rafmacalaba/datause-encoder-data huggingface dataset:
consumption and expenditure surveys (HCES) that are our main data source for the seven adequacy dimensions. Also, we outline how we harmonize the individual variables in these HCES to ensure comparability across countries.

# **_3.1 Housing Adequacy Dimensions_**

The construction of the _AHI_ is based on the aggregation of seven adequacy dimensions which have frequently been found to be a key determinant for socio-economic and health outcomes (cf. Section 2.2), which are the key dimensions reflected in the Agenda 2030, and which are consistently available in micro-level household surveys which are our primary data source (Section 3.2): (1) access to improved water; (2) access to improved sanitation; (3) adequate living space; (4) durable material and good structural quality; (5) security of tenure; (6) access to electricity; and (7) access to clean cooking. All seven adequacy dimensions are coded as dummy variables differentiating adequate access (1) and inadequate access (0). The following paragraphs outline how we define and operationalize adequate access and how we harmonize these variables across surveys. We apply a uniform coding framework that allows for comparability across countries (a full, countryspecific coding framework is available in the supplementary material upon request).

We align our coding of access to water (1) to the classifications of the WHO/UNICEF Joint Monitoring Programme (JMP). JMP defines improved drinking water sources as having the potential to deliver safe water by nature of their design and construction, including piped water, boreholes, or tube wells, protected dug wells, protected springs, rainwater, and packaged or delivered water (JMP 2020, Annex 1). We consider HHs that have access to improved drinking water to be adequate. We assume that HHs have inadequate access to water if they only have unimproved facilities at their disposal. Some countries’ HH surveys capture the difference in access to water for dry and wet season (e.g., Tanzania or Nigeria). For these countries, we consider access to water as adequate if HHs have access to improved facilities in _both_ wet and dry seasons. In Tanzania, for instance, more HHs use inadequate water sources during dry season. Adequate water sources HHs use during wet season (e.g., rainwater collection) seem to be largely unavailable to them in dry season. In contrast, few HHs revert from improved water sources in the dry season (public tabs, pipes, or boreholes) to unimproved water sources (surface water or unprotected springs) in the wet season, potentially due to the distance they have to cover to access public pipes in the dry season.

