rafmacalaba/data-use-mentions-extended huggingface dataset:
eat. Food diversity is the total number of food categories (out of 7) that the household spent any money on in the past 7 days. Reduced coping strategies index is the world food programme’s reduced coping strategies index, defined as follows: 1*(days in last week relied on less preferred foods) + 2*(borrowed food) + 1*(limited portion size) + 3*(restrict adult consumption so children can eat) + 1*(reduce number of meals eat in a day)

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

33
above 20% - implying that our instrument barely passes the weak instrument test (Stock and Yogo <u>2005). Therefore, the second stage results of the IV analysis need to be treated with caution.</u> Indeed, there are limits to how far cross-country comparisons, with limited degrees of freedom, can be considered conclusive about the effects of social protection policies on economic outcomes in a causal inference setting.

# **7. Conclusions**

In response to the COVID-19 pandemic, countries across the world implemented an array of social protection measures, some of them aiming at protecting the income and livelihoods of families, others aiming at protecting employment through job retention schemes. To assess the short-term impacts of these policies on post-COVID-19 economic recovery, we put together a database on the social protection response expenditure during the pandemic. It reveals that, on average, countries spent about 2 percent of GDP on social protection measures, although high-income countries spent almost five times more than low-income countries. Expenditure on income protection measures was about 1.2 percent of GDP, with less variation across country income groups than expenditure on job protection. That spending averaged 0.8 percent of GDP but ranged from almost zero in low-income countries to 1.9 percent of GDP in high-income countries.

Our analysis shows that countries that allocated larger shares of their stimulus budget to job protection measures seem to experience more robust GDP growth, higher employment, and lower inactivity and poverty rates than countries that allocated a larger share of the stimulus package to income protection measures. These results could be partly explained by the pre-pandemic coverage of social insurance programs. In countries with broader coverage, the income and job protection programs implemented during the pandemic had a smaller economic effect. The social protection response had a significant effect in countries with limited pre-pandemic social insurance coverage. In countries with advanced social protection systems, some social protection programs implemented during the pandemic may have been redundant as automatic stabilizers were already in place. In addition, job protection programs
The previous results are now used to shed some light on the evolution of the wage premium between 1996 and 2015.

# **The Supply of Skilled Workers Increased, but So Did Misallocation**

To set the stage, table 12 presents census data to track the evolution of the size and type distribution of firms and employment between 1998 and 2013. Notice first that average firm size _fell_ . Second, the number of small and very small firms increased more than the number of medium and large firms; in parallel, the number of formal firms _fell_ (by almost 8 percent), while that of informal firms _rose_ quite substantially, by 64 percent. In parallel, recall from section 2.4 that the share of six-digit sectors where informal firms are a majority increased during this period in manufacturing, commerce and services.

**Table 12. Size and Type Distribution of Firms and Employment, 1998–2013**

|||_thousands_<br>||
|---|---|---|---|
||**1998**|**2013**|**% change**|
|**Total**||||
|Firms|2,693.5|4,099.1|52.2|
|Employment|11,805.0|17,394.3|47.3|
|Average firm size|4.38|4.24|0.0|
|**Firms by size**||||
|1–5|2,460.77|3,754.0|52.5|
|6–10|118.7|186.4|57.0|
|11–50|91.5|127.9|39.7|
|51+|22.8|30.6|34.2|
|**Firms by type**||||
|
_Backwardjt_ captures the foreign presence in the sectors that are supplied by

> 6 Sector-specific ex-factory price indices for industrial products came from China Urban Life and Price Yearbook (2008, Table 4-3-3). The price indices are published for 29 individual sectors.

> 7 Price indices for fixed investment and industry-wide intermediate inputs are obtained from the Statistical Yearbook (2006) (obtained from the website of the National Bureau of Statistics of China).

> 8 In some specifications, we run regressions with domestic firms only. In these cases, we use the sample of pure domestic firms, which have zero foreign investment. Then we regress either the log of the firm‘s output or productivity on sector-level FDI without the variable ―Foreign Share‖.

