rafmacalaba/datause-encoder-data huggingface dataset:
WPS3909

# **Trading on Time**

Simeon Djankov Caroline Freund Cong S. Pham*

**Abstract:** We determine how time delays affect international trade, using newlycollected World Bank data on the days it takes to move standard cargo from the factory gate to the ship in 126 countries. We estimate a modified gravity equation, controlling for endogeneity and remoteness. On average, each additional day that a product is delayed prior to being shipped reduces trade by at least 1 percent.  Put differently, each day is equivalent to a country distancing itself from its trade partners by 70 km on average. Delays have an even greater impact on developing country exports and exports of timesensitive goods, such as perishable agricultural products. In particular, a day’s delay reduces a country’s relative exports of time-sensitive to time-insensitive agricultural goods by 6 percent.

_JEL codes: F13, F14 and F15_ _Keywords: trade facilitation, time costs, gravity models_

## World Bank Policy Research Working Paper 3909, May 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _____________________________

* The authors are from the World Bank. Corresponding author: Caroline Freund, World Bank, Mailstop MC3-301, 1818 H Street, NW, Washington, D.C. 20433, ph:(202) 333-1351, email: <u>cfreund@worldbank.org. We thank Mary Amiti, James Harrigan, Bernard Hoekman, Alan</u> Winters and seminar participants at the New York Federal Reserve Bank and the World Bank for comments, and Marcelo Lu and Darshini Manraj for collecting the data. The results presented here are part of a broader research project on “Trade Facilitation and Economic Growth: The Development Dimension” in the Development Economics Research Group – International Trade team of the World Bank.  The project is funded through a Trust Fund established by the U.K. Department for International Development (DFID) to explore the impact of trade costs and barriers on development.  The findings and conclusions here do not necessarily represent the views of U.K. government or DFID.
on Trade and Development. The tariff data are generally available for the period of analysis, but the extent of time coverage differs considerably across countries. The product-level trade and tariff data were also mapped into the 16 sectors we use in the analysis, as detailed in Table A1 in the Appendix.

# **4.2 Descriptive statistics**

To set the stage for the analysis that follows, we first document trends in robotization. Robot utilization varies widely not only across sectors, but also across countries. Figure 2 plots the evolution of robot adoption by country, and shows that robotization is most advanced in highwage countries such as Germany, Denmark and Belgium. It also shows that robotization remains limited in lower wage countries such as Romania and Bulgaria, though these countries are adopting progressively more robots. Figure 3 provides evidence that robot adoption across countries is closely aligned with GDP per capita. This evidence is consistent with the prediction of our theoretical model that incentives for robot adoption are greater among countries with higher productivity and labor costs.

Figure 4 shows the evolution of the stock of robots per million hours across sectors. Automotives, rubber and plastics, and metal are the sectors in which robotization is most pronounced and has been advancing most rapidly. By contrast, the adoption of industrial robots in education, agriculture, construction, utilities and other non-manufacturing sectors remains limited. These differences in the extent of robot adoption across sectors reflect in part the technological feasibility adoption. Figure 5 plots average adoption of robots across sectors over our entire sample period against the share of jobs in a sector that are replaceable. The association between adoption and replaceability is clearly positive, with sectors where a larger share of tasks can potentially be performed by robots indeed using more robots, as also shown by Graetz and Michaels (2018).

In sum, robot adoption has increased significantly over time, although at different speeds in different countries and sectors, which is convenient for identification. Figures A1 and A2 in the Appendix display clearly the extent of variation in robot adoption across sectors, countries and time. In addition, the strong correlations of robot usage with both replaceability and initial GDP per capita suggest the triple interaction between the degree of sectoral replaceability, the initial GDP per capita and the price of robots (proxied by the global robot stock) is a suitable instrument for robotization. Indeed, Figures A3 and A4 in the Appendix show that the change in robot density observed between the initial and final years of our sample period is positively 16
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of Chinese railways, calculating the distance from counties to straight lines connecting historic cities and ports. Note that technically speaking, the two rail lines in Tanzania, TRL and TAZARA, are not connected to each other, because they operate based on different systems of rail gauges (i.e., Narrow Gauge for TRL and Cape Gauge for TAZARA). Therefore, proximity to TRL line does not necessarily mean proximity to TAZARA.

