rafmacalaba/datause-encoder-data huggingface dataset:
# **List of Tables**

Table 1: Sri Lanka. Characteristics of the Population and the Labor Force

||**2002**|**2006/07**|**2009/10**|**2012/13**|**2002-**<br>**2012/13**|
|---|---|---|---|---|---|
|Urban|13.42|14.46|13.69|17.41|29.75|
|Rural|80.63|79.86|80.65|77.24|-4.20|
|Estate|5.95|5.68|5.66|5.35|-10.09|
|Average household size|4.19|4.06|3.98|3.86|-7.96|
|Share of children (age<15) in the household|22.94|22.23|21.99|21.82|-4.87|
|Share of adults (age15+) in the household|77.06|77.77|78.01|78.18|1.45|
|Share of elderly (age 65+) in the household|8.33|9.49|10.16|11.27|35.36|
|**Gender (pop 15+)**||||||
|Female|51.74|52.87|53.48|54.11|4.59|
|Male|48.26|47.13|46.52|45.89|-4.92|
|**Age structure (pop 15+)**||||||
|15-29|35.77|32.14|30.28|27.90|-22.00|
|30-49|37.59|37.02|37.01|36.99|-1.59|
|50-64|17.54|20.60|21.70|23.04|31.30|
|65+|9.09|10.25|11.00|12.07|32.76|
|**Labor Force Participation rate (pop 15+)**||||||
|All|62.08|57.66|57.07|54.26|-12.59|
|Women|45.22|37.99|37.95|34.89|-22.85|
|Men|80.14|79.72|79.06|77.10|-3.80|
|**Share of Employed Individuals (pop 15+)**||||||
|All|52.19|50.85|49.67|51.25|-1.80|
|Women|34.84|31.17|30.29|32.53|-6.63|
|Men|70.80|72.91|71.94|73.33|3.58|
|**Share of Unemployed Individuals (pop 15+)**||||||
|All|9.88|6.82|7.42|3.01|-69.56|
|Women|10.38|6.82|7.67|2.37|-77.21|
|Men|9.35|6.81|7.12|3.77|-59.70|
|**Education levels (pop 15+)**||||||
|None or Incomplete primary|17.27|16.45|14.01|13.18|-23.68|
|Complete primary|23.59|22.25|20.78|18.85|-20.12|
|Complete lower secondary|29.96|29.13|32.07|32.50|8.46|
|Complete senior secondary (O/L)|17.74|18.86|18.50|19.44|9.57|
|<br>Complete collegiate|9.48|10.93|11.66|12.88|35.87|
|<br>Post Secondary|1.96|2.32|2.65|3.13|60.05|
|<br>**Type of Settlement (pop 15+)**||||||
|Urban|13.78|14.66|13.88|17.79|29.10|
|Rural|80.44|79.96|80.78|77.27|-3.94|
|Estate|5.78|5.38|5.33|4.94|-14.59|
|**Labor Relation (pop 15+)**||||||
|<br>Salaried farm|6.5|5.4|4.7|4.8|-26.49|
|Salaried non farm|24.6|25.0|24.1|24.7|0.29|
|Self-employed farm|11.6|8.2|9.1|8.5|-26.73|
|Self-employed non farm|6.9|8.6|8.3|8.8|27.68|
|<br>Unpaid|2.8|3.7|3.5|4.6|65.69|
|Not employed|47.8|49.2|50.3|48.8|1.97|

