rafmacalaba/data-use-mentions-extended huggingface dataset:
continuing fiscal and investment incentives provided by public and private institutions on R&D and human capital.

According to Bjorvata and Eckel (2006), with many countries competing for FDI and trade, particularly in the presence of an asymmetric competition among countries, there are undesirable welfare effects for developing countries.

Authors like Suyanto et al. (2009) suggest that policies for strengthening the absorptive capacity of domestic firms through investments in knowledge and human capital formation may be superior to policies that provide concessions for FDI and trade.

In this context more general policies should be pursued, that not only attract FDI but also benefit domestic firms, for example, building modern infrastructure, increasing and strengthening the institutions for accelerating and sustaining economic growth.

# **References**

Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977): ―Formulation and Estimation of Stochastic Frontier Production Function Models‖, _Journal of Econometrics,_ (6). 21-37.

Aitken, B. J., and Harrison, A. E. (1999). Do domestic firms benefit from direct foreign investment? Evidence from Venezuela, _The American Economic Review_ , 89(3), 605–618.

Battese, G. E., and Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. _Empirical Economics_ , 20(2), 325–332.

Battese, G.E. and Coelli, T.J. (1988), Prediction of Firm-Level Technical Efficiencies with a Generalised Frontier Production Function and Panel Data. _Journal of Econometrics_ , (38). 387-399.

Battese, G.E. and Coelli, T.J. (1993): ―A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects‖, Working Papers in Econometrics and Applied Statistics No. 69.

Bjorvata, K., & Eckel, C. (2006): ―Policy competition for foreign
into poverty due to CR. > ln IIPP.

# **3 Data**

As we mentioned, we exploit five years of survey data per country to expand the variability of data, specifically in small countries such as the Dominican Republic, El Salvador, Honduras, and Panama. The surveys provide information at the individual and household levels. The data source is the SEDLAC database, which provides harmonized and standardized household survey data for LAC countries. Although most household surveys are representative at the state (province) level, some allow to identify lower levels, such as districts or localities where households reside. Thus, we also exploit information of climate hazards at the administrative unit level where each household is located. In our model, to construct the first group of variables (the dependent variable, IPCF, and household characteristics regressors, ), we need information at level 1. Hence, we use survey data at household level. The second group of variables is at level 2 and corresponds to community characteristics XX (regressors of aggregate household characteristics climate change and community natural hazards ). The aggregate data is prepared at FAO Global Administrative Unit Layers (GAUL) using ZZ, II, ADM 1, ADM 2, or ADM 3, depending on the imputation level. NN 10
31 Fay, M., D. Leipziger, Q. Wodon and T. Yepes (2003), Achieving the Millennium Development Goals: The Role of Infrastructure, Policy research working paper 2390, Washington, D.C.

Government of Vietnam (2001), “Infrastructure and the MDGs” Government-NGO poverty - task force, available at http://www.vdic.org.vn/eng/pdf/infra <u>eng.pdf</u> Jayasuriya, R. and Q. Wodon (2003), “Efficiency in Reaching the Millenium Development Goals”, World Bank Working Paper, No9 Komives, K. & Whittington, D. & Wu, X. (2001), "Infrastructure Coverage and the Poor: A Global Perspective," World Bank, Policy Research Working Papers, No 2551.

Manning, R. (2004), How can the development community help achieve greater progress towards the MDGs”, WIDER public lecture, February 9, 2004 Ravallion, M. (2003), The Debate on Globalization, Poverty, and Inequality: Why Measurement Matters”, World Bank, Policy Research Working Papers, No 3038 Sachs, J. (2004), “Doing the Sums on Africa”, The Economist, May, available at: <u>http://www.undp.org/mdg/Economist_May%2020%202004.pdf</u> United Nations (2001-2004), Country Reports on MDGs, available at: <u>http://www.undp.org/mdg/country_regionalreports.html</u> Wang, L. (2002). “Health Outcomes in Poor Countries and Policy Implications: Empirical Findings from Demographic and Health Surveys _”,_ World Bank, Policy Research Working Paper 2831

# **Bibliography for Financing needs**

Briceno, C., A. Estache and N. Shafik (2004), “Infrastructure Services In Developing Countries: Access, Quality, Costs
the fight against poverty. Without research, failed policy orthodoxies would often persist without the critical inspection needed to show that they have failed and successful policies would be dropped for the wrong reasons.

