rafmacalaba/datause-encoder-data huggingface dataset:
- **Customs transactions data** From Madagascar customs we obtained administrative data tracking imports at the transaction level for the period January 2015-November 2018. For each import declaration, the data covers the HS 8-digit products included (designated as items), their source country, the dates/times of registration, assessment, and clearance, the broker, the importing firm, and, the customs inspector assigned to handle the declaration. For each item, the data contain information on both the initially declared and the finally registered import value, weight, and taxes paid (tariff and value added tax as well as exemptions). These variables enable us to evaluate inspector modifications of value, weight, and tax liabilities. In addition, for each declaration we can track any modifications made to the IT system’s initial inspector assignment by the customs port manager.<sup>14</sup> This will allow us to disentangle the role of IT department staff from that of the customs port manager in generating deviations from official rules in inspector assignment.

- **Fraud records** Fraud records were provided by the Legal Department ( _Service des Affaires Juridiques et du Contentieux_ ). For each declaration, we know both whether and if so what type(s) of fraud was detected and the amount of taxes recovered (if any). Information on whether and how much inspectors modified tax yield is important for assessing the role of inspectors.

- **Risk management data** From the customs risk management unit we received for each import declaration information on the initial and finally-used clearance channel (documentary control/yellow channel, physical inspection/red channel or no inspection/blue channel). From GasyNet we received the risk score assigned to each import declaration (related to the risk of non-compliance with customs regulations ranging from 1 to 9) and valuation advice in case it was issued.

- **Container weight measurement data** We obtained from the company in charge of managing Toamasina’s container terminal - Madagascar International Container Terminal Services Limited (MICTSL) - data on the weight of containers that arrive in Toamasina as measured by weighing at a scale upon arrival for the period 2015-2017. This port authority weight data is merged to the customs data at the declaration level, for declarations whose goods fill completely one or more containers. For declarations that share containers with other declarations this information is missing. These port authority weight data provide a useful benchmark for verifying whether the weight registered by the broker is correct.

- **UN COMTRADE data** We rely on an international trade data source UN COMTRADE to obtain export flows - values and quantities (weight) - at the country-HS 6-digit-year level for all of Madagascar’s trading partners in 2015-2018. We use this mirror data for flows imported by Madagascar

- 14This data was obtained from the customs administration’s internal control systems and merged to the transaction data.

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- Pollitt, E., K. Gorman, P. Engle, J. Rivera, and R. Martorell (1995, 05). Nutrition in early life and fulfilment of intellectual potential. _The Journal of nutrition 125_ , 1111S–1118S.

- Powell, C. A., H. Baker-Henningham, S. M. Grantham-McGregor, S. P. Walker, T. J. Cole, and J. M. M. Gardner (2005, 08). Zinc supplementation and psychosocial stimulation: effects on the development of undernourished Jamaican children. _The American Journal of Clinical Nutrition 82_ (2), 399–405.

- Prado, E. L. and K. G. Dewey (2014). Nutrition and brain development in early life. _Nutrition Reviews 72_ (4), 267–284.

- Ravallion, M. and Q. Wodon (2000, March). Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy. _Economic Journal 110_ (462), 158–175.

- Rockoff, J. E. (2004, May). The impact of individual teachers on student achievement: Evidence from panel data. _American Economic Review 94_ (2), 247–252.

- Rodriguez Herrero, H. (2005). Qualitative evaluation of the programa de apoyo alimentario. final report. _CIESAS, Available at www.diconsa.gob.mx_ .

- Roland G. Fryer, J. (2017, May). Management and Student Achievement: Evidence from a Randomized Field Experiment. NBER Working Papers 23437, National Bureau of Economic Research, Inc.

- Santibañez, L., J. F. Martinez, A. Datar, P. J. McEwan, C. M. Setodji, and R. BasurtoDavila (2007). Breaking ground: Analysis of the assessment system and impact of mexico’s teacher incentive program" carrera magisterial." technical report. _RAND Corporation_ .

- Skoufias, E., M. Unar, and T. G. de Cossio (2013, December). The poverty impacts of cash and in-kind transfers: experimental evidence from rural Mexico. _Journal of Development Effectiveness 5_ (4), 401–429.

