rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5 Findings**

# 5.1 Descriptive statistics and correlation analysis

**Descriptive statistics and correlation analysis allow for an initial view of the relationship between the employment rates of older and younger persons.** In short, this section presents summary statistics and correlations from Sakernas data, which allows for a preliminary understanding of the patterns within the data, focusing on the variables of interest. It provides insight into, for example, the average employment rate within different age groups and genders, and the average hours worked and income earned across these same groups. Correlation analysis provides an initial view into the relationship of interest, that is, between the employment rate of older persons and younger persons across provinces. However, unlike regression analysis, it does not take into account other factors that could be driving the employment rate of younger persons such as the level of development within a province. Therefore, while correlation analysis is useful, it is not sufficient to draw conclusions on the relationship of interest.

**The employment rate is highest for prime-aged persons (25 to 54) compared to youth (15 to 24) and older persons (55 to 64).** The descriptive statistics for the main variables used in the analysis are presented in Annex 1. These reflect 272 province-level observations for 34 provinces including the Special Capital Region of Jakarta for the years 2016 to 2023. The average employment rate of prime-aged persons for the period is 75.5 percent, while the average employment rate of youth and older persons are 38.5 percent and 68.6 percent respectively. The average formal employment rate is lower than the average employment rate across all age groups. Both the average employment rate and formal employment rate for men is higher than that for women, with the exception of the average formal employment rate of youth which is higher for women (28.5 percent) compared to men (21.8 percent). This reflects the higher level of tertiary education attainment among women. In 2023
market imperfections. They also find the overall level and composition of debt to be very sensitive to tax incentives.

Overall the body of evidence suggests that institutional factors such as legal institutions, the level of banking and stock market development are important determinants of firms‟ leverage choices and choices short-term versus long-term debt.

> 23 Rajan and Zingales (1995) use balance sheet data of large listed companies from G7 countries and find similar results - factors identified in previous studies as correlated with firm leverage in the US are similarly correlated in other countries.

> 24 There is a large literature in international economics on how globalization (both financial liberalization and financial crises events) might affect debt maturity structures (e.g. Rodrik and Velasco (1999), Schmukler and Vesperoni (2006), Caballero and Krishnamurthy (1998), Calvo (1998), Calvo and Mendoza (1996), Calvo and Reinhart (2000), Chang and Velasco (1999, 2001a), Furman and Stiglitz (1998), and Sachs, Tornell, and Velasco (1999)

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responsibility for administration and cross-checking delegated to the farmer support fund of Ukraine. To support sign-up and applications, a call center was established and, between Aug. 12 when the SAR was launched and Dec. 31, 2022, received 26,197 calls. The SAR has also been used to manage distribution of short-term grain storage by FAO, for distribution of various type of emergency support, and for follow-up programs to support recovery. The latter include US$ 50 million out of a US$ 320 million grant combined with a US$ 230 million loan under the World Bank’s ARISE project. SAR is also planned to be used as the platform for farmers to apply for partial credit guarantees to be provided by an independent agency that is jointly capitalized with recourses from the World Bank and the EU.

> 13 The ‘cow program’ would provide UAH 3,600 (equivalent to US$ 120 at the time of program formulation) for each head of cattle registered in the farmers’ name in the government’s Animal Registry and confirmed to be still alive. While the government had concerns that fear of becoming visible to the state for taxes would prevent individuals and FOPs from registering in SAR, sign-up data show that those fears were largely unfounded.

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combined to create three sectors: farmer, middle and city. The farmer and city categories speak for themselves, while the middle category is a residual category of anyone who is not a farmer and not living in a city. These could be non-agricultural rural dwellers or residents of secondary towns.

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing/personal items, transfers out, and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Conservatively, rent is also excluded from the aggregate to avoid large differences in prices for similar quality housing being the driver of any measured urban-rural disparities. The aggregates are temporally and spatially deflated using data from the price questionnaires included in each survey round. As household size may differ between urban and rural households, consumption is expressed in per adult equivalent units rather than per capita. The poverty line is set at 326,474.2 Tanzanian shillings (TSh)<sup>13</sup> , calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2007 National Household Budget Survey estimate for rural areas (37.6 percent).

Combing the occupational, spatial and consumption changes between 1991/94 and 2010 (Table 2), it emerges that the agricultural share of the sample reduced from 82 percent in 1991/94 to 48 percent in 2010. Tanzania is clearly undergoing a structural transformation.<sup>14</sup> Second, categorizing the households into 6 groups depending on their starting point (farming and middle) and sectoral shifts (farm-farm, farm-middle, farm-city) shows that most people start from farming and remain in farming (1,368 out of 3,301), while the second largest group
63 Hemlata Rao reached generally similar conclusions, although using different analytical techniques, in his study of fiscal transfers in India. 8 He developed a composite index of state economic development In order to identify "backward" states; this index was then used to analyze the degree to which various transfers from the center were related to development. Transfers through the Finance Commission under the Second, Third, Fourth and Fifth national plans were analyzed using regression techniques with the conclusion reached that, although the Finance Commission stated that the main purpose of the allocations was to augment state resources in an equitable manner, "they miserably failed to achieve this."t49 Similar conclusions were reached from the regression analysis of Planning Commission transfers.

