rafmacalaba/datause-encoder-data huggingface dataset:
# **Measuring Financial Inclusion:**

## **The Global Findex Database**

Asli Demirguc-Kunt and Leora Klapper<sup>*</sup>

**This Version: April, 2012**

**Abstract:** This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The data show that 50 percent of adults worldwide have an account at a formal financial institution, though account penetration varies widely across regions, income groups and individual characteristics. In addition, 22 percent of adults report having saved at a formal financial institution in the past 12 months, and 9 percent report having taken out a new loan from a bank, credit union or microfinance institution in the past year. Although half of adults around the world remain unbanked, at least 35 percent of them report barriers to account use that might be addressed by public policy. Among the most commonly reported barriers are high cost, physical distance, and lack of proper documentation, though there are significant differences across regions and individual characteristics.

_Keywords_ : Financial Inclusion; Financial Institutions; Emerging Markets _JEL Codes_ : G2, G21, O16

* Demirgüç-Kunt: World Bank, ademirguckunt@worldbank.org; Klapper: World Bank, lklapper@worldbank.org. We thank Franklin Allen, Oya Pinar Ardic Alper, Thorsten Beck, Massimo Cirasino, Robert Cull, Maya Eden, Asli T. Egrican, Tilman Ehrbeck, Michael Fuchs, Xavi Gine, Markus Goldstein, Ruth Goodwin-Groen, Raul Hernandez-Coss, Richard Hinz, Jake Kendall, Aart Kraay, Alexia Latortue, Sole Martinez Peria, Ignacio Mas-Ribo, Jonathan Morduch, Nataliya Mylenko, Mark Napier, Douglas Pearce, Bikki Randhawa, Richard Rosenberg, Armida San Jose, Kinnon M. Scott, Peer Stein, Gaiv Tata, Jeanette Thomas, Klaus Tilmes, Augusto de la Torre, Rodger Voorhies, and Alan Winters for their valuable and substantive comments during various stages of the project.  The team is also appreciative for the excellent survey execution and related support provided by Gallup, Inc. under the direction of Jon Clifton. We are especially grateful to the Bill & Melinda Gates Foundation for providing financial support making the collection and dissemination of the data possible. This paper was prepared with outstanding assistance from Douglas Randall.  This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent.
# Figure 3. Long-term inflation expectations

## A. Inflation expectations, advanced economies

<!-- Start of picture text --> Percent Median<br>8 Interquartile range<br>6<br>4<br>2<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018<br><!-- End of picture text --> C. Central bank transparency, advanced economies

## B. Inflation expectations, EMDEs

<!-- Start of picture text --> Percent Median<br>8 Interquartile range<br>6<br>4<br>2<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2018<br><!-- End of picture text -->

## D. Central bank transparency, EMDEs

<!-- Start of picture text --> Index (15=best) Median Index (15=best) Median<br>15 Interquartile range 15 Interquartile range<br>12 12<br>9 9<br>6 6<br>3 3<br>0 0<br>E. Share of economies with declines in inflation F. Inflation expectations, EMDEs<br>expectations, 1995-2018<br>Percent of countries Percent High transparency<br>80 8<br>Low transparency<br>70<br>6<br>60<br>4<br>50<br>40 2<br>30 0<br>20<br>Advanced economies EMDEs<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2014<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2016<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2014<br><!-- End of picture text --> Source: Consensus Economics; Dincer and Eichengreen 2014; International Monetary Fund; World Bank. Note: EMDEs = emerging market and developing economies.

A.B.E.F. Inflation expectations are five-year-ahead expectations of annual inflation.

A. Based on a sample of 24 advanced economies during 1990H1-2018H1.

- B. Based on a sample of 23 EMDEs during 1995H1-2018H1.

- C. Based on a sample of 24 advanced economies.

D.F. Based on a sample of 23 EMDEs. F. High (low) transparency countries are defined as those with central bank transparency above the 75th (below the 25th) percentile of EMDEs.

20
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Kennedy, Paul. 1994. _Preparing for the Twenty-First Century_ . New York: Vintage.

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70
interventions at the beginning of each day.

Enumerators attempted to interview all acutely ill patients with malaria-like symptoms at clinic intake on these observation days. We classified a patient as “acutely ill” if they were feeling sick (neither preventive care nor follow-up visit for earlier treatment) and exhibited any of the following symptoms: fever, chills, excessive sweating, nausea, vomiting, diarrhea, poor appetite, headache, cough, weakness, fatigue, or reduced consciousness. We interviewed the patient (or a caretaker, in the case of children or the very sick) both before and after meeting with a provider. The survey covered demographic characteristics, ex-ante perceptions about their condition, symptoms, any prior treatment and/or diagnosis, medications prescribed and purchased, and blood tests taken at the clinic. Finally, the survey collected data on the price of consultation and treatment.

