rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- Afzal, U., G. D’Adda, M. Fafchamps, S. Quinn, and F. Said (2018): “Two Sides of the Same Rupee? Comparing Demand for Microcredit and Microsaving in a Framed Field Experiment in Rural Pakistan,” _The Economic Journal_ , 128, 2161–2190.

- Andrews, C., A. de Montesquiou, I. Arevalo Sanchez, P. V. Dutta, B. V. Paul, S. Samaranayake, J. Heisey, T. Clay, and S. Chaudhary (2021): “ The State of Economic Inclusion Report 2021: The Potential to Scale.” World Bank: Washington DC.

- Angelucci, M. and G. De Giorgi (2009): “Indirect effects of an aid program: How do cash transfers affect ineligibles’ consumption?” _American Economic Review_ , 99, 486–508.

- Bandiera, O., R. Burgess, N. Das, S. Gulesci, I. Rasul, and M. Sulaiman (2017): “Labor markets and poverty in village economies,” _Quarterly Journal of Economics_ , 132, 811–870.

- Banerjee, A., E. Duflo, R. Glennerster, and C. Kinnan (2015a): “The Miracle of Microfinance? Evidence from a Randomized Evaluation,” _American Economic Journal: Applied Economics_ , 7, 22–53.

- Banerjee, A., E. Duflo, N. Goldberg, D. Karlan, R. Osei, W. Pariente, J. Shapiro, B. Thuysbaert, and C. Udry (2015b): “A multifaceted program causes lasting progress for the very poor: Evidence from six countries,” _Science_ , 348, 1260799–1260799.

- Beaman, L., D. Karlan, and B. Thuysbaert (2014): “Saving for a (not so) Rainy Day: A Randomized Evaluation of Savings Groups in Mali,” NBER Working Paper, Cambridge, MA.

- Beaman, L., D. Karlan, B. Thuysbaert, and C. Udry (Forthcoming): “SelfSelection into Credit Markets: Evidence from Agriculture in Mali,” _Econometrica_ .

- Bedoya, G., A. Coville, J. Haushofer, M. Isaqzadeh, and J. Shapiro (2019): “No Household Left Behind: Afghanistan Targeting the Ultra Poor Impact Evaluation,” NBER Working Paper, Cambridge, MA.

31
Lall (2003), Kelly and Swindell (2000) and Parks (1984), show a statistically significant correlation. But studies have shown that factors _other_ than quality also influence satisfaction, including demographic factors like age, gender, education, income and ethnicity, as well as attitudes and predispositions related to political beliefs, consumer expectations or past experiences (see Amin et al, 2008 for a fuller discussion). Many of the demographic factors may be easy to observe and control for, but others such as expectations, experiences and predispositions are harder to measure or proxy. This makes it difficult to isolate the impact of quality on satisfaction and makes the interpretation of satisfaction data a complex exercise.

These problems have led many to argue that perception-based instruments may be useful for initiating public debate about government performance and enhancing accountability, but not for measuring actual quality of public services. This paper examines if this is true in the Indonesian context, using GDS-2 data. This survey is uniquely suited for such analysis, given that it is one of those rare instruments that provide data on satisfaction of households along with (from a facility level survey) objective indicators of quality of the facilities the households are using. The analysis also sheds some light on how satisfaction data can be used and interpreted as a measure of quality – a complex question for all the reasons discussed above.

Finally, our detailed analysis of the determinants of satisfaction will also shed some light on a question likely to be of interest to policymakers in Indonesia: What are the specific _dimensions_ of quality that seem to matter more than others for user satisfaction? For obvious reasons, indicators of satisfaction would be unable to address this question by themselves. The unique opportunities offered by GDS-2 allow us to address this question in a framework that corrects for some of the most typical problems in using and interpreting satisfaction data.

