rafmacalaba/data-use-mentions-extended huggingface dataset:
(i.e., the role of the landlord), then when she does not own it (i.e. the role of the tenant). This would suggest that the second version of the question (WTP), may mitigate the possibility of misreporting due to sentimental attachment to the property. In the selected surveys, we observe both types of self-assessed rent questions previously discussed. In particular, in the case of Albania and Peru, the questionnaire uses the first version: (a) “ _Assume that you want to rent [out] this dwelling what would be a monthly rent you will be able [to] ask for? If you rented out this dwelling, how much do you think you would make?_ ”, respectively. In the case of Iraq, the instrument uses the second version: (b) “ _How much would you pay if you were to rent a dwelling similar to the one you are currently living in?_ ”<sup>11</sup> For the reason explained above, we expect that the question as it is asked in Iraq is likely to mitigate the possibility of misreporting due to sentimental attachment to the property, while in Albania and Peru selfassessment might be inflated. Table 2 reports questions asked in each country and year.

As noted by Deaton and Zaidi (2002, 37), rental value from self-assessment _must be treated with caution and carefully inspected prior to use_ , as home-owners might not have a precise idea of the market value of their dwelling. The scope of this analysis is to illustrate whether and to what extent different methods can lead to different poverty and inequality estimates. Therefore, we do not ascertain the accuracy of self-reported rental values by homeowners and non-market tenants. Ceriani, Olivieri and Ranzani (2019) test the accuracy of home-owners’ selfassessed rental value in an application to Peruvian data.

# **4. Results**

In this section, we analyze the distributional impact of including the flow of services from dwellings
is not surprising, then, that fuel price increases correspond more strongly to productivity increases for foreignowned firms than domestic ones (panel II). Furthermore, electricity price hikes are negatively and significantly correlated with domestic firms’ productivity and profit margins. Regarding substitution and absorption effects, foreign firms’ substitute more strongly in response to increases in fossil fuel prices (panel III) while they sharply reduce employment following electricity price hikes (panel IV).

> 14We provide summary statistics for the size cut-offs used in the analysis in Table B.4. In ENIA the classification by firm size varies between the raw and combined data sets and complicates the firm matching algorithm described in Appendix C slightly; see Table C.7 for more information on the respective cut-offs in the two ENIA data sets.

> 15We define a firm as _domestic_ if it records a foreign capital formation of zero in all periods; see Table B.4 panel II for the corresponding summary statistics.

16
shows regression point estimates at the student level.<sup>46</sup> We find no significant impact on learning outcomes for either the MEET or SMS interventions.<sup>47</sup>

# **4.2 Average ITT effects: Support to elections and capacity of SMC members**

The third and fourth estimations in each of **Tables 7-11** present the impact of the MEET and SMS interventions combined with the ELECTIONS-CAPACITY intervention.

# **4.2.1 School functioning**

We do not observe significant positive impacts on school functioning from the ELECTIONSCAPACITY intervention. In fact, the addition of the intervention appears to have had a negative impact which offsets the impact of the MEET and SMS interventions. In the comparison of means **(Table 5)** and transition probability analysis **(Table 6)** , the impact of MEET in reducing the likelihood of school closures is smaller and no longer significant in MEET+ELECTIONS schools. Similarly, the impact of SMS in increasing the likelihood of schools reopening is reduced and no longer significant in SMS+ELECTIONS schools. The same pattern is observed in the point estimates **(Table 7)** . However, the impacts of SMS+ELECTIONS on schools reopening, while not significantly different from control, are significantly larger than the impacts of MEET+ELECTIONS in equivalence testing.

> 46 We also conduct estimations at the village level and find no difference in results.

> 47 The findings are subject to two potential limitations: the passage of time since the interventions, and the negative impact of out-of-school children. To address these, we also present estimates based on the same tests during school surveys, conducted with enrolled students only in 2015; we again observe no significant impacts from either treatment (Table A5, in Appendix A).

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, 56.3% were claims of maturity up to and including one year. This figure was an average of all the claims to banks, the public sector and the non-bank private sector. For the claims to banks, it is likely that a higher percentage was of short-tern. According to the Turkish Treasury, the short-term external debt of the deposit money banks reached US$8.4 billion at the end of 1996 and US$8.5 billion at the end of 1997, out of total external debt of the deposit money banks of US$1 1.1 billion and US$13.6 billion at the end of 1996 and 1997 respectively<sup>1 7</sup> . Thus in 1996 75% of deposit money banks' foreign borrowings were short-term, and in 1997 the ratio declined to 62%.

