rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4b presents an alternative way of characterizing the trading partners affected by Turkey’s TTB use by differentiating between whether the TTB-impacted imports were from a PTA member or non-member. Over the first decade of the 2000s, a substantially higher share of imports from PTA nonmembers are subject to Turkey’s TTBs than are its imports from PTA members. This is consistent with cross-country results from Prusa and Teh (2010) that PTA outsiders are more likely to face the incidence of antidumping than PTA insiders. Nevertheless, the general trend since 2002 is that Turkey’s imports from both PTA members and PTA non-members are increasingly becoming subject to Turkey’s TTBs.

One potential economic concern is therefore that Turkey’s imposed TTBs increasingly (implicitly) discriminate against PTA non-members. In addition to Turkey’s consumers facing costs by having to pay higher initial tariffs to import from PTA non-members than from Turkey’s PTA members (e.g., due to the preferences embodied in the customs union with the EU), imports from these PTA non-members may also be more likely to be subject to additional antidumping, safeguard, or countervailing duties. The efficiency concern with Turkey applying an increasingly discriminatory trade policy that further differentiates between PTA members and non-members is if it creates additional scope for _trade diversion_ (Viner, 1950), by which Turkish welfare suffers because consumers end up sourcing from relatively inefficient foreign suppliers because of discriminatory preferences.

# **3.2.3 Turkey’s particularly important TTBs in effect in 2011: “Cascading” use of flexibilities?**

According to the World Bank’s _Temporary Trade Barriers Database,_ Turkey had 127 antidumping measures, 10 safeguard measures and 1 countervailing measure in effect at the end of 2011. Turkey applies TTBs to import products in a number of different industrial sectors, including sizeable shares of imports in textiles and apparel, metals, electrical machinery, plastics and rubber, and stone and glass (Karacaovali, 2011).

Table 3 presents a ranking of Turkey’s “top 10” TTBs in effect in 2011, by estimated size of impacted imports.<sup>18</sup> Not surprisingly, four of the top 10 of Turkey’s TTBs concern the global safeguards policy – a set of import restrictions that negatively affects multiple foreign export sources

> 18 These are upper bounds to the true amount of impacted trade given that this is based on bilateral import data at the 6-digit Harmonized System level and TTBs are frequently applied at a much more disaggregated level. Furthermore, while the approach takes care to base the estimates on bilateral data and application of policy, it does not adjust for the possibility that trade diversion from non-targeted sources may replace bilateral imports destroyed because of the imposed TTB.

15
2.14 Data Interpretation: The most common education indicators used in the reviewed literature are education spending as a share- of the public budget or of GNP, and the gross enrollment rate. These indicators, by themselves, explain little. Whether or not a decline in education's share of the total public budget reflects specific government priorities depends on a number of factors:

* Debt brden: Few reports assess public education spending as a share of discretionary government spending (excluding interest payments).41 The growing debt burden may reduce all sectors' shares of the budget. For example, Fuller (1989) reports that debt service accounts for 38 percent of government recurrent spending in Malawi.

- Recession: A dec!ine in education's share of GNP may result from a smaller public budget (as a share of GNP).

* Real expenditures: Figures on budget shares indicate priorities within the government budget, but do not reveal how real expenditures changed. If the budget grows, education's budget share could decrease even though real education expenditures increase, and vice versa. An important difficulty in estimating real education expenditures is the lack of a suitable deflator for nominal expenditures. The common practice of using the GDP deflator is highly questionable. For example, if food prices triple while all other prices remain constant, the GDP deflator will falsely suggest that real education expenditures declined. Determining an appropriate deflator is not trivial. Because the largest outlays in education are teachers' salaries, real wages should figure prominently in this deflator. If expenditures decline because teacher salaries are cut, education supply will not change unless teachers quit or teaching quality declines.

a Regional or local government financing; Public eddcation fliancing figures often ignore regional and local contributions. If figures for education financing refer to central government expenditures only, they may provide little information on sector financing. For example, tl.- bulk of education financing in Nigeria comes from local and state governments.

