rafmacalaba/data-use-mentions-extended huggingface dataset:
#### Box 5.  Summary and ways forward: Earnings and decision making

- Particularly in contexts where seasonal/irregular wage employment is common, using community data on average wages for men and women, across more disaggregated categories/sectors of employment, can help complement individual-level data on wage/salary earnings — and enhance an understanding of expected labor market returns.

- Within household farms/businesses, given difficulties in valuing individual family members’ labor contributions to household income, decision-making questions over earnings and management of operations can be helpful. Reported decision-making roles, however, may not be consistent across different household members, and trying to ensure the same respondents across modules is important (for example, across labor and non-farm enterprise modules). Asking about contributing family workers’ decision-making roles is also important for policies on rural women.

# V.   Rural transition and barriers to economic mobility

Rural economies are in transition around the world, although country experiences vary in the pace of growth. In some countries, rural women have benefited from improved access to improved inputs and technology, as well as greater access to labor and output markets and accelerated growth in production stemming from agricultural commercialization (see IFC, 2016 for case studies from Bangladesh and Indonesia). However, rural women have been left out in many other cases. This section discusses areas of data collection needed to better understand, for policy making, how (a) rural women’s employment is affected by structural changes occurring in rural economies and (b) factors affecting their ability to take advantage of new economic opportunities.

## Additional features of women’s employment amid rural transitions

### _Migration_

Rapid out-migration to urban areas has significant implications for rural women. Rural women often face greater barriers to migrating for work, but nonetheless find their work and time allocation are affected when other male household members do so. Mu and van de Walle (2011) and Slavchevska et al (2016), for example, discuss how rapid economic development and rural-urban migration
the required physical infrastructure, to match the expected increases in student enrollments was not unforeseen. Munene (2016) includes a number of case study examples in which this was a first-order challenge in the lead-up to, and during the implementation of, FPE in several countries.

Several studies have documented reductions in quality associated with FPE. For example, using data from the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) and school census data, Valente (2019) finds that after fees were removed in Tanzania, student-teacher ratios increased and observed teacher training, experience, and subject-specific

> 4 A number of these studies include a “first stage” in which a measurable impact of FPE on school enrollment or attainment is documented.

4
home environment by 0.155 SD (measured by a combination of the FCI and HOME indicators, constructed using a principal component analysis). This estimate remains statistically significant even after accounting for multiple hypothesis testing. All results are robust to the inclusion of additional controls (see Table A8 in Appendix 2).

Statistically significant changes are found in Affection but not for Interaction, measured by the PBC nurturing scales. We find that NEP-I leads to a decrease in negative discipline (-0.077 SD), measured by the PBC discipline scale, but this estimate cannot be distinguished from zero with multiple hypothesis testing. Impacts of NEP-B on these variables are positive, but smaller in magnitude than those of NEP-I, and never statistically significant.

Regarding caregiver perceptions, we find that NEP-I significantly increases perceived self-efficacy (measured by the Parental Sense of Competence Scale) by 0.1 SD, and parental perceived impact of own behavior on child development by 0.1 SD, results which are robust to multiple hypothesis testing. We also find that perceived social support from friends and the community is also increased by 0.082 SD. While we find no statistically significant impacts of NEP-B or NEP-I on parental attitudes towards child-rearing measured through parental styles, and we see statistically significant impacts of NEP-I on the average age at which parents believe their children would be able to achieve key language developmental milestones in 0.1 SD.

Finally, we find no significant impacts of NEP in caregiver mental health (CESD) or stress (PSI).

# 5.2 Instrumental Variables

We now discuss IV estimates of the impact of participating in NEP-B and NEP-I on child and household outcomes. Based on information from administrative program records, the overall participation rates in NEP-B was 24.9% and in NEP -I was 30.8% among eligible individual. There is also imperfect compliance in the control group. The original plan was to start offering the program to the control group one year after the start of the study and given the delay
but no information on meal frequencies.<br>(2) Bolivia (2003) does not include information on dietary diversity.<br>(3) Indonesia (RKD) only has information on household level dietery diversity, and no information on meal frequencies.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Children in resource‐poor households are less likely to have access to adequate food security than children in resource‐rich households, but in general the differences are small. Although in all the countries analyzed children in resource‐poor households were less likely to have access to adequate food, there were differences in the degree of disparity (Figure 5c).<sup>13</sup> The largest disparity

> 13 We use the most recent year of data for the comparisons between resource‐poor and resource‐rich households.  The only exception is the Helen Keller data for Bangladesh where we use 2010 and not 2011, the reason being that no vaccination information was available for 2011.