It is noteworthy that short-term loans and credits from abroad have been subject to a levy of 4 percent by the Resource Utilization Support Fund.'<sup>18</sup> Thus despite the existence of 17 There is sorne discrepancy between Treasury data and Central Bank data of the foreign liabilities of the banking system. The Treasury data show larger foreign liabilities of the banks than Central Bank data. The discrepancy may have to do with the definition of "banks" in their prospective coverage.

**18** This levy was lifted in early 1999 in response to the Russian crisis.
different age groups and demographic subgroups, such as by region, state, rural-urban, north-south and gender given the data availability.<sup>6</sup> Table 1: Definitions used to identify severe deprivations from GHS and MICS datasets

|Deprivations|Definition|
|---|---|
|Severe Food Deprivation|“children whose heights [or] (...) weights for their age [or<br>weight for height] were more than –3 standard deviations<br>below the median of the international reference<br>population,that is,severe anthropometric failure”<sup>7</sup>|
|Severe Health Deprivation|“children who had not been immunised against any<br>diseases”<sup>8</sup>|
|Severe Education Deprivation|“children aged between (...) [5 and 17] who had never<br>been to school and were not currently attending school<br>(noprofessional education of anykind)”<sup>9</sup>|
|Severe Safe Drinking Water Deprivation|“children who only had access to surface water (for<br>example, rivers) for drinking or who lived in households<br>where the nearest source of water was more than 15<br>minutes away (indicators of severe deprivation of water<br>qualityorquantity)”|
|Severe Sanitation Facilities Deprivation|“children who had no access to a toilet of any kind in the<br>vicinity of their dwelling, that is, no private or communal<br>toilets or latrines”<sup>10</sup>|
|Severe Shelter Deprivation|“children in dwellings with more than five people per<br>room (severe overcrowding) or with no flooring material<br>(for example,a mud floor)”<sup>11</sup>|
|Severe Information Deprivation|“children aged between (.
<sup>0</sup> C increase in temperature (Somanathan et al., 2021). In a U.S. study, Deryugina and Hsiang (2014) show that productivity on an individual day declines by 1.7 percent for each 1<sup>0</sup> C (1 _._ 8<sup>0</sup> F) increase in daily average temperature above 15<sup>0</sup> C (59<sup>0</sup> F). Many of these studies that examine the productivity impacts of temperature focus on a few individual countries, hence providing only limited variations in terms of both the impact of temperature changes on productivity and variations across countries by income and geography.

LoPalo (2023) addresses this challenge in an innovative study that extends the analysis to 46 countries, examining the effects of WBT on the productivity of Demographic and Health Survey interviewers. She finds that hot and humid temperatures significantly impact worker productivity. Data quality problems, such as missing responses and flags for poor data quality, become more frequent on hotter days and interviewers become less productive. The number of interviews completed per hour worked declines by 13.6 percent on the hottest days. However, interviews and data collection make up a unique context. LoPalo (2023) provides interesting evidence on the link between temperature and survey workers’ productivity from a broad set of countries and regions, allowing for heterogeneous impacts across countries. However, the findings cannot be generalized to enterprises since data collection is a small share of economic activity, and the usually
%|21%|22%|22%|
|De jure female hh|99%|77%|96%|32%|9%|20%|
|De facto female hh|85%|80%|89%|25%|7%|11%|
|No earners|83%|98%|94%|50%|62%|58%|
|Remittance recipients<br>only|100%|99%|96%|69%|76%|73%|
|Female single earner|98%|91%|92%|38%|24%|32%|
|Male single earner|96%|93%|94%|14%|4%|7%|
|Majority female earners|88%|86%|83%|28%|9%|14%|
|Equal contribution|93%|81%|83%|16%|17%|17%|
|Majority male earners|78%|56%|57%|19%|15%|16%|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

# **8. Conclusion**

This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity
substantially cut the sample size.

# **4. Data**

Data used in this study come from several sources. The CO2 emissions and energy consumption data are from International Energy Agency (IEA) Fuel Combustion Statistics database and World Energy Statistics and Balances database, respectively. The databases report CO2 emissions and final energy consumption due to fuel combustion of transportation activities at the country level. The transportation sector is further divided into road, rail, domestic navigation, domestic aviation, and pipeline transport. The study period is from 1990 to 2018. The full sample contains 145 countries for which data are available. In 2018, the full sample of countries contributed to 84 percent of global transport carbon emissions. International aviation and marine bunkers account for the remaining 16 percent. The list of countries included in the study is presented in Table A1 in the appendix. We obtain PPP-based GDP (based on 2015 PPP rates) and population data from IEA.