* Private financing: Figures for public education spending ignore the contribution from private sources that is quite significant in some countries (80 percent in Haiti). If private spending increased while public spending declined, total sector resources may have remained constant or even increased.

* Intrasectoral allocations: Figures for total education spending conceal changes in resource allocations by level of education (which subsectors were vulnerable or protected) and by function (recurrent versus capital, salaries versus materials).

6<sup>1</sup> Exceptions are Sahn (1989); Gallagher (1990); Kakwani, Makonnen, and van der Gaag (1990); and World Bank (1990a).

18
Table 11: Impact of adding cash to therapy on adolescent’s own and perceived household response to COVID-19.

|(1)<br>**A**|(2)<br>**dolescent’s **|(3)<br>**own respons**|(4)<br>**e**|(5)<br>**Perceived H**|(6)<br>**H response**|
|---|---|---|---|---|---|
|Get angry<br>more<br>quickly|Arguing<br>more often|Talk<br>about<br>problems|Worry<br>about<br>health|Increased<br>stress in<br>HH|HH lost<br>some<br>income|
|_Panel A: Midline (12 month follow-up)_||||||
|IPT-G+<br>0.088|-0.024|0.019|0.009|0.049|0.008|
|(0.026)|(0.034)|(0.026)|(0.013)|(0.027)|(0.049)|
|IPT-G mean<br>0.480|0.384|0.670|0.924|0.637|2.529|
|IPT-G SD<br>0.500|0.487|0.471|0.265|0.481|0.758|
|Observations<br>1045|1047|1047|1047|1047|1044|
|H0: IPTG+=0 p-values<br>0.001|0.495|0.459|0.472|0.079|0.872|
|FDR adj. q-values<br>0.006|0.594|0.594|0.594|0.236|0.872|
|_Panel B: Endline (24 month follow-up)_||||||
|IPT-G+<br>0.110|0.122|0.019|0.032|0.067|-0.033|
|(0.037)|(0.042)|(0.026)|(0.034)|(0.035)|(0.078)|
|IPT-G mean<br>0.321|0.253|0.670|0.767|0.493|2.654|
|IPT-G SD<br>0.468|0.435|0.471|0.423|0.501|0.811|
|Observations<br>842|840|1047|842|843|842|
|H0: IPT-G+=0 p-values<br>0.004|0.005|0.459|0.350|0.059|0.675|
|FDR adj. q-values<br>0.014|0.014|0.594|0.524|0.119|0.787|

Notes: This table shows ITT estimates of the impact of adding Cash to IPT-G on the households’ response to COVID-19, comparing the IPT-G only group to IPT-G+ group; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Outcomes in columns 1 to 4 are binary indicators for the adolescent respondents saying that they responded to COVID-19 by: “Getting angry more quickly”, “Arguing more often”, “Talking more often about problems with your family to find solutions”, “Fearing and worrying about your own health and the health of your loved ones”; Columns 5 shows whether the respondent agrees or partially agrees that COVID-19 has increased stress in their household; Column 6 indicates whether the respondent thinks their household lost some, most, or all of their income due to COVID-19; The rows marked as _H0:..._ show the p-value for a t-test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of adolescents.

41
**Table 8: Spatial Fixed Effects Estimates of the Impact of LTR on Inheritance Dynamics**

