rafmacalaba/datause-encoder-data huggingface dataset:
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE cancelled in 2000. More recently, in 2009, a 25-MW build-operate-run-transfer (BORT) was granted to ONE (Morocco) for the rural electrification of 550 villages along the Saint Louis–Dagana–Podor axis. Rights to serve rural communities are also being awarded to small-scale providers under concession arrangements with a public subsidy. For power generation, a build-operate-transfer (BOT) agreement was signed in 1997 for a 56-MW combined-cycle power plan, and one signed in 2005 for a 65.7-MW heavyfuel diesel plan (for an independent power producer [IPP] at Kounoune). Under a different contractual arrangement, there was a rental agreement signed in 2005 for the 40-MW Aggreko Dakar Temporary Station to supply temporary power to SENELEC for two years.

**Figure 14. Power prices and costs in Sub-Saharan Africa**

<!-- Start of picture text --> a. Average tariff<br>50<br>40<br>30<br>20<br>10<br>0<br>b. Costs<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>Source: Power price: Briceño-Garmendia and Shkaratan 2010; Power costs: Eberhard and others 2008.<br>Note: DRC = Democratic Republic of Congo.<br>US cents per KWh<br>Liberia Chad Cape ... Senegal Burkina ... Mali Uganda Kenya DRC Rwanda Benin Niger Cote ... Namibia Madagas... Cameroon Ghana Botswana Mozambi... Lesotho Tanzania South ... Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br>US cents per KwH<br>Mali Niger Congo Benin Cape Verde Cameroon Rwanda Burkina Faso Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia DRC Zambia South Africa<br><!-- End of picture text --> Also Senegal has systematically increased its participation in regional initiatives. The country is active in the Senegal River Basin Organization,<sup>8</sup> and it is also interconnected to the West African Power Pool (WAPP). Its increased participation in the regional electricity market is an important step toward addressing the lack of sufficient electricity supply to the economy, which may be helped by importing hydroelectricity, mainly from Guinée through the OMVG<sup>9</sup> , and also gas-fueled electricity from Côte d’Ivoire.

> 8 Organisation pour la Mise en valeur du Fleuve Sénégal (OMVS).

> 9 Gambia River Basin Development Organisation.

27
interest rates began to drop post-2000, private corporate bond issuance rose dramatically. The value of new issues for both security types is shown below in Table 1.

**Table 1: New Corporate Issuance, in US$ millions**

|||<br>**Equity**||**De**|**bt**|
|---|---|---|---|---|---|
||IPOs<br>C|apital Increases|Intl.|Local|Intl.|
|1989|20.0|-||n.a.|n.a.|
|1990|0|-|98.3|n.a.|n.a.|
|1991|20.0|-|0.0|n.a.|n.a.|
|1992|121.5|-|129.2|n.a.|n.a.|
|1993|179.5|7.0|290.0|n.a.|321.85|
|1994|91.1|22.0|798.5|n.a.|155.0|
|1995|237.4|321.0|224.3|70.5|300.0|
|1996|418.5|199.9|296.9|178.2|1090.0|
|1997|170.0|133.6|563.4|109.1|1478.0|
|1998|0|14.6|72.4|827.0|1063.0|
|1999|0|133.5|85.0|776.6|1432.0|
|2000|0|52.7|0|1345.0|300.0|
|2001|0.0|0|n.a.|2978.9|1606.0|
|2002|100.0|0|n.a.|1812.4|982.7|
|2003|32.3|696.8|n.a.|2504.8|2300.0|

**Source:  SVS, Larrain Vial Brokerage, Euromoney, Bloomberg, Bondware**

Relative to many emerging markets, Chilean corporates have in general enjoyed good access to international capital markets. That said, there have been on average four bond issuances per year by Chilean companies that are domiciled in Chile; 19 companies have issued the roughly 40 distinct issues in the past 10 years. As the data in the above table illustrates, international bond issuance doubled in 2003 compared to the last 10 years. Mirroring domestic equity issuance, global equity (non-ADR) issues by Chilean companies had their peak in the early 1990s, dried up upon the Russian crisis, followed by a single issue in 2002 and 2003. ADRs have been slightly steadier, with a marked absence in 1998, with four new issues in 2003.