Regarding access to sanitation (2), we consider HHs to have adequate sanitation if they hygienically separate excreta from human contact. Adequate sanitation facilities include flushed toilet or pit latrines with slaps. JMP classifies adequate sanitation facilities which are shared between households as “limited service” on the sanitation ladder. Due to the absence of a large-
**Appendix Table 1:  Household consumption and biomass relationships for overall, non-poor, poor,** **<u>north, center and south.</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Rural|Non-Poor|Poor|North|Central|South|
|Femalehouseholdhead|-0.148|-0.104|-0.061|-0.117|-0.149|-0.158|
||[0.014]***|[0.017]***|[0.013]***|[0.041]***|[0.022]***|[0.020]***|
|Age of hh head:26-35 years|0.058|0.049|0.026|0.032|0.053|0.059|
||[0.017]***|[0.017]***|[0.015]*|[0.045]|[0.025]**|[0.025]**|
|Age of hh head: 36-45 years|0.072|0.064|0.024|0.029|0.028|0.118|
||[0.019]***|[0.020]***|[0.017]|[0.049]|[0.028]|[0.028]***|
|Age of hh head:46-55 years|0.010|0.016|0.014|-0.029<br>|-0.018|0.041|
||[0.019]|[0.021]|[0.018]|[0.053]|[0.030]|[0.028]|
|Age of hh head: 56-65 years|-0.053|-0.039|-0.029|-0.026|-0.102|-0.024|
||[0.020]***|[0.022]*|[0.019]|[0.055]|[0.032]***|[0.029]|
|Age of hh head: 66+years|-0.099|-0.069|-0.023|-0.032|-0.134|-0.091|
||[0.022]***|[0.023]***|[0.020]|[0.056]|[0.032]***|[0.033]***|
|Widowedhouseholdhead|0.058|0.053|0.028|0.079|0.039|0.052|
||[0.019]***|[0.023]**|[0.017]*|[0.056]|[0.028]|[0.028]*|
|Household size|-0.290|-0.216|-0.090|-0.255|-0.299|-0.301|
||[0.025]***|[0.013]***|[0.013]***|[0.028]***|[0.014]***|[0.042]***|
|Household size squared (/100)|1.313|1.182|0.288|1.079|1.461|1.286|
||[0.208]***|[0.103]***|[0.092]***|[0.209]***|[0.097]***|[0.361]***|
|Numberofchildren0-4|-0.059|-0.032|-0.027|-0.061|-0.047|-0.060|
||[0.009]***|[0.010]***|[0.008]***|[0.024]**|[0.013]***|[0.015]***|
|Numberofchildren5-10|-0.028|0.012|-0.017|-0.002|-0.029|-0.028|
||[0.009]***|[0.010]|[0.007]**|[0.021]|[0.012]**|[0.014]**|
|Numberofchildren 11-14|-0.001|0.015|-0.003|-0.005|-0.013|0.012|
||[0.010]|[0.012]|[0.008]|[0.025]|[0.014]|[0.016]|
|Highest education: some<br>primary|0.064|0.030|0.081|-0.057|0.072|0.064|
||[0.022]***|[0.021]|[0.019]***|[0.109]|[0.030]**|[0.030]**|
|Highest education: completed<br>|0.161|0.101|0.122|-0.072|0.196|0.176|
|primary|||||||
||[0.026]***|[0.025]***|[0.022]***|[0.112]|[0.036]***|[0.036]***|
|Highest education: post<br>primary|0.364|0.219|0.194|0.074|0.357|0.443|
||[0.026]***|[0.025]***|[0.022]***|[0.112]|[0.035]***|[0.037]***|
|Chronicillnessin household|0.059|0.021|0.034|-0.033|0.046|0.082|
||[0.011]***|[0.012]*|[0.010]***|[0.036]|[0.015]***|[0.016]***|
|Religion:Islam|-0.001|-0.008|-0.014|0.113|0.024|0.010|
||[0.025]|[0.028]|[0.021]|[0.157]|[0.038]|[0.043]|
|Religion: Catholic|-0.001|-0.018|-0.039|-0.103|0.017|-0.008|
||[0.021]|[0.023]|[0.018]**|[0.095]|[0.025]|[0.040]|
|Religion: CCAP|0.061|0.019|-0.022|-0.082|0.083|0.083|
||[0.022]***|[0.024]|[0.020]|[0.092]|[0.028]***|[0.045]*|
|Religion: OtherChristian|-0.000|-0.021|-0.025|-0.117|0.008|-0.004|
||[0.019]|[0.021]|[0.017]|[0.090]|[0.023]|[0.038]|
|HH has wage/salaryincome|0.114|0.060|0.052|0.117|0.107|0.113|
||[0.011]***|[0.013]***|[0.010]***|[0.036]***|[0.017]***|[0.016]***|
|Household has a non-farm<br>|0.142|0.053|0.066|0.181|0.136|0.134|
|enterprise|||||||
||[0.011]***|[0.012]***|[0.010]***|[0.030]***|[0.017]***|[0.015]***|
|HH grew tobacco in last|0.072|0.032|0.007|0.107|0.079|0.029|
|cropping season|||||||

45
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data

# _B. Prediction of True Sales_

The main objective of this paper is to estimate a firm’s true sales and therefore the extent of underreporting in sales to the tax office and in the survey. Prediction of the latent variable true sales in the MIMIC model can be done conditional on either the causal variables only or on both and the indicators.<sup>32</sup> Previous MIMIC empirical literature predicts the latent variable(s) conditional on the causal variables _X_ only (e.g., Giles 1999a, Chaudhuri _et al_ . 2006, Dell'Anno _et al_ . 2007). In order to increase precision, we choose to predict conditionally on both the causal variables _X_ and the indicators and and calculate<sup>33</sup> Next we calculate the unconditional underreporting to the tax office and in the survey by each firm in our sample using the formula , . The results are reported in Table 4.

