rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> o Sales in past month Log sales in past month<br>5 2<br>So<br>2 7<br>Qo<br>oaie) ee<br>s =——<br>=]<br>5TM<br>02 4 6 8 1 0 2 4 6 8 1<br>quantile quantile<br>Index of business practices<br>ie)<br>a| dW _<br>2 —— Quantile treatment LowerCl<br>o Upper Cl ——__ IIT<br>oa PV<br>0 2 4 6 8 1<br>quantile<br><!-- End of picture text -->
a purchase, which is again double the rate of non-RSE households. Ni-Vanuatu RSE households were 27-30 percent more likely to have made such a purchase, which more than doubles the 20 percent of non-RSE households making such a purchase. In both countries the impact is statistically significant.

In addition to households purchasing assets, RSE workers sometimes returned with durable goods acquired abroad. DVD players were the most common such asset. Each round of the survey also directly asked about ownership of certain durable goods, enabling us to capture the net effect of purchases, sales, and durable goods that migrants bought back with them. The Tongan RSE households are significantly more likely to have acquired a cell phone, television, DVD player, and bicycle over the two-year period than similar non-RSE households. RSE households also seem to have sold their kerosene ovens and purchased gas or electric ovens instead. The ni-Vanuatu RSE households are significantly more likely to have acquired a radio or stereo, a DVD player, a computer, a gas or electric oven and a boat over the two-year period than similar non-RSE households. We do not see any significant impact on livestock ownership in either country.

# **4.4 Impact on children’s education and business ownership**

In addition to raising household incomes and assets, an important motivation for many households to participate in the RSE was the chance to raise money to pay for school fees. In our baseline survey 85 percent of Tongan RSE households and 98 percent of ni-Vanuatu RSE households said that earning money to pay for school fees was a very important or somewhat important motive for participating in the RSE. In addition, school fees are one of the most common special purposes that households report using remittance income from RSE workers for in our surveys, and when households were asked in the final round survey what the most important use of the money earned in the RSE has been, 40% of Tongans and 28% of ni-Vanuatu said school expenses. The question is then whether this translates into higher schooling attainment for children.

Table 5 shows the impact of being in an RSE household on school attendance in the final round survey, conditional on baseline school attendance status and age (equation 3). In Tonga, schooling is compulsory between ages 6 and 15, and there is near universal school enrolment for children of these ages. It is therefore no surprise that we see no impact of the RSE on children who were aged 6 to 14 at baseline since over 97 percent of children of this age are attending school whether or not their household is in the RSE. In contrast, the last three columns of panel A show large positive effects of the RSE on school attendance of children aged 15 to 18 years at baseline. These effects are statistically significant for the full sample and PS-1 samples, and of similar magnitude but not significant in the smaller PS-2 sample. The magnitude of the effect is sizeable – a 10 to 14 percentage point increase in the proportion attending school, relative to 60 18
**Table 11a: Predicted change in the probability of selecting each animal from AOGCM climate scenarios for small farms**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|Baselin|e probability|2.20%|15.40%|29.60%|18.27%|34.53%|
|2020|||||||
||CCC|0.2%|-1.0%|0.7%|2.6%|-4.1%|
||CCSR|0.3%|-1.7%|0.9%|2.8%|-3.0%|
||PCM|-0.1%|2.0%|1.5%|0.6%|-6.0%|
|2060|||||||
||CCC|0.0%|2.4%|1.7%|1.6%|-6.7%|
||CCSR|-0.1%|4.9%|-3.9%|3.1%|-5.0%|
||PCM|0.5%|5.7%|-4.5%|7.8%|-9.5%|
|2100|||||||
||CCC|1.0%|20.0%|-8.5%|1.1%|-18.6%|
||CCSR|0.0%|2.8%|-1.3%|0.6%|-5.7%|
||PCM|0.1%|4.3%|-0.3%|1.6%|-8.3%|