7
**Figure 6: Change in FLFP Rate Over Time Across Social Groups**

<!-- Start of picture text --> 5 0.9<br>0<br>‐5 ‐3.5 ‐3 ‐3.2 ‐1 ‐1.6‐2.3‐3.9<br>‐10 ‐6.4‐5.5 ‐6 ‐7 ‐7<br>‐9.2<br>‐15 ‐12 ‐13 ‐12.4<br>‐15.1 ‐15<br>‐20<br>‐18.6<br>ST SC OBC UC ST SC OBC UC<br>Rural Urban<br>1993‐94 and 2004‐05 2004‐05 and 2011‐12 1993‐94 and 2011‐12<br><!-- End of picture text --> Source: Authors’ own estimate based on NSSO data.

# **3.5 Household Income and Female Labor Force Participation**

A comparision across income deciles suggests that during the period between 1993-94 to 200405, there was a rise in the FLFP rate across almost all deciles, in both rural and urban areas. However, the drop is more for the lower deciles, and it is the large drop in the FLFP rate which occurred in the second period (2004-05 to 2011-12) that led to an overall decline in the total FLFP rate across the entire period.<sup>6</sup> The drop is more pronounced at the lower strata of the consumption decile, and it gradually declined with increases in per capita consumption during the second — period a trend that is not visible during the first period.

As can also be seen from Table 12, the change in the FLFP rate during 1993-94 to 2011-12 was brought about largely by a change in the FLFP rate in rural areas, rather than in urban areas. In all deciles, the contribution of the decline
*b. Nonexperimental Evidence: Mincer Regressions**

# _Mincer regressions and their critics_

We take a straightforward approach to estimating the increase in labor earnings generated by attendance at public schools. We estimate a Mincer regression of (log) labor earnings on years of schooling and years of experience in a cross-section—the 2016/17 Ghana Living Standards Survey (2016/17 GLSS)—and use those estimates to calculate the increase in the present discounted value of lifetime earnings for each additional year of school. Such estimates are traditionally made with ordinary least squares (OLS): 13
being fully formal, which makes it a multidimensional concept (Perry and Maloney, 2007; Ulyssea, 2018, 2020).

The literature uses both macro and micro methods to measure informality.<sup>3</sup> Our work measures informality from the firm’s side and relies on microeconomic approaches that produce disaggregate indicators at the sectoral, regional, and enterprise levels. Given the obvious problems of self-reporting by firms about their non-compliance, proxies (e.g., self-employed and microenterprises) are widely used to measure informality, though with the caveat that very small firms can still comply with the law and be formal (Restrepo Echavarria, 2015). Firm-level surveys that include direct questions about the various forms of informality, such as the one used in our work, can be considered a lower bound of the size of informality (Putnin, š and Sauka, 2015), given that some firms might misreport (to avoid being on the radar of the government).<sup>4</sup> However, in most cases, researchers rely on only one aspect of informality ignoring the multidimensional nature of the issue (Restrepo Echavarria, 2015; Perry and Maloney, 2007).

# **2.2 Stylized Facts on Informality**

The informality literature has identified a set of empirical regularities about informality (non-necessarily causal) across developing countries. _First_ , the share of informality in developing countries is large and persistent Ulyssea (2020), accounting for 70%-80% of the labor force and about 20% of GDP (Loayza, 2018). _Second_ , informal firms tend to be a drag on growth not only because these firms have lower productivity (Perry and Maloney, 2007; La Porta and Shleifer, 2014; La Porta et al., 2008) but also because they use valuable resources leading to misallocation and aggregate productivity losses. _Third_ , informal firms tend to employ low-skilled or low-educated managers and workers (La Porta and Shleifer, 2014; De Mel et al
much more prominent role in rural India. In our analysis, we pay close attention to the implications of these cross-country differences.

The standard model of intergenerational educational mobility where parent’s education is the sole indicator of family background is not suitable for our analysis because it ignores occupational dualism in the rural economy.<sup>6</sup> Children born into a nonfarm household may face different educational opportunities compared to the children born into a farming household even when the parents have similar educational background. At a given level of education, the parent’s nonfarm occupation may affect investment in children’s education through two major channels. First, higher income from nonfarm occupations may relax binding credit constraints on investment in schooling.<sup>7</sup> Second, the probability of a child getting a nonfarm job may be higher when the parents themselves are employed in nonfarm occupations because of network, referral, and role model effects (Emran and Shilpi (2011)), and this would increase the optimal investment when returns to education are higher in nonfarm occupations.

