rafmacalaba/datause-encoder-data huggingface dataset:
Intelligence Network database provides data on vessel-specific fixtures, namely time charter and spot rates. Vessel-specific rates are not particularly representative of a given market segment (i.e., containerships, dry bulk, tankers, auto carriers, etc). For example, Brancaccio et al. (2020) report that Clarksons tend to oversample newer and medium-sized vessels in dry bulk freight markets. That said, Clarksons does provide a representative summary of the freight earnings time series (from an annual to a weekly reporting frequency) for main trade routes and vessel benchmarks: e.g., Feeder Containership 2,7500 TEU gearless 6-12 month Timecharter rates ($/day) or SCFI Shangahi-West Coast America (spot) freight rates ($/FEU).

Another readily available data source of transport costs for large cross-sections of countries is the Doing Business (DB) survey, compiled by the World Bank.<sup>18</sup> It contains information on international transport for 189 countries since 2006. The section "Trading across Borders" provides data on the number of documents, time, and import and export costs. More specifically, it reports the US dollar cost container to and The data in the DB is collected a World per import export. survey using Bank questionnaire completed by trade facilitators and freight-forwarders across countries. For the comparability across countries, the survey presents the respondents with a detailed, stylized import or export case. The cost per container is for dry-cargo, 20-foot, full container load, and the products shipped do not require any special standards. Given the description in the survey, the most likely product categories are textile yarn and fabrics (SITC 65), articles of apparel and clothing accessories (SITC 84), and coffee, tea, cocoa, spices (SITC 07). The respondents are also asked to identify the likely port of loading (Djankov et al., 2010). Therefore, the variation in transport costs across products, routes, or firms is rather limited (Blonigen and Wilson, 2018). The advantage of these data is that it allows for a comparison across many countries and some variation over time in the cost of a container and provides a more accurate measure of transport costs than, for example, the previously discussed c.i.f over f.o.b ratio or physical distance.

Finally, some studies use more focused data. For example, Simonovska (2015) used the DHL shipping rates, Limao and Venables (2001) used shipping rate quotes for a standard 20-container from

> 18The survey has been recently discontinued. See WB Statement for more details.

20
Issler, J. V., C. Rodrigues, and R. Burjack. 2014. “Using Common Features to Understand the Behavior of Metal-Commodity Prices and Forecast them at Different Horizons.” _Journal of International Money and Finance_ 42 (C): 310-335.

Jammazi, R., and C. Aloui. 2012. “Crude Oil Price Forecasting: Experimental Evidence from Wavelet Decomposition and Neural Network Modeling.” _Energy Economics_ 34 (3): 828–841.

Jiang, Z.-Q., W.-J. Xie, and W.-X. Zhou. 2014. “Testing the Weak-Form Efficiency of the WTI Crude Oil Futures Market.” Physica A: Statistical Mechanics and its Applications 405 (C): 235244.

Jin, X. 2017. “Do futures prices help forecast the spot price?” _Journal of Futures Markets_ 37 (12): 1205-1225.

Kaboudan, M. A. 2001. “Compumetric Forecasting of Crude Oil Prices.” Conference proceedings, 2001 Congress on Evolutionary Computation, Seoul, 2001.

Kahraman, E., and O. Akay. 2022. “Comparison of exponential smoothing methods in forecasting global prices of main metals.” _Miner Econ (2022). https://doi.org/10.1007/s13563-022-00354-y_ Karlsson, S. 2013. “Forecasting with Bayesian Vector Autoregression.” In _Handbook of Economic Forecasting Volume 2_ , Part B, 791-897, edited by G. Elliott and A. Timmermann. Amsterdam: Elsevier.

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Khoshalan, H. A., J. Shakeri, I. Najmoddini, and M. Asadizadeh. 2021. “Forecasting Copper Price by Application of Robust Artificial Intelligence Techniques.” _Resources Policy_ 73 (C): 1-11.

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Kwas, M., and M. Rubascezk. 2021. “Forecasting Commodity Prices: Looking for a Benchmark.” _Forecasting_ 3 (2): 447-459.

Lalonde, R., Z. Zhu, and F. Demers. 2003. “Forecasting and Analyzing World Commodity Prices.” Bank of Canada Working Paper 03–24, Bank of Canada: Ottawa.

