rafmacalaba/data-use-mentions-extended huggingface dataset:
from social protection program)_

> 27 Barrientos (2012). Discussing the choice of specific instruments and their relative role in each country is beyond the scope of this paper; see World Bank (2012b).

> 28 World Food Program school feeding global survey 2012.

> 29To look at the coverage by a whole set of social protection programs, their adequacy, and impacts on poverty and inequality, one needs household survey data that provides information on the poverty status of beneficiaries, as well as on the transfers from a variety of social protection programs.  We rely on the World Bank’s ASPIRE data set. It is based on household surveys that are relatively recent (after 2005), contain social protection questions or modules which allow the identification of beneficiaries of social programs, and are harmonized, and are available to the World Bank ( www.worldbank.org/spatlas).  Among all countries in the developing world as of 2013 only 56 meet these criteria. The data includes 17 countries in Low-Income Groups, 20 in Lower middle-income and 15 in upper middle income (classification is from WB Atlas). In terms of regional break down 7 counties are in SubSaharan Africa (AFR), 8 in East Asia & Pacific (EAP), 17 in Eastern and Central Europe (ECA), 16 in Latin America and the Caribbean (LAC), 3 in the Middle East and northern Africa (MNA), and 7 in South Asia (SAR).  The ASPIRE data set is expanding rapidly- this year 10 more countries will be added and another 15 are in the pipeline. See Annex 2 for list of countries and surveys used.

10
.627AIG - 0.016ARG + 47.0 DUM - 0.721DUM*AIG (19.37)* (0.065)* (0.060) (0.162)* (0.080) (23.93)** (0.446)

Adjusted for autocorrelation R' = 0.92, D.W. = 1.45, F-stat = 28.14, No. of obs. = 25, AR(1) = 0.53* Notes. These regressions are based on annual data from 1970-1996, obtained from the same source as Table 1. The figures in parentheses denote standard errors. The asterisk * (**) indicates that the coefficient is significantly different from 0 at the 5% (10%) level. A denotes differences.

- Regression equations (1) and (2) indicate that public investment has a negative and significant impact on private investment, suggesting that it exerted a crowding out, rather than crowding in effect in Mexico. Furthermore, the size of the crowding out effect has been significant: Regressions (1) and (4) indicate that private investment is crowded out by a factor of around one-half of the change in public investment.<sup>8</sup>

- To examine whether the relation between private and public investment might be nonlinear, the change in public investment was added in quadratic form to the list of explanatory variables in all the equations in Table 3, but the associated coefficients were not significant.

- 8While the regression equations in Table 3 gave the best fit, it is necessary to point out that the crowdingout coefficient is very sensitive to the inclusion or exclusion of other variables, notably GDP, among the explanatory variables. This suggests that there may be significant cyclical covariation in the variables.
and considerably larger than that of actual growth) in the initial years of the sample period, but remains sizeable over the whole period.

## [ Figure 1 here ]

# **5.2 Average effects on firm performance and worker compensation**

We now turn to the main focus of the empirical analysis: the impacts of unexpected demand shocks on worker compensation. Table 2 reports the point estimates on the effects of the weighted forecast error and weighted forecast growth on various measures of firm performance and average labor costs, using data from SCIE. Columns (1) and (2) reveal that unexpected demand shocks

> 12A total of 18 firms, four of which are foreign owned, have plants located outside Portugal.

10
Second, we find that the regions that monetary poverty and the FCS are identifying as having the worst deprivations are corroborated by other food access surveys. Figure 1 illustrates that both measures identify the north of Nigeria as being the poorest and having the worst food access. Importantly a wide variety of other sources identify the north as having the worst food access (e.g., FRAYM 2020; IPC 2021b; etc.). By contrast, Figure 1 also illustrates that the FIES identifies the south of the country as having worse food access, which is inconsistent with monetary poverty, the FCS, and other sources.<sup>29</sup>

# **Section 6b. Potential Reasons for Poor Alignment between the FCS and Other Welfare Indicators**

Combined, the results suggest that one of the reasons that the FIES and the FCS are poorly aligned is that the FIES is likely identifying a segment of the population as having the worst food access that does not have the largest macro- and micro-nutrient deprivations. We further investigate differences in the patterns described in Table 4 by the component questions of the FIES to try to infer why this might be the case. Specifically, we re-estimate Specification (2), but use each of the component questions of the FIES as the dependent variables. The results are presented in Table 6.

There are two ways in which the question-specific patterns in Table 6 differ from the patterns using the entire scale presented in Table 4. First, the more subjective questions in the scale, that are more likely to rely on individual-specific scales that are difficult to compare across individuals, performed particularly poorly. Specifically, the share responding affirmatively to the first FIES question, which asked whether any adult worried about food consumption, was essentially indistinguishable between the lowest expenditure decile and all higher deciles except for the top one (Column 1). And the share answering affirmatively to the fifth FIES question, which asked whether any adult ate less than they thought they should, actually increased for higher
Overview and Country Assessments.” Institutional Paper 107, EC, Brussels.

