rafmacalaba/datause-encoder-data huggingface dataset:
**Policy, Planning, and Research**

<!-- Start of picture text --> Trade and Transport<br><!-- End of picture text -->

- This paper a product of the Transport Division, Infrastructure and Urban Development Departnentis part of a larger effort in PPR to establish an effective framework for helping developing countries adjust to changing distribution logistics practices in intemational trade markets. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Teresa Lim, room S 10-029, extension 34894 (49 pages with tables). Containerization and multimodal transport * Develop and manage efficient subsystems arrangements are key features of the radical refor shipping, railway, road, and air transport structuring of transport logistics in international sectors as well as port and warehouse systems. trade in recent years.

To increase trade, India must tie into this highly organized intemational trade logistics network - but it has been totally unprepared to cope with the demanding arrangements common among its major trade partners. Peters recommends the following agenda for reform:

* Prepare a strategy for tying into intemational trade and transport logistics, particularly through containerization. This means organizing India's fragmented systems planning, control, and management, and involving the private sector in organizing logistics networks.

- Create a joint public-private sector task force to establish an agenda for market surveys and system analyses to identify system shortcomings and needs.

- Lift import duties on vital equipment and spares.

* Revise the regulatory and control net that now strangles transport. Above all, simplify customs procedures.

- Adapt trade-related banking and insurance arrangements.

* Promote the national freight forwarding industry by relaxing regulations that govem it.

* Encourage the participation of the private sector, particularly through equipment leasing and privatization of such facility operations as container terrninals.

The PPR Working Paper Series disseminates the findings of work under way in the Banks Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **Section1. Introduction**

Measurement of food access typically relies on more than a single indicator, particularly when making decisions about how and where to target emergency food assistance (e.g., Barrett 2010; IPC 2021; etc.). In part, this is because food access metrics may capture different aspects of poor food access and a wider range of metrics maximizes the chances that the various manifestations of poor food access be captured (e.g., Maxwell et al. 2014). But what exactly is captured by each indicator and which specific indicators should be included in a suite of indicators to best target food assistance and measure progress towards reducing food insecurity is far from settled (e.g., Headey and Ecker 2013; ICRC 2021; etc.). This is an urgent issue given rising levels of food insecurity in recent years and the need to distribute increasingly scarce resources to populations with the worst food access (e.g., UN 2020; FAO 2021; World Bank 2022; etc.).

Despite the clear need to track a number of different food access indicators, national statistical systems typically only collect data on a small number of food access indicators. In part, this is because the multipurpose household surveys in which food access indicators are often included are already long and survey fatigue can threaten overall data quality.<sup>1</sup> Within the small number of food security modules included in these surveys, there is a growing trend of including the FIES as one of the few food access measures. Nearly 100 countries have either incorporated the FIES into their national statistical systems or are in the process of doing so as of 2019.<sup>2</sup> Furthermore, in tracking the impacts of the COVID-19 pandemic, there were a very large number of surveys that included the FIES as the _only_ food access measure (e.g., FAO 2020; United Nations 2020; World Bank 2020; Adjognon et al. 2021; Amare et al. 2021; etc.). Part of the reason for the extensive reliance on the indicator is likely its inclusion as an official measure for tracking progress towards Sustainable Development Goal 2 (zero hunger).

However, the FIES needs to be more extensively validated beyond the work that

> 1For example, in the nationally representative survey analyzed here — the 2018/19 Nigerian Living Standards Survey (NLSS) — the only food access modules included are the Food Insecurity Experience Scale (FIES) and the Food Consumption Score (FCS).

> 2See ”https://www.fao.org/in-action/voices-of-the-hungry/background/en/” for a list of countries that have or are in the process of adopting the FIES.

