rafmacalaba/datause-encoder-data huggingface dataset:
# **_1. Introduction_**

The COVID-19 pandemic was a shock that provided impetus for increased usage of digital payment services, and perhaps spurred deeper financial inclusion. In fact, between 2017 and 2021, the share of adults reporting having used the internet to make or receive digital payments in the previous year rose, on average, by 7.7 percentage points. However, the standard deviation of these changes is also high, at 8.5 percentage points, indicating substantial variation across countries. Because it was unanticipated and because countries had differing responses in terms of increased use of digital payments, the episode provides an opportunity to identify the drivers of digital payments and digital financial inclusion. Using the 2021 round of the Global Findex survey and other new datasets describing bank regulation and supervision and legal and regulatory enablers and safeguards for digital commerce and (personal) data sharing, we measure the influence on digital payments of dimensions that have not been quantified well to date, while also measuring the influence of drivers such as human capabilities (educational attainment), infrastructure (electricity, internet usage), and the usage of mobile phones.

The literature on the effects of digital payments, and digital financial services (DFS) in general, is growing and surveys of it were available prior to the COVID-19 pandemic (Karlan et al., 2016; Abbasi and Weigand, 2017) and in the context of the pandemic (Agur et al., 2020). Key themes that emerge from the literature on impact evaluation, much of which is focused on field experiments (i.e., randomized controlled trials or ‘RCTs’) are that: (1) digital payments and transfers can strengthen informal risk-sharing networks, providing a source of resilience to unanticipated shocks that can contribute to higher household savings and consumption (Lee et al., 2021; Jack and Suri, 2011, 2014, 2016; Suri et al., 2012); (2) digital systems are proving a cost effective and secure method for governments to disburse funds to citizens in multiple contexts 2
Table 7: Heterogeneous treatment effects by natives’ parental education

||Natives|with high|parental ed|ucation|Native|s with low|parental edu|cation|
|---|---|---|---|---|---|---|---|---|
||Langua|ge score|Math|score|Langua|ge score|Math|score|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Panel A:|||||||||
|Share of recent|-1.131|-1.184|-0.935|-0.620|-3.34**|-3.35**|-1.13|-1.12|
|immigrants in grade|(1.291)|(1.223)|(1.178)|(1.171)|(1.457)|(1.446)|(1.402)|(1.379)|
|Grade-level controls||✓||✓||✓||✓|
|Grade effects|✓|✓|✓|✓|✓|✓|✓|✓|
|School-by-year effects|✓|✓|✓|✓|✓|✓|✓|✓|
|N. ofgrade-level observations|11,062|11,062|11,062|11,062|11,664|11,664|11,664|11,664|
|Panel B:|||||||||
|Share of other|0.202|0.156|0.090|0.118|-1.503|-1.462|-0.608|0.587|
|immigrants in grade|(1.425)|(1.375)|(1.502)|(1.563)|(1.657)|(1.276)|(1.643)|(1.576)|
|Grade-level controls||✓||✓||✓||✓|
|Grade effects|✓|✓|✓|✓|✓|✓|✓|✓|
|School-by-year effects|✓|✓|✓|✓|✓|✓|✓|✓|
|N. ofgrade-level observations|11,062|11,062|11,062|11,062|11,664|11,664|11,664|11,664|

_Notes._ ***: significant at the 1% level, **: significant at the 5% level, *: significant at the 10% level. Robust standard errors clustered at the school level are reported in parentheses. Controls for mean characteristics at the grade level include: the share of students by level of parental education, the share of female students in the grade, the share of disadvantaged students according to the Dutch weighting system, the share of students that repeated a grade, the average class size in the grade, teacher’s years of experience, the total number of students in the grade and its square. Low parental education refers to having a father that did not complete upper secondary education while high levels of parental education are defined as having a father that completed upper secondary education or more. Controls for linear school trends are also included. Estimates that do not control for linear school trends are quantitatively similar and available upon request.
Table A.2 : Estimates of Regional Gaps at the mean and at different quantiles

