rafmacalaba/data-use-mentions-extended huggingface dataset:
.266|2.266|2.266|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.0230|0.112|7.416|0.103|0.0140|0.766|0.730|0.0620|16.470|

_Note:_ The table presents the results of equation 1 for our outcomes related to migration. Columns 1–5 focus on outcomes for international migrants. Columns 6–9 focus on internal in-migration flows. All information was obtained from the Population Census of 2007. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Finally, we also looked at temporal migration for work by analyzing whether individuals work in a different census tract or municipality from where they live across the boundaries (Table D.22). We do not find that residents of former guerrilla areas are more likely to work outside their community, providing further evidence that out-migration may not drive the results.

It is puzzling that these people do not migrate today given better economic opportunities nearby. It is possible that they prefer not to leave their village due to strong social ties, more “rootedness,” and because they do not trust outsiders. This idea aligns with our results in Table 4 (Column 5), where we show that individuals in former guerrilla areas are more likely to trust members of their
# **Appendix C: Robustness Tests**

Table C.1. Excluding Border Departments

||_Likeliho_|_od of having chil_|_dren of_||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.034***|-0.072***|-0.015|-0.002|
||(0.011)|(0.013)|(0.010)|(0.002)|
|Observations|3,588|3,588|3,588|3,588|
|Observations by wave|1,196|1,196|1,196|1,196|
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic Sampling<br>|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). The analysis excludes migrants in the departments bordering Venezuela. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Premigration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a
Remittances offer one medium for risk sharing between households. Table 1 provides a summary of the average remittance flows over the past 12 months in 2010 between the migrant households and households living in or near their baseline villages. While non-migrant households were net receivers of remittances, Table 1 shows that transfers flow both ways. This could lead one to think – mistakenly as the analysis below reveals – that these are relationships of reciprocal risk sharing. The data in Table 1 are self-reported and it is interesting to note that migrants claim to send more home than non-migrants acknowledge. A similar discrepancy does not exist in migrant-migrant or stayer-stayer dyads<sup>14</sup> .

# _[Table 1 here]_

Table 2 provides an overview of the reasons for leaving the baseline village. More than one-third of the female respondents but none of the male respondents cited marriage as the reason for migrating, which is what one would expect in a culture with patrilocal marriages. Less than 15 percent of the female respondents reported that they left because of work. In contrast, almost 45 percent of the male migrants reported to have moved because they had found work or went looking for work.

# _[Table 2 here]_

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The aggregates are temporally and spatially deflated using data from a price questionnaire included in the survey. Consumption is expressed in annual per capita terms using 2010 Tanzanian shillings.<sup>15</sup> Table 3 provides the summary of the consumption and poverty developments of the panel respondents with respect to their 2010 location. On average, consumption levels in the sample almost doubled over 19 years. Individuals who stayed in their community saw their consumption increase by more than 40 percent. Consumption growth for migrants was much higher: those who left Kagera saw their consumption nearly triple over the same two decades. The poverty statistics tell the same story:

