rafmacalaba/datause-encoder-data huggingface dataset:
|72‐83 Metals|33|510|543|
|---|---|---|---|
|84‐85 Mach/Electronics|360|397|757|
|86‐89 Transport|56|76|132|
|90‐97 Miscellaneous|167|213|380|
|98‐99 Special|0|1|1|
|TOTAL|1236|3661|4897|

Source: Authors’ calculations based on Athukorala (2010) and Sturgeon and Memedovic (2011) Note: These product codes were conducted using trade data at the HS1988/92 six‐digit level of classification.

Entering a sector at one position in the chain may not require the same capability bundle as another position in the chain. For this reason, we further divide certain sectors into final and intermediate stages. While this does not account for all the steps in a value chain, it does account for some of the most important differences. Table 3 provides a breakdown of final products and intermediate products<sup>9</sup> within each of the main GVC sectors. Four sectors with a substantial number of both final and intermediate GVC products – transport, footwear, machinery and miscellaneous manufactures – are divided into final and intermediate sub‐sectors in the following pages. Since wood and chemicals comprise only one product and make up only 0.3 percent of the value of global GVC trade, we exclude them from subsequent analysis.

**Table 3: Number of Products by Position in Value Chain within GVC Sectors**<sup>**10**</sup>

|**Sector**|**Total Number of**<br>**GVC Products**|**Final Products**|**Intermediate**<br>**Products**|
|---|---|---|---|
|39‐40 Plastic/Rubber|12|0|12|
|41‐43 Hides,Skins|5|4|1|
|50‐60 Textiles|320|0|320|
|61‐63 Clothing|233|231|2|
|64‐67 Footwear*|42|29|13|
|68‐71 Stone/Glass|6|0|6|
|72‐83 Metals|33|0|33|
|84‐85 Machinery/Electronics*|360|50|310|
|86‐89 Transport*|56|14|42|
|90‐97 Miscellaneous Manufactures*|167|90|77|
|TOTAL|1234|418|816|

Source: Authors’ calculations

*These four sectors are divided between final and intermediate stages in subsequent analysis.

This remainder of this paper is structured as follows. First, using the framework outlined above, we investigate which capabilities matters most for participation in GVCs – overall and within the 14 sectors identified (the 10 main final assembly sectors plus 4 intermediate sectors). Then, we use the example of countries within the Southern African Customs Union (SACU) to illustrate how capabilities of these countries compare to other regions more involved in GVCs. This analysis gives a sense of the sectors SACU countries might target to advance their participation in GVCs, and which capabilities they might need to

> 9 Sturgeon and Memedov labeled their products as either final or intermediate. Athukorala’s list of parts and components were all designated as intermediates.

> 10 Wood and chemicals sectors were removed from this list because as they were only represented by one GVC product.

9
gain from granting preferential access. If it is not, then the developed country and the world could lose from a preferential arrangement.

How important the gains and losses are for different countries is ultimately an empirical question, and to address this question, we will use a multi-country Computable General Equilibrium (CGE) model. But the smallness of SSA - it accounts for a less than I percent share of QUAD imports - suggests that any impact of preferential access on the welfare of other countries and the world is unlikely.to be large.

# **3. The pattern of Sub-Saharan Africa exports**

In 1995, SSA exported 31.5 billion dollars worth of merchandise other than oil. About two-thirds of exports went to the European Union, around 7 percent to the United States and a fifth to other developing countries (ROW in Figure 2 below). Canada, Japan and other developed countries together accounted for only 6 percent of SSA exports (4 percent for Japan, 1 for Canada and 1 for the rest of the OECD). Thus, almost 80 percent of SSA exports were concentrated in the QUAD.

Oil exports accounted for around 43 percent of SSA's total merchandise exports (explained by Nigeria's presence in the SSA aggregate). Nearly half of the oil was exported to the United States and about a third to the European Union. However, since oil is subject to hardly any protection in the QUAD countries, its presence has little impact on our simulations (see Table 2).

