rafmacalaba/datause-encoder-data huggingface dataset:
trade policy adjustments, that disincentivize private traders. A government commitment not to intervene directly on food prices is credible so long as government retains some options to protect the poor and vulnerable. So safety nets are essential to break non-cooperative interactions between private traders and governments. This will not ensure that countries with more safety nets will avoid price stabilization policies completely. As we observed in the 2007/08 food crisis, even countries with large safety net systems (e.g., India) used stabilization policies and are planning to increase storage facilities. Hence, an important research question would be to better understand this trade-off between stabilization policies and safety nets.

As countercyclical trade policy interventions are unlikely to decrease soon and the reliability of the world market is equally unlikely to increase, it should not be excluded that storage policies may still play a part in the policy mix in the future. While buffer-stock policies have proved difficult to manage and rarely delivered any additional stabilization, emergency stocks may appear to be a valuable alternative. Emergency stocks are stocks allowing to meet situations when there are short-run physical constraints on production and import preventing supply of needs. Although the topic of buffer-stock policies has been well researched, this is not the case for emergency stocks. The World Food Programme's (2011) feasibility study for the G20 on regional food reserves and the assessment of the Ethiopian Strategic Grain Reserve by Rashid and Lemma (2011) provide insights into the design of and the benefits that can be expected from emergency stocks. Research on emergency grain stocks could also be inspired by studies related to the management of strategic petroleum reserves and their disposal in the case of supply disruption or the embargo that emerged in the 1980s.

# **References**

- Abbott, Philip C. 2012a. “Stabilisation Policies in Developing Countries After the 2007-08 Food Crisis.” In _Agricultural Policies for Poverty Reduction_ , ed. J Brooks, 109–168. Paris: OECD Publishing.

- ———. 2012b. “Export Restrictions as Stabilization Responses to Food Crisis.” _American Journal of Agricultural Economics_ 94 (2): 428–434.

- Ahmed, Raisuddin, Steven Haggblade, and Tawfiq-e-Elahi Chowdhury, eds. 2000. _Out of the Shadow of Famine: Evolving Food Markets and Food Policy in Bangladesh_ . Baltimore: The Johns Hopkins University Press for IFPRI.

- Alderman, Harold, and Trina Haque. 2006. “Countercyclical Safety Nets for the Poor and Vulnerable.” _Food Policy_ 31 (4) (August): 372–383.

- Anderson, James E, and John G Riley. 1976. “International Trade with Fluctuating Prices.” _International Economic Review_ 17 (1): 76–97.

37
extracted from equation (5). If the 100-day ISD implies that early exercise is optimal, then we used the 100-day ISD from the previous day.<sup>6</sup> Models like this have been estimated before (see, for example, Day and Lewis, 1992), but only in the context of tests for market efficiency. They have not been used in forecasting exercises. The idea behind the market efficiency test is that if the options market is efficient then the ISD backed out from a properly specified options pricing model should capture all of the volatility of the spot prices that can be predicted based on the current information set. nhe implication is that all of the coefficients in the variance equation of model (11, should be zero except for the coefficient on the ISD term. If either a or p3 remains significant upon inclusion of the ISD term, then there is information in the past time series of volatility which is not incorporated in the market's expectations of future volatility, but is relevant in predicting future volatility. This implies that the options are mispriced and past volatility data can be used to take advantage of the mispricing.

## Forecasts from this model are made with the following equation:

Again, we forecast ht+. for each period between now and our forecast horizon, and the square root of the sum of these forecasts is our forecast of volatility:

# (13)

where E(ht+il!t) is compu.ed from equation (12). We call this forecast COMBt,T because it combines the ISD and GARCH forecasts. This gives us six forecasts of volatility: 3 ISD forecasts, 2 time series forecasts, and a combination ISD and GARCH forecast.