In recent years, the World Bank research program has increasingly been involved in two broad cross-cutting types of research: evaluative research and methodological research.<sup>36</sup> Evaluative research rigorously assesses whether development policies are effective, and under what circumstances they tend to be more effective. This embraces both “micro” interventions in specific sectors and macro policies. The second type of cross-cutting research that has increasingly occupied Bank research in the past decade can be termed methodological research. The Bank has become a major producer of development data, and a number of the Bank’s most successful data initiatives started as research projects. The best example is probably the _Living Standards Measurement Study_ (LSMS), which started as a research project in the mid-1980s with the aim of greatly improving household survey data in developing countries. The LSMS database now houses more than 75 LSMS datasets with all the relevant documentation. The World Bank is also a leader in governance indicators: enterprise surveys (also known as Investment Climate surveys), and Purchasing Power Parity measurement through the International Price Comparison Project.

# **Shifts in Country Allocations, 1961 ­ 2010**

Before the early 1990s, with a few exceptions (such as P.T. Bauer), the economic literature on the effectiveness of aid in general, and IDA in particular, was inconclusive and consisted mostly of general studies and cross-country regressions.<sup>37</sup> Initial crosscountry results, which emphasized growth to reduce poverty based on the Washington consensus, sound fiscal and monetary policies, openness, liberalization of markets and deregulation. However, as it has been difficult to find robust evidence from cross-country data that the policy prescriptions of the Washington consensus – hence, aid based on those - generate growth, subsequent empirical work by researchers from the World Bank

> 36
elderly female houseowners and find that those that have taken out RM are more, not less likely than observably similar subjects to sell their house before death. Shan (2011) confirms this picture, showing that RM borrowers leave their houses at a much faster rate than comparable non-borrowers. The author argues that an underlying reason for this phenomenon is that RM are often signed for medical emergencies, which in turn reduce life expectancy. While this may be good news for lenders, it raises serious concerns on the consumer protection side.

Moral hazard is a more serious market failure that may constrain supply. Homeowners who sign an RM have less incentive to keep up their house and pay property taxes. The borrower will never have to repay more than the house value, and thus may seek to avoid expensive house repairs (Miceli & Sirmans, 1994; Shiller & Weiss, 2000; Caplin, 2002). Moreover, since RM debt cannot be passed on to the next generation, heirs who face a house value near or below the amount of funds received have little incentive to take steps to maximize the sales price, raising the risk of corruption in private house sales. Foreclosure by the lender is the most common outcome, but this procedure entails high transaction costs for the lender (Caplin, 2002). The California Reinvestment Coalition (CRC) reported that involuntary RM terminations are expected to double in 2016 compared with the previous year. And loan industry data show that about 22% of foreclosures were triggered by tax and insurance default (Miller, 2018). In order to address this moral hazard issue, since 2015 RM borrowers in the United States are required to undergo a credit assessment and pay a so-called Life Expectancy Set-Aside (LESA) to ensure property taxes and insurance are paid if their credit score is below a certain threshold.

Regulatory responses to moral hazard are available but costly. In the United States, the Federal Housing Agency (FHA) provides mandatory insurance against cross-over risk. Lenders can avoid downside risks by selling RM that
# **Appendix Summary of main articles reviewed in the paper**

