.0814|0.0641|0.0312|
||(0.47)|(0.56)|(0.19)|(0.49)|(0.38)|(0.22)|
|Urban Development|‐0.0224|‐0.0185|‐0.0719|‐0.0619|‐0.111|‐0.0761|
||(‐0.11)|(‐0.09)|(‐0.36)|(‐0.28)|(‐0.49)|(‐0.38)|
|Average Inflation over project life, log(%)||0.0630|||0.0660|0.0760*|
|||(1.52)|||(1.42)|(1.83)|
|Log net commit. (m$)|||‐0.105**||‐0.121**|‐0.119***|
||||(‐2.53)||(‐2.53)|(‐2.83)|
|TTL performance||||‐0.00251|0.0243||
|||||(‐0.04)|(0.42)||
|Observations|1042|1020|1042|843|827|1020|
|R squared|0.033|0.036|0.039|0.039|0.050|0.043|

Note: t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01 31
8 data point to the possibility of substantial dualism among SMEs, with an educated minority successfully "graduating" to become direct exporters, and the majority more-or-less permanently engaged in the international marketplace, if at all, only indirectly as subcontractors.

# II: EXPORT MARKETIG AND lTS SUPPORT SYSTEMS

Even with favorable market conditions, exports do not proceed spontaneously, but through the medium of institutions, and via exchanges which involve both ecx ante and ecx post transactions costs. Ex ante transactions costs comprise the costs of search by buyers and sellers to identify a party with whom to transact Ex post transactions costs comprise the costs to one party that result from non-performance by the other. SME success in export markets thus requies the presence of mechanisms which reduce the costs of search to SME suppliers and to their buyers, and which can reliably signal the reputaion of suppliers to putative buyers (and vice versa).

A central goal of this stLdy is to leam what was the role of collective marketing support - systems how extensively they were used by SME exporters, how much value SMEs ascnrbed to them, and what specific lknds of collective support were most highly valued by SMEs. Yet firms do not make their decisions on the use of collective marketing support in a vacuum. There also exist a range of private channels which reduce the tasactions costs to firms of - participating in export markets trading intermediari, search efforts by foreign buyers, opportnities for indirect exporting via subcontracting, and the presence of other prc-enistg private networks that can ease entry into export markets. Where they exist, these private networks represent substite channels which reduce SME demand for collective support. - Additionally, SMEs eager to export can make direct efforts of their own to win orders although collective support Cif it Is availale) is likely to complement, rather than substitute for, such efforts.

IbThn **e** xt subsections analyze comparatively for Indonesia, Japan, Korea and Colombia the supply of private mechanisms of export support, and the impact of their availalit on the
<!-- Start of picture text --> x Gaps Schanae Prices<br>4 50<br>Output gap Marginal cost<br>3 — Unemploymentgap 40 — Producer price<br>2<br>30<br>1<br>0 20<br>-1 10<br>-2<br>30<br>4-10<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->
for other state characteristics.

Since the overall impact of serving in a non-Yoruba state may mask ethnic specific effects, using Eq.(2), Table 6 presents similar outcomes as Tables 2-5 but provides ethnicspecific outcome variables as well as ethnic-specific treatments. For example, as a parallel to column 1 in Table 2 where the outcome is having more than half of daily interactions with Yorubas, the outcomes in rows 1-4 of Table 6 are having more than half of daily interactions with Hausas, Northern minority groups, Igbos, and Southern minority groups, respectively. In addition, instead of having a single explanatory variable of serving in a nonYoruba state, each regression (row) in Table 6 has four explanatory variables for serving in a Hausa state, Northern minority state, Igbo state, or Southern minority state. Appendix Tables A3-A7 present parallel results to Tables 2-6 but for the full survey sample.

# **6.1 Degree and Types of Interactions with Host Communities**

NYSC location has a strong impact on the degree of interethnic interactions individuals had during their service year. I measure participants’ patterns of interethnic interactions during NYSC with the question: _“Please think about the people you mostly interacted with during NYSC, at home and at work, about what share of them were from each ethnic group [Hausa, Igbo, Yoruba, Other Northern group, Other Southern group]?”_ For each group, the possible answers are: None, Just a few, Less than half (but more than a few), More than half (but not almost all), Almost all.<sup>20</sup> For ease of exposition, I use an indicator variable to classify participants as having a significant level of interactions with other groups if they report having “less than half (but more than a few),” “more than half,” or “almost all” interactions occurring with a particular group. Alternatively, I classify participants as

> 20Piloting the survey revealed that respondents found it easier to respond in this manner than with actual percentages. Enumerators were trained to ensure that the responses were consistent e.g. there could not be more than one group with a “more than half” or “almost all” response.

