rafmacalaba/datause-encoder-data huggingface dataset:
_'5_ _Import Substitution and Export Posibilities:_ For some countries like South Korea and India, which have a sound well developed financial system and adequate technical and professional manpower, there is a potential that cari be developed, through institutionalized expertise and financial technology, for acquiring the capacity to provide financial services to domestic borrowers, intending to have operations in the international market, as well as to export such services to other developing countries. These services are generally skillintensive and much le,, capital-intensive than some of the manufactured products. leveloping the capacity for providing such services on the basis of their dynamic comparative advantage would strengthen their payment position by saving on foreign exchange cost relating to their transactions in the international market (import substitution advantage) as well as by earnings from the export of such services. This potential somehow has not been exploited; the necessary institutional and policy changes, hence, have not been identified and implemented. For their effective and efficient participation in the international market, it is essential to seize all opportunities for the actualization of this poter,tial.

# **I3L Perequites for** Par"i:latlon: Agemda **for Research**

Participation in the international capital market is possible only if certain pre-conditions or pre-requisites are satisfied through an appropriate institutional and policy framework. This framework has to be such as to:

## *establish and maintain creditworthiness;

obring about progressive integration of the domestic and international markets; and opromote competitive impulses and pressures for improving the effectiveness and efficiency of domestic financial markets in an environment of financial stability ensured through strategic and prudential regulation of the financial system.

These are inter-related and inter-linked policy measures and it is not possible to take action on one front without at the same time relating it to the
# **3.2.2 Profile of the bottom 40 percent of population**

In the previous section, we observed that if spatial deflators are measured consistently across two periods in time, the impact on shared prosperity indicators is rather minimal for most countries unless the spatial price adjustments were carried out inconsistently over time. Even if growth rates do not change, it is still important to adjust for spatial price differences because purchasing power of the same amount of expenditure or income differs significantly if the price level differs. Furthermore, the welfare ranking of people can change significantly based on spatial price adjustments. Spatially adjusted prices may change where certain people in the bottom 40 percent are located in the welfare distribution. We explore this concern empirically using a subsample of our database – data for Kazakhstan, Rwanda, Tajikistan and Iraq.

Table 3 shows the percentage of the population remaining in the same decile regardless of the choice of welfare aggregate: nominal or spatially deflated. Substantial reshuffling of people occurs starting from the second decile. In Rwanda for example, only 60 percent of the population in the fourth decile stays if the welfare aggregate is spatially deflated. A further 20 percent of those are found in the fifth decile if regional variation in prices is taken into account.

As a result of the change in rankings, the share of the population in the bottom 40 can shift across regions. This is illustrated in figure 8 using data for Kazakhstan. In ten of sixteen regions, the share of the population from the bottom 40 percent is significantly different depending on whether a nominal or spatially deflated welfare aggregate is used. For example, only 8 percent of the total population in Almaty was from the bottom 40 percent if nominal consumption per capita is used; however, the ratio increases to 14 percent if the welfare aggregate is spatially deflated (results for Iraq and Rwanda are provided in the annex).

**Table 3. Percentage of population staying in the same decile regardless of welfare aggregate: spatially deflated or nominal**

|Deciles|Kazakhstan,2010|Iraq,2012|Rwanda,2010|
|---|---|---|---|
|bottom|89|86|90|
|2|72|66|72|
|3|62|57|64|
|4|57|54|60|
|5|56|49|58|
|6|57|49|57|
|7|60|55|61|
|8|65|59|69|
|9|76|68|81|
|top|91|88|93|

Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.

13
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individual selected may be on various spectrums of likelihood to migrate: some may have migrated in the past, and others have not. Our analysis is conducted separately for men and women; therefore, the results are not affected by the gender imbalance. The average age of respondents is 29.8 years and the average education, measured by years of schooling is 9.4, with similar levels for women and men (9.3 and 9.7 respectively). Of the female respondents, 81 percent are married compared to only 53 percent of the male respondents.

