rafmacalaba/datause-encoder-data huggingface dataset:
strongly and FDI-related profit remittances are expected to rise. On the other hand, a continued strengthening of Latvian exports to the EU, together with diversification in the export base under broad-based FDI, is projected to materialize during the period 2004-2006. As FDI is projected to

**Figure 4: Savings-Investment Balance, 1995-2001 (percent of GDP)**

remain buoyant— because of the forthcoming privatization of the remaining large public enterprises like Ventspils Nafta and Lattelekom and the sale of new telecommunications licenses—the external account is expected to be largely covered by nondebt-creating flows, thereby containing Latvia’s already favorable debt indicators.

<!-- Start of picture text --> 35 0<br>30<br>-2<br>Gross Domestic<br>25 Investment<br>-4<br>20 Gross National<br>-6 Savings<br>15<br>-8 Foreign Savings<br>10 (current account<br>deficit)<br>-10<br>5<br>- -12<br>199519961997199819992000200120022003*<br><!-- End of picture text --> Despite a wider current account deficit and lower FDI coverage in 2002 than in previous years, Latvia’s debt indicators—including those for short-term debt—remain moderate. Public sector external debt represented 11.4 percent of GDP in 2002 and total external debt (excluding currency and deposits) represented 53 percent of GDP. While the most comprehensive measures of short-term debt, including foreign liabilities of the banking system, suggest that Latvia’s short-term debt is high (47.8 percent of GDP), these liabilities are largely matched by high-quality, liquid foreign assets.<sup>6</sup> In addition, the Latvian banking sector is sound and well supervised.

Latvia has made great strides in building the institutions needed for developing a market economy through its aggressive structural reforms. For example, prices, trade, and other markets have been liberalized, and the necessary legal reform, institutional development, and social safety net improvements are being implemented. The remaining structural reform program is directed at attracting foreign investment and sustaining growth. The focus of the agenda is to implement measures identified as catalysts to improve the business climate, to foster competition in the energy and telecommunications sectors, to complete the privatization of large-scale public enterprises, and to combat corruption.<sup>7</sup> The restructuring and privatization process for state enterprises has been completed with the notable exceptions of several large companies—Latvenergo (power), Lattelekom (communications), and Ventspils Nafta (oil pipeline company). Recent privatization transactions included the sale of

> 6  According to a recent IMF study, over two-thirds of Latvian short-term debt consists of nonresident deposits in commercial banks.  These deposits are largely redeposited in foreign banks or placed in other secure liquid foreign assets in OECD countries chosen to match the currencies and maturities of the deposits.  When assessing vulnerability, it is reasonable therefore to exclude nonresident deposits from short-term debt or to net the liabilities against their corresponding assets.

> 7  See FIAS (2003).

9
characteristics shifted over time, considering that the composition of the labor force remained unchanged, relative to 2002.<sup>27</sup> Figure 16 presents results from the decompositions for 19 quantiles of the wage distribution. Two features are worth noticing. First, between 2002 and 2012, real hourly wages grew at all levels of the wage distribution. However, the increase was higher in the bottom of the wage distribution, which led to a reduction in wage inequality. Second, wage structure effects are more relevant than composition effects at all levels of the distribution. However, composition effects are important to explain the larger increase in wages at the bottom of the distribution than at the top, and, thus, contributing to the reduction in inequality (see Table 2).

**Figure 16. Wage structure and composition effects (plus error, Oaxaca) (2002-2012)**

<!-- Start of picture text -->
1.4<br>Total<br>1.2<br>Composition<br>1.0<br>WageStructure<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>5 15 25 35 45 55 65 75 85 95<br>2012)<br>Change in log of hourly wages (2002‐<br><!-- End of picture text -->

Source: Own elaboration based on RLMS (Rounds XI and XXI). _Notes:_ decompositions results are based on the subsample of currently employed wage workers in the 18-60 age group, reporting more than 140 worked hours in the last 30 days and with non-missing data in wages. The distribution of hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers.

An additional advantage of this methodology is that it allows us to disentangle the contribution of each individual variable (or group of variables) to the composition and wage structure effects.

> 27 Firpo, Fortin and Lemieux (2011) use a different base year; fixing the characteristics of the last year to obtain the wage structure effects, and fixing the returns of the first year to obtain the composition effects. Along this line, we ran our model changing the reference year; fixing the 2002 returns to obtain the composition effect, and the 2012 characteristics to obtain the wage structure effect. Most results remained qualitatively the same under this specification, with a few differences. For example, firm ownership has a larger negative effect on the composition effects, while the geographic component plays a larger role in the overall wage structure effects, contributing to the reduction in inequality.

