rafmacalaba/data-use-mentions-extended huggingface dataset:
a monetary welfare measure is used, such as consumption expenditures per adult equivalent). RR ββ̂ The extent to which hazards experienced by households in the sample are associated with sufficiently significant declines in their welfare depends in part on the degree to which there is variability in the exposure to and intensity of the shocks experienced. For example, if the value of k is the same for all in the sample and not just for those within a given cluster, then there is no way to identify the parameter 2 . Hence, it is critical to derive estimates of 2 based on data that include significant variability in RRk . This task is a particular challenge when analyzing the impact of drought because these events are spatially correlated and not localized. Increasing the number of years ββ and the number of countries included increases the variation in ββ̂ RR k . For this reason, the analysis prioritizes inclusion of countries where multiple rounds of survey data were available. In addition, by estimating this relationship at the level of the livelihood zone and not the country, the analysis increases the number of years and RR variation in hazards available.

Based on the general but unknown function _f_ , the effect of exposure to the hazard on welfare depends on the specific values of the determinants of welfare summarized by the variables k , and , i.e., <u>hk</u> = k d <u>( k hk hk )</u> ddWW k ~~.~~ The linear specification in regression equation (2) assumes a constant effect of the hazard on RR , XXhkk EEhkk ddRR RR =rr,XX =xx,EE =ee welfare (i.e., ddRR <u>hk</u> = 2 ) that is independent of the level of the hazard and of specific household characteristics k ddWW and environmental factors . It is important to relax this assumption in two ways: first, by allowing the ddRR ββ impact of the hazard to vary by the size of
In column 1 we include the measure of distance to major cities and the interaction with the homeland indicator. Both covariates are statistically significant. The closer a community is to a major city, the stronger is its growth in income. This effect is even more pronounced for communities located inside homeland areas. A potential concern is that this measure might not properly account for heterogeneity in transport infrastructure across regions. To account for this, in column 2 we include distance to main roads and its interaction with the homeland indicator. We observe that these measure are not statistically distinguishable from zero. Finally, column 3 includes both measures together and the results remain unaltered. These results provide further evidence supporting the importance of market access for economic growth. Furthermore, notice that the estimates for our variables of interest remain unchanged throughout.

> 27In order, the cities are Cape Town, Durban, Johannesburg, Soweto, Pretoria, Port Elizabeth, Pietermaritzburg, Benoni, Tembisa, and East London.

19
# **Figure 1**

# **Annual and Quarterly GDP Growth Rates for China**

# **and Other Major Economies, 2000–09**

_(percent)_

# _6.1a Annual GDP Growth Rates, 2000–08_

<!-- Start of picture text --> 14<br>12<br>10<br>China<br>8 India<br>World<br>6<br>United States<br>4 EU<br>2 Japan<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>-2<br><!-- End of picture text -->

# _6.1b Quarterly Annualized GDP Growth Rates, January 2008–June 2009_

<!-- Start of picture text --> 15<br>10<br>China<br>5 India<br>United States<br>0 EU<br>2008 Q1 2008 Q2 2008 Q3 2008 Q4 2009 Q1 2009 Q2<br>Japan<br>-5<br>-10<br><!-- End of picture text --> _Source_ : IMF 2009. Source: IMF 2009.

19
Technology may help with these issues.

First, technology can help to identify and value properties. In cases where there is no cadaster, satellite images may be used to produce a map of buildings, with additional ground-truthing to populate information on property characteristics. Traditional valuation approaches such as those based on construction costs are costly and time consuming. In other settings, property owners are asked to self-declare their property value but this is subject to manipulation. Alternatively, a fixed fee may be imposed on each dwelling, but this approach is likely to be regressive. Computer assisted mass appraisal methods that use statistical models to assign an estimated market value to property using data on property characteristics (location, size, amenities etc.) and sale price from recent transactions are widely used in middle- and high-income countries, but in many African contexts with weak property markets, the required data on recent purchase prices are often not available.

An alternative approach aided by technology is a simplified “points-based” system recently adopted by the Freetown City Council in Sierra Leone (Fish, 2018), with support from the International Growth Center, the International Centre for Tax and Development and the UK government (Grieco et al., 2019). The project involved the city’s authorities using satellite imagery to identify and measure properties in Freetown. They then deployed teams to collect data on easily observable characteristics such as the quality of walls, roofs and windows. Following this, they used existing data on rental values to build a simple model for calculating the taxable value of each property. This approach almost doubled the number of properties on the tax register from 57,000 to about 110,000. Crucially, the new system produced much higher tax bills for the most valuable properties, owned by the wealthiest taxpayers, which have been historically under-taxed, thereby increasing the progressivity of the tax system (Kamara, Meriggi and Prichard, 2020).

