rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Estimates of the effect of the randomly assigned movie on knowledge of BREGS by gender**

|||Full Sample|||Women|||Men||
|---|---|---|---|---|---|---|---|---|---|
||Film in villa|ge<br>Constant|Obs|Film in villa|ge<br>Constant|Obs|Film in village|Constant|Obs|
|Heard|0.028**|0.915***|5012|0.047***|0.867***|2782|0.006|0.976***|2230|
|Work score|0.497***|2.465***|4655|0.368***|2.006***|2464|0.696***|2.977***|2191|
|Days|0.118***|0.221***|4597|0.117***|0.104***|2429|0.130***|0.351***|2168|
|Gender|-0.022|0.654***|4635|-0.007|0.622***|2452|-0.037|0.690***|2183|
|BPL|0.042*|0.615***|4636|0.048|0.559***|2453|0.039|0.676***|2183|
|Work lag|0.062***|0.088***|4629|0.038*|0.064***|2450|0.094***|0.114***|2179|
|Unempl.|0.094***|0.286***|4635|0.081**|0.201***|2451|0.117***|0.381***|2184|
|Wage rate|0.116***|0.322***|4637|0.082***|0.239***|2452|0.165***|0.413***|2185|
|Wage lag|0.048***|0.106***|4609|0.025|0.093***|2438|0.078***|0.122***|2171|
|Demand|0.049**|0.605***|4989|0.027|0.515***|2767|0.083***|0.717***|2222|
|Contractor|0.047**|0.184***|4634|-0.011|0.132***|2449|0.121***|0.242***|2185|
|Facilities score|0.02|1.317***|4655|-0.068|1.140***|2464|0.144|1.514***|2191|
|Crèche|0.075***|0.128***|4644|0.073***|0.100***|2458|0.081***|0.160***|2186|
|Water|-0.041*|0.604***|4645|-0.063*|0.546***|2458|-0.009|0.668***|2187|
|Shade|-0.026|0.445***|4644|-0.067**|0.394***|2457|0.027|0.502***|2187|
|First aid|0.016|0.142***|4642|-0.008|0.102***|2457|0.049|0.186***|2185|
|Board<br>_Note_: Svy regressio|0.023**<br>ns usinghouseho|0.028***<br>ldweights andind|4996<br>ividualo|-0.004<br>utcomes. Bas|0.014***<br>ed on R2 data only|2773<br>. If an in|0.058***<br>dividual has not|0.045***<br>heard about B|2223<br>REGS, an|

_Note_ : Svy regressions using household weights and individual outcomes. Based on R2 data only. If an individual has not heard about BREGS, answers to other questions are coded as missing. *** p<0.01, ** p<0.05, * p<0.1, based on robust standard errors. Full definitions of the knowledge questions are given in Table 1.

21
# Behavioral Aspects of Healthy Longevity

Ana Maria Rojas `r` * Ana Maria Munoz Boudet `r*` , Ellen Moscoe `r*` , Julian Jamison `r^` , Carlos Rumiallo Herl<sup>1</sup> JEL-Classification: D90, D91, I12, I15, I18 Keywords: NCD, non-communicable diseases, healthy longevity, behavioral sciences, decision-making and habit formation.

> 1 Affiliations: *World Bank, Poverty and Equity Global Practice, ^University of Exeter, Erasmus University, Rotterdam. Authors' names have been randomized using the AEA author randomization tool (code: Yv7HMzfP1pPb). The paper was prepared as part of the World Bank’s Healthy Longevity Initiative.  We thank for their valuable comments have Sir George Alleyne, Damien De Walque, Akshar Saxena, Paul Isenman and the participants of the Healthy Longevity Initiative Technical Workshops (Mexico City, May 2022, Washington DC, October 2022).
Table 4: Estimated Elasticities of Fishing Hours and Distance Traveled with Respect to Subsidy Received

||||Dependen|t variable|:||
|---|---|---|---|---|---|---|
||log|(fshing h|ours)|log|(km trav|eled)|
||RF|FS|Elasticity|RF|FS|Elasticity|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Conventional|1.125|0.517|2.177|1.674|0.958|1.746|
||(0.233)|(0.101)|(0.622)|(0.219)|(0.189)|(0.414)|
|Bias-Corrected|1.565|0.583|2.682|1.872|1.071|1.749|
||(0.233)|(0.101)|(0.615)|(0.219)|(0.189)|(0.371)|
|Robust|1.565|0.583|2.682|1.872|1.071|1.749|
||(0.276)|(0.154)|(0.850)|(0.290)|(0.283)|(0.536)|
|Bandwidth|0.199|0.199|0.199|0.199|0.199|0.199|
|N|1,050|1,050|1,050|1,227|1,227|1,227|

