rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9892

# **Abstract**

This paper uses a “mystery client” approach and visits the websites of national statistical offices and international microdata libraries to assess whether foundational microdata sets for countries in the Middle East and North Africa region are collected, up to date, and made available to researchers. The focus is on population and economic censuses, price data and consumption, labor, health, and establishment surveys. Following the exercise, a new microdata access indicator that measures the degree of opennes of microdata and the ease with which microdata users can understand and navigate the websites of national statistical offices is presented. The results show that about half of the expected core data sets are being collected and that only a fraction is made available publicly. As a consequence, many summary statistics, including national accounts and welfare estimates, are outdated and of limited relevance to decision makers. Additional investments in microdata collection and publication of the data once collected are strongly advised. National statistical offices in the region should make considerable improvements to the outlook of their websites to make them more user friendly. Specifically, microdata libraries and updated survey calendars should be a standard feature of the websites to ensure easy access to available microdata.

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 uekhator@worldbank.org and jhoogeveen@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
Still, lineup estimates of poverty in Syria based on a distribution neutral nowcasting approach using growth in Household Final Consumption Expenditure (HFCE – constant prices, unit-passthrough) have so far been used for the production of global and regional poverty estimates.

**In the context of data scarcity, this paper aims to propose a methodological approach to address the knowledge gap regarding welfare in Syria over the last decade** . In particular, the objective of this paper is to (i) fill the knowledge gap on the extent of monetary poverty pre-conflict using international poverty lines for 2007 and 2009; (ii) assess the strengths and weaknesses of poverty estimates based on household survey data collected in 2021 and 2022 under the Humanitarian Needs Assessment Programme (HNAP); (iii) conduct a sensitivity analysis of distribution neutral nowcasting approaches, identifying the most credible approach given NA data limitations; (iv) triangulate estimates obtained through various approaches with indirect evidence on economic activity based on georeferenced data; and lastly (v) propose a methodological approach to assess the evolution of monetary poverty in Syria during the conflict period.
# Figure A.8: Financial Inclusion in Uruguay and the World Over Time Pace of Progress in Uruguay Relative to Other Countries

<!-- Start of picture text --> A. Bank Account Ownership<br>Uruguay 2017<br>Uruguay 2011<br>6 8 10 12<br>Log GDP per Capita (Constant 2010 USD)<br>2011 2017<br>B. Debit Card Ownership<br>Uruguay 2017<br>Uruguay 2011<br>6 8 10 12<br>Log GDP per Capita (Constant 2010 USD)<br>2011 2017<br>150<br>100<br>50<br>Bank Account Ownership (% age 15+)<br>0<br>100<br>50<br>Debit Card Ownership (% age 15+)<br>0<br><!-- End of picture text --> Notes: Similarly to figure A.1, this figure plots the cross-country relationship between financial inclusion indicators from the World Bank Global Findex Database and GDP per capita for 2011 and 2017. This figure is discussed in Section 3.3.

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# I. Introduction

The global economy headed into the COVID-19 pandemic and the Russian invasion of Ukraine after a decade of slowing growth. The pandemic-induced global recession of 2020 further deepened this slowdown and Russia’s invasion of Ukraine in February 2022 has already left additional scars. These adverse shocks have reduced not just actual global output growth but have also dampened potential growth—the rate of increase of potential output, defined as the level of output an economy can sustain at full capacity utilization and full employment. Potential growth is a critical determinant of a wide range of macroeconomic and development outcomes. Sound fiscal and monetary policy decision about stimulus or austerity cannot be taken without being grounded in a firm understanding of potential growth.

Potential growth is of fundamental importance to short- and long-run macroeconomic analysis and policy but it is not directly observable. In an extensive literature, three main methods of estimating potential output growth have been employed, each of which has its advantages and disadvantages. Thus, measures of potential growth based on production function estimates make it possible to study the contributions of the fundamental drivers of growth—namely, the growth of the factors of production and technical progress—but involve assumptions that may be viewed as restrictive. A second method uses economic analysts’ long-term (five-year-ahead) output growth forecasts, which may be assumed to incorporate their judgments. The third method obtains measures of potential growth from statistical filters of actual growth data; it may be best at ensuring consistency between estimates of potential growth and output gaps, on the one hand, and indicators of domestic demand pressures, on the other.

