rafmacalaba/datause-encoder-data huggingface dataset:
- Third, each of the active Venezuelan phones was assigned to the primary sampling unit (PSU) where the owner was most likely to reside.

# II.1 Identifying active mobile phones

A phone was tagged as being active if, during the past 90 days, it generated at least 60 of the following events in 30 different days: being turned on or off, making or receiving voice calls, sending or receiving text messages, or accessing the Internet.

# II.2 Identifying Venezuelan mobile phones

Active mobile phones were tagged as Venezuelan if they were registered under the name of a Venezuelan national or if, during the past 30 days, they were used at least 30 times to make/receive a voice call to/from the República Bolivariana de Venezuela; send/receive a text message to/from the República Bolivariana de Venezuela; or access a website of interest to Venezuelans (e.g., visa applications, job opportunities, etc.).

# II.3 Georeferencing the phones

Cellphones are mobile by nature. For this exercise, a phone's residence was based on the events generated between 8 pm and 6 am – the evening hours, which can be reasonably assumed to be the time when the owner is at home.<sup>12</sup> Each of the events uses one (or occasionally more than one) of Telefonica's antennas, and each of the antennas is characterized by a single territorial point – the centroid of its coverage area. The antennas used by phone in the evening hours during a given number of days are first clustered into groups of neighboring antennas, and the cluster most frequently used during that period is selected as the cluster that defines the owner's residence. The phone's location is computed as the average of centroids of antennas in that cluster, weighted by the number of events used by each antenna. Finally, the phone is assigned to the PSU where that point belongs.

# II.4 Extrapolating to the overall population

The above process determines how many of Telefonica's phones reside in each PSU, and how many of them are likely to be Venezuelan. The figures were adjusted by canton-wise adjustment factors based on Telefonica's market shares (known from other sources), to estimate the total number of phones from all companies and the total number of Venezuelan phones from all companies in each sector. Another adjustment factor based on the fraction of the population that uses mobile phones was used to make the figures consistent with the total number of households reported by INEC in each sector at the time of the 2010 Census.

# II.5 Results

At the country level, the total number of Venezuelan migrants estimated in 2019 by Telefonica de Ecuador was 470,095. This population represented about 3 percent of the total population in Ecuador in 2019 and

> 12 The workplace was also determined by monitoring cellphones’ activity from 9 am to 5 pm. The idea was to see whether Venezuelans move far away from their homes to seek for a job or not.

5
**Table 7. India Revenues and Percentage Offshore Services Industry**

|**FY**<br>**e**<br>|**2005**<br>**e**<br>|**FY 2**<br>**e**<br>|**006**<br>**e**<br>|**FY**<br>**e**<br>|**2007**<br>**e**|**FY 2**<br>**e**<br>|**008**<br>**e**|**FY 2**<br>**e**<br>|**009E**<br>**e**|
|---|---|---|---|---|---|---|---|---|---|
|**Revenu**<br>**(USD**<br>**bn)**|**% of**<br>**total**<br>**Revenu**<br>|**Revenu**<br>**(USD**<br>**bn)**|**% of**<br>**total**<br>**Revenu**<br>|**Revenu**<br>**(USD**<br>**bn)**|**% of**<br>**total**<br>**Revenu**|**Revenu**<br>**(USD**<br>**bn)**|**% of**<br>**total**<br>**Revenu**|**Revenu**<br>**(USD**<br>**bn)**|**% of**<br>**total**<br>**Revenu**|
|IT Services<br>10|56%|13.3|56%|17.8|57%|23.1|57%|26.9|57%|
|BPO<br>4.6|26%|6.3|27%|8.4|27%|10.9|27%|12.8|27%|
|Engineering<br>Services and R&D,<br>SoftwareProducts<br>3.1|18%|4|17%|4.9|16%|6.4|16%|7.3|16%|
|**Total**<br>**17.7**|**100%**|**23.6**|**100%**|**31.1**|**100%**|**40.4**|**100%**|**47**|**100%**|
|<br>Source: NASSCOM<br>**Figure 9.** **India Offshore**<br>33%<br>37%<br>29%<br>30%<br>35%<br>40%|**Services**<br>34%<br>33|**Indust**<br>%|**ry Gro**<br>30%<br>3|**wth Rat**<br>0%<br>31%|**e, 2006-**|**2009**||||
|0%<br>5%<br>10%<br>15%<br>20%<br>25%<br>FY 2006<br>**Growth Rate**<br>IT Services<br>|FY 20<br>BPO<br>En|23%<br>07<br>gineering Ser|FY<br>vices and R&|2008<br>D, Software|16%<br>F<br>Products|17%<br>14%<br>Y 2009E||||

