rafmacalaba/datause-encoder-data huggingface dataset:
should be large enough in order to minimize the bias arising from using pseudo panel data and not genuine panel data.<sup>25</sup>

- _Step 2._ Estimate the residual variance of the logarithm of the asset index within each cell of the pseudo panel corresponding to cohort _c_ at time _t_ . Practically speaking, we regress for each cell at the household level the logarithm of the asset index on a set of variables (including gender dummy, age and age squared, education dummies, household size, number of children under 5 years old, urbanization dummy or localisation dummies) and estimate the residuals. The residual variance over cohorts corresponds to the variance of the residuals of the previous regression.

- _Step 3._ Regress the residual variance on cohort dummies and a polynomial function of age. Then, draw the estimated age effects on a graph to obtain the age-profile of the residual variance.<sup>26</sup> Estimate the slope of this age-profile for each cohort _c_ ˆ 2

- which represents the estimated variance of the shocks faced by household,  _ct_  1 .

- _Step 4._ Draw a value  ~ _itc_  1 in the normal distribution with mean zero and variance ˆ 2

-  _ct_  1 within each cohort _c_ and combine it with the estimated coefficients of the observable characteristics to predict the vulnerability index _v_ ˆ _itc_ for each household _i_ at time _t_ belonging to cohort _c_ . For that purpose, _xitc_  1<sup>can be predicted</sup> _c_

- deterministically from _xit_ by incrementing age or assuming that characteristics are time invariant.

# **_Creation of a Pseudo-Panel_**

In order to have a look at the dynamic of asset-poverty, we regroup households from the DHS into homogeneous cohorts: households whose heads have the same date of birth (we define five-year cohorts), the same level of education (no education, primary and secondary and more) and the same place of residence (ten departments and urban/rural distinction) are regrouped into cells. After regrouping some low-sized cells, 261 cells were constituted for each year of the DHS dataset, with an average size of around 150 households and 950 individuals in each cell.

# **_Aggregate Estimates_**

Our estimates of the vulnerability index follow the different steps recalled previously. First, log per capita asset index is regressed on various household’s characteristics such as log of household size, age of the head and its square, education and gender of the head, location and the presence of children under 5 years old. Residuals are

> 25 As exposed by Verbeek and Nijman (1992), the bias in the standard within estimator based on pseudo panel data is decreasing with the number of individuals in each cell, more than with the number of cells. However, Verbeek (2008) notes that there is no general rule to judge whether cell size is large enough. Deaton (1985) also suggests that measurement error decreases as a function of the size of the cells.

> 26 As in Deaton and Paxson (1994), we can normalize so that the fitted age effect at, for instance, age 35-40 equals the average residual variance of the logarithm of the asset index for 35-40 year-olds over all cohorts.

16
$5,000 annual remittances for Ghanaians, $2,100 for Micronesians, $625 for New Zealanders (monetary remittances data only), $7,232 for Papua New Guineans, and $4,300 for Tongans. These amounts are significant relative to the per capita incomes of the developing countries, with Ghanaian and Papua New Guinean remittances equivalent to about seven times per capita GDP. Nevertheless, the amounts remitted are only a fraction of what the migrants would have been earning at home (Table 2).

# **4.2 Involvement in Trade and Foreign Direct Investment**

After remittances, the financial channels through which high skilled emigrants are often thought to have most positive economic benefits for their home countries are through their involvement in trade and foreign direct investment. The experience of Indian, Taiwanese, and Chinese information technology firms has been used to suggest that a highly skilled diaspora can use their knowledge of the destination country to lower the costs of transacting across countries (Rauch and Trinidade, 2000; Kugler and Rapoport, 2005, Javorcik et al, 2010), and that emigrants can provide venture capital for starting new firms (Saxeenian, 2001, 2002). However, such studies and anecdotal evidence tends to focus on the cases where these linkages have occurred, and do not provide any information as to how common such experiences are, or to whether the experiences of high skilled IT workers from large economies are translatable to the types of countries where brain drain rates are much higher and domestic markets much smaller.