This study introduces the most comprehensive international database yet for the nine most commonly used measures of potential growth, based on these three methods, for the largest available sample of countries over the period 1981-2021. In addition, this study addresses the following questions. First, how has potential growth evolved in recent decades? Second, how have recessions and other adverse developments affected potential growth? Finally, through which channels have such developments affected
|2.8|2.7|2.6|2.5|2.4|2.2|2.1|2.0|1.7|2.3|
|Moldova|Consumptionper capita(US$)*|1,434|1,916|2,209|2,460|2,710|2,973|3,309|3,764|4,402|6,496|3,166|
||Share of tobacco expenditures**|5.4%|5.3%|4.9%|5.1%|4.7%|4.5%|5.9%|4.8%|5.3%|4.9%|5.1%|
||Share of smoker households***|12.9%|13.5%|18.3%|15.2%|11.6%|15.8%|17.7%|17.2%|14.7%|17.8%|15.7%|
||Population|14,684,116|14,700,576|14,577,382|14,671,621|14,616,341|14,694,883|14,642,140|14,640,598|14,650,698|14,621,629|146,499,985|
||Household size|4.0|4.0|4.1|3.5|3.6|3.4|3.5|3.3|3.0|2.8|3.5|
|Russian<br>Fdti|Consumptionper capita(US$)*|2,124|3,506|4,436|5,384|6,415|7,682|9
invest in the group account in the game. We report marginal effects for continuous explanatory variables and the effect of a change from 0 to 1 for dichotomous explanatory variables. In column 1 we control for demographics, and in column 2 we add individual measures of social capital. Given the significant differences in behavior across districts, in column we add community characteristics generated by the school survey and the LSMS. Finally, in column 5 we add dummies for the Coastal, the Mountain, the Central and Tirana geographical areas.

The estimates in Table 4 show that wealthier individuals are more likely to invest in the group account, whereas no significant differences based on age, gender and education exist. Among the social capital measures, the number of relatives in the session increases cooperation in the game. This is not surprising, as the number of relatives among other participants is likely to affect one‟s beliefs about others‟ willingness to cooperate. This is consistent with altruism,<sup>30</sup> amplified by conditional cooperation,<sup>31</sup> being stronger within families. The fact that the presence of “friends” in the session does not have a similar effect confirms that Albania is a close-knit, family oriented society.

Table 4 columns 3 and 5 indicate that religious heterogeneity significantly reduces parents‟ cooperativeness in the game. However, the religious heterogeneity dummy becomes insignificant (p =

> 29 In three districts the majority of the population is Catholic, and in one district the majority is Orthodox. 30 See Andreoni (1990).

> 31 See Fischbacher et al. (2001).

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the taxes: ―The feeling of being controlled in a negative way, and being suspected of tax cheating, tends to crowd out the intrinsic motivation to act as an honorable taxpayers and, as a consequence, tax morale will fall. In contrast, if the tax official makes an effort to find out the reason for the error by contacting the taxpayer in an informal way, the taxpayer will appreciate this respectful treatment and tax morale will be upheld‖ (p. 4). They divide respectful treatment, by the tax administration into two components (p. 5): (i) transparent and clear procedure, (ii) respecting taxpayers‘ character. Their empirical analysis shows that treating taxpayers respectfully reduces tax evasion.

