rafmacalaba/datause-encoder-data huggingface dataset:
auto‐enrollment in Turkey for example, maintained a greater role for existing private pension providers partly to keep a clear separation between the public and private pillars. The market structure was still changed importantly by introducing auto‐enrollment, routing decisions via the employers to reduce the role of sales agents and switching at the individual level and enhancing the role and effectiveness of default employment strategies.

# **3.3.2 Investment strategy in private pensions and the potential impact of the NDC rule**

This section has so far identified a range of important considerations for developing private pension pillars that are relevant whatever the form of the public pension pillars. But the creation of an NDC pillar requires the creation of the NDC formula that will create the return on the notional capita. There are many options – and this section highlights the importance of modeling the likely **joint** distribution of outcomes between the NDC and funded pillars. If the NDC system is based on a GDP growth rate, or has a wage indexation or price indexation formula, how is that correlated with likely returns from different investment strategies? Are asset prices positively or negatively correlated with the formula driving the NDC payout? This is relevant both to the accumulation phase of private pensions but also to the decumulation phase. How do the payout rules for private pensions (annuity, re‐pricing or variable annuity, phased withdrawal or lump sum) interact with the NDC income formula – and other pension pillars – such as ‘Social Pensions’ providing minimum income for all?

This issue is highlighted by the fact that a well‐established, but often forgotten, feature of real equity market returns is that they tend to be negatively correlated with real per capita GDP growth. The result was established in Dimson, Marsh and Staunton’s “Triumph of the Optimists” using data from 1900 to 2000 and subsequent updates of their Global Investment Returns Year Book. This result goes against simple intuition for many people, but as explored in detail in Ritter (2005, 2012) there are in fact sound reasons for the result. They help to shed light on the nature of equity market returns – and the importance of good corporate governance in helping to ensure that companies return cashflows to shareholders if they do not have rigorously evaluated investment projects that will yield a positive net present value.

The result for 19 developed countries between 1900 and 2011 is a correlation between real returns on equities and growth of real per capita GDP that is minus 0.39. That is, if someone was seeking the highest real returns on equities when choosing between a sample of countries in 1900 they would have done best if they had chosen the countries that subsequently had the lowest growth in per capita GDP rather than those that had the highest growth. This result is shown in Figure 4. The ‐0.39 correlation is for real equity returns in local prices. If US dollars are used then the correlation is still negative at ‐0.32 (Ritter, 2012).

15
Religion similarity is proxied with an index built as follows: for each country, data on the percentage of population adhering to one of eight major religions is retrieved (data from the CIA World Factbook dataset)—these eight religion groups differ slightly from Melitz and Toubal (2014). Then the following formula for each country pair is computed, which results in a variable ranging from 0 (no believers in common) to 1: _Religion_Sim.ij =_ <u>(</u> _%muslimi * %muslim j_ <u>)+ (</u> _%catholici * %catholic j_ <u>)+</u> ( _%orthodoxi * %orthodox j_ )+ ( _%protestanti * %protestant j_ )+ (3) ( _%hinduismi * %hinduism j_ )+ ( _%buddhisti * %buddhist j_ )+ . ( _%easterni * %eastern j_ )+ ( _%judaismi * %judaism j_ ) I also control for the intensity of economic linkages between countries using the share of bilateral trade (exports plus imports, EXP+IMP) between a given pair over their total trade (COMTRADE data).<sup>16</sup> Trade is a conduit of information that may foster technological partnerships too, while it might be linked to the presence of migrants at the same time. I also account for the common technological specialization of countrypairs introducing an index of technological distance measured as where<sup>_f_</sup> _ih_<sup>stands for the share of patents of one technological class h according to the</sup> IPC classification of country i and _f jh_ for the share of patents of one technological class h of country j. Values of the index close to the unity indicate that a given pair of countries are technologically different, and values close to zero indicate that they are technologically similar (Jaffe 1986). Again, PCT patents are used to compute this index.

16. Changing this variable to trade in absolute numbers does not alter the main results and conclusions, though it makes the variable EXP+IMP non-significant—results provided upon request from the author.

19
above 1.5% of GDP (Jordan, Lebanon, and Morocco). MENA‟s life insurance sector is even smaller, with an average penetration ratio of only 0.3% of GDP and is an outlier by comparison with other regions. Only three countries (Lebanon, Bahrain and Morocco) display penetration ratios comparable to those in other emerging markets. Bahrain‟s penetration level reflects its historical regional role and significant health insurance portfolios.

