rafmacalaba/datause-encoder-data huggingface dataset:
that uses 100% biomass briquettes imported from India but the kiln operator indicates importing it from India is challenging and it is not readily available in the domestic market.<sup>14</sup> A study by (Lakho and Zardari, 2016) finds that the bricks fired with rapeseed husk and a mixture of sugarcane bagasse, rick husk, and used cloths at a commercial HK in Sind, Pakistan exhibited compressive strength of as high as 89% and 93% of those baked with coal, respectively. The saving in cost of bricks fired using these fuels ranges from 18% to 25% compared to those fired with coal. The rise in coal prices has been unprecedented in Nepal over the past few years mainly due to restrictions on low-priced coal export from India to its neighboring countries, high-priced coal import from overseas, and the Russian Federation’s war with Ukraine. For example, between 2020 and 2022, the average CIF import price of coal per kg at the Indian border almost doubled from NRs 14 to 24 (Figure 8). In the past ten years or so, the share of coal imports from India also declined while it has increased from overseas (Figure 8). With additional government taxes and fees, and transportation costs, the market prices of coal reached as high as NRs 42 per kg by the end of 2021 (Prasain, 2021).

<!-- Start of picture text --> 25 100<br>20 80<br>15 60<br>10 40<br>5 20<br>0 0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>India Australia Indonesia SA<br>USA Others Coal price<br>Coal import share (%) stack bar graph<br>CIF coal import price (NRs/kg) line graph<br><!-- End of picture text -->

**Figure 8. Import price of coal (line graph) and the share of coal imported from major countries to Nepal (stack graph).** Source: (NTIP, 2023)

> 14 In the field survey, technology-wise, most of the brick kilns are of natural and forced draft ZZK types (68%), followed by FC BTK (23%), and each one of CK and HK types (see Table A1.2). Coal is the dominant external fuel used for firing bricks. Out of the 22 surveyed kilns, 21 brick kilns use coal as the main fuel source for firing bricks. They also use a small amount of other fuels, mainly sawdust, rick husk, fuelwood chips, and bagasse, to supplement coal in firing bricks.

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# **Online-Only Online Appendix to “Coping or Hoping? Livelihood Diversification and Household Welfare in the COVID-19 Pandemic”**

# **A Additional Data Considerations**

## **A.1 Diversification Indices**

Following our pre-analysis plan, in addition to the indices specified and discussed in the main text, we also present findings from four additional indices. These are summarized in Table A1.

Each of the indices, here and in the paper, has advantages and limitations. The fractional indices consider engagement in income-generating activities. These measures include binary responses and the dichotomous nature of these variables allows for comparison in income-generating activities over time with the inclusion of the post-outbreak HFPS rounds. However, these fractional indices do not consider the amount of income earned from each source. As such, these indices are a less nuanced representation of household income diversity than the Herfindahl-Hirschman Index (HHI) measures. For example, suppose Household A was engaged in casual employment for one week in 2019. During that week, the household earned five percent of their total annual income and the remaining 95 percent was generated through farm work. Suppose their neighbor, Household B, was also engaged in casual labor and farm work but generated equal incomes from these two sources (a 50 percent split). In our data, Households A and B would receive the same fraction score, even though Household A is much more dependent on a single income source than Household B and as a result Household A would have a higher HHI than Household B. There is a trade-off between using all of the data, pre- and post-outbreak, and just the pre-COVID data. The former gives us more observations over time but less detail about income. The later provides more detail about income but is only a snap shot in time.