Our survey directly asked emigrants whether, in the past year, they had helped a home country firm make a trade deal, and if so, the value of this deal, and whether they had themselves directly exported goods from their home country to sell overseas, and the value. Panel C of Table 5 summarizes the results. We see that involvement in trade is very uncommon for this group of the best and brightest, with 3 percent of the Ghanaians, 4 percent of the Micronesians and Tongans, and 6 percent of the New Zealanders being involved in trade – the 10 percent figure for Papua New Guinea represents only 1 out of the 10 non-student migrants from this country who answered this question carrying out such activities. One might argue that a low incidence of involvement may still have large overall impact if the occasions where deals are made involve large transactions. For example, one of the Ghanaian migrants in the sample facilitated a 500,000 cedi ($350,000) trade deal with a Ghanaian company, using his knowledge of Ghana to carry out 16
Table 2.1: Insurance Penetration for Selected Countries (Gross Written Premium as Percentage of GDP, 1998)

|||Insuran<br>|ce Penetr<br>|ation|||
|---|---|---|---|---|---|---|
|Countrles/Regions||No|n-Life|||AverageEconomicLosses|
||Life|Catastrophe|Other|Total|Total|(%ofGDP)'|
|Tnnidad&Tobago|0.89|1.80|2.20|4.00|4.89|0.01|
|DominicanRepublic|0.21|0.68|0.83|1.50|1.71|0.58|
|Jamaica|3.43|1.13|1.38|2.50|5.93|0.98|
|Barbados|1.18|2.07|2.53|4.60|5.78|0.21|
|Average|1.43|1.42|1.73|3.15|4.58|0.44|
|Memo:|||||||
|LatinAmerica|0.20|||1.41|1.61|0.35|
|UnitedStates|6.63|||4.30|10.93||
|Japan|6.40|||2.30|8.70||
|France|3.76|||2.80|6.56||
|UnitedKingdom|8.72|||3.10|11.82||
|IndustrializedEconomlesV|4.52|||2.95|7.60||

The figure for average economic losses is derived by dividing total economic losses reported in Table 1.6 for the period 1970-99 by 30 years.

2J Includes 12 industnahzed economies.

Source: Swiss-Re, Economic Research & Consulting and Background Papers.

14
- 7b -

<!-- Start of picture text --> N' N<br>| | I I I I<br>eiur 2: Th.efetof<br><!-- End of picture text -->

- Figure 2: The effect of a production subsidy, an export subsidy and TAR on demand for domestic and imported inputs.

Le&end

- DOD initial input demand cutve

- D'D'- input demand with an export subsidy

- OB - initial demand for domestic input

- OA a demand for domestic input with a tariff cut (production subsidy)

- BC - initial imports

- BD - Imports with an export subsidy

- AD - imports with a tariff cut

- AE - imports with TAR
larger initial payouts in the early years, combined with longevity insurance when the deferred annuity is received.

# **3.2 The Demand for Retirement Products and Level of Annuitization**

The number of retirees choosing annuities has increased considerably in the past 20 years. As shown in Table 9, only 3 percent of the number of pensioners chose annuities in 1985, while in 2007 this percentage increased to 58 percent, including the small number of TW holders. Excluding disability and survivor pensioners, the share of retirees who selected annuitization reached 71 percent. No data are published on the size of accumulated balances that are used for PWs and thus there are no published data on the share of accumulated balances that is annuitized. However, this proportion is also likely to be close to 70 percent.

These numbers imply one of the highest levels of annuitization in the world. The average annuity payment is significantly higher than the average PW payment, as shown in Table 10, showing that the average income of the annuitant population is higher. The average TW payment is much higher, reflecting a segment of higher income annuity holders.

Unlike other countries, such as Denmark and Sweden, Chile has very detailed and comprehensive data on the use of annuities (Table 11). Most annuities are joint life, reflecting the regulation that forces married males to take this type of annuity.<sup>24</sup> The share of deferred annuities in the flows of annuities increased slightly from 20 to 30 percent, but the period of deferment has remained short, roughly around one year. The short period of deferment helps explain the low share of TWs in the total stock of retirement products.

The share of guaranteed annuities is large and most of these annuities are guaranteed for periods of 10-15 years and even longer. The strong demand for guaranteed period annuities reveals a voluntary insurance/income smoothing arrangement within the family unit as well as a preference for bequests. The main beneficiary accepts a lower payment in exchange for the maintenance of the same payment to the surviving spouse during the guaranteed period (when the guarantee expires the payment is reduced to 60 percent of the main annuity). If both die, the heirs keep receiving the payments during the guaranteed period. In the case of single annuities, payments go directly to the heirs during the guaranteed period.

