rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1.   Introduction

Armed conflict can have severe consequences for household welfare in conflict-affected areas. These negative consequences for local populations are indisputable (Blattman and Miguel 2010; Verwimp, Justino, and Brück 2018). However, the dynamic effects of victimization on household welfare remain understudied because longitudinal data about victimization are still scarce. Furthermore, the differential impacts of different types of victimization, ranging from violent attacks to attacks on property, are not well-known. Not all attacks against civilians in conflict-affected areas are committed by the main actors of the conflict. In conflict situations, crimes become more prevalent when law enforcement weakens and the opportunity cost of criminal activity decreases. Yet little is understood about how attacks committed by different types of perpetrators affect household welfare.

In this paper, we present microeconomic evidence on the effect of different types of victimization and different types of perpetrators on household welfare. Using panel data collected before, during, and after a large increase in conflict, we exploit plausibly exogenous variation in the timing, location, and intensity of household exposure to conflict to analyze the effects of victimization on household welfare. We conduct the analysis using panel data from the three most-conflict-affected regions in Nigeria, which all have witnessed an increase in conflict and violence since 2010.

Our first contribution to the literature is to quantify the effects of victimization on household consumption and food insecurity. We find that one additional conflict event leads to a 3–4 percent decrease in consumption. Both food and nonfood consumption are affected by victimization. By defining poverty as a threshold level of US$1.90 per capita per day consumption, we also find that poor households are more likely to stay poor after becoming victimized, whereas nonpoor households manage to prevent themselves from falling into poverty. We also find evidence of increased health and decreased educational expenditures relating to the herder-farmer clashes. This is in line with literature that documents a relationship between conflict and decreased schooling (Ichino and Winter-Ebmer 2004; Chamarbagwala and Moran 2011; Shemyakina 2011; Swee 2015; Monteiro and
between October 2018 and February 2019 (World Bank, 2019). Uganda now hosts about 1.4 million refugees, from 17 different countries (UNHCR, 2020). In this study, the refugee data set captures a cumulative total of 3,391,194 refugees in the years 2000 to 2016. Arguably, Uganda currently hosts the most refugees in Sub-Saharan Africa and is the third largest refugee hosting nation globally after Turkey and Pakistan (REACH Initiative, 2018; WHO, 2018; World Bank, 2019; UNHCR, 2020). The highest cumulative total number of refugees received in Uganda between the years 2000 and 2016 are from Sudan, South Sudan and the Democratic Republic of Congo (DRC) which represent about 78% of the cumulative total number of refugees received in the country (Figure 1).<sup>2</sup> <!-- Start of picture text -->
1.5e+06<br>1.0e+06<br>500000<br>0<br>Total No. of Refugees<br>Sudan South Sudan Dem. Rep. of the Congo Rwanda Somalia Burundi Kenya Eritrea Ethiopia Central African Rep. Liberia Nigeria Congo Malawi United Rep. of Tanzania Sierra Leone Iran (Islamic Rep. of)<br><!-- End of picture text -->

Figure 1: Total number of refugees by country of origin ( _Source: Authors’ illustration using study dataset_ ) Refugees in Uganda are mostly settled in the western flank of the country (Figure 2). Of the 127 districts of Uganda, the refugees are found in Adjumani, Arua, Koboko, Moyo and Yumbe in the West Nile region; Lamwo/Kitgum in the Northern region; Kiryandongo, Hoima, Kyegegwa, Kamwenge and Isingiro in the (South) Western region and; Kampala district in the central region of the country (World Bank, 2019). Therefore, from 2000 to 2016, the refugees have been distributed over 14 districts with Adjumani district
sensitivity analysis to the methods used to estimate the contributions and benefits is required to obtain robust results.

To study the effects of government’s transfers we present the incidence across pre-fiscal income deciles in columns [1] through [10] of Table 6. In addition, **Figure 6** shows the concentration coefficient of the programs analyzed in the study, as well as the Gini index of different income concepts. All programs are progressive and most of them have negative concentration coefficients.

