rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Impact on Risky Sexual Behavior

|**Panel A: Indexes**||||
|---|---|---|---|
||_Attitudes towards risky_<br>_sexual behavior_|_Risky se_|_xual behavior_|
|_Dep. Var. (Yt):_|(1)<br>(2)|(3)|(4)|
|Treated|0.149<br>0.148|0.175|0.148|
||(0.091)<br>(0.090)|(0.151)|(0.145)|
||[0.205]<br>[0.196]|[0.337]|[0.317]|
|Treated*Y_t−_1|0.001||-0.117**|
||(0.034)||(0.056)|
||[0.982]||[0.085]|
|Y_t−_1|0.292***||0.322***|
||(0.028)||(0.046)|
|R-squared|0.021<br>0.099|0.100|0.152|
|P-value test joint sig<sup>(</sup><sup>_a_)</sup>|0.104||0.318|
|Observations|4986<br>4986|3070|3070|
|Sample|Full Sample|Sex|ually Active|
|Mean Dep. Var. (Control)|0.00186||-0.0631|
|Std Dev Dep. Var. (Control)|3.452||3.625|
|**Panel B: Individual outcomes**||||
||_Has NOT had multiple_|_Used c_|_ondom last time_|
|_Dep. Var. (Yt):_|_concurrent partners_|_he/_|_she had sex_|
|Treated|0.027*<br>0.113***|-0.003|-0.021|
||(0.015)<br>(0.041)|(0.019)|(0.032)|
||[0.232]<br>[0.018]|[0.844]|[0.506]|
|Treated*Y_t−_1|-0.103**||0.038|
||(0.041)||(0.053)|
||[0.028]||[0.574]|
|Y_t−_1|0.288***||0.319***|
||(0.035)||(0.047)|
|R-squared|0.119<br>0.172|0.073|0.194|
|P-value test joint sig<sup>(</sup><sup>_a_)</sup>|0.007||0.513|
|Observations|3339<br>2623|3084|2308|
|Mean Dep. Var. (Control)|0.780||0.494|
|Controls<sup>(</sup><sup>_b_)</sup>|Yes<br>Yes|Yes|Yes|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively. FWER adjusted p-values in square brackets, based on 10,000 replications. Increasing values of the dependent variable correspond to outcomes more consistent with the messages of Shuga. (a) P-value test of joint significance: p-value for the null that the sum of the coefficient on Treated and Treated*Y _t−_ 1 (evaluated at the mean) is zero. (b) Controls in each regression include: female, age, currently attending school, years of education, english spoken at home, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language. 38
cooperative members because they can easily sell their shares if the stock price rises. Moreover, cooperative members typically don’t receive a distribution of profits when they leave; mutual members can withdraw their deposits or cash in their life insurance policies but the amount they receive do not depend on the current condition of the firm.<sup>22</sup> Equallyimportant, shareholding reduces the problem of free riding because an investor can buy up a large block of shares; cooperatives offer no similar method of concentrating the benefits of influencing management.

Skeptics also argue that any nominal savings in the cost of capital for cooperatives or non-profits do not represent a real savings but a shifting of risks normally born by shareholders to the firm’s customer/members and/or taxpayers. Glas Cymru and Network Rail are both financed entirely with debt, for example, and their debt receives a lower rate of return than the weighted average return on the debt and equity of the conventionally financed firms they replaced. But the lack of equity limits the firms’ options if they get into financial trouble. If their predecessor firms got into trouble, they could raise prices or cut the returns to equity before they would be forced to ask the government for a bailout or go bankrupt. But the new firms, lacking equity, can only raise prices before they resort to a bailout or bankruptcy. The new firms pay a higher interest rate on their debt than the old firms did, reflecting the added risk debt holders face without equity investors to help cushion shocks. But the customers and the taxpayers also assume added risks for which they may not be adequately compensated.<sup>23</sup> Very few studies empirically compare the performance of cooperatives with other types of enterprises, and most of these focus on comparisons with private firms rather than with SOEs.<sup>24</sup> On the optimistic side, Vining and Boardman’s (1992, p. 226) study of the 500 largest non-financial Canadian firms found that the 16 cooperatives in the sample performed as well as their private counterparts and significantly better than the SOEs. All 16 were producer cooperatives in the agricultural or general retailing areas.

> 22 There are often penalties and limitations on cashing out insurance policies, particularly for whole life contracts.

