rafmacalaba/datause-encoder-data huggingface dataset:
_32_ _Price Support at Any Price?_

# Table 5.2 summarizes main achievements in restructuring the Polish agriculture.

# **Tab.5.2 EU membership - adjustment path Simulation experiments: POLICY EFFECTS**

||Fast path<br>reduc<br>(EU pr. by 20|/output<br>tion<br>00, quotas)|Gradual int<br>full fin|egration<br>ancing|Gradual in<br>budget co|tegration<br>nstrained|
|---|---|---|---|---|---|---|
||2000<br>F|2010|2000|2010|2000 7|2010|
|Income Relation|||||||
|(farmer's income/wage)|||||||
|index, 91 level=100|134.1|133.9|117.8|178.4|111.9|_151.5_|
|index, base sol.=100|123.8|120.3|108.8|161.0|103.3|136.8|
|Number of farmers|||||||
|in millions|3.084|2.651|2.487|1.508|2.738|1.922|
|index, base sol.=100<br>|106.6|109.7|85.9|62.4|94.6|79.6|
|Employment in<br>agriculture as % of total||17.3%||9.9%||12.6%|
|Average farm area|||||||
|<br>in hectares|6.7|7.8|8.3|13.7|7.6|10.8|
|index, base sol.=100|93.8|91.1|116.4|160.2|105.7|125.7|
|Output per farmer|||||||
|<br>index, 91 level= 100<br>|118.4<br>|164.1<br>|153.5<br>|296.1<br>|139.5<br>|232.4<br>|
|l index, base sol.= 100|_<br>89.7|88.9|116.4|160.4|105.7|125.9|

_Source: Model POLAGR, simulation Jan.1995_ The FULL FINANCING scenario leads to an equilibrium position of the agricultural sector characterized by less than 10% of total employment, a share that allows for avoiding a big gap in iabor productivity between agriculture and non agricultural sectors. The number of farmers is reduced by two thirds, and the productivity tripled (a result 60% above the base run level). The average farm area grows to 13.7 ha (in the base run 8.6 ha, in the feasible price supporting policy experiment 7.8 ha).

The BUDGET CONSTRAINT scenario leads to similar, but much more modest results (25 _%_ productivity growth, farm area 10.8 ha).

Both scenarios (and particularly the FULL FINANCING scenario) allow for a big reduction of the initial gap between agricultural sectors of Poland and the EU. They both also have a positive macroeconomic impact, shown in table 5.3.
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**Table 9: Ranking of delegations in the Kasserine and Médenine governorates**

||Indicator|Rank|
|---|---|---|
|Djerba Houmet Souk|0.437|58|
|Kasserine Nord|0.437|59|
|Djerba Midoun|0.390|85|
|Zarzis|0.365|102|
|Médenine Nord|0.322|128|
|Médenine Sud|0.298|142|
|Djerba Ajim|0.278|154|
|Ben Gardane|0.273|156|
|Sbeitla|0.213|179|
|Ezzouhour|0.185|201|
|Feriana|0.177|204|
|Sidi Makhloulf|0.152|226|
|Thala|0.144|230|
|Beni Khedech|0.139|237|
|Sbiba|0.115|244|
|Foussana|0.112|247|
|Djedeliane|0.108|250|
|Kasserine Sud|0.095|255|
|Hidra|0.078|259|
|Majel Bel Abbès|0.044|261|
|El Ayoun|0.005|263|
|Hassi Ferid|0.000|264|

Source: ITCEQ (Tunisian Institute of Competitiveness and Quantitative Studies)

**The Médenine governorate has a relatively broad range of economic activities, including tourism and farming focused on olive oil production and fisheries in the Djerba and Zarzis delegations.** According to a study of regional development in Tunisia by the Ministry of Regional Development and Planning, the unemployment rate in the governorate (11%) is well below the national average (15%), although there is a significant difference between the various delegations that make up the region (see Figure 3) (Ministère du Développement Régional et de la Planification 2012b and 2012c).

