rafmacalaba/datause-encoder-data huggingface dataset:
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 14

# **Table 5.  Proportion of total land area, by land category and low population density**

||**Land ca**<br>**Suitab**<br>**rainfed**<br>**not for**|**tegory**<br>**le for**<br>**crops,**<br>**ested**|**Unsuita**<br>**rainfed**<br>**not for**|**ble for**<br>**crops,**<br>**ested**|**Suitab**<br>**rainfed**<br>**fores**|**le for**<br>**crops,**<br>**ted**|**Unsuita**<br>**rainfed**<br>**fores**|**ble for**<br>**crops,**<br>**ted**|
|---|---|---|---|---|---|---|---|---|
||**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|
|Argentina|37%|36%|60%|60%|1%|1%|2%|2%|
|Bolivia|31%|30%|28%|28%|37%|37%|4%|4%|
|Brazil|35%|34%|15%|15%|35%|35%|15%|15%|
|Chile|5%|5%|68%|67%|4%|4%|23%|23%|
|Colombia|21%|20%|20%|19%|28%|28%|31%|30%|
|Costa Rica|10%|10%|18%|18%|17%|17%|54%|48%|
|Cuba|75%|66%|3%|2%|14%|13%|9%|8%|
|Ecuador|28%|25%|37%|35%|14%|14%|21%|21%|
|Guatemala|10%|9%|24%|15%|31%|31%|34%|26%|
|Guyana|10%|10%|2%|2%|63%|63%|25%|25%|
|Honduras|12%|12%|25%|24%|17%|16%|45%|44%|
|Mexico|18%|16%|58%|56%|9%|9%|15%|15%|
|Nicaragua|23%|21%|16%|15%|32%|32%|28%|28%|
|Paraguay|54%|53%|25%|25%|18%|17%|4%|4%|
|Peru|5%|5%|41%|40%|23%|23%|31%|31%|
|Panama|21%|19%|18%|17%|18%|17%|44%|42%|
|Uruguay|97%|97%|3%|3%|0%|0%|0%|0%|
|Venezuela|37%|35%|7%|6%|28%|28%|28%|28%|
|_LCR Totals_|_30%_|_29%_|_29%_|_29%_|_25%_|_25%_|_16%_|_16%_|

Sources: 1) Population density is from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total area as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” includes GAEZ classifications of ‘marginal’, ‘very marginal’, and ‘not suitable’.
Our results indicate that TFP in non-state-owned Belarusian machine building firms exceeds the corresponding level of productivity in state-owned enterprises (see Table 6).<sup>12</sup> This finding is robust for all four different estimation procedures for the Cobb-Douglas production function, with the exception of the fixed effects estimator. Moreover, during our observation period (2005 to 2010), the difference in TFP levels across state- and non-state-owned firms has increased. The latter result is based on the comparison of TFP growth rates in the respective groups of firms. Again, this result is robust with regard to alternative estimation strategies for our simple production function.

Table 6: T-Test for Differences in TFP between different types of firms

||State-<br>owned|Non-State-<br>owned|Diff.|Privately-<br>owned|Non-Privately-<br>owned|Diff.|Ministry-<br>reporting|Non-Ministry-<br>reporting|Diff.|
|---|---|---|---|---|---|---|---|---|---|
||||||**Levels in TFP**|||||
|OLS|1.054|1.377|-0.323***|1.574|1.198|0.376*|0.988|1.494|-0.506***|
|FE|2.009|1.522|0.487***|1.225|1.791|-0.566***|2.065|1.389|0.676***|
|OP|17.232|20.784|-3.552***|22.014|18.941|3.073|16.937|21.641|-4.704***|
|LP|4.703|5.824|-1.121***|6.522|5.201|1.321*|4.513|6.189|-1.676***|
||||||**Growth in TFP**|||||
|OLS|0.032|0.173|-0.141**|0.268|0.096|0.172|0.124|0.104|0.020|
|FE|0.029|0.170|-0.141**|0.276|0.092|0.184|0.119|0.104|0.015|
|OP|0.082|0.247|-0.165**|0.326|0.161|0.165|0.193|0.164|0.029|
|LP|0.043|0.201|-0.158**|0.287|0.117|0.170|0.150|0.122|0.028|

_Notes:_ *(*)[***] stands for significance at the 10%(5%)[1%] level. The groups of state-owned and non-state-owned firms comprise 369 and 512 firm-year observations, respectively. The groups of privately-owned and non-privately-owned firms comprise 102 and 779 firm-year observations, respectively. The group of firms directly reporting to the Ministry of Industry and the group of non-reporting firms comprise 439 and 442 firm-year observations, respectively.

