rafmacalaba/datause-encoder-data huggingface dataset:
Using a new dataset assembled by the World Bank Payment Systems Group on the cost of remittances across 119 country corridors, this paper explores the factors that drive remittance costs.<sup>6</sup> The corridors studied include 13 major remittance sending countries and 60 receiving countries, representing approximately 60 percent of total remittances to developing countries. Because our data are by corridor, we are able to conduct a bilateral analysis of costs, as opposed to simply looking at costs aggregated at the receiving or sending country level. Furthermore, contrary to previous studies that have only focused on a certain type of remittance service providers (in particular the largest international money transfer operators), the data used here pertain to the largest providers in each corridor, be they money transfer operators, banks, post offices, etc.<sup>7</sup> At the same time, we are able to conduct our analysis both averaging across all types of providers and separately for banks and money transfer operators, thus allowing us to compare the determinants of the costs of remittances across different institutions. Finally, by analyzing the costs charged by Western Union across 98 corridors (80 percent of the sample), we are able to abstract from concerns of bias due to differences across firms (since we are looking at the same provider across corridors) and we are able to shed light on what drives the costs charged by a leading remittance service provider with worldwide operations.

We distinguish between three groups of factors as potential drivers of the cost of remittances. First, we consider the role of socio-economic characteristics of sending and receiving countries that might influence fees through their impact on costs incurred by remittance service providers, including the number (stock) of migrants, the level of economic and financial development, and the share of rural population within each corridor. Second, we examine the role of factors that might affect the ability of remittance service providers to set prices like the extent of competition, the market structure, and the level of education of the migrant population. Third, we assess the impact of government policies in different areas including exchange rate policies, capital controls, and regulation of remittance service providers.

Estimations of the cost of remittances across all types of remittance service providers show that corridors with a larger number of migrants and more competition exhibit consistently

> 6 The original World Bank database contains information on 134 corridors. We lose 13 corridors - those where Russia is the sending country- due to missing exchange rate spread data,  plus 2 other corridors where there is missing information for some explanatory variables.

> 7 On average, in each corridor between 8 and 10 providers are included. In some corridors, primarily those including the US and Spain as sending countries, the number of providers surveyed exceeds 10.

3
and universal enrollment in primary school, by age 12 about 54% of children still have not completed primary education (sixth grade). Grade repetition affects 25% of primary school students and about 20% of those in secondary school. On average repeaters lose around 1.5 years in primary school and 1.2 years in secondary school. In the age group 24-29 around 20% of individuals declare never having started junior high school. Among those who started this school cycle around 16% declare not having completed it. Data in ANEP (2005) show markedly more pronounced repetition rates among children from more disadvantaged backgrounds. Based on a socio-cultural indicator of schools, children in the bottom quintile of the distribution of that indicator are around three times more likely to repeat than children in schools in the top quintile.

In an attempt to reverse the poor performance of the education system, in the mid-1990s, the Government of Uruguay took direct actions to achieve universal pre-primary education for 4 and 5 years old (ANEP, 2000). The motivation for this reform was twofold. First, this was meant to achieve an increase in the number of years of schooling without raising school leaving age. This appeared the most viable policy option given the inability of the system to retain a large proportion of teenagers. Second, this program hoped to ease children's insertion into and transition through the primary school system, by providing them with some basic foundations before the start of the primary cycle and socializing them (and their parents) to school from an early age.<sup>2</sup> The hope was that this policy would reduce the high incidence of repetition among primary school children, hence making the transition through the primary school cycle speedier and in turn reducing the incentive for early drop out.

