rafmacalaba/data-use-mentions-extended huggingface dataset:
Kim, Bonggeun, John Gibson, and Chul Chung. (2017). "Using Panel Data to Estimate Income Under‐Reporting by the Self‐Employed." _Manchester School_ , 85(1): 41-64.

- Kopczuk, Wojciech, Emmanuel Saez, and Jae Song. (2010). “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data since 1937”. _Quarterly Journal of Economics_ , 125: 91–128.

- Kuhn, Randall and Steven Stillman. (2004). "Understanding Interhousehold Transfers in a Transition Economy: Evidence from Russia." _Economic Development and Cultural Change_ , 53(1): 131-156

- Lokshin, Michael and Barry M. Popkin. (1999). “The emerging underclass in the Russian Federation: income dynamics, 1992–1996”. _Economic Development and Cultural Change_ 47: 803–829.

- Lokshin, Michael and Martin Ravallion. (2004) “Household Income Dynamics in Two Transition Economies”. _Studies in Nonlinear Dynamics & Econometrics,_ 8(3), Article 4

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Figure 6: _Private sector participation in consultations_ <!-- Start of picture text --> Share of economies (%)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Before After During Not consulted<br><!-- End of picture text --> _Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ Private sector engagement in the process of policy formulation can broadly be simplified into two stages: pre- and post-legislation enactment. Most economies engage the private sector in the legislative process, but not throughout the entire cycle of policy formulation, implementation, and evaluation. In fact, economies across all income groups tend to consult before the formulation of the legislation, rather than after its enactment. In close to three-fourths of all economies with reform committees, the private sector is involved before the formulation of the business regulatory and legislative proposals, through in-person or online discussions or surveys. Similarly, in approximately 40 percent of economies with reform committees the private sector is less frequently engaged during reform implementation and provides feedback on practical challenges in the implementation stage (Figure 6). Importantly, in most of these cases the consultations are usually focused on implementation issues rather than impact evaluation. An exception is Slovenia, which in its ex-post evaluation efforts focusses on reducing administrative burdens for businesses.

For example, in Colombia the private sector is involved in the formulation of new legislation and in presenting the main challenges of the sectors. In Hong Kong SAR, China, the Business Facilitation Advisory Committee (BFAC) and its three Task Forces provide a platform for the trade actors and the government to communicate and exchange views on new regulatory proposals at all levels and take forward regulatory reviews or other initiatives to facilitate business compliances. Through the BFAC and Task Force meetings, the government consults the business sector on new regulatory
Each of these three progressivity concepts, and the metrics that can be adopted under each, are examined in more detail in the next three sections of the paper. Effective progressivity is considered first as it is the most commonly adopted approach.

# 2 Measuring effective tax progressivity

The most common and widely applicable type of progressivity measurement is effective progressivity measurement. Effective progressivity metrics consider the actual tax liabilities of individuals or households rather than just the tax schedules. Subject to data availability, effective progressivity metrics can be calculated for the PIT, SSC, VAT as well as various excise taxes (such as those on fuels used for heating and transport, electricity, and on alcohol, tobacco and sugar-sweetened beverages).

This section first discusses data requirements and key methodological issues before presenting the most commonly used global measures of structural progressivity. It then presents examples of their use, focusing where possible on IDA countries, and finally summarizes the strengths and weaknesses of effective progressivity metrics.

## 2.1 Data, welfare metric, and unit of analysis

To calculate effective progressivity metrics, data is needed that provides an appropriate welfare metric to rank individuals or households (or at least of a set of disaggregated groupings of individuals or households) from poorest to richest, and enables the tax liabilities of each individual or household (or group) to be ascertained. Ideally, microdata is used, although (simplified) calculations can also be made with semiaggregated data, such as average income levels across percentiles or deciles.

