rafmacalaba/datause-encoder-data huggingface dataset:
present new evidence on how mutual funds behave and explore how the organization of financial intermediaries can affect the investment decisions they make across countries and regions.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 studies how U.S. mutual funds allocate their portfolios internationally. Section 4 analyzes the factors behind the degree of international diversification. Section 5 studies whether there are potential costs and gains to the international diversification strategies of global funds. Section 6 concludes.

# **2. Data**

To conduct the analysis, we use data on U.S. equity mutual funds established to purchase assets around the world.<sup>16</sup> The U.S. mutual fund industry is very large (in 2005 there were 8,044 mutual funds with a market capitalization of US$ 8 trillion, or 69% of U.S. GDP), has a strong international presence (according to some estimates, U.S. mutual funds represent more than 70% of the assets held worldwide by all mutual funds), channels a significant share of retirement savings (mutual funds captured 24% of retirement savings in the U.S. in 2004), and is a relatively mature and sophisticated industry. We use two types of data: mutual fund holdings and mutual fund returns.

Mutual fund holdings are available from Morningstar, a company that collects mutual fund data. We analyze reports from March 1992 (when they became available) until June 2006. While some mutual funds provide monthly reports, most do so quarterly or semiannually (depending on the reporting SEC rules at the time). Given this heterogeneity in the release of new information, we construct our database with the last reported portfolio information for each fund in any given year. For example, our sample of mutual fund holdings for 2005 contains portfolio data for the Fidelity Worldwide Fund as of October 2005 and portfolio data for the Scudder Global Fund as of December 2005. In sum, we collect end-of-year detailed information on portfolio holdings between 1991

> 16 Funds that focus on both debt and equity are excluded from the analysis, even though they do invest a significant share of their portfolios in foreign stocks.

10
41 The survey did not ask about cooking fuels and had only one question on energy sources—the quantity and amount spent on the fuel in the previous month and in the past 12 months, except for firewood where no quantity was reported. At the lower quintiles, very few households purchased LPG and scaling up to national numbers is likely to be subject to large statistical uncertainty. LPG was moderately used only at the highest urban quintile level. The quantities purchased per household were comparable both across quintiles and between rural and urban households. Per capita consumption, however, tended to be larger in rural areas where households were smaller than in urban areas in each quintile (Table A2.7).

Table A2.7: Number of households purchasing LPG and average quantity purchased per month by purchasing households in Indonesia

|**Quintile**|**Number of**<br>**Rural**|**purchasing househo**<br>**Urban**|**lds (percent)**<br>**Total**|**Kg purchase**<br>**Rural**|**d per househol**<br>**Urban**|**d (per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|3,808 (0.1)|9,142 (0.7)|12,950 (0.2)|15.0 (2.5)|12.0 (0.2)|12.9 (0.9)|
|**2**|24,637 (0.3)|0 (0.0)|24,637 (0.3)|10.2 (1.8)|No users|10.2 (1.8)|
|**3**|88,127 (1.4)|97,511 (2.4)|185,638 (1.8)|9.6 (2.3)|11.7 (2.2)|10.7 (2.3)|
|**4**|185,614 (3.6)|385,297 (6.6)|570,911 (5.2)|10.8 (3.0)|11.3 (2.4)|11.1 (2.6)|
|**5**|330,909 (11)|2,532,273 (27)|2,863,182 (24)|12.1 (3.5)|12.3 (3.2)|12.2 (3.3)|
|**All**|633,095 (2.2)|3,024,223 (13)|3,657,318 (7.1)|11.3 (3.1)|12.1 (3.1)|12.0 (3.1)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The share of total spending allocated to LPG by user households was higher for rural quintiles than for the corresponding urban quintile, but did not show any clear trend across income levels, possibly because of the small number of purchasers in the lower quintiles (Table A2.8). The coefficient of variation for the calculated prices of LPG was 0.16, indicating that 95 percent of prices were within 70–130 percent of the national average price.

Table A2.8: Shares of total household expenditure on LPG for all households and for purchasing households in Indonesia (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|<br>**Rural**|**User households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.0|0.0|0.0|4.4|1.5|2.4|
|**2**|0.0|0.0|0.0|3.6|No users|3.6|
|**3**|0.1|0.1|0.1|4.0|3.6|3.8|
|**4**|0.1|0.2|0.2|4.0|3.2|3.5|
|**5**|0.3|0.6|0.5|2.8|2.1|2.2|
|**All**|0.1|0.3|0.2|3.4|2.3|2.5|

_Source:_ Authors’ calculations.