The lack of teaching infrastructures was a major constraint to a further expansion of the system and for this reason, in 1995 ANEP ( _Administration Nacional de Educación Pública_ ), the government agency in charge of public education, started an ambitious building plan that aimed at expanding preschool provision in public primary schools. By 1999, 414 new classrooms had been 6
# **3. Data and descriptive statistics**

The main data set we used for this study is the Tunisian Statistical Business Register, namely the _Répertoire National des Entreprises_ (RNE), collected by the National Institute of Statistics in Tunisia (Institut National de la Statistique). The RNE is an exhaustive data set, compiled from merging different administrative folders from the social security fund (Caisse Nationale de la Sécurité Sociale – CNSS), which is the source for the employment data, the Tunisian Customs, the Tunisian Ministry of Finance, and the Tunisian Investment Promotion Agency (l’Agence de Promotion de l’Industrie et de l’Innovation – APII). A major and unique advantage of the Répertoire is that it accounts for all enterprises and covers a relatively long period.

This database contains information on the number of employees, age, trade activities (exports and imports), foreign ownership, as well as trade regime categorization (i.e. offshore) of all registered private firms. The RNE database allows us to track the exit and entry of firms over time. We also exclude firms that report less than one employee to consider only active firms.<sup>10</sup> A detailed description of the RNE is presented in Rijkers et al (2014). Our analysis considers the 1996-2015 period. The data cover approximately 60,000 registered firms per year, for which about 1.2 million firm-year observations were available.

Table 2 describes the main variables used in the analysis. On average, Tunisian firms are about 14 years old, while a median firm is 11 years old. The main variable used in the analysis, to define HGF status, is employment. On average, Tunisian firms have about 15 employees, while a median firm has only 2 employees. Less than 10 percent of the firms have more than 20 employees.

Table 2 – Descriptive statistics of firm-level data for Tunisia (1996-2015)

|**Variable**|**N**<br>|**mean**|**p50**|**p10**|**p90**|
|---|---|---|---|---|---|
|Age|1,167,050|13.79|11|3|28|
|Age square|1,167,050|294.90|121|9|784|
|Employment|1,167,050|15.01|2|1|19.75|
|Export status|1,167,050|0.06|0|0|0|
|Import status|1,167,050|0.10|0|0|1|
|Exports (3-years)|539,550|0.21|0|0|0|
|Imports (3-years)|539,550|0.40|0|0|2|
|Foreign|1,167,050|0.03|0|0|0|
|Offshore|1,167,050|0.06|0|0|0|

Note: Exports (3-years) and Imports (3-years) refer to the sum of export or import statuses over 3 years. The calculation is performed only for those firms with 3 or more years of existence for each year, which explains the differences in the number of observations.

> 10 The National Institute of Statistics found that 8% of the registered self‐employed firms (reporting no employees) are “falsely active.”

5
MONGOLIA'S INFORMAL SECTOR Size and Trends 16

|**Estimates**|**of the Size of the In**|**formal Sector**||
|---|---|---|---|
|Method|Source(s) of Data|Reference|Estimate|
|Estimate the ratio of informals who<br>are not counted as employed to those<br>that are counted as employed;<br>multiply this ratio by the number of<br>people responding that they work for<br>sole proprietorships during the 1996<br>labor force census.|Informal sector<br>surveys conducted<br>by the author;<br>Annual Labor Force<br>Balance|Ulaanbaatar|23-32% of the working<br>age population were<br>employed solely in the<br>informal sector at the end<br>of 1996 and beginning of<br>1997;<br>including moonlighters<br>and working pensioners,<br>30-37%|
|Use the percentage of households<br>with informal income and the<br>average number of working age<br>household members to infer the<br>percentage of the working age<br>population with informal income.|SSO Household<br>Survey data.|Ulaanbaatar|30-33% of the working<br>age population of<br>Ulaanbaatar in the last<br>quarter of 1996.|
|Estimate the aggregate percentage of<br>informal income in aggregate total<br>income using household survey data.|SSO Household<br>Survey data.|Ulaanbaatar|32% of aggregate<br>household income in<br>Ulaanbaatar in the last<br>quarter of 1996.|
|Attribute the increase in the ratio of<br>currency to M2 between 1990 and<br>1996 to an expansion in the<br>"underground" economy; combine<br>with the velocity of M2, the current<br>level of official GDP, and the<br>(assumed) pre-transition level of<br>"underground" economic activity, to<br>arrive at an estimate of the current<br>size of the "underground" economy.|SSO and<br>Mongolbank data.|Mongolia|33-38% of the "above-<br>ground" economy at the<br>end of 1996.|

**Box 2**
_Dependence on the sales and promotion efforts of dealers._ Smetrix is dependent on dealers to drive volume through the platform. Apart from revenues from the Expanded Small Investors Program, heavy volume will make other organizations more likely to entrust their e-business initiatives to Smetrix.