There is substantial evidence that structural change in favor of non-farm occupations is an important source of increasing income inequality in villages of many developing countries.<sup>8</sup> According to the estimates of Lanjouw et al. (2013) based on the data from Palanpur in the Indian state of Uttar Pradesh, the contribution of non-farm income to income inequality was only 4 percent in 1974/75, which increased to 67 percent in 2008/09.<sup>9</sup> The evidence on China also suggests that nonfarm income contributes to income inequality in rural areas (Rozelle ( 1994), Yang and An (2002)). Such a rise in inequality associated with the expansion of the nonfarm sector is of special concern when it reflects lower intergenerational mobility.

We develop a theoretical model in the tradition of Becker and Tomes (1986) that incorporates the role played by parental farm and nonfarm occupations in shaping
<!-- Start of picture text --> 175-<br>150 ao Nao 28 28 9 26 9 229 198 23 24 19 22 B22 Bl 19 fh 20 20 19 fh 19 ft 18 13818 Boo Blog Bos<br>125 44 45 ft 39 fl 33 fl 29 fl 27<br>g 9 45<br>= 45 44 s0 52 «a 55 ff 57 95° B50 f° 50 ff 50 9 52 46 B45 948<br>E3 100<br>1S)<br>Pas2.o 75 474488, = oo W 8c Not ESSE “ 91<br>Zz5 50 6697 740877 8 75 8 790 1 B82 Bh go 92 92 § 90<br>25 iy Ea) Bs 32<br>23925 BoB 19 9 208 19 Bohs Mok Maoh 138 168 OPN OEE<br>;<br>FPF FFF FFF HSH Hrs? gr GP gg PP MM<br>Year<br>ME High Inequality (old data) WW High Inequality (recent data)<br>Ml Low/moderate inequality (recent data) Hl Low/moderate inequality (old data)<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2<br>-4<br>-6<br>-8<br>FEFSYSN GHGVoLPPMG& H© FEoOKP dhGD PHPHP© PVPOG WOWO PNMDYRW NELDV nD» PPPrKNE1 WNDO PSMW PP1SBL1D GHDPDO MisoNYD<br>year<br>i Classification Switched to High Inequality [J Classification Switched to Not High Inequality<br>—- Fitted Values<br><!-- End of picture text -->
- **Blau, David M, and Philip K Robins.** 1998. “Child-care costs and family labor supply.” _The Review of Economics and Statistics_ , 70(3): 374–381.

- **Bloom, David E, David Canning, G ̈unther Fink, and Jocelyn E Finlay.** 2009. “Fertility, female labor force participation, and the demographic dividend.” _Journal of Economic Growth_ , 14(2): 79–101.

- **Blunch, Niels-Hugo, Sudharshan Canagarajah, Dhushyanth Raju, et al.** 2001. “The informal sector revisited: A synthesis across space and time.” _World Bank Social Protection Discussion Papers_ , 119.

- **Boca, Daniela Del, Silvia Pasqua, and Chiara Pronzato.** 2005. “Fertility and employment in Italy, France, and the UK.” _Labour_ , 19(1): 51–77.

- **Bronars, Stephen G, and Jeff Grogger.** 1994. “The economic consequences of unwed motherhood: Using twin births as a natural experiment.” _The American Economic Review_ , 84(5): 1141–1156.

- **Bruijns, Sanne.** 2014. “Children and Their Parents’ Labor Supply: Evidence from 2012 Dutch Data.” Working Paper, University of Amsterdam.

- **B ̈utikofer, Aline.** 2010. “Sibling Sex Composition and Cost of Children.” Mimeo.

- **Cain, Glen G, and Martin D Dooley.** 1976. “Estimation of a model of labor supply, fertility, and wages of married women.” _Journal of Political Economy_ , 84(4, Part 2): 179–199.

- **Chun, Hyunbae, and Jeungil Oh.** 2002. “An instrumental variable estimate of the effect of fertility on the labour force participation of married women.” _Applied Economics Letters_ , 9(10):
. Furthermore, we allow for the errors to be correlated through clustering within each industry for which the production data is used to ensure the estimation is also robust to arbitrary within-industry correlation.<sup>38</sup> The variable I<sup>any0</sup> in table 2 takes the value one for goods imported at a duty-free preferential rate

> 37 The share of between-industry variation relative to total variation in the MFN tariff rates for EU is twice as high as in the US for instance. This is the case both in levels (0.62-0.69) and in changes (0.34). This might be partly due to the fact that the original CET in 1968 was an average of the tariffs of the founding members. When the averaging is done for each product across countries, the within variation for any given industry falls relative to what it was for any individual country. Thus, the share of the variation between industries after the averaging tends to increase relative to what it was for any given country. Moreover, from a political economy perspective, it is easier to build consensus for tariffs at the industry level (most countries are likely to produce some product in any given industry) rather than at the product level (some countries do not produce some products and would clearly loose when the CET on it is raised). In effect, the consumers are relatively organized in a CU because each government is likely to represent them when the country is a net importer. Producers from different countries that join for EU-wide lobbying on trade policy are more likely to maintain their coalition together if they focus on a level of protection that is similar for all of them, that is a level of protection with little within industry variation. A similar effect arises when ministers from different countries representing a given sector, say agriculture, must agree on an EU-wide policy. Each demand protection for some products and will support protection favored by other countries in other products, if they expect this to increase
81||0.55||
|<br>If yes, storage||0.02|0.00|0.00|0.03||0.08||
|<br>If yes, credit 5-7-9||0.02|0.00|0.00|0.03||0.00||
|<br>If yes, fertilizer||0.75|0.17|0.58|0.84||0.85||
|<br>If yes, seed||0.59|0.67|0.58|0.58||0.63||
|<br>If yes, other||0.16|0.17|0.17|0.16||0.06||
|<br>No. of obs.||136|32|21|83||128||

_Source:_ Own computation based on PSG records. _Note:_ The types of support other than PSG was a multi response question and hence they do not add up to one, and the other category includes cash grant. Other purposes for which SAR was used include checking information on land parcels or prices. _Δ_ Sig is the result of a t test for _ΔPSG_ - _ΔNon-PSG_ = 0 where _ΔPSG_ and _ΔNon-PSG_ is the difference between values in 2023 and 2022, for PSG participants and eligible non-participants, respectively.

30
ms||||
|||All rms||Lo|w-_K_ rm|s|H|igh-_K_ rm|s|
||_I_<0|_I_=0|_I_>0|_I_<0|_I_=0|_I_>0|_I_<0|_I_=0|_I_>0|
|Data|3.87|35.63|60.49|3.85|41.84|54.31|3.90|29.42|66.68|
|Simulated data|8.45|43.72|47.83|3.25|31.21|65.54|13.65|56.23|30.12|

Notes: Comparison of data from EPH (Encuesta Permanente de Hogares, Permanent Household Survey) and from EIA (Encuesta Industrial Anual, Annual Industrial Survey), and simulated data based on parameter estimates.

51
(GWP) average subjective well-being (SWB) Cantril’s Ladder scores circa 2000,<sup>9</sup> at the country level, to obtain the initial level of well-being u0(r) and separately identify the fundamental amenities a�(r). Subsequently, the model is simulated one period forward; the estimated migration frictions m2(r) are the values such that the model-implied population distribution for the subsequent year matches the one observed in the data. To convert the ordinal SWB scores into cardinal values, Desmet et al. (2018) show that for a given time period t, the following relationship holds: , where ψ is ii ii ψψS SStt(rr) the inverse of the estimated coefficient on the log of real income in a cross-country regression of SWB tt uu (rr) = ee of an individual _i_ residing in location _r_ on the log of their real income (Deaton and Stone, 2013; Kahneman and Deaton, 2010).<sup>10</sup> Table 1 presents the values of all internally and externally calibrated parameters along with their sources.

# **3. Migration frictions: Adjusting for spatial variation in initial utility in Latin America**

When calibrating the migration frictions, Desmet et al. (2018) assume that the initial level of wellbeing u0(r) is the same in all geolocations within a country. This assumption might be appropriate in the case of advanced economies, but not necessarily in the case of developing countries (Burger et al., 2021; Bryan and Morten, 2019; Chauvin, Glaeser, Ma, and Tobio, 2017). Hence, we relax the assumption of uniform initial level of utility in Latin America. Using geo-coded data on subjective well-being from several survey rounds of the Gallup World Poll (GWP) and Latinobarómetro, we first compute the average subjective well-being scores for different subnational (level-1) administrative regions in each LAC country.<sup>11</sup> We then estimate a
using repeated cross-section surveys, hence are much less data intensive. This is the reason why in the present paper we focus on analyses (i) and (iii). See Aaberge et al. (2011) for an analysis of long-term inequality of opportunity along the lines of concept (ii).

