rafmacalaba/datause-encoder-data huggingface dataset:
|Author (Year)|Study Type & Location|Participants|Diet Assessed<br>|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Askari et al. (2020)|SRM of 14 studies (13 cross<br>sectional, 1 cohort), 7 in<br>Brazil, 1 in Guatemala, rest<br>in HICs|Male and female<br>participants 10-<br>64 y (n=189,966)|UPF(defned by NOVA<br>classifcation system in<br>13 of 14 studies)|Overweight (10 studies),<br>obesity (6 studies)|UPF intake associated with overweight<br>(efect size 1.02; 95% CI 1.01, 1.03)<br>and obesity (efect size 1.26; 1.13,<br>1.41)|
|Qin et al. (2022)|Meta-analysis of 32 studies<br>(12 cross-sectional; 19<br>cohort; 1 case control),<br>includes Chile, China, India,<br>the Islamic Republic of Iran<br>and Philippines;rest in HICs|Adults|Fried Food Consumption|overweight, obesity (11<br>studies), T2DM (10<br>studies), hypertension<br>(11 studies)|Fried-food consumption is associated<br>with increased risk of OWOB (RR 1.16;<br>1.07, 1.25) and hypertension (RR 1.20;<br>1.05, 1.38) but not T2DM (RR 1.07;<br>0.90, 1.27).|
|Jiang et al. (2022)|SRM of 23 studies (18 cross<br>sectional, 1 cohort), China|Adults|Dietary patterns<br>(Traditional Chinese diet<br>(10 studies) vs.<br>modern/western diet (8<br>studies))|Obesity, overweight|Traditional Chinese dietary pattern was<br>associated with a lower risk of<br>overweight/obesity than Western diet|
|Mu et al. (2017)|Review of 21 studies (17<br>cross-sectional, 4 cohort), 2<br>in Mexico, 1 in Colombia, 1<br>in Cameroon|Adults|Dietary patterns<br>(prudent/healthy (n=17)<br>vs. Western/unhealthy<br>(n=18)<br>defned diferently for<br>each study: healthy had<br>high loadings of fruit,<br>vegetables, poultry, fsh,<br>low-fat dairy, whole<br>grains. Unhealthy had red<br>and/or processed meats,<br>refned grains, potatoes,<br>sweets and high fat dairy|Obesity, overweight|Highest categories of prudent/healthy<br>dietary pattern associated with<br>reduced overweight/obesity risk (OR<br>0.64; 95% CI 0.52, 0.78).<br>Increased overweight/obesity risk in<br>the highest vs lowest categories of a<br>western/unhealthy dietary pattern (OR<br>1.65; 95% CI 1.45, 1.87)|
|Dinu et al. (2017)|SRM of 108 studies (86<br>cross-sectional, 10 cohort<br>prospective)|Healthy adults,<br>18-81 y|Vegetarian and vegan<br>dietsvs omnivore diet|BMI (71 vegetarian<br>studies; 19 vegan<br>studies)<br>Blood glucose (27<br>vegetarian studies; 4<br>vegan studies)|_Cross-sectional studies_: Vegetarian<br>diet (WMD -1.49kg/m<sup>2</sup>; 95% CI -1.72, -<br>1.25) and vegan diet (WMD -1.72; -<br>2.21, -1.22) associated with lower BMI;<br>Lower blood glucose observed with<br>vegetarian (WMD -5.08 mg/dL; -5.98, -<br>4.19) and vegan (WMD -6.38; -12.35, -<br>0.41)diet|
|Daneshzad et al.<br>(2021)|SRM of 21 studies (17<br>cross-sectional, 3 cohort, 1<br>case-control),6 in Europe,|General<br>population (6 y<br>and above)|Red meat consumption|Overweight (3 studies)<br>Obesity (7 studies)|Red meat consumption not associated<br>with overweight (efect size: 1.19; 0.97,<br>1.46)or obesity (efect size: 1.16;0.93,|

38
32

# **6. Conclusions**

Using a nationally representative sample of workers in all sectors of the economy, this paper has sought to quantify the impacts of the 1988-1995 trade liberalization episode on the Brazilian wage distribution. Our results confirm previous findings that changes in industry wage premia and industry-specific skill premia did not meaningfully contribute to changes in the distribution of hourly wages. Trade reforms _did_ contribute to the observed reduction in inequality, but this happened through other channels. Chief among them were trade-induced changes in employment levels across sectors, industries and formality categories (formal, informal, self-employed, employer). The reallocation of workers that our model predicts to have arisen from changes in levels of protection, exchange rates, import penetration and export shares between 1988 and 1995 accounts for more than half of the observed changes in three out of four measures of inequality in hourly wages.

