rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4 GDP per capita in 2010 and 2025 (2007 PPP USD in thousands).**

<!-- Start of picture text --> 2010 2025<br>45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>Cameroon Côte d’Ivoire Ghana Nigeria Senegal Ethiopia Kenya Madagascar Malawi Mauritius Mozambique Tanzania Uganda Zambia Zimbabwe Botswana Namibia South Africa Rest of SSA World SSA HICs LDCs LDCs exc. China<br><!-- End of picture text -->

**Figure 5  Sectoral structure of SSA output in 2007 and 2025.**

<!-- Start of picture text --> 13%<br>2025 Agriculture<br>10% 7%<br>Crude and refined oil<br>10%<br>3% Natural resources<br>3%<br>48% 46%<br>Food<br>2007<br>8%<br>8%<br>Manufacturing<br>Infrastructure<br>21%<br>2%<br>Services<br>19%<br>2%<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

As average incomes of developing countries slowly converge to OECD levels, richer consumers will demand more services, whose income elasticity is greater than one. At the same time, productivity growth in services is generally slower relative to manufacturing and that will contribute to the growing value share of services in output. The increase in prices and demand for services in low- and middle7
-8-

Table 3 Structure of the bankinQ system (1984-87)

||1974|1975|1976|1977|1978|<br>1979|1980|<br>1981|1982|<br>1983|<br>1984|<br>1985|<br>1986|<br>1987|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Number of banks||||42|49|<br>49|<br>55|<br>_61_|<br>50|<br>45|<br>45|<br>45|<br>41|<br>41|
|Public|1|<br>1|1|1|<br>1|<br>1|<br>1|<br>1|<br>1|<br>1|<br>1|<br>1|<br>1|<br>1|
|Private domestic|19|18|19|20|21|<br>21|<br>23|<br>24|<br>20|<br>18|<br>18|<br>19|<br>16|<br>15|
|Foreign|1|<br>1|2|3|<br>6|<br>9|<br>13|<br>19|<br>19|<br>19|<br>19|<br>18|<br>20|<br>21|
|Finance companies|NA|NA|NA|18|21|<br>18|<br>18|<br>17|<br>10|<br>7|<br>7|<br>7|<br>4|<br>4|
|Total assets j.|||||100.0|100.0|100-0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|Public(%)<br>Private|||||27.4|22.9|21.2|18.9|16.2|18.1|18.5|21.5|<br>20.8|<br>20.0|
|domestica(%)<br>|||||66.7|71.4|72.5|77.7|82.0|67.8|68.3|64.3|63.2|62.8|
|Foreign(%)<br>Finance||||||||||12.8|12.2|13.2|<br>15.1|<br>16.3|
|companies<br>(%)|||||5.9|5.7|6.3|3.4|1.8|1.3|1.1|<br>0.9|<br>0.9|<br>0.9|

# Source: Superintendency of Banks (Informacion Financiera).

R. Includes Bancos de Fomento, IFICOOP and foreign banks (for 1978-82).
Traffic growth has slowed down with the international crisis but it is expected to increase at a fast pace in the upcoming years together with the economy and containerization levels.

According to customs data<sup>11</sup> , the distribution of dwell times is indicated in graph 2.<sup>12</sup> <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0-5 days 6-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 > 90<br>days days days days days days days days days days<br><!-- End of picture text -->

**Graph 2 - Dwell time distribution in the Port of Douala in 2009 (time between vessel discharge and gate exit)** Source: Cameroonian Customs data

An important observation is that dwell time variance is quite significant, with a standard deviation equal to 160% of the mean value. A sequential analysis of delays shows this variance is mainly the consequence of variance between vessel arrival and customs declaration lodging (referred to as “Arrival to Lodging” delay). Delay between payment of customs dues and gate exit (“Payment to Gate”) does also largely vary according shipment. These two intermediary delays account for about 75% of the total dwell time (“Arrival to gate”) in average. In contrast delay between lodging and payment of customs dues (“Lodging to payment”) is quite low and homogeneous in the whole sample.