# **How Are Business Risks Being Mitigated?**

As a financial services institution, the Treasury has unique experience when it comes to shared services. The chronic national budget deficit has deprived the government of resources to pursue a proprietary automation agenda. Given its limited budget, the Treasury has had to focus on a single critical mission: creating the Registry for Scripless Securities, and outsourcing remaining operations to private providers. This strategy can be regarded as a variant of privatization. Privatization is often pursued to increase efficiency, but the Treasury’s goal is to overcome budget constraints.

A downside to this strategy was that the Treasury has undergone business interruptions—such as the termination of the Small Investors Program in 1998—as a result of its shared services approach. Thus it has developed its own risk management approach so that systems can be back up if private service providers back out.

For clarity, the discussion of risks in this section deals with those specific to this variant of the shared services strategy. Hence the sections below often discuss operational risks that have had to be addressed under the Expanded Small Investors Program and related technology.

# **Operational continuity of the Registry of Scripless Securities**

The unreliability of funding for annual expenditures does not permit the Treasury to switch to a fully digital process. There is no “mirror” server in the Registry of Scripless Securities that can immediately take over if the primary server fails. The contingency plan is to revert to manual processes while registry operations are restored.

Backups of data and printouts of investor holdings and outstanding securities are maintained on a daily basis. In addition, accounting staff, facsimile machines, and telephones support the operations in the Treasury operations center.

The Registry of Scripless Securities (RoSS) provides registry and custody services for institutional investors and dealers that translate into about 3,000 accounts. This could balloon into several hundred thousand accounts by the time the program reaches full maturity in the next two years after the program becomes operational. Although the Treasury has upgraded the registry to hold up to 1 million individual accounts, it has had no occasion to stress test at maximum levels.

# **Partner interdependency**

The operations of the Expanded Small Investors Program require the availability of multiple systems. Because these systems are not under the control of the Treasury, the Treasury depends on the operational and security policies and procedures of its main partners: securities dealers and the Data Center. To foster an environment that ensures predictable services for investors, the Treasury signed 40
agency. Some countries have instituted a separate supervisory agency (e.g. Jordan, Syria, Tunisia), some countries have placed insurance supervision inside the central bank (Bahrain, Saudi Arabia), and other countries have merged insurance supervision with the capital market authority or other non-banking regulators (Egypt, Oman).

|**Table 11: Insurance Supervision Structures in MENA **|
|---|

|**Country**|**Supervisor **|
|---|---|
|**Algeria**|Ministryof Finance|
|**Bahrain**|Central Bank of Bahrain|
|**Egypt**|Egyptian FinancialSupervisoryAuthority|
|**Jordan **|Insurance Commission|
|**KSA**|Saudi Arabian MonetaryAuthority|
|**Kuwait**|MinistryofCommerce and Industry|
|**Lebanon **|Ministryof Commerce|
|**Libya**|PublicCommittee for EconomyTrade and Investment|
|**Morocco**|Ministryof Finance|
|**Oman**|Capital Market Authority|
|**Qatar **|Ministryof Business and Trade|
|**Syria**|Syrian InsuranceSupervisory Commission|
|**Tunisia**|ComitéGeneral des Assurances|
|**UAE**|Ministryof EconomyPlanning|
|**Yemen**|Ministryof Industryand Trade|

**Supervisory capacity varies considerably over the region.** Leading established supervisory jurisdictions include Bahrain, Jordan, Morocco, and Tunisia.  Egypt and Libya are in transition in parallel with their market liberalization programs. These countries have all benefited from strong government support. Newly formalized markets that have rapidly demonstrated a strong intervention capacity include KSA and Syria although these countries are still building their resources and supervisory models. Most catch up work is required in some oil and gas rich countries where to date insurance development has been seen as a secondary issue.

