rafmacalaba/datause-encoder-data huggingface dataset:
export-oriented foreign investors would certainly help propel Vietnam towards more laborintensive growth.

Of course, foreign investment also creates indirect jobs through forward and backward linkages -in particular through construction, services, and subcontracting relationships. The number of jobs created in such way is impossible to assess accurately but is usually estimated at around 1.5 times the number of employees directly hired by foreign enterprises (see Aaron and Andeya, 1998). Even so, the labor market impact of foreign investment would remain low. The capital intensive nature of foreign investment tends to minimize both employment creation and spillovers into the rest of the economy.

# _d) Manufacturing exports and the Asian crisis_

Despite the capital-intensive nature of much of Vietnamn's growth, there have been encouraging signs in the last few years of the emergence of a labor-intensive export sector. Changes in trade policies have been an essential component of the _doi moi_ policy adopted by the Government of Vietnam in 1986. Such changes have been implemented on several fronts. Most importantly, licensed private companies have been allowed to engage directly in international trade, breaking the trade monopoly of a small number of State-owned enterprises operating under central or provincial authorities. However, until very recently, private enterprises had to satisfy a number of fairly restrictive conditions to obtain the necessary licenses. The recent removal of trade licensing, allowing companies to freely engage in trade within the registered scope of their business activities, should further improve the environment for export-oriented industries. In addition, over the years, most export quotas have been lifted and export taxes have been reduced to generally very low levels (CIE, 1998 and UNDP, 1999b).

These reforms -together with sound macroeconomic management- have allowed Vietnam to start exploiting its comparative advantage in labor-intensive manufactures, putting the country _on track_ for export-led growth. As can be seen in table 9, between 1992 and 1997 the dollar value of imports and exports has roughly quadrupled, increasing the share of trade in GDP from about 52% to 86%. This is a high level by international standards. Table 10 shows that Vietnam's export growth was led by an impressive surge in light manufacturing exports, which in real terms grew by no less than 45% a year and whose share in total exports rose from 13.5% in 1992 to 36.7% in 1997. Also remarkable is the strong rise in the value of agricultural exports, which mainly reflects the spectacular take off in rice and coffee production. In only few years Vietnam turned from being a net rice importer into the world's second largest exporter with over 3.5 million tons in 1998.

**Table 9: The high share of trade in GDP**

||1992|1997|
|---|---|---|
|Value of exports (million $)<br>|2,581|9,185|
|Value of imports (million$)|2,540|11,592|
|(Exports + Imports)/ GDP|51.6%|85.7%|
|_Source:_GSO (1998)|||

10
choices are not significant in the IV models, however. Women’s schooling is significantly non-zero in each of the three maternal health care relationships, with a positive sign as hypothesized in each case. In all three cases, the IV estimate of the schooling effect is considerably larger than the standard probit estimate. With the exception of the hospital care model, the null hypothesis of no endogeneity is rejected in all cases, providing justification for use of the IV for schooling.

_Table 9: Marginal effects of schooling on fertility and maternal care use outcomes_

|**Dependent variable**|**IV-PROBIT**|**PROBIT**|
|---|---|---|
|Probability of having a child before age 21|-0.0310|-0.0232|
|Age at first birth|0.0794|0.0735|
|Probability of seeking ante-natal care in the first trimester|0.0666|0.0238|
|Probability of delivering in a hospital|0.0400|0.0185|
|Probability of having skilled assistance at delivery|0.1222|0.0527|

# Notes:

Marginal effects calculated at the mean values of the schooling variable after probit or ivprobit estimation. The marginal effect represents the change in the outcome in response to one additional year of schooling, at the mean level of schooling of 6.2 years The maternal care analysis excludes a sub-sample of women who completed their childbearing fairly early on and were not observed in the survey. The analysis of fertility outcomes indicates that more educated women are indeed more likely to start childbearing later. It is, therefore, reasonable to conclude that the maternal care analysis presented in this paper overestimates the actual impact of schooling by focusing on a subsample of women who chose to start childbearing later due to the influence of schooling and other factors associated with modernization and change. Estimation of the impact of 52
A third pairwise comparison looks at the balance between restructuring and private sector participation measures (Figure 7.9). In 24 percent of countries private sector participation has gone ahead of restructuring reforms. For example, almost 11 percent of countries have private sector participation in a utility that remains vertically integrated. By contrast, only about 6 percent of countries seem to have introduced reform on competition without taking parallel measures to incorporate the private sector.

