rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3 Singapore’s Total Factor Productivity Record Contribution to Changes in GDP and Productivity**

|Indicator<br>1993|-1998|1998|-2003<sup>p</sup>|1993|-2003<sup>p</sup>|
|---|---|---|---|---|---|
|.<br> <br>||(%per a<br>|nnum)<br>|||
|Changes in GDP<br>6.7|(100)|3.4|(100)|5.1|(100)|
|Attributed to<br> <br>|:<br>|||||
|- Productivity<br>1.9|(29)|2.6|(75)|2.2|(44)|
|- Employment<br>4.8|(71)|0.9|(25)|2.8|(56)|
|Changes in Productivity<br>1.9|(100)|2.6|(100)|2.2|(100)|
|<br>Attributed to|<br>:|||||
|- Capital Intensity<br>2.0|(105)|2.1|(84)|2.1|(93)|
|- TFP<br>-0.1|-(5)|0.4|(16)|0.2|(7)|

Source: Singapore Department of Statistics website _P - preliminary_ Whether this lends support to the venture capitalist approach is not immediately obvious, however. An interesting comparison may be made with the experience of Hong Kong. In contrast to Singapore, the Hong Kong government has adopted a much more hands-off approach in innovation policy. It does not take the same kind of risk associated with “winner-picking”. Neither does it invest heavily in frontier-pushing innovation. By and large, it appears to be content to let the private sector lead the way and create value wherever it sees opportunities. This may not make Hong Kong a world leader in innovation or technology creation. Indeed, in most cases, Hong Kong businesses tend to create value out of existing technology and business practices. If the Singapore approach is likened to a venture capitalist in the private equity market, the Hong Kong approach may be compared with portfolio investment in the public market.

Yet, it is not at all clear that the Hong Kong approach has resulted in poorer performance for the economy as a whole. It does not seem to have emerged worse than Singapore in the various studies on productivity growth. In the studies by Young cited earlier (Young, 1992; 1995), Hong Kong TFP was found to have grown 2.3% annually compared with Singapore’s 0.2% during the period 1966-1991. A more recent study comparing the productivity levels in Asian economies painted a similar picture (see Ong, 2002). The author noted that from 1973 to 1999, Singapore’s productivity level consistently stayed below that of Hong Kong, although the gap has narrowed in more recent years. In 1975, for example, Singapore’s overall productivity 34
Composition of Official Foreign Exchange Reserves (COFER) database, both compiled by the IMF.<sup>1</sup> Our data sets on bilateral investment positions capture most of the aggregate IIP at the country level as commonly used in the literature (e.g., Lane and Milesi-Ferretti 2018). In particular, aggregating our bilateral positions across all country pairs yields values close to those obtained by aggregating countrylevel data across all countries.

The main findings of the paper document the rise of the South in global finance. First, the South has increased its participation in global finance across all investment types, as a share of world gross domestic product (GDP) and as a share of global investment. The sum of North-to-South, South-to-North, and South-to-South investments as a share of the global total increased by roughly 10 percentage points (p.p.) between 2001 and 2018 for international loans and deposits, portfolio investment, and FDI. The pace of financial integration within the South was particularly fast. Although South-to-South investment remained the smallest block in terms of value throughout, it increased the fastest over this period. For FDI and loans and deposits, the share of South-to-South investment in the global total doubled between 2001 and 2018, from about 6 to 13 percent. For portfolio investment and international reserves, growth within the South was even more marked, albeit from lower levels: each type of investment increased from roughly 0.9 to 3.7 percent of the global total. The expansion of the South in global aggregates is also observed at the country-to-country level, indicating that these trends are not driven by a few large countries in the South.

Second, the rise of the South has proceeded along the intensive and extensive margins. South countries invested increasing amounts in countries with whom they were already connected at the beginning of the sample. The value of investment in the North-to-South, South-to-North, and Southto-South blocks combined represented about 4 percent of world GDP for portfolio investment and

> 1 The data on international reserves are not bilateral, but we estimate the destination of international reserve holdings at the country-to-region level. This limitation is not a serious problem because virtually all reserves are held in assets issued by a few countries in the North.