> 14 By
Table A9: Survey Instruments and Construction of Sensitive Outcomes

|Outcome|Source(s)|Outcome construction|Item – Original version|Item – Spanish version|
|---|---|---|---|---|
|High stress|Knowledge<br>Incorpora-<br>tion<br>Survey<br>(KI)|Dummy indicator that takes the value of 1 if<br>the individual responded ”Very stressed,” 0<br>otherwise|How stressed did you feel during the<br>past week?<br>Response options: Very stressed, Mod-<br>erately stressed, Mildly stressed, Not<br>stressed at all|¿Qu ́e tan estresado se ha sentido esta<br> ́ultima semana?<br>Opciones de respuesta:<br>Muy estresado,<br>Estresado, Poco estresado, Nada estresado|
|High stress|Baseline/<br>Follow<br>up<br>Surveys–<br>(DASS-21)|Each of the 7 items in the category of stress<br>in the DASS-21 are measured on a scale of<br>0–3 points (_Never, Rarely, Almost Always or Al-_<br>_ways_). We estimate the total stress score fol-<br>lowing Lovibond and Lovibond (1996). The<br>outcome is a dummy indicator that takes the<br>value of 1 if the individual has “High Stress,”<br>that is, if the score was greater than 18.|During the past week, how often did<br>you experienced the following?<br>1. I found it hard to wind down<br>2. I tended to over-react to situations<br>3. I felt that I was using a lot of nervous<br>energy<br>4. I found myself getting agitated
dummy)|407|0.029|0.008|113|0.018|0.012|0.012|0.017||
|Experience with Jihad|||||||||0.161|
|No (dummy)|432|0.951|0.010|125|0.960|0.018|-0.009|0.021||
|Yes (dummy)|432|0.049|0.010|125|0.040|0.018|0.009|0.021||

_Notes:_ Variables are constructed from self-reported information provided by recruits that is available in the Daesh personnel records database. The “analysis” sample consists of observations included in the analysis; the “left-out” sample consists of observations dropped because of missing information on either delegation of residence, education, or age. In the last column, for each group of variables, the F-statistic reports the result of a test of joint significance of all variables in that group.

27
Hay et al., 2009 _PLoS Medicine_ Health in Action article <u>http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000048</u> Longnecker MP, Klebanoff MA, Zhou H, & Brock JW (2001) Association between maternal serum concentration of the DDT metabolite DDE and preterm and small-for-gestational-age babies at birth. Lancet, **358** (9276):110-114.

Longnecker MP, Klebanoff MA, Dunsonc DB, Guod X, Chenc Z, Zhou H, & Brock JW (2005) Maternal serum level of the DDT metabolite DDE in relation to fetal loss in previous pregnancies. Environmental Research, **97** (2): 127–133.

McGlynn KA, Abnet CC, Zhang M, Sun XD, Fan JH, O'Brien TR, Wei WQ, Ortiz-Conde BA, Dawsey SM, Weber JP, Taylor PR, Katki H, Mark SD, & Qiao YL (2006) Serum concentrations of 1,1,1-trichloro-2,2bis(p-chlorophenyl) ethane (DDT) and 1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene (DDE) and risk of primary liver cancer. J Natl Cancer Inst., **98** (14): 1005-1010.

MicrobiologyBytes , 2009 http://www.microbiologybytes.com/introduction/Malaria.html Pavuk M, Cerhan JR, Lynch CF, Kocan A, Petrik J, & Chovancova J (2003) Case-control study of PCBs, other organochlorines and breast cancer in Eastern Slovakia. J Expo Anal Environ Epidemiol., **13** (4): 267275.

Pesticide Action Network-UK. 2011. DDT Factsheet. http://www.pan-uk.org/ Ribas-Fito N, Gladen BC, Brock JW, Klebanoff MA & Longnecker MP (2006) Prenatal exposure to 1,1dichloro-2,2-bis( _p_ -chlorophenyl)ethylene ( _p_ , _p_ ′-DDE) in relation to child growth. Int
39. The following provides an overview of potential measures to improve the access to formal financial sector services for the low income population in Colombia. It draws on the areas for correction which became apparent from the supply and demand side analysis and potential regulatory steps, including experience from other countries.

# **4.1 REGULATORY MEASURES**

40. As the experience of other countries shows (Chapter 2, Box 2), regulation to enhance financial access can take various forms. Models such as the US Community Reinvestment Act track the extent which banks to provide specific services to low income communities. In this case, service provision is enhanced through detailed information and disclosure requirements, and affects approval on other matters, such as bank branches or mergers and acquisitions. Other measures include the provision of basic services, as in Germany, where banks provide a voluntary undertaking to open accounts to all clients under a few basic conditions. This is monitored by consumer protection associations with the right of appeal of clients who have been rejected by a bank. A middle-of-the-ground model, such as Canada, provides government guidelines or directives for the provision of basic account services to the poor, with enforcement through monitoring by a government agency.

