rafmacalaba/datause-encoder-data huggingface dataset:
Table 2.1 shows the average diversification performance for selected countries for the period 1980-2010. T&T appears to have made significant progress in terms of diversification over the last three decades (its HH index did contract by more than 70 %). It has also undertaken a very rapid transformation in terms of diversification over the review period relative to countries with similar structural characteristics. There seems to be greater limitations in terms of diversification for oil net exporters as the average HH index for this group of counties is above the HH average for all other groups. Since the end of the 1990s, T&T has been placed below the net oil exporters’ average concentration index. It was also observed that while T&T has become more diversified over the past 30 years it remained less diversified than the average of country groups with which it shares significant structural characteristics.

**Table 2.1: Export Diversification Performance**

<!-- Start of picture text --> HH Index<br>Country 1980 1990 2000 2010<br>Trinidad and Tobago 0.8706 0.4597 0.3059 0.2480<br>Indonesia 0.3743 0.1305 0.0543 0.0560<br>Saudi Arabia 0.9414 0.7424 0.8377 0.6916<br>World 0.2553 0.2168 0.2399 0.1957<br>LAC 0.3573 0.2427 0.1872 0.1816<br>Caribbean 0.5328 0.3508 0.2227 0.1607<br>Oil net exporters 0.3580 0.3068 0.3684 0.2691<br>Island countries 0.3059 0.2286 0.2652 0.2429<br>Oil net exporter Islands 0.3566 0.2441 0.1516 0.1161<br>Source: WITS & authors’ calculations<br>Figure 2.1: HH - Energy Sector Figure 2.2: HH - Non Energy Sector<br>1.2 0.35<br>0.3<br>1<br>0.25<br>0.8<br>0.2<br>0.6<br>0.15<br>0.4<br>0.1<br>0.2 HH - Energy 0.05 HH - non energy<br>0<br>0<br>Source: WITS. Authors' Calculations Source: WITS. Authors' Calculations<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Though T&T has made significant strides in terms of diversification, in 2010 the energy sector still accounted for 66% of exports and 44% of GDP. Most of the country’s diversification has indeed happened within the energy sector as can be seen in Figures 2.1 and 2.2. Oil production has been following a declining trend since the 1980s, and has been replaced by natural gas as the 6
# **C Descriptive Statistics**

Table A.5. Descriptive Statistics - Baseline Controls

||Observations|Average|STD|Min|Max|
|---|---|---|---|---|---|
|_A. Demographic variables_||||||
|Time in Colombia (months)|1,385|53.318|10.628|26.067|189.433|
|Female [=1]|1,412|0.409|0.492|0.000|1.000|
|Age (years)|1,412|34.287|9.142|7.000|70.000|
|Number of children|1,412|1.548|1.481|0.000|10.000|
|_B. Socioeconomic variables (Info. In Venezuela before m_|_igration)_|||||
|Years of education before migration|1,412|13.278|2.808|0.000|19.000|
|Ever worked [=1]|1,412|0.977|0.151|0.000|1.000|
|Employed at private frm [=1]|1,412|0.605|0.489|0.000|1.000|
|Employed with Government [=1]|1,412|0.152|0.359|0.000|1.000|
|Self-employed or employer [=1]|1,412|0.181|0.385|0.000|1.000|
|Written contract [=1]|1,412|0.513|0.500|0.000|1.000|
|Had smartphone [=1]|1,412|0.581|0.493|0.000|1.000|
|Owner of dwelling in Venezuela [=1]|1,412|0.868|0.338|0.000|1.000|
|Electricity in Venezuela [=1]|1,412|0.994|0.075|0.000|1.000|
|Running water in Venezuela [=1]|1,412|0.860|0.347|0.000|1.000|
|Sewage in Venezuela [=1]|1,412|0.936|0.246|0.000|1.000|
|_C. Migration Variables_||||||
|Venezuela: parents or siblings [=1]|1,412|0.443|0.497|0.000|1.000|
|Household Venezuela: partner/spouse [=1]|1,412|0.552|0.497|0.000|1.000|
|Knew of job opportunity before migrating [=1]|1,412|0.346|0.476|0.000|1.000|
|Gap between last job and migration (months)|1,412|1.119|4.504|0.000|30.000|
|Migrated for health reasons [=1]|1,412|0.101|0.302|0.000|1.000|
|Friends or familyin Colombia [=1]|1,412|0.733|0.443|0.000|1.000|

11
any _p_ > 0. Note also that _NBG_ is negative for large values of _p_ , irrespective of whether _BG_ is equal to _BG1_ or _BG2_ . This is one result on which the new brain drain literature and this paper agree.

