rafmacalaba/datause-encoder-data huggingface dataset:
**<u><mark>Table A16: Likelihood to encourage others</mark></u>**

|**Likelihood**|BurkinaFaso|Malawi|Nigeria|Tanzania|
|---|---|---|---|---|
|1. Very likely|22.9|80.7|72.1|76.4|
||(18.2 to 27.7)|(75.0 to 86.4)|(67.3 to 76.9)|(70.7 to 82.2)|
|2. Somewhat likely|58.4|13.0|23.6|18.4|
||(52.9 to 63.8)|(8.2 to 17.8)|(19.0 to 28.3)|(13.2 to 23.5)|
|3. Neither likely nor unlikely|12.1|3.8|1.8|2.5|
||(8.5 to 15.8)|(1.2 to 6.4)|(0.6 to 3.1)|(0.0 to 5.0)|
|4. Somewhat unlikely|6.5|0.3|1.4|1.4|
||(3.6 to 9.4)|(0.0 to 0.6)|(0.2 to 2.5)|(0.0 to 3.1)|
|5. Very unlikely|0.1|2.2|1.1|1.3|
||(0.0 to 0.2)|(0.3 to 4.0)|(0.3 to 1.8)|(0.0 to 3.4)|
|_N_|690|584|921|402|

Note: Likelihood to encourage others to get vaccinated among those already vaccinated. All values in percent. 95% confidence intervals in parentheses.

28
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# 3. Data

In this section, we discuss the data used to measure the depth of a trade agreement and describe in detail our source for bilateral investments at the sector and activity levels.

## a. Preferential trade agreements

We first estimate overall the effects of deep trade agreements by using a novel measure of depth. Then we move to the estimation of the effect of the specific disciplines included in PTAs.

Our variables of depth and disciplines come from the World Bank database on the content of deep agreements. It covers all 279 PTAs that are in force and notified to the WTO as of 2015. (Hofmann, Osnago, & Ruta, 2018) give a thorough description of the database and the way the mapping of PTAs has been implemented. The methodology is based on the work of (Horn, Mavroidis, & Sapir, 2010), which was also used in the World Trade Report 2011 (World Trade Organization, 2011). This comprehensive data informs us on the specific disciplines, or policy areas, that are covered in each agreement. In total, 52 disciplines are identified across the 279 PTAs (see Table A.1 for the list of disciplines). On top of the area coverage, the data provide information on the legal enforcement of each discipline within an agreement. The enforcement coding is based on the analysis of the legal language of the treaty text and the possibility of recourse to dispute settlement.