Underreporting to the tax office is on average larger than in the survey (38.6% versus 11.9%). With respect to underreporting to the tax office, firms that are small (42.8%), firms that are located in Darkhan or Edernet (46.6% and 46.2%), and firms that do pay bribes and/or provided informal gifts when dealing with government authorities (41.9%), tend to underreport significantly more than other firms. Also, and surprisingly, firms in the construction sector tend to underreport less (33.9%). Firms facing credit constraints underreport slightly more on average than firms without such constraint, but the difference is not significant (39.9% v. s 37.8%).

32 Joreskog and Goldberger (1975) derived the formula of prediction conditional on both causal variables and indicators. But since we impose an inequality constraint, their formula for the conditional expectation will have to be modified. Another (minor) difference is that we normalize by taking instead of .

33 For the exact derivation of this conditional expectation see appendix C.

30
A validation exercise which is typically conducted to check the overall quality of a welfare measure based on consumption expenditure is to rank households based on such metric and then check whether Engel’s Law is satisfied, i.e. check if poorer household devote a higher share of total consumption to food.<sup>19</sup> As shown in Table 5 below, the expected negative relation between food shares and total household expenditure is only confirmed in 2022 data, possibly suggesting that the changes apported to the questionnaires improved the correspondence between the measure of welfare based on expenditure and households’ living conditions.

_Table 5: Analysis of relation between food share and total household expenditure_

|**Dependent variable: share of total con**|**sumption devoted to food**<br> <br>|
|---|---|
||**2021 data**<br>**2022 data**|
|(Log) Total monthly expenditure|0.0253<br>-0.0205|
||(13.51)<br>(-13.15)|
|Household size|-0.002<br>0.0014|
||(-2.75)<br>(2.38)|
|Nr. Observations|23166<br>26074|
|R<sup>2</sup>|0.0079<br>0.0068|

_Note_ : Model include a constant term _Source_ : World Bank staff calculations, based on HNAP 2021 and 2022 summer round of the Demographic and WASH survey Despite such improvements, the measure of consumption based on the 2022 Demographic and WASH survey is still far from being an accurate welfare metric for poverty estimation in Syria, particularly for the purpose of assessing the evolution of poverty over time using pre-conflict estimates from the HIES as a baseline.

In fact, the consumption module in each of the three rounds of HIES conducted prior to the conflict in 2003, 2007 and 2009 was based on an extensive diary account of household expenditures covering more than 500 items (goods and services). According to Beegle and others (2012), use of diary versus recall, and differences in the number of consumption items collected in surveys drastically affect poverty and inequality measures.

- 3.2 Monetary poverty estimates based on the HNAP 2022 Demographic and WASH survey

While differences in survey instruments between the HNAP 2022 and the HIES constrain the possibility of obtaining accurate pre-post conflict poverty comparisons, it is important to assess whether poverty estimates from the HNAP 2022 can be used to assess the profile of monetary poverty in Syria in 2022.

The welfare metric used for poverty analysis is constructed aggregating all available information on household expenditure, with the sole exclusions of expenditures recorded under the “Other” categories for both monthly and annual consumption items (see Table 4). Compared to a “standard” consumption aggregate for welfare measurement, the consumption aggregate based on information collected in the HNAP 2022 has three major drawbacks:

> 19 Engel’s law – the fact that the expenditure share dedicated to food consumption decreases as income rises – has been proved to be extraordinarily consistent across time and space. As such, the predictive character of this relationship has been widely used in poverty measurement, from the estimation of poverty lines to PPP assessment.
-6-

benefit of new information.7/ They are formally defined as follows: PR(X2 1yvXi,T,z) = Min E[P(y + t; z)IXI,T] - Min E[P(y + t; z IX,T1 (5) CR(X21y,X1<sup>,T,z)oMax(T-</sup> £si) conditional on (6) Min E[P(y + s; z)|X| < Min E[P(y + t; z)IX,,T] where Xi is the previous set of information, X2 is the new information, and X, is the combined set of information.81 Equation (5) is the difference between the minimization of expected poverty given the information in Xi and the minimization of expected poverty given that in X. It is always non-negative and should be positive if the additional information in X2 is useful. Equation (6) shows how much money can be saved when additional information becomes available which allows the government to more accurately target transfers to achieve a pre-specified poverty level.