# **Table 11b: Predicted change in net income per animal from AOGCM climate scenarios for small farms (US$)**

|||**Beef cattle**|**Dairy cattle**|**Goats**|**Sheep**|**Chickens**|
|---|---|---|---|---|---|---|
|<br>Baseline|income|151.38|140.98|6.55|12.89|0.86|
|2020|||||||
||CCC|-29.98|4.08|-0.89|0.52|-0.12|
||CCSR|-37.88|-1.41|-1.09|1.67|-0.12|
||PCM|-29.57|-14.45|0.14|-0.06|-0.07|
|2060|||||||
||CCC|-41.28|-12.05|-0.88|-0.54|-0.12|
||CCSR|97.12|0.50|-0.55|0.72|-0.19|
||PCM|-68.07|5.34|-1.01|3.04|-0.14|
|2100|||||||
||CCC|-94.27|21.20|-1.92|10.95|-0.09|
||CCSR|-86.76|8.88|-0.90|5.91|-0.06|
||PCM|-57.88|-12.26|-0.94|1.16|-0.15|

36
# APPENDX

## **Definitions and Sources**

|**Variables**|**Definition**|**Sources**|
|---|---|---|
||Ratio of total GDP to total population.<br>||
|Real<br>Per<br>Capita<br>GDP<br>and<br>Growth Rates (1 985US$PPP)|GDP is in 1985 PPP-adjusted US$. Post<br>1990 GDP per capita<br>growth rates are<br>obtained from constant<br>1995 US$ per<br>capita GDP series. Post 1990 GDP per<br>capita levels were calculated<br>applying<br>growth rates to 1985 PPP-adjusted series.|Data provided by Loayza,<br>Fajnzylber<br>and<br>Calderon<br>(2002). Based on Summers<br>and<br>Heston<br>(1991)<br>and<br>World Bank (2002)|
|NRX/Total Exports|Primary<br>exports*<br>divided<br>by<br>total<br>merchandise exports|WDI and UN COMTRADE|
|NRX/GDP|Primary Exports* divided by GDP|WDI and UN COMTRADE|
|Net NRX/Labor Force|Net Primary Exports**<br>divided by the<br>labor force|WDI and UN COMTRADE|
|Openness(S&W)<br>Openness(S&W)|Percentage of years with open economic<br>~regimeSahanWrer(95|Sachs and Warner (1995)|
|Investment|Natural<br>Log of the<br>ratio<br>of gross<br>domestic investment<br>(in 1995 US$) to<br>GDP(in 1995 US$).|From<br>Loayza,<br>Fajnzylber<br>and Calderon (2002)|
|Growth of Terms of Trade|Growth of the external terms of trade,<br>defined as the ratio of an export price<br>index to an import price index|WDI|
|Log Years of Schooling|Natural log of years of schooling|Barro and Lee (2000)|
|Real Exchange Rate Volatility|Standard<br>deviation<br>of monthly<br>inter-<br>annual changes in real effective exchange<br>rates|Authors' construction using<br>IMF<br>and<br>JP<br>Morgan<br>databases|
|Export Herfindahl|Herfindahl index of export value.|WDI and UN COMTRADE|
|Grubel-Lloyd IIT index.|Grubel and Lloyd intra industry trade<br>index.|WDI and UN COMTRADE|

* Primary Exports comprise the commodities in SITC sections 0,1,2 (excluding 22), 3,4 and 68. **Net Primary Exports also include sections 63, 64 and 94
- Arrow, Kenneth J. (1962). “Economic Welfare and the Allocation of Resources for Invention”. In _The Rate and Direction of Inventive Activity: Economic and Social Factors_ , volume 13 of _NBER Special Conference Series_ , pp. 609–625. Princeton, NJ: Princeton University Press

- Bank, World (2009). _World Development Report 2009: Reshaping Economic Geography_ . Washington, DC: The World Bank

- Banzhaf, John F. (1965). “Weighted Voting Doesnt Work: A Mathematical Analysis”. _Rutgers Law Review_ 19(2) (Winter): 317–343

- Barro, Robert J. (1996). “Democracy and Growth”. _Journal of Economic Growth_ 1(1) (March): 1–27

- Barro, Robert J. & Jong-Wha Lee (1993). “International Comparisons of Educational Attainment”. _Journal of Monetary Economics_ 32(3) (December): 363–394

- Beck, Thorsten & Ross Levine (2005). “Legal Institutions and Financial Development”. In Claude M ́enard & Mary M. Shirley (editors), _Handbook of New Institutional Economics_ , pp. 251–280. Dordrecht, The Netherlands: Springer