- Tagliati, F. (2019, October). Child labor under cash and in-kind transfers: evidence from rural Mexico. Working Papers 1935, Banco de España;Working Papers Homepage.

- Vazquez Mota, J. (2004). Rules of operation for the programa de apoyo alimentario. _Government of Mexico manuscript, Available at www.diconsa.gob.mx_ .

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# **(b) Total investment required for electricity system expansion (Billion €, undiscounted unless specified otherwise)**

||2015-2020|2020-2030|2030-2040|2040-2050|2015-2050<br>(Discounted)|
|---|---|---|---|---|---|
||WithoutE|nergy efficienc|yinthe demand|side (normald|emand case)|
|Baseline|7.4|14.2|17.5|27.6|27.6|
|Green|7.4|14.1|22.9|39.3|31.9|
|Supergreen|7.4|28.7|48.1|65.4|55.6|
||With E|nergy efficiency|inthe demand|side (low dema|nd case)*|
|Baseline|7.4|12.9|15.0|20.9|24.5|
|Green|7.4|12.5|19.5|31.6|28.2|
|Supergreen|7.3|22.5|38.3|51.9|45.3|
|Energy<br>Efficiency**|1.6|5.3|5.9|6.8|8.3|

* Each number under all three scenario should be added to corresponding energy efficiency costs to get total energy supply costs including energy efficiency investments

** Includes energy efficiency in measures using electricity

# **3.4. CO2  Emissions**

The reductions of CO2 emissions from the energy sector as whole and power sector specifically are presented in Figure 8(a) and (b). In all scenarios, energy related CO2 emission in Romania would be lower in years between 2015 and 2050 from the level in 2005. If energy efficiency measures are not implemented, total energy related GHG emissions in 2030 and 2050 under the baseline scenario would be, respectively, 9% and 2% lower from 2005 level. It is very important to note that historically energy related CO2 emissions in Romania are dropping from the level of 2005. For example, according to emissions data published by IEA (IEA, 2014b), Romania’s 2012 emissions are 16% lower compared to 2005 level. If efficiency measures in the demand side are implemented, the baseline emissions in 2030 and 2050 would be 16% below from the 2005 level in both years. Under the green scenario, total energy activity related CO2 emissions would be 26% below the 2005 level in both years. Under the stringent super green scenario total national CO2 emissions from energy activities in 2030 and 2050 would be respectively, 47% and 43% below the 2005 level.

The power sector will experience a large reduction of CO2 in all scenarios. Since it is an ETS sector and mandated to reduce emission even in the baseline, its emissions in the baseline scenario 20
3. For each IO code, pick the UN code that has the largest number of HS6 shared. This will guarantee that all IO codes will be covered.

4. For UN codes that are matched with multiple IO codes, manually choose a unique UN code for the match. It happens in only one case.

5. Then add up the values of intermediate inputs for each pair of upstream-downstream relationship. A matrix of 20 groups by 20 groups will be built.

6. Recompute the IO coe¢ cients based on the UN industry sector classi...cation.

# 1.1.2 Computing Domestic Upstream Variety

To compute domestic upstream variety, we use the weighted average of the number of HS6 products exported by non-processing ...rms across all upstream industries as a proxy for domestic upstream varieties, since data on domestic varieties are not available. The belief is that a ...rm’s export product scope is a subset of its domestic product scope.<sup>54</sup> Speci...cally, we compute the weighted average of the number of upstream varieties by Vjt =<sup>PI</sup> i=1<sup>sijVit,</sup> where sij is the share of industry i<sup>0</sup> s goods used in total input costs of industry j, according to the Chinese input-output table for 2002. Vit is the number of HS6 products exported by non-processing ...rms in industry i in year t. Since the HS classi...cations have changed twice (in 2002 and 2007, respectively) during our sample period, we use the concordance ...le created by Cebeci et al. (2012) to de...ne a consistent set of varieties over time. As reported in Table A5, the number of varieties available to the downstream processing exporters is increasing over time for most industries. Some industries have systematically higher input varieties (e.g. machinery, mechanical, and electrical equipment). This industry-speci...c feature is already

> 54There could be export varieties that were not sold domestically or vice versa. There could also be domestic varieties produced by non-exporters that were not exported. In these regards, our proxy should be considered as a lower bound of the number of domestic varieties.

