rafmacalaba/data-use-mentions-extended huggingface dataset:
Source: Authors’ tabulations for the population age 15 and older using the 2018 Honduras Multipurpose Household Survey (EPHPM). Each of the three simulations assign employment to women who fit the following conditions: 1) she reports being too busy with household tasks as the reason for not searching for work, 2) she is not enrolled in school, and 3) there are no children under the age of 1 in the household. Simulation 1 applies only to spouses of a household head who reports having an agricultural enterprise; Simulation 2 applies to other rural women who are either household heads or spouses of the household head; Simulation 3 applies to other rural women beyond spouses and household heads.

# Conclusion

In this study, we investigate some reasons behind the low labor force participation rates of women in rural Honduras, where only about two of five women of working age report being in the labor force. In this, Honduras is not an outlier – there are comparably low rates of labor force participation of rural women across all countries of Central America and most countries in Latin America. Poverty rates are high among these households and hence understanding the reasons behind these low levels of economic activity are critical for identifying pathways out of poverty. From a policy-making perspective, it is important to understand the extent to which these statistics are accurate. If they are accurate, what are the reasons why women are not involved in economic activities? If they are not accurate, then what are the activities in which women are engaging and what can be done to boost their income potential?

The main finding from this study is that women in rural communities in Honduras do not accurately report their economic activities for pay or for own-consumption in response to the two questions asked in the official household survey to measure employment. Simulations suggest that the rural female labor force participation rate in Honduras is underestimated by 6 to 23 percentage points. Significant discrepancies arise once one compares the household survey responses with tasks completed by rural women. Women reported activities ranging from agricultural production
.66%|3.10%|5.14%|9.93%|
||(5.85)|(12.29)|(10.30)|(10.45)|(15.36)|

_Note:_ This table presents descriptive statistics for firm characteristics, using RAIS data from 2016 to 2020. Means and standard errors, in parentheses, are calculated for the subset of private firms and BOS, and for each BOS type: public, mixed capital, and private firms with state participation. Innovative firms are defined as firms with at least one worker employed in technical or scientific occupations (PoTec), while innovation intensity is measured as the share of PoTec workers relative to firm employment. Standard errors in parentheses.

and private firms were altered in 2020, across all BOS types and ownership shares. Once more, robustness checks show that our results are qualitatively unchanged by restricting the sample to competitive industries (columns (4), (5), and (6)), and excluding micro firms from the analysis (Table A9).

15
# _(b) Food consumption score equation_

Tables 10 and 11 also show regression estimates for the impact of households’ characteristics on food consumption score. Both types of assets appear to be positively correlated with food consumption score, whereas agricultural assets losses have a negative effect, adding or not other controls. Interestingly enough, asset losses no longer have a significant effect on food consumption score in the June survey, while it was significantly negative in the February survey. Households thus seem to have been able to cope with the shock, some with their own means and others with the help of assistance programs.

Indeed, it is interesting to note that food assistance appears to have a positive effect on food consumption score in June, whereas it wasn’t significant in February. So it appears that five months after the disaster, assistance programs may have partially succeeded in responding to the shock, whereas the effects of food assistance on food security was not perceptible a few weeks after the seism.

Beside this, aid from abroad, savings and bank accounts all have a strong positive effect on food consumption score, although they are likely capturing the effect of other unobserved variables.

# _(c) Joint estimates of both equations_

There is a strong possibility that emergency assistance is actually endogeneous to food consumption score. Therefore, we try to use instrumental variables to estimate the probability of receiving assistance. Housing damage, location variables (i.e., where people sleep) and associations membership are potential instruments. However, when using multiple instruments in a single equation, it is not possible to reject the over-identification assumption with a standard Sargan test. Indeed, the instruments might be correlated with the error term in the food consumption score equation. Furthermore, it is not possible to reject the endogeneity of the assistance variable with a Hausman test when using any combination of instruments. So, this could also be an indication of the instrumental variables not being completely valid.

