rafmacalaba/datause-encoder-data huggingface dataset:
enabling targeted recruitment of workers with the requisite skills, and include provisions for worker rights, such as wages, working conditions, and access to services (Biehler, Kipp and Koch, 2024, Chilton and Woda, 2022). While BLAs relax some regulatory barriers, they do not address all informational or financial barriers that limit broader migration.

Though conceived as a tool to facilitate migration, it is not obvious that BLAs necessarily increase migration for various reasons. First, significant numbers of migrants move between countries outside the framework of BLAs and BLAs could be designed to restrict or control the flow of migrant workers (Livnat and Shamir, 2022). Second, instead of increasing net migration flows, BLAs could be a policy tool to regularize the irregular migration already taking place (Bither and Ziebarth, 2018, Clemens, 2024). Third, even if BLAs may be intended to increase migration, the efficacy of such policies – like with many policies – is not obvious ex-ante. Such concerns may be particularly strong in settings with high costs of migration, high extent of misinformation, and low actual or perceived returns to migration as well as in settings where low capacity and monitoring limits the efficacy of BLA implementation.<sup>1</sup> Finally, even if BLAs were effective in facilitating migration, it is not clear _how_ effective they can be as a policy tool. Determining the magnitude of migration impacts and its persistence is important for migration policies.

To understand the impact of BLAs on migration, we combine two comprehensive data sources on migration and BLAs across all country pairs. The global bilateral migration data captures the migration (measured by people living in a country that is different from the country of birth) between all country pairs for each decade from 1960 to 2020, compiled by the World Bank and the United National Department of Economic and Social Affairs (UNDESA, 2020, World Bank, 2022). A comprehensive dataset on all BLAs for the same period comes from Chilton and Posner (2018), Peters (2019 _a_ ) and Chilton and Woda (2022). We merge the two sets of databases by converting the BLA database into country-pair _×_ decade format for analysis. For each of the BLAs, we designate the country with a lower income per-capita as the _origin_ country and the other as the _destination_ country.

> 1The enforcement of BLAs is often measured through reports from international agencies such as the International Labor Organization, labor inspection data from national governments, monitoring of complaints from migrant workers and worker welfare organizations, or academic research studying the impact of BLAs in specific corridors (United Nations (2022)). However, factors such as lack of data, limited monitoring capacity of national governments, under-reporting of issues by migrant workers, and difficulties in cross-border mechanisms of enforcement can make monitoring effective implementation of BLAs challenging (Chilton and Woda, 2022, United Nations, 2022).

3
The More Stringent, the Better? Rationing Car Use in Bogotá with Moderate and Drastic Restrictions _Jorge A. Bonilla_ JEL codes: D62, R41, Q53, Q58 Jorge A. Bonilla is an assistant professor at the Department of Economics at Universidad de los Andes, Bogotá, Colombia; his email address is: jobonill@uniandes.edu.co. Financial support from the Swedish International Development Cooperation Agency (SIDA) and Universidad de los Andes is gratefully acknowledged. The author thanks the Air Quality Monitoring Network of Bogotá, the Transport and Finance Agencies of Bogotá, and Econometría S.A. for allowing access to their databases. Helpful comments on earlier versions of this paper from Jessica Coria, Joakim Westerlund, Thomas Sterner, and seminar participants at the University of Gothenburg and Universidad de los Andes are greatly appreciated. The author thanks two anonymous referees and the editor for their valuable comments and suggestions. All remaining errors are the responsibility of the author.
# **Table 5: Estimates of** ~~~~ **for Countries without Cross-sectional CPI Data**

|country|Survey<br>period|~~~~|Cumulative<br>CPI‐bias|
|---|---|---|---|
|Burkina Faso|1998‐2003|0.231|‐0.447|
|Cameroon|2001‐2007|0.226|0.040|
|Cote d'Ivoire|2002‐2008|0.223|0.517|
|Madagascar|2001‐2010|0.202|0.718|
|Madagascar|2005‐2010|0.208|0.281|
|Mauritius|2006‐2012|0.167|0.330|
|Nigeria|2011‐2013|0.216|0.878|
|Rwanda|2005‐2010|0.234|0.623|
|Senegal|2005‐2011|0.231|0.627|
|South Africa|2005‐2010|0.205|0.955|
|Tanzania|2000‐2007|0.220|0.942|
|Tanzania|2008‐2012|0.234|0.877|
|Tanzania|2010‐2012|0.236|0.623|
|Togo<br>* Note: There is in<br>D.R. Thus, we use<br>this country.|2006‐2011<br>sufficient data t<br>the average of al|0.239<br>o estimate<br>l Ῡ estimat|0.899<br>Ῡ for Congo,<br>es (0.22) for|

