rafmacalaba/data-use-mentions-extended huggingface dataset:
to any negative impacts (Hallegatte et al., 2016). Many of them also overly depend on ecosystems and natural resources for income generation (as shown for Vietnam in Narloch, 2016) and to cope with shocks (Angelsen et al., 2014; Barbier, 2010; Wunder et al., 2014). On the one hand, being exposed to environmental risks forces poor people to opt for lower‐risk, but lower‐return strategies, which can make it harder to escape poverty. On the other hand, the dependence on natural resources can increase ecosystem fragility and environmental risks. This downward spiral could result in poverty traps (Barbier, 2010; Barrett et al., 2011; Carter et al., 2007).

Environmental risks are unevenly distributed across space – depending on geographic and climate conditions, as well as socioeconomic factors that condition them. Moreover, poor people are often concentrated in remote and disadvantaged areas, as for example the mountain areas in Vietnam. Such conditions could result in spatial poverty traps through geographic pockets of fragility, risks, marginalization and poverty (Barbier, 2012). An earlier spatial analysis at the provincial level, however, finds that the relationship between poverty and environmental risks is hard to generalize in Vietnam (Dasgupta et al., 2005).

This paper revisits this relationship, taking advantage of recent data, which allow the analyses of spatial relationships between environmental risks and poverty and consumption changes over time. First, the paper uses high‐resolution data representing environmental risks across space. These data are combined with national maps showing the incidence of poverty at the district level (Lanjouw et al., 2013). In addition, this study computes environmental risks at the commune level<sup>2</sup> to relate it to household information based on the Vietnam Household Living Standard Surveys (VHLSS) for 2010, 2012, and 2014. Benefiting from the panel

> 1 This number is based on the national poverty line, which is at is
|150|150|104|104|104|94|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1031<br>05, *** p<0.0<br>(WDI), Open|1031<br>1. Standard er<br>Data Watch (O|1031<br>rors are clustere<br>DIN), Global|362<br>d at the country<br>Data Barometer|362<br>level. Data fr<br>(GDB), and O|362<br>om the Wor<br>pen Data Ba|94<br>ld Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

70
|0.61|0.54|
|Southeast Asia|-1684.83|0.09|0.07|
|China|-482.96|0.01|0|
|North Africa|-492.5|1.29|1.11|
|Sub-Saharan Africa|-0.06|10.2|12.43|
|Rest of the World|-34.24|1.26|1.53|

Source: Modeling results based on OECD.STAT and GTAP data base

# **6. Modeling  results**

Effectiveness requires that AfT policy achieves its stated goals. Following the WTO/OECD report (WTO/OECD, 2011), the common priorities between donor and recipient countries are to expand trade and, jointly, to alleviate inequalities. We use trade balance, computed as total exports less total imports as trade indicator; whereas, welfare and income indicators are

**17**
the heterogeneity of impacts across income groups while others concentrate on poverty at the municipal level. Hertel et al. (2010)<sup>3</sup> use a global Computable General Equilibrium model (the Global Trade Analysis Project or GTAP) to mimic the effect of productivity shocks in agriculture (model the consumption and production for all commodities of the national economy), and then link those estimates to household data. They use three

> 2Climate change is defined as the slow change in average temperatures and average precipitation predicted to result from the build-up of greenhouse gases in the atmosphere and not of climate variability.

> 3Hertel, T., M. Burke and D. Lobell. (2010) “The Poverty Implications of Climate-Induced Crop Yield Changes by 2030” GTAP Working Paper No. 59.

2
farmers intend to sell their produce at the start of the season. We also find that farmers are less likely to report producing only or mainly for sale at the beginning than at the end of the agricultural season. In Malawi, these changes over time in the intended destination of output are mostly driven by labor reallocations across agricultural products, particularly between subsistence crops and livestock — where the latter is more commercialized. However, changes in the self-reported intended use of output also play a role, as farmers, in the country contexts analyzed in this study, were more likely to report a given crop as being intended for sale at the end than at the beginning of the agricultural season. In addition, women appear to be less likely than men to report producing only or mainly for sale. In-depth analysis using the data from Malawi suggests that this primarily reflects differences in male and female farmers’ labor allocation across crop and, particularly, livestock products, which differ in their intended use of output. Last but not least, the correspondence between the activity-level and product-level estimates suggests that farmers understood the questions about the intended use of their output broadly in the way they were intended, i.e. they assessed whether their combined products, on average, are more likely to be sold or to be used for family consumption, despite the potential for cognitive bias inherent to such questions.

