rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Data in This Study**

# **3.1  Rationale for These Indicators of Trade Facilitation**

The first essential task in the quantitative analysis of trade facilitation is to develop measures of trade facilitation. WMO present four distinct areas of focus that meet policymakers’ needs for specificity on how to approach trade facilitation efforts. They are: (1) port efficiency, (2) customs environment, (3) own regulatory environment, and (4) service sector infrastructure.

Port efficiency is designed to measure the quality of infrastructure of maritime and air ports. Customs environment is designed to measure direct customs costs as well as administrative transparency of customs and border crossings. Regulatory environment is designed to measure the economy’s approach to regulations. Service sector infrastructure<sup>4</sup> is designed to measure the extent to which an economy has the necessary domestic infrastructure (such as telecommunications, financial intermediaries, and logistics firms) and is using networked information to improve efficiency and to transform activities to enhance economic activity.<sup>5</sup> Besides the observation that these categories match areas for policy-maker attention, these trade facilitation measures also match several GATT articles and appear in the list of Singapore issues in the Doha Development Agenda, and therefore have salience for WTO negotiations. The _port efficiency_ measure has been constructed in accordance to GATT article V (freedom of transit). This article says that freedom of movement is to be assured for goods, which should be allowed to move via the most convenient route, should be exempt from customs or transit duties, and should be free from unnecessary delays or restrictions. _Customs environment_ here consists of components that have their basis in the GATT article VIII. GATT article VIII states that in order to minimize impediments to trade due to customs procedures, fees charged by customs officials must be limited to the approximate cost of customs services. Also, there should not be substantial penalties for minor breaches of customs regulations such as clerical errors. _Regulatory environment_ issues are contained in GATT article X which discusses Publication and Administration of Trade Regulations. This article comes from the basic transparency obligation that requires prompt publication of laws and regulations affecting imports and exports so that foreign governments and traders may clearly understand them.

# **3.2  Constructing the Measures Used in This Study**

This paper builds on the WMO methodology and categories of trade facilitation. However, because this paper broadens the set of countries for analysis to 75, the crosscountry survey data on business and policy climate that are used to construct the four indicators for each country are somewhat different from the data used to construct the

> 4 WMO used a different terminology- e-business usage- for this category.

> 5 For further discussion of the relationship between domestic infrastructure and e-commerce, see Mann, Eckert, and Knight (2000).

6
6 _Insdtwo Nacloial de Estdsdca._ The survey covers about 11,000 households in all parts of the country. Our analysis is limited to 2,974 enrolled primary school children between the ages of 10 and 14.

The key dependent vaiablc aong the two groups of primary school children is REPEATER, or movrae', or age/grade distorton." A repeater is a student whose age is above the normal for fte grade he/she just completed or is currently attending. Using a variable called _schoolng-for-age (SAGE)_ (see Pcharopouos and Yang 1991), we assess the progress of a child in the school system and esimate rpetition, or 'ovenge'. Using the formula: SAGE( Yea of Schoolinz Age - E where E represents the usual school entry age in the country, age six in Bolivia, seven in Guatemala. We consider all those with a score under 100 as being below normal progress in the school system because of grade repition or lte entry.

However, as ths measure of repeition may be captuing late entrants, it can be described more as a measure of "age-gade distortion,' or *overage.' ScFtefelbein (1992: 31) noted that 60 pent of children in Bolivia enter school on time, that is, at age six; first grade repetition is 38.4 percent. **The** sample we are using, however, is urban only; therefore, !ate entrants may be fewer. Moreover, our method does not overmate repetition - it underestimates it. According to our modd, only 16.8 percent of Bolivian school children are estimated to be first ,rade repeaters.

**The** Guatemalan **educaton** sysem, however, is caczed by high rates of failure at all grade levels, accompad by repeating. While 52 percent of children enroll on time, more than 70 percent of rural stutens are older than the expected age for their grade, compared to S9 perct in urban areas. Sill, only 72 percent of Guatemalans ever enroll; and the rate of repetition is 47 percent (Schiefelbein 1992), although higher in rural areas. Our method gives a much higher rate, but it should be noted that this has much to do with the fact that the Guatemalan sample consists of children are eady oveaged; the sample is limited to those ten years of age and older (the Guatemalan survey did not collect information on those under ten years of age). Also, our sample, however, includes students between the ages of 7 and 14 (10 and 14 in Guatmala), in al prmary school grades, so that a number of 'late entrants" will have dropped out, and will consequendy not be included in our sdy.