2
|In (Middlc Management)||0.002<br>(5.5134)|||0.002<br>(55A4)||
|---|---|---|---|---|---|---|
|In (Upper Management)||0.794<br>(0.635)|||0.750<br>(0.635)||
|In (Wage Bill of<br>Temporary Workers)|6.2E-4<br>(42E4)|4.6E14<br>(3.9E.4)|1313-4<br>(2.OE-4)|62E-4<br>(42E-4)|4.6-4<br>(3.9E-4)|1.IE-4<br>(2.oE-4)|
|InK|0.152<br>(0.613)|-0.761<br>(0.486)|0204<br>(0.266)|0.164<br>(0.614)|-0.795<br>(0.486)|0239<br>(0268)|
|Time|-0.074<br>(0.069)|-0.051<br>(0.045)|-0.053<br>(0.033)|-0.076<br>(0.070)|-0.049<br>(0.045)|-0.05W<br>(0.033)|
|R2|0.946|0.923|0.923|0.962|0.945|0.939|
|N|51|51|62|51|51|62|

Significant at the 5 % statistical test leveL + giciant at the 10 % statistical test leveL Our sample in these industries consists of 11 fums.
**_Table 6: Dissatisfaction with Basic Social and Infrastructure Services, by income quintile, Cali, September 1999 (percent of population, by quintile)_**

|-------|---------------------------------------|**_1_**<br>-----------|**_2_**<br>---------|**_3_**<br>---------|**_4_**<br>----------|**_5_**<br>----------|**_average_**<br>---------------|
|---|---|---|---|---|---|---|---|
|dissa|tisfaction with|||||||
|-|electricity service|8.2|11.6|9.3|5.4|6.2|8.1|
|-|education (students)|9.1|9.2|7.0|9.5|8.2|8.6|
|-|water service|8.7|8.6|7.2|12.3|7.2|8.8|
|-|garbage collection|9.2|7.7|10.1|12.8|11.2|10.2|
|-|health (those using them)|24.7|16.2|17.9|16.0|17.5|18.4|
|-|sewerage|33.8|23.1|21.9|26.0|20.1|25.0|
|-|environmental cleanliness|61.2|66.8|60.5|64.3|60.9|62.7|

--------------------------------------------------------------------------------------------------------------Source: Own calculations based on EPSOC (1999).

However, while dissatisfaction levels varied significantly across services, the EPSOC also showed that there was relatively little variation of ratings across income groups. Dissatisfaction with electricity, education, water and garbage collection was always significantly lower than discontent with health services, sewerage and environmental cleanliness. The variation of ratings across income groups is particularly important for the inclusiveness of city social policies: if, as seen here for Cali, the service ratings do not show a marked variation across income groups, it is possible for the city administration to focus on the needs of the poorest population group while at the same time meeting preferences of the other groups within the city.

# _4.4. Budget Priorities_

It is of particular importance for the development of anti-poverty policies to understand the priorities of the poor – and to assess whether such priorities differ from the non-poor population. The EPSOC survey therefore included a module probing for such priorities. Tables 7 and 8 contain the results, again broken down by different income groups. Table 7 reports households’ priorities of the type of program (of the 13 included in the table) that were to be expanded if the municipality had available resources.<sup>21</sup> Table 8 then reports on the reverse scenario in which respondents had to choose one program for which they would suggest a reduction in municipal resources if this had to take place.

> 21 Respondents could only state one single preference.

21
# **Distributional Effects of Tobacco Taxation:**

# **A Comparative Analysis**

**Alan Fuchs, Fernanda González Icaza and Daniela Paz**<sup>1</sup>

JEL Codes: H23, H31, I18, O15

> 1 Fuchs: Poverty and Equity Global Practice, World Bank (email: <u>afuchs@worldbank.org). González Icaza: Poverty</u> and Equity Global Practice, World Bank (email: <u>mgonzalezicaza@worldbank.org). Paz: Kennedy School of</u> Government, Harvard University (email: <u>dap320@student.hks.harvard.edu). Support in the preparation of this</u> report has been provided by the World Bank’s Global Tobacco Control Program, co-financed by the Bill and Melinda Gates Foundation and Bloomberg Philanthropies. The authors are grateful to Patricio Marquez for comments and support. The findings, interpretations, and conclusions in this research note are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.
# References

Adelman, S., D. O. Gilligan, and K. Lehrer. 2007 “How Effective is Food For Education Programs? A Critical Assessment of the Evidence from Developing Countries” Unpublished manuscript. International Food Policy Research Institute, Washington, DC.

