rafmacalaba/data-use-mentions-extended huggingface dataset:
_GI_<sup>,</sup> Using the point estimates of the prevalence of stunting and underweight, we drew the concentration curves for different levels of geographic aggregation as shown in Figure 10(a). The concentration curves for underweight look similar and are given in Figure 10(b). There are four points to note here. First, the commune-level targeting is a large improvement over the stratum-level targeting, which is the best policy-makers can do with the DHS data only.

> 18An example of a self-targeting program is the food-for-work program, because only poor people without better outside option are willing to participate in the program to receive food. Food-for-work targeted to a particular region is an example of the combination of geographic targeting and self-targeting.

31
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PSI-PMI program|0.788**|0.585**|0.717|0.443|0.188*|0.154*|0.207*|0.157*|
||(0.334)|(0.206)|(0.445)|(0.324)|(0.101)|(0.079)|(0.118)|(0.086)|
|Student-level covariates|NO|YES|NO|YES|NO|YES|NO|YES|
|School-level covariates|NO|YES|NO|YES|NO|YES|NO|YES|
|Average WASSCE mathematics<br>score for control|20.18|20.18|20.74|20.74|0.05|0.05|0.03|0.03|
|Standard deviation of WASSCE<br>mathematics score for control|15.6|15.6|15.15|15.15|0.23|0.23|0.18|0.18|
|Observations|873|873|1,037|1,037|873|873|1,034|1,034|
|R-squared|0.096|0.434|0.077|0.247|0.073|0.258|0.097|0.181|

_Note:_ The results are based on equation 1. The outcome variables are the standardized WASSCE mathematics and English exam scores and students receiving satisfactory (“credit”) score on the WASSCE in both mathematics and English. Robust standard errors clustered at the school level are in parentheses. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination. *** p < 0.01, ** p < 0.05, * p < 0.1.

_School covariates_ variables include
|0.8|1.4|0.4|
|Mobile phone|subscriptions/100<br>people|32.7||24.4|33.1|50.6|
||Tanzania 2008|Low-inc|ome cou|ntries 2008|Sub-Saharan Africa<br>2008|Tanzania 2010|
|Price of monthly mobile basket|13.7||11.0||12.1|11.3|
|Price of monthly fixed line<br>basket|11.7||10.4||12.4|9.1|
|Price of 20-hour fixed broadband<br>Internet package|<br>66||287||209|32|
|Price of a 1 minute call to US|0.25||0.70||0.7|0.28|
|Price of inter-Africa tel. calls,<br>mean|0.34||0.94||1.0|0.39|

_Source:_ TCRA, TTCL, Vodacom and AICD database. Note: Low-income countries refer to those in Sub-Saharan Africa.

## **Challenges**

As of 2010, just over three quarters of Tanzania’s population lived within range of a GSM signal (table 8), falling somewhat short of the performance of east African neighbors such as Kenya and Uganda, where more than 90 percent of their population is covered by mobile networks. Detailed spatial analysis of potential costs and revenues for GSM service across the country suggest that around 95 percent of Tanzania’s population could be served on a commercially viable basis. There is thus a market 23
which cause its profits to move around further.

Ultimately it is therefore an empirical question as to whether this assumption seems reasonable over the periods of typical economic experiments, and especially over short-term neighborhoods of a few months around a particular horizon of interest. Figures 1A, 1B, and 1C investigate this assumption for microenterprise profits and for household expenditure. Figure 1A shows autocorrelations for monthly profits from the Sri Lankan microenterprises in de Mel et al. (2008) which were measured at three month intervals, and uses data from similar microenterprises measured monthly in de Mel et al. (2009) to show the autocorrelation in monthly profits at the one month and two month horizons. A horizontal line is drawn at 0.38, which is the average 10
8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>0.0 Children in the household 0-3 years<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->

