rafmacalaba/datause-encoder-data huggingface dataset:
Döös, B. (1997) Can large-scale environmental migrations be predicted? _Global Environmental Change_ 7(1): 41-61.

EACH-FOR (2008) _Preliminary Findings from the EACH-FOR Project on Environmentally Induced Migration_ . Environmental Change and Forced Migration Scenarios Project, viewed 10 December 2008, <www.each-for.eu> Easterling, W.E., Aggarwal, P.K., Batima, P., Brander, K.M., Erda, L., Howden, S.M., Kirilenko, A., Morton, J., Soussana, J.F., Schmidhuber, J. and Tubiello, F.N. (2007) Food, fibre and forest products. In M. Parry, O. Canziani, J. Palutikof, P. van der Linden, and C. Hanson (eds.), _Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change_ . Cambridge University Press, Cambridge and New York: 273-313.

Edin, M. (2003) Local state corporatism and private business. _Journal of Peasant Studies_ 30: 278-295.

Ellis, F. (2003) _A Livelihoods Approach to Migration and Poverty Reduction_ . Paper commissioned by the Department for International Development, London.

Erza, M. (2001) Demographic responses to environmental stress in the drought-and famine prone areas of Northern Ethiopia. _International Journal of Population Geography_ 7: 259-279.

FAO (2006) _The State of Food Insecurity in the World 2006_ . Food and Agriculture Organization of the United Nations, Rome.

Fearnside, P. (1997) Transmigration in Indonesia: lessons from its environmental and social impacts. _Environmental Management_ 21(4): 553-570.

Fernandes, W. (2000) From marginalization to sharing the project benefits. In Cernea, M. and McDowell, C. (eds.) _Risks and Reconstruction: Experiences of Resettlers and Refugees_ . The World Bank, Washington: 203-225.

Ferris, E. (2007) _Making Sense of Climate Change, Natural Disasters, and Displacement: a Work in Progress_ . Paper presented for Calcutta Research Group Winter Course, 14 December 2007, University of Bern.

Few, R., Brown, K. and Tompkins, E. 2007. Public participation and climate change adaptation: avoiding the illusion of inclusion, _Climate Policy_ 7: 46-59.

Findley, S. (1994) Does drought increase migration? A study of migration from rural Mali during the 1983-1985 drought. _International Migration Review_ 28(3): 539-553.

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103

# Budget DefIcitt()/GoP Countbes with per capita Income more Ihan $400

# Current Acocunt/GOP Countries with per capita Incomo more @1aN $400

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# Domeesc Savings/GDP Countrles wlth per capita Income more than $400

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# Real GDP Growth Rate Countries with per capita Income more than $400

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# _Evaluation of many FPD programs is possible_

The studies highlighted above have demonstrated a variety of methods that can be used for evaluating FPD policies – randomized experiments, difference-in-differences, and regression discontinuity designs. There are a variety of other evaluation methods available which when used carefully can also be informative as to policy impacts. We highlight here three of these other methods which are also likely to be useful in evaluating a broad array of FPD policies.<sup>20</sup> Propensity-score matching is a commonly used method for estimating a treatment impact. An example in the FPD literature is seen in Oh et al. (2008) who evaluate the impact of a credit guarantee policy used by the Korean government to support small and medium enterprises in the aftermath of the Asian financial crisis. The authors use plantlevel panel data on manufacturing firms and match firms which received credit guarantees to similar firms which did not, finding that the guarantee program positively affected both survival rates and sales and employment growth of the firms receiving the guarantees. A concern with propensity score matching is that it assumes the process of which firm receives a guarantee and which does not can be adequately captured by a set of observable variables which the firms are matched on. How plausible this is will be a judgment call in any given setting, and benefit from detailed knowledge of how the program was actually implemented. In general the literature has found the results to be closer to those obtained in an experimental setting when a rich set of data can be used for the matching, including multiple periods of pre-program data to control for existing trends. The data used by Oh et al. (2008) do not meet these criteria, with only data from one year (2000) for a relatively limited set of firm characteristics being used. This suggests one should be cautious in accepting their results.

