rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 13. Wheat open interest & prices<br>(000 contracts and $/ton)<br>600 600<br> Open Interest<br>500 Prices 500<br>400 400<br>300 300<br>200 200<br>100 100<br>0 0<br>Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08<br>Source: DECPG<br><!-- End of picture text -->

# **V. Summary and Conclusions**

The increase in internationally traded food prices from January 2002 to June 2008 was caused by a confluence of factors, but the most important was the large increase in biofuels production from grains and oilseeds in the U.S. and EU. Without these increases, global wheat and maize stocks would not have declined appreciably and price increases due to other factors would have been moderate. Land use changes in wheat exporting countries in response to increased plantings of oilseeds for biodiesel production limited expansion of wheat production that could have otherwise prevented the large declines in global wheat stocks and the resulting rise in wheat prices. The rapid rise in oilseed prices was caused mostly by demand for biodiesel production in response to incentives provided by policy changes in the EU beginning in 2001 and in the U.S. beginning in 2004. The large increase in rice prices was largely a response to the increase in wheat prices rather than to changes in rice production or stocks, and was thus indirectly related to the increase in biofuels. Recent export bans on grains and speculative activity would probably not have occurred without the large price increases due to biofuels production because they were largely responses to rising prices. Higher energy and fertilizer prices would have still increased crop production costs by about 15-20 percentage points in the U.S. and lesser amounts in countries with less intensive production practices. The backto-back droughts in Australia would not have had a large impact because they only reduced global grain exports by about 4 percent and other exporters would normally have been able to offset this loss. The decline of the dollar has contributed about 20 percentage points to the rise in dollar food prices.

Thus, the combination of higher energy prices and related increases in fertilizer prices and transport costs, and dollar weakness caused food prices to rise by about 35-40 16
<!-- Start of picture text --> “<br>s<br>= fC,<br>eo Ao So<br>wd] eT<br>°<br>1986 1988 1990 1992 yy 1996 1998 2000 2002<br>——— Quota price<br>sorrtr7> Market price<br>(7) 5/95 percentile - market price<br><!-- End of picture text --> <!-- Start of picture text --> cs<br>©<br>g<br>8<br>s*<br>2<br>°<br>°<br>1992 1994 1996 1998 2000 2002<br>year<br>—— Quota income<br>(7) 5/95 percentile<br><!-- End of picture text --> <!-- Start of picture text --> a<br>“<br>s .<br>z Po YO<br>eo Ue ST<br>© ennaa<br>°<br>1986 1988 1990 1992 yy 1996 1998 2000 2002<br>——— Quota price<br>corretee Market price<br>(= 7] 5/95 percentile - quota price<br><!-- End of picture text --> <!-- Start of picture text --> ©a<br>Bas<br>=<br>3=<br>5<br>=a.<br>30<br>°<br>1992 1994 1996 1998 2000 2002<br>year<br>—— Quota tax as percentage of income<br>(7) 5/95 percentile<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4858

# **Abstract**

The authors analyze five rounds of National Sample Survey data covering 1983, 1987/8, 1993/4, 1999/0, and 2004/5 to explore the relationship between rural diversification and poverty. Poverty in rural India declined at a modest rate during this period. The authors provide region-level estimates that illustrate considerable geographic heterogeneity in this progress. Poverty estimates correlate well with region-level data on changes in agricultural wage rates. Agricultural labor remains the preserve of the uneducated and also to a large extent of the scheduled castes and scheduled tribes. Although agricultural labor grew as a share of total economic activity over the first four rounds, it had fallen back to the levels observed at the beginning of the survey period by 2004. This all-India trajectory masks widely varying trends across states. During this period, the rural nonfarm sector grew modestly, mainly between the last two survey rounds. Regular non-farm employment remains largely associated with education levels and social status that are rare among the poor. However, casual labor and self-employment in the non-farm sector reveal greater involvement by disadvantaged groups in 2004 than in the preceding rounds. The implication for poverty is not immediately clear—the poor may be pushed into low-return casual non-farm activities due to lack of opportunities in the agricultural sector rather than being _pulled_ by high returns offered by the non-farm sector. Econometric estimates reveal that expansion of the nonfarm sector is associated with falling poverty via two routes: a direct impact on poverty that is likely due to a pro-poor marginal incidence of non-farm employment expansion; and an indirect impact attributable to the positive effect of non-farm employment growth on agricultural wages. The analysis also confirms the important contribution to rural poverty reduction from agricultural productivity, availability of land, and consumption levels in proximate urban areas.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to analyze processes and causes of poverty reduction in the developing world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rmurgai@worldbank.org and planjouw@ worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 10642

