rafmacalaba/datause-encoder-data huggingface dataset:
markets may have more enduring effects, the manufacturing sector may quickly be able to restore high levels of export growth.

||Table|12:**Is the crisis ov**|**er?**|
|---|---|---|---|
||Real growth<br>In 1998 (%)<br>(constant 97<br>Dong value)|Nominal growth<br>In 1998 (%)<br>(dollar value)|Nominal growth (%)<br>8 months 1998 -<br>8 months 1999<br>(dollar value)|
|Total exports|1.0|2.4|18.1|
|-Agriculture|6.6|7.6|5.2|
|<br>-Oil and mining|-12.9|-12.2|35.0|
|<br>-Manufactures|0.6|1.6|30.7|

_Source:_ General Departnent of customs

# **n. Looking towards the future: Vietnam's potential for export-led growth**

The low growth elasticity of employment in industry -i.e. the fact that industrial growth generates few jobs- is typical of countries that have promoted capital-intensive and local market oriented industrialization (see Krueger, 1983). Indeed, as documented above, capital-intensive industries that grow behind high import barriers offer few employment opportunities for a developing country's labor force. By contrast, during the last three decades, countries that have followed export oriented growth strategies were able to raise industrial employment at annual rates close to 80% of their industrial growth (see for example Gillis et al., 1990). In labor abundant countries, export-led growth policies which rely on comparative advantage are instrumental not only for generating strong and sustainable growth but also for establishing a labor-intensive, employment generating, _pattern_ of growth. This raises the two following interrelated questions: to what extent is Vietnam's export potential under-exploited and could Vietnam move towards a labor-intensive and export-led pattern of growth in the near future?

## _a) Trade Theory_

Recent evidence has shown that the composition of a country's exports is to a large extent determined by its availability of natural and hunan resources (see Wood and Berge, 1997, Owens and Wood, 1997, or Wood and Mayer, 1998). This confirms the predictions of the Heckscher-Ohlin trade theory, according to which the composition of exports in open market economies is largely influenced by a country's endowment in factors of production. The theory predicts that countries tend to export the kind of goods whose production requires an intensive use of their relatively abundant -and thus cheap- resources.

In particular, the higher a country's availability of skilled labor relative to land, the larger its share of manufactures in exports. This is because the production of manufactures is skill-intensive and primary production is land-intensive. That is, the production of manufactures requires many educated workers and little land, while the reverse is true in agriculture. Thus, countries that have a large pool of well-educated workers and little land have a comparative advantage in manufactures. Indeed, a high endowment of skill per worker and a low availability of land should be associated with a high share of manufacturing to primary exports. On the other hand, a high endowment of land and a low availability of skilled workers is expected to be associated with a high share of primary to manufacturing exports.

12
<!-- Start of picture text --> 70 000 80,000<br>70,000<br> 60 000<br>60,000<br> 50 000 50,000<br>40,000<br> 40 000<br>30,000<br> 30 000 20,000<br>10,000<br> 20 000<br>0<br> 10 000<br> -<br>Total livestock import Cattle import Sheep import<br>Source: FAOStat, estimates Source: FAOStat, estimates<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> The livestock import of the Central Africa region, essentially composed of cattle, has been growing over the period, reaching more than US$70 million in 2014 with a steep fall noticed since 2015 (Figure 4). Central Africa mainly imports cattle, sheep, and goats. The cattle import trend closely follows the path of the overall livestock demand in Central Africa. In 2018, 75 percent of the overall livestock imports of the region was composed of cattle. Sheep imports rank second. They have been increasing since the 2000s, from about $ 2 million in 2000 to nearly $ 10 million in 2018. But the demand for goats is much lower and has remained below US$1 million since 2010, except in 2014.