# _(d) Impact of displacements_

Focusing on households in
O*NET (2010) O*NET® Green Task Development Project. The National Center for O*NET Development.

O*NET. (2013) Greening of the World of Work: O*NET® Project’s Book of References. The National Center for O*NET Development.

ONET Green Tasks Statmenets excel https://www.onetcenter.org/dictionary/20.1/excel/green_task_statements.html Skills approach Burning Glass Technologies (2021). After the Storm. The Jobs and Skills that will Drive the Post-Pandemic Recovery ESCO. Skill/Competence S.3.3.2 follow environmentally-sustainablework practices http://data.europa.eu/esco/skill/a992f345-7c06-4982-8fc9-5fab55e316af ESCO. Skill/Competence S.3.3.2 complying with environmentalprotection laws and standards http://data.europa.eu/esco/skill/S3.3.2 Excel shared by Burning Glass with a list of green job titles terms

# **Papers, reports, and websites**

Bontadini, F.; Vona, F. (2021) Anatomy of Green Specialization: Evidence from EU Production Data, 1995-2015. Sciences Po OFCE Working Paper, n° 21/2020 Bowen, A.; Kuralbayeva, K.; and Tipoe, E.L. (2018) Characterizing green employment: The impacts of ‘greening’ on workforce composition. Energy Economics, 72: 263-275.

Chen, Marin, Popp, Vona (2020) Green stimulus in a post-pandemic recovery: the role of skills for a resilient recovery Consoli, D.; Marin, G.; Marzucchi, A.; Vona F. (2016): Do green jobs differ from nongreen jobs in terms of skills and human capital? In: Research Policy, 45(5), 1046- 1060.

Elliott, R.; Kuai, W.; Maddison, D.; Ozgen, C. (2021) Eco-Innovation and Employment: A TaskBased Analysis. IZA Discussion Papers
1%<br>1,000,000 -38.4%<br>-13.8% 2,000,000 -30.4%<br>-35.6% -23.7% +6.6%<br>0 -7.2% 0<br>< 0.5 0.5 to 1 1 to 2 2 to 5 5 to 10 10 to 20 to > 50 < 1 1 to 2 2 to 5 5 to 7.5 7.5 to 10 to 2020 to 50 > 50<br>20 50 10<br>Source: Authors based on CDR data<br><!-- End of picture text --> Changes in the distribution of distances meant a greater concentration of mobility flows on a smaller number of routes in Bogota but an increased dispersion in AMBA. In 2020, the number of OD pairs in which trips were recorded fell by 28 percent in Bogota on weekdays, and the median number of trips traveled per OD pair grew (+13 percent), indicating a greater concentration of mobility flows. In contrast, in AMBA, the pandemic caused a greater spatial dispersion of mobility flows, especially on weekdays. The number of OD pairs in which trips are recorded declined less than the total number of trips, leading to a drop in the median number of trips per OD pair by 8.5 percent on weekdays.

The pandemic changed the hourly profile of weekday travel, with more intense trip reductions at peak times, reaching 32 percent in Bogota and 38 percent in AMBA in the morning peak hours (Figure 6). The reduction in the number of trips during peak hours in AMBA was concentrated in the flows between residential areas in the northern districts of CABA and Commune 1, an attractor of forced mobility due to the large number of offices located in the area. In both metropolitan areas also the decline in public transport use specifically was greatest during peak hours. This is
|Any HH member drank alcohol|1 if respondent from a HH with any HH<br>member who had a drinking habit|0.105|0.308|0.074|0.263|0.070|0.256|
|---|---|---|---|---|---|---|---|
|**_Household characteristics:_**||||||||
|Number of children under 5 years old|Number of children who were under 5 years<br>old|0.407|0.793|0.387|0.694|0.251|0.556|
|Number of current HH members|Number of HH members living in the HH|4.575|2.263|4.855|2.075|4.244|1.819|
|HH owned house|1 if HH owned the house|0.958|0.201|0.961|0.194|0.916|0.278|
|HH owned land|1 if HH owned any land|0.175|0.380|0.172|0.378|0.112|0.316|
|Urban residence|1 if HH was located in urban area|0.461|0.499|0.492|0.501|0.446|0.497|
|HH belonged to poor group (B40)|HH wealth index generated by Principal<br>Component Analysis. 1 if HH wealth index<br>belonged to lower two quintiles|0.271|0.445|0.328|0.470|0.489|0.500|
|HH belonged to non-poor group (T60)|HH wealth index generated by Principal<br>Component Analysis. 1 if HH wealth index<br
for more T in experiments”, _Journal of Development Economics_ , forthcoming

