rafmacalaba/data-use-mentions-extended huggingface dataset:
# Figure 8.  Commodity exporters with high and low external debt

<!-- Start of picture text --> Low External Debt High External Debt<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>CE with high external debt = 0.48***<br>CE with low external debt = 0.48***<br>-0.4<br>-0.6<br>AzerbaijanYemen Côte d'IvoireUkraine<br>Rwanda Congo, Rep.Timor-Leste NicaraguaEcuador GuyanaLiberia Argentina<br>NigerBolivia KenyaZimbabwe FijiAngola<br>Brazil<br>Sudan SenegalMali Burkina Faso<br>Ghana Burundi Benin Turkmenistan MozambiqueChad Comoros The Gambia Colombia Tanzania Central African RepublicGabon EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Rep. of Madagascar<br>Kyrgyz RepublicRussian Federation Papua New GuineaLao P.D.R. Cabo Verde UgandaCameroon Indonesia Honduras Togo<br>Congo, Dem. Rep.Belize<br>NigeriaPeru Solomon Islands<br>Armenia<br>Sierra Leone Kazakhstan Guinea-Bissau Mauritania Costa Rica Botswana<br>Guinea<br>Zambia<br>Malawi<br>Guatemala<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high and low external debt, defined as external debt, as percentage of GNI, above (below) the sample median. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Debt
Lambsdorff 2003; Mauro 1995, 1998; Wei 2000).<sup>2</sup> This impact can be heightened when corruption is unpredictable (Campos, Lien, and Pradhan 1999; Malesky and Samphantharak 2008). Finally, the structure of corruption, specifically if there is a single agency demanding rents or if multiple agents all have opportunities for corruption, affects both the level and the impact of corruption (Olken and Barron 2009).

Unfortunately, measuring corruption requires novel research design strategies for a number of reasons. First, firm managers may be generally reluctant to answer politically sensitive questions (Lensvelt-Mulders et al. 2005; Azfar and Murrell 2009). This reluctance can stem from social factors, where firm managers may be hesitant to express personal beliefs that are considered taboo.<sup>3</sup> Second, and more relevant for this paper, the political environment can affect responses to firm level surveys. Jensen, Li, and Rahman (2010) analyze cross-national firm-level _Productivity and Investment Climate Surveys_ (PICS) administered by the World Bank. They find evidence for nonresponse and false response on corruption questions in the PICS survey. Specifically, in countries with lower levels of Press Freedom and Political Freedom, a larger percentage of firms tend to provide false response to the corruption questions and a large number of responses fit patterns of false response. They find that in countries with lower levels of press and political freedom, firm-level survey data tends to underreport levels of corruption.

In this paper we take seriously the potential for nonresponse and false responses in the firm level surveys. In fact, we design a survey methodology to examine patterns of nonresponse and false response on survey questions on corruption. Our methodology

> 2 Corruption can also affect firm entry strategies (Henisz 2000; Rodriguez et al. 2005; Uhlenbruck et al. 2006) or alter other aspects of their operations (Kwok and Tadesse 2006; Luo 2006).

> 3 For example, see Berinsky (1999) for work on public opinion
# **Contents**

|**S1**|**Road Improvements by Baseline Woreda Nighttime Lights**|**S2**|
|---|---|---|
|**S2**|**RSDP Road Dataset**|**S3**|
|**S3**|**Nighttime Lights Data**|**S4**|
|**S4**|**Trends in Woreda-Level Outcome Variables Over Time**|**S5**|
|**S5**|**Kebele Information**|**S8**|
|**S6**|**Universal Electricity Access Program versus RSDP**|**S9**|
|**S7**|**Two-Way Fixed Efect Models**|**S10**|
|S7|.1<br>Results using Kebeles as Unit of Analysis<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|S10|
|S7|.2<br>Results using 1x1km Pixels as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . .|S11|
|**S8**|**Additional IV Results Analyzing the Impact of Areas Incidentally Connected to**<br>**the RSDP**|**S12**|
|S8|.1<br>Treated and Control Summary Stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.2<br>OLS Results . . . . . . . . . . . . . . . . . . .
# **Precipitation**