Similarly, there is a trade-off between the simplicity of the fractional indices and the HHI. The fractional indices lack detail because they encode that detail in simple “yes” or “no” answers. Conversely, the HHI indices consider the portion of total income generated from each source, providing a more detailed measure of income diversity. However, these values are influenced by outliers in the data. Income calculations often involve multiplication of different variables: _wage earnings_ = _hourly income ∗ hours worked per week ∗_ _weeks worked per month ∗ months worked per year,_ aggregation across income sub-categories: 34
_Reviewing the quality of risk management in MENA_

**MENA guarantee schemes seem to be strengthening their risk management capacity.** Most guarantee schemes in the region report using credit scoring models to assess loan applications. The recent development of private credit bureaus in the region and the upgrading of some public credit registries will be instrumental in improving the quality of their credit risk management.<sup>11</sup>

**Regulation and supervision of MENA PCGs is not common.** This can have negative consequences e.g. in terms of inadequate risk management by PCGs. Another adverse consequence of the lack of supervision is that PCG guarantees may not be used as credit mitigation.

_4.9 Capacity Building to Participating Institutions_ _General guiding principles and international experience_

**Many PCGs provide technical assistance to participating banks and borrowers, and this service can contribute significantly to the effectiveness of the scheme.**<sup>**12**</sup> This capacitybuilding potential is often overlooked in the literature, but can constitute a major positive externality of guarantee schemes since PCGs can improve significantly lending and risk management technologies, resulting ultimately in improved outreach, additionality, and sustainability. For example, France’s OSEO shares risk management tools with participating institutions and trains bank staff in this area.  Many schemes such as Korea’s KODIT and Taiwan China’s SMEG also provide assistance to SMEs in the areas of accounting, business plan preparation, management and marketing. Capacity building for financial institutions in the areas of credit evaluation and risk management are especially important in countries where SME lending is limited and banks have inadequate expertise in this business line.

_Reviewing capacity building efforts in MENA_

**Most MENA PCGs still do not provide assistance to participating institutions.** Although the objectives of several PCGs (such as Iraq, Saudi Arabia, Syria) state explicitly that the scheme intends to contribute to an improvement in SME lending know-how in the financial sector (e.g. credit evaluation and risk management), capacity building is usually not included in the services offered. In this regard, the Palestine PCG is a noticeable exception and a good example of a scheme that provides substantial assistance to participating banks and borrowers (Box 1). Introducing similar practices is highly recommended to other PCGs in the region since banks have less experience with SME lending than in advanced countries.

> 11 Maddedu (2010) provides a review of credit reporting systems in MENA.

> 12 See e.g. Green (2003) for a comprehensive discussion on other elements of PCG design.

23
||Table of Contents||
|---|---|---|
|I.|Introduction|1|
|II.|Brief review of the literature|2|
|III.|Case studies|6<br>|
||III.A Country cases|6|
||III.B Project cases|8|
|IV.|Towards a framework of analysis|11|
|V.|Data and measurement issues|17|
|VI.|Results of the statistical analysis|19<br>|
||VI.A Unsatisfactory projects and economic policies|19|
||VI.B Results on longer-term sustainability|22|
||VI.C Results from probit analysis|25|
|VII.|Summary and conclusions|27|
|Tabl<br>1.|es<br>Factors influencing the project performance in social sectors|16|
|2.|Summary: Impact of economy-wide policies on the performance of social<br>and nonsocial projects|20|
|3.|Project sustainability of social and non-social projects|24|
|4.|Incidence of unsatisfactory social projects and policies:<br>results from crosstabulation|30|
|_5._|Education: incidence of unsatisfactory projects and policies:<br>results from. crosstabulation|31|
|6.|Probit: impact of policies on the performance of all social projects|32|
|7.|Probit: Multi-policy model for all social projects|34|
|8.<br>|Probit: impact of policies on the performance of education projects<br>|35<br>|
|9.|Probit: Multi-policy model for education projects|37|