24
.991|0.269|0.832|0.422 (0.063)|_p_=0.000|
|8. Benchmark (indiv, freq)|14.283|1.000|0.324|1.000|||

_Notes:_ The λ<sup>ˆ</sup> are from separate regressions for each module, where the independent variable is village-averaged log annualized total household consumption from the individually-kept, frequent visit diary (the benchmark).

# _N_ =168.

a Standard errors in parenthesis.

- 7 Beegle _et al_ . (2012) find just 13 of 420 pairwise comparisons to have statistically significant differences, at the five percent level, for 15 baseline household characteristics.

- 8 Beegle _et al._ (2012) show understatement by the other designs occurs through both food and non-food; average food consumption is statistically significantly lower than the benchmark for six of the modules, average frequent non-food consumption is significantly lower for four of the modules and average non-frequent non-food consumption is significantly lower for three of the modules.

8
using household consumption per capita for the sub-set of countries for which this is feasible. Surveys that contain detailed household consumption data as well as anthropometrics for women and children are not common, but some do exist including within the LSMS (specifically the LSMS Integrated Surveys on Agriculture) as listed in Table 1.<sup>28</sup> The consumption variable is spatially deflated and expressed in per capita terms.

In an attempt to test whether controlling for additional information, including education and labor assets, enhances predictive power, we draw on household and individual covariates from both surveys. Variables based on the consumption surveys are constructed to be as similar as possible to those used in the DHS data.

> 27 Using the World Bank’s international line of $1.90 a day at 2011 purchasing power parity, 43% of the population of SubSaharan Africa are found to be poor in 2013 (based on <u>PovcalNet).</u>

> 28 Only the consumption survey from Ghana is not one of the Integrated Surveys on Agriculture within the LSMS.

14
income between rounds instead.

> 11 Data from Mongolia and Myanmar, for instance, indicate that the majority of those who stop working do not have a job to return to or have a job to return to but have experienced a reduction in wages. Thus, the assumption of lower labor income appears to be reasonable.

> 12 Mobility is measured using data from the Google Mobility Reports, and the mobility index is here defined as the average percentage change in mobility to retail and recreation, transit stations, parks and workplace, relative to a pre-pandemic baseline level. We also analyzed the stringency index - as reported by the Oxford Coronavirus Government Response Tracker (OxCGRT)

8
The main variables used in the paper are the following. Real GDP per-capita corresponds to the PPP adjusted measure and was obtained from the Penn World Tables (version 6.1). Commodity price fluctuations are captured with the Deaton-Miller commodity-based terms-of-trade index (henceforth DM index; see Deaton and Miller (1996)). The updated data for this index was obtained from Dehn (2001). The broad terms-of-trade index corresponds to the ratio of export to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1). Among these two measures of terms-of-trade, in the analysis we will focus on the DM index for two reasons. First, as correctly pointed out by Aghion et al. (2005) broad terms-of-trade indexes capture fluctuations in the exchange rate that are less arguably exogenous to the business cycles than fluctuations in commodity prices. Second, from an empirical perspective, by using the DM index we give a better chance to exogenous shocks to actually play a role because, as it will be shown later, this index has a larger explanatory power for output fluctuations than the broader terms-of-trade index. Real per capita aid flows include the flows of official development assistance (ODA) and official aid in constant 1995 US dollars, and was obtained from the WDI. Aid as a fraction of Gross National Income was also obtained from the WDI.

The variables capturing the incidence of natural disasters are non-standard and deserve further discussion. Data for natural disasters was obtained from the Emergency Disasters Database (EMDAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED). This is a comprehensive database that includes data on the occurrence and effects of over 12,800 massdisasters in the world since 1900, compiled from a diversity of sources. As a general principle, to enter into the database an event has to meet any of the following conditions: there are 10 or more people reported killed; there are 100 or more people reported
(Number 8924/2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon.

> 4 Father’s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016).

> 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.