<!-- Start of picture text --> Figure 5: ECCAS imports of meat and edible offal Figure 6: ECCAS imports of meat and edible offal<br>from Africa (in thousands of US dollars) from the rest of the world (in thousands of US<br>dollars)<br> 40 000 450 000<br> 35 000 400 000<br> 350 000<br> 30 000<br> 300 000<br> 25 000<br> 250 000<br> 20 000<br> 200 000<br> 15 000<br> 150 000<br> 10 000 100 000<br> 5 000 50 000<br> - -<br>Source: FAOStat, estimates Source: Trademap<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Imports by the Economic Community of Central African States (ECCAS) of edible meat and offal have generally been on an upward trend for the past 20 years. Imports from Africa ranged from US$ 650,000 to US$ 34 million between 2001 and 2019, an average annual growth of 25 percent. Imports from the rest of the world are even more important (ranging from US$50 million to US$400 million between 2001 and 2019).

# **2.2. Market Potential in Nigeria**

Nigeria’s livestock imports over the past two decades has not varied much, following a steady trend (Figure 7). Cattle have the largest share in Nigeria's demand for livestock. FAO estimates indicate that the demand is relatively stable and is around US$ 100 million. As for sheep, demand is less significant and 6
to the observed patterns in the distribution of potential outcomes. The lower panel of figure 3 presents quantile treatment effects on the population of youth complying with assignment to enter formal apprenticeship.<sup>33</sup> The figure clearly shows a positive and increasing effect for low quantiles, but then a declining pattern and a change in sign. Quantile treatment effects at large quantiles are negative and significant. Although these quantile treatment effects cannot be estimated as effects at quantile (unless assuming rank preservation), the observed patterns are consistent with heterogeneity in treatment effects (see Heckman et al. (1997); Djebbari and Smith (2008)).

# **4.2 Value of Work from Apprentices and Profits in Firms**

We now analyze how the increase in inflow of apprentices indirectly affects firms, including through changes in labor input (time worked), the value of work provided by apprentices, as well as firm profits. During the follow-up survey, firm owners were asked questions about each apprentice who entered the firm since randomization. We measure how these apprentices contribute to firm activities, their hours worked, and whether they are involved in productive tasks. We aggregate apprentice-level measures in each firm across all apprentices who joined since randomization.

Table 5 documents impact on total labor input from apprentices who entered firms since randomization. The total time worked by apprentices at the time of the follow-up survey increase slightly. Firms see a small increase in labor input by apprentices of 6.9 days or 55.9 hours per month. This effect represents a 23 percent increase in days worked by apprentices. These results are significant, but given the size of the increase in the number of apprentices at follow-up (0.613, as can be seen from Table 2), they are actually relatively small. For example, the number of days worked per new apprentice entering firms is 6 _._ 9 _/_ 0 _._ 613 _≈_ 11. One simple explanation for this limited impact is that there are more youth working, but they work less hours. In section 5, we use disaggregated apprentice-level data to show that absenteeism (a pervasive phenomenon for apprenticeship) is in fact higher among formal apprentices.

One critical question pertains to the overall value of work provided by apprentices. In the

> 33We estimate unconditional instrumental variable treatment effects as developed by Frölich and Melly (2013), in which entry into formal apprenticeship is instrumented by the assignment variable.