**Table 6: Cumulative CPI Bias, by Country, Sub-Saharan Africa, 1998–2013**

|Country|Survey period|Cumulative<br>CPI bias|Standard<br>error|P‐value|Average annual<br>CPI bias|Correction<br>factor|
|---|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|(5)|
|Burkina Faso|1998‐2003|‐0.447|0.055|0.000|‐0.089|1.447|
|Cameroon|2001‐2007|0.040|0.038|0.298|0.007|0.960|
|Congo, D.R.|2004‐2012|0.514|0.020|0.000|0.064|0.486|
|Cote d'Ivoire|2002‐2008|0.517|0.024|0.000|0.086|0.483|
|Ethiopia|2004‐2010|0.169|0.012|0.000|0.028|0.831|
|Ghana|2005‐2012|‐0.407|0.164|1.000|‐0.058|1.407|
|Madagascar|2001‐2010|0.718|0.013|0.000|0.080|0.282|
|Madagascar|2005‐2010|0.281|0.025|0.000|0.056|0.719|
|Mauritius*|2006‐2012|0.330|0.009|0.000|0.055|0.670|
|Mozambique|2002‐2008|0.378|0.135|0.005|0.063|0.622|
|Nigeria*|2011‐2013|0.878|0.060|0.000|0.439|0.122|
|Rwanda*|2005‐2010|0.623|0.014|0.000|0.125|0.377|
|Senegal|2005‐2011|0.627|0.010|0.000|0.105|0.373|
|South Africa|2005‐2010|0.955|0.002|0.000|0.191|0.045|
|Tanzania|2000‐2007|0.942|0.007|0.000|0.135|0.058|
|Tanzania|2008‐2012|0.877|0.021|0.000|0.219|0.123|
|Tanzania|2010‐2012|0.623|0.033|0.000|0.311|0.377|
|Togo|2006‐2011|0.899|0.012|0.000|0.180|0.101|
|Uganda<br>_* national CPI b_|2009‐2012<br>_ias_|‐0.373|0.110|0.001|‐0.075|1.373|

24
2 The AAS asks the question: whether it is the failure of adjustment policies that has resulted in such disappointing results for Africa or is it the failure to adjust i.e. the countries did not implement these policies consistently and vigorously.

# External vs Internal Factors

Before answering this question, the study revisited the issue of the main contributory factor for Africa's economic decline. If the main cause of the decline is external shocks, then adjustment policies cannot be an effective answer to the problem. But if domestic policy weaknesses are the main culprit, then altering these policies should make a difference.

The debate on the relative weight of the factors explaining the poor performance of Sub-Saharan Africa has been lingering for the past fifteen years. The Berg report of 1981 argued that the domestic policy failures of African governments were mainly responsible for the continent's continuing decline, while the ECA and many other observers blamed the hostile external environment as a significant source of Africa's malaise.

The AAS acknowledges that the terms of trade decline -from the early 1970s to the mid 19SOs-had a negative influence on the economic growth of the African countries but accounted for a relatively small part (10 percent) of Africa's decline in the growth rate. A similar result was derived by El-Farhan,<sup>4</sup> who applied an OLS regession model to cross sectional data on 32 SSA countres over 1960-86 in order to test for various influences on GDP growth in Sub-Saharan Africa. According to her study, changes in the commodity terms of trade had the expected positive sign but were not found to be significant. The strongest explanatory variable was the growth of export volume. An analysis of SSA export perfonnance by Svedberg<sup>5</sup> found term of trade movements (up or down) to be the main influence on export earings only in 11 out of 33 countries in 1970-85. Pickett assessed the effects on GDP growth of long-run terms of trade for a sample of 20 SSA low income countries over 1966-86. In all the 20 countries he examined, the income effects of the terms of trade were negative, but in most cases the effect was quite modest.

> 4E2-Faxhan, H., Analysis of the **Gmwth** Performance of **Sub-Saharan Africa** Countrics, London: SOAS (Ph. D. Thcsis Unpublished).