- McKenzie, David 2012. “Learning about migration through experiments”, _CReAM Working Paper_ no. 07/12.

- Mohapatra, Sanket , Dilip Ratha and Ani Silwal 2011. “Outlook for Remittance Flows 2012-14”, _Migration and Development Brief No. 17_ , World Bank: Washington D.C.

- Seshan, Ganesh and Dean Yang 2012. “Transnational household finance: A field experiment on the cross-border impacts of financial education for migrant workers”, Mimeo. University of Michigan.

- Woodruff, Christopher, and Rene M. Zenteno 2007. Migration networks and microenterprises in Mexico, _Journal of Development Economics_ , 82(2): 509-28.

- World Bank 2006. _Global Economic Prospects 2006: Economic Implications of Remittances and Migration._ World Bank: Washington, D.C.

- World Bank 2010a. _Enhancing Access to Finance for Migrant Workers in Indonesia: Evidence from a survey of three provinces._ World Bank, Jakarta.

- World Bank 2010b. _Improving Access to Financial Services in Indonesia._ World Bank, Jakarta.

- Yang, Dean. 2008. “International Migration, Remittances, and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks.” _The Economic Journal,_ 118: 591630.

19
# **1 Introduction**

Almost three years into the COVID-19 pandemic, the world is still recovering from disruptions in lives and welfare that are unprecedented in recent times. In 2020, economic activity – contracted in nine out of ten economies worse than during the Great Depression of the 1930s, World War II or the global financial crisis of 2007-08 – while global poverty increased for the first time since 1998, increasing the number of extreme poor by 70 million in 2020 (World Bank 2022b). Labor market impacts have been particularly severe with workplace disruptions due to social distancing requirements and global supply chain disruptions affecting many sectors and occupations. Recovery has been slow and challenging, impeded by inflationary pressures and the war in Ukraine.

Analysis of household phone surveys early in the pandemic suggested that the impact of COVID-19 on employment and incomes was large and unequal across and within economies. Employment and incomes dropped sharply almost everywhere but particularly in middleincome countries (MICs) during the first few months of the pandemic when governments adopted sweeping containment measures, including lockdowns, quarantines and social distancing. Within countries, restrictions on mobility and economic activity were associated with larger losses in employment and income among more vulnerable population groups, including those with lower levels of education, women, and urban informal workers (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021).

The objective of this paper is to update the analysis of the early employment and income impacts of the pandemic over a considerably larger sample of countries,<sup>1</sup> and to describe how the welfare impact of the economic crisis associated with the COVID-19 pandemic evolved throughout 2020 and 2021. Toward those ends, we utilize a comprehensive database of high-frequency phone surveys (HFPS) that covers 80 economies in five out of six World Bank regions, representing a combined population of over 2.5 billion.<sup>2</sup> The HFPS database spans April 2020 through December 2021, with at least
somewhat larger with the overall return to schooling rising to 11.2% per year of schooling compared to 7.9% using the cohort-level data.