27
||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES||Heig|ht-for-age z-s|cores||
||(0.121)|(0.120)|(0.121)|(0.121)|(0.122)|
|Completed O-levels|0.123|0.050|0.123|0.093|0.129|
||(0.183)|(0.181)|(0.183)|(0.184)|(0.184)|
|Completed A-levels|0.614**|0.613**|0.616**|0.601**|0.624**|
||(0.291)|(0.289)|(0.291)|(0.283)|(0.291)|
|Technical college/University|0.259|0.186|0.261|0.210|0.270|
||(0.328)|(0.326)|(0.329)|(0.334)|(0.328)|
|Water source: Private connection||||||
|to pipeline|0.064<br>(0.245)|0.027<br>(0.241)|0.066<br>(0.244)|0.027<br>(0.245)|0.070<br>(0.245)|
|_Toilet Facility : Reference category_|_- Flush toilet_|||||
|<br>Latrine|<br>-0.557**|-0.531**|-0.571**|-0.454*|-0.568**|
||(0.247)|(0.243)|(0.247)|(0.261)|(0.249)|
|No toilet|-0.601**|-0.552*|-0.606**|-0.485|-0.612**|
||(0.287)|(0.283)|(0.288)|(0.298)|(0.288)|
|Ethnic Minority|0.014|0.027|0.010|0.017|0.010|
||(0.086)|(0.086)|(0.086)|(0.086)|(0.086)|
|_Asset index: Reference category - p_|_oorest_|||||
|Asset index 2|0.027|-0.016|0.028|0.008|0.035|
||(0.098)|(0.098)|(0.099)|(0.099)|(0.098)|
|Asset index 3|0.166|0.084|0.168|0.119|0.181|
||(0.112)|(0.112)|(0.112)|(0.113)|(0.111)|
|Rural|-0.372***|-0.352***|-0.379***|-0.345***|-0.384***|
||(0.115)|(0.113)|(0.115)|(0.116)|(0.114)|
|Kampala|0.118|0.099|0.118|0.043|0.136|
||(0.225)|(0.223)|(0.227)|(0.232)|(0.224)|
|Central without Kampala|0.085|0.098|0.085|0.066|0.099|
||(0.120)|(0.118)|(0.124)|(0.120)|(0.119)|
|Eastern|0.186|0.170|0.188|0.176|0.207*|
||(0.121)|(0.116)|(0.130)|(0.116)|(0.116)|
|Northern|<br>0.233*|<br>0.277**|<br>0.255**|<br>0.284**|<br>0.268**|
||(0.125)|(0.117)|(0.123)|(0.116)|(0.117)|
|Constant|<br>0.463|<br>0.544|<br>0.491|<br>0.286|<br>0.462|
||(0.387)|(0.381)|(0.390)|(0.398)|(0.387)|
|Observations|1,908|1,906|1,908|1,908|1,908|
|R-squared|0.073|0.079|0.073|0.076|0.072|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 1-SSEXR is the expenditure share of non-starchy staple food in total food expenditure, FCS is the food consumption score, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure in total expenditure.

110
column, we use the average SII of the U.K., another developed country with competitive services sectors. The results are similar to those when the U.S. data are used: the magnitude of the _D*SII_ ’s coefficient is similar to what is reported in Table 2 (30.3 vs. 27.72).<sup>16</sup> Next, we test for the second hypothesis, which states that countries may bypass their own inefficient domestic services sectors by relying on imported foreign services. As defined in equation (11), the share of embodied foreign services in total embodied services ( _forsh_ ) is used to measure the degree of a country’s access to foreign services markets. Because our story is relevant only to the sectors that use a significant amount of services as inputs, we consider only the first seven manufacturing sectors with high services input intensity as listed in Appendix 4,<sup>17</sup> and expect to see a stronger bypass effect than from sectors with lower services input intensity. We examine how the interaction between foreign services and domestic services development affects manufacturing export RCA based on specification (2) and report the results in Table 5A. As in the previous table, in the first three columns, we include separate country, year, and sector fixed effects; the first two regressions consider financial and business services respectively; and the third regression combines the two types of services. The coefficients of _D*forsh_ are always negative and significant at the 1% or 5% level. This shows that the benefit of foreign services inputs on manufacturing export RCA decreases with the level of domestic services development, suggesting that foreign and domestic services inputs are at least partially substitutable. Together with a positive coefficient of _forsh_ , this also implies that the access to foreign services can help a country to bypass under-developed domestic services provision. In the last three columns of Table 5A, we include time-varying country and sector fixed effects. As a result, we cannot estimate the coefficient of _D_ any longer. The absolute value of the estimated coefficients of the interaction term is even larger, although it is statistically less significant for financial services.