Figure 23. CME at mean, children in the household ages +3-6, ages 15-59 <!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household +3-6 years<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Figure 25. CME at mean, Household per capita<br>income, ages 15-59<br>1.0<br>0.9<br>0.8<br>0.7<br>160 350 500 1000 2000 3000<br>Household per capita income (thousands COP)<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Pr(labour force=1)<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 25. CME at mean, Household per capita income, ages 15-59
The recent surge in the international migration of high-skilled workers not only raised standard concerns about adverse brain-drain effects for developing countries but also led to worries about native high-skilled workers in advanced destination countries.<sup>1</sup> Domestic workers with higher education levels fear that their wages will decline in response to increased competition from similarly qualified migrants. Whereas debates on migration in the past have centered on asylum rights and low-skilled migrants, over the years, politicians and the mass media have discovered the issue of high-skilled immigration. For instance, in Switzerland and Austria, the discussion has recently become emotionally charged owing to significant inflows of tertiary-educated workers, particularly from Germany.<sup>2</sup> For the United States, Hanson, Scheve and Slaughter (2009) find that skilled natives tend to oppose immigration in states with a relatively skilled mix of immigrants more than in states in which the skill composition of immigrants features a high proportion of low-skilled immigrants. Similarly, a recent panel study by Müller and Tai (2010) for Europe suggests that higher-skilled workers have less favorable attitudes toward immigration when immigrants are more skilled relative to the average skill level in the destination country.

This paper examines whether domestic skilled workers have reason to oppose high-skilled immigration and, vice versa, whether nonmigrating high-skilled workers win or lose from brain drain in source countries. We argue that the international migration of high-skilled workers triggers productivity effects at the macro level such that the wage rate of skilled workers may rise in host countries and decline in source countries. By exploiting data on international bilateral migration flows from Docquier, Marfouk and Lowell (2007), we empirically examine the impact of an increase in high-skilled 2
cost of capital (including the reduction of profit margins and interest-rate spreads) in the domestic financial system. These indicators are discussed in the section above.

The second group of outcome indicators refers directly to international financial integration. Some of the most important indicators are, first, an increase in the participation of foreign banks and other financial institutions in the domestic market, measured for instance by the ratio of foreign to total number of banks or the market share of foreign-owned banks. _Bankscope_ is the premier data source about banks around the world. The second indicator is an expansion in the stock of domestic assets held by foreigners, and likewise, an increase in foreign asset holding by domestic residents, with the resulting rise of gross capital flows in the balance of payments. It is not clear, however, what type of asset should increase the most as result of financial globalization, whether gross foreign direct investment (FDI), portfolio investment, or debt. This should be determined by their relative scarcities in the portfolio of assets. In recent reform experiences, FDI has been the predominant form of foreign assets. The most important source for data on capital flows is _Balance of Payments Statistics_ published annually by the International Monetary Fund. For foreign asset stock data, see Lane and MilesiFerreti (1999) and Kraay et al. (2000).

Finally, measures of external vulnerability can represent outcome indicators of the government’s failure to provide for adequate supervision, to ensure macroeconomic equilibrium, or to eliminate implicit insurance or guarantees for speculative flows. Some of them are, high black-market premium on foreign exchange, substantial real exchange rate misalignment (which, particularly in the case of overvaluation, might indicate the need for a drastic adjustment), large domestic-foreign interest rate spreads (which, after accounting for risk premia, may indicate the presence of controls), and the classic indicators of high levels of external debt (particularly of short duration) relative to GDP, exports, or foreign reserves. For a discussion of leading indicators on
Mexico). We exploit the survey data on 585 small and medium enterprises (SMEs) in the manufacturing and services sectors. Of these 585 firms, 145 firms are from Eastern Croatia. The survey was conducted from January 2019 to June 2019. The FINA database provides the sample frame for this survey, where the sample was stratified by region and sector.<sup>2</sup> Firms were randomly sampled and interviewed face to face. Firm responses to questions on managerial practices are aggregated into a single management score,

> 1 In 2008 and 2009, the European Bank for Reconstruction and Development (EBRD) in cooperation with the World Bank (WB) conducted a new survey, the EBRD-WB Management, Organization and Innovation (MOI) survey.