The other key channel through which trade reform is likely to have affected the distribution of wages is through changes in the economy-wide skill premium. This is the channel on which Gonzaga et al. (2006) focused, and they argued that changes in the skill premium mandated by a Stolper-Samuelson model of trade would account for more than the actual change in skill-premium _in manufacturing_ during 1988-1995. While our approach is unable to identify changes in the economy-wide skill premium which are due to trade variables from those which are not, our findings are consistent with the Gonzaga et al. results: returns to education fell over the period, contributing to a decline in inequality which did overshoot the observed decline. If there was any skill-biased technical change, or if other forces for greater demand for skill were at work, they were more than offset by the joint force of the Stolper-Samuelson effect of trade liberalization in an economy that used to protect skill-intensive industries, and of increases in the supply of more educated workers.

Overall, even if one does not attribute the decline in the economy-wide returns to education to the trade reforms (despite evidence from other sources that part of it is
4 We think that Swiss experience is useful for the design of occupational pension pillars and their regulation. The main caveat of our analysis is the difficulty in obtaining representative and reliable information along several important dimensions, such as the asset and liability management of pension funds. Due to the high fragmentation of the second pillar (several thousand individual funds), it is very difficult to obtain a good overview of the (financial) situation of occupational pension schemes, as well as of the value that present and future annuitants get out of the system.

# **1.2 Empirical Background: Demographics**

As most industrialized countries, Switzerland experienced a baby boom and a subsequent decline in birth rates to very low levels. It also witnessed a high level of immigration leading to a 20 percent share of foreigners in the resident population. Figure 1 depicts the size of the population and its growth rate since 1970. High immigration and a net birth surplus until the mid-1970s was followed by a negative migration balance as a consequence of the oil shock recession (see also Figure 2). Immigration has accounted for approximately 80 percent of the population growth rate in recent years. Long-term projections on population growth and the age structure of the economy are thus difficult to make, especially as migration is also related to the Swiss business cycle and immigration policy. The recent opening of the Swiss labor market to citizens of EU accession countries, for example, has led to a new inflow of immigrants.

**Figure 1  Population and its Growth Rate**

<!-- Start of picture text --> 7'600'000 1.50%<br>7'400'000<br>7'200'000<br>1.00%<br>7'000'000<br>6'800'000<br>0.50%<br>6'600'000<br>6'400'000<br>0.00%<br>6'200'000<br>6'000'000<br>-0.50%<br>5'800'000<br>5'600'000<br>5'400'000 -1.00%<br>Population Grow th<br>197019721974197619781980198219841986198819901992199419961998200020022004<br><!-- End of picture text --> Source: Bundesamt für Statistik (2005, August), p.4 and Bundesamt für Statistik (2004, August), p.6
(cabbage to citrus or vice versa). If they decide to plant new citrus, they can choose a more advanced irrigation technology (drip), which would cost more but will save water. Cabbage producers can decide to have 1 cycle, 2 cycles or 3 cycles of cabbage production per year on their fields. Budgets and water consumption data for citrus and cabbage are provided to farmers at the beginning of the game. Village managers receive, and pay for, bulk water from the WUA that manages the entire water in the catchment and provide water services (including water distribution) to the households of each village. They start with a given ratio of water sources for the households of their village. These water sources are: river water; collective tap; indwelling tap. Each water source has a different cost (investment + operating cost) that has to be added to the cost of the bulk water the managers “buy” from the WUA. Village managers can charge their inhabitants with a per capita tariff for the water services they provide, and this corresponds to their annual income. The households derive a certain level of satisfaction (utility) from their income that can be spent on consumption goods. Because households have different levels of effort associated with the various water sources they are provided with, they also obtain different levels of utility from each of the three sources of water.

The village manager’s objective is twofold. First to maximize his or her profit resulting from the difference between the tariff collection and the water provision cost + bulk water cost and at the same time maximize the sum of households’ utility. Elements of budgets and utility values for households are provided to local village managers at the beginning of the game.

A number of factors varying annually, such as rainfall, market prices and population dynamics can influence players’ strategies.