## IV. Data.

The forecasting methods presented above are evaluated using daily data for cocoa, corn, cotton, gold, silver, sugar and wheat. The time span covered for each commodity varies slightly, depending on data availability, but usually extends from about January 1987 - November 1990. The ISD-based forecasts require data on futures prices, interest rates and options prices. The futures data is daily closing prices, obtained from Knight-Ridder Financial Services. The interest rates we use are 1 **_l_**

> **6** We **225-day ISDs because they are much more heavily traded and are much more commonly Vnalyzed in the literaturpfound that the horizon of the ISDs did not matter in equation (11). We therefore used 100-day ISDs instea l ,.**

9
Our data consist of primarily the date of the trip, the class of the trip (divided into coach, business, or first), the origin city, the destination city, and the number of flight miles. It is important to note the data do not consistently include the origin or destination airports (rather than cities), and also do not include aircraft types. Also, instant upgrades upon check-in are not reflected in the data.

A second data set was obtained from the World Bank’s Diio’s SRS database subscription, comprising most scheduled flights globally for CY-09, including the equipment code for each flight. The annual data has 2,122,509 records, reflecting some 26 million scheduled flights world-wide.

In order to obtain the equipment code for an AMEX-booked flight, the origins and destination cities needed to be mapped to their relative origin and destination airport codes, which would then allow mapping the scheduling data with the actual flights using the date, origin, destination, airport code, airline code, and flight number. About 30 cities in the AMEX data (out of 1,049) could not be matched with the SRS data. A total of 108,344 exact matches of flight records could be accomplished, representing 157,297 trips as the sample was prepared for Phase I. These flights were than matched with tables provided by ICAO mapping most of the flights to an aircraft code that had a total fuel consumption value for a given distance.

# **5.2 Features of HQ-Based Travel in CY-09**

Three steps can be identified in the calculation of the carbon footprint of headquarters (HQ)based WBG staff air travel for CY-09, as follows:

**Step I:** Calculations were first made using a random sample of the cabin class configurations of aircraft used for the relevant flights. By using detailed information from several websites where such data are available, a fairly accurate match between the flights and the configuration of the aircraft used by the specific airline was made.<sup>24</sup> Of 1,219 airline/aircraft combinations, 304 could be directly verified, covering 123,972 flights. Estimates were made by counting the number of seats occupied in the same sized space between classes, which meant that multiples based on economy seats could be established for business and first class seats, reflecting not only the dimensions of the seats themselves, but also isle width and row spacing. From the 304 verified configurations, assumptions were made regarding the remaining 915 airline/aircraft configurations, making a total of 157,297 flights usable for analysis. These were then matched with known fuel consumption factors with tables provided by the International Civil Aviation Organization (ICAO).  In this phase of the calculations a single industry standard load factor of 71 percent (corresponding to the overall industry average for 2009) was applied for the carbon footprint calculations.<sup>25</sup>

> 24 The information sources included seatguru.com and seatmaestro.com.

> 25 The “load factor” corresponds to the share of available seats that is actually occupied on any given flight. Load factors can vary widely, by travel route and not least by flight class.

17
Policy Research Working Paper 7161

# **Abstract**

This paper analyzes the determinants of export flow survival in Georgia. The paper uses a unique Georgian firm-level data set, in which firms’ characteristics and output dynamics are matched with their customs’ export transactions, for the period 2006–12. A discrete survival model is used to explore the role of firm level characteristics, diversification strategies, and network effects on the survival rates of export flows. Low survival rates at the product level are found to limit the ability of Georgian firms to consolidate new products in international markets. The analysis finds that it is production efficiency, rather than size, that boosts export survival chances, that firms’ diversification strategies matter for the prospects of survival, and that there is strong evidence of network effects in export survival. The analysis also finds that ratified foreign trade agreements contribute to increase the survival of export flows by reducing policy-induced trading costs and increasing information about destination markets.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at gvarela@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
in phase 4 is 242 students. While there may be multiple (competing) explanations for this pattern, length of exposure to the program is among them.

