rafmacalaba/datause-encoder-data huggingface dataset:
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent of the market. Two other operators each hold over a quarter of the market, however, so that the overall market concentration is quite good with a Herfindahl index of 0.31.

Liberia’s mobile footprint (the percentage of the population living within range of a mobile signal and hence able to subscribe to service) grew substantially between 2003 and 2009, from 18 percent to 32 percent. At the start of the period, there were many more potential subscribers than actual subscribers, with penetration rates of less than 0.1 percent in 2003 (figure 11). This shortfall took some time to bridge, but by 2008 penetration rates were around 20 percent and had almost caught up to the mobile footprint. The latest figures for 2009 suggest that penetration has exceeded the population within the signal area. This is possible if individuals subscribe to more than one service, which is not unusual in Africa. The evidence based on industry statistics is corroborated by Liberia’s 2007 Demographic and Health Survey, which found that 29 percent of households had a mobile telephone—a coverage level many times higher than the electrification rate for the country. In fact, Liberia’s mobile penetration rates are now on par with other low-income countries in Africa (table 10).

**Figure 11. Liberia’s mobile footprint has grown steadily while penetration has burgeoned**

<!-- Start of picture text --> subscribers within range of a GSM signal<br>% of population<br><!-- End of picture text --> Source: Mayer and others 2008.

While no official statistics on mobile prices could be found for Liberia, there is evidence to suggest that prices have fallen over time. The cost of a SIM card, for example, is reported to have fallen from $65-85 in 2001 to $3-5 at present. The average revenue per user (ARPU) for the main operator fell from $43 per month in 2003 to $11 per month by late 2009. This is in line with trends elsewhere in SubSaharan Africa. Falling prices following entry of new operators may account for the increase in penetration rates described above (figure 11). Between 2003 and 2004, three new mobile operators were licensed, and it is in the two to three years that followed that penetration really accelerated _._ 22
areas at the time of the survey. For the migrant survey, we are interested in household heads/spouses and their parents. We focus on the household heads/spouses 18 years of age or older. To capture the missing long-term migrants from the rural survey, we set four criteria for our migrant subsample. First, the household head/spouse must have an identified rural _Hukou_ . Second, they have stayed in the urban area for at least one year or longer by the time of the survey, which helps to identify the long-term rural migrants possibly missed in the rural survey. Third, they still have family member(s) living in the rural area. This ensures that the the families that are now fully urban are not included. Fourth, they did not have a strong economic tie with their original home back in the village. This is to avoid double counting (by both rural survey and migrant survey) and to prevent over-sampling of the migrants. We use remittances sent back to the village as a measure of the strength of their relationship with the household of origin. We examine the distribution of their income and the remittances. The 80th percentile of the remittance rate is 20.8 percent of income. We then set the cutoff for the remittance rate at 20.8 percent and limit the migrants to those who had remitted less than 20.8 percent of their urban income back to rural home. The low remittance rate represents a weak economic tie and it is most likely that these rural migrants are not be captured in the original rural survey. Using the four criteria, we identified 1,355 adult household heads/spouses and their parents along with the necessary information on household heads/spouses’ age, education,and their parents’ age, education and occupation. By reclassifying the migrants according to the province of their agricultural _Hukou_ , and adding them to the rural survey sample, we have a total of 7,264 valid children-parents pairs (5,909 rural pairs plus 1,355 migrant pairs). In our sample, most of the children (84 percent of daughters and 83 percent of sons) went to school after the reform was implemented in 1978. We thus can call them the ‘children of reform’.

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_Private Sector Approaches to Effecive Family Planning_ _30_

# FIGURE 3

Impact of CSM on commercial pill market in the Dominican Republic <!-- Start of picture text --> 300_<br>200<br>100 Mwrrr<br>0 1tI I- ,, N....r 1,,,r _<br>l9s5 1S8S 198? 9I1g 198l<br>Proetwilo 2W Other orat e Wyeth Total orcat<br><!-- End of picture text --> on attracting users new to the contraceptive method. Consumers purchasing CSM products in pharmacies and shops are interviewed on the spot for their socio-demographic characteristics and previous use of contraception. However, it is not immediately clear that new users have adopted the CSM method and brand because the program is promoting low-priced commodities or because buyers are at the stage of life to begin using contraception and purchase the CSM brand over another. New users have been reported to be younger than women who switch from other brands in the DominiRepublic, Honduras, and Peru; the alternative hypothesis that they would have begun contracepting without the CSM project cannot be discarded (Brown et al. 1988). In intercept studies in the Dominican Republic and Peru, 34 percent of the CSM brand purchasers in each country reported that they had not used any family planning method before using the CSM brand. However, these figures do not distinguish between first-time purchasers and long-term users: 46 percent of the respondents in Peru had been using the brand for over one year, and 11 percent for five years or more. These studies should disaggregate first-time purchasers for separate analysis.