# **Abstract**

This paper leverages Sri Lanka’s 2021 chemical fertilizer import ban to quantify the costs of restricted fertilizer access for agriculture, trade, and welfare. By leveraging trade and production data, satellite-based yield estimates, and event studies, the authors document sharp declines in fertilizer imports, agricultural output, and exports. A quantitative spatial model of trade and agriculture estimates the ban’s average welfare cost at 7.3%, with farmers, estate workers, and fertilizer-intensive regions bearing disproportionate losses. Model-implied partial-equilibrium elasticities line up with the experimental evidence, but once general-equilibrium price and wage adjustments are accounted for, the elasticities are much smaller—implying that using partial-equilibrium estimates to assess large, economy-wide fertilizer shocks (like those seen during recent conflicts) would substantially overstate their effects. Domestic and trade policy interact: by curtailing fertilizer use, the import ban effectively contracted the country’s fertilizer subsidy program and its implicit transfers from mobile workers to farmers, thereby mitigating the former’s welfare losses.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dghose@worldbank.org, epinheirofraga@worldbank.org, and afernandes@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **3.  Quantifying Habitat Protection Factors**

To implement the model, we quantify the allocation factors using the database developed in Dasgupta, et al. (2012a). For each tiger habitat area, we develop composite measures as follows. Variables are denoted by letters from the previous equations.

- (1) Subspecies extinction risk (ρ).  Our measure has three components:

- a. Species numbers (T):  Table 1 provides recent estimates of surviving tiger populations, by subspecies and country.  Our measure for each subspecies is its share of the total tiger population.

- b. Overall habitat loss (D): For each habitat area, we compute natural forest cover remaining by August, 2011.  This combines measures from Hansen, et al. (2006) for 2000, Hansen, et al. (2008) for 2000 – 2005, and FORMA data for January, 2006 to August, 2011.  Across all areas, we total original and remaining forest cover by subspecies<sup>5</sup> , and compute the overall percentage of original forest that has been subject to clearing.

- c. Species distribution across countries (N):  In standard portfolio terms, spreading the remaining tigers across polities should reduce aggregate risk.  Our index of “portfolio risk” is the intercountry entropy measure (E) for each subspecies, where

> 5 See the Introduction, para. 1, for the geographic ranges of Bengal, Indochinese, Malayan and Sumatran tigers.

7
**Integrating Qualitative Methods into Investment Climate Impact Evaluations**<sup>**1**</sup>

# Alejandra Mendoza Alcántara

Trade and Competitiveness Global Practice World Bank

# Michael Woolcock

Development Research Group World Bank Key words: investment climate, regulation, impact evaluation, mixed methods, triangulation JEL codes: B5, K2, M13

> 1 This paper was written as part of the Investment Climate Impact Program. For detailed and helpful comments on an earlier draft we are grateful to David McKenzie, Audrey Sachs and Paul Shaffer (though of course we remain solely responsible for enduring errors of fact and/or interpretation). Email addresses for correspondence: <u>amendoza1@worldbank.org</u> and mwoolcock@worldbank.org.
# b. CONFLICT OUTBREAKS IN AFGHANISTAN

On September 28, 2015, the Taliban overran the Afghan military forces and took control of the city of Kunduz. During September alone, more than 500 people were killed or injured. The city was recaptured by government forces in a counteroffensive on October 1, but three days later the Taliban claimed to have regained control of most of it. Heavy fighting continued for several days before the Taliban finally withdrew on October 13. As can be seen clearly in Figure 7, the battle for Kunduz had a strong and long-lasting effect on nightlights intensity. Nightlight intensity halved in October and remained low until March 2016.

_Figure 7: Nightlight intensity in Kunduz in 2015/2016._ <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16<br><!-- End of picture text --> A more systematic analysis of the impact of conflict on economic activity is conducted combining nightlight data with conflict data at the district level. The number of casualties scaled by the local population, provides a defensible measure of the local intensity of conflict. In parallel, we again predict GDP at the district level, based on local nightlight intensity and rural population. With this information, it is possible to estimate whether surges in local conflict affect local GDP in the same month, quarter or year. The results shown in Table 4 suggest that each additional dead or injured person per 1,000 people reduces local GDP growth during the same month by close to 9 percentage points. Quarterly GDP 19
<!-- Start of picture text --> Total Household Income and Revenues Proxy (Nominal USD), Month<br>oO8 97 ~~-~-- UP Control Non-UP<br>©<br>lo} 61ee64<br>S OTT Sse<br>i ne<br>¢ 7<br>°<br>N<br>°<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Any Adult Ever Skipped or Cut Size of Meals in Past Month<br>o Percentage of Households<br>= —----UP Control ——— Non-UP<br>83<br>4 oe<br>ae 68<br>B55 ==<br>t+ 33<br>fo}g<br>oO<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Index of Per Capita Food Consumption Proxy<br>Control UP Households at EndlineH == 1, Constant<br>w+ 198<br>2<br>---=- UP Control ——— Non-UP<br>©<br>=4 1.36<br>TTS _ 1.30<br>N Tass >fsL<br>= ie<br>- “77>...1.00~<br>Midline Endline<br><!-- End of picture text --> <!-- Start of picture text --> Any Child Ever Skipped or Cut Size of Meals in Past Month<br>o Percentage of Households<br>= -----UP Control ——— Non-UP<br>3 76<br>Uoeeoc7<br>8 Ueno 57<br>oOt+ _-7<br>ay TT2<br>oO<br>Midline Endline<br><!-- End of picture text -->
non-adopters in A villages, and households who expressed interest to adopt if the technology is introduced and who were uninterested in B villages) were selected<sup>12</sup> .