_Program benefits_ : The main program benefit is an enrollment-related subsidy: the school receives a monthly per-student cash subsidy amount of 300 rupees (US$3.5) up to a maximum of 750 students (i.e., the total amount is capped at 225,000 rupees or US$2,647).<sup>11</sup> Given a mean school size of 215 students in phase-3 and phase-4 program schools at the time of application to the program, the mean monthly subsidy payment is roughly 64,500 rupees (US$759) at program entry. Enrollment information for determining the subsidy amounts is submitted by program schools to PEF on a monthly basis using standardized reporting forms; if enrollment has increased by 50 students or more over one month, PEF visits the school to verify the information before raising the subsidy amount. PEF indicates that, when schools report large changes in enrollment, these reports tend to be at the start of the academic year in April.

PEF reports that the subsidy level was set low for two reasons. First, it confines the attractiveness of the program to low-cost private schools. Second, it raises the political palatability of the program as the per-student subsidy amount is less than half of the estimated per-student expenditure in the public school system at the time the program was introduced.<sup>12</sup> The subsidy benefit is paid for all twelve months of the year. To facilitate timely and regular payments, starting in August 2007, the benefit amounts have been transferred electronically to the bank accounts of program schools.

> 11 The program also offers two cash bonus benefits. The first is a teacher bonus for a high level of school test performance: once every academic year, a maximum of five teachers in each program school where at least 90% of students in tested classes obtain a score of 40% or higher in the QAT receive an award of 10,000 rupees (US$118) each. The second is a competitive school bonus for top school test performance: once every academic year, the program school in each of the seven main program districts which has the highest share of students with a score of 40% or higher in the QAT is awarded 50,000 rupees (US$588).

> 12 The exact subsidy amount was heavily guided by a survey conducted by PEF in 2005 in selected districts which showed that the vast majority of private schools that operate in rural areas and disadvantaged urban neighborhoods charge between 50–400 rupees per month (US$0.6–4.7) in fees. Based on this information, the subsidy amount was set at the upper-segment of this price range (Malik 2007).

11
poverty are of a similar magnitude, and in general larger. In terms of inequality, accounting for FAFH reduces the Gini coefficient by 3.4 percent, a statistically significant reduction.

Finally, we explore whether these effects change our general understanding of who the poor are. We confirm that by accounting for FAFH, poverty profiles and general characteristics about the household such as demographics, education or labor market differ due to the fact that individuals are moved around when we account for FAFH. As a result, not only the number of poor individuals, but the characterization of the poor population is significantly different.

In all, our findings suggest that ignoring the increasing importance of FAFH in household surveys around the world – even when poverty is measured based on income but with a poverty line that is estimated using a food basket - can seriously affect welfare measures and our general understanding of who the poor are. Given that the direction of this bias is unknown ex ante, accounting for FAFH is even more important. Future research should aim to expand and validate these findings in more settings, and deepen the analysis by better understanding not just how much people are spending on FAFH but potentially on what those FAFH meals include.

25
Since SCPs are more popular in Colombia than other Latin American countries, the ample supply of SCPs in Colombia provides an excellent context to study these programs’ contributions.<sup>9</sup> We collect countrywide administrative data on student background characteristics and outcomes, including learning outcomes at the end of the program (reading and quantitative reasoning test scores) as well as graduation and labor market outcomes (formal sector employment and wages). We combine the student-level data with detailed information on characteristics and practices from the universe of programs and SCP providers in the country and build novel, complementary measures of local SCP supply and the competitive environment facing institutions and programs. This rich data allows us to minimize self-selection when estimating program contributions (Hoxby, 2020).