**<u>Table 9: Breakdown of Number of Pensions, by Type of Instrument, 1985-2007</u>**

|Year|Total|P|Ws|T|Ws|Ann|uities|
|---|---|---|---|---|---|---|---|
|||Number|% of Total|Number|% of Total|Number|% of Total|
|1985|7,609|7,373|96.8%|-|0.0%|236|3.2%|
|1990|57,119|36,696|64.2%|148|0.3%|20,275|35.5%|
|1995|190,400|98,699|51.8%|6,803|3.6%|84,898|44.6%|
|2000|343,965|147,532|42.9%|6,632|1.9%|189,801|55.2%|
|2005|557,712|214,239|38.4%|6,917|1.2%|336,556|60.4%|
|2007|626,405|252,095|40.2%|8,578|1.4%|365,732|58.4%|

Source: SP

> 24 Since 2008 this requirement is imposed on married females as well.

15
Due to differential access to transport facilities, it was traders rather than farmers who were able to best take advantage of these inter-district price and tax variations. The available evidence suggests that local (e.g. first handler) trader operations were quite profitable this season as a result of the set TCMB buying prices and the guideline producer prices. An indication of this is provided in Table 18. The first column is for a Masasi-based trader who procured nuts within a 25 km radius of the town and then sold to TCMB in Masasi. For this low risk operation, this trader obtained a margin of 12.8%. The second column concerns a trader in Nachingwea who transported nuts to Lindi to sell to TCMB at much higher inter-store price. The trading margin was considerably higher.

Table 18: Private Cashew Trader Margins on Sales to TCMB (1992/93 Season) (All Values are Tsh./Kg.)

|Cost or Price|Masasi Trader Supplying<br>TCMB in Masasi|Nachingwea Trader Supplying<br>TCMB in Lindi|
|---|---|---|
|Producer Price|127.0|120.0|
|Levies/Cesses/Duties|14.4|9.40|
|Village Secretary Conmmission|N.A.|1.00|
|Transport Costs|2.0|12.0|
|Packaging Costs|4.35|4.35|
|Finance Charges|N.A.|3.60|
|Depreciation (On Vehicles)|N.A.|4.10|
|Overhead, Labor, Insurance|1.70|_5.50_|
|Total Costs|149.45|159.95|
|TCMB Inter-Store Price|166.45|190.00|
|Trader Margin|19.20 (12.8%)|30.05 (18.8%)|

Source: Author's Trader Interviews Overall, private traders accounted for about 43% of the crop procured directly from either farmers or cooperative societies and, by the completion of sales, will probably account for a similar proportion of the export volume. However, with the TCMB and several of the cooperative unions not obtaining finance until December or January, the export-oriented private traders (through their buying agents) were able to procure most of the high quality nuts from the 1992/93 harvest. That is, all or a large proportion of the nuts from Tinduru and Nachingwea Districts (all known for high-quality nuts) were eventually procured by private traders. In contrast, most of the nuts in Newala District (the quality of which is uneven or poor) were left for the TCMB and MARCU. In Masasi District, while procurement was more or less equally divided between MARCU and eight private traders, the majority of MARCU's stock was classified as undergrade. Traders supplying both private exporters and TCMB tended to provide the former with standard grade nuts and the latter with undergrades (selling them as standard grade if possible).

While the TCMB was still negotiating with foreign buyers/brokers in March, most of the private 37
8 where N is the number of articles in the corpus and N� is the number of articles in which word _i_ is present. Hence, this weighting gives more weight to words that appear more rarely across documents.

To illustrate our dictionary-based approach to measuring sentiment, here is an example of an article in our corpus in which the tonal words are underlined.<sup>6</sup> It suggests that although our sentiment measure s� does not capture all the nuances in the text, it provides a good indication of its overall tone.

<u>Title: Argentina’s Peronists defend Menem’s labor reforms.</u> <u>Timestamp: 1996-09-02</u> Text: BUENOS AIRES, Sept 2 (Reuters) - The Argentine government Monday tried to counter **criticisms** of President Carlos Menem’s proposals for more **flexible** labor laws, **arguing** that not just workers would contribute to new **unemployment** insurance. Menem **angered** trade unions, already in **disagreement** over his fiscal **austerity** programs, by announcing a labor reform package Friday including **suspending** collective wage deals and replacing **redundancy** payouts with unemployment insurance.