To examine the progressivity of the PIT, income survey data will typically be used, which can be weighted from the representative sample to reflect the population. Income and household characteristics are included to enable the actual tax liabilities of taxpayers to be modelled, including non-standard reliefs where the required information is included in the survey. To examine the VAT or excise taxes, household budget survey data can be used as these surveys typically provide a breakdown of total consumption across a large number of consumption categories, enabling differentiation between consumption subject to different VAT
school (Kazeem and Musalia, 2016). Also studying the education dimension of poverty, Bertoni et al. (2018) measured the impact of Boko Haram violence on school enrollment and attendance rates in Nigeria’s Borno state and found a negative impact of violence. Likewise, Nwokolo (2019) and Rotondi and Rocca (2019) studied the effect of Boko Haram violence on nutrition and birth weight, and found that violence reduces birth weight.

Popoola and Adetola (2016) studied five dimensions of multi-dimensional poverty (safe drinking water, sanitation, housing, health, and nutrition) based on the DHS survey of 2008, but limit the analysis to children under 5. Not distinguishing between adults and children, Ajakaiye et al. (2016a,b) measured multi-dimensional poverty based on a slightly different set of dimensions (including housing, water, sanitation, electricity and education) over four waves of the DHS from 1999-2013. All three studies, however, show irrespective of dimensions included that poverty is concentrated in the North and in rural areas. In a state level analysis, Ajakaiye et al. (2016a) also showed that the most deprived states are, while concentrated, not solely in the North.

To the best of the authors’ knowledge, Adetola and Olufemi (2012) and the World Bank’s 2016 poverty assessment are the only available studies, which analyzed multi-dimensional poverty indicators in relation to monetary poverty with data from 2008 and reaching up until 2013 respectively, whereby latter does not have a specific focus on child poverty. This analysis hence aims at filling the gap in the literature by providing more up to date monetary and multi-dimensional child poverty estimates and insights on how these measures overlap.

# 2.3 Children’s Rights in Nigeria

The Federal Government of Nigeria adopted the Child’s Rights Act in 2003 in line with both the United Nation’s Convention on the Rights of the Child and the African Charter on the Rights and Welfare of the Child to provide and protect the rights of Nigerian
1997 2000 2003 2006 2009<br><!-- End of picture text --> Source: World Development Indicators Technology is also part of the story. From both theory and practice, we expect developing countries to lag the high income countries in technology, since the latter are at the technological frontier in most products that they produce. That said, one would expect the more dynamic middle-income countries to begin to move up the technological ladder as they move up the per capita income ladder. In addition, there are sectors in middle income countries that are reaching (or have already reached) the technological frontier – for example, Brazil‘s deepwater oil drilling capacity. As a result, one would expect these countries to begin to invest more in research and development and begin to produce more homegrown technological innovation.

22
In 2001, the government adopted a balanced budget rule that constrained public expenditures, to ensure that the structural balance, measured as a share of GDP, met a specific target or range (De Gregorio 2009b; Llédo et al. 2017). The fiscal targets are were then regularly adjusted in line with changes in potential growth and forecasts of longterm copper prices.<sup>3</sup> Two independent committees, one focused on potential output and the other on copper prices, advise on the practical calculation of the structural balance.

Results. Despite some large fluctuations of inflation around the target range, long-term inflation expectations in Chile have been remarkably well anchored since the adoption of inflation targeting, and the sensitivity of inflation expectations to shocks is among the lowest in EMDEs. During the early years of the inflation targeting regime, inflation fell and became less volatile. Even under the partial inflation targeting regime, there was a sustained decline in headline inflation and inflation expectations (Figure A.5.2). Average inflation fell from 15.5 percent in 1991-94 to 5.7 percent in 1995-98. Moreover, the exchange rate pass-through to inflation dropped significantly starting in the mid-1990s and continued falling after the adoption of formal inflation targeting in 1999 (SchmidtHebbel and Tapia 2002).

A period of low inflation in 2003 and 2004 challenged the credibility of Chile’s inflation target. In the second half of 2003, Chile experienced a significant and unexpected deceleration in inflation, as the peso appreciated and competition in the retail sector intensified (Central Bank of Chile 2004). Although survey-based inflation expectations remained close to the target of 3 percent (that is, well anchored), five-year-ahead, marketbased inflation expectations declined significantly.

