rafmacalaba/datause-encoder-data huggingface dataset:
# **Data**

The data required for the study can be divided into two groups: (i) data needed for the global CGE model and (ii) data needed for GIDD model. A detailed discussion on the data for the global CGE model is available in Timilsina et al. (2010), so in this section we focus data required for GIDD model.

The main sources of data in the GIDD model include: (a) the dataset assembled for the production of the World Bank World Development Report (WDR) for developing countries, which are drawn largely from the Living Standards and Measurement Study (LSMS) and the African Institute for Sustainability and Peace (ISP)-Poverty monitoring group; (b) the Europe and Central Asia (ECA) databank and the different World Bank sources for Eastern Europe countries, and (c) the Luxembourg Income Studies (LIS) database for most of the developed countries.

There are two versions of the GIDD model. The original version includes data for the year 2000, while the second includes updates for the year 2005. In the original version, nationally representative household surveys of various countries nearest to 2000 were chosen. For household surveys of countries valued not in year 2000, the following adjustments were applied in order to “value” them in 2000. Local consumer price index (CPI) in the country was used to adjust income and consumption to 2000 domestic values. Furthermore, the values in the household surveys were converted to international dollars in year 2000 using the Purchasing Power Parity (PPP) factor obtained from the Penn World Tables. A correction factor was also applied to the population weights to make the population level consistent to year 2000.

In the updated GIDD model, the values for 2000 were updated to 2005 using updated population and 2005PPP factor. Also, the mean income at the country level was adjusted to match the latest poverty rates reported in POVCAL<sup>3</sup> . Thus, the poverty rates at the country level are consistent with the 2005PPP poverty estimates in Chen and Ravallion (2008).

The GIDD database covers all regions in the world. Eastern Europe and Central Asia is 100 percent covered; Latin America 98 percent; South Asia 98 percent; East Asia and Pacific 96

> 3 POVCAL is a tool developed at the World Bank for calculating poverty and inequality indices.

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inequality; (ii) allows for the possibility of income and inequality dynamics which in turn allows for poverty dynamics; (iii) relies on a large database of non-overlapping five-year averages that mitigates to some extent the problems encountered when the distribution of surveys across countries is very unequal (countries with 40 surveys against others with just 1 or 2); and (iv) allows for fixed effects in a dynamic panel framework.

The findings of the paper suggest the likelihood of inequality convergence, and that the speed of convergence for inequality is faster than the speed of convergence for per capita income levels. On the growth-inequality links, we find a Kuznets type of relationship by which inequality would increase with income levels up to about $3,000 (1985 US dollars) and decrease thereafter, and find no significant evidence of inequality affecting growth. Admittedly, the point estimate for inequality comes close to being significant at the 10 percent level, suggesting that higher inequality would lead to lower growth.

On the policy front, we find that improvements in education and infrastructure and lower inflation levels would lead to both growth and progressive distributional change. Financial development, trade openness, and cuts in the size of the government, all policies that would lead to faster growth, would be associated with increases in inequality. We also find that financial crises would be associated with reductions in inequality.

On the interaction between growth and inequality, the paper argues that in the short run, the positive impact on growth of the identified win-lose policies would not be enough to offset the negative impact they have on inequality and therefore, in the absence of propoor policies that accompany those reforms or additional feedback effects from growth (such as improvements in education or infrastructure), poverty could actually increase. In the long run, however, we find that the growth impact of these policies would offset the negative impact on distribution, and therefore poverty would fall as a result of the implementation of pro-growth policies. These findings would justify the adoption of: (i) a pro-growth policy package at the center of any poverty reduction strategy, and (ii) propoor measures that complement such a package and avoid to the extent possible potential short-run increases in poverty.

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reduction in formalism. Second, this is the first study to formally document a direct, causal link between case-level judicial efficiency and firm-level outcomes.

Finally, this paper motivates a focus on bureaucratic efficiency as a development outcome. Inefficient bureaucracies withhold services from the poor (Finan et al., 2017) and increase trade costs (Djankov et al., 2010). Slow, backlogged courts prevent the application of financial reforms such as bankruptcy law reforms (Alencar & Ponticelli, 2016). Combining detailed, high-frequency administrative records to evaluate the causal impact of reforms on bureaucratic efficiency is a fruitful area for future research.