Adelman, S., H. Alderman, D. O. Gilligan, and K. Lehrer. 2008 “The Impact of Alternative Food for Education Programs on Learning Achievement and Cognitive Development in Northern Uganda” Unpublished manuscript. International Food Policy Research Institute, Washington, DC.

Ahmed, A. and C. del Ninno. 2002 “The Food for Education Program in Bangladesh: An Early Evaluation of its Impact on Educational Attainment and Food Security” FCND Discussion Paper 138, International Food Policy Research Institute, Washington, DC.

Ahmed, A. 2004 “Impact of Feeding Children in School: Evidence from Bangladesh” Unpublished manuscript. International Food Policy Research Institute, Washington, DC. Alderman, H., H. Hoogeveen, and M. Rossi. 2009. “Preschool Nutrition and Subsequent Schooling Attainment: Longitudinal Evidence from Tanzania.” _Economic Development and Cultural Change_ . 57(2):239-260.

Bhutta, Z., T. Ahmad, R. Black, S. Cousens, K. Dewey, E. Giugliani, B. Haider, B. Kirkwood, S. Morris, H. Sachdev, and M. Shekar. 2008 “What works? Interventions for Maternal and Child Undernutrition and Survival” _Lancet_ . 371(3):417-440.

Burges, G. 1995. “The Case for Randomized Field Trials in Economic and Policy Research” _Journal of Economic Perspectives_ ,.9 (2): 63-84.

de Onis, M., A. Onyango, E. Borghi, A. Siyam, C. Nishida, and J. Siekmann. 2007. “Development of WHO Growth Reference for School-Age Children and Adolescents,” _Bulletin of the World Health Organization_ . 85: 661-668.

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The AI Index 2024 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024.

**<mark>Noy, S., and Zhang, W.</mark>** <mark>2023. Experimental evidence on the productivity effects of generative artificial intelligence.</mark> _<mark>Science</mark>_ <mark>,</mark> _<mark>381</mark>_ <mark>(6654), 187-192.</mark>

**Pizzinelli, C., Panton, A., Tavares, M.M., Cazzaniga, M., and Li, L.** 2023 Labor Market Exposure to AI: Cross-country Differences and Distributional Implications. IMF Working Paper, WP/23/216.

**Svanberg, M.S., Li, W., Fleming, M., Goehring, B.C., and Thompson, N.** 2024. “Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?” MIT FutureTech, Working Paper. <u>http://dx.doi.org/10.2139/ssrn.4700751</u>

**<mark>Van Roy, V., Vertesy, D., and Damioli, G.</mark>** <mark>2020. AI and robotics innovation.</mark> _<mark>Handbook of labor, human resources and population economics</mark>_ <mark>, 1-35.</mark>

**Webb, M.** 2019. The Impact of Artificial Intelligence on the Labor Market. SSRN Scholarly Paper. Rochester, NY.

**World Bank** 2022. The State of Global Learning Poverty: 2022 Update. Washington, DC: World Bank.

**World Bank** 2024. Digital Progress and Trends Report 2023. Washington, DC: World Bank.

**World Economic Forum** 2023. Future of jobs report.

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# Figure 7. Risks to EMDE growth prospects: Financial stress

A. Share of global bonds rated BBB or below B. Share of EMDEs with negative growth around currency crises <!-- Start of picture text --> Percent of global index Percent<br>50 100<br>45 80<br>40 60<br>35<br>40<br>30<br>20<br>25<br>0<br>20 Currency Currency Currency,<br>and banking banking, and<br>sovereign debt<br>C. Number of countries with large D. U.S. term premium<br>currency depreciations<br>Number of countries Percent<br>6 10-20 percent 20-30 percent 6<br>>30 percent 1995-2000 average 5<br>5<br>2000-10 average 4<br>4<br>3<br>3 2<br>1<br>2<br>0<br>1<br>-1<br>0 -2<br>2005 2005 2006 2007 2008 2009 2010 2010 2011 2012 2013 2014 2015 2015 2016 2017 2018<br>2012 2013 2015 2016 2018 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br><!-- End of picture text -->

# C. Number of countries with large

currency depreciations Source: Dealogic, Federal Reserve Bank of New York, Laeven and Valencia (2018), World Bank. A. Last observation is July 2018.