33
pressures. Technological transfers and greater access to export markets indeed improve domestic business opportunities and raise the demand for skilled workers locally. Yet, within this setting, raising possibilities to migrate (comparing S1t with S2t and with S3t) does not lead to a net domestic accumulation of human capital.

The impact <u>of Diaspora on the likelihood for brain gain</u>

|Migration Possibilities|Low<br>(S1t)|Medium<br>(S2t)|High<br>(S3t)|
|---|---|---|---|
|_Annual growth rates_||||
|Population|1.2%|1.2%|1.1%|
|Real Gross Domestic Product|3.6%|3.6%|3.5%|
|Per capita real disposable income|3.2%|3.3%|3.4%|
|Per capita domestic human capital|2.7%|2.5%|2.2%|
|Real physical investment|5.7%|5.8%|5.8%|
|Real educational investment|6.5%|6.6%|6.7%|
|Labor supply, unskilled|1.0%|1.0%|1.0%|
|Labor supply, skilled|4.0%|3.7%|3.4%|
|Wage, unskilled|3.0%|3.1%|3.2%|
|Wage, skilled|0.3%|0.8%|1.3%|
|Physical capital remuneration|-1.4%|-1.4%|-1.3%|
|Educational investment costs|0.5%|0.8%|1.0%|
|Physical investment costs|0.1%|0.2%|0.2%|
|Consumer price index<br>_Others_|0.2%|0.3%|0.3%|
|Proportion of domestic skilled workers after 25 years|10.7%|10.1%|9.4%|
|Skilled workers migration rate after 25 years|2.4%|3.0%|3.6%|
|Elasticity of per capita domestic human capital accumulation t|o skilled mig|ration|-0.48|
|Source: authors’ calculations.||||

Could this mechanism be pushed further to generate net brain gain? In the previous simulations, we assumed that a 10 percent increase in the size of the Diaspora would lead to a 1 percent change in the prices of imports and exports faced by the source country. This in turn means that raising migration possibilities (from S1e to S3e) would induce an increase (decrease) of 3 percent in the price of exports (imports) for a Diaspora 30 percent higher after 25 years. Interestingly, pushing further this mechanism (using elasticities 3 times higher for the response of technological transfers to the diaspora size) lowers elasticities of domestic human capital accumulation to skilled migration by a 20
in tariffs levied by broad groups of WTO members, and then turn to the implications for their access to partner markets.

The key implications of the Doha Agenda negotiations for the tariffs levied by four major groups of countries are presented in Table 1. These are given for total trade, for agricultural market access, and for non-agricultural market access (NAMA). The first three columns refer to the applied rates that influence actual market outcomes. The last three show the bound rates that are negotiated in the WTO. In each set of three columns, the first refers to the base rates in the absence of a Round; the second (“Formula”) shows the rate if the market access formulae being negotiated were implemented without exceptions, and the third (“Formula plus flex”) the rate after allowing for country exceptions, such as those for small and vulnerable economies (SVEs) and for product flexibilities such as those for sensitive and special products in agriculture.

**<u>Table 1. Weighted Average Applied and Bound Rates Levied by WTO members</u>**

||<br>|<br>AppliedRate|<br>s<br>Formula|<br>|<br>Boundrate|<br>s<br>Formula|
|---|---|---|---|---|---|---|
||Base|Formula|plusflex|Base|Formula|plusflex|
|**Total**|%|%|%|%|%|%|
|All countries|3.7|2.5|2.9|9.9|5.7|6.9|
|High income countries|2.5|1.4|1.7|5.2|3.1|3.8|
|Developing - non LDC|6.9|5.3|6.2|21.8|12.6|14.4|
|LDCs|11.1|8.7|11.1|na|na|na|
|**Agriculture**|||||||
|All countries|14.5|8.9|11.8|40.3|20.7|29.9|
|High income countries|15.0|7.5|11.0|31.9|13.5|20.2|
|Developing- non LDC|13.4|11.5|13.3|53.9|33.0|45.4|
|LDCs|12.5|12.2|12.5|94.1|51.6|94.1|
|**NAMA**|||||||
|All countries|2.9|2.1|2.3|7.8|4.7|5.3|
|High income countries|1.7|1.1|1.1|3.5|2.5|2.7|
|Developing- non LDC|6.4|4.8|5.6|19.1|10.9|11.8|
|LDCs|10.9|8.0|10.9|na|na|na|

Source: Laborde, Martin and van der Mensbrugghe (2008). Note: Country groups defined using World Bank and UN definitions.

