rafmacalaba/datause-encoder-data huggingface dataset:
# **_the top 5 percent of exporters, the average number of products and destinations per exporter, and entry and exit rates are explained to an important extent by those characteristics._**

We conduct an ANOVA decomposition of the same set of Database measures shown in Table 2 taken at the country and HS 2-digit sector level –averaged across the 2006-2008 period– to determine the explanatory power of country effects and sector effects.<sup>43</sup> The results are shown in Table 7 and indicate that country and sector effects combined explain an important share of the observed variation in the number of exporters, the average number of products and destinations per exporter, the share of the top 5 percent of exporters, the number of exporters (export base), and in exporter entry and exit rates.<sup>44</sup> In contrast, country and sector effects do very little to explain total exports in the country-sector, the average or median size of an exporter and exporter survival rates in the country-sector. The pattern of total exports across sectors in a country reflects comparative advantage. Since total exports in a country-sector are by definition determined by the number of exporters in the country-sector and their average size, the importance of country and sector effects in determining the number of exporters but not the size of exporters implies that comparative advantage works primarily via firm size.

There are also interesting differences in the importance of sector versus country characteristics in explaining exporter behavior. Sector characteristics do more to explain the share of top 5 percent and the number of products, while country characteristics explain better the number of exporters, the number of destinations, and entry and exit rates. While many of these differences are to be expected, the importance of sector for the share of top 5 percent implies that market structure drives a good part of concentration as opposed to country rules and

> 43 The ANOVA decomposition is a type of statistical hypothesis testing, whereby the observed variance of a given variable is partitioned into components attributable to different sources of variation. The ANOVA provides a statistical test of whether the means of the variable for different groups are all equal.

> 44 Kuwait and Portugal are part of the sample used for the ANOVA decomposition in Table 7 despite their data covering a period other than 2006-2008. However, similar findings are obtained if they are excluded from the sample.

30
mal proof).<sup>18</sup> This property does not apply to other value-added decompositions of trade that have been presented in the literature and, as far as the value-added originated abroad is concerned, this feature stems from the specific notions of ‘foreign value-added’ and ‘foreign double counted’ we adopted.<sup>19</sup>

# **3.3 Country-level perspective sectoral breakdown**

The bilateral decompositions presented above can be easily extended to consider the sectoral dimension.<sup>20</sup> It is worth recalling that here we do not change the perimeter for defining double counting, which is represented by the exporter’s borders as a whole. Thus, an item first exported by a certain sector and then re-exported by a different sector is accounted for as ‘value-added’ on one occasion and as ‘double counted’ on another.<sup>21</sup> By keeping this country-level perspective, we preserve the additivity of value-added components (i.e. the sum across all sectors and all bilateral partners yields the total GDP embedded in a country’s exports).<sup>22</sup> Here we focus on three different sectoral breakdowns: _i_ ) by origin of the value-added, either domestic or foreign, _ii_ ) by exports (the only one considered in the work of Wang et al., 2013) and _iii_ ) by final absorption.

To get a decomposition by sectors of origin, it is necessary to substitute in all the indicators of Sections 3.1 and 3.2 the 1 _×N_ vector **V** _j_ ( _j_ = _s, t_ ) with its diagonalized form **V**<sup>�</sup> _j_ (i.e. the _N ×N_ diagonal matrix with the direct value-added coefficients along the principal diagonal and zeros elsewhere):

> 18More specifically all the following equivalences hold true: � _Gr_ = _s_<sup>**DVA**</sup><sup>_sourcesr_</sup> = _G G G G_ = = � _r_ = _s_<sup>**DVA**</sup><sup>_sinksr_,</sup> � _r_ = _s_<sup>**FVA**</sup><sup>_sourcesr_</sup> � _r_ = _s_<sup>**FVA**</sup><sup>_sinksr_,</sup> � _r_ = _s_<sup>**VAX**</sup><sup>_sourcesr_</sup> _G G_ � _r_ = _s_<sup>**VAX**</sup><sup>_sinksr_</sup> � _r_ = _s_<sup>**REF**</sup><sup>_sourcesr_= �</sup><sup>_G_</sup> _r_ = _s_<sup>**REF**</sup><sup>_sinksr_.</sup>

> 19See Section 5.1 for more detailed discussion on this issue.