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by 43 percent in 2020, an overall gain by 15 percent. If legislative measures, outside the purview of the PHP, are implemented, further reduction in the BOD from injuries and COPD would be possible.

For methodologies, see C.J.L Murray and A.D Lopez in their report, "Global Burden of Disease" Bulletin of the World Health Organization, 1990, 72 (3).

The government should increase its spending level to meet these service requirements not only because such interventions are the most cost-effective but also because of a salutary impact of government expenditure on improving health status of Chinese population, as revealed by our regression estimates with panel data. Moreover, resource requirements are modest compared with 3 These figures capture roughly 2 to 7 percent of those countries GNP. See World Development Report 1993, World Bank, Oxford.
**Table 6. Health Attainment: Output-Efficiency scores by regions across the world**

**- Single Input-Single Output**

||AFR|EAP|ECA|LAC|MNA|SAS|
|---|---|---|---|---|---|---|
|LifeExpectancy at birth|.63|.87|.91|.92|.90|.83|
|Immunization DPT|.62|.83|.95|.87|.90|.75|
|Immunization Measles|.63|.83|.95|.91|.90|.71|
|DALE|.56|.83|.90|.90|.86|.79|

# **III.3.  Multiple Inputs and Multiple Outputs**

Both education and health attainment are not solely determined by public spending. Other inputs, such as private spending also affect the output indicators. For health, the World Bank WDI database reports a comparable statistic across countries. Unfortunately, a comprehensive database of this variable does not exist for education: For the education production technology we have multiple indicators of educational attainment, and three inputs (public spending, teachers per pupil, and adult literacy rate). In health, besides public spending, two other inputs were included: private spending and the education level of adults. The analysis was limited to include up to three outputs Too many output indicators will complicate the analysis, biasing efficiency scores towards one, increasing the variance of the estimators, and reducing their speed of convergence to the true efficiency estimators (Simar and Wilson, 2000; Groskopff, 1996) In education, the selected input-output combinations produce ranking that are somewhat similar: the average rank correlation coefficient is .53. The frequency distribution of the efficiency estimators is similar in all the models, and as the model shifts from a basic two-input two-output model to a more complex three-input
# 3.2.1 The voracity effect

Tornell and Lane (1999) develop a model in which a number of fiscal claimants (ministries, provinces, unions, and so forth) attempt to appropriate resources in good times without taking into account the effects of their actions on other claimants (like fishing from a common pool), which they refer to as the “voracity effect.” As the intensity of fiscal competition increases in good times, the rise in government spending could be higher than the windfall. Government spending will thus be procyclical. Further, the more claimants there are (which they interpret as power being more dispersed), the higher the level of government spending in good times (i.e., the more procyclical government spending is).

While the voracity effect is certainly an attractive idea to think about procyclical government spending and captures a key political friction, how to quantify power dispersion for econometric purposes is far from trivial. Specifically, the empirical measure used by Lane (2003) is the Political Constraint Index (POLCON), taken from Henisz (2000) and Henisz and Zelner (undated).<sup>22</sup> This (0,1) index counts the number of veto points in the political system and the distribution of preferences across and within the different branches of government.<sup>23</sup> As Lane (2003) himself discusses, the empirical complications stem from the fact that this index can be interpreted as capturing the voracity effect or actually measuring the opposite effect. On the one hand, we could argue that a larger number of veto power points implies that power is more dispersed and hence should lead to a larger voracity effect (i.e., power dispersion is bad). In such a case, we would expect that the higher is POLCON, the larger is the correlation between the cyclical components of government spending and GDP. On the other hand, Henisz (2000) concludes that POLCON is positively correlated with
. The contrast between Latin American and African adjustments in the 1980s is often mentioned: the increase in producer prices for small African farmers resulting from real devaluations in several countries helped improve income inequality there, whereas Latin America experienced deteriorating income inequality.<sup>12</sup> Third, the endogenous economic mechanisms for adjusting to shocks differ from country to country. For example, Behrman et al. (2000) show that labor market adjustment in the 1997-8 Thai crisis involved considerable increases in involuntary unemployment but no decline in real wage rates and no evidence of the increased employment that happened at

> 11Note, however, that, in contrast to the before-and-after comparison of measured inequality, the microsimulation model of Robilliard et al. (2002) implies that the pattern of price and wage changes, credit crunch, etc., of the Indonesian crisis would have increased inequality slightly (discussed further in the Annex).

