rafmacalaba/datause-encoder-data huggingface dataset:
As the Indonesian financial crisis and Tonga-New Zealand migration entailed large negative and positive income changes respectively, these studies appear to suggest that income shocks may play an important role in mental health outcomes. However, since both of these events also led to many other covariate changes, it is impossible to directly identify the effect of income shocks on mental health from these studies. Gardner and Oswald (2007) provide more direct evidence on the impact of positive income shocks on mental wellbeing by showing that British lottery winners exhibit significant improvements in mental health. However, as Gardner and Oswald (2007) note, their results suffer from a potential endogeneity concern, as it cannot be ruled out that there are structural differences between lottery winners and the individuals in their control group.<sup>3</sup> This paper uses a randomized cash transfer experiment to identify the causal effect of positive income shocks on the mental health of school-age girls in Malawi. Considering the importance of mental health during adolescence in educational achievement and risky behaviors, school-age beneficiaries are a target group of particular interest.<sup>4</sup> The unique design of the intervention allows us to compare the impact of _unconditional_ cash transfers (i.e. pure income shocks) with the impact of cash transfers made _conditional_ on attending school. Multiple overlapping randomized treatment layers also allow us to investigate the elasticity of mental health with respect to transfer size, to examine whether the identity of the recipient matters, and to identify spillover

> 3 There is much more evidence on the relationship between income _levels_ and mental wellbeing. This literature was given impetus by Easterlin (1974). He showed that within countries there is a link between income and happiness. However, across countries average levels of happiness do not exhibit much variation with respect to income. For references to this extensive literature we refer the reader to the introduction of Gardner and Oswald (2007).

> 4 There is a fair amount of evidence on the causes of poor mental health among adolescents. Patel et. al. (2007) identify a range of risk factors including poverty and social disadvantage, unstable romantic relationships, violence and abuse, poor physical health, and inadequate education. Das et. al. (2008) are unable to confirm a relationship between poverty and mental health using survey data on mental health from 5 developing countries. Engaging family and education environments (such as parents encouraging children to express their emotions and schools providing a safe learning atmosphere), on the other hand, can reduce the probability that adolescents develop mental disorders (Lewinsohn, Rohde and Seeley 1998; Patel et. al. 2007; Saluja et. al. 2004).

3
typically one maize harvest per year. The Ministry of Agriculture communicates with maize farmers through a national public, decentralized agricultural extension system. All agrarian areas are in principle staffed by an Agricultural Extension Development Officer (AEDO), but in practice, these frontline positions are chronically understaffed. Each AEDO is responsible for providing extension services to 1,465 households on average, and moreover, many AEDO vacancies remain unfilled. At the outset of our study in 2009, only 56% of all AEDO positions were filled across our study districts, according to Ministry records. As a result, only 18% of farmers participated in extension activity according to the 2006/2007 National Agricultural and Livestock Census. Informational deficiencies remain a key challenge that hinders adoption of yield-enhancing technologies.

In addition, the traditional extension network is male-dominated: the average ratio of male to female AEDOs from 2005-2010 was 8:1 (Masango and Mthinda 2012). The Ministry had adopted a ‘lead farmer’ approach to extend the reach of extension, in which a few farmers are chosen to act as extension partners to communicate with their neighbors. These positions are typically filled by men. These gender imbalances suggest that involving more women to communicate with farmers about new technologies may be a promising way to address informational deficiencies, especially since 48% of maize farms in our study area are cultivated by women. This creates a rich setting to study the role of innate ability vs. social norms and attitudes in the relative performance of men versus women in a field-based task, by involving women in the delivery of extension advice through experimental manipulation.

# **2.2 Experimental Design**

We partner with the Ministry of Agriculture and Food Security (MoAFS) in Malawi to conduct a large-scale field experiment with 142 maize farming communities in 8 districts across the country. In 2009, the MoAFS was preparing to roll out a new extension activity<sup>4</sup> in these eight districts, and we convinced them to incorporate ideas from social network theory to improve delivery. Under this approach, a few village residents are chosen as extension partners or _communicators_ , and are asked to serve as the interlocutor between the AEDO assigned to the

> 4 A decentralized T&V model of extension is set up such that information on new technologies flows from researchers to extension workers and on to producers via farmer communicators, points of contact between extension agents and other farmers (Kondylis et al. 2014).