A similar example comes from Senegal where Knebelmann, Pouliquen and Sarr (2021) report that, at most
# **4.3 Timeline and Data**

## **4.3.1 Study Timeline**

Figure 2 shows the study’s timeline and data collection phases. In the three study states, the MNM campaign was live for almost 5 months, from August 19th 2020 to January 6th 2021. To assess its effectiveness, we recruited two separate survey samples over distinct periods: (i) a longitudinal (experience) survey from early September 2020 to end of January 2021 to gauge effects _during_ the campaign; and (ii) a cross-sectional sample from January to late March 2021 to study accumulated and medium-term effects _after_ the campaign had ended. As outlined in Section 6, we also carried out an additional survey wave in April 2021 as part of an individual-level _feed_ experiment, aimed at distinguishing between limited effectiveness and limited reach of the ad campaign for vulnerable populations. Finally, we augmented these survey samples with administrative health facility records covering the period from four months prior to the campaign’s initiation (April 2020) to five months after its completion (May 2021).

Figure 2: Study Timeline <!-- Start of picture text --> Administrative data: recorded incidence<br>Preventative/response behaviors Self-reported incidence Analysis of<br>during campaign after campaign mechanisms<br>Longitudinal sample: Longitudinal sample: Cross-sectional sample: Individual-level<br>Recruitment & Baseline Endline Recruitment & Survey experiment<br>Experience surveys<br>(8 waves)<br>August 2020 January 2021 March 2021 April 2021<br>Ad campaign is live<br><!-- End of picture text -->

## **4.3.2 Survey Samples and Administrative Data**

During the campaign, the longitudinal (experience) survey assessed malaria-related preventive behaviors (such as bed net usage) and response actions (such as seeking medical care when experiencing fever symptoms). This survey involved a baseline questionnaire (before or at the start of the campaign
# ANNEX 3. Outcome Measures

## Primary Indicators

**Depression and anxiety:** The primary outcomes are the prevalence and intensity of depression and anxiety symptoms, as measured using the Patient Health Questionnaire Anxiety‐Depression Scale (PHQ‐ADS), which combines the 9‐item Patient Health Questionnaire (PHQ‐9) and the 7‐ item Generalized Anxiety Disorder (GAD‐7) scales (Kroenke and others 2016; Chilcot and others 2018). The PHQ‐9 incorporates Diagnostic and Statistical Manual of Mental Disorders (DSM)‐ Version IV depression diagnostic criteria with other major depressive symptoms (Kroenke and others 2001). Participants rate their responses on a 4‐point Likert scale ranging from ‘not at all’ to ‘nearly every day’ (0‐3), with the score ranging from 0 to 27. It measures whether a person had difficulty in sleeping, felt hopeless/down, felt tired, had trouble falling asleep, had poor appetite, felt bad about him/herself, had difficulty concentrating, had been slow or restless, and had suicidal thoughts. The GAD‐7 is based on the DSM IV diagnostic criteria for generalized anxiety disorder and has 7 items (Spitzer and others 2006). As with the PHQ‐9, each item is scored on a 4‐point Likert scale ranging from ‘not at all’ to ‘nearly every day’ (0‐3), with the score ranging from 0 to 21. The GAD‐7 measures whether a person has not been able to control worrying, has trouble relaxing, feels nervous and on the edge, is easily irritable, feels afraid that something terrible might happen, etc. The PHQ‐ADS score is calculated as the sum of the PHQ‐9 and GAD‐7, with the total score ranging from 0 to 48. Higher scores indicate higher intensity. The cutoff scores of 10, 20, and 30 indicate mild, moderate, and severe levels of depression and anxiety, respectively
transition probabilities is small compared to the difference in unemployment rates between these regions. In Latgale unemployment was 18 percent during this time, while in the Riga and Zemgale regions it was 11 percent.

From the evidence, one may conclude that Latgale suffers primarily from a low rate of job creation and skills mismatch among the long-term unemployed, rather than from a high rate of job destruction. On the other hand, the predicted overall rate of exit from employment (both to unemployment and inactivity) is highest for Vidzeme. Combined with the relatively high proportion of recently discouraged workers there, these findings point towards emerging problems in the Vidzeme labor market. For a summary of districtspecific information on employment transitions, see Appendix Table 21.

**The rate of job destruction is significantly higher in cities** . About 4 percent of employed urban residents aged 20 to 54 transit from employment to unemployment within a year.  In the countryside this proportion is about 2 percent (see Appendix Figure 15). Because some individuals commute to work, differences would be smaller if measured by job location rather than residence. However, information on the location of an individual's job in 2001 is not available in the LFS 2002. Interestingly, there is no difference between urban and rural areas in terms of predicted rates of exit from the labor force.

