rafmacalaba/datause-encoder-data huggingface dataset:
used to approximate the Ganges water flow from India in 2050 (see Annex 1). The water flow for the Brahmaputra in 2050 was approximated by its dry season water flow in 2012.<sup>24</sup>

# Projections for Sea Level Rise

IPCC AR4 projected increased global SLR between 0.18m and 0.59m across various emission scenarios over the next 100 years.<sup>25</sup> However, these projections were calculated from projections of SLR caused by thermal expansion and melting of glaciers—with the Greenland and Antarctic ice sheets calculated as being _close to mass balance_ —and _excluded_ rapid dynamic changes in ice flow (IPCC 2007b). Hence, it should be noted that the SLR estimates by IPCC AR4 are conservative.

The most recent evidence suggests that global SLR could reach one meter or more during this century (Hansen and Sato 2011; Vermeer and Rahmstorf 2009; Pfeffer et al. 2008; Hansen 2006, 2007; Rahmstorf 2007; Overpeck et al. 2006). These results focus on the dynamic implications of ice sheet instability and produce estimates significantly beyond the upper limit of the range cited by IPCC AR4 (IPCC 2007a): a 90 percent confidence interval of 18 to 59cm based principally on thermal expansion, with an additional 10 to 20cm allowed for a potential dynamic response from the Arctic and Antarctic ice sheets.<sup>26</sup> At present, regional estimates of SLR are scarce. In the absence of a scientific SLR estimate for the Bay of Bengal near the coast of Bangladesh, upon consultation with experts in Bangladesh, we use an estimate of between 27 and 32cm global SLR by 2050. This range was derived from various climate change scenarios as reported in Rahmstorf (2007).<sup>27</sup>

> 24 Brahmaputra is a trans-boundary river and one of the major rivers of Asia. The water of the Brahmaputra is currently shared by China, India, and Bangladesh. Predicting the water flow of the Brahmaputra is complex and should be a subject of future research.

> 25IPCC AR3 suggested an SLR of 0.09 to 0.88m by the year 2100 unless greenhouse gas emissions are reduced substantially (IPCC 2001).

> 26For a review of the scientific literature on sea level rise, see Dasgupta and Meisner (2009). 27This 27 to 32cm SLR is also the interval estimate of “medium sea level rise” by 2050 from a study by the World Bank (2011). See Neumann (2009) for details.

19
nity, three villages were sampled at random, and then households were sampled at random within the villages from lists enumerated on site. The baseline target sample size within each community was 15, with some variations to satisfy needs for a complementary study (analyzed in [[reference withheld]]). Non-response was typically infrequent enough that sampling targets were met. In six communities, however, the non-response rate was higher than 33%. The household level data are weighted to ensure balance in the numbers of treated and control households within each of the matching strata. The household survey was conducted in December 2009 through January 2010. We also obtained community level outcome measures from surveys with local chiefs and from the Table 1 reports summary and balance statistics for the raw matching covariates. The top panel shows how peacekeeping deployment (PKO) communities compared to non-PKO communities in general. We see important differences on most covariates. Relative to non-PKO communities, PKO communities tend to be less heavily concentrated in the Kru areas, closer to major roads, have much higher populations, fewer schools and health posts per capita, and much higher wartime conflict exposure. As such, simple comparisons across the two types of communities would likely produce false impressions about the effects of PKO deployments. The lower panel shows how balance is improved by the matching procedure. The gaps between the covariate distributions are closed considerably. Nonetheless, there remains some residual imbalance on the distance to major roads and, to a lesser extent, in the population sizes.<sup>16</sup> We account for these residual imbalances by including these (as well as all of the other) covariates in all of the regressions reported below.