> 20See also Borin and Mancini (2016) on this point.

> 21 Depending on the choice between the source and the sink based decomposition, the ‘valueadded’ is attributed to a certain export flow or to another.

> 22In Section 4 we present a decomposition based on a pure sectoral-bilateral perspective which means that a certain item is considered as double counted only when it is exported multiple times to the same partner within the same sector. In this framework, the additivity property does not hold, but these alternative measures may be very useful when addressing some specific issues such as trade policy analysis.

26
the EU can also provide small Balkan economies with greater stability and financial security, facilitating their ability to invest in long-term capital (i.e. investments in infrastructure, education, research and development, and other productive sectors of the economy) that ultimately promotes sustainable development and contributes to reducing poverty.

Against this background, this paper quantifies the potential benefits of more international integration. It uses various sources of microdata and simulates different scenarios using recent advancements in quantitative spatial models.

The paper is structured as follows. First, it describes the different data sources used in the analysis. The paper uses various sources of information, such as the I2D2 database, which reports employment, earnings, place of residency, industry variables, and aggregate information from the Working Time Statistics database held by the International Labour Organization (ILO). In doing so, this paper combines datasets specific to the Western Balkans with other sources for the EU. Second, the paper presents a range of descriptive statistics relevant to the discussion. Finally, it uses a quantitative spatial model (QSM) to understand the impact of market integration policies in the region. Using this QSM model, the paper simulates different counterfactual analyses to understand the impact of interventions such as reducing transit time at border crossings, resulting from the implementation of the National Single Window for Trade, investments in border management systems, and upgrading of border crossing points; accession of the Western Balkan economies to the EU; and the provision of improved road infrastructure. Section 4 describes the model and section 5 explains the counterfactual analyses.

Overall, the paper finds that market integration policies can effectively generate economic gains in the region, especially if cross-border coordination exists in the policy reform agenda and across policies. For example, reducing transit times at border crossings generates gains in terms of real income of around 2.5% in the region. Both cross-border coordination in the trade facilitation reform agenda and EU accession amplify these gains significantly. Similarly, EU accession boosts the benefits of road infrastructure improvements. In conclusion, the main policy message from the paper is that cross-border and cross-reform coordination is necessary to take advantage of regional integration opportunities.

# **2 Data**

The study focuses on the six Western Balkan economies, but it also includes data for the 27 European Union countries, the most important economic neighbor of the Western Balkans region. Since we are interested in assessing how impacts differ within countries and/or 2
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE have done better, and some have attracted private investors not only in the ICT sector but also in the the transport and power sectors. Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured private investment commitments between 1.8 and 2.5 percent of their GDP.

**Figure 33. Burkina Faso needs to attract more private investment, especially outside the ICT sector**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Water Transport Power ICT<br>Percentage of GDP<br>Senegal Kenya Uganda Nigeria Liberia Sierra Leone Mozambique Tanzania Rwanda Ghana Zambia Burkina Faso Mali Benin Seychelles South Africa Cameroon Niger Lesotho Gabon Madagascar Guinea Malawi Mauritania Cote d'Ivoire Zimbabwe Angola Burundi Congo, Rep. Mauritius Chad Botswana Togo Swaziland Namibia<br>Sao Tome and ... Guinea-Bissau Central African ...<br>DRC Congo, Dem. ... Equatorial Guinea<br><!-- End of picture text --> _Source_ : AICD calculations _Note:_ ICT = information and communication technology; GDP = gross domestic product.