The survey questionnaire was administered to 5,541 individuals in 955 rural households in each round who were selected through this multi-stage sampling approach. The survey collected detailed information on demographic characteristics, agricultural production, other income-earning activities, expenditure patterns, time allocation, individual food intakes, health, morbidity, and education. It also collected information on family background, marriage history, assets at marriage, transfers at marriage, inheritance, women mobility, and empowerment.

IFPRI sampling required that the households were representative of adopters and non-adopter households in A villages and likely adopters and likely non-adopters in B villages, and not necessarily representatives of rural Bangladeshi households. Nonetheless, a comparison of IFPRI sample with that of 1995-96 National Household Expenditure Survey (HES) of Bangladesh Bureau of Statistics of the Government of Bangladesh indicates that the IFPRI sample is broadly comparable to the nationally representative HES rural sample of 5,020 households based on household size, per capita expenditure, landholding, and poverty rates (Table 1).

# **5.1 Variables and Descriptives**

Table 1 provides the descriptive statistics of the variables used in the econometric analysis, and the variables are described below.

**Survey round and site dummies** : Survey round dummies (with 4th round omitted) are included to control for any agricultural seasonality that may affect nutrient consumption as in lean seasons there may be lack of food availability and labor market activities that can affect household food expenditure and income. As there are two major rice cultivation seasons, there are also two lean seasons that reduce employment and income earning opportunities. The major lean season is from mid-September to mid-November preceding the Aman harvest, which falls in round 2. The other lean season falls in round 3, which is from mid-March to mid-April, prior to Boro harvest<sup>13</sup> . Site dummies (with Jessore as omitted site) are included to control for any location specific effects, such as infrastructure, location endowment, market condition, health facility, etc., which may affect income earning opportunities, food availability and prices, health, and nutrition.

> **Village prices** : The survey collected data on household food expenditure and quantity

> 12The IFPRI evaluation concluded that adoption of these programs, had neither improved the micronutrient status of the adopting households through better quality diet nor increased their incomes. For a detailed description of the survey, see (IFRPI-BIDS-INFS, 1998).

> 13Aman and Boro are the rice grown in monsoon and dry season, respectively.

15
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Presumably, the impact on productive sectors would also be large. Given the considerable price adjustments necessary to eliminate subsidies and the consequent impact on household welfare, a gradual approach to subsidy reform would be preferable, even if a cash compensation scheme is put in place.

As in the case of food subsidies, we simulate two scenarios: 1) a 30% cut in subsidies for each product and 2) a 100% decrease (total elimination) of subsidies. Recall that a 30% cut in subsidies would result in a different price increase for each product since price levels vary across products. Table 21 reports for all energy products considered the initial subsidized price, the unit subsidy, the price following a 30% reduction in subsidy (Final price, scenario 1) and the price after the elimination of all subsidies (Final price, scenario 2). This last price is equivalent to the market reference price that we consider for each product.

The elimination of subsidies (scenario 2) would lead to exceptionally large price increases. The price of kerosene would need to rise 13.1 times to match the market price; that of gas LPG would need to rise by a factor of 11.5; and those of gasoline, diesel and electricity would need to rise by about eight times. The product whose price is currently the “closest” to market price (electricity) would still undergo a price increase of 7.8 times to match the market price. These are the largest gaps observed between subsidized and market prices in North Africa and the Middle East region and represent a real challenge for reform.

**Table 21: Energy Subsidy Reform: Two Scenarios (LYD per unit)**

||Initial price|Subsidy|Final<br>price(S1)|Final Market<br>price(S2)|Final price<br>(S2)/ initial<br>price|
|---|---|---|---|---|---|
|Gasoline|0.15|1.07|0.47|1.22|8.15|
|Diesel|0.15|1.11|0.48|1.26|8.40|
|Electricity|0.02|0.14|0.06|0.16|7.80|
|LPG|2.00|20.94|8.28|22.94|11.47|
|Kerosene|0.09|1.09|0.42|1.18|13.10|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

The direct cost of a complete elimination of subsidies to households is estimated at 2.5 billion LYD (Table 22), equivalent to the total amount of direct subsidies received by households. This is a very large sum, representing almost 20% of total household expenditure. A 30 percent reduction in subsidies on each product would cost households 0.75 billion LYD. These costs would be rather evenly distributed across quintiles with the exception of the first quintile which would bear a much lower cost than the rest. Interestingly, the quintile that would bear the greatest cost is the third quintile. In per capita terms, removing subsidies would cost more to the upper quintiles, as expected given the result that energy subsidies are regressive. Nonetheless, because energy expenditure represents a higher share of total expenditure for the poor, the per-capita loss of the lower quintiles represents a larger share of their total per-capita spending (Table 23), although the difference is not as stark as we found it to be in the case of food subsidy reforms.

25