In the case of regulation (Figure 7.10, 7.11), strictly speaking a regulatory entity is not essential when the sector is organized as a publicly‐owned vertically‐integrated monopoly. This is because the whole rationale for state ownership is to act in the public interest and there are no wider market relations to be regulated. Moreover, with both the regulator and the public utility reporting directly to the line ministry, it can be difficult for the regulator to exercise authority. Nonetheless, 13 percent of developing countries find themselves in the situation of having created regulators when their utility remains an integrated state‐owned monopoly. On the other hand, it is critical to have a regulator once private ownership or operation has been introduced to any part of the sector, and as the reforms of competition continue. However, 13 percent of developing countries find themselves in the situation of lacking regulatory entities despite introducing private sector participation, while 12 percent of developing countries lack regulatory entities despite having commenced along the competition process.

**Figure 7.7 – 7.11: Coherence between various reform measures**

**7.7 Competition and restructuring reform**

|Percentage of<br>countries|Vertically<br>integrated|Partially<br>vertically<br>unbundled|Fully vertically<br>unbundled|Fully vertically<br>and<br>horizontally<br>unbundled|Total|
|---|---|---|---|---|---|
|Monopoly|28.2**|2.4**|0.0|0.0**|30.6|
|IPPs|22.4**|2.4|1.2|1.2**|27.1|
|Single Buyer<br>Model|2.4|5.9|0.0|10.6|18.8|
|Bilateral<br>Contracting<br>with TPA|0.0|0.0|1.2|2.4|3.5|
|Wholesale<br>market<br>competition|1.2|2.4|0.0|1.2|4.7|
|Retail market<br>competition|1.2|7.1|1.2|5.9|15.3|
|Total|55.3|20.0|3.5|21.2|100.0|