> 16Distance to roads is measured in decimal degrees, where 1 decimal degree equals about 100 kilometers. Thus, the mean for PKO communities is about 5 km, whereas for non-PKO it is about 8km.

xiii
**Table 4:** Weighted summary statistics of covariates by sample type for 2018 campaign.

|||Mean|Sd|Min|Median|Max|N|
|---|---|---|---|---|---|---|---|
|**Self-reported Yi**|**elds (kg/ha)**|||||||
||_X−cc_|791.81|639.81|0.00|702.76|7052.24|1096|
|**Millet**|_Xcc_|829.18|651.81|0.00|695.98|6002.40|519|
||_X−cc −Xcc _|_−_37_._368||||||
||_X−cc_|933.42|950.33|0.00|725.57|8100.00|1435|
|**Sorghum**|_Xcc_|888.75|896.81|0.00|700.98|8130.08|693|
||_X−cc −Xcc_|44_._664||||||
||_X−cc_|3205.33|3475.40|0.00|2100.00|21000.00|899|
|**Rice**|_Xcc_|2963.22|3459.49|0.00|2035.28|19983.21|241|
||_X−cc −Xcc_|242_._103||||||
||_X−cc_|1845.10|1665.25|0.00|1443.02|18000.00|1465|
|**Maize**|_Xcc_|1890.11|1792.05|0.00|1428.57|18646.90|644|
||_X−cc −Xcc _|_−_45_._007||||||
||_X−cc_|535.53|454.56|0.00|427.38|2711.86|265|
|**Cowpea**|_Xcc_|553.83|484.77|0.00|434.66|2500.00|187|
||_X−cc −Xcc _|_−_18_._296||||||
||_X−cc_|1053.99|918.97|0.00|769.23|5378.57|1317|
|**Groundnut**|_Xcc_|1020.61|806.37|0.00|769.23|4911.59|554|
||_X−cc −Xcc_|33_._379||||||
|**Plot area (ha)**||||||||
||_X−cc_|2.52|2.85|0.05|1.84|44.09|1096|
|**Millet**|_Xcc_|2.16|2.18|0.07|1.50|21.01|519|
||_X−cc −Xcc_|0_._366<sup>_∗∗∗_</sup>||||||
||_X−cc_|1.79|2.26|0.02|1.18|52.05|1435|
|**Sorghum**|_Xcc_|1.75|1.65|0.03|1.31|13.61|693|
||_X−cc −Xcc_|0_._038||||||
||_X−cc_|1.21|1.59|0.01|0.76|30.00|899|
|**Rice**|_Xcc_|1.51|1.91|0.01|0.63|10.00|241|
||_X−cc −Xcc_|_−_0_._299||||||
||_X−cc_|1.53|1.60|0.01|1.07|22.97|1465|
|**Maize**|_Xcc_|1.48|1.59|0.02|1.00|17.80|644|
||_X−cc −Xcc_|0_._051||||||
||_X−cc_|0.74|1.08|0.00|0.48|8.01|265|
|**Cowpea**|_Xcc_|0.84|1.72|0.02|0.40|14.63|187|
||_X−cc −Xcc_|_−_0_._097||||||
||_X−cc_|0.72|0.90|0.00|0.49|14.18|1317|
|**Groundnut**|_Xcc_|0.77|1.08|0.03|0.48|9.48|554|
||_X−cc −Xcc_|_−_0_._046||||||

_Notes:_ Number of observations may differ by variable (missingness) depending on the level (plot or household).<sup>_†_</sup> denotes a dummy variable. Difference is assessed with t-statistics based on design-adjusted standard errors corrected for clustering at the enumeration area. Statistical significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01.

21
gifts.<sup>22</sup> That is, the expenditure measure we use reflects wellbeing after taking into account any self-production or any coping mechanisms used by households to smooth consumption (such as selling assets, help from friends and relatives, or benefits from government programs). The extent to which these impacts have long-run implications on the poverty status of the future welfare and poverty status of the household is beyond the scope of this paper.