5Svedbcrg, P., 1991, **The** Export **Performance** of **Sub-Saha Africa", in** J.H. FzimpoW.Ansh, ct al., Tmde **and** Development ia SubSaharan **Afric,** Manchester V. **Pess, Manchester.**

> **6Pickctt,** J., 1990, **The Low-Income Economiw** of **_Sub-Sharan Aica:_ Piubkm and Pznap:ca",** in J. **Picket?** and H. **Singer** (cds.), **Towards Economic** Recovery in Sub-Sahe **Arica:** London: Routhledge.
_promotora_ selection, nobody knew which intervention package the _promotoras_ or anybody else would end up receiving.<sup>17</sup>

# **3. Data and empirical strategy**

# _3.1. Data_

The data come from a household panel in the control and treatment communities. In treatment communities, data were collected from all households. In control communities, a random sample of households was selected so that the sample size in each control community was equal to onethird of the population in the intervention community that belonged to the same pair. This resulted in a control group of equal size as each of the three intervention groups (of about 1000 households).<sup>18</sup> The follow-up data was collected 9 months after the start of the CCT component of the program. The attrition rate of the second round was very low - 1.3 percent of the original households – due to tracking of households that had migrated. Attrition is uncorrelated with treatment - in a regression of attrited households on a dummy for treatment the coefficient is - .004, with a standard error of .005.

A number of survey instruments were used for data collection. The main household survey, collected in both rounds, contains household and individual level data on various socio-economic indicators for approximately 4400 households. In the follow-up survey, additional modules were added to specifically capture information about social dynamics and information sharing. A community survey was also collected to track, among other things, possible price changes and the presence of new programs.

During the follow-up survey, a separate team of female enumerators administered an additional questionnaire on early childhood development and women’s socio-emotional state and attitudes.

> 17 Due to the transparency of the process, the lottery process was widely perceived as fair. Participation by the invited beneficiaries to the assemblies and lotteries was near 100%.

> 18 If the number of households in the control community was less than one-third of the population of the treatment community, additional control households were sampled in a nearby control community. About 1100 households were sampled in the control communities. Out of those about 1000 are identified as “eligible” households using the same proxy means method as used for the treatment. These are the control households included in the intent-to-treat estimates.

12
that developing countries account for 35 percent of fuel use (see above), their exemption from the agreement might limit the global emissions reduction to about 2–3 percent.<sup>32</sup> A similar level of emissions reductions might occur if all countries participated, but at a price of $15 per tonne rather than $25. On the other hand, the emissions reduction might be around 5 **–** 8 percent under a CO2 price of $40 per tonne, encompassing all countries.<sup>33</sup>

# **C.   Revenue**

37. **Total projected revenues in 2020 are $12 billion under the $25 per tonne pricing scenarios, $7.5 billion for the $15 per tonne scenario, and $18.7 billion under the $40 per tonne scenario** (Figure 2a). Revenues are calculated as follows. Baseline emissions in 2020 are taken to be 500 million tonnes.<sup>34</sup> This is scaled back by the midpoint of the range of proportionate emissions reductions for the different pricing scenarios just discussed, and multiplied by the corresponding emissions price.

38. **With compensation schemes, developing countries would raise significant amounts of domestic revenue** (Figure 2b). For example, with a $25 per tonne CO2 price developing countries as a group raise revenues of $4.2 billion and even with the $15 per tonne price, they raise revenues of $2.6 billion (potential compensation relative to countries‘ GDP is discussed below).

39. **Revenues for climate finance and other uses total $4.8–$7.8 billion in the more realistic pricing scenarios ($15 to $25 per tonne of CO2) with developing country compensation** (Figure 2c). Of these, perhaps 5–10 percent might be remitted to implementing agencies, national or international, a precedent being that EU Member States are allowed to keep 10 percent of customs revenue to cover administration costs. Some might also fund clean technology development programs for the aviation and maritime industries. However, the amount that might be warranted on economic grounds to correct market failures, that is, the potential inability of innovators to appropriate spill over benefits of new technologies to other firms, would likely be a small fraction of the revenue raised. At most, therefore, international aviation might contribute around $4–7 billion for climate finance in these scenarios.

> 32 This ignores the (small) risk of offsetting emissions increases as the pattern of flights shifts from developed to developing countries.

> 33 For comparison, AGF (2010b) assumed no emissions reductions in response to similar charges to aviation.

> 34 This is based approximately on the projection in ICAO (2009a), scaled back by 20 percent on the assumption that ICAO‘s aspirational emissions mitigation goals (see above) will be met. It is assumed that charges are levied on flights that it is proposed to include in the EU-ETS. Issues arising from the coordination of such a charge with the EU-ETS are discussed in Section V.