41. In light of the low penetration of basic financial services, there are a number of initiatives that could help improve access to financial services in Colombia, from the enforcement of minimum service requirements that are already in the regulation, to the introduction of a basic account available to low income clients, or to encouraging that certain types of payments (such as pension payments and payroll) are done through bank accounts. Such basic accounts have been recently adopted elsewhere in Latin America, for example, in Brazil.

42. Given the extent of financial exclusion in Colombia as revealed in the Bogotá survey and banks’ risk-averse behavior following the crisis, it is unlikely that private banks will provide basic account services at their own initiative, and thus, Regulation could be catalyst. The Superintendencia Bancaria could support this by
# **1. Introduction**

Access to financial services—including payments, savings, credit, insurance, and investment products—allows households and small businesses to mitigate economic shocks, as well as build healthy and productive futures. Digital financial services, when used responsibly, makes these benefits more widely available, thereby driving financial inclusion for women and other underserved groups.

Expanding access to finance through digital channels and methods is one of the primary strategies for meeting United Nations Sustainable Development Goal 8.10, which targets ownership of an account at a formal financial institution, such as banks, regulated microfinance institutions, and mobile money service providers. Account ownership is just the first step, however. To truly reap the benefits of financial inclusion, account holders must be able to confidently and responsibly use their account to store money, save for the future, access credit, and make payments.

In this paper, we discuss the academic evidence on the benefits of digital financial services for expanding financial inclusion. We bring data from the demand-side Global Findex showing where technological enablement is associated not only with increased account ownership but also—and more significantly—with increased _usage_ of financial services. Direct digital payments out of and into an account are the most significant and dispersed way in which adults are using their accounts, and an example of how digital approaches help deliver the benefits of financial inclusion for more people. The data also points to opportunities to expand financial access and usage by supporting payment digitalization, including with stronger consumer protection.

# **2. Account ownership**

Account ownership is important as a financial foundation for reducing poverty and empowering individuals. Its benefits include a greater ability to save money (Breza, Kanz, and Klapper, 2020; Blumenstock et al. 2018), greater female empowerment (Ashraf et al., 2010; Field et al., 2021), and improved agricultural outputs (Brune et al., 2016). Importantly, account ownership is associated with increased financial resilience, based
# **5. Electricity Consumption and Economic Activity**

# _5.1 Cointegration and Causality_

First, we determine the long-run association between electricity consumption and economic activity based on annual data. We perform the Johansen cointegration test on two bivariate annual models: (i) real GDP and electricity consumption, and (ii) industrial production and electricity consumption. To uncover the long-run equilibrium and causal relationships between the selected variables, data from 2000 to 2020 are analyzed. Each of the estimated models contains one cointegrating vector, suggesting that an equilibrium relationship holds in the long run. The existence of a cointegration relationship also implies that at least one causal relationship exists among the variables. To check pairwise causality, we employ the TodaYamamoto (1995) Granger causality test, which is robust to the identified cointegration relationship.<sup>5</sup> It uncovers unidirectional causality from GDP to electricity consumption ( = 6.24, p = 0.04) and bidirectional causality between industrial production and electricity χχ<sup>2</sup> consumption.<sup>6</sup> Second, we determine the short-run association between electricity consumption and economic activity based on monthly data. Since Bangladesh publishes only annual GDP data, we can only use industrial production. For the analysis, we employ monthly data on industrial production and electricity consumption from August 2012 to April 2021. In line with the annual data, we find bidirectional Toda-Yamamoto Granger causality for electricity consumption and industrial production with monthly data. Growth in industrial production Granger causes growth in electricity consumption ( = 7.52, p = 0.01) and vice versa ( = 3.414, p = 0.07). The results suggest that electricity consumption can provide statistically significant χχ<sup>2</sup> χχ<sup>2</sup> information about economic activity.