Alm and Torgler (2006) combine a large number of Western European countries into an empirical study using multiple regression analysis. They differentiated between Romanic and Northern Countries. The results indicate that people from Northern Europe possess higher tax morale than people from Southern Europe. The marginal effects indicate that being from a Romanic country rather than from Northern Europe reduces the probability of stating that tax evasion is never justified by 1.3 percentage points. Similarly, Weck (1983), Weck, Pommerehne, and Frey (1984), and Frey and WeckHannemann (1984) use cross-country survey results for the years 1960, 1965, 1970, 1975, and 1978 in order to develop an aggregate, country-level ―tax immorality‖ index for various countries. Their index indicates that Romanic countries have a higher tax immorality than most other countries. In this context, Kirchgässner (1999) argues that state and religious authority were largely held by one person in the northern states of Europe (in contrast to the majority of Catholic countries in the south). Offenses against the state were therefore also religious offenses and consequently a sin. Torgler and Schneider (2007b) looking at Switzerland, Spain and Belgium observe that cultural and regional differences within a country also affect tax morale in both Switzerland and Spain.

Looking at Europe and
# **2010:M6**

Source: Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010. Shaded area marks period of recession.

Our interest is in the coefficients related to the dummy variables for the various quarters; more specifically, we would like to know whether there was a significant change in expenditure per capita between 2007 and 2008, 2008 and 2009, and 2009 and 2010. The results based on the estimates in Table 1 are summarized in Table 2. They show that real per capita consumption was lower in 2008 than in 2007 (p-value = 0.00, and -1,058 is more negative than -291), significantly higher in 2009 than in 2008, and significantly lower again in 2010.

In other words, _real expenditure per capita in Thailand was, on average, higher during the main recession year (2009) than in either the previous year or the subsequent year_ , after holding other influences constant. The main explanation is relatively straightforward: prices fell sharply during the recession, which boosted purchasing power, while households were able to smooth their consumption relative to their incomes; we discuss these effects more fully below.

For each section of the population of interest – for instance, urban dwellers, or poor households – we did similar computations, first estimating regressions based on the sample for the group in question, and then testing for changes in expenditure per capita over time. Tables 3-5 report the results; for all cases where there was a significant change in per capita expenditure from one year to the next (at the 5% level), the numbers are shown in bold face; for the cases were real expenditure was lower in 2009 than in 2008 the cells are highlighted in yellow.

The experience of Greater Bangkok was out of synch with the rest of the Thailand. In all of the regions except for Bangkok, real expenditure per capita fell in 2008, rose in 2009 relative to 2008, and then fell again in 2010 (Table 3),
Figure 1. Gender gap in time use by activity (men minus women). Hours per day Panel A. Market work Panel B. Unpaid domestic work <!-- Start of picture text --> 6.0 0.0<br>5.0 -1.0<br>4.0 -2.0<br>3.0 -3.0<br>2.0 -4.0<br>1.0 -5.0<br>0.0 -6.0<br>Panel C. Personal care Panel D. Leisure, social and study<br>0.8 2.0<br>0.4 1.5<br>0.0<br>1.0<br>-0.4<br>0.5<br>-0.8<br>0.0<br>-1.2<br>-0.5<br>Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br>Albania Bangladesh Colombia Ecuador Ghana Guatemala Moldova Mongolia Pakistan WBG Peru South Africa Turkey Uruguay USA Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Turkey Uruguay USA<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data.

Notes: The dashed line in each panel represents the average gender gap (hours per day) for each activity across countries.

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The relationship between labor demand and supply is given in equation A63 below. Aggregate supply _LS_ is a function of the working age population, average annual hours, and an index of labor quality: _DIV_ denotes dividends from the households’ share of capital income and is explained below in A75. _G_I_ and _G_transfer_ represent interest and transfers from the government, and _R_transfer_ is transfers from the rest-of-the-world. _T LUMP_ is the lump sum tax that is used in policy simulations.

Household income is allocated between consumption ( _VCCt_ ) and savings. In this model we use a simple Solow growth model formulation with an exogenous savings rate ( _s t_ ) to determine private savings ( _Stp_ ): Total consumption expenditures are allocated to the 33 commodities identified in the model. We do this with a demand function estimated over household consumption survey data. This consumption data is at purchaser’s prices and follows the expenditure classification; these have to be linked later to the IO classifications and the factory-gate prices of the IO system. We arrange the demand system in a tier structure shown in Table 1. At the top tier total expenditures is allocated to Food, Consumer Goods, Housing and Services. In the sub-tiers these four bundles are allocated to 27 items.