Both domestic and foreign issuance is dominated by highly rated companies and/or securities. With the exception of a few state owned company issues, all securities issued abroad were in the range of a BBB+/- rating. Figure 1 below shows an even bigger clustering of credit ratings for the domestic outstanding issues, where the bulk in both size and quantity lie in the AAA to A range.

7
_3_ _Family Planning Programs in Sub-Saharan Africa_

**Table 1. Family pla-ining program effort and components for selected African countries: Scores and percent of maximum possible score**

|_C_|_omponent and_<br>|_maximum p_<br>_Service_<br>|_ossible score_<br>|<br>|
|---|---|---|---|---|
||_Policy and_|_and_|_Records_|_Availabil-_|
|_Program_<br>_effort_<br>_Country_<br>_(120)_|<br>_stage_<br>_setting_<br>_(32)_|_service-_<br>_related_<br>_(52)_|_and_<br>_evaluation_<br>_(12)_|_ity and_<br>_access_<br>_(24)_|
|_Effort score_|||||
|Niger<br>_5.5_<br>|3.0<br>|1.1<br>|0.2<br>|1.2<br>|
|Rwanda<br>27.6|16.7|8.3<br>|2.0<br>|0.6<br>|
|Kenya<br>33.7|13.7|12.7|3.8|3.5|
|Benin<br>13.7|3.9|_5.0_|2.9|1.9|
|Ghana<br>21.3|10.1|7.6|2.1|0.9|
|Lesotho<br>16.7|8.6|6.3|0.9|0.9|
|Mauritania<br>4.2|1.3<br>|1.4<br>|0.0<br>|_1.5_<br>|
|Sudan<br>9.0|5.7|2.8<br>|0.2<br>|0.3<br>|
|Senegal<br>27.2|10.3|10.4|3.0|_3.5_|
|Global<br>69.5|21.0|27.8|6.1|14.6|
|_Percent of maximum_|||||
|Niger<br>_5_|9|2<br>|2<br>|_5_<br>|
|Rwanda<br>23|52|16|17<br>|3<br>|
|Kenya<br>28|43|24|32|15|
|Benin<br>11|12|10|24|8|
|**Ghana**<br>18|32|15|18|4|
|Lesotho<br>14|27|12|8|4|
|Mauritania<br>4|4|3|0|6|
|**Sudan**<br>8|18|5|2<br>|1<br>|
|Senegal<br>23|32|20|25|15|
|Global<br>58|66|53|51|61|

Adapted **from** R. J. Lapham and W. Parker Mauldin, "Contracepdve prevalence:<sup>The influence of</sup> organized family planning programs," _Studies in Family Planning_ 16(3),<sup>1985:117-137, Table 1.</sup> judged successful "have survived' most have developed new programmes;<sup>all have expanded in</sup> geographical coverage; and many are providing a valuable service by<sup>offering consultation and</sup> training support to government and other private agencies." Program<sup>survival, expansion,</sup> demonstration and replication were among the criteria applied to the three<sup>programs described</sup> in this paper as indicators of their successes in making family planning services available<sup>under</sup> difficult circumstances.
Table 12b: Impacts of PROSPERA on Type of High School 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 School Fixed Effects

||General<br>HS|Technical|Tec Prof|Private|
|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|
|PROSPERA beneficiary 2008|0.076***|-0.074***|-0.001|-0.001|
||(0.013)|(0.013)|(0.003)|(0.002)|
|Female beneficiary|-0.031*|0.032*|0.002|-0.002|
||(0.017)|(0.018)|(0.004)|(0.003)|
|Female|0.01|-0.012|0|0.002|
||(0.016)|(0.016)|(0.003)|(0.003)|
|Constant|0.254***|0.716***|0.007|0.023*|
||(0.068)|(0.069)|(0.015)|(0.012)|
|Observations|11,200|11,200|11,200|11,200|
|R-squared|0.263|0.261|0.135|0.157|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 12c: Impacts of PROSPERA on Type of High School: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008