# **_4.1 Negotiation results_**

This section illustrates some outcomes of a RPG session held in the Kat catchment in November 2005. The set-up of the game and the players participating in the session are presented in the right-hand side rectangle of Figure 1. The initial values characterizing each player are presented in the left column of Table 2, which also includes final values (end of year 6 of the negotiation game). In the tables that follow, year 1 is the initial state and was set by the game facilitators; years 2 to 6 were actually played.

Table 2 shows the initial and final values of the exogenous factors controlled by the game operators. The game facilitators introduced a general trend of increasing water scarcity. This 8
Figure<sup>4: Total and Desired Fertility</sup> <!-- Start of picture text --> Total Fertility, 1988<br>10<br>--TFR -GDFS<br>8<br>6<br>4 -(<br>2-<br>70%<br>60%<br>0 40% 60%<br>30%<br>20%<br>10%<br>0%<br>Female Secondary Enrollment, 1976<br><!-- End of picture text -->
# **1. Introduction**

Policymakers around the world face the challenge of providing reliable and affordable infrastructure services to their people. The oft-quoted statistic is that one billion people do not have access to safe water, two billion people lack electricity and safe sanitation facilities and three billion have never used a telephone; a majority of these people reside in Sub-Saharan Africa or South Asia (Brook and Smith, 2001; Anand, 2006). The adoption of the Millennium Development Goals (hereafter MDGs) by the development community and a surge in academic literature reaffirming the role of infrastructure in development has brought infrastructure sharply into focus.

A strong network of public infrastructure is a precondition for national and regional economic growth and a channel through which private enterprise invests in developing countries. Infrastructure directly affects productivity and output by enlarging the size of product and labor markets (Prod’humme, 2004). Infrastructure supports pro-poor growth by enhancing overall expansion, removing barriers that hurt poor people, and encouraging poor people’s participation in the growth process (OECD, 2006). In an analysis covering 100 countries to study the relationship between infrastructure development and growth and income distribution from 1960 to 2000, Calderon and Serven (2004) find that the infrastructure stock positively affects growth and that superior quality and quantity of infrastructure reduces income inequality (for a useful literature review on many of these issues, see Estache, 2004 as well as Estache and Wodon, forthcoming).

The private sector was expected to step in the early 1990s to help improve infrastructure when infrastructure gaps were acknowledged as a key constraint to meeting growth targets and achieving welfare improvements for the population of Africa. Between 1995 and 2005, the private sector invested almost $37 billion in infrastructure in Africa according to data from the Private Participation in Infrastructure Database at the World Bank). By contrast, the donor community retreated from infrastructure investments. In the late 1990s, infrastructure was considered a ‘sunset’ sector, with an all-time low of only 30 percent of total World Bank lending dedicated to infrastructure. Unfortunately, the private sector appetite for investments in Africa’s infrastructure has been on the decline in recent years due to difficult political economy constraints for cost recovery and several failed and renegotiated infrastructure transactions. The MDGs and the renewed emphasis on the direct and indirect relationships between MDG targets 2
been inadequate and short-lived. Future programs of a similar type can become more effective by focusing even more on – and improving the content and length of – such training.

Intervening through community organizations is probably the most effective way to increase demand for information, as members and leaders of such organizations are relatively better off, more educated, more engaged in entrepreneurial activities, and thus more knowledgeable and successful in requesting information about government actions. On the other hand, channeling information requests through community leaders may run the risk of capture by local elites, which could in turn imply that the concerns of the poorest or most disadvantaged citizens are inadequately represented. While the risk of elite capture is something this study has not analyzed, it is a risk that merits consideration in the design of future ATI-type interventions. Whether elite capture of CBOs has occurred in these areas will also be a subject of upcoming research using the evaluation survey data.

Since CBOs are likely to have limitations in terms of outreach among citizens, should future programs use media sources, such as television, more intensively? Recent literature shows the potential for ICT to enhance government outcomes (Zhen-Wei Qiang et al, 2006). The process evaluation report reveals that information campaigns through TV and radio were carried out only for one quarter of the calendar year, towards the end of the program implementation period. Despite this, the quantitative baseline survey reveals that when individuals are asked to respond spontaneously about how they heard about the FOI law, 60 percent answered TV, 27 percent radio, 18 percent family and friends, 11 percent newspapers, 5 percent community meetings, 5 percent CBOs, and less than 1 percent from other activities such as workshops. These numbers were similar in the follow up survey, but with the difference that the percentage of those who heard about the law from community meetings or CBOs increased to 9 and 7 percent respectively. Given this, future projects of this type should consider using media channels more intensively to disseminate information, to complement efforts through CBOs.