# Appendix

# Table A-1 Definition of Variables

# References

|38<br>39|
|---|

This work was initiated during preparations of the World Development Report 1991, and has benefitted from discussions and inputs from other members of the WDR team and from OED and operational Bank staff involved in the social sectors.
Table 8: Average Job Quality between 2007-2021, by demographic groups

||Average|Ar|ea|Ge|nder||Age Group||
|---|---|---|---|---|---|---|---|---|
|||Urban|Rural|Men|Women|15-24|25-54|55-64|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|2007|0.45|0.57|0.24|0.51|0.37|0.33|0.50|0.37|
|2008|0.43|0.53|0.25|0.50|0.34|0.32|0.47|0.37|
|2009|0.46|0.56|0.27|0.52|0.38|0.34|0.50|0.40|
|2011|0.46|0.56|0.27|0.54|0.37|0.34|0.51|0.44|
|2012|0.47|0.57|0.29|0.54|0.40|0.36|0.51|0.43|
|2013|0.49|0.58|0.28|0.55|0.41|0.35|0.53|0.43|
|2014|0.45|0.56|0.24|0.52|0.37|0.34|0.50|0.39|
|2015|0.46|0.55|0.28|0.52|0.37|0.35|0.50|0.50|
|2016|0.47|0.56|0.27|0.53|0.38|0.34|0.51|0.40|
|2017|0.46|0.56|0.26|0.53|0.38|0.33|0.51|0.38|
|2018|0.46|0.56|0.26|0.52|0.38|0.32|0.51|0.41|
|2019|0.46|0.55|0.28|0.53|0.38|0.29|0.51|0.42|
|2020|0.46|0.56|0.25|0.52|0.38|0.29|0.51|0.42|
|2021|0.45|0.52|0.31|0.51|0.38|0.31|0.49|0.42|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA.

Columns (6) and (7) reveal that younger workers not only have lower job quality on average across age groups but have also experienced a decline in job quality over time, decreasing from 0.35 in 2015 to 0.29 in 2020. In contrast, workers between the ages of 25 and 54 have better job quality on average and have not experienced significant declines except during the 2013-2014 period. The oldest population shows a similar trend to that of workers aged 25-54.

Regarding the COVID-19 pandemic, job quality declined in 2019, 2020 and 2021 as a consequence of the economic crisis. Although the national average in the quality of jobs slightly decreased, different groups were disproportionately affected. For instance, while job quality increased between 2019 and 2020 from 0.55 to 0.56 for workers in urban areas, it experienced a major decrease in 2021, dropping to 0.52. On the other hand, job quality initially declined from 0.28 to 0.25 for workers in rural regions, but later increased to 0.31. Similarly, job quality sequentially decreased for men from 2019 to 2021, with a variation of -0.01 points each year, while it remained unchanged for women (0.53 to 0.51 vs. 0.38, respectively, for all years). In the case of age-groups, the pandemic affected younger workers more - exacerbating their pre-existing declining trend- in comparison to older workers who experienced little to no changes. However, job quality for the youngest cohort recovered in 2021, returning to the pre-pandemic level.

20
WPS7324 Policy Research Working Paper 7324 Do Improved Biomass Cookstoves Reduce Fuelwood Consumption and Carbon Emissions? Evidence from Rural Ethiopia Using a Randomized Treatment Trial with Electronic Monitoring _Abebe Beyene Randy Bluffstone Zenebe Gebreegzhiaber Peter Martinsson Alemu Mekonnen Ferdinand Vieider_ Development Research Group Environment and Energy Team June 2015
1 if firm _i_ exports to destination _j_ in year _t_ and did not do so in _t_ -1, equal to 0 if the firm did not export to destination _j_ in year _t_ -1 and does not start in year _t_ , and it is missing otherwise.<sup>13</sup> The explanatory variables that capture firm _i_ ’s export experience in markets other than destination _j_ are constructed as follows: where _Exp Other Gij_ ( _p_ ) _,t−_ 1 indicates whether firm _i_ was exporting (product _p_ ) to any destination _k_ that has an extended gravity relationship _G_ with country _j_ . We construct different variables to identify the spillover effect of RTAs in country _j_ on export participation and entry of Costa Rican firms. We also construct variables to account for spatial exporting patterns that capture if firm _i_ was exporting (product _p_ ) to any country sharing a border, common language, or colonial history with country _j_ .

