rafmacalaba/data-use-mentions-extended huggingface dataset:
be made to allocate the CIT to taxpayers based, for example, on tax return microdata specifying capital income (see, e.g., Nunns, 2012; Rohaly et al., 2005), this uncertainty regarding incidence, and hence the appropriate amount of CIT to allocate to capital income earners, makes it an extremely challenging exercise. A further complication arises regarding how foreign ownership should impact progressivity measurement. For example, it is arguable that any CIT borne by a foreign owner could be considered to have a neutral distributional impact as it is not paid by poor or rich domestic households. This may be a particular issue in developing countries where many large companies may be predominantly foreign owned.

5
nonetheless, the empirical evidence on the existence of this effect is mixed (Javorcik and Spatareanu, 2005). We expect competition effects to be dominant within the same four-digit sector classification, while potential technology and knowledge transfers might come from foreign presence in the same two-digit sector.

**Table 6: Competition and Spillovers Within and Between Four Digit Sectors**

|Dependent Variable: Firm Produc<br>Sample: Domestic Firm|tivity<br>A|ll Firms|Con|tinuingFirms|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Spillover Competition_|-0.077***|-0.015***|-0.057***|-0.018***|
||(0.005)|(0.004)|(0.005)|(0.005)|
|_Spillover Knowledge_|0.346***|0.225***|0.329***|0.119**|
||(0.032)|(0.006)|(0.008)|(0.009)|
|Observations|36,638|33,354|16,192|16,492|
|Firm Fixed Effects|yes|yes|Yes|yes|
|Sector2dig-Year Fixed Effects|NO|YES|NO|YES|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey. _Notes:_ Estimation performed by Generalized Least Squares (GLS) where weights are the square root of the firm mean squared predicted residuals. Standard errors clustered at the corresponding level specified in the table are reported in parentheses. Results are obtained based on the sample of firms with no foreign ownership (i.e., firms that were never acquired (in any percentage) by
19.2|
|EM3tI|16.6|22.0|20.2|29.7|24.7|31.1|18.3|25.6<br>|19.8<br>|26.9<br>|
|Latin<br>Aerica|12.2|12.1|13.7|15.0|16.0|13.1|12.9|13.5|13.6|13.4|
|tU- unKmeGtries<br>a/|6.9|7.5|8.4|10.8|9.7|13.0|7.6|9.0<br>|8.1<br>|9.9<br>|
|High LIdebted cotries<br>b/|12.8|12.8|15.0|16.6|16.6|14.2|13.8|14.6|14.5|14.5|
|35 SAL Countries c/|12.8|13.8|15.9|18.1|19.1|18.2|14.2|15.9|15.3|16.4|
|15 Major SAL Countries d/|13.2|15.0|16.6|20.3|23.3|22.0|14.8|17.5|16.7|18.5|
|Dereloping auntry<br>Average e/|13.2|14.4|17.6|20.1|20.7|21.1|15.3|17.1|16.5|18.0|

Based on XDR 30 _low_ ln cointries. ased on Wrld Debt T&blm classification.

Based on the Ibrld **_Bsiks_** stnctural
# **1. Introduction**

In countries where formal pension schemes have wide coverage and guarantee high replacement ratios, individuals can rely on these programs to transfer consumption safely from their productive years to their retirement years. However, 61.2 percent of employed individuals around the world worked informally in 2019, without contributing to long-term saving schemes (Bonnet et al. 2019). They must therefore either save on their own while their income is high, as predicted by the life cycle hypothesis (Modgliani and Brumberg ,1954, 1979), or rely on transfers to consume in old age.