> 23 The customers are compensated in that the expected prices for the firm’s services are lower, although more volatile.  The question then is whether the customer is prepared to assume the risk of price volatility or would prefer to have the stockholder assume it in return for a higher expected price.  For a fuller discussion see Gomez-Ibanez (2005a, pp. 7-9).

> 24 There are a few theoretical comparisons of cooperative organizations and government departments of which the best by far is by Elinor Ostrum, Larry Schroeder and Susan Wynne (1993).

45
<!-- Start of picture text --> (.079,.401]<br>(.015,.079]<br>(.006,.015]<br>[0,.006]<br>No data<br><!-- End of picture text --> Figure 2: Index of decentralization 2005 - World Map Note Source: Ivanyna and Shah (2010). Each country is colored according to its index of decentralization, given by the formula (3). The lighter is the color the less decentralized the country is.

nothing to report. Besides, these extreme values are effectively smoothed in our final formula. At the same time, if our main decentralization variable lg ~~e~~ xpdec is not available then the country is excluded from further analysis. As a result, the index is built for 158 countries worldwide. Together they comprise 98% of the world’s GDP, and 99% of the world’s population.

The Figure 2 depicts distribution of the decentralization index on the World map. The darker the color of a country, the more decentralized it is. European countries, North America, Brazil, China are the most decentralized. Countries from Latin America, former USSR, and East Asia receive average decentralization index, while Middle East and African countries are the least decentralized. Denmark (0.4), China (0.35), Sweden (0.31), Finland (0.26), and Norway (0.22) have the five highest scores.

# 4 Measuring the incidence of corruption

In our main estimation we use 5 different measures of corruption. 2 of them are taken from the Transparency International Global Corruption Barometer (GCB) survey, 2 - from the World Bank Enterprises Surveys (WBES) and the 5th is TI Corruption Perception Index (CPI) - a measure, which is commonly used in empirical research on corruption. The definitions of corruption variables are given in Table 2, and their descriptive statistics are given in Table 4.

The Global Corruption Barometer is an annual survey of households conducted by Transparency International, and commenced in 2003. The data we have is from 2005 - 55 thousands households were surveyed in 68 countries from all continents, and all income groups. Apart from demographic characteristics of a respondent, the GCB questionnaire have questions in the following groups: which sectors and 14
<!-- Start of picture text --> S) 98.1 Regrenough water<br>S 96.9 33. me 98:2 Slope = 473<br>89.5 R°=.261<br>83.1<br>89 80.8<br>£ 67.3 67.3 67.3<br>D 63.5 65.4<br>2£& 59.6<br>© 462 49.7<br>D 415° 40.<br>£¢ 36. 5 4<br>® 29. 30.<br>5 6.0<br>a oO 19.<br>N<br>10. 11. 25<br>7. 5d 8.4 8.7<br>15, 19 2.8<br>oO % © © © © © © © © © 0 .© © 2. 0. WO ©<br>x Ss s s RS s RS s AS x x Ss ss e) s AS RS<br>SPB DH GM Pia MOS WP TWN PM OS<br>MEE Proportion of households that report conflict<br>ME Proportion that report enough water<br><!-- End of picture text -->
Figure 13 Trend in Reading and Mathematics Test Scores in PISA International Standardized Test <!-- Start of picture text --> 600 Brazil<br>Hong HongKong-China Kong-China 550 Indonesia Indonesia<br>500<br>450<br>Japan Japan Mexico Mexic e o<br>400<br>350<br>300<br>Korea Korea 250 Thailand Thailand<br>Poland Poland Greece Greece<br>Spain Spain Portugal Portugal<br>Russian Russian Federa Federation tion<br>Reading 2000 Reading Reading200 2003 3 Math Math200 2000 0 Math Math200 2003 3<br><!-- End of picture text --> _Source:_ Based on data from the OECD 2003.

Low quality of schooling raises questions as to the adequacy of the secondary school system in delivering returns and improving employability and income prospects. Lack of quality education is an issue especially for poor rural migrants to urban areas. While there is a trade-off in terms of allocating resources to improving enrollment rates in education, investments in teaching quality are necessary in tandem in order to increase returns to education. Indonesia’s performance in this international rating reinforces the fact that the education system is not relevant to the needs of the country’s development and returns to investment in the sector are not maximized.