5
However, the 6-digit HS classification has the advantage that it enables analysis of comparable trade data for almost all countries, and over time.<sup>5</sup> To identify HS 6-digit products associated with the value chains of solar PV, wind turbines and EVs, we followed three steps: First, we undertook a review of the academic and grey literature, finding key papers that have previously identified HS 6-digit products associated with wind turbines, solar PV, and EVs (see Table SI 1 for key sources used). Second, after collating the various HS 6-digit codes for each technology, we drew on further desktop research to classify each product into four value chain segments: raw materials, processed materials, subcomponents, and end products (see Table 5 for a definition of each segment). Third, we validated our product mapping for each green value chain with industry specialists in the supply chains of wind, solar and batteries for EVs. Reviewing the technical specifications of products in these value chains with our product description helped us trim the list of HS 6-digit products for each value chain – and ensured consistency across the three value chains. The final mapping and classification of HS 6-digit codes into value chain segments can be seen in Table 4.

**Table 4: Definitions of value chain segments**

|Value Chain Seg-<br>ment|Definition|
|---|---|
|Raw Materials|Basic materials that are mined, extracted or harvested from the earth. Also referred to as<br>‘unprocessed material’, examples include raw biomass and iron ore. In this link of the sup-<br>ply chain, value added comes from extracting, harvesting, and preparing raw materials for<br>international marketingin substantial volumes.|
|Processed Materi-<br>als|Materials that have been transformed or refined from basic raw materials as an intermedi-<br>ate step in the manufacturing process. Processed materials include steel, glass
provides operational guidelines for a successful customs modernization strategy, drawing on several customs reform projects undertaken by the Bank. Performance Indicators related to Customs can employ a simple but universal methodology that includes benchmarking, targets and a process of monitoring and review<sup>43</sup> .

The UNCTAD strategy involves building the right environment for TF through a combination of reforms, use of local knowledge and building deeper regional trade and transport linkages with global logistics systems. Its activities include research and analysis, training and capacity building, information systems development, knowledge creation and dissemination. UNCTAD also cooperates closely with all UN Regional Commissions and UN agencies (ITC, UNIDO, IMO, WB, IMF); inter-governmental organizations (WTO, WCO, OECD); and business sector institutions (ICC, FIATA).

Given the number of capacity building and technical assistance activities that are ongoing, coordination and cooperation between these organizations is essential to avoid duplication of efforts. Multilateral partnerships such as the GFP and the UN trade

> 42 Refer to presentation by Simon Royals and Matsudaira of <u>WCO</u> on WCO tools and technical assistance. 43 Some basic macro indicators include revenue collected/customs staff, declaration/staff, trade volume/staff, total customs costs/revenue collected, salaries/revenue collected, and were used to evaluate the TTFSE project.

22
Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|53|
|Table8|Impact on Revenue of Regional Free Trade . . . . . . . . . . .|54|
|Table9|Impact on Revenue of Regional Free Trade with Sensitive<br>Lists<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|55|
|Table10|Robustness Tests with Higher Elasticities . . . . . . . . . . . .|56|
|Table11|Change in Price, Production, Revenue, and Protection<br>. . . .|57|
|Table14|Statutory and Applied Tariff Rates<br>. . . . . . . . . . . . . . .|58|
|Table12|Major Exporters to Nepal,2008&2010 . . . . . . . . . . . . .|58|
|Table13|Number of Products in the Sensitive Lists<br>. . . . . . . . . . .|58|
|Table15|Summary of Data used in Table1Regressions . . . . . . . . .|59|
|Table16|List of Countries and Related Tax Data,2002-2006<br>. . . . . .|60|

iv
reliable indication of the extent of unemployment. The scope of employment office statistics is therefore very difficult to ascertain and, in general, these statistics are not comparable from country to country.

- Official estimates, which are provided by national authorities and are usually based on combined information drawn from one or more of the above sources.

_Non-Agricultural employment_ This concept covers all major divisions of economic activity other than the major division 'Agriculture, hunting, forestry and fishing".

## Public Sector Employment

The conceptual definitions used in this study are consistent with the Intemational Standards put forth in the System of National Accounts, to which the majority of our sources, especially International Organizations, adhere.

