rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Figure 2. U.S. Vehicle miles traveled 1945-2000 (millions)</mark>**

<!-- Start of picture text --> U.S. Vehicle miles travelled 1945-2000<br>(millions)<br>3,000,000<br>1945-1965<br>2,500,000 1966-2000<br>Linear projection of<br>2,000,000<br>1945-65 trend<br>1,500,000<br>1,000,000<br>500,000<br>0<br>1940 1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> _Source: U.S. Federal Highway Administration_ <mark>33. The Interstate Highway System also made it cheaper to develop land farther away from city centers, thus playing a major role in an induced second “hump” in transport infrastructure investment and</mark> energy use associated with accelerated suburbanization post-1990 (Figure 3).<sup>28</sup>

**<mark>Figure 3. Total Highway Mileage in the U.S. 1960-2006</mark>**

<!-- Start of picture text --> Total Highway Mileage U.S. 1960-2006<br>4,100,000<br>4,000,000<br>3,900,000<br>3,800,000<br>3,700,000<br>3,600,000<br>3,500,000<br>1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> _Source: U.S. Bureau of Transportation Statistics_<sup>_29_</sup> <mark>34. The conjunction of long-lived physical infrastructure, modified logistics patterns and resulting systemic changes in the way businesses operate, and modified land-use in cities has led to a durable</mark> 28 In fact, the increase in urban highway mileage over the 1980 – 2006 period is higher than the increase in total highway mileage because the mileage of rural roads has been decreasing over the same period. 29 A total of 43,000 miles of Bureau of Federal Land Management roads—excluded starting in 1998 from official U.S. BTS statistics—have been reintegrated in this graph to avoid break in time series.

14/39
financing.<sup>11</sup> Realism suggests that aid budgets will come under increasing pressure as OECD governments seek to reduce expenditures following the fiscal expansion of the last 18 months. While aid for trade is not linked to the DDA, concluding the round could help translate the aid for trade commitments into additional resource transfers.

# **“QUANTIFYING” THE VALUE OF DOHA**

Much of the discussion and debate about the “value” of Doha has centered on the results of global modeling exercises. A serious problem with any effort to assess the value of what was on the table using empirical models is that these can only assess the implications of proposed agreements for _applied_ levels of protection. Thus, they completely ignore the benefits of new policy disciplines and the effects of additional policy bindings. As a result, discussions of the value of the DDA that are based on numerical models will by definition be missing a big part of what is on the table.

Given this very important caveat it is nonetheless useful to briefly discuss what empirical studies suggest will be the impact of what is on the table on average levels of applied protection in trade in merchandise. Recent analyses by Laborde, Martin and van der Mensbrugghe (2009a, 2009b) that incorporates new methodological advances that allow much more disaggregated trade data to be used in general equilibrium simulation models – some 5,100 tariff lines/product categories – suggests that overall global gains would be up to $160 billion for the agricultural and nonagricultural market access agreements alone, even after allowing for exceptions for sensitive and special products. Compared to recent models that use more aggregate data, the gains are about 65 percent higher for developed countries and some 100 percent higher for developing countries as a group. The reason disaggregation is important is that the analysis results in a better measure of the welfare effects of tariff peaks. As the welfare cost of protection goes up with the square of the tariff, using more aggregate data on the average level of the tariff for a broad group of products results in lower welfare cost estimates (because the average is by definition much lower than the peak tariffs that often apply to specific tariff lines).

Decreux and Fontagné (2009) identify a US$57 billion world GDP gain as a result of implementing what was on the table in July 2008 in terms of liberalizing trade in merchandise, based on the modalities that had emerged and again including the likely exceptions and the differentiated nature of the commitments that will be made by different groups of countries. If the

> 11 These numbers are large because they include investments in infrastructure and span both low- and middleincome countries. However, even if a much more narrow definition of aid for trade is used, such assistance has approximately doubled between 2002-05 and 2008. See World Bank (2009).

