rafmacalaba/data-use-mentions-extended huggingface dataset:
the averages over the<br>period 1971-1985. Source: Penn World Tables (Mark 5.6).|
|GDP Growth|"Annual percentage growth rate of GDP at market prices based on constant local currency."<br>Data are the averages for available years over the period 1971-1985.|
|Lending Interest Rate|"Lending interest rate is the rate charged by banks on loans to prime customers." Real<br>lending rate is computed using GDP deflator. Data are the average over all available years in<br>the period 1971-1985.|
|Imports/GDP|"Imports of goods and services represent the value of all goods and other market services<br>provided to the world. Included is the value of merchandise, freight, insurance, travel, and<br>other nonfactor services. Factor and property income (formerly called factor services), such as<br>investment income, interest, and labor income, is excluded." Data are the averages for<br>available years over the period 1971-1985.|

Data source for explanatory variables is the 1999 _World Development Indicators_ on CD-ROM unless otherwise noted.

17
IMF (International Monetary Fund) and World Bank. 2017. Review of the Debt Sustainability Framework in Low-Income Countries: Proposed Reforms. International Monetary Fund, Washington DC.

Kaiser, J. 2013. “One Made it Out of the Debt Trap. Lessons from the London Debt Agreement of 1953 for Current Debt Crises.” International Policy Analysis, Friedrich Ebert-Stiftung, Bonn.

Kaufmann, D. A. Kraay, and M. Mastruzzi. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” Policy Research Working Paper 5430, World Bank, Washington, DC.

Kilic Celik, S., M. A. Kose, and F. Ohnsorge. 2020. “Subdued Potential Growth: Sources and Remedies.” In Growth in a Time of Change: Global and Country Perspectives on a New Agenda, edited by H.-W. Kim and Z. Qureshi. Washington, DC: Brookings Institution.

Kose, M. A., S. Kurlat, F. Ohnsorge, and N. Sugawara. 2017. “A Cross-Country Database of Fiscal Space.” Policy Research Working Paper 8157, World Bank, Washington, DC.

Kose, M. A., P. S. O. Nagle, F. L. Ohnsorge, and N. Sugawara. 2020. “Can This Time Be Different? Policy Options in Times of Rising Debt.” Policy Research Working Paper 9178, World Bank, Washington, DC.

Kose, M. A., P. Nagle, F. Ohnsorge, and N. Sugawara. 2021. Global Waves of Debt: Causes and Consequences. Washington, DC: World Bank.

Kose, M. A., and F. Ohnsorge. 2019. A Decade after the Global Recession. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, and N. Sugawara. 2021. “A Mountain of Debt: Navigating the Legacy of the Pandemic.” Policy Research Working Paper 9800, World Bank
relationship between IFFs and tax revenues—related with the mere measurement of IFFs and reverse causality—we exploited the Financial Action Task Force (FATF) data in an impact assessment analysis. Propensity score matching estimations revealed a significant tax revenue loss in countries associated with important IFFs with respect to comparable countries that are not signaled as being related to important IFFs. This causal effect—that was estimated to be economically meaningful—is supported by a large robustness analysis, and in particular remains unchanged when using several “doubly robust” estimators. Lastly, we unveiled heterogeneities in the impact of IFFs on tax revenues, related to the type of tax—significantly lower indirect taxes but not direct taxes—and the considered environment.

Our findings should be understood in two ways. First, it is not because the number of countries on the FATF list decreased that the damaging effect of IFFs on tax revenue mobilization was wiped off: although probably of a lower magnitude, IFFs still continue to 24
3 to ensure that questionnaires are designed to comply with the international standards laid out by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS).<sup>7</sup> Our findings are relevant to the literature on knowledge gaps on FDP. Though few, there are prior studies undertaken to review existing evidence and data gaps around migrants and displaced populations (e.g., Baal 2021; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023; EGRISS 2023). Berretta et al. (2023) provide a systematic review of existing studies on the causes of migration. Most recently, the _Compilers’ Manual on Forced Displacement Statistics_ by the Expert Group on Refugee, Internally Displaced Persons and Statelessness Statistics (EGRISS 2023) features some of the recent attempts to collect micro-level data on refugees and IDPs through censuses and household surveys.

