rafmacalaba/data-use-mentions-extended huggingface dataset:
medium-scale farms for 2021, 2022, and 2023. Drops in output prices together with higher input costs more than halved farm profitability in 2022. In 2023, most output prices continued to decline, so lower input costs brought only limited respite. Price changes affect all farm sizes equally, suggesting that diversification into higher value crops that are less sensitive to transport cost remained limited.

# **4.1 Descriptive evidence**

To complement image-based data on crop cover and conflict incidence, we use data on prices of key inputs and outputs that were collected in a consistent format every year from 2021 to 2023 for the country’s main farming areas. In line with the literature that highlights the disruption caused by the loss of links to export markets through Ukraine’s major Black Sea ports (Wilson _et al._ 2024), three trends can be discerned from panel A in table 6. First, output prices for the main crops almost halved in 2022 due to higher export costs, which can be attributed to the lower capacity at Ukraine’s main ports and higher cost of alternative access points to global markets through Poland or Romania. Second, with unit transport cost increasing by 48% over pre-war levels in 2022, prices for imported fertilizers more than doubled and those for other inputs also increased markedly directly after the invasion. Prices for locally produced services or inputs, although less affected, also increased markedly. Third, while most input prices returned to within 10% of their prewar levels by 2023, output prices for all crops except soybean declined further from their 2022 levels.

Although micro-data on agricultural profitability as used, for example, by Deininger et al. (2018), would be ideal for making inferences on the effects of the conflict, the State Statistics Service of Ukraine (SSSU) stopped making such data available in 2019. To assess the impact of the conflict on changes in farm profitability, we thus resorted to conducting our own survey of small and medium-scale farmers in areas not affected by the
> Physical Units", Acta Oeconomica,<sup>Vol. 2, No. 1-2, Budapest.</sup>

- Eurostat (1983), Comparison<sup>in Real Values of the Aggregates of ESA: 1980,</sup> Luxembourg.

- S. Fabricant (1940),<sup>The Output of Manufacturing</sup> Industries, 1899-1937, NBER, New York.

- A.W. Flux (1933), "Industrial Productivity in Britain and the United States", Quarterly Journal<sup>of Economics, November.</sup>

- M. Frankel (1957),<sup>British and American Manufacturing</sup> Productivity, University of Illinois,<sup>Urbana.</sup>

- Fundacao Getulio Vargas<sup>(1984), Contas Nacionais do Brasil: Metodologia e</sup> Tabelas Estatisticas, Rio.

- W. Galenson (1955),<sup>Labour Productivity in Soviet and American Industry,</sup> Columbia University<sup>Press, New York.</sup>

- A. Ghosh (1984),<sup>"Some Problems Relating to the Selection of Representative</sup> Commodity Baskets,<sup>the Problem of Quality Differences and the Treatment</sup> of Comparison Resistant<sup>Services in the ICP", Bellagio, processed,</sup> September.

- A. Ghosh (1985), "Comparison of Structurally Divergent Economies,<sup>Problems</sup> and Limitations", Economic and Politicial<sup>Weekly, January l9th.</sup>
2009. Similar to the analysis performed for Jordan, we define the sample as women and men aged 15 or more in 2006 and up to 64 in 2009. The sample includes 4,261 women and 3,641 men who were present in both waves of the survey. We define an indicator of household internet adoption or continuation between 2006 and 2009 as the explanatory variable of interest and the change in labor force participation as the outcome variable. Table 9 shows that women’s labor force participation increased from 49 to 52 percent between 2006 and 2009, while men’s rate improved from 78 to 84 percent. Internet access at the household level moved from approximately 20 to 37 percent.

In Chile, the public fixed telephone network has been the most significant component of the internet access infrastructure at the beginning of the 2000s (Cominetti C. (2002)). Our hypothesis is that access to a fixed telephone line at that time is a good predictor of the distance to the system’s backbone in future year, and thereby to internet access. We propose to use the share of households in each province that had a fixed telephone line in 2002 as an instrumental variable for the indicator of internet adoption or continuation between 2006 and 2009. We used information from the national 2002 Census which shows that this share ranged between 0.13 and 0.75.

We estimate OLS and IV models using individual and household observable characteristics in 2006 as control variables. We include the same set of regressors as in the previous models for Jordan –age, educational level, marital status, indicator of urban area, household size, and per capita household income. Models include region fixed effects and standard errors are clustered at the province level. Our main results appear in Table 10. Panel A presents the second stage estimates, while Panel B summarizes first stage results. The share of household that in each province had a fixed telephone line in 2002 is a good predictor of internet access or continuation between 2006 and 2009,
||71.4||
|2010||70.0|||67.8||
|2011||70.2|||68.1||
|2010/11|||72.0|||69.9|
|2018/19|80.8|||78.5|||

Note: the table shows a summary of the main estimates of number of people living below the US$1.90 poverty line using different population data. The left-hand side panel shows the number of extreme poor (in million) using the NBS population data, the right-hand side panel uses WDI population data (used throughout the rest of the paper).