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65
# **IV Empirical Strategy**

Identifying the effects of IDP inflows on firms’ behavior is challenging, since migrants do not move to random locations. The displaced populations, for instance, may have chosen to move to more populated, less violent areas where local authorities have better control of the territory, where there are better economic opportunities, and where illegal armed groups are not active. To correct for these biases, we use a panel-instrumental variable approach. Our main specification is given by the following equations: where _j_ stands for firm, _m_ stands for municipality, and _t_ stands for year; _Y_ represents the firm decisions (including production, prices, and input demands);<sup>11</sup> IDP _mt_ represents the ratio of IDP inflows to population of working age (multiplied by 100 to ease interpretation);<sup>12</sup> _Xmt_ is a vector of municipal controls;<sup>13</sup> _γt_ , _γj_ , _θt_ , and _θj_ represent year and firm fixed effects; and 11For production we use the variables of gross production (which includes the value of all products produced by the firm valued at factory-gate prices plus inventories in production process), intermediate consumption (which includes the value of all inputs used in the production process), energy consumption (measured in kilowatts), and probability of firm entry and exit; for prices we use the variables of nominal input and output prices (where firms’ prices are defined as the plant-product-year observation estimated by dividing the value of revenues or expenditures by physical quantities); and input demands include employment (total, blue- and white-collar), gross investment (measured as the sum of new and used assets minus the sales of assets) and net investment (which excludes depreciation).

12Working age population includes individuals 12 years and older as defined by the Colombian statistics agency.

13The variables in this vector include interactions of year dummy fixed effects and i) armed attacks by illegal armed groups in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) public expenditures in 1995, v) central government transfers to health, education, and other expenditures in 1995, vi) number of financial institutions in 1995, vii) number of tax collection offices in 1995, viii) 2000 GDP share in agriculture, services, and industry, ix) municipal tax income in 1995, and x) night light density in 1995; department and year fixed effects; homicide rates; clashes between armed groups; and armed actions.

13
report also finds a clear gender gap in working time—men tend to work longer average hours than women worldwide, with women working shorter hours in almost every country studied. The explanation, according to the report, lies probably in women bearing the primary responsibility for unpaid work and care work for the children and the elderly, but this portion of the working time is not documented in the study.

Also relevant to our work is the empirical literature that shows that who controls cash income in the household has important consequences for how the income is spent. As shown among others by John Hoddinott and Lawrence Haddad (1995; see also Lawrence Haddad, 1999), Maurizio Bussolo et al. (2009) and Prospere Backiny-Yetna et al. (2009), a higher share of labor income earned by women within a household tends to increase the share of total spending allocated by the household to investments in human capital, especially for children. In turn, these investments tend to reduce poverty in the long run. Therefore, the fact that women are working mainly at home on domestic chores without being paid may have in itself negative implications for future poverty reduction.

Within the empirical literature, our work is part of a broader renewed interest observed in recent years in analytical work on the economics of time use (see for example the papers in Daniel Hamermesh and Gerard Pfann, 2005). The allocation of time has implications in a wide range of areas, as illustrated for example by work on transportation (Junyi Zhang et al., 2005) and taxation (Patricia Apps and Ray Rees, 2004). In developing countries, the issue of time use has been discussed in relationship among others to the ability of household members to increase their supply of labor (Constance Newman, 2002), given strict time constraints due among others to limited access to basic infrastructure services. The role of illness in limiting the ability of women to take advantage of economic opportunities due to the burden of care has also been highlighted (Nadeem Ilahi, 2000, 2001). A broader discussion of the implications of time use issues for growth and development is available in the report “Engendering Development” by the World Bank (2001; see also Mark Blackden and Chitra Bhanu, 1999, Alan Gelb, 2001, Apps, 2004, United Nations Development Programme,<sup>3</sup> 1995, and Blackden and Wodon, 2006).

> 3 According to the 1995 Human Development Report (United Nations Development Programme, 1995), using data from a sample of 31 countries, women’s share in total work was 53 per cent in developing countries and 51 per cent in industrial countries. Moreover, the distribution of women’s time was two thirds in unpaid work and only one-third in paid work (for men these percentages are reversed). In developing countries, men were found to spend even less time in unpaid work.