27
Policy Research Working Paper 6359

# **Abstract**

This paper analyzes the reliability and consistency of subjective well-being measures. Using the Life in Transition Survey, which was administered in 34 countries of Europe and Central Asia in 2006 and 2010, the paper evaluates subjective well-being measures (satisfaction with life and subjective relative income position) against objective measures of welfare based on consumption and assets. It uses the different formulations of life satisfaction in the survey to test robustness to alternative framing and scaling. It also explores within-household differences in subjective wellbeing assessments. The analysis finds that subjective relative income is weakly correlated with household relative welfare position as measured by consumption or assets. Life satisfaction, by contrast, is highly correlated with objective and subjective measures of household welfare. It generally reflects cross-country differences in average consumption, assets, or per capita gross domestic product, although Central Asian countries report much higher life satisfaction levels than their incomes would suggest. Two alternative measures of life satisfaction are highly correlated and the correspondence between verbal and numeric scales is strong within a country or groupings of similar countries. Within households, subjective assessments of relative income are roughly consistent but measurement error is correlated with individual characteristics (gender and age of respondents), which could cause systematic biases in the analysis.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. 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 authors may be contacted at acojocaru@worldbank.org or mfdiagne@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
as well as airfreight cost indices from the U.S. Bureau of Labor Statistics. The GSCPI also uses supply chainrelated components from the PMI. The index is normalized, so zero represents the historical average, while negative values are representative of a very low stress situation and positive values express a level of stress.

The Pressure and Stress indices reflect major events, but the latter is more granular regarding containerized shipping specifically. Conversely, the GSCPI US-centric composition may explain its relative insensitivity to the Red Sea crisis as compared to the Stress Index.

Figure 9. Global Supply Chain Pressure Index and Stress Index (MTEU), 2016-2024 <!-- Start of picture text --> 2.5 5<br>4<br>2<br>3<br>1.5<br>2<br>1<br>1<br>0<br>0.5<br>-1<br>0 -2<br>Stress, MTEU (LHS) Pressure Index (RHS)<br>1/1/2016 4/1/2016 7/1/2016 10/1/2016 1/1/2017 4/1/2017 7/1/2017 10/1/2017 1/1/2018 4/1/2018 7/1/2018 10/1/2018 1/1/2019 4/1/2019 7/1/2019 10/1/2019 1/1/2020 4/1/2020 7/1/2020 10/1/2020 1/1/2021 4/1/2021 7/1/2021 10/1/2021 1/1/2022 4/1/2022 7/1/2022 10/1/2022 1/1/2023 4/1/2023 7/1/2023 10/1/2023 1/1/2024<br><!-- End of picture text --> Source: Federal Reserve Bank of New York and the World Bank.

# **6. Stress and Rates: Competing for Scarce Capacity**

## **Convergence between GSCSI and Freight Rates**

The striking parallelism between global stress and freight rate trends, while expected, merits further elaboration (Figures 10 and 11). It should be noted that prevailing freight rate indices reflect _spot_ rates, imperfectly capturing the actual shipping costs faced by large trade volumes under annual contracts negotiated between carriers and major shippers. They may imperfectly represent the actual shipping cost of trade, as spot rates correspond to rates paid by ad-hoc shippers competing for remaining slots, which can be limited. As such, they dynamically reflect real-time supply/demand tension in terms of their marginal cost.

The following factors help explain the boom/bust rate cycles:

1. Freight demand exhibits low rate elasticity, as freight cost represents a relatively small share of the landed price at destinations. The Free On Board (FOB) value of the content of a 40-foot container of manufactured goods from China ranges from USD 50,000 to several million. An increase in spot freight rates amounts to an ad valorem markup of only a few percent of the cargo value. Even at elevated rates, shippers willingly pay premiums to expedite deliveries and cut lead time. However, there are notable variations by commodity sector (standard international trade classification (SITC) categories), implying that sectors such as furniture have less elasticity than others such as office machines.

14
Policy Research Working Paper 7498

# **Abstract**

Can regionalism do what multilateralism has so far failed to do—promote greater openness of services markets? Although previous research has pointed to the wider and deeper legal commitments under regional agreements as proof that it can, no previous study has assessed the impact of such agreements on applied policies. This paper focuses on the Association of Southeast Asian Nations (ASEAN), where regional integration of services markets has been linked to thriving regional supply chains. Drawing on surveys conducted in 2008 and 2012 of applied policies in the key services sectors of ASEAN countries, the paper assesses the impact of the ASEAN Framework Agreement on Services (AFAS) and the ambitious ASEAN Economic Community Blueprint, which envisaged integrated services markets by 2015. The analysis finds that over this period, ASEAN did not integrate faster internally than vis-à-vis the rest of the world: policies applied to trade with other ASEAN countries were virtually the same as those applied to trade with rest of the world. Moreover, the recent commitments scheduled under AFAS did not produce significant liberalization and, in a few instances, services trade policy actually became more restrictive. The two exceptions are in areas that are not on the multilateral negotiating agenda: steps have been taken toward creating regional open skies in air transport, and a few mutual recognition agreements have been negotiated in professional services. These findings suggest that regional negotiations add the most value when they are focused on areas that are not being addressed multilaterally.