**Table 1. Sample description**

|**Variable**|**Whole sa**|**mple**|**Women**||**Men**||**Min**|**Max**|
|---|---|---|---|---|---|---|---|---|
||_Percent_||_Percent_||_Percent_||||
|Female|61%||N.a.||N.a.||N.a.|N.a.|
|Married|70%||81%||53%||N.a.|N.a.|
||_Mean_|_Std. Dev._|_Mean_|_Std. Dev._|_Mean_|_Std. Dev._|||
|Age|29.76|6.61|30.20|6.43|29.06|6.83|18.00|40.00|
|Years of schooling|9.42|3.52|9.28|3.52|9.65|3.50|0.00|18.00|
|Number of children<br>living with Respondent|0.79|1.15|0.97|1.22|0.49|0.98|0.00|7.00|
|Total work income (in<br>millions,IDR)|7.4|21|4.4|15|12|27|0|960|

Source: Authors’ calculations from DESMIGRATIF (2018) This study aims to predict individuals’ interest in migration, captured by a self-declared response to the question of “How interested are you in working abroad?” This question is in a 5-points Likert scale format, ranging from “not interested” to “very interested” (see Figure 1).

**Figure 1. Interest in migration in Likert scale by gender**

_Source_ : Authors’ calculation.

9
# **B. Institutional tools:**

As mentioned before, countries of the region present higher scores in regulatory tools than in those indicators grouped under institutional tools. Leading countries are Guatemala, Brazil, Jamaica, and Peru. Countries with the lowest scores are Panama, Ecuador, and Honduras.

**Figure 23: Institutional Tools Index**

<!-- Start of picture text --> Institutional Tools Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1) .4<br>.2<br>0<br>GUA BRA ARG JAM PER BOL T1 URU T&T COL ESV NIC C R R D MEX CHI BAR T2 PAN ECU HON<br><!-- End of picture text --> There are large disparities between countries at the top and the bottom of the index. Guatemala, Brazil, Jamaica, and Peru have certain regulatory quality standards tools (cost-benefit analysis, regulatory impact analysis, and administrative simplification), the full use of performance-based payments for their employees, the publication of both annual reports and institutional strategies and, with the exception of Peru, a structure of posts and salaries. By contrast, worst performing countries lack regulatory quality standards, do not use incentives for their employees, and have not developed institutional strategies. In addition, the registration of consumer complaints is facilitated through paper-based mechanisms, not using electronic devices to perform that task.

It is worth highlighting the use of regulatory impact analysis (RIA) by countries such as Mexico and Peru. In fact, Mexico’s regulatory impact analysis was praised by the OECD, which has considered the use of Mexican’s RIA as a benchmark in the management of cost-benefit analysis of regulations (OECD, 1999). The impact analysis of regulations in Mexico is done by a centralized body of the public administration, the Federal Commission for Regulatory Improvement (COFEMER), which performs cross-sectoral assessments on the impact of administrative decisions.

# **5. - ROBUSTNESS:**

We practiced two exercises to test the robustness of the Electricity Regulatory Governance Index (ERGI). Both exercises involved giving different weights to each of the variables of our regulatory governance framework. First, we assigned arbitrary weights to some variables. We assigned autonomy 40 percent of the total weight, and transparency, accountability, and tools were each given 20 percent. Moreover, within autonomy, the question related to tariff regulation (agency’s decision responsibilities in tariff structure and level) bears 50 percent of the total weight of the index. The rationale to this approach

- 34 -
South African agriculture has the appearance of being sophisticated and highly successful. A closer look at the present structure and performance of South Africa's agricultural sector, however, reveals that despite the appearance of efficiency, the sector has followed a pattern of growth that is far from normal. Although agriculture is generally characterized by constant returns to scale and an inverse relation between farm size and productivity (Binswanger, Deininger and Feder, 1993), the sector is dominated by relatively large farms that are owned and operated by a comparatively small number of individuals. International evidence indicates that a large-scale mechanized farm sector generally is inefficient, especially when compared to small-scale family type farm models. Although there may exist very real economies of scale, they are mostly 'false' because they are usually the result of policies which favor larger farms over small farms.

