rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1: Intimate Partner Violence

||||Full|Sample|||Rural|Urban|
|---|---|---|---|---|---|---|---|---|
|||OLS|||2SLS||2SLS|2SLS|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Physical viole**|**nce inde**|**x**|||||||
|Fraction of female leaders|0.064|0.054|0.075|0.348*|0.358*|0.398**|0.547**|-0.018|
||(0.056)|(0.054)|(0.054)|(0.190)|(0.187)|(0.190)|(0.230)|(0.211)|
|Observations|65,292|65,292|62,122|65,292|65,292|62,122|43,981|18,141|
|Outcome mean|0.000|0.000|0.006|0.000|0.000|0.006|0.031|-0.052|
|**Panel B: First-stage reg**|**ressions**||||||||
|_Dependent variable: Fractio_|_n of fem_|_ale leaders_|||||||
|Fraction of female leaders||||0.908***|0.902***|0.900***|0.885***|1.018***|
|in close elections||||(0.096)|(0.099)|(0.098)|(0.098)|(0.094)|
|Observations||||65,292|65,292|62,122|43,981|18,141|
|Outcome mean||||0.087|0.087|0.089|0.089|0.089|
|First stage _F_-stat||||89.238|83.740|83.818|81.159|116.175|
|State fxed efects|x|x|x|x|x|x|x|x|
|District characteristics||x|x||x|x|x|x|
|Individual characteristics|||x|||x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. Columns (1)–(6) show estimates for the full sample of women, while columns (7) and (8) restrict the sample to those living in rural and urban areas, respectively. Columns (1)–(3) present OLS estimates and columns (4)–(8) present 2SLS estimates as specified in equations 1 and 2. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

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Finally, economists doing research in this area have tended to focus exclusively on household decisionmaking, while taking governrment outlays as given and simply ignoring other factors-such as the macroeconomic, political economy and institutional environments. As shown by Pitt and others (PSP), an understanding of government budget allocation and program placement rules may be fundamental to coming to grips with public spending impacts. This will require that effort be invested in the collection of new sorts of data, such as time series of the stock of spatially available infrastructure and of the "history" of government investments. Such new data needs to be accompanied by imaginative ways of getting at, and incorporating into measurement methodologies, the context within which households and governments make their decisions.

The overall macroeconomic environment clearly has bearing on the effectiveness and sustainability of specific poverty alleviation strategy and spending decisions. Public expenditure plans are made within the context of general policies and the state of the economy. For example, the capacity to tax is pertinent to public spending objectives and outcomes. A growing economy which is generating broad-based employment provides a very different context for discussing public spending decisions than one that is in recession. Governments also worry about the political consequences of their policies. Political economy considerations are critical to understanding motivations, constraints and outcomes of public spending policy. Details on institutional and service delivery capacity-of which there has also been a lack of consideration-are likewise relevant to explaining the distribution of public spending. Household survey data and government budgetary data throw little light on these relevant features. The above information will be important in determining how to improve incidence but also in assessing why benefits are distributed in the way that they are. For example, there may exist bounds to how progressive public spending benefits can be due to political economy, macroeconomic and institutional factors, which too often are ignored. Incorporating information on 21
# **8.2 Did the treatments lessen misinformation?**

One possible explanation for the positive treatment effects on prosociality and attitudes toward migrants is that treated individuals have better information about the characteristics of the Venezuelan population and their rights in Colombia. Although this was not the objective, by considering migrant characteristics, treatment participants might indirectly update their information about this group. We test this hypothesis for seven variables we collected that aimed to measure misinformation related to the general characteristics of the Venezuelan population and their rights in Colombia (Table F.2). Importantly, the video provided factual information only about the number of forced migrants who left Venezuela. The correct answer to each question and the average response of the control group are reported at the bottom of the table.

We observe changes closer to the correct average values for two of the seven variables examined, relative to the average responses of the control group. In particular, the video improved the accuracy of the estimates participants held about the share of Venezuelans in the Colombian it 3.9 relative to the answer population, reducing by percent average of the control group (16.8 percent, column (1)). Furthermore, the game improved participants’ assessment of Venezuelans’ access to the subsidized health care program. We observed no statistically significant changes in correct answers for any other outcomes.

Hence, although the treatments indirectly decreased misinformation, these effects are neither generalized nor consistently observed across the treatments and outcomes.