Since precipitation is an important determinant in runoff, a simple precipitation runoff model is calculated for the catchment study areas in order to compare accuracy and level of significance against the BWI runoff model. We hypothesize that the BWI regressed with gauging station flow is better than precipitation predicting flow pass a gauging station, which we test using a R<sup>2</sup> test. The hypothesis is based on the realization that runoff is compounded by many factors: such as snow melt, soil texture, irrigation, solar isolation, relative humidity, and wind speed. All these factors are difficult to measure separately and they vary spatially from _in situ_ observations. The satellite captures the spatial structure, and the BWI integrates all the above factors into one observation at each grid point (Basist et al. 2001).

# **Runoff**

We identify the correspondence between regional BWI and the rate of flow at various gauging stations. The findings allow us to evaluate the relationship between the two variables and determine if useful results can be observed from the statistical models of stream flow. In order to perform the estimates, the measuring station must have a sufficiently long observation period of record (minimum of four years – as long as possible) and the period of record corresponds with the SSM/I data, which is available from 1988 until present<sup>5</sup> , except mid-1990 through 1991.

# **Integrating BWI and runoff in GIS**

Since both the BWI and runoff are geo-referenced, the model calculates the BWI over the area upstream of the gauging station. The Geographic Information System allows for the spatial delineation of the upstream area, using HydroSHEDS<sup>6</sup> (Lehner et al. 2006) for a model of flow accumulation and direction. Since impoundments and/or diversions affect the amount and timing of water reaching the gauge, we used GIS to display the geo-referenced dam data

> 5 We have used data until 2009
Figure 9. Contribution to the decline in National Moderate Poverty Headcount <u>(share of total decline in poverty)</u> <!-- Start of picture text --> 34<br>17<br>74 74<br>18 21<br>-15 -24<br>-10<br>Thailand, 2000 to 2009<br>Occupation share<br>Transfers<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>7<br>97<br>9<br>38<br>- 6<br>-7<br><!-- End of picture text --> <!-- Start of picture text --> 17<br>74<br>18<br>-15<br>Peru, 2004 to 2010<br>Occupation share<br>Transfers<br><!-- End of picture text --> <!-- Start of picture text -->
-44<br>Bangladesh, 2000 to 2010<br>Consumption-income ratio<br>Labor Income<br><!-- End of picture text -->

<!-- Start of picture text --> Labor Income Capital Transfers<br>Other Non-labor Unexplained<br>Source: Shapely value estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES<br>2000 -2010.<br>Figure 10. Changes in the Structure of Employed Population gure 10. Changes in the Structure of Employed Population ure 10. Changes in the Structure of Employed Population ges in the Structure of Employed Population es in the Structure of Employed Population ployed Population loyed Population yed Population ed Population pulation ulation<br>A. Occupational Structure B. Economic Sector<br>(percent change in employed
of the survey villages have rain station data, but there are rain stations in and around Nahouri province, we use an interpolation procedure that enables us to use these nearby rain stations to estimate rainfall in each village for each year. We purchased rain station data from the Burkina Faso Direction de la Météorologie and from the Ghana Meteorological Agency for the 50 rain stations within 100 kilometers from any of our survey villages (26 in Burkina Faso and 24 in Ghana). On average across the survey villages, the nearest rain station is 9.6 kilometers away.