At least tNo questions related to the productivity relations in South African agriculture -both commercial and subsistence-- and the effects of size on these relations, which have not been adequately addressed, are important when considering land reform along the lines proposed for South Africa:

- Are large mechanized farms and the present commercial white farms economically efficient relative to smaller holdings?

- What is the role of past policies in determining these observed productivity relations?

If larger farms are not efficient relative to smaller farms, then smaller farms and equalizing the ownership distribution would enhance both efficiency and equity, and if policy created artificial economies of scale, they should be adjusted..

This paper has as objective to explore these issues by briefly reviewing the sources of economies of scale and international evidence on these issues, as well as analyzing representative farm-level data in both the commercial and former homeland sectors. These analyses are conducted against the policy environment and changes therein, as well as other factors which influence farm production.

> 2 In its recent study, the World Bank (1994), supported by seveal othr studies (see, for example, Van Zyl and Groenewald, 1988; Thirtle, et al, 1993 and Van Zyl, 1994), concluded that agriculture in South Africa appears to be a highly sophisticated and successful sector, but this appearance hides severe distortions and inefficiencies. Evidence that is often cited in support of the former view is the fact that South Africa is self-sufficient with respect to most of its major agricultural commodity requirements. At the same time, the sectors relatively small and declining share of GDP is seen as indicating a pattern of secular decline of agricultural prduction that is consistent with a normal pattern of economic growth and development (Van Zyl, Nel and Gromnewald, 1988). A closer look at the present structure and performance of South Afica's agricultural sector, however, reveals that despite the appearance of efficiency, the sector has followed a patten of growth hat is far from normal (Van Zyl and Groenewald, 1988).