# _5.2 Regression Models_

To confirm the meaningful relationship between electricity consumption and economic activity in Bangladesh, we next estimate the following linear
of different lenders. Workers’ Compensation Funds<sup>50</sup> associated with large scale employers show the highest approval rates, while their funding is restricted in purpose and amount per client. The second best option is borrowing from a friend or relative. While the formal institutions (banks, finance corporations and cooperatives) offer lower active interest rates, they are also much more selective, turning down three out of four loan requests. As figure 3.13 shows, the banked population is far more likely to get a bank loan approved than the unbanked population. Higher loan approval rates in the informal sector therefore drive the unbanked towards borrowing at higher interest rates, reflecting a significant cost of being unbanked.

**Table 3. 5  Loan approval rates by different credit providers**

||Commercial<br>Bank|Finance<br>Corporation|Cooperative|Friend or<br>Relative|Prestamista<sup>51</sup>|Store|Workers’<br>Compensation<br>Fund|Informal<br>Saving|
|---|---|---|---|---|---|---|---|---|
|Approval Rate|21%|23%|25%|95%|87%|87%|100%|80%|
|Annual Int. Rate|15%|16%|13%|N/A|150%|150%|15%|N/A|

_Source: DANE household study of Bogotá, 2003_

**Figure 3. 13  Loan approval rates of banked and unbanked individuals**

<!-- Start of picture text --> 100%<br>90%<br>77.4%<br>80% 72.1%<br>70%<br>60%<br>51.0%<br>50% 43.
two sample groups could explain – at least in part – the differences between the full sample and PSM sample results. This is plausible because smaller firms, which tend to be more financially constrained, could react more to equity internationalization events than larger corporations.

To verify this formally, we disaggregate the connected firms in the PSM sample by size (defined by total assets in 2010-12). We then re-estimate the difference-in-differences equation for each subgroup (Table 5 and Figure 5). We find that the smallest firms (in the lowest quartile) in the PSM sample raised the most equity post-2012 (as a fraction of total assets in 2012), and the magnitude of the impact decreases monotonically in firm size (Figure 5). Consistent with this pattern, we also show that the smaller connected firms (below the median) increased their equity issuances relative to the larger connected firms (above the median) during 2014-20 (Table 6). The correlation between equity issuance reactions and firm size survives even when excluding stateowned enterprises (SOEs) from the sample. This is important because SOEs are relatively large corporations whose investment reacted less to the Stock Connect than privately owned firms (Ma et al., 2021).<sup>27</sup>

# _Robustness and Extensions_

We perform and report robustness tests for the full and PSM samples (Table 7). We (1) control for lagged assets and sales growth, (2) control for the size of bond issuances, (3) exclude firms with margin trading stocks, and (4) exclude firms with stocks purchased by the government during 2015-19.<sup>28</sup> Controlling for lagged total assets and sales growth allows us to ensure that changes in

> 27 We define as SOEs firms for which the main (top 1) shareholder is a government-related entity (following Ma et al., 2021). We merge our main dataset with 2007-20 firm-level data on firm ownership structure downloaded from Wind. We consider SOEs to be all firms
WB)<br>Taxation Capacity (ICTD)|<br>•<br>Freedom of Expression (BS)<br>•<br>Media Freedom (RSF)<br>•<br>Media Impartiality (V-Dem)|
|•<br>•|Revenue Collection (AfDB/WB)<br>Revenue Collection (AfDB)|•<br>Freedom of Expression (V-Dem)<br>•<br>Censorship (GI)|
|•|Revenue Collection (WB)|•<br>Media Censorship (GI)|
|•|<br>Transparency of State-owned Companies (GI)|<br>•<br>Online Censorship (GI)<br>•<br>Freedom of Association & Assembly (BS/GI)<br>•<br>Freedom of Association & Assembly (BS)<br>•<br>Freedom of Association (GI)<br>•<br>Freedom of Association (GI)<br>•<br>Trade Unions (GI)<br>•<br>Digital & IT Infrastructure (EIU/ITU)<br>•<br>IT Infrastructure (EIU)<br>•<br>Mobile Phone Subscribers (ITU)<br>•<br>Household Computers (ITU)|