With the data from the 50 rain stations, we use ArcGIS (geographic information system) to implement an inverse distance weighted (IDW) interpolation procedure, which uses the measured rain data from the stations surrounding a given survey village to predict rainfall in the village. Those rain stations that are closest to the village will have more influence on the predicted value by being weighted more heavily, while those further away will have less influence.<sup>11</sup> The intuition behind the IDW procedure is that locations closer together should have more similar rainfall patterns than those further apart, and therefore in estimating the village‟s rainfall, additional weight is put on those closer rain stations.<sup>12</sup> For several reasons, we believe the IDW interpolation procedure provides a more accurate estimated rainfall measure than alternatives such as using the nearest rain station. First, in much of West Africa, there is a strong north-south rain gradient with locations in the north generally receiving less rain than those areas further south. If we were to use only the nearest station and that station were further south than the village of interest, then we would potentially overestimate rainfall in the village. On the other hand, if the closest station were north, then we might underestimate actual rainfall. By using the rain stations in all directions surrounding a given village, this interpolation procedure allows us

> 11 Weights are proportional to the inverse distance squared, which is
collaboration with the Agency for Communication under the Government of Tajikistan. The messages were sent to all mobile phones and were not “targeted” to particular users or groups at elevated risk.

The campaign used existing SMS-systems maintained by the country’s three largest mobile operators<sup>8</sup> From May to December 2020, the project sent COVID-19 related public health messages to approximately 5.5 million mobile phone subscribers in both Russian and Tajik languages (Table 1). The content of the messages was developed in consultation with the MoHSP based on recommendations from the World Health Organization’s COVID-19 SMS database. The messages were directly attributed to the Ministry in each message and focused on preventing the spread of COVID-19. The SMS messages focused on specific behaviors from the list including handwashing, avoiding handshakes and hugs, more frequent cleaning and disinfecting items, not touching one’s face and mouth, wearing masks, quarantining sick individuals, and social distancing. For a full list of the messages sent, see Appendix A.

**Table 1: SMS-messaging through Mobile Engage in Tajikistan, May to December 2021**

|Mobile operator|Period|Approx. subscribers|Messages<br>(Tajik and<br>Russian)|Total SMS sent|
|---|---|---|---|---|
|TCell (round 1)|May 2020|2,477,274|8 (T/R)|39,636,384|
|TCell (round 2)|Jul-Sept 2020|2,477,274|7 (T/R)|34,681,836|
|Babilon M|Nov-Dec 2020|1,056,586|6 (T+R)|6,339,517|
|Megafon|May2020|~2,000,000|<br>1(T+R)|2,000,000|
||Total|5,533,860|
at destination, as well as regulations in sending countries which in some cases explicitly restrict female migration to avoid abuses (Ahmed and Bossavie 2021). Given the very low incidence of migration among female workers, we restrict the remainder of our analysis of return migrants’ characteristics and labor market outcomes to males.

As shown in Table 1 and Figure 1, return migrants are somehow higher skilled than nonmigrants in all three countries. The difference in the average years of schooling attained by return migrants and nonmigrants is substantial and statistically significant in Bangladesh, Nepal and Pakistan. In particular, the share of return migrants who have not attended school is much lower than for nonmigrants in all three countries, and workers with lower secondary education are over-represented among return migrants. However, return migrants are less likely to have some tertiary education than nonmigrants, especially in Nepal and Pakistan.

> 8Summary statistics for the full samples of male workers in Bangladesh, Nepal and Pakistan are reported in Table A1.

> 9In Bangladesh, according to the Bangladesh Return Migration Survey (BRMS), about 4 percent of return migrants from overseas are females, a similar share as the proportion of female outmigrants from the country. In Nepal, the proportion of females among return migrants is also around 4 percent and lower than among current migrants, which partly reflects an increase in female migration in recent years.

8
country bank regulatory authorities (Beck et al., 2004). In addition to regulatory matters, this covers information on branch numbers and on the number and size of different types of deposit and loan accounts broken down by type of intermediary and by class of customer (MSMEs and others, urban vs. rural) and the number and value of payments transactions. Despite concerns that many regulatory authorities might not now have the information necessary to respond fully to this questionnaire, the response rate has been good. Branching and ATM information has been obtained for almost 70 countries and average loan and deposit size information for about 30 countries..