1
|Table 4: Comparison of|gender gaps|in education: DH<br>|S, UNESCO, B<br>|arro-Lee.|||||
|---|---|---|---|---|---|---|---|---|
||Female le|Primary net e<br>vel (years)|nrollment rate<br>Male/ fem|ale ratio|Average<br>Female le|years of schoo<br>vel (years)|ling in populatio<br>Male / fe|over 15<br>male ratio|
||DHS<br>(average of<br>DHS years)|WDI (1990-<br>1999<br>average)|DHS<br>(average of<br>DHS years)|WDI (1990-<br>1999<br>average)|DHS<br>(average of<br>DHS years)|BL (1990)|DHS<br>(average of<br>DHS years)|BL (1990)|
|Benin|30.4|39.8|1.6|1.82|1.2|1.0|2.5|2.36|
|Burkina Faso|27.3|22.3|1.3|1.56|0.8||2.0||
|C.A.R.|41.9|42.3|1.3|1.52|1.7|1.2|2.2|2.12|
|Cameroon|61.4|.|1.1|.|2.8|2.5|1.6|1.47|
|Chad|.23.7|32.8|1.5|1.80|0.7||3.5||
|Cote d'lvoire<br>|42.6|46.4|1.3|1.34|1.9|.|2.0||
|Ghana|73.0|.|1.0|.|4.4|2.0|1.5|2.60|
|<br>Mali|<br>25.4|17.6|<br>1.3|1.60|<br>0.8|<br>0.5|<br>1.9|2.54|
|Niger|173|176|16|174|06|05|21|251|
|<br>Senegal|.<br>32.4|.<br>47.3|.<br>1.3|.<br>1.26|.<br>1.4|.<br>1.7|.<br>1.9|.<br>1.73|
|Togo|64.9|66.8|1.2|1.37|2.0|1.7|2.1|2.54|
|Egypt|78.3|84.7|1.1|1.14|4.9|3.2|1.5|1.71|
|Morocco|49.1|56.3|1.36|1.35|2.0|.|1.83||
|Bangladesh|75.1|59.7|1.0|1.14|2.5|1.4|1.7|2.13|
|<br>India|61.5|.|1.22|.|3.0|2.8|1.91|1.96|
|Nepal|57.3|.|1.3|.|1.3|0.7|2.7|3.36|
|Pakistan|39.7|.|1.4|.|1.8|2.8|2.3|1.93|
|Comoros|41.5|46.6|1.1|1.23|2.2|.|1.6||
|Kenya|81.2||1.01|.|5.2|2.9|1.3|1.58|
|Madagascar<br>|60.4<br>|62.7<br>|1.0<br>|0.92<br>|3.2<br>|.<br>|1.2<br>||
|Malawi|74.4|59.2|1.0|0.99|2.2|2.1|2.0|1.65|
|Mozambique|54.3|35.2|1.1|1.30|1.6|0.6|2.0|1.88|
|Namibia|91.1|92.7|1.0|0.93|5.0|.|1.0||
|Rwanda|61.0|70.3|1.0|1.01|2.8|1.3|1.3|1.78|
|Tanzania|52.4|49.8|1.0|0.98|3.6|2.1|1.3|1.54|
|Uganda|66.6|.|1.0|.|3.2|1.4|1.6|1.49|
|Zambia|72.9|74.9|1.0|1.02|4.7|3.5|1.4|1.71|
|Zimbabwe|84.3|.|1.0|.|5.9|2.7|1.2|1.54|
|Dominican Rep.|72.1|82.7|0.9|0.96|6.8|4.5|1.0|0.99|
|<br>Guatemala|66.5|.|1.11|.|3.8|2.7|1.22|1.28|
|Haiti|70.2|22.6|1.0|0.96|2.8|2.0|1.4|2.00|
|Nicaragua|83.1|78.2|1.0|0.97|5.4|3.7|1.0|1.00|
|Indonesia|91.1|95.0|1.0|1.05|5.2|4.1|1.3|1.27|
|Philippines|90.0|.|0.97|.|8.3|6.9|0.99|0.99|
|Bolivia|91.6|86.7|1.0|1.09|6.1|4.2|1.3|1.33|
|Brazil|94.7|.|0.99|.|5.8|3.7|0.97|1.04|
|Colombia|88.1|.|1.0|.|6.4|5.1|1.0|0.83|
|Peru|88.2|90.3|1.0|1.01|6.9|5.9|1.2|1.11|
|Kazakstan|90.4|.|1.0|.|9.5|.|1.1||
|Turkey|70.8|92.8|1.05|1.05|4.2|3.1|1.48|1.45|
|Uzbekistan|62.8|.|1.0|.|9.7|.|1.1||
|Unweighted mean*|58.6|58.3|1.1|1.22|3.7|2.6|1.6|1.73|
|<br>Unweighted std. Dev.'<br>|23.0|24.7|0.2|0.28|2.1|1.6|0.5|0.58|
|Maximum'|91.6|95.0|1.6|1.82|8.3|6.9|2.7|3.36|
|Minimum*|17.3|17.6|0.9|0.92|0.6|0.5|1.0|0.83|
|Median*|61.0|59.2|1.1|1.14|3.4|2.6|1.5|1.68|
|Unweighted mean|63.4||1.1||3.8||1.6||
|Unweighted std. Dev.|21.7||0.2||2.4||0.5||
|<br>Maximum|94.7||1.6||9.7||3.5||
|Minimum|17.3||0.9||0.6||1.0||
|Median|66.5||1.0||3.2||1.5||

Countries with data from both sources only.
a result similar to those reported in previous specifications. Thus, private firms appear to use fewer employees, _ceteris paribus_ , than public firms. The value of the coefficients suggests that the modal private firm operates efficiently and that the modal public firm operates inefficiently, in the sense of using more employees to produce a given vector of outputs.

# _Between-firms variations_

The between-firms variations model uses a subset of firms consisting of those that have constant ownership type. Such a sub-sample guarantees that the dummy variable accounts only for between-firm variations in ownership, which are more likely to capture a long-run effect of ownership type on efficiency.

OLS estimates of the between-firms variations model are reported in column (2) of Table 3. The results are similar to those in the full sample case: the coefficient on the private dummy is negative and significant at the 1% level, suggesting that private firms are more efficient in the use of labor than their public counterparts.

