rafmacalaba/datause-encoder-data huggingface dataset:
Angola is an example of an economy requiring most of these procedures and where the number of days needed to fulfill them adds considerably to the overall time necessary to start a foreign business (Figure 14). While examining the burden imposed on foreign companies in the country compared to domestic companies, we notice that the amount of procedures more than doubles.

<!-- Start of picture text --> 200<br>Figure 14. Starting a foreign business in Angola – procedures required of 1 1<br>foreign firms<br>Obtain a trade license 60<br>150<br>Procedures<br>Legalization of 1 1 1 4 required of<br>documents overseas domestic and<br>15<br>and in Angola foreign<br>12 companies<br>100 1<br>Additional<br>procedures<br>Approval of capital<br>Foreign required<br>importation<br>investment exclusively of<br>50 100 approval from foreign<br>ANIP companies<br>(including<br>0 1<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Procedures<br>Source : FDI Regulations database 2012<br>Days<br><!-- End of picture text --> _Authentication of parent company documentation overseas_ The most common procedure required exclusively of foreign companies is the legalization of the foreign parent’s incorporation document abroad. Eighty-four percent of the 102 surveyed economies require some sort of authentication of the parent company documentation overseas. The process consists of a multi-step authentication before various authorities<sup>10</sup> . Only 52 economies are party to the 1961 Hague Apostille Convention<sup>11</sup> , 12 of which have adopted one of the components of the electronic Apostille Pilot Program (e-App). One of the concerns faced by foreign investors is the uncertainty surrounding the time needed to complete said authentication. The Apostille Convention facilitates and expedites the legalization requirements of foreign public documents between states which are party to the Convention. The legalization process is meant to satisfy a foreign court or person that the document is indeed what it declares itself to be. The convention has replaced the cumbersome formalities of this lengthy process with the issuance by a single government entity of an apostille certificate that authenticates the origin of the public document.

> 10 For methodological purposes, the _FDI Regulations_ indicators quantify this step as adding only one day to the overall establishment process since the time involved mainly depends on the authorities in the country of origin of the parent company which differs from one country to another.

> 11 Full text of the Hague “Convention Abolishing the Requirement of Legalization for Foreign Public Documents,” of October 5, 1961, as well as additional details, can be found at <u>http://www.hcch.net/index_en.php?act=conventions.pdf&cid=41</u>

18
autocorrelation over the different interval measures. Pointwise confidence intervals for these autocorrelations are obtained via 1000 bootstrap replications. While Figure 1A does suggest some tendency for the autocorrelation to fall as the time horizon increases, the data do not deviate much from the 0.38 level over all time horizons between 1 month and 18 months, and so the assumption of constant autocorrelation seems a reasonable approximation here.

Figure 1B shows the autocorrelations in monthly microenterprise profits in urban Ghana, which were measured at three month intervals in the experiment of Fafchamps et al. (2010b), and Figure 1C shows the autocorrelation in three monthly household expenditures from the same experiment, also measured at quarterly intervals. Horizontal lines are drawn at 0.32, the mean autocorrelation over these horizons for profits, and 0.20, the mean autocorrelation over these horizons for household expenditures. Again both figures suggest the assumption of constant autocorrelation holds approximately over these horizons.<sup>6</sup> Gibson et al. (2003) also provide evidence from Zambia and urban China that the autocorrelation of expenditure does not change very much within a year as one increases the number of months between surveys.

Table 1 then considers the evidence for the second part of the assumption, which is that the autocorrelations are the same for treatment and control, using the microenterprise profits data from Sri Lanka and Ghana. The point estimates in most periods are reasonably similar between treatment and control, and the bootstrapped confidence intervals for the difference in autocorrelation between the two always contain zero. Evidence from another setting comes from using the test score data of Banerjee et al. (2007). The verbal test score autocorrelation is 0.58 for the treatment group and 0.61 for the control group; and the math test score autocorrelation is 0.47 for the treatment group and 0.55 for the control group. Therefore in these samples this assumption seems reasonable.

