rafmacalaba/datause-encoder-data huggingface dataset:
is acquired from the ERA-5 reanalysis dataset (Hersbach 2018). We derive estimates for the maximum and minimum temperatures at the daily level by taking the maximum and minimum of the hourly values for each day.

- **Future temperature projections from climate model simulation data:** Maximum and minimum hourly temperatures over the previous 24 hours are acquired from Earth system model simulations from the Coupled Model Intercomparison Project, phase 6 (CMIP6). We consider three shared socioeconomic pathways, namely SSP1 (Sustainability, taking the green road), SSP2 (middle of the road), and SSP5 (fossil-fueled development). SSP2 currently appears to be the most likely scenario from an emissions perspective (Pielke Jr et al. 2022). And while SSP1 and SSP5 currently appear to be unlikely, understanding climate projections under these relatively extreme scenarios provides us with a view of the full range of possibilities. Moving forward, we refer to these scenarios as “low emissions” (SSP1), “middle of the road emissions” (SSP2), and “high emissions” (SSP5). We consider projections from the following global climate models: AWI-CM-1-1-MR, EC-Earth3-VegLR, FGOALS-g3, GFDL-ESM4, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KACE-10-G, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, and NESM3 since each of these models has less than 2% missing daily values, over the full simulation period of 1979 to 2100, for each of the three socioeconomic scenarios considered here.

- **Economic data:** Gross domestic product (GDP) estimates, based on purchasing power parity, at a resolution of 30 seconds in both latitude and longitude (i.e., a spatial resolution of just under 1 km in the region of Southeast Europe) from Kummu et al. (2018). We use estimates from the year 2015 since these are the most recent available estimates.

# **Quantile Delta Mapping Post-Processing for CMIP6 Simulations**

We post-process the Earth system model simulations using a quantile mapping method, which corrects systematic distributional biases in climate model output data by removing historical biases relative to observations. Specifically, we apply the Quantile Delta Mapping (QDM) method (Cannon et al. 2015) in order to preserve the trends in the climate model simulations in such a way as to ensure that the climate sensitivity (i.e., the temperature response to greenhouse gas forcing) of the model is not affected by bias correction. QDM adjusts the distribution of simulated climate data to more closely match the distribution of observed (or reanalysis) climate data, while 8
partially effective vaccine and gains access to a mosquito net. Even with perfect data availability, it may then be dubious to say that it was either the vaccine or the mosquito net that saves her life - and it may be dubious to say that there are some numbers x and y such that her survival is x % caused by the vaccine and y % (where y = 100 – x) caused by the net. To illustrate this point, an analogy can be made with genetics. If a person with a gene that increases the chance of dying of cancer actually gets and dies of cancer, it would be wrong to think that there are some numbers x and y such that her death is x % caused by the gene and y % caused by the environment. It is the interaction between one’s genes and one’s environment that leads to her cancer/death, and there is little to be said about the quantitative extent to which her death is due to one cause as opposed to the other (Selgelid 2008:140).

The causal attribution problem is a significant threat to the viability of health impact measurement by either QALYs or DALYs (Disability-Adjusted-Life Years). In turn, it is a threat to the viability of the HIF as such. This is so because the crucial reimbursement process of the HIF is exactly premised on the idea that pharmaceutical companies are rewarded in proportion to the effect that their products have on the size of the global disease burden. The significance of the problem is further underscored by the fact that many successful medical interventions are ones that involve a number of different active ingredients (e.g. antiretroviral treatment for HIV/AIDS).

A simple prize scheme, AMCs and the HIF are not the only examples of incentivizing schemes that are currently being discussed by academics, policy-makers and members from the think tank and NGO sectors as changes/amendments to the TRIPS regime. Other such mechanisms include wild-card patent extensions (Spellberg et al. 2007; Outterson, Samora, and 32
financing high-return investment opportunities, and disciplining policies. However, they have been shown to typically fail at fulfilling these expectations. It is, thus important to check the extent to which alternative sources of foreign exchange such as remittances and ODA are, in contrast to private flows, more reliable macroeconomic tool in the hands of policymakers. We aim to improve on previous comparisons of these capital inflows by providing a description of their behavior according to additional measures to those used in prior studies.