|Year|Coefficient<br>M|ean|||Quantiles|||
|---|---|---|---|---|---|---|---|
|2000|||5th|25th|50th|75th|95th|
||Rural-Less Integrated|6.460|5.795|6.156|6.415|6.731|7.282|
||t-value|705.3|232.2|362.3|351.9|291.4|172.4|
||Urban|0.25|0.08|0.13|0.24|0.37|0.51|
||t-value|19.68|2.32|4.12|6.25|8.84|7.31|
||Integrated|0.12|0.07|0.10|0.13|0.13|0.15|
||t-value|10.19|0.19|3.88|4.54|3.46|2.66|
|2005||||||||
||Rural-Less Integrated|6.831|6.163|6.511|6.775|7.096|7.681|
||t-value|853.8|384.5|461.3|489.4|389.8|251.5|
||Urban|0.177|0.007|0.069|0.145|0.280|0.436|
||t-value|16.424|0.303|3.124|5.761|7.183|7.860|
||Integrated|0.143|0.153|0.154|0.132|0.138|0.131|
||t-value|13.487|7.648|7.228|6.060|4.733|2.527|
**Table 2: Country-Specific Sibling Correlation Estimates (By Cohorts)**

|**Country**|**1970**|**1980**|**1990**|**Country**|**1970**|**1980**|**1990**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.674|0.657|0.685|Benin|0.653|0.595|0.579|
|Indonesia|0.659|0.616|0.523|Burkina Faso|0.584|0.638|0.506|
|Philippines|0.614|0.616|0.550|Burundi|n.a|0.584|0.500|
|Vietnam|0.682|0.616|n.a|Cameroon|0.608|0.629|0.629|
|||||Chad|0.743|0.727|0.760|
|Europe & Central Asia||||Congo, Rep.|0.466|0.532|0.625|
|Albania|n.a|0.502|0.389|Cote d'Ivoire|0.633|0.592|0.640|
|Armenia|0.468|0.592|0.492|Ethiopia|0.793|0.656|0.630|
|Kyrgyz Republic|0.379|0.438|0.342|Gabon|0.475|0.614|0.576|
|Tajikistan|n.a|0.461|0.424|Ghana|0.592|0.617|0.500|
|Turkey|0.550|0.561|0.433|Guinea|0.597|0.515|0.559|
|||||Kenya|0.560|0.584|0.503|
|Latin America & Caribb|ean|||Lesotho|0.592|0.592|0.483|
|Bolivia|0.692|0.653|n.a|Liberia|n.a|0.535|0.522|
|Brazil|0.709|n.a|n.a|Madagascar|0.824|0.790|n.a|
|Colombia|0.666|0.582|0.546|Malawi|0.609|0.602|0.585|
|Dominican<br>Republic|0.595|0.538|0.435|Mali|0.626|0.619|0.573|
|Guatemala|0.750|0.697|0.680|Mozambique|0.526|0.522|0.681|
|Haiti|0.697|0.674|0.678|Namibia|0.554|0.535|0.516|
|Peru|0.668|0.591|0.546|Niger|0.670|0.684|0.645|
|||||Nigeria|0.643|0.666|0.741|
|Middle East & North A|frica|||Rwanda|0.546|0.550|0.522|
|Egypt, Arab Rep.|0.523|0.581|0.588|Senegal|0.588|0.584|0.588|
|Jordan|0.474|0.468|0.551|Sierra Leone|n.a|0.517|0.515|
|||||South Africa|0.521|0.383|0.501|
|South Asia||||Tanzania|0.559|0.563|0.484|
|Afghanistan|n.a|0.523|0.591|Togo|0.446|0.570|0.519|
|Bangladesh|0.673|0.614|0.609|Uganda|0.648|0.666|0.552|
|India|0.658|0.631|0.631|Zambia|0.664|0.681|0.620|
|Nepal|0.638|0.618|0.588|Zimbabwe|0.569|0.609|0.625|
|Pakistan|0.595|0.633|0.679|||||

_Notes:_ This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) dis-aggregated by different decades of birth cohorts (the 1970s, 1980s, and 1990s).

36
Manufacturing Industries.” _Journal of International Economics_ 71(1): 52–71.

- Fern ́andez, M., and J. Messina. 2018. “Skill Premium, Labor Supply, and Changes in the Structure of Wages in Latin America.” _Journal of Development Economics_ 135: 555–73.

- Ferreira, F., S. Firpo, and J. Messina. 2017. “Ageing Poorly? Accounting for the Decline in Earnings Inequality in Brazil, 1995-2012.” Policy Research Working Paper 8018, World Bank, Washington, DC.

- Ferreira, F., P. Leite, and M. Wai-Poi. 2010. “Trade Liberalization, Employment Flows and Wage Inequality in Brazil.” Chapter 8 in: M. Nissanke and E. Thorbecke, editors. _The Poor under Globalization in Asia, Latin America and Africa._ Oxford, United Kingdom: OUP.

- Fieler, A., M. Eslava, and D. Xu. 2018. “Trade, Quality Upgrading, and Input Linkages: Theory and Evidence from Colombia.” _American Economic Review_ 108(1): 109–46.