This paper develops a systematic approach to identify publicly available micro-level datasets on FDP. While there are several previous studies that attempt to assess data gaps on FDP,<sup>8</sup> they are limited in scope in terms of both geographical and temporal coverage and often lack a rigorous and systematic approach to guard against reviewer selection bias (Sida 2020; James et al. 2016; Campbell Collaboration 2015; Clapton et al. 2015; Gough et al. 2012; Rico and Camilo 2022).<sup>9</sup> This current analysis systematically reviews all datasets on FDP from the UNHCR and WB MDLs. While our paper has its limitations, the proposed methodology is the first attempt to develop a replicable and scalable approach to identify the universe of publicly available FDP microdata.

Our approach is innovative in that we leverage the entire collection of datasets found in the UNHCR and WB MDLs with the aid of a natural language processing tool and text mining. This involves scraping metadata from thousands of publicly available datasets in those databases, evaluating whether they include a representative sample of refugees and IDPs, and assessing various attributes of each dataset
|[0.012]|[0.030]|[0.040]<br>|[0.029]|[0.031]|
|\gamma_{oth._tuber}|-0.089***<br>[0.011]|-0.035***<br>[0.013]|-<br>0.110***<br>[0.027]|-0.119***<br>[0.020]|-0.158***<br>[0.028]|
|\gamma_{oth._oth.}|0.181***|0.016***|0.122***|0.143***|0.210***|
||[0.011]|[0.005]|[0.028]|[0.021]|[0.041]|
|Observations|21,239|6,766|4,785|5,743|3,945|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Source: 2012 1-2-3 Survey, DRC Tables 3 and 4 present estimates of the parameters of the QUAIDS for rural and urban households by poverty status. Most estimates are statistically different from zero, showing an overall good fit of the model. Estimated parameters differ in magnitude and sometimes in sign between rural and urban areas, highlighting the need to conduct a stratified analysis by area of residence and poverty status. These estimated parameters do not have a straightforward economic interpretation. The fact that most  coefficients are statistically different from zero further supports the choice of the QUAIDS that captures well the nonlinear relationship between the budget share devoted to aggregate commodities and logged expenditure, which is noticed in figures 3.
(1981), hereafter the HT estimator, and the Amemyia and MaCurdy (1989), hereafter the AM estimator. These are discussed below.

_The Within Estimator:_ To simplify notation, rewrite equation (6) as: where **xi,** is a _K_ x I vector collecting all the time-variant explanatory variables in equation (7),z, is a G x 1 vector collecting all the time-invariant explanatory variables, and **it1** and **it 2** are conformably dimensioned parameter vectors. We assume that the disturbances **si,** are _iid N(0,a6_<sup>_2_</sup> _)_ and the individual effects **m1** are _iid_ N(O,u,,<sup>2</sup> ). The time and household-variant component **6,,** are assumed to be orthogonal to both the explanatory variables and the individual effects, whilerli may be correlated with parts of x and z.

Combining all _NT_ observations we can write (8) as: where y and _£_ are _NT x_ 1, x _is NT x K,_ z is _NT x_ G, and V is an _NT x N_ matrix of individualspecific dummy variables. Now define the matrix Pv _= V(V'V)-'V_ as the projection onto the column space of V. Thus, _Pv_ is a matrix that transforms a vector of observation into a vector of individual means across time: i.e., _Pvy,,_ = _(=I_)zyi,_ yi. Then, Qv _=_ **_I_** - _Pv_ is defined as the projection onto the null space of _V,_ i.e., Q, produces a vector of deviations from individual means: i.e., _Pvy,= yj, -yp._ The within estimator is computed by projecting (9) onto the null space
largest predicted adequate housing deficit include India (73.3 million), Ethiopia (13.4 million), and Bangladesh (9.6 million). Together, these three countries account for about 48 percent of the overall predicted adequate housing deficit in rural areas in the sample. The five countries with the largest forecasted rural population by 2030 (India, Pakistan, Indonesia, Nigeria, and Ethiopia) are likely to have a cumulative adequate housing deficit of 112.9 million housing units.

