rafmacalaba/datause-encoder-data huggingface dataset:
- Feyrer, James (2009). Distance, trade, and income-the 1967 to 1975 closing of the Suez Canal as a natural experiment. Technical Report. National Bureau of Economic Research.

- Geertman, Stan CM and Ritsema Van Eck, Jan R (1995). GIS and models of accessibility potential: an application in planning. _International Journal of Geographical Information Systems_ , **9,** number 1, 67–80.

- Geurs, Karst T and Van Wee, Bert (2004). Accessibility evaluation of land-use and transport strategies: review and research directions. _Journal of Transport Geography_ , **12,** number 2, 127–140.

- Gonzalez-Navarro, Marco and Quintana-Domeque, Climent (2016). Paving streets for the poor: Experimental analysis of infrastructure effects. _Review of Economics and Statistics_ , **98,** number 2, 254–267.

- Guy, Clifford M (1983). The assessment of access to local shopping opportunities: a comparison of accessibility measures. _Environment and Planning B: Planning and Design_ , **10,** number 2, 219–237.

- Hansen, Walter G (1959). How accessibility shapes land use. _Journal of the American Institute of Planners_ , **25,** number 2, 73–76.

- Harris, Chauncy D (1954). The market as a factor in the localization of industry in the United States. _Annals of the Association of American Geographers_ , **44,** number 4, 315–348.

- Head, Keith and Mayer, Thierry (2011). Gravity, market potential and economic development. _Journal of Economic Geography_ , **11,** 281–294.

- Heikkila, Eric J and Peiser, Richard B (1992). Urban sprawl, density, and accessibility. _Papers in Regional Science_ , **71,** number 2, 127–138.

- Henderson, J Vernon, Storeygard, Adam and Weil, David N (2011). A bright idea for measuring economic growth. _American Economic Review, Papers and Proceedings_ , **101,** number 3, 194–199.

- Henderson, J Vernon, Storeygard, Adam and Weil, David N (2012). Measuring economic growth from outer space. _American Economic Review_ , **102,** number 2, 994– 1028.

40
19 In short, with the exception of the early 1970s,<sup>prices of local services kept up, and even</sup> surpassed, inflation. In the market for long-distance<sup>services, prices have been declining</sup> consistently in real terms, largely to eliminate<sup>cross-subsidization. The question is whether these</sup> t3riffs provided firms with adequate returns on their<sup>investment.</sup>

**Table 2. Telephone price schedule, 1989-93**

(September 1989 rates = 100)

||1989|1990|1991|1992|1993|
|---|---|---|---|---|---|
|Local<br>metered<br>service||||||
|Monthly charge|100|123<br>|138<br>|154<br>|171<br>|
|Variable charge|100<br>|100<br>|100<br>|100<br>|100<br>|
|Local flat rate service|100|114<br>|125<br>|136<br>|147<br>|
|National long-distance|100<br>|80<br>|74<br>|65<br>|65<br>|
|International long-distance|100|100<br>|100<br>|100<br>|100<br>|
|Allocation fee|100|82|25|12|0|

Source: CTC.

_Profit._ Since 1958, firms have been assured a<sup>rate of return of 10 percent--even higher since</sup> 1989--on the sunk cost of useful capital. However,<sup>trends of profitability on revalued assets</sup> reveal significant differences between CTC and ENTEL,<sup>and over time (figures 9 and 10).8/ To</sup> the extent that the reported rates of return are believable,<sup>several observations can be made.</sup> CTC's rates of return were positive in the 1960s, but below<sup>10 percent. In the early 1970s, the</sup> company even made losses. Only in the period following<sup>privatization in 1988 did CTC have</sup> rates of return higher than 10 percent. ENTEL, by contrast,<sup>consistently had rates of return of</sup> more than 10 percent in the 1980s. In recent years, the<sup>company's rates of return have even</sup> exceeded 40 percent on revalued assets, despite the<sup>decline in its prices.</sup> The rates of return on revalued assets have to be interpreted<sup>with caution, however. They</sup> underestimate the rates of return actually received by<sup>the companies by the extent to whiclh the</sup> denominator is inflated by t' .m. Nor can these<sup>rates be used to establish whether CTC and</sup> ENETL received the 12 and 14 percent rates of return to<sup>which they have been entitled since</sup> 1989 because they are based on revenue from both<sup>regulated and unregulated services.9/</sup>

> 8/ No information could be obtained before 1960<sup>for CTC and before 1979 for ENTEL.</sup>

> **9/** It was not possible to correct for these factors.
4 each percentile of Brazil‟s income distribution. The net effect is computed as the sum of three components: an “expenditure effect”; a “market income effect”; and a “transfer income effect”.