If we include some additional sectors with medium levels of services input intensity, the above results are still robust, although a bit weaker as expected. For instance, including also sectors 9 and 7 does not lead to a dramatic change in the results, except that the interaction term turns

> 16 We use the export RCA as our preferred measure for the dependent variable. This measure has the advantage of being comparable across sectors and countries. Nevertheless, we also try an alternative measure for the dependent variable – domestic value added in manufacturing exports in logarithms. Our previous findings are robust to this alternative measure.

> 17 The sector rankings are identical if we consider only financial or only business services, or if we consider both embodied domestic and foreign services.

22
based on Summers and Heston (1988). All variables are of the anticipated sign and initial income, the investment share, primary school enrollment rate, government consumption share, revolution and coups and the continent dummies are significant. Equation 3 is based on Kormendi and Meguire (1985) and includes initial income, the investment to GDP ratio, population growth, the average annual growth rate in the share of government consumption to GDP over the 196089 period, the average annual growth rate of domestic credit, the standard deviation of domestic credit growth, the average growth rate in the share of exports to GDP, and a measure of civil liberties. As in Kormendi and Meguire, this equation uses World Bank and IMF data. The coefficients have the anticipated signs and initial income, the investment share, population growth, average growth rate of domestic credit and standard deviation of the growth rate of domestic credit are significant at the 0.05 level. The equations explain 68 and 61 percent respectively of the cross-country variation in growth rates.

Since both equations appear reasonable but include different independent variables, readers may be wary of the findings of each study. To highlight this quandary, we combine the two equations using the union of the two sets of explanatory variables used by Kormendi and Meguire (1985) and Barro (1991). These results are shown in equations 4 and 5, based on World Bank and Summers and Heston data respectively. Only the investment share, initial income, and continent dummies remain significant with both data sets. The standard deviation of domestic credit growth remains significant with World Bank data while the government consumption share and the revolution and coup indicator is significant with the Summers-Heston data. Since the continent dummies simply suggest the importance of omitted variables, the results imply that only the share of investment in GDP and the initial income level - out of the long list 13
21 Table 6- Sex and Schooling ratio of Girls to Boys Aged 0-14, HICES 2005-06

||No. of Girls / No. of Boys|Girls at school /Boys at school|
|---|---|---|
|All Ethiopia Sample*|0.895|0.807|
|Nuclear sample of Table 5|0.902|0.807|

* Inclusive of number of non-biological children of the household head or spouse.

|Table 7|-Child genderbias eff<br>|ect onadult goods (A<br>|bsolute t-ratiosinbrac<br>|kets)/**_Child<=12_**<br>|
|---|---|---|---|---|
||Personal services|Adult clothing|Coffee|Tobacco|
|Constant|-0.05757(4.12)|-0.00593(1.81)|-0.09443(1.99)|0.01610(4.58)|
|Ln. totx|0.01606(4.28)|0.00075(2.02)|0.02852(2.24)|-0.00131(2.89)|
|Ln.totx2|-0.00105(4.14)|-|-0.00198(2.31)|-|
|Hhdsize|0.00011(0.35)|0.00104(2.26)|0.00295(2.43)|0.00173(3.31)|
|Ch0_2|-0.00011(0.11)|0.00146(0.99)|-0.00436(1.68)|-0.00046(0.40)|
|Ch3_6|-0.00161(2.10)|0.00218(1.40)|-0.00673(2.61)|-0.00072(0.61)|
|Ch7_9|-0.00008(0.09)|0.00099(0.84)|-0.00487(1.95)|-0.00051(0.41)|
|Girlsprp|0.00160(4.01)|0.00122(2.24)|0.00448(3.03)|0.00215(3.10)|
|Urb/rur|-0.00096(1.86)|-0.00088(0.83)|-0.00808(5.17)|-0.00393(6.44)|
|Safenet|0.00285(4.31)|0.00041(0.47)|0.00276(1.83)|-0.00581(7.86)|
|Sch. Fed|0.00012(0.18)|0.00181(1.49)|0.00316(0.89)|0.00266(2.54)|
|Tigray|-0.00148(1.83)|0.00041(0.25)|-0.00418(0.97)|-0.00367(1.31)|
|Affar|0.00510(3.82)|-0.00472(3.48)|-0.01023(2.70)|0.00384(1.48)|
|Amhara|-0.00017(0.21)|0.00106(0.93)|-0.01352(5.32)|-0.00199(0.87)|
|Oromiya|0.00051(0.92)|-0.00363(3.74)|0.01005(4.72)|0.00018(0.14)|
|Somali|-0.01097(1.50)|-0.00474(3.91)|-0.03001(1.17)|0.00182(0.81)|
|Benish.|0.00227(1.91)|-0.00253(1.53)|-0.00948(2.32)|0.00075(0.52)|
|SNNP|0.00034(0.47)|-0.00087(0.94)|0.05322(16.23)|0.01444(12.34)|
|Harari|0.00139(1.27)|-0.00451(2.85)|-0.01379(3.38)|0.00326(1.13)|
|DireDw.|0.00904(4.23)|-0.00239(1.30)|-0.01545(3.68)|-0.00372(0.81)|
|R<sup>2</sup>|0.0326|0.0202|0.2411|0.1136|
|RMSE|0.01203|0.01586|0.04064|0.02000|
|Sample|6964|6964|6964|6964|
|w<sup>-</sup>|0.00393|0.00220|0.01333|0.00614|
6 Table 5: PHN FIVE-YEAR LENDING PROGRAM BY SUBSECTORa (Number of projects)