Employment data cover both full-time and part-time employees. Whenever possible, the differences in employee status were highlighted in the country notes accompanying the tables.
The earliest empirical work did not observe shocks, and relied on the cross-sectional distribution of consumption to impute the prospective distribution for each household. Chaudhuri (2003)) notes that the main drawback of this approach is that the method implicitly assumes that shocks to consumption experienced by different households are drawn from the same distribution. Later work modeled the variance of consumption as depending on household and community characteristics (Christiaensen and Subbarao, 2005), but did not adequately allow for the variance in consumption that results from covariate shocks such as weather or prices. Survey data necessarily only observe one (if cross-sectional) or a small number (if a panel) of weather and price outcomes. As a result relying only on the variation within survey data alone does not provide a complete estimate of the variation in consumption that arises from these shocks. As we show, this variation can be substantial and omitting this source of variation results in an underestimate of vulnerability.

For the idiosyncratic shocks of death and job loss, we assume that the cross-sectional distribution of the shock for a group of households with similar characteristics represents the probability distribution of this shock occurring in a future period. This is similar to other studies on vulnerability (Klasen et al., 2015). Household characteristics such as assets, schooling and gender of the household head, plus land, cattle, highland and pastoral in the rural areas are used to group households and estimate the probability of the shock occurring (given all of the idiosyncratic shocks were defined as binary events).

For drought shocks, the 17 year historic distribution of crop losses due to inadequate rainfall for each woreda using the LEAP dataset is used to define the distribution of future rainfall shocks. To our knowledge nobody has used the historic distribution of rainfall data to create future consumption distributions. For price shocks, the number of times large increases in the CPI have been observed since 2005 is used to calculate the probability that monthly inflation exceeds 1.5 percent on average in a given year. We do not assume
# **Endnotes**

1 See, for example, de Walque (2006) on Cambodia, Ibáñez and Vélez (2008) on Colombia, Blanc (2004) on Eritrea, and Verwimp (2003) on Rwanda.

2 In a separate set of regressions, we tested the predictive power of our instruments from 1994 in explaining the growth in conflict intensity over the 1996-2006 time period. These regressions are not reported in the paper and are available upon request.

3 We acknowledge that in the form used in this study‟s regressions (third column of the final panel of Table 1), the partial F for the “Forested” variable falls below the generally accepted benchmark value of 10. Our results should be interpreted with this in mind.

4 We coded as self-employed those individuals who reported that they worked for themselves or for their family. The reference group is individuals who worked for someone else.

5 Note that the types of occupations do not differ much between less and more forested areas.

6 This specification excludes the woman's potential wage, a variable that could also determine women‟s participation in the labor market (as in Dex _et al._ 1995 and Prieto-Rodriguez and Rodriguez-Gutierrez 2003 in their studies of the added worker effect). Including this variable would necessitate an instrumental variables technique to estimate the potential wage. Because the Nepal DHS does not include information on cash earnings in the three years of our analysis, we cannot follow this approach.

7 Because the two sub-samples of women with husbands not present are not representative of all women, the estimates in columns 3-6 may be subject to selectivity bias. We address this issue by noting that male migration was already well-entrenched in Nepal before the conflict began. Furthermore, a specification check of the average proportion of husbands who had migrated regressed on the forest/year interaction terms and the full set of regressors at the year/sub46
discrimination in educational attainment (Kingdon, 1998; Drez and Kingdon, 2001; Zimmerman, 2011, to name a few). One plausible explanation for the recent drop in FLFP, therefore, is that with the recent expansion of secondary education (due to sustained efforts by the central and state governments), as well as rapidly changing social norms in India, more working age young females (15-24 years) are opting to continue their education rather than join the labor force early.<sup>3</sup> If this argument is valid, it implies that the reduction in FLFP should at least be compensated by an increase in educational enrollment for young women. In other words, the sum of the ‘percentage loss’ of working age females from the labor force and the ‘percentage gain’ in schools should at least be the same or rising over the same period. We call this ‘ _sum’_ the combined participation rate (CPR). To calculate the combined participation rates, we add up the labor force participation rates and educational participation rates as provided in Table 8.<sup>4</sup>

**Table 8: Female Labor Force Participation and School Attendance**

||FLFP<br>(|Rate (%)<br>1)|% Young Females (15-24) in<br>Schools (2)|Combined Particip<br>(CPR) (1|ation Rate (%)<br>+2)|
|---|---|---|---|---|---|
||Rural<br>(R1)|Urban<br>(U1)|Rural<br>(R2)<br>Urban<br>(U2)|Rural<br>(R1+R2)|Urban<br>(U1+U2)|
|Panel I: Young Cohort (15-24)||||||
|