- Galiani, S., G. Cruces, P. Acosta, and L. Gasparini. 2017. “Educational Upgrading and Returns to Skills in Latin America: Evidence from a Supply-Demand Framework.” NBER Working Paper 24015. Cambridge, United States: National Bureau of Economic Research.

- Galiani, S., and P. Sanguinetti. 2003. “The Impact of Trade Liberalization on Wage Inequality: Evidence from Argentina.” _Journal of Development Economics_ 72(2): 497–513.

- Ge, S., and D. Tao Yang. 2014. “Changes in China’s Wage Structure.”. _Journal of the European Economic Association_ 12(2): 300-336.

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40
context.<sup>1</sup> Three, it needs to be borne in mind that countries emerge from conflict under differing and unique conditions and any general lessons outlined here must be adapted to realities on the ground. In particular, the nature of the post-conflict regime varies. In some countries, such as in Liberia currently, the new government is explicitly committed to reducing poverty and works closely with the international community. But in others, the new regime may have little interest in improving its citizens’ prospects. In these situations, any of the conclusions in the last section of this paper are rendered irrelevant.

> 1 However, all the conflicts discussed here meet the criteria of the Uppsala Conflict Data Program, which defines an armed conflict as concerning government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths. See http://www.pcr.uu.se for more details.

8
authority's total contract value decreased slightly from 148 million MKD to 142 million MKD, while the median supplier's total contract value increased considerably from around 21 million MKD to 45 million MKD. This is also accompanied with an observed decrease in the average CRI score from 0.48 to 0.43. These changes suggest a shift in procurement patterns and possibly a change in the types of suppliers being awarded contracts due to redistribution of resources after the government change.

**Table 5: Summary of Procurement and Network Statistics before and after the government change in North Macedonia**

||**Jan 2011-May 2017**|**Jun 2017-Dec 2022**|
|---|---|---|
|**A) Procurement Statistics**|||
|**Total contract value**|~ 221 billion MKD|~ 254 billion MKD|
|**Number of procurement authorities**|1,215|1,356|
|**Number of suppliers**|9,527|6,940|
|**Median # contracts (PA)**|179|193|
|**Median # contracts (Supplier)**|34|68|
|**Median Total contract value (PA)**|~ 148 million MKD|~ 142 million MKD|
|**Median Total contract value (Supplier)**|~ 21 million MKD|~ 45 million MKD|
|**Average CRI**|0.48|0.43|
|**B) Network Statistics**|||
|**Number of ties**|39,939|53,173|
|**Average degree**|15.12|19.39|
|**Clusters**(Louvain algorithm)|225|62|
|**Modularity**(stronger clustering of buyer-supplier<br>ties)|0.405|0.350|
|**Closeness Centrality***(higher values given to<br>buyers/suppliers that are closer to other nodes in the<br>network)|0.256|0.272|
|**Betweenness Centrality***(higher values given|||
|to buyers/suppliers that create shorter paths between<br>two nodes)|185.186|9.401|

* These aggregated centrality measures are network measures based on the centrality scores of each node in the network, The higher the centrality scores, the more likely for nodes that are further apart to interact together through central agencies/firms

According to Panel B of Table 5, the number of distinct ties in the procurement network increased from 39,939 to 53,173 after the government change. The average degree in the network also rose from 15 to 19 connections, indicating an increase in network connectivity. However, there was no noticeable change in the closeness centrality of the network, although the average betweenness centrality score dropped sharply from 185.19 to 9.4. This suggests that after the government change, fewer nodes acted as brokers between different communities in the network. The Modularity score also decreased from 0.41 to 0.35, indicating a reduction in the number of 40
# **Table 2. Summary Statistics: Capital Ratios**

Banks in our sample operate in 12 different OECD countries (see Table A1 for the list of countries and distribution of banks across countries). The sample period for the measures of capital (lagged one period in the regression) is 2005 to 2008. The yearly data are obtained from Bankscope. RWRt is the total capital adequacy ratio under the Basle rules. It measures regulatory capital divided by risk-weighted assets and off balance sheet risks. RWRt1 is the Tier 1 risk-weighted capital ratio, defined as shareholder funds plus perpetual, non-cumulative preference shares plus retained earnings, as a percentage of risk weighted assets and off balance sheet risks measured under Basel rules. RWRt2 is the Tier 2 capital ratio, defined as subordinated debt, hybrid capital, loan loss reserves, and valuation reserves divided by risk-weighted assets and off balance sheet risks measured under Basle rules. LRt is the leverage ratio defined as regulatory capital divided by total assets. LRt1 is the Tier 1 leverage ratio and LRt2 is the Tier 2 leverage ratio. Common equity is shareholder funds, and other capital is regulatory capital minus common equity scaled either by risk-weighted assets (RWA) or un-weighted total assets (TA). Summary statistics are reported for the whole sample and the sample of large banks. Large banks are defined as banks with total assets above 50 $ billion (the 20th percentile of assets).

