rafmacalaba/datause-encoder-data huggingface dataset:
Second, the program allows for noncompetitive bidding by small investors, so direct competition with securities dealers is not present in primary auctions. But if this distribution channel becomes more attractive to retail investors than buying securities through banks (because of lower administrative costs), a degree of competition could be introduced. Moreover, this technology could ultimately result in an auction system for distributing securities that avoids the use of banks as intermediaries and distribution agents. The desire to avoid such an outcome would give Philippine banks a strong incentive to make their savings products more competitive. (see table 2) Today, however, banks have few incentives to cannibalize their own base of savings products to promote the Expanded Small Investors Program.

# **Table 2: A Comparison of Savings Products for Retail Investors***

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||||**Electronica**|**lly distribute**|**d**|||**Convention**|**al**|
||**Prima**|**ry T-Bill**|**Secondary**<br>**T-Bill**|**TDs**|**Common T**|**rust Funds**||**Time Depos**|**its**|
||**E-**|**SIP**||**Special**<br>**Savings**<br>**Accounts**|**ST Fixed**<br>**Income****|**Premium**<br>**Fund****||||
|**_Minimum Amount_**|5,000|100,000|100,000|100,000|100,000|100,000|10,000 -<br>100,000|> 100,000|> 1,000,000|
|**_Grossyield(p.a.)_**|6.84|6.84|6.38|5.00|6.83|7.31|1.50 - 3.50|1.50 - 3.87|1.875 - 4.25|
|**_Processing fee_**|0.79|0.08||||||||
|**_Brokerage fee_**|0.50|0.50||||||||
|**_Participationfee_**||||||||||
|**_Investment_**<br>**_management fee_**|||||0.75|0.75||||
|**_Early redemption_**<br>**_fee_**|||||0.25|0.25||||
|**_Minimum holding_**<br>**_period_**||||30|30|90|90|90|180|
|**_Tenor_**|91|91|91|||||||
|**_Net yield before_**<br>**_taxes_**|5.55|6.26|6.38|5.00|5.83|6.31|1.50 - 3.50|1.50 - 3.87|1.875 - 4.25|

* The table compares the net effective yield after all charges in the case of several savings products available to retail investors in the Philippines. <u>Sources: Bureau of Treasury web-site, Commercial bank websites.</u>

# **Potential risks for debt management posed by the program**

# _Potential downside of high demand_

By introducing a channel for distributing government securities directly to retail investors, the government risks fragmenting the securities market—by increasing the portion of domestic debt held 27
# **4** **<u>Accounting for missing top incomes in the computation of global inequality</u>**

So far in the analysis we used our main cross-sectional sample and only the information contained in the surveys, i.e. we did not adjust survey means to national accounts. In this part of the analysis, we test the robustness of our conclusions to (1) anchoring to national accounts, that is distributing the “excess” income from the gap between national accounts consumption and the household survey mean either across the entire distribution or to the top decile only, and (2) using a Pareto interpolation to “elongate” the distribution of the top decile.

|**Table 4: Robustness check on th**|**e globa**|**l Gini i**<br>**Benc**|**ndex: A**<br>**hmark**|**ccoun**<br>**years**|**ting fo**|**r missing top i**<br>**1988-2008**|**ncomes**<br>**1993-2008**|
|---|---|---|---|---|---|---|---|
||**1988 **|**1993 **|**1998 **|**2003 **|**2008**|**change (pp)**|**change (pp)**|
|**(1) Survey data only**|72.5|71.8|71.9|71.9|69.6|-2.9|-2.2|
|**(2) Private consumption**||||||||
|**instead of survey mean**|71.5|70.5|70.6|70.7|67.6|-3.9|-2.8|
|**(3) Private consumption with**||||||||
|**Pareto imputation**|71.8|70.8|71.0|71.1|68.0|-3.7|-2.8|
|**(4) Private consumption with**<br>**top heavy Pareto imputation 1/**|76.3|76.1|77.2|78.1|75.9|-0.5|-0.2|
|**Number of surveys**<br>_1/ The entiregap is allocated to th_<br>_Notes: Observations are weighted_<br>_of 520 country-yearsfor whichpri_|63<br>_e top 1_<br>_using_<br>_vate co_|105<br>_0%._<br>_popula_<br>_nsump _|112<br>_tion. All_<br>_tionfro_|126<br>_calcul_<br>_m nati_|114<br>_ations a_<br>_onal ac_|_re done across_<br>_counts is availa_|_the sample_<br>_ble._|