|Fiscal<br>year|**Rgion**|Total|**Healthb**|Population|lutritLon|<br>Health**&**<br>Population°|**HeaLth &**<br>Nutrition|**PHO**|**Focusedd**|
|---|---|---|---|---|---|---|---|---|---|
|1989|AFRICA|**4**|**0**|**0**|0|2|1|0|1|
||ASIA|2|2|0|0|0|0|0|0|
||_MENA_|2|2|0|0|0|0|0|0|
||LAC|2|1|0|0|0|0|0|1|
|1990|**AFRICA**|9|0|0|0|2|1|2|4|
||ASIA|4|1|1|1|0|0|0|1|
||EMENA|3|2|0|0|0|0|1|0|
||LAC|2|2|0|0|0|0|0|0|
|1991|AFRICA|10|3|1|0|2|0|3|1|
||ASIA|6|2|1|0|2|1|0|0|
||vMENA|3|2|1|0|0|0|0|0|
||LAC|3|0|0|0|1|0|1|1|
|1992|AFRICA|10|0|0|0|4|0|6|0|
||ASIA|6|2|2|0|1|0|0|1|
||_EMENA_|**1**|0|0|0|1|0|0|0|
||LAC|4|2|0|0|1|0|0|1|
|1993|AFRICA|6|0|1|0|0|0|2|3|
||ASIA|3|1|0|0|1|0|0|1|
||EMENA|1|1|0|0|0|0|0|0|
||LAC|6|5|0|0|0|0|0|1|
|TOTAL||87|28|7|1|17|3|15|16|

**a** CategorLsatLon **ia** based mainly on **the** name of **the** project, supplemented by project information vhen **available. Reserve status** projects **are** not included.

b **Many** of **these** projects **may** include small femLly plannLng and/or nutrition components. **c** Includes **family** health.

- d **AIDS, malaria, social** development, human resources, health financing, national essential drugs, poverty alleviation, health system fund, social sector adjustment, **integrated** healthleducation, chronic **disease**

11. Nutrition. Projects that focus on nutrition have been underemphasized in PHN. Some of the reasons for this follow: (a) Before Reorganization, nutrition was not given high priority in the PHN Department. The Department turned down certain unsolicited requests for nutrition projects (e.g., Paki.stan).

(b) Nutrition is not a sector in the conventional sense. It cuts across disciplines -- health, agriculture, education and economics -- and across organizational charts, thus requiring more than the usual analysis and preparatory time for projects.