An umpoant charactec of the Latin American primary school, as well as a factor detemining repetition, is te pmrence of childrn of various ages in the same grade, some of whom are themselves repeaters (Schiefebin 1992: 10). Estimates of the uoveragedw in primary schools in Lain Ameca and the Caribbean range from 43 percent in Brazil, 34 percent in the ret of South Amaica, and 32 perent in Centrl Ameica (32 percent), to just 11 percent in the Anglophone Caribbean (OAS 1992). In general, age/grade distortion is much greater il. rural
**Table 2.7 Liquid-Bulk Traffic, 2008 and 2012**

|**Port**|**Ton**|**nes**|**Gro**|**wth**|
|---|---|---|---|---|
||**2008**|**2012**|**Change (%)**|**AAGR (%)**|
|St. John’s|—|—|—|—|
|Woodbridge Bay/Roseau|43,967|48,536|+10|+3|
|St. George’s|85,076|81,061|-5|-1|
|Basseterre|68,161|81,296|+19|+18|
|LongPoint|17,869|14,599|-18|-5|
|Castries|2,218|4,060|+83|+34|
|Vieux Fort|35,310|38,291|+8|+2|
|Campden Park/Kingstown|53,393|63,453|+19|+5|
|**Total**|**305,994**|**331,296**|**+8**|**+5**|

_Source:_ Authors’ own elaboration on primary data. _Note:_ AAGR = average annual growth rate; OECS = Organization of Eastern Caribbean States. — Not available.

**Liquid-bulk traffic patterns remained relatively steady in the OECS in 2008–12.** The Port of Basseterre (St. Kitts) was the only port that experienced a significant decrease in liquid-bulk imports in 2010 and a sharp increase in 2011 of more than 50 percent, from 41,000 tonnes to 90,000 tonnes (figure 2.6). Liquidbulk imports to the Port of Castries doubled between 2008 and 2012, from 2,000 to 4,000 tonnes.

**Figure 2.6 Liquid-Bulk Traffic, 2008–12**

_Source:_ Authors’ own elaboration on primary data. _Note:_ OECS = Organization of Eastern Caribbean States.

# **f. Passenger Traffic**

**Passenger traffic across the OECS declined by 6 percent in 2008–12.** The OECS ports have not yet fully developed their infrastructure to satisfy current and potential capacity needs, forcing ports to use their cargo

19
~ ~ gives us _M_ 2 < _M_ 1 , since θ 1 _p_ 1 =θ 2.( _p_ 2 + _F_ ) and _p_ 1 − _p_ 2 − _F_ > 0 , by assumption. This implies a lower premium for the consumer. The consumer would clearly be better off since he now pays the same out-of-pocket expenditure, but a lower premium if he agrees to go abroad. Further, the size of the inefficiency due to moral hazard is also reduced from the area of triangle _BCD_ to that of _GEB_ , if the consumer goes abroad. Thus, if the moral hazard problem cannot be independently controlled, the lower premium serves as an instrument to give the consumer an incentive to travel abroad, while the higher coinsurance rate reduces the efficiency loss from moral hazard when the consumer obtains cheaper treatment abroad.

# _Summing up_

If the cost of health care is lower overseas, then the insurer can design an insurance contract that is unambiguously welfare-enhancing. There are two health plan design possibilities depending on whether or not ex-post moral hazard can be independently controlled for.

# _a)  Possible to independently control for moral hazard_

If it is possible to independently control for moral hazard leading to over-consumption abroad e.g., through utilization reviews, then it is sufficient for the insurer to offer consumers identical terms of coverage for health care obtained locally and abroad, provided that the foreign price is defined to include also the travel and other costs of obtaining treatment abroad.

Consider a numerical example. As we noted above, a knee surgery costs $10,000 in the US, but only $1,500 (inclusive of travel costs) in Hungary. Let us say that insurers establish that the standard psychological cost of going abroad for treatment is $1,000. Under a uniform 20 percent coinsurance rate (assuming a zero deductible for simplicity), a consumer would pay $2,000 out-of-pocket if they underwent treatment in the US and the insurance company would pay the US provider $8,000. On the other hand, if the consumer chose to go abroad, he would be reimbursed 80 percent of the foreign price inclusive of travel and psychological costs ($2,500). The consumer would end up making a net monetary gain of $500 (instead of an expenditure of $2,000 in the US) and the insurance company would pay $2,000 for the treatment (instead of $8,000 in the US).<sup>12</sup>

# _b) Not possible to independently control for ex-post moral hazard_

On the other hand, if it is not possible to independently control for moral hazard leading to over-consumption abroad, then the insurer can offer the consumer a choice between

> 12 Interestingly, there are insurance laws in some countries, e.g., Belgium, that prohibit making the insured richer than they were before purchasing insurance (source: Vanbreda).