**Patients: Home Follow-Up Survey.** In order to collect independent data on patients’ underlying malaria status and satisfaction, we conducted a follow-up survey of patients at their homes. This survey targeted a random subset of 1,669 patients who participated in the in-clinic survey. In addition to conducting an RDT to learn the true malaria status of the patient and asking a series of questions designed to measure satisfaction with care received at the clinic, the survey also collected data on any additional medications or tests taken after the clinic visit. Appendix Table A3 shows that the probability of taking the home survey and home-based RDT are uncorrelated with the extended training and patient information treatments. It also shows that 87 percent of patients selected for the home survey were successfully interviewed, and 64 percent took a home-based RDT.

**Health Workers: Endline Survey.** We selected up to three care providers per clinic for a post-intervention endline survey. We interviewed the clinic director and randomly selected one other doctor and one other care provider for an interview, including nurses, health technicians, and midwives (subject to staffing). The survey included questions on providers beliefs about the performance of RDTs and microscopy tests, perceived patient knowledge, patient requests for medications, and personal preferences regarding malaria diagnosis and treatment.

**Timeline.** The health worker trainings were conducted on November 2-4, 2016. In-clinic data collection ran from November 14 to December 30, 2016, with the provider endline following shortly thereafter, from December 10, 2016, to January 6, 2017. Thus, our experiment can only capture the short-run (0-2 month) effect of the extended provider training.

# **3.4 Predicting Malaria Risk**

One of our primary research aims is to understand how improving information about RDTs impacts the quality of care at clinics. While home-based RDTs give a very precise signal 11
Table 1: Correlation Matrix for Dependent Variables Used in the Cooperation Equations

||_NPRJ_|_TCO2_|_VINV_|_TCO2YR_|
|---|---|---|---|---|
|Numberof Projects|1||||
|TotalCO2 Abatement|0.812|1|||
|Volume of Investment|0.913|0.946|1||
|TotalCO2 Abatement peryear|0.757|0.995|0.919|1|
|Annual Investment cost|0.870|0.961|0.993|0.945|

Table 2: Project-level descriptive statistics

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|AnnualCO2abatement (ktCO2e)|2956|140.64|487.32|4|10437|
|Total CO2 abatement during<br>projectlife (ktCO2e)|2956|1120.42|3583.17|3.5|73059|
|CDM-based electricity generation<br>capacity (MW)|2474|31.20|100.09|0|1560|
|Investment cost<sup>a</sup> (US$)|1199|3.31E07|6.22E07|7222|7.87E08|

Notes:<sup>a</sup> All monetary values in this paper are 2000 constant US$.

26
We also find that other governance mechanisms have supported the growth of Indian firms. First, Burkart, Panunzi, and Shleifer (2003) link the degree of separation of ownership and control to different legal environments, and show that _family-run_ (professionally managed) firms will emerge as the dominant form of ownership structure in countries with _weak_ (strong) minority shareholder protection. Our survey evidence in this section and empirical results on the listed firms in section IV above, along with similar evidence in other Asian countries, suggest that family firms are a norm in India and other Asian countries. In fact, the combination of family firms and reputation-based informal mechanism may be one of the important factors behind the success of many family and group-based (listed and unlisted) firms in India (e.g., Khanna and Palepu 2000; Khanna and Yafeh 2005; Gopalan et al. 2005) in spite of weak standard corporate governance mechanisms, as reputation concerns motivate all managers (affiliated with the founder’s family) and member firms to take actions that maximize firm/group value, which in turn benefit non-controlling shareholders as well. Second, Allen and Gale (2000a) show that, if cooperation among different suppliers of inputs is necessary and all suppliers benefit from the firm doing well, then a good equilibrium with no external governance is possible, as internal, mutual monitoring can ensure the optimal outcome. In section III and our survey of SME firms, we presented evidence on the importance of trade credits as a form of financing for firms in wholesale and retail industries. Cooperation and mutual monitoring can ensure payments (as long as funds are available) among business partners and group-affiliated firms despite the lack of external monitoring and contract enforcement. The importance of trade credits is also found in other emerging economies (e.g., survey-based results of Vietnamese firms in McMillan and Woodruff (1999a) and AQQ (2005) survey of Chinese firms), as well as in developed countries (see Berger and Udell 1998 for evidence on small firms in the U.S.).

