rafmacalaba/datause-encoder-data huggingface dataset:
employ gravity models are not conclusive. Some studies find a decrease in the elasticity of trade to distance, though most point to little change or a modest increase (Disdier and Head 2003). Gravity equation estimates from panel data over long horizons also tend to find an increase (Brun et. al. 2003, Coe et. al. 2003). These results suggests that distance has become more important over the last 40 years.

We seek to contribute to this literature by rigorously analyzing how distance affects trade over time and across industries. While the gravity equation is a useful tool for cross-section analysis, we show that the change in the coefficient on distance over time could be due to misspecification that results from industrial differences in how distance affects trade. In particular, there are vast differences in the distance elasticity of trade across products, implying that the increasing importance of distance might be explained by a shift in the composition of trade toward products with high distance elasticity.

We use bilateral trade data for 766 SITC2 industries in 73 countries reporting trade<sup>1</sup> at the 4-digit SITC level to examine whether changes in the composition of trade or changes in the distance elasticity of trade across industries has been responsible for the increased importance of distance since 1985. We find that changes in the composition of trade are relatively unimportant. However, there has been a significant and increasing impact of distance on trade in almost 25 percent of industries. The increase in the elasticity of trade to distance among such a large share of industries suggests that there has been a shift in the relative cost of trade for these industries favoring proximate countries. We also find that homogeneous goods—as defined by Rauch (1999)—are

> 1 These countries report trade with 196 partners.

3
# **1 Introduction**

Time use survey data are an important input to policy analysis. They provide information on the allocation of time to household production of substitutes for market output, as well as on the allocation of time to leisure activities. This information is typically missing in other household survey datasets. Ostensibly, the economic analysis of policy is based on models of individual and household behavior that show how welfare depends on consumption and leisure. However, without access to information on time use outside the market, estimation of the models is inevitably limited to datasets that do not distinguish between the leisure time of a family member and the time each family member spends on household maintenance, management, and care activities for other family members.

Time use survey data indicate that, in most countries, women work longer than men when the time they spend on domestic work is added to the hours they work outside the home and in family enterprises. The data for developing countries show that girls tend to spend more time on domestic chores than boys and thus have more limited opportunities for education and leisure activities.<sup>1</sup> These gender differences in time use can be expected to have welfare and policy implications that are missing in empirical work that does not distinguish between domestic work and leisure. The data also indicate considerable heterogeneity in the allocation of time between market and domestic work across households with the same observed market opportunities and demographic characteristics, in both developed and developing countries. The welfare and policy implications of this are also lost in studies using household survey data with missing information on work at home and leisure.

The aim of this paper is to explain why time use data are essential for policy analysis in a wide range of areas and, in particular, for analyzing the following:

- (i) the intra-household allocation of resources and distribution of real income (ii) household living standards

> 1 For a survey of these studies, see World Bank (2001).

1
67 that public health programs distribute their benefits progressively across low income populations, according to **levels** of adult education.

Other studies examine interactions of health input choices themselves and environmental constraints on child mortality. Where household water and sanitation facilities **are** poor in Malaysia, breast-feeding is associated with larger reductions in infant mortality (Butz, St al., 1984). Estrey and Habicht (1987) found that **safe** water supplies **reduced** child mortality by a greater amount for more educated mothers, whereas access to toilets in the household is less effective in reducing child rortality for more educated mothers. In Costa Rica, Haines and Avery (1982) found **the** effect of a year of education for the mother is to reduce her child mortality between 6 and 7 percent, holding constant for household sanitation and dwelling quality and local levels of child mortality and health care facilities. Haines and Avery confirm that the child hea,lth gains **related** to mother's education are smaller in urban areas, a result also noted by Schultz (1980a) in Colombia and Behm (1976) in several Latin American capital cities.

