rafmacalaba/datause-encoder-data huggingface dataset:
related to parenthood for female financial inclusion. Equal treatment in _Entrepreneurship –_ i.e., in signing a contract, registering a business, and legally opening a bank account – increases the likelihood for a woman to open a blank account. This same holds true for _Assets,_ which captures laws related to women’s property ownership and inheritance rights. This positive link is consistent with a compendium of literature that demonstrates that women’s legal rights to own and manage property are linked to labor supply, level of income, access to credit, capital formation (e.g., Joireman, 2008; Gonzales et al., 2015; Hallward-Driemeier and Gajigo, 2015). Finally, the coefficient of the _Female * Pension_ interaction is not significant. It may be that there is no detectible relationship here as such laws, which impact a woman’s economic wellbeing at the end of her working life, are applicable at too late a stage in the course of her career. Overall, the positive and significant coefficients of the _Female * WBL_ interaction terms in all models indicate that, on a global level, legal gender equality is positively correlated with women’s financial inclusion.

The estimated coefficients on the control variables are in the direction expected. First, being a woman significantly dampens the likelihood to be financially included, no matter the legal framework. In line with Gosh and Vinod (2017), we find that _Age_ has a significant positive effect on formal account ownership. We observe that lower levels of income are negatively correlated with access to financial services in line with Demirgüç-Kunt and Klapper (2013). Moreover, being part of the lowest educated segment of the population significantly decrease the likelihood of accessing financial services. Regarding the country-level variables, only _lnGDPpercapita_ has a robust effect on financial inclusion. This might be explained by the cross-sectional structure of our data and the inclusion of country fixed effects that may capture the effect of our macroeconomic controls.

# **4.2. Further estimations**

## **4.2.1. Mediating effect of norms and legal enforcement**

In order to test what the mediating impact of social norms might be on the relationship between legal equality and women’s financial inclusion, we use the inverse of the Equality variable (which we refer to in our results as the _Negative Attitudes_ variable) provided by the World Values Survey (WVS) in line with Davis and Williamson (2019). Data from the WVS have been used to measure the intensity of gendered social norms by Seguino (2007; 2011), Kenny and Patel (2017) and Klassen (2019), amongst others. The WVS data are collected by a group of social scientists, and they follow set rules and procedures for collecting data. Using 11
|||World||200|7SERVICE EX|PORTSHARE||
|---|---|---|---|---|---|---|---|
|TRADITIONAL SERVICES||||||||
|Transportation|8,161|9.629|11,990|21%|19%|32%|18%|
|Travel|7433|7,854|8,999|41%|22%|30%|58%|
|Communication|6,261|6,320|7584|4%|2%|3%|4%|
|Construction|14,510|10,534|10,464|2%|2%|3%|3%|
|Insurance|8,167|11,306|13,630|2%|2%|0%|1%|
|Personal Cultural and Recreational|14,510|10,534|10,464|1%|1%|3%|2%|
|MODERN SERVICES||||||||
|Financial|18,590|23,063|25,743|3%|9%|3%|1%|
|Computerand information|14,916|20,092|18,797|2%|5%|3%|1%|
|Royalties and license fees|10,263|43,293|14,707|1%|3%|1%|0%|
|Otherbusinessservices|7883|9437|13,162|15%|20%|19%|9%|

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_3)_ Table IV. International Banking And Capital Market Flows (billions of dollars)

|Items|1976|1980|1984|1987|
|---|---|---|---|---|
|International Bond Issues|25.1|35.3|108.1|n.a.|
|Syndicaated Bank Loans|27.5|82.9|36.6|255.0|
|Note Issuance Facilities|-|-|18.9|n.a.|
|_Total_|52.6|118.2|163.6|315.0|
|As a percent of World Exports|5.7|6.3|9.3|n.a.|

_Source:_ For 1976 to 1984, Alexandre Lamsalussy, Structural Changes in the International Financial Markets, _International Journal of Development Banking,_ Volume 4, Number 1 (January, 1986); for other years, World Bank Estimates based on Bank of International Settlements (BIS) data.
Health Organization 2022). These data are not reporting direct COVID-19 deaths only but rather are an estimate of the combination of direct and indirect COVID-19 mortality, as measured by excess deaths.