Spending and Local Revenue Effects of Grants. Of equal interest to the distributional characteristics of grants are their impacts on local government behavior, particularly the expenditure responses and local government revenue effort effects. Two issues are salient. Do grants result in an increase in spending greater than the amount of the grant, i.e., is the grant stimulative of expenditures, or are the grant revenues simply substituted for locally-raised revenues with no net increase in expenditures? Second, do the grants encourage certain types of spending, e.g., investment expenditures and/or discourage other types? Again there 4<sup>8</sup> Hemlata Rao, Centre-State Finantial Relations (New Delhi: Allied Publishers Private Ltd., 1981). 4<sup>9</sup> Ibid, p. 148.
relationships, to have many relationships, or to diversify relationships across bank ownership types.

Our Indian data set provides an excellent opportunity to test the hypotheses, with information on firms, banks, and their relationships, as well as data on local market conditions. The data set includes information for 3,423 nonfinancial firms for the year 2001 from the Centre for Monitoring Indian Economy (CMIE). The firms are either listed on a national exchange or have sales or assets exceeding the equivalent of about US $4 million. In addition to firm characteristics, the data include the identities of the banks with which these firms have relationships. Firms with multiple relationships assign priorities to them, and we designate the bank with the highest priority as the firm’s main relationship bank. For firms with only one relationship, we treat their only bank as their main bank.

These data are matched to information from the central bank on the size, financial condition, and ownership type of the commercial banks in the country. We identify four bank ownership types – foreign banks, two types of state-owned banks, and private domestic banks. The state-owned banks include the State Bank of India (SBI) and its associates, established in the 1950s with a substantial rural footprint. These are treated separately from the other type of state-owned bank: more recently nationalized large banks that are managed more as corporate entities and retained some prior management, staff, and relationships with large firms. For convenience, we refer to the four ownership types as foreign, SBI, nationalized, and private, respectively.<sup>1</sup> We estimate three models of bank ownership types and firm-bank relationships. These are reduced forms that embody the preferences and needs of the firms, the abilities of the different types of banks to meeting these needs/preferences, and local market characteristics. Our first empirical model examines the determinants of the ownership type of the firm’s main bank. The results are generally consistent with the hypothesis that foreign banks tend
Regarding the relation between GVCs and women’s empowerment, some strands of the literature studied the impact of GVCs on gender wage disparity. For instance, Deb (2021) uses the Trade in Value Added Database (TiVA) by the Organization for Economic Co-operation and Development (OECD) and conclude that neither backward nor forward linkages were able to improve the relative wages of female workers in India. Nevertheless, Jenkins (2005) confirmed that women wages and working conditions are better in GVCs. Furthermore, few papers have discussed the impact of GVCs on women’s employment in general. GVC participation has been proven to increase female employment especially for developing countries (Shepherd and Stone, 2013; Bamber and Staritz, 2016).

Yet, it is important to note that the impact of GVCs on female empowerment can be moderated through several external and internal factors. At the external level, the scope of drafting of regional and bilateral trade agreements has been largely expanded to accommodate several Sustainable Development Goals. A large set of agreements prioritized environmental matters through the inclusion of climate change and environmental protection provisions (Martínez-Zarzoso, 2018). Labor rights related provisions also gained a large interest as starting from 2016, more than 136 countries negotiated at least one free trade agreement (FTA) that encloses labor rights related provisions (ILO, 2017; Harrison, 2019). However, only few strides have been made in order to include more gender related provisions in FTAs since among all FTAs in force, only 20% of them include explicit chapters or clauses that endeavor to achieve gender equality and to empower women (Monteiro, 2021). Similarly, according to the gender and trade report (UNCTAD, 2020), only 74 out of 500 RTAs (around 15%) include provisions that prioritize gender issues. Yet, it is worth mentioning that it is not the number of provisions or length of chapters including gender considerations that matter but their enforcement. Bahri (2021) shows how some RTAs such as Canada-Chile and Canada-Israel Agreements include whole
methodological studies to investigate these issues to provide guidance on the timing and length of data collection, especially in countries with a large share of employment in agriculture. Second, under the new standards household surveys will have to collect data on multiple activities, including both employment activities and own-use production work. Finally, data producers need to design communication strategies to carefully explain the revised definitions to data users. Both academics and policy makers should be aware of where the changes in series originate from and avoid attributing ‘spurious’ changes in the level and sectoral composition of employment to policies on the ground.