We contribute to the literature in several ways. First, we conduct the analysis at the country-level, for a large number of countries and a wide time-frame. This is because some of the conventional wisdom on the stability of remittances both absolute and relative to ODA and FDI is based on aggregate (global) data even though policymakers are typically concerned with the behavior of these flows at their own country level. Furthermore, most empirical work focusing on the macroeconomic impact of remittances typically constrains the estimated effects to be the same for all countries in the sample.<sup>1</sup> Second, we treat stability, cyclicality and stabilizing impact separately, while most previous literature has typically equated counter-cyclicality with stabilizing impact. As elaborated in Section 2.3 and shown in Section 4, the former measure implies the latter one for most countries, but not necessarily for all. Although some studies have examined the stabilizing impact distinctly from counter-cyclicality, by estimating the impact of remittances on the variability of GDP (or GDP growth),<sup>2</sup> they tended to do so by imposing an identical relationship between these variables for all countries. In contrast, our methodology allows us to identify the behavior of the various foreign capital inflows separately for each country.

The paper is organized as follows: Section 2 reviews prior literature, Section 3 describes the data, Section 4 presents the empirical results and Section 5 concludes.

# 2. Selected Literature Review

Section 2.1 describes studies that examine the behavior of stability of remittances and other external inflows. Section 2.2 reviews research that uses statistical methods to analyze cyclicality of remittances in relation to output volatility. Finally, Section 2.3 discusses how the issue of stabilization has been approached hitherto in the literature.

> 1 One exception is Chami et al. (2005), who run regressions at country level. 2 IMF (2005), Acosta et al (2008)

3
capital, and it makes most sense for institutions with the option of raising capital by collecting savings deposits. With regard to equity, the only adjustment is an adjustment for inflation. We could go one step further by adding a few percentage points to the country’s deposit rate to account for the transactions costs entailed in collecting deposits. As a test of robustness, we instead consider the prime interest rate, the price for capital between banks and their most trustworthy customers, also taken from the International Monetary Fund database. To account for the perceived risk of lending to microfinance institutions, we conservatively added 2 percentage points to the prime rate and then re-ran the profit calculations. Not surprisingly, this higher estimate of the price of capital diminishes the appearance of profits and increases the value of implicit subsidies used by the institutions. The adjustment means that the median nongovernmental organization is no longer profit-making, though it remains that they do not lean too heavily on subsidies. Banks are much less affected by the adjustment. Much more needs to be done here; these first steps reinforce the broad arguments we made above using the benchmark MIX data: the financial performance of most of these leading microfinance institutions is remarkable from a historical perspective, but the bulk of institutions rely on subsidies and post returns that will be of interest mainly to social investors.

We leave this section with one final thought. The analysis here is “static” in a way that is somewhat unfair to the institutions. We have no way of adjusting for the fact that the institutions would likely shift strategies and re-allocate resources if their access to concessional funds and grants dries up. This is the hope of the believers in commercial microfinance: that, when pushed, institutions will be able to reduce their dependence on subsidy by economizing and becoming more efficient—i.e., that they are not as dependent on subsidies as the tables here suggest. This is only speculation, of course, and others fear that instead institutions will sacrifice part of their social missions if subsidies are reduced sharply. We frame these views in the next section.

# **The Logic of Subsidizing Micro-Finance**

The big question for policymakers and social investors is: Do the costs of subsidizing microfinance generate large, important social benefits? This question implies others. Does microfinance reach the poor in large numbers? Does it create meaningful changes in customers’ lives? Does it compare well to alternative interventions?

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# **1. Introduction**

Most firms in developing countries are informal, that is they operate without registering with the government (OECD, 2009, and data from IFC, 2010). This can pose disadvantages to firms since they may be subject to government penalties and they may not have access to low cost sources of financing, government contracts, and public contract enforcement (Jansson and Chalmers, 2001). It can also limit firms’ market size since they cannot issue formal receipts to customers (McKenzie and Sakho, 2010). From the government’s perspective, informality may result in lower tax collection, restricting the government’s ability to finance public services (Levy, 2008).

Historically, different views have been put forward to explain why many firms operate informally. One view, associated with De Soto (1989), argues that informal business owners are viable entrepreneurs who are being held back from registering their firm due to complex regulation. This regulation includes the initial procedures for obtaining an operating license, as well as ongoing compliance costs for registered firms, such as taxes and labor contributions. Another view, expressed for example by Tokman (1992), sees informal business owners as individuals who are trying to make a living while they search for a wage job.