If one limits oneself to a relatively small amount of information one can directly solve (4) using household survey data given the assumption that the distribution of incomes found in the survey is identical to that found in the population. This has been done by Ravallion (1988) and Ravallion and Chao (1988), who limit their transfer scheme by dividing up the population into 10 mutually exclusive groups and assuming that the only information available is the membership of each individual in each group and the distribution of income

- 7/ The cost reduction benefit is essentially the same as Ravallion's (1988) equivalent gain from targeting.

8/ In most cases additional information will consist of adding more variables to the vector xi.
independent variables in the estimation of the relationship between strategies and financial outcomes, because the development of financial sector indicators could have been directly influenced by the financial sector strategy. Because reforms can be implemented also in countries with no explicit financial sector strategies, we further analyze whether there is a direct connection between financial strategies and the evolution of financial policy reforms. To this end, we use several indicators compiled in the World Bank’s Doing Business database, including the distance to the frontier for getting credit, enforcing contracts, and resolving insolvency.

We find that the overall quality of financial sector strategies could be improved significantly. The strategies could pay greater attention to systemic risk associated with achieving the targeted development outcomes (financial depth, efficiency, and inclusion) as well as acknowledge and try to manage trade-offs in financial policy—particularly between the speed of financial development and systemic risk management. Financial sector strategies are more recent phenomena in ECA compared with East Asia and Pacific (EAP) or Latin America and the Caribbean (LAC). Nevertheless, ECA strategies do not address trade-offs between financial development and systemic risk management, and they fall behind EAP, LAC, and in particular Organization for Economic Co-operation and Development (OECD) economies in that respect.

Our regression analysis suggests that, overall, countries using financial sector strategies experienced stronger financial sector development over 1985–2014. Namely, the use of financial sector strategy was associated with increased financial deepening (the ratio of deposits to GDP), increased financial inclusion (deposit accounts per 1,000 adults), and reduced financial volatility (the standard deviation of deposits to GDP, and the output loss in case of banking crises). We did not find any significant effect of the strategies on financial efficiency (the ratio of overhead costs to income, or returns on bank assets). Interestingly, while the strategies significantly promoted the deepening and inclusion in bank deposits as well as the stability of deposit growth, they did 4
Figure 1. The Job Quality Measure for Wage Employees and the Share of Wage Employees, by Country <!-- Start of picture text --> 90%<br>80%<br>MEX LBN EGY CRIBWATUR URY<br>70% PNM<br>BRA<br>60% PHL LKA PRY DOMGEO MNG EAP<br>NEP ECU ECA<br>50% KEN IND THA HND PER IDN COLVNM LAC<br>40% RWA MMR BOL<br>PAK<br>SEN LAO MNA<br>30%<br>CIV<br>GNB BGD SAR<br>20% MLI<br>TGO MOZ<br>SSA<br>10% BFA BENNER<br>0%<br>1.00 1.50 2.00 2.50 3.00 3.50 4.00<br>Average JQM<br>Share of wage employees<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1) Despite the positive correlation between the scores on job quality and the share of the wage employed, figure 1 also shows that job quality may vary significantly among countries with similar shares of the wage employed, and the share of the wage employed may vary considerably among countries with similar job quality scores. For example, Bolivia (BOL), the Lao People’s Democratic Republic (LAO), and Türkiye (TUR) have similar JQMs, at 3.16, 3.19, and 3.25, respectively, but different shares of the wage employed, at 40 percent, 33 percent, and 69 percent, respectively.

Focusing on job quality among the wage employed alone, however, leaves out a substantial share of the population in developing countries who are self-employed or unpaid workers. To evaluate the extent of the change in the results by including other categories, the JQM is weighted by the share of the wage employed across countries. This is equivalent to assuming that self-employed and unpaid employment had job quality scores equal to zero. Figure 2 shows a strong correlation between these two measures, but also a nontrivial extent of variation.