# **Kenya (KIHBS 2005–2006)**

The data are from the Kenya Integrated Household Budget Survey (KIHBS) administered by the Kenya National Bureau of Statistics (Ministry of Planning and National Development) of the Government of Kenya. The fieldwork for this survey was carried out between May 2005 and April 2006. The total of all non-purchased items (both food and others) was imputed to be equivalent to 51 percent of total cash expenditure. However, about 95 percent of non-purchased
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Benchmarking port indicators: Porto Grande of Mindelo as compared with other West African ports**

||**Porto Grande,**<br>**Mindelo (Cape Verde)**|Tema (Ghana)|Apapa (Nigeria)|Dakar (Senegal)|Abidjan (Côte d’Ivoire)|Cotonou (Benin)|Mombasa (Kenya)|Durban (South Africa)|
|---|---|---|---|---|---|---|---|---|
|**Capacity**|||||||||
|Actual containers handled<br>(‘000sTEU/year)|**170**|471|336|306|500|158|437|1,899|
|Actual general cargo handled (‘000s<br>tonnes/year)|**699**|7,900|3,400|6,109|n.a.|1,100|12,980|16,100|
|General-cargo-handling capacity (‘000s<br>tonnes/year)|**800**|8,500|5,000|n.a.|n.a.|2,500|1,500|n.a.|
|**Efficiency**|||||||||
|Average container dwell time in terminal<br>(days)|**16**|25|42|7|12|12|5|4|
|Average truck-processing time for receipt<br>and delivery of cargo (hours)|**6**|8|6|5|2.5|6|4.5|5|
|General-cargo vessel preberth waiting<br>time (hours)|**24**|9.6|n.a.|24|2.9|48|36|n.a.|
|General-cargo vessel turnaround time<br>(hours)|**36**|48|n.a.|60|2.2|48|48|n.a.|
|Average container crane productivity<br>(containers loaded/unloaded per crane<br>hour)|**n.a.**|13|12|n.a.|18||10|15|
|Average general-cargo crane productivity<br>(tonnes loaded/unloaded per crane<br>working hour)|**6**|13.5|9|n.a.|16|15|20.8|25|
|**Tariffs**|||||||||
|Average container-handling charge, ship<br>to gate ($/TEU)|**100**|168|155|160|260|180|67.5|258|
|Average general-cargo-handling charge,<br>ship to gate ($/tonne)|**10.0**|10.0|8.0|15.0|13.5|8.5|6.5|8.4|
|Average dry-bulk-handling charge, ship<br>to gate or rail ($/tonne)|**5.0**|3.0|n.a.|5.0|5.0|5.0|5.0|1.4|
|Average liquid-bulk-handling charge, ship<br>to gate or rail ($/tonne)<br>_Source:_Ocean Shipping Consultants 2009. D|**2.5**<br>erived from th|1.5<br>e AICD ports da|1.0<br>tabase (htt|4.0<br>p://www.inf|n.a.<br>rastructure|n.a.<br>africa.org/ai|n.a.<br>cd/tools/data<br>)|n.a.<br>.|

_Source:_ Ocean Shipping Consultants 2009. Derived from the AICD ports database (http://www.infrastructureafrica.org/aicd/tools/data). _Note:_ TEU = 20-foot equivalent units. n.a. = Not applicable.

# Air transport

## **Achievements**

Given Cape Verde’s isolation and growing tourism industry, air transport plays an important role in the economy. Cape Verde now has four international airports (Sal, Praia, Boa Vista, and São Vicente) as well as three additional aerodromes. The international airports, including their buildings, facilities, and runways, have been recently renovated. The remaining three (Fogo, Maio, and Sao Nicolau) serve 8
Table 2: Summary Statistics