||**Arrival to gate**|**Arrival to lodging**|**Lodging to payment**|**Payment to gate**|
|---|---|---|---|---|
|**Min**|1|1|1|1|
|**1**<sup>**st**</sup>**quartile**|8|3|1|1|
|**Median**|14|7|3|4|
|**Mean**<br>|**24,0**|**13,0**|**4,7**|**11,4**|
|**3**<sup>**rd**</sup>**quartile**|26|15|5|8|
|**Max**|566|446|340|387|
|**Inter quartile range**|18|12|4|7|

**Table 2 – Quartile distrib ution of cargo dwell time sequential components (container imports in Douala, 2009)** Source: CAMEROONIAN CUSTOMS, 2009 – Values in days

> 11 Data set of all containerized imports through the port of Douala in 2009 destined to local consumption (regime IM4). 12 In addition to the different long dwell times categories observed in graph 1 there are a few hundred containers  that do not appear in statistics which were already in the terminal as of January 1st, 2009 but have not been cleared before December 31st, 2009 (Source: Interview with DIT, October 2010.)

9
|RURAL ACCESS PROJECT|Binyam Reja|
|---|---|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|Technical Assistance for Economic Reform Project|Rajni Bajpai|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|WASTEWATER DISPOSAL IN TOURISM CENTERS PROJECT|Ventura Bengoechea|
|Bahia Education Project|Andrea C. Guedes|
|Bahia Education Project|Andrea C. Guedes|
|Bahia Education Project|Andrea C. Guedes|
|Bahia Education Project|Andrea C. Guedes|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Ceara Basic EducationQualityImprovement Project|Ricardo Rocha Silveira|
|Rural Environment Protection GEF Project|Barbara Letachowicz|
|Rural Environment Protection GEF Project|Barbara Letachowicz|
|Rural Environment Protection GEF Project|Barbara Letachowicz|
|Rural Environment Protection GEF Project|Barbara Letachowicz|
|Rural Environment Protection GEF Project|Barbara Letachowicz|
|Road Network Improvement Project|PhuongThi Minh Tran|
|Road Network Improvement Project|PhuongThi Minh Tran|
|Road Network Improvement Project|PhuongThi Minh Tran|
|Road Network Improvement Project|PhuongThi Minh Tran|
|Road Network Improvement Project|PhuongThi Minh Tran|
# **Reasons for Low Migration Rates**

Three stylized facts presented below suggest three interrelated reasons for Egypt’s reduced internal migration rates. The first is low educational attainment, as migration rates tend to increase with level of education. Second, labor is absorbed by low-productivity agricultural activities, which relates to the third reason for low migration, that of households producing significant portions on their total food consumption, in what has been labled the “food problem” (Gollin, Parente and Rogerson, 2008).

# **Migration and Educational Attainment**

Migration rates increase with educational attainment (Table 14). The correlation may reflect both the larger numbers of workers in lower educational levels, and the greater tendency of highly-educated individuals to migrate. Migration rates among those with the least educational attainment (from illiterate to technical secondary) are low, with a maximum of 8.3 percent for graduates of technical secondary schools. As educational attainment increases, migration rates spike to reach more than 20 percent among migrants possessing post-graduate degrees.

Table 14 – Migration Rate by Educational Attainment

|Education Level|Non-Migrants|Migrants|Total|
|---|---|---|---|
|Less than 6 years|99.2|0.8|100|
|Illiterate|91.9|8.1|100|
|Read & Write|94.4|5.6|100|
|Less than intermediate|94.6|5.4|100|
|General Secondary|93.1|6.9|100|
|Tech. Secondary|91.7|8.3|100|
|Above Intermediate|88.8|11.2|100|
|University|87.7|12.3|100|
|Above University|78.9|21.1|100|
|Total|93.8|6.2|100|

Source: Authors’ calculations using Labor Force Survey Most migrants have had little education. Around 25 percent are illiterate, and 51.4 percent received less than intermediate level schooling. It is worth noting however, that a significant share of migrants (15.5 percent) possess college degrees.