6
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|Kigali c. 2010|87.0%|
|---|---|
|Nairobi c. 2000|90.3%|
|Nairobi c. 2010|92.3%|

# LAND COVER ANALYSIS RESULTS

## **Dar es Salaam, Tanzania**

Dar es Salaam’s rapid population growth of 5.6% annually from 2002‐2012 makes it Africa’s 3<sup>rd</sup> fastest growing city. In 1963 the Ministry of Lands, Housing and Urban development used aerial photography to estimate that there were roughly 7,000 units in unplanned areas. By 1972 that number had more than tripled to 28,000.<sup>33</sup> In 2002, Tanzania’s Community Infrastructure Upgrading Project estimated that about 68% of the city’s population lived in unplanned settlements.<sup>34</sup> The UN echoes this estimate, approximating that in 1995, 70% of Dar es Salaam’s population resided in informal settlements. While the methodology used in the study cannot quantify ‘slums’ or informal settlements as they are defined by the UN (it is impossible to determine land tenure or sanitation from satellite imagery) this methodology is able to detect residential neighborhoods that appear irregularly planned. This analysis therefore uses ‘irregular’ as a proxy for slum or shanty.

Figure 6 below shows the land cover classification for Dar es Salaam. Land with irregular residential characteristics is shown in red, and appears predominately towards the core of the city in the western and northern side. The center of the city is marked with a black dot. Just beyond the commercial/industrial city center, irregular housing begins to appear with many of them found clustered near the large industrial park that transects the city (shown in purple in Figure 6). Upon closer inspection, small pockets of irregular settlements are detected directly adjacent to large warehouses and transit infrastructure.

In order to quantify these observed trends, the classified data layer was summarized by distance to the city center. Figure 6 shows how the coverage of residential (regular and irregular) and commercial/industrial land changes as one moves from the center to the periphery of the city. As expected, the first few kilometers of built‐up land around the CBD is dominated by commercial/industrial and residential regular, with a noticeable low percentage of irregular residential. It is not until 3 to 4 kilometers beyond the city center that residential irregular begins to appear, it then peaks between 6 to 9 kilometers with residential irregular occupying roughly 18% of the built‐up land.

In contrast, residential regular neighborhoods are spread throughout the city. The residential regular class captures neighborhoods with moderately dense housing units, some lawns/trees and gridded or well organized road network. Given this rather broad/encompassing definition, the class includes both attached homes (townhouses) within the city center that have a gridded road, as well as large development single family houses near the periphery. While there is a slight increase in residential

> 33 UN Habitat (2010) Dar es Salaam: A Rapidly Urbanising Informal City.

> 34 CIUP, 2004. Dar es Salaam Infrastructure Development Programme. Community Infrastructure Upgrading <u>Project.</u>

14
_WATER WHEN IT COUNTS_ _5_ cient water relative to their crop requirements decreased by 14.0 percentage points (from a pre-information weekly average of 27.48% of plots). High-frequency data on plot-level water availability suggests that these efficiency gains are achieved as farmers dynamically adjust their use to closely fit the water requirements over the crop cycle.

Farmers who were randomized into receiving additional, individualized information on water allocations over the crop life cycle were no more likely to change their behavior. Taken together, the findings suggest that providing relatively cheap information on general water requirements leads to water-conserving practices, with no additional benefits to providing more expensive individual feedback.

The remainder of the paper is organized as follows. Section II summarizes the context, data innovations, and establishes stylized facts from the one-year water monitoring phase of the study. Section III provides a theoretical framework for inefficiency arising from rules of thumb motivated by the baseline stylized facts. Section IV describes a field experiment that tests the impact of generalized vs. individualized feedback tools on the efficiency of water allocation, while Section V outlines the empirical methodology and main experimental results. The final section concludes.

