rafmacalaba/datause-encoder-data huggingface dataset:
states in addressing the problems facing youth today, and in the future, it will be important to regularly re-calculate the index and observe its progress over time.

The three indices presented in this paper – the Youth Well-Being Index, Child-Youth Well-Being Index, and the General Youth Well-Being Index – are a Brazilian adaptation of the US-based Youth Well-Being Index, taking into consideration the factors that are important to the Brazilian context and the availability of data. They use different sets of variables to come up with a single measure of well-being for youth today (YWI) and for youth today and in the next generation (C-YWI). The GYWI takes into consideration the hypothesis from the ecological risk framework and includes in the measure environmental factors that affect child and youth development. These three indicators show very similar state rankings, suggesting that any of the three may be used to track youth progress. However, the similarity across indices also suggests that the situation of youth in Brazil is relatively static, since the states that have the poorest youth well-being indicators today are not making necessary investments to correct the situation for the future.

The index presented has some methodological shortcomings to address as the tool is used and refined. First, the quality of some of the indicators is questionable. Notably, the AIDS incidence indicators deserve further examination to ascertain their quality. Second, the alcohol and drug use indicators are from a special survey that reports results at the regional level. This survey will need to be repeated regularly and the results reported at the state level to provide greater variance and updating of the indicators for use in the index. A more random sample to include small cities and rural areas would also improve the quality of these indicators and thus the quality of the index. Third, information on new youth issues, such as obesity, incarceration/rehabilitation or youth participation, would further improve the applicability of the index to the Brazilian youth context.

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Table B5: Difference-in-Difference- Share of Informal Firms

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Outcome:|Informal|Informal|Inf.-non salary|Inf.-non salary|Informal|Informal|Inf.-non salary|Inf.-non salary|
|||_Pa_|_nel A: Continuou_|_s Treatment Measu_|_re_||||
|-lndistance_i_ x 1999|-0.005**|-0.005*|-0.004|-0.003|-0.005**|-0.004*|-0.004|-0.003|
||(0.002)|(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|(0.003)|(0.003)|
|-lndistance_i_ x 1999|-0.009***|-0.007**|-0.006*|-0.004|-0.009***|-0.009***|-0.006**|-0.005|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|-lndistance_i_ x 2004|-0.018***|-0.017***|-0.015***|-0.014***|-0.017***|-0.018***|-0.013***|-0.014***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.004)|(0.004)|(0.004)|
|Observations|11,504|11,504|11,504|11,504|11,504|11,504|11,504|11,504|
|R-squared|0.884|0.883|0.906|0.906|0.892|0.892|0.910|0.910|
|||_Panel B: _|_Treatment Measur_|_e using the dumm_|_y variable_||||
|_Ti_ x 1999|-0.014***|-0.013**|-0.007|-0.006|-0.010**|-0.009*|-0.007|-0.005|
||(0.005)|(0.005)|(0.006)|(0.006)|(0.005)|(0.005)|(0.006)|(0.006)|
|_Ti_ x 2004|-0.022***|-0.020***|-0.015**|-0.013**|-0.017***|-0.015**|-0.014**|-0.012*|
||(0.006)|(0.006)|(0.007)|(0.007)|(0.006)|(0.006)|(0.007)|(0.007)|
|_Ti_ x 2009|-0.032***|-0.029***|-0.022***|-0.019***|-0.026***|-0.023***|-0.019***|-0.016**|
||(0.006)|(0.006)|(0.007)|(0.007)|(0.006)|(0.006)|(0.007)|(0.007)|
|Observations|11,504|11,504|11,504|11,504|11,504|11,504|11,504|11,504|
|R-squared|0.883|0.883|0.906|0.906|0.892|0.892|0.910|0.910|
|Mean outcome before the shock|0.833|0.833|0.796|0.796|0.833|0.833|0.796|0.796|
|Distance Measure|Meters|Minutes|Meters|Minutes|Meters|Minutes|Meters|Minutes|
|Distance Controls|X|X|X|X|X|X|X|X|
|State-Time FE|X|X|X|X|||||
|Municipality-Time FE|||||X|X|X|X|

_Notes:_ This table reports the results of a regression relating changes in the share of informal workers in each location with the line B of the subway. Panel A reports the results for the continuous treatment measures, and panel B for the dummy variables. In the first four columns, I include state-time fixed effects, and in the fifth column to the eight column municipality-time fixed effects. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.
Two earlier studies using the same household survey data from Peru have investigated the wage rate function for men and women (Stelcner and others 1988, King 1988). They did not, however, assess differences in wages and returns to schooling, or evaluate whether correcting for sample selection or unobserved variables systematically modifies the comparison of the estimated returns to education and productivity of men and women.