23
proportions are nearly the double that for the age group 35–44. In LAC nearly half of 20year-old youth have not completed secondary education and as many as three out of four in rural areas have not completed this level (La Cava et al. 2004). In Haiti figures are worse than the LAC region’s average because more than half of 20-year-olds do not have secondary education and only one out of three has completed it in rural areas. As Verner (2005) shows, education has a large significantly positive wage return in Haiti. Moreover, education is a protective factor because it preempts most negative outcomes and leads to postponed age of starting families, better health, fewer children, prioritization of children’s education, better preparation for the labor market, and therefore better jobs and higher income. Yet, as Jiménez (2005) states, 85 percent of slum dwellers’ children in Rio de Janeiro had higher education than their parents, but just 59 percent had better jobs. Such frustrated expectations may lead to anger and violence. Therefore, it is also important to create opportunities for those who have completed their education.

|Table 4.2:|Level of Comple|ted Educatio|n 2001(percen|t)|
|---|---|---|---|---|
|Age|No education|Primary|Secondary|Tertiary|
|15–19|13.4|55.8|30.7|NA|
|20–24|17.6|34.7|47.0|0.6|
|15–24|15.2|46.6|37.9|0.3|
|25–34|28.7|30.3|37.4|3.6|
|35–44|48.1|28.5|20.8|2.5|

_Source:_ Own calculations based on ECVH 2001.

A series of factors, such as high costs, housework, or marriage, results in youth not attending school. Some youth never enroll in school, some drop out or are pushed out, and some leave after completing their education. In connection to the positive relation between schooling and general performance in life, it is of interest to examine the characteristics of the group not attending school. This helps to identify factors that could increase incentives and possibilities for an increased education level in Haiti. Table 4.3 shows that the main reason for youth not attending school is that the cost of schooling is too high.<sup>15</sup> More than nine out of ten parents expect their child to graduate. However, for children who do not graduate, two out of three parents give high education costs as the main reason for not expecting their children to complete their education. In addition, illness, repeated failure, marriage, restrictions for girls, family disintegration, and work in or outside the household are important factors for not attending school (see Table 4.3), as discussed below.

> 15 In 1980 the direct cost of schooling represented on average around 12 percent of per capita income. In comparison average expenditures in other low-income countries were 3 to 4 percent of household income (Salmi 2000).

11
argued that European influence in the Middle East and North Africa fueled more government centralization (Hanna, 1995). Aside from few courageous attempts at decentralization, the region inherited a heavily centralized system of taxation and public administration from the Ottoman Empire and other European States.

# **3. Comparison of Intergovernmental Structure in Selected MENA Countries**

In this section, we examine the intergovernmental structure in a selected number of MENA countries to demonstrate the centralized features of local government systems in the region. Our selection includes five countries representing both Maghreb and Mashreq regions. These are Egypt, Iran, Tunisia, West Bank/Gaza and Yemen. The central government in these countries is the senior partner in the intergovernmental relationship. The share of subnational expenditures in GDP is very low compared to OECD average of 17 percent<sup>5</sup> (see Table 1). In many countries in the MENA region, such as Yemen and Iran, a large share of the expenditures is disbursed through subnational governments that act as agents of central government ministries and departments. In these countries locally elected representatives have little decision-making power over expenditures.

# **3.1. Deconcentration and Decentralization in MENA**

Overall, local government systems in the MENA region can be characterized as a form of deconcentration rather than one of devolved local self-government. In general, the public administration system is highly centralized, equipped with an elaborate system of deconcentrated field offices of line agencies. Decisions for the most part, especially

> institutions and larger domestic markets caused by growing economies in Europe, and rising costs of war and public administration.

> 5 http://siteresources.worldbank.org/INTDSRE/Resources/WBFDISummaryNote17Aug2004.doc

7
# Current Benefits of Wildfire Smoke for Yields in the US Midwest May Dissipate by 2050

## A. Patrick Behrer,<sup>1</sup><sup>_,_2</sup><sup>_∗_</sup> Sherrie Wang<sup>1</sup><sup>_,_3</sup>

1 Stanford University Center on Food Security and the Environment, 616 Jane Stanford Way, Stanford, CA 94305, USA

> 2The World Bank, 1818 H Street N.W., Washington, DC 20433, USA

> 3UC Berkeley Goldman School of Public Policy, 2607 Hearst Ave, Berkeley, CA 94720, USA

> _∗_ To whom correspondence should be addressed; e-mail: abehrer@worldbank.org.