||**Geographic**|**Time**|**Data**|**Methodology**|**Findings**|
|---|---|---|---|---|---|
||**coverage**|**coverage**||||
|**Expansion of pu**<br>|<br>**blic transit**<br>|<br>||||
|Duranton &<br>Turner (2011)|United States|1983-2003|Average annual daily<br>traffic, road network from<br>the US Highway<br>Performance and<br>Monitoring System; 1995<br>Nationwide Personal<br>Transportation Survey and<br>2001 National Household<br>Survey|Exploiting 3 IV (routes<br>of major expeditions of<br>exploration between<br>1835 and 1950, major<br>rail routes in 1898 and<br>proposed routes of<br>interstate highways in<br>preliminary plan) to<br>predict incidence of<br>roads on traffic|Traffic volume increased<br>as a result of the expansion<br>of highway capacity|
|Chen &<br>Whalley (2012)|Taipei|1995|Air pollution and transit<br>ridership,<br>hourly air quality data (from<br>Taiwanese EPA air quality<br>monitoring network)|Regression<br>discontinuity|The opening of the subway<br>line reduced carbon<br>monoxide (CO) emissions<br>by 5–15 percent|
|Goel & Gupta<br>(2017)|Delhi|2004-2006|Pollution and weather data<br>(hourly, daily and weekly)|Regression<
measure IPV, though measures in relevant surveys, such as the Conflict Tactic Scale, do allow for the exploration of multiple levels of severity such as physical and psychological violence (Papageorge et al. 2019; Heath 2014; Heath, Hidrobo, and Roy 2020; Hidrobo and Fernald 2013). It appears that only one other paper explores the various sources of data on violence: Amaral’s study (2017) confirming that improved inheritance rights among women in India reduce self-reports of IPV in surveys, police reports of violence against women, and female mortality.

This study focuses on the most populous municipalities in Brazil because of data limitations and confirms that, in these populations, an increase in the gender wage ratio in favor of women is negatively associated with the most severe form of violence against women—female homicide. The impacts are more prominent among younger women (ages 15–29) and in municipalities with lower a human development index.

However, for lesser forms of violence captured in medical professionals’ reports of nonlethal abuse, context matters: the analysis finds that a reduction in the gender wage ratio triggers a decrease in reports only in municipalities in which women’s police stations are located, that is, police stations that specialize in crime against women and that are predominantly staffed by women police officers. The effect is opposite in municipalities that lack women’s police stations. It appears that, for less severe forms of violence, economic empowerment may trigger a backlash unless it is complemented by other programs with the direct objective of ensuring women’s safety.

# **Background: Measurement of IPV**

Empirically testing whether IPV increases or decreases with economic opportunity is not a trivial exercise. One of the limitations of collecting data on IPV through survey data is the likelihood of underreporting, which often happens because women fear stigma or retaliation or because they may normalize incidents of domestic violence in cultures where IPV is “acceptable.” Surveys that only include a module on violence result in a lower incidence of reported
Second, the annual nominal investment N is the change in the nominal capital stock between two consecutive years, that is, ii N = N ii ii<sup>−N</sup> ii−1<sup>(A3)</sup>

> 23 The 1997 IO table is used to construct the input deflators of 1998-2000, the 2002 IO table is used to construct the input deflators of 2001-2005, and the 2007 IO table is used to construct the input deflators in 2006-2007.