7
time: in the 1987-1995 period, only 46% of investigations had resulted in antidumping or countervailing duties.<sup>47</sup> The last column in Table 3 above shows that in 2002 34% of concluded cases had antidumping or countervailing duties in force. This percentage, far lower than the 58% previously mentioned, is due to the fact that after annual administrative or sunset reviews some duties are eliminated. Given the years gone by since the system has been implemented, a great part of its current activity focuses on proceedings other than new investigations, as shown by the UPCI’s statistics on resolutions (last two columns in Table 4).

**Table 4 - Resolutions Regarding Unfair Practices and Safeguards, 1987-2002**

|**Year**|**Rejections**|**Investigations**<br>**initiated**|**Special**<br>**proceedings**<sup>**1**</sup>|**Other**<sup>**2**</sup>|
|---|---|---|---|---|
|1987|N.A.|18|...|...|
|1988|N.A.|11|...|...|
|1989|N.A.|7|...|...|
|1990|N.A.|12|...|...|
|1991|N.A.|9|...|...|
|1992|8|26|...|...|
|1993|12|83|...|...|
|1994|16|26|...|...|
|1995|2|4|8|1|
|1996|6|4|16|5|
|1997|5|7|7|1|
|1998|0|11|6|16|
|1999|10|11|4|13|
|2000|5|5|10|22|
|2001|7|5|13|16|
|2002|0|12|10|9|
|2003|N.A.|11|N.A.|N.A.|
|Total|71|262|74|83|

1/ They include product coverage, benefit extension, new exporters and anti-circumvention.

2/ Reference price and duty elimination and review.

Source: _Informe Anual de Labores_ 2002, UPCI and DOF.

> 47 Miranda (1995), p. 195.

17
# **4 Experimental Results**

## **4.1 Insurance and Savings Preferences**

Our main empirical result is that most farmers prefer both pure savings and pure insurance to interior mixture of the two.<sup>16</sup> 1 the valu- any Figure displays average ation versus _γ_ , the insurance share of the WISA. Participants reported the highest average valuations of pure savings and pure insurance, with these bids statistically indistinguishable. Valuations for both interior mixtures of savings and insurance were significantly lower than those for pure products. Our finding of an interior local WTA minimum in the percentage of insurance is inconsistent with the standard intertemporal insurance model and Proposition 1.

Figure 1 shows within-subject differences in average valuations as a function of _γ_ . In addition, we group the subjects according to various patterns of the bids, which indicate distinct preferences over insurance or savings. These groups are shown in Table 2. Eighteen percent of respondents were indifferent, which means they had the same valuation for each product. Seven percent preferred savings, i.e., their bids were weakly decreasing in the percentage of insurance contained in the product. Thirteen percent showed a preference for insurance, meaning their bids were weakly increasing in the percentage of insurance contained in the product. Eleven percent preferred an interior mix, which means they had the highest bid for one of the mixture products, with the bids weakly decreasing as one moves away from the highest bid. A strong plurality of 39 percent of the subjects had preferences that corresponded to the average, meaning they showed a preference for both pure insurance and over of the mixtures. Twelve did pure savings any percent of subjects not express clear preferences, meaning that their bids changed directions twice as _γ_ increased.

Regressions of WTA on a quadratic in _γ_ and various controls support these descriptive patterns. Results are shown in Table 3. Column 1 contains only the linear

> 16Except where specified to the contrary, in this Section for simplicity all analysis we report involves the elicited same-day WTA. For the qualitatively-similar results using the post-monsoon WTA elicitations, see the Appendix. In addition, this Section pools across the between-subjects framing manipulations, to which we return in Section 5.

17
Kneller, R. A. and Z. Yu. 2008. Quality Selection, Chinese Exports and Theories of Heterogeneous Firm Trade. University of Nottingham, GEP, Discussion Papers. http://EconPapers.repec.org/RePEc:not:notgep:08/44.

Konings, J. and H. Vandenbussche. 2008. “Heterogeneous responses of firms to trade protection.” _Journal of International Economics_ , 76 (2) :371-83.