Source: NASSCOM Figure 9 shows that almost every Indian segment of the offshore services industry saw a decline in demand in 2009. The IT services segment had growth rates equal to or higher than 30% during the period 2006-2008, but during the economic downturn, the growth rate decreased to 16%. BPO activities follow a similar pattern, experiencing growth rates over 30% annually and decreasing to half of that rate in 2009. The third category, “engineering services and R&D, software products,” based on discretionary spending projects, is subject to greater fluctuation in demand and saw its growth shrink slightly more than the other segments. This is a category that combines two completely different activities, “engineering services and R&D” and “software products.” Offshoring of engineering and R&D services have shown strong growth in the past two years, leading one to assume that the fluctuations represent changing demand for new software products.

In the Philippines the industry is concentrated in BPO activities (see Figure 10). In 2009, revenues in this segment increased by 22% with respect to the previous year. The KPO segment increased by 35% and similar trends are expected for 2010 (BusinessMirror 2010). According to 24
**Table A1. Baseline Member Characteristics of  Mixed vs single gender COs**

||N. Obs.|N. Obs|Mea<br>Male|ns||N. Obs.|N. Obs|Me<br>Female|ans||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Mixed<br>gender<br>CO/CG|(2)<br>Single<br>gender<br>CO/CG|Mixed<br>Gender<br>CO/CG<br>(3)|Single<br>gender<br>CO/CG<br>(4)|(5)<br>P-val of<br>t-test<br>(3)-(4)|(6)<br>Mixed<br>gender<br>CO/CG|(7)<br>Single<br>gender<br>CO/CG|Mixed<br>Gender<br>CO/CG<br>(8)|Single<br>gender<br>CO/CG<br>(9)|(10)<br>P-val of<br>t-test<br>(8)-(9)|
|**_Individual Characteristics_**|||||||||||
|Age|82|1798|36.26|38.27|0.16|137|1434|34.06|37.85|0.00|
|Own Education|82|1798|7.22|5.22|0.06|137|1434|2.54|2.65|0.61|
|Business Owner (Yes=1)|82|1798|0.63|0.61|0.66|137|1434|0.59|0.58|0.84|
|Digit Span Recall|82|1798|3.87|3.85|0.94|137|1434|2.39|2.71|0.12|
|Risk Tolerance (0=Risk Averse; 10= Risk Lover)|82|1798|4.09|3.80|0.38|137|1434|2.80|3.43|0.02|
|Married (Yes=1)|82|1798|0.77|0.82|0.01|137|1434|0.83|0.85|0.48|
|Index of Optimism|82|1798|-0.23|-0.02|0.21|137|1434|-0.87|-0.29|0.00|
|Business Literacy|82|1798|0.97|0.66|0.07|137|1434|0.14|0.23|0.67|
|<br>Index Female Mobility|82|1798|-|-|-|137|1434|0.08|0.00|0.36|
|Index No Purdah|82|1798|-|-|-|137|1434|-0.30|0.19|0.00|
|Trust in Formal System|82|1798|-0.01|-0.03|0.31|137|1434|-0.28|-0.14|0.00|
|Months as Member|82|1798|16.48|28.74|0.00|137|1434|20.28|24.98|0.45|
|Holds office in Group (Yes=1)|82|1798|0.48|0.20|0.00|137|1434|0.06|0.18|0.01|
|Eligibility|82|1798|0.49|0.63|0.00|137|1434|0.53|0.63|0.00|
|**_Household Characteristics_**|||||||||||
|Household size|82|1798|7.88|7.93|0.70|137|1434|7.45|7.00|0.00|
|Years of Education of Spouse|82|1798|3.87|2.67|0.02|137|1434|4.93|4.88|0.91|
|Number of children under 9|82|1798|1.78|1.97|0.40|137|1434|1.69|1.40|0.05|
|Land|82|1798|4.38|6.01|0.39|137|1434|4.48|2.58|0.30|
|Fraction of Members of same Zaat (caste)|82|1798|0.41|0.43|0.12|137|1434|0.45|0.21|0.00|
|Ever in Business (Yes=1)|82|1798|0.60|0.62|0.11|137|1434|0.54|0.61|0.76|
|Log of Value of Livestock|82|1798|5.77|9.36|0.00|137|1434|6.99|4.78|0.00|
|Distance to meeting place|82|1798|6.24|7.50|0.07|137|1434|5.91|9.17|0.00|
|<br>Credit Constraints (Yes=1)|82|1798|0.09|0.12|0.28|137|1434|0.19|0.16|0.31|
|Expenditures (in 1,000 Rupees)|82|1798|5.42|4.98|0.91|137|1434|4.86|4.58|0.36|
|Decision Making (0-8)|82|1798|3.10|3.35|0.71|137|1434|1.95|1.83|0.39|
POLICY RESEARCH WORKING PAPER 2953

