rafmacalaba/datause-encoder-data huggingface dataset:
land prices and prices of other local goods may increase in the locations affected by the oil extraction activities. The local economy will not benefit from the resource if higher prices increase the cost of living and discourage businesses from operating in affected districts.

Another channel through which oil extraction affects the local economy is through oil windfalls. Some governments redistribute oil windfalls through income transfers and provision of public goods such as roads, health facilities, classroom blocks and public water supply. The provision of public goods benefits both households and businesses. On the other hand, the presence of oil revenues can create corruption (Ades and Di Tella, 1999), rent seeking behavior (Brollo et al., 2013), and even conflicts (Angrist and Kugler, 2008). Vicente (2010), for example, finds that offshore oil discovery in São Tomé and Príncipe led to perceived increases in corruption.

# **4. Research Design**

In this paper, we examine the impact of natural resources on the local economy by focusing on the effects of oil extraction on the employment and standard of living of households in affected districts. We use consumption and poverty status of the household as a measurement of the standard of living besides income.

# _4.1.  Data_

We use three rounds of the Ghana Living Standards Survey (GLSS) covering the period between 1998 and 2013 (1998/1999-GLSS4, 2005/2006-GLSS 5 and 2012/2013GLSS 6).<sup>7</sup> GLSS is a nationally representative household survey and is collected by the Ghana Statistical Service (GSS). It reports detailed information on demography, income, expenditure and other household and individual characteristics. With the support of the Ghana Statistical Service, we created a complete dataset of location information of enumeration areas of the household surveys. The GLSS 6 sample includes 818

> 7 We use GLSS 5 (2005/2006) and 6 (2012/2013) in the main analysis, and GLSS 4 (1998/1999) in a test for similarity in pre-trends. We exclude GLSS 4 from the main analysis because it contains only 102 districts.