‐53‐
Federle, J., A. Meier, G. Muller and V. Sehn. 2022. "Proximity to War: The Stock Market Response to the Russian Invasion of Ukraine." CEPR Discussion Paper 17185.

Fergusson, L., A. M. Ibanez and J. F. Riano. 2020. "Conflict, Educational Attainment, and Structural Transformation: La Violencia in Colombia." _Economic Development and Cultural Change_ 69 (1): 335-371.

George, J., A. Adelaja and D. Weatherspoon. 2020. "Armed Conflicts and Food Insecurity: Evidence from Boko Haram's Attacks." _American Journal of Agricultural Economics_ 102 (1): 114-131. Glauben, T., et al. 2022. "The War in Ukraine, Agricultural Trade and Risks to Global Food Security." _Review of European Economic Policy_ 57 (3): 157-163. Ibanez, A. M., A. Moya and A. Velasquez. 2022. "Promoting Recovery and Resilience for Internally Displaced Persons: Lessons from Colombia." _Oxford Review of Economic Policy_ 38 (3): 595-624. Korovkin, V. and A. Makarin. 2023. "Conflict and Intergroup Trade: Evidence from the 2014 Russia-Ukraine Crisis." _American Economic Review_ 113 (1): 34-70. Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A., 2017. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters 14, 778-782. Kussul, N., A. Shelestov and G. Lemoine. 2023. "Using Freely Available Imagery to Identify Damage from Military Action: A Machine Learning Approach Applied to Ukraine ", _Paper Presented at the 9th International Conference on Agricultural Statistics_ , World Bank, Washington DC. Lark, T. J., R. M. Mueller, D. M. Johnson and H. K. Gibbs. 2017. "Measuring Land-Use and Land-Cover Change Using the U.
<br>(associative study)|Higher female<br>literacy in a district<br>is associated with<br>lower odds of a<br>gender gap in child<br>mortality. These<br>results hold across<br>all regions.|
|Ren (1995)|What covariates are<br>associated with<br>changes in the<br>gender-gap in infant<br>and child mortality<br>rates?|China In-depth<br>Fertility Survey<br>(1985 and 1987)|Multi-variate logit<br>regression<br>(associative study)|When mothers are<br>educated, girls had<br>relatively higher<br>odds of neo-natal<br>survival than boys|
|Pande and<br>Astone (2007)|What covariates are<br>associated with<br>lower levels of son<br>preference?|1992-93 NFHS|Ordered logistic<br>regression<br>(dependent variable<br>is son preference<br>measured in an<br>ordinal scale)<br>(associative study)|Female education,<br>particularly at<br>secondary plus<br>levels, was<br>associated with<br>lower son preference<br>regardless of family<br>size (or other<br>controls)|

> 19 See Stout et al.’s (2011) experimental study on how random exposure to successful women in science, technology, engineering, or mathematics (STEM) changed young girlsgirls’ perceptions about the ability of females to succeed in STEM careers. Also see Marx and Roman’s (2002) experimental study on how women’s math test scores improved in the presence of women who represented stereotype defying information about women’s mathematical abilities in general.

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_Figure 1: Poverty versus vulnerability to poverty in El Salvador, 2019_ <!-- Start of picture text --> Poverty versus Vulnerability Rate (2019)<br>Poverty Rate Vulnerability Rate<br>41.2%<br>32.6%<br>23.7%<br>21.7%<br>15.0% 13.5%<br>All Rural Urban<br><!-- End of picture text --> _Note: The poverty rate reported here is the estimated poverty rate from the model and not the observed poverty rate. Source: World Bank estimates based on EHPM (2019)._ _Figure 2: Vulnerability Decomposition_ <!-- Start of picture text --> Poverty versus risk-induced vulnerability (2019)<br>Poverty-induced Risk-induced<br>0.24<br>0.15<br>0.18 0.10<br>0.09<br>0.04<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2019)_