22
**TABLE 2: Financial performance of the IFC private equity portfolio as of June 30, 2019.** The public market equivalent (PME) is measured following Kaplan and Schoar (2005) as the ratio of cash in (disbursements) to cash out (client capital calls), where each series is discounted according to a public market index. The discount rate is given by the total return of the index, including dividends and price appreciation. IRR is internal rate of return. TVPI is total value to paid-in capital. GPME is measured following Korteweg and Nagel (2016), where cash flows are discounted according to a stochastic discount factor _Mt_ +1 = exp( _a − brm,t_ +1) and _rm,t_ +1 is the log market return at time t+1. For the full sample of investments since 1961, the terms are estimated as _a_ = 0 _._ 015 (standard error = 0.005) and _b_ = 3 _._ 478 (0.697). Investment-level cash flows for each month and market index values are observed on the last date of each month. For investments with non-zero holding valuation, the fair value is treated as a positive cash flow in June 30, 2019, as if the investment is sold on that date. An investment is realized if it has zero holding valuation.

||Financ<br>with y<br>since...|ial perfo<br>ear of <br>|rmance<br>frst cash|of equi<br> fow|ty invest<br> includin|ments<br>g and|
|---|---|---|---|---|---|---|
||1961|1970|1980|1990|2000|2010|
|Panel A) Complete portfolio|||||||
|PME vs. S&P 500|1.15|1.12|1.16|1.14|1.07|0.70|
|PME vs. MSCI World||1.21|1.26|1.23|1.12|0.78|
|PME vs. MSCI Emerging Markets||||1.3|1.18|0.98|
|IRR (%)|13.4|13.8|14.5|13.4|12.9|3.6|
|TVPI|1.71|1.70|1.69|1.61|1.47|1.15|
|GPME vs. S&P 500|2.08|2.17|2.23|2.21|2.71|-0.11|
|_P-value for GPME = 0_|[0.17]|[0.18]|[0.18]|[0.20]|[0.32]|[0.74]|
|Number of investments|2,509|2,429|2,304|2,053|1,433|803|
|Panel B) Separating direct and fund investments|||||||
|PME vs. S&P 500, direct investments only|1.19|1.16|1.20|1.21|1.10|0.69|
|PME vs. S&P 500, fund investments only|0.96|0.96|0.96|0.89|0.92|0.76|
|PME vs. S&P 500, realized direct investments only|1.24|1.22|1.31|1.34|1.37|0.64|
|PME vs. S&P 500, realized fund investments only|1.11|1.11|1.12|1.01|1.43|0.71|
|Panel C) Less hypothetical 2% of annual fee and 20% o|f realize|d proft|over 8%|hurdl|e||
|PME vs. S&P 500, direct investments only|0.96|0.92|0.96|0.97|0.85|0.42|

33
decades, allowing us to study the process of structural transformation and the evolution of gendered labor market outcomes over a long period of time. The six economies span a wide income spectrum (Figure OA.7 in the Online Appendix) and cover around 25-30% of the world population. For Indonesia, we complement the IPUMS census data with the SAKERNAS labor force survey to extend the time coverage of wage data to 2018. For India, we use survey data from the National Sample Surveys (NSS), the Employment-Unemployment Survey (EUS), and the Periodic Labor Force Survey (PLFS) up to 2018.

**Sector and Occupation Classification.** We define three market sectors – agriculture, manufacturing, and services – by assigning the more detailed industry codes from IPUMS and the labor force surveys to these three categories. The categorization follows the literature (Herrendorf et al. (2013); Herrendorf and Schoellman (2018)) and is outlined in Online Appendix C, Table OA.6. We define a fourth sector as the “home sector” to which we attribute unemployed and inactive individuals, following Hsieh et al. (2019).