# **9 Robustness Tests and Heterogeneous Effects**

# **9.1 Assessing sensitivity to adding control variables**

We test the robustness of our main estimates to the inclusion of the covariates in Table 1 by reestimating the treatment effects on the indices of prosociality, attitudes, and beliefs while controlling for these covariates. The results are in Table G.1. Since we report FDR 23
<!-- Start of picture text --> Go, D. S., H. Lofgren, F. Mendez-Ramos, and S. Robinson. 2016. "Estimating Parameters and Structural<br>Change in CGE Models using a Bayesian Cross-Entropy Estimation Approach."<br>Golan, A., G. Judge, and S. Robinson. 1994. "Recovering Information from Incomplete or Partial<br>Multisectoral Economic Data."<br>Granger, C. W. J. 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S.<br>Grimit, E. P., T. Gneiting, V. Berrocal, and N. A. Johnson. 2006. "The Continuous Ranked Probability Score<br>for Circular Variables and its Application to Mesoscale Forecast Ensemble Verification."<br>Hassapis, C., and S. Kalyvitis. 2002. "Investigating the Links Between Growth and Real Stock Price Changes<br>Haubrich, J. G., and A. M. Dombrosky. 1996. "Predicting Real Growth using the Yield Curve."<br>Hersbach, H. 2000. "Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction<br>Jay Choi, J., S. Hauser, and K. J. Kopecky. 1999. "Does the stock market predict real activity? Time series<br>Johnstone, D. 2007. "Economic Darwinism: Who has the Best Probabilities?"<br>----------, 2007b. "The Value of a Probability Forecast from Portfolio Theory."<br>Johnstone, D., V. R. R. Jose, and R. L. Winkler. 2011. "Tailored Scoring Rules for Probabilities."<br>Kao, Y.-C., C.-M. Kuan, and S. Chen. 2013. "Testing the Predictive Power of the Term Structure Without Data<br>Kling, J. L. 1987. "Predicting the Turning Points of Business and Economic Time Series."<br>Kling, J. L., and D. A. Bessler. 1989. "Calibration-Based Predictive Distributions: An Application of<br>Prequential Analysis to Interest Rates, Money, Prices, and Output."<br>Kramer, W. 2006. "Evaluating Probability Forecasts in Terms of Refinement and Strictly Proper Scoring<br><!-- End of picture text -->
**_7_**

_Price Support at Any Price?_ The Polish farming sector is not homogenous. Private farms may be divided into two categories: a **_dynamic sector,_** about 45% of farms, which increase productivity and output, producing about 60% of the value added, and a **_stagnating sector,_** where productivity and output are falling (Hunek, 1995).

<!-- Start of picture text --> Polish Agriculture viv-a-vis EU<br>(indices, EU-1 2 average-1 00)<br>Average farm size<br>Value Addedifarmer (PPP adjusted XR)* 1 5<br>Value Addedifarmer (current XR)*<br>Gr.output/farmer (physical measures)*<br>Gross output/ha (physical measures)*<br>Employment In agrlc.per 100 ha __ __ __<br>Value added In agric. (% of GDP) -<br>Farmers (% of total empl.) _<br>* Polish agriculture 1991 0 100 200 300 400 500<br>[I] of which: developing sector (see text)<br>* 1966.1UOSYUSQ. ~ ~ ~ ~ ~ ~ ~ ~~~~~HMOK1I rI us<br>Figure 2<br><!-- End of picture text --> Figure 2 provides a comparison of the current situation of the Polish agricultural sector and the EU average. The black bars show the average indices for the whole sector, the white bars refer to the dynamic sector.

The figure shows that gross output per hectare is similar for Poland and the EU if measured in physical measures. However, the relation deteriorates dramatically if related to the number of farmers: the Polish farmer produces only a small fraction of the output and value added produced by the average EU farmer (around 25-30%). Also the average farm area represents less than 40% of the
**Figure 10:  Coverage of social protection programs in Africa**

<!-- Start of picture text --> Share of population covered by social protection vs extreme poverty<br>South AfricaEswatini<br>Lesotho<br>Egypt, Arab Rep.GhanaBotswana Burkina Faso<br>Mauritius<br>Gabon<br>MauritaniaNamibia<br>Morocco Malawi<br>Zimbabwe<br>Côte d'Ivoire<br>Cabo Verde Kenya Rwanda<br>DjiboutiEthiopia NigerNigeria<br>Tunisia Senegal Sierra LeoneTanzaniaLiberiaAngola Congo, Dem. Rep.<br>GambiaSudanComorosGuineaCameroon UgandaChadMaliBeninTogo Congo, Rep.ZambiaMozambiqueMadagascarSouth Sudan<br>0 20 40 60 80 100<br>Share of population living in extreme poverty<br>Linear fit Share<br>100<br>80<br>60<br>40<br>20<br>Share of population covered by social protection<br>0<br><!-- End of picture text --> _Source_ : ASPIRE (Atlas of Social Protection Indicators of Resilience and Equity) database, World Bank. <u>https://www.worldbank.org/en/data/datatopics/aspire.</u> There is considerable diversity in the size and administrative design of the programs. Some are larger in size and have highly centralized management (e.g., Productive Safety Net Program in Ethiopia, National Social Safety Net Program in Nigeria, and the Child Support Grants Program in South Africa) while most programs have a narrow geographic coverage or target specific social groups. The most common delivery designs are cash transfers, followed by public work programs and school feeding programs (Beegle et al., 2018; Gentilini et al., 2020). Cash transfer programs account for 64 percent of social protection programs in Africa. The composition of programs, however, varies by income level of countries. In lowincome countries, programs are concentrated in cash transfers, public works and emergency transfers whereas in higher income countries programs are delivered primarily in the form of cash transfers and social pensions (Gentilini et al., 2020). During the pandemic, cash transfer programs were used to protect poor and vulnerable households against the risk of falling into poverty; to offset the income losses of informal and self-employed workers due to the pandemic; to compensate for school feeding programs, among others. Likewise, in-kind/ public work programs have been used to protect food security, expand education access and improve health outcomes. The size of transfers during the pandemic has been relatively generous compared to pre-COVID-19 levels, though it has gradually been scaled down as the economic pressures of the pandemic eased (Gentilini et al., 2020; Gentilini et al., 2021).