Keywords: Climate change, Wildfires, Agriculture, Crop yields, Pollution JEL codes: Q10, Q53, Q54
**Decoupling and recoupling**

TIC data collect cross-border holdings of securities measured at market value through security-level surveys (that is, information is reported separately for each security) that conflate data from custodians, issuers, and investors. As the data is collected by the US Treasury, it comprises transactions between U.S. residents and counterparties located outside the United States involving six types of securities, including four domestic (U.S. Treasury bonds and notes, bonds of U.S. government corporations and federally-sponsored agencies, U.S. corporate and other bonds, and U.S. corporate and other stocks) and two foreign (foreign bonds and foreign stocks). We compute liability flows coming from the US as the difference between gross purchases and gross sales of foreign securities by US residents. In turn, EPFR data report the fund net flow to individual emerging economies, as well as the assets under management (AUM) by country.

There are differences between TIC and EPFR data. On the one hand, unlike annual TIC data, EPFR data is available at monthly (and, for a subset of funds, weekly) frequency. More importantly, the simple within-country correlation between both flow measures is rather low ( **Table 8** ), reflecting the distinct behavioral pattern between both sources: as we stated above, global funds tend to keep close to their benchmark, inducing correlation (as a result of global swings in risk appetite and exposure) and possibly exacerbating financial contagion, while the average TIC respondent would tend to trace less and to exhibit a more selective sensitivity to relative price changes.

A degree of complexity in the measurement of de facto FG is introduced by the normalization. On the one hand, normalization by the (US dollar) GDP looks natural for issues related with the country ́s wealth diversification away from domestic shocks (and exposure to external shocks). On the other hand, normalization by local market capitalization seems to be more appropriate when assessing cross-border flows as a source of price volatility and international contagion. The differences are not trivial: as we show in a companion paper (Levy Yeyati and Williams (2010)), whereas in the 2000s globalization in EM remained stable in terms of foreign market participation, it grew in terms of GDP reflecting the valuation effect of the equity rally before and after the 2008 slump. To avoid capturing these spurious price effects, here we normalize equity liability flows by local equity market capitalization and debt liability flows by the total outstanding debt of the recipient country.

<u>13</u>
are openly administering the territory, providing services and security, and running the local courts, respectively. LWJ may assess a district controlled by the Taliban or by the U.S. government if the district center frequently exchanges hands, and the Taliban or the government only controls a few buildings or villages in the district.

- Contested districts: places where the government is in control of the district center or buildings within the district center, or a base, but little else, while the Taliban controls large areas or all of the areas outside of the district center. Or, the Taliban may control several villages, mines and other resources, runs prisons in the district, or administers areas of the district.

With the classification of the LWJ, we construct the share of districts controlled by the Taliban, the U.S-Allies and the Contested territories in each of 31 provinces in Afghanistan.

# **B.C Social Desirability Bias**

Individuals tend to answer to how their will be viewed usually according responses by others instead of answering what they really believe, and this phenomenon is known as social desirability bias. For this purpose, we measure social desirability bias by using four questions from Marlone and Crowe’s social desirability scale (see Crowne (1964) for details). The questions assess whether or not respondents are concerned with social approval. A high number of socially desirable responses suggests the respondent is concerned with social approval.

Each question on the scale includes a statement to which the respondent has to answer true or false. The four questions we included are: ”It is sometimes hard for me to go on with my work if I am not encouraged (false corresponds to higher social desirability)”, ”There have been times when I was quite jealous of the good fortune of others (false corresponds to higher social desirability)”, ”I am always willing to admit when I make a mistake (true corresponds to higher social desirability)”, and ”I have never intensely disliked anyone” (false is associated with higher social desirability)”. Each statement gets a score of zero or one, and the total level is calculated by adding up the scores of the four questions. To facilitate its interpretation, the total value is standardized.

