rafmacalaba/data-use-mentions-extended huggingface dataset:
, and help maintain soil fertility, water filtration, and the supply of raw material for vital, productive sectors (Armstrong, Siegmann, Alonso Mendieta, Reyes-Retana, 2018). However, the sustainability of Mexico’s forests is threatened by socioeconomic stresses, unsustainable agricultural practices, and climate change (CONAFOR, 2020). Drought, irregular rainfall, deforestation, and the degradation of natural resources all increase the risk of natural disasters and the loss of biodiversity (Armstrong, Siegmann, Alonso Mendieta, Reyes-Retana, 2018).

Many of the people living in these forests are poor and vulnerable: 88% of Mexico’s 12 million forest dwellers live in highly marginalized localities, and 62% live below the poverty line (INEGI, 2009; DOF, 2016). At the same time, 60% of forests in Mexico are property of communal organizations, with authorization to use the forest resources (Madrid, Núñez, Quiroz and Rodriguez, 2009). Hence, promoting impactful sustainable practices through community forest management provides an important opportunity to sustain forest resources while enabling local populations to create income generating opportunities.

A persistent gender gap exists in productive activities in forest landscapes, the distribution of benefits from these activities, and the associated public sector programs. For example, while important efforts have been made to provide more funding to women in Mexico, in 2020 only 1.15% of the resources of the main forest sector support program funded productive projects led by weomen. At the same time, according to the Agrarian National Registry, only 26% of all ejido 2
, Wendy, Menno Pradhan, Richard de Groot, Estelle Sidze, Hermann Pythagore Pierre Donfouet, and Amanuel Abajobir. 2021. “The Short-Term Economic Effects of COVID19 on Low-Income Households in Rural Kenya: An Analysis Using Weekly Financial Household Data.” World Development 138: 105280.

- Josephson, A., T. Kilic, and J. D. Michler. 2021. Socioeconomic impacts of COVID-19 in lowincome countries. _Nature Human Behaviour_ 5: 557–565.

- Kansiime, Monica K., Justice A. Tambo, Idah Mugambi, Mary Bundi, Augustine Kara, and Charles Owuor. 2021. COVID-19 implications on Household Income and Food Security

34
_Ingo Borchert and Mattia Di Ubaldo_

# **2 Related literature**

Services are an increasingly important part of a country’s production and trade, as reflected by the growing attention to services in domestic regulation, as well as multilateral and bilateral policy instruments.

From a general equilibrium perspective, entering a services PTA is likely to generate welfare gains akin to those arising from entering PTAs liberalizing trade in goods, as the PTA is expected to lower services input prices, final goods prices, and allow more consumption than would otherwise be possible. Further, as barriers to services trade typically consists of regulatory measures which act behind a country’s border, their removal does not come at the expense of a loss in tariff revenues, making services PTAs even more desirable (Egger and Shingal, 2020).

The economic literature on how PTAs affect services trade is a great deal less abundant than work on goods trade. Partly the reason is data availability: it is only with the publication of the OECD database on bilateral services trade in the early 2000s that a literature attempting to estimate determinants of bilateral services trade rapidly emerged, finding generally a positive and significant impact of PTAs (Ceglowski, 2006; Kimura and Lee, 2006; Walsh, 2008; Marchetti, 2011; Shingal, 2010; Egger et al., 2012).<sup>3</sup> Another reason why analyses on PTAs and services are scant, is that most of the PTAs explicitly featuring services chapters have been negotiated in the last two decades. For this reason, early work investigated the impact of goods PTAs, rather than services PTAs.<sup>4</sup> More recently, work focusing explicitly on the impact of services PTAs confirms

> 3Authors also investigated whether the standard gravity equation can explain bilateral services trade patterns, some confirming that proximity (geographical and linguistical) is a key determinant of services trade (Ceglowski, 2006; Kimura and Lee, 2006), other concluding that the impact of distance is negligible (
computed for any country/sector for each database available in `icio` . This database is available on the official WDR 2020 website in the data section.<sup>17</sup> Other basic examples of questions on GVC participation and the related syntax are:

- _Which share of the German exports related to GVC is produced in Italy?_ `icio, origin(ita) exporter(deu) output(gvc)`

- _Which share of the German exports is related to backward and forward GVC?_ `icio, exporter(deu) output(gvcb) icio, exporter(deu) output(gvcf)`

# **6 Conclusions**

In this paper we described the new Stata command `icio` for value-added trade and GVC analysis. It’s most important features are the following:

- It exploits the most famous Inter-Country Input-Output (ICIO) tables - the World Input-Output Database (Timmer et al. 2015), the OECD TiVA database (OECD), and the Eora Global Supply Chain Database (Lenzen et al. 2013) - but also allows to load any user-provided ICIO table.