6
We analyze the tax-to-GDP ratios of 27 economies during the period surrounding their transition from a low-income to a lower-middle-income status. To conduct this study, we utilize the World Bank income group classification to identify countries that have changed their status from one income group to another based on data from 1987 to 2021. We identify 53 countries that transitioned from low to lower-middle-income status, 67 countries from lower-middle to upper-middle-income status, and 41 observations from upper-middle to high-income status (table B1).<sup>7</sup> However, due to constraints in the availability of tax-to-GDP data, we track only a subset of these transitioning countries: 27 from low-income to lower-middle income (L to LM), 45 from lower-middle to uppermiddle income (LM to UM), and 29 from upper-middle income to high income (UM to H), with additional limitations in temporal coverage.

Economies rising from low to lower-middle income status achieve a critical tax-to-GDP ratio of 15 percent, reflecting a 3-4 percentage point or GDP increase over the decade before transitioning. Our analysis indicates that the average tax-to-GDP ratio is 15 percent when countries shift from a lowincome to a lower-middle-income category (figure 9A). This average increases to 23.4 percent and 25 percent when transitioning from lower-middle-income to upper-middle-income and from uppermiddle-income to high-income groups, respectively. Notably, as countries progress from low to lower-middle income status, we observe that their median tax capacity in the ten years preceding the transition expands by about 4 percentage points (figure 9B). In contrast, when countries transition within middle income status to upper middle income there is no notable increase in tax collection in the ten years preceding that transition.

**Figure 9 Taxes during income group transitions**

A. Tax-to-GDP at transition point B. Tax-to-GDP prior to transition to middle income status, median <!-- Start of picture text --> Percent of GDP Percent of GDP<br>30 Median Mean 19<br>18<br>17<br>25<br>16<br>15<br>20 14 12.9<br>13<br>15 12<br>27.9<br>25.0 11<br>23.4<br>10 21.3 10 9.1<br>15.0 9<br>12.9<br>5 8<br>7<br>6<br>0<br>L to LM LM to UM UM to H<br>Years<br>T-10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 T<br><!-- End of picture text --> _Sources:_ International Monetary Fund; Authors’ calculations. A. “L” denotes low income, “LM” denotes lower-middle income. “UM” denotes upper-middle income, and “H” denotes high income based on World Bank income classification. B. T denotes the year of transition from low to lower-middle income group. Dashed line denotes the inter-quartile range.

7 Some countries have transitioned more than once, likely due to volatility in estimated per capita income levels. To address this, we consider only the most recent year in which a country transitioned.

17
with Hurricane Andrew in the USA were greater in cost in current dollar terms (see Figure 1).

**Figure 1: Insured Catastrophe Claims 1970 – 2002 (Amounts are in $US indexed to 2002)**

<!-- Start of picture text --> 40<br>Man-made Catastrophes<br>35<br>Natural Catastrophes<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Year<br>1970 72 74 76 78 1980 82 84 86 88 1990 92 94 96 98 2000 02<br>$US Billions<br><!-- End of picture text --> Source: Swiss Re Sigma 2/2003.

Insurance companies are influenced by catastrophes through the combination of the frequency of occurrence of the events themselves and the level of the insurable exposure attached to such events.

Recent experience has seen an increasing number of man-made catastrophes as economic activity expands and terrorism becomes an issue. There is also some evidence of an increase in the frequency of natural hazards. One explanation may be the increased reach of information systems (including increased insurance penetration). Another cause may be long-term climatic trends.

Increasing costs from catastrophic events are also a function of increased insurance coverage. The primary costs of a catastrophe (ie property loss and 6
**4-**

Figure 1. Policy, incentives, and agricultural exports in Sub-Saharan Africa <!-- Start of picture text --> Index 1970 = 100<br>120 -<br>110 -<br>9 0<br>8 0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>arithmetic mean for 26 countries<br>- REER agricultural exports real producer price<br><!-- End of picture text -->
not completely excessive, and much lower than the group of new EU entrants where the average was 644 per 100,000 in 2005 (see Table 8). In fact, the number of beds per 100,000 people in Albania and Bosnia and Herzegovina is about half that of the EU. Moreover, it is only in Serbia and in Bosnia and Herzegovina that health systems seem to be dominated by a few large hospitals with enormous bed capacity. Elsewhere, capacity is very similar to the EU – at least on a population-wide basis.