The paper is structured as follows. Section Two explains th model specification and estimation strategy. Section Three discusses the data and highlights the differences between males and females in terms of wagerelated characteristics. Section Four reports the results. Policy implications are in the concluding sect:on.

# 2. Model SRecification and Estimation Strategy

This section outlines a model framework to address participation in the labor market, the private economic returns to education, and a wage gap between males and females that is influenced by differences in job-related characIteristics. It also discusses ways to reduce the impact of unobserved variable and sample selection bias from the wage estimates.
gram, based on lists of members obtained in advance, with responses given by the household head. We chose to sample equal numbers of households within each community because one goal was to obtain a community average effect on socioeconomic indicators (not weighted by population). Characteristics of the respondents are given in Table A4. The primary reported economic activity of over half of the respondent households was agriculture or livestock, most household heads have not completed secondary school, and 72% of respondents understood the survey questions “very well” (as assessed by the enumerator).

The sample is representative of the universe of possible applicants. When we compare the communities sampled with the universe of possible applicants in all states during these years, we find few differences in observable covariates. Table 2 shows the means and normalized differences in means across the sampled versus the not sampled applicants between 2011 and 2014. Most of the normalized differences are quite small (less than a quarter of a standard deviation). The covariate for which there is a substantial difference is percent indigenous in the municipality, which is larger for applicants in the sample both for beneficiary and non-beneficiary communities<sup>7</sup> . Sampled beneficiaries are also somewhat farther from major cities.

Leaders and households were surveyed separately, with some questions overlapping across the instruments. Leaders responded to questions about the PSA program, including the location of the parcel and its characteristics, a referendum question regarding how they would vote on program participation at different prices (see description below), the way in which payments were divided within the community, the types of forest management, and use of the PSA parcel. In addition, they responded to a battery of questions on community characteristics (infrastructure, productive activities, common property size, etc.). A significant number of questions were also asked with the intention of measuring social capital for use in a complementary study (Alix-Garcia, Sims, Orozco-Olvera, Costica, Medina & Monroy 2018).

The household survey also began with questions about the PSA program, including the same referendum question asked to the leaders (though with different random prices) and household use of the PSA parcel. A basic household roster and demographic characteris-

> 7When we test within our sample for a difference in estimated WTA between those with above 50% indigenous and those below, we find a difference of 3 pesos on average, and a t-test of this difference across subsamples yields a p-value of 0.61. This assuages concerns about possible bias from a high presence of observations from majorityindigenous municipalities.

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4

# **2. ABOUT THE DATA**

The Services Trade Restrictions Database contains information on policies that affect international trade in services. Services trade is defined, as is now customary, to include the supply of a service through cross-border delivery, through establishing a commercial presence, or through the presence of a natural person.<sup>2</sup> The perspective is one of a foreign supplier who wishes to provide services to consumers in a particular country, and we focus mainly on policy measures that discriminate against foreign services or service providers. Domestic regulations that affect domestic and foreign agents alike are only covered in the few instances where they clearly have a major impact on trade.

It is important to clarify at the outset what the database does not (yet) cover, which should be the focus of future data collection efforts. First, the database does not include information on the existing market structure – e.g. the number of firms, their market share and ownership – across sectors and countries, which means that our policy measures capture the restrictions on entry into markets but do not capture the prevailing extent of competition between domestic and/or foreign firms. Second, we do not collect data on outcome variables such as prices, quality, or diversity of services. Third, we are able to cover the state of prudential and pro-competitive regulation only in a rather limited way, which makes it hard to assess how far these ostensibly non-discriminatory measures offer _de facto_ protection to domestic service providers. This gap also makes it hard to assess how far the gains from market-opening depend on the state of complementary regulation, and we emphasize that a mechanical elimination of trade barriers is not necessarily desirable in all sectors. Fourth, we capture only limited information on the implementation of policies. For instance, we make an effort to identify certain aspects of the process by which services providers are granted licenses, such as transparency and accountability, but the process remains opaque and it is hard to determine whether the processes in themselves offer protection to domestic providers. Finally, the database focuses on countries‟ Most-Favored Nation (MFN) policies, which in (paradoxical) trade parlance means their non-preferential policies, and contains only limited information on their preferential policies. We discuss this issue further below under the heading „Dealing with preferential policies.‟

> 2  We do not cover “consumption abroad,” a mode of delivery that is particularly important in services like tourism, education and health – sectors not covered by the database.
# **4.5 Price adjustments for poverty measurement**

The shortcomings of price indices used in the computation of real (spatially adjusted) welfare indicators are well known; the most serious and frequently discussed issues have to do with limitations of price data, namely the lack of reliable information on non-food prices, and the flaws of unit values as a proxy for market prices (Deaton 1988, Deaton and Dupriez 2011, Gibson and Rozelle 2005, Gibson and Kim 2019, Amendola, Mancini, Redaelli and Vecchi 2024). The result is that price indices used in poverty measurement typically have limited coverage (they are _food_ price indices) and their reliability is often called into question. As serious as this problem is, viable solutions are few and far between. The proposed paper should assess strategies to overcome the problems raised by limited-coverage price indices in poverty measurement, including, potentially, the calculation of more comprehensive Paasche indices (incorporating for example rent prices), the use of alternative methods (such as the CPD method, as seen in Chen et al. 2020), and even the switch to an implicit approach based on multiple poverty lines (see WP 3).