Table 2 provides autocorrelations from other data sets for a range of different economic outcomes. The purpose of this is twofold. First, it demonstrates that for many economic outcomes, the autocorrelations are typically lower than 0.5, with many around 0.3. An exception is test scores, which have higher autocorrelations. Second, it provides some parameters that researchers can use when conducting their own power calculations, since it is rare to have

> 6 The Sri Lankan microenterprise surveys only measured household expenditure annually, which is why household expenditure is not shown for this sample.

11
Table 2. Output growth during global downturns

|||Globa|l downturn|s||Global|Non-|All|
|---|---|---|---|---|---|---|---|---|
||1958|1998|2001|2012|Average|recessions|recessions|years|
|**World**|||||||||
|Output|2.2|2.5|1.9|2.5|2.3|0.3|3.9|3.7|
|Output per capita|0.2|1.1|0.7|1.2|0.8|-1.3|2.2|2.0|
|Output (PPP)|2.7|2.4|2.3|3.2|2.7|0.8|4.2|4.0|
|Output per capita (PPP)|0.7|1.0|1.1|2.0|1.2|-0.8|2.5|2.3|
|**Advanced economies**|||||||||
|Output|1.6|2.7|1.5|1.2|1.8|-0.4|3.5|3.3|
|Output per capita|0.4|2.1|0.9|0.6|1.0|-1.1|2.7|2.4|
|Output (PPP)|1.5|2.7|1.5|1.2|1.7|-0.4|3.5|3.3|
|Output per capita (PPP)|0.3|2.1|0.9|0.6|1.0|-1.1|2.7|2.5|
|**EMDEs**|||||||||
|Output|6.2|1.8|3.2|4.9|4.0|2.1|4.9|4.7|
|Output per capita|3.8|0.3|1.8|3.5|2.4|0.2|3.0|2.8|
|Output (PPP)|6.4|1.9|3.4|4.9|4.2|2.4|4.9|4.8|
|Output per capita (PPP)|4.0|0.4|2.0|3.6|2.5|0.5|3.0|2.9|

Note: All variables show percent changes. “Global recessions” refers to average growth rates during the four global recessions (1975, 1982, 1991, and 2009). “Non-recessions” refers to averages during 1950-2019, which are shown as “All years,” excluding years of global recessions.

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assistance programs. We explored the role of the following seven community level resources or programs in mitigating negative welfare impacts of shocks in rural areas of Java: (1) presence of technical irrigation in the community<sup>7</sup> , (2) Kampung Improvement Program (an informal housing area upgrading program that provided basic services and infrastructure through community based organizations), (3) Infrastructure Development Program (a community-based infrastructure development program), and (4) availability of credit through the INPRES Poor Villages Program, (5) the village has a _Padat Karya_ program, a loose collection of laborintensive programs sponsored by various government departments (Sumarto et al. 2002), (6) the village had a PDM-DKE (Regional Empowerment to Overcome the Impact of Economic Crisis) program, a block grant program for villages to support public works or revolving funds for credit (Sumarto et al. 2002), and (7) the _Inpres Desa Tertinggal_ (IDT) (Program for Underdeveloped Villages), another block grant program targeting extremely poor villages (Sumarto et al. 2002). Data on the first four community level programs above were available in both the 1997 and 2000 IFLS surveys, so we pooled the data to increase the number of observations. However, data on the last three community-based programs above were only available in the 2000 IFLS survey, so we could only use the single year of observations in evaluating those programs.

As discussed earlier, recognizing that government assistance programs are often targeted to poor areas, we use propensity score matching to infer the moderating impact of some community level interventions on the impact of the shock. For each of the community-based programs, we estimate the average treatment effect of the intervention on per capita household expenditures components (total, nonfood, and food) among households exposed to the shock and located in communities with the program of interest (i.e. SATT, or the sample average treatment effect for the treated). The results in Table 3 are shown as the percent difference in mean per capita expenditures between the treatment and comparison groups. The panel on the left side of Table 3 relates to the sample of households of rural Java that were exposed to the post-onset low rainfall shock regardless of occupational status, while the panel on the right focuses on the subsample of households exposed to the shock that were engaged in a farm business.<sup>8</sup>

> 7Data only indicated whether technical irrigation existed in the community, not household use of technical irrigation. 8 We also attempted to extend this analysis to only farmers indicating rice as the most valuable crop, but the data thinned out and precluded application of this approach to this sub-sample.