As part of the same exercise, 300 banks in 60 countries are also being surveyed by mail with an 11-page access questionnaire asking about such matters as minimum deposit

> 25 The CGAP database draws on and subsumes the data assembled by the Microcredit Summit (DaleyHarris, 2003).

> 26 About one fifth of this total comes from a very approximate imputation for savings banks based on their total assets and certain assumptions about the distribution of savings balances.

> 27 The largest entities included in the CGAP inventory are: the Postal Savings Banks of India, China, Korea and Egypt (the first two alone account for 234 million savings accounts – about a third of the grand total); India’s Regional Rural Banks (network of almost 200 banks); China’s Rural Credit Cooperatives (network of 35,000 state-owned intermediaries); Indonesia’s National Family Planning Coordinating Board, Bank Rakyat Indonesia (BRI) and the pawn-brokerage Perum Pegadaian; Agricultural Bank of Turkey and Thailand’s Bank for Agriculture and Agricultural Co-operatives (BAAC).

19
anthropometric indicators derived from the international growth reference. _American Journal of Clinical Nutrition_ 46:749-62.

- Dyson, Tim, and Mick Moore. 1983. “On kinship structure, female autonomy and demographic behavior in India”, Population and Development Review 9(1):35-60.

- Engle, Patrice L., Purnima Menon, and Lawrence Haddad. 1999. Care and Nutrition: Concepts and Measurement, _World Development_ 27(8):1309-1337

- Esrey, Steven A., James B.Potash, Leslie Roberts, and Clive Shiff. 1990. Health benefits from improvements in water supply and sanitation: survey and analysis of the literature on selected diseases, Washington DC: USAID, WASH Technical Report No. 66.

- Filmer, Deon, and Lant Pritchett. 2001. Estimating Wealth Effects without Expenditure Data --or Tears: an application to educational enrollments in states of India, _Demography_ 38(1):115-32.

- Gillespie, Stuart (ed.).1997. Malnutrition in South Asia : a regional profile, Kathmandu: UNICEF Regional Office for South Asia.

- Gillespie, Stuart and Lawrence Haddad. 2001. Attacking the double burden of malnutrition in Asia, Manila: Asian Development Bank and IFPRI, ADB Nutrition Development Series No 4.

- Gillespie, Stuart, Milla McLachlan, and Roger Shrimpton (eds.). 2003. _Combating Malnutrition: Time to Act_ , Washington DC: The World Bank, Human Development Network, Health Nutrition and Population Series (World Bank-UNICEF Nutrition Assessment).

- Gopalan, C. “Growth Charts in Primary Child-Health Care: Time for Reassessment.” Indian <u>Journal of Maternal and Child Health</u> 3.4 (1992): 98-103.

- Government of India, Ministry of Rural Development (no date) http://rural.nic.in/book01-02/ch- <u>2.pdf. See also http://rural.nic.in/book00-01/ch-2.pdf</
lower castes and tribes. Household wealth groups show less differences in improvement during1992-98.

Girls’ position deteriorated relative to boys between 1992 and 1998: they had similar or marginally lower proportions stunted and underweight as boys in 1992, but by 1998 girls were nutritionally more disadvantaged than boys across all socio-economic groups. Girls from the highest socio-economic groups showed improvement in nutritional status between 1992 and 1998. Amongst most of the other socio-economic groups, girls showed much more modest declines in the prevalence of underweight. There was an _increase_ in the proportion underweight amongst girls in the poorest wealth tertile and the scheduled tribes, and in the prevalence of stunting amongst girls in all the lower socio-economic groups.

This suggests that levels of discrimination against girls may actually have _risen_ amongst the lower socio-economic status groups between 1992 and 1998: including uneducated mothers and poorer households. It has risen sharply amongst the tribal populations. This ⎯ could be because people are reducing family size in India which, combined with

> 19 See, for example, Kumar and Stewart.

> 20 World Bank 2005, citing data from the National Sample Survey Organisation, 2001.