26
representative sample of teachers declared to have introduced changes in their teaching to acquaint students with test requirements. This aspect was also taken into account while choosing textbooks and other supporting teaching materials. To find out if these changes could have influenced the PISA results one should compare the scope (content) of both PISA and gymnasium exams. It should be noted that 26 percent of the teachers expressed the opinion that the unsatisfactory results of their students were not caused by their poor knowledge or low skills, but by their lack of experience in taking such tests. Therefore, teachers concluded that it was important to practice taking tests. Konarzewski (2008) shows that in literally all gymnasia, a substantial amount of time is devoted to solving test type tasks and doing mock exams. Approximately 5 percent of the respondents have changed their assessment schemes making them more test-like. In his conclusion, Konarzewski (2008) writes: “The test exam, being so predictable as ours, each year less and less measures the competences of gymnasium leavers but more and more the effort and time spent by schools on training students to do the exams.”

# **3. Relative Increase in Scores**

Improvements in student outcomes in Poland measured by PISA have been impressive. In math, Poland improved its score from 470 points in 2000, to 490 in 2003, and to 495 in 2006 (see Table 1). Reading has seen a steady improvement over time, from 479, to 490, to 508 in the latest round. In fact, in the first assessment Poland ranked below the OECD country average in reading. In 2003, Poland reached the OECD average. Moreover, by 2006, the latest assessment, Poland scores above average, ranking 9th among all countries in the world. In science, the scores are 483, 498 and 498.

Table 1: Top 10 Reading <u>over Time, PISA</u>

|2000|2003|2006||
|---|---|---|---|
|1 Finland|549 Finland<br>543|Korea|556|
|2 Netherlands|537 Korea<br>534|Finland|547|
|3 Canada|535 Canada<br>528|Hong Kong|536|
|4 Hong Kong|532 Australia<br>525|Canada|527|
|5 Australia|528 Liechtenstein<br>525|New Zealand|521|
|6 Ireland|528 New Zealand<br>522|Ireland|517|
|7 New Zealand|526 Ireland<br>515|Australia|513|
|8 Japan|525 Sweden<br>514|Liechtenstein|510|
|9 United Kingdom|524 Netherlands<br>513|Poland|508|
|10Korea|522 HongKong<br>510|Sweden|507|

# **4. Hypotheses for Explaining Change over Time**

Several factors could explain these changes; however, it is hard to say whether there any causal relationships. In other words, in most cases validity of given explanations is doubtful. The claim of this paper is that many statistics produced from PISA are not fully comparable across years if specific empirical questions are to be answered. If one is interested in international comparisons to assess the effectiveness of countries’ educational policies then only samples equivalent in a distribution of important student and family characteristics could be 8
this section we describe each survey, summarize the preparation of our data, and provide some descriptive statistics.

# **2.1 The FAT data**

The FAT data is a nationally representative survey to measure firm-level technology adoption, which was first implemented in Cear ́a-Brazil, Senegal, and Vietnam on businesses with 5 or more employees in agriculture, manufacturing, and services ( _17_ ). This new survey collects data on the technologies used more often (from a list of relevant technologies available) in every general and sector-specific business function. The potential technologies associated with each business function follow a ladder of sophistication ranging from the most basic to the most sophisticated (from handwritten processes to Enterprise Resource Planning in the business administration function, for example). In this paper we exploit the firm-level technology sophistication measure for general business functions (GBFs) proposed by ( _17_ ).

GBFs are tasks common across firms regardless of their sector. They include: business administration, production planning, sourcing and supply chain management, marketing and product development, sales, payments, and quality control.<sup>1</sup> With the exception of the most basic option (e.g., handwritten processes for business planning), the technologies available to perform GBFs are predominantly digital. Thus, our measure captures the level of sophistication of digital technologies, whenever they are used to perform these tasks. The technology measure for each business function ranges from 1 to 5 to capture the different levels of sophistication of the most frequently used technology for every business function, and then averages these sophistication measures across GBFs within each firm. This average is our measure of pre-pandemic technology sophistication.<sup>2</sup> The FAT data also includes information about the characteristics of the manager of the firm and

> 1Business administration corresponds to human resources, finance, and accounting. Appendix A lists each technology associated with each GBF in the FAT survey. See also ( _17_ ).