14
baseline survey – see column (5). In the case that we include the variable without imputation – see column (6) – the estimated coefficient is slightly lower but the statistical significance does not change.

Finally, we also conduct robustness checks of the effects of survey mode on secondary outcomes after including other control variables that can proxy for income level. Specifically, we test if the lack of differences in the consistency of reporting sensitive information by survey mode changes after the inclusion of educational level as control variable. As we show in Table A5, differences are still statistically insignificant after including this proxy for income level.

# **7 Discussion and Concluding Remarks**

We provide empirical evidence on how a phone-based mode affects survey completion rates relative to a self-completion mode. Considering the rapid increase in access to digital technologies, this represents a key parameter for policy makers who confront a set of trade-offs when collecting data. We find that phonebased surveys increase response rates by 42 percentage points—even more if targeted to women and older generations. Considering the costs of data collection and the likelihood of failing to find a significant effect in underpowered studies, this result provides a key piece of information, especially in countries similar to El Salvador, where we ran this experiment.

Importantly, along with differences in completion rates, we also document differences in responses across groups. This can be relevant when collecting data on sensitive issues. For example, we observe that phone-based survey respondents were less likely to report a high level of stress. We find suggestive evidence that differences in stress level by mode of survey were due to differences in willingness to report as opposed to changes in the composition of the group that completed the survey. This is important if a research team is considering combining phone-based and self-completion surveys in some part of the datacollection process, and it wants to evaluate how much its decision can affect both the integrity of the data collected and its comparability with other sources of information.

Finally, policy makers would like to know the trade-off between the two alternative methods we compare. Based on our findings and a simple back-of-the-envelope calculation, we observe that after consider22
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. All models are estimated via three regression models: ordinary least squares (OLS), robust regression and instrumental variable model. In dropping outlier model, Top 5% samples on each dependent variable. The details of instrumental variable model are shown as below.

*1 IV1: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 IV2: 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

*3 IV3: 1st excluded IV is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies , and 2nd is Independence of Supervisory Authority - Political respectively

# <u>Panel B: Supporting Institutional Framework</u>

|||Depe|OLS dropping<br>ndent Variable: N|OLS w/ the<br>umber of borrow|<br>ers as % ofpopu|<br>lation||
|---|---|---|---|---|---|---|---|
||OLS|Robust Reg|<br>top 5% of dep<br>var|same<br>observations<br>to IV|Instrumental<br>Variable 1*<sup>1</sup>|Instrumental<br>Variable 2*<sup>2</sup>|Instrumental<br>Variable 3*<sup>3</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Supporting Institutional Framework (Nominal)|0.0586|0.1096***|0.0717**|0.0782*|0.2130**|0.0858|0.2051**|
||[0.106]|[0.000]|[0.031]|[0.080]|[0.049]|[0.242]|[0.048]|
|Inflation, GDP deflator (annual %)|-0.1409|-0.1622*|-0.1174|-0.108|-0.1102|-0.1588|-0.1199|
||[0.220]|[0.072]|[0.254]|[0.491]|[0.494]|[0.122]|[0.464]|
|GDP growth (annual %)|0.071|0.0645|0.051|-0.0673|0.1558|0.0096|0.0486|
||[0.733]|[0.690]|[0.784]|[0.817]|[0.648]|[0.966]|[0.889]|
|Rule of law|-2.0331*|-1.8579**|-1.8531*|-1.7743|-1.4295|-1.8137*|-1.5643|
||[0.054]|[0.025]|[0.051]|[0.139]|[0.248]|[0.062]|[0.208]|
|Bank overhead costs / Total assets|-13.373|-2.3468|-10.3053|-10.2426|-10.4817|-14.3801|-17.1873|
||[0.346]|[0.830]|[0.417]|[0.538]|[0.539]|[0.375]|[0.397]|
|Ease of doing business index (1=most business-friendly regulations)|-0.014|-0.0097|-0.0145|-0.0121|0.0037|-0.0081|0.0056|
||[0.317]|[0.372]|[0.248]|[0.471]|[0.858]|[0.594]|[0.790]|
|Constatnt|2.3956|-0.969|1.5935|1.586|-6.5612|1.0207|-5.4174|
||[0.401]|[0.660]|[0.533]|[0.661]|[0.350]|[0.827]|[0.416]|
|Observations (countries)|47|47|46|34|34|36|31|
|R-squared|0.189|0.425|0.241|0.244|-0.031|0.276|0.017|
|Adjusted R-squared|0.067|0.339|0.124|0.076|-0.260|0.126|-0.228|
|F statistics for excluded instruments on 1st stage|||||2.685|4.377|2.688|
|Shea's adj. partial R-sq|||||0.171|0.238|0.189|
|P-values of Hansen J statistics|||||0.027|0.616|0.042|