21
Policy Research Working Paper 8754

# **Abstract**

This paper examines the public sector wage premium using nationally representative household surveys from 91 countries. The public sector generally pays a wage premium compared to all private sector salaried employees, but the size of the premium is sensitive to the choice of the private sector comparator and varies considerably by worker characteristics. For most countries, the average premium disappears when the public sector is compared to only formal sector private employees, especially when controlling for occupation. The public sector wage premium is higher for women and low-skilled workers. In contrast, highskilled public sector employees are most often paid the same as their private sector counterparts or may even pay a penalty for working in the public sector. Consistent with this, the public sector premium is greater for employees with less education, those working in lower paid occupations, and those whose earnings fall in the lower part of the conditional earnings distribution. Across countries, the wage premium is only weakly associated with countries’ level of development. These findings nuance the existing consensus that public sector workers tend to enjoy a significant wage premium over their private sector counterparts, and that this premium is especially large in low-income countries.

This paper is a product of the Governance Global Practice and the Poverty and Equity Global Practice. 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 zhasnain@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 5199

# **Abstract**

The poor in Bangladesh are more likely to belong to households with a larger number of dependents and lower education among household members, be engaged in daily wage labor, own little land, and be less likely to receive remittances. This poverty profile for 2005 is similar to the profile in the mid-1980s and hence at first glance it would appear that little has changed over time. A closer look at national household survey data suggests a more nuanced story. This paper uses the latest two rounds of the Bangladesh Household Income and Expenditure Survey to decompose the micro-determinants of poverty reduction between 2000 and 2005, closely following a similar analysis using five earlier rounds of the Survey. The comparison of results shows that the spatial distribution of poverty seen in earlier decades has changed with time and the drivers of poverty reduction are different in several respects.

This paper—a product of the Poverty Reduction and Equity Group, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to monitor and analyze trends and determinants of poverty and inequality. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at akotikula@worldbank.org, anarayan@worldbank.org and hzaman@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
35

# Table 3: Parameter estimates for selected models

||(1)||(2)||(3|)|(5)||
|---|---|---|---|---|---|---|---|---|
||coef|t|coef|t|coef|t|coef|t|
|_Nonproject areas_|||||||||
|Wealth quintile no. 2|-0.003|-0.227|-0.002|-0.197|-0.014|-1.547|-0.013|-1.503|
|Wealth quintile no. 3|-0.023***|-2.812|-0.023***|-2.822|-0.020**|-2.506|-0.020**|-2.446|
|Wealth quintile no. 4|-0.026***|-3.128|-0.025***|-3.070|-0.024***|-3.523|-0.023***|-3.512|
|Wealth quintile no. 5|-0.011|-0.634|-0.011|-0.674|-0.019**|-2.090|-0.019**|-2.008|
|2002 dummy|0.010|0.706|0.001|0.117|0.005|0.676|-0.000|-0.085|
|Govt. health expend. (GHE)|0.035|0.151|-0.046|-0.296|-0.150**|-2.070|-0.120*|-1.807|
|GHE x 2002 dummy|-0.147|-0.952|||||||
|Constant|0.026|1.575|0.030**|2.375|0.045***|5.192|0.042***|6.435|
|_Project areas_|||||||||
|Wealth quintile no. 2|-0.021*|-1.789|-0.021*|-1.792|-0.014|-1.547|-0.013|-1.503|
|Wealth quintile no. 3|-0.018|-1.578|-0.018|-1.591|-0.020**|-2.506|-0.020**|-2.446|
|Wealth quintile no. 4|-0.022**|-2.301|-0.022**|-2.302|-0.024***|-3.523|-0.023***|-3.512|
|Wealth quintile no. 5|-0.030***|-2.961|-0.030***|-2.949|-0.019**|-2.090|-0.019**|-2.008|
|2002 dummy|0.029*|1.793|0.023|1.600|0.005|0.676|-0.000|-0.085|
|Govt. health expend. (GHE)|0.137|0.385|-0.046|-0.296|-0.150**|-2.070|-0.120*|-1.807|
|GHE x 2002 dummy|-0.716*|-1.861|-0.535**|-2.071|-0.161|-1.229|||
|Constant|0.040***|2.903|0.045***|3.997|0.045***|5.192|0.042***|6.435|
|_Selection equation_|||||||||
|GDP|-1.564***|-3.387|-1.564***|-3.387|-1.563***|-3.382|-1.564***|-3.383|
|Revenues as % expenditures|1.759*|1.836|1.759*|1.836|1.760*|1.839|1.759*|1.836|
|Constant|5.116***|4.474|5.116***|4.474|5.112***|4.468|5.115***|4.468|
|lnnonproject|-2.038***|-23.934|-2.037***|-23.950|-2.035***|-23.800|-2.036***|-23.845|
|lnproject|-1.835***|-23.108|-1.835***|-23.102|-1.834***|-23.067|-1.834***|-23.039|
|nonproject|-0.023|-0.336|-0.021|-0.362|0.050|1.525|0.019|0.810|
|project|-0.013|-0.198|-0.012|-0.182|0.008|0.107|0.019|0.810|