**Table 1: Insurance Premiums and Assets (% of GDP) 2009**<sup>**1**</sup>

|Country<sup>**2**</sup>|Non-Life Premium<br>(% of GDP)|Life Premium<br>(% of GDP)|Assets<br>(% of GDP)|
|---|---|---|---|
|Algeria|0.60|0.05|0.8|
|Bahrain|1.43|0.70|12.1|
|Egypt|0.42|0.37|3.9|
|Jordan|1.53|0.21|4.8|
|KSA|0.46|0.07|-|
|Kuwait|0.34|0.09|1.8|
|Lebanon|2.18|0.98|7.2|
|Libya|0.48|0.01|-|
|Morocco|1.54|0.89|19.0|
|Oman|0.97|0.20|2.4|
|Qatar|0.67|0.01|2.7|
|Syria|0.92|0.01|0.0|
|Tunisia|1.38|0.25|0.6|
|UAE|1.34|0.28|3.1|
|Yemen|0.24|0.02|-|
|MENA|0.97|0.28|5.3|
|GCC .|0.87|0.23|4.4|
|Non-GCC|1.03|0.31|6.1|
|Oil|0.80|0.16|3.8|
|Non-Oil|1.22|0.45|7.1|

Sources: AXCO Reports, Swiss Re. Sigma, IMF/WB databases, national sources

**Table 2: Average penetration rates and assets (% of GDP), MENA and other regions**

||**MENA**|**East Asia**<br>**& Pacific**|**New**<br>**EU-10**<br>**Countries**|**Other**<br>**Europe &**<br>**Central**<br>**Asia**|**High**<br>**income**<br>**OECD**|**Latin**<br>**America**|**South**<br>**Asia**|
|---|---|---|---|---|---|---|---|
|**Average non life premium **|0.97|0.8|1.7|0.9|2.3|1.3|0.5|
|**Average life premium **|0.28|1.6|1.1|0.1|4.0|0.7|1.2|
|**Average Assets**|4.9|14.8|5.2|2.7|45.0|4.9|6.4|

Source: Swiss Re. Sigma, AXCO Reports, WB calculations

**In terms of the ratio of insurance assets to GDP, the region would seem to compare better, but this result is due to very few countries.** Excluding only one country (Morocco) the MENA average would drop to 3.5% of GDP, below the average of other emerging regions. Excluding Bahrain the average would drop further to 2.7% of GDP.<sup>3</sup>

> 1 Separately identified health insurance is not included in the following econometric analysis – see Box 1.

> 2 Iraq has not been included because results are affected by conflict.  Djibouti has not been included because of lack of data.

> 3 Bahrain‟s assets reflect its expatriate population.

5
the level of disaggregation of the estimates (regions, 0.5 ° x0.5 ° grid cells, 0.25 ° x0.25 ° grid cells, etc.). Several reviews of the literature suggest using small geographic units and quasi-experimental design to avoid biases arising from unobserved heterogeneity or competing mechanisms (Collier and Hoeffler, 2007; Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015). However, these gains for the identification strategy may also depend on the existence of subnational root causes of conflict and the degree of precision of the data at that scale (for a discussion, see Laville, 2019). To test for the influence of the size of the spatial unit on the results, we include a dummy variable indicating whether the estimates apply to grid cells of size less than or equal to 0.5 ° x0.5 ° at the equator (approx. 55km Ö 55km), and the natural logarithm of the sample size.

# **Conflict Characteristics**

We use several variables to control for heterogeneity in the estimates resulting from differences in the empirical definition of the conflict outcome. First, we control for the origin of the conflict data. Conflict studies often rely on data from the Uppsala Conflict Data Program (UCDP/PRIO) (Sundberg and Melander, 2013) or Armed Conflict Location and Event Data (ACLED) (Raleigh et al., 2010). The institutions behind these datasets have the capacity to collect daily data on the timing and location of violent events, while maintaining a consistent definition of violent phenomena (which supports the comparability of estimates across studies). However, their respective definition of violent phenomena and the potential omission, inflation or misrepresentation of events could systematically influence the literature due to their widespread use (Miller et al., 2022). We therefore include a dummy variable indicating if the regression relies on UCDP/PRIO or ACLED data. Second, we control for the type of conflict outcome by including three dummy variables indicating estimates of conflict incidence (if the outcome variable is the total number of conflict per day/month/year, or if it takes the value _one_ when at least one conflict is observed), onset (if the outcome variable only takes the value _one_ at the starting day/month/year of the conflict), or duration (if the outcome variable is the number of days, months or years of active conflict). Third, we include a dummy variable indicating if the spatial dimension of the dependent variable is local (below Admin1 level or at small grids level), or larger (above Admin2 level or at large grids level). Fourth, we include a dummy variable indicating if the conflict implied human casualties, indicating a relatively more intense form of conflict. Finally, we control for the type of conflict using dummy variables in27
**Table 11. Global Welfare Effects from Unilateral Tariff Liberalization**