8
|P124075|2011|2014|12/6/2013|16 Closed|Bhutan|
|---|---|---|---|---|---|
|P124075|2011|2014|6/4/2014|17 Closed|Bhutan|
|P124075|2011|2015|12/2/2014|18 Closed|Bhutan|
|P124076|2013|2014|5/20/2014|1 Closed|Cote d'Ivoire|
|P124076|2013|2016|11/24/2015|2 Closed|Cote d'Ivoire|
|P124077|2011|2012|6/7/2012|1 Closed|Benin|
|P124077|2011|2013|12/24/2012|2 Closed|Benin|
|P124077|2011|2013|6/20/2013|3 Closed|Benin|
|P124081|2011|2011||0 Closed|Eastern Africa|
|P124081|2011|2012||0 Closed|Eastern Africa|
|P124081|2011|2013||0 Closed|Eastern Africa|
|P124085|2012|2012||0 Active|Congo,Republic of|
|P124085|2012|2013|3/20/2013|1 Active|Congo,Republic of|
|P124085|2012|2014|11/3/2013|2 Active|Congo,Republic of|
|P124085|2012|2014|6/28/2014|3 Active|Congo,Republic of|
|P124085|2012|2015|2/13/2015|4 Active|Congo,Republic of|
|P124085|2012|2016|8/18/2015|5 Active|Congo,Republic of|
|P124085|2012|2016|11/26/2015|6 Active|Congo,Republic of|
|P124085|2012|2016|3/11/2016|7 Active|Congo,Republic of|
|P124085|2012|2017|7/27/2016|8 Active|Congo,Republic of|
|P124085|2012|2017|1/24/2017|9 Active|Congo,Republic of|
|P124085|2012|2018|7/24/2017|10 Active|Congo,Republic of|
|P124085|2012|2018|1/23/2018|11 Active|Congo,Republic of|
|P124085|2012|2018|4/2/2018|12 Active|Congo,Republic of|
|P124085|2012|2019|11/5/2018|13 Active|Congo,Republic of|
|P124085|2012|2019|5/21/2019|14 Active|Congo,Republic of|
|P124085|2012|2020|12/23/2019|15 Active|Congo,Republic of|
|P124085|2012|2020|6/12/2020|16 Active|Congo,Republic of|
|P124090|2011|2012|9/8/2011|1 Dropped|Yemen,Republic of|
|P124091|2012|2012||0 Closed|Sri Lanka|
|P124091|2012|2013|1/16/2013|1 Closed|Sri Lanka|
|P124091|2012|2014|10/3/2013|2 Closed|Sri Lanka|
|P124091|2012|2014|2/10/2014|3 Closed|Sri Lanka|
|P124091|2012|2015|7/28/2014|4 Closed|Sri Lanka|
|P124091|2012|2015|12/19/2014|5 Closed|Sri Lanka|
|P124091|2012|2015|6/23/2015|6 Closed|Sri Lanka|
|P124091|2012|2020|4/30/2020|7 Closed|Sri Lanka|
|P124092|2011|2012|9/8/2011|1 Dropped|Syrian Arab Republic|
|P124102|2011|2012|3/9/2012|1 Closed|Tajikistan|
|P124102|2011|2014|7/4/2013|2 Closed|Tajikistan|
|P124106|2011|2011||0 Closed|Central Asia|
|P124106|2011|2012||0 Closed|Central Asia|
|P124107|2011|2011||0 Closed|Grenada|
|P124107|2011|2012|1/5/2012|1 Closed|Grenada|
|P124107|2011|2013|9/7/2012|2 Closed|Grenada|
|P124107|2011|2013|4/24/2013|3 Closed|Grenada|
|P124107|2011|2015|11/25/2014|4 Closed|Grenada|
<!-- Start of picture text --> Figure 3. Demographic characteristics<br>Household sizes have declined... ..while the share of adults has increased<br>6.0 90<br>5.2<br>5.0 4.5 4.4 4.2 4.4 4.1 80 73.3<br>4.0 68.4 68.0 70.7 69.7<br>3.1 2.9 3.0 2.9 70 63.4<br>2.7 2.6<br>3.0<br>60<br>2.0<br>50<br>1.0<br>40<br>0.0<br>2000 2010 2004 2010 2000 2009 30<br>2000 2010 2004 2010 2000 2009<br>Bangladesh Peru Thailand<br>Bangladesh Peru Thailand<br>Average household size<br>Number of adults per household Share of adults per household<br>Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4. Labor Income Growth Incidence Curves<br>Bangladesh, 2000 to 2010 Peru, 2004 to 2010 Thailand, 2000 to 2009<br>6.00 0.14 0.8<br>5.00 0.12 0.6<br>4.00 0.1 0.4<br>0.08 0.2<br>3.00<br>0.06 0<br>2.00<br>-0.2<br>0.04<br>1.00 Labor Income per capita<br>-0.4<br>0.02<br>0.00 Total Income per capita<br>-0.6<br>0<br>-1.00 1 11 21 31 41 51 61 71 81 91<br>1 11 21 31 41 51 61 71 81 91<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentile Percentile Percentile<br>Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br>% change<br>% change<br>% Change<br><!-- End of picture text --> 23
the relative attractiveness of wage employment and self-employment. Still, the results showing that participants to the entrepreneurship track are more optimistic suggest that graduates feel more confident to be able to benefit from new opportunities opened to them. As such, they point to the broader impacts of employment programs on youth behaviors and attitudes towards the future.

# **<u>References</u>**

- Almeida, R., J. Arbelaez, M. Honorati, A. Kuddo, T. Lohmann, M. Ovadiya, L. Pop, M. Sanchez-Puerta, and M. Weber. (2011). “Improving Access to Jobs and Earnings Opportunities: The Role of Activation and Graduation Policies” Social Protection and Labor Discussion Paper No. 1204. Washington, DC, World Bank.

- Almeida, R., and E. Galasso. (2010). "Jump-Starting Self-employment? Evidence Among Welfare Participants in Argentina." _World Development,_ 38 (5).

- Almlund, M., A. L. Duckworth, J. J. Heckman and T. Kautz (2011). “Personality Psychology and Economics.” NBER Working Paper No. 16822.

- Angel-Urdinola, D; A. Semlali, and S. Brodmann (2010). “Non Public Provision of ALMPs in AMCs: An Inventory of Youth Programs”. Social Protection and Labor Discussion Paper No. 1005. Washington, D.C., World Bank.

- Attanasio, O. P., A. D Kugler., and C. Meghir. (2011) “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial.” _American Economic Journal: Applied Economics_ , 3 (3).

- Banerjee, A. and E. Duflo. (2008). “What is middle class about the middle classes around the world?” _Journal of Economic Perspectives,_ 22(2).

- Baumol, W. (1968). “Entrepreneurship in Economic Theory.” _American Economic Review_ , _Papers and Proceedings_ , 58 (2).