13
Table 2: Impact of NTMs _vs_ Import Tariffs

||(1)<br>TFPQ|(2)<br>Markup|(3)<br>TFPR|
|---|---|---|---|
|Output NTM|0.008|-0.064|-0.057|
||(0.029)|(0.111)|(0.125)|
|Input NTM|0.044|0.100|0.144|
||(0.080)|(0.134)|(0.190)|
|Output tarif|-0.280|0.262*|-0.0184|
||(0.197)|(0.158)|(0.279)|
|Input tarif|-1.112***|-0.804**|-1.915***|
||(0.402)|(0.389)|(0.600)|
|Observations|59,791|59,791|59,791|
|R-squared|0.981|0.694|0.955|
|Plant FE|yes|yes|yes|
|2-dig ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|5-dig HH-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of NTMS and import tariffs, applied either to output or inputs, on plant’s TFPQ (col. 1), markup (col. 2) and TFPR (col. 3) in the years between 2008 and 2015. _Output NTM_ is the share of HS-10 digits products subject to at least one NTM within the corresponding five digit industry. For each five digit industry, _Inputs NTM_ is the weighted sum of output NTM in all five digit supplying industries. _Output tariff_ is the average tariff rate in the 5 digit industry of a plant. _Inputs tariff_ is the weighted average of the tariffs rates applied to the inputs used by a plant. The coefficients are obtained by the estimation of Equation 1. All specifications include plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

19
**Table 2: HIV prevalence and education. General Population Cohort (Medical Research Council), Rural Uganda.** Round 1 (11/89 & 9/90) & Round 11 (11/99-9/2000), Age 18 and above Dependent variable: HIV positive =1, HIV negative = 0 **Logit estimation: Mean of Marginal Effects, observation by observation**

|**Round 1**<br>**By education**<br>**categories**<br>**Bothgenders**||**Round 11**<br>**By education**<br>**categories**<br>**Bothgenders**|**Round 11**<br>**By education**<br>**categories**<br>**Males**|**Round 11**<br>**By education**<br>**categories**<br>**Females**|**Round 11**<br>**By years of education**<br>**Bothgenders**||**Round 11**<br>**By rank in the educati**<br>**distribution**<br>**Bothgenders**|**on**|
|---|---|---|---|---|---|---|---|---|
|**age 18-29**|0.116|0.207|-0.084|0.262|**age 18-29**|0.16|**age 18-29**|0.207|
||[1.12]|[1.77]|[-0.54]|[1.77]||[1.38]||[1.38]|
|**primary age >29**|-0.014|0.054|0.088|0.36|**years of education**|0.001|**2nd third age >29**|0.007|
||[-0.75]|[2.36]|[1.98]|[1.30]|**age > 29**|[0.37]||[0.49]|
|**secondary age>29**|0.008|0.039|0.028|0.051|**years of education**|-0.005|**3rd third age > 29**|-0.011|
||[0.29]|[1.51]|[0.59]|[1.57]|**age 18-29**|[-1.84]||[-0.64]|
|**primary age 18-29**|-0.015|-0.028|0.062|-0.037|||**2nd third, age 18-29**|-0.021|
||[-0.68]|[-1.14]|[0.89]|[-1.30]||||[-1.33]|
|**secondary age 18-29**|-0.005|-0.059|0.043|-0.078|||**3rd third, age 18-29**|-0.044|
||[-0.19]|[-1.84]|[0.54]|[-1.88]||||[-1.75]|
|N =|2601|2861|1028|1361||2861||2861|
|Observed Probability|0.102|0.081|0.083|0.08||0.081||0.081|
|Pseudo R-2|0.093|0.146|0.178|0.151||0.141||0.141|

<u>Note: Robust z-stat in square brackets.</u> Controls for gender, marital status, religion and wealth are included and are interacted, like the education variables, with the dummies for the two age groups. Age and village dummies included. The rank in the education distribution is calculated by gender and 5 years age group. Primary is some primary education (grades 1 to 7), secondary is some secondary education (grades 8 to 13) or above. The type of housing materials (soft roof and house, mixed or hard roof and house) serves as a proxy for wealth and is measured at Round 12, in the case of Round 11.

40
# **1. INTRODUCTION**

A large and empirically well-identified body of evidence has demonstrated the ability of Conditional Cash Transfer programs (CCTs) to raise schooling rates in the developing world. Due in large part to the high-quality evaluation of Mexico’s _Progresa_ , CCT programs have become common in Latin America and are beginning to spread to other parts of the world. As of 2007, “...29 developing countries had some type of CCT program in place (in some cases, more than one) and many other countries were planning one.” (World Bank, 2009) However, designing a new CCT program remains a complex task. Many difficult decisions need to be made regarding the selection of beneficiaries, the nature (and enforcement) of conditions, and the level and structure of payments. While numerous evaluations of CCTs have been conducted in Latin America, most evaluations consider a policy with a single, fixed set of contract parameters. Therefore, the evidence base needed by a government to decide _how_ to design a new CCT program is either limited or non-existent in several critical dimensions.

