rafmacalaba/data-use-mentions-extended huggingface dataset:
br>IEs<br>0 50 100 150 200 250 300<br>Clients Healthcare providers Health system managers Data services<br>Others<br>Knowled ge and beliefs<br>Health status (aggreg ated/su mmary units)<br>Health status (natural units)<br>Quality of care<br>are n<br>Healthc utilisatio<br>utic)<br>Process outcome (therape<br>(non utic)<br>Process outcome therape<br>on<br>Client or provider satisfacti<br>ur<br>Behavio change<br>c s<br>Economi outcome<br><!-- End of picture text -->

**Figure 3 Outcomes reporting from the DHI Evidence base by intervention type**

# 3.1.6 Economic outcomes

Cost data for the interventions are infrequently reported. About 80% (n=632) of the studies did not report any cost data and out of those that reported more than half simply provided cost data without performing further analysis such cost-effectiveness analysis. A majority of
- **Karolyi, G Andrew and Ren ́e M Stulz** , “Are financial assets priced locally or globally?,” _Handbook of the Economics of Finance_ , 2003, _1_ , 975–1020.

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Line|10.92%|0.022|0.007|
|c. $5.50 International Poverty Line|40.58%|0.121|0.050|
|3. Household consumption minus Samurdhi payment plus||||
|Rs.1732 (all per capita) for eligible households identified by<br>the PMT formula||||
|a. National Poverty Line|1.63%|0.003|0.001|
|b. $3.20 International Poverty Line|5.50%|0.009|0.002|
|c.$5.50 International PovertyLine|37.01%|0.087|0.030|

Note: The poverty line for this exercise is set at Rs. 4,166 per person per month, the official national poverty line in 2016. The international poverty line is in 2011 PPP dollars per person per day converted to 2016 Rupees. The α refers to the parameter in the FGT index. Analysis based on HIES 2016. Scenario 3 allows for some amount of mistargeting since the redistribution is based on the PMT formula. The $3.20 and the $5.50 lines refer to the thresholds for Lower Middle‐Income Countries and Upper Middle‐Income Countries, respectively, according to World Bank definitions.

The last three columns of Table 5 present the following FGT measures for the scenarios we examine: poverty headcount index (α=0), poverty gap index (α=1), and poverty severity index (α=2). We see that the poverty headcount rates set at the national poverty line and the $3.20 line are 4.08 percent and 9.73 percent, respectively, in the baseline scenario. In this scenario, households report receiving Samurdhi benefits in the HIES 2016. In the second scenario, we remove the Samurdhi payments that households may have been receiving. As a result, we see that
Policy Research Working Paper 8605

# **Abstract**

This paper intends to inform the effort of the Sri Lankan government to reform the targeting efficacy of its social protection programs, in particular, Samurdhi, which currently distributes benefits based on self-reported income. The paper develops a proxy means test for Sri Lanka based on the Household Income and Expenditure Survey 2016 and evaluates its performance for targeting benefits of Samurdhi. The paper considers a range of models and policy parameters that could be applied depending on data availability and country preferences. The results indicate that switching to a proxy means test could considerably improve the targeting performance of Samurdhi and would significantly improve the poverty impact of the program. The analysis finds that the performance of the proposed proxy means test model suffers when the coefficients are estimated from samples smaller than 1,000 households. However, the analysis does not find a similar loss of model performance when the model is estimated from seasonal data, provided the sample size is sufficiently large. The proposed model could be applied to targeting a variety of safety net programs after validating and refining the model by conducting a pilot survey.

This paper is a joint product of the Poverty and Equity Global Practice and the Social Protection and Jobs 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/research. The authors may be contacted at twalker@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
cannot address this problem in the same way as described above in the case of refugees and natives. Instead, we choose to drop the age at arrival dummies from the estimating equation.

A final issue concerns controls for education levels. In our main sample, which is drawn exclusively from administrative data, we do not observe individuals’ education levels, as mentioned above. However, for a subsample of natives and refugees, we are able to match the administrative dataset with data on education levels from the Swiss Structural Survey. Therefore we estimate all equations on the two samples: first, by using the main sample without education variables, second, by using the restricted sample, taking dummy variables for education levels into account. We also check if the restricted sample can be considered a random sub-sample of our main sample by estimating the equation using

> 23. To facilitate the interpretation of the estimation results, we do not drop a reference category for the _YSMitj_ dummies when we interact them with the refugee dummy _Ri_ . For this reason, the _Ri_ dummy itself does not appear in the equation since it is perfectly collinear with the interaction terms. 24. The comparison between natives and migrants is based on the same equation as (1), except that the refugee dummy is replaced by a dummy for migrant status.