|Country|Sample size<br>Avg.|eq.|income|Std. dev.|Gini|
|---|---|---|---|---|---|
|AT|6,220||25,451|13,971|0.268|
|BE|6,011||23,291|10,948|0.249|
|BG<br>|7,154<br>||3,714<br>|2,491<br>|0.333<br>|
|CH|7,583||42,208|24,486|0.279|
|CY|4,589||21,058|11,454|0.279|
|CZ|8,711||9,006|4,320|0.250|
|DE|12,683||22,221|12,273|0.276|
|DK|5,897||32,027|13,836|0.232|
|EE|5,338||6,922|3,912|0.330|
|EL|6,184||13,184|8,651|0.334|
|ES|15,481||17,088|10,597|0.329|
|FI|9,743||27,517|13,891|0.246|
|FR|11,078||24,299|14,583|0.288|
|HR|6,969||6,627|3,819|0.306|
|HU|13,330||5,327|2,863|0.276|
|IE|4,318||24,867|14,307|0.296|
|IS|3,684||22,190|9,232|0.210|
|IT|21,070||18,786|11,730|0.309|
|LT|5,403||4,774|3,150|0.344|
|LU|6,765||37,911|19,977|0.271|
|LV|6,423||5,334|3,618|0.363|
|MT|4,701||13,006|6,747|0.277|
|NL|11,411||25,210|11,414|0.235|
|NO|5,026||43,260|16,971|0.202|
|PL|15,545||6,103|3,690|0.316|
|PT|5,899||10,781|7,296|0.334|
|RO|7,867||2,562|1,646|0.337|
|SE|6,599||26,346|10,700|0.215|
|SI|13,183||13,772|5,994|0.225|
|SK|6,779||7,304|3,416|0.257|
|UK|7,391||25,936|16,815|0.320|

_Note_ : Summary statistics for the 31 countries in the 2011 wave of EU-SILC. Income variables are measured in Euros.

partition is similar but more parsimonious than the one used in Checchi et al. (2016), who base their analysis on a total of 96 types.

Lastly, we compare our estimates against the latent class approach as proposed by Li Donni et al. (2015). The eligible set of circumstances is the full set of observable circumstances, Ω.<sup>ˇ</sup> We follow Li Donni et al. (2015) in using Schwartz’s Bayesian Information Criterion (BIC) to select the most adequate number of latent types.

# **4.3 Estimates of Inequality of Opportunity**

Table 3 shows inequality of opportunity estimates for our country sample according to five different estimation procedures. Columns 2-4 list results using the parametric, the non-parametric, and the latent class approach, all of which have been proposed in the extant literature (see section 4.2). Columns 5 and 6 list results from conditional inference trees and conditional inference forests, respectively. For all methods, inequality of opportunity estimates are obtained by calculating the Gini index in the counterfactual distribution _Y_<sup>_EA_</sup> .

Of all methods under consideration the parametric approach delivers the highest estimates. For 29 out of 31 countries the inequality of opportunity estimates are higher than the results 14
maximum systematic risk proportion for Model 2 is about one half of the maximum of same risk for Model 1.

Again, these results suggest that international diversification via NIFs is desirable for the U.S. investor. It also points out that, on average, the NIFs are not a good proxy for their corresponding local market index (i.e., NIF-based diversification gains would be lower than theoretical diversification gains based on (inaccessible) local market indices).

# **11.3** - Mean-Variance Efricient Frontiers

Although there are 18 EM and 14 DM funds that trade on the NYSE representing 13 EMs and 10 DMs, in this subsection, we include all NIFs that continuously traded over the period July 1989 to June 1991. Thus, the data consists of weekly returns on 8 EM and 6 DM national index funds traded on the NYSE and the corresponding data on 7 EM and 6 DM market portfolios. The efficient frontiers are defined as usual as the set of portfolios that have less risk than any other with comparable expected return, and more return than any other with comparable risk. These frontiers were developed using standard packages using historical time series of returns.'

# 11.3.1 - Idealized Diversification Gains

We first investigate whether the diversification benefits documented in the past studies carry through to the more recent period and for our sample countries. Specifically, we inquire whether the benefits (in terms of mean-variance (M-V) efficiency) to the passive U.S. investor sequentially increase as (s)he diversifies into developed and emerging markets. As in the previous studies, we use various market indices and assume no barriers to international

> **10** Since the local market indices do not include dividends, we do not include distributions of the sample NIFs in computing returns. We use the S&P500 without dividends as a proxy for the U.S. market return.

11
Figure 13: Average Net foreign investment inflows From OECD countries <!-- Start of picture text --> 1990 1995 2000 2005 2010<br>year<br>post conflict average conflict average<br>2000<br>1500<br>1000<br>500<br>net positive investment inflow (in million USD) 0<br><!-- End of picture text --> positive investment flow during recovery. Moreover, the large boom in inflows in the post 2005 period is less marked. Note, that the deviation between the two groups rises around a time in which globalization took off, i.e. countries in recovery joined a globalized network of production and expanding multinational firms.

However, this is obviously not conclusive evidence for the role played by recovery from conflict as the samples of these two groups changes slightly across time. In order to get around this problem we will use country fixed effect regressions. Country fixed effects regressions allow us to look at changes of inflows in the same country across time. Figure 14 displays the example of Uganda. The graph shows battle-related deaths as red bars and investment flows as a blue line. The timing of the increase in inflows coincides quite clearly with the end of violence. Fixed effects regressions help to identify this sort of dynamics.