Notes: In each regression, the data are binding vessels built before 2016, the unit of observation is a vessel-month-year, we cluster standard errors at the vessel-level, we use default options from the rdrobust package, and we control for gear fixed effects, gross tonnage, engine horsepower, length, and year built. RF (reduced form) is the effect of the policy on log fishing hours (Column 1) or log distance traveled (Column 4), FS (first stage) is the effect of the policy on log fuel subsidy received, and elasticity is the reduced form divided by the first stage. We calculate the elasticity standard errors with the delta method. We use the reduced form optimal bandwidth in estimating the first stage so that the set of vessels used to calculate the instrumental variables elasticity is the same.

As in Column 3, this estimate is somewhat imprecise (conventional p-value is 3 _×_ 10<sup>_−_5</sup> but randomization inference p-value is 0.140). Figure A6 displays the placebo randomization inference coefficients against the true first stage and elasticity coefficients.

The large magnitude of these elasticities could be attributed to their long-run nature, as fishers were aware that the fuel subsidy policy would remain in effect for at least five years (Section 2). This knowledge may have facilitated more significant adjustments in fishing behavior compared to situations with policy uncertainty or a shorter policy duration. While we are unaware of studies reporting long-run elasticities for input subsidies, in the context of electricity consumption, researchers have found that long-run price elasticities are an order of magnitude larger than short-run price elasticities (Buchsbaum, 2022; Deryugina et al., 24
# **Table of Contents**

|**Forew**|**ord........................................................................................................................................ii**|
|---|---|
|**Abstra**|**ct......................................................................................................................................... iv**|
|**I.**|**Introduction................................................................................................................... 1**|
|**II.**|**The Administration of Stamp Duties in India............................................................ 2**|
|A.|The Basic Features of Stamp Duties............................................................................... 2|
|B.|Administrative Problems with Stamp Duties.................................................................. 6|
|C.|Stamp Duty Rates: Some International Practices ......................................................... 10|
|**III.**|**The Distributional Effects of Stamp Duties.............................................................. 11**|
|**IV.**|**The Effects of Stamp Duties on Resource Allocation.............................................. 13**|
|**V.**|**The Revenue Performance of Stamp Duties............................................................. 17**|
|A.|Stamp Duty Collections and Trends............................................................................. 17|
|B.|Stamp Duty Revenue Losses from Undervaluation...................................................... 21|
|C.|Links to Other Taxes..................................................................................................... 22|
|D.|The “Black Economy” Effect ....................................................................................... 26|
|E.|Stamp Duty Rates and Revenue Performance: Evidence for a Link Between<br>Undervaluation and Stamp Duty Rates......................................................................... 29|
|F.|The Impacts of Tax Reform.......................................................................................... 31|
|**VI.**|**Conclusions: The Case for Stamp Duty Rate Reform............................................. 32**|
|**Annex**|**I: Stamp Duties and Property Transfer Taxes for Selected Countries...................... 35**|
|**Annex**|**IIA: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 66**|
|**Annex**|**IIB: Stamp Duty Rates for States and Union Territories FY81-FY2003 .................. 67**|
|**Annex**|**IIC: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 68**|
|**Annex**|**IID: Stamp Duty Rates for States and Union Territories FY81-FY2003.................. 69**|
|**Annex**|**III: Time Series Analysis of Stamp Duty Revenues in West Bengal.......................... 70**|
|**Annex**|**IV. Actual vs. Predicted Growth of Stamp Tax Revenues.......................................... 71**|
|**Annex**|**V: Stamp Tax Revenue Data Across States.................................................................. 72**|

i
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Figure 3: Investment in digital, informality, and uncertainty <!-- Start of picture text --> (a) Investment in digital vs. informality (b) Investment in digital vs. uncertainty by type of informality<br>.4 Round 1 .35 Necessity<br>Round 2 De Soto<br>Round 3 Parasite<br>.3 Fully formal<br>.3<br>.2<br>.25<br>.1<br>.2<br>0<br>.15<br>-.1<br>0 .2 .4 .6 .8 -20 0 20 40 60<br>Informality (PCA score) Uncertainty<br>Probability of investing in digital technologies Probability of investment in digital technologies<br><!-- End of picture text -->