Long-term inflation expectations varied only slightly during the global financial crisis, despite large gyrations in actual inflation. From mid-2007 to late 2008, headline inflation in Chile experienced upward pressure from international factors—namely, rising food and energy prices. Headline inflation peaked at 9.9 percent (year-on-year) in October
Policy Research Working Paper_ .

- DellaVigna, S. and La Ferrara, E. (2015). Economic and Social Impacts of the Media. In Anderson, S. P., Waldfogel, J., and Strömberg, D., editors, _Handbook of Media Economics_ , pages 723– 768. Elsevier.

- Deszö, C. and Ross, D. G. (2012). Does Female Representation in Top Management Improve Firm Performance? A Panel Data Investigation. _Strategic Management Journal_ , 33:1072–1089.

- Dharmalingam, A. and Morgan, S. P. (1996). Women’s Work, Autonomy, and Birth Control: Evidence From Two South Indian Villages. _Population Studies_ , 50:187–201.

- Duckworth, A. L. and Quinn, P. D. (2009). Development and Validation of the Short Grit Scale. _Journal of Personality Assessment_ , 91(2):166–174.

34
dynamics. Thus, equation (15) assumes that _C_<sup> ̄</sup> 0 = 0 and remains so over time, whereas equation (16) assumes that _C_<sup> ̄</sup> _t−_ 1 reaches _C_<sup> ̄</sup> _∞_ in

> 23While time-varying demographics, _Nit_ , were not collected prior to the experiment, the estimated household fixed effect _θ_<sup>ˆ</sup> _i_ incorporates the household average demographics _N_<sup> ̄</sup> _i_ over the course of the experiment.

> 24Note that, in an alternative household fixed specification, we could not distinguish cohort (month of connection) and time (aggregate shock) effects.

28
- End of picture text --> Source: Authors’ analysis using Babajob data.

Note: Text clouds are visual representations of the most frequently used words in job descriptions. The size of the word corresponds with the number of times it was used. Of the 186,829 job advertisements published on Babajob in 2014, 32,665 had job descriptions, 7,396 of which were for professional-level positions and 25,269 of which were for non-professional positions.

15
# **1. Background and Introduction**

Integrated water resource management (IWRM) and organizing it primarily at the river basin level are two of the most common and widely repeated recommendations in the water resources literature of the last decade if not longer (Allee 1988; Galloway 1997; McDonald and Kay 1988; World Bank 1993). Basin management is often associated with the concept of decentralization, of managing water resources at the “lowest appropriate level.” (See, e.g., International Conference on Water and the Environment 1992; Mody 2001.) Several conceptual arguments have been presented in favor of decentralization in water resource management, and basin-level management in particular: that the whole array of resources and use patterns in the basin will be taken into account, public participation will be greater and broader, management decisions will be based on better knowledge of local conditions, and so on.

Empirical studies of river basin management systems provide opportunities to examine the claims made for basin-level integrated resources management, and to explore factors that appear to influence its implementation and outcomes. In this research project, with the support of the World Bank, the project team has searched for those factors and their relationships to river basin management in two ways: with a survey of river basin organizations throughout the world, and with case studies of eight river basins analyzed in greater detail. Some of those eight cases have emerged recently, as in the Warta River basin of Poland and the Fraser River basin of Canada. Others have long histories of basin-scale institutions, such as the Guadalquivir River basin in Spain and the Murray-Darling Basin in Australia.

This paper focuses on analysis of the evolution and current status of river basin management institutions in the Murray-Darling Basin. Only brief descriptions of the basin’s physical characteristics, social and economic profile, and historical development are included in this paper.

# **2. Analytical Framework**

To analyze the data gathered for this project from the case studies and from the survey of river basin organizations
political violence, with the magnitude of each episode ranging between 1 (lowest) and 10 (highest).<sup>15</sup> The link from conflict, political violence, and fragility to growth is well established (e.g., Benhabib and Rustichini 1996; Rodrik 1999; Amodio and Di Maio 2018). Our measure exhibits considerable variation over time, which is a key advantage over most common measures of institutional quality, which often come out highly insignificant in growth regressions due to a high noise-to-signal ratio for individual countries over time (e.g., Araujo et al. 2016).