Finally, the impact of increased prevalence of CSM methods should be assessed against the background of total contraceptive prevalence. It appears that in the Dominican Republic and Panama, women may have begun to postpone sterilization because of greater reliance on oral contraceptives (Brown et al. 1988). In this case, CSM could be seen as promoting greater user choice and quality of care, but its impact on fertility would be diminished.

In Honduras, pill prevalence remained unchanged three years after the introduction of the CSM project (12.7 percent in 1984 vs. 13.4 percent in 1987) (Honduran Ministry of Public Health et al. 1989). The social marketing share of the pill market more than doubled, from 6.5 to 15.4 percent. CSM drew clients from the public sector (20.9 to 17.8 percent), the commercial sector (26.4 to 21.0 percent), and the CBD program (46.0 to 41.3 percent). Thus project success in attracting previously subsidized users (public and CBD clients) was nearly offset by source substitution from the self-supported commercial sector.

The degree to which CSM projects increase total method prevalence and/or effect source substitution from subsidized and commercial outlets probably depends in part on baseline prevalence
to compile macroeconomic statistics and social data reporting and estimation practices. This dimension measures the quality aspect of the data system. Under the assumption that international guidelines provide the benchmark of ideal data systems, adherence to such standards may imply that the quality of data systems meets well-established standards. Countries are evaluated against a set of criteria such as use of an updated national accounts base year, use of the latest balance of payments manual, the external debt reporting status, updated consumer price index, updated industrial production index, updated import/export prices, accounting basis for reporting government financial data, vaccine reporting to WHO (discrepancy between WHO and government estimates), subscription to IMF’s Special Data Dissemination Standard, and enrollment data reporting to the United Nations Educational, Scientific, and Cultural Organization (UNESCO).<sup>6</sup> Each criterion has equal weight.

The periodicity and timeliness dimension measures the availability and periodicity of key socioeconomic indicators, of which nine are Millennium Development Goals (MDG) indicators. This dimension attempts to measure the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The periodicity of the main indicators considered, each receiving equal weight, includes: (i) Income poverty indicator, (ii) Child malnutrition indicator, (iii) Child mortality indicator, (iv) Immunization indicator, (v) HIV/AIDS indicator, (vi) Maternal health indicator, (vii) Gender equality in education indicator, (viii) Primary completion indicator, (ix) Access to water indicator, (x) GDP growth indicator.

Several studies have used the SCI to evaluate the quality of data systems and investigate its relationship with other macroeconomic variables (Devarajan, 2013; Kubota and Zeufack, 2020). Tables 3 and 4 provide summary statistics for the SCI indicator for both the panel and cross-section data sets, respectively. Tables 5 and 6 present the correlation between the SCI and other variables for both cross-sectional and panel data,

> 6 The discrepancy between vaccination rates from government administrative records and household surveys has been documented by Sandefur and Glassman (2015) as an indicator of bad government data.

16
market have hovered between 1’500 and 2’500 tons per year worldwide with no clear trend (Figure 1).<sup>2</sup> Figure 1 Worldwide natural vanilla export tonnage <!-- Start of picture text --> 3500.0<br>3000.0<br>2500.0<br>2000.0<br>1 500.0<br>1 000.0<br>500.0<br>0.0<br>19651967196919711973197519771979198119831985198719891991199319951997199920012003<br>Export volume in tons<br><!-- End of picture text --> Source: Blarel and Dolinsky, COMTRADE.

# **2.1 The vanilla sector: stylized facts**<sup>3</sup>

## **2.1.1 Organization and market structure**

Figure 2 shows that the production of vanilla beans consists of three stages: (i) vanilla growing, which produces the “green” beans; (ii) curing, the stage at which

> 2 Like other primary products, natural vanilla faces a constant threat from technical change. For instance, a German  flavor and biotech company, Symrise, was recently reported to have made advances in the development of natural vanillin by fermentation with genetically modified bacteria of Eugenol, the conventional (and cheap) source of artificial vanillin. Although European hostility toward GMOs has convinced Symrise’s management to put its GM vanillin project temporarily on hold, once on the market, this technology could increase competition for natural vanilla (see www.genet-info.org).