The revised standards have significant implications for our characterization of rural employment in Sub-Saharan Africa, in terms of its prevalence and sectoral distribution. In the two countries for which we have nationally representative data (Malawi and Nigeria), measured rural employment-to-population ratios drop by about 20 to 30 percent. In Malawi, the decline in rural employment is larger for the females than for males, while the opposite pattern holds true in Nigeria. The extent of the decline in the rural employment-to-population ratio partly reflects the extent to which male and female farmers are engaged simultaneously in non-farm household enterprises and/or wage work. In other words, many farmers who produce only
of parameters that are very computationally intensive given the size of the data set.<sup>61</sup> In the first stage, the index is constructed at both the monthly and quarterly levels for each product group (125 groups) and store. Stores that do not appear throughout the entire sample period are dropped, retaining around 23,500 stores in the sample. Therefore, the results are not affected by store entry and exit. Among the stores that are in the sample in 2006, 84% remain throughout the entire sample period. Although the scanner data are weekly, they are aggregated up to the monthly or quarterly level to decrease missing values and reduce chain drift, as pointed out by Ivancic et al. (2011). Each base observation is a monthly or quarterly unit value for each store-product, that is, monthly or quarterly revenue divided by the total number of units, which is equivalent to a quantity-weighted average price. Products are defined as UPC codes. Alternatively, weekly prices can be sampled from each store-product-period. Only goods that appear consistently across an entire year are included, such that around 50% and 70% of all sales are used in constructing store-level monthly and quarterly indices, respectively.<sup>62</sup> Quantities are directly observed and used as weights, which is a major advantage relative to the CPI, which collects price quotes at the store level but not quantities, so that they use quantities that are lagged three to four years and are obtained from the BLS CEX. Quantity weights are updated yearly to avoid chain drift, and the weights (denoted as _qi,y_ ) are lagged one year to ensure price changes are not a result of changing consumption patterns in response to current prices or shocks. The CPI weights are updated every two years, which is less frequent than the scanner index. Hence, the CPI is more subject to substitution bias and the basket is less relevant over time. The price index _Pj,t,y_<
|General Public Services|10,475.73|19.9|
|Public Debt Services|4,744.17|9|
|Religion & Cultural Services|2,578.06|4.9|
|Public Order& Safety Services|2,657.87|5|
|Total|52,712.06|100|

_Source:_ Annual Financial Statements of the Royal Government of Bhutan. Ministry of Finance.

Social spending mainly covers education and health services while social protection programs are limited in Bhutan. Public education is free from primary to tertiary level. Health services are predominantly provided by the public sector and are also available free of charge. On the other hand, social protection programs are very limited. _Kidu_ is a key program aimed at alleviating the hardships of the most vulnerable population but information on Kidu receipt is not collected in the household survey.<sup>9</sup> Most recently during the COVID-19 pandemic, the Druk Gyalpo’s Relief Kidu provided temporary income support to vulnerable people, including the self-employed, who had suffered from livelihoods losses due to the COVID-19 crisis.

# **_Education Spending_**

> 9 The Bhutan Living Standards Survey does not collect information on Kidu receipt.

6
. According to EACI-17 and EAC-18 data, groundnut plots, which are managed by women at a higher rate than average (18% compared to the overall average of 7%), also have a high rate of proxy respondents. Specifically, 42% of the information about these plots is not provided directly by the manager.

# **4 Empirical approach**

## **4.1 Validation approach**

We have structured our empirical framework to identify the limitations of imputation accuracy and to offer recommendations for capturing the essential data set. This includes the necessary variables, the most effective machine learning (ML) modeling approach, and the minimum sample size for robust statistical analyses of crop yields when crop-cut data is either completely or partially missing.

To determine the required set of variables and the most effective ML modeling approach, we train our model on 75% of the plots with crop-cut data, then make predictions on the remaining 25%. This process is carried out separately in the EACI 2017 and EAC 2018 datasets.

To explore the limitations imposed by survey sampling when using crop-cut data to train an imputation model, we experiment with the ML approach, and with varying sample sizes of

> 7Among the six crops analyzed, cowpea has the highest rate of intercropping, with 27% of the plots cultivated with cowpea being intercropped.

