rafmacalaba/datause-encoder-data huggingface dataset:
The motivation for focusing on trade facilitation measures comes from existing studies that show that with the decline of tariff and non-tariff barriers around the globe, it is the trade facilitation measures that are becoming increasingly more important for the overall expansion of international trade (see, for example, Wilson et al. 2003). There is no standard definition of trade facilitation in public policy discourse. In a narrow sense, trade facilitation efforts simply address the logistics of moving goods through ports and the documentation associated with cross-border trade. Some of the factors included under trade facilitation are Internet availability (Freund and Weinhold 2002), time to clear shipments at ports (Djankov et al. 2010) and standards harmonization and automated customs procedures (Herter et al. 2001). The present paper focuses on a regulatory aspect of trade facilitation, the number of documents required to export and import as measured by the World Bank Doing Business project. There is a lot of variation in the measure across countries. For example, as of May 2010, while two documents are required to export a container from France, it requires 11 documents to do the same in Namibia. Is some of this cross-country variation in the number of required documents due to differences in country size? The present paper attempts to answer this question.

Much like the studies mentioned above, our focus is on a single element of trade facilitation. We would like to caution that trade facilitation is a multi-faceted phenomenon and there is no guarantee that the results presented in this paper for the number of documents required to export and import would necessarily extend to other aspects of trade facilitation. A careful analysis of the various trade facilitation measures one by one may be required to understand the determinants and effects of these different measures.

The motivation for exploring the possibility of non-linearity in the relationship between country size and trade facilitation is largely empirical in nature. That is, there is no strong theoretical reason to expect the stated relationship to be non-linear or linear. Without a formal theoretical underpinning, it is difficult for us to establish with any reasonable degree of confidence exactly how and why the non3
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treatment.<sup>11</sup> One potential shortcoming of any survey data is that it is self-reported. We therefore complement the survey data with an observed measure of job search. Specifically, study participants in Experiment 2 receive a notification about a vacancy and are asked to submit their full application via email in case they are interested. This message was sent from a third party email address not associated with the research project in order to mitigate concerns about surveyor demand effects.<sup>12</sup> This allows us to test whether participants apply and whether they submit the reference letter as part of their application.

# **3.2.3 Experiment 3: Barriers to Obtaining Letters**

Results discussed in more detail below suggest that reference letters substantially increase the probability of receiving an employer response. This raises the question of why only about 2% of job seekers in the control group use reference letters in their job search. Experiment 3 tests different barriers to obtaining reference letters.

During follow-up surveys, a significant share of participants could not provide us with a reason why they have not tried to obtain the letter or cited reasons like having “no time” or that they do not need it. This may be a sign that job seekers do not believe they benefit from these reference letters or are in other ways insufficiently incentivized to obtain them. We design two interventions to test potential explanations for low take-up: (i) provide job seekers with information on the effectiveness of letters and (ii) compensate participants with 100 Rand (about half a daily wage) in cell phone airtime if they obtain a letter.

A group of 438 job seekers, previously encouraged to obtain a letter,<sup>13</sup> receives a follow-up text message to their cell phone and (if provided) email address reminding them of how

> 11Table A.4 shows that attrition rate increases from about 6% in wave 1 to 17% in wave 2, likely due to survey fatigue and participants switching phone numbers. Attrition is clearly not random: younger and less educated participants are more likely to attrite, but importantly rates do not differ between treatment and control group.

> 12Participants were informed about a vacancy in a specific sector. Among those with work experience in multiple sectors, we randomly chose which sector we notify them of. For job seekers for whom we do not have information on previous sectors, we send a general notification about a vacancy. Sectoral shares were balanced by treatment status. Applications were submitted to actual vacancies after the end of the last survey wave so that it would not confound employment estimates.

> 13About half of these participants were drawn from the sample in Experiment 1. The other half were participants that were drawn from the ESSA database solely for this experiment. Importantly, there is no overlap with the Experiment 2 sample.

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sex ratio, the predictions are generally based on fewer births. In other words, it appears that the probability of having a boy is exactly the same in India as it is in other places.

For the group most likely to use sex selection, highly educated, urban women in the 1995–2006 period, the predicted percentage boys is also almost perfectly aligned with the expected percentage boys (Figure 6c). Hence, there is no evidence that Hindus in India use sex selection on first births. This cast serious doubts on the data used by Jha et al. (2006) and their results.