The most progressive programs are the Food transfers, the _Plan de Equidad_ and the noncontributory pensions. In fact, these programs were conceived for the lower-income population and both are mean-tested, so the results are in line with the programs’ designs. The rest of direct transfers are progressive though they are not specially designed for redistributive purposes among all the population: they comprise benefits that cover risks of formal workers such as illness or unemployment.

As shown by the Kakwani index, progressivity of all _in-kind_ benefits is lower to the progressivity observed of all direct transfers. However, in-kind benefits are more heterogeneous. The concentration coefficient of educational programs ranks from -0.457 for childcare to 0.316 for tertiary level. Finally, the concentration coefficient of health services is close to zero.

**Table 6.** Incidence of Taxes and Transfers on Income Distribution. Percentages. Uruguay, 2017.

|Decile|Direct<br>taxes<br>[1]|Contributi<br>ons to<br>Health<br>System<br>[2]|_Plan de_<br>_Equidad_<br>[3]|Non<br>contribut<br>ory<br>pensions<br>[4]|Food<br>transfers<br>[5]|Other<br>direct<br>transfers<br>[6]|All direct<br>transfers<br>[7]|Education<br>[8]|Health<
# **Multi-Hazard Groundwater Risks to the Drinking Water Supply in Bangladesh: Challenges to Achieving the Sustainable Development Goals**

Mohammad Shamsudduha<sup>2</sup> , George Joseph, Sabrina S. Haque Mahfuzur R. Khan, Anwar Zahid and Kazi Matin U. Ahmed

# **1. Introduction**

Groundwater is the largest store of freshwater that provides drinking, irrigation, and industrial water supplies globally (Taylor et al., 2013). In Bangladesh, approximately 32 km<sup>3</sup> of groundwater is withdrawn annually of which 90% is used for irrigation, and 10% for domestic and industrial purposes combined (Figure 1) that is equivalent to ~4% of global groundwater withdrawal (Hanasaki et al., 2018). However, the sustainability of groundwater resources is threatened by hydrological and socioeconomic factors such as poor water quality, overabstraction, inadequate governance, and impacts of changing climate that are not well understood. Effective management of groundwater resources is critical in meeting national and international agendas for improved public health, economic development, and poverty alleviation (Conti et al., 2016).

About 98% of drinking and 80% of dry-season irrigation water supplies come from groundwater at shallow depths (<150 m below ground level, bgl) (Shamsudduha, 2018). However, widespread arsenic (As) contamination of shallow aquifers affects some 26% of all tubewells across Bangladesh (World Bank 2018), making it the largest mass poisoning of a population in history (Smith et al., 2000). Furthermore, a recent World Bank led study estimates that nearly 40% of all tubewells have dangerous detections of fecal bacteria (World

> 2 Corresponding author; George Joseph, Global Water Practice, The World Bank, gjoseph@worldbank.org, Mohammad Shamsudduha, m.shamsudduha@ucl.ac.uk,  1Institute for Risk and Disaster Reduction, University College London, London WC1E 6BT, UK, , Sabrina S. Haque, sabrina.
about one‐quarter of 6 When more granular data such as enrollment rates by age are available, UNESCO uses them to calculate SLE. When they are not, UNESCO calculates SLE using school level‐specific gross enrollment rates (which are the most widely available in the UNESCO data), using Equation (5). Whether age‐specific or level‐specific enrollment rates are used makes little practical difference for the calculations. Using the data on gross enrollment rates and duration by level of school as reported by UNESCO in Equation (5), and restricting attention to primary and secondary, reproduces the UNESCO estimates of SLE for primary plus secondary almost exactly.

> 7 To see why, consider an education system in which a fraction a� of each cohort of students start school at the official age; a fraction l� starts one year late and therefore are above age‐for‐grade; and a fraction 1 �a� �l� students do not start school at all.   Gross enrollment in first grade at time t is the number of students in first grade divided by the cohort of children aged 6 who are supposed to be in first grade, i.e. GER� � �� ���������� ����� where

N� is the number of children in the cohort of new six year‐olds at time t . With a stationary population N� �N��� and stationary starting age shares a� �a and l� �l , it follows that GER�a�l�1 , even though there could be many over‐aged children in the grade. With stationary starting age shares the only way to generate GER�1 is if a�l is close to one (i.e. near‐universal enrollment) and N��� �N� i.e. a shrinking school‐aged population. The other way to generate GER�1 is with time‐varying starting age
IMF (International Monetary Fund). 2019. Fiscal Monitor: Curbing Corruption. April. Washington, DC: International Monetary Fund.