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programs surveyed by Coady et al.<sup>20</sup> The median _CGH_ across the 122 programs is 1.25. By this measure, DB is a clear outlier in targeting performance internationally.

Turning to our fourth measure of targeting performance we find that while 29% of the poor receive DB, this is only true of about 2% of the non-poor; thus we find that _TD_ =0.27. The mean DB payment across all those with _Y_ < _Z_ is 87.61 Yuan per person per year, while the

> 19 These are sample means.  If one weights by city population then the proportion receiving DB rises to 4.69% of which 2.64% had incomes below the DB line.  The estimated proportion below the DB line falls slightly, to 7.64%, of which 5.00% were not receiving DB.

> 20 _Trabajar_ is a combination of a workfare program and social fund, whereby participants are offered low-wage work to do things of value to poor communities; see Jalan and Ravallion (2003). Coady et al., calculate _CGH_ from _Trabajar_ from Jalan and Ravallion (2003) who estimate that 80% of _Trabajar_ participants come form the poorest 20% of the Argentine population ranked by income net of _Trabajar_ receipts (which are roughly constant across recipients).  The corresponding _CGH_ for the poorest decile is much higher, at about 6.0, though still less that for DB.

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at risk from household air pollution as they inhale and absorb higher levels of particles given the same exposure as adults (Bruce et al., 2013; Kurt et al., 2016; Sturm, 2012). To assess health among this population, a trained enumerator measured height (or recumbent length for children under two years of age) which was then standardized into a z-score relative to a representative, well-nourished child of the same age in months and gender using the Centers for Disease Control and Prevention growth tables (CDC, 2012). Height, conditional on age and gender, is both straightforward to measure and a well-established indicator of health status during very early childhood, reflecting both the genetic endowment and the influence of the disease environment during the in utero period and the first two to three years of life, commonly referred to as the “1<sup>st</sup> 1,000 days” (Cusick and Georgieff, 2016; Martorell and Habicht, 1986; Victora et al., 2010; Waterlow et al., 1977). As there is limited potential for catch-up growth to offset early-life deficits, child height is a powerful predictor of attained height as an adult and is thereby associated with reduced mortality and morbidity, as well as greater economic prosperity, educational attainment, and cognitive function (Adair et al., 2013; Fogel, 2004; Glewwe and Miguel, 2008; Hoddinott et al., 2008; LaFave and Thomas, 2017; Strauss and Thomas 1998).

Understanding the biological mechanisms linking biomass smoke exposure and child growth is an active area of inquiry (e.g. Burnett et al., 2014; Gordon et al., 2014; Rylance et al., 2015) with a recent National Institutes of Health review noting the link between household air pollution and child growth a specific priority (Martin et al., 2013). Exposure to particulate matter from biomass smoke is a risk factor through in utero exposure as well as direct inhalation during early life
economics and how recent developments in economics can actually offer new instruments to model forced displacement. This is done from the perspective of utility theory and modeling choice. Next, we discuss data issues that are currently preventing economists from working on forced displacement. Section 5 will provide a concrete example of how economics research can serve policy makers and improve the lives of refugees, IDPs and their hosts. Section 6 concludes.

# 2. Some facts

The United Nations High Commissioner for Refugees (UNHCR) reports that at the end of 2015 there were 65.3 million forcibly displaced people worldwide.<sup>5</sup> This number includes 21.3 m. refugees and about 10 m. stateless people with the rest mostly being accounted for by Internally Displaced Persons (IDPs). The forcibly displaced population accounts therefore for about 0.9 percent of the world population estimated at 7.3 bn people in 2015. The latest estimate of forcibly displaced people is the largest on record, since the creation of the UNHCR in 1950. The growth in these numbers is evidently associated with episodes of conflict and violence. The Somalia, South‐Sudan and Syrian conflicts have been responsible for the major outflows of refugees and movements of IDPs over the past decade, with the Syrian conflict being responsible for the latest spike (Figure 1).

_Figure 1 ‐ Number of Forcibly Displaced Persons (1951‐2015)_ <!-- Start of picture text --> 70000000<br>60000000<br>50000000<br>40000000<br>30000000<br>20000000<br>10000000<br>0<br>1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Source: Constructed from UNHCR population data <u>(http://popstats.unhcr.org/en/time_series)
technological/economic constraints, one is unlikely to possess the population of the underlying data. By contrast, in this exercise we have access of the full distribution of trades (the full LocalBitcoins trade data set). This allows us to control for “false positives” by estimating the expected share misidentified as vehicle trades by our algorithm through randomized sampling from our overall data set, in a way that by construction, does not contain any vehicle trades.