40
9

**Table 2.** Goodness of Fit Estimates for Synthetic Jordan, EFW Index

||**OIC All**|**OIC Minus PAK**|**OIC SAL**|
|---|---|---|---|
|_Pre-treatment fit_||||
|(1) RMSPE|0.097|0.104|0.119|
|_Permutation tests_||||
|(2) Pr(post RMSPE≥Jordan)|0.154|0.077|0.000|
|(3) Pr(pre/post RMSPE≥Jordan)|0.308|0.231|0.000|
|(4) Pr(pre RMSPE≥Jordan)|0.692|0.769|0.875|

_Source_ : Authors’ analysis of the Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Table presents goodness of fit metrics for Synthetic Jordan with the EFW index as the outcome of interest. Goodness of fit for Synthetic Jordan is denoted by its Root Mean Squared Prediction Error (RMSPE). Placebo tests reassign the 1990 refugee surge to each control country and report the fraction of countries with a posttreatment RSMPE greater than Jordan’s RMPSE, normalized ratio of pre- to post-treatment RMSPE greater than Jordan’s RMSPE, and pre-treatment RMSPE greater than Jordan’s. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).

**Table 3.** Predictor Balance for Synthetic Jordan

||**Real**<br>
HNLSS. Evidence suggests that changes to the survey questions used to capture consumption can alter poverty estimates considerably (Deaton 2005, Beegle at al. 2012). The poverty headcount rate estimated using the 2009/10 HNLSS is 56.4 percent. Given these differences, it would be erroneous to compare this estimate with the latest poverty headcount rate of 39.1 percent estimated using data from the 2018/19 NLSS.<sup>4,5</sup> This paper explores other analytic solutions to fill this gap and finds alternative methods to estimate a long-run poverty trend in Nigeria.

> 4 Beyond changes in the consumption module, the data collection for the 2018/19 NLSS has substantially improved compared to previous years (detailed survey documentation can be found in the World Bank Microdata Library available here: https://microdata.worldbank.org/index.php/catalog/3827/related-materials). Among other improvements, the 2018/19 NLSS switched to CAPI allowing for better real-time monitoring of data collection. 5 The regional and global poverty estimates currently published by the World Bank are based on an interpolated poverty trend between these two estimates for Nigeria. In effect, this trend erroneously compares the two estimates and results in estimating a drop of around 17 percentage points in Nigeria over the decade between 2009 and 2019. The evidence presented in this paper unveils a much lower reduction in poverty and stresses the importance of collecting frequent and comparable data when measuring poverty over time.

5
therefore they are included only in commitments.<sup>5</sup> We differentiate commitments and disbursements, as the former denote more the moment of the IFC investment decision, while the latter are more related to the client’s decision to request a

> recipient countries. Burnside and Dollar (2000) show that aid accelerates growth if a sound policy framework is in place. Hansen and Trap (2000) determine that the growth effect of aid is significant but not linear. Easterly et al. (2004) contend that Burnside and Dollar’s finding that aid positively affects growth is not robust to the inclusion of new data. More recently, Andersen et al. (2020) demonstrate that a share of aid is diverted to private bank accounts in offshore financial centers, indicating that elite aid-capture reduces the impact of aid on growth.

> 4 Over 80 percent of commitments and disbursements data for the study’s sample countries is in debt.

> 5 Over 60 percent of commitments and over 90 percent of disbursements data for the study’s sample countries is in long term financing instruments.

6
by addressing water quality in the existing network. Finally, the analysis was carried out in the context of a considered restructuring of an existing World Bank investment called the Lusaka Sanitation Program (LSP), and identifies the most efficient combined initiative was partial sanitation investment scale-up and investment in piped water in 10 priority wards, costing US$134 million, and reducing the risk of cholera city-wide by some 48%.

In the following sections, this paper outlines the relevant data for Lusaka that was assembled to complete the analysis, such as case location data on cholera cases, water supply and sanitation infrastructure and other potential predictors of cholera to conduct a geospatial analysis for addressing three important objectives:

- (i) use cholera case location data and geospatial covariates to generate a high-resolution map of cholera risk across the city;

- (ii) seek to explain patterns of risk in relation to putative causal factors;

- (iii) compare current water and sanitation infrastructure and access to the pattern of cholera risk, identify priority areas and compare alternative strategies for improved infrastructure.
effective are taxes and government spending in reducing inequality and poverty? and (4) What is the impact of fiscal reforms that change a particular tax or benefit's size and/or progressivity? (Lustig 2018).