- Buiter, Willem H. & Ebrahim Rahbari (2011). “Global Growth Generators: Moving beyond “Emerging Markets” and “BRIC””. _Global Economics View_ Feb 21, New York, NY: Citigroup Global Markets

- Coe, David T. & Elhanan Helpman (1995). “International R&D spillovers”. _European Economic Review_ 39(5) (May): 859–887

- David, Paul A. (1993). “Knowledge, Property, and the Systems Dynamics of Technological Change”. In Lawrence H. Summers & Shekhar Shah (editors), _Proceedings of the World Bank Annual Conference on Development Economics_ , pp. 215–247. Washington, DC: The World Bank

- Decker, Jessica Henson & Jamus Jerome Lim (2008). “What Fundamentally Drives Growth? Revisiting the Institutions and Economic Performance Debate”. _Journal of International Development_ 20(5) (July): 698– 725

- Eaton, Jonathan & Samuel Kortum (2002). “Technology, Geography, and Trade”. _Econometrica_ 70(5): 1741– 1779

- Ethier, Wilfred J. (1986). “The Multinational Firm”. _Quarterly Journal of Economics_ 101(4) (November): 805–833

- Findlay, Ronald & Kevin H. O’Rourke (2007). _Power and Plenty: Trade, War, and the World Economy in the Second Millennium_ . Princeton, NJ: Princeton University Press

- Fingleton, Bernard (2001). “Equilibrium and Economic Growth: Spatial Econometric Models and Simulations”. _Journal of Regional Science_ 41(1): 117–147

- Fosfuri, Andrea, Massimo Motta & Thomas Rønde (2001). “Foreign Direct Investment and Spillovers Through Workers’ Mobility”. _Journal of International Economics_ 53(1) (February): 205–222

- Frankel, Jeffrey A. & David H. Romer (1999). “Does Trade Cause Growth?” _American Economic Review_ 89(3) (June): 379–399

- Fujita, Masahisa, Paul R. Krugman & Anthony J. Venables (1999). _The Spatial Economy: Cities, Regions, and International Trade_ . Cambridge, MA: MIT Press

26
Policy Research Working Paper 9156

# **Abstract**

Several new statistical tools and analytical frameworks have been developed recently to measure countries’ and sectors’ involvement in global value chains. Such wealth of methodologies reflects that different empirical questions call for distinct accounting methods, along with different levels of aggregation of trade flows. This paper is a companion to the conceptual framework presented in <u>Borin and Mancini (2019). The paper describes a new Stata module, icio, that</u> allows the user to construct the most appropriate measure for given empirical questions on trade in value-added and participation in global value chains of countries and sectors. By exploiting inter-country input-output tables, icio provides decompositions of aggregate, bilateral, and sectoral exports and imports according to the source and destination of their value-added content. As different measures are suited to address distinct economic questions, icio is designed to be flexible also in this respect.

This paper is a product of the World Bank’s _World Development Report 2020_ Team, Development Economics. 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://www.worldbank. org/prwp. The authors may be contacted at federico.belotti@uniroma2.it, alessandro.borin@bancaditalia.it and michele.mancini@bancaditalia.it.

_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
The majority of the most common nationalities and preferred destinations remain the same in 2017 and 2018. Interestingly, Egypt appears as an origin and a destination country and Libya is the destination country for a large number of migrants.

# **5.2 Spatial and network analysis of migration flows**

In the following sections, we use network analysis and spatial statistics to document a set of stylized facts regarding migration flows in Libya.

As the first step, we characterize the determinants of migration flows across Libyan provinces. To this end, we use a Separable Temporal Exponential Random Graph Model (STERGM), which is an extension of the Exponential Random Graph Model (ERGM). The ERGM model estimates the probability distribution from which the observed network of migration flows is likely to be drawn. Specifically, the model is used to infer which factors are most informative in explaining the process of formation of the network; for example, a migration flow is more likely to be observed between two Libyan provinces if the province of arrival has experienced fewer episodes of violence than the province of origin. The STERGM extends this analysis to the longitudinal case, i.e. observing the network at different points in time, and is used to estimate the probability of creation and of dissolution of a migration flow between two provinces.