# 2.2. Heterogeneity

# 2.2.1. Individual Heterogeneity

Assume, for simplicity, that ability—or talent--is distributed uniformly and that an individual’s ability affects the benefit of education but not its cost which is a constant _C_ . This is shown in Figure 2, which draws on Commander et al. (2004). Ability is measured on the horizontal axis and declines from right to left, with the highest ability equal to _AMAX_ . The benefit and cost of education are measured on the vertical axis.

Figure 2 also shows three parallel lines declining from right to left, which depict the benefit of education under different circumstances. The lower line shows the benefit of education obtained in the absence of migration, i.e., the domestic wage. The top line shows the benefit of education obtained by migrants in the destination country, i.e., the foreign wage. The middle line shows the expected benefit of education, which is equal to a weighted average of the foreign and domestic wages. The weights are _p_ for the foreign wage) and (1– _p_ ) for the domestic age, where p is the migration probability (share of migrants in the skilled population).

In the absence of migration, the equilibrium is at _A_ *. Under migration, equilibrium is at _A_ **, with a brain gain equal to ( _A** - A*_ ). However, one cannot simply compare ( _A** - A*_ ) and ( _A* - AMAX_ ) because the two groups have different ability levels. Recalling that the distribution of abilities is uniform, individuals who acquired education 8
- 9 -

social relationships (networks, support, trust); social order (freedom from fear, safety); and social and material well-being. The last of these is an essential (pre) condition for social cohesion but is considered in this paper under the “living standards” category. As noted above, links between labor market institutions and social cohesion are not well researched. However, the following indicators address different aspects of social cohesion and may be influenced by labor market institutions.

- Voice/participation in the workplace/community

- Employment of immigrants

- Youth indicators (employment/unemployment; relative earnings)

- Income inequality/polarization (ideally household)

- Income/employment security

- Balancing work and family

This framework is applied to a broad sample of studies that look at the impacts of labor market institutions included in this paper: minimum wages, the regulation of contracts, regulations regarding unions and collective bargaining, and mandated benefits. This sample attempts to broadly cover the literature but does not pretend to be exhaustive. A particular effort has been made to include studies of institutions in developing countries.<sup>11</sup> Finally, it has not been possible to assess the data and methodology of all of the studies included in this review so readers should be aware that differences in quality may be an unobserved explanatory factor.

> 11 In assembling the studies for this review, certain existing reviews have been particularly helpful.  These include OECD (2004, 2006); Boeri, Helppie, and Maccis (2008); and Freeman (2010).  The latter two were important sources for developing country studies.
on marginal lands. The key seems to be to enhance growth in the initial phases of the plant by using additional inputs (Jongschaap et al. 2007 and references therein). The table also provides typical oil contents.

**Table 4: Achievable Dry Seed Yields for Jatropha** **_curcas_**

|Reference|Achievable Yield<br>(t/ha/yr)|Growing conditions|
|---|---|---|
|Heller (1996), Francis et<br>al (2005)|2-3*|Semi-arid area and wasteland|
|Francis et al (2005)|5|Good soils, annual rainfall of 900-1200mm, optimal<br>management|
|Jongschaap et al(2008)|7.8|Potential|
||Oilcontent (%)|Plant part|
|Kandpal and Madan|33-39|Seed|
|(1995), Ginwal et al<br>(2004)|46-58|Kernel<sup>a</sup>|

*With water availability of 500-600 mm/yr Euler and Gorroz (2004) reported yields of less than 1 t/ha.<sup>a</sup> Accounts for roughly 65% of the seed.

Notice that the ranges of yields and oil contents of these seeds make for very wide ranges of oil yields per hectare. Typical production levels could be placed at 3.75 t/ha, with and oil concentration of 30-35% by weight, leading to oil yields of 1.2 t/ha (Gonsalvez 2006). Biodiesel yields between 1800 and 2800 liters per hectare have been mentioned as realistic for current conditions by industry sources (F.O. Licht 2009b).