- Betcherman, G., K. Olivas, and A. Dar. (2004). "Impacts of Active Labor Market Programs: New Evidence from Evaluations with Particular Attention to Developing and Transition Countries." Social Protection and Labor Discussion Paper No. 0402. Washington, D.C., World Bank

- Betcherman, G., M. Godfrey, S. Puerto, F. Rother, and A. Stavreska (2007) “A Review of Interventions to Support Young Workers: Findings of the Youth Employment Inventory.” Social Protection and Labor Discussion Paper No. 0715. Washington, D.C., World Bank.

- Blattman, C. and J. Annan. (2011). “Reintegrating and employing high risk youth in Liberia: Lessons from a randomized evaluation of a landmine Action and agricultural training program for ex-combatants”. IPA, Yale University.

27
those individuals who could not be interviewed in person during the second phase and had not been successfully interviewed by phone in phase 1.

We also attempted to obtain migration outcomes using WhatsApp location sharing. During the baseline (August 2019), respondents were shown how to share their current location using WhatsApp. Respondents were asked via audio messages to share their current locations and for each location share, were compensated with mobile credit equivalent to 50 Gambian dalasi. This exercise was repeated during the endline survey (in August 2020) and afterwards (in January and February 2021).

We employed the same survey firm used for the baseline, to maximize the trust in enumeration and the chance of relocating individuals. Surveys were done on tablets with multiple consistency checks, and geo-coded and time-stamped to verify where and when surveys were done. After the data cleaning exercise, a total of 2,761 youths, 370 household heads and 391 elders were successfully surveyed through face-to-face interviews. We ended up with a final panel of 3,640 youth which were either interviewed face-to-face (n=2,761) or through the phone (n=475), or for which we got information from their household head (n=370), the elder survey (n=28) or only their WhatsApp location (n=6). Only 1 individual, a teacher was lost because he could not be contacted by phone and the whole household had moved out.

# **4.3 Estimating treatment impacts using the follow-up data**

We use outcomes from the follow-up survey and run the following linear regression to estimate intention-to-treat effect. As before, let _Information_ , _Senegal_ and _Vocational Training_ be dummy variables that take value one if the individual is assigned to the information intervention, Senegal migration alternative, and offer of vocational training respectively. Then for outcome _y_ for individual _i_ in settlement _s_ we estimate: 1I I I I 2S 3 I I + ∑ii,ss98 1�s � (3) ss ss ss yy = αα+ ββ aaI + ββ IIS aaSS + ββ VVIIVVaaI aaSS TTIIaaI SS where jj=1 δδjj are randomization strata fixed effects (following Bruhn and McKenzie, 2009), and the ssI aaIIaajj + εεii,ss standard errors jjjj are clustered at the settlement level.<sup>20</sup> As mentioned above, note that everyone δδ standard errors jjjj are clustered at the settlement level.<sup>20</sup> As mentioned above, note that everyone 20 ii,ss Our clustered random assignment allows for spillovers within settlements but assumes no spillovers across εε settlements. The vocational training vouchers and transport incentives were given to individuals and unlikely to be 25
# **Figure 3.  Saving and Real Exchange Rate Gaps 1990-2012**

<!-- Start of picture text --> 2<br>1<br>EAP<br>MNA HI<br>0<br>ECA<br>SSA<br>LAC<br>-1<br>-2<br>-1 -0.5 0 0.5 1<br>Real Exchange Rate<br>National Saving<br><!-- End of picture text --> ♦LAC 1990-2012 Average ●Other countries per Period ♦ Other groups of countries 1990-2012 Average Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure shows the national saving and real exchange rate gaps for the whole sample. Each period is a three-year average. See Section 3 for the details of how the benchmarks are calculated.

31
25 De Rus, G. and G. Nombela (1999), "Variable Term Concessions for Highways: Are the Most Efficient Concessionaires Always Being Selected?", mimeo, The World Bank Institute. Washington D.C.

Engel, E., R. Fisher and A. Galetovic (1 997a), "Highway Franchising: Pitfalls and Opportunities". _American Economic Review_ Papers and Proceedings. 87:2. pp.68-72.

Engel, E., R. Fisher and A. Galetovic (1997b), 'Privitizing Roads: A New Method for Auctioning Highways'. _Viewpoint Note, N. 112,_ The World Bank: Washington D.C. Reprinted in S. Smith (ed.). _The Private Sector in Infrastructure._ The World Bank, Washington D.C. pp.109112.