||No. ofObserv.|Mean|Std.Dev.|25thpercentile|50thpercentile|75thpercentile|95thpercentile|
|---|---|---|---|---|---|---|---|
|**Whole sample**||||||||
|RWRt|4254|12.6|2.8|10.7|11.9|13.7|19.5|
|RWRt1|4073|10.2|2.8|8.1|9.7|11.6|16.5|
|RWRt2|4049|2.3|1.5|1.2|2.4|3.1|4.9|
|LRt|3779|8.1|2.5|5.9|7.8|9.8|13.0|
|LRt1|3814|6.7|2.4|4.7|6.3|8.3|11.4|
|LRt2|3726|1.4|1.1|0.7|1.3|1.9|3.4|
|Common equity/RWA|3655|9.6|5.5|6.3|9.1|11.9|19.5|
|Common equity/TA|5381|7.1|4.5|3.8|6.2|9.5|16.8|
|Other capital/RWA|3654|1.2|3.3|-0.6|0.2|2.2|8.6|
|Other capital/TA|3700|0.8|2.3|-0.4|0.1|1.4|6.0|
|**Large banks sample**||||||||
|RWRt|887|12.2|2.4|10.6|11.7|13.1|19.5|
|RWRt1|827|8.6|1.9|7.2|8.2|9.5|12.7|
|RWRt2|827|3.2|1.5|2.7|3.3|4.0|5.2|
|LRt|741|7.2|2.2|5.4|6.5|8.5|12.4|
|LRt1|769|5.1|1.7|3.7|4.6|6.0|8.8|
|LRt2|736|2.0|1.0|1.3|1.9|2.8|3.8|
|Common equity/RWA|745|7.2|3.9|3.4|7.2|10.5|13.4|
|Common equity/TA|973|4.8|3.3|1.9|4.1|7.5|10.7|
|Other capital/RWA|745|1.4|3.3|-1.1|0.8|3.5|7.3|
|Other capital/TA|748|0.7|2.0|-0.6|0.5|1.9|4.3|

17
- 11 -

supported by casual evidence in highly dollarized countries, such as Bolivia and Peru, which indicates that the vast majority of wages continues to be paid in local currency, with only few exceptions (such as for some top executives). '1 Figure 3. Financial and Real Dollarization (in percent) <!-- Start of picture text --> 0.7<br>0.6 -<br>0.1<br>0 20 40 60 80 100<br>Foreign Cumncy Depositb tD Total Deposits<br><!-- End of picture text -->

# **III.** WHAT CAUSES DOMESTIC DOLLARIZATION?

# A. **Theory**

Explanations for payments dollarization have been amply discussed in the currency substitution literature, based on inflation differentials that penalize the holdings of domestic currency. 1<sup>2</sup> While some holdings of foreign currency deposits may also simply reflect the convenience of having transactions balances in the currency of payments, especially by corporates involved in foreign trade, this cannot explain dollar holdings in the financial " Nonetheless, dollar indexation in the real sector appears to have made inroads over the years. In Bolivia, for example, it now affects most utility prices, pensions, parts of the tax system, accounting standards, and some supplier contracts. In Peru, a number of services, including residential and commercial leases, real estate, professional services, and insurance premia, are priced in dollars.

12 While the persistent use of the dollar once inflation has abated (hysteresis) has been amply documented in the currency substitution literature, based on transaction costs and "network" externalities, the opposite phenomenon, the persistent use of the local currency for payments, even at the height of hyperinflations or when financial dollarization has become practically universal, is equally noteworthy. The wide availability of local cash, which is supplied at no cost, and the injection of local currency through public sector payments activates Gresham's Law, e.g., the public disposes of the "bad" local currency it receives by using it for payments and keeps the "better" currency (the dollar) under the mattress.
findings obtained from the qualitative analysis, GRADE started using mixed methods in its impact evaluations. The most common design now used is to initially conduct a qualitative study to understand the context and develop the questions for surveys and find insights regarding the outcome variables that should be taken into account. After the quantitative analysis is conducted, a second qualitative analysis is used to explain or dig deeper into the results found.<sup>13</sup>