> 2 The Financial Agency (Fina) is the leading Croatian provider of financial data services. The database comprises of all tax registered firms in Croatia.

2
Eligibility for the BRMS was restricted to migrants who had returned from employment overseas to Bangladesh since 2010. This restriction was put in place to reduce possible issues that might arise regarding the accuracy of migrants’ recollection of past migration experiences, and, at the same time, to allow for some variability in the timing of return of the migrants. The sampling frame used for the survey was the Bangladesh Census of Population and Housing for 2011. The survey used a two-stage, stratified-cluster sample design: In the first sampling stage, a total of 250 unions throughout Bangladesh were randomly selected from the rural and semi-urban unions of the census. In the second sampling stage, households with returned migrants were randomly selected from a listing carried out in each selected union to identify households with return migrants.

The dataset has several features that enable us to fill important data gaps identified in prior work. First, the survey was designed with the understanding that temporary migration decisions are part of a life-cycle-optimization process by the workers. As a result, the survey includes detailed retrospective questions on the entire employment histories of migrants both in Bangladesh (before and after the migration) and while they were abroad. The data allow us to construct the full employment trajectories of the workers in the sample with detailed information on each migration episode, including destination country, dates and duration of stay, labor market outcomes (such as wages and occupation), and the reasons for returning.

The survey records the costs of each migration episode, finely disaggregated by cost categories and the source of financing. Such detailed data are rarely collected in household surveys that include migration modules.<sup>12</sup> Those expenses play a critical role in migration decisions in the case of Bangladesh and other South Asian countries, where these costs are known to be very high relative to workers’ earnings (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017).<sup>13</sup> The survey asks workers about the expectations that they
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to the small effect on landed import prices (see below). However, other emissions reduction channels include modifying operations (e.g., reducing speed to reduce resistance of the ship‘s hull, faster loading techniques, improved routing, decreased turnaround times and improved maintenance including hull and propeller cleaning with adequate intervals); technology adoption to improve the efficiency of new ships (e.g., larger ships, hull and propeller optimization, more efficient engines that include waste heat recovery systems), and novel, low-resistance hull coatings); and replacing heavy fuel oil with less carbon-intensive marine diesel or liquefied natural gas. There is much uncertainty over the extent to which higher fuel prices will encourage these mitigation possibilities, though large responses are not projected for the scale of emissions prices considered here.<sup>41</sup> A reasonable conjecture is that emissions will decline by up to 5 percent over the medium term in response to the $25 per tonne price, and proportionately more/less for the higher/lower carbon price scenarios.

> 38 A liter of bunker fuel produces 0.003 tonnes of CO2 (EIA, 2011a) which is about 22 percent higher than the CO2 coefficient for jet fuel.

39 Data taken from Platts, August 29, 2011, see <u>http://www.platts.com/Shipping/BunkerFuel/?WT.srch=1&gclid=CP_0hLHT9KoCFeMD5QodLkuIOw</u>

> 40 Given the potential increase of marine fuel prices by 2020, including a likely significant increase due to the upcoming low sulphur regulations, the anticipated price impact (increase) will likely be below 10 percent.

> 41 See, for example, McCollum et al. (2009). IMO (2009) concluded that a significant potential for CO2 reduction exists through technical and operational measures, many of which appear to be cost-effective, although both financial and nonfinancial barriers may discourage their implementation. IMO (2011) suggest a smaller emissions reduction than assumed above,
poverty slightly increased after a slow drop in 2001; again, the size of the middle class increased. Overall, these figures suggest that the evolution of the absolute middle class has followed the same pattern than the official poverty incidence (see figure 3), which is both counter intuitive and contradictory to the definition of middle class presented here. As we argue, the lack of a conceptual framework to support a definition of thresholds results in descriptive statistics rather than meaningful analysis.

**Figure 3: People living on $2-13 a day; on $2-10 a day; and incidence of poverty in Peru**<sup>**/a**</sup>

_Percentage of households_ _Source_ : Author‘s calculations based on data from ENAHO cross-sectional databases. /a The left axis draws the size of the middle class according to B&D and Ravallion, while the incidence of poverty is drawn in the right axis.