# Abstract

Health facility surveys come in various guises. One dimension in which they vary is their motivation. Some seek to understand better links between households and providers. Others seek to understand better provider behavior and performance. Still others seek to understand the interrelationships between providers, while yet others seek to shed light on the linkages between government and providers. Health facility surveys differ too **in** the data they collect, **in** part due to the different motivations. Surveys also vary in the way they collect data, some relying on direct observation, some on record review, and some on interview. Some quality data are collected through clinical vignettes. Facility data have been put to a variety of uses, including planning and budgeting; monitoring, evaluation, and promoting accountability; and research. Lindeldw and Wagstaff review some of the literature under each heading and offet some conclusions regarding the current state of health facility surveys.

This paper-a product of Public Services, Development Research Group-is a background paper prepared for the World Bank workshop on Health Facility Surveys, December 2001. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hedy Sladovich, room MC3-607, telephone 202-473-7698, fax 202-522-1154, email address hsladovich@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mlindelow@worldbank.org or awagstaff@worldbank.org. January 2003. (47 pages)

> _The Policy Research Working Paper Series disseninates 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._

Produced by the Research Advisory Staff
# **Table 10: Data sources used in the estimation of the number of MOFI accounts**

This table shows the distribution of data sources used to estimate the number of accounts in mutually owned financial institutions (MOFI) around the world. The third column represents the percentage in terms of countries. The last column represents the percentage in terms of accounts.

|**Data Source**|**# countries**|**% sample**|**% final**<br>**count**|
|---|---|---|---|
|FA Survey '09|56|35%|56%|
|WOCCU|52|33%|3%|
|Predictions|15|9%|6%|
|Set to 25th percentile|21|13%|2%|
|EACB|13|8%|13%|
|CGAP internal numbers|1|1%|20%|
|Totals|158|100%|100%|

Notes: WOCCU is World Council of Credit Unions, ECBA is European Cooperative Banking Association, 25th percentile used only in "preferred estimate" 50th percentile used in "high estimate"; predictions based on regression, and CGAP internal numbers are for Japan and come from direct communication with regulator.

# **Table 11: Data sources used in the estimation of the number of SSFI accounts**

This table shows the distribution of data sources used to estimate the number of accounts in SSFIs around the world. The third column represents the percentage in terms of countries. The last column represents the percentage in terms of accounts.

|**Data Source**|**# countries**|**% sample**|**% final**<br>**count**|
|---|---|---|---|
|FA Survey '09|68|33%|78%|
|CGAP "Big Numbers" *|141|67%|22%|
|Totals|209|100%|100%|

Notes: CGAP "Big Numbers" refers to data gathered for CCGAP, Occasional Paper no. 8, 2004 47
of funds, verify compliance regulations, and assess the scope and sources of delays in budget execution. The survey found considerable improvements in the amount of funding that schools received compared to the 1996 study. In part, these improvements can be attributed to measures to increase transparency and accountability - e.g., through publication of school and health facility in local papers and on local notice boards (Reinikka 1999). However, the study also documents considerable delays in the transfer of funds. The study found that these delays do not occur uniformly throughout the system. Central ministries, the Central Bank, and the head quarters of the government owned Uganda Commercial Bank (UCB) all transferred resources in full and without delays. At lower levels, however, transfers were held up by local branches of UCB and by districts administrators.