Besides the weather shock variables we include variables that capture household composition (number of children in the household, number of adult males in the household, number of adult females in the household), characteristics of the household head (years of schooling of the household head, gender of the household head, and the age of the household head), an asset index,<sup>23</sup> and the characteristics of the housing unit (presence of a kitchen, access to tapped water indoors, presence of a toilet, access to piped sewage or septic tank, electricity, and flooring material). These variables are all thought to explain expenditures. To control for the agro-climatic conditions and other time invariant characteristics we introduce locality fixed effects. To account for any systematic change between the two survey periods, we control for the survey wave. Furthermore to account for the potentially different amount of resources available depending on the season in which the household responded to the survey, we introduce a season indicator variable. Appendix 2 gives the descriptive statistics of the variables used in the analyses.

# _5.1 Impacts of climatic variability on expenditures_

We run six different specifications with different measures of welfare and different measures of weather shocks based on equation (1). Given differences in the average climatic conditions in the North, and the Centre and South regions of the country besides including all rural households, we limit the sample to only those households in the North and to only those households in the Centre and South. The first set of specifications uses the (ln) per capita expenditures on all non-health items and the second set uses the (ln) per capita expenditures on

> 22 Given the way in which the expenditure survey was administered, we are unable to separate the value of consumption from own production from the value of goods received as gifts.  For about 7% of the rural households more than 50% of their food comes from non-purchased sources.  On average for a rural household about 7% of all food comes from non-purchased sources.

> 23 The asset index is based on the principal factor analysis of how many parcels of land the household owns, whether or not the household owns their residence, another house, bicycle, motor vehicle, an electric device, a washing machine or a stove, a domestic appliance, machinery or a tractor, bulls or cows, horses or mules, pigs or goats, or poultry.

18
# **Figure 2: Key model components for growth in Bangladesh**

<!-- Start of picture text --> Percentage points Percent<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>1985-1989 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014 2015-2019<br>Structural Improvements (SI) Persistence of SI<br>Other Persistence Global Factors<br>Residual GDP Growth (RHS)<br><!-- End of picture text --> _Note:_ GDP = gross domestic product; SI = structural improvements. The persistence is decomposed into the contribution of past structural improvements of the last three periods (“Persistence of SI”) and remaining persistence (“other persistence”).

Figure 3 shows a further disaggregation of the structural improvements into the six above-mentioned groups of variables. First, more openness to trade and FDI played a fundamental role in supporting growth starting in the late 1990s. Easing FDI regulations were complemented by the creation of new export processing zones (EPZs) and other trade liberalizations, which contributed to a boost in ready-made garment exports.<sup>18</sup> Overall, exports doubled from 10 percent of GDP in the mid1990s to over 20 percent of GDP in 2012, when exports as a share of GDP peaked. Due to the strong increase in RMG exports, which have accounted for over 80 percent of all exports in recent years, Bangladesh’s exports became more specialized over time. Our model suggests a positive impact on growth from this specialization in Bangladesh. More FDI contributed as much as trade openness and increased specialization. Although the government had a skeptical view of foreign investment in the 1970s and 1980s, it started encouraging FDI through policy changes and investment incentives in the 1990s. Among others, these included tax holidays, a duty-free facility for imports of capital machinery, investments in special economic zones, as well as allowing full foreign ownership and profit repatriation. As a result, FDI inflows increased from negligible amounts to 0.4 percent of GDP at the end of the 1990s. Most of the structural improvements date to the late 1990s and early 2000s, with slower reform progress in recent years.

> 18 The first EPZ (Chittagong) was already fully functional in 1984, but the second (Savar near Dhaka) was only established in 1993. Many more EPZs were created subsequently.

-17-
Uruguay Round Agreement in 1993, Thai government indicated no sign to greatly expand the export subsidy scheme. This was presumably because of both the budgetary constraint and the danger to raise food prices for the urban poor.

Since the mid1980s, the Thai government has introduced a commodity credit program akin to a program operated by the Commodity Credit Corporation in the United States in the past. By this program farmers can receive low-interest loans from the government for the pledge of their rice until the rice price will go up to a target level and, in the event that the price will not sufficiently rise, they can relinquish their debt by submitting the pledged rice to the government. This is a high-cost program unsustainable even in the United States. It is doubtful if this program can be expanded to such a scale as to render sufficient income supports for farmers in a middle-income country.