Adopting lower-cost technologies could substantially reduce the cost of meeting the posited infrastructure targets, and thus eliminate the funding gap. For example, meeting the MDGs for water supply and sanitation with lower-cost technologies than previously used (such as stand posts, boreholes, and improved latrines) could reduce the associated price tag from $208 million to $165 million each year. Similarly, meeting transport connectivity standards using lower-cost road surfacing technologies (such as single-surface treatment) could reduce the associated annual price tag from $144 million to $105 million. The overall savings from these measures would amount to $82 million, which could eliminate the country’s infrastructure funding gap, underscoring the importance of technology choices (table 21).

**Table 21. Adopting alternate strategies to meet targets might represent important savings**

<u>$ million</u>

||Before<br>innovation|After innovation|Savings|Savings as % of<br>sector funding<br>gap|Savings as % of<br>total<br>funding gap|
|---|---|---|---|---|---|
|Power trade|142|142|0|0%|0%|
|WSS appropriate technology|208|165|43|50%|72%|
|Roads appropriate technology|144|105|39|89%|66%|

_Source_ : AICD calculations _Note:_ WSS = water supply and sanitation.

If Burkina Faso had no means of raising additional finance for infrastructure, any additional way to meet the targets identified here would take a longer period of time than the decade contemplated at the outset of this exercise. If the country were to make efficiency gains while holding spending at current 44
**Table A2:** Exclusion error % by model and number of question for various coverage rates (approx. no. of questions in brackets)

|Number of questions|PMT|Truncated|Early|stop|TrES|t|
|---|---|---|---|---|---|---|
|_10% coverage_|||||||
|37.0|–|50.01 (37.0)|–||–||
|30.0|–|50.08 (30.0)|–||–||
|22.8|50.16 (22.8)|50.23 (23.0)|–||–||
|20.0|–|50.30 (20.0)|50.01|(20.5)|–||
|15.0|–|50.60 (15.0)|–||50.09|(12.1)|
|10.0|–|51.70 (10.0)|50.15|(10.5)|50.21|(10.3)|
|5.0|–|54.09 (5.0)|51.85|(5.1)|50.62|(4.9)|
|0.0|–|63.82 (0.0)|63.08|(0.8)|63.34|(0.1)|
|_20% coverage_|||||||
|37.0|–|39.56 (37.0)|–||–||
|30.0|–|39.76 (30.0)|–||–||
|22.8|40.05 (22.8)|39.76 (23.0)|39.56|(23.0)|–||
|20.0|–|39.87 (20.0)|39.56|(19.8)|39.82|(19.1)|
|15.0|–|40.16 (15.0)|39.75|(15.3)|39.70|(14.8)|
|10.0|–|41.07 (10.0)|40.86|(9.8)|39.89|(10.0)|
|5.0|–|43.35 (5.0)|45.88|(5.2)|40.86|(5.0)|
|0.0|–|53.99 (0.0)|–||53.79|(0.1)|
|_30% coverage_|||||||
|37.0|–|32.03 (37.0)|–||–||
|30.0|–|32.03 (30.0)|32.03|(29.7)|–||
|22.8|32.34 (22.8)|32.12 (23.0)|32.06|(23.2)|–||
|20.0|–|32.17 (20.0)|32.07|(20.2)|–||
|15.0|–|32.31 (15.0)|32.37|(14.9)|32.14|(15.0)|
|10.0|–|33.24 (10.0)|33.36|(10.2)|32.34|(9.8)|
|5.0|–|35.11 (5.0)|37.33|(4.7)|33.59|(5.0)|
|0.0|–|45.10 (0.0)|–||44.97|(0.1)|
|_40% coverage_|||||||
|37.0|–|25.99 (37.0)|–||–||
|30.0|–|26.00 (30.0)|25.99|(29.5)|26.09|(27.2)|
|22.8|26.44 (22.8)|26.09 (23.0)|26.01|(23.2)|26.08|(22.6)|
|20.0|–|26.09 (20.0)|26.05|(20.3)|26.10|(20.1)|
|15.0|–|26.28 (15.0)|26.44|(14.7)|26.19|(15.0)|
|10.0|–|27.02 (10.0)|27.07|(10.3)|26.35|(10.1)|
|5.0|–|28.55 (5.0)|28.53|(5.6)|27.41|(5.0)|
|0.0|–|37.79 (0.0)|–||35.19|(0.5)|
|_50% coverage_|||||||
|37.0|–|20.85 (37.0)|–||–||
|30.0|–|20.86 (30.0)|20.85|(30.5)|20.91|(30.2)|
|22.8|21.17 (22.8)<br>|20.91 (23.0)<br>|20.90|(22.5)|20.96|(22.7)|
|20.0|–<br>3|20.95 (20.0)<br>8|20.95|(20.3)|20.98|(19.8)|
|15.0|–|21.05 (15.0)|21.21|(15.4)|21.00|(15.0)|
|10.0|–|21.69 (10.0)|22.09|(9.8)|21.17|(9.9)|
|5.0|–|22.89 (5.0)|23.77|(5.2)|22.16|(5.0)|
|0.0|–|31.19 (0.0)|–||28.99|(0.6)|
## **Annex 5: City Indicator Initiatives from Six Different Cities:**