To the best of our knowledge, there are no reliable estimates of the global extent of jatropha cultivation. The British company D1 (D1 Oils 2008) is reported to acquire almost 260 thousand hectares distributed across India (73%), South-East Asia (21%), and Africa (6%). The site Jatrophabook.com asserts that over 3 million hectares will be in production within 3 years, mostly in India and Pakistan.<sup>8</sup>

# _2.2.2. Micro-algae_

Microalgae are a diverse group of aquatic photosynthetic microorganisms that grow rapidly and have the capability to yield large quantities of lipids adequate for biodiesel production (Li et al 2008; WWI 2007). Algae as a potential source of fuel was initially investigated during the gas scare of the 1970s (Li et al 2008). The NREL started its algae feedstock studies in the late 1970s, but their research program was discontinued in 1996.

> 8 <u>http://www.jatrophabook.com/statistics_jatropha_curcas_projects.asp.</u>

12
years around 8-12% of total assets, deposits and loans (see Figure 1.2). While collecting 11% of deposits, public banks provide 8% of total loans of the financial sector, or 24% of total lending if public second-tier institutions are included. Public banks intermediation ratio is of 57%.

# **1.3 SUPPLY OF MAIN FINANCIAL SERVICES**

15. The following provides a brief overview of the supply structure of basic financial services in Colombia based on the absolute development of financial services and the relative importance of the different intermediaries in providing these services.

**Figure 1. 4  Breakdown of deposits and loans by type of product 1998-2003**

<!-- Start of picture text --> Total deposits by type of product 4 70 Gross loans by type of product<br>60 4<br>3 3 60<br>50 2 3 22<br>40 23 23 24 21 5040 16 16 13 12 22 1010<br>30 23 24 30 10 7 7 8 8<br>21<br>20 17<br>10 14 16 15 20 28 28 28 28 31 33<br>6 7 9 11 12 13 10<br>0<br>0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br> Current Account Savings Account<br>CDT (*) Others Commercial loans Consumer loans Mortgage loans<br>(*) CDTs: Certificados de Depósito a Término<br>COP trillion<br>COP trillion<br><!-- End of picture text --> _Source: World Bank calculations based on Superintendencia Bancaria data_

**Figure 1. 5  Deposits and loans to GDP, 1998-2003**

<!-- Start of picture text --> Loans to GDP, Deposits to GDP<br>50 44 45<br>41<br>40 38 33 37 35 34 36 34 39 38<br>30<br>20<br>10<br>0<br>Dec 98 Dec 99 Dec 00 Dec 01 Dec 02 Dec 03<br>Gross Loans/GDP Total Deposits/GDP<br>%<br><!-- End of picture text --> _Source: World Bank calculations based on Superintendencia Bancaria data_ 16. **Deposits have continuously increased, despite the 1998 crisis.** As Figures 1.4 and 1.5 show, the level of deposits, both in absolute numbers and as a percentage of GDP, has grown steadily in the last six years. This is a significant development, given the 1998-99 banking crisis, 22
_Table 1. OLS Regression Results: Explaining Wage Growth_

