rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Program Outreach and Sustainability**

||**BK**|**K**|**LK**|**P**|**PHB**|**K**|**P4**|**K**|**BK**|**D**|
|---|---|---|---|---|---|---|---|---|---|---|
|**________________________________**|**1993**|**1995**|**1993**|**1995**|**1993**|**1995**|**1993**|**1995**|**1993**|**1995**|
|**Outreach**||||||_|||||
|Average loan size percent of<br>GDP/capita|9|10|<br>9|7|10-13|7-13|7<br>l|7|8|7|
|Annual number of loans issued<br>_<br>(thousands)a|18|<br>35|3|4|10|27|113|<br>164|1,713|1,607|
|,Annual loan volume (US$ millions)|1.4|3.4|0.2|0.3|1.6|_5.5_|6.2|10.9|110.6|113.4|
|Loan volume real growth rate (percent)|NA|42|-20|28|7|73|162|<br>-14|8|-8|
|Women percent of borrowers|NA|40|NA|62|NA|50|NA|_50_|NA|40|
|**Sustainability**||||**I**|||||||
|Average real interest rate (percent)|49|50|137|137|<br>89|89|16|<br>16|111|111|
|Real interest rate required to eliminate<br>subsidies (perceent)I|118|<br>50|<br>288|172|<br>550|244|68|81|NA|NA|
|Annual default rate (percent)|23|<br>3|<br>12|<br>6|5|1|NA|2-3|4.4|2.6|
|Loan volume in arrears percent of<br>volume outstanding<br>or Number of groups with arrears<br>percent of groups with outstanding<br>loans|27|6|<br>17|<br>20|20|11|6|19|18|<br>18|
|I Voluntary savings percent of loans|16|<br>31|<br>35|18|<br>NA|NA|NA|NA|5|_5_|

Information was not available for items labeled "NA" in the above table.

a/ For PHBK and P4K figures are estimates based on number of loans issued to groups and estimated average number of members in each group. For BKD, the figure is an estimate based on the number of loans issued in December of each year.

## **Lessons Learned and Recommendations**

The programs show that microfinance initiatives can:

- provide low-income people with a valuable service at an initial, affordable cost to governments or donors;

- obtain strong financial performance through the use of incentives for staff and clients;

- reduce, and even eliminate, the need for subsidies by charging high real interest rates, aggressively pursuing repayments, and achieving a significant volume of business;

- face political pressures that undermine their commitment to sound banking practices;

- be weakened by poorly-designed supervision systems;
# **Table A3 – Testing for Pre-Trends, Given Exposure to the Energy Sector in August 2014**

This table tests for the existence of pre-trends across banks with varying exposures to the energy sector in June 2014 on a series of bank-level characteristics. Observations are at the bank-month level. Dependent variables are listed in the columns. _Loansb,m_ are total monthly loans of bank _b_ in logs. _CDS Spreadsb,m_ is the log of CDS spreads of five-year maturity bonds of bank _b_ in month _m. Stock Priceb,m_ is the log of the stock price of bank _b_ in month _m. Delinquencyb,m_ is the share of delinquent loans of bank _b_ in month _m_ . _ExposureEnergyb,m_ represents lending to the energy sector of bank _b_ as a share of its tier 1 capital in month _m_ . Standard errors are double clustered at the bank and month levels. Detailed variable definitions <u>are provided in Table A1. The sample period is from January 2013 to August</u> 2014. *** p<0.01, ** p<0.05, * p<0.1.

||ExposureE<br>|nergyb,m<br>|Total Le<br>|ndingb,m<br>|CDS Sp<br>|readsb,m<br>|Stock P<br>|riceb,m<br>|Delinqu<br>|encyb,m<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|ExposureEnergyb,Aug14|0.333***||0.001||-0.032||-0.015||-0.027||
||(0.103)||(0.029)||(0.032)||(0.047)||(0.023)||
|ExposureEnergyb,Aug14*Trendm|0.006|0.004|-0.000|-0.000|0.003|0.003|-0.000|-0.000|0.001|0.001|
||(0.009)|(0.003)|(0.003)|(0.000)|(0.002)|(0.002)|(0.004)|(0.000)|(0.003)|(0.001)|
|Observations|306|306|306|306|124|124|170|170|306|306|
|R-squared|0.362|0.946|0.163|0.996|0.205|0.817|0.006|0.998|0.046|0.863|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|SD(ExposureEnergyb,Aug14)|6.5|6.5|6.5|6.5|6.5|6.5|6.5|6.5|6.5|6.5|