7
|5|0|0|0|0|0<br>0|
|**characteristics and**|Access to electricity (%)|97|98||98||98|9|8|97|98|98|9|8|9|8|9|8|98|0|0|0|0|0<br>0|
|**access to services**|Accessto improved water(%)|87|89||89||90|9|0|90|88|90|9|0|9|1|9|1|90|1|1|1|1|1<br>1|

Source: SEDLAC (World Bank and CEDLAS).

Note: Note: Since the numbers presented here are based on SEDLAC, a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable for a given country in a given year, values have been interpolated using WDI data or using microsimulations to calculate regional measures. Type of employment and sector limited to working individuals ages 15–64.

20
almost one-third (World Bank 2018d).

# **Timeliness**

When data are timely, they can lead to better emergency response when disasters hit, whether environmental, financial, health, or conflict related. For example, weather data, especially weather forecasts, can help people anticipate and prepare for extreme events. The value of such data can be illustrated by two intense cyclones in the Bay of Bengal that occurred 14 years apart. The 1999 cyclone caught the Indian state of Odisha by surprise, causing massive devastation, killing more than 10,000 people, and destroying housing and public infrastructure. Since then, the Odisha State Disaster Management Authority and the government of Odisha have invested in weather forecast data and disaster response measures. When another cyclone hit in 2013, nearly 1 million people were evacuated to cyclone shelters, safe houses, and inland locations, and only 38 people died during and immediately after the storm (Hallegatte et al. 2017). These impressive results would not have possible without the weather data that gave sufficient advance warning of the cyclone.

Mobile technologies have the potential to speed up emergency response. In Uganda, an SMS-based health reporting program that provides real-time health data to beneficiaries, health professionals, and the Ministry of Health was able to halve response time to disease outbreaks.

12
# **3.3.3 Data models, digital services trade, and development variables**

Digital trade is strongly connected with a country’s level of development. Figure 5 shows that richer countries tend to exhibit a greater level of per capita digital services trade. The dashed vertical line in the figure marks the difference between high-income and middle-income countries. Interestingly, the three data models are applied by countries across all income groups, except for countries following the limited model for which Brunei is the only high-income country with this model in place.<sup>15</sup> The other countries applying a limited model are middle income countries, falling below the dashed vertical line.

A further interesting insight from this graph is that almost all countries sharing the limited model are placed below the fitted values line, indicating that they trade less digital services than it would be expected on the basis of their per capita income levels. This is not always the case for countries sharing the other models, where in fact countries are placed both above and below the dashed fitted values line.

Figure 5: Digital services trade, data models, and level of development (2015) <!-- Start of picture text --> Digital services trade and data models<br>By level of development<br>100<br>10<br>1<br>.1<br>4000 8000 16000 32000 64000 12800<br>GDP per capita PPP USD (log)<br>Open Conditional Limited<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data and World Bank WDI. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

Digital services trade is also strongly associated with technological capabilities of a country, which in turn is typically connected with the level of development. Figure 6 shows that countries with a higher level
39.3] [-8.5] [2.6] [2.7] [-0.8]<br>R? 0.01 0.02 0.01<br>Employment status (omitted category: not employed)<br>Employed -0.51*** -0.55*** -0.31*** -0.33 *** -0.34*** — -0.25***<br>[-59.0] [-58.0] [-14.8] [-28.3] [-27.6] [-8.1]<br>R? 0.05 0.05 0.01 0.14 0.14 0.09<br><!-- End of picture text -->

||||||One cha|racteristic|at-a-time|All ch|aracteristic|s jointly|
|---|---|---|---|---|---|---|---|---|---|---|
|re||e|||All<br>—|Emerging|Developed|All<br>—|Emerging|Developed|
||All|Emerging|Developed|Individualandhouseholdcharacteristics|(1)|(2)|(3)|(4)|(5)|(6)|
||(1)|(2)|(3)|Householdincome(omittedcategory:quinti|le3)||||||
|||||Quintile 1|0.52***|0.48***|0.70***|0.46***|0.41***|0.63***|
|Unconditionalaverage|