Knoop, T. A. 2004. Recessions and Depressions: Understanding Business Cycles. Westport: Praeger.

Koepke, R. 2019. “What Drives Capital Flows to Emerging Markets? A Survey of the Empirical Literature.” Journal of Economic Surveys 33(2): 516-540.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2020. Global Waves of Debt: Causes and Consequences. Washington, DC: World Bank.

Kose, M. A., and F. Ohnsorge, eds. 2019. A Decade After the Global Recession: Lessons and Challenges for Emerging and Developing Economies. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, and N. Sugawara. 2020. “Global Growth Next Decade: Optimistic Expectations, Disappointing Outcomes.” Policy Research Working Paper, World Bank, Washington, DC.

Kose, M. A., C. Otrok, and E. Prasad. 2012. “Global Business Cycles: Convergence or Decoupling?” International Economic Review 53(2): 511-538.

Kose, M. A., C. Otrok, and C. H. Whiteman. 2003. “International Business Cycles: World, Region, and Country-Specific Factors.” American Economic Review 93(4): 1216-1239.

Kose, M. A., C. Otrok, and C. H. Whiteman. 2008. “Understanding the Evolution of World Business Cycles.” Journal of International Economics 75(1): 110-130.

Kose, M. A., and E. S. Prasad. 2010. Emerging Markets
# **Introduction**

By its nature corruption is hard to measure. Yet, over the last two decades a series of data sets and indices have emerged that attempt to capture the phenomenon of corruption. Work with indicators on corruption typically confronts two basic issues. First, it is not always clear what types of corruption are measured. Second, it is not clear whether and to what degree perception measures of corruption reflect biases of respondents who either try to hide the truth or make biased guesses. The practical result is that perception measures of corruption can raise awareness but are not actually helping design policy. As argued by Kenny (2006) it could be that new more fine-grained firm-level indicators of corruption might generate more useful findings.

In this paper we explore whether the juxtaposition of several data sets can help disentangle some of the issues. We start by considering potential bias in aggregate perception indicators of corruption by confronting them with indicators generated from firm surveys. We then confront the firm surveys of corruption with data on regulatory obstacles and find plausible stories both about the link between regulation and corruption and about the effectiveness of removing obstacles through corruption, including obstacles to obtaining infrastructure service.

Turning to infrastructure, we find just a few data and case studies. The firm-level indicators suggest that infrastructure sectors are plagued by corruption, but not more than other sectors of the economy. If there is a sector that is particularly plagued by corruption it is construction, including for infrastructure projects. But there are important stylized facts about price levels and access to infrastructure services that shed light on mechanisms of corruption and are relevant for policy-making. We argue that the first line of defense for policymakers against the major illeffects of corruption in infrastructure is the restriction of government transfers to firms and reliance on consumer prices that effectively cover full cost. Corrupt elites may have an interest in going along with such policy change, because it might make them better off too.

We conclude with some basic thoughts about a sensible attitude towards measurement of corruption and policymaking.
provides summary statistics for the study sample of 678 farmers and their registered plots. Panel A reports farmer characteristics. Incomes were low by Mexican standards with an average self-reported annual income of 29,414 pesos (US$ 2,200). Panel B of Table 2 for all farmers and column 1 of Appendix Table A3 for farmers in the control group, report the agricultural practices at baseline. Farmers cultivate about 2 plots and an average total area of 5.7 hectares. In 2014 (the year before the intervention) fertilizer use in their registered plot was near universal (97%) with farmers carrying out 1.6 fertilizations on average though only 6% fertilized at sowing. Average self-reported yields were about 2 mt/ha and about half sold maize in the market. Only 6% had used agricultural extension services in the past and only 15% had paid for a soil analysis.