> 3 This section draws from de Melo et al. (2000) and Blarel and Dolinsky (1995). Ecott (2005) narrates the fascination with vanilla since the time when the Aztecs demanded vanilla as a tax from the people of the central and high plateaus of the country we now call Mexico. In addition to a history of vanilla’s origins and its development, his book describes in great details the organization of vanilla production and its use by the flavoring industry (see the narrative of his visit to the Nielsen-Massey factory (Ecott 2004, pp. 11-19).

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domestic policy reforms,<sup>4</sup> This phenomenon was alluded to in Park & Fatemi (1993) in their analysis of equity returns. It is this issue of the competition among emerging markets in an endeavor to attract portfolio investnent from abroad that this paper sets out to address, using portfolio flow data.

The paper is organized as follows: Section I provides a description of the magnitudes involved in these emerging market portfolio flows and delineates the foundation for the hypotheses that are tested econometrically. Section 2 discusses the data and methodology used for arriving at the empirical results, which are provided in Section 3 of the paper. The main conclusions of the study and the policy implications for developing country policy makers in the context of their access to portfolio investment from abroad are provided in the last section of the paper.

# 1. TRENDS IN PORTFOLIO FLOWS TO EMERGING MARKETS<sup>5</sup>

Private capital flows to developing countries have, for the first time since the international debt crisis of the early 1980s, surpassed the volume of official flows to developing countries in 1992-93. This shift in the creditor composition of extemal financing to developing countries has been accompanied by a similar shift in the borrower composition. Private to private flows now account for almost 60 percent of net flows to developing countries. These flows have been primarily in the form of portfolio investment (through bonds and equities) and foreign direct investnent.

Portfolio flows increased from about $7.5 billion in 1989 to $36.8 billion in 1992, and topped an estimated $55.7 billion in 1993 (Table 1). Since 1989, more than half the

> 4 1hese portfolio switching' activitics between emerging markets coexist with the overall increase in the share of foreign investors! portfolios that arc allocated to emerging markets in genral. These two effects are analogous to the "substitution cffccr and 'income effcctC associated with a change in relative prics of commodities. 5 Sources: World Bank, _World_ **_Debt Tables,_** 1993-94, Volume l; _Qanrerty_ **_Review_** _of Financial Flows_ **_to_** _Developing_ **_Counires_** (various issues); and _GlobalEconomic Prospects, 1993._

7
<!-- Start of picture text --> Male LFP Female LFP<br>w 2<br>foe)<br>~ S<br>ct ~~co<br>3 3<br>Ow ort<br>N<br>Oo oOo<br>-.2 -1 0 A 2 -.2 0 2<br>Deviation from actual value Deviation from actual value<br>Male unemployment Female unemployment<br>Oo fo]<br>wo fee)<br>< 8<br>3° ae<br>S& (Ss)<br>° a<br>Oo oO<br>-1 -.05 0 .05 A 15 -.05 0 .05 A 15<br>Deviation from actual value Deviation from actual value<br>In sample (SAE) Out of sample (SAE) Direct estimate<br>All deviations are winsorized by one percent in each tail for the sake of presentation.<br><!-- End of picture text -->
# **Table 1: Calibration of the NK Model to a Typical Commodity Exporter in Sub-Saharan Africa**