Tables 1 and 2 provide descriptive statistics of the main variables from our expanded firm-level and product-level data sets, respectively. The first two rows in both tables report unconditional statistics, showing (as expected) that the expanded data set is largely filled with zeros and exporting is a rare event. Export entry, by definition, is a less frequent event than export participation and has fewer observations due to missing values. The subsequent rows report descriptive statistics conditional on exporting.

Table 1: Descriptive statistics: Firm level data set

|**Variable**|**N**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|**Unconditional (N=22,283,550)**||||||
|Export participation dummy|22,283,550|0.005|0.071|0|1|
|Export entry dummy|20,694,907|0.002|0.045|0|1|
|**Conditional on exporting (N=112,835)**||||||
|Export entry dummy<br>|42,311|1|0|1|1|
|Export Other ... (t-1)||||||
|... RTA|106,407|0.572|0.495|0|1|
|... RTA _×_ Depth|106,407|0.284|0.342|0|1|
|... RTA _×_ Depth MFN|106,407|0.404|0.429|0|1|
|... RTA _×_ Depth Pref|106,407|0.466|0.459|0|1|
|... RTA _×_ SPSorTBT|106,407|0.434|0.496|0|1|
|... Border|106,407|0.399|0.49|0|1|
|... Language|106,407|0.521|0.5|0|1|
|... Colony|106,407|0.016|0.124|0|1|

In Table 1, the number of non-missing observations for export entry conditional on exporting suggests that an exporting firm’s average duration of exports to a given destination is about 2.7 years. Of the firms that exported to a given destination in year _t_ , 57% also exported to an RTA partner of that destination in year _t_ -1, 40% exported to a destination that shares a border, and 52% to a destination with a common language.

> 13We allow multiple entries for a given firm-destination, as in Koenig, Mayneris, and Poncet (2010) and Fernandes et al. (2017).

6
- 4 -

Figure 4. Electricity Distribution Losses by Region <!-- Start of picture text --> 25<br>1998<br>2003<br>20<br>15<br>10<br>5<br>0<br>East Asia & Europe & Latin Middle East South Asia Sub-Saharan<br>Pacific Central Asia America & & North Africa<br>Caribbean Africa<br>Source: World Development Indicators .<br>Electric power transmission and distribution losses (%)<br><!-- End of picture text --> Based on these aggregate quality data, Calderón and Servén (2004) examines the growth impacts of infrastructure development in both volume and quality terms. Using a GMM technique, it is shown that growth is strongly affected by the infrastructure stocks and very weekly influenced by the quality of infrastructure.<sup>1</sup> The coefficient of infrastructure quality has been found statistically insignificant, possibly because of the dominant effects of infrastructure quantity. But they also show that the quality of telecommunications is likely to matter on a growth path, when only one infrastructure proxy is employed.

One important fact ignored by country-level analyses is that there is large heterogeneity in received infrastructure service quality within a country. Unfortunately, there are few indicators for the within-country heterogeneity that can be used for international comparison. However, it is intuitively obvious that the quality of infrastructure is often worse in rural areas, particularly in developing countries. Figure 5 reflects the access rates to improved water in urban and rural areas by income level. It is apparent that the quality infrastructure provision in the water sector tends to be uneven in lower income countries.

> 1 Calderón and Servén (2004) creates an index of the quality of infrastructure services using the waiting time for telephone main lines, the rate of electricity transmission and distribution losses and the share of paved roads.
competitiveness. In fact, there is much evidence that Portugal‘s business climate is especially weak and further deteriorated following the adoption of the euro.

31. In the aftermath of the global financial crisis, most European countries entered a recession and the bubble in the real estate market burst, leading to sharp increases in nonperforming loans and subsequent government-financed bail-outs for the financial sectors. The increase in public debt resulting from these bail-outs was further compounded by the ballooning of government deficits resulting from the sharp fall in revenue as a result of the drop in output. Figures 9 and 10 show the evolution of fiscal balances and of the public debt burden following the crisis.