||**Know who**<br>**will inherit**|**Children**<br>**inherits**|**Son inherits**|**Daughter**<br>**inherits**|
|---|---|---|---|---|
|Treatment indicator|0.094**|0.133**|0.102**|0.096**|
||(2.186)|(2.276)|(2.552)|(2.039)|
|Treatment X Female headship|-0.044|-0.046|-0.052|-0.158**|
||(0.741)|(0.779)|(0.735)|(2.326)|
|Number of years possessed|0.001|0.000|0.001|0.001|
||(0.745)|(0.112)|(0.708)|(0.396)|
|Parcel was purchased|-0.008|-0.021|0.043|0.008|
||(0.344)|(0.964)|(1.642)|(0.361)|
|Parcel was inherited|0.026|0.004|0.072**|0.038|
||(0.719)|(0.136)|(2.247)|(0.995)|
|Acquired through other means|-0.051|0.025|0.167*|-0.030|
||(0.369)|(0.276)|(1.928)|(0.254)|
|Parcel size in hectares|-0.023**|-0.012|0.006|0.004|
||(2.375)|(1.248)|(0.567)|(0.284)|
|Head's age|0.001|0.003**|0.002|0.004**|
||(0.820)|(2.347)|(1.285)|(2.476)|
|Female headed household|0.071*|0.094***|0.210***|-0.003|
||(1.789)|(3.003)|(5.764)|(0.062)|
|Head has at least primary education|0.018|0.024|0.031|-0.001|
||(0.818)|(1.337)|(1.297)|(0.025)|
|Number of male children age 14 and less|0.028***|0.053***|0.155***|-0.017|
||(2.779)|(5.292)|(11.481)|(1.417)|
|No. of female children age 14 and less|-0.003|0.028***|-0.041***|0.161***|
||(0.303)|(3.178)|(3.064)|(13.739)|
|Number of adult male age 15 to 60|0.014|0.029**|0.144***|-0.056***|
||(1.319)|(2.362)|(10.160)|(3.165)|
|Number of adult female age 15 to 60|0.010|0.014|-0.040**|0.137***|
||(0.729)|(1.157)|(2.499)|(9.142)|
|Male members age 60 and above|0.068|0.078**|0.123***|-0.160**|
||(1.296)|(1.992)|(3.077)|(2.113)|
|Female members age 60 and above|-0.035|-0.027|0.026|0.091|
||(0.600)|(0.481)|(0.385)|(1.572)|
|Constant|-0.002|-0.002|0.228***|0.212***|
||(0.209)|(0.237)|(10.160)|(11.360)|
|Number of observations|6325|6325|4053|4053|
|R2|0.018|0.057|0.345|0.057|

**Note:** The geographical neighborhood of each household is defined at a distance of 1000 meters. Absolute value of t-statistics corrected for spatial dependence (1500 meters).* significant at 10%; ** significant at 5%; *** significant at 1%.

24
# **<u>Table 2: Descriptive statistics on rights for agricultural land</u>**

|||<br>**Agricultu**|**ral Land**||
|---|---|---|---|---|
||**All**|**Matrilineal**|**Mixed**|**Patrilineal**|
|**Panel A: IHPS**|||||
|_Household level_|||||
|Parcel size (ha)|0.421|0.437|0.346|0.393|
|Member has right to make decision|0.870|0.861|0.923|0.908|
|Member has right to invest|0.840|0.834|0.812|0.913|
|Member has right to transfer/mortgage|0.749|0.734|0.751|0.821|
|Member has right to bequeath|0.728|0.712|0.685|0.817|
|Member has right to sell|0.657|0.658|0.569|0.670|
|_Parcel level_|||||
|Parcel (co)-owned by female(s)|0.746|0.750|0.801|0.711|
|Females can decide on crop cultivation|0.847|0.834|0.924|0.884|
|Females have right to invest|0.758|0.745|0.738|0.845|
|Females can transfer/mortgage|0.626|0.614|0.579|0.645|
|Females can bequeath|0.604|0.592|0.510|0.652|
|Females can sell|0.530|0.539|0.393|0.484|
|Parcel (co)-owned by men|0.440|0.421|0.486|0.560|
|Males can decide on crop cultivation|0.880|0.875|0.864|0.926|
|Males can invest|0.889|0.893|0.807|0.943|
|Males can transfer/mortgage|0.794|0.803|0.761|0.820|
|Males can bequeath|0.782|0.769|0.739|0.910|
|Males can sell|0.698|0.715|0.602|0.705|
|No. of parcels|2,207|1,617|181|218|
|**Panel B: IHS4**|||||
|_Household level_|||||
|Parcel size (ha)|0.407|0.409|0.396|0.403|
|Member has right to bequeath|0.757|0.747|0.705|0.838|
|Member has right to sell|0.642|0.645|0.642|0.641|
|_Parcel level_|||||
|Parcel (co)-owned by women|0.704|0.715|0.637|0.683|
|Females can bequeath|0.691|0.706|0.588|0.672|
|<br>Females can sell|0.551|0.588|0.519|0.398|
|Parcel (co)-owned by males|0.529|0.468|0.651|0.751|
|Males can bequeath|0.736|0.702|0.708|0.850|
|Males can sell|0.639|0.629|0.667|0.661|
|No. ofparcels|13,011|9,745|1,051|2,110|

_Source:_ Own computation from 2015/16 Malawi IHPS and IHS4.