Konings, J. and H. Vandenbussche. 2009. Antidumping Protection Hurts Exporters: Firm-level Evidence from France. CEPR Discussion Paper No. DP7330.

Krupp C. and P. Polard. 1996. <mark>"Market Responses to Antidumping Laws: Some Evidence from the U.S. Chemical Industry.”</mark> _<mark>Canadian Journal of Economics</mark>_ <mark>29 (1): 199-227.</mark> Manova, K. and Z. Zhang. 2009. Quality Heterogeneity across Firms and Export Destinations. NBER Working Paper No. 15342.

Miranda J., Torres R. and M. Ruiz. 1998. “The International Use of Antidumping: 1987-1997.” _Journal of World Trade_ 32 (5): 5-71.

Molina D. and M. Muendler, 2009, Preparing to Export. Working paper, University of California San Diego (http://www.econ.ucsd.edu/muendler/papers/brazexp.pdf) Pierce J. 2011. “Plant-Level Responses to Antidumping Duties: Evidence from U.S. Manufacturers.” _Journal of International Economics_ 85 (2) :222-33.

Prusa T. 1992. “Why are so Many Antidumping Petitions Withdrawn?” _Journal of International Economics_ 33 (1-2) :1-20.

Prusa T. 2001. “On the Spread and Impact of Antidumping.” _Canadian Journal of Economics_ 34 (3): 591-611.

Prusa, T. and S. Skeath. 2002. “The Economic and Strategic Motives for Antidumping Filings.” _Weltwirtschaftliches Archiv_ 138 (3): 389-413.

Staiger, R. and F. Wolak. 1994. “Measuring Industry Specific Protection: Antidumping in the United States.” _Microeconomics_ 1994: 51-103.

Vandenbussche, H. and X. Wauthy. 2001. “Inflicting Injury through Product Quality: How European Antidumping Policy Disadvantages European Producers.” _European Journal of Political Economy_ , 17: 101-116 Verhoogen E. 2008. “Trade, Quality Upgrading and Wage Inequality in the Mexican Manufacturing Sector.” _Quarterly Journal of Economics_ 123: 489-530.

17
allocation. In other words, all individuals within the household are assigned the same consumption per capita, and this by construction constrains variability in characteristics like gender that vary heavily within the household.<sup>12</sup> Parental education is grouped into four categories defined the same way across countries – None, Primary, Secondary, and Tertiary – and treated as ordered to avoid the algorithm grouping non-consecutive categories (to prevent, for example, a type grouping none and secondary separately from primary and tertiary). Parental industry is also grouped into four categories defined the same way across countries – Agriculture, Industry, Services, and Other. For birth region, we use the administrative division that yields between 5-15 regions in each country.<sup>13</sup> Being born in another country is treated as a separate category. In some cases, we assume the respondent’s previous location is their birthplace. In the case of South Africa, ethnicity is not available, and we instead use race, which takes four categories. Refer to the Data Appendix for details on assumptions made during harmonization.

Given the varying number of response categories across countries for birth region, ethnicity, and religion, caution is required when comparing IOp across countries. For example, the number of ethnic groups varies from 11 (Guinea-Bissau) to 66 (Gabon). This is not a problem for interpretation of IOp to the extent that it reflects true differences in ethnic diversity across countries. However, it may instead reflect differences in the granularity of survey responses.<sup>14</sup> Likewise, the number of religions varies from 4-8. Religions are separated into subclassifications in some countries (for example, Christian is sometimes divided into Catholic, Protestant, Other Christian, etc.), and as with ethnicity, this could reflect differences in surveying as opposed to differences in the relevance of those subclassifications in each country.

In some cases, circumstances are missing for some observations. An advantage of conditional inference trees is that they avoid dropping observations with missing circumstance information. Instead, where information on circumstances is missing, the

> 12 As a result, IOp changes very little when we include gender as a circumstance. We also exclude age, which like gender varies heavily within the household, but furthermore because age is a characteristic that varies over the lifecycle.