|||Model A|||Model B|||ModelC||
|---|---|---|---|---|---|---|---|---|---|
||Total|Unskilled|Skilled|Total|Unskilled|Skilled|Total|Unskilled|Skilled|
|∆Export Index|.012<br>(.0098)|.030**<br>(.013)|-.0007<br>(.0088)|.017**<br>(.0079)|.028***<br>(.011)|.0011<br>(.0079)||||
|∆Export US Index|||||||.019<br>(.013)|.041**<br>(.017)|-.010<br>(.012)|
|∆Skilled/unskilled status|-.054|.11|-.026|-.086|.055|-.071|-.085|.055|-.069|
||(.062)|(.094)|(.063)|(.065)|(.092)|(.078)|(.065)|(.092)|(.078)|
|∆Marital status|-.12*|-.15*|-.014|-.10*|-.14**|.0025|-.10|-.14*|.0016|
||(.072)|(.079)|(.093)|(.063)|(.069)|(.091)|(.063)|(.069)|(.092)|
|Initial wage in log|-.25***|-.36***|-.26***|-.40***|-.49***|-.33***|-.40***|-.49***|-.33***|
||(.020)|(.029)|(.034)|(.029)|(.033)|(.043)|(.030)|(.033)|(.043)|
|Female||||-.097***|-.24***|.022|-.097***|-.24***|.021|
|||||(.024)|(.034)|(.036)|(.024)|(.035)|(.036)|
|Minority||||.0024|-.12|.099|-.00001|-.13|.099|
|||||(.087)|(.12)|(.072)|(.088)|(.12)|(.072)|
|Education||||.041***|.023***|.028***|.041***|.023***|.027***|
|||||(.0035)|(.0045)|(.0068)|(.0035)|(.0046)|(.0067)|
|Potential Experience||||.0015<br>|-.00010<br>|.00062<br>|.0016<br>|-.00006<br>|.00058<br>|
|||||(.0015)|(.0016)|(.0018)|(.0015)|(.0016)|(.0018)|
|Urban||||.044|.060|.0079|.043|.060|.0068|
|||||(.031)|(.042)|(.042)|(.031)|(.042)|(.042)|
|<sup>a</sup>||||-.41*|-.27|-.72***|-.35|-.19|-.72***|
|Agriculture share 2002||||(.23)|(.30)|(.26)|(.23)|(.30)|(.25)|
|<sup>a</sup>||||-.28|.072|-.71|-.099|.28|-.61|
|Industry share 2002||||<br>(.44)|<br>(.63)|<br>(.51)|<br>(.47)|<br>(.63)|<br>(.45)|
|Ctti h 2002<sup>a</sup>||||.14|1.51|-1.08|-.0051|1.33|-1.15|
|onsrucon sare||||(.73)|(.97)|(1.07)|(.71)|(.97)|(1.07)|
|<sup>a</sup>||||-1.27|-.20|-2.81**|-1.30|-.24|-2.85**|
|Government share 2002||||(1.21)|(1.39)|(1.22)|(1.23)|(1.42)|(1.23)|
|Constant|3.50***|4.91***|3.85***|5.45***|6.53***|5.12***|5.39***|6.44***|5.10***|
||(.27)|(.37)|(.46)|(.44)|(.48)|(.66)|(.44)|(.48)|(.65)|
|Observations<sup>b</sup>|1718|1051|647|1718|1051|647|1718|1051|647|
|R<sup>2</sup>|.115|.182|.146|.210|.274|.184|.209|.272|.184|

_Notes_ : The dependent variable is the log difference of monthly wage ( _∆Ln W_ in Equation (2)). The classification between skilled and unskilled workers is based on the status in 2002.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively.

The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within province correlation (clustering).

a The share of workers employed in the service sector is the omitted category.

b The total sample includes those who worked for “Army force”. As the skill category is not assigned for the latter workers, the number of skilled and unskilled workers do not add up to total number.

44
redemption **by** mutual-fund investors during crisis periods is not large, and outflows that occur tend to be small and short-lived (at least during Mexico's crisis-see Marcis et al. **1995** and Rea **1996).** Froot et al. **(1998)** present a similar picture based on aggregated flows that mix mutual funds with other types of international investor. They find that net inflows during the Mexican and Asian crises decreased, but there is little evidence of net outflows.<sup>2</sup> Our paper departs from the more aggregated analysis above **by** effecting analysis at the portfolio level. We develop a novel data set that includes individual portfolios, which allows us to examine trading strategies at much higher resolution. The data include the quarterly holdings of **13** mutual funds from April **1993** to January **1999. All 13** funds are dedicated Latin America funds. (At year-end **1998,** there were **_25_** Latin America funds; the **13** we track account for **88%** of the value of these **_25_** funds.) We use these data to address two sets of questions. The first set relates to whether funds engage **in** momentum tradingsystematically buying winning stocks and selling losing stocks (Jegadeesh and Titman **1993,** Grinblatt et al. **1995).** The second set of questions relates to whether funds engage in contagion trading, **by** which we mean systematically selling stocks from one country when stock prices are falling in another. In addressing this second set of questions, we establish a first, direct empirical link between contagion and trading strategies.

The methodological contribution of the paper is our approach to attributing actions to fund managers versus underlying investors. Despite a vast literature on the behavior of domestic (i.e., **U.S.)** funds, to our knowledge we are the first to disentangle the two. In effect, the trades of mutual funds reflect both institutional and individual decisions. To understand those trades, particularly in the international context, ensuring that the decisions are not commingled is an important step.