|**Parameters**|(1)<br>**Symbol**<br>|(2)<br>**Value**|(3)<br>**Source/Target**|
|---|---|---|---|
|Panel A. Generalparameters||||
|Quarterly discount factor|β<br><br>|0.99|1% interest rate at qtly frequency|
|Risk aversion|1/σ<br><br>|1|Nakamura & Steinsson (2014)|
|Frisch elasticity|1/φ<br><br>|1|Nakamura & Steinsson (2014)|
|Elasticity of subst. across varieties|ε<br><br>|7|Nakamura & Steinsson (2014)|
|Elasticity of subst. btw H and F|η<br><br>|2|Nakamura & Steinsson (2014)|
|Price rigidity parameter|1 −ι<br><br>|0.25|Galí & Monacelli (2008)|
|Taylor rule reaction to PPI|πH<br><br>|1.5|Galí & Monacelli (2008)|
|Debt-elasticity of country premia|ψ<br><br>|0.007|Schmitt-Grohe & Uribe (2016)|
|Payroll subsidy rate|τ<br>|-0.5%|Farhi & Werning (2012)|
|Share of hand-to mouth HHs|ω|0.5|Galí et. al (2007)|
|Panel B. Sub-Saharan Africa||||
|_AR(1) Commodity Price (Quarterly):_||||
|AR coefficient of price|ρ<br>|0.975|Match half-life of 6.7 years|
|Error % STD of price|σp<br>|0.09|Match annual STD of 22%|
|_Structural Parameters:_||||
|Size of H economy/rest of the world|<br>n<br>|1%|Small SSA country|
|Degree of openness|α<br><br>|0.4|2000-16 SSA imports 40% GDP|
|_Steady State (% Qtly Non-Res GDP)_||||
|Resource GDP|Ys<br>R<br><br>|20%|2008-12 comm. exports in SSA|
|Public assets|As<br><br>|-244%|2000-18 external debt in SSA|
|Private assets (Ricardian HHs)|Bs<br><br>|0%|No asset revaluation effects|
|Transfers to Hand-to-mouth HHs|Ts<br>′′<br><br>|16%|Equal S.S. cons. across HHs|
|Transfers to Ricardian HHs|Ts<br>′′<br>|2%|Equal S.S. cons. across HHs|
|Profits|Ds|14%|(Endogenouslydetermined)|

**SSA-specific parameters.** Panel B of Table 1 displays parameters calibrated to match key features of a typical country in SSA.<sup>20</sup> On average in SSA, commodity exports represented 15.2% of GDP in 2008–2012, so we normalize the steady-state non-resource quarterly GDP s ) to 1 and set resource GDP ( s ) to 0.2 such that ZZ RR s s s ) ≈ 15%. The degree of openness is calibrated so that steady-state (YY YY RR RR ZZ consumption of foreign goods is equivalent to 40% of GDP ( , based on YY /(YY + YY the median imports-to-GDP ratio in SSA during in 2000–2018 (see Appendix αα= 0.4)

> 20 Panel B of Appendix Table 1 displays the list of countries in SSA with available export data from the UN Comtrade Database. Column (1) shows the average commodity exports in 2008–2012 as a percent of GDP. Columns (3)–(8) display the two main commodities exported by each country and their respective shares of GDP.

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9 distribution of income. Fortunately, an approach is available that does just this. Developed in the public finance and income redistribution literature in the early 1990s [8], the approach was used to measure equity in health care finance in several OECD countries in the latter part of the 1990s [6, 7]. This section outlines the approach, and the next section applies it data on out-ofpocket payments from Vietnam.

# _DECOMPOSING REDISTRIBUTIVE EFFECT_

The redistributive effect of a tax-i.e. the impact of a tax on the distribution of incomedepends on four key factors [8]. The same reasoning can be applied to health care payments. In the case where income inequality is measured using the Gini coefficient, redistributive effect, _RE,_ is simply the difference between the Gini coefficient for prepayment income _Gpre,_ and the Gini coefficient for postpayment income, _Gp,,,._ This has been shown by Aronson et al. to be equal to: (2) _RE = V-H-R,_ where _H la_,GF(X)_ and _RGy p -Cx p_ An explanation of these various indices and their interpretation follows.

# _VERTICAL INCOME REDISTRIB UTION-THE ROLE OF PROGRESSIVITY_

The term V in eqn (2) represents _vertical_ income redistribution-the change in income inequality that would have been brought about by health care payments if everyone at each prepayment income level had paid the same amount towards health care. V depends on two things, one of which is _K._ This is Kakwani's [9] index of progressivity, but computed on the assumption that at each income level everyone spends the same amount on health care. Kakwani's index, illustrated in Fig 3, is defined as twice the area between the prepayment Lorenz curve, _Lpre,_ and the payment concentration curve, _Lpay,_ the latter being the graph of the cumulative share of health care payments against the cumulative share of the population, ranked by prepayment income. Equivalently, K is the difference between the payment concentration index (defined analogously to the Gini coefficient but with reference to the payment concentration curve) and the Gini coefficient. A positive value of K indicates a progressive payment structure (the case illustrated in Fig 2), whilst a negative value indicates a regressive structure.
# **F. Is the VSL “Individuated”?**

We use the coefficients of the interval-data Weibull model of table 5, panel 1, to predict the mean WTP for different types of road users in Delhi, and hence their VSL. The resulting figures, displayed in table 6, allow us to determine whether the VSL is “individuated,” i.e., whether the VSL differs for specific groups of beneficiaries.