> 13 The exceptions are Malawi, which only has 3 regions, and Tanzania, which has 31 regions.

> 14 A possible solution becoming increasingly popular in the literature is to link ethnic groups to a linguistic tree to aggregate them into comparable levels of linguistically defined ethnic divisions. However, this requires having an ethnic mapping specific to the surveys we use – for example, an ethnic group like “Asante” will have different meanings in different surveys. To our knowledge, such a mapping has not been created for the specific surveys we use in our analysis.

10
market (with at least 30 percent of exports claiming AGOA preferences) experienced large decline (over 65 percent) in their exports by the second year of suspension.

**Table 2: Pre- and post-suspension exports to the US**

|**Country (year of suspension)**|**Exports**<br>**suspens**<br>**(Millio**|**in pre-**<br>**ion year**<br>**n $US)**|**Share of**<br>**AGOA**<br>**Exports**<br>**(Percent)**|**Exports in the**<br>**second year of**<br>**suspension**<br>**(Million $US)**|**Change in**<br>**total exports**<br>**(Percent)**|
|---|---|---|---|---|---|
||**Total**|**AGOA**||||
||(1)|(2)|(3)=(2)/(1)|(4)|(5)=(4)/(1)-1|
|Burundi (2016)|8.4|0|0|9|7|
|Cameroon (2020)|330|0.4|0|208|40|
|Central African Republic (2004)|2|0.2|10|5.7|185|
|Côte d’Ivoire (2005)|706|88|12|701|-2|
|Congo, Dem. Rep. (2011)|435|147|34|133|-69|
|Gambia, The (2015)|0.3|0.06|20|0.7|133|
|Guinea (2010)|67.3|0.21|0.3|80.7|20|
|Guinea-Bissau (2013)|0.1|0|0|0.1|0|
|Madagascar (2010)|253|210|83|87|-66|
|Mali (2013)|3.7|0.2|5|3.7|0|
|Mauritania (2019)|13|0.02|0.2|6|-54|
|Niger (2010)|106.3|0.11|0.1|26.5|-75|
|South Sudan (2014)|0.1|0|0|0.2|100|
|Eswatini (2015)|82|59|72|17|-79|

_Source: Authors’ calculations, based on USITC data._

# **3.2 Empirical Specification**

The empirical approach to identify the impact of the AGOA suspension exploits the variation in country and product eligibility and in the timing of suspension across countries. More specifically, following Frazer and Van Biesebroeck (2010) and Hakobyan (2020), we adopt a triple difference-in-differences regression model to identify the impact of suspension on exports from the suspended countries.

12
12 reported). In the year-by-year regressions (not reported), the significance of the ownership concentration variables actually increases over time, suggesting an improvement in the role of owners in corporate governance. In general, these results are strong and show that more concentrated ownership has a positive and increasing association with firm value and profitability.

**Table 2: Estimation Results*** (random effects model)

|Explanatory Variable|_Regress_|_ion i_|_Regress_|_ionii_|_Regres_|_sion_al|
|---|---|---|---|---|---|---|
||<br>Tobin's Q|<br>Profit|<br>Tobin's Q|<br>Profit|<br>Tobin's Q|<br>Profit|
|Leverage|0.076<br>(16.077)**|-0.002<br>(1.702)***|0.072<br>(15.677)**|-0.002<br>(1.262)|0.073<br>(15.745)**|-0.002<br>(1.284)|
|Dummy for First Wave|0.112<br>(3.521)**|0.018<br>(0.225)|0.321<br>(0.822)|0.025<br>(0.218)|0.362<br>(0.925)|0.018<br>(0.178)|
|Concentration (Herfindahl Index)|0.062<br>(1.691)***|0.104<br>(4.889)**|0.064<br>(0.817)|0.028<br>(0.729)|0.108<br>(0.732)|0.002<br>(0.741)|
|Bank Sponsored IPFs|||0.196<br>(2.425)**|0.006<br>(0.186)|0.157<br>(1.906)***|0.009<br>(0.358)|
|Non-Bank Sponsored IPFs|||-0.006<br>(0.065)|0.028<br>(0.975)|-0.007<br>(0.086)|0.023<br>(0.985)|
|National Property Fund|||0.181<br>(1.492)|0.006<br>(0.155)|0.176<br>(1.512)|0,005<br>(0.148)|
|Local Strategic Investors|||-0.095<br>(0.872)|0.056<br>(1.835)***|-0.104<br>(0.915)|0.057<br>(1.854)***|
|Foreign Strategic Investors|||-0.092<br>(0.714)|0.058<br>(1.565)|-0.105<br>(0.784)|0.062<br>(1.584)|
|Conflict-of-interest Dununy|||||0.057<br>(2.085)**|0.002<br>(0.721)|
|Sector Dununies|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dunmnies|Yes|Yes|Yes|Yes|Yes|Yes|
|R-square|0.139|0.083|0.167|0.098|0.169|0.098|