7
**Figure 11: Survival Rate of Russian Exports by Destination, EACU and Other Countries, 2002–12**

<!-- Start of picture text --> 0 5 10<br>Analysis Time<br>BLR KAZ CH N EU Rest Eu rope MEN A North A merica Rest Asia non C IU ROW<br>.8<br>.6<br>.4<br>probability<br>.2<br>0<br><!-- End of picture text -->

# _Source_ : COMTRADE data.

_Notes_ : Analysis time is in years, with year 0 = 2002 and year 11 = 2012. BLR = Belarus; KAZ = Kazakhstan; CHN = China; MENA = Middle East and North Africa; nonCU-CIS = members of the Commonwealth of Independent States, except the original three members of EACU; ROW = Rest of World; EU = European Union.

0
imputation methods, energy shares can be imputed into the EU-SILC survey. Utilizing the resulting synthetic dataset, one can overlay energy poverty with official monetary poverty. The findings reveal a substantial portion of the energy-poor population (using the LIHC measure) that does not fall under the category of official income poverty. Consistent with the findings in this paper, the results vary significantly depending on the energy measure used. This research contributes to the expanding body of literature exploring the potential of statistical matching to enhance the current data landscape in the European Union.

# 3.2. Consensual Approaches and Outcome-based Metrics

**We next explore consensual and outcome-based measures in Bulgaria and rely on those indicators mentioned in Table 1, which are included in the EU-SILC and available in Bulgaria.** Two aspects of the main EU indicators for material and social deprivation (MSD) and severe material and social deprivation (SMSD) are directly applicable to the monitoring of energy poverty using this approach. They are commonly employed as proxies for assessing energy poverty in the EU: the inability to adequately heat one's home and the incidence of arrears on utility bills.<sup>40</sup> Both factors are integral components of the AROPE headline indicator, which has been fundamental to the EU2020 poverty and social exclusion target. It continues to play a key role in supporting the 2030 EU poverty and social exclusion reduction target.

**<mark>Figure 13 shows</mark> that the energy poverty incidence rate fluctuates between 11.0 percent and 46.5 percent, depending on the indicator used.** Consequently, the overlap between monetary and energy poor differs significantly by <mark>indicator (Figure 14). Not surprisingly, the leakage</mark> and type of dwelling indicators<sup>41</sup> report relatively low overlaps. At the same time, nearly half of the monetary poor are also energy poor in the case of the affordability indicators. A high share of households are only energy poor
|17.0||

_Source:_ Eberhard and others 2008. Other sources include access data coming from the Demographic and Health Surveys 1996 and 2001; utility data from the AICD electricity database (www.infrastructureafrica.org/aicd/tools/data) _Note:_ kWh = kilowatt-hour; MW = megawatts; kVA = kilovolt ampere. — = Not available.

Senegal has been moving from a unique vertical integrated provider (SENELEC)—which generates, transmits, and distributes electricity—to a model where generation, transmission, and retail distribution is liberalized and there is room for promising alliances with the private sector.

Senegal has had already a long history of private sector participation in the sector, under various kinds of contracts and with differing results. The first concession for SENELEC was granted in 1999 but 26
<!-- Start of picture text --> Labor productivity 2010 US$ ('000)<br>160 = Interquartile range 60<br>120 Country count (RHS)<br>100 40<br>80 30<br>40<br>20 10<br>0g<br>Club 1 (High) Club 2 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> Labor productivity 2010 US$ ('000)<br>160 = Interquartile range 60<br>120 Country count (RHS)<br>100 40<br>80 30<br>60 20.<br>4020 40<br>0 ee ——__e 0<br>Club 1 (High) Club 2 Club 3 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> Labor productivity 2010 US$ ('000)<br>160 Ly = Interquartile range 60<br>120 Country count (RHS)<br>100 40<br>80 30<br>40<br>20 10<br>ee,<br>Club 1 (High) Club 2 Club 3 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> Labor productivity 2010 US$ ('000)<br>160 = Interquartile range 60<br>120 Country count (RHS)<br>100 le | 40<br>0 30<br>40 20<br>200 xx —>— 010<br>Club 1 Club 2 Club 3 Club 4<br>(High) (Low)<br><!-- End of picture text -->
- **Card, David.** 2001. “Immigrant Inflows, Native Outflows, and the Local Labor Market Impacts of Higher Immigration.” _Journal of Labor Economics_ , 19(1): 22–64.