13
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136. Second, the historical focus of both policy makers and researchers on manufacturing seems misplaced. The spatial, time series and consumption data uniformly point to commerce and services as the key growth sectors over the course of the coming rural structural transformation. Some manufacturing activities will grow, particularly tailoring, carpentry and metal work. But the exclusion of services and commerce from many promotional schemes on the grounds that they are unproductive is unfortunate. Services in particular are the best insulated from competition from urban areas. Service and commercial enterprises require credit, management and technical assistance, as well as access to key infrastructure if they are to attract the clientele commensurate with their potential.

137. Third, rural towns may act as a focal point for the development of the rural non-farm economy. For them to play their role in a balanced rural development process it will be essential for them to be assured of adequate economic and social infrastructure to support emerging modern non-farming activities. Electricity, telegraph and telephone communications, water and roads will all be important. Fourth, and closely related to the above, key institutional infrastructure will be essential in fostering the transition to a more specialized productive rural economy. Rural financial markets will be of particular importance in this context.
Following the literature on within country migration, we use the share of the population that had arrived in destination j more than 5 years ago that was born in state of origin i as a proxy.

<u>Population: is the population by state in 1995 as reported by the Census. As Greenwood</u> (1997) summarizes, population is often used as a measure of the availability of public goods. However, it is also true that larger states offer more “connection points” than small states and will hence, in a random reallocation, attract more migrants. Shultz (1982) is also correct in arguing that larger states may have smaller rates of out migration simply by virtue of having more places to migrate to within. The 1995 value is used to eliminate any problems of simultaneity with migration flows that happen in subsequent years.

<u>Labor Market Variables:</u> The unemployment and nominal wage variables are generated as the average of their quarterly values in 1995, 1996 and 1997 from the National Urban Employment Survey (Encuesta Nacional de Empleo Urbano- ENEU).

<u>Cost of Living:</u> Though presumably the potential migrant should be focusing on real as opposed to nominal expected wages, the actual deflator used may not be that of the current place of work. For instance, if a migrant plans to return to a low cost area to retire, he may generate real savings measured in his retirement destination faster by earning a lower real wage in a high cost area (See Lucas 1997 for a survey of this literature). Further, high cost of living may point to a larger potential income over the long run even if not experienced even by the individual making the decision (Spencer 1989, Pagano 1990). Certainly, in an intertemporal or intergenerational context, taking a lower real wage in the US is still likely to offer the migrant’s descendents far better options than would have been the case in Mexico.<sup>5
0.239|0.484|0.490|0.468|0.239|0.484|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Notes: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university or teaching institute, or has master. _Respondent young_ is a dummy that takes value 1 if the respondent’s age<=32 and 0 otherwise. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master. _Respondent high educated_ is a dummy that takes value 1 if the respondent has completed primary education and 0 otherwise. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master. _Work experience as enumerator_ is the number of months previously worked as an enumerator. _Respondent poor_ is a dummy that takes value 1 if the value of the asset index for the household is lower than the mean value of the asset index for the population and 0 otherwise. Each regression includes as additional control variables: _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ . Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0
# _Variables in the “Small Model” (Largest Availability)_

**Inflation** is measured as the log change of _v_c/q_c_ (household consumption at current national prices / household consumption at constant national 2017 prices) from the national account module of PWT10.0, which has larger availability than inflation data from WDI. We add 1 to this variable to avoid negative numbers and then take logs to obtain _linflation_na_ .<sup>15</sup>

The **real exchange rate** , _lrer_ , is calculated as the log of the GDP price level (at purchasing power parity) over the nominal exchange rate: _pl_gdpo/xr_ , both taken from PWT10.0. Since _xr_ is measured as national currency/US$, an increase in _lrer_ reflects a real appreciation, which is expected to negatively affect output and growth through various channels (e.g., Rapetti, 2019; Levy-Yeyati et al., 2013). We also calculate (absolute) deviations of the real exchange rate from a HP-filtered trend, _mad_lrer_deviation_ , which is included in the data set as alternative, but not included in our models.