17
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21

# **VI.   FIGURES**

# **Figure 1**

<!-- Start of picture text --> Poland, Hungary, Czech Republic and Slovakia ‐ Bulgaria, Latvia, Lithuania, Romania and Slovenia ‐<br>Exports to Germany Exports to Germany<br>30 30<br>96‐03: y = ‐1.54x + 2.90<br>25 96‐03: y = ‐1.52x + 16.60R2 = 0.21 20 R2 = 0.46<br>20 10 04‐12: y = ‐1.38x + 8.74<br>04‐12: y = ‐0.24x + 4.55 R2 = 0.13<br>Average 1996-2003 15 R2 = 0.01 0<br>Average 2004-2012 10 -15 -10 -5 -10 0 5 10 15 20<br>5 Average 1996-2003 -20<br>0 Average 2004-2012 -30<br>-10 -5 -5 0 5 10 15 -40<br>-10 -50<br>REER (%yoy) REER (%yoy)<br>Source: IMF, JP Morgan and National Sources.<br>Figure 2<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>Real Gross Exports (%yoy) Real Gross Exports (%yoy)<br>Elasticity (two SE bandwidth)<br>1996-2001 1996-2002 1997-2003 1998-2004 1999-2005 2000-2006 2001-2007 2002-2008 2003-2009 2004-2010 2005-2011 2006-2012<br><!-- End of picture text --> Note: These are the slopes on Real Effective Exchange Rate Change elasticity of manufacturing exports estimated for the different samples in the horizontal axis using the regression framework discussed in empirical strategy.
# **DECONSTRUCTING HERDING: EVIDENCE FROM PENSION FUND INVESTMENT BEHAVIOR**

Claudio Raddatz and Sergio L. Schmukler<sup>*</sup>

## **JEL Classification Codes:** G11, G12, G23, G28, O16

**Keywords:** institutional investors, portfolio allocation, investment pattern, capital market development

* We are indebted to the Chilean Superintendency of Pensions for giving us unique data and support that made this paper possible. For very useful comments, we thank Solange Berstein, Matias Braun, Pablo Castañeda, Eduardo Fajnzylber, David Musto, Gonzalo Reyes, Luis Serven, and participants at presentations held at the LACEA Annual Meetings (Buenos Aires, Argentina), NIPFP-DEA Workshop (Delhi, India), Superintendencia de Pensiones and Universidad Alfonso Ibañez (Santiago, Chile), and World Bank Group (Washington, DC). For excellent research assistance, we are especially grateful to Ana Gazmuri and Mercedes Politi. We also thank Alfonso Astudillo, Leandro Brufman, and Francisco Ceballos, who helped us at different stages of the project. The World Bank Research Department and the Knowledge for Change Program provided generous financial support. The views expressed here do not necessarily represent those of the World Bank. Authors are with the World Bank, Development Economics Research Group. Email addresses: <u>craddatz@worldbank.org</u> and <u>sschmukler@worldbank.org.</u>
<!-- Start of picture text --> o<br>e<br>5<br>-w----------<br>2 @) Target<br>rs) Financial Gap @<br>Financial and policy<br>INF 7-</' options using @<br>Gap existing resources<br>¢ Pind<br>Level of investments ies/ _?<br>(% of GDP)<br>Today Time<br><!-- End of picture text -->
Moody’s) to 21 being lowest (C for all three agencies).<sup>11</sup> Table 2 shows the correspondence in the ratings between the three rating agencies. We exclude cases of sovereign default or selective default in our regression analysis since it is difficult to assign a specific numeric rating to such extreme credit events. Although default or selective default appears to be just another step down the road of getting a rating downgrade; assigning a specific value to such an event would run the risk of ignoring the degree of distress (e.g., a temporary liquidity crisis versus a systemic crisis).