The variables selected to be included in the model using the goodness-of-fit procedure are: i) the difference in the intensity of night lights between two provinces (to proxy for the different levels of economic activity); ii) the (log) distance between two provinces; iii) the difference in the number of conflict events taking place in two provinces<sup>10</sup> ; iv) the difference in the number of migrants hosted in two provinces; v) the marginal propensity to create intransitive relations among provinces, which is proportional to the number of stars detected in the network: i.e., province _i_ is linked to provinces _j_ and _k_ , which are not connected to each other; and vi) the number of links in the network (which is used in this model as a sort of intercept). Each variable is a proxy for a potential driver of migration. Variables i) and ii) are used to measure the economic and physical distance between two provinces, as in the gravity approach; variable iii) controls for potential risk factors pushing migrants to leave one province; variable iv) identifies provinces that are highly attractive for migrants; and variable v) identifies the presence of provinces that act as a bridge between unconnected provinces, having the advantage of a brokerage position (Burt, 1992) and potentially playing the role of hub in the network: i.e. provinces

> 10 Data on conflict events are from the ACLED data set (Raleigh et al. (2010).

10
allow adequacy-based data transfer, while upper-middle- and lower-middle-income countries generally take into account only one condition.<sup>12</sup>

**Figure 6. Countries by income group adopting good regulatory practices on cross-border data flows (percentages)**

<!-- Start of picture text --> High income Upper middle income<br>Lower middle income Low income<br>Conditions for cross-border<br>data transfer<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Existence of adequacy level Existence of data transfer<br>governing cross-border arrangements with foreign<br>data transfer countries<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

# _4.3.1 Enablers: e-commerce/e-transactions_

Country performance on each of the key regulatory dimensions (summarized above) can be further explored with reference to the scores on the “component” question underlying each dimension. Such analysis makes it possible to pinpoint where the data governance framework may be deficient.

Firstly, supporting the development of e-commerce calls for a regulatory environment that is adapted to fast-evolving technologies to allow a full participation in the opportunities brought by e-commerce without increasing digital inequalities (UNCTAD 2019). The e-commerce dimension encompasses the following elements: existence of an overarching e-commerce law; legal equivalence of paper-based and e- communications; legal recognition of e-signatures; adoption of principles of technological neutrality of e- communications; and implementation of a digital ID system so users can access e-government services.

Country performance varies significantly on technological neutrality in electronic communications and a digital ID system for accessing e-government services (figure 7). The technological neutrality principle advocates a flexible regulatory framework. Applied in the arena of e-commerce/transactions, it ensures openness to technologies needed for e-signatures and e-communications. Rules that are neutral regarding technology accommodate innovation and change without further legislative work. Regulations should define the expected output without imposing requirements favoring, or consolidating the position of, technological winners (Maxwell and Bourreau 2014). For instance, the Ukrainian Law on Electronic Trust Services stipulates technological neutrality of e-services.<sup>13</sup> The principle of technological neutrality has been implemented in the regulation of 53 percent and 57 percent of low- and upper-middle-income

> 12 The DBI project measures how many adequacy conditions can be found on average in each jurisdiction. This average score is calculated on the basis of the full set of countries; the score is not restricted to countries that regulate adequacy-based data transfer only.

> 13 See art. 1 (42). The text of the law is accessible at: https://zakon.rada.gov.ua/laws/show/2155-19.

19
- **Card, David, Jorg Heining, and Patrick Kline** , “Workplace Heterogeneity and the Rise of West German Wage Inequality,” _The Quarterly Journal of Economics_ , August 2013, _128_ (3), 967–1015.

- **Carozzi, Felipe and Sefi Roth** , “Dirty Density: Air Quality and the Density of American Cities,” _SSRN Working Paper 3590889_ , May 2020.

- **Carranza, Eliana, Robert Garlick, Kate Orkin, and Neil Rankin** , “Job Search and Hiring with Two-sided Limited Information about Workseekers’ Skills,” _Working Paper_ , 2020.

- **Chang, Tom, Joshua Graff Zivin, Tal Gross, and Matthew Neidell** ,“Particulate Pollution and the Productivity of Pear Packers,” _American Economic Journal: Economic Policy_ , August 2016, _8_ (3), 141–169.

- **Chay, Kenneth Y. and Michael Greenstone** , “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession,” _The Quarterly Journal of Economics_ , 2003, _118_ (3), 1121–1167.