Following Melguizo et al. (2016) and Smith and Stange (2016), we use program fixedeffect methods to estimate multiple value-added models. Like Minaya and Scott-Clayton (2022), we assess how estimates differ across models. We first control just for individual characteristics ( _I_ ), then _I_ and peer characteristics ( _Z_ ), and subsequently include _I_ and _Z_ along with measures of local SCP supply ( _S,_ which includes measures of program supply, capacity, selectivity, and cost) at the student’s high school city. We find that including additional controls beyond _I_ yields substantially different estimates of the contribution to learning outcomes but not to graduation or labor market outcomes. This implies that, for the latter, self-selection is addressed appropriately by including only variables for individual characteristics, _I_ . Since the specification that includes _I, Z, S,_ and program-level fixed effects as regressors yields the lowest root mean squared residual by outcome, our analysis relies on this model.

> 9 While less than 10 percent of higher education students in Latin America and the Caribbean were enrolled in SCPs in 2017, this share was about 30 percent in Colombia (Ferreyra et al. 2021), close to that in the United States.

3
conducting a full census is nevertheless cost-prohibitive, a related option would be first to identify the poorest areas based on poverty maps at higher levels of geographic aggregation where they tend to be more reliable, and then to survey all households in these geographic areas. We provide evidence suggesting the targeting gains from this approach would also be relatively larger than those from improving the PMT estimates by adding more difficult to observe assets.

Although beyond the implications of our study, another complementary strategy deserving of future investigation would be to transform the household targeting system registration into a more open process in order to allow greater entry. Other developing countries, like Colombia and the Philippines, combine a complete census in the poorest areas with on-demand applications (also referred to as selftargeting) in other areas, in an attempt to survey as many poor households as possible while maintaining relatively low total registration costs. In an on-demand approach, households that consider themselves eligible for a given program are allowed to apply for inclusion in the registry. This approach may contribute to improve the performance of registries in terms of undercoverage in a situation where there is substantial churning in and out of poverty by allowing households to apply when their circumstances change.<sup>25</sup> In a randomized pilot experiment in Indonesia, Alatas et al. (2016) find that self-targeting leads to similar undercoverage as full enumeration at a lower overall cost, since it surveys fewer households. However, further research is needed to assess the costeffectiveness of these different strategies, especially given evidence that self-targeting excludes some of the poorest households (that do not apply) and leads to higher costs directly incurred by poor households.

Either of these strategies could be further bolstered with greater focus on improving the costeffectiveness of the household registration process. For instance, one cost-effective alternative may be to shorten the targeting questionnaire to allow a larger number of households to be surveyed at a lower cost, which might not necessarily come at the expense of targeting accuracy. Indeed, Bah

> 25 In addition, in order to take advantage of local knowledge about households’ welfare, an on-demand application system should be combined with local appeal committees comprising community members. These appeal committees could in particular reassess the eligibility status of households that feel they have been wrongly excluded from certain programs, ‘out of the PMT box’.

26
across treated firms that adopt the IHC program and control firms that do not adopt the IHC program in the periods before and after the firms adopt the IHC program. Our benchmark DiD regression includes a treatment dummy defined for each treated firm as 1 for any month after the firm has begun to clear imports using the IHC program and 0 otherwise and defined for each control firm as 0 in every month, as well as firm and month fixed effects. The DiD regression is a simplified version of Eq. (2) where ��� �� (i.e., there is a single average treatment effect), �� is constant over time (i.e., firm fixed effects are included), and the vector of conditioning variables is ignored (��=0). Our DiD regressions are estimated based on a sample that includes all 21 treated firms and a set of control firms (that did not adopt the IHC program), from within the same deciles (4-10) of total 2010 import value as the treated firms. This choice of control firms is useful for comparison to the SCM results. The DiD regression results are reported in Table 6. The estimates show a significant negative effect of the IHC program on customs clearance times at various points in the distribution but no effect on inspection rates or import value.