**Idiosyncratic shocks are more prevalent in El Salvador than covariate shocks.** Risk-induced vulnerability can arise from idiosyncratic or covariate shocks. The ratio of idiosyncratic to covariate vulnerability is 2.5 in El Salvador (see Figure 3). The ratio of idiosyncratic to covariate vulnerability is 2.2 in rural areas and 3.4 in urban areas. These results suggest that shocks at the household level are more marked for urban areas than municipality-level shocks. In addition, households in urban areas are relatively more vulnerable to crime, illnesses, or price increases than rural households. Still, rural households are also more affected by these individual shocks than those happening at the municipality level.

_Figure 3: Sources of vulnerability_ <!-- Start of picture text --> Idiosyncratic versus covariate vulnerability (2019)<br>Idiosyncratic Covariate
Heterogeneity in the returns to R&D activities is of paramount importance for policy. Firms are expected to invest more in those innovation activities that yield positive returns; in other words, if the returns are low, as in the case of many developing and middle-income countries (Goñi and Maloney 2017), it is unlikely that firms will invest in innovation activities. Policies that aim to encourage investments in R&D but cannot affect these returns will fail, especially in sectors and regions with low returns. Therefore, understanding what factors explain these low returns and the heterogeneity in attaining them is critical for policy design and effectiveness.

In this paper, we examine the hypothesis that several contextual factors determine both the set of technological possibilities and the probabilities the innovation will succeed (in terms of higher productivity), as well as firms’ capacity to appropriate the rewards of innovative investments in their production process. We call these factors, respectively, _knowledge complementary factors_ and _market complementary factors._ We also hypothesize that the impact of these complementary factors is mediated by the firm’s innovative capacity, which is manifested in specific management strategies that can maximize the returns when these complementary factors are available.

This study contributes to previous literature because it is the first to empirically analyze the relationship between contextual factors and R&D returns in Argentina with micro-level data in a comprehensive set-up.<sup>4</sup> In Argentina, firms’ context is frequently mentioned as an important factor incentivizing firms’ defensive or short-term low-risk strategies, instead of pursuing longer-term innovative strategies that could boost productivity levels (Katz 2000, Katz and Bernat 2011, Arza 2013, Kosacoff 2000, Chudnovsky 2001, Fanelli 2002). The novelty of our approach lies in the comprehensive evaluation of this hypothesis in an econometric framework. This allows us to estimate the relationship between innovation returns and the context dimensions (which we group into “knowledge” and “market” factors), while also considering firm-specific innovative capacity. In addition, this is one of
**Children’s Birth Location:** For any household member who are 15 or older, the IFLS has information about the location types of birth: village, small town, or a big city. If the respondents were born in a village, we define the birth location as rural and otherwise, urban. The IFLS collects this information in all waves. For any respondent, we use the birth location in the latest wave in which the respondent reported his/her birth location type.

**Child Cognitive Ability:** We use the Raven test scores and two memory tests to construct the cognitive ability index. An advantage of these measures is that they do not require any knowledge of numeracy or literacy to do well in the tests. To arrive at the index, we first take the first principal component of the raven test scores and two memory tests. Then we regress that on age and age square. A critical issue is that cognition outcomes are positively correlated with age because of the Flynn effect. We, therefore, use residual of the regression as the cognitive ability index.

**Education Expenses:** The IFLS has separate education modules for children (age below 15) and adult (age equal to or above 15). The expense data includes all types of school-related expenses. We aggregate the expenses Using the Panel ID, we match individuals of our children sample (18 to 40 years old in wave 5) with their education expense data in the previous waves. The expenses are then adjusted for inflation taking 1993 as the base year; we obtain the yearly inflation of Indonesia from the World Bank web site.

In the investment sample, we also control for number of school age of children. To calculate the number of school children in wave, we first obtain the fathers’ ID from the roster. We then calculate how many other individuals in the education sample also listed as the same ID as the father. That number provides us with the number of school age