The top panel of table 6 suggests that perceived exposure to road traffic risks affects the VSL dramatically. Holding age, education, and income constant, and assuming a risk reduction of about 13 in 100,000—the average risk reduction across all scenarios and all variants of the questionnaire—the VSL is three times larger for “high exposure” people than for “low exposure” individuals. For the former, which we define as individuals who travel as part of their job and drive a two-wheeler, the VSL is 148,873 PPP$, while for the latter it is 46,325 PPP$. The bottom panel of table 6 shows that the VSL does increase with income. If we focus on a high-exposure respondent with a highschool diploma, the VSL is roughly 123,000 PPP$ if this individual has a low income, 149,000 PPP$ if this individual is middle-income, and over 179,000 PPP$ if this individual falls in the high-income group.<sup>16</sup> Our preferred estimate of the VSL is about 150,000 PPP$ and is based on a highexposure individual. This figure is roughly equal to 1.75 times the discounted flow of personal income over the rest of the working life of the average respondent in our sample, using a discount rate of 12%.<sup>17</sup>

> 16 We note that the VSLs reported in table 6 are almost identical if they are computed using a model that drops answers to the pedestrian scenario—the least successful of our scenarios—which elicited a zero WTP response from 50% of respondents.  The respective VSLs are: 121,000 (low income), 149,000 (middle income), 168,000 (high income).

> 17 The Planning Commission of India currently uses a social discount rate of 12%.

- 20 -
<!-- Start of picture text --> Figure 1: FCV Pass-through rate for Syria 2009-2020<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Share of time since 2009 as FCS Pass-through Rate<br>Source: Corral et al. (2020)<br><!-- End of picture text --> An alternative nowcasting approach, which bypasses the distribution neutral assumption, relies on growth elasticities of poverty (GEP). Growth elasticities of poverty reveal the percentage change in poverty associated to a 1 percent growth in GDP per capita. Implicitly, the elasticity takes into account both the impact of growth on average welfare as well as changes in the distribution of welfare (Shorrocks, 2013; Datt and Ravallion, 1992). Still, this approach relies on the strong assumption that the relation between growth and poverty does not change over time.

In some cases, poverty nowcasting builds on more articulated microsimulation models. More complex examples of microsimulation models are based on computable general equilibrium (CGE) or general equilibrium macroeconomic models that rely on social accounting matrices or time series macroeconomic data to create “linkage aggregate variables” (LAVs) that are fed into microsimulation models that build on household survey data. However, the high information demands of these models makes them hard to apply in data deprived contexts. A less data demanding approach to microsimulation relies on building behavioral models on household surveys (typically linking labor market outcomes and remittances to individual and households’ incomes and consumption) which are then linked to aggregate and sector level macro-economic projections.<sup>8</sup> Table 3 below provides a summary of the results and methodologies employed in the limited number of studies that have attempted to estimate poverty in Syria during the post-conflict period. Despite their notable limitations, all these studies consistently indicate substantial increases in poverty.<sup>9</sup> _Table 3: Comparative Overview of Post-Conflict Poverty Estimation Studies in Syria_

|**Author**|**Poverty line**|**Estimates**|**Methodological approach**|
|---|---|---|---|
|World<br>Bank<br>(2017)|Lower national PL (LPL)|2007: 12.3%<br>2016: 54.5 – 66.5%|Extrapolation using a growth<br>elasticity of poverty<br>estimated over the period<br>2003-2007.|
|ESCWA (2016)|Lower national PL (LPL)|2010: 14%; 2015: 50%|NA|
||Upper national PL(UPL)|2010: 28 %;2015: 83.4%||
|ESCWA<br>(2020b)|$1.90 (2011 PPP) intl PL<br>$3.50(2011 PPP)intl PL|2010: <1%; 2019: 40%<br>2010: 19%;2019: 77%|Distribution neutral<br>projections based on the<br>growth ofprivate household|

8 Olivieri et al. (2014).

> 9 For a detailed description of each study see Annex 1.