**Labor force transitions by employment status and occupation.** Results displayed in Appendix Figure 16 suggest that the self-employed are significantly less likely to become unemployed or to leave the labor force than otherwise similar employees. Individuals in white collar occupations are also relatively unlikely to make transitions to unemployment or to non-participation, in comparison with semiskilled and unskilled manual workers and market and sales workers. Skilled manual workers are similar to other blue collar workers as regards the risk of becoming unemployed, but more similar to white collar workers as far as the probability of leaving the labor force is concerned.

**Inter-sectoral differences** . There are significant differences between sectors of the economy in terms of job
33 are absent on the day of the visit, the number of classrooms, and the share of classrooms where the survey team could see teaching activity. In addition, the household survey asks respondents to give their assessment of how active the Parent-Teacher Association is. Although all schools report having a PTA, households vary significantly in the degree to which they report PTA activities aimed at improving schools. Since community action to secure greater school inputs would likely occur through the PTA, this is a plausible proxy for collective action.

To see if these inputs were significantly higher in villages with greater access to community radio, we estimate the following, , where the control variables are the same as in the base specification (column 2) of Table 3. Table 6 displays the main results of the estimation. Community radio access has no effect on any inputs. Tangible inputs into education (teachers, classrooms and books) are no different across villages with less or more access. Government oversight of the deployment of those resources, measured by teacher absenteeism, is also no different. Access seems to have no effect on collective action: perceived levels of PTA activity are unrelated to access to community radio. The PTA result does not change when using an estimate of PTA activity based only on the perceptions of households with children in school. In addition, there is no correlation between village radio access and the proportion of households reporting specific PTA participation in the recruitment of new teachers, monitoring of teacher presence in the classroom, conflict resolution, school supervision, or the collection of contributions.<sup>12</sup> These results stand in contrast to earlier research on transfer programs, where access to media did affect government behavior.

12 These results are not reported here in the interest of brevity, but are available upon request.
, exporter-sector-time, and pair-sector fixed effects.

Panel (A) includes data for the three broad sectors of agriculture, manufacturing, and services and the years 2000 to 2015. When using all provisions of the World Bank’s DTA data to determine the depth of trade agreements, we find that deep agreements increase overall trade by 25% while shallow ones and agreements notified under the Enabling Clause do not activate trade (column (1)). The size of the effect is very standard in the literature. The size of the sample used to obtain these results is large: _N_ = 474 _,_ 312 sector-pair combinations. If one accounts for ad valorem tariffs from the standard WITS data set in the specification, the sample shrinks to _N_ = 360 _,_ 522 observations as there are many missing values.

Specification (2) suggests that deep TAs continue to remain statistically significant determinants of bilateral trade, with a trade-creating effect of 16%. Shallow TAs and the enabling 13
Figure A3. The Progression of Severity: Lower Cases <!-- Start of picture text --> Argentina Bolivia China Cameroon<br>10.0<br>2<br>7.5<br>3<br>5<br>5.0<br>2.5 11<br>18<br>0.0<br>Colombia Czech Republic Algeria Greece<br>10.0<br>2<br>7.5<br>3<br>3<br>5.0<br>7<br>2.5<br>0.0<br>Philippines Poland Romania Russian Federation<br>10.0<br>7.5<br>5.0<br>6 3<br>7 4<br>2.5<br>0.0<br>Apr 1 May 1 Apr 1 May 1 Apr 1 May 1<br>Saudi Arabia<br>10.0<br>7.5<br>5<br>5.0<br>2.5<br>0.0<br>Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

32
). In poor countries with weak institutions, such as postconflict countries, decentralization may worsen poverty (Jütting et al. 2004). The effects on the poor have also been tied to the sequencing and pace of fiscal, administrative, and political decentralization (von Braun and Grote 2009). Some studies find that decentralization has an equalizing impact (Sepúlveda and MartinezVazquez 2011; Tselios et al. 2012), while others show the opposite (Sacchi and Salotti 2014).

This paper aims to contribute to the literature by identifying the causal effects of a fiscal transfer fund on social welfare. To explore the distributional effects of fiscal federalism, the paper examines the case of a large earmarked social infrastructure fund in Mexico, the Social Infrastructure Transfer Fund ( _Fondo de Aportaciones para la Infraestructura Social,_ FAIS), equivalent to 2.53 percent of “assignable federal tax revenues” ( _Recaudación Federal Participable)_ .<sup>1</sup> Estimating the effects on poverty and inequality of earmarked funds is relevant in numerous developing economies, including those in which a large share of the funding of subnational governments is generated by the taxation of national resources. This paper exploits a novel dataset of 20 years of municipal poverty maps by combining the dataset with local public finance variables. The dataset also includes information from the population and establishment censuses to proxy local economic activity. The results are important for identifying who benefits within municipalities and which part of the distribution is affected and for differentiating effects between urban and rural areas, as well as in terms of the monetary and nonmonetary components of welfare.