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**Table 4: Impact of formal preschool (children ages 2–5 years at baseline)**

|||||ITT|P-||TO|T|P-|
|---|---|---|---|---|---|---|---|---|---|
||N|C|T-C|SE|val|N|T-C|SE|val|
|**Peabody Vocabulary test**||||||||||
|TVIP: Raw score|1536|1.785|0.02|(0.060)|0.736|1535|0.09|(0.250)|0.719|
|TVIP: Age equivalent|1528|3.156|0.001|(0.060)|0.99|1527|0.003|(0.253)|0.989|
|TVIP: Age equivalent – age|1528|-0.99|0|(0.069)|0.997|1527|0.001|(0.290)|0.997|
|**Ages and stages**||||||||||
|ASQ communication|1527|5.219|-0.07|(0.076)|0.339|1526|-0.332|(0.361)|0.358|
|ASQ gross motor|1525|7.606|0.078|(0.054)|0.151|1524|0.354|(0.256)|0.167|
|ASQ fine motor|1526|3.837|0.004|(0.059)|0.942|1525|0.02|(0.257)|0.939|
|ASQ problem solving|1525|4.14|-0.15**|(0.060)|0.013|1524|-0.682**|(0.296)|0.021|
|**Woodcock Johnson**||||||||||
|WJ: Raw score, out of 72|1528|1.522|-0.02|(0.061)|0.77|1527|-0.081|(0.266)|0.761|
|WJ: % answers correct|1528|3.383|-0.08|(0.065)|0.247|1527|-0.344|(0.279)|0.218|
|**Strength and difficulties**||||||||||
|SDQ emotion|1539|2.483|-0.05|(0.056)|0.359|1538|-0.226|(0.240)|0.346|
|SDQ conduct|1539|1.673|0.053|(0.080)|0.508|1538|0.235|(0.329)|0.475|
|SDQ hyperactivity|1539|3.828|0.089|(0.076)|0.238|1538|0.394|(0.312)|0.207|
|SDQ peer|1539|1.983|-0.01|(0.069)|0.921|1538|-0.03|(0.292)|0.917|
|SDQ pro-social|1539|2.761|-0.122*|(0.072)|0.091|1538|-0.539|(0.341)|0.115|
|**Anthropometrics**||||||||||
|Height for age z score|1519|-1.81|-0.03|(0.046)|0.469|1519|-0.152|(0.204)|0.458|
|Weight for age z score|1523|-1.68|0.026|(0.058)|0.652|1523|0.119|(0.255)|0.641|
|**Caregiver mental health**||||||1391|-0.093|(0.444)|0.834|
|CESD(caregiver)|1392|1.587|-0.02|(0.105)|0.841|1535|0.09|(0.250)|0.719|

Note: For ITT columns, "T-C" presents results of the regression of the dependent variable on the treatment group. For TOT columns, "T-C" presents results of the regression of the dependent variable and the treatment status instrumented by the treatment group. “ITT” is intent to treat, “TOT” the treatment on the treated. Standard errors are always robust to heteroskedasticity and account for intravillage correlation (max. 45 clusters). All test score are standardized using the standard error of the control group. All regressions include age, sex, age sex interaction dummies, height for age at baseline, and geographic fixed effect. * 10% significance; ** 5% significance; *** 1% significance.