The consensus in the development literature has long been that life cycle saving is limited in developing countries, but this question should be reexamined considering major economic and demographic trends observed in recent decades. It is often assumed that individuals in developing countries work into old age and rely on children to support them in their unproductive years, reducing the needs for asset accumulation. However, three major socioeconomic trends could have strengthened longer-term saving in developing countries since that diagnostic was established. First, life expectancy (and thus the number of unproductive years to be covered) has increased significantly throughout the developing world. In Pakistan, the context of this study, it went from 60 years in 1990 to 67 in 2018 (World Bank, World Development Indicators 2018). Second, high fertility rates meant that the elderly could expect to rely on large cohorts of active individuals within their family and village networks for support in old age. While still relatively high compared to peer countries, the fertility rate in Pakistan has almost halved over the last thirty years, from 6.2 births per woman in 1990 to 3.5 in 2018, a trend that is common to many developing countries. Third, income growth is also correlated with higher saving rates (Loayza et a. 2000). Large increases in GDP per capita (USD371.7 in 1990 vs. USD1,284.7 in 2019 in Pakistan) could have also broadened the
economic and political systems, to fight it using all the means under its control. The direct means for these challenges is through government mobility restriction policies, which could deter the spread of the pandemic. A country can also try to contain the pandemic by drawing on its formal institutions such as voluntary compliance with social distancing based on the legitimacy of the government under democracy, or direct control under authoritarian systems to address these challenges. A country can further draw on its cultural norms, such as collective cultural traits and general trust among the citizens.

This ongoing pandemic allows us to examine the roles of the government, democratic institutions, and cultural norms in containing the pandemic. If we classify the tasks confronting a society into normal activities—such as growing an economy and provision of basic public goods— and handling unusual crises, dealing with the pandemic is clearly the latter. In dealing with these distinct tasks, the roles of the government, institutions and culture differ greatly. For one thing, in normal times the government could use its formidable bureaucratic machinery to manage many routine tasks. But in crisis, the bureaucratic machinery matters less because of the work stoppage, and the role of leadership would matter much more (Fukuyama 2020), as would institutions and culture. Indeed, recent research suggests that a large portion of social distancing was explained by private individual choices rather than government lockdown policies (for example, Goolsbee and Syverson, 2020). It is thus intriguing how the roles of the government, democratic institutions and culture matter for the pandemic outcomes.

In this paper, we combine the COVID-19 data by the Center for Systems Science and Engineering (CSSE) at JHU, downloaded on November 19, 2020, with cross-country data from the World Bank and other sources to examine the key factors that are correlated with the two indicators of pandemic severity: the pandemic spread (i.e., the number of confirmed COVID-19 cases per 1,000 residents), and the pandemic mortality rate (among the confirmed cases)
we use bi-annual data of 25 countries: 11 advanced economies and 14 emerging markets during 2006H2-2018H2. Advanced economies include Australia, Canada, the Czech Republic, Denmark, Japan, the Republic of Korea, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Emerging markets include Bulgaria, Colombia, China, Hungary, India, Indonesia, Malaysia, Mexico, Peru, the Philippines, Poland, Thailand, Turkey, and South Africa. The Euro area countries are omitted from advanced economies for the following reasons. As discussed by Ichiue and Shimizu (2015) and Wright (2011), this is because their term structures had been highly correlated with those of Germany since the introduction of the euro. Therefore, it would be difficult to identify how much long-term interest rates in the Euro area have reflected the fundamentals of these countries. In this regard, the share of foreign private investors in the euro area has been higher relative to others (Arslanalp and Tsuda (2014a)). In the euro area, the zero percent risk weight has been a major driver of banks’ holdings of foreign debt securities denominated in the local currency due to Basel capital regulation. Bonner (2016) shows that capital regulation encourages banks to substitute other bonds with government bonds. Because of this regulatory impact, the impact of foreign investors on interest rates for the euro area could be different from that of other countries. Hence, euro area countries are omitted.<sup>12</sup>

> 12Brzoza-Brzezina and Kotlowski (2018) employing the PSTR model also use data from both advanced economies and emerging markets. However, they use actual data, not forecast data

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” (for employees). The SEBJ calculated using ILO data is multiplied by the employment-to-population ratio from the ILO used in the basic GEGI.<sup>24</sup> The secondary source is the JOIN database microdata. At the time of data collection, the public JOIN data set provides data by Status in Employment or Economic Activity, but not both. As such splitting by both (with the latest surveys) needs to be constructed manually from the underlying microdata.<sup>25</sup> 20 For the backward-looking application, one could use the human capital measure from the Penn World Tables directly. This is expressed in productivity units relative to unskilled labor—like h —based on the years of schooling of the workforce today. 21 Recall the SEBJ is defined as the number of employers plus non-agricultural employees, as fraction of total employment. 22 Downloaded from https://ilostat.ilo.org/data/browse-by-subject/ on 20 February 2020 (with ICSE-93 option).