This paper is a product of the Trade and International Integration 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 authors may be contacted at bgootiiz@worldbank.org and amattoo@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
Policy Research Working Paper 8557

# **Abstract**

The high cost of nutritious foods can worsen poor diets and nutrition outcomes especially among low-income households. Yet little is known about the spatial and temporal patterns of the cost of nutritious diets in South Asia, where malnutrition in multiple forms remains high. Using existing food price data from Sri Lanka, Pakistan, Afghanistan, and India, two methods are applied to assess the affordability of nutritious foods: Cost of a Recommended Diet (CoRD) and Nutritious Food Price Index (NPI). The analysis finds that the cost of a nutritious diet is 38 percent higher in Sri Lanka using CoRD compared to the cost of a (calorie-based) diet that meets basic food needs, and 15 percent higher in Afghanistan. In addition, CoRD varies across cities due to variability in the price of dairy and vegetables. Comparison of the NPI and the food Consumer Price Index (CPI) indicates that, for some countries, the price of a nutritious food basket varies more by season and has been increasing at a faster rate than the price of a typical food basket. This phenomenon is largely due to the variable cost of vegetables.

This paper is a product of the Agriculture Global Practice and the South Asia Chief Economist Office. 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://www.worldbank.org/research. The authors may be contacted at fdizon@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
Table **la.** Base and Final Subsidy Conuniiitents for Selecred Coimiodities in Major Subsidizing Countries 000 **iitetic** tons

|Country|Whea|t||
|---|---|---|---|
||Stibsidized Exports|||
||1986-90|1995|2000|
|US|18,382.4|20,238.3|14,522.1|
|EU|17,008.1|19,118.6|13,436.4|
|Canada|11,204.8|13,590.3|8,851.8|
|Turkey|2,306.0|2,600.2|1,461.2|
|Hungary|1,444.0|1,393.0|**1,141.0**|
|Total (top 5)|50,345.3|56,940.3|39,412.4|
|Total Export Sibsidies|53,018.3|||
|World Trade 1991/92|108,289.0|||
|Top S as % of Total Export Subsidies|95.0|||
|Top S as % of World Trade in 1991/92|46.5|||

|Country|Rice|||
|---|---|---|---|
||Subsidized Exports|||
||1986-90|1995|2000|
|Indonesia|299.8|295.6|257.8|
|EU|183.7|177.3|145.1|
|Uruguay|53.2||45.7|
|US|48.8|271.7|38.6|
|Colotiubia|18.9||16.3|
|Total (topS)|604A|744.5|503.4|
|Total**I/**|604.5|||
|World Trade|14,080.0|||
|Top S as % of Total Export Subsidies|100.0|||
|Top S as b ofWorld Trade in 1991/92|4.3|||

|Country|Veget|able Oil||
|---|---|---|---|
||Subsidized Exports|||
||1986-90|1995|2000|
|Brazil|552.1|544.3|474.7|
|Hungary|185.0|179.0|**146.0**|
|**uS**|178.9|5875|141.3|
|Canada|117.4|113.3|92.8|
|Turkey|72.2|94.5|76.5|
|Total (top5)|1,105.6|1,518.7|931.3|
|Total**I/**|1,197.2|||
|World Trade|21,470.0|||
|Top 5**as %**of Total Export Subsidies|92.4|||
|Top 5**as*** of World Trade in 1991/92|**5.1**|||
|Country|Co|arse Grains||
||Subsidized Exports|||
||1986-90|1995|2000|
|Eli|12,624.5|12.182.6|9,973.4|
|Canada|4,392.0|4,418.9|3,617.6|
|Mexico 1/|3,577.8|3,513.1|2,951.0|
|ITS|1,975.4|1,906.3|1,560.6|
|Rep. of South Africa 2/|1,893.5|1,827.3|1,495.9|
|Total (topS)|24,463.2|23,848.2|19,598.5|
|Totall/|28,328.6|||
|World Trade|91,680.0|||
|Top S as**%**of Toral Export Subsidies|86.4|||
|Top**5**as**%**of World Trade in 1991/92|26.7|||

**1/** Cors and sorghum Subsidy volumes have been added

2/ Barley, mianize and simaize products, oats, and grain **sorghuni** subsidy voluittes have been added together Soturce: Author's calculations: basic data are from Uruguay Round country schedules.