- **Card, David.** 2009. “Immigration and Inequality.” _American Economic Review: Papers Proceedings_ , 99(2): 1–21.

- **Chari, Amalavoyal V., Elaine M Liu, Shing-Yi Wang, and Yongxiang Wang.** 2021. “Property Rights, Land Misallocation, and Agricultural Efficiency in China.” _Review of Economic Studies_ , 88: 1831–1862.

- **Chari, Amalavoyal V., Peter Glick, Edward Okeke, and Sinduja V. Srinivasan.** 2019. “Workfare and Infant Health: Evidence from India’s Public Works Program.” _Journal of Development Economics_ , 138: 116–134.

- **Chen, Chaoran, Diego Restuccia, and Ra ̈ul Santaeul`alia-Llopis.** 2022. “The effects of land markets on resource allocation and agricultural productivity.” _Review of Economic Dynamics_ , 45: 41–54.

- **Cheung, Steven N.S.** 1969. _The Theory of Share Tenancy._ University of Chicago Press.

- **Christiaensen, Luc, and Lionel Demery.** 2018. “Agriculture in Africa: Telling Myths from Facts. Directions in Development.” _Washington, DC: World Bank_ .

- **Cohen, Robin,** ed. 1995. _The Cambridge Survey of World Migration._ Cambridge University Press.

- **Colmer, Jonathan.** 2018. “Temperature, Labor Reallocation, and Industrial Production: Evidence from India.” _American Economic Journal: Applied Economics_ , 101–124.

- **Cui, Xiaomeng, and Wei Xie.** 2022. “Adapting Agriculture to Climate Change through Growing Season Adjustments: Evidence from Corn in China.” _American Journal
estimate in the main sample was 4 percentage points). The penalty for an children aged 4 to 6 years old is 3 percentage points (instead of 2 percentage points), and that of children aged 7 to 12 years is less than 1.3 percentage points (instead of less than 1 percentage point).

# **6. Conclusion**

During most of the 2000s, Guatemala grew at a relatively high rate, fertility rates declined, and female educational attainment rose without precedent. Despite these promising trends, Guatemala remained the country with the lowest FLFP rate in the LCR. This under-utilization poses a large cost to this upper-middle-income country where most women are not engaged in paid jobs. This paper explored a wide set of micro data sets between 2002 and 2018 to investigate the role of different factors in influencing the women’s decision to participate in paid employment to shed new light on the reasons behind the trend over the 2000s and the drivers of its large within-country dispersion. We assessed the role of: (i) individual and household socioeconomic characteristics (such as the level of education or the number of children), (ii) local social norms and attitudes towards women (at home and in the community), (iii) social public policies (such as investments in pre-schools), and (iv) local labor market characteristics (such as the ratio of male to female employment or the composition of employment across sectors of activity). Traditionally, a small state, weak governance, and chronically low tax revenues lead to limited public investment in infrastructure and human capital, largely explaining the lack of developmental progress and large gaps in social outcomes. We conjecture that regional differences in these factors influence women’s willingness and ability to participate in paid employment.

Even though our sample only covers data up to 2018, the impact of COVID-19 raises an even gloomier scenario for FLFP in Guatemala. Globally, women suffer larger work stoppage rates than men (Alon et al., 2021; Kugler et al
link (f). Ignoring these two effects likely results in systematic underestimation of the long-run _benefits_ and short-run _costs_ of trade liberalization, respectively. However, determining the ultimate importance of these two linkages requires specification of a stochastic, dynamic model, which is beyond the scope of this study.

6 Some of the other studies in this general category are: Devarajan and van der Mensbrugghe (2000); Sadoulet and de Janvry (1992); Dorosh and Sahn (2000); Coxhead and Warr (1995); Bautista and Thomas (1997); Harrison, Rutherford, and Tarr (2003); Levin (2000); and Cockburn (2001). Summaries are provided in the tables of Reimer (2002). See also Löfgren, Robinson, and El-Said (2003) for a brief introduction to this topic. Cloutier, Cockburn, and Decaluwé (2004) provide an extensive modeling survey of CGE analysis of poverty and trade liberalization.