Table 4 presents our results. Since here we replace survey means with private consumption from national accounts (only when the latter is greater, otherwise we keep survey means<sup>66</sup> ) we lose country-years for which we do not have national accounts information. This leaves us with 520 surveys across the five benchmark years instead of 565. In addition, we now treat China, India and Indonesia as single countries because we do not have separate national accounts information for rural and urban areas. The new baseline Gini for this new sample (row 1 in Table 4) is very similar to the Gini for the full sample (Table 3): the difference is less than 0.5 Gini points except

> 66 This means that we use the maximum of per capita private consumption and the survey mean. In other words, when the survey mean is greater than private consumption we keep the survey mean. Atkinson (2007) replaces survey means by national account aggregates in all cases. We adopt a different approach because we focus on the underestimation of top incomes in household surveys. In other words, we assume that surveys cannot overestimate, but only underestimate, overall income of households.

36
44

# Table 16: Brazil: Outcomes For Children By Type of Household, Metropolitan Area, 1984 (percent of children)

|||Recife||**S**|**Ao**Pau|lo|Por|to**Ale**|**gre**|
|---|---|---|---|---|---|---|---|---|---|
|Children's Age<br>and Status|**FFH**|AU|T-st|**FHH**|All|T-st|**FHH**|All|T-st|
|Age 7-9<br>- not in school|23|14|3.6|8|8|0.0|12|10|1.1|
|Age**10-14**<br>**-**not in school|22|14|_5.1_|12|8|2.9|18|12|3.1|
|- in labor force|10|7|2.9|13|8|3.1|16|9|3.9|
|Age**10-14 In**||||||||||
|**the labor force**<br>**-**not in school|69|56|2.2|42|36|0.9|69|67|0.2|

Source: author's tabulations Note: T-st refers to the T-statisics for testing whether the difference between FHHs and all households is zero.
- Stavins, R., 1999. The Costs of Carbon Sequestration: A Revealed-preference Approach. The American Economic Review 89 (4), 994–1009.

- Steinbuks, J., Hertel, T. W., 2013. Energy Prices Will Play an Important Role in Determining Global Land Use in the Twenty First Century. Environmental Research Letters 8 (1), 014014.

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33
# **1.  Introduction**

Most discussions of the digital divide treat it as a “North-South” issue, but the conventional dichotomy doesn’t apply to cell phones in Sub-Saharan Africa. As Table 1 shows, huge gaps in cell phone coverage divide Sub-Saharan countries that are all poor by international standards. The table provides statistics on country-level percentages of urban and rural populations within range of cell phone towers.<sup>1</sup> In both rural and urban sectors, population coverage in March, 2004 varied from 0 to 100%. Rural coverage was negligible or nonexistent in 17 countries<sup>2</sup> , but above 47% in the top quartile. Urban coverage was below 56% in the lowest quartile, but above 98% in the top quartile.

In this paper, we investigate the sources of these huge disparities in African cell-phone coverage, with a particular focus on the role of differences in competition policy. Moving beyond cross-sectional analysis of country-level differences, we take a spatially-disaggregated approach that enables us to investigate the local determinants of coverage at thousands of grid points in SubSaharan Africa. This approach has several advantages. First, it enables us to control much more precisely for area-specific demand and supply factors that affect firms’ decisions to install cellphone towers. These include proximity to main roads and local population centers, as well as local topography. Such factors are potentially-important determinants of cross-country differences in coverage, because African countries differ greatly in geography, population distributions and infrastructure endowments. Second, introduction of better spatial controls permits clearer tests of the impact of competition policy on cell-tower expansion. Finally, the micro-spatial approach

> 1  We have produced the estimates by overlaying a digital map of GSM (Global System for Mobile Communications) cell phone towers and coverage areas with a map of urban and rural populations.  Cell phone tower data in GIS format have been provided by GSM World, Inc.  Spatial population distribution data have been drawn from CIESEN’s Gridded Population of the World, version 3.

> 2  Djibouti, Angola, Congo, Comoros, Somalia, Guinea, Guinea, Sudan, Central African Republic, Mali, Eritrea, Niger, Guinea Bissau, Sierra Leone, Chad, Ethiopia and Liberia.