Lacking actual measures of direct and indirect COVID-19 mortality by sex, we rely on estimates of excess deaths by sex. We check the reliability of these data by comparing sexmortality ratios from the estimate of excess deaths and those from reported COVID-19 deaths for a subset of countries for which this is available. This ratio of ratios is not always 1. However, there is no clear pattern suggesting that the ratio based on excess deaths is different from that constructed from reported COVID-19 mortality (not shown here but available upon request).

This dataset also contains population counts from the World Population Prospects by country, year, sex and age (UN 2022). The dataset covers 194 countries in 2020 and 194 in 2021. For this analysis, only countries with excess death estimates based on actual all-cause deaths (not predicted) by sex and age-group are included. This limits the sample to 75 countries in 2020 and 62 countries in 2021. Additionally, we exclude countries with total excess deaths (both sexes combined) below 2,000 in either 2020 or 2021. After applying these two criteria, we have 66 countries in this analysis: 54 in 2020 and 57 in 2021 (Table 1). Lastly, we analyze mortality data only for adults 45 years and older, since excess death rates are extremely low for younger ages.

[TABLE 1]

# _Estimating age-mortality patterns_

To characterize the age-mortality patterns drawing on the data from multiple countries, we estimate a model of the age-mortality curve with interactions by sex for each country. The advantages of working with a model are twofold. First, it produces a slope of the agemortality curve by sex. Second, it enables the use of predicted values for some estimated quantities which minimize the influence of outliers (discussed above) and, importantly, allows calculation of confidence intervals. The models are estimated separately for 2020 and 2021 by fitting a Poisson regression using deaths as the response variable and population as an offset.

where the age and sex specific number of deaths is for the 10-year age group age to age for sex in country The data xx,ss,ii DD xx points correspond to the group consisting of all ages 85 years and above. To interpret death counts as mortality rates (for the age-sex specific population), an exposure term xx+ 9, xx= {45,55,65,75,85} PP= {M MMee, FFeeFFM ee} PP. xx= 85 was 3 xx,ss,ii uu
_Development Economics_ , 143. https://doi.org/10.1016/j.jdeveco.2019.102380.

- Black, R.E., Allen, L.H., Bhutta, Z.A., Caulfield, L.E., de Onis, M., Ezzati, M., Mathers, C., Rivera, J., 2008. Maternal and child undernutrition: global and regional exposures and health consequences. _Lancet_ 371, 243–260.

Buchy, M. & Basaznew, F. (2005). Gender-blind organizations deliver gender-biased services: The case of Awasa Bureau of Agriculture in Southern Ethiopia. _Gender, Technology, and Development_ , 9(2): 235-251. doi: 10.1177/097185240500900204.

Donald, A., Goldstein, M., & Rouanet, L. (2022). Two heads are better than one: Agricultural production and investment in Côte d’Ivoire. _Policy Research Working Paper 10047_ . World Bank.

Donald, A., Doss, C., Goldstein, M., & Gupta, S. (2024). Sharing responsibility through joint decisionmaking and implications for intimate-partner violence: Evidence from 12 sub-Saharan African countries. _Review of Economics of the Household_ , 22: 35-66. <u><mark>https://doi.org/10.1007/s11150023-09646-w</mark></u>

- Doss, C. & Meinzen-Dick, R. (2015). Collective action within the household: Insights from natural resource management. _World Development_ 75: 171-183. <u>http://dx.doi.org/10.1016/j.worlddev.2015.05.001</u>

Farnworth, C. R., Jumba, H., Otieno, P.E., Galie, A., Ouma, E., Flax, V.L., Schreiner, M-A., & Colverson, K. (2023). Gender roles and masculinities in leveraging milk for household nutrition: Evidence from two districts in Rwanda. _Food Policy_ , 118. https://doi.org/10.1016/j.foodpol.2023.102486

- Fletschner, D., & Mesbah, D. (2011). Gender disparity in access to information: Do spouses share what they know? _World Development_ , 39(8), 1422-1433. doi:10.1016/j.worlddev.2010.12.014.

- Frank, E. (1999). Gender, agricultural development and food security in Amhara, Ethiopia: The contested identify of women farmers in Ethiopia. USAID. https://pdf.usaid.gov/pdf_docs/pnacg552.pdf

- Han, X., Ding, S., Lu, J., and Li, Y. (2022). Global, regional, and national burdens of common micronutrient deficiencies from 1990 to 2019: A secondary trend analysis based on the Global Burden of Disease 2019 study. eClinicalMedicine, 44: 01299. doi: 10.1016/j.eclinm.2022.101299.

Harris-Fry, H., Prost, A., Beaumont, B., Fivian, E., Mohanty, S., Parida, M., Pradhan, R., Sahu, S., Padhan, S., Mishra, N., Rath, S., Rath, S., Koniz-Booher, P., Allen, E., & Kadiyala, S. (2023). Intrahousehold power inequalities and cooperation: Unpacking household responses to nutrition-sensitive agriculture interventions in rural India. _Maternal Child Nutrition_ , 19(3): e13503. <u>https://doi.org/10.1111/mcn.13503.</u> Hassen, H., Ali, J., Gebreyesus, S., Endris, S., Temesgen, A. (2020). National incidence, prevalence and disability-adjusted life years (DALYs) of common micronutrient deficiencies in Ethiopia from 1990 to 2017: Estimates from the global burden of disease study. _Global Health Action_ , 13 (1): 1776507, DOI: 10.1080/16549716.2020.1776507.

Isler, J., Sawadogo, N.H, Harling, G., Barnighausen, T., Adam, M., Sie, A. & McMahon, S.A. (2020). ‘If he 17
**Table 1: Theory-based hypotheses and expected findings**

||**Hypothesis**|**Expected finding**|
|---|---|---|
|**H1**|_There is a critical wealth threshold below which_<br>_households fall into climate immobility traps._|Negative impacts of weather anomalies on migration<br>for households with lower endowments (e.g., asset<br>levels).|
|**H2**|_Households exposed to recurrent shocks are_<br>_more likely to experience resource-constrained_<br>_immobility._|Concave relationship between the estimated effect of<br>contemporaneous weather shocks on migration and<br>lagged weather conditions.|
|**H3**|_Local adaptation and migration are substitute_<br>_risk management strategies that households_<br>_adopt to deal with climate risk._|Negative relationship between the estimated impact of<br>weather shocks on migration and pre-shock levels of<br>local adaptive capacity.|
|**H4**|_There also exists a wealth threshold above which_<br>_climate mobility is no longer needed or_<br>_desirable._|Negative impacts of weather anomalies on migration<br>for households with higher endowments (e.g., asset<br>levels).|

# **3. Data**

Our main data source is the recently released _Uniform Panel Dataset_ which is derived from the past longitudinal rounds of Nigeria's General Household Survey - Panel (GHS-Panel).<sup>5</sup> The GHSPanel is a national, longitudinal, multi-topic household survey that has been implemented by the Nigeria National Bureau of Statistics since 2010, in collaboration with the World Bank Living Standards Measurement Study (LSMS). The past GHS-Panel rounds were implemented in 20102011, 2012-2013, 2015-2016, and 2018-2019. Each round is composed of two visits, one conducted after crop planting (post-planting visit) and one after the harvest (post-harvest visit). We focus on households who participated in the agricultural questionnaire—i.e., households engaged in agriculture—due to the predominant importance of the agricultural channel in determining climate-migration relationships in low and lower-middle-income countries (Cai et al., 2016; Cattaneo et al., 2019).

We construct our migration outcome variable using survey questions regarding household

> 5 The data can be requested here.

11
See Appendix 5 for itemized cost estimates for sea facing and interior polders.

# _<u>Coastal afforestation measures:</u>_

As mangrove forests exist along some segments of the coastline of Bangladesh, only the additional cost of further afforestation is computed. At present, estimation from Google Earth using GIS methodology indicates 897 km length<sup>54</sup> of existing sea polders would require mangrove forests for protection. This in turn implies that for a 500m wide protective forest belt, as recommended by the IWM officials, a foreshore area of 448.5 sq. km (= 897km x 0.5 km ) would need afforestation. At the current cost of afforestation of $168,000 / sq km<sup>55</sup> , the projected cost is US $ 75 million.<sup>56</sup>

# _<u>Multipurpose Cyclone Shelters</u>_ **:**

Current consensus is in favor of multipurpose cyclone shelters with elevated space for livestock and overhead water storage, which can serve as a primary school / office space at times other than an emergency.

In coastal Bangladesh, 8,059,185 people were exposed to storm surge related inundation depth of more than 3m in 2001. In 2050, with a projected growth of population<sup>57</sup> , 13,571,667 inhabitants will be exposed to storm surge related inundation depth of more than 3m even without climate change. In a changing climate with the projected expansion of the inundation zone as well as an increase in inundation depth coupled with a projected population growth, an additional 9,122,762 inhabitants will be exposed to a similar inundation risk. In estimation of the costing of multipurpose cyclone shelters, current cost and capacity of cyclone shelters have been collected from the World Bank funded projects. At present, a World Bank‐funded multipurpose cyclone shelter, under‐construction, with provisions for 1,600 people costs $214,000. In order to accommodate 9,122,762 inhabitants

> 54 At present, total length of 49 sea facing polders is 957 km, of which only 60 km has forest belts.

> 55 The cost of afforestation per hectare used is from the CERP II project.

> 56 With the addition of 500 m forest belt, requirement to enhance the elevation of embankments of sea polders  may reduce by 30 cm in specific locations.

> 57 Population for the coastal region in Bangladesh 2050 has been projected under the assumption that Bangladesh will attain replacement fertility rate by 2021, and 68.4% growth of coastal population between 2001 and 2050.
**Table 7:  Robustness check using alternative measures of country endowments**

||I|ntermediate ver|sus all other exp|orts|Inter|mediate versus|consumption|exports|
|---|---|---|---|---|---|---|---|---|
||A.1|A.2|A.3|A.4|B.1|B.2|B.3|B.4|
|qxQ (institutions)|0.061***|0.058***|||0.066***|0.061***|||
||(10.131)|(9.842)|||(9.500)|(9.059)|||
|qxQxI|-0.005|-0.002|||-0.005|-0.001|||
|1 ti t f|(-0.780)|(-0.354)|||(-0.831)|(-0.170)|||
|qxQ (me o enorce<br>contract)|||-0.025***||||-0.021***||
||||(-4.397)||||(-3.297)||
|qxQ1xI|||0.005||||0.001||
|xQ2 (cost to enforce|||(0.838)||||(0.122)||
|q<br>contract)||||-0.039***||||-0.051***|
|||||(-6.270)||||(-7.008)|
|qxQ2xI||||0.008||||0.016**|
|||||(1.368)||||(2.327)|
|txT (infrastructure)|||0.036***|0.036***|||0.022***|0.020***|
||||(7.350)|(7.356)|||(3.312)|(3.059)|
|txTxI|||0.019***|0.019***|||0.041***|0.043***|
||||(4.122)|(4.138)|||(6.521)|(6.797)|
|txT1 (time to export)|-0.035***<br>(-7.730)||||-0.019***<br>(-3.123)||||
|txT1xI|-0.007||||-0.027***||||
||(-1.591)||||(-4.356)||||
|txT2 (road,rail,port,air)||0.039***||||0.032***|||
|||(8.863)||||(5.120)|||
|txT2xI||0.011**||||0.022***|||
|||(2.305)||||(3.615)|||
|kxK (capital)|0.017***|0.015***|-0.001|0.004|0.025***|0.023***|0.005|0.012***|
||(4.497)|(4.089)|(-0.239)|(1.109)|(5.738)|(5.379)|(1.257)|(2.939)|
|hxH (human capital)|0.017***|0.019***|0.022***|0.020***|0.016***|0.019***|0.020***|0.018***|
||(5.247)|(6.415)|(6.544)|(6.121)|(4.538)|(5.384)|(5.199)|(4.859)|
|I (intermediate dummy)|-0.078***|-0.081***|-0.075***|-0.075***|-0.014**|-0.016***|-0.01|-0.01|
||(-16.464)|(-17.295)|(-15.481)|(-15.487)|(-2.277)|(-2.588)|(-1.555)|(-1.554)|
|Country effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.712|0.711|0.708|0.708|0.695|0.694|0.69|0.691|
|Number of observations|34,868|36,023|33,352|33,352|29,196|30,158|27,925|27,925|
|Number of countries|99|102|93|93|99|102|93|93|
|Number of industries|342|342|342|342|309|309|309|309|

Notes: The dependent variable is the natural log of intermediate or all other (consumption) exports in industry k by country i to the World. Coefficients are beta standardized and t-values for robust standard errors are reported in brackets. * Significant at 10%; ** at 5%; *** at 1%.

15
<!-- Start of picture text --> Figure 1b: Côte d'Ivoire<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>2003 160 2003 0.47<br>10-49 Workers 2008 190 10-49 Workers 2008 0.50<br>2014 374 2014 0.60<br>2003 107 2003 0.31<br>50-199 Workers 2008 108 50-199 Workers 2008 0.28<br>2014 151 2014 0.24<br>2003 44 2003 0.13<br>200-499 Workers 20082014 4961 200-499 Workers 20082014 0.100.13<br>> 500 Workers 200320082014 323333 > 500 Workers 200320082014 0.050.090.09<br>Source: Côte d'Ivoire Registrar of Companies for the Modern Enterprise<br>Source: Côte d'Ivoire Registrar of Companies for the Modern EnterpriseSectors, 2003, 2008, & 2014. Sectors, 2003, 2008, & 2014. Shares may not sum to one due to roundingerrors<br>Total Employment Employment Share<br>2003 3,662 2003 0.05<br>10-49 Workers 2008 4,503 10-49 Workers 2008 0.06<br>2014 7,943 2014 0.09<br>2003 11,114 2003 0.16<br>50-199 Workers 2008 10,099 50-199 Workers 2008 0.13<br>2014 14,276 2014 0.16<br>2003 13,466 2003 0.19<br>200-499 Workers 20082014 14,07818,472 200-499 Workers 20082014 0.190.20<br>> 500 Workers 20032008 41,06446,198 > 500 Workers 200320082014 0.550.590.62<br>2014 50,682<br>Source: Côte d'Ivoire Registrar of Companies for the Modern Enterprise<br>Source: Côte d'Ivoire Registrar of Companies for the Modern EnterpriseSectors, 2003, 2008, & 2014. Sectors, 2003, 2008, & 2014. Shares may not sum to one due to roundingerrors<br><!-- End of picture text --> 36
the old NDC target or a randomly selected carbon tax. Second, it compares a carbon tax policy with mandated emission constraints for China. Most existing studies for China either analyze the emission constraints or carbon tax; they do not compare them. Third, it examines the impacts of carbon tax more deeply by considering nine alternatives for recycling carbon tax revenues; whereas the existing studies for China consider only a few schemes. Fourth, it analyzes the criteria used to reallocate carbon tax revenues for a given purpose, particularly when the carbon tax revenue is recycled to cut corporate income taxes. No existing studies for China have gone this much deeper while designing a carbon tax. Fifth, the study uses the latest data (the social accounting matrix or SAM for year 2017); the existing studies use older SAMs.

The paper is organized as follows. Section 2 briefly describes the basic structure of the model and data used for model calibration. The policy scenarios are designed in Section 3. This section also offers detailed explanations of how these scenarios are incorporated into the model. Main results from model simulations are discussed in Section 4, followed by key conclusions and policy implications in Section 5.

# **2. The Model and Data**

## **2.1 Basic structure of the model**<sup>**4**</sup>

A recursive dynamic CGE model is developed to simulate the economic effects of the policies considered in this study. The model consists of four economic agents: producers, households, government, and international trade. It captures the interactions within and between the agents. The production sectors are classified into 27 sectors, including six sectors for electricity generation. Table 1 defines these sectors.

**Table 1 Sector description in the CGE model**

|**Sector**|**Description**|
|---|---|
|Agriculture|Agriculture, forestry, animal husbandryand fishery|
|Coal mining|Miningand washingof coal|
|Oil and gas|Extraction of crudepetroleum and naturalgas|

> 4 The detailed model is presented in the Appendix.

4
# **4.2  Forest Clearing**

Hansen et al. (2013) publish annual high-resolution estimates of global forest clearing. The data are currently available at 30 m spatial resolution for 2001-2016. We use the Hansen estimates to compute forest clearing rates through 2016 in 500 m cells.<sup>2,3</sup> Figures 2 and 3 display our results for Lao PDR in 2000 and 2016, aggregated to 0.1-degree cells for ease of interpretation. Each cell is color coded by total percent cleared in the relevant year. Figure 2 (2000) reveals an initial pattern of forest clearing in road corridors that becomes much more pronounced in Figure 3 (2016), particularly in areas of high road network density.

# **5.  Estimation of the Forest Clearing Model**

The estimation exercise in this paper draws on the insights of previous research and the theoretical model in Section 3 to incorporate five critical determinants of forest clearing in road corridors: distance from the road, road quality, legal protection status, transport time to the nearest market center and terrain elevation. Following Damania et al. (2018) and Dasgupta and Wheeler (2016), we extend previous work on road transport cost and deforestation by introducing explicit estimation of road quality effects.

> 2 We use the 500 m approximation for expositional convenience.  Our cell sides are .005 decimal degrees in length. This translates to 556.6 meters on a side, yielding cell areas of 309,804 square meters or 30.98 hectares. We produce Hansen estimates at .005-degree resolution by resampling Hansen 30 m raster values (1 if clearing occurred from 2001 to 2016; 0 otherwise).

> 3 Our estimate also incorporates the Hansen 30 m estimates of tree cover in 2000.

12
17 Table 4

# Proportion of Pfrsons Affected by the MW Classification by Personal Characteristics (percentages)

||1980|1985|
|---|---|---|
|Experience (years)s|||
|4- 10|45.6|47.0|
|10- 20|11.3|13.4|
|21- 50|2.2|4.7|
|51- and more|8.5|19.5|
|Education (years):|||
|<br>less than 8|26.5|39.5|
|less than 12|8.3|12.3|
|12|0.0|0.0|
|12 and more|0.0|0.0|
|Males|12.0|14.0|
|Females|7.4|9.6|
|Employed|8.4|9.6|
|Unemployed|17.5|16.1|
|Inactive|14.7|19.9|

Table 5

||Compute|d Density Functio|ns||
|---|---|---|---|---|
||E(w)|DF(1980)|E(w)|DF(1985)|
|Years of experience:|||||
|4- 10|-1.54|0.50|-1.29|0.39|
|11- 20|-0.89|0.27|0.04|0.18|
|21- 50|-0.48|0.10|0.58|0.04|
|51 and more|-1.82|0.47|-2.88|0.03|
|Schooling<br>years:|||||
|less th,an<br>8|-2.18|0.32|-2.16|0.06|
|8|-1.13|0.38|-0.51|0.43|
|12|-0.28|0.06|-0.82|0.01|
|12 and more|0.35|0.004|1.81|0.00|
|log w|-1.59||-0.86||
|a|0.62||0.63||

DF - Density Function, estimated as f( _a (v))_