> 16 Specifically, study farmers came from 54 localities ( _localidades_ ) of which 29 were quite small and thus merged with the closest _localidad_ using distance between centroids from INEGI’s geographic databases. If there were ties, we chose to merge _localidades_ with the closest altitude. Two of the small _localidades_ were merged into one to give us a total of 26 clusters. The median cluster had 21 farmers, the 25th percentile had 15, and the 75th had 32.

> 17We note that with individual level randomization, there could be spillovers across farmers in different treatments. We study the potential spillovers in Section 5.2.

> 18We lack data for some farmers because they did not sow maize in a particular year (less than 5%), or because they could not be located for the interview. Appendix Table A2 column 1 shows that attrition was not differential across experimental arms.

7
rural population living in extreme poverty, the problem remains broad and deep. The P0 measures the proportion of people below a certain poverty line but takes no account of how far they are below that line—the degree of poverty—or whether they are becoming even poorer. To address the situation of the poorest and to evaluate whether their economic situation has improved, the poverty gap (or P1) measure is used. In rural areas with less than 15,000 inhabitants the P1 poverty measure reveals that the extreme poverty depth fell 10 percentage points to 12 percent during 1996-2002 (Figure 3.3 and Table 3.1). However, the poverty gap measure reveals that poverty depth remained virtually unchanged in the 1992-2002 period.

The regional poverty gap figures reveal that there are also large regional differences in the depth of poverty in rural localities (Figure 3.4 and Table 3.1). In the North and Capital regions P1 was below 5 percent while in the Center, Gulf, and South P1 reached more than 15 percent in areas with less than 15,000 inhabitants in 2002. During 1992-1996 the poverty gap increased the most in the regions where poverty was less deep in 1992, such as the Capital and North regions where P1 reached 17 and 16 percent, respectively. The depth of poverty was at its highest for all the regions in the middle of the 1990s.

13
# **1. Introduction**

The TRIPS Agreement, by ensuring stronger intellectual property rights (IPR) regimes around the world, has globalized the IP system and stimulated greater cross-border trade in knowledge assets. Technology transfer, a term still not defined in the WTO framework, has come to be seen as the primary strategy for developing countries to gain access to much needed, but legally protected, knowledge assets owned in developed economies. It is not particularly surprising that the IMF’s Balance of Payment statistics show that most developing countries pay much more in licensing and royalty fees to third parties than they receive. The transfer of technology from the developed to the developing world through licensing arrangements is an important process that merits discussion. The role of technology transfer in development has received considerable attention (Teece 1977; Liu 1995; Bozeman 2000; Hewitt-Dundas 2011; Saggi 2002; Schrank 2004). However, while technology transfer remains a major issue in global economic governance debates, it is equally important to recognize the alternative IP commercialization techniques available to actors in both the developed and developing world. By recognizing these alternatives, developing nations may eventually begin to close the gap that prevails in IP commercialization globally.

As IP ownership grows, it is increasingly significant to identify the various mechanisms available to rights holders in developing countries to commercialize their intellectual property rights (IPRs). This study reviews several IP monetization strategies in order to emphasize the ways that IP can be used to generate domestic innovation and growth within developing nations. Essentially, we are asking: what methods enable developing country innovators to commercialize their technology within their own nation and abroad? In doing so, we explore the variety of IP monetization techniques that exist and assess the extent to which they promote active markets for technology in developing countries. IP defines a wide spectrum of rights including copyright, trademarks, design rights, patents and geographical indicators. Our analysis remains focused on patent monetization strategies. This is to retain analytical clarity and also because patents generate the most economic value
<!-- Start of picture text --> Figure 8: Average deal size across governing laws<br>(dollar-denominated central government bonds )<br>1,800 $ millions<br>1,600 U.K. Law U.S. Law<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br><!-- End of picture text --> Source: Authors’ calculations based on Dealogic data.