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# **3. Econometric Analysis**

To examine the effectiveness of different disclosure formats for loans and savings products, we first look at the impact of the different formats on the probability of choosing the best product. We run logit regressions using data from Peru and Mexico separately and then combined. For Peru we use the following specification: BestChoiceijk = αj + β1Simplifiedjk + β2Mktjk + β3LowProfileik + Xik′ γ + εijk (1) where BestChoiceijk is a dummy variable that takes value 1 if participant _i_ in round _j_ and session _k_ chose the cheapest credit product or the savings product with highest yield given the profile assigned. Simplifiedjk and Mktjk are dummy variables that take value 1 if participants in round _j_ and session _k_ were given the standardized KFS or the marketdesigned KFS, respectively. The omitted treatment is the one that uses marketing materials such as brochures. LowProfileik takes the value of 1 if participant _i_ in session _k_ was assigned to Profile 1 described in the previous section. The vector of characteristics Xik includes whether participant _i_ in session _k_ is a male, whether he or she has postsecondary education, age and age squared (divided by 100) and our proxy for financial literacy. All specifications include round fixed effects αj and standard errors are clustered at the participant-session level.

In Mexico, selected participants received either a phone call or a series of SMS messages on behalf of CONDUSEF one or two days prior to participating in the experiment. These phone calls and text messages contained simple information on key financial terms that were used in the disclosure formats and would help participants select the best product. Online Appendix OA2 contains the scripts of the calls and messages. In addition, we follow the literature (Hasting and Tejada, 2008; Gigerenzer et al., 2003 and Shu and Townsend, 2010) and varied whether the total amount to be paid for the loan or the total amount earned in the savings accounts was displayed in peso amounts or in percentages. In particular, the marketing brochures and the simple table with 5 products had the total amount in peso amounts for participants with an even-numbered ID and in 12
**Figure 10: Macro and micro mean income historical comparison – growth rate**

<!-- Start of picture text --> 15%<br>10%<br>5%<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-5%<br>-10%<br>-15%<br>-20%<br>Annual mean income per capita GDP per capita<br><!-- End of picture text --> Source: WB staff calculations based on data from BCU and ECH. _Note:_ For comparability across time, all numbers in the figure are representative of urban areas (more than 5,000 inhabitants), since ECH became representative at the national level only after 2006.

# **5 Simulation results**

Two micro simulations were constructed for each year in order to compare the results on poverty, shared prosperity and the overall income distribution between the benchmark/no-crisis and crises scenarios. The simulation parameters as well as the results of the simulation are presented in the following sections.

# **5.1 Simulation parameters**

The simulations are based on the comparison of benchmark to crisis scenario. The scenarios developed for the baseline year (2012), year 1 and year 2 were then mapped to the 2011 household survey _Encuesta Continua de Hogares_ released by INE. The macroeconomic parameters used in the simulations are presented in Figure 9. They represent the percentage change of each indicator from its level in 2011. In year 1, under the benchmark scenario, annualized real GDP growth is 4.1 percent higher than in 2011 whereas under the crisis scenario, it is 2.0 percent lower. Moreover, different sectorial growth rates are simulated for each of the scenarios, which explain the dissimilarities in the unemployment rate. For year 2, under the crisis scenario, GPD is expected to contract by 1.0 percent (annualized growth rate) with respect to the 2011 levels, while under the nocrisis scenario real GDP would be 4.0 percent higher, in annual terms, than it was in 2011.

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We employ two widely used methodologies in the survival analysis literature: Kaplan-Meier (KM) survival curve estimates and Cox Proportional Hazards regression. The KM estimator provides the unconditional probability of the occurrence of an event – in this case, the first time a policy measure in a specific category is taken – during a certain interval (Kaplan and Meier, 1958). This non-parametric approach allows us to estimate and compare separate survival curves for country groups with different characteristics (e.g. high versus low income).