||Neares<br>|t-Neighbor Matching <br>|Models<br>|<sup>h</sup>|
|---|---|---|---|---|
|Variables|12th grade<br>General|12th grade<br>Technical<br>T|12th grade<br>echnicalprofessional|12<sup>t</sup>grade<br>Private|
||Propensity score mat|ching with exact matc|hing on age and state||
|Girls|0.046***|-0.046***|0.004|-0.0037|
||(0.017)|(0.018)|(0.004)|(0.0037)|
|Observations|4,931|4,931|4,931|4,931|
|Boys|0.074***|-0.066***|- 0.0062|-0.0015|
||(0.017)|(0.017)|(0.004)|(0.0037)|
|Observations|4,751|4,751|4,751|4,751|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Finally, Table 13a through 13c shows impact on achievement tests for indigenous students and shows some mixed evidence. Impacts on ENLACE scores in Spanish are positive and significant ranging up to 0.13 standard deviations on achievement tests up through ninth grade. In fact, these effects are larger than those observed for the overall population of beneficiary children in marginalized areas, consistent with the higher enrollment impacts that were also observed. However, effects on mathematics achievement are smaller and less precise/significant.

32
|**Data Appendix**|**92**|
|---|---|
|Uppsala Confict Data Program Battle-Related Dataset . . . . . . . . . .|. .<br>92|
|Geographical Research On War, Unifed Platform (_Grow_<sup>_up_</sup>) Dataset . . .|. .<br>93|
|Polity IV Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>95|
|FDI data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>96|
|Refugees data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>98|

# **List of Figures**

|1|Violence per Capita and Growth . . . . . . . . . . . . . . . . . . . . . .|8|
|---|---|---|
|2|Locally Afected Population and Growth (African Countries) . . . . . .|9|
|3|Estimated output loss in the period 1946-2010 (cross-country analysis)|12|
|4|Estimated Output Loss in the period 1992-2012 (ethnic group level) . .|16|
|5|Length of Recovery Period on the Ethnic Group Level . . . . . . . . . .|21|
|6|GDP per capita in 2013 and Peace Stability at the Country Level . . .|24|
|7|Confict and Economic Performance . . . . . . . . . . . . . . . . . . . .|25|
|8|Comparison Between Uganda and Kenya . . . . . . . . . . . . . . . . .|27|
|9|Confict and Aggregate Productivity Loss . . . . . . . . . . . . . . . . .|36|
|10|Simulated Output in Nigeria . . . . . . . . . . . . . . . . . . . . . . . .|41|
|11|Firms and Political Instability . . . . . . . . . . . . . . . . . . . . . . .|44|
|12|Firms and Political Instability . . . . . . . . . . . . . . . . . . . . . . .|44|
|13|Average Net foreign investment infows From OECD countries<br>. . . . .|46|
|14|Investment Flows into Uganda Post Confict . . . . . . . . . . . . . . .|47|
|15|Refugees and Economic Activity: Uganda and Sierra Leone . . . . . . .|49|
|16|Refugees and Economic Activity: Nicaragua and Cambodia . . . . . . .|49|
|17|Peace and Foreign Infow Across Cut-ofs and Data Sources . . . . . . .|51|
|18|FDI Infows in Post-Confict . . . . . . . . . . . . . . . . . . . . . . . .|52|
|19|Trust and Confict in the Cross Section . . . . . . . . . . . . . . . . . .|70|
|A1|Night light in 2013 and Recovery Length . . . . . . . . . . . . . . . . .|86|

# **List of Tables**

|1|Confict Incidence and Economic Performance: Country-level data . . .|11|
|---|---|---|
|2|Confict Incidence and Country Economic Performance: Ethnic-level data|15|
|3|Recovery Growth After Confict: Country Level . . . . . . . . . . . . .|19|
|4|Recovery Growth After Confict: Ethnic Group Level . . . . . . . . . .|20|
|5|Growth and Confict in the Cross-section . . . . . . . . . . . . . . . . .|22|
|6|Impact of Civil Confict on the Stock of Refugees: Country-level Analysis|31|
|7|Output and Confict Related Aggregate Loss in Human Capital<br>. . . .|40|
|8|Peace and Foreign Investment . . . . . . . . . . . . . . . . . . . . . . .|52|
|9|Fragility and Investment Flows<br>. . . . . . . . . . . . . . . . . . . . . .|55|
|10|Risk Factors and Investment Flows . . . . . . . . . . . . . . . . . . . .|57|
|11|Robustness Check: Risk Factors and Investment Flows . . . . . . . . .|58|

2
|2006||0.377||0.569||||
|---|---|---|---|---|---|---|---|
|2007|0.810||||0.275||0.701|
|_Change in average_|**_0.040_**|**_-0.037_**|**_0.00_**|**_0.34_**|**_0.071_**|**_0.171_**|**_0.420_**|
|_Skill Premium_|**_(0.007)_**|||||||

*The industry variable in the 1994 Sakernas is at the one-digit level;** Lower secondary and above for Cambodia; *** Upper secondary and above for Cambodia.

