rafmacalaba/data-use-mentions-extended huggingface dataset:
|**Table**<br>H0|**9.**Test of validity and w<br>Statistic|eakness of instrument<br>MENA|s<sup>+</sup><br>OECD|
|---|---|---|---|
|Instruments are valid|OIDR<br>p-value|2.82<br>0.42|0.54<br>0.46|
|Instruments are weak|CDME<br>Critical value|9.60<br>4.32|27.27<br>5.44|

+ OIDR: Overidentifying restrictions test statistic (Anderson and Rubin, 1950); CDME: Cragg and Donald (1993) minimum eigenvalue statistic (Stock and Yogo, 2005); Critical value is the highest critical value relating to the LIML size of nominal 5% Wald test (Stock and Yogo, 2005).

# **_PSM robustness check_**

Let us now go one step further in the investigation of the effects of Provision and Protection on Participation by performing a propensity score matching (PSM) analysis (Rosenbaum and Rubin, 1983; Abadie and Imbens, 2006, 2011, 2016; Dehejia and Wahba, 2002, Imbens and Wooldridge, 2009), which is known to be an alternative approach to IV for handling endogeneity. PSM is also known as efficient in solving self-selection problems.<sup>46</sup> PSM allows one to analyze the impact of a treatment on an outcome by reconstructing the original dataset within the framework of a quasi-experiment. Indeed, two groups of individuals are defined, namely, a treated group and a control group. Then, a matched sample is constructed based on "propensity scores," that is, the probabilities that the individuals receive the treatment given a set of observed covariates. As stressed by Rosenbaum and Rubin (1983), propensity scores are balancing scores in the sense that
MENA countries, whether or not they are actually enforced.<sup>13</sup> Most MENA countries use disclosure requirements (70 percent) rather than usury ceilings in practice, to try and ensure that interest rates are transparent and understood. Currently, most MFIs charge interest rates that are much higher than the interest rate caps, in order for micro-level products to be viable, and in so doing they run a risk of being challenged in court.  A more transparent approach would be to remove interest rate caps and instead strengthen consumer protection against abusive practices such as excessive fees on overdrafts, miss-selling of financial products and lack of consumer redress.

# _Consumer Protection and Financial Inclusion_

**Consumer protection and financial literacy are increasing priorities worldwide** , as financial inclusion increases, with almost two thirds of middle income countries assigning responsibility for these areas to the financial regulator.  MENA lags other regions in terms of assigning responsibility for consumer protection or financial literacy to a financial regulator. However a majority of MENA countries introduced consumer protection reforms in 2009. Of 11 MENA countries that responded to the CGAP and World Bank Financial Access survey in 2010 survey, 7 had introduced consumer protection reforms, indicating that MENA is broadly in line with global trends in terms of increasing focus on consumer protection.

> 13 To date, however, there have been no known legal challenges to interest rates in this region.

21
# _Instrumental Variables Approach_

A second methodological approach is to find instrumental variables that correlate with the measure of bargaining power but not directly with the outcome measure. Many of the studies discussed throughout this paper that use income or assets as an indicator of bargaining power use this approach. Several authors use rainfall patterns as an instrument for agricultural production (Doss 2001b; Duflo and Udry 2004). Brown (2003) investigates how the size of a woman’s dowry affects the intrahousehold allocation of time between household chores and leisure and the share of household spending that goes to women’s goods. To account for unobservable confounding factors that affect both dowry and women’s bargaining position in the household, the author instruments dowry using regional grain shocks in the year preceding marriage and sibling sex composition of the bride and groom. Higher dowries are associated with more potential leisure time for wives. One of the advantages of this approach is that often large sample household survey data sets include variables that can be used as instruments.

# _Randomized Experiments_

The third and final methodological approach is to use experimental games or randomized control trials (RCTs) to isolate the relationships between bargaining power and outcomes. The experimental games discussed above are a means to analyze the processes of decision-making. In RCTs, participants are randomly assigned to a treatment or control group. In practice, the randomization may be at the community level, such as when programs are rolled out over time; but the choice of which communities receive the program first is randomly assigned. Many RCTs, but relatively few of them are designed explicitly to analyze bargaining within the household.

14
. This analysis aims to provide an updated picture of multidimensional poverty in four EU countries. The analysis is cross-sectional in nature and relies on the more recent 2022 EU-SILC cross-sectional wave available for these countries. The analysis includes estimates of the incidence of multidimensional poverty and the degree of overlapping deprivations, delves into the underlying factors contributing to higher risks of multidimensional poverty, and identifies at-risk population groups to inform targeted policy interventions tailored to different European contexts. Finally, it extends the AROPE measure to include key additional dimensions (access to education and health) and a broader measure of social and material deprivations, which includes some aspects of living conditions (i.e., overcrowding).