13
Using the approach based on the risk of falling into poverty, as proposed by Lopez-Calva & Ortiz-Juarez (2014), is conceptually more accurate in defining the lower bound of the middle class for country-specific analysis. Interestingly, it has been found that despite using an alternative approach, this paper’s results align closely with their findings, which is a valuable contribution to the discussion on middle-class thresholds. Moreover, while the risk-based approach is highly relevant and useful for defining the lower end of the middle class, it does not address the upper end of the spectrum. The proposed methodology in this paper simultaneously tackles the lower and upper bounds of the middle class, using a consistent methodological approach. Doing so provides a comprehensive and nuanced understanding of the middle class in Latin America, which can be of great value to policy makers and researchers alike.

Despite offering valuable insights into the socioeconomic structure of the region, this methodology has certain limitations that should be acknowledged. First, the synthetic panel construction using labor market surveys is subject to potential biases and measurement errors inherent in self-reported income data. These biases may impact the accuracy of the estimated bounds for the middle class. Additionally, the synthetic panel data may not capture the full extent of income mobility based on cross-sectional surveys rather than longitudinal data. Second, this approach does not explicitly account for factors such as social protection systems, access to quality education and health care, or other non-income dimensions contributing to an individual's vulnerability or resilience to falling into poverty. As a result, the proposed methodology may not fully capture the complex dynamics that define the middle class in Latin America. It may overlook essential aspects related to social inclusion, economic security, and quality of life. Lastly, this method may need to comprehensively understand the heterogeneity within the middle class. This group is diverse, encompassing households with varying levels of vulnerability. This approach may not adequately capture these nuances, which are essential for designing effective policies to foster inclusive growth.

While the present approach offers a sound perspective on defining the middle class in Latin America, it is important to recognize its limitations and consider complementary methodologies to better understand the complex dynamics of this socioeconomic group. By acknowledging these limitations, this paper aims to contribute to a richer and more informed debate on the region's identification and characterization of the middle class.

23
Table A4: Median of Materials to Sales Ratio by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.785|0.774|0.779|0.724|0.833|0.784|0.797|0.774|
|06:chemical products (28-38)|0.813|0.824|0.777|0.790|0.814|0.771|0.787|0.772|
|07:plastics & rubber (39-40)|0.806|0.791|0.791|0.799|0.830|0.806|0.798|0.798|
|08:raw hides & skins (41-43)|0.806|0.810|0.788|0.766|0.772|0.792|0.763|0.741|
|09:wood & articles (44-46)|0.801|0.788|0.769|0.741|0.776|0.801|0.796|0.815|
|10:pulp of wood (47-49)|0.800|0.796|0.778|0.785|0.818|0.799|0.769|0.771|
|11:textiles (50-63)|0.791|0.782|0.770|0.771|0.769|0.758|0.753|0.736|
|12:footwear & headgear, etc. (64-67)|0.795|0.778|0.754|0.770|0.763|0.745|0.749|0.720|
|13:stone, plaster, cement, etc. (68-70)|0.795|0.768|0.735|0.777|0.750|0.777|0.739|0.753|
|14:precious metals (71)|0.780|0.754|0.739|0.749|0.744|0.711|0.724|0.762|
|15:base metals (72-83)|0.826|0.817|0.797|0.782|0.812|0.791|0.787|0.810|
|16:machinery, mech, elect & eqmt (84-85)|0.800|0.803|0.773|0.773|0.804|0.796|0.780|0.780|
|17:vehicles & aircraft (86-89)|0.811|0.829|0.800|0.776|0.811|0.787|0.809|0.788|
|18:optical, photographic, etc. (90-92)|0.806|0.785|0.750|0.759|0.773|0.753|0.753|0.727|
|20:misc manufacturing (94-96)|0.796|0.776|0.757|0.764|0.783|0.755|0.758|0.761|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey.

