rafmacalaba/data-use-mentions-extended huggingface dataset:
Viscusi, 2010; Dávila, Mora and González 2011; <mark>Zavodny, 2015), but also in Spain (D</mark> íaz-Serrano, 2010; Solé, Díaz-Serrano and Rodríguez, 2010), Germany (Giuntella et al., 2015), Canada (Premji et al., 2010; Smith, Kosny and Mustard, 2009) and Australia (Corvalan, Driscoll and Harrison, 1994).<sup>2</sup> The economics literature has shown that immigrant workers are concentrated in occupations that are more sensitive to business cycle fluctuations, exposing them to economic downturns and shocks (Dustmann, Glitz and Vogel, 2010; Orrenius and Zavodny, 2010). In this paper, we show that the greater exposure of immigrant workers to employment-related risks also manifested itself in the specific context of the COVID-19 pandemic, and we provide descriptive evidence of the drivers behind this greater risk exposure.<sup>3</sup> The remainder of the paper is organized as follows: Section 2 describes our measurements of occupational vulnerability to COVID-19. Section 3 describes the datasets used in the paper. Section 4 provides descriptive statistics on occupational exposure to COVID-19 in the EU member countries. Section 5 discusses the empirical strategy. Section 6 explores the main contributors of the greater occupational exposure of immigrant workers to the COVID-19 shock. It investigates whether systematic differences in observable characteristics between native and migrant workers can explain differences in exposure, and it explores additional factors, such as the effects of networks and the imperfect transferability of skills. Section 7 examines actual job losses in the first year of the pandemic and their correlates by migrant status, using recent data from 2020. Section 8 concludes.

# 2. Measurement of Occupational Exposure to COVID-19

## 2.1 Different Measures of Risk Exposure

Risk exposure is an ex-ante measure; it needs to be assessed _before_ the harmful events take place and people react to them. Otherwise, people’s endogenous responses to the events and resulting changes in their behavior will
returns to education for girls reflect women’s comparative advantage in skill-intensive occupations as opposed to brawn-intensive occupations and structural change in the economy in favor skill-intensive occupations. However, the evidence also consistently shows that there is a substantial gender wage-gap against the women due to the fact that _Y_ 0<sup>_cd_</sup> _< Y_ 0<sup>_cs_</sup> in most of the cases. It is well-known in the literature that return to education plays a prominent role in intergenerational educational persistence (Becker (1991), Solon (1999), Solon (2004)). As we will see below, in contrast, the intercept _Y_ 0<sup>_cj_</sup> matters much less.<sup>14</sup> For the main analysis of the mechanisms underlying intergenerational persistence in schooling, we assume that the parents are aware of the fact that the labor market returns are higher for girls. We are not aware of any studies on eliciting parental belief about returns to education for sons vs. daughters in our study countries.<sup>15</sup> In fact, data on what parents state (which may differ from the true belief) when asked about relative returns to education in a survey are also not available in the major data sets we are aware of in India (such as NSS, IHDS, REDS, NHSF) and China (CHIP, CFPS, CHNS). The only data set we are aware of is the Gansu Survey of Children and Families in China. An analysis of the Gansu survey shows that about 55 percent of mothers disagree that returns to education (in terms of higher income) are higher for sons. We also explore the implications of the alternative assumption where parents believe that the returns to education are lower for girls notwithstanding the evidence noted above. This alternative assumption, however, leads to implausible conclusions, as we discuss below in the context of urban India in section (4.1). The consumption
Table A.2.1 Studies on advanced economies (continued)