2
efficiency of product and output development and they also train co-workers with lesser skills. In industrialized countries, governments have increasingly emphasized the importance of employer-led training. Training provides workers with the skills necessary for improving competitiveness, adaptability and GDP-growth. Furthermore, skill acquisition may reduce wage inequalities.

**Table 1**

|**Highest Level of Com**|**pleted Educati**|**on**(%)|
|---|---|---|
|All|Women|Men|
|No education<br>1.6|1.6|_1.5_|
|Primary<br>40.9|35.0|42.3|
|Secondary<br>53.5|57.0|52.5|
|Vocational<br>_1.5_|1.7|1.5|
|University<br>_1.5_|2.3|1.3|

Data Source: RPED data.

The incidence of training off-the-job in Zimbabwe is low as compared to industrialized and semi-industrialized countries. Data indicate that a small number of firms in Zimbabwe train workers formally. Around 17 percent of the employees receive formal training in-house or by outside providers. The low number may indicate that manufacturing enterprises in our sample underinvest in formal training. Biggs _et al._ (1995) compare Zimbabwe to Japan and find that only about one-fifth of the number of people trained in Japan are trained in Zimbabwe, and around half of the number in WestGermany. Tan and Batra (1995) find that in Taiwan 38 percent of large firms and 4 percent of small firms train employees in-house.

In Zimbabwe, external training is carried out in technical schools and training courses, and sometimes abroad. The metal sector trains its employees more than any other manufacturing subsector. More than 40 percent of the firms supply in-house training to their employees. The metal sector also engages more than other sectors in external training. This may be explained by the technological complexity in the metal sector. The 11
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The typical applicant is an average age of 33 at the time of application, with two-thirds of them married, and an average education level of between 11 and 12 years of schooling (McKenzie et al, 2010). Just over half of the applicants were living in households in which all members would be eligible to migrate if they won, while the rest were living in households that contained extended family such as their parents, siblings, nephews and nieces (Gibson et al, 2011). The context is thus one in which migration results in a nuclear household moving permanently abroad, and potentially leaving behind extended family members who they had been living with at the time, but would not necessarily continue living with as they got older even if they stayed.

# _2.1 Sampling Design_

Our population of interest consists of entrants to the 2002 to 2005 PAC migration lotteries. There were a total of 4,696 applicants, of whom 367 were randomly selected as ballot winners (Figure 1). Official records provided by the New Zealand immigration authorities in late 2012 show that 307 of these winners (84%) had approved residency applications and had ever migrated to New Zealand after winning. The remaining 60 ballot winners did not migrate, and are thus non-compliers to the treatment of migration.

Our prior studies measuring the short-term impacts of migration through this lottery began with a 2005/06 survey that included only 66 migrant households in New Zealand (McKenzie et al, 2010) who had entered the 2002-04 ballots.<sup>5</sup> This sample was expanded to incorporate 101 migrant households by including also winners of the 2005 ballot, as well as tracking some of the individuals who had won the 2002-04 ballots but had been slower to move (Stillman et al, 2009). A follow-up survey three years later tracked 89 of these migrants (Stillman et al, 2015). This sample is still only one-third of the ballot winners who eventually moved to New Zealand. The main reason for this was that the New Zealand government’s

> 5 Note that at the time of this first survey only 33% of the ballot winners had moved to New Zealand.

7
living in extreme poverty by between 20 million and 70 million over the same projected period (Figure 3).

The set of simulations focused on ROCs and NROCs predicts 2030 poverty headcount results with uncertainty—the standard deviation of simulated outcomes—at 2.9 and 0.6 percent, respectively. By 2030, on average and across the four studied sample periods, the ratio of this uncertainty between ROCs and NROCs is approximately 5 to 1. This comparison of the size of the uncertainty between ROCs and NROCs shows how difficult it is to predict the poverty headcount performance of resource-output oriented economies; this is in line with the recent historical variability of economic growth in ROCs and NROCs (Figure D 2 in Appendix D).<sup>34</sup>