For our baseline measure of depth, we follow previous works that use the content of this data set or other similar sources, by counting the number of disciplines included in a PTA. We focus on the number of legally enforceable disciplines as they are expected to be more impactful than disciplines that are just mentioned in treaties but without any sign of substantial commitment from the parties. We define the variable T T T T i = ∑52 i - i.e. the simple count of legally enforceable disciplines kk ( i ) included in the agreement between country TTh ii kk=1 TTD DD ii and at time . Once an kk ii TTD DD DD jj TT 8
3. Entry costs, export costs, and distance all impact export diversification negatively and significantly (at the 1% level), while own tariffs have a positive impact which is statistically significant at the 5% level under OLS and at the 1% level using the negative binomial. EU tariffs enter with an unexpected positive sign, but are not statistically significant in any of the three models. 5 Conclusions and Policy Implications We have used highly disaggregated mirror data from the EU, along with new information from the World Bank’s Doing Business database, to show that trade costs and entry restrictions are inversely related to export diversification in a sample of 118 developing countries. We find that export costs, international transport costs (proxied by distance), and domestic market entry costs all impact negatively and significantly on export diversification, with the strongest effects working through transportation (with an elasticity of -0.4) and export costs (-0.3). By contrast, the elasticity on market entry costs is only -0.1. We find much more mixed evidence in support of the expected positive impacts of lower EU tariffs and higher own tariffs. Our core results turn out to be quite robust to alternative diversification measures, model specifications, and country samples. Although we are cautious in drawing causal inferences based on cross-sectional data for a single year, instrumental variables estimates using geography and colonial history to identify exogenous variation in trade and entry costs would appear to provide preliminary support for such a conclusion. This interpretation would moreover be in accordance with the theoretical structure we have developed in the paper, incorporating heterogeneous firms and asymmetric trade costs. Subject to that caveat, our results can be used to sketch out some preliminary implications for development policy. On the one hand, policies aimed at reducing trade costs related specifically to exporting, as well as international transport costs, would appear to be promising avenues for developing countries aiming to promote export diversification. Trade facilitation measures, in the broad sense in which Wilson et al. (2005) use that term, would therefore be attractive. Similarly, <u>are obtained using interval regression for Tobit,</u> while boostrapping (200 replications) is used for the negative binomial. 28
<!-- Start of picture text --> Figure 8. Output informality and underdevelopment<br>A. Informality and GDP per capita (2019) B. Informality, labor productivity, and human<br>capital<br>10,000 2017 PPP $ / Year<br>EMDE (in blue) 12 EMDEs with below-median informality<br>EMDEs with above median informality<br>10<br>Indonesia 8<br>6<br>AEs (in red) 4<br>2<br>6 8 10 12 0<br>Ln GDP per capita Labor productivity Years of schooling<br>60<br>50<br>40<br>30<br>20<br>10<br>MIMIC-based output informality (% GDP)<br><!-- End of picture text --> Sources: World Bank World Development Indicators, Elgin, et al. (2021 updated), and World Bank Staff estimates. A. Informality is captured by MIMIC-based informal output as a share of GDP. B. The sample is constrained to EMDEs, and data are averaged over 2011-19 to avoid the impact of the global recession around 2020 and 2008/09. EMDEs are grouped into “above-median” and “below-median” self-employment as a share of total employment.

33. **The link between informality and underdevelopment is supported by evidence in Indonesia.** First, output informality in Indonesia is most prevalent in the agriculture sector, followed by labor-intensive low-value-added service sectors (such as wholesale, retail, and hospitality sectors). Informal output in the agriculture sector is almost double the share of total informal output in the economy. It has declined slightly compared to its level in 2001 (figure 9). Wholesale, retail trade and hospitality sectors also pose a relatively larger share of informal output compared to other sectors though the share declined significantly compared to 2001. This is similar to a broad informality trend in other countries where informality tends to be higher in sectors that are labor-intensive, low-skilled, less productive, and with relatively low wages.

<!-- Start of picture text --> Figure 9. Output informality in Indonesia, simple definition (share, weighted)<br>Percent of total output in the corresponding sector<br>80 2019 2020 2021 2001<br>60<br>40<br>20<br>0<br>Agriculture, Wholesale Transportation, Constr- Mining Community, Manuf- Electricity, Finance,<br>forestry, and retail trade, storage and uction and social and acturing gas, and insurance,<br>livestock, restaurants communications quarrying personal srv water supply real estate<br> and fishing and hotels and bus.srv<br>Source: BPS, WB staff estimation.<br>Note: weighted estimates (using the sample weights from the SAKERNAS) are used.<br><!-- End of picture text --> 34. **The regions with the largest output informality are also regions with the largest rural population, weaker urbanization, prevalence of agriculture, and lower income.** For example, West Sulawesi and West Nusa Tenggara are two provinces in the top five largest output and employment informality. The share of the agriculture sector in those two provinces is the largest 14
<!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>control treatment<br><!-- End of picture text --> Figure III Fraction of Teachers Admitted to the Certification Process, At, or Before the Indicated Year _Notes:_ Teachers were admitted to the certification process at different points in time. The first batch of teachers was admitted in 2006. The intervention took place in 2009, which created a difference between treatment and control schools in terms of the fraction of teachers admitted to the certification program. The bars represent fractions of teachers who were admitted to the certification program at, or before the indicated year. For example, around 60% of teachers in treatment schools were admitted to the certification program in the year 2009 or before, against roughly 30% in control. We use baseline data to construct the 2006, 2007, 2008, 2009 bars, Y2 data to construct the 2010 and 2011 bars, and Y3 data to construct the 2012 bar.