# **II. Irrigation water use in Central Mozambique**

# _A. Study background_

Mozambique’s population primarily lives off low-productivity agriculture: 80% of workers are engaged in the agricultural sector, while its contribution to GDP is merely 25% (World Bank, 2011). In an effort to transform the agricultural sector, government and aid resources have been designated to support diversification of production into horticulture and other export-oriented crops. One of the main challenges farmers face is
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33
**4**

However, political and economic reforms have also brought enormous economic and social dislocations, often because of policy inconsistencies. Old command structures have been dismintled before new structures or rules were put in place or even agreed. An advance in some areas has often led to unforeseen consequences, accelerating, widening or, sometimes, delaying reforms somewhere else. A case in point is the loss of macro-economi **c** ontrol during 1992z aid 1993 that led to high inflation and rapid depreciation of the ruble.

It is too early to say whether the Russian reforms will succeed. The transformation of the Russian society into a democracy and market economy is a far greater challenge than nearly anywhere else. Nowhere else has central planning been taken as far, nowhere else were economic, structures as centralized and distorted as in the formner Soviet Union (see the World Bank, 1992 and IMF 1993). But in comparison to Russia's much more difficult starting point, considerable and unexpected progress has been made. The unsuccessful attempts at macroeconomic stabilization to date should not be allowed to cloud the judgement. Neither Germany nor Japan were able to attain price stability during their massive economic reallocation. following the war.. Hyperinflation facilitated then a new start by wiping out old debts. The same is true now in Russia. With structural reforms in place, there is a realistic chance for stabilization now to succeed.
# 8. BIBLIOGRAPHY

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|**Table 9: Microent**|**erprise financi**<br>|**al elements**<br>|**by activity ty**<br>|**pe (’000 Tk**<br>|**./year): 2000**|**-2010**|
|---|---|---|---|---|---|---|
|**Financial elements**|**Manuf. and**|**Transport**|**Trading**|**Service**|**Misc.**|**All**|
||**processing**||||**activities**|**activities**|
|||**200**|**0**||||
|Revenue|161,406.8|76,204.2|202,254.1|206,668.1|156,504.5|183,408.2|
|Operating cost|125,850.8|43,580.4|105,655.5|158,918.9|119,173.0|138,497.0|
|Family labor cost|5,262.4|4,243.2|6,148.4|5,950.2|5,097.6|5,619.0|
|(imputed)|||||||
|Profit (not accounting for|35,556.0|34,446.0|123,818.3|48,643.8|37,726.9|46,367.1|
|family labor)|||||||
|Profit (accounting for|30,337.4|30,230.3|118,330.9|42,862.6|32,823.0|40,903.5|
|family labor)|||||||
|Working capital|1,020.8|1,526.3|996.1|809.1|1,388.6|969.7|
|Value of the enterprise|82,046.1|134,102.0|450,225.8|121,976.2|78,898.2|121,537.8|
|||**200**<br>|**5**<br>||||
|Revenue|220,174.2|39,119.9|246,975.2|254,255.0|132,028.1|230,789.4|
|Operating cost|179,708.3|17,683.9|214,706.8|215,058.3|10,718.8|193,208.7|
|Family labor cost|10,423.7|5,113.1|10,110.5|7,795.9|7,562.6|7,942.6|
|(imputed)|||||||
|Profit (not accounting for|40,465.9|21,436.0|32,268.4|39,196.8|30,309.3|37,580.7|
|family labor)|||||||
|Profit (accounting for|32,399.8|16,651.8|24,461.2|32,078.7|23,247.2|30,497.9|
|family labor)|||||||
|Working capital|2,999.4|670.6|646.1|3,525.7|407.3|3,047.3|
|Value of the enterprise|65,783.5|36,464.2|123,537.4|79,658.5|56,944.0|75,458.1|
|||**201**|**0**||||
|Revenue|149,489.2|54,570.3|88,782.1|223,631.1|134,237.4|178,780.4|
|Operating cost|115,637.7|23,861.8|57,037.3|180,705.6|95,778.3|140,388.7|
|Family labor cost|8,410.7|6,134.3|8,665.5|9,610.7|19,220.5|9,847.2|
|(imputed)|||||||
|Profit (not accounting for|33,851.5|30,910.9|31,744.8|42,938.7|38,459.2|39,426.4|
|family labor )|||||||
|Profit (accounting for|32,399.8|16,651.8|24,461.2|32,078.7|23,247.2|31,263.9|

41