**Notes:** Controls for size, sector, age, gender of the CEO, country, panel firms, and initial digital readiness. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

Figure 4: Shapley decomposition investment digital <!-- Start of picture text --> 45.2<br>28.9<br>24.5 24.9<br>22<br>20.1<br>15.7<br>8.5 8.5<br>1.6<br>Round 2 Round 3<br>Informality External attributes<br>Severity of the lockdown (mobility) Severity of demand shock<br>Constraints<br>60<br>40<br>20<br>Pseudo R2 explained by group<br>Relative Shapley Value (%) - Probability of investing in digital<br>0<br><!-- End of picture text --> Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

# **3.4 Informal firms are less likely to make employment adjustments and respond with less job growth even as sales begin to recover.**

High levels of uncertainty, lack of working capital, and financial constraints also affect firms’ decisions on employment adjustments. On the one hand, informal firms have more flexibility to adjust as they are also informal in their hiring practices and can easily change the terms of informal contracts with employees or even fire without notice. During crises, adjustments on both intensive and extensive margins are, therefore, expected to be more 17
# **TABLE 2: DEMOGRAPHIC CHARACTERISTICS**

|Variable|Nobs|Mean|Std. Dev.|
|---|---|---|---|
|**Socioeconomicpro**|**file**|||
|Age (years)|1200|35.09|11.03|
|Male|1200|0.95|0.22|
|Currently married|1200|0.77|0.42|
|Household size|1200|5.01|2.54|
|Completed High School & above|1200|0.48|0.50|
|Completed Bachelor's Degree & above|1200|0.28|0.45|
|Personal Income (Annual, Rupees)|1200|90,190|84,624|
|Household Income (Annual, Rupees)|1200|134,970|125,960|
|Low Personal Income (< Rs. 8,000 p.m.)|1200|0.44|0.50|
|Middle Personal Income (Rs. 8,000 – Rs. 20,000 p.m.)|1200|0.48|0.50|
|High Personal Income (> Rs. 20,000 p.m.)|1200|0.08|0.28|
|Primarywage earner|1200|0.67|0.47|
|**Commuting and Vehicle O**|**wnership**|||
|Commute time (minutes)|1200|36.02|28.39|
|Travel while on the job|1200|0.31|0.46|
|Monthly commuting cost (Rupees)|1200|490.55|771.49|
|Drive two-wheeler|1200|0.51|0.50|
|Drive two-wheeler to work|1200|0.25|0.43|
|Drive car to work|1200|0.07|0.26|
|Take a bus to work|1200|0.26|0.44|
|Walk to work|1200|0.26|0.44|
|HH owns a motor vehicle|1200|0.50|0.50|
|HH owns a two-wheeler|1200|0.43|0.50|
|HH owns a car/jeep/van|1200|0.15|0.35|
|**Accident History**||||
|Ever had an accident|1200|0.23|0.42|
|Ever been injured in an accident|1200|0.17|0.38|
|Know a friend or family member who has had an accident|1200|0.14|0.34|
|Believe higher than average risk as:||||
|<br>Pedestrian|1200|0.23|0.42|
|Driver|1200|0.17|0.37|
|Passenger|1200|0.25|0.43|
|Wear seatbelts when in front seat of car|1200|0.60|0.49|
|Wear and straphelmet when ridinga two-wheeler|1200|0.48|0.50|

- 24 -
IFiiune Wgage WGrowtn by CoUImtnry GTO3nps <!-- Start of picture text --> 35<br>o' 30-<br>25 25<br>c20-<br>~15-<br>2 10<br>0~<br>Non-globalizers Rich countries Globalizers<br><!-- End of picture text --> _Source:_ Based on work in progress by Freeman, Oostendorp and Rama (2001). Wages are measured in current U.S. dollars, as averages across a common set of occupations, using data from Freeman and Oostendorp (2000). Countries are classified as non-globalizers, rich or globalizers as in Dollar and Kraay (2001). Wage data for the 1980s are from period 1983-1989, whereas wage data for the 1990s are for 19901998.