48
|P095240|2007|2013|4/1/2013|9 Closed|Madagascar|
|---|---|---|---|---|---|
|P095250|2007|2007||0 Closed|Moldova|
|P095250|2007|2008|12/20/2007|1 Closed|Moldova|
|P095250|2007|2008|6/26/2008|2 Closed|Moldova|
|P095250|2007|2009|12/22/2008|3 Closed|Moldova|
|P095250|2007|2009|6/29/2009|4 Closed|Moldova|
|P095250|2007|2010|7/29/2009|5 Closed|Moldova|
|P095250|2007|2010|2/11/2010|6 Closed|Moldova|
|P095250|2007|2011|1/11/2011|7 Closed|Moldova|
|P095250|2007|2012|12/3/2011|8 Closed|Moldova|
|P095250|2007|2012|6/27/2012|9 Closed|Moldova|
|P095250|2007|2013|12/30/2012|10 Closed|Moldova|
|P095250|2007|2013|6/26/2013|11 Closed|Moldova|
|P095250|2007|2014|2/4/2014|12 Closed|Moldova|
|P095250|2007|2015|8/27/2014|13 Closed|Moldova|
|P095251|2007|2007||0 Closed|Congo,Republic of|
|P095251|2007|2008|12/20/2007|1 Closed|Congo,Republic of|
|P095251|2007|2008|5/24/2008|2 Closed|Congo,Republic of|
|P095251|2007|2009|12/12/2008|3 Closed|Congo,Republic of|
|P095251|2007|2009|6/11/2009|4 Closed|Congo,Republic of|
|P095251|2007|2010|12/15/2009|5 Closed|Congo,Republic of|
|P095251|2007|2010|6/10/2010|6 Closed|Congo,Republic of|
|P095251|2007|2011|3/29/2011|7 Closed|Congo,Republic of|
|P095251|2007|2012|7/7/2011|8 Closed|Congo,Republic of|
|P095251|2007|2012|1/3/2012|9 Closed|Congo,Republic of|
|P095251|2007|2012|3/20/2012|10 Closed|Congo,Republic of|
|P095251|2007|2013|10/19/2012|11 Closed|Congo,Republic of|
|P095251|2007|2013|5/29/2013|12 Closed|Congo,Republic of|
|P095251|2007|2014|11/25/2013|13 Closed|Congo,Republic of|
|P095251|2007|2014|6/17/2014|14 Closed|Congo,Republic of|
|P095251|2007|2015|1/9/2015|15 Closed|Congo,Republic of|
|P095251|2007|2015|6/29/2015|16 Closed|Congo,Republic of|
|P095251|2007|2016|2/1/2016|17 Closed|Congo,Republic of|
|P095251|2007|2016|6/5/2016|18 Closed|Congo,Republic of|
|P095251|2007|2017|12/31/2016|19 Closed|Congo,Republic of|
|P095251|2007|2021|7/21/2020|20 Closed|Congo,Republic of|
|P095275|2010|2010||0 Closed|Vietnam|
|P095275|2010|2011|2/28/2011|1 Closed|Vietnam|
|P095275|2010|2012|3/20/2012|2 Closed|Vietnam|
|P095275|2010|2013|12/1/2012|3 Closed|Vietnam|
|P095275|2010|2013|6/22/2013|4 Closed|Vietnam|
|P095275|2010|2014|2/6/2014|5 Closed|Vietnam|
|P095275|2010|2015|7/19/2014|6 Closed|Vietnam|
|P095275|2010|2015|12/9/2014|7 Closed|Vietnam|
|P095275|2010|2015|5/29/2015|8 Closed|Vietnam|
|P095275|2010|2016|11/18/2015|9 Closed|Vietnam|
|P095275|2010|2016|6/26/2016|10 Closed|Vietnam|
<!-- Start of picture text --> Figure A.1:Comparison of Freight rates: Pandemic vs. pre-Pandemic<br>Coefficient and 95% Cl<br>Nov_2020 7 —————-e————_-<br>Dec_2020 ——_e———_-<br>Jan_2021 ——_e—————_<br>Feb_2021 ————“(9 —<br>6 8 1 1.2 1.4<br><!-- End of picture text --> <!-- Start of picture text --> Figure A.2: Trucking's Share in Product Price at Destination<br>gx<br>a<br>as)<br>5<br>me}<br>2<br>ase)<br>co<br>2<br>o<br>££<br>a7)<br>oO<br>aN<br>£<br>a7<br>fo}<br>oO<br>Do<br>&<br>x<o*,. —<br>Fed<br>a<br>_—<br>o<br>2<br>Oo<br>ao<br>0 1000 2000 ; 3000 4000<br>Distance between origin and destination (km)<br><!-- End of picture text -->
## Figure 16

# **CHILEAN ENERGY SECTOR: IN THE FUTURE**

**(2005)**

## THE GENERATION MATRIX

<!-- Start of picture text --> OTHER INTERCONECTION<br>*2% W13%<br>~~ ~~ 19%<br>HYDROELECTRICITY<br>A<br>/4_.<br>* 28% NATURAL GAS<br><!-- End of picture text --> SOURCE: NATIONAL ENERGY COMMISION 29
otherwise. These bank branches refer to Airtel partner banks and the distance was calculated as the closest distance from any point on the cluster boundary. The third stratification variable was based on a strategic priority rating based on business potential provided by Airtel, ranging from one to four. In clusters with greater priority, more time was spent rolling out agents than in areas with low priority.

# **3.2 Agent Rollout**

The Airtel Money agent rollout took place in treatment clusters between January and June 2017. A professional services firm assisted with identifying potential agents.<sup>3</sup> This firm also helped agents with the logistics of signing-up to become Airtel Money agents and provided them with the necessary equipment, training, and marketing materials. However, the firm did not provide support in signing up households for Airtel Money services. The research team closely supervised the professional services firm and quality-checked information provided by them.

During the rollout, the professional services firm activated 400 agents who reportedly undertook a successful first transaction. To verify activation, we matched the phone numbers associated with these agents with Airtel Money transactions data. Only 370 of the 400 agents were found in Airtel’s data, which could potentially be due to typos in the recorded phone numbers.