But the studies of Malaysia and Costa Rica treat the household's water and sanitation infrastructure **'as** well as the mother's breast-feeding as _mg&2SM_ or, in other words, as not affected by maternal education or unobserved variables that might **themselves** influence child health outcomes. Barrara's (1988b) study of household and community data from the Bicol Province of the Philippines extends these earlier studies. He assumes that water and sanitary facilities of the household are andooMus, choice variables and may be thus potentially correlated with unexplained variation in child health. Consequently, he first analyzes the relation between maternal education and child health conditional on comantiZ averagg levels of water and
6 towards them in production. Such substitution behavior is likely to take place both between food and other goods, and among food commodities whose relative prices change. The substitution effects, which clearly depend on own- and cross-price elasticities both on the demand and on the supply-side, cause the total welfare impact of price changes to deviate from the first-order approximation. Attempts to estimate these deviations, through second-order Taylor expansions of the expenditure function, can be found, for example, in Friedman and Levinsohn (2002) and Robles and Torero (2010).

Friedman and Levinsohn (2002) investigate the welfare impact of large price increases during the Indonesian currency crisis of 1997. Following Deaton (1988, 1990), they use spatial variation in quality-adjusted unit values within a single cross-section of the Indonesian SUSENAS household survey to estimate the matrix of cross-price elasticities for 21 food groups (and one non-food category). They find overall welfare impacts that are generally large, but that differ considerably not only between urban and rural areas, but also across different provinces. Effects were generally more regressive in urban than in rural areas. Although allowing for substitution behavior made substantial level differences, the authors argue that “the distributional consequences were the same whether we allowed households to substitute towards relatively cheaper goods or not” (Friedman and Levinsohn, 2002, pp. 419-420).

Robles and Torero (2010) - probably the paper whose subject is closest to our own - investigate the effect of the 2007-2008 “food crisis” on four Latin American countries: Guatemala, Honduras, Nicaragua and Peru. Like Friedman and Levinsohn (2002) they too estimate a second-order Taylor expansion of the household net expenditure function around initial prices. They classify food commodities into six groups, and find much smaller welfare effects than Friedman and Levinsohn found for Indonesia. The average national compensating variations were of the order of 1.5 – 2.5% of initial expenditure in Guatemala, Honduras and Peru, and 7% in Nicaragua. In every country, the effects were somewhat larger in urban than in rural areas, reflecting the protective effect of agricultural activities. They were also systematically regressive, with the compensating variation falling across income quintiles as income rose. These estimates implied poverty incidence increases of approximately one percentage point in Guatemala, Honduras and Peru, and about four percentage points in Nicaragua. On average, substitution effects (i.e. allowing for the second term in
Table 3.2

# Water Pollution Abatement in Chinese Factories

Table 3.2a: Distribution of Effluent and Influent Concentration (mgll)

||**Min**|Quartile**I**|Quartile 2|Quartile 3|**Max**|
|---|---|---|---|---|---|
|TSS Effluent|0.00|43.50|93.30|199.20|10806.00|
|TSS Influent|0.00|155.00|<br>567.00|1754.00|100000.00|
|COD Effluent|0.00|82.14|183.78|500.00|63950.00|
|COD Influent|0.00|344.24|850.00|1649.25|100000.00|
|BOD Effluent|0.00|16.77|<br>48.90|145.00|3886.00|
|BOD Influent|15.88|<br>167.93|<br>264.00|699.80|63075.00|
|Other Effluent|0.00|0.04|2.98|8.64|2500.00|
|Other Influent|0.00|1.73|<br>12.00|<br>107.43|37000.00|

Table 3.2b: Distribution of Abatement (%)

||**Max**|**Quartile 3**|**Quartile 2**|**Quartilel**|**Min**|
|---|---|---|---|---|---|
|TSS|100.00|94.30|80.10|55.00|0.00|
|COD|100.00|88.20|72.60|52.70|0.00|
|BOD|100.00|93.10|77.60|55.80|0.00|
|OTHER|100.00|100.00|100.00|94.20|0.00|