Source: Own estimations based on HIES 2002, 2006, 2009 and 2012/13 22
For Ethiopia, Uganda, Kenya and Jordan, data exist on refugees residing in camps and among host communities (Figure 5). This data allows one to consider self-reliance in slightly more detail. Two aspects stand out. Refugees residing in camps tend to have low levels of self-reliance. In urban areas, by contrast, the degree of self-reliance tends to be much higher. Self-reliance is highest in Addis Ababa and Kampala, while in Nairobi, where refugee (work) freedoms are restricted by an encampment policy and significant constraints on the right to work (Betts, 2021), self-reliance is noticeably lower. Under such conditions, it is not surprising that refugees in Nairobi are less self-reliant than those in the comparatively freer city of Kampala. Nor is it surprising that refugees in Addis Ababa have such high levels of self-reliance. Here, the country’s Out-of-Camp Policy requires refugees to demonstrate they can cover their cost of living and provide a sponsor _before_ they are allowed to move out of camp, ensuring high levels of self-reliance (Kassa et al. 2019). Self-reliance is also higher among refugees in Jordan’s capital city, Amman, than in the country’s camp-based settings of Azraq and Zaatari. Yet refugee self-reliance in Jordan remains relatively low for a middle-income country. This is unsurprising given the restrictions the country imposes by requiring refugees to obtain work permits, which confine refugees to jobs in low-skill sectors only (Hoogeveen and Obi, 2024).

In addition to clear disparities between urban and camp settings, significant variation exists between camps, with only 4 percent of refugees self-reliant in camps in Ethiopia, compared to 39 percent in Dadaab in Kenya. Such variation is also found within countries, with camps located in more isolated areas with fewer economic opportunities characterized by lower levels of self-reliance. For example, self-reliance in the isolated camp of Azraq is lower than in Zaatari which is 10km east of the northern Jordanian city of Mafraq. While in Kenya, self-reliance in Kakuma and Kalobeyei is less than in Dabaab, which is much closer to population centers and trade routes.

**Figure 5: Difference in refugee self-reliance by location within countries**

<!-- Start of picture text --> 100 91 91<br>80<br>68 66<br>60 53<br>39 41 39<br>40 29 31<br>23<br>20 15 14 16<br>9 7<br>4<br>0<br>Ethiopia Uganda Kenya Jordan<br>All refugees In camps (*) Addis Ababa All refugees Outside Kampala (*) Kampala All refugees Kakuma (*) Kalobeiye (*) Dabaab (*) Other urban Nairobi All refugees Azraq (*) Zaatari (*) Outside Amman Amman<br><!-- End of picture text -->

**Self-reliance is negatively correlated with aid.** As shown in Figures 6 and 7, self-reliance is negatively correlated with aid at both national and sub-national levels. One way to interpret this is that aid provides a disincentive to work, an explanation made less plausible by the fact that the amount of aid is limited and

13
labor intensive and job creation is usually led by this sector. In the CIS-7, growth has been driven instead by the production of a few export items mostly in extraction and heavy industry, by retail trade and commerce and, to a minor extent, by services.

**Table 2.2 CIS-7 Output shares by sector**

|||**Beg. Trans.**|**End reces.**|**Latest**|
|---|---|---|---|---|
|**Azerbaijan**||**1992**|**1996**|**2002**|
||Agriculture|40.3|37.6|40.2|
||Business Services|18.6|24.5|24.2|
||Manufacturing|20.5|14.3|10.5|
||Public Services|20.7|23.6|25.1|
|**Georgia**|||**1998**|**2002**|
||Agriculture||48.5|53.8|
||Business Services||17.7|18.3|
||Manufacturing||8.9|6.8|
||Public Services||24.9|21.1|
|**Kyrgystan**||**1992**|**1996**|**1999**|
||Agriculture|38.2|47.1|52.4|
||Business Services|14.9|18.2|16.8|
||Manufacturing|21.4|13.4|10.3|
||Public Services|25.6|21.2|20.5|
|**Moldova**|||**1999**|**2002**|
||Agriculture||48.9|49.6|
||Business Services||17.7|17.7|
||Manufacturing||12.2|12.7|
||Public Services||21.2|20.0|
|**Tajikistan**||**1992**|**1997**||
||Agriculture|49.4|49.8||
||Business Services|9.9|8.6||
||Manufacturing|21.1|17.1||
||Public Services|19.6|24.5||

Source: Constructed from http://www.cisstat.com In fact, the CIS-7 output structure shifted away from production of goods towards services. In Georgia, production of goods decreased from 48.4% to 40.7% of GDP between 1998 and 2002 while services grew from 47.7% to 53.6%. Construction, trade and transport were the sectors that grew faster during the period. In Armenia, production of goods decreased from 60.2% to 55.6% between 1997 and 2001 while services expanded marginally from 34.3% to 36.8%. In Moldova, production of goods decreased from 54% to 41% of output between 1995 and 2001 with the share of services increasing from 37% to 49%.