||**_Glob_**<br>**_From 1992_**|**_al change in equ_**|**_ivalent variation_**<br>**_From 2005_**||
|---|---|---|---|---|
||**_US$ mn_**|**_%_**|_US$_**_mn_**|**_%_**|
|USC|816|2.2|1,054|1.7|
|EU|4,947|13.6|7,907|13.0|
|JPN|14,145|38.8|18,635|30.7|
|NICs*|5,734|15.7|11,158|18.4|
|CHN|0|0.0|0|0.0|
|IDN|187|0.5|405|0.7|
|MYS|3,076|8.4|7,477|12.3|
|PHL|1,032|2.8|1,804|3.0|
|THA|1,788|4.9|4,467|7.4|
|LTN|1,044|2.9|1,422|2.3|
|SSA|-143|-0.4|-154|-0.3|
|SAS|1,748|4.8|3,441|5.7|
|ROW|2,100|5.8|3,069|5.1|
|**_TOTAL_**|36,473|100.0|60,685|100.0|

Note: *Only South Korea undertakes tariff liberalization

**Table 12. Decomposition of MFA liberalzation in 2005, export tax shocks**

||**_From 199_**|**_2_**|**_Changze_**<br>**_in eq_**|**_uivalent va_**<br>|**_riation_**<br>**_From_**_2005_|||
|---|---|---|---|---|---|---|---|
||||_To 1992 ta_|_x_**_level_**|**_1992 lev_**|**_el to 0_**|**_Total_**|
||**_US$ mn_**|**_%_**|_US$ mn_|**_%_**|_US$ mn_|**_%_**|_US$ mn_|
|USC|9,726|60.1|3,629|33.2|19,040|68.7|22,709|
|EU|8,100|50.1|5,075|46.4|18,132|65.4|23,245|
|JPN|204|1.3|-407|-3.7|1,068|3.9|660|
|NICs|-3,498|-21.6|1,462|13.4|-8,135|-29.4|-6,480|
|CHN|1,914|11.8|5,540|50.7|1,677|6.1|7,207|
|IDN|1,407|8.7|68|0.6|1,877|6.8|1,944|
|MYS|-64|-0.4|88|0.8|-360|-1.3|-271|
|PHL|192|1.2|99|0.9|-166|-0.6|-67|
|THA|382|2.4|325|3.0|160|0.6|484|
|LTN|-757|-4.7|-1,799|-16.5|-1,426|_-5.1_|-3,231|
|SSA|-115|-0.7|-288|-2.6|-256|-0.9|-545|
|SAS|1,926|11.9|772|7.1|1,743|6.3|2,506|
|ROW|-3,240|-20.0|-3,635|-33.3|-5,641|-20.4|-9,285|
|**_TOTAL_**|16,177|100.0|10,929|100.0|27,711|100.0|38,876|
|_%_**_of income_**|0.08||0.04||0.10||0.13|

30
aspects of national wellbeing. The surveys are not designed for the purpose of international comparisons and global monitoring; they are designed to serve the specific needs and interests of each particular country. Different countries use different concepts, methods and questionnaire designs for estimating household consumption and income, and they frequently change the questionnaires over time. This heterogeneity limits the comparability of estimates between countries, and sometimes also within countries over time.

One issue of comparability is linked to the fact that essentially all countries in the PovcalNet database measure wellbeing (either in the form of consumption or income) at the level of the household. So one decision that needs to be made is how to convert a household level measure into an individual level measure. For many countries, making the wellbeing aggregate comparable across individuals means adjusting for differences in needs between different ages of people, typically based on adult-equivalence scales. It may also mean adjusting for household economies of scale, which aims to account for the possibility that a household of four people may need less than twice what a household of two needs to reach the same level of wellbeing. The basic idea is that there may be some important goods, such as housing, that can be consumed by multiple individuals at the same time at a very low additional cost (ie. non-rivalrous goods).<sup>15</sup> The key issue for global poverty measurement is that, strictly speaking, a country that allocates household consumption to individuals within the household on a per capita basis has a consumption measure that is not comparable with a country that allocates consumption to individuals based on adult-equivalence.<sup>16</sup> To treat all countries the same, all consumption and income measures in PovcalNet are in per capita terms.<sup>17</sup> 15 For discussion of these issues see, for example, Buhmann et al. (1988) and Lanjouw and Ravallion (1995).