One limitation of KM estimates is that they do not allow for multivariate analysis. We, therefore, employ Cox proportional hazards regression (Cox, 1972; 1975), a semi-parametric approach. A key assumption underlying Cox regression is that the independent variables scale a baseline hazard function 0 that only depends on time. In our case, the unit of time _t_ is a day. λλ (tt) ) (1) cc cc 00 cc where the subscript λλ (tt|XX , ββ) = λλ (tt) × e _c_ denotes a country andee (ββXX is the conditional hazard rate. The Cox model does not impose any functional form on 0 . The multiplicative term cc ) (also known as scale factor) λλ comprises a vector of country characteristics ( ) that affects the hazard function cc . The relevant country λλ (tt) exp (ββXX characteristics are described in the next section. cc cc XX λλ

# _3.2 Country characteristics_

In this section, we describe four groups of country characteristics that may help explain the policy response to COVID-19 for which there are sufficient data across countries: domestic exposure to COVID-19; COVID-19 containment and economic support policies; economic development; macro-fiscal fundamentals; and banking sector characteristics.

**Domestic exposure to COVID-19.** Higher exposures to COVID-19 call for a stronger policy response. We compute the number of days elapsed since the WHO declaration, for the cumulative COVID-19 cases to exceed 100 people as a proxy for the sense of urgency to respond to the spread. We also test the sensitivity of our estimates using the cumulative number of confirmed COVID-19 cases per 100,000 people as a relative measure of exposure to COVID-19. The data are taken from the World Health Organization.<sup>12</sup>

> 12 A description of the data and the methodological underpinnings can be found here: https://covid19.who.int/.

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Figure 3: Change in the Value and Growth Rates of Exports to the US and Share of Services in Exports, Selected Countries and Regions <!-- Start of picture text --> ARG<br>IND<br>JPN FRA HKG<br>CHN MEX AsiaPacBRA TWNKORBEL GERSGPEuropeNLD AUS<br>GBR<br>ITA<br>SCAmerica<br>CAN<br>ZAF<br>MiddleEast<br>Africa<br>VEN<br>0 10 20 30 40 50<br>Pre-Crisis Share of All Services<br>in Total Exports (%)<br>Change in Export Value During Crisis<br>Fitted Values (slope = .42)<br>ARG<br>JPN BEL GERFRA NLD<br>HKG<br>IND<br>MEX<br>AsiaPacBRA TWNKOR ITA SGPEurope AUS<br>GBR<br>CANSCAmerica<br>CHN<br>Africa<br>ZAF<br>MiddleEast<br>VEN<br>0 10 20 30 40 50<br>Pre-Crisis Share of All Services<br>in Total Exports (%)<br>Change in Export Growth Rate During Crisis<br>Fitted Values (slope = .62)<br>10<br>0<br>-10<br>-20<br>-30<br>Change in Total Exports (%) -40<br>-50<br>0<br>-20<br>-40<br>Change in Total Export Growth Rate<br>-60<br><!-- End of picture text --> Source: BEA, US International Transactions Accounts Data, Tab.12: US International Transactions by Area, millions of dollars, not seasonally adjusted. The slope coefficient in the upper graph is significant at the 2 percent level, the one in the lower graph at the 1 percent level.

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<u>Other Indicators:</u> We also explore the possible impact on other indicators associated with social capital (trust, respect, relationships between different ethnic groups, community disputes, etc.) and female empowerment/decision-making. The analysis does not find any significant impact over the 2 year period of the evaluation.

<u>Direct effects and Spillover effects: The two main reasons for using villages as units of our</u> analysis are: random phase-in is much more feasible across villages as opposed to individual households, and village-level randomization is a better option in presence of possible spillover effects. In the case of PAF intervention, the treatment villages have both direct beneficiaries and non-beneficiaries. Through social and economic interactions, non-treated households in the treatment villages may potentially be affected by PAF grants to treated households in the same villages. For example, if there is sharing of such grants among relatives or if increased demand for goods/services among treated households affects either earnings or prices faced by non-treated households, spillover (indirect) effects are plausible. Using a method suggested in Banerjee et al. (2007), we find that spillover effects are small and the net program impact is mostly driven by direct effects on the treated households. When we consider individual households as units of analysis, the magnitude of the net impact of PAF, for instance, on per capita consumption is about 38 percent change, close to double that of the estimate using villages as units of analysis<sup>10</sup> . This suggests that the impact estimates that we present in this paper using village level analysis are in fact lower bound estimates. Moreover, the lack of indirect effects and the dominance of direct effects also imply that the household level targeting in the PAF program is justified.