From the first row of Table 1, we see that the average tariff applying in the absence of a Doha agreement would be 3.7 percent, while the average bound tariff would be 9.9 percent. If the formulae for agriculture and NAMA were applied without exceptions, the average applied tariff would fall to 2.5 percent and the average bound rate to 5.7 percent. The exceptions and flexibilities reduce these cuts, so that the average tariff falls to 2.9 percent (a reduction of 27 percent), while the average bound tariff falls to 6.9 percent (a reduction of 30 percent).

2
value (Phoenix uses a top elasticity of 0.5). We follow Vennemo et al. in using a nuclear resource supply elasticity of 2.5.

The specification of the hydro power production function is another complicated matter. Rivers suitable for hydro power is an obvious input, and one might think of the stock of such water resources and the cost of using the marginal river. If the next unused water source requires a more costly structure (or implementation costs including relocation of people) than existing dams, then one may represent this as a production function with a lower TFP factor, or as a paying more for a fixed quantity of effective water resource R. For simplicity we have chosen the latter approach and set up the hydro function like that of nuclear, with a rising supply curve for the water resource input, _R_<sup>_HYDR_</sup> . In the same spirit, our wind and solar output functions follow those of nuclear and hydro with specific Resource inputs. This is illustrated in Figure A2(b). The parameterization of the resource input shares follows that given in EPPA 4 (Paltsev et al. 2005 Table 11); the resource share for wind and solar is set at 0.05 and we assume the resource share for hydro is also 0.05 of total gross output.

The cost functions for the renewables – nuclear, hydro, wind, solar and “other” – are the same The following equations are for the sub-aggregates – output price _PbEGEN_ , _t_ , intermediate input price _PM bBL_ , _t_ , value-added-resource price _PBLb tVR_ , , value-added-energy price _PBLb tVE_ , , the energy price _PEb tBL_ , : ‐48‐
<!-- Start of picture text --> 0.07<br>0.06<br>0.05<br>0.04<br>LOW INCOME<br>0.03<br>LOWER MIDDLE INCOME<br>UPPER MIDDLE INCOME<br>HIGH INCOME<br>0.02<br>Net Interest Margin<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> 29
58 actions with Special Economic Zones, but this cannot be systematically assessed). Data quality is assessed, as in Fernandes et al. (2016), by a comparison of total exports and total imports obtained from aggregating the transaction-level data at year level with total yearly exports and imports for Sri Lanka from World Integrated Trade Solution (WITS) and from the Central Bank of Sri Lanka. The ratios to WITS in 2017-2021 are in the 97%-100% range for exports and the 83%-94% range for imports. To obtain our final datasets, we drop exports and imports of oil (HS Chapter 27), which are generally not well captured in customs datasets. To focus on true commercial export and import transactions by firms, we also drop transactions of currency paper notes (HS 490700) conducted only by the Central Bank of Sri Lanka, of large machinery entering and exiting Sri Lanka temporarily for construction projects such as dredgers (vessels) (HS 890510, HS 890190), and of arms and ammunition (HS Chapter 93).

We aggregate transaction-level values (in USD) and net weights (in kg) up to the HS8 productmonth-year level for our import analysis by summing across firms and source countries. We aggregate transaction-level values (in USD) up to the firm-HS8 product-year level for our export analysis by summing across destination countries. For our export analysis, we focus on agricultural products, which are listed in HS Chapters 06-24.

# **A.B.1 Cleaning Firm Names**

The names of Sri Lankan firms engaged in export or import transactions are noisy. Names appear with multiple spellings or with spelling errors, and some entries are a number or an address instead of a proper names. In brief, the steps for the cleaning of names are as follows:

1. Eliminate from the list of firm names those entries that are not proper names (numbers, addresses, and the expressions ‘NIL’, ‘N/A’, or ‘To the order’).

2. Remove from firm names a list of stop words, geographic stop words, and symbols that are not letters nor numbers. The list of stop words was selected based on text analysis indicating the most common words appearing in firm names, such as ‘corporation’, ‘international’, ‘limited’, and ‘llc’. We also remove prefixes and suffixes (such as ‘co’, ‘sa’, and ‘pvc’) from firm names.

3. Based on the list of pre-processed firm names obtained in Step 2, we use N-gram similarity (using cosine distance metrics) to identify potential pairs of similar firm names (using a lower similarity threshold of 0.6, where 1 means two names are identical).