10
- Dang, T.H., Nguyen, T.A., Van, M.H., Santin, O., Tran, O.M.T., Schofield, P. (2021). Patient-Centered Care: Transforming the Health Care System in Vietnam With Support of Digital Health Technology. _Journal of Medical Internet Research_ , 23(6): e24601.

- Deaton, A. (1988). Quality, Quantity, and Spatial Variation of Price, _American Economic Review_ , 78: 418-430.

- Deaton, A. (1990). Price elasticities from survey data: extensions and indonesian results. _Journal of Econometrics_ , 44: 281-309.

- Deaton, A. and J. Muehllbaur (1980). An Almost Ideal Demand System, _American Economic Review_ , 70: 312-326.

- Filippini M, Pachauri S (2004) Elasticities of electricity demand in urban Indian households. _Energy Policy_ , 32: 429–436.

- Gibson, J. and Kim, B. (2019). Quality, quantity, and spatial variation of price: Back to the bog, Journal of Development Economics, 137: 66–77.

- Gibson, J., Kim, B. (2013a). Quality, quantity, and nutritional impacts of rice price changes in Vietnam, _World Development_ , 43(1): 329–340.

- Gibson, J., Kim, B., (2013b). Do the urban poor face higher food prices? Evidence from Vietnam. _Food Policy_ , 41: 193–203.

- Gibson, J., Romeo, A. (2017). Fiscal-food policies are likely misinformed by biased price elasticities from household surveys: evidence from Melanesia. _Asia & the Pacific Policy Studies_ , 4(3): 405– 416.

Hausman, J. A. (1978). Specification tests in econometrics. _Econometrica_ 46: 1251–1271.

- Hien, P.D. (2019). Excessive electricity intensity of Vietnam: Evidence from a comparative study of Asia-Pacific countries. _Energy Policy_ , 130: 409-417.

- Hoang, H.K. (2018). Analysis of food demand in Vietnam and short-term impacts of market shocks on quantity and calorie consumption. _Agricultural Economics_ , 49: 83–95.

- Holly, A., and D. Sargan. 1982. Testing for exogeneity in a limited information framework. Cahiers de Recherches Economiques 8204, Universit ́e de Lausanne.

- Hwang, J., Cho, Y., Long, N.V. (2009). Investigation of factors affecting the diffusion of mobile telephone services: An empirical analysis for Vietnam. _Telecommunications Policy_ , 33: 534–543.

- Lazaridis, P. (2003). Household meat demand in Greece: a demand system approach using microdata. _Agribusiness_ , 19: 43–59.

- Le, C.Q. (2008). An empirical study of food demand in Vietnam. _ASEAN Economic Bulletin_ , 25(3): 283-292.

- Lecocq, S. and J.M. Robin (2015). Estimating almost-ideal demand systems with endogenous regressors, _The Stata Journal_ , 15(2): 554–573.

- Lin, Q., Rizov, M., Wong, M. (2014). Residential electricity pricing in China. _The Chinese Econonomy_ , 47(2): 41–74.

- McKelvey, C. (2011). Price, unit value and quality demanded, _Journal of Development Economics_ , 95(1): 157–169.

- Mergenthaler, M., Weinberger, K., Qaim, M. (2009). The food system transformation in developing countries: A disaggregate demand analysis for fruits and vegetables in Vietnam. _Food Policy_ , 34: 426–436.