Several papers have developed theoretical models supporting either view<sup>2</sup> . Other papers examine empirically which view is correct, leading to different conclusions (Maloney, 1999 and 2004, and La Porta and Shleifer, 2008 and 2011). Recently, some have emphasized that informal firms are heterogeneous and that a mix of both views may be correct (OECD, 2009). Selfreported statistics support this mixed view. In World Bank Enterprise Survey data on informal firms in Madagascar, Côte d’Ivoire, and Mauritius, about 62 percent of business owners report that they started their firm to take advantage of a business opportunity, while the remainder says they were not able to find a satisfactory job elsewhere (Amin, 2009). Maloney (2004) also presents similar numbers for Brazil and Mexico.

De Mel, McKenzie, and Woodruff (2010) investigate heterogeneity in the informal sector in Sri Lanka through discriminant analysis, a tool used by biologists to separate animals or plants into species on the basis of easily measured characteristics. The authors classify a sample of self-

> 2 See for example Bennett and Estrin (2009), Bennett (2010), and Straub (2005) for models supporting the De Soto view and Fields (1975) and Fields (2004) for models supporting the Tokman view.
Second, we examine whether any of the interventions changed internal and regional migration. Take-up of the Dakar travel facilitation was low, with only 16 percent of this treatment group contacting our Dakar representative, and only 12 youth meeting with them in Dakar. We find no impact of this intervention on migration to Dakar. However, the offer of Senegal as a destination appears to have stimulated migration to other parts of Senegal, and we find a 2.5 percentage point increase in the likelihood of the youth being in Senegal at the time of the follow-up survey with this treatment (relative to a control mean of only 1.6 percent), with them doing less internal migration to the capital city of Banjul and other urban areas. Take-up of the vocational training intervention was also low, with 12 percent beginning, and only 5 percent completing training, in part due to delays in implementation and closures in training facilities during COVID-19. The offer of this vocational training also led to an increase in migration to Senegal and reduction in internal migration, with similar magnitudes to the Dakar treatment.

Finally, we test for changes in irregular migration behavior. The vocational training treatment resulted in a statistically significant, but small reduction in the number of steps taken towards backway migration during this period, while there was no significant effect of information or the Dakar offer. None of the three treatments had any significant effect on attempted backway migration or on making it to Europe. Irregular migration from the Gambia dramatically dropped during our study period, with Frontex data showing only 394 irregular migrants in European border data in 2019 and 285 in 2020, compared to 8,522 in 2017.<sup>4</sup> This appears to stem from a change in government in The Gambia reducing the chance of asylum in Europe, coupled with the effects of border closures and a reduction in the desire to migrate due to the COVID-19 pandemic in the second half of our follow-up period (Bah et al, 2021). The consequence was that, despite focusing geographically on the highest migration regions and then studying the demographic group most likely to migrate, only 1.1 percent of our control group attempted backway migration in the 18month follow-up period. This limited the scope for our interventions to reduce irregular migration, since ex post there was very little to reduce.

> 4 Frontex data may underestimate the total amount of irregular migration by not collecting data on migrants who are not intercepted by border patrol, those who change their country of origin, or who do not file for asylum status upon arrival. However, even if the levels are an understatement, we believe they capture the trend in migration well. Our survey data questions on migration over time closely track the Frontex trends.

5
**Table 7. Uganda's irrigation potential, 2005**

||**L**|**arge-scale**||**S**|**mall-scale**|||**Total**||
|---|---|---|---|---|---|---|---|---|---|
||**Investment**|**IRR**|**Area**<br>**increase**|**Investment**|**IRR**|**Area**<br>**increase**|**Investment**|**IRR**|**Area**<br>**increase**|
|**Cutoff**<br>**(%)**|$million|%|**Ha**|$million|%|**ha**|$million|%|**ha**|
|**0**|1035|2.4|530,522|3203|32.0|619,916|4239|18.4|1,150,438|
|**6**|36|6.9|18,498|2926|37.0|566,165|2962|36.0|584,663|
|**12**|0|0.0|0|2300|45.0|445,041|2300|45.0|445,041|
|**24**|0|0.0|0|687|72.0|133,009|687|72.0|133,009|

_Source:_ Derived from You and others (2009).