8
**Table 2 Selected Statistics of Remittance Recipient Households in Jamaica -** **<u>(1995 2002, selected years)</u>**

|||1995|1998|2001|Total(1995-2002)|
|---|---|---|---|---|---|
|Percent of households rec<br>remittances<br>|eiving<br>|31.2|24.6|27.1|24.0|
|Percent of remittances to<br>household income<br>|the total<br>|4.4|4.7|6.1|5.2|
|Average amount (current<br>monthly household remitt|US$) of<br>ance<br>|135|235|250|178|
||Remittance<br>recipients|||||
|Percentage of<br>households with male<br>household heads|Yes|52.0|54.8|50.4|51.2|
||No|58.0|56.2|56.1|58.1|
|Average hours worked<br>by male household head<br>per week|Yes|35.2|31.8|31.6|32.7|
||No|38.3|34.7|35.3|36.9|
|Average hours worked<br>by female household<br>head per week|Yes|21.2|21.9|18.7|20.9|
||No|25.8|24.9|23.3|25.3|

There is the hypothesis that high levels of remittances are hurting Jamaica’s competitiveness because they raise the reservation wages of recipients and the equilibrium real wages in the labor market. This hypothesis follows a common conjecture that the remittances have a negative impact on the labor supply of recipient households. There are very few studies that explicitly investigate the relationship between labor supply and remittances. One exception is the study on Nicaragua by Funkhouser (1992). He found that an increase in remittances would have a negative impact on labor force participation but also a positive impact on self-employment. His study indicates that remittances change the distribution of employment in sectors. Rodriguez and Tiongson (2001) conducted a same exercise using household level data in Manila in 1991. They find remittances decrease labor participation of recipient households. A recent study by Acosta (2006) using El Salvador data shows that remittances can increase the household budget and reduce liquidity constraint problems, allowing more consumption and investment. After controlling for household wealth and using selection correction techniques, remittances are negatively related to child labor and adult female labor supply, while on average adult male labor force participation remains unaffected. That additional income derived from migration increases girls’ education and reduces women’s labor 8
“When a Dalit [formerly an Untouchable] argued with an upper caste farmer..., the upper caste villagers attacked 80 Dalit families in retaliation. When the same Dalit man then went to the police to report the incident, a social boycott was imposed on all of the Dalits from [his village]; they were thrown out of their village and denied every opportunity to earn their livelihood.” Tejeshwi Pratima, “Dalits thrown out of their village for raising their voice against discrimination,” June 29, 2006<sup>1</sup> Every society requires restraints on opportunism. The conventional simplifying assumption in economics is that government provides these and that individuals obey them because it is in their self-interest to obey the law. More recently, scholars have questioned the usefulness of this convention, arguing that social norms are a key source of restraints and that without them even formal rules would be unenforceable.<sup>2</sup> Underneath the level of behavior that most of economics is concerned with are the _social capabilities_ to enforce restraints on opportunism, underpinning even modern societies with well-developed legal institutions. Economic historians such as Douglass North (1990) and Joel Mokyr (2008) have emphasized the role of appropriate social norms for the enforcement of contracts and property rights and, thus, for economic development and growth.<sup>3</sup> As social norms are part of cultural traditions that may inhibit or enhance trade and production by affecting honesty, trust and trustworthiness, and cooperation, these arguments bring cultural factors into the focus of economists’ attention ( _e.g_ . Gintis 1972, 2008; Bowles 1998; Bowles and Gintis 1998; Guiso, Sapienza and Zingales 2004, 2006; Banerjee and Iyer 2005; Fisman and Miguel 2006; Herrmann, Thöni and Gächter 2008; and Tabellini 2008).