# **6. Conclusion**

In this paper, we introduce a new _Adequate Housing Index_ , which allows for a micro-founded comparison of housing adequacy across 64 emerging economies. To the best of our knowledge, this is the first paper to present cross-country microdata pooled from household surveys on housing conditions on a series of countries across different income groups and continents.

20 Due to the absence of location marker in Albania’s and Turkey’s HCES, urban estimations are excluded here.
Haddad, L., J. Hoddinott and H. Alderman (eds) (1997), _Intrahousehold Resource Allocation in Developing Countries,_ Johns Hopkins University Press, for the International Food Policy Research Institute.

Kakwani, N. (1993), Statistical inference in the measurement of poverty, _Review of Economics and Statistics_ , vol. 75, pp.632-639.

Klasen, S. and R. Lahoti (2016), “How Serious is the Neglect of Intra-Household Inequality in Multidimensional Poverty Indices”, CRC-PEG Discussion Papers No. 200.

Lanjouw, P. (2005), “Constructing a Consumption Aggregate for the Purpose of Welfare Analysis: Issues and Recommendations Concerning the POF 2002/3 in Brazil”. Development Economics Research Group, World Bank.

https://www.researchgate.net/publication/255573370_Constructing_a_Consumption_Aggregat e_for_the_Purpose_of_Welfare_Analysis_Issues_and_Recommendations_Concerning_the_POF_200 23_in_Brazil.

Ndiaye, S., M. Ayad (2006), "Enquête Démographique et de Santé. Sénégal 2005", Ministère de la Santé et de la Prévention Médicale. Centre de Recherche pour le Développement Humain. Dakar.

Ndoye, D. _et al_ . (2009), "Tendance et profil de la pauvreté au Sénégal de 1994 à 2006" _<u>Perspective Afrique</u>_ <u>, Association Africaine pour les Sciences sociales, vol. 4(1-3), pages 1-29.</u> Nichols, A. (2008), « Erratum and discussion of propensity-score reweighting”, _Stata Journal_ , 8, n°4, pp.532-539.

Ravallion, M., (2000). “Should Poverty Measures Be Anchored to the National Accounts?” _Economic and Political Weekly, 35_ (35/36), 3245-3252.

Rose, E. (1999), “Consumption Smoothing and Excess Female Mortality in Rural India”, _Review of Economics and Statistics,_ 81:1, 41-49 Scott, C. and B. Amenuvegbe (1991), “Effects of Recall Duration on Reporting of Household Expenditures
Nairobi, we obtain the distribution of employment using data from Henderson et al. (2018), where for each grid square we know the footprint and height of every building in Nairobi in 2015 – from aerial photo and Lidar data – and can calculate building volume. We match these buildings with land use maps, before taking total employment of the city and smearing it into grid squares according to each grid squares share in total volume of nonresidential buildings in Nairobi. The alternative would be to smear it into commercial and industrial buildings, ignoring public buildings. Crucially, unlike Landscan we do not need to base smearing on inferences from satellite images of what uses buildings have; instead, we know the use and the volume fairly accurately.

For each city we create a measure of the ambient population according to the following equation: We base our replication of the ambient population just on places of work and residence, where we assume for 14 hours of a day (nocturnal) all people are in their grid square of residence to sleep, eat, and recreate. For 10 hours a day, we add in the employment in the grid square, allowing people time to work, hangout, and finish commuting. We then add in the non-working population of the grid square assuming they remain in that square kilometer and subtract out the resident workers (since we have already counted employment). If everyone works in their grid, then we just have total grid square population; but, for downtown grid squares where few people live, most have replication numbers from employment. We make no allowance for the time people are on roads or shopping outside the grid square. We have no information on which to base such inferences, especially in a context where so many people commute by walking.