39
-9-

# II. DATA DESCRIPTION

13. Long, consistent-quality time series on tropical timber prices are rare. The length of the series is of importance when performing integration and coirtegration tests since the critical values for both are based on large samples; moreover, since cointegration is a test for long-term relationships, the number of observations in the sample is not as important as the time span Lhat the observations cover. For example, a sample of 100 quarterly observations (4 times 25 years would be preferable to a sample consisting of 180 monthly observations (12 Limes 15 years).

14. The time series were chosen to cover several species and products in the different markets. While it was possible to find some long series for logs in the two markets, usable price series for sawnwood and plywood were found only lor the Southeast Asian region and then only for one species for sawnwood and one type of plywood.

15. A brief description of each price series follows: Logs

- Meranti (Malaysia). Besc quality, dark red meranti from Sabah. This price is the sale price charged by importers in Japan. Data from 1956 to 1989.
|Sensitive variables|Parameters of the<br>model<br>|lower value|upper value|
|---|---|---|---|
|Economic growth|ln(Yt/Y0)<br>t|1.45%.year<sup>−1</sup>|3%.year<sup>−1</sup>|
|Emissions|Parameter of decar-<br>bonization ψ0|0.5%/year|1.5%/year|
|Abatement costs|BK2008, ν, ζ, α|110$/t CO2, 3, 0,<br>1%|1023$/t CO2, 4, 15,<br>1.35%|
|Ethical preferences<br>on future|Pure time prefer-<br>ence ρ|0.1%|2%|

Table 1: Value ranges for the socio-economic scenarios 3%], with higher growth rates over the decades. Its impact on the volume of carbon emissions is determined by the carbon intensity σt which in turn is driven by ψt which captures the joint impact of technical change and fossil resources depletion: σt = σ0e<sup>−ψtt</sup> with ψt > 0. For a given population level, carbon emissions level is proportional to E0e<sup>(g−ψt)t</sup> . As long as g > ψt (with ψt set at its initial level), carbon emissions would remain strictly growing with time for most (ψ0, g) couples. To guarantee that emissions decrease by the end of the century, as predicted by the overwhelming majority of available scenarios, ψt progressively increases so that it can become higher than g: with β > 0, the speed of growth of ψt. Thus, Beliefs about mitigation costs are captured through the price of the backstop carbon free technology BK2008 capable to achieve total abatement of CO2 emissions. This technology is supposed to be available at each time period, with a cost declining over time, but it penetrates only when it becomes cost-efficient at a rate function of the rate of capital turnover. Costs of backstops are not explicitly mentioned by the last IPCC report but can be determined in order to fit with its published cost data (Figure 3.25 page 205). We retained an initial range of [110$/tCO2, 1023$/tCO2] and decreases at a rate of 1% and 1.35% for optimistic and pessimistic opinions respectively.

As regards to pure time preference ρ, it is not the place to close the dispute between the advocates of normative choice of that parameter and the advocates of a positive approach consistent with observed behaviors and virulent ethical controversies rooted in old intellectual traditions (Ramsey (1928) 8
Several of these surveys were harmonized by the Economic Research Forum (Egypt’s 20122013, 2015, 2017-2018 HIECSs; Iraq’s 2007 and 2012 IHSESs; Jordan’s 2010-2011, and 20132014 HIESs; the West Bank and Gaza’s 2009 and 2011 PECSs; and Tunisia’s 2005 and 2010 NSHBCs. The most recent surveys for Egypt (2019-2020), the West Bank and Gaza (2016-2017), Tunisia (2015 and 2021), and the Mauritanian EPCVs were obtained from national statistical agencies CAPMAS, PCBS, INS and ONS, respectively. We implemented careful harmonization of these surveys with the previous survey years and translated the variables from Arabic or French to English.