|Authors|Questions|Economies<br>(sample period<br>and frequency)|Methodology|Results|
|---|---|---|---|---|
|Kumar et al.<br>(2015)|Are firms’ inflation<br>expectations well<br>anchored?|New Zealand<br>(2014Q3-2014Q4)|<sup>Quantitative survey of cross-section of firms</sup>|Firm managers display little anchoring of inflation<br>expectations, despite 25 years of inflation targeting by the<br>Reserve Bank of New Zealand. Managers are generally<br>poorly informed about recent inflation dynamics. Their<br>forecasts of future inflation reflect high levels of<br>uncertainty and are volatile at both short- and long-run<br>horizons.|
|Johnson<br>(2003)|Does inflation targeting<br>affect long-term inflation<br>expectations?|Australia,<br>Canada, New<br>Zealand, Sweden,<br>and the United<br>Kingdom (1990s,<br>monthly)|<br>Comparison of forecasts of actual inflation<br>with predicted forecasts, which are derived b<br>multiplying the estimated coefficients of<br>Phillips curves using the same independent<br>variables before and after the target<br>announcement|y<br>Inflation targeting stabilizes long-term inflation<br>expectations.|
|Strohsal,<br>Melnick, and<br>Nautz (2016)|<br>Did the global financial<br>crisis affect inflation<br>expectations?|United States<br>(2004-14,<br>monthly)|Regression of deviations of long-term inflatio<br>expectations from the inflation target on<br>observed inflation or news-driven short-term<br>inflation expectations using a
randomly selected across the country.<sup>8</sup> Within each of the selected districts, 100 households with at least one child born between 2001 and 2002 (index child) were chosen randomly to participate in the project. The baseline round was conducted in 2002, when the index

> 7 The YL survey is also being conducted in Vietnam, Ethiopia, and India (Andra Pradesh region). As of now, 5 rounds of data have been collected.

> 8 Because the YL project is particularly interested in children living in poorer households (Wilson et al 2006), the sampling frame excluded the top 5 percent of districts as measured by a district poverty ranking. Despite excluding the least poor, it has been documented that the data reflect the Peruvian population in a broad range of indicators (Young Lives-GRADE 2008).

6
29 percent of deleveraging were to occur via the loan portfolio, the impact would be €501 billion, or 30 percent of EMDE credit (Scenario 2). If banks are able to raise equity by 20 percent―as they did after the 2007–08 crisis―and the residual deleveraging occurs fully by loan reduction, the estimated decline in EMDE credit could amount to €676 billion, or 41 percent of EMDE credit (Scenario 3). Scenario 4 shows the impact if half of the asset shedding occurs through loans (€338 billion, or 21 percent).

**Figure 21. European Banks' EMDE Credit (€billion and percent)**

<!-- Start of picture text --> ASIA, 241.1 CIS, 99.1<br>15% 6%<br>MENA, 132.8<br>8%<br>CEE, 529<br>32%<br>AFR, 125.5<br>8%<br>LAC, 519,<br>31%<br><!-- End of picture text --> Source: IMF, Financial Soundness Indicators.

**Figure 22. EBA Recapitalization Impact on EMDE Credit across Simulation Scenarios**

<!-- Start of picture text --> Result of EBA Capital Shortfall (€billion)<br>300.0<br>€300<br>LAC<br>€250 245.1 250.0 Middle East<br>€200 1 Asia<br>200.0<br>109.0 CIS<br>€150<br>2 130.5 150.0 CEE<br>€100<br>Africa<br>3<br>€50 4 100.0 56.7<br>5 27.1<br>€0 50.0 96.4 29.1<br>10% 20% 30% 40%
among the prime-age population was stable throughout the whole period.

Since our primary focus is on the heterogeneous impact of the policy, it is worth discussing whether different types of firms face different labor market institutions. In Hungary, it is relatively easy to hire or dismiss workers (Tonin, 2009). Wage bargaining takes place mostly at the individual level. The rare collective wage bargaining is based on firm-level agreements and the coverage of these policies is low. The unionization rate was around 10% in this period, one of the lowest in the OECD (Hungarian Central Statistical Office, 2016; Borb ́ely and Neumann, 2019; OECD, 2022 _b_ ). The weak labor market institutions and the lack of any size-specific regulations imply that firms with different size or productivity face similar institutional constraints in setting wages and employment.

# **3.2 Data**

We use linked employer-employee administrative data from Hungary covering years 2010–2015 on a random 50% sample of the 2003 population. Since our sample is drawn from the whole

> 18A new pension policy for women was introduced in 2011, which granted an early retirement option for women with 40 years of work credits, regardless of age.

14
at 0.22, while Kerala has the lowest, at 0.01.