> 1 These “assignable federal tax revenues” correspond to taxes collected from federal income tax, value added tax, and ordinary taxes and fees from oil extraction and production. In practice, with the exception of extraordinary fees from oil and other extraordinary fiscal revenues by the federal government, most of the federal taxes are subject to sharing with subnational governments through earmarked and unconditional fiscal transfers.

2
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# Technical Notes

## Population labor and Emoloyment

_Population and Labor Force:_ Population data are based, in most cases, on a de facto definition. Refugees not permanently settled in the country of asylum are generally considered to be part of the population of the country of their origin.

The total labor force consists of the 'economically active' population - a restrictive concept that includes the armed forces and the unemployed but excludes homemakers and other unpaid caregivers.

_Participation_ **_rate:_** This concept refers to the percentage of the population that is economically active.

_Unemployment:_ This concept is in accordance with the Resolution concerning statistics of the economically active population, employment, unemployment and underemployment adopted by the Thirteenth Intemational Conference of labor statisticians (1982). It comprises all persons between the ages of 15 and 64 who, during the reference period, were without work, currently available for work and seeking work.

There are, however, ample differences in the way each individual country treats this definition when compiling its own statistics. In a number of cases, national definitions of unemployment differ from the recommended standard definition. Differences relate to the age limits, treatment of persons temporarily laid off, and of persons seeking work for the first time.

Furthermore, intercountry comparisons are also affected by the variety of types of sources used to obtain information on unemployment and the differences in scope and coverage of such sources. In this study, the data will relate to the entire country in question. Rural areas, however, have tended to be less well covered than other areas in unemployment statistics.

In general, there are four main sources of unemployment statistics which may be distinguished.

- Labor force sample surveys or general household sample surveys generally provide very complete statistics on unemployment since, in particular, they include groups of persons who are not covered in unemployment statistics by other methods.

- Social Insurance Statistics which come from compulsory unemployment insurance schemes, which as a rule have broad industrial coverage and relate to
Following existing international banking studies, we capture the potential for _scale economies and profit opportunities_ in two ways. First, we include the log of constant dollar GDP. Second, we include the growth of GDP as a measure of growth opportunities in the country. Economic growth may also be interpreted as a measure of default risk, since in countries that do not grow, borrowers might have a harder time repaying their obligations to banks. Data on both of these variables come from the World Bank _World Development Indicators_ or the IMF _International Financial Statistics_ , depending on the country.

In international banking, pricing risks materialize when the value of the claims or return on the claims held by foreign banks declines as a result of exchange rate or other price changes. We measure price risk by controlling for inflation in our estimations.<sup>14</sup> Data on inflation comes from the _World Development Indicators._ While both cross-border and local foreign claims are exposed to price and default risks, local claims are also subject to expropriation risks. We capture the extent of expropriation risks by including a measure of property rights in the host countries. This measure is an index compiled by the Heritage Foundation and reported as part of the _Index of Economic Freedom_ . Higher levels of this index indicate worse property rights in the country. In some estimations, we replace the measure of default, price, and expropriation risks for one composite country risk measure as collected by the International Country Risk Guide (ICRG) in its _country risk index._<sup>_15_</sup> We estimate the following equation to investigate the implications for financial stability of the mix (cross-border versus local) of foreign claims: _Foreign claims volatilityi,j_ =γ0 + γ1 _Share of local claimsi,j_ + γ2 _Growth volatility,j_ + γ3 _Price volatilityj_ + γ4 _Volatility of claims to other countriesi,j_ + γ5 _Banking crisis indicatorj_ + μ _i,j_ (2)

> 14 Results remain unchanged if we used the change in the exchange rate
Policy Research Working Paper 8846

# **Abstract**

The low take-up of cost-effective and highly subsidised preventive health technologies in low-income countries remains a puzzle. One under-studied reason is that the design of subsidy schemes is such that households remain financially constrained. This paper analyses whether, and how, micro-finance supports a large public health subsidy program in the developing world—the Swachh Bharat Mission—in achieving its aim of increasing uptake of individual household latrines. Exploiting a cluster randomised controlled experiment of a sanitation micro-finance program that coincided with the launch of the SBM program, and unique survey data matched to administrative data, findings reveal that the complementarity runs on two levels: First, micro-credit allows households officially ineligible for the subsidy to invest in sanitation by alleviating credit constraints. Second, micro-credit also helps subsidy eligible households to overcome short-term liquidity constraints induced by the remuneration-post-verification subsidy design to invest in sanitation. Subsidy eligible households living in areas experiencing large delays in subsidy disbursement, or high toilet costs, are more likely to take a sanitation loan, but less likely to use the loan to construct a toilet.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at bet_c@ifs.org.uk.

_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