<!-- Start of picture text --> .8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>Y0 Y2 Y3 Y0 Y2 Y3<br>control treatment control treatment<br><!-- End of picture text --> Figure IV Completing the Certification Process (Left) and Being Paid the Certification Allowance (Right) _Notes:_ The left panel presents the fraction of teachers who completed the certification process. The right panel presents the fraction of teachers who completed the certification process and were paid the certification allowance.
**<u>Table 3. Summary of SHG activities</u>**

|||**2001**|||**2003**|||**2006**||
|---|---|---|---|---|---|---|---|---|---|
||**Control **|**Treated**||**Control **|**Treated**||**Control **|**Treated**||
|**Participation rate of program SHGs**||||||||||
|Overall participation rate|0.0%|10.5%|***|29.5%|42.2%|***|33.4%|45.6%|***|
|% participated of very poor households|0.0%|12.6%|***|29.4%|49.8%|***|36.9%|54.1%|***|
|% participated of poor households|0.0%|11.7%|***|38.2%|49.0%|***|41.8%|51.5%|**|
|% participated of middle or rich class|0.0%|8.7%|***|24.8%|34.2%|**|27.1%|37.7%|**|
|**SHG functioning**||||||||||
|Meet at least monthly|0.44|0.48||0.46|0.71|***|0.79|0.75|*|
|Members make savings in meetings|0.97|0.88|***|1.00|1.00|*|0.92|0.88|**|
|**Other activities**||||||||||
|Activities to reduce vulnerability|0.13|0.09||0.24|0.49|***|0.46|0.71|***|
|If access to food credit program|0.00|0.00||0.05|0.40|***|0.24|0.54|***|
|<br>Marketing activities undertaken|0.03|0.04||0.05|0.10|***|0.14|0.22|***|
|<br>Employment program /job training|--|--|--|--|--|--|0.17|0.22|**|
|**Micro-credit activities**||||||||||
|_Practice internal lending_|0.30|0.28||0.38|0.55|***|0.90|0.86||
|if yes, median internal loan size|7050|4500|--|6200|5926|--|10000|10900|--|
|if yes, median internal loan length|--|--|--|12|10|--|12|12|--|
|share for consumption smoothing|0.49|0.45||0.53|0.48||0.44|0.47||
|share for investment|0.34|0.47|**|0.40|0.44||0.47|0.45||
|_Have access to CIF_|0.00|0.04|***|0.00|0.25|***|0.55|0.62|**|
|if yes, median size|--|38143|--|--|34000|--|21000|40500|--|
|if yes, median length (months)|--|--|--|--|20|--|12|20|--|
|consumption smoothing main purpose|--|0.03|--|--|0.07|--|0.07|0.05||
|investment main purpose|--|1.00|--|--|0.95|--|0.90|0.90||
|<br>_Access bank loans_|0.55|0.41|***|0.37|0.45|**|0.87|0.88||
|if yes, median size|15000|15000|--|30000|24000|--|51000|50000|--|
|if yes, median length (months)|--|--|--|12|12|--|20|20|--|
|<br>consumption smoothing main purpose|0.49|0.37|**|0.35|0.39||0.32|0.07|***|
|<br>investment main purpose|0.67|0.77|*|0.73|0.81|*|0.64|0.88|***|
|_Group’s account balance_|0.01|0.01||0.09|0.22|***||||
|Number of observations(SHGs)|179|691||294|1092||278|1085||

_Source:_ Own computation from 2004 and 2006 SERP/CESS impact evaluation surveys.