In order to investigate differences in productivity, we additionally compare privately-owned firms with purely state-owned and mixed-ownership firms. The corresponding findings are reported in columns 4, 5 and 6 of Table 6. For this latter classification of different firm groups our results are somewhat weaker. The group of privately owned firms only consists of 102 firm-year observations, which considerably lowers the power of our statistical tests. Qualitatively, our results are in line with the view that, in comparison to privately-owned firms, state-owned and mixed-owned firms have lower levels of TFP. In this regard, the fixed effects estimator again deviates from all other estimated models. Focusing on TFP growth rates, we are not able to obtain any significant differences across privately-owned and non-privately-owned

> 12Table 6  provides the results of simple t-tests with unequal variances in the sub-populations for different types of firms. The upper part compares the level of TFP, while the lower part focuses on differences in TFP growth rates across the respective groups. The first two columns compare state-owned and non-state owned firms.

19
Glassman, C. A. (2000), Customer Benefits from Current Information Sharing by Financial Services Companies, a study for the Financial Services Roundtable, December.

Gootiiz, Batshur, and Aaditya Mattoo (2017), Services in the Trans-Pacific Partnership: What Would be Lost?, Journal of World Trade, Volume 51(5), pp. 787-816.

Hoekman, Bernard (1996), Assessing the General Agreement on Trade in Services, in: Will Martin and L. Alan Winters (eds.), The Uruguay Round and the developing countries, Cambridge: Cambridge University Press, pp. 88-124.

Hoekman, Bernard, and Aaditya Mattoo (2013), Liberalizing Trade in Services: Lessons from Regional and WTO Negotiations, with Bernard Hoekman, International Negotiations, vol. 18, pages 131–151.

Johnson, Simon, and Jeffery J. Schott (2013), Financial Services in the Transatlantic Trade and Investment Partnership, Peterson Institute for International Economics Policy Brief No. PB1326, Washington DC.

Kitchenman, W. F. (1999), “U.S. Credit Reporting: Perceived Benefits Outweigh Privacy Concerns,” The Tower Group.

Krajewski, Markus (2014), Balancing legal certainty with regulatory flexibility, Chapter 5 in Lim, Aik Hoe, and Bart De Meester (eds), (2014), WTO Domestic Regulation and Services Trade: Putting Principles into Practice, Cambridge University Press, Cambridge.

Lim, Aik Hoe, and Bart De Meester (eds), (2014), WTO Domestic Regulation and Services Trade: Putting Principles into Practice, Cambridge University Press, Cambridge.

Mattoo, Aaditya, and Pierre Sauve (2003), Domestic Regulation and Services Trade Liberalization, Oxford University Press and World Bank, Washington, D.C.

Mattoo, Aaditya (2005), Services in a Development Round: Three Goals and Three Proposals, Journal of World Trade, vol. 39, 1223-1238.

Mattoo, Aaditya, and Deepak Mishra (2009), Foreign Professionals in the United States: Regulatory Impediments to Trade, with Deepak Mishra, Journal of International Economic Law, vol. 12, 435-456.

Mattoo, Aaditya, and Joshua P. Meltzer (2018), International Data Flows and Privacy: The Conflict and Its Resolution, World Bank Policy Research Working Paper 8431.

NASSCOM‐DSCI (National Association of Software and Services Companies ‐ Data Security Council of India) (2013.), Survey of the Impact of EU Privacy Regulation on India’s Services Exporters.

30
government stability, socio-economic environment, ethnic tensions, etc. We also collect information on the index of _investor protection_ which measures the strength of minority shareholder protections against misuse of corporate assets by directors for their personal gain. This includes information on the transparency of transactions (extent of disclosure), liability for self-dealing (extent of director liability), and the shareholders’ ability to sue officers and directors for misconduct (ease of shareholder suit index). The data is obtained from Djankov et al. (2008). Table A.2 presents a more detailed description of the sources of data for all the variables used in our econometric analysis.