**Third** , output trends in industry and agriculture have been consistent with employment trends. Throughout the transition period, employment in agriculture continued to grow in relative terms and employment in industry - particularly manufacturing - continued to decline. In Georgia, employment in agriculture between 1998 and 2002 increased from 48.8% of total employment to 53.8% while employment in manufacturing decreased from an already very low level of 7% to 4.6%. Even among services, it is only employment in trade that shows a significant improvement during the period. In Armenia, agriculture is the only major employment sector that shows relative growth between 1997 and 2000 with industry declining and services remaining approximately stable. In Kyrgyzstan, the employment share of agriculture increased uninterrupted between 1989 and 2000 while employment in manufacturing declined uninterrupted and by a staggering two-thirds during the period.

At the end of the recession in 1996, agriculture was by far the largest employment sector in all CIS-7 countries with Georgia leading the way with about 52% of the employed population work15
# **L-egislati-ve** Erameworks

# **Used to Foster Petroleum Development**

### By William T. Onorato

## **I.** Introduction and Summary

In the course of my ten years as the Bank's legal adviser on petroleum matters, I have had the opportunity to advise on the creation of appropriate legislative framewos aimed at fostering the development of petroleum resources in over forty member countries, worldwide'. This has been done principally through active participation in the legal reform and technical assistance (TA) components of petroleum exploration promotion projects (PEPPs), which, inter a, support the development of enabling legislation to help Bank members with petroleum potential attract foreign direct investment and risk-capital into this crifical sector of their economies. Most recently, under the Technical Cooperation Program for the former Soviet Union (FSU) and certain of its adhering Republics<sup>2</sup> , such expert legal TA was provided to both the Republics of Russia and Kazakhstan. Its objective was to aid them in the development of acceptable, international-standard, legal, contractual and fiscal frameworks to encourage a quickening of the pace of petroleum .BA, JD, Ph.D. (Cantab); Principal Coursel, Enrrgy & Mlning, The World Bank; Adjunct Prfoesor of Law, Georgetown Univerlty Law Cenbtr.

1. Prlncipaly In Sub-Saharan Africa, Eastem Europe, Latin America and the Caribbean and seveal Asian countries. In this regard, please see the Anmex to this paper detailing the Bank group's petroblum sector loans, credls, Investmnts and legal TA activitie between 1980 and 1994.

> 2.Tethnlcal Cooperation Agreement dated November S,1991, beteenthe Bank and the USSRtowhich the Republs of Russia, Kazakhstan, the Kyrgyz Repubfc and Belanu adhered, putsuant to the terms of the Agreement,
# **Trade Integration as a Way Forward for the Arab World: A Regional Agenda**

## **Jean-Pierre Chauffour**<sup>**1**</sup> **World Bank**

**Keywords** :  Arab world, PAFTA, trade integration, preferential trade agreements, non-tariff measures, services liberalization, knowledge platforms, trade rules and discipline

**JEL classification:** F1, F5, L60, L80

> 1 Lead Economist, World Bank’s Poverty Reduction and Economic Management Network, Middle East and North Africa Region. This paper benefited from inputs from Jean-Francois Arvis, Olivier Cadot, Kevin Carey, Abdelmoula Ghzala, Gerard McLinden, Douglas Pierce, Carlo Maria Rossotto, and Sebastian Saez, and was prepared in consultation with Simon Bell, Bernard Funck, Mona Haddad, Bernard Hoekman, and Jonathan Walters. Useful comments were received from Shamshad Akhtar, Ibrahim Al-Ghelaiqah, Najy Benhassine, Philippe De Meneval, Ndiame Diop, Santiago Herrera, Elena Ianchovichina, Claudia Nassif, Stefano Paternostro, Miria Pigato, and Fahrettin Yagci. Special thanks to Paul Brenton and Borko Handjiski for their thoughtful peer reviews.
Policy Research Working Paper 5268