_Source: Mo Ibrahim Index of African Governance Indicators, 2017_ 38
# **6.1 Plausible Mechanisms**

Several plausible mechanisms can explain the improvement in health, cognition, and labor market outcomes due to exposure to a village midwife early in life. First, a number of studies find that the midwife programs are associated with greater antenatal and postnatal care (Frankenberg et al., 2009; Rao, 2014). Accordingly, the effect of the program on antenatal care is investigated. Here, the panel feature of the IFLS is particularly advantageous, as it gives information on the receipt of such inputs for individuals who were exposed to the program early in life and then were interviewed in 2014.<sup>34</sup> Table 6 shows the mean effect of the program expansion on three outcomes: use of antenatal care, skilled delivery at birth, and one postnatal input, breastfeeding. While all the regressions control for birth year fixed effects and community fixed effects, the last four columns additionally control for province-specific linear trends. Column (1) of Table 6 shows the effect on the use of antenatal care—individuals who were exposed to the midwife program during pregnancy were 10 percent more likely to receive antenatal care compared to individuals who were not exposed to the program during pregnancy or exposed during their first four years of life. In the same table, column (2) shows the effect on attendance of midwife during birth. Individuals exposed to the program are more likely to be attended by a midwife during their birth. Finally, there are no effects of the midwife program on breastfeeding practices for any group.<sup>35</sup><sup>_,_36</sup> The results are very similar when both province-specific linear trends and 1983 district health facilities interacted with year of birth

> 34In this case, a slightly different econometric specification is used than in equation (1)–the children who are exposed at age 1-3 and age 4 are treated as controls and everything else is same as the main specification. The reason is
.|.|
|Computer Use Rate|0.42|0.37|0.44|0.16|0.23|0.22|0.24|0.07|0.60|0.55|0.62|0.26|
||(0.31)|(0.30)|(0.31)|(0.24)|(0.22)|(0.23)|(0.22)|(0.14)|(0.35)|(0.33)|(0.35)|(0.32)|

_Source_ : Sample consists of Current Population Surveys Merged Outgoing Rotation Group Workers for 1983-2002. Affiliate (or offshore) employment data are taken from the Bureau of Economic Analysis annual survey of US firms with multinational affiliates for the same period. Low income countries are defined according to the World Bank income categories. Import penetration and export share are taken from Bernard et al. (2006). Computer use rates are taken from October CPS supplements during the sample period. Investment good prices and total factor productivity measures, and the real price of shipments are taken from the NBER productivity database.
- 22 -

comfort, as the persistence of high fees has been one of the main criticisms of the Chilean system. These are the levels of fees charged by expensive active/high turnover equity mutual funds in the US. The fees charged by indexed (passive) equity mutual funds, bond funds, and money market mutual funds are much lower than 1 percent of assets, and so are the fees charged by occupational pension funds in most countries.<sup>15</sup>

# **Figure 7: Long Run Total Fees over Contributions for the Average MPF**

<!-- Start of picture text --> 40%<br>37.10%<br>35%<br>30.61%<br>30%<br>25%<br>20%<br>15%<br>10%<br>6.49%<br>5%<br>0%<br>TOT C OP C AM C Past<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Note: real wage growth = 4%, rate of return = 8%, asset management fees = 0.97% of assets and operating fees: 6.49 % of contributions.

Source: Own calculations on HFSA data.

# **Figure 8: Long Run Total Fees over Assets for the Average MPF**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>1.18%<br>0.97%<br>0.21%<br>0%<br>TOT C OP