The results from the surveys also suggest that efforts to raise awareness about right to information laws are likely to be more successful if the type of information that is disseminated is customized or tailored to fit more closely with the immediate concerns and 25
Table 2: Marriage penalty by urban-rural residence

|Dependent variable|||Working|||
|---|---|---|---|---|---|
|Sample|SAR|BGD|IND|MDV|NPL|
||(1)|(2)|(3)|(4)|(5)|
||_Panel A: _|_Rural_||||
|Post-marriage _×_ No child sample|-0.104***|-0.084***|-0.108***|-0.0543|-0.0334|
||(0.005)|(0.014)|(0.007)|(0.053)|(0.019)|
|Post-marriage _×_ Ignore child sample|-0.134***<br>(0.005)|-0.087***<br>(0.013)|-0.138***<br>(0.006)|-0.206***<br>(0.052)|-0.110***<br>(0.017)|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|10845|2075|5770|655|2331|
|R-squared|0.588|0.482|0.489|0.627|0.341|
||_Panel B: _|_Urban_||||
|Post-marriage _×_ No child sample|-0.131***|-0.0804**|-0.136***|-0.134|-0.072***|
||(0.008)|(0.025)|(0.011)|(0.075)|(0.019)|
|Post-marriage _×_ Ignore child sample|-0.192***|-0.134***|-0.198***|-0.191**|-0.110***|
||(0.007)|(0.023)|(0.010)|(0.070)|(0.017)|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|9137|1794|4957|357|2016|
|R-squared|0.595|0.233|0.594|0.513|0.536|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching postmarriage observations in the full sample. Cohort FE refer to birth cohort. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

24
Table 4: Regressions for transmission channels associated with foreign claims by EU and US banks in developing countries, 2004Q1–2011Q1<sup>_†_</sup>

||_I1_|_I2_|_I3_|_I4_|
|---|---|---|---|---|
|Lagged foreign claims|0.788<br>(0.03)<sup>_∗∗∗_</sup>|0.796<br>(0.03)<sup>_∗∗∗_</sup>|0.797<br>(0.03)<sup>_∗∗∗_</sup>|0.799<br>(0.02)<sup>_∗∗∗_</sup>|
|Libor-OIS|-0.040|-0.035|-0.027|-0.029|
||(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗∗_</sup>|(0.02)|(0.02)|
|1st lag|||0.005<br>(0.01)||
|2nd lag|||0.015<br>(0.02)||
|3Rd lag|||-0.017<br>(0.01)||
|Libor-OIS _×_ crisis||||-0.006<br>(0.05)|
|Global bank CDS|0.007|-0.000|-0.003|0.008|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Global bank CDS_×_ crisis||||-0.008<br>(0.02)|
|Asset volatility|-0.024|-0.029|-0.039|0.029|
||(0.02)|(0.03)|(0.03)|(0.06)|
|Asset volatility _×_ crisis||||-0.048<br>(0.07)|
|Lending rate|0.110|0.641|0.873|-1.520|
||(0.11)|(1.82)|(1.73)|(2.44)|
|Infation|-0.007|-0.061|-0.085|0.157|
||(0.02)|(0.19)|(0.18)|(0.31)|
|Growth|-0.075|-0.045|-0.044|-0.140|
||(0.15)|(0.13)|(0.14)|(0.19)|
|GDP|0.261|0.286|0.301|0.174|
||(0.04)<sup>_∗∗∗_</sup>|(0.11)<sup>_∗∗∗_</sup>|(0.10)<sup>_∗∗∗_</sup>|(0.12)|
|Crisis|0.106<br>(0.02)<sup>_∗∗∗_</sup>|0.104<br>(0.03)<sup>_∗∗∗_</sup>|0.081<br>(0.03)<sup>_∗∗∗_</sup>|0.175<br>(0.14)|
|Adj. _R_<sup>2</sup>|0.801|0.793|0.792|0.788|
|Kleibergen-Papp _rk_||12.915<sup>_∗∗_</sup>|11.812<sup>_∗∗_</sup>|11.917<sup>_∗∗_</sup>|
|Hansen _J_||2.343|1.999|0.209|
|Estimator|FE|IV|IV|IV|
|Instruments||13|19|16|
|N|2,849|2,713|2,713|2,782|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelationrobust standard errors are reported in parentheses, with the exception of specification ( _I4_ ), where errors are bootstrapped. A constant term was included in the regressions, but not reported. The excluded instruments are the aged dependency ratio and its lags for four quarters.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