46
The sectoral results of Table 3 presage our subsequent poverty analysis. The most important difference between the scenarios in this context has to do with food prices and domestic output responses. Under the UL scenario, food prices are suppressed by import liberalization and farmers suffer directly and indirectly. In the case of FL, all primary food prices rise and farm output and income respond accordingly. Clearly, a low-income agrarian country like Vietnam needs to see significant agricultural returns from any multi-lateral trade agreement, if its poor rural majority is to benefit in the short or medium term.

# [Table 3 about here]

# **5.2 Changes in poverty rates after trade liberalization**

As noted in the previous section, our analysis starts by looking at the spatial distribution of poverty under the Baseline (ex ante) scenario. We estimated relevant parameters in Eq(5)–Eq(8) using the VLSS data set.

For Eq(5), we simply ran OLS for each wage-earner of the eight labor segments to find coefficients. The _R_<sup>2</sup> statistic varied from 0.24 to 0.42, depending on the labor segment. For Eq(6), we ran OLS of logarithmic non-wage income for urban and rural areas, capturing about 35 percent and 38 percent of variations. There are about 1.2 percent of households without any non-wage income, and they were excluded from the estimation. Multinomial logit regressions were run to estimate Eq(7) and Eq(8) for each labor segment. We were able to predict 73 percent of the individuals correctly after applying the relevant weights. Detailed estimation results are reported in the Appendix.

The macroeconomic CGE results also gives us the aggregate wage income for each combination of skilled/unskilled, male/female and urban/rural as well as the non-wage income for rural and urban households. This allows us to calculate the adjustment coefficients by solving Eq(13)–Eq(16). The adjustment coefficients for each scenario are also reported in the Appendix.

20
Monte Carlo evidence from Table 1, row 2B suggests the estimators in Equations 9-11 are extremely accurate: the average across Monte Carlo replications is extremely close to μμ ii εε the true values in the Monte Carlo of σσ� , σσ� , σσ� = 5%. Consequently, the μμ ii εε performance statistics (R and bias ) and average shrinkage coefficients (σσ = 1.5%, σσ = 1%, σσ and ) are very μμ ii ii similar to those when the variance components {RREE λλ̂ , } are known in Table 1, row B1. ψψ γγ μμ ii εε σσ σσ , σσ

**Endogeneity** The key empirical challenge in estimating the effect of leaders on growth is that “leader transitions are often non-random, and may in fact be driven by underlying economic conditions” (Jones and Olken 2005, p836). While we cannot address this issue through a quasiexperimental design like Jones and Olken (2005) for hundreds of leader estimates, we can show that the most plausible kind of endogeneity in the data does not affect the performance of our empirical methodology. The first step in this approach is to estimate a linear probability model of how growth affects the probability of a leader transition in the actual leader-growth panel data:

25 Wooldridge (2001, p260) advocates using the covariance between errors within the same unit as a measure of the individual effect. Chetty et al. (2014) and Kane and Staiger (2008) also estimate teacher effects via the covariance between average test scores in each class taught by the same teacher each year. An alternative approach used in previous versions of this paper (and in Stata’s _xtreg, re_ and _xtreg, sa_ commands) is to first estimate the between 2 −1 2 variance (in our context, the variance across leader growth averages = ∑ ), and then subtract the iid NNLL 2 error variance in Equation 9. However, we found that this method was upward biased ( oo LL ii=1 ii too large) in Monte Carlo σσ� NN gg̅ 2 2 simulations, quite noisy, and often had < 0 , which was rounded to zero. μμ σσ� oo εε,ii σσ� −σσ� 18
# **1. Introduction**

In the past decade, mobile phones have become a ubiquitous feature of life in developing countries. Between 2005 and 2011 mobile cellular subscriptions nearly tripled in the developing world, increasing from 1.2 to 4.5 billion. In Africa, the region with the fastest mobile subscription growth rate, mobile cellular subscriptions increased from 87 million in 2005 to 433 million in 2011. A number of factors contributed to the luxury-to-commodity transformation of mobile phones. Technological advances along with economies of scale in network equipment and handsets in the developed world led to price declines. Additionally, the introduction of pre-paid billing systems lowered service costs by eliminating the need for operators to send bills and collect debts, thus making mobile phones accessible to lower income consumers. Finally, deregulation created a competitive marketplace, further reducing prices and bolstering mobile phone adoption (Economist 2009). Accompanying the growth in handset ownership has been growth in mobile applications, including digital money transfers and payments, citizen polling station surveillance, remote health care consultation and diagnosis, and transmission of timely market information to farmers.