The question of whether the observed effects of a CCT program are a result of the “income effect” associated with the transfer or the “price effect” from the condition remains largely unanswered. This issue is of much more than academic interest, because it has direct implications on program design. The ideal experiment to answer this question – i.e. a randomized controlled trial with one treatment arm receiving _conditional_ cash transfers, another receiving _unconditional_ transfers, and a control group receiving _no_ transfers – has not yet been conducted anywhere. The evidence that can be gleaned so far is either from model-based simulation exercises (e.g. Bourguignon, Ferreira, and Leite, 2003; Todd and Wolpin, 2006) or from interventions with implementation glitches in Mexico (De Brauw and Hoddinot, 2007) and Ecuador (Schady and Araujo, 2008).

With regards to transfer size, while “...the key parameter in setting benefit levels is the size of the elasticity of the relevant outcomes to the benefit level” (World Bank, 2009, pp. 182), random variation in transfer size among program participants is rarely, if ever, observed. Nor has the related issue of to whom the transfer should be made been studied extensively. While there are a few studies examining the effect 2
<!-- Start of picture text --> oO<br>id<br>oO<br>ive)<br>” 4<br>4<br>s 3) /<br>a | /#<br>Oo /<br>tld<br>oO<br>N<br>oO<br>0 50000 100000 150000 200000 250000 300000<br>Cumulative distribution of savings stock<br>—-*--—- Control ——*—— Treatment 95% Cl of the difference<br><!-- End of picture text -->
25 <u>Table 6: Median progressivity index values for out-of-pocket expenditures on health, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|0.10|0.17|0.07|-0.18|0.08|
|Europe & Central Asia|0.17|0.10|0.07|-0.06|-0.01|
|Latin America & Caribbean|0.11|0.02|0.07|0.12|0.08|
|Middle East & North Africa||0.08|0.08|-0.12|0.04|
|North America||||-0.24|-0.24|
|South Asia|0.05|0.09|||0.07|
|Sub-Saharan Africa|0.04|0.04|0.02||0.04|
|Total|0.05|0.07|0.07|-0.08|0.04|

<u>Table 7: Median incidence of catastrophic out-of-pocket health expenditures at the 10% threshold, latest year</u>

||Low-<br>income|Lower<br>middle-<br>income|Upper<br>middle-<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|East Asia & Pacific|11|3|10|6|5|
|Europe & Central Asia|12|8|9|5|8|
|Latin America & Caribbean|7|10|10|10|10|
|Middle East & North Africa||8|8|13|9|
|North America||||5|5|
|South Asia|21|8|||12|
|Sub-Saharan Africa|6|7|3||5|
|Total|7|8|8|5|7|
terms “trust” and “confidence” and respondents’ definition of government (OECD, 2013). Cultural factors and societal norms influence how individuals define public institutions, making valid crossnational comparisons of trust difficult (OECD, 2013). These issues affect all trust-related, international surveys. However, this study takes advantage of the detailed information on individual-level food insecurity provided by the FIES. This information, combined with the globally standardized trust-related questions in the GWP, make it possible for the first time to examine the direct impact of food insecurity on both dimensions of trust around the world.

# **_2.4. Measuring Food Insecurity_**

FAO created the FIES to gather consistent and comprehensive information on the prevalence and severity of global food insecurity. The GWP/FAO FIES survey module contains eight questions designed to assess the adequacy of individuals’ access to food, adapted from the long-established United States Household Food Security Survey Module (US HFSSM) and the Latin American and Caribbean Food Security Scale (ELCSA). The FIES survey questions focus on respondents’ behaviors and experiences when they have encountered difficulties in meeting their basic food needs over the past 12 months. Similar to other food insecurity surveys, the FIES questions are ranked by severity. Each question specifies that food insecurity results from a lack of money or other resources to obtain adequate food (see Appendix, Table A1).

In this analysis, we use a binary measure of individuals’ severity of food insecurity. The measure, _food insecurity_ , is coded as one if an individual experienced moderate or severe food insecurity in the past 12 months; zero otherwise. This measure captures experiences ranging in severity from a reduction in food quality and variety to the reporting of physiological hunger (Nord et al., 2016; Cafiero et al., 2018).