We present the poverty lines for the six countries in Tables A.2-A.7 (Appendix A), compiling them from official sources and World Bank publications. We used region-specific poverty lines within each country to account for spatial differences in consumption (expenditure) patterns and price levels.<sup>7</sup> Since our focus is on poverty analysis, we used consumption values and national poverty lines in local currency units and in survey-year prices to sidestep conversion issues (e.g., with the PPP or market exchange rates) and adjustment for inflation.

# **3.2. Living-standards indicator**

Expenditure is widely regarded as a better indicator of permanent income when households, particularly in poorer countries, exercise consumption smoothing and use savings to augment unstable incomes due to seasonal or informal employment or unexpected shocks (Deaton, 1997; Deaton and Zaidi, 2002; Mancini and Vecchi, 2022). We use household consumption expenditures

> 7 We were able to do this for all countries in our sample except for Jordan. According to DOS reports, Jordan’s Department of Statistics (DOS) did not publish region-specific poverty lines and used a single poverty line for all of Jordan in 2010 and 2013. Jordan’s DOS does not publish region-specific Consumer Price Indices so we were unable to take spatial price differences into consideration.

16
<!-- Start of picture text --> Age-groups: 45-54 years, 55-65, 65-74, 75-84, 95»<br>All-cause deaths (expected): 2020 Excess deaths: 2020 Excess deaths: 2021<br>N countries = 66 N countries = 54 N countries = 57<br>10<br>a °<br>8 o (|? — °<br>° i re} y, |\ O °<br>° mAOTO ° 5<br>omy } ' aE v I °<br>° : 0 e<br>ane J 4 5 5° i<br>4 +c i Oo incO y=lo £9 \ 2)52 eee = 0 0 D o: = — om<br>° & OoOo eawehishiy/ Bam D> <1 RAY Ao£25 5S° Ooooer °o. o 0 peicra2 ‘:<br>° LEE eve: = O 30s Meee ae 0 ? 0 ° elle l= ° 05 e<br>° ° 8 ) i?) role} ° 6) ° ° 8<br>foo<br>0 ° 8<br>$20,000 $40,000 $60,000 $20,000 $40,000 $60,000 $20,000 $40,000 $60,000<br>GNI per capita (PPP)<br>Note: Results are based on the country-specific Poisson model described in the text applied to the WHO Global<br>Excess Deaths dataset. Each point represents the median predicted relative risk of mortality between men and women<br>for a single country from a simulation of 1,000 parameter draws of the Poisson model. The lines for each group are<br>loess fits of this median ratio of predicted mortality, using 85% of the points in the neighborhood at each point<br>along the x-axis. The shaded areas show the lower 25th and upper 75th percentiles of the predictions. The y-axis<br>is censored at 10 in the figure; in 2020, there are 2 countries with data points above this cut-off: Dominican<br>Republic (all age-groups) (sex ratio 11-22, GNI per cap $18,080), Netherlands (age 45-54) (sex ratio 12.9, GNI per<br>cap $58,980)<br><!-- End of picture text -->
WPS3290

# **Analyzing Building Height Restrictions: Predicted Impacts, Welfare Costs, and a Case Study of Bangalore, India**

by Alain Bertaud _Consultant, World Bank 166 Forest Road Glen Rock, NJ 07452-3017 email: duatreb@msn.com_ and Jan K. Brueckner _Department of Economics and Institute of Government and Public Affairs University of Illinois at Urbana-Champaign Champaign, IL 61820 email: jbrueckn@uiuc.edu_ Abstract This paper analyzes the effects of building height restrictions, providing a concrete welfare-cost estimate for the city of Bangalore. Relying on several theoretical results, the analysis shows that the welfare cost imposed on its residents by Bangalore's building height restriction ranges between 3 and 6 percent of household consumption. This burden represents a significant share of individual resources, and its presence may push many marginal households into poverty.