The expenditure effect is estimated as each household‟s compensating variation, in the standard way: the difference between the expenditure functions at the new and old price vectors. Estimation of the market income effect takes advantage of the detailed information available in the Brazilian household survey on the specific agricultural activity (by crop) where each person works, and relies on different scenarios for the pass-through from food prices to the wages of workers engaged in the production of specific agricultural commodities. Finally, the transfer income effect accounts for the fact that the Brazilian government increased the transfer amounts for two important social assistance benefits (at least in part) in response to the „food crisis‟: Bolsa Família and the Benefício de Prestação Continuada (BPC). Using pre-crisis data on transfer recipients, we impute the expected effect of the rising transfers.

Our main findings are that, despite living in one of the world‟s largest food exporters, most – but not all – of Brazil‟s population experienced a decline in welfare as a result of the price shock in 2007-2008. The expenditure effect is large and regressive, with an average compensating variation of 7% of baseline expenditure, and a range from 11% at the lowest percentiles to just under 2% at the top of the distribution. On the other hand, both market and transfer income effects are progressive, with larger gains at the bottom of the distribution. These effects are small in large urban areas, but can be substantial in rural areas: If agricultural wages rose in the same proportion as food prices, this would lead to falling poverty in Brazil‟s rural areas. For the country as a whole, the net effect is U- shaped, with small welfare gains for the bottom ventile of the population, and the largest losses (roughly of the order of 5%) accruing to the three middle quintiles.

There are three reasons why the results from this exercise should be treated as suggestive, rather than definitive. First the expenditure effect is calculated as a first-order approximation to the compensating variation, with no allowance made for consumer substitution effects. Second, data limitations prevent us from examining food sales at the household level. While such family-farm sales are thought to account for a small fraction of the country‟s agricultural output, it would obviously still have been good to have information on them. But this information is simply not available for Brazil. Third, the market income effect is assessed on the basis of relatively crude assumptions about the pass-through from food prices to agricultural wages. Despite these
14

- Thus direct plus indirect policy influences mean that developing country farmers faced prices that were an estimated 30 percent below what they would have been without distortionary farm price, trade and exchange rate policies on average; and

- Within the agricultural sector, the producers of exportables tended to be taxed by agricultural policies (average DRA of -12 percent) and those producing importcompeting farm products tended to be protected (average DRA of 16 percent) but, when the indirect impact is included (which reduces the DRA by 23 percentage points), the TRA for both sub-sectors were negative on average for the 17 countries and for all but Group IV countries (South Korea and Portugal).

For the K/S/V sample of 17 developing countries and the period 1960-84, there was therefore a severe anti-trade bias in farm policies and also a severe anti-agricultural bias that was reinforced by non-farm policies, particularly manufacturing protection; and the biases against farmers – especially those capable of exporting – tended to be greater the lower the national per capita income.

The unweighted NRAs and RRAs in the recent World Bank project, for the same developing countries and years, are shown in part (b) of table 2. The estimates of agricultural NRAs, however, include a bigger sample of covered products (more than twice as many as in K/S/V including livestock products which were mostly ignored in K/S/V). The total agricultural NRAs (but not the NRAs for exporting and import-competing farmers) also include, unlike in K/S/V, non-product-specific assistance and guesstimates of assistance to the roughly 30 percent of the value of farm products that has not been included in the new study’s explicit price comparison exercise. Recall too that the NRA estimates incorporate the trade-taxing effects of multiple exchange rates; hence they can be expected to have more of an anti-trade bias than K/S/V’s DRA measure.

The new NRA and RRA estimates reinforce the conclusions from K/S/V for the period to 1984. Specifically, the new agricultural NRAs are very similar to the comparable DRAs (both averaging -8 percent, and within 4 percentage points for the four income groups). Second, non-agricultural policies were even more important in depressing the new RRA than agricultural policies, with their NRA averaging 34 percent compared with the average NRA for agriculture of -8 percent. Third, the estimated direct plus indirect policy influences on farmers’ incentives on average are very similar in the two studies: a TRA of -30 percent by K/S/V, and an RRA of -29 percent from the new study. And fourth, as anticipated (because of the inclusion of the impact of multiple exchange rates), within the agricultural
percent increase in port efficiency (from a productivity perspective) reduces trade costs by 0.06 percent.