3
How about the use of market (rather than PPP) exchange rates in global inequality calculations? This is a useful complement because it gives us a different insight into inequalities. If one is interested in global purchasing power or ability to affect the world economy, then conversion of local incomes into actual US dollars makes sense. But there are relatively few instances where we are interested in this, and most of our interest in global inequality is really based on the desire to compare living standards of different people. For that purpose PPP exchange rates are, of course, better.

# 3. How big is global inequality?

There is a general agreement about the size of global inequality, and there is a general disagreement about the recent direction of change of global inequality. Table 1 shows the results for global inequality obtained by a number of authors using quite different techniques: most of them mix national accounts information (using GDI per capita as mean income) and household survey information, and only a few use household surveys directly. In all the studies however, the recipients are individuals (inequality is expressed on a per capita basis), and national incomes are converted in international (PPP) dollars although the PPP exchange rates may be drawn form different sources. All Gini values for the 1990s, with the exception of the two extremes (61 and 71) lie within a relatively narrow range between 63 and 68. The similarity in the results is even more remarkable when one realizes that the standard errors of these estimates are between 2 and 3 Gini points,<sup>25</sup> and that consequently most of the estimates are within one standard error of each other.

As for the direction of change—comparison between the 1990s and 1980s—there is no unanimity. Sala-i-Martin and Bhalla, using very similar methodologies, argue that global inequality has declined by between 3 and 4 Gini points. Dikhonov and Ward, and Bourguignon and Morrison find an increase of about 1 Gini point. Sutcliffe concludes

> 25 One has to be careful in the interpretation of the standard error. The standard errors are obtained using simple bootstrapping techniques so they basically show how sensitive is the estimated Gini coefficient to any single observation (Milanovic, 2002). These results do not include any information about the reliability of the underlying national income distributions  ( _viz_ , how correctly incomes are measured).

14
**Figure 6. Skill Shares of Low- Skills and High- Skills Demand Firms**

<!-- Start of picture text --> 80<br>Low‐skill firms<br>60 High‐skill firms<br>40<br>20<br>0<br>Prim. & Upper Sec. Tertiary TVET Other Sales & MPT occs.<br>Lower Sec. occs. opers.<br>Education and Occupational Skills<br>Skills Shares (%)<br><!-- End of picture text -->

|**Table 3. Dete**|**rminants of S**<br>|**hares of Edu**<br>|**cational Sk**<br>|**ills**<br>|
|---|---|---|---|---|
||Primary &<br>Lower<br>Secondary|Upper<br>Secondary|Tertiary<br>Educated|TVET<br>Qualifications|
|ExplanatoryVariables|Coef.|Coef.|Coef.|Coef.|
|**_Firm Attributes_**|||||
|TopManager Education|-6.43**|2.42*|4.027**|4.88*|
|Foreign Owner|-2.94|-2.13|5.07|4.26|
|Female Owner|-0.63|1.20|-0.57|1.82|
|Multi-Unit Firm|-10.01**|2.85|7.16**|2.70|
|YoungFirm|-0.40|-0.40|0.81|2.33|
|**_Demand Drivers_**|||||
|Exporter|-5.57|-1.21|6.78*|-0.49|
|Innovator|-8.58**|4.58**|3.99|1.07|
|Constant|99.08**|8.76*|-7.82|-1.09|
|Sample Size|388|388|388|326|
|R-squared|0.3290|0.0968|0.3182|0.2559|
|Note: * and ** denotes statistical|significance at th|e 5% and 1% le|vels respectiv|ely|

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**– – Figure 5: Growth Incidence Curves, HBS 2007 2011/12 and NPS 2008/09 2012/13**

<!-- Start of picture text --> Growth incidence, Tanzania Mainland, 2007-11/12 Growth incidence NPS, Tanzania Mainland, 2008/09-12/13<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Growth rate by percentile Growth rate in mean Growth rate by percentile Growth rate in mean<br>10 0<br>8<br>-2<br>6<br>4 -4<br>2<br>-6<br>0<br>-2<br>-8<br>-4<br><!-- End of picture text --> _Source:_ HBS 2007 and 2011/12. _Source:_ NPS 2008/09 and 2012/13.