One area in which mobile technology holds promise is for implementing surveys. Despite recent advances, in many developing countries there is a lack of reliable and timely socioeconomic data. Today, there is an opportunity to leverage mobile phones for routine data collection to help fill this gap and support policy decisions, strengthen civil society, and facilitate program management (Croke et al. 2012). This paper examines the opportunities and challenges of using mobile phones as a research tool, based on the experience with the South Sudan Experimental Phone Survey (SSEPS).

The paper is organized as follows. Section 2 of the paper describes options for data collection via mobile phones, Section 3 describes the South Sudan survey, Section 4 assesses patterns of non-response and survey results, and Section 5 concludes.

# **2. Mobiles for Data Collection**

For mobile phones to be useful as a survey device, they need to satisfy a number of criteria. A mobilebased survey should 1) yield a sample size large enough to draw statistically reliable inferences about a population of interest, 2) be economically viable compared to alternative data collection, and 3) generate data of adequate quality. The third criterion relates to sampling, attrition, and non-response as well as respondent behavior when interacting with a mobile phone interface.

2
# **Table 11**

# **FG and financial recoupling: Robustness tests and causality**

This table presents estimations of the relationship between financial recoupling and financial globalization. All results are from Arellano and Bond difference estimator. FG Flows are absolute values of net inflows to a country by global and global emerging mutual funds normalized by market capitalization. Dependent variables are dlog returns from EM assets (MSCI country indexes, sovereign spreads, and exchange rate, LCU/USD). Sample goes from 2005-09/2010 due to data availability. Normal and turmoil divides the sample between positive (or zero) and negative returns from the global asset respectively. Different type of instruments were used. External indicates that instruments for country i were FG flows to EM minus FG flows to country i. Internal indicates that the first lag of the dependent variables were used as instruments. Robust standard errors in parentheses. *, **, and *** denotes significance at the 10, 5 and 1% level respectively. Source: MSCI, GEM World Bank, Bloomberg, US Treasury, EPFR, Barclays Capital, GEM World Bank, IMF IFS Statistics.

|Sample|Full Sample|Turmoil|Normal|Full Sample|Turmoil|Normal|
|---|---|---|---|---|---|---|
|**Instrument Type**|**External**|**External**|**External**|**Internal**|**Internal**|**Internal**|
|||**Equity**|||||
|S&P Returns|0.255***|0.047|0.503***|0.474***|0.432***|0.564***|
||(0.089)|(0.140)|(0.103)|(0.090)|(0.113)|(0.129)|
|S&P Returns*FG Flow|7.192***|8.136***|3.580***|4.596***|5.193***|2.620**|
||(1.020)|(1.366)|(0.788)|(0.636)|(0.765)|(1.155)|
|FG Flow|0.031|0.192***|0.046|0.053***|0.119***|0.017|
||(0.026)|(0.049)|(0.041)|(0.016)|(0.025)|(0.034)|
|Observations|1,428|567|861|1,428|567|861|
|||**Bond**|||||
|US HY Returns|0.464***|0.234***|0.620***|0.448***|0.400***|0.499***|
||(0.0430)|(0.0446)|(0.166)|(0.0507)|(0.0546)|(0.103)|
|US HY Returns*FG Flow|4.999***|8.295***|1.719|3.857***|4.408***|1.528|
||(0.956)|(1.609)|(3.061)|(0.783)|(0.877)|(1.444)|
|FG Flow|-0.470***|-1.309***|-1.120***|-0.350***|-0.456***|-0.585***|
||(0.165)|(0.337)|(0.378)|(0.123)|(0.171)|(0.107)|
|Observations|1,005|495|510|1,005|495|510|
|||**FX**|||||
|Broad Dollar Index Returns|1.077***|1.094***|0.774***|1.140***|1.277***|0.859***|
||(0.190)|(0.243)|(0.199)|(0.185)|(0.219)|(0.165)|
|Broad Dollar Index*FG Flow|5.494|6.055|-0.983|1.100|1.381|-1.758|
||(3.773)|(4.760)|(3.482)|(1.449)|(1.957)|(1.170)|
|FG Flow|-0.0283|-0.241***|-0.0917**|-0.0624**|-0.0352|-0.128***|
||(0.0292)|(0.0757)|(0.0434)|(0.0308)|(0.0442)|(0.0242)|
|Observations|1,156|493|663|1,156|493|663|