B. Share of countries that experienced negative growth in the current or next year following a currency crisis, a currency and banking crisis, or a currency, banking, and sovereign debt crisis between 1975 and 2017.

C. Figure shows three-month moving averages. Depreciations are defined as negative quarterly changes in the effective exchange rate. The sample comprises 138 EMDEs. Last observation is December 2018.

D. Based on Adrian, Crump, and Meonch (2013) model of the term premia at a 10-year maturity. Last observation is August 2019.
Other Risks are not well-covered by supervisors in many developing countries that have been assessed. In practice there is then a need to enhance supervision here and Basel II may provide an incentive for extra resources to be provided. Of course, whatever the precise regulation adopted, the real job of supervisors is to consider whether the bank has sufficient capital for its overall risk-profile.

In terms of simply checking the capital adequacy against the regulation, the Basic Indicator approach would not require large resources if bank’s income statements are believed. The Standardized Approach is slightly more problematic as banks may have the incentive to massage their income statements such that greater gross income appears against the business lines that attract the lower operational risk. If supervisors have little experience or expertise, low effective control over and confidence in the veracity banks’ income statements, there may be an argument to staying with the Basic Indicator Approach.

However, as countries have experimented with different levels of the basic capital requirement for credit risk, it may also be that countries will wish to consider whether the actual percentages suggested by Basel II (currently 15% under the Basic Indicator or the various percentages under the Standardized Approach) are relevant for their particular circumstances. The 15% figure was calibrated largely on the basis of G10 banks. However, gross income in developing country banks tends to be high compared to their G10 counterparts, perhaps reflecting higher risks but also perhaps higher costs. It is understood that the results of Basel’s quantitative impact study (known as QIS3) indicated that the operational risk charge for some developing country banks could be very high. The appropriate calibration of the operational risk capital requirement for developing countries is then an issue for continuing future research.

# **2.4.5 Securitization Risk**

The securitization of bank-originated claims has grown enormously in recent years. While, in terms of size, the markets in G10 swamp those in developing countries, some developing countries have made significant advances in terms of the volumes of these transactions. Moreover, this is an area that is generally deemed to be one where developing countries should advance further in order to enhance the supply of credit in certain areas, reduce the risks in the banking sector (where long term assets are frequently funded by 24
for maize, soybeans and wheat prices in a reduced form econometric model. However, Byrne, Fazio, and Fiess (2013) using commodity data spanning over a century concluded that a common-factor drives comovement among ten prices and related such factor to the real interest rate. Yet, to extent that interest rates may alter both consumption of commodities and stockholding behavior, the _S/U_ ratio variable may capture the effect of interest rate as well. Such mixed evidence could also reflect that fact that low interest rates (by the United States) depress the U.S. dollar. Thus, the exchange rate elasticity could potentially capture part of the interest rate effect. However, this is an unlikely explanation for the commodities examined here because even in the cases where exchange rate did not matter much (wheat) or it was insignificant (palm oil), the parameter estimate of the interest rate was not significantly different from zero.

# **Assessing Post-2004 Price Movements**

The impact of the drivers on the post-2004 food price movements were further assessed in four ways. First, the model was re-estimated by excluding the post-2004 observations in order to examine by how much (and in what direction) the parameter estimates changed from the limited sample model (1960-2004) to the full sample model (19602012). Second, by using the parameter estimates of the full sample model, we measured the relative contribution of all explanatory variables to food price changes from 19972004 to 2005-12—these two 8-year periods can be viewed roughly as pre-boom and boom periods. Third, to check the forecasting power of the model, the parameter estimates from the 1960-2004 model were used to derive predicted prices for the 2005-12 period (that is, out-of-sample forecasts) and compare them with predicted prices from the full sample model (that is, in-sample forecasts). Fourth, the robustness of the model was further examined by re-estimating the equations as a panel by examining both conventional and cointegration statistics. The rest of this section elaborates on these extensions.