The last two instruments represent geographic conditions at each firm location: _ELEV_ is the level of elevation in meters, and _SLOP_ is the slope angle of the terrain where a firm exists. These are likely relevant to transport connectivity because rail infrastructure is normally aligned to relatively flat terrain. However, these are less likely to affect firm productivity. The data are extracted from a global spatial data set, the Shuttle Radar Topography Mission (SRTM), which provides 90m resolution elevation data of the world. The validity of the instruments will be tested with the actual data.

# **IV. DATA**

To collect firm-level data covering a wide range of locations, an enterprise survey was carried out in February to May 2016. The survey aimed at about 500 firms focused on light manufacturers in major urban areas of 18 regions (see **Figures 9, 11 and 13** above).<sup>11</sup> The identification of firms to be included in the survey was started with the customer databases provided by the two rail companies. As discussed above, only a small number of firms are currently directly using railway services for shipping their freights, because of the diminished rail operations and the poor quality of the services.<sup>12</sup> The rest were randomly selected in each major urban area. The subsample size was determined in proportion to the size of each city.

> 11 Arusha, Dar es Salaam, Dodoma, Iringa, Bukoba, Kigoma, Moshi, Babati, Musoma, Mbeya, Morogoro, Mwanza, Sumbawanga, Shinyanga, Singida, Songea, Tabora, and Tanga.

> 12 From the empirical point of view, the current sample frame may not be ideal because it is significantly unbalanced. It is not easy to identify clear rail transport users. Still, there is economic potential to (re)develop rail transportation in large countries, such as Tanzania. Thus, the current study was designed as a potential baseline for further surveys. As the government is investing in rail (and lake) transport infrastructure, the follow-up surveys are expected to show more visible impacts of improved rail transport.
countries and at borders. While demand for some food products may decrease, for instance as restaurants cannot operate, this effect might be outweighed by government policies, including stimulus packages, aimed at supporting demand and by fears of food scarcity that can generate an increase in demand due to consumers’ hoarding. As the global supply of food shrinks and inventories decline, world prices would eventually rise as the gap with global food demand widens. Evidence based on high-frequency data points to an increase in export prices of rice since February 2020 for several exporters (Figure 2).

**Figure 2: Evolution of prices for rice (January 2020 – April 2020)**

<!-- Start of picture text --> 600<br>550<br>500<br>450<br>400<br>350<br>300<br>China Thailand United States of America Viet Nam<br>export price by exporter (USD/tonne)<br><!-- End of picture text --> _Source_ **_:_** ITC Market Price Information.

We provide a first assessment of the initial shock that Covid-19 could have on world food markets, which considers its potential heterogeneous effects across food products. Intuitively, supply conditions will impact food products differently, as workers’ morbidity and the need for social distancing will affect more the supply of products that are more labor intensive, such as paddy rice, or the supply of products that have crucial labor-intensive stages, such as the processing of fish. For these products, Covid-19 may be expected to cause shortages to be more severe than in food sectors that do not require workers to be in close proximity, for instance due to more extensive automation (World Bank, 2020a). Moreover, as the extent of automation to produce the same food product varies widely across countries, we would expect for the same product larger reduction in supplies in countries with less automation.

To account for the heterogenous impact of Covid-19 on food supply across countries and products, we rely on data at the country-product level on the share of low-skill workers in the total value added in exports (Auguiar et al., 2019). We assume that the initial shock in supply proportionally increases with the weight of low-skill labor in total value added in exports. In our baseline scenario, we use information from Chinese food exports in January and February 2020 to define the potential upper bound for the decrease 3
7FIGURE 3: GULF SHARE INDEX <!-- Start of picture text --> 200 220<br>100<br>120<br>300<br>30<br>60\<br>40<br>20<br>0 l . . . . . . . . . . . . . . . . . . . . .<br>3981 1932 t983 1984 1965 IS6 3907 195o<br>Source: Al-Amwal Co. WLL, Kuwait.<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 4: GULF SHARE INDEX VOLUKE<br>(In millions of shares)<br>3.7 ---<br>'.,<br>1.3 -<br>1.2<br>0.3 0.I~~~~~~~~I<br>0.79 000<br>0.6~~~~~~~~<br>0.4-<br>0.3 C<br>0.2 -01 I<br>9138 1952 3913 3914 I9N5 INS 3957 INS<br>Soure:t AI-Amwal Co. VLL, Kuwait.<br><!-- End of picture text -->
# **Appendix Table 5.3: Practice by Practice Impacts on Sales and Marketing Practices in Round 2**