- **Chen, Shuai, Paulina Oliva, and Peng Zhang** , “The Effect of Air Pollution on Migration: Evidence from China,” _NBER Working Paper w24036_ , 2017, p. 69.

- **Collier, Paul** , “African urbanization: an analytic policy guide,” _Oxford Review of Economic Policy_ , 2017, _33_ (3), 405–437.

- **Combes, Pierre-Philippe and Laurent Gobillon** , “Chapter 5 - The Empirics of Agglomeration Economies,” in Gilles Duranton, J. Vernon Henderson, and William C. Strange, eds., _Handbook of Regional and Urban Economics_ , Vol. 5 of _Handbook of Regional and Urban Economics_ , Elsevier, 2015, pp. 247–348.

- **Conley, T. G.** ,“GMM estimation with cross sectional dependence,” _Journal of Econometrics_ , September 1999, _92_ (1), 1–45.

- **Dargay, Joyce, Dermot Gately, and Martin Sommer** , “Vehicle Ownership and Income Growth, Worldwide: 1960-2030,” _The Energy Journal_ , 2007, _28_ (4), 143–170.

- **de Mel, Suresh, David McKenzie, and Christopher Woodruff** ,“Returns to Capital in Microenterprises: Evidence from a Field Experiment,” _The Quarterly Journal of Economics_ , November 2008, _123_ (4), 1329–1372.

- **, , and** , “Labor Drops: Experimental Evidence on the Return to Additional Labor in Microenterprises,” _American Economic Journal: Applied Economics_ , January 2019, _11_ (1), 202–35.

- **Deryugina, Tatyana, Garth Heutel, Nolan H. Miller, David Molitor, and Julian Reif** , “The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction,” _American Economic Review_ , December 2019, _109_ (12), 4178–4219.

- **Duranton, Gilles and Diego Puga** , “Chapter 48 - Micro-Foundations of Urban Agglomeration Economies,”in J. Vernon Henderson and Jacques-Francois Thisse, eds., _Cities and Geography_ , Vol. 4 of _Handbook of Regional and Urban Economics_ , Elsevier, 2004, pp. 2063–2117.

- **Ebenstein, Avraham, Maoyong Fan, Michael Greenstone, Guojun He, Peng Yin, and Maigeng Zhou** , “Growth, Pollution, and Life Expectancy: China from 1991-2012,” _American Economic Review_ , May 2015, _105_ (5), 226–231.

- **Ellison, Glenn, Edward L Glaeser, and William R Kerr** , “What Causes Industry Agglomera-

33
# **The Effect of Local Governance on Firm Productivity and Resource Allocation: Evidence from Vietnam**<sup>**1**</sup>

## **Shawn W. Tan**

## **Trang T. Tran**

## **World Bank**<sup>**2**</sup>

## **JEL Classification** : L25, D24, D02, O53

Keywords: Productivity, resource allocation, Vietnam, business environment, governance.

> 1 This paper is a background paper for the World Development Report 2017 on Governance and the Law.

> 2 Both authors are economists in the Trade and Competitiveness Global Practice in the World Bank. Please direct your comments and questions to both Shawn Tan <swtan@worldbank.org> and Trang Tran

> <u><ttran6@worldbank.org>. They thank Siddharth Sharma for his helpful comments and Wenyu Zhu for his excellent</u> research assistance.
# **7.6. Comparing Z-score distributions**

**Figure 16. Box plots of Z scores in both tests (N=475, adjusted)**

<!-- Start of picture text --> Fluency standardized score<br>ECE-06 standardized score (over 19 items)<br>ECE-06 standardized Rasch score<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text -->

# **7.7. Comparison of distributions by groups**

**Figure 17. Distribution of Fluency and Comprehension Scores by First Language and Type of School** **_a. Fluency by First language b. Comprehension by First language_**

<!-- Start of picture text --> Spanish Quechua Ashaninka Spanish Quechua Ashaninka<br>-4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4<br>Fluency standardized score ECE-06 standardized score (over 19 items)<br>Percent normal zfluidez Percent normal zmed19<br>Graphs by First language Graphs by First language<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>Percent Percent<br>30 30<br>20 20<br>10 10<br>0 0<br><!-- End of picture text --> 77