Social norms are enforced by informal sanctions that are often imposed by those who obey the norm even though sanctioning is costly and yields no material benefits to the punisher (Fehr and Fischbacher 2004). Such punishment can have very powerful effects because in the absence of sanctioning opportunities, norms tend to erode and eventually may collapse. Thus, the

> 1 Cited in New York University Center for Human Rights and Global Justice and Human Rights Watch, 2007, p. 60. (hereafter, NYU). The incident took place in June 2006,

> 2 See, _e.g.,_ Hayek (1973, ch. 2), Greif (1993), Platteau (1994), Weingast (1997), Lindbeck, Nyborg and Weibull (1999), Basu (2000), and Ostrom (2000).

> 3Mokyr argues that the unusual strength of Britain’s social norms against dishonest behavior in business helped elevate Britain to the leading position it took in the Industrial Revolution.  In 18<sup>th</sup> century Britain, “opportunistic behavior was made sufficiently taboo that only in a few cases was it necessary to use the formal institutions to punish deviants” (p. 532).  In this secure environment, unprecedented levels of cooperation occurred between individuals with commercial acumen and those with technical skill: “Boulton found his Watt, Clegg his Murdoch, Marshall his Murray, and Cooke his Wheatstone” (p. 535).
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE medium-term cash deficit through an increase in its working capital by restructuring its concession agreement and current shareholder structure.

Between 2005 and 2009, Transrail’s traffic volume was only half that of Sitarail, the other West African railway concession with a very similar type of a binational network (table 5). This is despite the fact that Transrail had access to greater hinterland demand. The main reasons for Transrail’s underperformance was a lack of financing, dilapidation of tracks, and outdated rolling stock. The relatively high rate of derailments (0.45 derailments per million TU) and the low locomotive reliability (15 mainline locomotive breakdowns per 100,000 km) are indicators of these problems. These numbers should not come as a surprise, however, as Transrail’s assets are aging. Some portions of the track are over 70 years old, and the average locomotive age is 30 years. The segment between Tambacounda and Dioubeba in Senegal close to the border with Mali (approximately 464 km) is in poor condition, which hampers access to the port of Dakar.

**Table 5. Railway indicators for Mali and selected other countries, 2005–09**

||**TRANSRAIL**<br>**(Senegal -**<br>**Mali)**|SITARAIL<br>(Côte d’Ivoire –<br>Burkina Faso)|Camrail<br>(Cameroon)|Madarail<br>(Madagascar)|
|---|---|---|---|---|
|Concessioned (1)/ state-run (0)|**1**|1|1|1|
|Freight traffic volume (million ton-km)|**393**|794|1,061|113|
|Passenger traffic volume (million passenger-km)|**91**|210|377|3|
|Total traffic volume (million TU)*|**429**|878|1,212|114|
|**EFFICIENCY:**|||||
|Staff: thousand TU per staff|**247**|558|547|118|
|Derailments per million TU|**0.45**|0.01|0.15|2.31|
|Mainline locomotive breakdowns per 100,000km|**15**|6|9|6|
|**TARIFFS:**|||||
|Average unit tariff, freight, US cents/ton-km|**7.0**|6.3|8.1|6.0|

Source: Bullock 2009. Derived from AICD rail operator database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available. Note: * With 2.5 passenger-km equivalent to 1 TU, 1 ton-km equivalent to 1 TU Transrail’s short-term prospects are grim unless tracks and rolling stock are rehabilitated and its concession contract is restructured. Trasnrail recently proposed an emergency investment plan that is deemed to be insufficient given Transrail’s operational needs. Transrail faces critical cash flow and insolvency issues. The company is almost bankrupt with significant public service obligations and build up in arrears. The precarious operational situation leads to unpredictability of performance.

Mali’s railways, if successfully rehabilitated, could offer the best long haul transport to and from Dakar. The Dakar–Bamako railway, which was the primary mode of transport during colonial times, should be revitalized to provide a more economical mode of transport than roads. The concession for the management by a private operating company has shown that the downfall due to mismanagement and years of neglect can be partially offset, but it is not substitute of a needed investment in rehabilitation.