# **Toronto**

|**Initiatives from the city hall**|**Initiatives from the private**<br>**sector**|**University**<br>**based**<br>**initiatives**|**Other initiatives**|
|---|---|---|---|
|**Toronto Economic Indicators**is a monthly<br>publication produced by the Economic Development<br>Division of the City Hall. It provides a monthly<br>snapshot of the regional economy, and includes data<br>on employment, unemployment, real estate activity,<br>transit rider ship, social indicators, financial<br>indicators,innovation and others.|“**Urban Indicators**” is a website presenting<br>free data analyzed in two contexts the great<br>Toronto and the city itself. The variables are<br>population, economy, development and<br>crime. Each has its own division as such:<br>total population and ethnicity, industry and<br>employment, building and infrastructure|**University of Toronto**has The<br>**Environmental Security Database**<br>which<br>contains<br>information<br>on<br>books, journal articles, papers, and<br>newspaper clippings relating to the<br>study<br>of<br>the<br>links<br>between<br>environmental stress and violent|At the provincial level the Ministry<br>of Municipal Affairs and housing<br>collects each year from all the<br>**Ontarian municipalities a set of**<br>**information for the Municipal**<br>**Performance**<br>**Measurement**<br>**Program**. However the results do|
|The City Hall of Toronto financed a research on<br>social indicators and priority areas. This report<br>provides a description of social conditions in the new<br>City of Toronto and focuses attention on indicators<br>of risk, or social vulnerability. The concentrations of<br>socially vulnerable people. In particular, the trends<br>we see are; increasing income disparity within the<br>City and between the City and the GTA regions; new<br>priority areas emerging; large migrations of people<br>into and out of the City; impeding issues due to a<br>concentration of seniors; rejuvenation in parts of the<br>City that relate in some case to the highest priority<br>locations. This analysis helps to point out the areas<br>for intervention and strategic reinvestment.<br>**Toronto Environmental Database**(TED): Design<br>with support form ICLEI, the TED was conceived as<br>an aid for the city to make more informed decisions<br>regarding the investments it makes in improving the<br>urban environmental quality of Toronto. The TED is<br>based housed in the W&ES department.|and finally total crimes and homicides. This<br>data is very useful for having a quick,<br>general view of the city.|conflict indeveloping countries<br>.|not show how each municipality is<br>performing, therefore results from<br>Toronto are not evident.|

51
not told anyone about the domestic abuse they experienced; this type of person would be unlikely to admit to suffering domestic abuse on a brief survey.

There are no other nationally available surveys which include questions on domestic violence, and given the problematic reporting concerns, we turn to another measure of domestic abuse: the female homicide rate. We choose this variable for two reasons. We will argue that this measure is a good proxy for most extreme forms of domestic violence, which lead to women’s deaths. Conditional on this variable being a good indicator for the most severe form of domestic violence, there is a strong imperative to focus on this outcome first, as death is the ultimate limitation of freedom.