16 Consider two identical households each with two adults and two children and total household consumption of $120. Assume one household lived in a country that used adult-equivalence adjustments that assume a child consumes half that of an adult. In this country, the individuals of this household would be assigned $40 as their measure of consumption (ie. $120 divided by 3 adult equivalents). Assume the other household lived in a country that defined individual level wellbeing based on a per capita measure of consumption. This household would be assigned $30 as their measure of wellbeing. While both households are identical, the household living in the country that used an adult equivalence adjustment for allocating household measures to the individual would appear to be significantly richer. For a description of adult-equivalence adjustments, see James and Schofield (1990).

17 The adoption of a per capita scale imposes cross-country comparability and is easy to explain. It does not, however, address the deeper issue of what the “best” equivalence scale might be in each country.

11
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey workers from exporting to non-exporting firms. They show that firms that do not engage in exporting activities are precisely the ones that, on average, pay lower wage premia, resulting in wage compression.<sup>14</sup> They estimate that the largest observed increase in commodity prices, between 2002 and 2011, is associated with a reduction in skill premium of 7 log points.<sup>15</sup> They confirm the proposed cost and wealth channels, accounting for an estimated 2/3 of the reallocation across sectors and between exporter and non-exporter Finally, the commodity boom may have impacts on the wage distribution simply due to the resulting currency appreciation. As the value of domestic currency raises, exporting firms suffer losses, which may trigger the mechanism described in the model developed by Helpman et al. (2010). Since exporting firms are larger and pay higher wages, and because interfirm wage dispersion is lower within the nontradable sector, the reallocation of labor from the exporting to the nontradable resulting from currency appreciation should result in decreased dispersion of wages. Messina and Silva (2017) argue that this can be a potential mechanism in explaining decreased wage inequality in Brazil, where 2/3 of the decrease in inequality among workers in the same sector and occupation occurred through a reduction in the variance of wages between firms. However, they do not provide an empirical analysis of this hypothesis. Some evidence against it is provided by Benguria et al. (2018), who investigate the possible confounding effect of commodity prices and exchange rate in their specification, showing that this is not what is driving their results. However, their focus is limited to skill premium and reallocation of workers, and may not account for other mechanism responsible for determining interfirm wage differentials. Further research is necessary to clarify the role of exchange rate appreciation on inequality.

# **3.3 Effects of trade liberalization on regional, racial, and gender wage gaps**

Barros et al. (2010) and Ferreira et al. (2007a) had already noticed the relevance of gaps between groups defined by location, race and gender in explaining changes in overall wage inequality. More recently, Ferreira et al. (2017) reinforce their conclusion, showing that racial, gender, and regional gaps were important factors in accounting for the reduction in wage inequality between 1995 and 2012. Changes in the composition of the workforce with respect to race, gender, and regional and urban situation combined

> 14Notice that the employment reallocation from exporting and non-exporting firms is compatible with the inequality reducing mechanism modeled by Helpman et al. (2010) and Helpman et al. (2017). 15Fern ́andez and Messina (2018) document a decline in the college-premium with respect to secondary and primary school of 56.6 log points between 1995 and 2013 for the whole Brazilian economy.