> 24 Since _China Statistical Yearbooks_ report the growth rate of nominal capital stock in the two-digit industry from 1986, we assume that firms established before 1986 are established in 1986.

``` 41 ```
# **2.7 Uncertainty Estimates for ML Estimators**

For EBP, we model welfare at the sub-area level, which is then aggregated up to the target area level. Because the model specification contains a random effect at the target area level, this procedure accounts for the hierarchical nature of the data. The procedure uses a parametric bootstrap to estimate uncertainty, drawing from the estimated distributions for the random effects and the error terms, and accounting for the arcsin transformation implemented for EBP. For the three ML estimators, we also estimate the models at the subarea level.

While Friedberg et al. (2020) prove a central limit theorem for local linear forests that allows for the construction of uncertainty estimates, variance estimation has not yet been implemented for Boosted Regression Forests. As an alternative, we propose a non-parametric bootstrap procedure that draws from previous work on residual bootstraps with hierarchical data (e.g. Luo and Lai (2021)).<sup>13</sup> ˆ For subarea _sa_ , consider the sample direct estimate of the outcome: _ysa_<sup>_direct_</sup> . In addition, there is the prediction from the machine learning algorithm, _y_ ˆ _sa_<sup>_ML_.With these two estimates, we calculate subarea-specific “residuals”</sup> as: We can likewise calculate residuals at the area level, by aggregating _y_ ˆ _sa_<sup>_ML_</sup> and _y_ ˆ _sa_<sup>_direct_</sup> to the area, weighting by estimated population from WorldPop: The proposed bootstrap continues in two steps. First, note that we can only calculate this residual for in-sample subareas and in-sample areas. After estimating predictions, we first calculate the residuals in Equation 2. Then, we randomly draw one residual from the vector _R_<sup>ˆ</sup> _sa_ for each in-sample subarea and add that residual to the prediction: _y_ ˆ _sa_<sup>_ML_</sup> + _R_<sup>
(i) Government Data,

> 2 - See “Big Data Universe beginning to explode,” at http://www.csc.com/insights/flxwd/78931 <u>big_data_universe_beginning_to_explode. Accessed September 15, 2015.</u>

> 3 - - Open Data Charter, <u>https://opendatacharter.net/wp content/uploads/2015/10/opendatacharter charter_F.pdf. Accessed January</u> 21, 2019.

> 4 Open Data Handbook, http://opendefinition.org/od/index.html (May 12, 2012). Accessed September 15, 2015.

> 5 McKinsey Global Institute, “OGD: Unlocking Innovation and Performance with Liquid Information,” McKinsey Center for Government (October 2013).

> 6 A. Stott, “OGD for Economic Growth: Transport and ICT Global Practice,” World Bank (June 25, 2014).

> 7 Graham Vickery, “Review of Recent Studies on PSI Reuse and Related Market Developments,” Information Economics, Paris.

> 8 United Kingdom, <mark>Department for Business, Innovation & Skills,</mark> “Shakespeare Review: An Independent Review of Public Sector Information,” London (2013).

> 9 Dinand Tinholt, _Characterization Study of the Infomediary Sector_ , Cap-Gemini, Spanish Open Data Portal Report 2013.

> 10 Open Data Charter, <u>https://opendatacharter.net/wp-content/uploads/2015/10/opendatacharter-charter_F.pdf.</u> Accessed January 21, 2019.

2
particularly the disabled received diverse benefits, such as free or reduced-price utilities and transportation services.

Since most of those who received such categorical benefits were demonstrated to actually be the non-poor (see various World Bank poverty assessments), categorical targeting received significant and warranted criticism from external and internal advisors and policy makers. However, the problem with categorical targeting may have been in the poor choice of categories more so than the idea of using an indicator or combination of indicators (a proxy means test) to identify the poor. The choice of categories was dictated by political considerations-not by a carefil study of who was poor and what determined poverty.

In this section, we try to determine whether a combination of indicators can identify the poor, which in turn would provide the necessary information for effective targeting of cash or in-kind benefits, or for active labor market policies. The indicators used here are the same economic and demographic variables which were used as regressors in our previous models, with some additional variables. In practice, in the FSU, and particularly in Russia and Ukraine, increasingly benefits are being awarded to applicants who meet a categorical filter _and_ an income-test. Typically, this means-test is based only on official income. As noted in the poverty profile section, in the FSU, official income is a particularly poor predictor of household welfare, due to the pervasive informal sector and the general unwillingness of households to disclose such sensitive information.

Preliminary evidence from the housing allowance subsidy programs in Ukraine and Russia, which are based on official income (wages plus transfer income) suggests that an 92