- Fraser 2016. “Economic Freedom of the World – 2016 Annual Report”. Fraser Institute.

- Gal, P., and A. Theising. 2015. “The Macroeconomic Impact of Structural Policies on Labor Market Outcomes in OECD Countries: A Reassessment.” Economics Department Working Paper 1271, OECD, Paris.

- Greenwood, J., J. M. Sanchez, and C. Wang. 2013. “Quantifying the Impact of Financial Development on Economic Development.” Review of Economic Dynamics 16 (1): 194‒215,

- Haile F., and L. C. Moller. 2019. “Explaining the WAEMU Growth Sprut – the role of financial deepening and macro policy.” World Bank working paper.

- Hansen, A. H. 1939. “Economic Progress and Declining Population Growth.” _American Economic Review_ 26: 1‒15.

- Hauk, W. R., Jr., and R. Wacziarg. 2004. “A Monte Carlo Study of Growth Regressions.” NBER Working Paper No. 296, National Bureau of Economic Research, Cambridge, MA.

- IMF. 2015. “Fiscal Policy and Long-term Growth.” Policy Paper 04/2015. International Monetary Fund, Washington, DC.

- ———. 2017. World Economic Outlook database.  International Monetary Fund, Washington, DC.

- ———. 2019. “World Economic Outlook. Global Manufacturing Downturn, Rising Trade Barriers.” International Monetary Fund, Washington, DC.

- Irwin, D. A. 2001. "Tariffs and Growth in Late Nineteenth Century America." _The World Economy,_ 24(1): 15‒30.

- Johansson, A., Y. Guillemette, F. Murtin, D. Turner, G. Nicoletti, C de la Maisonneuve, P. Bagnoli, G. Bousquet, and F. Spinelli. “Long-term Growth Scenarios.” Economics
the characteristics of production occupations compiled by the World Bank for Indonesia, Thailand and Malaysia, suggests that secondary education is the typical educational attainment for all of the occupations at greater risk of automation across countries with similar economic characteristics.<sup>7</sup> The information provided in the World Bank occupation profiles is similar to the Occupational Information Network database (O*NET) for the United States, but it is based on analyses of labor force survey data for comparable Asian economies. An example of the Occupation Profile for “Welders and Flame Cutters” is provided in Figure A3. According to the World Bank, this is an occupation at high risk of automation. The key piece of information provided by the Occupation Profiles is the typical educational attainments of the occupation: Junior High

> 6E.g. Goos et al. (2014); David and Dorn (2013); Goos et al. (2009); Goos and Manning (2007)).

> 7We thank Mauro Testaverde for sharing these unpublished profiles with us. More details on the Occupation Profiles are presented in Section B.

7
as large compared to when educational quality is not accounted for.

The second and third columns of Table 5 show the corresponding development accounting results. Human capital per worker gaps between the United States and poor countries are substantially higher compared to Table 1. As a result, the _Success_ measure jumps from 8% to 32%.

In a subsequent study, Hendricks and Schoellman (2018) use data on wage gains at migration to provide yet another perspective on cross-country differences in human capital. This approach treats human capital as a residual: wage gains at migration are informative on combined effect of technology and physical capital, and the cross-country dispersion that remains after acocunting for these factors can be attributed to human capital. Formally, from the production function in (4) , the wage of a migrant from country _c_ (and corresponding human capital _hc_ ) when working in the US can be written as while the pre-migration wage of the same worker is (10) with _kUS, yUS_ and _AUS_ replaced by the corresponding quantities for country _c_ . It follows that the wage gain upon migration is so that the relative human capital per worker can be found as This approach is not subject to concerns on migrants’ selection, as wage gains are constructed at the level of individual migrants (therefore keeping constant both observable and unobservable human capital). Moreover, the authors do not need to impose any structure on the human capital production function. Since _hc_ is measured as a residual, its variation across countries might reflect a combination of the quantity and quality of education, as well as of all other country-specific inputs relevant for human capital formation.