Our results show that emerging-market funds do indeed engage in momentum trading. Their strategies exhibit positive momentum-they systematically buy winners and sell losers. This is due to momentum trading at both the fund-manager level and the investor level (through redemptions/inflows). We further distinguish between

> 2 Though a lovely data set, the Froot el al. **(1998)** data do not include transactions settled in foreign currencies, e.g., ADR trades in New York and Brady bonds. These trades can be especially important in times of crisis when local-market liquidity is at a minimum. Our fund-portfolio data include these trades.

2
_“Africa looks to China and sees success: according to the World Bank, the Chinese have lifted 400 million of their own people out of poverty in the past two decades. All the while, no one forced the Chinese government to have elections or allow its opponents to start newspapers. [...] The Chinese come to Africa as equals, with no colonial hangover, no complex relationship of resentment. China wants to buy; Africa has something to sell. If African governments could respond in a way which spreads the new wealth – a large if, of course – then China might provide an opportunity for Africa which Europe and America have failed to deliver.”_ (Lindsey Hilsum)<sup>2</sup>

# **1. Introduction: Africa’s Position in International Trade Today**

Africa has seen its share of international trade decline ever since the end of World War II (figure 1). The poorest continent is rich in natural resources such as minerals, timber and oil, but trade with the rest of the world is often complicated due to rundown infrastructure, political instability, poor governance and the disastrous impact of HIV-AIDS on the working age population.<sup>3</sup> <u>Figures 1 & 2</u> <!-- Start of picture text --> Figure 1: Africa's Share of World Exports<br>8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>1948 1953 1963 1973 1983 1993 2004<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: Destination of Sub-African Exports (2004)<br>9.3%<br>8.8%<br>36.4%<br>18.2%<br>27.3%<br>E.U. U.S. Asia Africa Others<br><!-- End of picture text --> <u>Source: Derived from IMF,</u> _Direction of Trade Statistics Yearbook_ , 2005 Historically, Africa has had strong trade ties with Europe. This process of cooperation formally started when the first Yaoundé Agreement was signed in 1963 as a mark of European solidarity and to foster economic cooperation with French-speaking Africa. With the accession of the United Kingdom into the EEC, the agreement was widened to include 46 ACP (Africa – Caribbean – Pacific) countries, and the Lomé Convention replaced the earlier agreement. In 2000, the E.U. and Africa signed the Cotonou Partnership Agreement, which regulates ACP-EU cooperation for the next twenty years. The other large world market, the

> 2 Lindsey Hilsum, “We Love China,” _Granta: The View from Africa_ , January 2006. Online available at: http://www.granta.com/extracts/2616

> 3 British Broadcasting Cooperation, _Africa’s Economy_ , online available at: <u>http://news.bbc.co.uk/2/shared/spl/hi/africa/05/africa_economy/html/trade.stm</u> (accessed on 5/30/2006)

2
other cases (e.g., computer hardware), importers complain about the lack of consistency of accepted values for each item and prefer a shipment-level administrative value (“mercurial value”). Other importers prefer more differentiation. Fraud and corruption practices seem widespread, and monitoring practices could better target and adjust according to the risk profile of importers.

Finally, the acquisition of formal broker licenses is inadequately regulated and monitored because licenses can be rented, sold, or ceded to a third party without effectively controlling for financial and management skill. Some formal brokers partner with associates who do not have the necessary skills and experience to operate. Compared with countries in East Africa, in Cameroon the number of official brokers is limited (see table 7), and therefore the barrier to entry seems to be higher (which also could reflect the level of guarantee associated with obtaining a license).

However, it is also important to note that licenses are never removed in practice. Therefore, the guarantee level is questionable because it only acts as a barrier to entering the profession and is not used as a sanctioning tool. As a barrier to entry, the guarantee does not separate brokers with good practices from others. The guarantee only screens out those with limited financial resources.

**Table 7 Comparison of Number of Brokers and Freight Forwarders and Ratio of Imports to Number of Brokers, Cameroon and East Africa**

|Country|Number<br>of<br>customs<br>brokers<br>and<br>freight<br>forwarders|Ratio of imports<br>(US$, millions) to<br>number of brokers|
|---|---|---|
|Burundi|43|18.8|
|Kenya|1,200|12.6|
|Rwanda|124|12.6|
|Tanzania|480|21.5|
|Uganda|339|15.3|
|**Cameroon**|**200**|**32.8**|

_Sources:_ Cameroon: World Bank; East Africa: Arnold et al. 2011.

22