19
# Migration, families, and counterfactual families<sup>∗</sup>

Simone Bertoli<sup>a</sup> , David McKenzie<sup>b</sup> , and Elie Murard<sup>c</sup>

> a _Universit ́e Clermont Auvergne, CNRS, IRD, CERDI, IZA_

> b _Development Research Group, World Bank_

> c _University of Trento, IZA, NOVA SBE_

**Keywords:** Migration; family formation; counterfactual reasoning; status quo bias; remittances. **JEL classification codes** : J12, J15, J61, O15.

> ∗We thank our discussants Yajna Govind and Sulin Sardoschau, and participants at the 2 _nd_ International Workshop on Migration and Family Economics and 16<sup>_th_</sup> AFD-World Bank International Conference on Migration and Development; Simone Bertoli gratefully acknowledges the support received from the _Agence Nationale de la Recherche_ of the French government through the program “ _Investissements d’avenir_ ” (ANR10-LABX-14-01); the usual disclaimers apply.
Not At All Familiar 1 2 3 4 5 6 7 8 9 10 Very Familiar Score:_________ B2. How familiar are you with the literature on Choice Experiment (CE) surveys?

Not At All Familiar 1 2 3 4 5 6 7 8 9 10 Very Familiar Score:_________ B3. Approximately how many CV surveys, if any, have you worked on (alone or in cooperation with others)?

_________ (write zero if you have not done any CV surveys) B4. Approximately how many CE surveys, if any, have you worked on (alone or in cooperation with others)?

_________ (write zero if you have not done any CE surveys) B5. Approximately how many SP (CV and/or CE) surveys of biodiversity and ecosystem services, if any, have you worked on (alone or in co-operation with others)?

_________ (write zero if you have not done any SP surveys of biodiversity and ecosystem services) B6. Approximately how many empirical studies using valuation methods other than SP ( _e.g._ , hedonic pricing, travel cost analysis) have you worked on (alone or in cooperation with others)?

________studies (write zero if you have not done any empirical studies using other methods than SP)) B7. Approximately how many benefit transfer exercises have you conducted (i.e. transferring estimates from primary environmental valuation studies to a new geographical site to perform e.g. cost-benefit analysis (CBA)).

________studies (write zero if you have not done any benefit transfer exercises for use in e.g. CBA)) B8. Approximately, what fraction of your research would you say was on contingent valuation, choice experiments, other valuation methods, and what fraction on other topics? (Sum to 100%) _______% Contingent valuation _______ % Choice Experiments _______% Other valuation methods 44
II

## REGRESSION #1 - THE LISTING DECISION

|LISTED|I|Coef.|Std. Err.|t|P>Iti|
|---|---|---|---|---|---|
|MAMMAL|1<br>|<br>1.11<br>|.42|2.679|0.008|
|BIRD|1<br>|<br>1.21<br>|.38|3.224|0.001|
|REPTILE|1|<br>.92|.44|2.102|0.036|
|AMPHIBIAN||-1.51<br>|.45|-3.339|0.001|
|NCRANK|1|<br>-1.47|.16|-9.238|0.000|
|LNLENGTH||.25|.14|1.713|0.087|
|MONOTYPIC|j|.84|.39|2.177|0.030|
|CONSTANT|<br>j|1.07|.42|2.550|0.011|

Dependent variable is LISTED. Method of estimation is logit. 511 observations.

# These results indicate that many forces play a role in the listing process.

1) The coefficient on LNLENGTH is positive and significant at the 10 percent level; other thffings equal, a 1 percent increase in physical length translates into approximately a .05 percentage point increase in the likelihood of listing.<sup>1</sup> ' 2) Similar translations yield statistically significant estimates for mammals, birds, reptiles and amphibians (relative to fish) of 20, 22, 17 and -27 percentage points, respectively. All of these results are significant at the five percent level?

"As a first approximation, logit coefficients can be transated into probability terms by multiplying by p(1p), where p is the mean of the dependent variable. In this case, p = .24 and p(l-p) w .18.