**Finally, it conducts a further analysis of multidimensional poverty among the Roma population in Bulgaria, the only country for which disaggregated data by ethnicity is available; this also includes longitudinal analysis to assess the extent of chronic income poverty and chronic**

> 10 Source: 2021 FRA Survey. Roma in 10 European Countries Report.

> 11 Longer temporal trends are not possible due to the methodological changes in the AROPE indicator, as explained in more detail in Section II.

> 12 We focus on the latest countries to join the European Union: Croatia, which became a member on July 1, 2013, and Bulgaria and Romania, which both joined on January 1, 2007. Poland joined the European Union on May 1, 2004, as part of a significant enlargement that included nine other countries. Despite sharing some common characteristics, these countries have made uneven progress in social and economic dimensions. Bulgaria and Romania still face significantly high rates of poverty, social exclusion, and inequality, whereas Croatia and Poland have made substantial improvements.

4
Table B.1: **Integrity of the Assignment Variable** – Audit of Electronic Land Records The table reports summary statistics for the sample of 2,064 audited electronic land records by treatment status. The first column reports number and percentages (in brackets) for accounts with audited landholdings greater than the land reported in bank lists. The second column reports on cases where land holdings reported in bank beneficiary lists are lower than the land reported in the household’s electronic land record.

||Electronic land record||Total|
|---|---|---|---|
||_≤_bank record (N=1,942)|_>_bank record (N=122)||
|Treatment|1,112|82|1,194|
|%|[.93]|[.07]|.58|
|Control|830|40|870|
|%|[.95]|[.05]|.42|
|Total|1,942|122||
||[.94]|[.06]|2,064|

land. In these cases, the bank-reported landholding is greater than the total land held by the farmers. This also is considered legitimate and does not violate the fundamental identification assumption. Third, rounding and conversion errors were common, as landholding can be recorded ina variety of complex and region-specific units of measurement. Since official land documents almost never reported landholding in the same units as banks, there were nearly always opportunities for rounding and conversion errors. In assessing whether an official landholding record matches the corresponding bank report, we therefore allow for a _±_ 5% margin for error. In addition, since landholding documents sometimes report distinct plots of land, we allow for either total-land or partial-land matches: if any combination of listed plots adds up to the size reported by the bank, within _±_ 5%, we consider this
Policy Research Working Paper 10093

# **Abstract**

In Iraq, the Multi-Purpose Cash Assistance programs have been instrumental in reaching the households most affected by the conflict with the Islamic State of Iraq and the Levant in areas where the coverage of the government’s social safety net (SSN) programs remain limited. In the evolving context, however, short-term Multi-Purpose Cash Assistance programs will require eventual integration in some form with the government’s social safety net programs to continue reaching vulnerable households affected by the conflict. As an initial step, this paper proposes an analytical pseudo-proxy-means test (PPMT) tool to bridge the targeting differences between the government’s cash transfer program and the humanitarian Multi-Purpose Cash Assistance programs. Using the common proxies between the cash transfer targeting formulas of the humanitarian agencies and the government, the pseudo-proxy-means test provides each Multi-Purpose Cash Assistance beneficiary’s probability of being eligible for the government’s cash transfer program under different expansion scenarios. When applied to the existing humanitarian beneficiary database, the results of the pseudo-proxy-means test tool suggest the potential for both significant referral numbers and a sequenced referral strategy.

This paper is a product of the Poverty and Equity 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 lphadera@worldbank.org; dsharma5@worldbank.org; mwaipoi@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
surveyed households fall below the poverty line of USD 1.90 per person per day, 49% had no bread to eat in the 7 days prior to the survey and 70% had no meat or dairy products to eat.

**Economic activity (age > 14). Figure A4b** shows detailed information on economic activities individuals engaged in the 7 days prior to the study. The most frequent activity was non-agricultural self-employment, which 41% engaged in, followed by help in family non-farm businesses (30%), salaried labor (26%), and agricultural self-employment (24%). On average, workers engage in about two separate economic activities and for 24% the main sector is agriculture ( **Table 1** ).

**School attendance (age > 14). Figure A4c** shows the distribution of school attendance by age, from 6 to 14 completed years of age, the range for which schooling is compulsory. School attendance rates are above 75% for ages 11 and over, and tend to decrease slightly for younger ages. Stated reasons for non-attendance vary widely, but 14% say that the child “is still too young” to attend school, the second most frequent answer ( **Table 1** ). By far the most common reported reason is a “lack of financial resources” (66%).