Why is the female homicide rate a good proxy for most extreme forms of domestic abuse, resulting in death of the victim? Much evidence supports the assumption that domestic violence is responsible for most of the female homicides in Brazil. Movimento Nacional de Direitos Humanos (MNDH) estimates that close to 70 percent of murdered Brazilian women are victims in the sphere of their domestic relations. These are not that different internationally: in South Africa, Israel, Canada or the US, 70% of female homicides are from domestic violence (United Nations Office on Drugs and Crime, 2011).

Other statistics support the MNDH estimate: 66.3 percent of the accused in homicides against women are their partners (AGENDE 2007). Of all female murders, 40 percent occur inside the family home or residence, where domestic violence is most likely to occur (Waiselfiz 2012). By comparison, the portion of male murders occurring inside the family residence was 14.7%. Brazil is also developing a questionnaire for victims of violence in the health system. This is not yet scaled up nor consistently applied within all hospitals, however, in 2011, of the almost 50,000 women in the database who indicated the identity of an attacker, 70 percent were family. Notably, in California, 70% of female hospital visits due to aggression are from domestic violence (Aizer 2010).

Despite the reporting bias in the PNAD data, discussed above, the answers of those who were willing to voice their experience of abuse also render support to our hypothesis. 70 percent of all perpetrators of aggression admitted by women were a partner, family, or known by the female victim. Men, in comparison, are more likely to admit having been a victim of an unknown person; 58% of reported perpetrators against men were individuals with whom the man did not have an 8
**Table 1. E-commerce development and total household expenditure per capita**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West||
|---|---|---|---|---|---|---|---|
|Z-score of purchase/<br>|0.303***|0.247***|0.718***|0.267***|0.313**|0.538***||
|population||||||||
||(0.067)|(0.061)|(0.098)|(0.083)|(0.119)|(0.157)||
|Lag log household<br>|-0.596***|-0.548***|-0.664***|-0.581***|-0.582***|-0.641***||
|expenditure per capita||||||||
||(0.015)|(0.019)|(0.016)|(0.026)|(0.024)|(0.025)||
|Average age of household<br>|-0.057***|-0.049***|-0.099***|-0.064***|-0.056***|-0.068***||
|members/10||||||||
||(0.009)|(0.014)|(0.014)|(0.015)|(0.017)|(0.019)||
|Square of average age of|0.001***|0.000***|0.002***|0.001***|0.001**|0.001***||
|household members/100||||||||
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)||
|Household dependent ratio|-0.131***|-0.045|-0.244***|-0.103**|-0.133***|-0.185**||
||(0.033)|(0.039)|(0.044)|(0.049)|(0.043)|(0.090)||
|Urban/rural area dummy<br>(Urban=1)|0.242***|||0.254***|0.204***|0.302***||
||(0.028)|||(0.055)|(0.043)|(0.050)||
|West|0.033|0.047|0.035|||||
||(0.051)|(0.061)|(0.059)|||||
|Central|0.063|0.035|0.111**|||||
||(0.046)|(0.055)|(0.048)|||||
|Constant|6.092***|5.795***|7.152***|5.963***|6.038***|6.720***||
||(0.169)|(0.227)|(0.187)|(0.258)|(0.292)|(0.297)||
|Observations|11,107|5,183|5,924|4,457|3,486|3,163||
|Adjusted R-squared|0.319|0.285|0.319<br>0.368|<br>0.285<br>0.321|<br>0.368<br>0.301|0.321<br>0.338|0.301|

Note: Robust standard errors clustered at the county level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 We examine the pattern of association between e-commerce development and each of the 18 categories of subcomponents of household consumption in Table 2. For most of the 18 categories of consumption, the association with e-commerce development is positive, stronger in the less developed regions and in rural areas. Three patterns are apparent from the table.