13
Table A.1: Baseline statistics and balance test

||||Test of ra|ndomizati|on balance|(continued|) (N=999)||
|---|---|---|---|---|---|---|---|---|
||Sample|Assigne|d therapy|Assign|ed cash|Assign|ed both|F-Test|
|Baseline covariate|Mean|Coef.|_p_ value|Coef.|_p_ value|Coef.|_p_ value|_p_ value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Age|25.40|-0.16|0.68|0.19|0.59|-0.18|0.68|0.18|
|Married or partnered|0.16|-0.03|0.65|-0.04|0.67|0.04|0.76|0.93|
|# of partners|0.53|0.06|0.43|0.12|0.17|-0.21|0.12|0.11|
|# of children<15 in household|2.20|-0.59|0.07|-0.50|0.19|0.62|0.30|0.33|
|Sees family often|2.36|0.13|0.00|0.25|0.02|-0.30|0.01|0.01|
|Muslim|0.10|0.02|0.64|0.00|0.96|0.01|0.87|0.18|
|Years of schooling|7.72|-0.19|0.68|0.04|0.95|-0.01|0.99|0.55|
|Currently in school|0.06|-0.03|0.08|-0.03|0.13|0.04|0.11|0.16|
|Literacy index (0-2)|1.21|0.14|0.11|0.13|0.30|-0.27|0.08|0.12|
|Math score (0-5)|2.79|-0.10|0.25|-0.03|0.85|-0.15|0.39|0.89|
|Health index (0-6)|4.87|-0.09|0.11|-0.19|0.17|0.31|0.15|0.28|
|Has any disabilities|0.08|0.04|0.29|0.00|1.00|-0.04|0.48|0.19|
|Depression index (0-17)|7.09|0.18|0.41|-0.01|0.97|-0.11|0.80|0.45|
|Distress index (0-21)|7.51|0.16|0.43|0.00|0.99|-0.40|0.31|0.40|
|Relations to commanders index (0-4)|0.45|0.00|0.93|0.07|0.42|-0.06|0.55|0.72|
|Ex-combatant|0.41|0.06|0.09|0.08|0.11|-0.09|0.12|0.15|
|War experiences index (0-12)|5.85|0.38|0.24|0.19|0.48|-0.73|0.12|0.32|
|Weekly cash earnings (USD)|17.02|-1.89|0.03|-4.85|0.03|5.48|0.00|0.02|
|Summary index of income, z-score|0.00|-0.22|0.05|-0.12|0.48|0.26|0.21|0.07|
|Homeless in past two weeks|0.25|-0.01|0.82|0.00|0.93|-0.02|0.74|0.33|
|# of days slept hungry, last 7 days|1.26|0.25|0.10|0.28|0.05|-0.32|0.09|0.14|
|Savings stock (USD)|33.83|-10.08|0.26|-12.74|0.31|15.71|0.31|0.53|
|Can get loan of 50 USD|0.52|-0.02|0.62|-0.05|0.32|0.04|0.51|0.57|
|Can get loan of 300 USD|0.11|-0.03|0.27|-0.03|0.34|0.06|0.07|0.13|
|Hours in illicit activities|13.55|1.21|0.68|-0.86|0.67|0.06|0.99|0.14|
|Hours/week in agriculture|0.36|0.34|0.26|-0.10|0.35|0.13|0.84|0.01|
|Hours/week in low-skill wage labor|19.39|0.54|0.88|1.24|0.73|-0.43|0.90|0.94|
|Hours/week in low-skill business|11.53|0.16|0.92|-1.53|0.60|5.76|0.13|0.50|
|Hours/week in high-skill work|1.51|-0.05|0.91|0.94|0.03|0.11|0.85|0.01|
|Years of experience in agriculture|0.78|-0.21|0.29|-0.34|0.07|0.25|0.32|0.15|
|Years experience in non-agricultural|2.96|-0.35|0.36|-0.80|0.05|0.97|0.08|0.04|
|business|||||||||
|Years experience in high-skill work|0.96|-0.29|0.13|-0.27|0.41|0.62|0.12|0.02|
|Sells drugs|0.20|0.01|0.69|0.00|0.92|0.00|0.93|0.92|
|Drinks alcohol|0.75|0.08|0.19|0.07|0.23|-0.07|0.23|0.31|
|Uses marijuana|0.59|0.12|0.02|0.09|0.02|-0.14|0.01|0.01|
|Uses marijuana daily|0.44|0.08|0.14|0.04|0.12|-0.09|0.21|0.34|
|Use hard drugs|0.26|-0.01|0.82|0.02|0.59|-0.01|0.82|0.83|
|Uses hard drugs daily|0.15|-0.04|0.21|0.02|0.52|0.01|0.90|0.37|