59
# **1 Introduction**

The recent backlash against globalization and resurgence of protectionist tendencies have renewed interest in the distributional impacts of trade protection. To inform trade and social protection policy reform, identifying who gains and who loses from trade and quantifying by how much is of crucial policy interest.

Trade reform impacts households as consumers, producers, wage earners, and, possibly, taxpayers. As a consequence, how a particular household is impacted by a trade reform depends on its income and consumption portfolios. This implies that trade reforms typically have very heterogeneous effects on household well-being. These heterogeneous impacts operating at the household level are difficult to measure with readily available data.

This paper presents a new cross-country _household survey_ data set, the Household Impacts of Tariffs (henceforth HIT) data set, that enables researchers to investigate how tariff changes impact the real incomes of households across the income distribution. The data set covers 54 developing countries, and was constructed by harmonizing representative household surveys with import tariff data from UNCTAD. The sample comprises all low-income countries for which relevant nationally representative household survey data—i.e., data with information on both households incomes and consumption spending is available—and a number of middle-income countries. In addition, we use the HIT database to assess trade policy and establish stylized facts about agricultural trade protection using a simple agricultural household model and a first-order effects approach as in Deaton (1989). In mostly agrarian economies, as the ones covered in our data set, agriculture is a major source of gains and losses from trade, especially for the poor.

We find substantial gains from own agricultural tariff liberalization, amounting to 2.50 percentage points of real household income per capita across our sample of 54 countries. Because of differences in consumption and income portfolios as well as in initial tariffs, there is huge heterogeneity in the gains from trade both across countries and across households within countries. For example, the average standard deviation of the gains from trade across countries is 1.01 percentage points, but it can be as high as 2.68 percentage points. Furthermore, in 29 countries, agricultural tariff liberalization would be pro-rich in the sense 1
-35-

# TABLE A-2 PUBLIC SECTOR EMPLOYMENT

[in perewd of aborlome)

|LATIN AMERICA<br>|<br>Ge<br>|PUBL<br>neral Civillan<br>|IC SECTO<br>Govemme<br>|R EMPLO<br>nt Employ<br>|YMENT<br>ment<br>||
|---|---|---|---|---|---|---|
|AND<br>|Govemment A<br> <br>|dministratio<br>|Social S<br>|ectors<br>|Total<br>||
|THE CARIBBEAN|<br>Central<br>Govemment|Non Central<br>Govemment|Education<br>|Health|General Civilian<br>Govemment|Armed<br>Forces|
|Antigua & Barbuda|29.4|0.3|.||||
|Argentina|2.3|7.3|<br>2.1|0.9|<br>12.6|<br>0.5|
|Bahamas|6.3|0|<br>3.4|3.0|<br>12.6|____|
|Barbados|7.8|0|2.2|3.1|<br>13.1||
|Belize<br>||||||1.5|
|Bermuda||||||1.2|
|Bolivia|3.0|0.4|<br>2.8|0.7|<br>6.8|0.4|
|Brazil||I|_1.8||||
|Chile|0.8|0.4|<br>0.2|0.9|<br>2.4|1.0|
|Colombia|0.3|1.3|<br>2.1|0.5|<br>4.2||
|Costa Rica||||||0.8|
|Dominican Republic|<br>0.0|0.0||||1.6|
|Ecuador|5.1|1 .2|3.1|0.8|<br>1 0.3|1 .4|
|El Salvador|1.8||1.5|1.0|<br>4.4|1.2|
|Guatemala||||||0.5|
|'Guyana|3.8|1.0|0.1|0.6|<br>5.4|1.0|
|Honduras|2.9|0.4|<br>1.8|0.3|<br>5.3|0.3|
|-<br>__<br>___<br>__<br>jJamaica|_|__<br>_<br>_<br>_<br>_<br>__|_<br>___|_<br>_||0.8|
|Mexico||||||0.8|
|Nicaragua|1.3|||1.4||1.1|
|Paraguay|8.0|||||1.4|
|<br>Peru<br>_|_ __4.2|||||1.2|
|Suriname|_||__<br>_||_ _|0.4|
|Trinidad and Tobago||||||1.8|
|Uruguay|5.3|2.7|4.8|1.8|<br>14.6|1.0|
|Venezuela|3.0|1.3|<br>2.2|0.3|<br>6.9||
|AVERAGE|2.9|1.8|2.1|0.8|<br>7.6|1.0|