# **2.2 Estimation Technique**

The proposed panel data regression faces two challenges for estimation. The first one is to control for the presence of unobserved period- and country-specific effects. The inclusion of period-specific dummy variables can account for the time effects while country dummies deal with country-specific effects. The second challenge is that financial openness is likely to be jointly endogenous with shocks to domestic financial market development; hence, we need to control for the biases resulting from simultaneous or reverse causation in our financial development equation.

Our regression equation of domestic financial development has the following specification: where our dependent variable, _FDit_ , is the measure of financial development of country _i_ in period _t_ . The matrix **_X_** it represents the matrix of explanatory variables, and _FO_ it is the measure of financial openness — _i.e._ our variable of interest. The matrix **_X_** it comprises information on the control variables for our financial depth regression equation (real output per capita, inflation, trade openness, and dummies for dual and freely falling exchange rates).<sup>6</sup> All the control variables are lagged so as to avoid likely reverse causality issues. _FO_ it also captures the degree of international financial integration of country _i_ in period _t_ as measured by the ratio of foreign assets to GDP, foreign liabilities to GDP, or foreign assets and liabilities to GDP.

> 6 Note that the selection of explanatory variables follows the empirical literature on the determinants of financial intermediation (Aggarwal et al. 2006; Baltagi et al. 2009; Chinn and Ito, 2006; Do and Levchenko, 2007; Huang, 2005; Huang and Temple, 2005; Ito, 2006)

9
Policy Research Working Paper 5640

# **Abstract**

Skill shortage remains one of the major constraints to continued growth of the Indian economy. This employer survey seeks to address this knowledge-gap by answering three questions: (i) Which skills do employers consider important when hiring new engineering graduates? (ii) How satisfied are employers with the skills of engineering graduates? and (iii) In which important skills are the engineers falling short? The results confirm a widespread dissatisfaction with the current graduates—64 percent of employers hiring fresh engineering graduates are only somewhat satisfied with the quality of the new hires or worse. After classifying all skills by factor analysis, the authors find that employers perceive _Soft Skills_ ( _Core Employability Skills_ and _Communication Skills_ ) to be very important. Skill gaps are particularly severe in the higher-order thinking skills ranked according to Bloom’s taxonomy. In contrast, communication in English has the smallest skill gap, but remains one of the most demanded skills by the employers. Although employers across India asks for the same set of soft skills, their skill demands differ for _Professional Skills_ across economic sectors, company sizes, and regions. These findings suggest that engineering education institutions should: (i) seek to improve the skill set of graduates; (ii) recognize the importance of _Soft Skills,_ (iii) refocus the assessments, teaching-learning process, and curricula away from lower-order thinking skills, such as remembering and understanding, toward higher-order skills, such as analyzing and solving engineering problems, as well as creativity; and (iv) interact more with employers to understand the particular demand for skills in that region and sector.

This paper is a product of the Education Team, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ablom@ worldbank.org and hsaeki@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|93.<br>Kirkenes<br>Port|Norway|New<br>seaport<br>(156)|Proposed|Asia-Pacific region and<br>North America.<br>Railway would link this<br>port to Baltic via Finland,<br>opening up to Europe.<br>Would compete with|10-Oct-<br>18<br>(157)|
|---|---|---|---|---|---|
|||||New Dvina Port.||

# DATABASE REFERENCES

1. _Программа создания экономического коридора Китай_ - _Монголия_ - _Россия_ . Ufa : Ministry of Industry and Trade of Russia, 2015. "Program for the creation of the China-Mongolia-Russia Economic Corridor", document signed in Ufa by three governments, projects listed. In Russian..

2. Judge, Connor. What does the China-Mongolia-Russia Economic Corridor mean for Mongolia? s.l. : Belt and Road Advisory, September 30, 2018.

3. Mongolian Mining Journal. Эрдэнэт-Овоот чиглэлийн төмөр замын шавыг тавилаа. Mongolian Mining Journal. June 20, 2017. "Erdenet-Ovoot railway line has been laid".

4. Parliament of Mongolia. Төрөөс төмөр замын тээврийн талаар баримтлах бодлогын хэрэгжилтийг хангах зарим арга хэмжээний тухай улсын их хурлын тогтоолын талаарх товч танилцуулга. Ulaanbaatar : s.n., November 2014. Updated Mongolian national rail policy. In Mongolian..