## World Bank Policy Research Working Paper 3290, April 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Analyzing Building Height Restrictions: Predicted Impacts, Welfare Costs, and a Case Study of Bangalore, India
12 Table 5: Employees in the Formal Sector. 1965-84, by Citizenship and Economic Sectork<sup>81</sup>

||1965|1970|1975|1980|1984|
|---|---|---|---|---|---|
|Agriculture,Forestry,Fishing<br>|32700|<br>34610|<br>36100|32630|<br>35400|
|Zambian<br>|32070|<br>34090|<br>3479n|31580|<br>34660|
|Non-Zambian|630|520|1310|1050|<br>740|
|Mining and Quarrying|52360|<br>57640|<br>64750|63070|<br>58470|
|Zambian|44820|52130|<br>54440|57110|<br>54390|
|Non-Zambian|7540|5510|10310|5960|<br>4080|
|Manufacturing|27770|38160|44330|47760|<br>48200|
|Zambian|24030|35230|<br>41230|45860|<br>46720|
|Non-Zambian|3740|2930|3100|1900|<br>1480|
|Construction|45320|68740|71750|43750|<br>33610|
|Zambian|43100|66220|67790|42070|<br>32570|
|Non-Zambian|2220|2520|3960|1680|<br>1040|
|Transport & Communications|14560|<br>22340|<br>22050|23940|<br>24000|
|Zambian|11870|<br>19720|<br>20490|23040|<br>23340|
|Non-Zambian|2690|2620|1560|900|<br>660|
|Services(b)|88350|121480|154510|168150|165510|
|Zambian|72290|108200|142430|159780|159490|
|Non-Zambian|16060|<br>13280|<br>12080|8370|<br>6020|
|TOTAL|261060|342970|393490|379300|365190|
|Zambian|228180|315580|361170|359440|351170|
|Non-Zambian|32880|<br>27390|<br>32320|19860|<br>14020|

# Notes;

- a - Data are for fourth quarter of each year shown, except those for 1965, which are averages of all quarters, and for 1984, which are for June. Prior to 1975, the citizenship breakdown refers to Africans and non-Africans, rather than Zambian/nonZambian, respectively.

- b - Includes utilities, financial and business services, restaurants and hotels and other services.

- Sources: GRZ 1980b, and Meesook et al 1986: Appendix Table 1.
_8 Railway_ **_Refonrr_** _In the Central and Enstern European (CEE) Economies_

**Figure 12**

<!-- Start of picture text --> Shares of the U.S. Public Passenger<br>Market<br>(Percent of PassengerXKm)<br>Share N<br>sao<br>29 34 39 44 4S 54 58 84 69 74 7b; 84 89<br>Yewr<br>-Rail E3us B Air o WOW<br><!-- End of picture text -->

**Figure 13**

<!-- Start of picture text --> ohares of West European Freight Market<br>(Percent of Tonne-Kn)<br>SharARS6)<br>60<br>40<br>20<br>1985 1970 1975 1980 1985 1969<br>Year<br>Rail -- Trudck<br><!-- End of picture text -->
Table 5.1 – Welfare Decomposition by individual and household characteristics Dependent variable: log of welfare change.