Notes: .01 - ***; .05 - **; .1 - *. t-statistics reflect standard errors adjusted for clustering at the province level. Selection equation also includes variables in outcome equation. Model numbers correspond to those in Table 2.
Policy Research Working Paper 8694

# **Abstract**

This paper takes a first look at the trade effects of China’s Belt and Road Initiative, also referred to as the New Silk Road, on the 71 countries potentially involved. The initiative consists of several infrastructure investment projects to improve the land and maritime transportation in the Belt and Road Initiative region. The analysis first uses geo-referenced data and geographical information system analysis to compute the bilateral time to trade before and after the Belt and Road Initiative. Then, it estimates the effect of improvement in bilateral time to trade on bilateral export values and trade patterns, using a gravity model and a comparative advantage model. Finally, the analysis combines the estimates from the regression analysis with the results of the geographical information system analysis to quantify the potential trade effects of the Belt and Road Initiative. The paper finds that (i) the Belt and Road Initiative increases trade flows among participating countries by up to 4.1 percent; (ii) these effects would be three times as large on average if trade reforms complemented the upgrading in transport infrastructure; and (iii) products that use time sensitive inputs and countries that are highly exposed to the new infrastructure and integrated in global value chains have larger trade gains.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 mruta@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
The same trend is seen in Ethiopia, where Chinese investment made up 11.5% of the countries’ total FDI inflows in 2000-2005, and rose to top place in 2006-2011 contributing 25% of total FDI in the country. In Zambia, too, Chinese investment held a more fluctuating but generally significant place in total FDI during the same period.

**Figure 6: The Percentage of Chinese Investment of Total FDI in Ethiopia and Zambia, 2000-2011**

<!-- Start of picture text --> Ethiopia Zambia<br>55.60%<br>40.00%30.00% 25.00% 29.00% 32.00% 24.00% 27.00% 26.00% 60.00%40.00% 31.27% 23.60%<br>20.00% 11.50% 20.00% 13.80% 17.26% 11.24% 8.26%<br>10.00%<br>0.00%<br>0.00%<br><!-- End of picture text -->

# _Source: Shen and Zhai_

In Ghana, Liberia and Rwanda, Chinese investment is proportionally much less significant – except occasional spike years seen in each country, the average Chinese/Total FDI ratios linger at low levels, less than 1% in Liberia, 1.8% in Rwanda, and 3.5% in Ghana.

**Figure 7: The Percentage of Chinese Investment of Total FDI in Ghana, Liberia and Rwanda, 2000-2011**

<!-- Start of picture text --> Ghana Liberia Rwanda<br>36.50%<br>59.03% 30.00%<br>80.00% 40.00% 40.00%<br>60.00% 30.00% 30.00%<br>40.00% 5.23% 0.61% 0.34% 4.11% 8.68% 1.85% 20.00% 0.12% 0.04% 0.06% 0.12% 0.01% 0.10% 20.00% 2.10% 0.00% 4.10% 1.00% 2.60% 1.60%<br>20.00% 10.00% 10.00%<br>0.00% 0.00% 0.00%<br><!-- End of picture text -->

- **_Chinese firms tend to be wholly Chinese owned. They are relatively small in size.  They mainly complete with firms from other emerging economies of similar size and sector interest._**

Figure 8 based on the six countries’ data show that Chinese investments are mostly wholly Chinese owned, with only a small percentage forming joint ventures with local or other foreign business partners.

**Figure 8: Ownership Structure of Chinese Investment Projects**

14