> 2The list of key business functions and technologies associated with each of them were developed in collaboration of over 50 sector and technology experts. The FAT survey follows several procedures to minimize bias and measurement errors, both in its design and implementation. Moreover, several ex-post robustness checks for Brazil, Senegal, and Vietnam, including validation of the technology sophistication measures with external data are described by ( _17_ ). Data were collected through face-to-face interviews with top managers. The survey was implemented by the General Statistics Office (GSO) in Vietnam, by the State Industry Association (FIEC), in Cear ́a-Brazil, and by Kantar-public, in Senegal.

5
30 Kelley, Allen C., 1988, "Economic Consequences of Population Change il the Third World," _Journal of Economic Literature,_ 26, pp 1685-1728.

- Kelley, Allen C. and Robert M. Schmidt, 1994, "Population and income change: Recent evidence," World Bank Discussion Papers no. 249, World Bank: Washington DC.

- Kormendi, Roger and Philip Meguire, 1985, "Macroeconomic Deerminants of Growth: Cross Country Evidence," _Journal of Monetary Economics, 16,_ pp 141-63.

- Kremer, Michael, 1993, "Population Growth and Technological Change: One Million B.C. to 1990,1" Ouarterly Joumal of Economics, 108 (3), pp 681-716.

- Kuznets, Simon, 1960, "Population Change and Aggregate Output" in Demorravhic and Economic Change in Developing Countries, Princeton, NJ.

- , 1967, Onantative Asecs of the Economic Growth of Natons, Chicago Research

- Ceter in Economic Development and Cultural Change.

- Landau, Daniel, 1986, "Govermment and Economic Growth in the Less Developed Counties: An Empirical Study for 1960-1980, _Economic Development and Culur Change, 35,_ pp 35-75.

- Lcvine, Ross, and David Renelt, 1992, 'A Sensitivity Analysis of Cross Country Growth Regressions,' _Azerican Economic Review,_ 82(4), pp 942-63.

- Mankiw, N. Gregory, David Romer and David Weil, 1992, "A Contnrbution to the Enpirics of Economic Growth," _Quarterly Journal of Economics,_ 102, pp 407-37.

- National Research Council, 1986, _Population Growth and Economic Development: Policy Questions,_ Working Group on Population and Economic Developmet, Committee on Population, Commission on Behavioral and Social Sciences and Education. Washington, D.C.: National Academy Press, 1986.

- Nehru, Vilram, and Ashok Dhareshwar, 1993, "A New Database on Physical Capital Stock: Sources, Methodology and Results," _Revista de Analisis Economico,_ 8(1), pp _37-59._

- Nehru, Vikram, Eric Swanson and Ashutosh Dubey, 1993, 'A New Database on Human Capital Stock: Sources, Methodology, and Results," World Bank Working Paper WPS1124 (April).

- Otani, Ichiro, and Delano VillManueva, 1990, "Long-Term Growth in Developing Countries and Its Determinants: An Empirical Analysis," _World Development,_ 18 (4), 769-83.
# **1. Introduction**

Mobile money is the term used to describe tools to perform banking and financial transaction services using mobile phones or devices.<sup>1</sup> Mobile money can serve as a platform for bill payment, person-to-person transfers (P2P), government-to-person (G2P) transfers, payment of services such as public transport, etc. As mobile phones have been widely spread all over the globe, mobile money has flourished both in developed and developing countries in various forms reflecting country context, level of financial sector development, market and competition landscape, relevant regulations, etc. Business models for mobile money can be led by mobile network operators (MNO), banks and third parties and collaborative efforts of them.

Additive mobile money model refers to the efforts to provide customers with more convenient and easy access to financial services largely driven by banks. Transformational models rather aim at providing the unbanked population with financial services. According to data from Global Findex, most mobile money users in the world have a bank account, reflecting the dominance of the additive mode.<sup>2</sup> Nevertheless, in some developing countries with large segments of unbanked population such as Kenya, the transformative model has developed successfully. Mobile money has the potential to offer the poor and unbanked population the means to manage their limited cash resources in safer and more efficient ways. It has also emerged as a way to enable people in the most remote areas to transfer money using their mobile phone.

This study intends to increase our understanding of how different types of mobile money services have developed in very different environments. For this purpose we select two very different countries—the Republic of Korea and Uganda—in which mobile money has developed following different models in responce to their structural characteristics.

> 1 IFC, Mobile Money Study 2011.

> 2 <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=global-findex-(globalfinancial-inclusion-database)</u>

2