**_LOGCs, MOGCs, and HOGCs_** _._ The estimates of the 2015 poverty headcount in LOGCs, MOGCs, and HOGCs was approximately 50, 13, and 4 percent, respectively. The predictions of poverty in LOGCs in 2030 are quite susceptible to the sample period used to generate the Monte Carlo simulations (Figure 2). Using the 1980–2014 and 1990–2014 sample periods, the LOGCs are expected to decrease their poverty headcount by 2 and 4 percentage points, respectively, in the period 2015–2030. Using better performance periods of growth (2000–2014 and 2005–2014), LOGCs expect, in terms of the median values, to reduce their initial 2015 poverty headcount rates by more than 20 percentage points in the 2015–2030 horizon. In contrast, extreme poverty in absolute terms in LOGC economies over the horizon 2015–2030 is predicted to either increase by more than 30 million under the 1980–2014 and 1990–2014 sample periods or to decrease on average more than 9 million people under the 2000–2014 and 2005–2014 base periods. The simulations for the MOGC economies show more consistent results of poverty headcount hovering around median values between 7 and 9 percent by 2030. These poverty rates correspond to a decline in extreme poverty by 200 million and 100 million people between 2015 and 2030, respectively. HOGCs, in contrast, expect to decrease their poverty headcount rates, in terms of the median simulated outcomes, to levels of approximately 1 percent in 2030, lifting around of 70 million people out of extreme poverty conditions in the same 2015–2030 horizon.

Likewise, the uncertainty associated with the poverty headcount predictions for 2030 varies significantly depending on the country-output growth subcategory. Regardless of the study period,

> 34 Among the four studied sample periods, the information embedded in the period 2005–2014 (global pre- and postfinancial crisis period) leads to simulated outcomes of the poverty headcount with less dispersion  uncertainty  for both country typologies, ROCs and NROCs.

Page **22** of **45**
Saharan African countries in 2010–13 vis-à-vis 2015–16. Most countries in the region (36 of 44) have a reduced fiscal space—as proxied by an increase in the number of tax years needed to repay the public debt burden. From 2010–13 to 2015–16, the median increase is about 1.1 years for countries with tighter fiscal conditions. However, these central figures mask the wide variation across countries. For example, in over one-third of these countries (13 out of 36) the increase is more than one standard deviation above the median. The countries with the largest increase in the number of tax years required to pay off the entire debt burden from 2010–13 to 2015–16 were the Central African Republic (from 3.3 to 5.8), The Gambia (from 5.8 to 8.5), Mozambique (from 3.7 to 8.5), and the Republic of Congo (from 3.6 to 8.8). The findings suggest that in some countries (Sudan and Guinea-Bissau), it takes more than 9 years to repay their public debt burden (about 9.2 years), despite the reduction in this ratio for these countries when compared with 2010–13. For Botswana, Swaziland, and Lesotho, it would take at most one tax year to repay fully their general government gross debt stock.

**Figure 7. Fiscal Space Across Sub-Saharan African Countries, 2010-13 vs. 2015-16** _(General government gross debt as a ratio of average tax revenues)_

<!-- Start of picture text --> 10<br>GNBSDN<br>9<br>COG<br>MOZ GMB<br>8<br>MRT<br>7<br>CPV<br>GHA<br>SLE<br>6<br>CAFTGO ETH<br>5<br>TCD<br>MWIZWE GIN<br>4 NER MDG<br>COD<br>ZMBGABRWABENSENUGATZAKENMUSBDI<br>3 CIV<br>LBR BFA<br>CMR<br>SYC<br>MLI COM<br>2 ZAF<br>GNQAGO<br>NAMNGA<br>1 SWZLSO<br>BWA<br>0<br>0 2 4 6 8 10 12<br>Time to fully repay public debt (in number of tax‐years), 2010‐13<br>Time to fully repay public debt (in number of tax years), 2015‐16<br><!-- End of picture text --> _Notes: Red dots represent resource rich countries while green dots represent non-resource rich countries. Fiscal space is calculated by the ratio of general government gross debt to average tax revenues (Aizenman and Jinjarak 2010). Calculations based on data from Kose et al. (2017)._ 18
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14
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30
29 Tabte 10: A Sumry of Convertibility