# Excluding Post-2004

Table 4 reports parameter estimates based on 1960-2004 observations. In terms of model performance, the average _adjusted-R_<sup>_2_</sup> is the same as in the full sample model while the unit root statistics indicate a stationary error term. In terms of individual performance, the explanatory power of palm oil is lower while that of rice is higher. A comparison of the full sample with the pre-2005 model yields mixed results for the _S/U_ ratio; its elasticity increases for maize and palm oil but declines for wheat, rice, and soybeans (figure 2, panel a). Interestingly, the _S/U_ ratio elasticity for rice is higher in the 1960-2004 model compared to the full sample model, implying that during the post-2004 period, the _S/U_ ratio of rice played limited role in the determination of rice prices. The relatively small and insignificant _S/U_ ratio elasticity for rice has important policy implications, especial— 12 —
|Table 5: Effect of the Caribbean Basin Initiative<br>Dependent Variable<br>Ratio of Car. Price to Average US Price<br>~~1902-2000 (CBD | 2001-02 (CBTPA)~~<br>0.709**<br>0.642**<br>Tariff Difference<br>(0.071)<br>(0.166)<br>0.036**<br>0.034**<br>LogofExport Value<br>(0.004)<br>(0.008)<br>—0.019**<br>—0.009<br>LogofTotalUS Imports<br>(0.005)<br>(0.012)<br>—0.014<br>—0.383<br>Constant<br>(0.084)<br>(0.259)|
|---|

** Statistically significant at the 1% level. Standard errors in parentheses. Each estimation

includes separate product group, year, country fixed effects.

26
It is evident from Table 8 that except for Tanzania, the overall PPPs based on household level data are greater than the ICP PPPs. This is in line with the finding from Table 5, where for almost the entire non-African region and some African countries this feature is observed. There are variations in PPP values across income quintiles, the values of the coefficient of variation (CV) for Iraq, Malawi, Tanzania and Vietnam being 0.152, 0.085, 0.084 and 0.074, respectively. This complements the evidence in favor of subnational PPPs, and is consistent with the variation across expenditure percentiles in PPP between India and Vietnam reported in Majumder, Ray and Sinha (2015a). In other words, poverty level PPP s are quite different from the aggregate PPPs, and this should be recognized when drawing up international poverty lines. However, this variation in PPP has little effect on the overall inequality between households pooled over these four countries as evident from the values of the Gini coefficient presented in Table 9.

[Table 9 about here]

# **6. Concluding Remarks**

This paper is written in the wake of the recent release by the World Bank (2015) of the final results of the 2011 International Comparison Project (ICP) that provided estimates of the Purchasing Power Parities (PPP) between different countries’ currencies. PPPs play a central role in economic analysis because they are required in a host of international welfare comparisons, such as cross-country comparisons of poverty rates, real GDP and real consumption expenditures, and the calculation of the total number of people in poverty. The 2011 ICP exercise has generated renewed interest in the concept of PPP that can be traced back to the early work of Cassel, Balassa and Samuelson. PPPs are considered better measures of the ‘true’ purchasing power of a country’s currency and more relevant in comparisons of living standards than exchange rates, since PPPs are based on both tradeable and non-tradeable items unlike exchange rates, which reflect only the price differences of tradeable items. This distinction makes the PPP of particular importance for developing countries with a lower consumption share of tradeable items leading to a greater deviation of the PPPs from exchange rates in relation to the developed countries.

Since inaccurate PPP estimates are likely to distort the cross-country comparisons, much attention has been focused on the accuracy of the estimated PPPs yielded by the successive ICPs. If, as happened between 1993 and the 2005, the PPPs are revised upwards sharply, the 29