In contrast to the cohort-mean level analysis, the findings from individual-level empirical analysis consistently suggest that parents’ life expectancy has a statistically significant positive influence on both human capital investments and lifetime earnings in the pooled sample as well as in the male, urban and rural subsamples. Parents’ life expectancy effects on years in school are 19%-26% of the cohort’s own-life expectancy effect; the effects on earnings are even larger— 38.5% to 54.5% of the own-life expectancy effect. This is strong evidence of an intergenerational transfer effect with respect to increases in life expectancy at birth. Such high transmission across generations is not uncommon considering the recent findings by Lindahl et al. (2015) of intergenerational persistence in human capital and lifetime earnings across several generations.

# **7.1 Instrumental Variables Estimation**

Thus far, we have assumed that life expectancy at birth is exogenous. This assumption may be supportable by evidence that, in Sweden, all of the persistence in mortality across generations is attributable to pre-birth factors (Björkegren et al. 2019), and yet findings for one country is hardly generalizable. We investigate the possible biases due to endogenous life expectancy at birth by using a plausible instrument in equations (24) and (25): the observed mortality rate of children of age five or less. The presumption is that parents use the prevailing under-5 mortality rate as a signal of a newborn’s expected length of life when they make their fertility decisions, and so it should be a reasonable indicator of the exogenous component of the health endowment, 0. The instrumental variables estimates for completed years of schooling are presented in HH panel (a) of Table 8 while those for earnings are presented in panel (c). To facilitate a 26
the appendix in table A1.

We begin by examining the earnings differentials between self-employed workers and all wage and salaried employees. Self-employed workers include both own-account workers and employers. We are able to examine differences between self-employed workers and employees using 347 household surveys from 73 countries. 62% of these surveys are from Latin America, and over 90% of these surveys are from either Latin America or Europe and Central Asia.

We recognize that within the self-employed there may be large differences between employers, professional or technical own-account workers (i.e. lawyers, doctors, accountants, etc.) and non-professional own-account workers (unskilled self-employed). Therefore, where possible, we separate self-employed workers into two categories: (i) nonprofessional own-account workers and (ii) employers and own-account professionals. We are able to examine earnings differences between these two types of self-employed workers

> 4 The database is an updated version of that described in Montenegro and Hirn (2009). Version 4 of the I2D2, which was released in October 2013, was used for this study.

> 5 Self-employed workers include those who self-identify as either an own account worker or an owner/employer.  We use the ILO definition of own account workers as “workers who, working on their own account or with one or more partners, hold the type of job defined as a self- employed job, and have not engaged on a continuous basis any employees to work for them during the reference period”

9
continue to this day. The state of Punjab is included after 1966, when it attained separate statehood. Haryana is not included because data for this state is not available across many explanatory variables.

Currently, India has 25 states because several union territories have attained statehood over the years, the most recent converts occurring as recently as 1991. Therefore, to maintain consistency in our analysis over a reasonable time period, we only include those states that existed since 1960 and 1966.

121 am grateful to Tim Besley of the London School of Economics for providing me with some of this data that had already been compiled in his research group.

13Data on state roads was taken from a district-level data set put together by Robert Evenson at Yale University using official Government of India sources. Evenson, Pray and Rosegrant [1994| provide a detailed description of this data set.

14
she would apply. We then used the answers to compute the share of official CPG clinical content items that the provider mentioned without prompting from the interviewer.<sup>10</sup> Providers, however, do not necessarily deliver clinical services up to their level of knowledge. We define quality of care as the actual clinical services delivered to patients. We collected information on actual clinical services provided during prenatal care from exit interviews of patients leaving facilities and from the household surveys. We measured the quality of prenatal care by computing the share of actual clinical content items delivered during a prenatal care consultation to the items that should compose a typical prenatal consultation as recommended in the Rwandan CPGs.11 For the subsequent analysis we also break out tetanus vaccine as it is also one of the directly priced services (Table 1).