2
knowledge through imports. Coe and Helpman (1995) provide an empirical implementation of the open economy endogenous growth model. They construct an index of the foreign R&D to which a country has access as the trade-weighted sum of that country's trading partners' stocks of R&D. They find for a sample of developed countries that both domestic and foreign R&D have a significant impact on TFP, and that TFP increases with the general degree of openness of the economy and with openness towards the larger R&D producing countries.<sup>3</sup> Coe, Helpman and Hoffmaister (1997) examine the same issue for developing countries. They find that developing countries benefit more from foreign R&D spillovers, the more open they are and the more skilled is their labor force. These findings provide support for the hypothesis that trade is an important mechanism through which knowledge and technological progress is transmitted across countries.

This paper builds on Schiff, Wang and Olarreaga (2002). That paper expanded on Coe and Helpman (1995) and Coe et al. (1997) by examining these issues at the industry level in developing countries.<sup>4</sup> The idea is that importing countries learn from the knowledge embedded in the _inputs_ that they import. As is shown in Section 3 below, our measure of the stock of foreign R&D obtained by an importing country at the industry level explicitly incorporates the production structure of the economy as reflected in the input-output relationships.

> 3 Keller (1998) argues that Coe and Helpman's finding on trade as a channel for R&D spillovers is not entirely conclusive. Lumenga-Neso et al. (2001) show that Coe and Helpman's results do seem to hold once "indirect" trade-related R&D spillovers are taken into account.

> 4 Keller (2002a) did examine trade-related R&D spillovers at the industry level for the G-7 countries and Sweden.

_5_
# **<u>Annex Table 6. Marginal Effects from probit regressions. Dependent variable: Ever attended school, ages 6 to 17</u>**

||Burundi 2000|Cambodia 1999|Cambodia 2000|Indonesia 2003|Jamaica 1998|Mongolia 2000|Myanmar 2000|Romania 1995|Sierra Leone<br>2000|
|---|---|---|---|---|---|---|---|---|---|
|Disabled (0,1)|-0.189|-0.565|-0.314|-0.529|-0.185|-0.367|-0.002|-0.503|-0.035|
||(3.36)***|(7.84)***|(7.50)***|(22.39)***|(9.26)***|(11.01)***|(0.03)|(11.26)***|(0.34)|
|Age (years)|0.495|0.281|0.308|0.024|0.004|0.317|0.135|0.103|0.094|
||(19.89)***|(23.27)***|(45.00)***|(41.25)***|(6.04)***|(34.48)***|(14.92)***|(31.88)***|(6.69)***|
|Age-Squared|-0.021|-0.010|-0.012|-0.001|-0.000|-0.012|-0.006|-0.004|-0.004|
||(17.58)***|(19.44)***|(38.98)***|(38.75)***|(6.50)***|(30.38)***|(12.35)***|(29.38)***|(7.21)***|
|Male (0,1)|0.107|0.022|0.038|-0.001|-0.001|-0.015|-0.000|0.000|0.103|
||(7.58)***|(1.99)**|(5.92)***|(2.55)**|(1.32)|(1.82)*|(0.09)|(0.03)|(8.13)***|
|Urban (0,1)|0.198|0.078|0.057|0.002|0.001|0.019|0.083|0.004|0.094|
||(4.40)***|(6.67)***|(5.11)***|(5.66)***|(1.06)|(1.66)*|(11.13)***|(1.70)*|(4.93)***|
|Quintile 2|0.010|-0.053|0.035|0.002|0.002|0.026|0.002|0.017|0.010|
||(0.42)|(3.02)***|(3.94)***|(6.05)***|(2.50)**|(2.28)**|(0.23)|(7.08)***|(0.50)|
|Quintile 3|0.127|-0.018|0.075|0.003|0.001|0.061|0.015|0.021|0.088|
||(5.49)***|(1.01)|(8.42)***|(8.19)***|(0.96)|(5.85)***|(1.88)*|(9.58)***|(4.35)***|
|Quintile 4|0.151|0.052|0.154|0.004|0.002|0.066|0.002|0.022|0.162|
||(6.67)***|(3.12)***|(17.73)***|(11.37)***|(1.95)*|(5.74)***|(0.18)|(10.60)***|(7.81)***|
|Quintile 5|0.261|0.183|0.228|0.005|0.001|0.107|0.005|0.019|0.243|
||(10.65)***|(9.41)***|(22.74)***|(13.32)***|(1.62)|(9.18)***|(0.47)|(7.64)***|(10.22)***|
|Observations|5838|10880|23737|64136|6952|7619|26317|13776|7448|