# **_2.5. Control Variables_**

In line with the literature, we control for other determinants of trust that include individual and household characteristics. These include gender, age, and lifecycle stage (age squared), household composition, marital status, education, income, consumption, location (urban/rural), labor force status, whether or not the respondent is an immigrant, and the respondent’s religiosity. Lastly, we 7
8 forced Ivanic and Martin (2008) to ignore changes in a large number of food prices, both on the consumption and production sides. A good example is the fact that welfare impacts in Nicaragua are estimated without taking changes in the price of coffee (an important export) into account, because coffee was not among the seven staples whose prices are followed in the study.<sup>5</sup> Labor market and substitution effects are also absent from the analysis in Son and Kakwani (2009) – another study closely related to ours. These authors use the first-order approximation to the welfare effect of a price change to derive the elasticity of the Foster, Greer and Thorbecke (1984) class of poverty indices with respect to prices. They calculate these elasticities for various categories of goods in the large urban areas of Brazil, between 1999 and 2006, using two of the three data sets we use in this paper: the Household Budget Survey (POF) and the Consumer Price Index (INPC) microdata.

In this paper, we seek to contribute to this literature in four ways. First, we use a time-series of domestic consumer prices, captured monthly in eleven urban areas covering all five macro-regions of Brazil. We observe changes in the prices of 156 individual food items, which are then grouped into 16 food categories. These categories account on average for 97 percent of food consumption (and 22 percent of household consumption expenditure on all goods) recorded in the Household Budget Survey. This is a more finely disaggregated set of prices than has generally been used in the analysis of the 2007-2008 crisis. Second, we use occupational data that maps individual agricultural workers to the production of specific food groups, to compute estimates of income gains that might accrue to them under two different assumptions about the pass-through from food prices to agricultural wages.

Third, we attempt to shed light on the magnitude and effectiveness of the social policy response of the Brazilian government, in terms of the extent to which it helped mitigate the price increases. And fourth, we present the estimates for each of these effects – the expenditure effect, the market income effect and the transfer income effect – in a distributionally disaggregated way, by means of “price change incidence curves”, which are analogous to the growth incidence curves of Ravallion and Chen (2003). These curves depict each individual effect, as well as the net effect, for each percentile of the income distribution. They are computed and presented separately for the large urban areas where the prices are collected, for rural areas, and for the country as a whole. From the 5 The staples considered are beef, dairy, maize, poultry, rice, sugar, and wheat.
<!-- Start of picture text --> Sol,ewarch, and Extemal Afar<br><!-- End of picture text -->

- This paper a product of the Debt and Intemational Finance Division, Intemational Economics Department- is partof a largereffort in PRE to investigate the determinants of the terns, conditions, and volume of private-source international financial flows to developing countries. Other related work under way includes a review of the determinants of thc volume and destination of foreign direct investment from Gernany and Japan, a study of portfolio investment in developing countries, and a study of the determinants of commercial bank decisions in menu-based rescheduling and debt reduction exercises. Copics of this paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sheilah King-Watson, room S8-025, extension 31047 (31 pages with figures and tables).

Ozler studied changes in spreads on bank loans to developing countries during 1968-81.

She found that a borrower's experience had a significant impact on spreads. Spreads started at high values at low levels of loan experience and decreased as experience increased. Spreads at initial-experience levels were about 30 percent above benchmark rates and declined to the benchmark, with experience. The impact of experience became negligible with 30 prior loans.

Ozler suggests a greater role for intemational organization, in collecting and disseminating infortation about potential borrowers - because of the high initial fixed cost of collecting information about borrowers and the lenders' inability to distinguish different types **(t** borrowers in advance. Indeed, intemational institutions have been doing more of this factfinding since the onset of the debt crisis.

The study was based on loan-.evel data for 1968-81, when the Eurocunrency market was expanding. Data on renegotiated loans were not used. The results are robust to a number of altemative specifications. They are not a consequence of the behavior of spreads over calendar time, but are robust with altemative definitions of experience variables and altemative functional forms.