As the individual estimates point in different directions, the few coefficients that happen to be significant are most likely not very meaningful. To draw more robust conclusions, we created an index of all test scores (“all scores” in Table 5), accounting for all estimated treatment effects. That index is divided into several subindices deemed to capture the main outcomes that participation in preschool is expected to stimulate. The “ASQ” index encompasses all ASQ scores; “MOTOR” encompasses the gross motor, fine motor, and anthropometric estimates; “COGNITIVE” encompasses the TVIP, WJ, and cognitive competences of the 14
# **The Welfare Effects of Mobile Broadband Internet: Evidence from Nigeria**<sup>**1**</sup>

Kalvin Bahia<sup>2</sup> Pau Castells<sup>3</sup> Genaro Cruz<sup>4</sup> Takaaki Masaki<sup>5</sup> Xavier Pedrós<sup>6</sup> Tobias Pfutze<sup>7</sup> Carlos Rodríguez-Castelán<sup>8</sup> Hernan Winkler<sup>9</sup> .

**Keywords:** Poverty, Household consumption, Mobile broadband, Africa, Nigeria **JEL codes:** D12, F63, I31, L86, O12

1 The authors wish to thank Shubham Chaudhuri, Andrew Dabalen, Mark Dutz, Jonas Hjort, Samantha Lach, David McKenzie, Chandni Raja, Viviane Sanfelice, Emmanuel Skoufias, Daniel Valderrama and Tara Vishwanath for their comments and suggestions. The authors would also like to thank participants of the World Bank seminar organized by the Poverty and Equity and Digital Development Global Practices. The authors are also thankful to Gero Carletto and Siobhan Murray from the World Bank LSMS team for their support with data integration, as well as Aibek Baibagysh and Abul Azad from the World Bank Poverty and Equity Global Practice for their support on building the consumption aggregate for Nigeria GHS. The authors would also like to thank Mobile Network Operators in Nigeria for sharing their mobile network infrastructure data. This study received funding from the Digital Development Partnership and from UK aid. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or the UK government’s official policies, or those of the GSMA and GSMA Intelligence.

2 GSMA 3 GSMA 4 GSMA 5 World Bank 6 GSMA 7 Florida International University 8 World Bank and IZA 9 World Bank
by Scenario 3 and 4 on these major imported commodities are smaller and therefore do not outweigh the reductions in the imports of other sectors. Hence, they lead total imports to decline. Import price indices show no changes, while those of exports slightly increase under the four scenarios.

The overall country’s change in the balance of trade under the four scenarios is positive due to the fall in investment experienced across all scenarios. The fall in investment is due to the falling rates of return, which are largest when all of the subsidy is removed (-3.57, Scenario 1) and smallest when a subsidy is placed on domestic production (-0.85, Scenario 3). The resulting changes in the balance of trade under Scenario 1 through Scenario 4 in US$ million are 65, 36, 32 and 178, respectively. The larger increase in the trade balance in Scenario 4 is due to a lower decline in savings by private households relative to scenario 1. This is somewhat surprising, since the poor rural households that receive the additional income transfers from government do not have very high savings rates (see Appendix 1). The rise in relative savings in Scenario 4 is the result of a lower decline in income (see section 4.5 below) and hence savings of some of the Urban households, and in particular the Urban North-West.

# **4.5 The Impact on Households**

Changes in real household income, which are calculated from the MyGTAP model by adjusting changes in nominal households’ income for a household specific price index for private consumption expenditure, are shown in Figure 8. The simulation results indicate that real income decreases in Scenario 1, 2 and 3 for all households. In Scenario 4 that includes a government transfer to rural households, real income increases for all rural households with slightly varying extent, ranging from 2.3 percent increase for Rural-North West households to 7.6 percent for Rural-South West households. The varying degree of increase in the income of the households mainly results from different shares of government transfers in their base income. These findings indicate that Scenario 4 maintains the best policy for rural households in Nigeria among the four simulated policies. Under the first three scenarios, real household income declines due to the decreases in factor returns (wages and profits), which led the total disposable factor income to decrease by 1.5, 0.7 and 0.3 percent, respectively.

20