Based on the matched 370 phone numbers, panel A of appendix figure A1 shows the number of active agents in the Airtel Money transactions data from January 2017 to November 2017. The number of active agents increased steadily, reaching a high of 285 in August 2017 with a relatively stable number of agents through November 2017. Not all agents transacted each month, which is why the

> 3 As a starting point to identify agents, information from the 2015 business listing exercise was used. The following information was collated into an index: (i) number of employees; (ii) type of business (service, retail, others); (iii) annual turnover; (iv) proximity to closest bank branch; (v) business closed when visiting bank branch; and (vi) business used mobile money before. In treatment areas with more than one listed business, the business with the highest index score was selected to be the first business to be approached.

8
**Table 7A: Truncation Bias in the Intercept: Estimates from 13-50 Years Age Sample**

||**BA**|**NGLADESH**|||**INDIA**||
|---|---|---|---|---|---|---|
||**(δ0)**|**(ρ0)**|**(β0)**|**(δ0)**|**(ρ0)**|**(β0)**|
|**All Children Sample**|||||||
|**Father's Schooling**|**4.93%**|**26.97%**|**18.44%**|**6.76%**|**19.44%**|**17.78%**|
|**Mother's Schooling**|**9.27%**|**23.14%**|**13.91%**|**11.71%**|**15.63%**|**13.90%**|
|**Average Schooling**|**5.19%**|**24.67%**|**15.11%**|**8.43%**|**19.69%**|**18.03%**|
|**Father-Sons**|**-13.28%**|**13.69%**|**1.40%**|**-7.01%**|**7.24%**|**4.97%**|
|**Mother-Daughters**|**28.62%**|**35.64%**|**28.62%**|**10.96%**|**11.76%**|**12.92%**|
|**Table 7B: Truncati**|**on Bias in the**<br>**BA**|**Intercept:**<br>**NGLADESH**|**Estimates**<br>|**from 16-60**|**Years Ag **<br>**INDIA**|**e Sample**|
||**(δ0)**|**(ρ0)**|**(β0)**|**(δ0)**|**(ρ0)**|**(β0)**|
|**All Children Sample**|||||||
|**Father's Schooling**|**2.66%**|**28.31%**|**23.40%**|**5.11%**|**20.98%**|**20.40%**|
|**Mother's Schooling**|**6.81%**|**24.45%**|**19.29%**|**11.61%**|**17.07%**|**16.47%**|
|**Average Schooling**|**1.59%**|**24.63%**|**19.53%**|**7.15%**|**21.40%**|**20.83%**|
|**Father-Sons**|**-18.5%**|**13.37%**|**4.02%**|**-11.01%**|**7.38%**|**5.61%**|
|**Mother-Daughters**|**27.42%**|**37.30%**|**35.71%**|**0.41%**|**6.80%**|**12.94%**|

NOTES: (1) **( δ0 )** is the intercept of rank-rank, **( ρ0 )** is the intercept of IGC regression, and **( β0 )** is the intercept of IGRC regression. (2) The reported numbers are the percentage bias as defined in Table 3.
early implementation of these changes reduced the provincial share of revenues to roughly 20 percent of the total. In contrast, Liaoning province is increasing the provincial share. Even within a single province, the direction of change differs across municipalities. For example, in Liaoning, Anshan municipality is choosing to end its sharing of the VAT, enterprise income tax (EIT), PIT, and business tax collected by the counties. This decentralizing thrust in Anshan contrasts with that in Panjin and Yingkou municipalities, which are taking 40 percent of the local portion (after sharing with the province) of the VAT, EIT, PIT, business tax, and real estate tax collected by the counties (World Bank 2006a).

# _The “Three Rewards and One Subsidy” Program_

The “three rewards and one subsidy” program was rolled out in May 2005. It is the product of a high-level task force appointed by the National People’s Congress in 2004 to investigate the fiscal problems of counties and townships.<sup>59</sup> The program’s stated aim is to “solve” the problems of “fiscally poor” counties within three years. The program was funded with an allocation of RMB 15 billion from the central budget for 2005. The amount had risen to 23.5 billion in 2006, and in 2007 it was 33.5 billion. The program is designed to support and enhance completion of the TSS reform by boosting the incentives for revenue mobilization and provincial equalization efforts, as well as by the more painful downsizing reforms. It also rewards grain-producing counties to offset their fiscal disadvantages.