24
# **Annex 1: Selected papers on capital structure of regulated firms**

|Paper|Approach|Conclusion|
|---|---|---|
|Besley and<br>Bolton 1990|Survey of<br>opinions|“Besley and Bolton (1990) in a survey of 27 regulatory agencies and 65<br>utilities, find that approximately 60% of the regulators and utilities<br>surveyed believe that an increase in debt relative to equity increases<br>regulated prices (Spiegel and Spulber 1994: 425)”|
|Bradley and<br>others 1984|Empirical|“Regulated firms such as telephone, electric and gas utilities, and airlines<br>are consistently among the most highly levered firms (page 870)”.|
|Dasgupta and<br>Nanda 1993|Empirical|“Regulated firms can improve their bargaining positions and induce the<br>regulator to set higher prices for firm output by choosing more debt.”<br>Harsher regulatory environments lead to higher debt–equity ratios in the<br>US.|
|Spiegel 1996|Theoretical|“Debt financing is shown to alleviate this distortion [firms’ choosing high<br>variable-cost technology to reduce regulatory opportunism] because it<br>induces regulators to increase the regulated price to prevent the firm from<br>financial distress, thereby reducing the cost to the firm of adopting<br>technologies with low marginal costs. When regulators restrict the firm’s<br>ability to issue debt, the firm may have an incentive to goldplate (i.e. waste<br>resources). This incentive disappears when the firm can use its most<br>preferred mode of financing”|
|Spiegel and<br>Spulber 1994|Theoretical|“Our main finding is that lack of regulatory commitment to rates provides<br>the firm with an incentive to issue debt because debt mitigates the<br>regulator’s incentive to act opportunistically. Thus, debt reduces the<br>regulator’s incentive to lower rates as a response to the firm’s investment in<br>cost reduction (page 425)”.|
|Taggart 1981|Theoretical|“Rate-of-return regulation may create incentives for regulated firms to alter<br>their capital structures (page 392)”.|
|Taggart 1985|Empirical|“Taggart (1985) studies state electricity and natural gas regulation in the<br>period 1912–1922, and concludes that the establishment of regulation<br>increases the utility’s debt–equity ratio. Taggart attributes this in part to the<br>reduction in the firm’s risk due to regulation, but cannot reject a “price<br>influence” effect of debt on regulatory decisions (Spiegel and Spulber 1994:<br>425).|

60
Table 1: Transportation time data validation

|Dep Var:|_log_(<br>(1)|_Xij_)<br>(2)|Days_ij_<br>log(#)<br>(3)|Days_ij_<br>#<br>(4)|Days<sup>_MAR_</sup><br>_ij_<br>log(#)<br>(5)|Days<sup>_MAR_</sup><br>_ij_<br>#<br>(6)|
|---|---|---|---|---|---|---|
|# Days_ij_|-0.143***<br>(0.012)||||||
|# Days<sup>_MAR_</sup><br>_ij_||-0.105***<br>(0.009)|||||
|log(Distance)_ij_|||1.116***<br>(0.062)|7.472***<br>(0.408)|1.203***<br>(0.066)|10.090***<br>(0.535)|
|Contiguity_ij_|2.309***|1.983***|0.403**|-0.950|0.174|-4.492***|
||(0.415)|(0.453)|(0.165)|(1.037)|(0.182)|(1.624)|
|Language Proximity_ij_|0.441***|0.360***|0.016|0.273**|0.001|-0.417**|
||(0.062)|(0.062)|(0.013)|(0.126)|(0.013)|(0.175)|
|Colonial Ties_ij_|0.718**|0.768**|-0.007|0.192|0.004|0.741|
||(0.316)|(0.323)|(0.079)|(0.608)|(0.074)|(0.689)|
|Observations|2038|2,038|2,038|2,038|2,038|2,038|
|# Origins|20|20|20|20|20|20|
|# Destinations|110|110|110|110|110|110|
|Period|2018|2018|2018|2018|2018|2018|
|Transportation|no MAR|MAR|no MAR|no MAR|MAR|MAR|
|Tarif Equivalent|2.4|1.8|||||
|Distance|||||||
|Elasticity||||0.180||0.178|

_Note:_ MAR indicates that the transportation times are estimated considering maritime preferences. Exporter( _i_ ) and Importer ( _j_ ) fixed effects are included in all regressions. Standard errors in parenthe*** ** * ses are clustered by country-pair, p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. To compute tariff equivalents we assume _σ_ = 6. log(Distance) _ij_ , Contiguity _ij_ and Colonial Ties _ij_ variables are from Mayer & Zignago (2011); while for Language Proximity _ij_ we the use “lp” variable from Gurevich, Herman, Toubal & Yotov (2021) (to be more precise we use _log_ ( _lp_ + 0 _._ 01)).

9
introduce more spatial interpolation error. We explore the implications in our discussion of the estimation results.

# _<u>DHS Variables</u>_

Many studies have investigated the determinants of mortality risk for infants in Bangladesh and other low-income countries (Amouzou and Hill 2004; Bicego and Ahmad 1996; Chowdhury 2013; Das Gupta 1990; Hobcraft, et al. 1984, 1985; Jain 1985; Majumder, et al. 1997; Martin, et al. 1983; Murphy and Wang 2001; World Bank 1993). This paper draws on their findings to identify potentiallysignificant variables that are provided by the Bangladesh DHS. They include numerous variables for children (e.g., gender); parents (e.g., age, education); and households (e.g., gender of household head, wealth status, sanitation facilities, drinking water sources, cooking fuels).