<u>Topics: Labor/Personnel Issues, Corporate/Industrial News, Economic/Monetary Policy,</u> Economic News, Political/General News, Labor Issues, Domestic Politics <u>Locations: Argentina, Latin America, South America</u> After computing the sentiment of each article s�, we obtain a daily sentiment index for each country _c_ by taking the average of s� across articles published in any given day that contain country _c_ in its location tags.

# **B.** **<u>Asset prices and related variables</u>**

We explore the impact of news-sentiment measures on stock market returns. Equity returns are computed using the country’s main stock market index. Returns are computed in local currency to isolate the effect of sentiment on the local stock market from its effect on the exchange rate. Daily world returns, which are used as a global control, are computed using the Dow Jones World Index. The final coverage, along with the list of indexes and sources used to compute equity returns is reported in the Appendix.

To proxy for market liquidity, we also collect daily equity trading volumes reported by local stock exchanges. Following Campbell, Grossman, and Wang (1993) and Tetlock (2007), we compute the detrended log of daily trading volume, using the rolling average of the past 60

> 6 This article’s main location tag is Argentina and one of the topic tags is “Economic News”.
**Table 2: Gravity model regression results using the shifted log distance function.**

|**Dependent Variable:**|**No. of Flights per Week**|
|---|---|
||-0.001***|
||[0.000]|
|Observations|44,313|
|R2|0.854|

_Prob. values based on robust standard errors corrected for clustering by country-pair are in brackets below the coefficient estimates. Statistical significance is indicated by * (10%), ** (5%), and *** (1%). The model includes fixed effects by origin and destination country (estimates suppressed for brevity), and is estimated by Poisson. R2 is calculated as the squared correlation coefficient between the actual and fitted values._ The elasticity of substitution implied by the model (see equation 15) is about 5.5, which confirms a high impact of distance on transport flows. Although sectoral characteristics are obviously very different, it is useful to compare this order of magnitude with estimates of the elasticity of substitution from goods markets. Anderson and Van Wincoop (2003), for example, use a benchmark of for their counterfactual gravity model simulations. The simple average of the sectoral elasticities of substitution estimated by Broda and Weinstein (2006) ranges from four to 17, depending on the time period and aggregation scheme used. Our estimate might therefore be considered to be towards the low end of estimates for goods, which would be consistent with the intuition that different transport routes are less substitutable than products from different origins.

Another way of looking at the distance result is by converting the regression coefficient to a simple elasticity, familiar from standard gravity models in the trade literature. Evaluated at the mean, a one percent increase in distance is associated with an approximately 3% decrease in the number of flights per week. This elasticity is much stronger than is typically found in gravity models of international trade, particularly those estimated by Poisson (Santos Silva and Tenreyro, 2006). Nonetheless, this finding sits well with the nature of the transport industry, in which distance is obviously a primary commercial concern due to the importance of fuel in total operational costs.

27
price<sup>3</sup> and turnover should benefit local vendors, but disadvantage buyers (Cunha et al, 2011; D’Aoust et al, 2013). If, however, there exist financial, social or behavioral constraints that are relieved by demobilization transfers, demobilization allowances would be expected to be invested in productive assets. In this case, the impact on consumption and assets of beneficiary households should be positive and long-lasting, but the magnitude of the consumption increase should be rather small (Gertler et al, 2012). Spillovers on nonbeneficiaries at the community level are expected to be marginal. Examining which of the consumption or investment scenarios prevails is an empirical question, which we tackle in the empirical analysis.

We use a household panel dataset to capture the direct and indirect effect of the DDR program on beneficiary and nonbeneficiary households. The first wave of the panel was collected in 2006, while the reintegration phase of the demobilization of CNDD+ combatants was taking place. This wave is representative at the provincial level. The second wave was collected in 2010, in the midst of the reinsertion phase for FNL combatants, and about five years after the demobilization of CNDD+ combatants. The timeline below summarizes graphically the various components of our assessment (figure 1).