23 Note that I must use ICSE-93 rather than “Aggregate status in employment”, as the latter is missing data on employers. 24 In the initial sample based on data from Pennings (2020), Papua New Guinea, Kiribati and Tuvalu are missing data on Better Employment, and so were missing from the full GEGI. This was due to missing data on the number of employers, or missing data using the ICSE-93 classification. To ensure a more complete measure of employment gender gaps, I downloaded additional data on “Employment by sex, status in employment and economic activity (Thousands) -Annual” from <u>https://www.ilo.org/shinyapps/bulkexplorer7/ (accessed 30 September 2021), using non-ICSE-93 categories and also assuming</u> that the number of employers was zero (if it was missing). This allowed me to calculate the SEBJ for these three countries, and hence the Better Employment Gap and the full GEGI. SEBJ data for Tonga was
more extreme forms of deprivation where households reduce their intake of staple foods. Finally, we consider other markers of welfare, including subjective measures of poverty.

# 4.1. Overall monetary consumption and poverty

We begin by estimating Equation 1 with the log of consumption – in 2011 PPP-adjusted USD – as the independent variable. As described above, Chad is excluded from this analysis due to the timing of the EHVCM waves. Mali is also excluded from any specifications involving monetary consumption due to limitations on the consumption data.<sup>21</sup> Pooling the data from Burkina Faso, Niger, and Senegal, monetary consumption is approximately 10.5 percent lower in the lean season according to the specification without controls, a difference which is statistically significant even at the 1 percent level (Table 1).<sup>22</sup> At the mean level of consumption across those three countries, this represents a difference of 0.37 USD 2011 PPP per person per day. This estimate only drops to 9.9 percent when a full set of controls are included and remains statistically significant. Therefore, seasonal swings in welfare appear to be sizeable in the EHCVM sample.

**Table 1. Difference in the log of total monetary consumption per capita between the lean season wave and non-lean season wave**

||Burkin|a Faso|Ni|ger|Sen|egal|All except C|had and Mali|
|---|---|---|---|---|---|---|---|---|
||No<br>controls|Full<br>controls|No<br>controls|Full<br>controls|No<br>controls|Full<br>controls|No<br>controls|Full<br>controls|
|Lean season<br>wave|-0.1793***|-0.1397*
schemes, however, attempts to "privatize" the existing PAYG schemes and explicitly recognize the accrued public pension liabilities could pose significant fiscal risks. In these countries reform proposals have recommended limiting the level of and eligibility for public pension benefits within the existing system.

To deal with the expected rise in government pension liabilities the IMF (1996b) has advised governments to (a) shift indexation of pensions to prices rather than wages, provided that the adequacy of pension benefits is reviewed periodically; (b) raise the minimum retirement age rather than reduce pension benefits; and, if possible, (c) adopt a sensible immigration policy rather than active employment policies to boost participation rates and reduce unemployment. Because of the possible effect on unemployment of the increase in the retirement age pension reform should be undertaken in conjunction with liberalization of the labor market. The World Bank (1994) has suggested that indexation of pensions to the arithmetic average of the movement in wages and prices would both protect the elderly from poverty and reduce the fiscal effect of population aging.

Testing pension adequacy and setting an optimal public pension level is problematic because of cross-country differences regarding the function of public pensions and the range of methods for measuring income adequacy. Comparing the different income adequacy measurements in New Zealand, Brashares (1993) noted that the relative earnings related measure (65 percent of net average ordinary time weekly earnings, which had been accepted for setting the floor of the NZS) implies the most generous income adequacy level. Moreover, as income differentiation and the spread between average and median earnings rises, the comparative generosity of this standard increases. 6 Rigorous analysis of the link between private saving and government provision of pensions is beyond the scope of this paper. The literature survey in Box 1 offers a short summary of the most recent findings.