'Readers may notice that the order of listing preference suggested by this regression places fish ahead of amphibians, while an evolutionary tree would place humans closer to amphibians than io fEh We are not swe that an evolutionary tree is the correct measwe of what constitutes a 'higher form of life", and the main reason we ran the regression with dummies rather than a single ordered -evolutionary- variable was to remain agnostic on this issue. Nevertheless, the overall pattern of the coefficient estimates is consistent with a loose evolutionary interpretation of "higher" as being "more closely rlated to humans".
# **Table 11. Use of Capital, Extended Model**

|||Dependent|variable: cap|ital stock ra|tio (ln)|||
|---|---|---|---|---|---|---|---|
||(1)<br>**1996-2013**|(2)<br>**1996-2013**|(3)<br>**1996-2013**|(4)<br>**1996-2013**|(5)<br>**1996-2013**|(6)<br>**1996-2013**|(7)<br>**1996-2013**|
|**Pillar 1**|-0.002|-0.005*|-0.001|-0.004|-0.004|-0.004|-0.006*|
||(-0.07)|(-1.31)|(-0.00)|(-1.14)|(-1.14)|(-1.10)|(-1.58)|
|**Pillar 2 and 3**<br>Infra index<sup>a</sup>|||||||0.006***|
||||||||(2.39)|
|Infra index (WEF)|0.010|-0.001|0.009|-0.001|-0.001|-0.001||
||(0.78)|(-0.04)|(0.67)|(-0.50)|(-0.50)|(-0.30)||
|**Pillar 4**||||||||
|Output gap|0.006***|0.002***|0.005***|0.002***|0.002***|0.002***|0.002***|
||(5.52)|(15.27)|(4.75)|(14.37)|(14.37)|(13.65)|(13.09)|
|Change in terms of trade|-0.032|0.001|-0.037|0.003|0.003|0.002|0.003|
||(-0.80)|(0.19)|(-0.92)|(0.47)|(0.47)|(0.44)|(0.59)|
|**Pillar 5 and 6**|0.103*<br>(1.42)|-0.020***<br>(-4.29)|0.165***<br>(2.31)|-0.020***<br>(-4.32)|-0.020***<br>(-4.32)|-0.020***<br>(-4.26)|-0.024***<br>(-4.81)|
|**Pillar 7**||||||||
|Tax rate|-0.015|-0.001|-0.034|0.002|0.002|0.002|0.004|
||(-0.22)|(-0.02)|(-0.48)|(0.24)|(0.24)|(0.31)|(0.50)|
|Regulatory quality|<br>0.035|<br>0.007***|<br>0.055**|<br>0.005*|<br>0.005*|<br>0.006*|<br>0.006**|
||(1.13)|(2.12)|(1.76)|(1.50)|(1.50)|(1.64)|(1.79)|
|Regulatory trade barriers<sup>b</sup>|0.013**|0.002**|0.015**|0.002**|0.002**|0.002**|0.001*|
||(1.77)|(1.71)|(1.93)|(1.93)|(1.93)|(1.76)|(1.34)|
|**Pillar 8**||||||||
|Women|0.019|-0.007***|0.008|-0.007***|-0.007***|-0.007***|-0.007***|
||(0.54)|(-3.84)|(0.22)|(-3.85)|(-3.85)|(-3.99)|(-4.19)|
|CBR|0.100|0.005|0.100|0.005|0.005|0.004|0.002|
||(0.54)|(0.45)|(0.55)|(0.43)|(0.43)|(0.37)|(0.18)|
|**Pillar 9**||||||||
|Private credit|0.082***|0.014***||0.013***|0.013***|0.025***|0.024***|
||(3.52)|(4.80)||(4.47)|(4.47)|(4.85)|(4.87)|
|Private credit (t-1)|||0.001|0.002***|0.002***|0.002***|0.002***|
||||(0.26)|(2.46)|(2.46)|(2.33)|(2.48)|
|Credit market regulations<sup>c</sup>||||||-0.015***|-0.015***|
|||||||(-2.81)|(-2.89)|
|**Pillar 12**|0.027***|-0.001|0.031***|-0.001|-0.001|-0.001|-0.001|
||(2.23)|(-0.55)|(2.51)|(-0.26)|(-0.26)|(-0.19)|(-0.73)|
|Emerging market dummy|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time effect|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Capital stock ratio (ln, t-1)||0.998***||0.999***|0.999***|0.999***|0.998***|
|||(796.27)||(784.80)|(784.80)|(786.06)|(785.12)|
|Constant|Yes|<br>Yes|Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Obs|505|505|516|505|505|503|503|
|Wald Test|1195.6|877051.3|1148.9|870619.9|870619.9|873441.5|847098.7|
|Wald Test p-value|0|0|0|0|0|0|0|