It is worth mentioning how entrepreneurs and investors alleviate and overcome problems associated with government corruption. According to proponents of institutional development (e.g., 37
time (which is why we present the two coefficients separately rather than pool them). The treatment effect on profits two years after the training is still positive but no longer statistically significant. Regressions include a time dummy (not shown) to indicate the survey wave and standard errors are in parentheses, clustered at the firm level.

In panel B we present the results for the PI and BSED trainings, where we find no evidence of any impact on business performance outcomes with no statistically significant differences between treatment and control groups.

TABLE 5: IMPACT OF TRAININGS ON BUSINESS PERFORMANCE

||Business<br>closure|Average Monthly<br>Profits (ETB)|Log Monthly<br>Profits|Average Yearly<br>Profits (ETB)|Average Monthly<br>Revenues (ETB)|Business Costs<br>Monthly (ETB)|Number of<br>Employees|Hours owner worked<br>per week|Capital Value<br>(Machinery) (ETB)|
|---|---|---|---|---|---|---|---|---|---|
||OLS|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|ANCOVA|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**_Panel A: DOT study_**||||||||||
|DOT_after one year|-0.0323|1155.2**|0.362**|5709.3|5466.2|-9770.6|-0.0898|-1.224|5722.9|
||(0.0251)|(559.3)|(0.182)|(6194.4)|(5831.0)|(7332.2)|(0.234)|(2.348)|(16927.3)|
|DOT_after two years|-0.0142|964.7|0.238|8167.3|-1825.9|-8563.1|-0.270|-2.861|2474.5|
||(0.0318)|(667.6)|(0.162)|(6146.3)|(8289.2)|(9380.2)|(0.221)|(2.290)|(18500.2)|
|Observations|1455|1119|1119|1065|1147|1184|1184|1177|1181|
|Control Group Mean|0.150|4693.3|7.259|56299.1|36569.7|42594.8|2.042|64.39|81381.99|
||(0.0187)|(378.4)|(0.138)|(5164.8)|(5066.4)|(5824.7)|(0.241)|(1.792)|(13971.95)|
|p_value: DOT_after one year =<br>DOT_after two years|0.553|0.800|0.578|0.766|0.381|0.913|0.394|0.571|0.880|
|**_Panel B: PI/BSED_**||||||||||
|Personal Initiative (PI)|-0.0312|143.3|0.157|637.7|-7,339|-1462.89|-0.0495|0.814|18,714|
||(0.0226)|(768.2)|(0.129)|(9,876)|(8,004)|(5315.19)|(0.237)|(1.642)|(42,857)|
|Basic Skills (BSED)|-0.00599|895.5|0.283|2,566|-681.7|-3434.29|-0.162|-1.338|-8,227|
||(0.0225)|(576.0)|(0.268)|(7,979)|(10,249)|(6101.7)|(0.203)|(1.499)|(44,689)|
|Observations|1,777|1,701|1,701|1,612|1,676|1,777|1,777|1,771|1,748|
|Control Group Mean|0.171|12415.2|7.988|116422.5|76230.0|72147.46|4.168|48.94|256993.3|
||(0.0180)|(1070.8)|(0.143)|(10558.5)|(10239.8)|(8735.3)|(0.369)|(1.385)|(40320.5)|
|Controls used|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

(1) Controls include: received WEDP loan at baseline, age of owner, education more than secondary, number of children, number of workers and digitspan score at baseline. (2) ANCOVA regressions are shown for businesses operational at follow-up

# **4.3.2 Impact on psychological constructs**

Table 6 presents the impacts on psychological outcomes. We measure a change in business mindset by creating indices from a set of psychology statements that proxy for confidence and motivation. Panel A in
playgroup enrollment between poor and non-poor children.

Finally, we do not find significant differences in enrollment duration in kindergarten between treatment and comparison villages, either in 2013 or in 2016. On average, all children in the sample seem to have completed about 5 months of kindergarten by 2013 and about 10 months of kindergarten by 2016 – with no substantial variation by household wealth or parenting.