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complex. An objective of the scries 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, t the PRE Dissemination Center
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Weig|ht-for-age z-s|cores|||
|Within 30min of health post|-0.020|-0.017|-0.013|-0.022|-0.024|-0.017|-0.021|
||(0.049)|(0.049)|(0.049)|(0.049)|(0.049)|(0.049)|(0.049)|
|Within 30min of hospital|0.110*|0.107*|0.105|0.111*|0.113*|0.105|0.110*|
||(0.064)|(0.064)|(0.064)|(0.064)|(0.064)|(0.064)|(0.064)|
|Within 30min of dirt road|-0.060|-0.058|-0.064|-0.071|-0.076|-0.056|-0.069|
||(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|(0.051)|
|Within 30min of paved road|-0.073|-0.076|-0.080|-0.085|-0.088|-0.074|-0.096|
||(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|
|Piped water in house|0.046|0.041|0.035|0.033|0.039|0.007|0.026|
||(0.066)|(0.066)|(0.066)|(0.065)|(0.065)|(0.066)|(0.065)|
|Flush toilet|0.187***|0.183***|0.170***|0.162**|0.175***|0.147**|0.155**|
||(0.064)|(0.065)|(0.064)|(0.065)|(0.065)|(0.064)|(0.065)|
|Latrine|0.047|0.044|0.035|0.040|0.044|0.040|0.038|
||(0.063)|(0.063)|(0.063)|(0.064)|(0.064)|(0.063)|(0.064)|
|Child exclusive-ly breastfed|0.032|0.032|0.030|0.032|0.035|0.036|0.041|
||(0.055)|(0.055)|(0.055)|(0.055)|(0.055)|(0.055)|(0.055)|
|Months breastfed|-0.002|-0.002|-0.001|-0.002|-0.002|-0.002|-0.002|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Complementary food|-0.013*|-0.013*|-0.013*|-0.014**|-0.014**|-0.014**|-0.014**|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Suffer from chronic illness|-0.440*|-0.445*|-0.442*|-0.405*|-0.394|-0.407*|-0.408|
||(0.252)|(0.252)|(0.252)|(0.245)|(0.246)|(0.243)|(0.248)|
|Fell sick in the last 30 days|-0.073|-0.073|-0.071|-0.073|-0.071|-0.079*|-0.072|
||(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|
|Asset index 2|-0.009|-0.005|-0.007|-0.030|-0.026|-0.027|-0.029|
||(0.070)|(0.070)|(0.070)|(0.071)|(0.071)|(0.070)|(0.071)|
|Asset index 3|0.022|0.023|0.019|-0.004|0.004|-0.012|-0.003|
||(0.076)|(0.076)|(0.076)|(0.077)|(0.078)|(0.076)|(0.077)|
|Asset index 4|0.093|0.095|0.086|0.064|0.079|0.040|0.062|
||(0.084)|(0.085)|(0.084)|(0.086)|(0.087)|(0.086)|(0.086)|

67
# **Annex 4. Detailed Transport Questions in LSMS Surveys (18 Countries in LSMS-tagged Collection)**

|**Survey Question**|**Afghanistan**|**Armenia**|**Benin**|**Bhutan**|**Burundi**|**Cameroon**|**Colombia**|**Gambia**|**Ghana**|
|---|---|---|---|---|---|---|---|---|---|
|**_I. Education_**||||||||||
|1. Main mode of transport to and from education<br>facility|||X|X||||X|X|
|2. Time taken to get to school by this means of<br>transport||||X||||X<br>(and<br>distance)||
|3. Education cost on transportation||X|X|X|X|||X|X|
|4. Transportation difficulty as main reason for non-<br>attendance in education/missingthe classes||X||||||||
|**_II. Health_**||||||||||
|1. Main mode of transport to and from health facility||||||||X|X|
|2. Distance to the nearest health facility||||||||||
|3. Time taken to travel from the household to the<br>nearest health facility|X<br>(with different<br>transport<br>|||||||||
||modes)|||||||||
|4. Health cost on transportation|X|||X||||X||
|5. Transportation difficulty or high transportation cost<br>as the main reason for not consulting healthcare||||X||||X||
|duringillness||||||||||
|**_III. Employment_**||||||||||
|1. Main mode of transport to and from work|||||||X|X||
|2. Distance to the workplace||||||||||
|3. Time taken to reach the workplace|||||||X|||
|4. Transportation cost to work||||||||||
|5. Transportation problem as the main reason for not<br>|||||||||X|
|workingor not lookingfor work||||||||||
|**_IV. Food security_**||||||||||
|Not able to reach the market due to high<br>transportation costs as the main reason for food||||||||||
|insecurityin thepast 12 months||||||||||
|**_V. Agriculture_**||||||||||
|1. The main mode of transportation associated with<br>different agriculturalproductionprocedures||||||||||
|2. The total cost of transportation associated with<br>different agriculturalproductionprocedures||X|||||||X|

39