Continued on following page.

ii
_Regional (scenario I) and National (scenario II) irrigation water market liberalization simulations: Micro impacts_ The previous section estimated crop-level differences in water SPs caused by irrigated water quotas assigned to farmers based on the types of crops they grow (Figure 3)<sup>22</sup> . Although we apply the same crop-specific SPs throughout the country, differences in cropping patterns imply that average SPs vary across WMAs. As shown in Table 9, the shift from a crop-specific to a uniform market-based regional irrigation water price under scenario I ( _Regional Irrigation Market_ ) has different effects on average SPs across WMAs, with some regions’ prices rising and others falling. This outcome depends on initial crop patterns and water SPs. For instance, as expected initial water SPs are lowest in major water exporting regions (surplus WMAs – see Table 2) such as the Upper Orange, Usutu-Mhlatuze and Thukela. On the other hand, average base SPs in water importing regions such as the Berge, Olifants, Crocodile and Fish WMAs are relatively higher reflecting scarcity.

In addition to the water stress factor, current pattern of cropping also have important influences on average base SPs. For example, WMAs cultivating high shares of their land to high value crops (e.g. horticulture in Luvulvhu-Letaba, Olifants/Dom and Breede and oil seed in Limpopo - see Table 5) show relatively higher SPs. This is in contrast with the case of water importing WMAs such as Middle and Lower Vaal which show low SPs due to the fact that most of the land in these regions are planted to lower value field crops (e.g. summer and winter cereals – Table 5).

Table 9 shows changes in national agricultural production and water use. Crops with low initial SPs show the largest declines in production, such as fodder crops, summer cereals and sugarcane. Irrigated land allocated to these crops declines substantially such that all fodder production and most of cereals and sugar cane go under rainfed systems. By contrast, most horticultural crops have a high willingness-to-pay for irrigated water and their irrigated production expands significantly (especially citrus fruits and vegetables) after liberalizing local irrigated water markets ( _Regional Irrigation Market_ scenario).

> 22 This assumes that crop yield levels reported in the 2002 Census of Commercial Agriculture (StaSA, 2002) reflect yield levels achieved under particular per ha water quota allocations that are crop specific.