# **5. Conclusion**

The primary objective of this study is to evaluate the impact of Nepal Poverty Alleviation Fund on rural household welfare. PAF is a CDD-type social fund program and currently covers 40 poorest districts in Nepal, with plans to expand across the country. Between 2004 and 2011, PAF has supported some 15,000 Community Organizations and benefited more than 2.5 million people directly. Establishing causal effect of such a complex operation is difficult, particularly in a country setting where designing and implementing a robust impact assessment are not the norm. The Government took the initiative to build an impact evaluation component into the program and collaborated with an external research institution (Tribhuvan University) and a technical team from the World Bank to implement it.

> 10 This is consistent with the findings from earlier version of this paper that considered individual households as units of analysis, where treated households were compared with non-treated households.

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**3**

# I. **Basic Data and Methods**

# Labor Force Concepts and Job Type

Population censuses and household surveys frequently tabulate persons in the labor force by sex and by industry, as well as by "type of employment." This classification of Job. tve refers to (1) wage and salary workers, (2) employers, self-employed or own account workers, (3) unpaid family workers, and (4) others. The category "others often includes unemployed and "not specified elsewhere" as well as a heterogeneous mixture of groups that differ from country to country, such as in some cases the armed forces. This 'other" category is rarely more that ten percent of the labor force, and because it cannot generally be al'located by industry, it is excluded.

The fraction of a specific job type of "total" employment is thus defined as one job type over the sum of the first three job types, for example, the fraction of wage earners (W, - (1)/(1 + 2 + 3)) approximates the importance of firms as opposed to family based employment. Persons holding more than one type of job are allocated to their primary job, but in the case of agriculture those who cultivate their own, rented, or share-cropped land are categorized as selfemployed, though they may also work for wages, possibly outside of the agricultural sector. Because individuals cannot allocate their time proportionaliy across types of jobs or across sectors, these traditional tabulations of labor force by primary attachment are potentially misleading.

There does not appear to be a commonly accepted definition of a worker. Consequently, from country to country the definition of 'participating in gainful activity" varies, and has an effect on "measured" participation rates, particularly for women. Even within a country over time, definitions may be
1 6-E Figure 6.d

# **Devaluation and Parallel Premium** Ghana; 1970-89

<!-- Start of picture text --> 1000* 5000<br>800 --4000<br>600 3000<br>400 - 2000<br>200 1000<br>- --<br>0 -0-<br>-200 1972 1974 1976 1978 1980 1982 1984 1986 1988 1 000<br>J -<br>Devaluation (%) Premium (%)<br><!-- End of picture text -->
Figure 8. Policies to strengthen corporate balance sheets

# A. Potential output growth

# B. Long-term growth forecasts for fixed investment

<!-- Start of picture text --> Percent Percent<br>7 1998-2018 potential growth 12 EMDEs EMDEs excl. China World<br>6<br>5 10<br>4<br>3 8<br>2<br>1 6<br>0<br>4<br>2<br>World AEs EMDEs<br>C. Total factor productivity growth in D. Impact of supportive policies on potential<br>EMDEs output growth in EMDEs<br>Percent Percent Baseline Supportive policies<br>3.0 1998-2007 average 5.2<br>5.0<br>2.5<br>4.8<br>2.0 4.6<br>4.4<br>1.5<br>4.2<br>1.0 4.0<br>3.8<br>3.6<br>2013-17 2018-27 Impact<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>2003-07 2013-18 2019-27 2003-07 2013-18 2019-27 2003-07 2013-18 2019-27<br>2003-07 2013-17 2018-27<br><!-- End of picture text --> Source: Consensus Economics, Penn World Tables, UN Population Prospects, World Bank. A. Period average of annual GDP-weighted averages. Estimates based on production function approach. World sample includes 50 EMDEs and 30 advanced economies. AE = Advanced economies, B. 10-year-ahead forecasts surveyed in indicated year. Aggregate growth rates are calculated using constant 2010 U.S. dollar investment weights. Sample includes 23 advanced economies and 20 EMDEs. C. Shaded area indicates forecasts. GDP-weighted averages of production function-based potential TFP growth estimates. Sample includes 50 EMDEs.