**Displacement and local violence.** The average IDP household has been displaced for 3.7 years, has moved 1.3 times and the vast majority (92%) report that Boko Haram activity was their main reason for displacement ( **Table 1** ). The number of events of local violence individuals were exposed to over the 12 months prior to the survey ranges from 0 up to 44, with high numbers concentrated in small areas in Maiduguri. Of the sample, 39% were exposed to any local violence captured in the ACLED dataset, and the average individual was exposed about 7 documented
# **I. Introduction**

Governments of developing countries typically spend resources equivalent to between 15 and 30 percent of GDP. Hence, small changes in the efficiency of public spending could have a significant impact on GDP and on the attainment of the government’s objectives whichever these are. The first challenge faced by stakeholders is measuring and scoring efficiency. This paper attempts such quantification. Additionally it verifies statistically some empirical regularities that describe the cross country-variation in the estimated efficiency scores.

The paper has four chapters following this Introduction. The first one presents the methodology that defines efficiency as the distance from the observed input-output combinations to an efficient frontier. This frontier, defined as the maximum attainable output for a given input level, is estimated using the Free Disposable Hull (FDH) and Data Envelopment Analysis (DEA) techniques. The exercise focuses on health and education expenditure because they absorb the largest share of most countries’ budgets, and because of lack of data availability for international comparisons in other types of expenditures.

The second chapter estimates the efficiency frontiers for nine education output indicators and four health output indicators, based on a sample of 140 countries and data for 19962002 Both input-efficiency (excess input consumption to achieve a level of output) and output-efficiency (output shortfall for a given level of inputs) are scored. The chapter presents both the single input-single-output and the multiple-inputs multiple-outputs frameworks. In addition, this chapter explores how expenditure efficiency has changed over time.

The third chapter seeks to identify empirical regularities that explain cross-country variation in the efficiency scores. Using a Tobit panel approach, this chapter shows that higher expenditure levels are generally associated with lower efficiency scores. Similarly, countries in which the wage bill is a larger share of the total budget tend to have lower efficiency scores. Three other variables that explain the cross country variation in efficiency scores are the degree of urbanization (positively correlated with efficiency, the prevalence of the HIV/AIDS epidemic (negatively associated with efficiency scores), and inequality in income distribution (higher
Agbonifo, 2020), which emphasizes the relationships and interactions between different explanatory factors and across the individual and group levels for understanding behaviors and outcomes (Kapilashrami and Hankivsky, 2018). Specifically, we analyze how gender, displacement status and local violence shocks (the ‘factors’) shape coping behaviors at the household, adult and child levels (across ‘levels’), and pay particular attention to household composition by gender and age.

For the empirical analysis, we combine unique household survey data and high-quality conflict event data from North-east Nigeria. We use the new “Nigeria IDP Survey 2018” dataset (Pape and Kwenin, 2018), which is based on a household survey sample that is representative of internally displaced persons (IDPs) in North-east Nigeria. In enumeration areas located in host communities, the survey also collected information from a sample of non-IDPs that is representative of the nondisplaced population in host communities. The dataset contains observations from 17,951 individuals from 2,947 households: 8,524 IDPs (1,437 households), and 9,427 non-IDPs (1,510 households). The dataset provides detailed socio-economic information from all households and individuals, as well as displacement information for IDPs. We spatio-temporally match the survey data with conflict event data from the Armed Conflict Location & Event Data Project (ACLED). The ACLED data allows us to calculate an indicator of the local intensity of armed-group violence based on the number of documented violent events in the 12 months prior to the survey.

> 1 See: <u>https://news.un.org/en/story/2022/05/1118772 (accessed May 30, 2022).</u>

> 2 These are the Democratic Republic of the Congo, North-east Nigeria, the Sahel, South Sudan, the Syrian Arab Republic and the Republic of Yemen.

2
representative for the world as a whole even if recently there has been an apparent greater reluctance of the rich to participate in national household surveys. This in turn likely imparts a downward bias to national and perhaps to global inequality estimates.<sup>6</sup> Table 1 shows the coverage of world population by household surveys. The second row from the bottom shows the overall coverage which was in all years but one greater than 90%. This is quite good, but should not make us forget that the countries that are omitted because they do not conduct household surveys are not a random draw from among all countries in the world but are all poor countries such as Afghanistan, Sudan, Congo, Somalia, Eritrea, etc. This is reflected in substantially lower population and income coverage of Africa. While the population coverage of other continents never falls below 92 percent, African coverage at its peak is 78 percent (see Table 1). And, in a worrisome development, the number and availability of household surveys in Africa is currently less than five or ten years ago. If we could include all of the omitted countries, global inequality would increase. In other words, what we calculate here, the Gini of about 70, is a lower bound to global inequality, simply because we do not have data from many of the poorest countries. Thus both the decreasing participation of rich individuals in national surveys, and the fact that countries that do not have surveys are overwhelmingly poor, bias the global inequality numbers down.