**and** Foreign Trade Regimes **in** the CEE-3 (end **of** 1991)

|Institution<br>responsible<br>for setting<br>exchange rate|Exchange<br>Rate<br>Regime|Pegged<br>Currency|Access to Convertible<br>Currency Markets|Export<br>Measures|Tariffs|Licensing & Irport<br>Quotas|
|---|---|---|---|---|---|---|
|Czechoslovakia|||||||
|Council of<br>Ministers.<br>NationalBank<br>sets the<br>exchange rate<br>within +/-10X<br>of the official<br>rate.|Fixed|Currency<br>basket (DM<br>accounts for<br>45.5X)|Purchase unlimited.<br>Firms can buy CC<br>in banks.<br>Individuals<br>entitled<br>to<br>S175 per year.|No taxes and subsidies.<br>Around201of exports<br>subject to lIcersing<br>(covers weapons, essen-<br>tial<br>inputs and VERs).|Average around<br>**_51._**A tenporary<br>import surcharge<br>of 151. Around<br>96X is GATT-<br>bound.|No quantitative<br>controls.<br>Feu<br>import Licenses<br>covering weapons,<br>drugs, etc.|
|Hungsr|||||||
|Planning<br>Committee of<br>Council of<br>Ministers.<br>National<br>Bank<br>sets the<br>exchange rate<br>withfn+/-S5<br>of the central<br>rate.<br>. . .<br>.......................<br>Poland|Crawling<br>(<br>. ....|Currency<br>basket<br>50<br>USS,<br>501ECU)<br>....<br>..<br>..<br>..<br>..|Purchase unlimited.<br>Firms can buy CC in<br>banks for foreign<br>trade transactions.<br>Individuals<br>entitled<br>to S50 per year.<br>. .............<br>..<br>. ...<br>....<br>..<br>...|Subsidies on some<br>agricultural<br>products.<br>Restrictions<br>related<br>to OECD nontariff<br>restrictions.<br>.....<br>....<br>. .......<br>.............|Average around<br>13X. but other<br>charges (5-6X<br>advalorem).<br>.........|Licenses cover<br>around101of<br>total<br>imports.<br>1001 advance<br>ifport<br>deposit<br>required.|
|Council of<br>of Ministers|Downward<br>crawl|Currency<br>basket<br>(pre-<br>announced)|Full convertibility<br>for current<br>account<br>transactions.|No taxes and subsidies.<br>Export quotas related<br>to OECD restrictions<br>and selected<br>inputs.|Average around<br>14X. All procedu-<br>resin line with<br>GATT articles.|No restric-<br>tions<br>except<br>for alcoholic<br>beverages.|

Sources: Jan J. Michalek, The Opening-Up of the Polish Economy, PPRG Discussion Paper #11, University of Warsaw, December 1991; Dani Rodrick, Foreign Trade in Eastern Europess Transition: Early Results, NBER, mimeo, January 1992; and Gazeta Bankowa, No. 44, 11.03-11.09.1991.
<!-- Start of picture text --> 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile of the reference distribution<br>Decile-specific relative proportion<br>Relative density<br>95% confidence interval<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>Relative density/proprtion<br>0.5<br>0.0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Morocco’s household surveys.

The overall results show that, guided by an increase in consumption, the households shift from the lower deciles of the distribution to the middle and the upper parts. Indeed, the values of the deciles in the upper tail of the distribution are higher than 1, which means that there are more households in that decile of the distribution in the last year of analysis than there were in the first year.

Panel (b) presents the effect due only to the median shift, i.e. the pattern that the relative density would have displayed if there had been no change in distributional shape, but only a location shift of the density. The effect of the median shift was quite large. This alone would have moved a substantial proportion of Moroccan households in 2000 out of the two lowest deciles of the reference distribution and placed them in the other upper deciles of the distribution.

Looking at panel (c), which shows the shape effect, it is clear that there is a concentration in the tails of the distribution. Values above 1 indicate that, in relative terms, there are more households in that decile of the distribution at the end of the examined period than there were at the beginning. Therefore, relative to the initial period, households in the lowest deciles increased by about 14 percent (+1.4 over 1) in the lowest decile, and by 5 percent (+0.5 over 1). Therefore, the trend is more marked in the lower part of the distribution, while a similar but smaller change is observed for the upper tail, with an increase in the last two deciles. Thus, once changes in real median expenditure are netted out, a U-shaped relative density is observed; this supports prior findings on the more unequal and more polarized distribution of consumption expenditure in the course of the past two decades.

20