|**Table 6. Difference**|**-in-Differences R**<br>Median<br>clearance time|**egressions, Pooli**<br>Clearance time<br>75<sup>th</sup>pct.|**ng Across Treat**<br>Clearance<br>time 90<sup>th</sup>pct.|**ed Firms**<br>Inspection<br>rate|Import value|
|---|---|---|---|---|---|
|Treatment dummy|-0.223***|-0.508***|-0.549***|-0.007|0.035|
||(0.043)|(0.049)|(0.056)|(0.006)|(0.034)|
|Firm fixed effects|Yes|Yes|Yes|Yes|Yes|
|Month fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|50,738|50,738|50,738|50,738|50,738|
|R-squared|0.777|0.759|0.734|0.741|0.857|

Notes: Standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variables are 3-month moving averages of the variables in logs (with the exception of inspection rate that is in levels). The sample is limited to the treated firms and the control firms included in deciles 4-10 of firm total import value in 2010.

The DiD estimator, however, relies on strong identifying assumptions. It requires that, in the absence of treatment, the outcome variables for treated and control firms would have experienced similar time trends. In order to test this assumption, we consider data for the 21 treated firms and control firms in the pre-treatment period (defined to be all months prior to July 2011, when the first firm adopted the IHC program) and estimate a regression of each of the five outcome variables on a common time trend and the interaction of the trend with a dummy for future treatment indicating that the firm will adopt the IHC program within our sample period. As in Table 6, we estimate each regression for a sample including all treated firms and only control firms 30
Table 45

|Dependent variable: F|iscal adjus|tment whe|n needed b|ased on de|bt/revenue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|-0.011|-0.009|-0.010|-0.008|-0.009|-0.012|-0.018|-0.021|
|(% of GDP)|(0.007)|(0.008)|(0.008)|(0.008)|(0.012)|(0.022)|(0.018)|(0.014)|
|Primary gap (t-1)|0.001|0.006|0.001|0.004|0.004|0.004|0.004|0.005|
||(0.003)|(0.005)|(0.003)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.669||-0.271|-0.284|-0.276|-0.233|-0.301|
|||(0.835)||(0.872)|(0.879)|(0.874)|(0.892)|(0.868)|
|Inflation (t-1)||-0.038||0.031|0.026|0.031|0.046|-0.013|
|||(0.206)||(0.201)|(0.205)|(0.201)|(0.208)|(0.200)|
|U.S. Interest rate||-0.028*||-0.026|-0.026|-0.025|-0.030|-0.027|
|||(0.017)||(0.019)|(0.021)|(0.020)|(0.022)|(0.020)|
|**Political factors**|||||||||
|Gini|||0.006|0.006|0.006|0.004|0.006|0.006|
||||(0.007)|(0.007)|(0.007)|(0.014)|(0.007)|(0.007)|
|Rule of law|||-0.027|0.001|0.001|0.000|-0.062|0.003|
||||(0.061)|(0.071)|(0.071)|(0.070)|(0.108)|(0.071)|
|Margin of Majority|||0.456*|0.442*|0.440*|0.438*|0.442*|-0.086|
||||(0.234)|(0.231)|(0.231)|(0.234)|(0.232)|(0.379)|
|Election's year|||-0.031|-0.030|-0.030|-0.030|-0.030|-0.027|
||||(0.041)|(0.042)|(0.042)|(0.042)|(0.042)|(0.041)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||0.002<br>(0.016)||||
|Gini * Rigidity||||||0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||0.002<br>(0.004)||
|Majority * Rigidity||||||||0.023<br>|
|||||||||(0.019)|
|Constant|0.613***|0.689***|0.226|0.178|0.183|0.287|0.451|0.476|
||(0.184)|(0.231)|(0.511)|(0.525)|(0.528)|(0.715)|(0.568)|(0.545)|
|Observations|562|559|504|503|503|503|503|503|
|R-squared|0.005|0.013|0.014|0.021|0.021|0.021|0.021|0.024|
|Number of ifscode|84|84|75|75|75|75|75|75|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard erro|rs clustere|d by country|in parent|heses|||||
|***p<0.01,**p<0.0|5,*p<0.1||||||||

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