# **D. Comparison of HBS and NPS density functions**

To further explore the differences in expenditures between NPS and HBS, we plot the kernel density functions of consumption expenditure of both surveys in Figure 6.a and Figure 6.b. Figure 6.a compares the kernel density of log real monthly consumption expenditure per adult equivalent of HBS 2007and NPS 2008/09. The NPS 2008/09 density function is slightly to the right of the HBS 2007 density, indicating relatively higher consumption levels.<sup>2</sup> Figure 6.b compares HBS 2011/12 density with NPS 2010/11 and NPS 2012/13 densities. There appears to be no significant difference between HBS 2011/12 and NPS 2012/13 densities, but NPS 2010/11 lies significantly to the left, indicating lower welfare levels.

> 2 Consumption levels have not been adjusted by temporal (between years) price differences.

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## **Figure 2.1:  Distribution Between Food Poverty And** **<u>Chronic Malnutrition</u>**

<!-- Start of picture text --> Food Poor<br><!-- End of picture text --> <!-- Start of picture text --> 413<br> 6 %<br><!-- End of picture text --> <!-- Start of picture text --> 746<br> 12 %<br><!-- End of picture text --> <!-- Start of picture text --> 3256<br> (50 %)<br><!-- End of picture text --> <!-- Start of picture text --> Malnourished<br><!-- End of picture text --> <!-- Start of picture text --> Sample of<br> N = 6449<br><!-- End of picture text --> <!-- Start of picture text --> 2034<br> 32 %<br><!-- End of picture text --> _Source: June 5, 2012_

**.**

# **A. Household characteristics**

Poor, food poor and malnourished households are larger than the median household size in Mali. The median household in Mali is composed of 9 members. In comparison, the food poor households have 12 members and malnourished households have 15 members. The poor and vulnerable households are also characterized by a larger average number of dependent members (1.5-1.6 dependent members for every active member compared to 1.4 on average for the whole population). In addition, poor and food poor households are also more likely to have an older head of household (51-52 years old of median age compared to 48 for the whole country) and they are more likely to be headed by members with no primary level education. On the other hand, households with heads that have secondary or more education are much less affected by poverty, food poverty and stunting. Female headship does not appear to affect households negatively, as fewer of these households are food poor. This phenomenon is seen often in poverty analysis and follows largely from our definition of welfare<sup>vi</sup> (Table 2).

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# **MACROECONOMIC AND POLICY IMPLICATIONS OF POPULATION AGING IN BRAZIL**<sup>*</sup>

Ole Hagen Jorgensen<sup>**</sup> _JEL Classification:_ C32; C54; D58; D91; E21; E27; H55; E61; J10; J21; J26; O11; O20; O40; 050. _Keywords:_ Population aging; Brazil; saving; economic growth, labor supply; demographic dividend; retirement age; fiscal policy; debt.

* An earlier version of this paper was presented at the conference on “ _Brazil Country Study on Aging_ ”, The World Bank, Brasilia, Brazil, April 6-7, 2010. The paper was written for the World Bank _Brazil Aging Study_ (P116850). Michele Gragnolati and Tito Cordella, as well as the peer reviewers of the paper, Jan Walliser, Dino Merotto, and Svend E. Hougaard Jensen, deserve particular appreciation for their thorough comments which led to improvements of the paper. Without implication, I am also grateful for useful comments to the conference participants: Makhtar Diop, Rodrigo Soares, Cassio Turra, Bernardo Queiroz, Romero C. Barreto Rocha, and Tim Miller. I would also like to thank Rodrigo Chaves, Barbara Cunha, and Anna Fruttero for helpful suggestions. Excellent research assistance was provided by Apostolos Apostolou, Gintautas Bloze, Tammy Pertillar, Lars Lonstrup, and Casper Hansen.

** Ole Hagen Jorgensen, The World Bank, Latin America and the Caribbean Region, Economic Policy Unit (LCSPE), Email: ojorgensen@worldbank.org; oj@ojconsult.dk. The views expressed in this paper are the author’s alone, and not necessarily the views of the World Bank Group. A technical appendix for model derivations, data sets, and simulation models are available upon request.
and Serra, 2003; Buch, 2003, Buch and Lipponer, 2004; Wezel, 2004).<sup>5</sup> At the same time, a smaller number of papers have examined the determinants and behavior of cross-border claims (e.g., Dahl and Shrieves, 1999; Buch, 2000; Jeanneau and Micu, 2002; Kawai and Liu, 2002; and Buch and Lipponer, 2004).