Hendricks and Schoellman (2018), for confidentiality restrictions, display only the average wage gains across five groups of countries, corresponding to the five quintiles of the GDP per worker distribution in 2005. To implement my version of their development accounting exercise, I assign to each country in my sample the average wage gain of the relevant group (implictly assuming no change in this quantity between 2005 and 2010)
countries, they consistently rank lower than nonindigenous peoples on poverty indicators (Garcia-Moreno and Patrinos 2011). As noted by the World Bank Latin America and the Caribbean [LAC] Equity lab, “Even if the Latin American and Caribbean region has seen significant progress in terms of poverty reduction over the past decade, the indigenous populations have benefited little and remain among the poorest in LAC. In some areas, the difference in poverty between them and other groups has grown even wider.”<sup>15</sup> Also, a recent World Bank study shows that from the early to the late 2000s, although the proportion of indigenous people living in moderate poverty (US$4/day) and extreme poverty (US$2.50/day) has decreased, the poverty gap between indigenous and nonindigenous people increased by 32 percent in Bolivia, 13 percent in Ecuador, and 99 percent in Brazil, but in Guatemala the gap decreased 0.36 percent. As a result of those gaps, in the Latin American countries for which data are available, the proportion of indigenous households living in poverty today (a) still doubles the proportion of nonindigenous households living in poverty, (b) is 2.7 times as high for extreme poverty, and (c) is three times as high for people living on less than US$1.25 a day (see figure 1).<sup>16</sup>

> 13 UNOHCHR 2014b, paras. 17 and 31, <u>https://documents-dds-ny.un.org/doc/UNDOC/GEN/G14/100/72/PDF/G1410072 .pdf?OpenElement.</u>

> 14 Minority Rights Group International  2008.

> 15 See LAC Equity Lab, “Overview,” http://www.worldbank.org/en/topic/poverty/lac-equity-lab1/overview.

> 16 World Bank 2015a, 59.

Anti-Discrimination Law and Shared Prosperity 8
- ———. 2019. “Doing Business (database).” World Bank Group, Washington, DC. <u>http://www.doingbusiness.org/en/data.</u>

- Zashev, Peter and Stefan Ehrstedt. 2010. “The First Mover in New Emerging Markets: Balancing Risks versus Opportunities in the Case of Belarus.” _Journal of East-West Business_ 16 (3): 201–30.

28
of the employees. Segmentation is most directly measured by the extent to which the gender of owners influences their decision to hire workers of the same gender. For female-owned establishments, for instance, this is measured as the share of female employees in total employment, while for male-led businesses segmentation is computed as the share of male employees in total employment. A shortcoming of this measure, however, is that we need information on the gender of the owner, which, unfortunately, is unavailable for the organized manufacturing surveys.

In an alternative setting, we view gender segmentation through the perspective of an average employee. For an average employee, we compute the share of people that this employee works with who are of his/her gender. For example, for an average female worker, we measure segmentation as the share of other workers at her establishment that are also female, while we compute a similar metric for an average male employee. In our work, we sometimes also consider the segmentation of the combined group, that is, for an average employee we compute the percent of co-workers in the establishment who are of the same gender.

Since trade and domestic industry reforms have been widely known to impact primarily the organized manufacturing sector (e.g., Topalova, 2007; Tewari and Wilde, 2015), we measure segmentation in our regressions using metrics that are possible to calculate with the ASI data. Nevertheless, it is useful to clarify here that our industry level regression work includes industry level information from both the formal and the informal manufacturing sectors, as trade reforms could have indirectly impacted the informal sector via reallocation effects.

∆�� in equation (1) is the related change in the industry level reforms, which could take the form of change in tariff protection or an index measuring the change in de-reservation of products exclusively for SSI. We follow Topalova and Khandelwal (2011) in using three alternative indices for measuring trade protection: (i) output tariffs, (ii) input tariffs, and (iii
but also tax policy and public spending, as well as the interaction between these policies. In contrast, individual microsimulations focus only on a single program or policy and do not account for the broader fiscal context. Furthermore, the CEQ approach evaluates the combined impact of different policies on poverty and inequality, which is especially important given that many households receive multiple social transfers and are affected by multiple tax policies. By analyzing the entire fiscal system, policymakers can identify where the system is failing to address poverty and inequality adequately and can design more effective policies to address these challenges.

**Given the purpose of this paper, we refine and improve the modeling of social transfers to better identify social program beneficiaries using eligibility conditions.** In the 2018 CEQ (Vaughn & Cabrera, 2022), most social protection transfers were assessed using the direct identification method, an important limitation for assessing social transfers **.** This method primarily relies on the responses provided by households in the survey concerning social exclusion transfers. However, these responses typically lack the granularity needed to discern the exact nature of the transfers received. In other words, the dataset does not specify whether the reported transfers pertained to the heating allowance, the Monthly Social Allowance (MSA) scheme, or other non-contributory, means-tested programs.<sup>15</sup> . Furthermore, when comparing the reported figures related to social exclusion transfers, a significant discrepancy emerged between the total expenditure calculated from the dataset and the figures reported in the Survey on Income and Living Conditions (SILC). This discrepancy further complicates the accurate assessment of the effectiveness and impact of various social protection programs.