# **Abstract**

The impacts of international emigration and remittances on incomes and poverty in sending areas are increasingly studied with household survey data. But comparing households with and without emigrants is complicated by a triple-selectivity problem: first, households self-select into emigration; second, in some emigrant households everyone moves while others leave members behind; and third, some emigrants choose to return to the origin country. Allowing for duration-dependent heterogeneity introduces a fourth form of selectivity—one must now worry not just about whether households migrate, but also when they do so. This paper clearly sets out these selectivity issues and their implications for existing migration studies, and then addresses them by using survey data designed specifically to take advantage of a randomized lottery that determines which applicants to the over-subscribed Samoan Quota may immigrate to New Zealand. The analysis compares incomes and poverty rates among left behind members in households in Samoa that sent Samoan Quota emigrants with those for members of similar households that were unsuccessful in the lottery. Policy rules control who can accompany the principal migrant, providing an instrument to address the second selectivity problem, while differences among migrants in which year their ballot was selected allow for estimation of duration effects. The authors find that migration reduced poverty among former household members, but they also find suggestive evidence that this effect may be short-lived as both remittances and agricultural income are negatively related to the duration that the migrant has been abroad.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the group to study the determinants and consequences of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted atdmckenzie@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. Introduction**

In 2018, the Financial Stability Board (FSB) launched an evaluation of the effects of G20 financial reforms on the financing of small and medium-size enterprises (SMEs). According to FSB (2019), reforms such as the Basel III bank regulatory framework shall boost financial stability and systemic resilience in the long term. But they may be associated with some short-run costs. The global community is interested in learning if any such short-run costs may be incurred by SMEs— the main job providers and conduits of shared prosperity. Because banks provide SMEs with most of their external finance, the main transmission mechanism from the reforms to SME growth could work through bank credit provision to SMEs (FSB 2019).

Although the FSB evaluation focuses on the group of G20 jurisdictions, this paper conducts a similar evaluation for SMEs in countries covered by the World Bank Enterprise Surveys. More specifically, the paper uses firm-level panel and repeated cross-sectional data for 32 countries from the Enterprise Surveys together with banking system–level data from the World Bank’s FinStats, individual bank-level data from Fitch (to create matched firm-bank data for a subset of firms), and with the Bank of International Settlements Financial Stability Institute Survey of Basel III implementation in emerging markets and developing economies (EMDEs). The Enterprise Surveys offer some benefits in comparison with other available databases, for example, allowing us to examine a broad measure of access to finance in the EMDEs—like the European Central Bank’s Survey on the Access to Finance of Enterprises,<sup>1</sup> which was used as the leading data set in the FSB evaluation.

We examine whether Basel III capital requirements,<sup>2</sup> the first and most prominent regulation of the Basel III package, had any short- to medium-term effects on SME access to finance in EMDEs. Access to finance as a constraint reflects the firm’s overall perception of difficulties in accessing financing and is tracked before and after the Basel III implementation for matching firms. This subjective measure of access to finance has been used by, for example, Beck, Demirgüç-Kunt, and Maksimovic (2004) and Beck et al. (2006). Examining the access to finance measure from the World Bank Enterprise Surveys and comparing it with more objective measures of financial

> 1 https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.

> 2 BCBS (2019) provides a survey on the benefits and costs of higher bank capital.