D. Shaded area indicates forecasts. GDP-weighted averages of production function-based potential growth estimates. Sample includes EMDEs. Supportive policies assume that each country matches over the period 2018-27 its best historical ten-year improvement in educational attainment, schooling, life expectancy, female labor force participation, and investment.
# Policy Research Working Paper Series

|**Title**|Author|**Date**|Contact<br>for paper|
|---|---|---|---|
|WPS1411 Income Inequality, Wellarm, and<br>Poverty: An Illustration Using<br>Ukrainian Data|Nanak Kakwanl|January 1995|G. Evans<br>85783|
|WPS1412 Foreign Technology Imports and<br>Economic Growth in Developing<br>Countries|XiaomingZhang<br>Heng-iu Zou|January 1995|C.Jones<br>37754|
Fargher S. and Hallegatte S. (2020), <u>Best investments for an economic recovery from Coronavirus: An illustration based on the Fiji Climate Vulnerability Assessment to pinpoint stimulus options.</u> World Bank Group, Washington, DC.

Giannakis, E., & Bruggeman, A. (2017). Economic crisis and regional resilience: Evidence from Greece. _Papers in Regional Science_ , 96(3), 451-476. https://doi.org/10.1111/pirs.12206 Greco, S., Ehrgott, M., Figueira, J.R. (Eds.), 2016. Multiple Criteria Decision Analysis: State of the Art Surveys, International Series in Operations Research & Management Science. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4939-3094-4

- Hammer S. and Hallegatte S., <u>Planning for the economic recovery from COVID 19: A sustainability checklist for policymakers, World Bank blog on Development and a Changing Climate, 14 April 2020.</u>

Helm D. (2020), <u>The environmental impacts of the coronavirus.</u> _Environmental and Resource Economics_ 76:21–38.

Hepburn C., O’Callaghan B., Stern N., Stiglitz J. and Zenghelis D. (2020), Will COVID-19 fiscal recovery <u>packages accelerate or retard progress on climate change?</u> _Oxf Rev Econ Policy_ 36(S1):1–48.

Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M., Roehrl, A., 2011. OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development. _Energy Policy_ , Sustainability of biofuels 39, 5850–5870. https://doi.org/10.1016/j.enpol.2011.06.033 IEA (International Energy Agency), _<u>Sustainable Recovery</u>_ <u>. Paris, France, July 2020.</u> IMF, _<u>World Economic Outlook</u> – A Long and Difficult Ascent_ <u>. International Monetary Fund,</u> Washington, DC, October 2020.

IRENA (International Renewable Energy Agency), _<u>Post-COVID Recovery</u>_ <u>, June 2020. ISBN: 978-92-9260-</u> 245-1.

Jordan, A.J. and Turnpenny, J.R., 2015. _The Tools of Policy Formulation_ . Edward Elgar. doi: <u>10.4337/9781783477043</u> Klenert, D., Mattauch, L., Combet, E., Edenhofer, O., Hepburn, C., Rafaty, R. and Stern, N., 2018. Making carbon pricing work for citizens. _Nature Climate Change_ Vol. 8, August 2018, pp. 669–677. doi: <u>10.1038/s41558-018-0201-2</u> Kumar, A., Sah, B., Singh, A.R., Deng, Y., He, X., Kumar, P., Bansal, R.C., 2017. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. _Renewable and Sustainable Energy Reviews_ 69, 596–609. https://doi.org/10.1016/j.rser.2016.11.191 Liu, Y., Du, J., 2020. A multi criteria decision support framework for renewable energy storage technology selection. _Journal of Cleaner Production_ 277, 122183. <u>https://doi.org/10.1016/j.jclepro.2020.122183</u> Mastrocinque, E., Ramírez, F.J., Honrubia-Escribano, A., Pham, D.T., 2020. An AHP-based multi-criteria model for sustainable supply chain development in the renewable energy sector. _Expert Systems with Applications_ 150, 113321. <u>https://doi.org/10.1016/j.eswa.2020.113321</u> Miller, R. E., & Blair, P. D. (2009). _Input-output Analysis: Foundations and Extensions_ . Cambridge University Press.

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