14
Evidence from Canadian child benefit expansions. _Am Econ Rev_ 99 (2): 128-132.

- Molina-Millan, Teresa, Tania Barham, Karen Macours, John A Maluccio, and Marco Stampini. 2016. Long-Term Impacts of Conditional Cash Transfers in Latin America: Review of the Evidence Inter-American Development Bank Technical Note No. IDB-N-923.

- Molyneux, Maxine, With Nicola Jones, and Fiona Samuels. 2016. Can Cash Transfer Programmes Have ‘Transformative’ Effects? _The Journal of Development Studies_ 52 (8): 1087-1098.

- Munshi, Kaivan D and Mark R Rosenzweig. 2006. Traditional institutions meet the modern world: Caste, gender and schooling choice in a globalizing economy. _American Economic Review_ 96 (4): 1225-1252.

- National Statistical Office and ICF Macro. 2005. Malawi Demographic and Health Survey 2010

- Naudeau, Sophie, Rifat Hasan, and Anne Bakilana. 2015. Adolescent Girls in Zambia: Introduction and Overview. Policy Brief: Zambia:

http://elibrary.worldbank.org/doi/abs/10.1596/24597.

- Oster, Emily and Bryce Millett Steinberg. 2013. Do IT service centers promote school enrollment? Evidence from India. _Journal of Development Economics_ 104:123-135.

- Ozier, Owen W. 2015. The impact of secondary schooling in Kenya: a regression discontinuity analysis. _World Bank Policy Research Working Paper_ , no. 7384.

- Saavedra, Juan E and Sandra Garcia. 2013. Educational impacts and cost-effectiveness of conditional cash transfer programs in developing countries: A meta-analysis. _CESR Working Paper_ , no. 2013-007.

- Thomas, Duncan, John Strauss, and Maria-Helena Henriques. 1991. How does mother's education affect child height? _Journal of Human Resources_ 26 (2): 183-211.

- Woerner, Wolfgang, Bacy Fleitlich-Bilyk, Rhonda Martinussen, Janet Fletcher, Giulietta Cucchiaro, Paulo Dalgalarrondo, Mariko Lui, and Rosemary Tannock. 2004. The Strengths and Difficulties Questionnaire overseas: evaluations and applications of the SDQ beyond Europe. _European Child and Adolescent
impacts could be over or underestimated. This is much more likely to be the case for severe drought conditions, whereas those that are captured in our surveys tend to be less extreme events that are unlikely to cause movement or disintegration of households.

This brings us to the third potential source of bias in the results. Although the analysis uses survey data across many weather conditions in the analysis, there are very few observations in the survey data that experience drought conditions below two standard deviations from the mean. This limits the degree to which non-linearities can be estimated. It also means there is limited empirical support for predicting the welfare costs of extreme events.

A final source of bias comes from measurement error in the hazard data. Although care was taken to use the best available data and match hazard and household data carefully, any measurement error in the hazard variable will attenuate the estimated coefficients, thereby underestimating the welfare impacts. Additional uncertainty is 3
000|0.000|
|**P-val. Moth. ed.**|0.153|0.016|0.030|0.597|0.023|
|**P-val. Fath. ed**|0.118|0.160|0.210|0.087|0.296|
|**P-val. Moth. oc.**|0.442|0.757|0.611|0.569|0.418|
|**P-val. Fath. oc.**|0.781|0.347|0.769|0.448|0.670|

# _Source:_ Authors’ calculations

_Notes:_ *p<0.05; **p<0.01; ***p<0.001. Standard errors clustered on the school level.

# **_4.3 Inequality in inputs_**

We now turn to examining inequality in home and pre-primary inputs by SES. In Figure 5, we explore the patterns of pre-primary quality and home inputs by the families’ asset index, based on a local polynomial (triangle kernel). Figure 6 shows the variation in stimulation by mother’s and father’s education and father’s occupation. Table 2 shows OLS models for SES and the various home and pre-primary inputs (testing H2 and H3). There is substantial variation in the relationship between inputs and assets. Strong relationships were observed between home stimulation and preprimary environments, and the family asset index. A one SD increase in the asset index predicts a statistically significant 0.197 SD increase in home stimulation (consistent with H2). There are similar (and likewise significant) relationships of around 0.19 SD increases in the pre-primary environment or teacher attitudes for each SD increase in assets (consistent with H3). Other results for teaching practices (coefficient of 0.117), materials (-0.006)