2
**Figure 8:** Intergenerational Educational Mobility in LAC - Gender

<!-- Start of picture text --> BOL LCA<br>BOL gap = 3.535 + -.0018 cohort (1.854) (.0009) gap = -.8180 + .0004 cohort (.6631) (.0003)<br>BOL<br>GTM<br>PER GTM BOL<br>BRAGTMPRYPANURY ECU GTM MEXPRY780HTIBRAPANCHLCOLCRINICHNDVENARGURY GTM ECU PRYHTIHNDCRIARGPAN780BRAJAM URY CHLCOLDOMVENLCA ECUMEXJAMPRYPAN780CHLARGHND URYCOL CRINICBRAVENSLVLCA PERPANJAMSLVMEXECUPRYHTI192CHL URY ARG780CRIHNDCOL NIC BRADOMVEN PER192BOLPANECUMEX780SLV740CRIARG URY COLNICBRADOM BRAPANGTMURYPRY BRANICHTIPRYCOLPANVENHNDURY C ARGMEX GTM 780ECUBOLRIHL DOMVENHNDBRACOLPAN780CHL URYECU AGTMCJAMPRYHTIBOL R GI BRANICSLVVENHND COLURY CPPANCHLARGMEXJAMECU780PERLCAGTMBOL R YI DOMBRA NIC HNDVENCRIPRY URY HTIPANCOLARGSLV780MEXCHLECUJAM192PERBOL GTM NICBRADOMCOLSLV U APANCRIECUMEXPERBOL 7 19248 R0 G Y<br>1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>Birth decade Birth decade<br> countries with data for 1950s, 1960s, 1970s, 1980s countries with data for 1950s, 1960s, 1970s, 1980s<br> other countries other countries<br> mean among countries with data 1950s-1980s mean among countries with data 1950s-1980s<br>(a) Upward mobility (b) Downward mobility<br>.2<br>.2<br>.15<br>.1<br>.1<br>0<br>.05<br>-.1 0<br>male upward IGM - female upward IGM -.2 -.05<br>male downward IGM - female downward IGM<br><!-- End of picture text --> Notes: These estimates correspond to the probability of completing at least primary education for those whose parents did not finish primary school in the case of upward mobility and probability of not completing primary education for those whose parents completed primary school in the case of downward mobility. They are estimated by country, birth decade of the “children” and gender.

**Figure 9:** Intergenerational Educational Mobility in LAC across cohorts

<!-- Start of picture text --> Jamaica Cuba<br>Bolivia Suriname<br>Trinidad and Tobago Jamaica<br>Cuba Mexico<br>Mexico Bolivia<br>Uruguay Panama<br>Chile Uruguay<br>Suriname Chile<br>Costa Rica Costa Rica<br>Peru Trinidad and Tobago<br>Ecuador Colombia<br>Argentina Peru<br>Panama Argentina<br>Colombia Ecuador<br>Dominican Republic Dominican Republic<br>Paraguay Venezuela<br>Saint Lucia Paraguay<br>Brazil El Salvador<br>Venezuela Saint Lucia<br>El Salvador Brazil<br>Honduras Honduras<br>Nicaragua Guatemala<br>Guatemala Nicaragua<br>Haiti Haiti<br>0 .2 .4 .6 .8 1 0 .1 .2 .3 .4<br>IGM UP IGM Down<br>1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>(a) Upward mobility (b) Downward mobility<br><!-- End of picture text --> Notes: The estimates are done by birth decade cohort of the children.

21
20 Table 7: Panel Data Estimates: Equity Flows, 1988.01-1992.09 - Latin Ameriza (6 countries) Asia (7 countries).