_Note:_ Water for irrigation can be collected in two ways: through large, dam-based schemes or through small projects based on the collection of run-off from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed. The unit cost for large scale projects is set at USD3000/ha and for small scale projects at USD2000/ha.

## **Challenges**

_Uganda can maximize its potential and achieve rates of return of at least 12 percent by developing small scale irrigation projects in a relatively small area, but at a high initial cost._ Across most of Uganda’s territory, which covers over a million hectares in all, either large scale or small scale irrigation activity is economically viable (figure 16b; table 7). But if attention is limited to higher return irrigation schemes with internal rates of returns of at least 12 percent, the viable area shrinks to just under half a million hectares, but the average rate of return shoots up to 45 percent. This would be entirely composed of smallscale irrigation schemes on the eastern side of the country, as none of the large-scale irrigation schemes are capable of delivering returns above this threshold. However, the total investments needed to develop this area amount to a daunting $2.3 billion (table 7).

# Water supply and sanitation

## **Achievements**

_Developments in the water and sanitation sector have curtailed use of untreated surface water and reduced practices of open defecation_ . Use of untreated surface water halved between 1995 and 2006, falling from 28 percent to 13 percent. In fact, reliance on untreated surface water in Uganda is one-third of what is observed in low-income countries (LICs) in Africa. Similar progress was recorded in the sanitation sector. The practice of open defecation decreased from 20 percent to 12 percent between 1995 and 2006, and its rate of prevalence in Uganda is now only one-third of the average for other LICs and below the average of MICs (table 8). Most of the decrease in use of untreated surface water and reduction in open defecation was achieved between 1995 and 2001, although the rate of improvement has subsequently declined.

27
3 outcome. It is no accident that the greatest number of cost-efisctiveness and cost-benefit studies for nutrition interventions are for micronutrient projects, which have inputs and outcomes which are easier to measure and define.

More importantly, the most commonly used measure in health (cost per death-averted) is not very appropriate for nutrition interventions. It is rather an extreme outcome measure to use for nutrition interventions. Nutritional improvements have many effects other than lower mortality, such as decreased morbidity and hence decreased use of health care facilities, improved learning and ability, higher productivity and hence earnings, increased activity levels, etc. The effects on cases of severe malnutrition (both PEM and micronutrient deficiency) and hence on decreasing the probability of death, is therefore only the "tip of the iceberg". Huffman and Steel (1990) discuss what they call the "dark side of child survival", arguing that narrowly focussed health initiatives may decrease mortality but do little for the quality of life for survivors, whereas nutrition interventions have generalized effects on both morbidity and mortality. Thus an alternative cost-effectiveness measure which combines mortality and morbidity information into a measure of "healthy days saved" is probably preferable, but data requirements mean that such a measure has been only rarely used.

Using cost per death averted may make nutrition interventions compare unfavourably with health interventions. The simplest health interventions (immunization, ORT) have cost-effectiveness figures as low as $50-$100 (Stewart, 1988), whereas the lowest such figure for an actual feeding program is around $1500 (for Tamil Nadu's highly selective feeding program, in a region with very high prevalence of moderate and severe malnutrition, discussed in Ho, 1985). Estimates for prenatal maternal supplementary feeding are lower ($187 per death averted converted to 1988 dollars, for Narangwal, India). Many other health initiatives (e.g. water and sanitation projects, vaccines against cholera and rotavirus) have cost-effectiveness figures also around $1500 (Stewart, 1988). Nevertheless, it is clear that on a cost per death averted basis, interventions involving feeding do not
Policy Research Working Paper 8393

# **Abstract**

Interest rate caps can have far-reaching consequences on the composition and maturity of commercial bank loans and deposits. This paper carefully documents these impacts on the formal financial sector in Kenya after the recent interest rate caps of 2016. Using bank-level panel data from before and after the caps, the paper identifies a significant decline in aggregate lending, an increase in nonperforming loans, and a change in composition of lending away from small and medium enterprises and toward safer corporate clients. Banks also shifted away from offering interest on current account deposits to preserve their interest margins. These quantitative findings are supported by qualitative evidence through detailed interviews of commercial bank executives, and have important implications for economic growth and financial inclusion.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at bzia@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team