of a small change in GOVERNANCE on the quartile _τ_ . The intuition behind UQR/RIF-OLS is worth repeating: a negative co-efficient on _β_<sup>_RIF_</sup> implies that as GOVERNANCE increases, there is a downward shift in the distribution of Political Influence at a given moment ( _τ_ ). The thing that will be of most interest to many—previewing our results—is the relative size of these coefficients. For example, a larger magnitude of coefficients at higher moments of the distribution (e.g., _|βτ_<sup>_RIF_</sup> =75<sup>_| > |β_</sup> _τ_<sup>_RIF_</sup> =25<sup>_|_),asillustratedinFigure5),</sup> implies that changes in governance have a more dramatic relationship with the long rightward tail shown in our Political Influence Index, compared to movements at the leftward parts of the distribution.

We use several well-known measures of governance quality to estimate the movement of Political Influence with the underlying political environment. These include Transparency International’s Corruption Perceptions Index (CPI), which we reverse to have 0 indicate the lowest level of corruption and 100 the highest, as we find it more intuitive to have higher levels indicate greater levels of corruption. The second measure is Henisz (2002)’s political constraints score (POLCON), with higher values indicating more veto points in a political system, and thus more constraints on unitary action. Then, we take the six sub-indices from the World Governance Indicators (WGI), which include Voice & Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. For comparison with our index, we re-scale each of these sub-indices from 0 to 100, using the global minimums and maximums, where 100 indicates a higher level of governance quality.<sup>31</sup> Lastly, we include a dummy measure we denote as ‘Power
through dialogue and argumentation (World Bank, 2017). Examining these norms and preferences and how they evolve is key to understanding why conflicts of interest persist and how reformers can find common ground for socially beneficial reforms (Khemani, 2020). Broad participation and engagement could also increase the contestability of policy ideas. Consequently, reforms could be of higher quality, and efforts could be focused on the most-needed reforms. Available empirical evidence shows that citizen engagement largely improves outcomes at the local level (Baiocchi, 2005; World Bank, 2017). Citizen support can also be instrumental in improving the legitimacy of decisions. The Arab Spring is a powerful reminder of the importance of inclusiveness and public support.

Little has been done to analyze the engagement and support for economic reforms<sup>2</sup> in the GCC. This paper is an effort to fill in the needed gap using novel data from news and social media. The paper contributes to the existing literature on economic reforms, which has typically focused only on the effects of implemented reforms (see Prati et al., 2013, Alesina et al., 2020).

The paper uses text analysis techniques on publicly available big data sources: passed laws and decrees, social media, and news media. The use of data from newspapers and social media platforms has several advantages. They can be real-time and cover many countries. They can provide more granular and

> 2 Economic reforms are understood as changes introduced with the intention to improve the economy. For example, we do not examine political reforms in this paper.

2
personal income taxes paid by the labor force employed in productive sectors except fossil fuel and thermal electricity sectors. To incorporate this scenario, we first calculate a rate that will be used to adjust the wage rate and user cost of capital. The rate is calculated as follows: Labor price, PL and, capital price, PK are then adjusted by multiplying by (1 −pitcr) throughout the model. In the final set of scenarios, the carbon tax revenue is used to cut corporate income taxes of the firm (“Corporate Income Tax Cut” case). We have three cases under this scenario based on how the carbon tax revenues are reallocated to firms to cut their corporate income taxes. Under Scenario 4a (“CO2 Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors inversely proportional to their CO2 intensities. This means a sector with a higher CO2 emission intensity gets a lower carbon tax revenue as a rebate. The rebate rate used to adjust corporate income tax etri adjusted for sectoral carbon intensity weights as YGAP etri = carbii. (48) (sek.QKiPKi) Where carbii is a weight computed from the SAM and used to adjust the rebate for all non-energy sectors according to their carbon intensity such that sectors with higher carbon intensity have a lower rebate then etri is used to adjust corporate tax expenditure in Equation 31 as follows: EExp= sek. ∑(te−etri i). PK⋅QKi + TEG+ ∑TEHh h + TEW (49) In Scenario 4b (“Capital account share” case), carbon tax revenue is recycled to non-fossil fuel sectors in proportion to their capital accounts. A sector with a higher capital account gets a higher rebate in this scenario, the tax rebate for corporates is computed as: QKiPKi YGAP etri = . (50) ∑QKi iPKi (sek.QKiPKi) In the last case (“Export Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors in proportion to exports of their outputs