||Lati<br>|n America<br>||Asia<br>|
|---|---|---|---|---|
||Nominal interesi<br>rates|Real interest<br>rates|Nominal interest<br>rates|<br>Real interest<br>rates|
|Method|GLS|GLS|GLS|GLS|
|Credit rating<br>PCS|1.690<br>(8.174)|7.278<br>(7.480)|0.108*<br>(0.051)|0.106*<br>(0.052)|
|Return|-0.039<br>(0.316)|-0.C61<br>(0.316)|0.0020<br>0.001|0.002?<br>0.001|
|Price-Earning Ratio|-0.9850<br>(0.928)|-0.654<br>(0.894)|-0. 182**<br>(0.065)|-0. 185**<br>(0.066)|
|P11|-18.141*<br>(8.212)|-11.066°<br>(6.913)|-2.007**<br>(0.479)|-1.564**<br>(0.485)|
|Adjusted E?|0.292|0.290|0.0S3|0.036|
|_xi[_|-0.430|-3.633|0.976<br>(0.323)|0.000<br>(0.994)|
|NOBS|342|342|399|399|

Note: See Table 5.

Latin America: Argentina, Brazil, Chile, Colombia, Mexico and Venezuela. Asia: India, Korea, Malaysia, Philippines, Pakistan, Taiwan and Thailand. Return: differential of rates of return (rates of return of domestic stock markets - US rates of return). As in Table S, three dummies are used for equity flows in Latin America. "#", "o", "a", and "**f:significant at the _15%,_ 10%, _5%_ and I % levels, respectively.
**Table 7. Firm Efficiency and Government Facilitation: Adding Other Local Controls**

||**lnlp**|**lnlp**|**lnlp**|**lnlp**|
|---|---|---|---|---|
|loan help, city-ind mean|1.141***|1.055***|0.953***|0.909**|
||(0.339)|(0.350)|(0.330)|(0.353)|
|info facilitation, city-ind mean|0.596***|0.576**|0.456***|0.389*|
||(0.213)|(0.230)|(0.175)|(0.199)|
|PS long tenure|-0.028|0.044|0.024|0.054|
||(0.098)|(0.101)|(0.103)|(0.105)|
|Bank loan, city-ind mean||0.106||0.272|
|||(0.148)||(0.179)|
|Trade credit, city-ind mean||1.598***||1.328***|
|||(0.425)||(0.496)|
|Entertainment, travelling/sales, city-ind<br>mean|||-2.154|-2.791|
||||(2.678)|(2.716)|
|Property rights protection, city- ind mean|||0.436|0.518|
||||(0.304)|(0.347)|
|Share of dispute resolved by courts, city-<br>ind mean|||0.416|0.058|
||||(0.506)|(0.505)|
|Gov efficient services, city-ind avg|||0.774**|0.559|
||||(0.390)|(0.406)|
|Official ability, city-ind avg|||-0.235|0.170|
||||(0.594)|(0.631)|
|Official helping firms, city-ind avg|||-0.323|-0.836*|
||||(0.472)|(0.505)|
|ind dummies * ln(K/L), lnL|Yes|Yes|Yes|Yes|
|Ind dummies|Yes|Yes|Yes|Yes|
|Firm characteristics, log(GDP per capita)|Yes|Yes|Yes|Yes|
|CEO characteristics|Yes|No|No|Yes|
|r2_a|0.366|0.370|0.363|0.370|
|N|4,647|4,484|3,806|3,649|

*, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively.  White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, in parentheses.

CEO characteristics include CEO education, whether the CEO was educated abroad, CEO tenure as the CEO of the firm, CEO ownership of the firm, and whether CEO is also the chairman of the firm.

The results based on labor-productivity-styled regressions are similar.

38
- D. Public Sector Management and Institutions

12. Property Rights and Rule-based Governance 13. Quality of Budgetary and Financial Management 14. Efficiency of Revenue Mobilization 15. Quality of Public Administration 16. Transparency, Accountability, and Corruption in the Public Sector For each criterion, countries are rated on a scale of 1 (low) to 6 (high). A 1 rating corresponds to a very weak performance, and a 6 rating to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5 and 5.5 may also be given. For the years 1995-1997, countries were rated on a scale of 1 to 5. Scores have been rescaled for this research to a scale of 1 to 6. See OPCS (2009) for a detailed elaboration of the scoring procedure.

42