* All regressions are based on an unbalanced panel of 2490 observations. t-statistics in parentheses. ** Significant at the 99% level.

_***_ Significant at the 90% level.

We next investigate the role of different types of shareholders by running regressions where we include the equity share for bank sponsored investment funds, non-bank sponsored investment funds, the NPF, local and foreign strategic investors. We also include the Herfindahl
16 Table 1: Summary Statistics

|Total Number of Studies Reviewed|70|
|---|---|
|Freight|17|
|Passenger|49|
|Others|4|
|Single Modal Studies|37|
|Multimodal Studies|27|
|Others|6|
|Air"<br>|20|
|Auto<sup>4</sup>|18|
|Shipping"|5|
|Railu|24|
|Tmcka|8|
|Public Transit'|22|
|others'|3|
|Time Series|25|
|Cross Section|33|
|Panel Data and Pooled Time Series and Cross Section Data|3|
|Others (including unknown data sources)|9|
|United States|32|
|Canada|8|
|United Kingdom|8|
|Australia and New Zealand|7|
|Europe (excluding United Kingdom)|3|
|Brazil|2|
|India and Pakistan|2|
|Othersb|8|

'The number of studies in this classification do not sum to the total elsewhere because a single study is counted more than once in the case of multimodal studies.

bincluding multicountry studies and studies with unknown data sources.
**Table 22: Welfare Direct Effects (m. LYD)**

||**Gasoline**|**Diesel**|**Electricity**|**LPG**|**Kerosene**|**Total**|
|---|---|---|---|---|---|---|
|Quintile_1|-53.2|-1.4|-61.0|-16.1|-0.9|-132.6|
|Quintile_2|-64.4|-1.4|-69.4|-16.6|-1.5|-153.4|
|Quintile_3|-66.5|-1.1|-69.8|-16.2|-1.7|-155.3|
|Quintile_4|-66.7|-1.1|-67.5|-15.8|-2.0|-153.1|
|Quintile_5|-61.9|-1.3|-73.1|-14.9|-2.1|-153.3|
|Total<br>(Scenario 1)|-312.8|-6.4|-340.9|-79.6|-8.2|-747.7|
|Total<br>(Scenario 2)|-1,042.6|-21.2|-1,136.2|-265.2|-27.4|-2,492.5|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