- It provides breakdowns of aggregate, bilateral and sectoral exports and imports according to the source and the destination of their value-added content, with a careful treatment of double counted items. These decompositions can be used to:

- assess the exposure of countries/sectors to different kind of trade shocks, including tariffs.

- get indicators for any level of disaggregation of trade flows that are consistent with more aggregate measures, i.e. disaggregated indicators can be summed up to get correct measures in more aggregate trade flows.

- It can break down export flows in terms of “traditional” vs GVC-trade, at any level of aggregation, also distinguishing between backward and forward participation in GVC.

> 17Go to https://www.worldbank.org/en/publication/wdr2020/brief/world-development-report2020-data.

32
downgrades to downgrades of public enterprises, banks and private sector entities, by raising their funding costs and potentially triggering covenants (Augustin et al., 2016; Chow, 2015; Williams, Alsakka and ap Gwilym, 2013). The deterioration in balance sheets of public enterprises and banks could negatively feed back to the sovereign via credit guarantees or other financial supports, augmenting their sovereign-corporate linkages.<sup>3</sup> Since the relaxation of sovereign ceiling policy, corporate ratings that “pierce” the sovereign ceiling has become more common (Almeida et al., 2017), and bonds issued by such sub-sovereign entities could be more sensitive to changes in the sovereign rating.

Despite a growing literature on sovereign-corporate linkages, there is very little evidence on how far the ratings of sub-sovereign bonds would be generally positioned _above_ or _below_ the corresponding sovereign rating. In particular, as the sovereign ceiling policy has recently been relaxed, further empirical analysis is needed to identify key factors that determine the strength of sovereign and sub-sovereign rating linkage when bond issuers are constrained or unconstrained by the sovereign ceiling.

This paper revisits the effect of sovereign creditworthiness on sub-sovereign ratings using data on foreign currency bonds issued by sub-sovereign entities in 47 emerging markets and developing economies (EMDEs) during 1990-2013. We use censored and double-hurdle regressions to estimate relative contributions of each factor to determine the “distance” between sovereign and sub-sovereign ratings, separately for bonds rated at or below the sovereign ( _bounded bond ratings_ ) and above the sovereign ( _non-bounded bond ratings_ ). We also merge the bond-level dataset with balance sheet data from Bloomberg to estimate the importance of firm’s financial situation in affecting the distance for a subset of matched debt issuers.

The results shed light on the asymmetry in factors which determine the distance between bounded and non-bounded bond ratings. For bounded bond ratings, their distance is mainly determined by bond issuers’ ownership structure, size of their assets, and sector of issuer. The distance tends to be narrower as the country’s trade linkages
25

# **Appendix A1: Dealing with attrition**

A significant amount of households were lost in the third round of the household survey and there are some differences between the interviewed and lost households (Table A1-1). For example, households whose fields are further away or who lived in remote villages were less likely to be interviewed in round 3. Smaller households are more likely to be dropped from the sample. Male headed households and households whose members are elected officials or member of a social or economic group are more likely to remain in the sample.

**Table A1-1:Characteristics** **<u>of the interviewed and dropped households</u>**

||Attrition<br>|round2|Attrition<br>|round3|
|---|---|---|---|---|
||Not||Not||
||interviewed|Interviewed|interviewed|Interviewed|
|Household size (# of persons)|5.010|5.343|4.957|5.410|
|Dependency ratio|0.511|0.540|0.542|0.538|
|Gender household head (% male)|0.857|0.875|0.791|0.893|
|Age household head (year)|47|54|49|54|
|Percentage of illiterate household members|0.070|0.070|0.073|0.069|
|Member of hh has elected position in village<br>|0.189|0.271|0.166|0.290|
|Member of hh is a relative of people with elected positions in<br>village<br>|0.083|0.080|0.048|0.088|
|Member of hh is a member in a primary society, or other<br>economic group<br>
(1): 7–29. https://doi.org/10.1007/S10584-0100003-7.

- Ghosh, Tilottama, Rebecca L Powell, Christopher D Elvidge, Kimberly E Baugh, Paul C Sutton, and Sharolyn Anderson. 2010. “Shedding Light on the Global Distribution of Economic Activity.” _The Open Geography Journal_ 3: 148–61. https://doi.org/10.2174/1874923201003010147.

Gin, Richard and Bernard Lubin. 1989. _Psychological Aspects of Disaster_ .

- Giri, C., E. Ochieng, L. L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek and N. Duke. 2011. “Status and Distribution of Mangrove Forests of the World Using Earth Observation Satellite Data.” _Global Ecology and Biogeography_ 20 (1): 154–59. https://doi.org/10.1111/J.1466-8238.2010.00584.X.

- Hallegatte, Stéphane. 2014. _Natural Disasters and Climate Change: An Economic Perspective_ . _Natural Disasters and Climate Change: An Economic Perspective_ . Springer International Publishing. https://doi.org/10.1007/978-3-319-08933-1.