12Lustig (2000) shows that widening inequalities were seen in 15 of the 20 macroeconomic crises which she studies from Latin America 1980-95. In a provocative and not unrelated analysis, Behrman et al. (2001) argue that financial liberalization contributed to the worsening of income distribution in Latin America in the 1990s. They regress the Gini, and the log-difference between the mean income of the top decile (or of the median income), and the mean income of the bottom 30 percent on a number of measures of policy, including an index of financial liberalization, as well as measures of inflation, macroeconomic volatility and the real exchange rate. The financial liberalization index, from Morley et al. (1999), is based on whether or not there were controls on deposit and lending rates, and on the ratio of reserves to deposits in the banking system.

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crop damage; and column 4, an indicator of the share of VCs where publicly available data pointed to any type of conflict. This measure decreased from a high of 16.1% in period 2 to about 10% in the summer of 2022 and fluctuated at around 7%-8% for the remainder of the period. By comparison, the share of VCs experiencing field damage ranged from a maximum of 3% (in period 10) to a minimum of 0.4% (in periods 29 and 30), almost an order of magnitude smaller. With between 4,226 and 135,334 ha in periods 21 and 23, the total area damaged by conflict action at the national level in any two-week period remained small compared to a total cropped area of about 30 million ha. The correlation between this measure and the public information</mark> is low, suggesting that remotely sensed data contain information that is not captured by data from other sources.

<mark>To assess the effect of conflict-related damage on area cultivated with summer or winter crops, we match two-week periods to different phases of crops’ vegetative development (planting, growing, and, for winter crops, dormancy) to the data on conflict incidence and intensity by two-week intervals. Columns 5 and 6 in table 1 display the classification of these periods for summer and winter crops, respectively. Pa</mark> nel B provides the means over the planting or growing seasons for winter or summer crops in 2022 or 2023, which are the variables included in the relevant area regressions. The area damaged per VC dropped from about 550 ha per affected VC in 2022 to 236 and 306 ha per affected VC in the 2023 growing season for summer and winter crops, respectively. Aggregating information from ACLED in the same way, in columns 7 to 10, highlights that, with between 0.5% and 1% of VCs reporting (between 9.8 and 30.9) fatalities in any 7
data. Additional secondary outcomes include psychological wellbeing measured with the General Health Questionnaire 12 (GHQ-12), and the number of meals eaten in the last week that contained, meat, fish, or eggs.

_Sexual behavior._ All outcomes in the sexual behavior domain are secondary and selfreported. On the extensive margin, our sexual behavior outcomes include whether the core respondent has ever had sex, her number of lifetime sexual partners, and whether she was sexually active during the past 12 months. On the intensive margin, we look at the core respondent’s age at first sex, whether she had a sexual partner five or more years older, and her condom use during her most recent sexual intercourse.

_Empowerment and Aspirations._ Our primary measures of empowerment include an indicator of changes in life satisfaction and a super index of overall empowerment. This super-index of empowerment includes sub-indices (all secondary outcomes) that measure self-esteem, preferences for children’s education, an index of social participation, and aspirations. We also construct super indices of empowerment separately for the married and unmarried sub-samples, as well as a super index of economic control within marriage for the married sub-sample. These three indices are also primary outcomes.

_Employment and Wages._ In this domain, we examine the proportion of hours spent in selfemployment or paid work, the typical wage rate for work done in the past three months, and the opportunity cost of time which is constructed by asking the core respondents a series of hypothetical questions regarding whether they would accept employment at a given wage rate. Secondary outcomes include whether the core respondent participated in any wage work in the

> 36 Our pre-analysis plan suggested we would use a hazard model. We instead simply use OLS to examine age at first marriage and age at first birth in the intensive margin.

59
problems in the data. This calls for additional data work before adjusting the data with a balancing procedure. Unfortunately, we could not obtain additional data to improve our initial estimate of the input-output table. Instead, we assess the quality of the different parts of the data, and collect data on the structure of input-output tables for similar countries. We use the quality assessment and the auxiliary data to guide the necessary adjustments.

# **Balancing of the input-output table**

We now develop a balancing procedure that produces a balanced input-output table. All data points in the input-output table are included and may be adjusted.

The heart of the procedure is an optimization problem that in which the elements of the table are adjusted such that an objective function is minimized subject to side constraints. The objective function is of the popular form which adds the sum of the squared deviations from the initial estimates<sup>38</sup> . The side constraints include supply-use balance by sector, and a GDP accounting identity. The latter equates total GDP by measured by income with total GDP measured by expenditure.

> 37 We did a similar analysis using data from the supply-use table only (using 2006 data only and without updating to totals for 2007). The analysis revealed similar imbalances.

> 38 All of the squared deviations are scaled by the corresponding size of the original estimate to preserve the density of the input-output table.

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