**Table 23: Per-capita Welfare Direct Effects as percentage of total welfare (Scenario 1 and 2)**

||**Gasoline**|**Diesel**|**Electricity**|**LPG**|**Kerosene**|**Total**|
|---|---|---|---|---|---|---|
|Quintile_1|-2.89|-0.07|-3.31|-0.87|-0.05|-7.20|
|Quintile_2|-2.82|-0.06|-3.03|-0.73|-0.07|-6.70|
|Quintile_3|-2.58|-0.04|-2.71|-0.63|-0.07|-6.02|
|Quintile_4|-2.43|-0.04|-2.46|-0.57|-0.07|-5.58|
|Quintile_5|-2.01|-0.04|-2.37|-0.48|-0.07|-4.98|
|Total (Scenario 1)|-2.50|-0.05|-2.72|-0.63|-0.07|-5.97|
|Total(Scenario 2)|-8.32|-0.17|-9.06|-2.12|-0.22|-19.89|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Eliminating all energy subsidies (scenario 2) would create direct savings of 2.5 billion LYD to the government budget—the same amount as the total direct value of subsidies to households (Table 24). This is equivalent to 3.83 percent of total government expenditure. The removal of gasoline subsidies alone could create direct savings of 1.6% of government expenditure and the removal of subsidies on electricity about 1.75% (table 24). A 30 percent reduction in subsidies on all products (scenario 1) would create 1.22 billion LYD in direct savings to the government budget which is more than one-third of the decline in spending under the 100% reduction scenario (scenario 2). As already explained for the case of food subsidies, this is because with a partial reduction in subsidies we have two sources of reduced government spending, the first resulting from higher subsidized prices and the second resulting from lower quantities consumed by households at these higher prices. If subsidies were totally eliminated, this second effect would disappear given that no quantities would be sold at a subsidized price.

**Table 24: Reduction in Government Expenditure (Libyan Dinars)**

||**Gasoline**|**Diesel**|**Electricity**|**LPG**|**Kerosene**|**Total**|
|---|---|---|---|---|---|---|
|Quintile_1|84.3|2.2|101.4|27.9|1.6|217.4|
|Quintile_2|102.0|2.3|115.3|28.8|2.8|251.2|
|Quintile_3|105.4|1.7|116.0|28.1|3.0|254.2|
|Quintile_4|105.6|1.8|112.2|27.3|3.6|250.5|

26
# and adolescent forced migrants.

We contribute to the body of work examining the impacts of migration on children.<sup>3</sup> The vast majority of this literature has been centered on examining the impacts of migration on left-behind children (Ohinata and Van Ours 2013, Mu and De Brauw 2015, Edwards and Ureta 2003, Tumen 2021), native children (Cascio and Lewis 2012, Geay, McNally and Telhaj 2013, Hunt 2017, Figlio et al. 2021 C ̧ akır, Erbay and Kırdar 2023), and on how voluntary (as opposed to forced) migration impacts immigrant children (Macours and Vakis 2010, Felfe, Rainer and Saurer 2020).<sup>4</sup> We contribute to these studies by collecting the first longitudinal study that collects comprehensive data on forcibly displaced children and adolescents residing in host communities, rather than in refugee camps. This analysis has been limited by the data availability as it is difficult to find these migrants. To overcome these obstacles, we developed an innovative approach for constructing sampling frames for forcibly displaced migrants living outside of refugee camps, where traditional census data or official records are unavailable. Our methodology involves an extensive listing exercise that integrates information from various sources, including population censuses, satellite imagery of new migrant settlements provided by humanitarian organizations, contributions from migrant groups and donors, field verification, and referrals from within the migrant community itself. This approach mirrors similar efforts documented in studies on the impact of regularization programs (Ib ́a ̃nez et al. 2022), but on them satellite data on new settlements and on a expands by incorporating focusing demographic group that has been less studied in this context—children and adolescents.

We also contribute to the literature by collecting rich and comprehensive information on children’s and adolescent’s such as development covering aspects physical, cognitive,

> 3See Cortina, Jimenez and Rozo (2024) or a detailed literature review of findings from 113 studies produced between 1990 and 2023 focusing on the social and economic impacts of migration on various child groups affected through the migration path.

> 4A relevant focus in this group of studies has been centered on understanding the role of public policies, such as enforcement policies or school mixing, on the outcomes on migrant children (see AmuedoDorantes and Lopez 2015, Amuedo-Dorantes, Arenas-Arroyo and Sevilla 2018, Amuedo-Dorantes and Arenas-Arroyo 2019, Alan et al. 2021).

7