At the occupation level, we use 1-digit ISCO codes. We aggregate the top three ISCO codes, which results in the following seven occupation categories: (1) professionals, (2) clerks, (3) service workers, (4) skilled agricultural workers, (5) crafts and trades workers, (6) plant and machine operators, and (7) elementary occupation workers. To minimize measurement errors arising from small sample sizes in specific occupation-sector-gender cells, we impose two restrictions. First, we consider only two occupations within the agriculture sector: skilled agricultural workers and elementary occupations.<sup>7</sup> Second, we model the occupation of “skilled agricultural workers” only in the agriculture sector and we assign all workers in that occupation to the agriculture sector. More information is provided in Online Appendix C.

**Construction of the Data Set.** First, we calculate employment shares of men and women in each occupation-sector and country-year. We allocate part-time workers proportionally between the reported occupation-sector and the home sector based on their hours worked. This adjustment is particularly important because women are more likely to work part time. Second, we compute hourly earnings for men and women in each occupation-sector for the limited sample of countries that provide data on hours worked and earnings. Further details on the data cleaning and dataset construction are available in Online Appendix C.

> 7We attribute the small percentage of workers who work in different occupations in the agriculture sector proportionally to the same occupation in the other two sectors.

8
**Table 6. EU Emission Standards for Light Commercial Vehicles, g/km**

|**Category**|**Tier**|**Date**|**CO**|**HC**|**HC+NOx**|**NOx**|**PM**|
|---|---|---|---|---|---|---|---|
|**Diesel**||||||||
|N1, Class I ≤1305 kg|Euro 4|2005.01<br>|0.5|-|0.3|0.25|0.025|
||Euro 5|2009.09<sup>b</sup>|0.5|-|0.23|0.18|0.005<sup>e</sup>|
||Euro 6|2014.09|0.5|-|0.17|0.08|0.005<sup>e</sup>|
|N1, Class II|Euro 4|2006.01|0.63|-|0.39|0.33|0.04|
|(1305-1760 kg)||||||||
||Euro 5|2010.09<sup>c</sup>|0.63|-|0.295|0.235|0.005<sup>e</sup>|
||Euro 6|2015.09|0.63|-|0.195|0.105|0.005<sup>e</sup>|
|N1, Class III|Euro 4|2006.01|0.74|0.46|0.46|0.39|0.06|
|>1760 kg|Euro 5|2010.09<sup>c</sup>|0.74|0.35|0.35|0.28|0.005<sup>e</sup>|
||Euro 6|2015.09|0.74|0.215|0.215|0.125|0.005<sup>e</sup>|
|**Petrol (Gasoline)**||||||||
|N1, Class I ≤1305 kg|Euro 4|2005.01|1|0.1|-|0.08||
||Euro 5|2009.09<sup>b</sup>|1|0.10<sup>f</sup><br>|-|0.06|0.005d<sup>,e</sup>|
||Euro 6|2014.09|1|0.10<sup>f</sup>|-|0.06|0.005d<sup>,e</sup>|
|N1, Class II<br>|Euro 4|2006.01|1.81|0.13|-|0.1||
|(1305-1760 kg)||||||||
||Euro 5|2010.09<sup>c</sup>|1.81|0.13<sup>g</sup>|-|0.075|0.005d<sup>,e</sup>|
||Euro 6|2015.09|1.81|0.13<sup>g</sup>|-|0.075|0.005d<sup>,e</sup>|
|N1, Class III >1760 kg|Euro 4|2006.01|2.27|0.16|-|0.11||
||Euro 5|2010.09<sup>c</sup>|2.27|0.16<sup>h</sup><br>|-|0.082|0.005d<sup>,e</sup>|
||Euro 6|2015.09|2.27|0.16<sup>h</sup>|-|0.082|0.005d<sup>,e</sup>|

† For Euro 1/2 the Category N1 reference mass classes were Class I ≤ 1250 kg, Class II 1250-1700 kg, Class III > 1700 kg.

a - until 1999.09.30 (after that date DI engines must meet the IDI limits)

- b - 2011.01 for all models

- c - 2012.01 for all models

d - applicable only to vehicles using DI engines

- e - proposed to be changed to 0.003 g/km using the PMP measurement procedure

f - and NMHC = 0.068 g/km

- g - and NMHC = 0.090 g/km

- h - and NMHC = 0.108 g/km

Source: CONCAWE, 2006 21