6 For the recent developments in income distribution in New Zealand, see Barker (1995) 7
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**_Notes_ : 1997-2001: flows between Mays of corresponding years; 2001-2002 – annual average.** Flows exclude the impact of migration, mortality, and new entrants who were younger than 15 in the first of the two periods. In this way, the impact of economic change is identified.

_Source_ : Calculations based on LFS data (see Appendix Table 14).

**Low mobility between the main sectors**<sup>6</sup> . Considering that agricultural employment will likely continue to decline in Latvia, it is disturbing to observe that the recent gross labor flows out of farming were predominantly to non-employment (Appendix Table 15). This trend appears to be increasing over time. Those who found new work in 1997-2001 went predominantly to the service sector, but in 2001-2002 roughly equal numbers went to industry and services.

> 5 See Hazans (2004b), Figure 6.

> 6  This and several next paragraphs (until the end of subsection A) draw from OECD (2003a).

21
complete and includes over 40 questions on environmental factors, functioning and health conditions. Few countries may, however, have the capacity or resources needed to develop and implement the stand-alone survey or include the brief version in an existing survey, let alone repeat this over time for monitoring the situation of persons with disabilities post-2015 and in the context of the CRPD.

Yet another option consists in using a few questions to measure disability in a population census as well as in general or mainstream household surveys, such as the Living Standards Measurement Study. A population census or a mainstream survey makes it possible to measure prevalence as well as inequalities across disability status for general indicators such as employment rates and educational attainment. However, unlike the participatory and disability surveys above, it will not allow for an in-depth analysis of disability-specific barriers or challenges, nor facilitate understanding of the drivers of inequalities. However, it may give very useful information when it comes to tracking the situation of persons with disabilities in different aspects of their lives. Of course, measuring disability with a few questions is challenging, especially if it has to be done in an internationally-comparable manner for global SDG and CRPD related monitoring efforts. Since 2001, there has been an international effort to collect internationally comparable disability data by the United Nations’ Washington Group on Disability Statistics (the “Washington Group'' (WG)). This group, organized under the United Nations Statistical Commission, gathers over 100 representatives of National Statistical Offices as well as other government, non-government, and disabled people’s organizations. There was a broad consensus on the need for population-based measures of disability for country use and for international comparisons and the Washington Group on Disability Statistics was formed to address this urgent need. The main purpose of the Washington Group is to promote and coordinate international cooperation in the area of disability measures. The Washington Group has developed and tested questions and made recommendations for a short set of six questions 3
for Ethiopia and Nigeria, 8 rounds for Malawi, and 5 rounds for Uganda. The dates of data collection in each country, sample size and average stringency index for each round are presented in Appendix Table A2.

# **Variables and descriptive statistics**

Using the LSMS-ISA and HFPS data, we construct several indicators to analyze early-phase and one-year labor market impacts and income loss. Descriptive statistics on all outcome indicators and household and respondent demographics are reported in Appendix tables A3-A4.

To measure the early-phase labor market impacts of the COVID-19 pandemic, we analyze three indicators: job loss, re-employment, and change of industry. Job loss captures whether respondents who were working before the pandemic (as self-reported, retrospectively, during the first HFPS round) lost their job between the start of the pandemic and July 2020. This is measured using all HPFS rounds implemented up to July 2020.<sup>7</sup> Note that job loss includes the loss of paid employment, but also loss of other types of work, including self-employment and family farming. For the sub-sample of respondents who lost their job during this period, the re-employment indicator captures whether they were working during at least one of the HFPS rounds between July and October 2020. And finally, for those who were re-employed, we construct an indicator for “changing industry” by comparing their industry of work in the last pre-COVID-19 face-toface LSMS-ISA survey with their industry of work upon re-employment. We distinguish between agriculture (including mining); manufacturing and utilities; construction, transport and communication, financial and business services; commerce; public administration; and other services.