**In addition to identifying individual social programs, using self-reported information on household surveys to capture beneficiaries of social programs may lead to bias due to measurement error, as documented in the literature.** Survey data capturing social benefits, such as the SILC data, often undergo harmonization processes. Still, it may exhibit bias because lower-income households tend to underreport the
# **Data and Methodology**

The main data source of our analysis is the World Bank’s _Povcal_ database (http://iresearch.worldbank.org/PovcalNet). The _Povcal_ database provides information on mean expenditures or income (in 2005 USD PPP terms) and the Lorenz curve of expenditures/income distributions for specific countries and years, both of which are estimated from nationally representative household surveys. The _Povcal_ database includes data on 131 countries. Since no parametric information is provided for Latin America, we also draw information directly from nationally representative household surveys for 18 Latin American countries.

The _Povcal_ database reports Lorenz curves using two approximation methods: the _General Quadratic_ and the _Beta_ Lorenz curve approximations. Combined with information on mean expenditures or income, these approximations allow estimating poverty headcount and gap indexes quite accurately, as well as Gini coefficients (see Datt, 1998, and the Appendix for details on all approximations used in the analysis). For each country in _Povcal_ , we use the approximation that best fits the actual poverty headcounts (see below). Additionally, for the 18 Latin American countries, we first convert income into 2005 USD PPP using conversion factors reported by the _International Comparison Program_ , and then estimate ourselves the best parametric approximation of the income distribution.

To adequately represent poverty in all the countries analyzed, we use a 2.5 dollars a day poverty line. We base our estimation for each country either on income or expenditures, as reported by _PovCal_ or the surveys (given the focus of the analysis on poverty change _within_ countries, we do not apply any form of correction for harmonizing income and expenditure data). The timeframe we consider in our simulations is circa 1995 to 2008, which allows us to capture the largest number of countries. To correct for the fact that, for some countries, the time frame slightly diverts from these two reference years, we report results in terms of average annual changes. Moreover, to achieve comparability with the Millennium Development Goals (MDG)
The projected increase in saving rate is the result of two main factors. First, the change in the population structure leaves larger shares of the population in age-groups that save relatively more. Second, because the share of poor falls with age, the share of the population that is non-poor will increase with time (solid line in Figure 4b) — and this will lead to a higher weight on the saving rates by the non-poor in the average weighted saving rate. If inequality then falls, there will be even fewer poor people — across all age groups but also for the population as a whole. This will lead to an even higher weight on the non-poor’s saving rates in the average weighted saving rate.

# **_Robustness and Sensitivity_**

The baseline scenario for the change in the saving rate, when the population ages, is, as discussed above, illustrated by the solid line in Figure 4b. Three robustness scenarios are then performed: One where public pensions are still included in the estimations of income but where inequality remains unchanged; and two scenarios without public pensions where the poverty headcount either follows the trend or remains constant. These robustness scenarios are also displayed in Figure 4b. If the poverty headcount remains unchanged, fewer people will be saving a higher rate, so the saving trajectory shifts down.

The second and third scenarios testify to whether public pensions should or should not be counted as income which potentially alters the main finding. As Figures 2a,b depict, the saving rates evolve in line with the life-cycle hypothesis if public pensions are no included. Such a saving pattern would generate a weighted average of the saving rate that would fall over time when the population ages. It is our view, however, that these scenarios are not particularly credible, so while we present the implications for illustrative and transparency purposes, we choose rely on the income measurement that includes public pensions — and, thus, rely on the saving profiles that slightly rise over time.

Further robustness tests include whether fertility might
collected in weekly visits—our resource-intensive golden standard—was then compared with data reported after the harvest. After establishing the magnitude of recall bias, we investigated the mechanisms by which it arose.

We found strong evidence of recall bias in the reporting of family farm labor, but, because of competing forms of recall bias in the reporting of hours of labor, the number of plots, and the number of active household members, the degree of distortion in reporting depended on the level of data aggregation. Labor data collected on a weekly basis, whether in person or by phone, are similar, albeit sometimes not statistically identical. Likewise, the labor data reported by recall in our two recall designs are also quite alike. However, there are striking and economically meaningful differences between the weekly and recall data. Respondents in recall-style modules reported they worked up to nearly four times as many hours per person per plot, compared with respondents reporting labor on a weekly basis. If hours are aggregated to the household level, however, this discrepancy disappears. This is driven by two factors: underreporting by recall households of the number of both working household members and the plots under cultivation. Evidence suggests that these competing forms of recall bias are driven not only by failures in memory, but also by the mental burdens of computing data on a typical situation if agricultural work patterns are highly variable during the season.

> 2 See ABCDQ (Agricultural Bulletin Board on Data Collection, Dissemination, and Quality of Statistics) (database), Statistical Division, Food and Agriculture Organization of the United Nations, Rome, http://faostat.fao.org/abcdq/.

2