|age<br>educ|0.0000<br>(0.0000)<br>0.0016***<br>(0.0001)|AGUASCALIENTES<br>BAJA CALIFORNIA|0.0113***<br>(0.0039)<br>0.0042<br>(0.0034)|GUANAJUATO<br>GUERRERO|-0.0121***<br>(0.0033)<br>-0.0166***<br>(0.0033)|QUERETARO<br>QUINTANA ROO|-0.0117***<br>(0.0037)<br>-0.0495***<br>(0.0038)|
|---|---|---|---|---|---|---|---|
|gender|-0.0001<br>(0.0007)|BAJA CALIFORNIA<br>SUR|0.0159***<br>(0.0046)|HIDALGO|-0.0116***<br>(0.0034)|SAN LUIS POTOSI|0.0173***<br>(0.0033)|
|hh_size|0.0005***<br>(0.0001)|CAMPECHE|-0.0412***<br>(0.0040)|JALISCO|-0.0077**<br>(0.0032)|SINALOA|0.0153***<br>(0.0034)|
|logpcexp|0.0046***<br>(0.0004)|COAHUILA|-0.0075**<br>(0.0034)|MEXICO|-0.0361***<br>(0.0031)|SONORA|-0.0040<br>(0.0034)|
|urban|0.0022***<br>(0.0006)|COLIMA|-0.0080*<br>(0.0043)|MICHOACAN|-0.0112***<br>(0.0032)|TABASCO|-0.0456***<br>(0.0034)|
|||CHIAPAS|-0.0393***<br>(0.0031)|MORELOS|-0.0152***<br>(0.0035)|TAMAULIPAS|-0.0091***<br>(0.0033)|
|Obs|8999|CHIHUAHUA|-0.0072**<br>(0.0033)|NAYARIT|0.0028<br>(0.0038)|TLAXCALA|-0.0101***<br>(0.0039)|
|R-Squared|0.798|DISTRITO FEDERAL|-0.0133***<br>(0.0032)|NUEVO LEON|-0.0060*<br>(0.0033)|VERACRUZ|-0.0127***<br>(0.0030)|
|||DURANGO|0.0165***<br>(0.0036)|OAXACA|-0.0375***<br>(0.0031)|YUCATAN|-0.0432***<br>(0.0034)|
|||||PUEBLA|-0.0115***<br>(0.0031)|ZACATECAS|0.0144***<br>(0.0037)|

Note: White corrected standard errors are shown in brackets. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively. State variables are dummies.

Figure 5.4 – Density Function <!-- Start of picture text --> observed distribution no liberalization distribution<br>.47316<br>.042105<br>6 10<br>log per capita expenditures<br>density<br><!-- End of picture text --> 53
**Table A4: Number of people exposed, by region (millions)**

||Total<br>population|Flood|Drought|Heatwave|Cyclone|Any shock|Any shock<br>(% of<br>population)|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific|2,294|445|332|834|401|1,477|64|
|Europe & Central Asia|914|81|202|18|0|277|30|
|Latin America & Caribbean|628|56|96|48|35|205|33|
|Middle East & North Africa|420|47|44|102|0|165|39|
|North America|371|20|64|68|28|152|41|
|South Asia|1,829|246|332|1,481|132|1,589|87|
|Sub-Saharan Africa|1,119|66|314|186|5|471|42|

Notes: Geographic regions use the World Bank’s regional classification including high-income economies: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.</u>

**Table A5:  Number of people exposed and vulnerable in each dimension, over time by region**

**<u>(millions)</u>**

||||**20**<br>Social|**10**<br>Financial|||
|---|---|---|---|---|---|---|
||Income|Education|<br>protection|<br>inclusion|Water|Electricity|
|_Region_|||||||
|East Asia and the Pacific|311|182|501|492|36|30|
|Europe and Central Asia|13|24|42|63|11|0|
|Latin America and the Caribbean|29|25|79|104|10|12|
|Middle East and North Africa|11|14|22|71|5|3|
|Other High-Income economies|3|1||9|1||
|South Asia|828|289|324|961|26|344|
|Sub-Saharan Africa|236|164|236|210|101|242|
|_FCV_|178|126|219|156|96|188|
||||**20**<br>Sil|**19**<br>Fiil|||
||Income|Education|oca<br>protection|nanca<br>inclusion|Water|Electricity|
|_Region_|||||||
|East Asia and the Pacific|45|198|629|212|22|29|
|Europe and Central Asia|10|1|39|26|9|2|
|Latin America and the Caribbean|22|20|101|68|7|10|
|Middle East and North Africa|26|15|10|74|6|4|
|Other High-Income economies|4|1||1|0||
|South Asia|510|300|333|516|18|318|
|Sub-Saharan Africa|286|185|270|194|119|223|
|_FCV_|215|154|235|166|108|188|

Notes: Income refers to having less than 1.5*$2.15 per household member, education refers to no adult having primary schooling, social protection refers to not receiving any social protection, and financial inclusion refers to not having a bank account. Water and electricity refer to access to improved water and electricity.

51
scenarios of climate change on agricultural productivity (low, medium, or high productivity)<sup>4</sup> to evaluate the changes by 2030 on global commodity prices and the incidence on the poverty headcount rate (defined as the portion of the country’s population living on less than $1 a day).