**Table 2: Ratings - conversion from letter to numeric scale**

|**S&P**|**Fitch**|**Moody’s**|**Numeric Grade**|
|---|---|---|---|
|**Investment Grade**||||
|_Highest credit quality_||||
|AAA|AAA|Aaa|1|
|_Very high credit quality_||||
|AA+|AA+|Aa1|2|
|AA|AA|Aa2|3|
|AA-|AA-|Aa3|4|
|_High credit quality_||||
|A+|A+|A1|5|
|A|A|A2|6|
|A-|A-|A3|7|
|_Good credit quality_||||
|BBB+|BBB+|Baa1|8|
|BBB|BBB|Baa2|9|
|BBB-|BBB-|Baa3|10|
|**Speculative Grade**||||
|_Speculative_||||
|BB+|BB+|Ba1|11|
|BB|BB|Ba2|12|
|BB-|BB-|Ba3|13|
|_Highly speculative_||||
|B+|B+|B1|14|
|B|B|B2|15|
|B-|B-|B3|16|
|_High default risk_||||
|CCC+|CCC+|Caa1|17|
|CCC|CCC|Caa2|18|
|CCC-|CCC-|Caa3|19|
|_Very high default risk_||||
|CC|CC|Ca|20|
|C|C|C|21|

_Source:_ Standard and Poor’s, Moody’s Investors Service, and FitchRatings.

11 This conversion rule is the converse from the one mostly used in the literature (see Cantor and Packer 1996). Since our ultimate objective is to project ratings for poor countries; we want to leave open the rating spectrum downwards.

13
food that the peer adherence and nutritional support intervention group received for the duration of the experimental intervention.

In the subsequent pages, patient-level data are used to investigate the impact of peer adherence and nutritional support on measures of ARV treatment adherence and success.

# **2.2 Data collection**

Trained enumerators conducted structured, face-to-face baseline interviews with ART patients recruited into the study.<sup>2</sup> Written, informed consent was obtained from study participants by the nursing personnel at the respective clinics (for ARV patients), as well as by the enumerator (for ARV patients and ARV patient/comparison households). A facility survey was also conducted, during which semi-structured interviews were conducted with health care providers at each study site.

At follow-up, enumerators again obtained written, informed consent from study participants. Two rounds of follow-up interviews were conducted with patients. The median time between consecutive interviews was 11.7 months [IQR 10.6-15.9 months]. A total of 1,588 interviews were conducted with 653 individual patients, 422 of whom were interviewed at both follow-ups. By the second and final round of follow-up interviews, 218 patients had been lost to follow-up, primarily due to mortality among study participants (42.4%) and unknown whereabouts (34.1%), which translates into an aggregate attrition rate of 33.6%. Of the 337 patients that consented to enrollment in the adherence and nutritional support interventions at the commencement of the intervention, 288 or 85.4% were interviewed at the first round of follow-up interview, whereas 175 or 86.2% of the 203 study participants that completed the full 18-months of FEATS treatment were interviewed at the second and final round of follow-up interviews.

On completion of the study clinical bio-markers and other additional patient information were obtained from paper and electronic files. Patients at baseline, when first recruited into the study,

> 2 The study also included a household survey component, with interviews being conducted with patient and comparison households, this to achieve various other objectives of the larger study (refer CONSORT statement).