# **3.3 Examples of mixed-methods in evaluation**

One example that illustrates the iterative systematic approach is an assessment of the Kecamatan Development Project (KDP, a national community-driven development program) in Indonesia on local conflict dynamics (Barron et al, 2011). KDP’s objective was to provide block grants to local communities, who would then allocate this money to those projects community members themselves deemed most pro-poor, sustainable and cost-effective. This allocation process took place in community forums, but not every proposal was funded, generating the potential for conflict if villagers perceived that outcomes were a function of non-merit-based procedures (or worse). The evaluation’s objective was to assess whether and how these forums improved local governance; the hypothesis was that participating in KDP creates robust civic spaces and deliberative skills which enable local conflicts to be constructively addressed. One major challenge was that ‘conflict’ is notoriously hard to measure, and what little data there was had been collected from village leaders (who had obvious incentives to under-report the incidence of conflict on their watch). A mixed-method approach was used to find a novel way to measure conflict (which included a comprehensive analysis of local newspapers) and the mechanisms by which it is initiated or resolved (discerned via key informant interviews). In addition, it was critical for the evaluation to understand the causal chain of events, which was only possible with a deep qualitative analysis (which was generated by collecting dozens of cases of conflict pathways in program and comparable non-program villages).

An iterative strategy for integrating the quantitative and qualitative analysis was used. An initial period of qualitative fieldwork was pursued for three months. The villages were selected using a quantitative sampling frame [using propensity score matching (PSM) techniques derived from nationally representative household surveys], but the final selection of the best match of program

> 13 Information obtained from a telephone interview with Lorena Alcazar, November 2012.

17
INTRODUCn1ON XXV Table 6 Long-Run and Stationary Population, Momentum, Doubling Year, and Year When Net Reproduction Rate Reaches 1 for the World, Less Developed and More Developed Countries, and Continents

|||Populat|ion<br>(mill|tions)|Station<br>ary over|-<br>Popu-|Year<br> <br>when||
|---|---|---|---|---|---|---|---|---|
|Region|1985|<br>2050|<br>2100|Station<br> <br>ary|1985<br>- popula<br>tion|lation<br>-mi-<br>mentun|popul?-<br>tion<br>doubles|Year<br>when<br>NRR=1|
|Wortd|4,844|10,035|11,330|11,514|<br>2.4|<br>1.4|<br>2044|2060|
|Less developed|3,666|8,716|10.020|10,200|<br>2.8|<br>1.5|<br>2028|2060|
|More developed|1,179|1,319|1,310|1,314|<br>1.1|<br>1.1|<br>(a)|2030|
|Africa|559|<br>2,275|2,962|3,049|<br>5.5|1.&|<br>2009|2060|
|America|667|<br>1,146|1,192|1,201|<br>1.8|<br>1.4|<br>(a)|2030|
|Asia|2.823|5,728|6,288|6,374|<br>2.3|<br>1.5|<br>2047|2045|
|Europe<br>and U.S.S.R.|771|<br>843|<br>842|<br>846|<br>1.1|<br>1.1|<br>(a)|2030|
|Oceania|25|<br>42|<br>44|<br>45|<br>1.8|<br>1.3|<br>(a)|2030|

Note: More developed" compr.ses Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comiprises the rest of the world.

a. Population will not double with projected rates.

Africa is growing much faster, at over 3 percent annually, as opposed to only 1.9 percent for Asia. Africa currently has a smaller population than Asia, Europe and the U.S.S.R., and the Americas, but by the year 2000 will be second in size only to Asia. Annual growth rates in Africa are projected to stay above 2 percent at least up to 2025, by which year the continental population will have almost tripled.

America, with 14 percent of world population, includes both more developed and less developed countries and is growing as a whole more slowly than Africa and Asia. By 2025, its population will have increased by half, but its share of world population will have fallen to 12 percent. The small Oceania group is projected to experience growth essentially similar to America.

Europe and the U.S.S.R., now second in size to Asia, will be only fourth among these continents by 2025. Its share of world population will shrink from 16 **to** 10 percent. This projection assumes that fertility, currfntly below replacement level for **tl-** group and for many countries, will rise to replacement level by 2030. Should this not happen, tne projected 10 percent increase in the population by 2025 could be smaller, or could even become a decrease.

Long-run trends. The long-run implications of the projection assumptions are illustrated in Table 6, which shows projections up to 2100 as well as other indicatars. If every country and economy reaches replacement-level fertility by or before 2060, world population will be over 11 billion by the end of the next century. Another 300 million will be added before thc population becomes stationary, i.e., before the growth rate falls to zero.

Table 6 shows two ratios, that of the projected stationary population to the 1985 population, and that for population momentum. The la:ter is the ratio to the 1985 population of