# **4.6 Consistency and specificity in poverty measurement**

The idea of a trade-off between the consistency and specificity of poverty lines was popularized by Bidani and Ravallion (1994). A strand of literature that includes some recent contributions (Ravallion and Lokshin 2006; Arndt and Simler 2007, 2010; Mahrt, Herforth, Robinson, Arndt and Headey 2022) has investigated possible solutions, related to the imposition of revealed preference conditions to construct utility-consistent poverty lines that vary across households. However, these extensions are not yet widely popular in applied work. The proposed paper should take stock of this literature, assessing the different strategies available and working towards the establishment of a “best practice”.

# **4.7 How sensitive are poverty estimates to poverty measurement methods?**

The results of a poverty assessment are sensitive to seemingly minor choices made along the process that leads from data collection to final estimates. This is particularly concerning when such choices are controversial. The problem is hardly new: analysts are aware of the arbitrariness baked into some of the decisions involved in setting a poverty line, and some solutions have been investigated (ex. Pradhan et al. 2000); many poverty assessment reports 18
# **Appendix Table 6. World Health Organization guidelines for a healthy diet; World Cancer Research Fund recommendation regarding meat**

## **For adults**

A healthy diet includes the following:

- Fruit, vegetables, legumes (e.g. lentils and beans), nuts and whole grains (e.g. unprocessed maize, millet, oats, wheat and brown rice).

- At least 400 g (i.e. five portions) of fruit and vegetables per day _(2)_ , excluding potatoes, sweet potatoes, cassava and other starchy roots.

- Less than 10% of total energy intake from free sugars _(2, 7)_ , which is equivalent to 50 g (or about 12 level teaspoons) for a person of healthy body weight consuming about 2000 calories per day, but ideally is less than 5% of total energy intake for additional health benefits _(7)_ . Free sugars are all sugars added to foods or drinks by the manufacturer, cook or consumer, as well as sugars naturally present in honey, syrups, fruit juices and fruit juice concentrates.

- Less than 30% of total energy intake from fats _(1, 2, 3)_ . Unsaturated fats (found in fish, avocado and nuts, and in sunflower, soybean, canola and olive oils) are preferable to saturated fats (found in fatty meat, butter, palm and coconut oil, cream, cheese, ghee and lard) and _trans-_ fats of all kinds, including both industriallyproduced _trans-_ fats (found in baked and fried foods, and pre-packaged snacks and foods, such as frozen pizza, pies, cookies, biscuits, wafers, and cooking oils and spreads) and ruminant _trans-_ fats (found in meat and dairy foods from ruminant animals, such as cows, sheep, goats and camels). It is suggested that the intake of saturated fats be reduced to less than 10% of total energy intake and _trans-_ fats to less than 1% of total energy intake _(5)_ . In particular, industrially-produced _trans_ -fats are not part of a healthy diet and should be avoided _(4, 6)_ .

- Less than 5 g of salt (equivalent to about one teaspoon) per day _(8)._ Salt should be iodized.

## **For infants and young children**

In the first 2 years of a child’s life, optimal nutrition fosters healthy growth and improves cognitive development. It also reduces the risk of becoming overweight or obese and developing NCDs later in life.

Advice on a healthy diet for infants and children is similar to that for adults, but the following elements are also important:

- Infants should be breastfed exclusively during the first 6 months of life.

- Infants should be breastfed continuously until 2 years of age and beyond.

- From 6 months of age, breast milk should be complemented with a variety of adequate, safe and nutrient-dense foods. Salt and sugars should not be added to complementary foods.

If you eat red meat, limit consumption to no more than about three portions per week. Three portions is equivalent to about 300 to 500 grams cooked weight of red meat. <u>Consume very little, if any, processed meat.</u> Source: WHO (2020); World Cancer Research Fund and American Institute for Cancer Research 45
Policy Research Working Paper 8051