> 14 Since those variables have descending order of availability, we use stepwise prediction with a country fixed effects and country-specific time trend model to interpolate the missing data. Details are available upon request. The interpolated data are down-weighted in the index as explained below.

> 15 The Systemicpeace.org data do not include 2019.

-10-
We calculated the impact of higher investment growth. The investment growth path is taken from the Ministry of Economy and Development’s projections up to year 2025 and extrapolated afterwards. These forecasts imply that the ratio of investment to potential GDP is expected to increase from 23 percent<sup>6</sup> in 2017 to 34 percent by 2028. Since 1990, more than 70 countries have experienced such an increase. Other than the well-known example of China, other relevant examples include Indonesia, India, the Republic of Korea, Saudi Arabia and Turkey. All these countries experienced over a 11 percentage points increase in their investment rates in 11 years.

The increase in the investment/GDP ratio raises potential growth by 0.2 percentage point in 2020 and 0.6 percentage point in 2028. The cumulative impact of the higher investment rate is around 4 percent of potential output during 2018-2028.

# **D. Productivity**

Russian TFP growth began slowing down around the global financial crisis period in 2008-09, and declined to 1.3 percent in 2017. The decline of TFP growth in Russia is supported by firmlevel data (World Bank 2016): firm-level TFP has been declining since 2007 in all sectors. At the same time, productivity dispersion, especially in the service sector, has risen since 2005, which may imply increasing economic distortions. These economic distortions ultimately create an uneven playing field, limiting the entry and expansion of more efficient firms and the exit of less efficient ones. It is expected to decline further to 1.1 percent in 2028, as the declining share of young working age population will weigh on TFP.

We assumed substantial strengthening of institutions for regulation of private enterprises and successful implementation of policies focused on (1) leveling the playing field for firms by stimulating competitive markets and eliminating barriers to the entry of productive firms and the exit of unproductive incumbents; and (2) upgrading firm capabilities, such as innovation, and technology adoption and better managerial skills, in order to help
foreign asset holdings largely reflect cumulative flows, intense flows could be consistent with a limited geographical diversification of assets and liabilities. Conversely, the existence of large foreign asset holdings (for example, as a result of capital flight) does not necessarily imply frequent rebalancing and cross-market arbitrage. Thus,

> 15 This section borrows from Levy Yeyati and Williams (2010).

> 16 The literature generally distinguishes between de jure and de facto financial globalization (Kose, Prassad, Rogoff, Wei, 2009): where the former is based on regulations, restrictions and controls over capital flows and asset ownership, the latter is related to the intensity of capital flows and cross-market correlation and arbitrage.

> 17Kraay, Loayza and Ventura (2005) report a similar dataset on country ́s asset positions. An alternative approach to FG relies on price convergence, an application of the Law of one Price to financial markets. Measures within this group point at transaction costs and regulation that inhibit market arbitrage, and usually compare prices of identical or similar assets trading in different markets. On this, see Levy Yeyati, Schmukler and Van Horen (2008) and references therein. LMF and the Chinn-Ito index are the de facto and de jure measures of choice in recent work on determinants and implications of FG (see, i.a., Kose, Prasad, Taylor, 2009 and Rodrik, and A. Subramanian, 2009).

<u>11</u>
**Figure 7: Share of Workers Applying for Jobs in Only One Occupational Category by Main Occupation**

**Choice**

Source: Authors’ calculations using Babajob data.

Note: The analysis was conducted by excluding job seekers who applied for multiple jobs. Of 90,258 job seekers who submitted at least one job applications between January and December 2015, 88,098 (99%) submitted multiple applications.