(c) Nutrition often gets passed over in favor of easier, mshorterO routes to human development.
_ <!-- Start of picture text -->
*  t  ~~~~~~~  R  ~  ~~Polloy !"<br>Debt  and  Inllkdnal  Fbwnoo<br><!-- End of picture text -->

<!-- Start of picture text --> WPS 1186<br><!-- End of picture text -->

- This paper a product of the Debt and International Finance Division, International Economics - Tepartment is part of a larger effort in the department to study the integration of developing countries in world financial markets. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room S8-042, extension 31047. (Septemrber 1993, 27 pages). For individual country data, available on a floppy disk, contact Shelley Fu, extension 33885 (fax 477-)661).

Researchers and policymakers have in recent years paid considerable attention to the phenomenon of capital flight. Researchers have focused on four questions: What concept should be used to measure capital flight? What figure for capital flight will emerge, using this measure? Can the occurrence and magnitude of c-apital flight be explained by certain (economic) variables? What policy changes can be useful to reverse capital f-light?

Claessens and Naude focus strictly on presenting estimates of capital flight using a number of alternative methodologies. In their discussion of these methodologies, they show that although the approaches to measuring capital flight differ, the identities used in balance of payment data make thcm close in final measurement. In particular, the so-called World - Bank residual and Dooley methods presented in the past as very different approaches to - measuring capital flight actually produce similar measurements.

Claessens and Naudd discuss the data used for calculating capital flight and the adjustment that must be made. They present aggregate capital flight figures using the various measuires for o4 developing countries.

The figures show a pattem of increasing capital flight until 1988, followed by a return of flight capital between 1989 and 1991.

Claessens and Naude present regional aggregates of capital flight and rank countries and regions by the level of capital flight relative to GDP. They find that capital flight is more widespread than commonly assumed and, relative to GDP, is rather evenly distributed. The capital flight-GDP Lorenz curve is above the 45degree line, indicating that countries with a smaller GDP have more capital flight than one would expect if it were distributed proportionate to GDP.

The Policy Research Working Paper Series disseminates the findings of work under way in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusionLs inthese papers Jo not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Censer
**<u>Table 2: Descriptive Statistics at Block Level</u>**

||<br>**Year**|**Total**|**Matched**|**Sample for Short-Te**|**rm Effects**||
|---|---|---|---|---|---|---|
||||**Total**|**Treatment**|**Control**|**t-test**|
|No. of parcels|1990|1.35|1.262|1.244|1.280||
|registered|2000|3.20|3.015|3.184|2.846||
||2010|5.76|4.834|4.856|4.813||
|No. of registered|1990|0.040|0.025|0.025|0.024||
|transfers|2000|0.030|0.017|0.022|0.011|*|
||2010|0.060|0.051|0.049|0.053||
|No. of registered|1990|0.097|0.090|0.090|0.090||
|mortgages|2000|0.011|0.006|0.008|0.004||
||2010|0.076|0.057|0.042|0.072||
|Share of total area|1990|0.382|0.440|0.445|0.434||
|built|2000|0.445|0.511|0.521|0.501||
||2010|0.476|0.539|0.550|0.527||
|Share of registered|1990|0.052|0.049|0.051|0.047||
|area built|2000|0.085|0.080|0.082|0.078||
|Population density|1990|954|1,055|971|1,138||
|(indiv./km<sup>2</sup>)|2000|1,356|1,499|1,459|1,539||
||2010|1,570|1,731|1,691|1,770||
|Numberofblocks||1932|1514|1.073|441||
|<br>_Sources:_LAA land administration<br>mortgages registered. WSFE for b|database from 2011 and d<br>uilt area; and gridded popu|, <br>igitization of paper<br>lation of the world|, <br>records, 1981–20<br>v4 (CIESIN) for p|11 for parcels, transfe<br>opulation density.|<br>rs, and||

21