As industrialization in Thailand will continue to progress, comparative advantage in agriculture will decline further. For closing the rural-urban income gap, the government will continue to increase supports on farmers in various fronts, including subsidies on inputs and credits as well as price supports. Yet, it is unlikely that Thailand will able to expand the support programs to such a scale as to fully close the income gap before its economy will advance to a high-income stage.

# The experience of Japan

The current problem in Thailand at the middle-income stage, as reviewed in the previous section, may be better understood by comparing it with the economic transformation of Japan from the low-income to the high-income stage. Table 3 presents a synopsis of modern economic development in Japan from 1885 to 1995. Japan and Thailand opened to international trade at about the same time under the pressure of West; both were forced to sign unequal treaties – Thailand with the United Kingdom in 1855 and Japan with the United States in 1858. Despite this similarity, industrialization progressed much faster in Japan than in Thailand, probably owing to much scarcer endowments of natural resources, especially land for cultivation, making it more urgent in Japan to industrialize than in Thailand for surviving under open international trade (Bounlouane, et al., 2005). At any rate, in terms of per-capita GDP data in Table 3 (Column 1), it appears that Japan was able to approach the middle-income stage by the first decade of the 20 century. Until the First World War, 12
their experience with previous projects of this nature, but we have to mobilize them quickly. We have to meet our goal of a certain number of SHGs.”<sup>21</sup>

# **b) Social mapping to facilitate local knowledge versus achieving numeric targets**

Once key stakeholders in the village agreed on the merits of the project, the next step was to identify households to include in the project. This method of identifying the ‘poorest of the poor’ (or POP in JEEViKA’s parlance) involved using census data and hamlet-level information, but also brought in the community to identify the most vulnerable households in a way that established greater ownership and legitimacy over the process.

Facilitators in both Phase I and II demonstrated a comparable understanding of this process. What set them apart is that in Phase I villages, ACs and CCs shared a healthy skepticism towards the process. They knew that it was susceptible to being captured by a few groups, that some voices were louder than others when identifying target households, and most importantly that this list would have to be revised with time as they gained a greater understanding of the community, and the community understood more about the project. In a nutshell, decisions about the community were made _and_ changed in the field, with facilitators corrected by participants and vice versa, thereby establishing a model of engagement that was neither entirely bottom up nor top down but which evolved collaboratively on the frontlines.

In Phase II villages, facilitators collected data entirely as a means of arriving at a _number_ of target households. The more qualitative data collected in this stage were not utilized during the process of mobilizing women. Moreover, while the ACs and CCs did the mapping exercise and gathered the necessary data, mobilization was ultimately left to CRPs from another village to whom the numeric target was transferred. However, the qualitative data and the informal knowledge on village dynamics collected in the initial stages were not transferred to these CRPs. As a result, often the households identified by the ACs and CCs in the initial round as the poorest were not the same the households finally mobilized into the project by the CRPs.

> 21 Former AC, Nauganj.

20
- 2 -

# **I. INTRODUCTION**

In theory, railway transport generally has the advantage for large-volume long-haul freight operations. Because of the significant amount of fixed costs required, railway transport exhibits large economies of scale. It is a common view that 1 million net tons a year are needed to justify economic viability of railways (World Bank 2010). With an average haulage of more than 500 km, railways can achieve good financial performance (e.g., World Bank 2013). There is also an increasing recognition that railways are an important factor to foster sustainable green transportation. Theoretically, railways can be more than 50 percent greener than road transportation (Kopp et al., 2013). In the world, the rail sector accounts for 3.6 percent of total transport emissions, while it transports over 8 percent of the world’s passengers and goods (IEA and UIC, 2015).

Railway carries 6.3 percent of the global passenger transport demand and 9 percent of the total freight demand ( **Table 1** ). For passenger movements, road transport is a dominant mode in many countries, except for some countries, such as China, India, Japan and the Russian Federation, where railway accounts for about 15-30 percent of the total passenger transport ( **Figure 1** ). In Africa, railway passenger transport is minimal.