A more disaggregated analysis reveals a more complex picture, however. First, the growth rates depicted in figure 1 are only relevant for those who do earn a wage, whereas globalization could in principle lead to substantial job losses. The impact of globalization on job creation and job destruction will be addressed in section 4. Second, these growth rates refer to average wages, but some workers may gain more than the average, while others may not do as well, or may even lose. The differential impact of globalization across workers will be discussed in section 3. And third, the growth rates in figure 1 summarize a long-time trend, but the dynamics of globalization could involve a temporary decline in wages. The issue then is whether the present value of the gains from globalization is large enough to offset the short-term losses. The rest of this section deals with the dynamics of wage adjustment.

Figures 2 and 3 summarize the impact of openness on wages over a five-year period. These two figures were constructed based on a regression explaining the level of wages as a function of two openness indicators: the ratio of foreign trade to GDP and the ratio of foreign direct investment (FDI) to GDP. In addition to these two indicators, the regression controls for country, occupation and year. The wage data are, again, from Freeman and Oostendorp (2000). The solid line in figures 2 and 3 indicates the point estimate of the coefficient multiplying the openness indicator considered. The dotted lines demarcate the 95 percent confidence interval for that coefficient.

4
# **Are All Trade Agreements Equal? The Role of Distance in Shaping the Effect of Economic Integration Agreements on Trade Flows**

Rebecca Freeman and Samuel Pienknagura<sup>1</sup> Keywords: Trade Agreements, Integration Agreements, Geography, Economic Distance, Gravity JEL Codes: F1, F14, F15 1 Rebecca Freeman is a Consultant in the Office of the Regional Chief Economist for Latin America and the Caribbean, The World Bank and a PhD candidate in international economics at the Graduate Institute for International and Development Studies (rebecca.freeman@graduateinstitute.ch). Samuel Pienknagura is a Senior Economist in the Office of the Regional Chief Economist for Latin America and the Caribbean, The World Bank (spienknagura@worldbank.org). This paper benefitted from useful comments from Richard Baldwin, Nicolas Berman, Chad Bown, Aksel Erbahar, Michael Gould, Russell Hillberry, Daniel Lederman, Marcelo Olarreaga, Raymond Robertson, and participants in the Authors’ Workshop of the World Bank’s Regional Flagship Report on Regional Economic Integration and the IHEID Economics Department BBL Series. The authors acknowledge financial support from the World Bank’s Office of the Chief Economist for Latin America and the Caribbean.
# <u>Table 6. Logistic regression of playgroup sustainability on supplementary services</u>

|<br>|<br>(1)<br>Playgroup is open<br>in 2016 (1=Yes)|<br>(2)<br>Playgroup is open in<br>2016 with two trained<br>teachers (1=Yes)|
|---|---|---|
|Provided food program in 2010 (1=Yes)|0.08|0.14*|
||(0.05)|(0.08)|
|Change in provision of food program from 2010-2013|-0.11**|-0.19***|
||(0.04)|(0.06)|
|Provided vitamin in 2010 (1=Yes)|-0.09|-0.04|
||(0.07)|(0.17)|
|Change in provision of vitamin from 2010-2013|0.10|0.12|
||(0.07)|(0.13)|
|Provided anti-worming medication in 2010 (1=Yes)|0.00|0.04|
||(0.04)|(0.08)|
|Change in provision of anti-worming medication|0.02|0.05|
|from 2010-2013|(0.04)|(0.06)|
|Log-likelihood|-63.75|-152.46|
|Observations|245|245|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Robust standard errors clustered by playgroup. Coefficients are average marginal effects.

Figure 5. Probability of sustainability and change in provision of food program Note: Plot of predicted probability that a playgroup remained open (solid line) and remained open with 2 trained teachers (dashed line). Variable on x-axis was measured during the project period (2010 and 2013) and variable on y- axis was measured after the project period (2016). The model holds constant the following variables at its mean level: provision of food program in 2010, provision of vitamin supplements in 2010, provision of anti-worming medication in 2010, change in provision of vitamin supplements from 2010-2013, and change in provision of anti-worming medication from 2010-2013.