The early-phase labor market indicators are summarized in Table A3 and Figure 5. Among respondents who worked pre-COVID-19 (81% of all respondents), on average 34% lost their job in the first four months of the pandemic. The rate of job loss was much higher in Nigeria (61%) compared to the other countries

> 6 Ambel, McGee and Tsegay (2021) analyzed the survey sample
Finance Gap. https://www.ifc.org/wps/wcm/connect/03522e90-a13d-4a02-87cd- <u>9ee9a297b311/121264-WP-PUBLICMSMEReportFINAL.pdf?MOD=AJPERES&CVID=m5SwAQA.</u> IFC. 2018. “IFC Reach Data.” https://www.smefinanceforum.org/data-sites.

International Trade Centre, 2020. “SME Competitiveness Outlook 2020” (accessed on 1 August 2020).

- “ ’ ”

- Intrum, 2020. <u>One in five of Europe s SMEs say late payments are a threat to their business . (accessed 1</u> August 2020).

- Leer, John. “Analysis: Divergent Paths for Consumer Condence in U.S., EuropeUnderline Need to Control Coronavirus Spread.” Morning Consult. https://morningconsult.com/2020/07/29/analysis-consumer- <u>confidence-us-europe-spain/ (July 29, 2020).</u>

- Li, Lei, Philip E Strahan, and Song Zhang. 2020. Banks Are Lenders of First Resort: Evidence Form the COVID-19 Crisis.

Long, Heather. 2020. “Small Business Used to Define America’s Economy. The Pandemic Could Change - That Forever.” Washington Post. https://www.washingtonpost.com/business/2020/05/12/small <u>business-used-define-americas-economy-pandemic-could-end-that-forever/.</u> OAG (Open Access Government). 2019. World Bank blockchain pilot sows fresh narrative for Haiti’s farmers. https://www.openaccessgovernment.org/world-bank-blockchain-haitis-farmers/61205/ 24
# 4.5 Model Specification

We test the hypothesis that, in the absence of the BH insurgency, changes in the rates of IPV before and during the BH insurgency would have been the same in the BH affected areas and in areas unaffected by BH (hereafter non-BH areas). To estimate the effect of the BH insurgency on women’s experience of IPV, we specify the following difference-in-difference (DD) model. I i = 0 + 1 P + 2 P B B + 3 P * P B B + 4 i + i (1) ii ii ii ii ii ii ii The DD model exploits the variation in timing and location of the BH insurgency. This allows for II ββ ββ IIP PP ββ ββ IIP PP ββ XX εε the avoidance of biases from comparisons of IPV rates before and after 2009 that could be the results of factors other than BH attacks. The unit of observation for equation (1) is a woman. I i is the outcome variable indicating if a woman _i_ , interviewed in an area _l_ , and NDHS year _y_ , ii experienced IPV. P is a binary variable indicating the NDHS year the woman was II interviewed. It is 1 for observations from the 2013 NDHS during the BH insurgency and 0 ii otherwise. P B B IIP indicates whether the woman resides in a BH affected area. It is a binary PP variable equal to 1 if the woman resides within a 10 km radius of any BH event between 2009 and ii 2013 and 0 otherwise. The covariates listed in Table 2 are represented by i . Finally, i stochastic error term. ii ii<sup>is the</sup> XX εε The parameter of interest is 3, which measures the average treatment on the treated. That is, the effect of the BH insurgency on women’s experiences of IPV, as measured by the change in IPV between BH affected areas and non-BH areas before and after the BH insurgency. In other words, ββ it estimates the effect of the
are identified in the region of 0.64–0.79, even when the number of clusters reaches 15.<sup>5</sup> This “statistical performance gap” is analogous to the concept of the middle-income trap in the development economics literature (see Gill and Kharas 2007).<sup>6</sup> A similar gap between the top-performing NSSs and the rest of the world is evident in the Open Data

> 4 See World Bank (2021).

> 5 We used the Stata cluster _kmean_ routine to perform this analysis (StataCorp 2019).

> 6 The term _middle-income trap_ refers to countries that experienced rapid growth and quickly reached middle-income status but then failed to catch up to high-income countries. Statistical capacity may exhibit the same phenomenon. The SPI, the first capacity building indicator to measure all countries, is in its infancy. It will be some years before there are sufficient time series data to robustly test the statistical performance gap hypothesis.

8