24
|||such as subgroup analyses and<br>adjusted analyses||**Text S3**|
|---|---|---|---|---|
|**Results**|||||
|**Participant flow (a**<br>**diagram is**<br>**strongly**<br>**recommended)**|13a|For each group, the numbers of<br>participants who were randomly<br>assigned, received intended<br>treatment, and were analyzed for<br>the primary outcome|For each group, the numbers of<br>clusters that were randomly<br>assigned, received intended<br>treatment, and were analyzed for<br>the primary outcome|**Figure 3**|
||13b|For each group, losses and<br>exclusions after randomization,<br>together with reasons|For each group, losses and<br>exclusions for both clusters and<br>individual cluster members|**Results – Paragraphs 4 & 5**<br>**Discussion – Paragraph 2**<br>**Figure 3**|
|**Recruitment**|14a|Dates defining the periods of<br>recruitment and follow-up||**Methods – Paragraphs 6 & 13**<br>**Results – Paragraphs 1, 4 & 5**<br>**Figure 3**|
||14b|Why the trial ended or was stopped||**n/a **|
|**Baseline data**|15|A table showing baseline|Baseline characteristics for the||
|||<br>demographic and clinical<br>characteristics for each group|<br>individual and cluster levels as<br>applicable for each group|**Results – Paragraphs 1 & 2**<br>**Table 1**|
|**Numbers analysed**|16|For each group, number of<br>participants (denominator) included<br>in each analysis and whether the<br>analysis was by original assigned<br>groups|For each group, number of<br>clusters included in each analysis|**Figure 3**|
|**Outcomes and**<br>**estimation**|17a|For each primary and secondary<br>outcome, results for each group,<br>and the estimated effect size and its<br>precision (such as 95% confidence<br>interval)|Results at the individual or<br>cluster level as applicable and a<br>coefficient of intracluster<br>correlation (ICC or k) for each<br>primary outcome|**Tables 1, 3, 4 and 5**<br>**Results – Paragraphs 7 & 8**|
||17b|For binary outcomes, presentation<br>of both absolute and relative effect<br>sizes is recommended||**Tables 1, 3, 4 and 5**|
|**Ancillary analyses**|18|Results of any other analyses<br>performed, including subgroup<br>analyses and adjusted analyses,<br>distinguishing pre-specified from<br>exploratory||**Tables S1, S2, S3, S4, S5, S6, S7,**<br>**S8, S9 and S10**|
|**Harms**|19|All important harms or unintended<br>effects in each group (for specific<br>guidance see CONSORT for harms)||**Results – Paragraphs 8 & 9**|
|**Discussion**|||||
|**Limitations**|20|Trial limitations, addressing sources<br>of potential bias, imprecision, and,<br>if relevant, multiplicity of analyses||**Discussion – Paragraphs 3, 4, 5 &**<br>**7**|
|**Generalizability**|21|Generalizability (external validity,<br>applicability) of the trial findings|Generalizability to clusters<br>and/or individual participants (as<br>relevant)|**Discussion – Paragraph 6**<br>**Conclusion**|
|**Interpretation**|22|Interpretation consistent with|||

52
Hummels, D. (2007) “Transportation Costs and International Trade in the Second Era of Globalization,” _Journal of Economic Perspectives_ , vol.21 (3), pp.131-154.

Hummels, D. and Lugovskyy, V. (2006) “Are Matched Partner Trade Statistics a Usable Measure of Transportation Costs?” _Review of International Economics_ , vol.14 (1), pp.69-86. Limao, N. and Venables, A.J. (2001) “Infrastructure, Geographical Disadvantage, Transport Cost, and Trade,” _The World Bank Economic Review_ , vol.15 (3), pp.451-479.

39
**Table A2b: Electricity Generation Mix in 2020 and 2040 (%)**

||**Year**|**Diesel**|**Gas**<br>**Turbine**|**Hydro**|**Solar**|**Coal**|**Wind**|**Combi**<br>**ned**<br>**Cycle**|**Bioma**<br>**ss**|**Nuclea**<br>**r**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Afhit**|**2020**|0.0%|2.5%|92.6%|0.0%|0.0%|4.8%|0.0%|0.0%|0.0%|
|**gansan**|**2040**|0.0%|0.0%|99.8%|0.0%|0.0%|0.2%|0.0%|0.0%|0.0%|
|**Banladesh**|**2020**|0.3%|0.0%|1.6%|8.2%|30.3%|0.3%|59.4%|0.0%|0.0%|
|**g**|**2040**|0.1%|0.0%|0.5%|2.4%|92.3%|0.1%|3.4%|0.0%|1.3%|
|**Bhtn**|**2020**|0.0%|0.0%|100.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**ua**|**2040**|0.0%|0.0%|100.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**India**|**2020**|0.0%|0.1%|10.5%|1.1%|74.5%|5.9%|1.2%|2.1%|4.6%|
||**2040**|0.0%|0.0%|8.3%|0.8%|74.5%|4.8%|2.2%|4.1%|5.4%|
|**Neal**|**2020**|0.0%|0.0%|99.8%|0.2%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**p**|**2040**|0.0%|0.0%|99.9%|0.1%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Pkit**|**2020**|0.0%|2.3%|39.2%|0.0%|29.6%|12.7%|9.0%|1.6%|5.6%|
|**asan**|**2040**|0.0%|0.1%|21.8%|0.0%|57.1%|11.3%|2.1%|0.4%|7.2%|
|**Si Lk**|**2020**|0.0%|0.7%|19.1%|0.7%|33.6%|15.3%|29.0%|1.7%|0.0%|
|**r ana**|**2040**|0.0%|0.0%|9.0%|0.4%|63.5%|18.5%|7.2%|1.5%|0.0%|
|**Ttl**|**2020**|0.0%|0.3%|15.2%|1.3%|66.0%|6.3%|4.7%|1.9%|4.3%|
|**oa**|**2040**|0.0%|0.0%|12.1%|0.7%|70.9%|5.5%|2.2%|3.1%|5.3%|