Table 4 gives the rationing rates for the poor and non-poor. Consistently with the allIndia results in Table 3, we see that the non-poor are rationed more than the poor in almost all states (the only exceptions are Kerala and Rajasthan).

The _TD_ is determined by how the demand rates and the rationing rates vary between the poor and non-poor. We can use a simple decomposition method to show how much of the _TD_ is due to each factor:

> 16 To divide the population into poor and non-poor, we use poverty lines that deliver the same poverty rates using the abridged consumption module in the Employment Schedule, as those that are obtained from the Consumption schedule, and reported in Table 1.

11
CEPR Discussion Paper DP3026.

- [27] Mountford, A. (1997): Can a brain drain be good for growth in the source economy?, Journal of Development Economics, 53, 2: 287-303.

- [28] OECD (2002): Trends in international migration, Paris: OECD Editions.

- [29] Schi¤, M. (1996): South-North migration and trade - a survey, World Bank Reserach and Policy Report, n. 1696.

- [30] Stark, O., C. Helmenstein and A. Prskawetz (1997): A brain gain with a brain drain, Economics Letters, 55: 227-34.

- [31] Stark, O., C. Helmenstein and A. Prskawetz (1998): Human capital depletion, human capital formation, and migration: a blessing or a ’curse’?, Economics Letters, 60, 3: 363-7.

- [32] United Nations (2002): International Migration Report 2002, New York: United Nations.

36
# 2 ANALYTICAL FRAMEWORK

The principal concern in this paper is with the ability of the rural poor and other socially excluded groups<sup>2</sup> to gain access to and effective control over cultivable land. This calls for an analytical framework that allows for subtle nuances in the definition of property rights.

The analytical framework adopted here distinguishes individuals' rights, claims or interests in land according to three parameters: (i) whether or not they may legally be upheld, under prevailing legislation _(strict legality);_ (ii) whether or not they are socially perceived to be legitimate, irrespective of their strict legality _(social legitimacy);_ and (iii) whether or not they are actually exercised in practice, and therefore translate into effective control over land _(effective control)._ The last of these parameters refers to the degree of tenure security that is enjoyed in practice, regardless of the type of rights. For example, an individual with limited usufruct rights may enjoy security of tenure, if s/he is confident that s/he will actually be able to exercise those rights when necessary. Conversely, an individual with ownership rights to a parcel of land may find his or her claim to be vulnerable to land-grabbing by another individual with greater bargaining power, voice, and leverage over government officials, particularly if the original landholder's rights are not recorded in the official land records. The relative bargaining power of diverse agents strongly influences the extent to which individuals are able to enjoy effective command over land and other resources. The rural poor and other socially excluded groups, by definition, have less bargaining power vis A vis other agents (Leach, Mearns and Scoones 1998).

Generally speaking, ownership rights (which may be acquired through inheritance or the sale/purchase market) are the most secure. They are also the least likely to be enjoyed by the rural poor and other socially excluded groups. Individuals not owning land but still relying on land for at
<mark>Griffith, R., Redding, S., and H. Simpson. 2002. “Productivity Convergence and Foreign Ownership at the Establishment Level,” Institute for Fiscal Studies Working Paper 22, London.</mark> <mark>Grossman, G. M. and E. Helpman. 1991.</mark> _<mark>Innovation and Growth in the Global Economy</mark>_ <mark>, Cambridge, MA: MIT Press.</mark>

- <mark>Grossman, G. M. and E. Rossi-Hansberg. 2008. “Trading Tasks: A Simple Theory of Offshoring,”</mark> _<mark>American Economic Review,</mark>_ <mark>98 (5), 1978-97.</mark>

<mark>Haddad, M. and A. Harrison. 1993. “Are there positive spillovers from direct foreign investment?</mark>

- <mark>Evidence from panel data for Morocco”,</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 42, 51-74.</mark>

<mark>Hale, G. and C. Long. 2011. “Did Foreign Direct Investment Put an Upward Pressure on Wages in China?”,</mark> _<mark>IMF Economic Review</mark>_ <mark>, 59, 404-30</mark>

- <mark>Harding T. and B. Javorcik. 2012. “Foreign Direct Investment and Export Upgrading,”</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>, 94, 964-80.</mark>

- <mark>Harrison, A.E., Love, I., and M.S. McMillan. 2004. “Global capital flows and financing constraints,”</mark> _<mark>Journal of Development Economics</
beyond which the marginal economic return declines with additional rainfall. The impact of temperature is in line with previous findings at a small spatial scale (e.g., BHM 2015). We then demonstrate that as the data are aggregated to larger spatial scales, the impact of precipitation becomes smaller until it ultimately disappears when the spatial unit is the country level.

Our results further indicate that the relationship between rainfall and GDP growth is sharper in low-income countries. However, in contrast, economic activity remains affected by temperatures in the developed economies. As a likely mechanism, we show that the relationship between precipitation and GDP growth is stronger in cells with large areas of croplands.

# **2- Methods**

## **2.A Data**

We use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by the University of Delaware (Willmott and Matsuura 2001). This data set provides monthly mean precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each month between 1901 and 2014. These data are widely use in the economics literature, including in DJO and BHM. Annual precipitation at the cell level between 1990 and 2014 ranged between 3mL in Sudan to 10,187mL in India (659mL average, Figure 1-A). Annual temperature at the cell level between 1990 and 2014 ranged between -32.4oC in Greenland and 37.2 oC in Peru (14.8 oC average, Figure 1-B).

Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and Guillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli, _et al._ (2013) and cover 82 countries, representing 85% of the global population and 92% of global total GDP (PPP) in 2015.

In figure 1, we plot the average cell-level precipitation between 1990 and 2014 against the level of GDP per capita observed in 2014 (y-axis),
national poverty.

kjPc,J Pct_ This measure has direct relevance for policy. This enables us to understand the impact of various policy measures in different groups and regions of the country. Also the knowledge about the share of each region or group in total poverty is essential for targeted interventions.

Having discussed the framework of our analysis we now have to outline the implementation of the framework and issues ensuing from it. The data set for this study was obtained from the National Integrated Survey of Households, undertaken by the Federal Office of Statistics (FOS). We have already noted the coverage of the data set and its main characteristics and now turn to a more practical look at the data, given the proposed framework for poverty analysis.
age to start working as 10 years old. By considering these labor laws and regulations, a more comprehensive understanding of the Job Quality Index can be achieved, considering the legal framework that defines the characteristics of good-quality jobs in Bolivia.

# **4 Data and methodology**

## **4.1 Data and components of the JQI**

The main data used in this paper are derived from the National Household Survey, spanning from 2005 to 2021 and collected by the National Statistics Office. The National Household Survey is the primary survey in the country for measuring poverty, employing a probabilistic sampling procedure. Since 2011, the survey has been representative at both national and regional levels.<sup>10</sup> To ensure comparability across the years, we created the Job Quality Index starting in 2007, which coincides with the commodity boom in Bolivia. This period witnessed high economic growth until 2014, followed by slower growth.

To construct the job quality index, we only consider observations from individuals employed at the time of the survey and who reported the variables required for creating the different dimensions of the index. The sample comprised approximately 6,800 individuals between 2007 and 2009 and around 15,500 individuals since 2011. Table 1 compares demographic variables for the employed population between the complete sample from the National Household Survey and the sample used in our study. The patterns of evolution for different characteristics are roughly similar between both samples, indicating that the sample used in this paper adequately captures the observed changes in the labor market.

> 10For the case of the departments of Beni and Pando, it is necessary to include both regions together in order to obtain representative statistics.

7
% 4.7<br>37.3<br>70% 19.0 10.8 56.7 23.9<br>60%<br>10.2<br>50% 16.8 12.7<br>40% 10.6<br>30% 58.8 3.2 25.6<br>52.3<br>20% 38.0 15.3<br>10% 14.3 16.9<br>0% 4.1<br>Latvia Czech Republic Turkey France Croatia<br>Age Education Gender Income Urban<br><!-- End of picture text --> _Notes:._ LITS is household level. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS

# **iv. BORROWING**

In terms of borrowing, informal sources are more heavily used than formal ones in Turkey (Figure 19). Less than 5 percent of adults borrow from financial institutions in Turkey which is lower than the formal borrowing levels at both the BRICS and the rest of ECA. On the other hand, over 40 percent of adults use store credit and family/friends as an informal borrowing source. These levels indicate that individuals in Turkey are more than 8 times likely to use store credit than a financial institution whereas no other country groups uses store credit more than financial institutions as a lending source.<sup>4</sup> Individuals in Turkey do not seem to trust formal sources as viable borrowing source.