# _Model in first differences_

The previous specifications established a positive correlation between private ownership and firm-level efficiency. In the early stage of the reforms, however, governments may wish to demonstrate that privatized firms achieve higher efficiency. To do so they are likely to choose to privatize firms that are already operating relatively efficiently, so that when privatized they provide an example of high efficiency compared with their remaining public sector counterparts. This would imply that the preceding results might be biased in favour of finding that private ownership improves efficiency. If The hypothesis that inefficiency effects are not stochastic, _H_ 0 : _γ_ = 0, is also rejected at the 1% level (LR=76.05>8.27).

12
The Big-Five personality test aims to measure non-cognitive (socio-emotional) skills through the following five components: (i) openness (to experience), (ii) conscientiousness, (iii) extraversion, (iv) agreeableness and (v) neuroticism. Details on each component are below (Table 4.2 of Jonh, Naumann and Soto, 2008): _Openness to experience_ describes the breath, depth, originality and complexity of an individual’s mental and experiential life.

_Conscientiousness_ describes socially prescribed impulse control that facilitates task- and goal-directed behavior, such as thinking before acting, delaying gratification, following norms and rules, and planning, organizing, and prioritizing tasks.

_Extraversion_ implies an energetic approach toward the social and material world and includes traits such as sociability, activity, assertiveness and positive emotionality.

_Agreeableness_ contrasts a prosocial and communal orientation toward others with antagonism and includes traits such as altruism, tender-mindedness, trust and modesty.

_Neuroticism_ contrasts emotional stability and even-temperedness with negative emotionality, such as feeling anxious nervous, sad and tense.

# <u>Pilots</u>

The experiment was first developed through pilots. Two different pilots were introduced to test both the content of the assessment and the survey tools planned to be used. The first pilot was composed of a telephone survey of 100 job seekers in our target population, using the Big Five Personality Test (24 question version adopted in the World Bank’s Skills Toward Employment and Productivity (STEP) Measurement Survey (Pierre et al. 2014)). For this survey, Babajob’s Consumer Insights Team was instructed to call job seekers and give the questions exactly as they were written without any explanation, even if the respondent requested it. The survey was administered from June 22 to June 24, 2016. Half of the job seekers were told that the results of this survey would be shared with potential employers, while the other half were told that the survey was purely for research purposes and the results would not be shared. The goals of this pilot were to ensure that job seekers understood the questions of the assessment and how to answer them, and to investigate whether job seekers were likely to change their answers if they believed the results would be shown to employers. Overall, the job seekers understood the questions being asked (only 2.7% of all responses given were the “Respondent couldn’t answer” option), and there was not significant deviation between the two groups.

6
# Skills, Exports, and the Wages of Five Million Latin American Workers<sup>_∗_</sup>

Irene Brambilla<sup>_†_</sup> Rafael Dix Carneiro<sup>_‡_</sup> Daniel Lederman<sup>_§_</sup> Guido Porto<sup>_¶_</sup>

> _∗_ The authors gratefully acknowledge the financial support from the World Bank’s Latin American and Caribbean Studies Program and the World Bank executed Multi-Donor Trust Fund on Trade and Poverty. Invaluable insights and comments on previous versions of this paper were received from William F. Maloney, J. Humberto L ́opez, Augusto de la Torre, and especially Pravin Krishna. The opinions expressed herein do not represent the views of the World Bank, its Executive Directors, or the governments they represent. All remaining errors are the authors’ responsibility.

> _†_ Universidad de La Plata, Universidad de San Andres, and NBER. Calle 6 e 47 y 48, (1900) La Plata, Argentina. email: irene.brambilla@econo.unlp.edu.ar

> _‡_ Princeton University, email: rdc@princeton.edu

> _§_ The World Bank, 1818 H St. NW, Washington DC 20433. email: dlederman@worldbank.org

> _¶_ Universidad Nacional de La Plata, Calle 6 e/47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar