rafmacalaba/data-use-mentions-extended huggingface dataset:
November 1st to March 30th. For the 2018/2019 heating season, the corresponding benefit was BGN 74.83 monthly during the heating season (Vaughn & Cabrera, 2022).

**Before the reform, eligibility for the heating allowance was also linked to the GMI. Still, the minimum income thresholds to determine eligibility (Differentiated Minimum Income for Heating - DMIH) were 2 – 3 times higher than those in the MSA scheme (Differentiated Minimum Income).** More details of the eligibility conditions are in Annex 3. Therefore, the program covered about 8 percent of the population and roughly one in four poor in 2018. (World Bank, 2021, pp. 77–78). According to previous estimates from the World Bank (2020) with 2013/14 SILC data, the coverage of the heating allowance was in the range of 4.7 – 4.9 percent of the population in heating seasons 2012/13 and 2013/14, and the range of 15.4 – 15.9 percent of the poorest quintile respectively (World Bank, 2020).

**Nevertheless, the lack of a monthly electricity consumption threshold and the absence of GMI level indexing with inflation have constrained access to the heating allowance program, suggesting there is scope for policy improvements** . Energy poverty is estimated to impact the poor in Bulgaria significantly. In 2022, 42.7% of poor households (measured by the at-risk-of-poverty rate) could not afford adequate home heating, ranking second highest in the EU. Additionally, in 2021, over half of households spent more than 10% of their income on energy. Recent microsimulations on rising energy prices suggest a potential increase in energy and income <mark>poverty (Robayo-Abril and Rude, forthcoming).</mark> While Bulgaria's heating

11
managers to make choices that improve the firm's performance (Jensen (1986), (1989) and Wruck (1990)). However, a high leverage may also weaken the incentive to pursue efficiency, since borrowers' relative stake in the firm is smaller. 15 Finally, to test whether the maturity structure of debt has an impact on firms' investment policies, we estimate a simple augmented accelerator model, with the investment rate as dependent variable and the rate of growth in sales, the ratio of cash flow to total assets, leverage, and the maturity structure of debt as regressors. We allow the maturity variable to enter both contemporaneusly and lagged once. A significant cash flow variable is likely to capture the importance of financing constraints and may also reflect the fact that contemporary cash flow acts as a proxy for expected profitability.'<sup>6</sup> Also, higher leverage should lead to a greater premium on debt and then have a negative impact on investment, if the rate of interest is an increasing function of firm's leverage because of the existence of agency and/or financial distress costs.1<sup>7</sup> As mentioned in Section 2, the prediction on the relation between investment and debt maturity is instead ambiguous. However, the common wisdom is that the availability of long term finance should stimulate investment in fixed capital by reducing the fear of liquidation and by allowing firms to make long term plans. Moreover, if the interest

**15** The **adverse consequences ofthe conflict between borrowers and lenders is emphasized in Jensen and Meckling (1976), Myers (1977),** Stulz **(1990).**

16 **See Fazzari, Hubbard and Petersen** (1988) **for** an **empirical investigation of cash flow and financing constraints.** 17 **See Lang, Ofek and Stulz (1995) for an empirical analysis using US firms panel data.*
43 Table II: Preference Parameters

|Parameter|Value|Standard<br>Error|Source|Description|
|---|---|---|---|---|
|_φ_|0.0105|.|From HIES data|Asymptotic agricultural share|
|_ν_|0.12|.|Eckert and Peters(2023)|PIGL parameter|
|_η_|0.656|0.129|Estimated on HIES data|Engel elasticity|
|_σM_|2.528|.|Feenstra et al (2018)|EoS across origins (manufacturing)|
|_σA_|1.714|0.153|Estimated on HIES data|EoS across crops|
|_σK_|2.377|.|Match crop price increase|EoS across origins(within crop)|

_Notes_ : for each preference parameter in the model, the table displays the value we impose for it, the standard error of its estimate (when applicable), the source from which it is taken or estimated, and a brief description of the parameter.
over land – namely, rights to sell and bequeath— has a stronger association with time allocated to paid work among women employed in agriculture.

Fifth, a cross-cutting finding is that the associations is stronger with the use of dichotomous indicators of ownership of and rights to physical and financial assets, as opposed to individuals’ self-reported asset values. And while the data are not set up to examine causal effects, the findings are consistent across different regression specifications that are used to test the robustness of our results. These include household fixed effects regressions that control with household-level unobserved heterogeneity that may jointly impact individual time use and asset ownership.

Overall, the paper provides a comprehensive overview of the associations between individuals’ time use and individual wealth, as measured through newly available data on individual asset ownership. The findings underscore the importance of examining channels by different subpopulations, relevant for the targeting of policies around wealth and labor, and that can be explored further in future survey methodological work and experimentation.

# 2.  Country Context and Data

## 2.1  Employment trends in Cambodia

Cambodia has maintained high growth and labor force participation over the last decade, but more needs to be understood about factors associated with underlying gender inequalities in economic opportunities. Cambodia’s annual GDP growth rate is among the highest in East Asia and the Pacific region, and has remained relatively stable at around 7 percent annually, from 2013 to 2019. Labor force participation (LFP) rates were among the highest in the region in 2019, albeit with gender gaps. In 2019, the employment-topopulation ratio for men age 15 and older was about 88 percent, compared with 76 percent for women. In 2019, 58 percent of employed women versus 44 percent of employed men were in occupations the ILO has identified as vulnerable, including own-account work (self-employed with no employees) or contributing family work.<sup>5</sup> Furthermore, about three-quarters of the population live in rural areas, but activities have diversified rapidly away from agriculture. Since 2013
.00654]|
|Income|0.001|-0.0273***|-0.001|
||[0.00998]|[0.0105]|[0.00244]|
|Self employed|-0.006|0.020|0.006|
||[0.0158]|[0.0137]|[0.00528]|
|University degree|-0.0382***|-0.0559***|-0.002|
||[0.0130]|[0.0126]|[0.00409]|
|Method|Probit|Probit|Probit|
|Pseudo R2|0.08|0.08|0.05|
|Country fixed effects|yes|yes|yes|
|# Households|13,838|13,838|13,838|
|#countries|21|21|21|

Source: LITS survey and authors‟ calculation. Note: The dependent variables in this table are Cut consumption, Cut services and Sold assets. Cut consumption is 1 if the household reduced its consumption of goods in 2009 or 2010 and 0 otherwise. Cut services is 1 if the household reduced its use of health, utilities or telecommunication services in 2009 or 2010 and 0 otherwise. Sold assets is 1 if the household sold off an asset in 2009 or 2010 and 0 otherwise. The variable Job loss is 1 if any household member lost his job since 2008, and 0 otherwise. Closed business is 1 if a family business was closed since 2008, and 0 otherwise. Wage reduction is 1 if a family member's wage income was reduced since 2008, and 0 otherwise. Less remittance is 1 if remittances from abroad to the family were reduced since 2008, and 0 otherwise. All other variables are as defined in tables 3.3.1 and 3.3.2. All models report marginal effects of profit
their merchant business identification number (SIRET code). Using detailed card transactions<sup>8</sup> I calculate the number of cards and number of new cards for each merchant that I consider to be cards that have never made a transaction before 2019. Additionally, using merchant business identification number (SIRET code) and public data from the National Institute of Statistics (SIRENE) on French establishments, I obtain further information on each merchant: date of creation, sector activity (NAF code), number of employees, and geographical location (postal code or longitude and latitude).<sup>9</sup> The data set offers several key benefits for my study. First of all, one of the main advantages of my data is that I have card and mobile transactions information from all the banks in France, which allows me to fully capture all mobile and card sales for each merchant and then be able to generalize my findings. Second, digital payment is transforming the global payment system, household consumption behavior, and merchant selling behavior. Merchant card transactions give me the ability to directly verify the impact of a payment technology shock (such as an increase in the contactless payment limit) as well as a global shock (such as a lockdown due to the COVID19 epidemic) on the ability of merchants to mitigate them. Third, the high-frequency card-sales transactions data recorded with little measurement error, compared to the traditional survey-based data set can allow me to track the sales to each merchant through the transaction record, which is crucial for my study. Relative to very few studies utilizing merchant transaction sales, I am able to study a long time series (two-year period) of sales from a large scale of merchants in different categories.

In 2020, the world was affected by the COVID-19 pandemic. To stop the spread of the virus in France, the government imposed a first containment from mid-March to mid-May. In addition, in mid-May 2020, the French banks increased the limit for contactless payment in
In an earlier study, Arnold (1992) also used DHS data to assess the prevalence of female disadvantage across countries (not normalized by reference population mortality). He found that the female child mortality rate was equal or higher to that of males in 18 of the 26 countries he included. Similarly to Hill and Upchurch, Arnold found that there was no clear pattern of female disadvantage in the prevalence or treatment of diarrhea, fever, and ARI, nor in the nutritional status indicators available.

Using the precursor surveys to the DHS, the World Fertility Surveys (WFS) carried out between 1974 and 1980, Rutstein (1984) reports the female and male mortality between the ages of two and five (3q2). Of 40 countries, Rutstein finds a higher female than male mortality rate in 25 countries.20 The median female to male mortality ratio was 1.05 for the 36 non-South Asian countries, with a mean of 1.05 and a standard deviation of .23. The median female to male ratio was 1.17 for the four South Asian countries (Bangladesh, Sri Lanka, Nepal, and Pakistan) with a mean of 1.22 and a standard deviation of .18. Although Rutstein's findings indicate a somewhat higher rate for the non-South Asian countries than we do, the much higher level in the South Asian countries is consistent with our results.

# Conclusion

This descriptive work is a first step in a research agenda that aims to examine the causes of gender disparity, and where possible, to suggest policies aimed at reducing it. However, even from this preliminary work there are four conclusions.

> 20 Portugal was dropped in this assessment.

25
in this paper should be treated as a starting point for more in-depth analysis that would rely on unpublished data from local authorities. Furthermore, shifting from exposure analysis to a comprehensive assessment of naturerelated risks requires the development of relevant scenarios and a better understanding of sectoral and asset level economic and financial vulnerabilities, including the consideration of indirect impacts.

The analysis offers four key findings. First, nature-related financial risk exposure is generally high (above 50 percent on average) across the sample countries. Second, higher exposure tends to be negatively correlated with countries’ income level, highlighting the relevance of economic structure. When considering more indirect dependencies on ecosystem services via supply chains and trade, this correlation might change, however. Third, the highest dependencies on ecosystem services across countries tend to be on climate regulation and flood and storm protection, indicating the interconnectedness of climate change and nature loss. Finally, high dependencies on ecosystem services are concentrated in few sectors, with construction and real estate explaining more than 20 percent of all banking sector dependencies.

The remainder of this paper is organized as follows. Section 2 presents the methodology and the data used for the analysis. Section 3 presents the key results while section 4 concludes.

# **2. Methodology and data**

This paper conducts a multi-country assessment of the dependency of banking systems on ecosystem services in 20 emerging market economies. The analysis serves as an indication of banking sector exposure to nature loss, specifically to nature-related physical risks. In this section, we describe the data sources as well as the applied methodology.

3
among other measures.

Second, in view of the complex logistics, supervision requirements, and ultimately higher costs associated with crop cutting, the question is whether crop cutting can be implemented on a sub-sample basis and if so, the minimum share of plot observations that should be subject to crop cutting in a way that allows us to estimate an imputation model that provides reliable measures of imputed CC yields. We assessed the predictive accuracy of our preferred ML specification with different sizes of the training data set - experimenting with 10, 20, 25, 50, 67, and 75 percent of the crop cut measures, while leaving the remaining sample in each scenario to be the test sample. It appears (see top panel of Figure 8) that conducting crop cutting on 50 percent of the plot observations may be sufficient for deriving reliable imputed CC yields. After the 50 percent mark, the gains in accuracy appear to be marginal.

Third, a related question is whether it is necessary to randomly designate a share of plot observations for crop cutting in all primary sampling units or whether reliable predictions can be obtained by working with a subsample of primary sampling units and conducting crop cutting on all plots in the designated areas. Since ML algorithms can perform well out of sample, logistics and supervision of crop cutting can potentially be simplified by limiting our footprint to fewer areas in a given country. To answer this question, we create an alternative training data set composed only of crop cut measures obtained in one randomly chosen ERIVaS stratum – as opposed to working with one-third of plots randomly chosen across all strata. The test sample in this case are composed of plot observations from the remaining ERIVaS strata. The bar plot in the bottom left panel of Figure 8 shows that the choice of the stratum has an important effect on the accuracy of the ML measure. The choice of the first stratum as the source of the training data results in an the _R_<sup>2</sup> value of 0.24 from the bivariate regression of
passed a bill whose main block consists of changing the procurement rules so that purchases must give preferences to local (Ontario-based) firms.<sup>3</sup>

This phenomenon is particularly intriguing in the case of Europe. Even though the EU regulation based on “the single market spirit” should make the market for public procurement perfectly integrated, “Improving access to procurement markets” is one of the six strategic priorities to improve the public procurement system of the European Commission.<sup>4</sup>

**Research question.** In this paper, we ask: _What is the effect of governments’ home bias_

> 1All these numbers come from our own calculations using TED data (see also Kutlina-Dimitrova and Lakatos (2014)). For comparison, in Appendix C, we use our data together with input-output tables and data on trade flows across regions to show that import penetration rates in procurement are significantly lower than those in overall trade also when looking at narrowly defined sectors.

> 2The Buy American Act (1933) requires the US government to “prefer” US-made products and services in its purchases. “Joe Biden will mobilize the talent, grit, and innovation of the American people and the full power of the federal government to bolster American industrial and technological strength and ensure the future is made in all of America by all of America’s workers. Biden believes that American workers can out-compete anyone, but their government needs to fight for them.” (Link)

3 “By harnessing our immense buying power, BOBI (building Ontario businesses initiative) will allow our government to build our businesses in every corner of our province and support new jobs for our workers.”(Link) 4See for instance “Making Public Procurement work in and for Europe”. (Link) 1
Source: Authors’ calculations based on quarterly HBS.

The recovery in 2017 had a different dynamic: incomes of the lower‐income groups were recovering faster. There is a negative relationship between real income growth rates in 2017 and real income level in 2016 (Figure 9). The higher‐income groups like those with high education levels or residents of Minsk continued to experience income declines in 2017. The lower‐income groups, like households with 2 children, single‐ adult households with children, or residents of rural areas and small cities enjoyed recovering incomes in 2017. The only exception here is households with three and more children that continued to experience income declines even in 2017.

_Figure 9: Real income change during recovery in 2017 for major socio‐economic groups negatively depends on initial level of income_ <!-- Start of picture text --> 16.0%<br>14.0% Single-adult hh with<br>12.0% Hh with 2 ch children<br>10.0%<br>Small cities<br>8.0% Rural<br>6.0% Secondary spec edu<br>4.0%<br>2.0% Minsk<br>0.0%<br>-2.0% 150 200 250 300 350 400<br>-4.0% Hh with 3 and more ch Retiree hh<br>Higher education<br>-6.0%<br>Cash income in 2016<br>Real cash income change 2016-2017<br><!-- End of picture text --> Note: Cash income and change in cash income are measured in real 2014 roubles per capita. Source: Authors’ calculations based on quarterly HBS.