# **Abstract**

Who are the civil servants that serve poor people in the developing world? This paper uses direct surveys of civil servants—the professional body of administrators who manage government policy—and their organizations from Ethiopia, Ghana, Indonesia, Nigeria, Pakistan and the Philippines, to highlight key aspects of their characteristics and experience of civil service life. Civil servants in the developing world face myriad challenges to serving the world’s poor, from limited facilities to significant political interference in their work. There are a number of commonalities across service environments, and the paper summarizes these in a series of ‘stylized facts’ of the civil service in the developing world. At the same time, the particular challenges faced by a public official vary substantially across and within countries and regions. For example, measured management practices differ widely across local governments of a single state in Nigeria. Surveys of civil servants allow us to document these differences, build better models of the public sector, and make more informed policy choices.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at drogger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Ex-Ante Evaluation of Sub-National Labor Market Impacts of Trade Reforms

Maryla Maliszewska, Israel Osorio-Rodarte and Rakesh Gupta<sup>1,2</sup>

**Keywords** : economic growth of open economies; measurement and analysis of poverty; computable general equilibrium modeling; distributional impacts of trade; trade agreements; Sri Lanka

**JEL Classification** : F16, I32, F61, F63, O53, C68

> 1 Maryla Maliszewska, Israel Osorio-Rodarte, and Rakesh Gupta: The Word Bank. The paper is part of the background analysis for the World Bank Report, _Distributional Impacts of Trade (2020)_ . It was generated under the guidance of Caroline Freund, Antonio Nucifora and Carolina Sanchez-Paramo. We received comments and suggestions from Erhan Artuc, Paulo Bastos, Paul Brenton, Maurizio Bussolo, Erik William Churchill, Jakob Engel, Carmen Estrades, Michael Ferrantino, Sanjay Kathuria, Deeksha Kokas,  Tae Hyun Lee, Gladys Lopez-Acevedo, Mariem Malouche, Ambar Narayan, David Newhouse, Bob Rijkers, Raymond Robertson, Carlos Rodriguez-Castelan, Sebastian Saez, Hans Timmer and participants of the various World Bank seminars and trainings where this paper was presented.

> 2 This paper is a product of the staff of the International Bank for Reconstruction and Development/the World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This material should not be reproduced or distributed without the World Bank's prior consent.
**Graph 3: Distribution of Input Intensity**

<!-- Start of picture text --> Input Intensity<br>Manufacturers<br>-10 -5 0 5<br>Log of inputs usage per worker<br>Large Urban Urban Microenterprises<br>Rural Town Rural (other)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->

**Graph 4: Distributions of Value-added Per Worker**

<!-- Start of picture text --> Distribution of Value added per worker<br>Manufacturers<br>-5 0 5 10<br>Log of Value Added per Worker<br>Large Urban Urban Microenterprises<br>Rural Town Rural (other)<br>.5<br>.4<br>Density .3.2<br>.1<br>0<br><!-- End of picture text -->

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analytical tools (survey and census‐based approaches, comparative investment climate indices, etc.) that can be explored. Moreover, targetted institutional options are available, such as urban development funds. Special attention needs to be focused on the issues of political economy and national‐sub‐national fiscal relations, with the fragmentation of ministerial responsibility again a major issue to be considered. Finally, the policy and institutional challenge of small and medium‐size cities (as distinct from larger cities) is likely to be particularly pressing for large countries, and countries with high urban population growth and relatively poor institutional capacities (i.e., much of Africa).

# 3. <u>Research on selected instruments</u>

In addition to the instrument of comprehensive slum improvement programs (see above), Linn (2010) gives special priority to further research on two sets of instruments: conditional cash transfer programs and incentive‐based approaches: Conditional cash transfer (CCT) programs have been widely used in Latin America to combat poverty and to provide incentives for activities that create better human development (education, health, nutrition, etc.). However, many of the schemes have a rural focus and special problems have been encountered in implementing these schemes in urban areas. Exploring how such programs can best be designed and implemented to meet the needs and conditions of the urban poor is therefore an urgent priority as many countries are exploring the possibility of developing or expanding their CCTs.<sup>8</sup> CTTs are a special example of a broader set of instruments that employ incentives to change individual, community or institutional behavior. Tournament approaches, output‐based aid, innovation and replication funding mechanisms are other examples. A key potential benefit of their use is that, if properly designed, they can achieve improvements in performance on a significant scale and by large numbers of institutions or jurisdictions.<sup>9</sup> Research is needed to determine how and where these incentive‐based approaches can be best brought to bear on various aspects of urban poverty reduction.

# 4. <u>Research on selected analytical and planning tools</u>

Three analytical and planning tools deserve further research in how they can best be brought to bear on the problems of urban poverty: happiness and life satisfaction analysis; impact analysis; and strategic planning tools.

Instead of using the traditional economic tools of looking at revealed preferences as expressed in per capita income and consumption and their composition, happiness and life satisfaction analysis bases its measurement of well being on people’s

> 8 Linn points out that the example of CCTs in developing countries has also spurred their application in some industrial countries, including New York City.

> 9 See Zinnes (2009) for an exploration of the tournament approach in a number of urban settings, including some initiatives dealing with urban poverty reduction.

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