7
Figure 4: Prevalence of Shocks across Households in Sudan, 2009 and 2014/15 <!-- Start of picture text --> (a) Rural versus Urban (b) Household Livelihoods<br>80% 90%<br>72% 79% 83%<br>80%<br>70% 65% 70% 63%<br>60% 51%52% 54%57%61%61%65% 50%<br>60%<br>50%<br>51% 49% 35% 37%40% 37%39%39% 36% 37%<br>40%<br>50% 45%<br>30%<br>40% 36% 20%<br>10%<br>30% 0%<br>20%<br>10%<br>0%<br>Urban Rural Sudan<br>2009 2014/15<br>2009 2014/15<br>(c) Household Poverty (d) Quintile of Per Capita Consumption<br>80% 80%<br>71%<br>70% 68% 65% 70% 68% 69% 65%<br>63% 62%<br>60% 60% 58%<br>49% 51%<br>50% 48%<br>45% 50% 47%<br>43% 45%<br>43%<br>40% 38%<br>40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>Non-Poor Poor Sudan 0%<br>Poorest Poor Middle Rich Richest Sudan<br>2009 2014/15<br>2009 2014/15<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

Majority of the households affected by shocks live in the Central Darfur and Kordofan regions of Sudan. At state level, Southern Kordofan, Blue Nile, Western Darfur, and Northern Darfur have the highest incidence of shocks—more than 80 percent of households in these states reported at least one shock 2009 and more than 50 percent in 2014/15. On the other hand, low prevalence of shocks is observed in North16
<u>Table 6. Impact on test scores: treatment versus control</u>

|||NAPE|2011|||NAPE|2012||
|---|---|---|---|---|---|---|---|---|
|||O|LS|IV||O|LS|IV|
||Control Mean<br>and SD<br>(1)|Treatment-<br>Control<br>(2)|Treatment-<br>Control<br>(3)|Treatment-<br>Control<br>(4)|Control Mean<br>and SD<br>(1)|Treatment-<br>Control<br>(2)|Treatment-<br>Control<br>(3)|Treatment-<br>Control<br>(4)|
|A. Characteristics of Students and Househol|ds||||||||
|Students' age|16.22|-0.271**|-0.083||16.19|-0.301**|-0.094||
||(1.44)|(0.13)|(0.10)||(1.49)|(0.14)|(0.12)||
|Gender (1 male)|0.51|-0.045|-0.034||0.53|-0.049*|-0.008||
||(0.50)|(0.03)|(0.03)||(0.50)|(0.03)|(0.02)||
|Index infrastructure|1.69|0.06|-0.015||1.44|0.038|-0.004||
||(0.76)|(0.08)|(0.06)||(0.68)|(0.07)|(0.05)||
|Index assets|2.58|0.303|0.214||3.08|0.252|0.224||
||(1.97)|(0.26)|(0.24)||(1.70)|(0.19)|(0.23)||
|Inverse PLE Score (from low to high)|||||18.66|0.057|-0.066||
||||||(5.14)|(0.48)|(0.41)||
|B. Test scores results|||||||||
|Math (official)|0|0.112|0.127|0.384***|0|0.122|0.067|0.280**|
||(1.00)|(0.09)|(0.10)|(0.15)|(1.00)|(0.10)|(0.10)|(0.13)|
|Math (independent)|0|0.145*|0.154*|0.426***|0|0.167**|0.127*|0.312**|
||(1.00)|(0.08)|(0.08)|(0.15)|(1.00)|(0.08)|(0.08)|(0.13)|
|English|0|0.152|0.089|0.412***|0|0.260***|0.15|0.441***|
||(1.00)|(0.10)|(0.10)|(0.14)|(1.00)|(0.10)|(0.09)|(0.12)|
|Biology|0|0.074|0.088|0.312**|0|0.193*|0.074|0.293**|
||(1.00)|(0.11)|(0.12)|(0.14)|(1.00)|(0.11)|(0.12)|(0.13)|
|Region Fixed effects and baseline controls||No|Yes|Yes||No|Yes|Yes|
|Number of obs.|1126|2356|2356|2186|983|2249|2249|2101|

Note. Column (1) report mean and standard deviation for the control group. Columns (2) and (3) report the OLS estimate of effects of a regression of each outcome variable against the treatment indicator, the first one without covariates, the second one with regional fixed effects and baseline controls (school characteristics and average students characteristics) . Test scores are standardized with respect to the control group. Columns (4) report the results of the second stage of an IV estimation; first stage: receiving transfer (as reported in Check 2) as a function of treatment status. Standard errors are cluster at the school level.

23