> 6 The effects of non-participation in surveys on measured inequality is by definition difficult to estimate since income of people who refuse to participate is not known. It is only indirectly (e.g., by looking at the geographical distributions of refusals as in Korinek, Mistiaen and Ravallion, 2005) that we can conclude that it is the rich who comply less. The difficulty of figuring out the effects of rich’s non-participation exists despite the intuition that it must underestimate actual inequality
# **References**

- Aizer, A. (2010): “The gender wage gap and domestic violence,” _American Economic Review_ , 100, 1847–59.

- Anderberg, D., H. Rainer, J. Wadsworth, and T. Wilson (2016): “Unemployment and domestic violence: Theory and evidence,” _The Economic Journal_ , 126, 1947–1979.

- Anderson, M. L. (2008): “Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” _Journal of the American statistical Association_ , 103, 1481–1495.

- Anukriti, S. (2018): “Financial incentives and the fertility-sex ratio trade-off,” _American Economic Journal: Applied Economics_ , 10, 27–57.

- ——— (2020): “Female Sterilization and Women’s Well-being in India,” _Unpublished_

- _manuscript_ .

- Anukriti, S. and A. Chakravarty (2019): “Democracy and demography societal effects of fertility limits on local leaders,” _Journal of Human Resources_ , 54, 79–121.

- Asher, S., T. Lunt, R. Matsuura, and P. Novosad (2021): “Development Research at High Geographic Resolution: An Analysis of Night Lights, Firms, and Poverty in India using the SHRUG Open Data Platform,” World Bank Economic Review.

- Ashraf, N., E. Field, and J. Lee (2014): “Household bargaining and excess fertility: an experimental study in Zambia,” _American Economic Review_ , 104, 2210–37.

- Ashraf, N., E. M. Field, A. Voena, and R. Ziparo (2020): “Maternal mortality risk and spousal differences in the demand for children,” Tech. rep., National Bureau of Economic Research.

- Bardhan, P. K., D. Mookherjee, and M
# **IV. Estimation Results**

As a first step, we check whether our data sets are representative of the same underlying population (Assumption 1) by performing means difference tests across key predictors. Given that the PDM is a subsample of the Targeting or ProGres data sets, we need a test that is suitable for partially overlapping samples. Here we use the same test proposed by Verme at al. (2016, 58) applied to refugee ProGres data in Jordan. Table 1 provides the results. It shows that all the variables are not significantly different in terms of means, indicating that the two samples are representative of the same population.

To evaluate the performance of the welfare estimation model, we consider three models. Model 1 includes demographic and geographic variables (region of residence and country of origin). This is the most parsimonious model and uses the variables that are most readily available in the ProGres data set. Model 2 adds to Model 1 variables related to animal and asset ownership. Model 2 is richer than Model 1, but it is more demanding in terms of the control variables, which may also be less reliable or more likely to be missing in the census data. Model 3 adds to Model 2 variables measuring coping strategies. To test for multicollinearity, Table 2 reports the variance inflation factor (VIF) for the different models. It shows that no variable has a VIF that is over 5, and the mean VIF is smaller than this threshold. We conclude that multicollinearity is not an issue for any of the models considered.

Next, we test the out-of-sample performance and possible overfitting of the three models, using PDM data and the root mean square error (RMSE) and mean absolute error (MAE) as performance functions. To do so, the data set is split into five equal folds. In the first iteration, the first fold is used to test the model, and the rest are used to train the model. In the second iteration, the second fold is
**Table A.1.3. Co-insurance, payment coverage type, and coverage value distribution**

|**Country name**|**Co-**<br>**insurance**<br>yes=1 no=0|<br>**Co-**<br>**insurance**<br>**percentage**|**Payment**<br>per depositor=1<br>per deposit=0|<br> <br>**Percentage of**<br>**deposit value**<br>**covered**|**Information source**<br>**for coverage**<br>**distribution**|**Reference date of**<br>**data on coverage**<br>**distribution**|
|---|---|---|---|---|---|---|
|Albania|1|15|1||||
|Algeria|0|0|0||||
|Argentina|0|0|0|40|Garcia|2000|
|Austria|1|10|0||||
|Bahamas|0|0|0|39|IADI|2001|
|Bahrain|0|0|0||||
|Bangladesh|0|0|0|31|Garcia|2000|
|Belarus|1|20|0||||
|Belgium|1|10|0||||
|Bolivia|1|50|0||||
|Bosnia-Herzegovina|0|0|0||||
|Brazil|0|0|0|23.6|FGC|2003|
|Bulgaria|0|0|1|71|DIF|2003|
|Canada|