<u>Table 2. Impact on enrollment outcomes for the younger cohort</u>

|**Outcome**|**Survey**<br>**Year**||**All**|**Poor**|**Non-poor**|**Low**<br>**Parenting**<br>**Score**|**High**<br>**Parenting**<br>**Score**|
|---|---|---|---|---|---|---|---|
||||(1)|(2)|(3)|(4)|(5)|
|||Coeff.|0.499***|0.494***|0.504***|0.501***|0.498***|
||2013|(S.E.)|(0.022)|(0.029)|(0.024)|(0.025)|(0.026)|
|||Comp. mean|0.00732|0.0116|0.00421|0.00699|0.00767|
|Ever enrolled in<br>l||Obs.|2,778|1,201|1,577|1,540|1,238|
|project paygroup<br>until surve ear||Coeff.|0.498***|0.539***|0.470***|0.466***|0.532***|
|y y|2016|(S.E.)|(0.023)|(0.029)|(0.027)|(0.027)|(0.026)|
|||Comp. mean|0.0673|0.0499|0.0802|0.0686|0.0659|
|||Obs.|2,894|1,289|1,605|1,834|1,060|
|||Coeff.|-0.220***|-0.193***|-0.241***|-0.215***|-0.223***|
||2013|(S.E.)|(0.037)|(0.044)|(0.042)|(0.041)|(0.043)|
|Ever enrolled in||Comp. mean|0.334|0.284|0.371|0.324|0.345|
|non-project||Obs.|2,778|1,201|1,577|1,540|1,238|
|playgroup until||Coeff.|-0.221***|-0.190***|-0.241***|-0.209***|-0.232***|
|survey year|2016|(S.E.)|(0.037)|(0.045)|(0.042)|(0.041)|(0.045)|
|||Comp. mean|0.407|0.338|0.459|0.389|0.427|
|||Obs.|2,894|1,289|1,605|1,834|1,060|
|||Coeff.|-0.075|-0.074|-0.085*|-0.090|-0.059|
||2013|(S.E.)|(0.040)|(0.053)|(0.041)|(0.048)|(0.043)|
|||C|0532|0441|0598|0522|0542|
|E lld i||omp. mean|.|.|.|.|.|
|ver enroe n<br>kindergarten until||Obs.|2,778|1,201|1,577|1,540|1,238|
|<br>survey year||Coeff.|-0.086*|-0.068|-0.098*|-0.098*|-0.075|
||2016|(S.E.)|(0.040)|(0.054)|(0.038)|(0.044)|(0.046)|
|||Comp. mean|0.743|0.670|0.796|0.735|0.751|
|||Obs.|2,894|1,289|1,605|1,834|1,060|
|||Coeff.|7.781***|7.319***|8.129***|7.748***|7.843***|
||2013|(S.E.)|(0.396)|(0.511)|(0.447)|(0.454)|(0.469)|
|||Comp. mean|0.0622|0.122|0.0189|0.0559|0.0691|
|Months enrolled in<br>||Obs.|2,778|1,201|1,577|1,540|1,238|
|project playgroup<br>til||Coeff.|8.683***|8.840***|8.599***|8.365***|9.022***|
|un survey year||(S.E.)|(0.413)|(0.544)|(0.469)|(0.496)|(0.491)|
||2016|<br>Comp. mean|<br>0.327|<br>0.321|<br>0.331|<br>0.320|<br>0.334|
|||<br>Obs.|2,894|1,289|1,605|1,834|1,060|
|||Coeff.|-3.001***|-2.094***|-3.743***|-2.721***|-3.284***|
|||(S.E.)|(0.637)|(0.571)|(0.844)|(0.629)|(0.798)|
|Months enrolled in|2013|Comp. mean|4.635|3.290|5.613|4.228|5.082|
|non-project||<br>Obs.|2,778|1,201|1,577|1,540|1,238|
|<br>playgroup until||Coeff.|-3.187***|-2.476***|-3.706***|-2.913***|-3.443***|
|survey year|2016|(S.E.)|-0.64|(0.651)|(0.814)|(0.660)|(0.796)|
|||Comp. mean|5.346|4.241|6.167|4.975|5.741|
|||Obs.|2,894|1,289|1,605|1,834|1,060|