||Customer<br>research|Competitor<br>research|Market Potential<br>research|Product<br>promotion|Price<br>changes|Branding<br>strategy|Uses<br>CRM|Loyalty<br>program|Sales<br>force|
|---|---|---|---|---|---|---|---|---|---|
|Assigned to Insourcing|0.036|0.060|0.080|0.063|-0.010|0.064|0.152**|0.099*|0.206***|
||(0.056)|(0.057)|(0.056)|(0.058)|(0.059)|(0.060)|(0.060)|(0.059)|(0.058)|
|Assigned to Outsourcing|0.076|0.132**|0.107*|0.066|-0.011|0.108*|0.171***|0.109*|0.135**|
||(0.054)|(0.055)|(0.056)|(0.058)|(0.059)|(0.058)|(0.059)|(0.058)|(0.059)|
|Assigned to Training|0.003|0.085|0.026|-0.086|-0.142**|0.000|-0.032|-0.038|0.039|
||(0.057)|(0.057)|(0.058)|(0.060)|(0.059)|(0.060)|(0.060)|(0.057)|(0.059)|
|Assigned to Consulting|0.072|0.122**|0.079|0.013|-0.015|0.079|0.120**|0.010|0.151**|
||(0.054)|(0.054)|(0.056)|(0.059)|(0.058)|(0.058)|(0.059)|(0.057)|(0.059)|
|Mean of Control Group|0.697|0.652|0.652|0.614|0.636|0.538|0.455|0.356|0.356|
|Sample Size|678|678|678|678|678|678|678|678|678|
|P-value: all treatments zero|0.456|0.113|0.302|0.069|0.102|0.234|0.001|0.045|0.002|
|P-value: all treatments equal|0.491|0.494|0.523|0.035|0.077|0.293|0.002|0.031|0.041|
|P-value: In=Out=Consult|0.711|0.345|0.840|0.594|0.997|0.742|0.674|0.166|0.443|
|P-value: Insource = Outsource|0.447|0.174|0.616|0.951|0.986|0.452|0.747|0.858|0.228|

Notes: Regressions control for stratification batch fixed effects and baseline value of marketing practices index. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. Practices are all dummy variables, and estimation is for second follow-up survey. **Customer research** denotes carrying out structured research on customers; **Competitor research** denotes carrying out structured research on competitors; **Market Potential research** denotes carrying out structure research on market potential; **Product promotion** denotes promoting products through advertising or other means; **Price changes** denotes changing prices to increase sales or profits; **Branding strategy** denotes using a branding strategy; **Uses CRM** denotes uses a customer relationship management (CRM) system; **Loyalty program** denotes using a post-purchase loyalty program; **Sales force** denotes has a professional sales force. Reference period is last 3 months for all practices.

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straightforward way and therefore shift cooperatively-bargained household utility along the utility-possibility frontier away from _Uh_ to _Uw_ , leading to greater bargaining power for women within households.

# **III. Data**

The study is based on the second-round of a two-period panel of data collected in the India Human Development Surveys (IHDS), which were jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi, India. The nationally representative survey covers all of India’s key states and union territories except for Andaman and the Nicobar Islands, and Lakshadweep.

The first round of the survey was carried out in 2004/05 (mostly in 2005). It collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second survey, conducted in 2011/12 (mostly in 2012), reinterviewed 83 percent of the original households and split households (if located within the same village or town), and also interviewed 2,134 new households, for a total of 42,152 households.<sup>3</sup> Eligible women from each household (defined as women of age 18–49 who had been married at least once) were interviewed in both rounds. The second-round survey covers substantially more sets of topics on gender relations regarding women’s decision-making ability, mobility, financial autonomy, reproductive freedom, and social participation. Analysis of the effects of electrification on women’s empowerment is therefore based on the cross-sectional data of 2011/12.

The survey also covered detailed information on households’ electrification status, energy use, income, expenditure, education, health, and employment. In addition, the survey included key features of surveyed households’ villages. It is important to control for village-level

> 3 Some households surveyed during the first round split into multiple households by the time the second round survey was carried out. This is mostly because children became adults and formed separate households. For a detailed description of the survey, see the IHDS website at http://www.ihds.umd.edu.