Micco and Pérez (2002) find that increasing port efficiency from the 25th to the 75th percentile reduces shipping costs by more than 12 percent. They use transport cost data produced by the U.S. Department of Transportation for 1995–99 and an ad hoc port efficiency measure from the World Bank’s _Global Competitiveness Report_ .

The importance of port efficiency on reducing costs and improving trade is also illustrated by Blonigen and Wilson (2008). They argue that for small, less-developed countries, port inefficiency has a larger trade-reducing effect than other trade frictions. They identify the costs associated with loading and unloading freight at ports, which are associated with efficiency, through fixed effects. They estimate the effects of port efficiency on transport costs between 1991 and 2003. However, their measure is likely to capture the effects of determinants unrelated to efficiency that are not controlled for in the regression.

All of these measures of port efficiency are statistically significant and show that port efficiency is an important determinant of transport costs. They rely, however, on ad hoc measures that do not control for the actual use of port assets or measures that can be very noisy. To avoid these problems, this paper builds a measure of economic efficiency based on the use of port inputs to deliver port output. It relies on data envelop analysis (an input-output approach for measuring efficiency) to rank countries in terms of their port efficiency.

# **Empirical Framework**

By how much would improvements in port performance benefit countries in the Indian and Western Pacific Oceans? To answer this question, this paper (a) estimates port efficiency based on data envelop analysis (based on Herrera Dappe and Suárez-Alemán 2016); (b) presents a model of maritime transport costs that includes the impact of port efficiency (based on Fink, Mattoo, and Neagu 2002); and (c) uses a multilevel gravity model to test the impact of maritime transport costs on worldwide trade.

# **_Estimating Port Efficiency Using Data Envelop Analysis_**

Data envelopment analysis (DEA) is the most frequently applied nonparametric methodology for estimating efficiency in the port industry as the relationship between inputs to the port production process and the outputs derived from it.<sup>2</sup> This deterministic method uses mathematical programming techniques to envelop the data as compactly as possible.

> 2 Most empirical analyses can be categorized as using either parametric or nonparametric models to estimate port efficiency. Parametric models assume that a specific function underpins the data; within this category, stochastic frontier analysis is the most commonly applied methodology. Nonparametric

7
This strategy led to an overall response rate in the treatment group of 54.03 percent. The response rate in the control group was 34.17 percent, resulting in an Intention-To-Treat (ITT) effect of 19.86 percentage points. Due to the fact that out of the 248 individuals assigned to treatment, only 77 staff answered the phone call, had not yet filled out the survey but were willing to receive another survey link, the local average treatment effect (LATE) is even larger. The LATE – which in this case measures the average effect of treatment-on-the-treated (TOT) – corresponds to a vast increase of 64 percentage points.

The remainder of the note is structured as follows. In the next section we highlight the specificities of conducting a web-based survey in an international organization, discuss the survey design and elaborate on the method used to evaluate the impact of phone reminders on survey response rates. In section 3 we discuss the results while section 4 concludes.

# **2. Background and Method**

As part of a larger evaluation, the World Bank’s Independent Evaluation Group (IEG) implemented a web-based survey among professional staff to elicit their views on one of the Bank’s corporate goals. The survey was sent out to a sample of 3,698 eligible staff members.<sup>3</sup> It was launched on September 7, 2016 and closed on October 18, 2016. Surveying professional staff in an international organization comes with a number of challenges.<sup>4</sup> First, the World Bank Group has more than 130 offices worldwide, which complicates the timing of the survey and the telephone reminders. Relatedly, given its

> 3 We originally selected a sample of 3,736 units. However, 32 individuals were excluded as they had left the institution by the time the survey was implemented. We also excluded 6 IEG staff members from the sample who were mistakenly included (IEG was not part of the target population), leading to a sample of 3,698 eligible units. 4 One important advantage of a web-based survey targeting staff of an international organization is the availability of a nearly complete sampling frame, thus minimizing coverage bias.