What may be more of an issue is the appropriateness of the distribution of those beds across hospital sectors (general, specialized and tertiary care) and across the geographic area of the country. Bed numbers in specialized facilities may not necessarily correspond to the specific medical needs of the population; the bed numbers may not be located in areas of greatest population need while other population groups may have access to a number of medical facilities. In urban centers, for example, there are some very large hospitals that account for a large share of the total national bed availability, but very little access in rural areas. For example, while Macedonia averages 470 beds per 100,000 people, the distribution of these beds is such that bed capacity varies between 130 per 100,000 in Kochani and 520,000 per 100,000 in Shtip.

Another major source of inefficiency in health systems is the loss of productivity that can result due to underutilization of primary health care facilities, too-frequent referral from the primary care level to the secondary and tertiary care levels, and skipping of primary care treatment to seek care directly at the secondary and tertiary level. Physicians in Bosnia and Herzegovina, for example, only resolve about half of primary health care cases and refer the rest to the more expensive secondary and tertiary care, even though such referrals may be unnecessary. One contributing factor is the low number of primary health care units in some countries. In Bosnia and Herzegovina, Serbia and Montenegro, the number of primary health care units per 100,000 people is only around 30, which is half as many as there are in the EU-12 countries.

**<u>Table 7 Supply of medical facilities, 2005</u>**

|||**Hospitals**<br>**per 100,000**|**Primary Health Care**<br>**units per 100,000**|**Hospital beds**<br>**per 100,000**|
|---|---|---|---|---|
|Albania||1.6|76.55|297|
|Bosnia|and|0.95|30.28|304|
|Herzegovina|||||
|Serbia||1.38|-|593|
|Montenegro||1.93|29.52|418|
|Macedonia, FYR||2.65|91.05|470|
|Kosovo||-|-|-|
|Croatia||1.76|74.54|545|
|Slovenia||1.45|3.2|483|
|EU||3.04|-|585|
|EU-15||3.19|-|571|
|EU-12||2.63|62.22|644|

Source: WHO HFA-DB, 2007 Note: Data are for 2005, except for the EU-15 data which are for 2004 To improve hospital productivity, the average length of stay (ALOS) should be shortened, bed occupancy rates increased and the number of beds reduced. Further reductions may be possible by improving admission management, facilitating earlier discharge and strengthening coordination among levels of providers, especially when it comes to moving patients from inpatient to outpatient care.

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# **Timing and Duration of Exposure in Evaluations of Social Programs**

Elizabeth M. King and Jere R. Behrman Forthcoming, _World Bank Research Observer_ Keywords: Impact evaluation, duration of program exposure, social programs in developing countries, implementation delays Corresponding author: Elizabeth M. King, The World Bank, 1818 H Street, NW, Washington, DC 20433; <u>eking@worldbank.org</u>

* We are grateful to Laura Chioda and to three anonymous referees for helpful comments on a previous draft. All remaining errors are ours.  The opinions and conclusions expressed in this paper do not necessarily reflect those of the Executive Directors and officials of the World Bank or of its member governments.
whose public and private contracting environment includes reliable institutions of loss control. The difficulty is one of sequencing. Where financial controls are poor, explicit deposit insurance can spur financial development only in the very short run. Although formal guarantees make banks more eager to lend, they also undermine longstanding patterns of bank bonding and depositor discipline. Over longer periods, the displacement of pre-existing private discipline can encourage patterns of lending that increase financial fragility and deter financial development. In this case, excessive risk-taking leads to financial and nonfinancial insolvencies that destroy real economic capital.