7
# **Public transport in the Metropolitan Area**

5.19 As already indicated, the OPSM Survey only covers head of household commuter journeys in the Metropolitan area. The survey does not include any information on other journeys undertaken by the head of household, nor any journeys undertaken by other household members. Moreover, given the deep recession in Argentina, some heads of household did not report any commuting journeys due to being unemployed. Therefore, for the purposes of this study, a very simple definition of subsistence consumption is used, equivalent to 22 return journeys to work per month for the head of household. This level of subsistence consumption could be guaranteed by providing some kind of free or subsidized bus pass, that based on current fare structures would need to take a value of around US$14.91 per month.

# **RESULTS OF SIMULATIONS**

5.20 Based on the multi-dimensional poverty proxy and subsistence consumption thresholds developed above, it is possible to simulate a series of alternative social policies in each sector. In all cases, these simulations respect the current budget envelope for social expenditures on infrastructure services, and hence would require a fiscally neutral reallocation of existing expenditures. Two types of simulations are performed for use of service and connection subsidies respectively. In the case of use of service subsidies, it is assumed that all the resources would be spent on subsidizing the subsistence consumption of those households classified as eligible by the multi-dimensional poverty proxy, adjusting the percentage subsidy given to ensure that the overall budget constraint is met. In the case of connection subsidies, it is assumed that all the resources would be spent on a one-time fixed subsidy to unserved households classified as eligible by the multidimensional poverty proxy, adjusting the number of beneficiaries each year to ensure that the overall budget constraint is met.

# **Water**

5.21 In contrast to the other services, the water sector does not currently have any national financing mechanism for social policies that would allow for some degree of geographical redistribution of resources across jurisdictions. Nonetheless, a draft Federal Water Law developed in 1999 proposed the creation of such a national social fund for the water sector based on a 3% surcharge on water bills that could be credited against the sector’s VAT liability. Although this draft law was never sanctioned, this possibility continues to be debated within the water sector. Therefore, for purely illustrative purposes, the simulations presented in this study will limit themselves to the budget envelope associated with this proposal, which amounts to US$21.3 million per year. Two alternative uses of these hypothetical funds will be considered.

5.22 The first is the introduction of a national social tariff that offers a discount against the minimum unmeasured water tariff for all households that qualify according to the 40
|Education Sector Investment Project|Ousmane Diagana|
|---|---|
|Education Sector Investment Project|Ousmane Diagana|
|Education Sector Investment Project|Ousmane Diagana|
|Education Sector Investment Project|Ousmane Diagana|
|Education Sector Investment Project|Ousmane Diagana|
|Education Sector Investment Project|Ousmane Diagana|
|Education Sector Investment Project|Ousmane Diagana|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Vocational TrainingProject|Yasser Aabdel-Aleem AwnyEl-Gamm|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Reduction of Ozone Project(Montreal Protocol)|Tuuli Johanna Bernardini|
|Industrial Credit Project||
|HighwayProject(04)||
|HighwayProject(04)||
|Grain Storage Project||
|Grain Storage Project||
|Grain Storage Project||
|Grain Storage Project||
|Railways Project(02)||
# **Table 1: Baseline product level regressions**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Full|Homogenous|Referenced|Differentiated|Non-differentiated|
|(t-r)|0.667**|-0.090|0.565|1.481***|0.335|
||(0.297)|(0.770)|(0.556)|(0.498)|(0.472)|
|prc_sh|-9.405***|-19.994***|-10.645***|-7.611***|-12.242***|
||(1.399)|(5.515)|(2.743)|(1.857)|(2.405)|
|collpriv_sh|-17.978**<br>*|-14.072*|-14.931***|-21.337***|-14.751***|
||(2.254)|(7.954)|(3.947)|(3.140)|(3.499)|
|for_sh|-50.835**<br>*|-47.018***|-49.217***|-52.549***|-48.783***|
||(1.758)|(7.234)|(3.192)|(2.388)|(2.910)|
|HS4 fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|7,009|522|2,011|4,176|2,533|
|R-squared|0.546|0.708|0.473|0.563|0.537|

Notes: The regressions are at HS8 level. The dependent variable is the share of indirect exports (conservative measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm ownership type is SOE. The last column is for non-differentiated products, including both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

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