4 In fact, Turkey has the highest coverage rate in having store credit amongst the surveyed FINDEX countries. See appendix for further information on the EAC decomposition for borrowing form formal and informal institutions.

20
of the different sectors in consumption, we choose values similar to other studies like Betts et al. (2017), _ωa_ = 0 _._ 07, _ωm_ = 0 _._ 15. The elasticity of substitution between consumption goods comes from Herrendorf et al. (2009). Using a value-added approach and the U.S. data these authors estimate _ε_ = 0 _._ 002.

To obtain a value for the international interest rate, we follow a standard procedure by looking at the prediction given by the Euler equation for consumption assuming that aggregate consumption and output grow at the same constant rate.<sup>18</sup> If this is the case, condition (4) implies that where _ZYm_ is the rate at which these two variables grow. Equation (41) predicts that an interest rate net of depreciation of 6.79% is the one compatible with the 2.52% average annual growth rate of real GDP per capita for the Brazilian economy over the period 19702007 obtained fitting an exponential regression model to Penn World Tables data, version 8-0. The capital depreciation rates are the ones that generate an average investment share – in gross fixed capital formation in Brazil for the period 1971-2007 of 0.194 this number is calculated from the IBGE data. Applying the fact that estimated depreciation rates for public capital (including the electricity infrastructure) are usually about half those of private capital (see, e.g., Kamps 2006), we obtain _δk_ = 0 _._ 0452 and _δg_ = 0 _._ 0226.

As we mentioned previously, not all investment comes from manufacturing because the service sector is an increasingly important component. Next, we search for the share of investment that needs to be assigned to each of these two sectors. Following Herrendorf et al. (2014), we allocate investment value added to each sector using constant shares. As these authors argue, the quantitative relevance of this assumption should be relatively small because total investment is a
The SLQAT pass rate _z_ might be correlated with our outcomes of interest. Thus, the treatment assignment mechanism is clearly not random and comparing schools that receive the treatment ( _di_ <sup>_z_</sup> _i_  1) to schools that do not ( _di_ <sup>_z_</sup> _i_  0 ) will yield a biased estimate of the ATT. If, however, we consider schools with pass rates near the cutoff to be comparable, then the treatment assignment mechanism in the neighborhood of the cutoff can be viewed as if it was ―almost random‖.

More formally, let _e_  0 denote an arbitrarily small number. Comparing the outcomes of schools with pass rates just below the cutoff (marginal failers) with the outcomes of schools with pass rates at or just above the cutoff (marginal passers), yields

> _E_  _y_ 1 _i_ | _zi_  _c_  _e_   _E_  _y_ 0 _i_ | _zi_  _c_  _e_   _E_  _i_ | _zi_  _c_  _e_   _E_  _y_ 0 _i_ | _zi_  _c_  _e_  _E_  _y_ 0 _i_ | _zi_  _c_  _e_  .(4)

> Under the assumptions that (1) the limit lim _E_  _y_ 0 _i_ | _zi_  _c_  _e_  is well defined; (2) _E_  _y_ 0 _i_ | _zi_  _c_  _e_  0 is continuous in the assignment variable _z_ at the cutoff (i.e., the conditional expectations of the outcome variable exhibits local smoothness at the cutoff in the absence of the treatment); and (3) the density of the assignment variable _z_ is positive in the neighborhood of the cutoff, the difference in the mean outcomes between marginal passers and marginal failers identifies

This object is interpreted as the ATT at the cutoff (Hahn et al. 2001; Todd 2007).

A sharp RD design neatly fits the phase-4 SLQAT taker data, as the