In Figure 1 for Kampala, we show 4 items. As noted above, in the upper left panel of each figure is the population distribution over space and on the right upper panel is the employment distribution. In the bottom panel, on the
restrictions significantly differ across European countries as can be seen in Figure 2, no comprehensive study on the EU’s services sector and its effect on the wider downstream economy has until now been undertaken. This paper therefore performs a first step to fill that gap and takes the European market as a point in case. In sum, this is the first study to perform firm‐level analysis with a multiple country dimension using developed economies.

In particular, our study contributes to the existing literature as hitherto discussed in the following ways. First, we augment all the previous country‐specific papers by using firm‐level data that spans multiple (European) countries so as to have a consistent cross‐country (almost balanced) panel sample for both goods and services firms. This is important as it could provide us with close estimates of the real economic potential of future reforms across the entire European landscape rather than concentrating specifically on one country and / or on the manufacturing sector only. These are two valuable additions in our paper that have so far not been exploited. Previous firm‐level works related to our topic only takes up one country and only the manufacturing sector. Our rich data set allows for changing this tradition by not only concentrating on multiple countries, but also by taking into account all other downstream services sectors at a disaggregated level that are present in European economies.

Second, we exploit alternative TFP estimates which have been developed over recent years and have been applied individually in each previous study and never together.<sup>5</sup> The productivity literature has presented several estimates of how to construct a credible TFP indicators with estimation strategies from Olley and Pakes (2003) and Levinsohn and Petrin (2008) as the most commonly used ones.<sup>6</sup> This recent literature has also brought forward new TFP measures such as the one by Ackerberg _et al_ . (2006;

> 4 A study by Bourlès _et al_ . (2013) and Barone and Cingano (2011
planning have lost value during the transition. At the same time, the value of such characteristics as flexibility, adaptability, ability to learn, market-oriented education, foreign language and computer skills – all of which tend to be stronger for younger workers – has increased. As a consequence, both observed and estimated age-earnings profiles (based on available workers’ characteristics) in the transition economies tend to peak at a much earlier age than in other industrialized countries (see Hazans (2002) for a more detailed presentation).

The Latvian LFS 2002 can be used to estimate age-earnings profiles separately by gender and education<sup>11</sup> . Results are summarized in Figure 23 (models are not presented here). The male earnings profile peaks at 37 years of age when no postsecondary education is acquired and at 34 – 35 otherwise. After this peak, the value of postsecondary vocational education declines with age faster than the value of general secondary education. For females with higher education the peak of the age-earnings profile is 39 years. This profile is flatter than for males. Such a result contrasts with the evidence from Western countries, where age earnings profiles are generally steeper for women. By this measure, women of childbearing age are less disadvantaged in terms of relative wages than in developed market economies. For females with secondary or postsecondary vocational education the advantage of being young reveals itself even more strongly. Age-earnings profiles are remarkably flat. For females with basic education they are downward sloping.

Table 4 confirms that strong position of young employees in the Latvian labor market is a feature shared with the other two Baltic countries. Predicted earnings in the Baltic countries peak not only much earlier than in the West, but also than in the Czech Republic and Slovakia. Earnings peaks in the latter countries are found at 46 – 47 years in the private sector and at 53 in the public sector (Juraida (2001), based on 1998 data).

**<u>Table 4.  Predicted Age of Peak  Earnings<
stamp transfers. In estimating the MPC, the panel data (986 households over 1989-1991) was exploited. Following Ravallion, van de Walle and Gautam, (1995) the first difference of consumption was regressed on the first difference of FSP and other household characteristics between 1991 and 1989. This was used to estimate an 'average' MPC for the sample.