3
# **1. Introduction**

Gender gaps in earnings and in other economic outcomes have been documented extensively by economists (Blau and Kahn 2000). While gaps favoring men exist in virtually every country, disparities in income, health, education, and decision-making power are more pronounced in developing countries (Jayachandran 2015). Understanding the causes of these gaps has received a lot of attention in the literature, with most of the empirical work focused in developed countries.<sup>2</sup> The early literature documented cases of discrimination against women in the market as a source of disparity in outcomes (Altonji and Blank 1999), while a more recent literature has increasingly focused on gender differences in innate preferences, attitudes and ability as underlying causes, inspired by insights from psychology and experimental research (Bertrand 2011).

Research documenting innate gender differences in risk preferences or attitudes towards competition relies heavily on lab experimental approaches (e.g. Gneezy et al 2003, Niederle and Vesterlund 2007), with very few field experiments (e.g. Flory et al 2015). The literature on discrimination has developed in parallel, and somewhat independently, using observational and experimental data in field settings, employing methods such as audit studies (Neumark 1996), blinded natural experiments (Goldin and Rouse 1996, Moss-Racusin et al 2012), and field experiments with fictitious resumes or emails (Bertrand and Mullainathan 2004; Riach and Rich 2006; Petit 2007; Milkman et al 2012).

The central thesis of this paper is that the two literatures and classes of explanations may be intricately tied to each other: even if women have innate characteristics (raw ability and attitudes) that are comparable to those of men, they may perform differently in the market if the task requires them to interact with others in society, and they are subject to discrimination in those societal interactions. We set up a field experiment and data collection strategy to explore this thesis, and then add a cross-cutting experiment to explore whether the provision of small incentives to promote interactions between women and men helps eliminate such differences between male and female actors.

> 2 See Bertrand (2011) and Niederle (2015) for reviews of the literature.

2
2 Table 1 Public Pension Expenditure **as a** Percent **of** GDP In OECD _Natiorn_

||1960|1975|1980|1985|
|---|---|---|---|---|
|Australia|3.3|4.5|4.9|**4.90**|
|Austria|9.6|12.5|13.5|14.5|
|Belgium<br>|N/A<br>|10.5<br>|11.9<br>|N/A<br>|
|Canada|2.8|3.7|4.4|5.4|
|Denmark|4.6|7.8|9.1|8.5|
|Finland|3.8|6.1|6.5|7.10|
|France|6.0|10.1|11.5|12.7|
|Germany<br>|9.7<br>|12.6<br>|12.1<br>|11.8<br>|
|Greece|N/A|4.8|5.8|10.7|
|Iceland|N/A|N/A|N/A|N/A|
|Ireland|2.5|4.2|4.5|5.4|
|Italy|5.5|10.4|12.0|15.6|
|Japan|1.3|2.6|4.4|5.3|
|Luxembourg|N/A|N/A|N/A|N/A|
|NetheTlands|4.0|8.9|11.0|10.5|
|New Zealand|4.3|5.3|7.6|8.1|
|Norway|3.1|8.0|7.9|8.00|
|Portugal|N/A|4.1|6.1|7.2|
|Spaln|N/A|4.3|7.3|8.6'|
|Sweden|4.4|7.7|10.9|11.2|
|Switzerland|2.3|7.7|8.0|8.1|
|Turkey|N/A|N/A|N/A|N/A|
|Unied Kingdom|4.0|6.0|6.3|6.7|
|United States|4.1|6.7|6.9|7.2|
|OECD averageb|4.3|7.1|8.2|8.9|

# Notes:

N/A: data not available.

'Ratio of annual public expenditure on pensions to current-year GDP. Public pensions include both transfers through social programs and pension payments to retired govemment employees. Privately sponsored pensions are not Included, nor are tax expenditures granted to private and/or public plan savings. bOECD average represents the unweighted mean of available figures. 'Figures are for 1984.

# Source:

OECD (1090), Table 7, p. 33, and author's calculations. Figures for Germany refer to the former nation of West Germany.
share of the EU in tariff revenue was 16.2%, and thus less than its share in imports, with the opposite being the case for imports from other countries. Tariffs on total imports account for 19% of Ethiopia’s total tax revenue, with tariffs on imports from the EU contributing 3.1% of tax revenues.<sup>23</sup> When VAT and excise taxes levied on imports are taken into account, imports from the EU contribute 7.6% of tax revenues while total imports provide 43.3% of revenues. Trade is clearly a key source of tax revenues in Ethiopia.