There are also legal nuances and issuance trends that merit attention. The first relates to collective action clauses (CACs) that allow a qualified majority of creditors to impose a settlement on a minority of holdouts. Several studies attributed the difference in yields to the different voting requirements (Tsatsaronis 1999, Eichengreen and Mody 2004). However, the sign of the relationship between CACs and yields is ambiguous. Intuitively, one may think that since CACs facilitate orderly resolution of crises, they make bonds more attractive and their yields lower (Kletzer 2003). On the other hand, it would be reasonable to think that CACs drive up yields, because the prospect of orderly restructuring exacerbates moral hazard. As Eichengreen and Mody (2000) noted, the answer to this question is empirical. These authors found one effect or the other to prevail depending on the issuer’s creditworthiness. While “renegotiation-friendly loan provisions” reduce borrowing costs for higher-rated issuers (for whom default is a rare event), the opposite is true at the lower end of the rating spectrum (Eichengreen and Mody 2004). Earlier studies (Tsatsaronis 1999, Eichengreen and Mody 2004) normally used the governing law under which debt was issued as a proxy for the inclusion of CACs. In accordance with each jurisdiction’s traditional common practice, bonds issued under U.K. governing law were assumed to contain CACs, while those issued under U.S
11 effects of public capital on economic growth have identified a positive empirical link in this context. These have mostly relied on data from the Unites States and other developed countries, however, which limits their suitability for estimating the impact of changes in Mexico's public capital stock on output growth. The analysis below, therefore, estimates a growth equation based on data for 46 developing countries with similar levels of output per capita as Mexico.

## _Theory_

The growth equation below is derived from the (Robert) Solow-inspired analytical framework elaborated by Mankiw, Romer and Weil (1992) and Hulten (1996). That framework departs from a simple neoclassical production function, written here for simplicity in a Cobb-Douglas specification with labor-augmenting technical progress: (1) Y = F(K, EK, H, L) = (K" )aP(EKG)aG(H)aI(AL)(- ap- **aG- aH)** where, Y denotes aggregate output, K" and KG denote the private and public capital stocks, H stands for human capital, L is the stock of labor, A denotes the level of total factor productivity and E is an index of the efficiency of use of public capital. Dividing both sides of the equation by AL and taking natural logs yields, (2) y = f(k", k, e, h) = a, + ap k + aGk + aE e + aHh, where all variables except 'e' are now expressed in terms of "effective" units of labor.

The evolution of the capital stocks is assumed to obey the following sirnple formulation: (3) d(KZ/AL)/dt = szY/AL - _(KZI/AL)_ for z = P, K, H.

where sz represents the savings (and investment) rate associated with the capital stock z, and X represents the effective rate of depreciation, equal to the sum of the rates of population growth
_Ceará_ , and the results suggest that the large gains in student learning after introducing TA and reformulating the RBF rule were attained without increasing municipal public spending.

The paper is organized as follows. After this introduction, section 2 provides background information on _Ceará_ and explains the RBF reform that introduced education incentives for mayors. Section 3 describes the data and presents descriptive statistics. The identification strategy is discussed in section 4. Section 5 illustrates the RBF effects, presents estimation results on test scores, and disentangles the components of TA. Section 6 investigates several mechanisms explaining our results. Section 7 concludes.

# **2 Background**

The state of _Ceará_ is located in northeast Brazil and has approximately 9 _._ 1 million inhabitants (equivalent to Austria or Israel) living in an area of 148 _,_ 895km<sup>2</sup> (equivalent to Bangladesh or Greece). According to 2017 data, its GDP per capita reached _R_ $16 _,_ 395 (or US$7 _,_ 378 PPP), similar to Vietnam (US$7 _,_ 448) and Cabo Verde ( _US_ $7 _,_ 454), which translates into the fifth smallest GDP per capita in Brazil. Three-fourths of its population is concentrated in urban areas, infant mortality is around 13 _._ 2 per 1 _,_ 000 live births, and life expectancy is 74 _._ 3 years _−_ 2 years less than the national average.<sup>2</sup>

## _−_ **2.1 The Results Based Financing Reform in** **_Ceará_**

In this socioeconomic context, in December 2007 _Ceará_ initiated a reform modifying how state consumption tax revenues were redistributed to municipalities. The tax in question is the _Imposto sobre Circulação de Mercadorias e Serviços_ (henceforth, ICMS), an important source of

> 2The information about economic activity, demography, and socioeconomic characteristics of _Ceará_ is from Cavalcante et al. (