In Indonesia, the evolution of industry premiums over time suggests that the premium of belonging to an industry other than Agriculture has declined substantially over time, and has turned in favor of Agriculture sometime after 2003. In the Philippines, Thailand and Cambodia the opposite was the case; industry premiums increased over time and the proportional increase was largest in the case of Cambodia (premiums increased fivefold), followed by the Philippines. For the Philippines, most of the increase took place sometime between 1998 and 2001 and for Cambodia sometime between 1998 and 2003. In the case of Vietnam, small premiums in favor of Agriculture were observed over the entire period. In China, the reward of being in an industry other than Agriculture has declined sharply over time.<sup>6</sup> In Mongolia, the premium of belonging to an industry other than Agriculture has declined substantially over time.

The results on skill premiums confirm the previous country grouping, with generally stable premiums for Indonesia, Philippines and Thailand, and increasing ones for the other countries. Average skill premiums in Indonesia, for both definitions, remained fairly stable, exhibiting only a small increase over time during the 1996-2007 period (i.e., discarding 1994), especially for those with tertiary education. This was also the case for the Philippines were practically no change is observed. This finding for the Philippines is somewhere in-between what was reported in table 10a (no controls for industry affiliation) and table 10b (with controls for industry affiliation) where declines in skill premiums of 20 and 10 percent respectively are reported for those with at least some senior secondary and tertiary qualifications.

> 6 Note that, the data are for urban populations and, therefore, the agricultural sector is very small in comparison to the other countries examined.

35
|Table 1: Probabilityof being|Employed i<br>Nonagri<br>Emplo|n the Nonag<br>cultural<br>yment|ricultural Se<br>Low-prod<br>Nonagric<br>Employ|ctor,2001<br>uctivity<br>ultural<br>ment|High-produc<br>Nonagricult<br>Employm|tivity<br>ural<br>ent|
|---|---|---|---|---|---|---|
||dF/dx|t|dF/dx|t|dF/dx|t|
|Female*|0.50|12.41|0.13|5.75|0.11|3.74|
|Family Size|0.03|3.64|0.02|4.93|0.00|-0.71|
|Primary education*|0.17|3.97|-0.06|-2.46|0.21|5.70|
|<br>Secondary education*|0.33|7.41|-0.07|-2.63|0.37|8.48|
|Tertiary education*|0.35|4.99|-0.14|-3.84|0.68|6.55|
|<br>Migrated*|0.18|2.43|0.00|0.16|0.03|0.75|
|Social capital*|0.08|1.79|-0.01|-0.43|0.03|0.77|
|<br>Land per capita (ha)|0.00|5.12|0.00|2.84|0.00|4.93|
|Rural*|-0.30|-2.95|-0.12|-2.19|-0.28|-3.83|
|Rural female*|-0.05|-1.01|0.16|5.23|0.12|3.01|
|Rural fam size*|-0.02|-2.11|0.00|-0.30|-0.01|-1.41|
|Rural primary education*|0.02|0.32|0.08|2.47|-0.01|-0.35|
|Rural secondary education*|0.04|0.58|0.10|2.06|0.08|1.41|
|<br>Rural migrant*|0.04|0.44|0.08|1.92|0.09|1.69|
|Rural social capita*|-0.03|-0.61|-0.01|-0.49|0.06|1.37|
|Rural ha per capita|0.00|0.27|0.00|2.73|0.00|1.09|
|Southeast*|-0.61|-6.47|-0.07|-1.38|-0.21|-4.47|
|North*|-0.30|-3.10|0.19|4.18|-0.21|-5.25|
|Northeast*|-0.54|-5.44|0.23|3.93|-0.27|-6.43|
|Artibonite*|-0.49|-5.32|0.13|3.27|-0.29|-7.79|
|Center*|-0.58|-6.33|0.01|0.25|-0.29|-8.16|
|Southeast*|-0.49|-4.81|0.02|0.36|-0.22|-4.94|
|Grand-Anse*|-0.43|-4.26|0.16|3.31|-0.27|-6.84|
|Northwest*|-0.47|-4.69|0.01|0.29|-0.19|-3.92|
|Rural Southeast*|0.32|4.76|0.09|1.12|0.15|1.93|
|Rural North*|0.18|2.09|-0.03|-0.77|0.04|0.49|
|Rural Northeast*|0.29|4.04|-0.05|-0.89|0.20|1.79|
|Rural Artibonite*|0.23|2.79|-0.10|-2.72|0.19|2.79|
|Rural Center*|0.27|3.68|-0.06|-1.24|0.36|4.55|
|Rural South*|0.24|2.83|-0.03|-0.57|0.13|1.65|
|Rural Grand-Anse*|0.23|2.81|-0.10|-2.65|0.31|4.00|
|RuralNorthwest*|0.29|4.13|0.07|1.14|0.14|1.73|
|PseudoR2|0.5|0|0.1|6|0.37||
|Note: Excluded variables: No|education a|nd West reg|ion. Number|of obs = 4,3|49.(*) dF/dx is fo|r discrete|
|change of dummyvariable from 0|to 1;t is the|test of the un|derlyingcoef|ficient beinge|qual to 0.||