5. Aminaa, K. Mongolia, Russia and China discuss Eastern Railway Corridor. Montsame.mn. s.l. : Mongolian News Agency Montsame, Jan 23, 2018.

6. Deng, Xiaoci. Sino-Russian rail bridge to be completed this year . GlobalTimes.cn. s.l. : Global Times, Mar 9, 2018.

7. Строительство моста Нижнеленинское — Тунцзян продолжат после спада воды в Амуре. EastRussia.ru. s.l. : East Russia Info and Analytics Agency, August 2, 2018. "Construction of the Nizhneleninskoye-Tongjiang Bridge will continue after Amur River recedes". In Russian..

8. Chen, Qingqing. Russia’s Far East ambitions. s.l. : Global Times, September 26, 2018. GlobalTimes.cn.

9. Ministry of Far East Development, Russia. _Концепция развития международных транспортных коридоров_ « _Приморье_ -1» _и_ « _Приморье_ -2». [PDF] s.l. : Government of Russia., 2016. "Concept note for the development of "Seaside 1" and "Seaside 2" international transport corridors." In Russian. PDF Available at: http://static.government.ru/media/files/jqfm5NlYLGTTVhUn3AKqOAg9mJcb2Yxx.pdf.

10. _По транспортному коридору_ « _Приморье_ -2» _доставили груз из Японии в Китай_ . s.l. : East Russia Info and Analytics Agency, October 24, 2018. EastRussia.ru. "Freight delivered from Japan to China via Seaside-2 transport corridor." In Russian..

11. Russia-Mongolia-China Road Corridor to be Ready in 2018. s.l. : Silk Road Briefing, September 8, 2017. SilkRoadBriefing.com.

12. China, Mongolia, Russia start road freight test. s.l. : CCTV, August 18, 2016. CCTVPlus.com.

13. Asian Development Bank. Mongolia: The Road to Prosperity. November 8, 2018.

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into compliance with regulations, but may provide insufficient incentives to induce significant improvements by the worst performers or firms with good ratings. However, our results for Green Watch indicate significant impacts for firms with good (green and blue) ratings. The stronger result for our four cities in Jiangsu Province may stem from two additional benefits for green-rated firms: (a) Enterprises awarded green in a particular year can be given priority consideration in the selection of enterprises with the best economic and social performance records; and (b) an enterprise that has won green for three consecutive years is given preferential status by provincial environmental regulators. The Jiangsu experience suggests that stronger results can be produced by PRD programs that target highly-rated firms for benefits beyond reputational improvement.
**Figure 7: Export transformation and technological catch up**

Relative to Korea, China’s competitiveness has increased in two ways: the share of exports in which it has a RCA (greater than 1) is the same as Korea’s, and <u>the medium and high tech content of its exports has increased significantly more than Brazil or India.</u> <!-- Start of picture text --> South Korea: Export competitiveness and catch up in products with a China: Export competitiveness and catch up in products with a<br>revealed comparative advantage revealed comparative advantage<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>1980-84 1985-89 1990-94 1995-99 2000-04 2005-06 1980-84 1985-89 1990-94 1995-99 2000-04 2005-06<br>Low Tech 1 Low Tech 2 Med Tech 1&2 Med Tech 3 Low Tech 1 Low Tech 2 Med Tech 1&2 Med Tech 3<br>High Tech 1 High Tech 2 High Tech 1 High Tech 2<br>Figure –C-D:<br>Brazil: Export competitiveness and catch up in products with a India: Export competitiveness and catch up in products with a<br>revealed comparative advantage revealed comparative advantage<br>40% 60%<br>35%<br>50%<br>30%<br>40%<br>25%<br>20% 30%<br>15%<br>20%<br>10%<br>5% 10%<br>0% 0%<br>1980-84 1985-89 1990-94 1995-99 2000-04 2005-06 1980-84 1985-89 1990-94 1995-99 2000-04 2005-06<br>Low Tech 1 Low Tech 2 Med Tech 1&2 Med Tech 3 Low Tech 1 Low Tech 2 Med Tech 1&2 Med Tech 3<br>High Tech 1 High Tech 2<br>High Tech 1 High Tech 2<br>Source: COMTRADE and authors’ calculations<br>Share of products Share of products<br>Share of products Share of products<br><!-- End of picture text --> 19
# **3. How to attract more foreign investment? A cross country approach to the determinants of FDI**

## **3.1. An eclectic empirical approach to the determinants of FDI**

This section revisits the determinants of foreign direct investment in Eastern Europe and Turkey by adopting the cross country analytical framework proposed by Sánchez-Martín et al. (2014). This empirical approach combines different theories concerning foreign direct investment to identify relevant variables to be included in panel data regressions.