The quality index and the knowledge index cover the same items and therefore are comparable. While providers know two-thirds of the appropriate protocols, they only deliver about 45 percent of the protocols (Table 3 Panel E). This implies a gap about 18 percentage points between knowledge and practice, which translated into providers delivering about twothirds of clinical services they know they should deliver. It implies that there is substantial distance from the provider’s production possibility frontier in terms of technical efficiency.

# **d. Maternal and Child Utilization**

Our utilization measures conform as closely as possible to the paid indicators in Table 1. At baseline over 95 percent of mothers had some prenatal care during the previous pregnancy, but most did not begin their care until after the first trimester and few completed 4 or more visits (Table 5 Panel B). In addition, only about one-third of women delivered in health care institutions and only about 1 in 10 were using modern contraception at the time of the survey.

We separate children’s preventive care utilization into ages 0-23 months and 24-59 months as the younger group is expected to have higher preventive visit rates (Table 5 Panels E and F). Child
28 Similarly, Table 7 shows that nontariff barriers were also structured along lines that would reinforce the trade distorting effects of the agreements preferential tariffs. According to the UNCTAD data, nontariff restrictions were applied to about 21 percent of all tariff line items, which is almost onehalf the corresponding ratio (41.9 percent) for Mercosur's first decile products. Similarly, the trade coverage ratio for products falling in the first three deciles averaged almost 60 percent (about 3 times the ratio for all imports) and reached 84 percent for the first decile group.

# V. Policy Implications

This paper identified dramatic changes in the Mercosur countries' trade patterns over the period 1988-94 and particularly over its latter half. It also argued that these are substantially due to the changes in trade policy and probably mainly those introduced under Mercosur transitional arrangements. Statements of this kind depend on comparing actual data with a counterfactural. Two natural candidates present themselves. The counterfactual implicit in this paper is that, but for these policy developments, Mercosur's trade patterns would not have changed much and that the shift towards apparently uncompetitive capital-intensive intra-block trade would not have occurred. The implication is that if the Mercosur countries had achieved an equivalent degree of liberalization on a non-discriminatory basis they would have maintained a more efficient import structure, paying less andlor obtaining better goods, and they would have purchased more from their trading partners outside the block.21 Given the size of the trade effects identified, the evidence that preferences and the application of industrial policies within RTAs can be distortionary is both compelling and disturbing. It is true that one could imagine other explanations 21The reader should specifically note that trade patterns in 1994 are being compared with those in 1988 and not with some "free-trade" counterfactual. The assumption is that 1994 would have been no more distorted than 1988. We believe that the latter is less distorted geographically because, in the earlier period, trade barriers were applied on a most-favored-nation basis with the exception of a few sectoral arrangements.
)|(6)|
|log (Imports per worker), change|0.00249|0.00315|0.00259|-0.0145|0.00482|0.00641|
||(0.00416)|(0.00416)|(0.00416)|(0.0127)|(0.0120)|(0.0120)|
|Observations|1,980|1,980|1,980|1,980|1,980|1,980|
|Demographic and employment controls (year 2000)|NO|YES|YES|NO|YES|YES|
|Median income control(year 2000)|NO|NO|YES|NO|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p < .01 ** p < .05 * p < .1. The dependent variable is the change from 2000 to 2015 in the log of total labor income and log of average labor income. The explanatory variable is the change in the log of exportsto-workers and imports-to-workers from 2004 to 2014. Demographic and employment controls include the proportion of rural population, the proportion of population aged 15 and more who are literate, total population, and the share of employment in the primary, secondary and tertiary sectors of economic activity (all variables in the year 2000). Total and average labor incomes are expressed in 2015 Mexican pesos using the National Consumer Price Index.

# 3.3 Exports, imports and the migration channel

Our findings show that both export and import expansion per worker increase immigration to Mexican municipalities, but effects are stronger for exports (Tables 3 and 4). Expanding exports per worker by 10 percent increased the number of people migrating into the municipality in the previous five years by on average 7.2 people (see also Rodríguez-Castelán et al. 2020), while the effect of imports is less than half that magnitude (panel a, column 1