Ahmed et al. (2009) also analyze the climate-agriculture-poverty link. Their model is practically identical to that in Hertel et al. (2010) above, and is applied to 16 countries including Mexico. The main difference is the origin of the shocks to agriculture, which in this paper is derived from three different sources: (i) the percent of annual total precipitation due to events exceeding the 1961 to 1990 95th percentile; (ii) the maximum number of consecutive dry days; and (iii) the heat wave duration index. They analyze these effects for 30-year periods from 1970 to 2000 and 2071 to 2100 through simulations under the IPCC’s A2 scenario. When they simulate the poverty impacts from extreme dry events for the period 2070 to 2100, an additional 1.8% of the population in Mexico becomes impoverished by extreme climate in the future.

Andersen and Verner (2010, 2010a) employ municipal data in Mexico (and other Latin American countries) to estimate the cross-sectional relationship between climate and income in 2000. The estimated relationships are then used to simulate the impact of the climate changes for the next 50 years. They find that average temperatures and precipitation are not significantly related to income. They assume that a negative relationship exists between per capita income and poverty, and so their conclusion is that Mexico appears to be less vulnerable to climate change than most other Latin American countries. However, the analysis is very crude. They do not estimate the poverty-output elasticity, and do not consider income and population projections which are accounted for in this paper.

To assess the effects of climate change on poverty we propose a two-stage least squares regression (2SLS) where we first regress temperature and rainfall (along with municipal controls for elevation, longitude, latitude, distance from the municipality to the nearest road and state and year fixed effects) on municipal GDP per capita for 2000 and 2005, and then such predicted GDP per capita is used in a second equation to estimate municipal poverty on the same years.

Changes in GDP per capita affect poverty and vice versa. This is why we use rainfall and temperature in a first stage to instrument GDP and hence circumvent the problem of endogeneity. In other words, we explain the variation in GDP per capita through differences in

> 4The low productivity scenario depicts a world with rapid temperature change, high sensitivity of crops to warming, and a CO2 fertilization effect at the lower end of published estimates. The high productivity scenario represents a world with relatively slow warming, low sensitivity of crops to climate change, and high CO2 fertilization.

3
changed behavior as a result, with the main change being the use of the website to compare costs, and asking around for better rates. For the East Asians, 80 percent of those attending said it was useful and 75 percent would recommend it to others, with the most useful knowledge being about remittance fees. Only 21 percent said they had changed behavior, mostly in terms of examining the costs of sending money. Among the Sri Lankan sample, 91 percent of those answering the follow-up survey said the training had been useful, but only 2 people in the Sri Lankan sample who had attended training said they had changed their behavior as a result. These direct reports are therefore consistent with the empirical results, suggesting that there was a knowledge increase, and some changes in information seeking behavior for the Pacific Island group in particular, but no big changes in remitting behavior.

# **4. Discussion**

The training did succeed in increasing financial knowledge about the components of remittance costs, and in getting people to search for more information about the costs of sending money. It was fairly cheap to deliver – courses were typically taught in churches or other community spaces, and once the content was developed, the main costs were the time of the trainer, and snacks and refreshments for those attending – for an approximate cost of NZ$20-30 per attendee. Given these low costs, the benefits observed in knowledge and behavior may be enough to justify providing this course.

Nonetheless, despite the emphasis on the Westpac prepaid debit card as the lowest cost of remitting, and credit cards as a low cost of obtaining credit for the Pacific Island sample, we do not see any impact on these outcomes. The final round survey asked people why they were not using the cheapest method of remitting, and 41 percent replied that another method was more convenient for them, and 55 percent that another method was more convenient for the receiver. The latter is consistent with Gibson et al. (2007), who show the geographic spread of ATM facilities in Tonga covers a lower share of the population than Western Union offices. Further evidence of the lack of take-up of low cost technologies in the Pacific Islands comes from two other methods: the internet-only money transfer operator _KlickEx_ had the lowest costs overall but was unused by participants throughout our study, and in October 2011, _KlickEx_ linked with a major mobile phone provider, _Digicel_ , to offer remittance transfers into a mobile wallet in 19