19
An extraordinarily important factor in public health care is simply whether workers show up for work. Chaudhury and Hammer (2004) report shockingly high rates of absenteeism among doctors in rural Bangladesh: 40 percent of doctors in large clinics and fully 74 percent of doctors in small (single-doctor) clinics were absent. Chaudhury and others (2005) report figures on the absenteeism of health workers and teachers across six developing countries (Bangladesh, Ecuador, India, Indonesia, Peru, and Uganda). Figure 8 summarizes evidence from these studies and others that show similarly high rates of absence using different methods including surprise visits, time-in-motion studies, and clinical observations. Absenteeism has been captured in qualitative work as well (DiTella and Savedoff 2001).

**<u>Figure 8. Absentee Rates among Health Workers in Select Countries, 1989–2003</u>**

<!-- Start of picture text --> Bangladesh (2002) Rural MDs<br>Bangladesh (2002) All MDs<br>Bangladesh (2002) Health staff<br>Dominican Republic (1989)* Hospital MDs<br>Honduras (2000) Health staff<br>India, Udaipur (2002/03)** Health staff, rural clinics<br>India, Udaipur (2002/03)** Health staff, large rural centers<br>India (2002/03) Health staff<br>Indonesia (2002/03) Health staff<br>Mozambique (2002) Health staff<br>Papua New Guinea (2002) Health staff<br>Uganda (2002-03) Health staff<br>0 10 20 30 40 50 60 70 80 90 100<br>Health worker absence rates (%)<br>Notes: *Santo Domingo Hospital, Dominican Republic. **Udaipur district, Rajasthan, India.<br>Sources: Chaudhury and Hammer (2004); Chaudhury et al. (2006); Banerjee, Deaton and Duflo (2004); Lewis, La Forgia and Sulvetta (1996); Lindelow,<br>Kushnarova and Kaiser (2006); Lewis (2007).<br><!-- End of picture text --> Results from a range of countries—India, Tanzania, and Brazil—are instructive. A study in India finds that the public sector provides medical practitioners with attenuated incentives for good performance: Das and Hammer (2007) report the results of observing more than 4,000 doctorpatient interactions in Delhi and comparing clinical practices with what the doctor knew to be appropriate behavior (as appraised in previous vignettes).<sup>12</sup> They find that “public doctors exert much less effort than their private counterparts” (Das and Hammer 2007: 8). In addition, bettertrained doctors do not necessarily provide better service: Das and Hammer find that, although providers without medical degrees are less competent (that is, they know less about what _should_ be done in clinical situations), providers with medical degrees exert significantly less effort. Indeed, “clearly incentives are strong for MBBS [that is, degree-holding] doctors to do less than they know, and [they are] stronger still in the public sector.”

> 12. Vignettes are case studies that assess adherence to clinical protocols.

28
Policy Research Working Paper 8686

# **Abstract**

This study presents a novel set of indicators on outcomes of foreign direct investment spanning 63 developing countries and 10 areas that matter for development. Building on decade-long data collection by the World Bank Enterprise Surveys, the indicators highlight systematic differences between foreign multinational enterprises and domestic firms across countries in competitiveness outcomes such as productivity, innovation, export orientation, as well as the extent to which they promote inclusiveness through job creation, gender empowerment, or supply linkages. Although there appears to be no striking trade-off between competitiveness and inclusiveness of foreign multinational enterprises, their premia differ substantially across regions and income groups. Differences in some key drivers of competitiveness, such as productivity, innovation, and skills transfer, appear to be increasing with income, although premia in most outcomes are stronger in lower-middle-income or low-income markets, highlighting the relevance of foreign multinational enterprises for socioeconomic progress in these contexts. Moreover, outcomes of foreign multinational enterprises in areas such as export orientation, skills transfer, and physical capital accumulation are more consistent across countries, whereas in other areas the outcomes display wide variation, suggesting potentially higher sensitivity to investor motivations, sectors, seasonal trends, and business environments. Policy efforts should take these differences into account, to devise investment strategies that not only seek to increase capital flows, but also enhance the benefits that could be derived from them.

This paper is a joint product of the Economics and Private Sector Development Vice Presidency of the International Finance Corporation (IFC), member of the World Bank Group, and the Investment Division of the Organisation for Economic Cooperation and Development (OECD). 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 aragoussis@ifc.org and iza.lejarraga@oecd.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