20
Given the base category, 18 percent more of 98 Survey respondents were working than 94 Survey respondents were (Table 19). In the 94 Survey, the North, Center, and Pacific regions correlate with an increased probability of working. The probability of searching for a job is 17 percent more for the 1994 graduates than it is for the 1998 graduates. For 1994 female graduates, the probability of working is 16 percent higher than for 1998 female graduates. Also, the probability of searching for a job is slightly higher for the 1994 graduates than it is for 1998 graduates. The probability that a graduate continues to study is three percent higher in the 1994 than in 1998 Survey. For female graduates the probability of working at home as housewives is 16 percent higher in the 98 cohort (Table 19). The 94 Survey respondents earn higher hourly wages than the 1998 cohorts do (Tables A4.2, A4.3). A plausible explanation is that since 1994, real wages in Mexico have declined by almost 40 percent.

42
The analysis of TII in table 1 suggests that current trade flows may partly reflect the prevailing trade distortions that influence these flows. Using the GTAP 8 database, Column 1 in table 2a shows the U.S.’s Ad Valorem Equivalent (AVE) tariff rates against India’s goods, for which reductions would increase U.S. imports from India. The remaining columns of table 2a show the barriers which apply to other U.S. trading partners—these exports to the U.S. are likely to be reduced by an India-US FTA and the associated U.S. tariff revenues to be reduced, with consequent reductions in economic welfare.

An important feature of the GTAP 8 database is the use of protection data which include a comprehensive treatment of trade preferences resulting from preferential trade arrangements (PTAs) as well as the conversion of specific tariffs for both agricultural and non-agricultural goods (Guimbard, Jean, Mimouni and Pichot 2012). Overall, the average AVE rate of protection in the U.S. is relatively low at 1.3 percent. The variation of tariff rates by source countries reflects both preferential schemes and the composition of U.S. imports. The U.S. AVE protection against India’s goods of 2.7 percent is higher than average and the U.S. retains particularly high barriers in the textile and clothing sector (9.1 percent). In contrast, the U.S. grants duty-free access to NAFTA partners for their apparel and textile products and applies substantially lower tariffs against the SSA region (0.5 percent) which reflects particularly the preferential rates under the African Growth and Opportunity Act (AGOA). As the US FTAs with Morocco, Chile, and Central America all allowed for immediate duty-free access to textile and apparel products meeting the agreements’ rules of origin (Lawrence and Chadha 2004), a similar agreement with India— or, better, one that does not include restrictive rules of origin—would be likely to give Indian producers of textile and apparel products a competitive advantage in the US market.

13
consumption in proxying for household welfare.<sup>15</sup> Since poverty is a function of household consumption, wealth indexes offer biased estimates as well. These results intuitively follow from our assumption that household consumption provides the benchmark measure of household welfare, but can be formalized in the following proposition and corollary.

**Proposition 1:** _Wealth indexes tend to provide biased estimates of poverty rates (as measured by household consumption)._ **Proof** : Appendix 1.

# _Illustrative Example 1_

We provide an illustrative example where the wealth index is generated using both the simple aggregation (Table 2, Model 1) and the PCA method (Table 2, Models 2 and 3) on the VHLSSs in 2012 and 2014. (Further details on the implementation are provided in Appendix 2.) Each cell in the first five rows shows the proportion of each quintile of the consumption distribution that is correctly captured by each quintile of the wealth index. These quintile divisions may also be considered as different poverty lines. The list of assets for Model 1 includes (whether the household has) a car, a motorbike, a bicycle, a desk phone, a mobility phone, a DVD player, a television set, a computer, a refrigerator, an air conditioner, a washing machine, and an electric fan. Model 2 adds to Model 1 the construction materials for the house’s roof and walls, Model 3 adds to Model 2 the type of water and toilet the household has access to.

Estimation results concur with Proposition 1, where each of the quintiles based on the wealth index can only capture around half of the corresponding quintile based on the consumption distribution. For example, the poorest wealth index quintile in Model 3 can correctly capture only 57 and 47 percent of the poorest consumption quintile respectively in 2012 and 2014. Alternatively, we also show the proportion of the cumulative consumption distribution that is

> 15 In addition, assets take time to depreciate and to be sold/ bought so they are not as smooth as consumption in capturing household welfare.

15