11
PPR Working Paper Series

||Title|Author|Date|Contact|
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|WPS89|Export Quota Allocations,<br>Export<br>Earnings and Market Diversifications|Taeho Bark<br>Jaime de Melo|September 1988|<br>C. Cabana<br>51539|
|WPS90|AFramework for Analysis of Mineral<br>Tax Policy in Sub-Saharan<br>Africa|Robert F. Conrad<br>Zmarak M. Shalizi|September 1988|<br>A. Bhalla<br>60359|
|WPS91|<br>Israel's<br>Stabilization<br>Program|Nissan Liviatan|September 1988|<br>N. Liviatan<br>61763|
|WPS92|A Model of Cocoa Replanting<br>and New<br>Planting In Bahia, Brazil: 1966-1985|Pravin K. Trivedi|September 1988|<br>_0._Gustafson<br>33714|
|WPS93|The Effects of Education,<br>Health and<br>Social Security on Fertility In<br>Developing<br>Countries|Susan H. Cochrane|September 1988|<br>S. Ainsworth<br>31091|
|WPS94|The World Bank's Population<br>Lending<br>and Sector Review|George B. Simmons<br>Rushikesh Maru|September 1988|<br>S. Ainsworth<br>31091|
|WPS95|International<br>Trade and Imperfect<br>Competition: Theory and Application<br>to the Automobile<br>Trade|Junichl Goto|September 1988|<br>J. Epps<br>33710|
|WPS96|The Private Sector and Fami!y Planning<br>In Developing<br>Countries|Maureen A. Lewis<br>Genevieve Kenney|September 1988|S. Ainsworth<br>31091|
|WPS97|Export-Promoting<br>Subsidies and What<br>to Do About Them|Richard H. Snape|September 1988|J. Sweeney<br>31021|
|WPS98|Diversification<br>in Rural Asia|Agriculture<br>and Rural<br>Development<br>Staff|October 1988|S. Barghouti<br>38408|
|WPS99|Trade Policies and the Debt Crisis|Sam Laird<br>Julio Nogues|September 1988|S. Torrijos<br>33709|
|WPSIOO|Public Infrastructure<br>and Private<br>Sector Profitability<br>and Productivity<br>in Mexico|Anwar Shah|September 1988|A. BhalIa<br>60359|
# **Appendix Table A.1: Impact of Parents' Family Background on Children’s Years of Schooling** **<u>(junior middle school as the threshold for parents' education)</u>**

|**Estimates**||**(1)**<br>**Parents'**<br>**Education**|**(2)**<br>**Parents'**<br>|**(3)**<br>**Education and O**<br>|**(4)**<br>**ccupation**<br>|**(5)**<br>**Test of e**|**(6)**<br>**quality**|
|---|---|---|---|---|---|---|---|
|||**Full sample**|**Non-farm, low**<br>**edu**|**Farm, high**<br>**edu**|**Non-farm, high**<br>**edu**|||
|||||||**z score**|**z score**|
|**_Daughters_**||||||||
||OLS|**1.36**<br>9.74***|**0.57**<br>3.32***|**1.19**<br>6.21***|**1.78**<br>9.99***|-2.41**|-2.25**|
|**Matching**|A-I<br>matching||**0.72**<br>3.62***|**1.69**<br>6.42***|**2.17**<br>6.94***|-2.94***|-1.17|
|**estimator**|BC-RM||**0.66**<br>3.03***|**1.22**<br>5.37***|**1.93**<br>10.74***|-1.78*|-2.45**|
|**Propensity**<br>**score**<br>**weighted**|NIPW||**0.59**|**1.26**|**1.85**|-2.46**|-2.13**|
|**estimator**|||(0.31,0.87)|(0.96,1.61)|(1.53,2.14)|||

|**_Sons_**|OLS|**1.42**<br>12.21***|**0.81**<br>5.42***|**1.31**<br>8.31***|**2.08**<br>14.37***|-2.36**|-3.59***|
|---|---|---|---|---|---|---|---|
|**Matching**|A-I<br>matching||**0.70**<br>4.25***|**1.29**<br>6.33***|**2.07**<br>8.26***|-2.25**|-2.41**|
|**estimator**|BC-RM||**0.62**<br>3.54***|**1.41**<br>8.22***|**2.54**<br>14.76***|-3.22***|-4.65***|
|**Propensity**<br>**score**<br>**weighted**|NIPW||**0.82**|**1.41**|**2.21**|-2.64**|-3.87***|
|**estimator**|||(0.60,1.09)|(1.16,1.66)|(1.92,2.44)|||

## **_Notes_**

- 1) Dependent variable=children's years of schooling;

- 2) For the column (1), the interest variable=parents' education dummy, dummy =1 if at least one of parents more than junior middle school, 0=otherwise;

3) For all columns, the control variables are children's age, children's age squared, mother's age, mother's age squared, father's age, father's age squared; 4) For the columns (2) - (4), the interest variable is parents' education and occupation status dummy. The dummy=1 if treatment group, =0 otherwise; base group is parents (farm, low edu)

- 5) A-I matching=Abadie and Imbens (2002)

- 6) BC-RM = Biased corrected radius matching;

- 7) NIPW = normalized inverse probability weighted propensity score estimator of Hirano and Imbens (2001);

8) The computation of NIPW uses STATA program written by Millimet and Tchernis (2013); 9) Estimates and t statistics are shown. For NIPW, 90% confidence interval is given in parentheses, which is boostrapped using 250 replications; 10) "high edu" stands for more than junior middle schooling, "low edu" stands for junior middle schooling or less; 11) ***, **, and * Denotes statistical significance at 1%, 5%, and 10% level.