18
Table 3: Forced Migration and the Expansion of Non-State Armed Actor Activity

||FARC|+ ELN|Organize|d Crime|Total N|on-State|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: OLS**|||||||
|Migration Outfows|0_._003|0_._003|0_._200<sup>_∗∗∗_</sup>|0_._075<sup>_∗∗_</sup>|0_._203<sup>_∗∗∗_</sup>|0_._078<sup>_∗∗_</sup>|
||(0_._002)|(0_._002)|(0_._073)|(0_._035)|(0_._073)|(0_._035)|
|**Panel B: 2SLS**|||||||
|_Second Stage_|||||||
|Migration Outfows|0_._048<sup>_∗∗_</sup>|0_._043<sup>_∗_</sup>|0_._213<sup>_∗∗_</sup>|0_._228<sup>_∗∗_</sup>|0_._261<sup>_∗∗_</sup>|0_._271<sup>_∗∗∗_</sup>|
||(0_._024)|(0_._024)|(0_._100)|(0_._094)|(0_._109)|(0_._104)|
|_First Stage_|||||||
|Imputed Outfows|0_._206<sup>_∗∗∗_</sup>|0_._210<sup>_∗∗∗_</sup>|0_._206<sup>_∗∗∗_</sup>|0_._210<sup>_∗∗∗_</sup>|0_._206<sup>_∗∗∗_</sup>|0_._210<sup>_∗∗∗_</sup>|
||(0_._054)|(0_._052)|(0_._054)|(0_._052)|(0_._054)|(0_._052)|
|F Statistic (excl. instrument)|14.50|16.28|14.50|16.28|14.50|16.28|
|**Panel C: Reduced Form**|||||||
|Imputed Outfows|0_._010<sup>_∗∗_</sup>|0_._009<sup>_∗∗_</sup>|0_._044<sup>_∗∗_</sup>|0_._048<sup>_∗∗∗_</sup>|0_._054<sup>_∗∗_</sup>|0_._057<sup>_∗∗∗_</sup>|
||(0_._004)|(0_._004)|(0_._021)|(0_._018)|(0_._022)|(0_._019)|
|Observations|11,022|11,022|11,022|11,022|11,022|11,022|
|Dep. Var. Mean 1992|0.003|0.003|0.000|0.000|0.003|0.003|
|Municipality & Year FE|✓|✓|✓|✓|✓|✓|
|Controls _×_ Time Trends||✓||✓||✓|

_Notes:_ The table illustrates the estimates of specifications (1), (3), and (2). The dependent variable is the number of FARC and ELN events in columns (1) and (2), the number of organized crime events in columns (3) and (4), and total non-state armed actor events in columns (5) and (6). Panel A presents the OLS estimates of equation (2). Panel B represent the 2SLS estimates of equations (3) and (2). Panel C presents the reduced-form estimates of the outcomes in the instrumental variable. Odd-numbered columns exclude baseline controls and even-numbered columns include baseline municipal characteristics interacted with year fixed effects. The F-statistic is the Kleibergen-Paap Wald F-statistic. Controls in the baseline are interacted with time trends and include urban coverage, water bodies, and tree-cover loss for 2001; night-light density for 1992; and political repression records for 2004. Robust standard errors clustered at the municipality level are in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1.

The estimates indicate that forced displacement significantly expanded activity by non-state armed actors in origin municipalities. The F-statistic of 16.28 confirms a strong first stage. Our preferred specification is the 2SLS estimate for total non-state armed actor events with controls, reported in column (6) of Panel B. It indicates that a one-percentage-point increase in forced displacement outflows as a share of the 1990 municipal population is associated with 0.271 additional non-state armed actor events per municipality per year. Applying the 39
weights post-stratified by country of birth and education to match the cross-sectional distribution of African migrants to OECD countries in the year 2000.

Table 1 summarizes the characteristics of African migrants in each of our datasets. We present summary statistics for each of our surveys, as well as the pooled sample. Concentrating on the pooled data in the final column, a typical migrant is a 36 year-old male who has completed high school and has an annual household income of $26,000. However, there is considerable heterogeneity between the datasets. One-third of the pooled sample has completed an undergraduate university degree, but this ranges from 53% in the Belgian IRSHS survey to 3% in the Spain NIDI survey. The average years spent abroad over the whole sample is 9.4 years, ranging from a low of 3.6 years in the Australian data (which is a survey of recent immigrants) to 18.2 years in the French 2MO survey, which surveys remittance senders at French post offices. Family characteristics, especially family members living away from the migrant, are an important determinant for understanding remittance behavior. In our database 64% of migrants on average are married, and 10% of migrants on average have a spouse outside the country.

# **3. The incidence and level of remitting by African migrants**

We start with establishing the prevalence and the pattern of remittances by the migrants in our database. We consider two key dimensions of remittance behavior. Firstly, whether migrants choose to remit at all or not (the extensive margin), and secondly the total amount remitted (including as zero those who did not remit anything). There is considerable variation among the different surveys in sampling methodology, and target population, as well as differences arising from the self-selection of immigrants moving to different countries and in the income-earning opportunities they face once they arrive there. It is therefore useful to begin by illustrating the differences in the incidence of remitting and amount remitting by survey, which is done in Figures 1 and 2.

Figure 1 shows the extensive margin. There is substantial variability between the different surveys: for example, over 80% of migrants in the Belgium IRSHS survey remit, while only 20% of migrants in the French DREES survey remit. Figure 2 summarizes total remittances sent by migrants in each destination country. Total 6