# **At the same time, Financial Sector Assessments (FSAPs) conducted by the World Bank and the IMF in MENA countries have identified several common weaknesses in supervisory practices:**

- Weak financial reporting – problems in obtaining consistent, accurate and timely statistical, financial and reinsurance information;

- Lack of adequate enforcement of claims provisions and premium levels for MTPL insurance;

- Lack of early intervention and enforcement actions at industry and insurer levels;

- Weak corporate governance;

- Problems in dealing with illegal and excessive payments to agents and brokers while allowing them to operate commercially;

- Problems ensuring that bancassurance products and distribution rules meet adequate market conduct standards;

- Lack of functional consumer protection mechanisms;

23
# **Figure 1.2. Female adolescents reporting forced sexual initiation, as a percent of those reporting having had sex (Population-Based Surveys 1993-1999)**

<!-- Start of picture text --> New Zealand 7%<br>United States 9%<br>Mozambique 19%<br>Ghana 21%<br>South Africa 28%<br>United Republic of Tanzania 29%<br>Cameroon 37%<br>Peru 40%<br>Caribbean 48%<br><!-- End of picture text -->

**Source:** Population-Based Surveys 1993-1999. All figures cited in Jewkes, Sen, and Garcia Moreno, 2002.

Sexual violence within marriage is also common, with approximately 10-13 percent of women reporting having been forced by a partner to have sex against their will at some point (Heise, Ellsberg, and Gottemoeller, 1999). Sexual violence often accompanies physical battery by intimate partners, and in many settings, women and men believe that men have the right to beat their partners for refusing sex (Figure 1.3). Women who experience physical violence by intimate partners may be less able to negotiate when and how they have sex. Nonetheless, large variations exist in patterns and prevalence levels between and within countries. For example, a study from León, Nicaragua found that nearly all women who reported sexual violence had also experienced physical violence (Ellsberg et al, 2000). In contrast, research from Indonesia suggests that sexual violence often occurs outside the context of physical violence and may be even more common than physical violence (Hakimi et al., 2001).

**Figure 1.3. Percentage of men and women who say that men have the right to beat their wives when they refuse to have sex.**

<!-- Start of picture text --> 81%<br>57%<br>Men<br>43%<br>Women<br>33%<br>28%<br>10%<br>5%<br>Egypt (rural) Egypt (urban) Ghana West bank / Gaza Nicaragua (rural) Singapore<br>strip<br><!-- End of picture text -->

**Source:** Various population-based surveys, cited in Heise, Ellsberg, and Gottemoeller, 1999.

11
whether the family owns their house in 2003, the quality of home construction (whether the house uses permanent materials like concrete) in 2003 and the individual’s age in 2003.

- Second, we also conducted a sequential bivariate semi-nonparametric estimation for the correction term: we first predict attrition from the sample, and then whether the individual is included in our wage regression. To predict sample attrition, we used an indicator for whether the mother was born in the village, the number of boys in the family, whether the father was living in the household in 2003, and whether the male interview was conducted in another language than Punjabi in 2003. To predict inclusion in our regression analysis sample, we use the number of schools in the village in 2003. We use the Stata package developed by De Luca (2008) to carry out the sequential bivariate semi-nonparametric estimation for the sample attrition and selection.

# **Appendix C. Measurement of Cognitive Skills**

## **C.1. Adaptive Cognitive Test**

To capture the skills of our diverse pool of respondents, we worked with an organization to design an adaptive tablet-based test. The organization we partnered with developed 324 items ranging from early primary level to college level. The test classified respondents into six levels that correspond to different grades. The mapping between levels and grades is as follows:

- Level 1: Nursery, Grades 1 to 3 (early primary)

- Level 2: Grades 4 and 5 (late primary)

- Level 3: Grades 6 to 8 (middle school)

- Level 4: Grades 9 and 10 (high school)

- Level 5: Grades 11 and 12 (intermediate)

- Level 6: College

The items of the tests were designed to capture the following learning domains: (1) mastery over concepts and definitions (e.g., “what is a pronoun?”), (2) application mastery (e.g., “add 2+2”), and (3) evaluation mastery (e.g., “two boys meet two girls, one boy leaves; how many children are left?”). As we expected many respondents to have been out of school for a long time, items were designed to test general mastery (as opposed to specific terms or formulae). All the test items were multiplechoice questions with four possible answer choices and one correct answer.