For all education groups and for all periods more than ninety percent of women will have had their first child within 21 quarters of being married and the proportion is increasing in education. In addition, 70 to 85 percent of women will have had their first child within 2.5 years (ten quarters) of their marriage and the space between marriage and first birth has become shorter over time. The most likely explanation for the reductions in spell lengths and the increase in the number of women who have their first child before 21 quarters is improvements in health status. This is consistent with the differences between education groups where more educated women are also more healthy and therefore more likely to conceive. There are two implications of this. First, it reinforces the need for estimating the models separately for different education levels. Secondly, it confirms that the length of the first spell can serve as a suitable proxy for fecundity and that Hindu women in India have their first birth very soon after marriage, even among highly educated, urban women.

# **7.2 High Education Group**

If lower fertility is the driving force behind sex selection, women with the most education should be the earliest adopters and use it for lower parity children than those with less education. The predicted percentage boys for the second, third and fourth spells for women with eight or more years of education show that this is, indeed, the case. As for the first spell, the results are presented using a representative woman with the same characteristics as above, except that the ages at the beginning of the spell are 22, 24 and 25 years for the second, third and fourth spell and that the first spell length is set equal to 16 months.

Figures 7 and 8 show the second spell predicted percentage boys and the survival curves by 29
We complement the data from the BRMS with nationally representative household and firm surveys. Our modeling of both emigration and return choices accounts for selection into the sample of return migrants. The Bangladeshi Household Income and Expenditure Survey (HIES), which provides information on both non-migrants and current migrants, allows us to model the initial emigration decision. We make labor demand an explicit part of our model to account for the fact that emigration is restricted by the requirement to obtain a work visa. We document and utilize the fact that the volume of Bangladeshi emigration strongly depends on oil prices.<sup>4</sup> All major destination countries for Bangladeshi migrants are oil exporters, and we show that migrant numbers track fuel revenues very closely. Subject to the probability of obtaining a work visa and to not being dismissed in the destination country, migrant supply depends on individuals’ unobserved location preferences, which govern emigration and migration duration choices. We identify these preference parameters by fitting our structural model to data on both emigration rates and the time migrants spend abroad.

Our original data and analysis provide novel and direct evidence on the linkages between self-employment and migration. Comparing (within individual) activities before and after migration, we document a strong shift into self-employment upon return. Our survey directly asks how self-employment has been financed. Around half of the return migrants in our sample report that savings from abroad were their main source of financing. Migration-related expenses in Bangladesh, while typically lower than the capital needed for self-employment, are nevertheless substantial. We find that while low-skilled workers can often borrow to pay for migration expenses, they can rarely borrow to finance self-employment activities. One explanation is that migrant workers hold a formal job contract that makes loan repayment more secure and shirking more difficult compared to a loan repayment that depends on entrepreneurial profits. The credit market can be thought of as segmented, with a clear distinction between migration loans and entrepreneurship loans, with higher or even prohibitive interest rates for the loans supporting entrepreneurship. We document a causal, positive effect of earnings accumulated abroad and duration of migration on the probability of self-employment after return.

Why is self-employment, then, so attractive for low-skilled workers? Even though it has been argued that self-employment is a last resort for workers in some developing-country contexts, evidence from Bangladesh indicates that the vast majority of the self-employed have voluntarily chosen this activity (Gutierrez, Kumar, Mahmud, Munshi, and Nataraj 2019).

> 4The positive association between oil prices and emigration to the six member countries of the Cooperation Council of the Arab States of the Gulf (also known as the Gulf Cooperation Council, GCC) has also been documented by Wahba (2015) for Egyptian migrants.