_Note t_ statistics in parentheses * p<0.2, ** p<0.1, *** p<0.05

> a Weighted average of energy use pc (ln) and number of individuals using internet (ln), with equal weights.

> b Fraser 2016

> c Fraser 2016

34
# **Figure 1: Headcount indices of poverty for a common international poverty line**

# 1(a) Survey-based

<!-- Start of picture text --> Percentage of population living below $1.25 a day at 2005 PPP<br>100<br>China<br>80<br>India<br>60<br>40<br>Brazil<br>20<br>0<br>1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->

# 1(b) Mixed method

<!-- Start of picture text --> Percentage of population living below $1.25 a day at 2005 PPP<br>100<br>80<br>China<br>60<br>India<br>40<br>Brazil<br>20<br>0<br>1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> <u>Source: Chen and Ravallion (2009).</u> 30
among the 2,213 observations appearing in one or more tests reported below is 33%. (Appendix Table A1 provides summary statistics for all variables in the analysis.) The PDMS data constitute an unbalanced three-dimensional panel: surveys were conducted in 2006, 2008 and 2011, and designed to obtain information on aid flows for the previous calendar year (2005, 2007 and 2010 respectively) for each donor-recipient pair. Each successive survey round improved the quality and comprehensiveness of data. Guidance on definitions was strengthened and comparability of data reported by donors and by recipients was enhanced in later rounds (OECD, 2011; OECD 2008). The number of recipient countries choosing to participate in the survey increased from 34 in 2006, to 54 in 2008, and 78 in 2011. These 78 recipients account for about three-fourths of total aid worldwide (OECD, 2011).

Coverage of aid within these recipients is comprehensive over donors. Most of the “emerging” bilateral donors and a few of the smaller multilateral donors and vertical funds are dropped from the analysis, due to missing data on other donor variables from the DAC database or public opinion surveys. Dropped donors collectively account for a total of only 1.4% of aid reported in the survey.<sup>4</sup> The 34 donors remaining include the 22 traditional DAC donors, Korea (a new DAC member in 2011), Turkey, 7 multilateral organizations, and 3 vertical funds.<sup>5</sup> The panel is unbalanced not only with respect to time, but also with respect to donorrecipient pairs: not all donors provide aid to all recipients in each survey year. These features of the data create potential selection problems, considered in more detail below.

> 4 Their share of all aid worldwide is undoubtedly greater, although much of what is popularly termed “aid” from China and some other new donors would not meet the DAC‟s definition of official development assistance (ODA). 5 Bilateral donors are listed in Appendix Table A2.  Multilaterals in the sample include the African, Asian and InterAmerican Development Banks, the World Bank, IMF, EU and UN.  Vertical funds include the GAVI Alliance, IFAD, and the Global Fund to Fight AIDS, Tuberculosis, and Malaria.  The sample includes all donors with sufficient data for listing in the appendix of the OECD (2011) survey report, and all donors covered by Birdsall and Kharas (2010) in their rankings of donor performance.  (The OECD tables exclude Greece, while Birdsall and Kharas exclude Turkey, the IMF and the GAVI Alliance.)

8