The logic of the test was as follows.

- Everyone started at the same level – Level 2 for Urdu and Mathematics and Level 1 for English – and answered a batch of 6 questions.

- If the respondent got 5-6 questions right, they moved to the next higher level (or, if at Level 6, remained at Level 6).

- If the respondent got 3-4 questions right, they stayed at the same level.

53
# **Table 3: 2SLS**

||first stage|second stage|
|---|---|---|
|VARIABLES|(y: socialpolicy cond.)|(y: CPIAC change)|
|logarithm of population 2006|1.353*<br>(0.698)|.|
|FPS prior actions 2006-2014|1.094***<br>(0.238)|.|
|initial level of policy quality|5.810***|-0.371***|
||(2.054)|(0.0644)|
|average aid/GDP 2006-2014|29.70*|-0.198|
||(17.46)|(0.531)|
|average per capita growth 2006-2014|-0.397|0.0300***|
||(0.370)|(0.0102)|
|change in political freedom 2006-2014|-0.116<br>(1.122)|-0.0624**<br>(0.0291)|
|social policy prior actions 2006-2014|.|0.00913***<br>(0.00353)|
|Constant|-38.81***|1.172***|
||(14.49)|(0.226)|
|Observations|67|67|
|F-test of excluded instruments|12.900|.|
|F-test of endogeneity|.|3.124|
|Overidentification test|.|0.325|
|R-squared<br>Note: Robust standard errors in parentheses.|0.539<br>*** p<0.01, ** p<0.05, * p<|0.382<br>0.1|

<u>Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1</u> The second stage regression indicates that the coefficient on loans more than doubles in comparison with its OLS counterpart, suggesting that the net effect of endogeneity bias was negative. With a point estimate of 0.0091, it takes 30 social policy conditions on average to increase the CPIA cluster C score by 1 standard deviation. The 2SLS regression 12
al. 2014), mobile phone surveys also turn out to be remarkably flexible and adaptive. New questions can be introduced on a needs basis and in-depth qualitative interviews can be carried out at a moment’s notice. These qualities make mobile phone surveys well suited for monitoring welfare in volatile environments: they have been used for welfare monitoring during the ebola crisis in Liberia (Himelein 2014) and for welfare monitoring in conflict-affected areas such as South Sudan (Demombynes et al. 2013). Unique about using a mobile phone survey with a displaced, mobile population is that it allows tracking welfare during displacement, and upon return.<sup>8</sup> Three target populations were identified for the purpose of this survey: Internally Displaced Persons (IDPs) living in Bamako, refugees in refugee camps in Mauritania and Niger, and returnees in Gao, Timbuktu and Kidal, the capitals of regions that bear their names. The sample does not include those who were not displaced by the crisis nor those who returned to places other than the three regional capitals in the North. While the sub-sample of IDPs includes exclusively IDPs in Bamako, and the refugee sub-sample only refugees in Niger and Mauritania, the returnee group includes people who were displaced elsewhere (33%).

**Figure 1: Distribution of  the sample**

<!-- Start of picture text --> Camp Bamako<br>(Mauritanie) (Mali)<br>20% 20%<br>Gao (Mali)<br>Camp (Niger) 18%<br>16%<br>Tombouctou<br>Kidal (Mali)<br>(Mali)<br>10%<br>16%<br><!-- End of picture text --> The sample size was capped at 501 respondents (distributed as shown in Figure 1). Taking this into account as well as the coverage of the mobile phone network in northern Mali, the survey was 4