# **6.  Summary and Conclusions**

Government efforts to channel resources toward pro-poor, pro-rural programs continue to gather momentum. Addressing an assembly of the Political Bureau in Beijing in February 2008, President Hu Jintao urged government officials at all levels to devote themselves in building a government ‘by the people, for the people’, and one that ensures all Chinese enjoy the fruits of the country's economic development. "The government should provide education

> 59 This information is based on MOF Document No. 77, May 8, 2005, “Notification on the 2005 Central Budgetary Rewards and Subsidies to Local Governments in Providing Assistance to Counties and Townships to Resolve Fiscal Difficulties,” and interviews at MOF, February 2005.

57
# Table D4. Distribution of Manufacturing Establishments Boreholes by Employment Size, Thailand

## IREHOLE (X1 BOREHOLES FOR PRODUCTION) FIRENSIZE (EMPLOYEES per FIRM)

<!-- Start of picture text --> Frequency<br>Percent<br>Row Pct<br>Col Pct 1-19 120-49 50-99 1100-199 1200-499 1500-999 11000 4 I Total<br>II I II lover<br>................ --- ------------4 +......... + .... ........<br>YES 4 13 12 5.16 17 2.33<br>i .33| 4.33 4.00 S33 5.167 2.337 | 133 24.33<br>I 5.1U6 17.81 16.44 21.92 23.29 9.59 5.48<br>I 12.12 i 17.57 17.65 29.63 36:17 43.75 50.00<br>.............. ................ _........ ....... ....... ......<br>110o 29 61 1 56 38 30 1 9 1 4 227 ....<br>9 967 20.33 18.67 12.67 10.00 3 300 1 133 75.67 ....<br>12.78 26.87 24.67 16.74 13.22 3.96 1 176<br>87.88 2U.43 8 2.35 70.37 63.83 56.25 50.00<br>................ ............... ........ ........ ......-- ........ .....<br>Totat 33 74 60 54 47 16 a 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
attention on the ranking themselves rather than the underlying developments. . It provides instead a quantitative assessment of countries performance over time (with a quarterly time frequency). Third, it focuses on performance or competitiveness on export markets.

Second, there are databases of indicators of relative prices and costs, such as the EER published by the Bank for International Settlements (BIS), the “Harmonised Competitiveness Indicators” of the European Central Bank and Eurosystem<sup>11</sup> or the European Commission’s MIP scoreboard, created for the purposes of the EU macroeconomic surveillance and excessive imbalances procedure.<sup>12</sup> Examples of the specific measures used are relative inflation (HIPC) deflated real effective exchange rates (REER), unit labor costs, and house prices. These are very popular measures used by policy makers and macroeconomists to gather views and compare countries’ developments in competitiveness. Our database shares with the price and cost measures of competitiveness the important advantages of being based on widely available data (bilateral trade at the product level) and of offering a good coverage in time while also allowing for computations at higher than annual frequencies. It however avoids the typical shortcomings of some commonly used measures of price and cost competitiveness, including the failure to account for market and product composition differences and changes.

Both types of data, i.e. those that concentrate on countries’ institutional characteristics and those that provide measures of relative prices and costs, are largely complementary to our database, and together they can provide an increasingly comprehensive perspective on international competitiveness. In Section 4 we illustrate econometrically the correlations between the Export Competitiveness Database and other indicators of competitiveness.

Differences across these data sets in terms of goals, units of measurement and sampling period notwithstanding, we find the information from our performance component of the database to be reasonably consistent with other databases. For instance, in Section 4 we show that there is a tight correlation between the country-level performance component over time and the change in NEER and REER. In a separate note, available from the authors on request, we further show that countrylevel export changes (net of sectoral and geographical composition) are also positively correlated to several indicators included in the 12 pillars of the World Economic Forum Global Competitiveness Index.

> 11 <u>http://sdw.ecb.europa.eu/browse.do?node=6374972</u>

> 12 <u>http://ec.europa.eu/economy_finance/indicators/economic_reforms/eip/</u>

13
# **Shifting Comparative Advantages in Tajikistan: Implications for Growth Strategy**<sup>1</sup>

Souleymane Coulibaly<sup>2</sup>

# **J.E.L classification:** F14, O53, R10

# **Sector Board :** EP

**Keywords:** comparative advantage, diversification, horticulture, leading cities, enclave economy

**Acknowledgment:** This paper is derived from the trade chapter of the Country Economic Memorandum of Tajikistan led by Sudharshan Canagarajah. Comments received from the team members on the various drafts of the note are acknowledged. Vilas Pathikonda provided excellent research assistance and Galen Burr Evans produced the maps used.