**Figure 9: Evolution of general government deficits, percent of GDP**

<!-- Start of picture text --> 10<br>5<br>0<br>Ireland<br>-5 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Greece<br>-10<br>-15 Spain<br>-20<br>Portugal<br>-25<br>-30<br>-35<br>Source: Eurostat.<br>180 Figure 10: Evolution of general government gross debts, percent of GDP<br>160<br>140<br>120<br>Ireland<br>100<br>80 Greece<br>60 Spain<br>40<br>Portugal<br>20<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Source: Eurostat.<br><!-- End of picture text --> 16
Using LMDI (Ang, 2005), the additive decomposition of the change in transport sector CO2 emissions from year t-1 to t is expressed as: where Similarly, the multiplicative decomposition of the change in the transport sector CO2 emissions from year t-1 to t (again, following Ang (2005)) is given as: where with The first term on the right hand side of Equations (5) and (7) represents the emission coefficient (EC) effect. Note that only the coefficient of electricity is changing 26
a significant drop in the likelihood of childbearing, an impact observed immediately after the program’s implementation. The effects, which strengthened one year after the rollout, might have partially been driven by improved access to labor market opportunities and public services. The former raised the opportunity cost of childbearing and the latter lowered the price of child quality, inducing a quantity-quality trade-off.

These findings have profound implications for public policy due to increased forced migration worldwide and the reticence of host countries to facilitate these flows for several reasons, including the fear that natives view them as a threat to national identity. These concerns are particularly acute when incoming migrant groups have higher fertility rates than natives. Our illustrates how can such analysis regularization programs appease concerns. By facilitating access to labor market opportunities and public assistance— including educational services, health care, and financial aid—regularization programs hasten the of to that of natives while may convergence migrant fertility simultaneously promoting their integration and social contributions.

# **References**

- Abramitzky, Ran, Leah Platt Boustan, and Katherine Eriksson (2012) “Europe’s tired, poor, huddled masses: Self-selection and economic outcomes in the age of mass migration,” _American Economic Review_ , 102 (5), 1832–56.

- (2014) “A nation of immigrants: Assimilation and economic outcomes in the age

- of mass migration,” _Journal of Political Economy_ , 122 (3), 467–506.

- Altında ̆g, Onur and Stephen D. O’Connell (2022) “The short-lived effects of unconditional cash transfers to refugees,” _Journal of Development Economics_ , 160, 102942.

- Amuedo-Dorantes, Catalina and Francisca Antman (2017) “Schooling and labor market effects of temporary authorization: Evidence from DACA,” _Journal of Population Economics_ , 30 (1), 339–373.

16
Madrasah teachers). The samples were drawn in two stages. 91 clusters<sup>1</sup> were selected at the first stage as a subsample of the 361 clusters included in the Bangladesh Demographic and Health Survey (BDHS) of 2004. The second sampling stage selected one adult from each household. Opinion leaders were selected from among those who were resident in and around the cluster, having knowledge of and influences on the people of the cluster. On average 49 adults and 5-6 opinion leaders were interviewed in each cluster. Out of the 49 adults interviewed in a cluster, roughly 16 were married women age 15-25, 16 married women age 45-59 and 17 married men age 25-50. Interviews were conducted in April-May 2006.

We have two estimation samples: older women (1431 initial observations) and younger women (1543 initial observations). Explanatory variables are missing for some observations, which cause a drop in sample sizes for the final/effective analyses samples. Our final samples thus are: older women (1408 observations) and younger women (1534 observations). Sample drops of these magnitudes do not seem to be cause for concern regarding the representativeness of the estimation samples. The means for the analyses samples are reported in Table 2.

[Table 2 about here] In analyzing the difference in patterns between the two cohorts of women in the sample, we capture intergenerational change. Of course, it is entirely possible that the difference could well be a function of age and life-cycle and not of cohort. That being so, we believe that once we control for a number of demographic characteristics, we do capture the effect of change over time.

> 1 A cluster is a census defined village that corresponds roughly to a mouza village in rural areas and a census block (part of a mohollah) in an urban area.

11