A second method is the control function approach introduced by Heckman, which involves explicitly modeling how unobservables which affect the outcome are related to the observables, including the choice of participation in a program or policy regime. This approach is used along with propensity-score matching by Fajnzylber et al. (2006) to look at the impact of access to credit, training, and membership in business associations on

> 20 For a good recent general reference to different estimation strategies for impact evaluation, see Imbens and Wooldridge (2008). Instrumental variables is another common technique for evaluation which we won’t explicitly discuss here – McKenzie and Sahko (2009) provide an example in FPD.

- 18 -
31 Table 4 COUNTRY ARREARS TO THE IMF AS OF JANUARY 31, 1990 (In millions of SDR)

|Member|Ttl|Longest overdue<br>obligation|
|---|---|---|
|Kampuchea, Democratic|30.1|March 1975|
|Liberia|305.2|January 1985|
|Panama|181.2|December 1987|
|Peru|624.9|December 1985|
|Sierra Leone|69.0|January 1987|
|Somalia|111.7|July 1987|
|Sudan|895.3|July 1984|
|Viet Nam|46.4|February 1984|
|Zambia|882.4|June 1986|

Source: International Monetary Fund, Financial Statements of the General Department and the SDR Department and Accounts Administered by the IMF, (Washington, D.C.s International Monetary Fund, December, 1990), p. 16.
boarding schools—could leave a permanent impact on psychic costs of schooling because both instruments imply attending schools in distant areas.

The improvements in educational outcomes for men and women, and urban and rural residents, but particularly for those who traditionally lag behind, have important implications for both individual and social welfare. There is a growing literature on the causal effects of education on various demographic and labor-market outcomes in developed-country settings. However, we know very little about such causal relationships in developing countries, where such relationships could significantly differ. For instance, longer schooling of mothers could change infant health through its effect on the probability of out-of-wedlock pregnancy in a Western setting, whereas such a channel may not exist in a socially conservative developing country. Thus, it will be important to establish such causal relationships in the Turkish context in future works, using the policy in this paper as a source of exogenous variation in schooling. This study lays the groundwork for future works regarding the causal effects of schooling by illustrating the groups whose schooling outcomes are altered the most.

# **References**

Alderman, H., J. R. Behrman, D. Ross, and R. Sabot. 1996. “The Returns to Endogenous Human Capital in Pakistan’s Rural Wage Labor Market.” _Oxford Bulletin of Economics and Statistics_ 58 (1): 29–55.

Alderman H., and P. Gertler. 1997. “Family Resources and Gender Differences in Human Capital Investments: The Demand for Children’s Medical Care in Pakistan.” In L. Haddad, J. Hoddinott and H. Alderman, eds., _Intrahousehold Resource Allocation in Developing Countries_ . Baltimore, MD: Johns Hopkins University Press.

37
3 regions in the world (United States and OECD),<sup>4the paper providesestimates of the total</sup> level of migration from each of the<sup>24 study countries.Finally,the paper uses datafrom</sup> the 2000 U.S. Population Census and<sup>estimatesfrom the 2001OECD Continuous</sup> Reporting System on Migration to show<sup>the stock of migrants by educationallevel</sup> (primary, secondary and tertiary)<sup>5</sup> in<sup>the twomain labor-receiving regions. These data</sup> also show the fraction of the population<sup>in each educationalcategory in eachlabor-</sup> exporting country that has migrated<sup>to theUnited Statesor theOECD.6Thelatter</sup> estimates, which are quite rough and<sup>in need of further refinement,providesome sense of</sup> the magnitude of the brain drain from<sup>each of the 24 labor-exportingcountries.</sup> Results from the study show that international<sup>migrantsare well-educated:67</sup> percent of all immigrants to the United<sup>States and88 percent of those to theOECD have</sup> a secondary (high school) education<sup>or higher.While thesefiguresdo not includethe</sup> large numbers of illegal (and presumably<sup>less educated)internationalmigrants, it does</sup> appear that uneducated individuals<sup>havelimitedaccess to legalinternationalmigration.</sup> International migrants also tend to be<sup>much bettereducatedthat the rest of the population</sup> of their country of origin. However,<sup>in terms of actualbrain drain on theircountry of</sup> origin, international migration does<sup>not seem to takea very high proportion of the best</sup> educated (tertiary). For example, with<sup>respect to migration to the UnitedStates.for 14 of</sup>