_Source:_ Own calculations based on HLCS 2001.

9
- Mihretu, Mamo and Gabriela Llobet, (2017) _Looking Beyond the Horizon: A Case Study of PVH’s Commitment in Ethiopia’s Hawassa Industrial Park_ , World Bank.

- Moretti, Enrico. 2004. Estimating the social return to higher education: Evidence from longitudinal and repeated cross-sectional data. _Journal of Econometrics_ , 121(1), 175– 212.

- Munshi, Kaivan and Mark Rosenzweig. 2016. Networks and misallocation: Insurance, migration, and the rural-urban wage gap. _American Economic Review_ 106(1): 46-98.

- Murphy, Kevin M., Andrei Shleifer, and Robert W. Vishny. 1989. Industrialization and the big push. _Journal of Political Economy_ 97(5): 1003-1026.

- Neumark, David and Helen Simpson. 2015. Place-based policies. In: G. Duranton, J. V. Henderson, and W. C. Strange, _Handbook of Regional and Urban Economics, volume 5B_ . Amsterdam: Elsevier, pp. 1197-1287.

- Parry, Ian W.H., Margaret Walls, and Winston Harrington. 2007. Automobile Externalities and Policies. _Journal of Economic Literature_ 45(2): 373-399.

- Quatraro, Francesco and Marco Vivarelli. 2015. Drivers of entrepreneurship and post-entry performance of newborn firms in developing countries. _World Bank Research Observer_ 30(2), 277–305.

- Redding, Stephen J., Daniel M. Sturm, and Nikolaus Wolf. 2011. History and industry location: evidence from German airports. _Review of Economics and Statistics_ 93(3): 814-831.

- Redding, Stephen J. and Matthew A. Turner. 2015. Transportation Costs and the Spatial Organization of Economic activity. In Gilles Duranton, Vernon Henderson, and William C. Strange (eds.) _Handbook of Regional and Urban Economics, volume 5B_ . Amsterdam: Elsevier, 1339–1398.

- Shapiro, Jesse M. 2006. Smart cities: Quality of life, productivity, and the growth effects of human capital. _Review of Economics and Statistics_ , 88(2), 324–335.

- Schoar, Antoinette. 2010. The divide between subsistence and transformational entrepreneurship. Innovation Policy and the Economy 10 (1), 57-81.

- Shoup, Donald C. 2005. _The high cost of free parking._ Chicago: Planners Press.

- Siodla, James. 2015. Razing San Francisco: The 1906 disaster as a natural experiment in urban redevelopment. _Journal of Urban Economics_ 89: 48-61.

- Small, Kenneth A. 2012. Valuation of Travel Time. _Economics of transportation_ 1(1), 2-14.