**Table 4: Sources of current tariff revenue in Ethiopia**

||COMESA (FTA)|COMESA (non‐FTA)|EU27|Other|Total|
|---|---|---|---|---|---|
|Share of imports|1.2%|3.2%|23.2%|72.3%|100.0%|
|Share of total tariff revenue|0.0%|4.3%|16.2%|79.5%|100.0%|
|Share of tariff revenue/ total tax revenue*|0.0%|0.8%|3.1%|15.3%|19.2%|
|Share of total trade tax revenue*|1.8%|4.7%|17.6%|75.9%|100.0%|
|Share of trade tax revenue / total tax revenue*|0.8%|2.1%|7.6%|32.9%|43.3%|
|Trade weighted average applied tariff|0.0%|10.8%|5.7%|8.9%|8.1%|
|Trade weighted average statutory tariff|0.9%|16.0%|10.7%|11.5%|11.4%|
|Trade weighted average tariff exemptions|0.8%|5.2%|5.1%|2.6%|3.2%|
|Tariff collection rate|5.1%|67.6%|52.8%|77.4%|71.5%|

* Based on IMF data on total tax revenue

The trade weighted average applied tariff is 5.7 percent for the EU, below the overall average of 8.1 percent. Conversely, the tariff actually levied on imports from other COMESA countries and the rest of the world is above the overall applied average while applied tariffs to member countries of the COMESA FTA are close to zero even though Ethiopia is not yet a member of the FTA. It should be noted that these are trade weighted tariff averages, so a higher number may reflect that imports are concentrated on products with a high level of protection. The tariff collection rate (the ratio of actual tariff revenue to hypothetical tariff revenue had the statutory tariff rate been applied) is 52.8% for imports from the EU, which is below the average for all imports (71.5%) and highlights the importance of taking into account exemptions when analyzing trade policy changes towards the EU.

> 23 Total tax revenue is not a TRIST output. The data were taken from IMF Article IV reports.

16
Nigeria provide clear examples). Through a phenomenon known as Dutch Disease, oil booms can also have adverse consequences for the nonoil sectors of the economy, as an appreciating exchange rate caused by surging oil exports renders other products uncompetitive (Adam and Bevan 2003; Garber 2004; Same 2008). It will therefore be critical for Uganda to capitalize on oil revenues—particularly during boom periods—and use them to make investments in assets such as infrastructure that will help to promote economic diversification into more sustainable sectors.

# Why infrastructure matters

_Adequate infrastructure is key for economic growth and competitiveness in Uganda._ The country’s current inadequate infrastructure is impeding faster growth. Empirical evidence between the 1900s and early 2000s shows that infrastructure improvements in Uganda contributed over 1.5 percentage points to Uganda’s per capita growth rate (figure 1a). The ICT sector made the strongest impact on growth followed by power as a distant second. Looking ahead, if Uganda could improve its infrastructure to the level of Africa’s best performing country—Mauritius—growth performance could be enhanced by as much as 3.8 percentage points per capita, with the most significant contributions coming from upgrades to the power and ICT infrastructure (figure 1b).

**Figure 1. Infrastructure’s contribution to economic growth: Benchmarking Uganda against other SubSaharan nations 1995-2005**

**a. Infrastructure’s contribution to annual per capita economic growth in East African countries, in percentage points, 2001– 05**

<!-- Start of picture text --> 2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>ICT Power Roads<br>Changes in per capita growth<br>Congo, Rep Kenya East Africa Cameroon Cote d'Ivoire Tanzania Uganda Sudan<br><!-- End of picture text -->

**b. Potential contributions of infrastructure to annual per capita economic growth in East African countries, in percentage points**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>ICT Power Roads<br>Potential contributions of per capita growth<br>Cote d'Ivoire Kenya Cameroon Tanzania Sudan Congo, Rep of Uganda East Africa<br><!-- End of picture text --> _Source:_ Calderón 2009.

_Note:_ ICT = information and communication technology.

Evidence from enterprise surveys suggests that in 2006 infrastructure constraints were responsible for as much as 58 percent of the productivity handicap faced by Ugandan firms (figure 2a), with the 5