The analysis also examined the relationship between the behaviors of job posting and application. Yamauchi et al. (forthcoming) applied time-series analysis to Babajob data to understand the relationship between the demand for and supply of workforce skills. The numbers of advertisements per firm and applications per job seeker were used to represent the demand and supply sides, respectively, and both reflect trends on the intensive margin. Focusing on business-process outsourcing (BPO) jobs advertised on a daily basis between 2012 and 2015 in Bangalore, the analysis found that job seekers responded quickly to job advertisements, suggesting that the supply of required skills, at least in terms of quantity, was not a binding constraint, and that the quantitative skills gap between the demand and supply for BPO jobs in Bangalore appears to be relatively small. Moreover, an increase in the number of job advertisements was found to have a surprisingly fast and significantly positive impact on the number of job applications received, with most job seekers applying for a posted position within one to two days.<sup>12</sup> The above results suggest that (i) job advertisement information circulates very quickly through the Babajob job portal and (ii) the supply of required skills is not a constraint for BPO jobs.<sup>13</sup> Such job seekers’ responsive behavior may reflect the success rate of their job search activity. The analysis also examined employer preferences regarding the timeframe for completing the hiring process. From employers’ point of view, while they wish to recruit the candidate whose qualifications and skill set best matches their selection criteria, they
_Table 4a: Impacts on S/E Skills Index and Different Sets of Skills (Z-Scores)_

||Including All Skills<br>(1)|S/E Skills Index<br>Excluding Deliberate<br>Practice Beliefs and Grit<br>(2)|Only Including Deliberate<br>Practice Beliefs and Grit<br>(3)|
|---|---|---|---|
|Treatment 1|0.055**|-0.004|0.070***|
|“Student Self-Learning”|_(0.022)_|_(0.022)_|_(0.020)_|
|Treatment 2|0.128***|0.063***|0.133***|
|“Teacher Delivery”|_(0.021)_|_(0.021)_|_(0.021)_|
|N|18,718|18,718|18,718|
|N Control|7,286|7,286|7,286|
|N Treatment 1|5,424|5,424|5,424|
|N Treatment 2|6,008|6,008|6,008|
|_R2_|_0.337_|_0.345_|_0.308_|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level. All figures are rounded to three decimal places.

_Source:_ Own survey data, school year 2015/2016, own calculations.

Table 4a shows that our intervention had a significant, positive impact on our S/E skills
# **Table 2. Change in censored headcount ratio models results with restricted and unbalanced panel data.**

||Change<br>|_Depe_<br>in censor<br>|_ndent vari_<br>ed headco<br>~~(~~<br>ΔHci(t)<br>Δt<br>~~)~~<br>|_able:_<br>unt ratio p<br>|er year<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Initial poverty centrality (Sc(t))<br>||-0.010<sup>***</sup>|-0.009<sup>***</sup>|-0.009<sup>***</sup>|-0.009<sup>***</sup>|
|||(0.002)|(0.003)|(0.003)|(0.003)|
|Initial censored headcount ratio (Hc(t))|||-0.003||-0.001|
||||(0.006)||(0.016)|
|Initial headcount ratios of all other indicators<br>(∑<br>H<br>i≠c<br>ci<sup>(t)) </sup>||||0.003|0.002|
|||||(0.005)|(0.014)|
|Constant|-0.002<sup>***</sup>|-0.001<sup>*</sup>
10 dummies, one for each age decile, to allow for a non-linear age effect), student grade, and for the average age effect calculated as a mean age for each round and country. In the second approach, we re-estimated all regressions for countries from Wave 1 in PIRLS. These countries did not postpone the assessment due to the pandemic, and according to PIRLS organizers, their data are fully comparable with previous rounds without additional adjustments.

The pandemic could affect student results differently, not only through school closures. Disruptions could also affect health, economic and psychological situation, and general wellbeing. We distinguish these effects by comparing students in countries or schools closed for different periods. In this regard, we used information provided by principals on how their schools were affected by the pandemic. The baseline in the model is more than eight weeks of school closure, and we compare how they differed from a linear trend compared to schools that were closed for between 2 and 8 weeks and those affected for less than two weeks or not closed at all. We also used UNESCO-provided data on the number of weeks of school closures for 4