Engel, E., R. Fisher and A. Galetovic (1998), "Least-Present-Value-of Revenue Auctions and Highway Franchising". NBER Working Paper 6689. Cambridge Estache, A. (2000), "Privatization and regulation of Transport Infrastructure in the 1990s: Successes... and Bugs to Fix for the Next Millenium", World Bank Policy Research Working Paper No.2248, forthcoming in the _World Bank Research Observer_ Galvez, T.E. and S.R. Jara-Diaz (1998), "On the social valuation of travel time savings", _International Journal of Transport Economics,_ Vol. XXV, No. 2, June Gomez-Ibanez, J., W.B. Tye and C. Winston, ed. (1999), _Essays in Transportation Economics and Policy,_ Brookings, Washington, D.C.

Jara-Diaz, S.R. (2000), "Allocation and valuation of travel time savings", mimeo, Universidad de Chile Lefevre, C. and J.M. Offner (1990), _Les Transports Urbains en Question,_ Edition CELSE, Paris.

Mercer Management Consulting (1998), _"Commuter Rail Demand Studyfor the BuenavistaHuehuetoca Line_ ", prepared for the Mexican Communications and Transport Secretariat Ministere de l'Equipement, France (1999), "Etudes de trafic et de sensibilite au peage en Ile de France", internal document Ortuzar, J de D. (2000), Modelos de Demanda de Transporte", Segunda Edicion, Alfaomega Grupo Editor S.A., Mexico Ortuzar, J.de.D. and L. G. Willumsen (1994), _Modelling Transport,_ Wiley, New York Oum, T.H., W.G. Waters, II and J.S. Yong (1992), "Concepts of Price Elasticities of Transport Demand and Recent Empirical Estimates", _Journal of Transport Economics and Policy_ 26 (May), 139-54 Pickrell (1992), "A Desire Named Streetcar- Fantasy and Fact in Rail Transit Planning", _Journal ofAmerican Planning Association,_ Spring
9 as large if not greater than those in this approach. Thus, although this approach has neveral limitations which are discursed in the appendix, we believe it should be viewed as at least as goLd as any of the alternatives.

It should be noted that a significant advantage of the approach ir this paper is that it a-voids conversions through exchangt.; rates between roubles, dollars and forints. This fact is demonstrated in the appendix. Given the fact that the forint/rouble and rouble/dollar exchange rates are not market determined, it is desirable to avoid any conversion based on exchange Lates and the arbitrariness that involves.

# 3. THE RESULTS

The Chnge iAn the Income terms-of-trade The first measure discussed is the income terms-of-trade loss to Hungary of the switch-over. The basic data are presented in table 1. Based on 1988 quantity weights and on the price information for that year, the summary data are presented in column 7. An explanation of the other columns is presented *n the appendix. Column 7 indicates the value Hungarian exports would earn in dollars and the value Hungarians would have to pay in dollars for imports at world market prices tn 1988. Take machinery exports as an exampla. If the same quantity of Hungarian machinery exports to the Soviet Union were sold at world market prices estimated to prevail in 1988, the expected value of the earnings is $2.15 billion as opposed to 2.83 billion TR. If all exports were sold in dollars the expected earnings are $4.1 billion, and if all imports were paid for in dollars, the expected value of Hungarian payments is $4.9 billion. Thus, the difference of $0.8 billion is the expected value of the additional dollars that Hungary would have to pay as a result of the switch-over.
17 costs is not available, but the high public share would suggest that private industry costs have been lower than those in the United States and Europe. These numbers should be treated with caution, however, as existing statistics make it difficult to compare industry level data across countries, because of differences in coverage or definitions of industry.'" How much of the total private costs are borne by industry or the manufacturing part of it, depends on country circumstances. One study on the United States (Low 1991) indicated that in 1988 0.5% of manufacturing output was spent on pollution abatement. German data suggests that 0.7% of manufacturing output in 1990 was devoted to environmental expenditures. These and other estimates (see Table 10, Ugelow 1985) show that environmental expenditures are a small share of total industry costs - compared to costs of energy or labor, for example.

**Table 10: Levels of private environmental expenditure, selected industrial cauntries, mid 1980s**

||Private<br>environmental<br>exp. in GDP|For reference:<br>Shares in maan<br>Energy(1990)|ufacturing<br>costA of:<br>Labor(1990)|
|---|---|---|---|
|||percent||
|Fuband|0.8|3|20|
|Norway|0.3|**na.**|19|
|Sweden|0.3|n.a.|n.a.|
|Germany|0.7|2|25|
|uS|0.9|na.|**n.a.**|

Source: OECD (1990), National statistics.