4
**Table 1: Early-phase labor market impacts**

||JOB<br>April-Ju|LOSS<br>ly 2020|RE-EMPL<br>July-Oc|OYMENT<br>t 2020|CHANGE<br>July-O|INDUSTRY<br>ct 2020|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Respondent is female|0.0821***|0.0995***|-0.197***|-0.144***|-0.123**|-0.104**|
||(0.0239)|(0.0294)|(0.0533)|(0.0516)|(0.0567)|(0.0517)|
|Age|-0.000866**|-0.00134***|-0.00272***|-0.00146|-0.00239*|-0.00165|
||(0.000438)|(0.000490)|(0.000866)|(0.000935)|(0.00127)|(0.00116)|
|Primary education|-0.00513|-0.0224|-0.0278|-0.0500**|-0.0734*|-0.130***|
||(0.0155)|(0.0185)|(0.0213)|(0.0226)|(0.0429)|(0.0382)|
|Secondary education|0.0362|0.0128|-0.0236|-0.0296|0.00901|-0.114***|
||(0.0225)|(0.0259)|(0.0224)|(0.0233)|(0.0477)|(0.0405)|
|Tertiary education|0.0620**|0.0407|-0.0216|-0.0310|0.144***|-0.0330|
||(0.0256)|(0.0317)|(0.0262)|(0.0245)|(0.0448)|(0.0424)|
|Urban|0.0887***|0.0655***|-0.0697***|-0.0529***|0.0785**|-0.00334|
||(0.0163)|(0.0184)|(0.0158)|(0.0180)|(0.0366)|(0.0380)|
|Children 0-4 present|-0.00387|-0.0122|0.0268*|0.0332*|0.0137|0.0212|
||(0.0148)|(0.0178)|(0.0159)|(0.0182)|(0.0329)|(0.0305)|
|Female*Children 0-4|0.0218|0.00113|-0.0224|-0.0270|-0.0495|-0.0687|
||(0.0280)|(0.0325)|(0.0414)|(0.0420)|(0.0607)|(0.0583)|
|Children 5-18 present|0.0314*|0.0444*|-0.0176|-0.0219|-0.00253|0.0138|
||(0.0173)|(0.0227)|(0.0202)|(0.0229)|(0.0391)|(0.0363)|
|Female*Children 5-18|-0.0492|-0.0686*|0.128***|0.102**|0.0287|0.0221|
||(0.0334)|(0.0385)|(0.0477)|(0.0501)|(0.0599)|(0.0559)|
|Income diversification|-0.0451***|-0.0434***|0.0226|0.00501|0.0717**|0.0471*|
||(0.0162)|(0.0165)|(0.0169)|(0.0167)|(0.0300)|(0.0276)|
|Manufacturing & Utilities||0.0905***||0.00411||0.595***|
|||(0.0318)||(0.0391)||(0.0492)|
|Construction, Transport &|||||||
|Professional services||0.0480*||0.00594||0.372***|
|||(0.0277)||(0.0266)||(0.0558)|
|Commerce||0.0307||0.0163||0.238***|
|||(0.0193)||(0.0276)||(0.0526)|
|Public Administration||0.0818*||0.0368||0.363***|
|||(0.0480)||(0.0382)||(0.0729)|
|Other services||0.0575***||0.00441||0.429***|
|||(0.0201)||(0.0262)||(0.0449)|
|Constant|0.574***|0.571***|1.120***|1.072***|0.578***|0.390***|
||(0.0300)|(0.0346)|(0.0548)|(0.0603)|(0.0905)|(0.0846)|
|Observations|5,193|3,860|1,755|1,299|1,158|1,158|
|R-squared|0.132|0.150|0.114|0.116|0.107|0.234|
|Sample mean dep. Var.|||||||

Note: OLS estimation, standard errors clustered at EA level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All estimations include country fixed effects. Job loss is captured for the period between April to July 2020 (R1-3 for Ethiopia and Nigeria, R1-2 for Malawi, and R1 for Uganda). It takes the value of one if the respondent reported that they did not work at the time of the survey but was working pre-pandemic. For re-employment and changes in industries we analyze data up to October 2020 (R1-6 for Ethiopia and Nigeria, R1-4 for Malawi, R1-3 for Uganda).

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Pakistan had made little progress until recently on **_security of supply_** due to an absolute shortage of generation capacity, made worse by the failure to maintain and operate the nationally-owned generation, transmission and distribution capacity in the optimal fashion. Although IPPs have played an important role in adding new generation plant, their efforts have fallen short, since the government has not yet been able to make the sector sufficiently attractive to investors. Further, the government has no integrated energy plan that would identify the best energy mix. Renewables, although apparently encouraged by the regulatory system, have in practice found it difficult to enter the sector because of planning delays, and the recent unexpected fall in renewable energy prices has acted as a further brake on entry.

On **_access,_** the picture is clouded by uncertainty arising from contradictions between alternative data sources. There has been no obvious electrification plan designed to increase access during this period, nor were the distribution utilities in a strong enough position to undertake this on a large scale without government support. The evidence appears to support the view that little was done to improve access, and a current value of 70 percent grid access based on company connections data and a recent census is more plausible than the 98 percent rate based on household surveys. Accepting this view leads to the conclusion that there is a large amount still to do in connecting remote or poorly located households.

**_Affordability_** is strongly influenced by the subsidies provided to the distribution utilities to bridge the gap in revenue they incur by charging subsidized tariffs. There has been a clear trend of setting the rate of increase of tariffs for the lowest consumption bands well below that on the higher use bands, thus providing a relatively larger support to the poorest users. Calculations for the 2008 and 2011 tariffs indicated that the tariff structure was becoming more progressive. At the earlier date all households were receiving some subsidy on all the units they consumed, while by 2011 households consuming more than 300 kWh/month paid more than the cost of supply for some units, thus creating an element of cross subsidy. In 2008, the richest 20 percent of households received 38 percent of total subsidies and the poorest 20 percent received 9 percent, while by 2011 the share of the richest had fallen to 30 percent. By 2016 the poorest 40 percent of households would have had to spend 4.2 percent of their income to purchase the average quantity consumed in the country.