According to Dunning (1998), developer of the so-called eclectic Ownership-LocationInternalization (OLI) paradigm, beyond firm-specific determinants, labor endowments and locationbound factors play an important role in multinational decisions to invest. Thus, the empirical model incorporates _enrollment in tertiary education_ and _telephone and mobile phone lines per capita_ available from the World Development Indicator database of the World Bank (2015), as proxies for human capital and telecommunications infrastructure, respectively. The first is expected to influence foreign direct investment attraction, especially for companies requiring a skilled labor force, whereas the penetration of telecommunications would benefit all multinationals by lowering costs and improving business competitiveness.

Drawing from the OLI paradigm, the Knowledge-Capital Model (Markusen and Maskus, 1999) allows for multiple production facilities, separating cross-support centralized services and disperse production in some multinationals. This approach distinguishes three types of FDI depending on the motivation: export-oriented (vertical), market seeking (horizontal), and resource seeking. The empirical framework presented in this paper incorporates _GDP per capita growth_ as proxy for market expansion. It has been found to be one the most robust determinants of horizontal FDI inflow attraction in econometric studies (Artige and Nicolini, 2005). It also includes the level of _trade openness_ , defined as the sum of exports and imports as a percentage of GDP, as this would signal economies that are “international trade friendly”, which would positively contribute to vertical FDI attraction. Nonetheless, it is worth noticing that this macro-level cross country analysis does not explicitly distinguish between horizontal and vertical FDI inflows.

In addition, other macroeconomic and country risk variables, such as _inflation_ , a typical proxy for macroeconomic stability (Buch and Lipponer,2004), an _exchange rate stability_ index, an _economic risk rating_ and a _financial risk rating_ are also included as potential determinants of foreign direct investment, 14
|**Table 3: Impact on Business Practices of Cu**|**rrent Ente**|**rprises**|||||||
|---|---|---|---|---|---|---|---|---|
|||Total Prac|tices Score||Marketing|Stock Control|Record keeping F|inancial Planning|
||Round 2|Round 4|Round 5|All rounds|All rounds|All rounds|All rounds|All rounds|
|**Intent-to-Treat Effects**|||||||||
|Assigned to Cash if finish Training|2.530***|1.936***|2.109***|2.087***|0.379***|0.230***|0.872***|0.628***|
||(0.555)|(0.567)|(0.570)|(0.326)|(0.109)|(0.0603)|(0.154)|(0.132)|
|Assigned to Training only|1.719***|1.708***|1.075*|1.524***|0.433***|0.125**|0.483***|0.535***|
||(0.555)|(0.560)|(0.568)|(0.326)|(0.113)|(0.0638)|(0.148)|(0.140)|
|**Treatment on the Treated**|||||||||
|Received Training & Cash|3.588***|2.790***|3.122***|3.059***|0.552***|0.338***|1.281***|0.917***|
||(0.591)|(0.607)|(0.631)|(0.429)|(0.147)|(0.0819)|(0.204)|(0.174)|
|Received Training Only|2.192***|2.261***|1.489**|2.031***|0.574***|0.167**|0.645***|0.711***|
||(0.540)|(0.546)|(0.580)|(0.389)|(0.136)|(0.0783)|(0.178)|(0.168)|
|Observations|544|513|506|1,563|1,563|1,563|1,563|1,563|
|Firms|544|513|506|573|573|573|573|573|
|p-value for testing two treatments equal|0.154|0.690|0.080|0.099|0.622|0.091|0.011|0.533|
|<br>Baseline Mean:|4.96|5.02|4.98|4.96|1.66|0.53|2.10|0.64|
|Robust standard errors in parentheses clu<br>All specifications also include baseline ou<br>round dummies. Business practices were|stered at t<br>tcome valu<br>not measu|he firm lev<br>e and con<br>red in roun|el when al<br>trols for ra<br>d 3.|l rounds us<br>ndomizatio|ed, *** p<0.<br>n strata, and|01, ** p<0.05, *<br>columns 4-8 al|p<0.1<br>so include survey||

36