**Table 2. E-commerce development and various categories of household expenditure per capita by region**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Cosmetics and beauty|0.490***|0.449***|0.983***|0.510***|0.415***|1.391***|
||(0.121)|(0.125)|(0.235)|(0.162)|(0.152)|(0.476)|
|Food|0.355***|0.288***|0.842***|0.336***|0.352***|0.673**|
||(0.066)|(0.061)|(0.118)|(0.081)|(0.111)|(0.298)|
|Of which: Food at home|0.389***|0.348***|0.805***|0.357***|0.412***|0.606**|
||(0.073)|(0.066)|(0.144)|(0.094)|(0.111)|(0.263)|

·· 14
|**Table A3: Follow‐up Surveys Attrition**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Mean<br>[SD] in|Differe<br>and|nce betw<br>Controlg|een [...]<br>roup||P‐valu|e for diffe|rence...|P‐values<br>joint tests|
||Control<br>Group|Group1|Group2|Group3|N|Group 1<br>and 2|Group 1<br>and 3|Group 2<br>and 3|<br>G1=G2=G3<br>=0|
|**Two years follow‐up survey results (May‐J **|**une 2016**|**):**||||||||
|Surveyed and business still operating|0.712<br>[0.453]|0.006<br>(0.033)|0.012<br>(0.02)|0.009<br>(0.018)|3,596|0.878|0.928|0.896|0.937|
|Surveyed with short phone survey and|0.037|0.005|‐0.007|0.004|3,596|0.404|0.929|0.179|0.599|
|business still operating|[0.188]|(0.014)|(0.009)|(0.008)||||||
|Surveyed and business shut down|0.081<br>[0.273]|‐0.006<br>(0.021)|0.015<br> (0.013)|0.008<br> (0.012)|3,596|0.359|0.495|0.588|0.635|
|Surveyed and business owner deceased|0.012<br>[0.108]|0.015** <br>(0.007)|‐0.006<br> (0.005)|‐0.004 <br> (0.004)|3,596|0.012**|0.011**|0.691|0.058*|
|Survey attrition (refused, not found,|0.159|‐0.02|‐0.014|‐0.018|3,596|0.819|0.912|0.817|0.61|
|sickness, traveling, maternity leave..)|[0.366]|(0.026)|(0.016)|(0.014)||||||
|_Including refused to answer_|0.083<br>[0.276]|0.001<br>(0.02)|‐0.013<br>(0.012)|‐0.005 <br> (0.011)|3,596|0.531|0.758|0.533|0.759|
|**One year follow‐up survey results (April‐M **|**ay 2015)**|**:**||||||||
|Surveyed and business still operating|0.811<br>[0.392]|0.008<br>(0.029)|‐0.023<br> (0.018)|‐0.013 <br> (0.016)|3,596|0.328|0.458|0.579|0.575|
|Surveyed with short phone survey and|0.01|0.015|0.002|0.014***|3,596|0.184|0.898|0.031**|0.025**|
|business still operating|[0.1]|(0.009)|(0.006)|(0.005)||||||
|Surveyed and business shut down|0.057|‐0.002|0.008|0.007|3,596|0.607|0.635|0.877|0.844|
||[0.232]|(0.018)|(0.011)|(0.01)||||||
|Surveyed and business owner deceased|0.004<br>[0.065]|0<br>(0.004)|0.001<br> (0.003)|‐0.003 <br> (0.002)|3,596|0.908|0.51|0.206|0.57|
|Survey attrition (refused, not found,|0.118|‐0.023|0.012|‐0.005|3,596|0.179|0.459|0.227|0.517|
|sickness, traveling, maternity leave..)|[0.322]|(0.023)|(0.014)|(0.013)||||||
|_Including refused to answer_|0.066<br>[0.248]|‐0.007<br>(0.018)|0.006<br> (0.011)|0.009<br>(0.01)|3,596|0.504|0.382|0.831|0.749|

Notes: Column 1: Standard deviations presented in brackets. Columns 2‐4: coefficients and standard errors (in parentheses) from an OLS regression of the firm owner/firm characteristic on treatment dummies, controlling for strata dummies (dummies for each triplet). ***, **, * indicate statistical significance at 1, 5 and 10%. Sample sizes by group are the following: control group: 1,197, group1: 301, group 2: 899, group 3: 1,199.

44