4. Potential pairs of similar firm names identified in Step 3 go through an algorithm to more precisely determine if they are really similar based on Levenshtein distance (a measure of similarity between two strings). Distance cutoffs defining what is a “small” distance between a pair of names are determined through machine learning algorithms based on subsamples of firm name pairs whose similarity was determined through manual inspection.
Table 8: O.L.S. and I.V. Regression Results with only cohorts between (1965-1968) and <u>(1971-1974)</u>

|||||Dependent v|ariable: Pr(b|irth teen=1)||||
|---|---|---|---|---|---|---|---|---|---|
||W|hole sample||A|t least KCP|E|At leas|t secondaryc|omplete|
||OLS|IV|IV|OLS|IV|IV|OLS|IV|IV|
|First-stage(all S|...<br>econd-stage R|..<br>HS variables|..<br>included,b|...<br>ut not reported|..<br>)|..|...|..|..|
|Reform||0.207|0.069||1.292***|1.192***||2.143***|1.850***|
|||(0.26)|(0.24)||(0.24)|(0.23)||(0.35)|(0.34)|
|Second-stage||||||||||
|Education|-0.049***|-0.136|-0.236|-0.067***|-0.063*|-0.068*|-0.045***|-0.101***|-0.112***|
||(0.00)|(0.22)|(0.73)|(0.00)|(0.03)|(0.04)|(0.00)|(0.03)|(0.03)|
|Year|5.052|-1.714|-6.856|4.268|3.653|4.295|-0.767|0.261|1.244|
||(3.84)|(16.42)|(45.82)|(4.77)|(4.97)|(5.02)|(6.47)|(7.41)|(7.48)|
|Year<sup>2</sup>|-0.001|0.000|0.002|-0.001|-0.001|-0.001|0.000|-0.000|0.000|
||(0.00)|(0.00)|(0.01)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Muslim|-0.013||-0.346|-0.039||-0.040|-0.058||-0.122|
||(0.04)||(1.30)|(0.06)||(0.06)|(0.08)||(0.1)|
|City|-0.059||-0.102|-0.067||-0.066|-0.143**||-0.05|
||(0.05)||(0.20)|(0.05)||(0.06)|(0.07)||(0.09)|
|Town|-0.008||-0.037|-0.021||-0.021|-0.091||-0.017|
||(0.04)||(0.14)|(0.05)||(0.05)|(0.06)||(0.08)|
|Countryside|-0.017||-0.369|-0.055||-0.055|-0.086||-0.087|
||(0.04)||(1.35)|(0.04)||(0.05)|(0.06)||(0.07)|
|Abroad|-0.145*||-0.231|-0.053||-0.053|-0.168||-0.131|
||(0.09)||(0.42)|(0.10)||(0.10)|(0.10)||(0.16)|
|Embu|0.192***||0.449|0.112**||0.111**|-0.007||-0.086|
||(0.03)||(0.98)|(0.05)||(0.05)|(0.07)||(0.10)|
|Kalenjin|0.182***||0.600|0.134***||0.133**|0.060||-0.025|
||(0.04)||(1.61)|(0.04)||(0.05)|(0.06)||(0.09)|
|Kamba|0.145***||0.641|0.093**||0.092*|0.048||-0.005|
||(0.03)||(1.93)|(0.04)||(0.05)|(0.06)||(0.08)|
|Kikuyu|0.149***||0.638|0.092**||0.091*|0.093||-0.018|
||(0.03)||(1.89)|(0.04)||(0.05)|(0.06)||(0.09)|
|Kisii|0.186***||0.595|0.143***||0.142***|0.117**||0.03|
||(0.03)||(1.59)|(0.04)||(0.05)|(0.06)||(0.09)|
|Luhya|0.272***||0.518|0.235***||0.234***|0.189***||0.069|
||(0.03)||(0.95)|(0.04)||(0.06)|(0.06)||(0.10)|
|Luo|0.205***||0.440|0.134**||0.133*|0.007||-0.065|
||(0.04)||(0.92)|(0.06)||(0.07)|(0.08)||(0.09)|
|Masai|0.028||0.058|-0.004||-0.005|-0.119||-0.277**|
||(0.04)||(0.18)|(0.06)||(0.07)|(0.08)||(0.13)|
|Meru|0.062||0.328|-0.012||-0.012|-0.099||-0.171*|
||(0.04)||(1.03)|(0.06)||(0.06)|(0.06)||(0.10)|
|Mijikenda|0.077||0.160|0.028||0.027|0.078||-0.067|
|/Swahili|(0.05)||(0.35)|(0.07)||(0.09)|(0.09)||(0.13)|
|Recall|-0.001|-0.003|-0.000|0.002|0.000|0.002|-0.013***|0.008|0.013|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.00)|(0.01)|(0.01)|
|SurveyFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