16
# **Figure 5. Predicted FGT indexes, Tanzania 2019/20- 2020/21**

<!-- Start of picture text --> Panel 1: FGT 1 Panel 2: FGT 2<br>7 3<br>6 2.5<br>5 2<br>4 1.5<br>3 1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 3: FGT 3 Panel 4: FGT 4<br>1.5 1<br>.8<br>1 .6<br>.4<br>.5 .2<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Panel 5: FGT 5<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%) Estimated poverty gap (%)<br>Estimated poverty gap (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true poverty gap. Dashed lines represent the true poverty gap. Dotted lines represent confidence intervals of the true poverty gap. Estimates are obtained using the normal linear regression models.

44
<!-- Start of picture text --> Relative rate of deforestation 1996-2002<br>= -2.00% 1<br>7a@&4 500% 4----------- geRagen nnn<br>0<br>6 |<br>Ps C1 aa EOS<br>> RE<br>%93 2oisndy eeeeee eee EeeiEeS |<br>f=)*L 9.00% |<br>1996-97 1998-99 2000-01 2002<br>—¢- Amazon -® Transition —*& Cerrado<br><!-- End of picture text -->

<!-- Start of picture text --> Absolute rate of deforestation 1996-2002<br>2.00% 7- _<br>—z®0Es 1.50% +------ gancccneeeeeecccceceseeececcceeeeecececeeseeeeed<br>c |<br>©a 1.00% f---2--esrecseseseccs ego acc are]<br>2 |<br>5a 0.50% fon----nneeneeeceeee eee MTreeceee eeeeeed<br>5a |<br>0.00%<br>1996-97 1998-99 = 2000-01 2002<br>—@ Amazon -®Transition —*& Cerrado<br><!-- End of picture text -->
# **Table 13. Contemporaneous and Lagged Effects of US, EU, or EFTA-Partnered IPAS on U.S. Majority-Owned Affiliate Sales in Manufacturing**

||(1)|(2)|(3)|
|---|---|---|---|
|US/EU/EFTA IPAt|0.168**<br>(0.066)|0.171**<br>(0.083)|0.161**<br>(0.081)|
|High-IP × US/EU/EFTA IPAt|0.104|–0.058|–0.081|
||(0.091)|(0.160)|(0.167)|
|US/EU/EFTA IPAt–1||0.001|0.002|
|||(0.081)|(0.098)|
|High-IP × US/EU/EFTA IPAt–1||0.170<br>(0.166)|0.149<br>(0.172)|
|US/EU/EFTA IPAt–2|||–0.010<br>(0.084)|
|High-IP × US/EU/EFTA IPAt–2|||0.048<br>(0.101)|
|**Total effect time t (****_β_IPA +****_β_High-IP,IPA)**|**0.272*****|**0.113**|**0.081**|
|_F_-test of total effect|11.35|0.76|0.34|
|**Total effect t–1**||**0.171**|**0.150**|
|_F_-test of total effect||1.67|1.13|
|**Total effect t–2**|||**0.038**|
|_F_-test of total effect|||0.18|
|Observations|5,523|5,283|5,042|
|_R_<sup>2</sup>|0.906|0.912|0.920|
|Country-sector FEs|Y|Y|Y|
|Year FEs|Y|Y|Y|

Robust standard errors clustered by country-industry in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1 35
**TABLE A9: Direct investment regressions are robust when restricting to the sample of realized investments.** Specifications are identical to those in the referenced table in the main text, but use the sample only of realized investments. Constant, R-squared, and number of observations not reported for brevity. *** p<0.01, ** p<0.05, * p<0.1