11
|IV<br>_Two girls_|(6)|_2 (incl.)_|0.048<br>(0.032)<br>173.4<br>1.599<br>(2.180)<br>124.18<br>_6 (incl.)_<br>0.063<br>(0.050)<br>123.3|0.015<br>(0.027)<br>105.75<br>Yes<br>Yes<br>Yes<br>_red, Age at First birth_<br>cludes women aged 20<br>ith 2 or more children<br>** Indicates statistical<br>ge estimation.|
|---|---|---|---|---|
|**arent**<br>IV<br>_Same-sex_|(5)|_n before 197_|-0.011<br>(0.045)<br>94.65<br>-0.010<br>(3.043)<br>72.02<br>_n before 196_<br>0.008<br>(0.064)<br>65.95|-0.005<br>(0.032)<br>67.18<br>Yes<br>Yes<br>Yes<br>_ge, Age Squa_<br>f mothers in<br>ied women w<br>nce at 10%. <br> the frst-sta|
|**Older P**<br>OLS<br>—|(4)|_Mothers bor_|-0.031***<br>(0.006)<br>-0.007<br>(0.484)<br> _Fathers bor_<br>-0.036***<br>(0.010)|0.008<br>(0.005)<br>Yes<br>Yes<br>Yes<br>ovariates: _A_<br> The sample o<br>s of the marr<br>ical signifca<br>orresponds to|
||Obs.|_l A.2. _|4151<br>2843<br>_el B.2. _<br>2741|2336<br>lowing c<br>_y 2nd_. <br>husband<br>s statist<br>ments c|
||Mean|_Pane_|0.205<br>20.05<br>_Pan_<br>0.439|0.061<br>ude the fol<br> _1st_ and _Bo_<br>cludes the <br> * Indicate<br>uded instru|
|IV<br>_Two girls_|(3)|_1972_|0.058<br>(0.043)<br>108.15<br>10.584***<br>(3.723)<br>69.69<br>_1966_<br>0.070<br>(0.069)<br>96.03|0.079*<br>(0.042)<br>76.03<br>Yes<br>Yes<br>Yes<br> the models incl<br>dicators for _Boy _<br>ple of fathers in<br>ict fxed efects. <br>istic of the excl|
|**Parent**<br>IV<br>_Same-sex_|(2)|_born after _|0.032<br>(0.056)<br>71.82<br>14.292***<br>(5.066)<br>50.98<br> _born after _<br>0.083<br>(0.096)<br>61.55|0.095<br>(0.058)<br>47.54<br>Yes<br>Yes<br>Yes<br>ntheses. All <br>o include in<br>old. The sam<br>ey and distr<br>. The F-Stat|
|**Younger **<br>OLS<br>—|(1)|_.1. Mothers_|-0.024***<br>(0.007)<br>-1.002<br>(0.687)<br>_.1. Fathers _<br>-0.009<br>(0.013)|0.009<br>(0.007)<br>Yes<br>Yes<br>Yes<br>el are in pare<br>2) and (3) als<br>han 18 years <br>ted with surv<br>cance at 1%|
||Obs.|_Panel A_|3253<br>2019<br>_Panel B_<br>2719|2194<br>PSU lev<br>lumns (<br>unger t<br>s estima<br>al signif|
||Mean||0.111<br>11.95<br>0.349|0.056<br>d at the <br>dels in co<br>hildren yo<br>h model i<br>s statistic|
|t for Fertility:|variable||**_f-farm_**<br>_F-stat FS_<br>**_r week_**<br>_F-stat FS_<br>**_f-farm_**<br>_F-stat FS_|**_ccupation_**<br>_F-stat FS_<br>ed-Efects<br>xed-Efects<br> errors clustere<br>_ucation_. IV Mo<br>with 2 or more c<br> years old. Eac<br>%. *** Indicate|
|Method<br>Instrumen|Dependent||**_Worked o_**<br>**_Hours pe_**<br>**_Worked o_**|**_Second o_**<br>Survey Fix<br>District Fi<br>Controls<br>_Notes:_ Standard<br>and _Years of Ed_<br>to 49 years old <br>younger than 18<br>signifcance at 5|

44