36
Table 6: Garment Remittances and Agricultural Investments, IV Estimates

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable|Total agric.<br>costs|Crop<br>cultivation|Chemical<br>fertilizers|Draft<br>power|Livestock<br>& Poultry|Labor<br>productivity|
|Garment remittances|0.366***|0.473***|0.584***|0.134|0.466***|0.280**|
||(0.115)|(0.160)|(0.174)|(0.222)|(0.120)|(0.113)|
|Urban|-0.706***|-0.769***|-1.247***|-0.079|0.038|0.576***|
||(0.242)|(0.131)|(0.095)|(0.217)|(0.265)|(0.146)|
|Area|0.554***|0.733***|0.872***|0.567***|0.156*|0.645***|
||(0.088)|(0.110)|(0.132)|(0.133)|(0.086)|(0.106)|
|Bad harvest|4.418***|5.433***|8.162***|2.679|||
||(1.104)|(1.915)|(1.362)|(1.778)|||
|Nb. of livestock & poultry|0.006||||0.016**||
||(0.004)||||(0.006)||
|Chemical fertilizers||||||0.246***<br>(0.044)|
|Crop varieties||||||0.187***<br>(0.047)|
|Observations|131|122|108|107|114|105|
|Root MSE|0.878|0.900|1.181|0.837|1.282|0.765|
|Kleibergen-Paap F-stat|24.96|22.69|19.12|29.47|45.40|20.51|
|Hansen J p-value|0.367|0.505|0.586|0.713|0.081|0.090|
|Zone FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Authors’ calculations based on the 2011 Cambodia Socio-Economic Survey. Standard errors are clustered at the province level. *, ** and *** denote statistical significance at the 10%, 5% and 1% level respectively. Sampling weights are used in all regressions. Dependent variables: (1) _Total agric. costs_ : per capita total expenses related to agricultural activities, including crop cultivation and livestock and poultry-raising activities (riels, log); (2) _Crop cultivation_ : per capita total expenses related to the production of crop, including fruits and vegetables (riels, log); (3) _Chemical fertilizers_ : per capita expenses related to buying chemical fertilizers, pesticide, weedicide and fungicide for crop cultivation (riels, log); (4) _Draft power_ : per capita expenses related to payment to hired draft power (tractors/animals) including human labor for ploughing/harrowing (riels, log); (5) _Livestock & Poultry_ : per capita expenses of livestock and poultry-raising activities such as spending on animal feed and veterinary services (riels, log); (6) _Labor productivity_ : output per worker (log). Regressors: i) _Garment remittances_ : total value of remittances received by a household from its garment migrant(s), in riels (log); ii) _Urban_ : dummy equal to 1 for urban households; iii) _Area_ : total area in m<sup>2</sup> of plots owned or freely used by household (logs); iv) _Bad harvest_ : dummy equal to 1 if the village experienced a bad harvest during the past dry and/or wet season; v) _Nb. livestock and poultry_ : number of cattle, ovine and poultry animals owned on 31 December 2010; vi) _Crop varieties_ : varieties of crops grown by household. Instruments: average age of garment migrants, cost of domestically transferring money, price of ampicillin. _Root MSE_ : root mean squared error.

30
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|---|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Access to Basic Services in Rural Areas and CapacityBuildingProjectRoger Coma Cunill|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|Statistical CapacityBuildingProject<br>Arden JeremyFinn|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|GH eTransform Ghana<br>Kaoru Kimura|
|Enhancingthe Income and Asset Declaration System<br>Cecilia D. Vales|
|Enhancingthe Income and Asset Declaration System<br>Cecilia D. Vales|
|Enhancingthe Income and Asset Declaration System<br>Cecilia D. Vales|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|Mauritania Sustainable Landscape Management Project under the SNicolas Benjamin Claude Desramaut|
|MA-Second Skills and Employment DPL<br>Diego Angel-Urdinola|
|MA-Second Skills and Employment DPL<br>Diego Angel-Urdinola|
|MA-Second Skills and Employment DPL<br>Diego Angel-Urdinola|
|Lesotho Highlands - Botswana Water Transfer<br>Marcus J. Wishart|