# **2. The model**

# **2.1 Canonical models of inequality of opportunity**

The conceptual basis for the definition of inequality of opportunity is provided by the distinction, among the factors influencing the individual achievements, between individual efforts and predetermined circumstances – defined as those which lie outside the realm of individual responsibility. The EOp approach considers that inequality due to the former is not ethically offensive, whereas it suggests that differences in individual outcomes due to the latter represent a violation of the principle of equality of opportunity and should be removed. In what follows we will follow the simple framework introduced by Checchi and Peragine (2010) to measure inequality of opportunity.

Consider a distribution of income Y in a given population. Suppose that all determinants of Y, including the different forms of luck, can be classified into either a set of circumstances C that lie beyond individual responsibility, belonging to a finite set Ω, or as responsibility characteristics, summarized by a variable e, denoting effort,<sup>2</sup> belonging to the set Θ. The outcome of interest is generated by a function g: Ω �Θ →R such that:

> 1 Also the cross-country comparability is a relevant issue, given the potentially different definitions of outcome and circumstances involved in the analysis.

> 2 Effort could also be treated as a vector. However, we follow the literature and treat it as a scalar.

2
In order to assess the vulnerability of population and GDP within a coastal zone from storm surges under climate change – in the areas where mangroves may provide some protection‐‐ we overlay information on the number of people from Landscan 2005 (Bright et al. 2006) and GDP for 2005 from the World Bank/UNEP databases (World Bank/UNEP Global Assessment Report on Disaster Risk Reduction 2011) with the geographic area vulnerable to storm surges due to the loss of mangroves (Table 6). At the outset, it should be noted that no projections were made of population or GDP for 2100 in coastal zones; the analysis of human resources protected by mangroves uses baseline 2005 data. The estimates in Table 6 also do not include the additional areas and resources at risk that are not upstream of any mangroves.

Our estimates further indicate that under current climate and mangrove coverage, 3.5 million people and GDP worth roughly $400 million are at risk, partially protected by mangroves. Under the future impacts of climate change, resources at risk increase significantly, where GDP at risk increases nearly three‐fold and population at risk more than doubles (Table 6, Figure 3). These risks are especially acute in Latin America and Caribbean and East Asia. Densely populated South Asia has an increase of 60 percent and 70 percent for population and GDP, respectively. Although the top ten countries have a large share of the current total exposure of resources at risk, the exposure under the future impacts of climate change for the remaining countries increases nearly four‐fold for population and more than doubles for GDP. Among the top‐ten countries, the population of Indonesia and the Philippines are most at risk under all climate change impacts, but Mexico and Myanmar along with the Philippines will also experience large increases in vulnerability of population and GDP.

22
generate price effects which are directly and unequivocally linked to a trade reform. The changes in relative factor prices (particularly between labor and capital remunerations, and between skilled and unskilled labor wages) and relative goods prices (such as between food and non-food items) are then linked to the household survey to generate income distribution effects. This methodology does not maintain full

> 1 See Elizabeth Becker’s CAFTA article on the New York Times article, April 6th, 2004.

> 2 For similar modeling frameworks see Ravallion and Chen (2004), Nicita and Olarreaga (2004), Bussolo, Lay, and van der Mensbrugghe (2005), Bussolo and Medvedev (2005), Hertel _et al_ . (2004), Ianchovichina _et al_ . (2001).

2
per month from the Afghanistan Living Conditions Survey (2018). This poverty line consumption excludes legal, health, household construction, and repair expenditures, and includes expenses on consumer durables and housing. We use consistent consumption with this figure to classify households with respect to the national poverty line. The psychological well-being measures include the Center for Epidemiologic Studies Depression (CES-D) seven-point scale (Radloff, 1977) and questions on happiness and life satisfaction from the World Values Survey (WVS). All monetary amounts are in nominal USD. ER for 2016 of 68.87 from the IMF (annual average). SD = standard deviation; UP = ultra-poor; ER = exchnage rate.

12