- Small, Kenneth A. and Erik T. Verhoef. 2007. _The Economics of Urban Transportation_ . New York:Routledge.

- World Bank. 2009. _World Development Report 2009: Reshaping economic geography._ Washington, D.C.: World Bank

73
<!-- Start of picture text --> wD<br>=<br>So<br>a<br>o<br>Dio<br>oo<br>(= ))ae<br>oO<br>3<br>© re<br>Cc<br>[)<br><!-- End of picture text --> <!-- Start of picture text --> ue<br>N<br>2011 2012 2013 2014<br>Year<br>—e— 8,: No controls<br>—e— §,: Controls for non-monetary characteristics<br>Note: 95% confidence interval is shown by bars<br><!-- End of picture text --> <!-- Start of picture text --> uw<br>o<br>oS<br>a<br>o<br>ate)<br>ae<br>o =<br>D1S $$}<br>c<br>o<br>ee<br><!-- End of picture text --> <!-- Start of picture text --> N<br>2011 2012 2013 2014<br>Year<br>—e— 8,: Controls for non-monetary characteristics<br>—e— 8,: Controls for occupation and industry<br>. B7: Controls for non-monetary<br>characteristics AND occupation and industry<br>Note: 95% confidence interval is shown by bars<br><!-- End of picture text -->
# **Labor productivity and employment gaps in Sub-Saharan Africa** **_All figures and tables_**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>6 7 8 9 10 11<br>LN GDP Per Capita<br>Ag Employment Share:<br>LSMS Sample Other Africa Non-Africa<br>Ag Value Added Share:<br>LSMS Sample Other Africa Non-Africa<br>15<br>10<br>5<br>0<br>6 7 8 9 10 11<br>LN GDP Per Capita<br>LSMS Sample Other Africa Non-Africa<br>Share<br>Agricultural Productivity Gaps<br><!-- End of picture text --> Figure 1: Figure 1a (top) shows a global cross-section of agricultural labor and employment shares graphed against a log transformation of each country’s per capita GDP. Figure 1b (bottom) shows agricultural labor productivity gaps graphed against the log of GDP per capita (Source: Gollin et al, 2014). The horizontal dashed line represents inter-sectoral parity in labor productivity (value = 1).

21
we try to assess how much of this concentration is driven by technological dispersion versus demand. Eaton, Kortum, and Kramarz (2008) also relate trade patterns to productivity and demand shocks. But while they dissect trading patterns only for French ...rms, regardless of which products each ...rm exports (there could be more than one product per ...rm), we analyze trade at the product level for many countries.<sup>6</sup> In the next section we document concentration and distributions of exports for 151 countries in the product-destination dimension and perform preliminary analysis. In section 3 we estimate the contribution of technology versus demand to the distribution and concentration of exports. Section 4 concludes.

# 2 Empirical Facts

Our main data source is the UN Comtrade database. The U.N. classi...es exported commodities and manufactured products by source and destination at the six-digit level (roughly 5000 categories). We use the 1992 Harmonized System classi...cation (HS1992) for the year 2000, to maximize the available bilateral trade pairs. Using a less disaggregated classi...cation might have lead to better coverage of countries (say, 4-digit SITC), but would miss the extreme concentration within ...nely de...ned products.<sup>7</sup> We restrict our sample to manufactured categories, i.e. we drop from the sample all agriculture and commodities exports. Our focus on manufactured products stems from our interest on exports that are not dependent on country-speci...c natural endowments, and could potentially be produced everywhere in the world. We basically exclude products that rely directly on natural resources. Natural resources create strong comparative advantage for extracables and agricultural products. Therefore, a priori, focusing on manufacturing also reduces the degree of concentration, especially for developing countries.

Some importers in the original dataset did not correspond to well-de...ned destinations, so we dropped those destinations from the analysis.<sup>8</sup> Eventually, our sample contains 151 exporters, 2984 export categories, which may be shipped to at most 217 destinations (importers).

> 6 The distribution of exports across products is similar to what they ...nd for French ...rms.

> 7 An analysis of the distribution of product-destination export ‡ows at the 4-digit SITC level reveals similar patterns, but lower levels of concentration, as one might expect.

> 8 For example, “Antarctica”, “Areas, nes”, “Special Categories”, etc.

5