Cotunm averages encompass only those counties for which the information Is complete
# **Discussion**

Based on means and medians, the primary drivers of annual THE were hospitalizations ($78 and $51, respectively) and medications ($72 and $61, respectively). Based on the distribution of THE, the primary driver of annual THE was medications (75%). The main driver of hospitalization expenditures was fever and malaria (21% of total hospitalization expenditures). In addition, although we were unable to estimate health expenditures by health problem for outpatient care, fever and malaria were the most common health problems that did not require hospitalization. The prevalences of CHE (based on the ≥10% threshold) and IHE were 13% and 4%, respectively. Subsidizing medications alone would prevent 46% of CHE and 73% of IHE cases. Subsidizing medications alone would have a greater impact on CHEs and IHEs than subsidizing hospitalizations alone, which would prevent 4% and 3% of these cases, respectively.

In this study, we showed the importance of medication in Guinea. First, among individuals who had a health problem that did not result in hospitalization, 68% did not use health services because of selfmedication, and these health problems affected 74% of the population. Second, drug expenditures accounted for the largest share of THE and had a higher median cost than hospitalizations. Third, drug expenditures was the most important driver of CHE and IHE.

Since Guinea ratified the Bamako Initiative in 1988, drug costs have been borne by households, despite the implementation of a number of fee exemption programs in recent years.<sup>[13]</sup> One of the objectives of the Bamako Initiative was to minimize the cost of medicines by ensuring access to generic drugs.<sup>[14]</sup> However, the Guinean health system is currently unable to provide its population with adequate access to medicines and to ensure that the population has access to good quality medication.

The pharmaceutical sector in Guinea comprises a public sub-sector, a legal private sub-sector, and an informal and illegal private sub-sector.<sup>[5]</sup> In the public sub-sector, pharmaceutical products are supposed to be supplied by the Central Pharmacy of Guinea (CPG), which was set up in 1992.<sup>[15]</sup> However, stockouts are frequent and significant, even for essential medicines.<sup>[14, 15]</sup> According to 2016 data, only 39% of public health facilities' orders from the CPG were filled.<sup>[15]</sup> In 2017, the antibiotics ampicillin and amoxicillin and oral rehydration salts were available in only 38%, 28%, and 26% of public health facilities, respectively.<sup>[14]</sup> This situation forces health facilities to seek supplies from the private sector. The challenges of the PCG stem from, among other things, a lack of adequate tools and procedures to quantify 17
These factors can be included as elements of targeting programs. In contrast, addressing chronic poverty, especially chronic extreme poverty, might be most effectively achieved via a combination of interventions that lift long-term income prospects —such as measures aimed at assisting the disabled to participate in society—and measures that promote economic growth in sectors where the poor are active, such as agriculture. Measures that promote school attendance by children in these chronically poor families as well as building and maintaining rural roads are also important in this respect.

Of course, the results presented here may be suggestive, but they should be interpreted with the appropriate caution. Our profiling of population groups defined in terms of their poverty trajectories has been entirely descriptive, with little explicit attention to underlying causal mechanisms. For example, our finding that education is associated with escaping poverty or with being counted among the never-poor, could imply that education raises income levels. But it could also reflect the fact that ambitious and dynamic individuals are both likely to earn high incomes and seek to educate themselves. Our analysis has not attempted to dig into such processes. A great deal of additional work and analysis is needed before underlying mechanisms can be well understood. Further research could also usefully exploit the richness of the ESPS 1 and 2 data, and also bring in analysis from the 2001 household survey, thereby offering a longerterm perspective on welfare transitions in Senegal.

In providing an early application of the synthetic panel data method to study poverty dynamics in Sub-Saharan Africa, it is hoped that this paper can serve as a useful template for work elsewhere. Each country with its own specific socio-political economy structure may reveal an entirely different set of issues associated with poverty dynamics. Since one of the strengths of 30