To calculate the distributional effects of fiscal policy, the CEQ framework constructs different income concepts in sequential stages, starting from pre-fiscal income that includes only private income sources to final income that incorporates the (almost) full set of taxes and government benefits. In summary, the CEQ (Lustig and Higgins 2018) identifies four main concepts of household income (when the household surveys include variables of different sources of income).<sup>8</sup>

> 8 _Market income_ includes factor income (all wages and salaries received by workers and capital income), plus private transfers (remittances, private pensions, etc.) plus imputed housing rent, all income before taxes and social security contributions, government transfers and old-age pensions, minus the contribution to social insurance oldage pensions (only in cases where pensions are considered deferred. The contribution to old-age pensions is not subtracted when contributory pensions are financed by the state). _Disposable income_ is market income plus direct cash and near-cash transfers, minus personal income taxes and employee social security contributions (only when contributory pensions are treated as transfers). _Consumable income_ includes indirect subsidies (energy, food, and other general targeted price subsidies) and excludes indirect taxes (VAT, excise taxes, and other indirect taxes). _Final income_ (post-fiscal) is consumable income plus the value of in-kind transfers, such as free or subsidized government services in education and health with copayments and user fees subtracted; it reflects post-fiscal income.

7
from an analytical and policy perspective.

At the height of the crisis in 2014, Syria had an estimated 6.5 m IDPs, 4.4 m. refugees, and 1.5 m. additional migrants, which amounted to about half of the Syrian pre‐conflict population. Despite these numbers, relatively little was known about the wellbeing of these populations aside from anecdotal information and some data collected for the purpose of assistance of refugees. The UNHCR had the data to conduct such studies but not the time or the expertise and was understandably concerned about data protection whereas specialized organizations such as the World Bank did not have access to data and did not see refugees as part of their core mandate of reducing poverty worldwide. This changed in 2014 when the two organizations decided to join forces to prepare a welfare assessment of refugees in Jordan and 20
consistent with the definitions in use) for a product with a higher _PRODY_<sup>_11_</sup> as a continuous measure of product sophistication (Cirera, Marin and Markwald 2012). An alternative definition (to support the robustness of the concept) is for a contract with a higher _average selling price_ (adjusted for weight) of the product in the subsequent FPD. These two definitions were chosen over others, such as the technological content sophistication index from the OECD, because they are continuous and therefore allow for a finer level of analysis.

The expectation is that some of the experience variables will support upgrading over simple terminations (while still helping to reduce the hazard rate of death). For example, having already had experience with a destination or being very familiar with it would help sustain an export overall, and in the event of termination, make an upgrade more likely. This could be because the trust levels between traders have already been raised by a repeated interaction and the reliability of the Lao exporter was rewarded with the subsequent contract. Moreover, having a large number of peers could act as a “trap”, whereby the network would help a firm’s contracts survive but prevent it from upgrading through peer pressure that drags it back into the norm. Still, other variables could prove irrelevant for the death vs. upgrade choice model, and in such cases the coefficients would be indistinguishable (i.e. the two effects would be symmetrical).

# 4.3 **Estimation Strategy**

The use of duration models is well suited for a multivariate analysis of survival and its determinants. As discussed above, the analysis is run on an arrangement of the data into export units, which consist of monthly aggregates of firm-product-destination triplets (i.e. FPDs). This arrangement has already been used in the literature (Cadot, et al. 2010, Tovar and Martinez 2011), and allows for a simpler and more detailed analysis of various firm-, product- and
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 (e.g., geographical coverage, thematic focus, kinds of questions available for identification of displacement status). The innovation in our approach lays the groundwork for future data gap mapping exercises to be more objective, replicable, and scalable.<sup>10</sup> Additionally, this paper contributes to highlighting specific data gaps that have yet to be filled. To make further progress in filling data gaps, it is critical that we understand where those gaps exist today. This paper does exactly that by analyzing what and where data exists, so policy makers and development practitioners alike can gain a broad knowledge of the current data landscape on the forcibly displaced and aid their decision making based on available data.

# 2. Data

For this analysis, we leverage a rich catalogue of micro-level datasets from the UNHCR and World Bank MDLs. The WB MDL includes datasets from the World Bank, other international organizations, statistical agencies and other agencies in low- and middle-income countries. These datasets may also originate from population, housing or agricultural censuses or through an administrative data collection processes. This

> 7 See more information on EGRISS at https://egrisstats.org/.

> 8 See, for instance, Baal 2021; S 2020; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023.

> 9 For instance, Rico and Camilo (2022) assess the coverage of information on migrants by the censuses and regular household surveys, but their analysis is geographically confined to Latin America.