On the other hand, railway has a more vital role to play in freight transportation. With navigation excluded, rail carries the same amount of freight as road does. In non-OECD countries, the demand for rail freight transport has been increasing in recent years, while developed countries have been experiencing more stable rail freight activities ( **Figure 2** ). The rail dependency varies across countries: While rail freight accounts for more than 30 percent in India and the United States, the modal share of rail freight in Japan is less than 1 percent. In the Russian Federation, the share exceeds 85 percent.
- 37 -

in July, 1990, by 26 percent on average to remove the direct budgetary subsidy. Prices were controlled at the new higher levels, and consumers received partial compensation. In January, 1991, prices were liberalized, and jumped quickly by about 30 percent, before levelling off in March and starting to decline in response to excess supply, particularly of beef.

These price increases, although large by world standards, are modest in the East/Central European current context. Moreover, the larger Czechoslovak incomes and smaller share of food in family budgets eased the absorption of the shock. In Romania, in contrast, with partial price liberalization that pressures producer prices of grain to approximately half of world levels, the consumer price index for food is reported to have risen in April, 1991 to 255 compared to 100 in. October, 1990 _(Buletin Statistic de Pretun,_ _Nr._ 5, April 1991, Bucharest, Romania).

Consumers' adjustments in Romania and Bulgaria have been both admirable and painful, and more adjustment lies ahead. The puzzle of declining production and continued lines and "shortage" when prices are in theory free and have doubled in a **short** time is explained by the considerable degree of control that remains behind the retail level.

# Conclusion

The agricultural transition is an essential part of stabilization and adjustment in East/Central Europe because agncultural sectors are large aid food is important. The transition is not the story-book variety; agricultural producers cannot spin collectivized straw into market oriented gold and deliver it in the morning to the Minister of Finance. Like the miller's daughter, however, producers and their domestic and foreign advisors must name the thing they face before they have any power over it. An agricultural
In this study, we bridge the gap by proposing a comprehensive country- and product-level Discrepancy Index database that captures both the frequency of misreporting and its impact on overall recorded trade value. The database was created by leveraging the UN Comtrade database, capturing all available Harmonized System (HS) data from 1988 to 2022. Central to this approach is use of a bilateral Discrepancy Index (DI), which calculates the level of divergence between reported data and its corresponding mirror (data reported by trading partner).

This study makes several contributions towards measuring discrepancies and assessing reporter reliability in the UNSD Comtrade database. First, we provide a comprehensive coverage of discrepancy measurements across all periods of observation, reporting countries, products, and product detail for the HS nomenclature. Hence, resources provided here can assist researchers aiming to trace discrepancies to specific causes, whether logistic or otherwise. Second, we show that the functional form of DI presented here avoids some of the pitfalls of commonly used trade asymmetry measures and is therefore more suited for penalizing discrepancies that are least likely to be explained by some logistic factor. Third, we demonstrate how the international merchandise trade reporting discrepancy database can aid discrepancy analysis and resolve data reliability issues in the UN Comtrade database. By measuring discrepancy in reported values across time, reporting nomenclatures, trading partners and products, we identify an encouraging trend of an increasing quality of reported trade over time. We identify persistent serious quality problems linked to some reporters and note potential effects of reporting discrepancies on important trade indicators such as trade balances. Thus, this study also contributes to ongoing discourse regarding quality of data in international merchandise trade by providing measures and tools for its assessment and outlining important considerations in practical applications of trade data. More importantly, our results can equip trade analysts with the necessary tools to make informed decisions regarding the use of a country’s reported data or its mirror.

The rest of this paper is organized into five sections. Section 2 discusses the numerous factors contributing to discrepancies in international merchandise trade reporting. Section 3 discusses methods for measuring reporting discrepancies in international merchandise trade and provides details on main and aggregate indicators. Section 4 describes the data used in this analysis and provides details on accessing the discrepancy analysis tools developed in this study. Section 5 outlines the study’s key findings and results along with examples and country case studies.

4