For each trade _i_ , this discovery algorithm is a single hypothesis test with a size (under the null hypothesis) of ∗. ii θθ To control for false “discoveries”, and to establish our estimate of the share of crypto vehicle trades, , we introduce a measure of the number of trades one would expect to falsely discover as vehicle trades, φφ. ii cc where ∗. Over _i_ , describes the probabilities of a trade seeing a random match within five ii ii ii ii hours under the null model. Summing cc = αα θθ cc over _i_ thus represents a measure of the expected number of ii matches, assuming the null model holds. cc 13
|random effects,_p_-value|||0.0001|||0.0000|
|Root MSE|0.8500|1.2822|0.6209|2.1074|2.7608|0.9271|
|R-squared|0.9788|||0.8645|||
|# obs.[# countries]|34[12]|34[12]|34[12]|67[27]|67[27]|67[27]|

_Notes:_ [1] and [4] are Prais-Winsten regressions (See Davidson and MacKinnon (1993): 343-351) with country level heteroscedasticity and country-specific AR1 error correlation within panels. [2] and [5] are populationaveraged panel-data models with semi-robust SE‘s clustered on countries (see Zeger et al., 1988); in these models, the estimates are obtained by averaging country-level random effects (possibly correlated with the Xs) across population. [3] and [6] are mixed-effects linear models with country-level random effects (possibly correlated with the Xs), see Section 2 and Greene (2008: 233-238) for details. Countries covered: For models [1] – [3], see Notes to Table 5. For models [4] – [6]: EU-27 (except for Cyprus, Luxembourg, and Malta), Norway, Russia, and Ukraine. *, **, *** - estimates significant at 10%, 5%, 1% level, respectively. _Sources:_ Dependent variables: Calculations with ESS data (see Table 1 for details). Explanatory variables - see Annex 1 (Table A1) _._ Model estimates - own calculations.

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spells that are non-overlapping, and with limited durations (e.g. spells of 3 years or less, spells shorter than 5 years).<sup>16</sup> In all of our analyses, we treat the country as the unit of analysis and therefore weight each country equally. Where countries have varying number of observations of matched survey and national accounts data, each observation is weighted as the inverse of the number of observations for each country, so that the total weight given to each country, in each subsample, sums to one.<sup>17</sup> This ensures that our analysis does not assign more weight to countries simply because they have more household surveys available, which is what would occur in an unweighted analysis. Our decision to treat the country as the unit of analysis is based on our interpretation of this literature as being fundamentally about the performance of country-level statistical systems, not people. Nonetheless, we do provide, supplemental tables with populationweighted and unweighted gaps in both levels and growth rates between surveys and national accounts as this can help explain how these gaps affect aggregate measures such as global changes in living standards or poverty.<sup>18</sup>

# **3. Assessing the Gaps**

There are many reasons why there are gaps between survey and national accounts estimates, but they largely fall into three categories – (i) measurement error in surveys, (ii) measurement error in national accounts, and (iii) conceptual differences in what each are designed to measure, including differences in their primary objectives. The evidence of measurement error in surveys is vast and indicative of substantial levels of noise in estimated levels of consumption and income. Examples demonstrating the sensitivity of estimated consumption to questionnaire 16 The analysis excludes 1 percent (top 0.5 and bottom 0.5) of estimated gaps in our main analyses of both levels and growth. Even after careful review of deflator and currency conversion issues, a few outlier observations remain, which
multidimensionally poor when her income is below the low poverty line zj<sup>e′,regardlessof</sup> her achievements in the other dimensions. In contrast, when the income of an individual i is above the low poverty line zj<sup>e′butbelowthecutoffpovertylinezj′,iisidentified</sup> as multidimensionally poor only if she also suffers from (enough) deprivation in nonmonetary dimensions, as is the case in Figure 5.

<!-- Start of picture text --> x2 x2<br>xi′<br>xi′<br>z2 z<br>z2<br>z<br>xi<br>u<br>u<br>z1 e z1 x1 (money) z1 x1<br><!-- End of picture text -->

**Figure 5:** Deprivation-based identification makes fewer mistakes.

**Figure 6:** Achievement-based identification makes exclusion errors on extremors

Observe that Eq. (4) uses information on the depth of deprivation (in the cardinal variable) in order to improve the identification of the multidimensionally poor. This approach, which deviates from the “counting” approach, could also be used when measuring the intensity of multidimensional poverty, although here we only considered its incidence.

Finally, we warn against one key limitation of another solution to increase the information content considered in the identification methodology. A seemingly natural solution is to take a weighted sum of achievements, namely identifying the multidimensionally poor with<sup>�</sup> j∈J<sup>card wjxij≥k.ThissolutionwasproposedinRavallion(2011).This</sup> solution would also seem to be a natural way of identifying the multidimensionally poor from multidimensional well-being indices proposed by Benjamin et al. (2014b) or from the total consumption approach (see Section 3.4.3). The limitation of this approach is that it assumes an infinite substitutability across dimensions and thus does not acknowledge the essentiality of