# _<u>Model Estimation</u>_

To maximize degrees of freedom, we estimate our model using a combined sample from DHS 2004 and 2007 that contains information on 39,150 children. We perform all estimations using standard and robust estimators for both probit and logit models. The results across all four estimators are effectively identical, so we report only the standard probit estimates in this paper.<sup>11</sup>

# _DHS Variable Selection_

We identify potentially-significant child, parent and household variables in the DHS data for full sample estimation for infant mortality, excluding salinity because inclusion drastically reduces degrees of freedom by restricting the sample to clusters within 40 km of salinity monitors in the coastal region. Variables included in this step are gender of child; age and education of the mother; gender of household head and household wealth; and selected dummy variables for toilet facilities (flush toilets, pit latrines), water sources (tubewells, springs and other surface water) and cooking fuels (clean fuels - electricity and

> 11 The other estimates are available from the authors on request.  We have not estimated spatial variants of these models because the appropriate estimators are not yet available in Stata.

9
Section 5 presents the main findings, while Section 6 tests robustness to allowing for a wider range of covariates. Section 7 concludes.

# **2. Insights from the literature**

Several strands of the literature have bearing on how effective household poverty data can be expected to be in revealing the presence of poor individuals. Here we summarize relevant arguments and evidence.

A body of research on the economics of the household has focused on the wealth effect on nutritional status, i.e., how much nutrition improves as a household’s economic welfare—income, consumption or wealth—rises. One strand of this literature has estimated income elasticities of demand for food and (hence) nutrition; an influential early example is Behrman and Deolalikar (1987). Rather than focus on food consumption, as in consumer demand studies (such as Pitt, 1983), other work has instead studied the income effect on nutritional adequacy, taking account of requirements for good health and normal activities in society. A low income elasticity of demand for food can be consistent with a high responsiveness of nutritional adequacy to income gains, since even small gains in nutritional intakes can make a big difference at low levels (Ravallion 1990, 1992).<sup>10</sup> New evidence on this topic has emerged from analyses of the many micro data sets (including the DHS) that have become available to researchers over the last 20 years or so. A limitation of the DHS is that the surveys have not included the questions needed to measure consumption or income. (At the same time, most surveys of the LSMS-type have not included anthropometrics.) The DHS wealth index was developed to help address this deficiency (Filmer and Pritchett 2001). Some studies have argued that the DHS wealth index is a good predictor of various human capital and other outcomes (Filmer and Pritchett 1999, 2001; Filmer and Scott 2012; Sahn and Stifel 2003; Petrou and Kupek 2010). For example, on comparing DHS wealth indices, Filmer and Scott (2012, p. 359) conclude that “...inferences about inequalities in education, health care use, fertility and child mortality, as well as labor market outcomes, are quite robust.” Similarly, Sahn and Stifel (2003, p. 463) argue that their version of the wealth index “...is a valid predictor of a crucial manifestation of poverty—child health and nutrition.” However, other studies have been less supportive and have found only seemingly modest

> 10 While it is not an issue taken up here, it is now well recognized that nutritional intakes can also be too high from the point of view of good health and normal activity levels. A strand of the literature has focused on obesity and its relationship to wealth in both rich and poor countries; for a review see Ravallion (2016, Chapter 7).

5
TABLE A21: Impact of text messages on intermediary outcomes for children of leaders versus non-leaders

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|ITT|0.08|0.19<sup>_∗∗∗_</sup>|0.04|0.07|0.06|0.12<sup>_∗∗_</sup>|0.09<sup>_∗_</sup>|
||(0.05)|(0.05)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|
|Child of Leader|-0.14|0.31<sup>_∗∗∗_</sup>|-0.04|0.21<sup>_∗_</sup>|-0.03|0.21<sup>_∗_</sup>|0.05|
||(0.11)|(0.11)|(0.10)|(0.11)|(0.10)|(0.11)|(0.11)|
|ITT X Child of Leader|-0.09|-0.16|0.15|-0.11|-0.24<sup>_∗∗_</sup>|0.06|-0.07|
||(0.12)|(0.12)|(0.11)|(0.12)|(0.11)|(0.12)|(0.12)|
|Observations|2407|2408|2419|2403|2408|2303|2413|
|ITT + ITT X Child of Leader = 0|0.88|0.82|0.06|0.71|0.09|0.12|0.91|

_Note:_ All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Five villages without leaders were excluded from the sample. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 69