Figure 1: Timeline <!-- Start of picture text --> Conflict Coup Arusha Peace Demobilization Demobilization<br>Onset d’État Agreement CNDD-FDD+ FNL<br>1993 1996 2000 2004 2009<br>1 2 1 2<br>Data collection (2006, 2010)<br>1 Cash transfer<br>2 In-kind transfer<br><!-- End of picture text --> Our identification strategy exploits several key facts: (i) not all households hosted excombatants; (ii) not all households with ex-combatants benefited from allowances; (iii) the waves of the demobilization program took place before and in between the two survey rounds of the panel; and (iv) some communities in the panel had ex-combatants among their populations whereas other communities did not. Our outcomes of interest include indicators of consumption, nonfood spending, livestock, and prices. We call all households hosting an ex-combatant _returnees_ , and we call the subset of returnees which received demobilization allowances _demobilized_ . In order to measure the short-run impact of reinsertion allowances on beneficiaries,

> 3This holds only if the degree of openness and competition in the local economy is low, as is the case in rural Burundi.

2
**Table 4: Impacts of IPAs on Bilateral Patent Filings**

|Income Used for HI vs NHI:|Varies byYear|Earliest|Latest|
|---|---|---|---|
|_Panel A: WTO LE2_||||
|Intra (member to member)|-0.229|-0.244|-0.241|
||(0.182)|(0.187)|(0.187)|
|Extra1 (between HI member and|0.122**|0.116**|0.115**|
|HI non-member)|(0.051)|(0.052)|(0.051)|
|Extra2 (NHI destination in IPA from all|0.188*|0.145|0.191|
|non-member sources)|(0.104)|(0.147)|(0.176)|
|Extra3 (NHI source in IPA to all non-|0.024|0.180|0.210|
|member destinations)|(0.176)|(0.294)|(0.348)|
|Any IPR provision not meeting WTO LE2|0.012|0.004|0.007|
||(0.061)|(0.060)|(0.060)|
|Any bilateral investment agreement|<br>0.513***|<br>0.523***|<br>0.520***|
|(BIT or other IIA)|(0.167)|(0.171)|(0.171)|
|_Panel B: US/EU/EFTA_||||
|Intra (member to member)|-0.014|-0.014|-0.010|
||(0.128)|(0.130)|(0.128)|
|Extra1 (between HI member and|0.137*|0.137*|0.137*|
|HI non-member)|(0.070)|(0.074)|(0.070)|
|Extra2 (NHI destination in IPA from all|0.224***|0.235**|0.358***|
|non-member sources)|(0.065)|(0.104)|(0.122)|
|Extra3 (NHI source in IPA to all non-|-0.364**|-0.252|-0.630**|
|member destinations)|(0.159)|(0.340)|(0.310)|
|Any IPR provision not meeting WTO LE2|0.187*|0.187*|0.191*|
||(0.101)|(0.103)|(0.101)|
|Any bilateral investment agreement|0.228**|0.228**|0.229**|
|(BIT or other IIA)|(0.090)|(0.091)|(0.089)|
|_Panel C: CORETRIPS3_||||
|Intra (member to member)|0.235***|0.238***|0.237***|
||(0.076)|(0.074)|(0.074)|
|Extra1 (between HI member and|0.072|0.071|0.072|
|HI non-member)|(0.098)|(0.098)|(0.098)|
|Extra2 (NHI destination in IPA from all|0.252**|0.346***|0.332***|
|non-member sources)|(0.123)|(0.115)|(0.121)|
|Extra3 (NHI source in IPA to all non-|-0.371*|-0.290|-0.575**|
|member destinations)|(0.198)|(0.194)|(0.224)|
|Any IPR provision not meeting WTO LE2|0.319**|0.319**|0.319**|
||(0.157)|(0.156)|(0.157)|
|Any bilateral investment agreement|0.053|0.051|0.053|
|<br>(BIT or other IIA)|(0.053)|(0.053)|(0.053)|
|Observations|104,081|104,081|104,081|
|Origin-year FEs|Y|Y|Y|
|<br>Dest-year FEs|Y|Y|Y|
|Pair FEs|Y|Y|Y|
|Pseudo R2|0.997|0.997|0.997|

_Notes_ : Country incomes are based on World Bank income group classifications. Column 1 groups countries by their annual classification. Column 2 groups countries by their earliest observed income classification within our sample and Column 3 groups them by their latest observed income classification. Two-way clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 21
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Birth Years Omitted Birth Year 1957-58 © Comparison Cohort © Partially Exposed Cohort @ Fullly Exposed Cohort