Figure 2. JQM Main Specification Compared with JQM Adjusted for Share of the Wage Employed <!-- Start of picture text --> 3.00<br>2.50 BFA LAO CRIMNG URY BRA<br>MNG EGY TUR<br>2.00 MMR LBN LAO EAP<br>MEX PNM DOM GEO<br>PERNEP ECU ECA<br>1.50 COL<br>IND PAK VNM LAC<br>PHL BOL<br>PRY<br>1.00 HND LAO MNA<br>THA LKA MNG<br>IDN SAR<br>0.50 KEN RWASEN CIVGNB MLI BGDMOZ SSA<br>TGO<br>BENNER BWA<br>0.00<br>1.00 1.50 2.00 2.50 3.00 3.50 4.00<br>JQM<br>JQM - Adjusted for Share of Wage Employees<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see Table A.1 in the Annex) 16
**The EU-LFS data could not be used for this study as it only has information on the wage distribution in deciles rather than specific point estimates.** This poses a challenge when analyzing the data, as point estimates are often needed to estimate labor income accurately. To overcome this issue, one option is to use imputation techniques to fill in the missing values. Specifically, we could use the EUSILC data, which provides detailed demographic characteristics and income information, to impute the missing point estimates on the LFS data. This can help provide a more accurate representation of the wage distribution and enable a more nuanced analysis. However, it is important to carefully consider the imputation process's potential biases and limitations and evaluate the quality of the resulting estimates, especially when several data sources (including administrative tax data) need to be imputed.

**In this study, we match income data from the 2020 tax administrative dataset with the 2021 EU-SILC (reference income year 2020) using data matching techniques; then, we assess the degree of tax compliance and possible overestimation of the share of minimum wage earners in the tax data.** Following the literature, we assume that the "true"  income is reported in the survey, while "reported" income is reported to the tax authority. After imputing tax income into the EU-SILC, we assess the degree of tax evasion and the impact of tax evasion on the share of minimum wage earners.

**Before matching the administrative tax data and the EU-SILC datasets, it is essential to carefully** We must evaluate the **evaluate the comparability of the datasets before conducting any analysis.** comparability of the two datasets regarding the target population and income. This involves determining whether the datasets are compatible and suitable for comparison. The extent of comparability determines the accuracy of the results and the conclusions drawn from the analysis. Therefore, it is important to ensure that the datasets are matched appropriately and that the analysis considers any differences in the target population or income that may affect the results. Both data sources are compared in Table 1.

11
In recent years, new techniques have been developed to measure the extent of underreporting of sensitive behaviors using household surveys (Lara et al., 2004). They include— among others— the list randomization or item count technique (Blair et al., 2015; Tsuchiya et al., 2007; Houston and Tran, 2001; Anderson and Miller, 1990), randomized response method (Pollock and Beck, 1976; Goodstadt and Gruson, 1975; Greenberg et al., 1971; Warner, 1965), three card method (Droitcour et al., 2001), nominative method (Sirken, 1970), and Bogus Pipe method (Tourangeau et al., 1997). Several studies have used list randomization in other fields where the respondents have a tendency to withhold information about sensitive issues. Blair et al. (2014) have developed a statistical test and multivariate regression models for comparing and combining the results from list randomization and endorsement experiments, showing that when carefully designed and analyzed, the two survey experiments can produce substantively similar empirical findings. Blair and Imai (2012) have found similar results. When using the National Race and Politics Survey, Imai (2011) has employed the item count technique to measure the degree of racial hatred in the United States. The results indicate that the level of hatred is higher relative to that reported in conventional surveys. Similar results have been found for vote buying in Nicaragua (Gonzales-Ocantos et al., 2012). Finally, Rosenfeld et al. (2016) have found that while direct questioning leads to significant underestimation of sensitive votes against the anti-abortion referendum in Mississippi, indirect survey techniques yield estimates much closer to the actual vote count, with endorsement experiment and randomized response yielding the least bias.

Despite the urgent need to more accurately estimate the prevalence of gender-based violence (GBV), and despite the recent development of statistical and survey techniques discussed above to address this issue, research on the extent of underreporting of GBV is virtually non-existent for any country. Studies of physical violence and harassment on buses and other forms of transport are also virtually non-existent. Hence, by applying the list randomization technique to survey questions about GBV in Kerala, India, this study intends to fill this gap in the literature.

9