# **_<u>Investment Requirement for Power Sector Development</u>_**

- South Asia is projected to require 1,390 billion US$ for expanding electricity generation expansion during the 2015‐2040 period (to add approximately 750 GW of electricity generation capacity). Already committed and planned inter‐grid connection, especially within India would require around 29 billion US$.

- More than 67% of the total investment for electricity generation and 96% of grid interconnection expansion under the baseline would occur in India.

**Table A3: Investment Requirement for the 2015‐2040 Period (Billion US$, Undiscounted)**

||**Investment**<br>**(Generation)**|**Investment**<br>**(Interconnection)**|**Operation**|**Energy**<br>**Not Served**|**Total**|
|---|---|---|---|---|---|
|**Afghanistan**|16.36|0.18|0.47|0.61|18|
|**Bangladesh**|105.12|0.63|226.20|0.00|332|
|**Bhutan**|32.08|0.54|0.00|0.04|33|
|**India**|929.67|27.93|2,160.03|0.16|3118|
|**Nepal**|10.75|0.00|0.00|0.00|11|
|**Pakistan**|276.96|0.00|292.39|2.69|572|
|**Sri Lanka**|18.67|0.00|27.69|0.39|47|
|**Total**|1,390|29|2,707|4|4130|

39
business owners.” Each sample is randomized into three groups: a control group, a group invited to attend training, and a group invited to receive training and a grant of around $130 conditional on completing training. Individuals are not told whether they are eligible to receive the grant until the completion of training, so that the grant does not differentially alter the composition of who attends training between the two groups.

Among the sample of current business owners, we find that training alone leads to some changes in business practices, but has no effect on business profits, sales or capital stock. In contrast the combination of training and a grant leads to large and significant short-run improvements in business performance for the current enterprises, but these gains appear to have dissipated two years after completion of the training.

Among the sample of potential business owners, training – and especially training combined with a grant – speeds up the process of starting a business and has longer-term effects on the type of women who enter. Women assigned to either training treatment are more likely to be operating a business four months and eight months after the training course, but this entry effect disappears 16 months after training. Instead, later surveys show that training – either with or without the grant – causes women with lower initial analytical ability to open a businesses; the combination of training and the grant leads to entry by women with lower levels of household assets at baseline. Even with these selection effects, we find that the potential entrants assigned to training have higher profits and better management practices two years after completion of training. Business training therefore appears to enable more successful businesses to be started.

This study contributes to an existing and concurrent literature of business training experiments with women (or with men and women) in developing countries. Examples include Field et al. (2010) in India, Karlan and Valdivia (2011) and Valdivia (2011) in Peru, Drexler et al. (2011) in the Dominican Republic, Berge et al. (2011) in Tanzania, Bruhn and Zia (2011) in Bosnia-Herzegovina, and Gine and Mansuri (2011) in Pakistan. These studies typically involve microfinance clients – most of them existing business owners – whose progress is evaluated by a single follow-up survey measuring outcomes six months to one year after training. The stylized finding is that training leads to improvements in business practices, but no detectable effects on profitability or employment, with Karlan and Valdivia (2011) and Drexler et al. (2010) finding some evidence to suggest training helps improve sales in bad weeks or bad months, even if there is no impact on the average month.

3