Absolute value of t statistics in parentheses. *, **, *** indicates significant at 10%, 5%; and 1% levels. Models include region dummies 22
26 export variety by 16.7 percent in the petroleum & plastics industry, at the highest, and 3.7 percent in the mining & basic metals industry, at the lowest. Finally, most industries benefit from tariffs imposed on other industries due to reallocation of productive resources. For example, the textiles & garments industry expands its export variety due to tariffs imposed on agriculture industry and basic metals industry. Tariffs imposed on the basic metals industry also benefits the machinery & transport industry, and the electronics industry. Using these tariff impact estimates, along with the estimates of 1/(1 − σ n ) in Table 2 and the sample average industry shares, we calculate that a 10 percentage point increase in all U.S. tariffs would reduce the exporting country productivity by 2 percent due to the decreases in export variety of the industries. This implies that tariffs are both statistical and economically important in explaining export variety and country productivity.

The next section of Table 4 shows the marginal effects of four trade agreement dummies (CANFTA, NAFTA, CBI and ANDEAN) on export variety. Given that we already control for tariffs, these variables capture the effect of the reduction in non-tariff barriers due to the signing of such agreements on export variety. CANFTA is shown to have positive and significant impact on export variety in textiles & garments, wood & paper, machinery & transport and the electronics industry, while NAFTA is shown to have no significant additional effects on variety. On the other hand, the CBI increases the export variety in agriculture, textiles & garments, machinery & transport, and the electronics industry, while ANDEAN provides for variety expansion in textiles & garments and the petroleum & plastics industry.

The third section of Table 4 focuses on the effects of geography related trade costs variables such as distance (in log of kilometers) and transport costs.<sup>9</sup> In order to allow for

> 9 Distance from US is the geographical distance in kilometers between the capitol cities, as obtained from Nicita and Olarreaga (2004).
# **Table 2:  Regression Results:  Bolivia**

All variables in logs except the dummy variable for protected status Dependent variable: Log odds ratio, cumulative forest cleared %, 2014

||Rando|m Sample (10,000|obs.)|20% Rando|m Sample|All Obser|vations|
|---|---|---|---|---|---|---|---|
||OLS<br>(1)|Bootstrap<br>(2)|Spatial<br>(3)|OLS<br>(4)|Bootstrap<br>(5)|OLS<br>(6)|Bootstrap<br>(7)|
|Distance from road|-0.326<br>(15.39)**|-0.326<br>(12.03)**|-0.323<br>(15.34)**|-0.323<br>(129.25)**|-0.323<br>(21.37)**|-0.327<br>(292.22)**|-0.327<br>(25.03)**|
|Distance to nearest|-0.499|-0.499|-0.488|-0.531|-0.531|-0.530|-0.530|
|urban center (DU)|(11.79)**|(7.33)**|(11.67)**|(104.99)**|(15.56)**|(234.72)**|(16.77)**|
|DU x Primary road<br>share|0.215<br>(16.88)**|0.215<br>(11.78)**|0.209<br>(16.60)**|0.209<br>(137.29)**|0.209<br>(19.56)**|0.209<br>(307.60)**|0.209<br>(26.71)**|
|Slope (Std. dev.<br>eleveation)|-0.797<br>(7.79)**|-0.797<br>(5.01)**|-0.772<br>(7.59)**|-0.812<br>(66.22)**|-0.812<br>(11.48)**|-0.828<br>(150.89)**|-0.828<br>(12.65)**|
|Slope x Elevation|0.148<br>(10.54)**|0.148<br>(7.04)**|0.143<br>(10.26)**|0.147<br>(87.73)**|0.147<br>(16.25)**|0.149<br>(198.87)**|0.149<br>(17.63)**|
|Protected area|-0.245<br>(4.19)**|-0.245<br>(3.11)**|-0.246<br>(4.23)**|-0.236<br>(33.70)**|-0.236<br>(5.22)**|-0.236<br>(75.20)**|-0.236<br>(6.97)**|
|Agricultural<br>opportunity value|0.356<br>(14.84)**|0.356<br>(10.93)**|0.354<br>(14.84)**|0.367<br>(126.99)**|0.367<br>(20.63)**|0.364<br>(282.02)**|0.364<br>(24.57)**|
|Constant|-1.559|-1.559|-1.059|-1.357|-1.357|-1.341|-1.341|
||(6.69)**|(4.13)**|(4.13)**|(48.30)**|(7.59)**|(106.77)**|(7.11)**|
|Observations|9,998|9,998|9,998|705,260|705,260|3,526,359|3,526,359|
|R-squared|0.20|0.20||0.19|0.19|0.19|0.19|

Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

23
sources as well as a survey of deposit insurers. One of the main improvements is the introduction of historical data on coverage and co-insurance, introducing a time series aspect to the data. Another major data source is the survey carried out by the International Association of Deposit Insurers in 2002-03. The main cross-country part of the database comprises readily usable data for empirical and statistical analysis where most variables are coded as indicators along with explanatory details. We present the main database in the appendix section A.1. The details of the data for each country with references to the sources are covered in the appendix section A.2 and the detailed data sources are given in the appendix section A.3.

The electronic version of the full dataset<sup>6</sup> is available online at the Finance Research website of the Development Economics Research Group, World Bank. The complete database includes the full coverage ratio data spanning 1960 to 2003 for all countries, where applicable. In the following sections we describe the dataset and the included variables and discuss main features of explicit deposit insurance systems around the world.

# **3.1 Explicit versus implicit deposit insurance**

EDISs differ from IDISs due to their reliance on formal regulation through central bank law, banking law, or the constitution and so on. The relevant law explains the main ingredients of the deposit insurance such as the beginning date, coverage limits, how (if any) they are going to be funded, and how bank failures will be resolved.

If such regulation is not present for deposit insurance, we assume that the DIS is implicit, relying on the observation that every country establishes a de facto insurance system for banks.

The variables related to the type of deposit insurance available in each country comprise of the following: **_a) Type:_** This variable identifies the form of the deposit insurance – explicit or implicit – present in each country. The variable takes the value of one for countries with EDIS, and zero otherwise (Table A.1.1). **_b) Date Enacted /_**

> 6 The data is available as an Excel workbook consisting of three worksheets. The first worksheet includes the main cross-country dataset, the second worksheet provides historical levels of coverage limits and coinsurance, and finally the third worksheet provides the coverage ratios (coverage limits as a share of GDP per capita).

5
accurately. We then used the latter module as our data source to measure the main outcomes. We discuss this process in more detail in Section 3.

_Stage 3: Selection of households and respondents._ The experiment was conducted in two regions within the Departments of San Salvador and Usulután. We selected these regions in order to achieve high variation in the types of activities reported. Region 1, which is located in the Department of San Salvador, includes households located close to the Metropolitan Area of San Salvador, the main economic activities of which are commerce, construction, manufacturing, and sugar cane production. Region 2 is situated in the Department of Usulután, where the main economic activities include coffee production, fishing, and commerce. Using the 2007 Salvadoran Population Census (i.e., the most recent census available), we identified 276 enumeration areas (EAs): 114 in Region 1, and 162 in Region 2. Each of these EAs had at least 30 households and was classified as either rural or peri-urban. From the 276 EAs, we randomly selected 48 that were evenly distributed across the rural and peri-urban areas and then conducted a full household listing for each EA. We then randomly selected 21 households within each EA for a total of 1,008 households to constitute our study sample. Within each household, we randomly chose a minimum of two and up to a maximum of four working-age household members between the ages of 15 to 64 years, which yielded a total of 2,480 working-age individuals. Since our main hypothesis consists of the differential impacts of these survey methods by age and sex, we stratified the selection of the working age household members by these two variables, and we defined youth as individuals between the ages of 15 to 24 years.

_Stage 4: Random assignment of households to experimental groups._ We conducted a randomization at the EA-level to assign the 1,008 households with equal probability to one of the following three groups (Figure 1):

1. _LOA Module group (LOA)._ A group of households was randomly assigned to participate in the LOA module before responding to the standard labor module. This treatment arm allowed proxy respondents to provide information about the labor activities of other selected household members when they were not available for the interview.

2. _Enforced self-reporting group (ESR)_ . The selected adults of the households that were randomly assigned to this group responded to the standard labor module without previous exposure to the LOA module. Self-reporting was enforced in this group, and 8