# 3.2. Absolute and moderate poverty

The crisis of 2015‐2016 had a substantial effect on the well‐being of the low‐income population, but it was not accompanied by a corresponding increase in the
2005 World Bank estimates are discussed at length in Chen, Ravallion, and Sangraula (2010).

7. FDIC 2009.

8. Aterido, Beck, and Iacovone (2011) find no evidence of discrimination or lower inherent demand for financial services by women when key individual characteristics are taken into account.

9. Cole, Sampson, and Zia 2011.

10. The data on spending per student on primary education are from the World Bank’s World Development Indicators database.

11. Gallup World Poll data include two variables related to the urban-rural divide: municipality population data that are used to stratify the sample, and interviewer-coded data on area size category. Municipality population data are not available for all regions because strata are sometimes based on geographic categories. The analysis in this report and the country-level data release are based on the interviewer-coded urban-rural data. The correlation between the population-based and interviewer-coded categorizations is very strong.

12. The positive but imperfect correlations of account use with financial depth and national income level raise questions about the drivers of cross-country differences in financial use and access that are explored in Allen and others (2012).

13. Among respondents, 12 percent chose none of the given reasons for not having an account.

14. Estimated increases are based on the percentage of adults who report not having a formal account for a given reason and thus by how many percentage points account penetration could increase if a given barrier was eliminated. For example, 31 percent of adults without an account in Sub-Saharan Africa cite distance as a barrier. Since the unbanked make up 76 percent of the Sub-Saharan Africa adult population, 23 percent of all Sub-Saharan African adults report not having an account in part because of distance (31*0.76 = 23). So, if distance barriers were alleviated, this segment of the population could become banked, thereby increasing the percentage of banked adults by 23 percentage points. Clearly these statements are indicative at best and should not be interpreted as causal.

**28**

MEASURING FINANCIAL
- National average (by size and class) gap filling or equalization standard to replace Williamson’s index. Pool can be adjusted by affordability and allocation determined by standard.

- Gap filling or equalization by size and class of local governments and possibly equal per capita grants to villages.

- Fiscal capacity measurement based upon potential revenues plus tax shares and other transfers and 50% of resource revenues.

- Fiscal needs measurement to discontinue wages compensation (basic allocation), discontinue use of HDI and the Williamson’s index and other indexes. Instead consider need measurement based upon service/client population for each service category.

# **_Alternative 1: A Simpler Yet More Objective Gap Filling Approach_**

To simplify DAU and to ground it in local comparative context, one requires meaningful grouping of local governments. One alternative in this regard is to have the following classes or clusters. Please note that the use of sophisticated statistical techniques in clustering as recently advocated by the World Bank and the Indonesian academics simply adds more complexity without having any clear advantages to a simpler and transparent approach advocated here.

**Table 7. Characteristics of Each Cluster: Province, C1-C4, and D1-D4**

|**Cluster**|**No.**<br>**of**<br>**juris-**<br>**dicti-**<br>**ons**|**Total**<br>**popu-**<br>**lation**<br>**(mil.)**|**Min.**<br>**pop.**<br>**(mil.)**|**Max.**<br>**pop.**<br>**(mil.)**|**Group**<br>**ave-**<br>**rage**<
the only practical way we can achieve this identification is probably estimating the effects of business environment at the plant level. At the same time, it is the long run productivity effects that would measure the true cost of possible deficiencies in a location’s business environment. Moreover, longitudinal analysis of aggregate or industry level data seems to be a far

> 9 This is not, of course, necessarily in the sense of the physical dismantling of plants to set them up elsewhere, but in that the number of plants in areas of “poor” business environment would eventually fall through closures relative to areas of  relatively “good” environment, where more new plants would be setup than would be closed over the same period.

6