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|||Qu|antile Regr|ession||
|OLS|0.1|0.25|0.5|0.75|0.9|
|PME|PME|PME|PME|PME|PME|
|Panel A of Table 6: GDP per capita and legal origins||||||
|Ln(GDP per capita)<br>0.50|0.00|0.05**|0.03|0.04|-0.04|
|(0.40)|(0.02)|(0.02)|(0.04)|(0.08)|(0.28)|
|British legal origins (=1)<br>-0.43|0.00|0.04|-0.06|-0.04|0.02|
|(0.51)|(0.04)|(0.04)|(0.09)|(0.20)|(0.51)|
|Socialist legal origins (=1)<br>1.58|0.02|0.27***|0.42***|0.28**|0.14|
|(2.11)|(0.06)|(0.07)|(0.12)|(0.13)|(0.53)|
|Panel A of Table 7: Other country-level risk factors||||||
|Political Risk (PRS Group)<br>2.74|0.00|-0.09|-0.11|-0.52**|-1.16**|
|(2.82)|(0.09)|(0.08)|(0.13)|(0.26)|(0.51)|
|Corruption Perception (Transparency Int’l)<br>-0.66|-0.00|0.09|0.19|0.52**|0.97***|
|(0.88)|(0.15)|(0.15)|(0.15)|(0.26)|(0.23)|
|Economic Freedom (Heritage Foundation)<br>-1.98|-0.01|0.05|-0.11|-0.32*|-0.54*|
|(2.63|(0.08)|(0.07)|(0.13)|(0.18)|(0.32)|
|Panel A of Table 8: Capital market openness and develo|pment|||||
|De-jure fnancial openness index<br>-0.18|0.00|0.03|0.06|0.13|-0.17|
|(0.57)|(0.03)|(0.03)|(0.07)|(0.14)|(0.51)|
|Private sector credit (% of GDP)<br>-0.60|-0.00|-0.03|-0.13**|-0.23|-0.35|
|(0.78)|(0.04)|(0.05)|(0.06)|(0.18)|(1.53)|
|Panel A of Table 10: Changes in macroeconomic variabl|es before|frst cash f|ow|||
|Real GDP<br>0.110|0.000|0.013***|0.020**|0.019|-0.004|
|(0.147)|(0.004)|(0.005)|(0.010)|(0.015)|(0.058)|
|Local currency GDP defator<br>-0.047|0.000|-0.000|-0.005|-0.009|-0.021***|
|(0.032)|(0.001)|(0.002)|(0.004)|(0.006)|(0.006)|
|Local currency units per U.S. dollar<br>0.058|0.000|0.001|0.005|0.009|0.019**|
|(0.039)|(0.001)|(0.002)|(0.004)|(0.007)|(0.008)|
|Panel A of Table 11: Changes in macroeconomic variabl|es over lif|e of invest|ment|||
|Real GDP<br>0.168|-0.012|0.012|-0.033|0.098|0.256|
|(0.197)|(0.051)|(0.039)|(0.061)|(0.137)|(0.156)|
|Local currency GDP defator<br>0.680|0.013|0.053|0.155**|0.284**|0.350***|
|(0.451)|(0.052)|(0.044)|(0.063)|(0.118)|(0.124)|
|Local currency units per U.S. dollar<br>-0.643|-0.015|-0.066|-0.173***|-0.328***|-0.408***|
|(0.406)|(0.051)|(0.041)|(0.064)|(0.118)|(0.129)|

54
comprises 12 relevant years, when many developing countries introduced reforms (including opening up their financial systems), which was followed by years of high internationalization (up to the burst of the dotcom bubble).<sup>8</sup> In sum, we are able to analyze an important aspect of international integration using a new and large dataset of firms across countries, within countries, and over time. This contributes to new insights into both what type of countries have firms that go abroad and what type of firms access international markets.

The rest of the paper is organized as follows. Sections 2 and 3 describe the data and methodology. Sections 4 and 5 respectively present country- and firm-level summary statistics and results on the extent of internationalization. Section 6 concludes.