20
**Table 3.2: Decomposition of Annual Changes in Employment by Personal Characteristics**

||year en|dingthe secondqu|arter of...|
|---|---|---|---|
||2008|2009|2010|
|**TOTAL**|**960,040**|**-522,415**|**1,307,551**|
|**BY SEX**||||
|female|398,975|-221,542|603,825|
|male|561,065|-300,873|703,726|
|**BY EDUCATION LEVEL**||||
|Without instruction|38,222|-290,129|18,351|
|Primary|-117,628|-732,183|23,931|
|Secondary|568,820|-87,614|661,404|
|High school|323,360|296,169|409,459|
|Professional|147,266|291,342|194,406|
|**BY AGE GROUP**||||
|Less than 26|171,178|-541,611|500,242|
|From 26 to 35|109,625|-193,136|187,169|
|From 36 to 45|319,282|-57,089|163,450|
|From 46 to 55|165,732|285,681|148,266|
|More than 55|194,223|-16,260|308,424|
|**BY GEOGRAPHIC LOCATION**||||
|Capital|248,065|-66,564|314,352|
|Center region|69,142|-142,347|290,907|
|Center gulf region|18,055|-32,695|126,057|
|North central region|42,011|-30,037|65,428|
|North pacific region|41,830|-17,509|54,851|
|Central pacific region|159,689|-58,427|189,950|
|South pacific region|24,861|59,082|95,370|
|Peninsula region|74,524|9,541|59,828|
|Border|281,863|-243,459|110,808|

**Source: Own calculations using data from ENOE.**

The crisis produced net job losses in the primary and secondary sectors (where tradable goods are produced) and a much subdued job creation in the tertiary sector (shown in table 3.3). Manufacturing endured the largest employment destruction among all sectors, with 700,000 jobs lost. Construction activities experienced the second largest employment destruction (123,000 jobs lost), which indicates that public works were not able to create enough employment to at least partly compensate for the shock of the crisis. Interestingly, employment in restaurants and hotels also experienced job destruction of more than 45,000 (something not seen in the last ten years. This was perhaps due to the recession in developed countries and the outbreak in April 2010 of the H1N1 flu in Mexico, both of which must have reduced the number of tourists traveling to the country.

15
# APPENDIX 10: Databases of National Environmental Policies

_Databases of national environmental regulations, financial incentives, overarching policies and frameworks, and voluntary approaches_

|**Database**|**Website**|**Data Description**|
|---|---|---|
|Faolex|faolex.fao.org|National laws, regulations and policies relating to food, agriculture,<br>and renewable natural resources|
|Ecolex|www.ecolex.org|National environmental legislation,court decisions,and literature|
|Climate Change<br>Laws of the<br>World|web.law.columbia.edu/climat<br>e‐change/resources/climate‐<br>change‐laws‐world|Information on country‐specific climate change policies, laws, plans,<br>and initiatives by sector (climate, energy, air pollution, forest and land<br>use, environmental impact assessments, and adaptation and<br>resiliency)|
|New Climate<br>PolicyDatabase|climatepolicydatabase.org|Information on GHG mitigation policies by sector (electricity and heat,<br>industry,buildings,transport,and agriculture and forestry)|
|Legal Atlas|www.legal‐atlas.net/|Dynamic database of national environmental legislation that can be<br>compared across topics and countries|

60
Table 13: Main reason for having chosen the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|2.194|-0.075|1.967|0.298|-3.493|1.681|
||(2.210)|(2.848)|(3.953)|(6.851)|(3.129)|(6.271)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.058|0.073|0.015|0.036|0.049|0.059|
|N|3394|3388|3394|3388|3394|3388|
|Control mean|19.359|19.359|11.718|11.718|32.621|32.621|
||Good pro|spect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-13.431<sup>_∗∗∗_</sup>|-8.901<sup>_∗_</sup>|-0.465|-0.251|-2.470<sup>_∗∗_</sup>|-2.770|
||(3.193)|(4.791)|(0.368)|(0.253)|(1.174)|(1.761)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.076|0.066|0.023|0.022|0.047|0.084|
|N|3394|3388|3394|3388|3394|3388|
|Control mean|21.619|21.619|0.516|0.516|4.914|4.914|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|6.645<sup>_∗∗∗_</sup>|7.690<sup>_∗∗∗_</sup>|9.053<sup>_∗∗∗_</sup>|2.329|||
||(1.475)|(2.716)|(2.841)|(2.604)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.046|0.049|0.049|0.046|||
|N|3394|3388|3394|3388|||
|Control mean|0.721|0.721|8.533|8.533|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