**Trade openness** is conventionally captured as the sum of exports and imports over GDP. To account for higher trade shares of smaller countries and higher trade in times of higher demand from abroad, irrespective of trade policies, we calculate a _trade_resid_ as the residual of a regression of exports and imports over GDP on log of exporter population and global GDP, which acts as demand shifter. We then take the log of that residual which first requires transformation to positive values. This is done as: _ltraderesid_ = ln(trade_residmin(trade_resid)+0.01). All respective variables are taken from PWT10.0.

**Infrastructure** is captured by an index, as often is the case in the literature (surveyed in Välilä, 2020). We therefore combine information on phone lines, mobile phones, internet coverage, availability of secure
Tackling Gender Discriminatory Inheritance Law Privately: Lessons from a Survey Experiment in Tunisia Christina Sarah Hauser<sup>∗</sup> JEL classification: J16, N37, D31, K37 Keywords: gender discrimination, inheritance, Islamic law, information experiment

> ∗Department of Economics, European University Institute (EUI), Via delle Fontanelle 18, 50014 Fiesole, Italy. Email: christina.hauser@eui.eu. I gratefully acknowledge the financial and analytical support from the Office of the Chief Economist for the Middle East and North Africa (MNACE; TTLs: Nelly Elmallakh and Nazmul Chaudhury) under the regional Labor and Gender Research Programs of the World Bank. This research also benefited from a LEAP (Laboratory for Effective Anti-Policy Action) Student Grant and an Early Stage Research Grant of the European University Institute. IRB approval was obtained from the Ethics Committee of the European University Institute in December 2022. The study was preregistered at the AEA RCT registry on 9 January 2023 (AEARCTR-0010343). I am indebted to One-to-One for Research and Polling for superb data collection and to Friedrich Ebert Stiftung for hosting me during my research stay in Tunis. I wish to thank Jihen Abbes, Sule Alan, Haroun Ben Youssef, Tom Crossley, Souleima Dhaya, Leyla Hassen, Zeineb Mazouz, Youssef Meddeb, Imen Mezlini, Dietlind Stolle, Alessandro Tarozzi, Abir Tayari and Weiwei Zheng for helpful comments and advice. Any mistakes are my own.
mortality, river pollution, and other variables to be used as controls. In most of our analysis, we restrict our sample geographic area to the Ganga Basin, which is defined as the area that drains the Ganga and all of its tributaries. We make this restriction because of the singularity of this area in the context of our analysis. The Ganga Basin is not only a much more densely populated region than anywhere else in India, but also a region in which water issues have received special government attention: the National River Conservation Plan (NRCP) focused exclusively on the Ganga Basin (including the Ganga, Yamuna, Damodar, Gomti, and Mahananda Rivers) throughout its first 10 years (1986-1995). Extending the analysis beyond this region might confound the effect of the Supreme Court ruling with the effect of broad scrutiny in the Ganga Basin during this time period.

6
10 The Household Budget Survey (POF) is nationally representative and has extensive and detailed information on consumption, expenditure, and income. We use the 2002/2003 round, which is the only pre-shock detailed survey with the appropriate information for our analysis, and which surveyed 48,470 households throughout the country. The 2008/2009 round is post-crisis, and therefore unsuitable for the purpose of estimating the compensating variation of the 2007-2008 price change. The welfare measure is household per capita consumption expenditure, calculated excluding pensioners, household workers, and relatives of household workers. The unit of analysis is the individual. The concordance between food groups in the INPC and POF classifications works extremely well at the level of the 16 food groups, with 97% of total household food consumption expenditures in the 2002-03 POF matched to these groups.

Although the POF records some income information for household members as well, it does not include detailed data on two variables that are important for our estimation of the market and transfer income effects of rising food prices. The first variable is a disaggregation of the type of activity exerted by workers, with detailed information on the kind of crop or agricultural production performed by individuals reporting positive incomes from that sector. The second is information on the receipt of social assistance transfers. Both of those variables are therefore obtained from the National Household Income Survey (PNAD), an annual, nationally representative survey of income, employment and living conditions. Since the 2007 survey, fielded in September, would already have been contaminated by rising food prices, we use the 2006 wave. The PNAD survey lacks a comprehensive measure of expenditure, but is thought to capture wage incomes comprehensively, and it contains information on whether households are beneficiaries of social programs, specifically Bolsa Família and BPC. The method used to combine information from these different, unmatched surveys is explained in the next section, which describes our analytical framework and empirical approach.