> 4Unfortunately, no data are available on the level of migration to the third most important labor-receiving region in the world: the Arab Gulf The<sup>OECD includes 30 member countries:Australia, Austria,</sup> Belgium, Canada, Czech Republic,<sup>Denmark,Finland,France, Germany,Greece, Hungary, Iceland,</sup> Ireland, Italy, Japan, Korea, Luxembourg,<sup>Mexico,Netherlands, New Zealand,Norway, Poland, Portugal,</sup> Slovak Republic, Spain, Sweden,<sup>Switzerland,Turkey, United Kingdom,and UnitedStates.</sup>

_5_ Primary education is defined as 0 to<sup>8yearsof schooling;secondary is 9to12years;andtertiary is13</sup> years and above.

> 6 For the OECD, estirates of the fraction of population in each educational category from each study country are taken from Carrington<sup>and Detragiache(1998).</sup>
who came to the United States during the age of mass migration in the late 19<sup>th</sup> century returned home. Another common yet overlooked pattern is transit migration where migrants move from one destination to another. For example, close to 15 percent of the high‐skilled migrants in the United States actually arrived from a country different than their place of birth (Artuç and Özden 2016). This is especially common among migrants from developing countries who face high entry restrictions to the United States. In order to circumvent these policy barriers, they migrate to countries like the United Kingdom (for South Asian migrants) or Australia (for East Asians) and then move to the United States.

In some settings, high‐skilled immigrants have been found to be more likely to return home than low‐skilled migrants (Bijwaard and Wahba 2014).<sup>10</sup> Immigrants who decide to return to their home country tend to do so within a reasonably short time period (OECD 2008), and the likelihood to return has been connected to economic conditions (for example, Bijwaard et al. 2014), the difficulty of obtaining permanent residency or citizenship, and the ease of moving across national borders (Gundel and Peters 2008). Another pattern is that the relationship between return and skill levels is non‐monotonic. While high‐skilled workers are more likely to re‐migrate as a group, those at the very highest skill levels are often less likely to do so. For example, the OECD (2008) calculates that the United States retains about 65‐70 percent of those who studied for a doctorate at the five‐year mark after receipt of degree, higher than university graduates. Several comparative studies show in diverse settings that those with a PhD or high productivity levels are less likely to return migrate than other skilled workers (Finn 2007; Gibson and McKenzie 2009; Harvey 2009; Gaule 2014).

> 10 Germany loses about 50 percent and Netherlands as many as 60 percent of foreign students coming to complete a degree within five years of graduation (Bijwaard 2010). Gundel and Peters (2008) find that high‐skilled immigrants are more likely to re‐emigrate from Germany. Pohl (2006) argues that a large share of high‐skilled migrants exhibits short staying patterns, but once controlling for a number of other factors the average high‐skilled migrant is actually less likely to leave. Dustmann and Weiss (2007) use the British household panel and find that outmigration causes the remaining immigrant pool in the United Kingdom to become less skilled over time. In Canada, all immigrant groups display high re‐emigration rates, but those arriving in the skilled class and business class were most likely to leave again (Aydemir and Robinson, 2008). Dustmann et al. (1996) review policies related to return migration in Europe.