Most environmental costs are concentrated in a few basic industries. In Germany, for which complete data is available (Annex Table 4), the industries with the highest environmental expenditures are either in mining and utilities (10% of industrial output in 1990) or in basic industries like iron and steel, chemicals, petroleum and metals (24 % of industrial output). In mining and utilities, environmental expenditures were over 4 % of the value of output, while the t **Comparisona** of environmental **cost are** made difficult by uneven coverage acrou countries or differences in **classification. Some** countries equal private cost to those of industry (Japan), while others report a much more comprehenaive coverage (US). **As** utilities account for a large share of environmental expenditures, definition of industry can be important proper accounting of expenditures. Nordic countries do not include utilities in industry costs, whereas Japan, the US and Gernany do. Mining is sometimes included in definitions of indusry. 7his makes any comparative assessments especially at the industry level difficult.
The frequency of cropland reflects the suitability of conditions for rainfed agriculture. Table 1 reveals that mid and high elevation humid and subhumid forests have the highest fraction of cropland followed by mid elevation moist savannah. The AEZs with the lowest fraction of cropland are the deserts and lowland semi-arid areas. Cropland in these areas is largely irrigated and depends on the availability of water.

We obtained economic data on crop net revenue from a sample of over 10,000 farmers collected in 11 countries (Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa, Zambia, and Zimbabwe) (Dinar et al 2008). For each farm, we were able to identify the AEZ for the district. We then calculated the average net revenue per hectare of cropland, _R,_ , for each AEZ _j_ (see Table 1).

# _Rj=TRj/CRj_

# where _TRj_ is the total farm revenue generated in AEZ _j_ .

An estimate of the expected crop revenue per hectare of land in each AEZ is calculated by multiplying the average net revenue per hectare by the fraction of cropland in that particular AEZ. As shown in Table 1, the two AEZs that produce the most net revenue per hectare of land are high-elevation moist savannah and high-mid elevation sub-humid forest. The lowest valued AEZs are lowland semi-arid, lowland sub-humid forest, and high-elevation dry savannah. Note that the net revenue per hectare of cropland in the desert is very high because it is irrigated.

Data on climate was gathered from two sources. We relied on temperature data from polar orbiting satellites operated by the Department of Defense (Basist et al. 1998). The precipitation data comes from interpolations between weather stations made by the National Oceanic and Atmospheric Association’s Climate Prediction Center- Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003).

We have combined monthly climate data into winter and summer seasonal averages (Kurukulasuriya and Mendelsohn 2008). In the southern hemisphere, we define the average of November, December and January as summer and May, June and July is winter. The northern hemisphere seasons are the opposite. It is apparent that each AEZ has different climate characteristics. Table 2 shows the average climate variables for each AEZ in Africa. Lowland semiarid and lowland dry savannah AEZs are relatively hot. Deserts have average temperatures for Africa but they are dry. Mid and high elevation AEZs are cooler.

Soil data were obtained from the FAO (FAO 2003); providing information on the major and minor soils in each location as well as slope and texture. Predicted natural water flows in each district were predicted from a hydrological model for Africa (Strzepek and McCluskey 2006). Data on elevation at the centroid of each district was obtained from the United States Geological Survey (2004). The USGS data is derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).
a country _i_ and _j_ to construct an index of financial proximity (FP), such that: where here _Ci→j_ , _t_ refers to total cross-border claims from country _i_ to country _j_ .

**Foreign Direct Investments.** Data from the UNCTAD’s Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows, outflows, inward stock and outward stock by region and economy.<sup>13</sup> We use the inflows and outflows in order to construct a bilateral financial integration (FI) controls, such that:

**Sectoral composition.** If shocks have a sectoral component, then two countries that tend to specialized over time in the same sectors could have an increase in business cycle comovements over and beyond any trade effects. in order to account for such a mechanism, we create an index of “ _proximity in sectoral composition_ ” based on the World Development Indicators. We use the share in value added of main sectors: service and agricultural sectors and we decompose manufacturing sectors into 7 main sub-sectors.<sup>14</sup> We then compute the following index:

where _k_ refers to a particular sector. Pairs of country with very similar sectoral composition have an index close to 1, while countries that completely specialize in different sectors would have an index of 0.

**Third country index.** We finally attempt to control for third country effect. This is motivated by the fact that two countries trading mostly with similar partners are likely to comove. We

> 13Data are in principle collected from national sources. In order to cover the entire world, where data are not available from national sources, data from partner countries (mirror data) as well as from other international organizations have also been used. Data can be downloaded on the UNCTAD website.

> 14This includes textile, industry, machinery, chemical, high-tech, food and tabacco, other. Data are available here: `https://databank.worldbank.org/data/source/` .

7