Three regressions representing three definitions of household expenditure were estimated (see Table 7). With few exceptions (month of interview, dummy variables to capture the quality of housing), all other variables are first differences. The regressions also include variables for household structure (i.e. household composition and education of household head). Variables to capture quality of housing were included in the regression to capture household income effects. Aside from variables that captured housing quality and household structure, additional variables that influence program access were also - included in the regressions i.e. dummy for the presence of pregnant or lactating women, dummy variable for the presence of a person receiving food stamps because they ar disabled. However, a proxy variable that indicated whether there were handicapped individuals receiving Poor Relief transfers in a household was included in the regressions. This variable was found to be significant, but did not alter the estimate of the marginal propensity to consume out of food stamp transfers.

Table 7 summarizes the regression results. The results yield coefficients of .12 and .11 for the impact of food stamp transfers on expenditures-depending on whether expenditures exclude or include non-consumption expenditure on insurance, taxes, transfers, interest, and depreciation assets. There are several potential explanations for 19
Some interesting cross-sectoral patterns emerge from Table 3.<sup>36</sup> On average across all countries, the number of exporters is largest in electrical machinery and smallest in live animals and animal products and in arms and ammunitions. The degree of concentration as measured by the share of the top 5 percent of exporters is largest on average in precious metals and in plastics. Entry and exit rates are highest in transportation vehicles and mechanical machinery (more than 60 percent) and lowest in foodstuff (about 45 percent).

# **IV. The Exporter Dynamics Database: Six Stylized Facts**

In this section we present four new stylized facts (facts 1 through 4) and we generalize two stylized facts (facts 5 and 6) from earlier work. The distinguishing feature of these stylized facts is that they could not have been discovered using any other cross-country source of trade data available so far. Therefore, they show the usefulness of the Database and illustrate the possibilities of future policy-relevant analysis and research that can be done using the Database.

**_Stylized Fact 1: More developed and larger economies have a larger export base (number of exporters), larger average exporter size, more concentrated export sectors among firms, and more diversified exporters (in terms of their portfolios of products and destinations). In contrast, more developed and larger economies exhibit significantly lower exporter entry and exit rates. The survival rates of new entrants are not correlated with the level of development or the economic size of countries. Similar patterns are obtained for the importance of the export sector in the country._**

> 36 We exclude Kuwait and Portugal from the calculation of the averages across all countries that export that particular group of HS 2-digit sectors shown in Table 3 since those countries do not have data for the period 2006-2008.

22
# **A. Introduction**

Conflict-induced forced displacement—defined as situations where people are forced to flee their homes due to conflict, generalized violence and human rights violations—is gaining prominence as a topic of discussion, both internationally and within the World Bank.<sup>2</sup> This is partly due to the massive refugee flows generated by the Syrian conflict, but also motivated by a growing consensus that forced displacement poses a substantial threat to the achievement of the Sustainable Development Goals (SDGs), especially for countries of origin and host countries of refugees and IDPs. Within the Bank, the adoption of the twin goals of eliminating extreme poverty and enhancing shared prosperity requires increased attention to vulnerable and disadvantaged communities, among whom the displaced rank highly. Support for a development approach to displacement, which promotes durable solutions based on economic opportunity and selfreliance is gaining ground.

Establishing an accurate picture of the scale and dynamics of conflict-induced forced displacement is extremely challenging. Data are collected from a multiplicity of sources, employing different definitions and methodologies, leading to variations in the comparability, quality and reliability of reported data within and between countries. Numbers can change rapidly due to ongoing conflicts and monitoring can be challenging due to the fluidity of population movements and lack of access to conflict-affected areas. In a number of contexts, statistics may misrepresent the true scale of displacement due to practical difficulties associated with data collection, political incentives to manipulate figures or methodological issues. Reported data may be incomplete due to limited coverage of data collection activities, or because collected data are not fully reflected in aggregated statistics due to poor coordination of data compilation activities. Significantly, headline figures on forced displacement do not capture affected host communities and returnees who may still face specific vulnerabilities associated with their displacement.

Despite these challenges, large amounts of data on forcibly displaced populations are collected and disseminated each year and used to inform policy and programming. Substantial amounts of data are gathered and published by national governments, international organizations, and national and international