13
<!-- Start of picture text --> Figure 1a. Poverty headcount ratio<br>All individuals<br>Elderly from EHH1 & EHH2<br>Poverty line = 50% of median per capita income<br>Household incomes were equivalized using the OECD Equivalency Scale Adjusted household size [= 1+(# of adults-1)*0.5 + (# of children*0.3)]<br>An adult was a household member aged 18 years or more, & a child was a household member aged less than 18 years at the time of the survey<br>Elderly are household members aged 60 years or more<br>Households with >=1 elderly member are divided into 2 subsets: EHH1 (elderly living alone by themselves) & EHH2 (consisting of both elderly<br>and non-elderly members)<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>0.322<br>0.313<br>0.308<br>.3 0.287 0.276 0.284<br>0.252<br>0.238 0.239 0.235 0.234 0.233<br>0.227 0.226 0.228<br>0.218 0.217 0.221<br>0.212 0.212 0.209<br>0.194 0.197<br>0.188<br>.2 0.182 0.183 0.182<br>0.172 0.172 0.171 0.170<br>0.164<br>0.148<br>0.134<br>.1 0.077<br>0.060<br>0<br><!-- End of picture text --> Household incomes were equivalized using the OECD Equivalency Scale Adjusted household size [= 1+(# of adults-1)*0.5 + (# of children*0.3)] An adult was a household member aged 18 years or more, & a child was a household member aged less than 18 years at the time of the survey Elderly are household members aged 60 years or more 27
24 This is reflected in the destination of Gabon’s timber exports. China became a significant importer of Gabon’s timber in the mid-1990s (Figure 16). In volume, Chinese imports of the roundwood equivalent of sawnwood, veneers and plywood is currently more than three times those of French imports, but in value, China imports roughly the same value of timber products from Gabon. The difference between value and volume shares is accounted for by the fact that, as will be shown below, the Chinese market is almost exclusively a market for unprocessed logs, whereas an increasing (albeit still small) share of the EU market is serviced by processed timber products which has a higher unit value. Despite being the dominant importer of Gabon’s timber by volume, China’s dominance in Gabon’s timber trade is a little atypical of the global picture where its share of imports is even higher. The high relative share of France in Gabon’s exports reflects its close historical links with France and the long presence of French timber companies operating in Gabon.

**Figure 16: Chinese and French Tropical Wood Product Import Structures from Gabon – Log and Wood Product Volumes (in 1,000 m**<sup>**3**</sup> **RWE)**<sup>*****</sup>

<!-- Start of picture text --> 2000<br>1800<br>1600<br>1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>1991b1992b1993b 1994b1995b 1996b 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>Logs Wood products<br>CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR CN FR<br><!-- End of picture text --> _Source:_ FAOSTAT Forestry Trade Flow Data, accessed October 2009 * The calculation of the roundwood equivalent uses the following conversion factors, based on the responses of firms in Gabon. For EU: the sawnwood ratio is 2.5:1, for, veneer sheets 1.82:1, and for plywood 2.3:1. For China: the sawnwood ratio is 2.05:1 (reflecting lower quality requirements of buyers). Veneer sheets and plywood have the same conversion factor as in exports to the EU.

# **_3.4 Consequences_**

What have been the consequences for Gabon of the shift in the market for its timber exports, and what are the implications for the trajectory of its timber industry? Two major developments can be noted - the nature and extent of standards imposed on producers, and the accretion of value added to harvested logs

# <u>Standards</u>

Bluntly-speaking, the transition in market destination has led to a collapse in the standards required of producers (Figure 17). These standards have important implications for capability-building. Greater demands for quality require enhanced
# Table 18