The downside of installing explicit insurance is that it reduces incentives for depositors to monitor the riskiness of their banks. Depositors are prepared to tolerate aggressive bank lending whenever they believe that, even if borrowers cannot repay the bank, their deposit claims will be paid by the deposit insurer. Unless the insurer can effectively replace the (private) monitoring that government guarantees displace, aggressive banks can fund a portfolio of risky loans at a deposit interest rate that lies far below the yield at which the resulting exposure to loss deserves to be funded. In institutionally weak environments, effective deposit-insurance design is often blocked by political obstacles that end up intensifying rather than reducing the probability and depth of future crises.

For countries that have already installed or are in the process of designing an explicit deposit-insurance scheme, cross-country empirical research identifies six common-sense principles of good design. No government can afford to neglect these principles. Even in the strong institutional environments, weaknesses in deposit-insurance design and distortionary political pressures that support them can fuel financial fragility and lessen the 20
Figure 4. Evolution of world output and world output per capita <!-- Start of picture text --> A. World output B. World per capita output<br>Index, 1950 = 100 Index, 1950 = 100<br>1,600 500<br>Market weights Market weights<br>PPP weights 400 PPP weights<br>1,100<br>300<br>600<br>200<br>100 100<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: Feenstra, Inklaar, and Timmer (2015); Kose and Terrones (2015); World Bank.<br>Note: Shaded areas indicate global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998,<br>2001, and 2012. Data for 2019 and 2020 are estimates and forecasts, respectively.<br><!-- End of picture text --> 33
rather than the exporters themselves. There is evidence, based on the AGOA preference scheme, that the pass through of preference margins is indeed partial at best. Olarreaga and Özden (2005) find that the average export price increase for products benefiting from preferences under AGOA was about 6 percent, whereas the average MFN tariff for these products was some 20 percent. Thus, on average exporters received around one-third of the tariff rent. Moreover, poorer and smaller countries tended to obtain lower shares—with estimates ranging from a low of 13 percent in Malawi to a high of 53 percent in Mauritius.<sup>21</sup> In addition, based on our analysis in the previous section, we should expect administration costs related to these programs to also chew up some of the benefits. In the case of market power, the result is a simple redistribution of the benefits of preferences (rents) being transferred to importers. With administration costs, however, the share of the gains that is lost is not redistributed, but is really a deadweight loss. In both cases, the trade effects of preference programs will be less as well.

# **4.2 A numerical assessment**

We now turn to a numerical assessment of the likely magnitudes involved. While there has been a great deal of political weight attached to this issue, this debate has largely been taking place in a vacuum of real information on the costs and benefits involved. Moreover, the exceptions have focused almost exclusively on the effects of preference erosion on the exports of beneficiary countries.<sup>22</sup> Our assessment uses a global, multi-region general equilibrium model of trade. The model includes 34 regions/countries and 24 sectors (Table 5). The social accounting data come from the GTAP database (http://www.gtap.org) and are benchmarked to 2001. These data include national production and international trade flows.

The import protection data are based on a thorough and careful effort to include use of preferences in a matrix of global import protection (Bouet et al. 2004). These data are the product of a joint effort between the UN International Trade Centre, the United Nations Conference on Trade And Development (UNCTAD), the World Trade Organization

> 21 See Ozden and Sharma (2004) for a similar analysis of the CBI program. Tangermann (2002) also notes that exporters often do not capture all the rents.

> 22 See e.g., IMF (2003), Alexandraki and Lankes (2004), Brenton and Ikezuki (2004). Earlier efforts at quantifying the value of preferential access for LDCs such as Ianchovichina at al. (2003) did not have access to accurate information on preferences for these countries.

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# Measuring Poverty in Forced Displacement Contexts<sup>1</sup>

Utz Pape and Paolo Verme<sup>2</sup> <u>Keywords: poverty, refugees, IDPs.</u> JEL: I30 I32.

1 This is an expanded version of the previously published short note ‘Poverty Measurement for Forcibly Displaced Populations: Challenges and Prospects of a New Field’.

2 Authors listed alphabetically. UP: The World Bank and Georg-August-University Göttingen, upape@worldbank.org. PV: World Bank, pverme@worldbank.org. The authors would like to thank Theresa Beltramo, Felix Schmieding, Ciara Silke and Christina Wieser for their helpful comments as well as Benu Bidani for overall guidance. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. This work is part of the program 'Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership'. The program is funded by UK aid and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of the UK, WBG or UNHCR.