> 10 All analysis for this report is performed in R. The replication codes are all available from <u>https://github.com/takaakimasaki/DisplacementDataGap.</u>
, whereas in the services sectors, most establishments are in wholesale and retail. Data were collected face-to-face in 2019 for the state of Cear ́a. For the states of S ̃ao Paulo and Paran ́a, interviews were carried during 2022.

In addition to detailed information on the technology used for each business function, the FAT survey also includes information on several firms’ characteristics, which we use to control for other covariates likely to explain differences in technology adoption. For example, other than firms’ size, region, and sector, the database includes information on managers’ and workers’ education, the use of formal incentives and performance indicators, and in-

> 5In a small number of business functions, the technologies covered are used in various subgroups of tasks. For example, in the body pressing and welding functions of the automotive sector, the survey differentiates between technologies used for pressing skin panels, pressing structural components and welding the main body. In cases like this, we construct ranks of technologies for each subgroup of tasks within the business function, and then aggregate the resulting indices by taking simple averages across the task groups.

> 6The non-uniqueness of latent cardinal variables associated with an ordinal rank such as _r_ ˆ _f_ is common in many economic applications such as measures of institutional quality, quality of education, well-being, trust, social norms, and sophistication of management practices, to name a few. However, it is critical to demonstrate that these indices and results are robust to alternative plausible cardinalizations of the ordinal rankings they measure.

5
. This alternative measure, though, is less consistent than the obtained from the World Development Indicators and is incomplete for most former Yugoslavia countries, so we preferred to use the original from the WDI for the rest of the report. As reported earlier when citing the 2006 WDR, “One reason that inequality might make people less happy, even when controlling for absolute income levels, is that it violates their sense of fairness” (p. 82). The Gini, a less sensitive measure to changes in the tails of the distribution, can potentially not capture this correlation.

**Perceptions of fairness in moving ahead in life are associated with more positive perceptions about economic mobility.** The perception that effort, hard work and intelligence are the most important factors to succeeding in life, rather than criminal or corrupt ties, or political connections, reduces the probability of reporting movements down. Specifically, those who believe in fairness have 9 percentage points less probabilities of perceiving a worsening in their relative position. This is particularly the case in higher income countries in ECA (Table A2.3).

**Trust in public institutions is associated with more positive perceptions about economic mobility.** For any individual, moving from a neutral approach to trust to becoming trusting in public institutions reduces the likelihood of perceiving movements down by 7 percentage points. This effect is stronger (10 percentage points) in low and lower-middle income ECA countries.<sup>10</sup>

**The perception of having done better than colleagues in 1989 is associated with less probabilities of perceiving downward economic mobility** . Individuals who reported a relative improvement compared to their peers pre-transition have a 4 percentage point lower probability of reporting downward mobility. This

> 9 <u>http://www.wider.unu.edu/research/Database/en_GB/database/</u>

> 10 We also performed a robustness check, by replacing this indicator for trust in religious institutions. The rationale behind is that
relationship significance of FDIs and international reserves on stock prices (Barbic & CondicJurkic, 2011; Issahaku, Ustarz, & Domanban, 2013) or on the growth of economic output (Lazarov, Miteva-Kacarski, & Nikoloski, 2016; Lee & Chang, 2009). Be that as it may, the BOPNFA research indicator also captures the interactions of financial assets, portfolio investments, reserve assets, and debt investments, and thus provides for a broader joint factor impact on stock prices. So, while the results show a positive BOPNFA coefficient, the findings in this research are limited in the direction of the relationship of the results because the sub-component source of the associating relationship was not analyzed. Rather, the results are limited to the existence of a statistically significant relationship between the BOPNFA and SMI.

The empirical testing in this research shows non-significance in the relationship with the FX indicator. On the contrary, a panel study of 29 countries in the research period of 2000 to 2011 by Lee and Wang (2015) shows negative in the short run but positive in the long run in the statistically significant estimating relationship association of FX on stock indexes. Also contrary to the results of this empirical research, Alrub, Tursoy, and Rjoub (2016) found a positive longrun estimating relationship association of the industrial production index in Turkey on the Istanbul Stock Exchange in the study period of 2002 to 2013.

The results of this study’s empirical research on the non-significance of the GDPPC indicator are in line with former related research in Southeastern European countries (Fink, Haiss & Vukcic, 2005; Lazarov, Miteva-Kacarski & Nikoloski, 2016). The non-significance of FX and IPI indicators might represent specifics in the market structure, alluding to the lower importance of the domestic industrial sector on the stock exchanges and less of a prevalent presence of more FX sensitive portfolio investors seeking to rebalance the investment positions of their portfolios.

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