# **2. Data**

To perform our empirical analysis, we compile a comprehensive database of internationalization and collect data on the characteristics of internationalized firms as well as of those firms that remain domestic, which we use as a control sample. We assemble data on firms’ participation in the international equity markets as well as in the local markets for a total of 111 countries for the period 1989-2000. Details on the data collected and the specific variables are summarized in Appendix Table 1, while the list of countries covered and the groupings by income level are provided in Appendix Table 2.

As international financial markets, we mainly study the two largest financial centers, New York and London, but we also use data from the Frankfurt Stock Exchange. There are no comprehensive data available on the degree to which securities are being listed and traded abroad. We therefore combine a number of sources on international activity, four for international trading activity in the U.S. and two for in Europe.

For the U.S., the first source is the Bank of New York, which covers depositary receipt (DR) programs in the three major stock exchanges in the U.S.: NYSE, NASDAQ, and AMEX and contains information on the list of current DR programs and the effective date of each program. As of March 2005, there were a total of 2,259 listed programs. The DR Directory includes all currently active programs, dating back to January 1956, with most of them after 1980. The resulting database accounts for 1,990 firms with active DR

> 8 See de la Torre et al. (2006).

6
|Table 6.6: Coverage ofJefa<br>|s byDepartmental Populatio<br>|n,Argentina,2003<br>|
|---|---|---|
|Inhabitantsindepartments|Beneficiaries|Coverage|
|Fewer than 2,000|0.03|0.04|
|2,001–6,000|0.10|0.07|
|6,001–10,000|0.12|0.05|
|10,001–20,000|0.22|0.06|
|20,001–40,000|0.18|0.05|
|40,001–80,000|0.16|0.05|
|80,001–12,000|0.06|0.04|
|More than 120,000|0.14|0.05|

Source: Own calculations based on data from Ministry of Employment. Coverage= # beneficiaries/total Table 6.7: Share of Household Heads Receiving Jefas in 2003 Dispersed Rural Areas in Argentina <u>(Percent)</u>

|Mendoza<br>Santiag<br>del<br>Estero|o<br>Chaco|Santa<br>Fe|<br>Total|
|---|---|---|---|
|Share of Household Heads||||
|Receiving Jefas<br>2.5<br>5.6|7.3|1.0|3.9|

Source: Own calculation based on RHS 2003.

In Argentina, rural development programs are managed by the Secretariat of Agriculture (SAGPyA), the Ministry of Production, and the National Institute of Agricultural Technology (INTA). The majority of rural development programs that emerged in the 1990s were aimed at assisting small farmers in order to increase production. Rural development programs provide subsidies, credits, technical assistance, training, and organizational capacity. The national rural development programs PSA, PROINDER (see Box 3), PRODERNOA, and PRODERNEA use a holistic approach to improve the livelihood of small farmers by simultaneously providing several tools to improve their production and living conditions. PROINDER, PRODERNEA, and PRODERNOA specifically target the rural poor, identified by households with UBN. The program’s technical staff is extremely committed to reduce rural poverty, but programs lack resources to reach all needy households. Programs may benefit from evaluation and improved coordination. Apart from national level programs, provinces also have their own rural development programs. For example, programs in Chaco are primarily devoted to livestock and agricultural development.<sup>2122</sup>

> 21 For example, the _Programa Hortícola para el Este Provincial_ (Horticulture program for the East of the Province) was aimed at developing 5,000 ha of crops through investments in irrigation systems and machinery, the _Programa Frutícola para el Este Provincial_ (Fruit Program for the East of the Province) was aimed at planting 1.000 ha of citrus and other species, the _Programa Frutícola para El Impenetrable_ (Fruit Program for the Impenetrable Region) was aimed at investing in 200 fruit farms, and the _Programa Provincial de Siembra Directa_ (Provincial Zero Tillage Program) was aimed at modernizing farm technology. Moreover, Chaco has forestry, desertification, protection, and fishery programs. Chaco is also implementing a program to formalize and regularize the use and tenure of fiscal lands.

39