# **4. Analytical Framework and Empirical Approach**

The conventional starting point for an
Federal funding also supported the "professionalization" of the practice of evaluation. From this profession came the methodological modifications that enabled evaluation practice to move more closely to the needs of decision-makers, and away from large scale national research.

The administrative structure for M&E that has emerged is complex, with federal standards and guidelines, coordination and implementation from state agencies, and implementation at local levels. Staff support for the function has expanded at all levels. Vertical coordination has been establis1hed by mandated reporting requirements.

These requirements, as well as the minimum standards for student achievement established at the state level and criteria for accreditation have increased the specificity of the work of educational evaluators, district super<sup>4</sup> nt^ndents, headmasters and teachers.

At the same time, it is important to note the failure of the Vocational Education Data System in the first half of the 1980s. An overly
(iii) Interestingly, the strongest dominance is attributed to human capital. This appears conflicting with the conclusion from several growth regressions that stress the statistical insignificance of this variable, which also cannot be distinguished from a 0-correlation in our regression. But statistical insignificance can be the result of poor measurement of that variable, which has been pointed out in the literature (Pritchett 2001; Hanushek et al. 2017) and should not be confused with economic irrelevance (that dominance criteria aim to measure). More sophisticated measurement approaches usually confirm the importance of human capital for growth (e.g., Hanushek and Woessmann 2012; Maloney and Valencia Caicedo, 2022). In line with the potential problem of measurement error (which biases estimated coefficients towards 0), the standardized beta coefficient suggests a more modest economic relevance than the dominance statistics. This may also reflect that human capital is usually quite persistent and shows little variability over time, which lowers the standardized beta coefficients by construction (see previous footnote).

(iv) To a lesser extent, FDI and credit/GDP provide a good fit for income per capita levels in the sample but only show modest partial correlations with the latter (which cannot be distinguished from 0 in a statistical sense, except for FDI in the medium model, see Online Appendix A.2).

(v) Conversely, trade diversification,<sup>21</sup> the exchange rate, political violence, trade openness, and inflation show relevant partial correlations with income but are attributed a low dominance. In the medium model (see Online Appendix A.2), the partial correlation further increases for trade openness and financial crises but declines for the exchange rate.

Our overall reading of those results is that: _(i)_ they confirm the potentially important role of infrastructure for growth, which the recent literature has emphasized, _(ii)_ typically insignificant parameter estimates of human capital in growth regressions shall not lead to the conclusion that education does not matter,
in determining a household’s consumption level. In this context, controlling for ethnicity is especially important as poverty is increasingly concentrated among ethnic minorities, often located in Vietnam’s mountainous areas. While productive assets affect the capacity of a household to generate income and thus its consumption level, current asset levels may be simultaneously determined with consumption, as the disposal of assets (e.g. livestock) may be relied upon to cope with disasters, or they may reflect destruction by the disasters, leading to an underestimate of the welfare effects of the latter. In the absence of information of asset ownership prior to the survey, only those assets that display limited variation over time were included such as the amount of land owned and an indicator variable indicating whether the household owns a house or not.

30 Communes (the primary sampling units) were selected in the first stage with a probability proportionate to population size based on the 1999 Population census. On average, a commune contains 1,600 households. Subsequently, three enumeration areas (EAs), containing about 100 households each, were randomly selected within each commune. One of these EAs would be used to draw a sample of households in each VHLSS during the final (third) stage. In, 2002, this resulted in a sample of 29,530 households across 2,910 communes (EAs) (25 households per EA in 75% of the EAs and 5 in the remaining 25%). In 2004 and 2006, three households were selected per EA, resulting in 9,189 households each year in 3,061 communes. Phung and Nguyen (2008) provide a detailed description of the questionnaire and survey design.

31 0 refers to no degree, 1 to primary school, 2 to lower secondary school, 3 to higher secondary school, 4 to short‐term technical worker, 5 to long‐term technical worker, 6 to professional secondary school, and 7 to college diploma and above.