The problems highlighted in the Uganda studies are far from new. However, these studies provided important quantitative evidence on the scope and seriousness of problems in budget execution, and demonstrated how a new tool can be applied to analyze these issues. Other surveys have been carried out in Ghana, Honduras, Tanzania, and Uganda. These surveys have focused on leakage, "job capture" and other means of extracting private benefits from public positions may be systematically, the implicit allocation of resources at different levels of administration, and the costs and efficiency of health care delivery. For example, the study of public employment in the Honduran health sector showed that the best qualified health sector staff engaged in more shirking, 'ghost working', and job capture than lowerranked employees (World Bank 200 lb). In addition, a number of surveys are currently being planned or implemented. Many of these surveys are variants of school of facility surveys, and in some cases efforts are made to link the surveys with household data collection exercises.

## _Source_

- Dehn, J., R. Reinikka, and J. Svensson. 2001. "Basic Service Delivery: A Quantitative Survey Approach.". Development Research Group, The World Bank.: Washington, D.C. Processed.

# **References**

The word "processed" describes informally reproduced works that may not be commonly available through library systems.

- Ablo, Emmanuel, and Ritva Reinikka. 1998. "Do budgets really matter? Evidence from public spending on education and health in Uganda." Policy Research Working Paper 1926. Development Research Group, World Bank, Washington, D.C.

- Ackerberg, D. A., M. P. Machado, and M. H. Riordan. 2001. "Measuring the Relative Performance of Providers of Health Service." NBER Working Paper No. W8385. Cambridge, Mass.

- Akin, J., C. Griffin, D. K. Guilkey, and B. M. Popkin. 1986. "The Demand for Primary Health Care Services in the Bicol Region of the Philippines." _Economic Development and Cultural Change_ 34(4): 755-82.

39
are different for the period 1993 to 2008 (Appendix Table 4).<sup>80</sup> The country-deciles that have lost most between 1993 and 2008 are mostly from Sub-Saharan Africa and Latin America, with East European deciles having recovered. These results are consistent with the disastrous income declines in Eastern Europe between 1988 (the beginning of the reform process) and mid-1990s followed by, in some countries sharp, and in other countries, rather slow recovery.

Another simple way to evaluate the success of various country-deciles is to compare their positions in global income distribution. This can obviously be done for every country-decile and every year. In Figure 7, we do it for several selected countries (2008 values are always drawn as a solid line, 1988 values as a dashed line). The top left panel illustrates the already discussed remarkable upward mobility (in the global income distribution) of Chinese rural and urban deciles. In 2008, the Chinese top urban decile is at the 83<sup>rd</sup> global percentile while twenty years earlier it was at the 68<sup>th</sup> . In other words, if somebody stayed in that same decile in China over 20 years and his income followed the average growth path of the decile, he would have leapfrogged, in terms of income, more than 900 million people worldwide. It is interesting that in 2008, the Chinese top _rural_ percentile is at a higher global position than the Chinese _urban_ percentile was in 1988. The same development, although less dramatic, is illustrated by the improvements in the position of Indonesian rural and urban deciles shown in the upper right panel of Figure 7.

But when we turn to the bottom left panel the situation is exactly the reverse. There we see that Nigeria’s and Côte d’Ivoire’s deciles have almost uniformly slid down the global income distribution. It is only the top Ivoirian decile that has managed to preserve its 1988 position. Finally, in the bottom right panel, we show the evolutions in Germany and Brazil. The position of German deciles has remained very high and displays very little change. Brazil is an example of a reasonably fast growth across its income spectrum, and improvements in the distribution so much so that all its deciles, except the highest, are now placed globally higher than they were in 1988.