34
Access survey and the estimates generated through the methodology already described. The penetration of loans varies widely across countries and is closely correlated with economic development. Developed economies have the largest number of loans per thousand adults. What’s more, it is likely that this figure underestimates the true amount of loans in these countries due to the presence of a large number of highly-developed unregulated lenders. In Eastern Europe and Central Asia, there are, on average, 367 loans per thousand adults. Latin America and South Asia follow with 314 and 268, respectively. Middle East and Africa are the least develop markets.

**5. Which Country Characteristics Are Correlated with Outreach of the Financial System?** This section explores the empirical relationships between our financial access indicators and an array of country-level variables that intuition, theory, and previous empirical work suggest might be relevant. Cross-country analyses such as these suffer from a number fundamental problems (including omitted variables and reverse causality) which make it impossible to infer a causal relationship between the independent and dependent variables, even when the regression coefficients are statistically significant. In measuring the correlations and conditional correlations with regressions we do not hope to imply any causal relationships, but simply to map out the main relationships and features of the data. A similar exercise was carried out in Beck, Demirgüç-Kunt, and Martinez-Peria (2007). In that paper, the authors correlate deposit and loan penetration with various factors from the empirical literature to explore the determinants of financial development. We improve on their effort by using a larger sample of countries and newer data. As a rough guide, we rely on the framework outlined by Beck and de la Torre (2006) to determine which factors are likely to impact financial outreach and thus should be included in our analysis.

We divide the analysis into three areas: savings, credit, and physical outreach. Table 19 provides correlations between all of our indicators of account penetration, loan penetration and demographic penetration of branches and all explanatory variables. Tables 20-23 rely on a theoretical framework and build simple OLS specifications to estimate the relationship between a 26
recent study of Tunisian returnees, whose entrepreneurial activities in Tunisia were shown to be supported by transnational social networks and partnerships with business contacts in France and Italy, their countries of migration (Cassarino, 2000).

With respect to Albania, limited empirical evidence exists, and what is available is mostly anecdotal or based on qualitative research or case studies. The conclusions are also ambiguous. A number of studies (Nicholson, 2001 and 2002; Labrianidis and Hatziprokopiou, 2005) provide evidence that returnees use savings earned abroad to finance micro-enterprises and purchase productivity-enhancing equipment in existing activities. In addition, work abroad appear to be providing a learning opportunity for migrants to enhance their skills so that, once back home, they can replicate businesses in which they worked abroad. Despite these positive results, return migration continues to be associated with failure among Albanians (Labrianidis and Kazazi, 2006) and no solid evidence exists to substantiate the position that sizable investments are directed towards productive activities (Barjaba, 2000).

Based on our theoretical reasoning above and the available empirical evidence, we hypothesize that past migration experience of Albanian households will positively influence the probability of business ownership. In addition, this impact will be different depending on the host country, and the timing of migration. More specifically, we anticipate past migration experience in Italy and beyond, as well as earlier past migration experience, to be associated with higher probabilities of being involved in non-farm businesses.

# **3. Data**

The data for this study come from the 2005 Albania Living Standards Measurement Study (ALSMS05) survey conducted by the Albanian Institute of Statistics (INSTAT), with technical assistance from the World Bank, between April and November, 2005. The sampling frame for the survey was stratified into four regions – namely coastal, central, mountain and Tirana, the capital – and a total sample of 3,640 households from 455 census enumeration areas (EAs) was drawn based on a multi-stage cluster design.

The ALSMS05 includes a typical household questionnaire covering general household demographics, education levels, asset ownership, expenditures and labor market participation. In addition, the survey also provides community-level data, which include information on access to services and infrastructure in the locality, as well as price information. The household

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