Atkinson and Brandolini (2011) have a consistent view in the sense that they suggest that income distribution and income measures alone can no longer represent accurate definitions of the middle class. They argue for the inclusion of wealth holdings and position in the labor market into income definitions of class. The latter has been the longest tradition in sociological work.

# **4. The data**

The vulnerability-to-poverty approach to defining the middle class is constructed in three stages. The first stage identifies actual characteristics that are associated with movement in or out of poverty. Then, the second and third stages construct probabilities of falling into poverty, and an income level associated to those, respectively. Based on such models, the methodology results in an estimate of the value of the lower income threshold, in PPP terms. In these stages we exploit longitudinal data for Chile (2001-2006), Mexico (2002-2005), and Peru (2002-2006).

In the case of Chile, we use the panel version of the _Socioeconomic Characterization Survey_ (CASEN Panel) carried out by the Ministry of Planning (
the U.S. be high? The litigation costs of merely initiating a case as a complainant are not large, and that is the only indicator necessary for our analysis of whether a U.S. trade remedy is challenged at the WTO. Nevertheless, we do proxy for the foreign country’s capacity to incur significant legal costs by using measures of its real GDP per capita, with data derived from World Bank (2004). We feel that this is a reasonable measure and is likely better than one designed to capture the stock of lawyers in the foreign country, given that legal expertise is an internationally traded service. Nevertheless, we also proxy for a country’s legal capacity by using data on the number of delegates the WTO member had sent to the Secretariat in Geneva. The larger are each of these variables, the greater the country’s capacity to absorb legal costs and the more likely it will initiate a dispute, ceteris paribus.

# **3.4 Access to Alternative Retaliatory Instruments**

Our last explanatory variable included in this stage of the estimation is designed to capture the concern that a country may choose _not_ to use the formal dispute settlement process at the WTO to challenge a U.S.-imposed trade remedy because it has access to an alternative (and perhaps preferable) retaliatory instrument, i.e., because it is able to take matters into its own hands and hit the protected U.S. industry with a trade remedy of its own. Therefore, we include another measure which is the share of the U.S. _industry’s_ value of domestic production that is exported to the remedy-affected country, where the industry is the 6-digit NAICS industry that is receiving the U.S. trade remedy protection.<sup>22</sup> The higher is the share of the value of U.S. industry production exported to the foreign country, the _less_ likely is the foreign country to initiate a formal WTO dispute against the
inflation from domestic food price inflation is still weak but stronger than when the IMF food index is used.

**Table 7: Response of Domestic Inflation to International Food Price Inflation Shocks (using the FAO food index)**

||Model 1(hea|dline inflation)|Model 2(f|ood inflation)|
|---|---|---|---|---|
|Horizon|percentage<br>points|alternative<br>measure|percentage<br>points|alternative<br>measure|
|1|1.3|4.9|0.0|0.0|
|3|2.8|6.0|9.3|20.6|
|6|5.0|8.2|12.6|22.0|
|12|6.3|9.5|9.7|16.1|
|18|6.4|9.7|8.6|14.5|
|24|6.3|9.7|8.6|14.5|

**Table 8: Response of Core inflation to Domestic Food Price Inflation Shocks (using the FAO food index)**

||Core inf<br>|lation<br>li|
|---|---|---|
|Horizon|percentage<br>points|aternatve<br>measure|
|1|-2.9|-18.2|
|3|0.8|3.6|
|6|2.1|9.0|
|12|2.5|10.6|
|18|2.4|10.2|
|24|2.4|10.2|

# 5.2.2 Sources of inflation: Variance decomposition analysis

Although IRFs provide information on the impact of the fluctuations of
of Labor Statistics 1995 and refers to 1993.

Venezuela Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1993 and source **I:** _Labor force sample surveys_ and _General household sample surveys._ Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1990.

Data on GDP at market price, and wages and salaries of Consolidated Central Government are taken from IMF Government Finance Statistics and relate to 1994.

Middle East and North Africa
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