> 80 The bottom decile in Japan declined by 4.5% per year on average, which seems a very substantial decrease. Over the same period, average incomes in Japan (in our data) declined by 1.35% per annum, whereas GDP per capita (in 2005 $PPP) grew by 0.88% per annum. Only in the most recent benchmark year we were able to obtain micro data from LIS for Japan. Before that the data are based on tabulations, which might explain the very substantial decline between 1988 and 2008 for the bottom decile.

46
around barriers to services as binding commitments from trade agreements are eliminated.

- **Scenario 2 - Increase to bound tariffs** : in this scenario, we quantify the effects of an increase in tariffs up to the bound rates under WTO commitments and an increase in the cost of traded services of 3 percent. The difference between applied tariffs and bound rates, i.e. the binding overhang or the water in the tariff is the policy space that countries can use without breaching their WTO commitments. Average worldwide tariffs would more than triple increasing from the currently applied 2.7 percent to 10.2 percent. The increase in the cost of traded services captures the value of the increase in uncertainty around barriers to services as binding commitments are eliminated.

As a first step, we updated tariffs in the GTAP 9.2 database with effectively applied tariffs available from the World Bank WITS database<sup>7</sup> to reflect the latest available year (2015) of worldwide protectionism. Not surprisingly, as highlighted in Table 1, average import tariffs in advanced economies such as the EU and the United States are among the lowest and range from 1.5-1.6 percent. In a contrast, in emerging market and developing economies (EMDEs) in South Asia and Sub-Saharan Africa, effectively applied duties are five times higher than that of advanced economies with average rates ranging from 7.5-7.7 percent.

As in the case of applied tariffs, average bound tariffs are also the lowest for advanced economies amounting to 2.5 percent and 2.7 percent in the European Union and the United States, respectively. On the other extreme, average bound rates are the highest in South Asia at 45 percent, followed by Sub-Saharan Africa 36 percent and Latin America and Caribbean of 33 percent driven by higher than average bound tariffs in major EMDEs in these regions such as India (41 percent), Nigeria (126 percent) and Brazil (30 percent).

In terms of the difference between currently applied and MFN rates (scenario 1), Europe and Central Asia and Latin America and the Caribbean would increase tariffs the most, by 2.2 percentage points each. On the other hand, the loss of existing preferences would only result in small tariff increases in regions with no strong RTA commitments such as South Asia and China. In terms of raising tariffs to bound levels (scenario 2), the United States and the EU would be the least affected, followed by Europe and Central Asia and China. On the other hand, South Asia, Sub-Saharan Africa and Latin America and the Caribbean would be raising tariffs significantly by 37 percentage points, 29 percentage points and 26 percentage points, respectively.

> 7http://wits.worldbank.org/

12
**Figure 7: Real Spending per Student Has Generally Risen in Low-Income and Middle-Income Countries, but the Gap between Income Groups Has Widened**

_Public education spending per student (constant 2015 PPP $), 1998–2001 to 2014–17_ <!-- Start of picture text --> a. Primary education b. Secondary education<br> 3,500 3,500<br>UMIC<br> 3,000 3,000<br> 2,500 UMIC 2,500<br> 2,000 2,000<br> 1,500 1,500 LMIC<br> 1,000 LMIC 1,000<br> 500 500<br>LIC<br>LIC<br> - -<br>1998–2001 2002–05 2006–09 2010–13 2014–17 1998–2001 2002–05 2006–09 2010–13 2014–17<br><!-- End of picture text --> _Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ LIC = low-income country, LMIC = lower-middle-income country, and UMIC = upper-middle-income country.

# **_Recent Spending Increases Have Mostly Financed School Expansion in Low-Income Countries_**

Increases in government funding for education can go towards expanding the number of student places, increasing the average amount spent on each student, or a combination of the two. To determine if countries are finding a balance between quantity and quality, it can be useful to explore how policy makers have used additional public education funding and how their country’s level of education development has influenced their choices. The data can also help to identify the transition points where countries shift spending priorities from increasing access to improving quality. These transitions are where spending inefficiencies potentially can increase if policy makers fail to pay due attention to how increases in levels of spending per student are used. To understand how governments have historically used funds over the last two decades, we broke down public education spending by education level (primary, secondary, and tertiary) and decomposed it into spending per student and enrollment (Box 6).

19