# Antidumping: Share of Affected Economies in Total Cases Relative to Share in World Exports

(in % and ratios)

|Affiected Economies|Shar<br>World Ex|e in<br>ports %|Share in Total A<br>Investiga|nti-Dumping<br>tions %|Shar<br>Total Definitiv|e in<br>e Measures %|Ra<br>Investi|d<br>gations|Ra<br>Definitive|d<br>Measures|
|---|---|---|---|---|---|---|---|---|---|---|
||1989-1997|1995-1997|1987-1997|1995-1997|1987-1997|1995-1997|1987-1997|1995-1997|1987-1997|1995-1997|
|WTO Members|88.2|87.7|77.8|73.4|73.9|63.9|0.9|0.8|0.8|0.7|
|Developed|63.5|62.5|38.1|34.1|34.9|21.7|0.6|0.5|0.5|0.3|
|Developing|21.6|22.0|34.5|34.4|32.9|36.1|1.6|1.6|1.5|1.6|
|Transition and Other|3.0|3.2|5.0|4.8|6.2|6.1|1.7|1.5|2.1|1.9|
|Non WTO Members|11.8|12.3|22.2|26.6|26.1|36.1|1.9|2.2|2.2|2.9|
|"Non Market"|6.3|6.9|16.4|20.8|21.4|32.1|2.6|3.0|3.4|4.7|
|Other|5.5|5.4|5.8|5.8|4.7|4.0|1.0|1.1|0.8|0.7|

Explanation: R,d: Share in Investigations (Measures)/Share in World Exports _Source: WTO, IDB and Antidumping Data Base, J Miranda et. at., 1998._
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE open defecation. Even though these improvements are significant, access to flush toilets, at 32.2 percent, remains below the level of MICs (40.8 percent); while open defecation, at 23.2 percent, is higher than that of similar African countries.

**Table 7. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries||**Botswana**||Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|
|||Mid-2000s|1988|2000|2006|Mid-2000s|
|Access to piped water|% pop|9.3|19.5|43.1|64.9|61.1|
|Access to stand posts|% pop|17.1|68.6|48.6|26.3|22.1|
|Access to wells/boreholes|% pop|39.3|6.1|3.8|5.6|4.8|
|Access to surface water|% pop|34.2|5.8|4.6|3.3|10.9|
|Access to flush toilets|% pop|4.7|12.3|18.1|32.2|47.7|
|Access to improved latrines|% pop|18.3|23.5|30.5|26.3|33.7|
|Access to traditional latrines|% pop|38.5|23.6|30.6|18.5|6.9|
|Open defecation|% pop|38.3|40.6|20.8|23.2|11.0|
||||**2000**|**2005**|**2008**||
|Domestic water consumption|liter/capita/day|50.9|76.7|69.6|80.5|196.4|
|Revenue collection|% sales|94.1|95|95|95|99.3|
|Distribution losses|% production|34.8|21.0|12.6|10.6|28.8|
|Labor costs|connections per<br>employee|175.9|—|—|—|203.4|
|Total hidden costs as % of revenue|%|111|—|29|19|67|
|||**Bots**|**wana**|Count|ries with|Other|
|||**2005**|**2008**|scarc<br>reso|e water<br>urces|developing<br>regions|
|Residential tariff|U.S. cents per<br>cubic meter|45||45|60.26|30600|
|Nonresidential tariff|U.S. cents per<br>cubic meter|88||88|120.74|.–.|

Source: Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Note: Access figures from the Demographic and Health Surveys (1988 and 2006) and Multiple Indicators Survey (2000). Total cost recovery is calculated assuming a capital cost of 40 cents/m3. — = Not available.

Botswana’s progress in increasing access to improved water supply is spread across both urban and rural areas. Between 2000 and 2008 4.2 percent of the population gained access to piped water per year, while 0.2 percent stopped using surface water as their water source. In rural areas, 1.8 percent of the population gained access to piped water per year, and 0.4 percent stopped using surface water.

During the period covered by this report (2004-2008), the reform of the water sector was still under discussion. The water service came from one three entities Water Utilities Corporation (WUC), Department of Water Affairs (DWA) in the Ministry of Minerals, Energy and Water Resources or the local councils. Due to difficulties getting access to the data, the analysis of providers provided in this report only covers WUC. Since 2009, a number of important changes have taken place (box 1).

20