This paper assembles a database on foreign claims, both cross-border and local, extended by Italian, Spanish, and U.S. banks to over 100 countries around the world during the period 1997-2002, to investigate the determinants of the share of local claims across countries and its implications for financial stability. Banks from Italy, Spain, and the United States are dominant players in the international banking market and they jointly account for approximately 30 percent of all outstanding foreign claims vis-à-vis the countries in our sample.

In studying the mix of cross-border and local claims extended by international banks across countries, we rely on the conceptual/theoretical framework used in the trade/multinational firm literature to study the choice between exporting goods (the equivalent to cross-border lending) and producing them abroad for foreign markets (FDI). In particular, studies such as Brainard (1997) and Helpman et al. (2003) discuss the tradeoff faced by multinational firms between paying the higher sunk costs of establishing affiliates overseas vis-à-vis confronting the transportation costs and trade barriers that arise from exporting their goods instead.

Following the trade literature, we investigate how the share of local claims to total foreign claims extended by international banks across countries is affected by the entry requirements and startup sunk costs of setting up operations overseas. These costs have been

> 5 A growing literature has examined the implications of foreign bank presence in developing countries. See Clarke et al. (2003) for a review of this literature. Among others, Claessens et al. (2000), Barajas et al. (2000), Denizer (2000), and Martínez Pería and Mody (2004) discuss the implications on competition and efficiency in the banking sector. Dages et al. (2000), Detragiache and Gupta (2004), Peek and Rosengren (2000), de Haas and Levyveld (2002, 2004) compare the lending behavior of foreign and domestic banks during crises. Berger et al. (2001), Mian (2004), and Clarke et al. (2005) address the consequences on access to financing by small businesses.

4
them. The grid contains 7 GBFs: Business administration, production planning, sourcing and procurement, marketing, sales, payment methods, and quality control. Each function considers between 4 and 7 technologies. For example, an establishment can gather and analyze customer information for marketing purposes using face-to-face conversations, online chats via WhatsApp or the internet, structured customer surveys, customer relationship management (CRM) software to store contact information, interaction history, and communication preferences, or big data analytics and/or artificial intelligence to uncover trends and make informed marketing decisions. Figure 2 presents the grid for one sector-specific module, agriculture. The grid considers six SSBFs for agriculture which include land preparation, irrigation, weeding and pest management, harvesting, storage and packaging. For example, to prepare the land for cultivation, a farm can use manual labor with simple tools such as hand-held hoes, or rakes, animal-aided instruments such as ploughs, equipment manually operated such as tractors, motor tillers, or rotators, or equipment supported by digital technologies such as GPS, software or precision agriculture tools. Section A.1.1 of the appendix reports the grids for all other SSBFs and the implementation manual precisely defines each of the technologies in the grid.

# **2.3 Ranking of Technology Sophistication**

In addition to identifying key business functions and relevant technologies, industry experts ranked the technologies in each function based on their sophistication. More sophisticated technologies can perform a wider variety of tasks, more complex tasks, or perform tasks with greater accuracy and speed. The experts’ deliberations and resulting sophistication rankings, shown on the grid, were produced before the survey administration. This approach to ranking technologies resembles the World Management Survey (Bloom and Van Reenen, 2007), which relies on experts to rank management practices according to their quality.

Given the importance of the ranking for our analysis, we evaluated the coherence of the expert rankings through a three-stage validation process implemented in 14 of the 63 business functions on the grid including most of the GBFs and SSBFs in agriculture, food processing apparel, and retail. The three stages are as follows:

1. **Comparison of Key Features** : We compared the technologies in each business function along three dimensions invoked by the experts: functionality, integration, and automation. Functionality refers to the capabilities a technology offers to handle more complex tasks, in a faster way, on a larger scale, with greater accuracy and reliability. Integration reflects a technology’s ability to connect and interact seamlessly with other systems by exchanging data and coordinating processes. Automation enables the technology to execute

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