> 1 The findings, interpretations and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.

> 2 Senior Economist, South Caucasus and Central Asia country unit Poverty Reduction and Economic Management (ECSP1, scoulibaly2@worldban.org.
# Tabl, J

# Real Hourly Cost of Labor (Iadx 1950 a 100)

||1i6|1970|1976|1976|1977|1970|1979|1910|1|901|19is"|1|994 10|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Argentino|**91.3**|105.1|107.9|76.9|80.0|84.0|90.2|100.0|**72.5**|2.7|32.8|102.s|**68.3**|
|Brazil|61.4|31.8|95.1|106.7|110.5|122.9|117.4|100.0|10S.l|120.2|80.4|**72.3**|76.4|
|Chile|94.3|116.0|45.8|61.2|U4.2|94.8|100.3|100.0|151.2|129.2|79.7|67.6|**46.5**|
|Colombia|**$6.5**|76.6|59.9|68.9|72.0|**62.3**|92.1|.100.0|107.7|**12.**|12.2|126.8|106.6|
|_Grece_|83.4|52.4|69.7|75.6|83.0|97.8|108.1|100.0|39.o|91.2|39.4|6.9|t6.4|
|Hong Kong|47.7|61.0|77.4|34.6|94.4|100.4|99.0|100.0|94.1|91.8|**39.8**|91.3|100.9|
|India|106.0|94.7|94.8|97.s|91.7|108.4|101.5|100.0|**86.3**|79.0|73.7|75.8|79.6|
|Kenya|79.8|116.0|101.8|92.0|91.6|101.2|91.2|100.0|U.6|75.4|66.1|86.4|60.6|
|Korea|20.4|47.6|**51.8**|84.0|80.9|101.7|119.6|100.0|98.2|100.5|106.4|109.|112.9'|
|Malawi|6.6|110.1|92.4|65.6|**9.8**|**96.6**|94.9|100.0|91.1|111.1|79.1|**56.6**|NA|
|Mexico|39.2|80.0|109.5|110.0|87.4|37.2|**3.1**|100.0|112.0|73.8|51.3|57.7|**5.6A**|
|Morocco|NA|100.2|107.0|106.8|104.8|116.6|112.4|100.0|79.9|78.8|6.0|54.9|54.6|
|Nigeria|40.4|46.2|5.2|U5.4|9.0|59.1|79.8|100.0|65.1|90.2|37.2|90.3|39.1|
|Pakistan|118.6|160.7|91.9|**91.5**|114.8|182.9|122.2|100.0|gy.1|102.0|7.6|109.4|106.9|
|PFru|140.3|146.1|191.8|198.0|125.4|**96.3**|77.0|100.0|106.4|106.7|79.0|".1|3.9|
|Portugl|87.6|54.4|117.9|118.4|106.2|101.6|91.1|100.0|80.7|32.0|39.8|60.6|U.4|
|Slgpore|74.1|".|94.8|90.0|90.5|**92.6**|9 .7|100.0|100.0|117.1|180.5|142.2|144.4|
|Sri<br>Lanka|233.9|**209.M5**|1.S|127.7|1$7.6|156.3|190.7|100.0|92.4|**91.9**|15.7|100.8|109.0|
|Tanzania|NA|115.1|122.2|109.1|109.0|109.4|100.6|100.0|102.0|**91.8**|**91.9**|73.6|74.0|
|Zambia|39.1|97.1|102.6|12t.7|97.5|110.8|107.8|100.0|60.6|33.1|67.4|5$.1|49.3|
|_Ziebabe,_|79.4|79.4|97.5|.5|9..1|92.7|90.6|100.0|106.5|100.9|91.4|51.4|**09.5**|
|USA|NA|NA|99.4|108.6|109.3|107.9|104.6|100.0|100.9|109.3|109.1|110.2|<br>114.7|

* Original dt weor In nominal dollor term. The deflator uoid mm the WPI of t* UlSA. Source: Table 2 **_and_** Wenrd Sank Filel (Audre)