On **_efficiency and financial viability,_** the performance of many of the distribution utilities with high T&D losses and low collection rates has been weak and shows no signs of improvement, despite the use of efficiency targets set by the regulator. Notably, the one private sector company (Karachi Electric) was able to improve on its extremely poor performance pre-privatization, but at present performs no better than the average of the publicly owned utilities. The financial 59
**Table 9:  Robustness to endogeneity of quality of infrastructure, 2SLS IV regressions**

||Tot<br>|al Trade<br>|Inter<br>|mediate<br>|Con<br>|sumption<br>|
|---|---|---|---|---|---|---|
||Firststage|Second Stage|Firststage|Second Stage|Firststage|Second Stage|
|txT (infrastructure)||0.157***<br>(4.652)||0.201***<br>(4.095)||0.02<br>0.292|
|qxQ (institutions)|0.054***|0.173***|0.069***|0.169***|0.040**|0.211***|
||(6.411)|(5.774)|(5.741)|(3.901)|(2.512)|3.545|
|kxK (capital)|0.041|-0.108|0.074|-0.039|-0.078|-0.606**|
||(0.993)|(-0.747)|(1.223)|(-0.184)|(-1.037)|-2.16|
|hxH (human capital)|0.046***|0|0.039***|0.02|0.022|-0.005|
||(5.518)|(0.014)|(2.966)|(0.419)|(1.352)|-0.093|
|txIV (average<br>neighboring countries T)|1.197***<br>(103.02)||1.183***<br>(71.76)||1.226***<br>(55.16)||
|R-squared|0.983|0.772|0.985|0.77|0.986|0.762|
|Number of observations|3249|3249|1603|1603|882|882|
|Number of countries||13||13||13|
|Number of industries||342||246||137|
|Widstat||10612.605||5148.938||3042.99|

Notes: The dependent variable is the natural log of exports in industry j by country i to the World. txT is instrumented using txIV where the instrument is the average infrastructure of neighboring countries. Reported are the beta standardized coefficients of the second stage IV regression with t-values shown in brackets. * Significant at 10%; ** at 5%; *** at 1%.

As far as potential endogeneity problems of the quality of institutions are concerned, we also estimate equation (1) using an instrumental variable approach. As an instrument for the quality of institution of a country we use the average quality of institution across all countries with its same legal origin. In particular, we distinguish between British, French, German, Social or Scandinavian legal origin. It makes intuitive sense to assume that the different legal origins of countries are reflected in today’s quality of institutions of countries, but that legal origins themselves are not affected by comparative advantage in 2000.

Table 10 reports the results of these estimations. As shown, our instrument is significant and positive in the first stage regressions, thus supporting the view that legal origins do affect a country's present quality of institutions, and the coefficient for institutions as a source of comparative advanatage is positive and significant in the second stage regressions, thus showing that there is causal effect that runs from the quality of institutions to a country's specialization patterns. Furthermore, the comparison between the beta coefficients of the regressions for intermediate and consumption goods continue to support the view that while infrastructure are more important as a source of comparative advanatage in intermediate goods trade, institutional quality is not.

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improvement and greater social inclusion in education. Paqueo, Lopez-Acevedo and Parandekar (2003) proposed three formulas based on per student allocation,<sup>14</sup> and she found that any of these three approaches would improve not only efficiency but also equity.

For the health sector one should not analyze the allocation of the _Fondo de Aportaciones para los Servicios de Salud (FASSA)_ in an isolated way, but rather one should also consider the resources of the health component of Oportunidades _, Instituto Nacional Indigenista_ (INI), _Programa de Ampliación de Cobertura (PAC)_ , and the IMSS-Oportunidades. Figure 7 shows the allocation of these resources. With the exception of Campeche, it seems that richer states get more resources for their uninsured population than poor states. This trend, however, is driven by the shares of uninsured population in the states, where the richer states have the lowest shares and the poorest states the highest.

_Figure 7: Federal Health Resources per uninsured person-2002 Distribution_ _(states are arranged with the highest poverty rates on the left and lowest on the right, according to the CONAPO index of marginality)._ <!-- Start of picture text --> 2,500<br>2,000<br>1,500 IMSS-Oportunidades<br>Oportunidades<br>INI<br>FASSA<br>PAC<br>1,000<br>500<br>0<br>MxP per uninsured person<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source:_ SHCP and Bank staff calculations.

> 14  The three suggested formulas are: 1) uniform per student allocation, 2) productivity adjusted allocation, and 3) a multi-component formula based on the number of students and a supplementary component allocated according to the number of children in the state with special needs (rural, indigenous, or poor).

15