- Hamilton, Stuart. 2013. “Assessing the Role of Commercial Aquaculture in Displacing Mangrove Forest.” _Bulletin of Marine Science_ 89 (2): 585–601. https://doi.org/10.5343/bms.2012.1069.

- Hamilton, Stuart E. and Daniel Casey. 2016. “Creation of a High Spatio-Temporal Resolution Global Database of Continuous Mangrove Forest Cover for the 21st Century (CGMFC-21).” _Global Ecology and Biogeography_ 25 (6): 729–38. https://doi.org/10.1111/GEB.12449.

- Heger, Martin, Alex Julca and Oliver Paddison. 2008. “Analysing the Impact of Natural Hazards in Small Economies: The Caribbean Case.” _UNU/WIDER Research Paper 2008
observed difference in earnings is not explained by ethnicity, age, educational attainment, sector of employment, or employment in a rural (versus urban) area. Against this backdrop of different labor market characteristics between men and women, does discrimination play a role in the relatively low employment among women?

According to the latest report of the Department of Statistics of Malaysia, during the fourth quarter of 2022 there were 204,420 job vacancies posted by 42,592 companies. This number was slightly higher than the third quarter of 2022 when 190,170 job vacancies were posted by 39,472 companies. Of the vacancies posted in the fourth quarter of 2022, 43% were ads for professionals, and close to 50% of the job ads were located in the states of Kuala Lumpur and Selangor (DOSM, 2022). Job vacancies are commonly advertised online in Malaysia. We will exploit this feature by utilizing an online job platform for our study.

Our study focuses on entry level positions, that are particularly important to understand. For instance, using US data Wachter (2020) finds that early career opportunities are an important determinant of future career employment trajectories and earnings. This suggestive evidence likely holds in different contexts. In Malaysia, 35% of fresh graduates in Malaysia work in semi-skilled or low skilled occupations (DOSM, 2021). This fact suggests a mismatch between the demanded and the supplied skills in the labor market for young professionals.

# **3 Experimental Design**

In this section we describe the research protocol for implementation and data collection, which includes the following steps: profile creation (section 3.1), job search and classification (section 3.2), randomized assignment and job applications (section 3.3), and monitoring applications (section 3.5).

## **3.1 Profile Creation**

We create similar candidate profiles, each including a resume, that differ only by name (signaling both ethnicity and gender), and multiple signals of
|6.7|2.3|2.8|11.0|12.4|
|p|(0.17)|(0.16)|(0.07)|(0.08)|(0.29)|(0.27)|
|Pr nnr|12.7|12.9|8.5|8.9|15.7|15.6|
|oo - o-poo|(0.37)|(0.35)|(0.28)|(0.26)|(0.41)|(0.39)|
|Non-oor - oor|3.0|2.5|1.5|1.2|4.8|3.9|
|p p|(0.07)|(0.05)|(0.04)|(0.03)|(0.1)|(0.08)|
|Non-poor - non-|78.5|77.9|87.7|87.2|68.6|68.0|
|poor|(0.54)|(0.49)|(0.36)|(0.33)|(0.69)|(0.62)|

Notes: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Standard errors in parenthesis.

# **5 Conclusions**

The paper applies a methodology recently developed by Dang and Lanjouw (2023), that delivers point estimates of poverty transition probabilities in the absence of longitudinal data. The analysis focuses on the dynamics of poverty in Bhutan between 2017 and 2022. This case study is interesting because the observed and documented reduction in poverty appeared particularly significant (the poverty headcount ratio was 28 percent in 2017, and only 11
what is stopping mainstream financial institutions from adopting the needed innovations. Perhaps here too a lack of competition may removed the pressure to innovate.) Such mismatches are even more apparent when it comes to some of the main services typically offered by mainstream institutions such as the traditional demand deposit/current account with checkbook. This is a costly-to-provide product bundling powerful transactions services with high liquidity for those whose creditworthiness allows them to have their cheques widely accepted. But it is accordingly costly and very risky for poor borrowers, given the price imposed for bounced cheques (NSF charges) – a material risk for low-income households. In reality, poor customers do not need all of the bundled services and will often reject this product but do have a higher demand for a limited service transaction account as has been advocated in advanced economies (Kempson et al. 2000), and implemented in some developing countries. Likewise, common complaints to mainstream institutions about such features as high minimum balance and compulsory collateral for loans can be seen as the consequence of rigid product design (rather than being complacently attributed to unavoidable transactions costs).<sup>9</sup> Other examples of unsuitable product and service design from mainstream institutions are exclusive reliance on interactions that demand onerous paperwork from illiterate or barely literate customers.

Accepting this framework, it is clear that household characteristics, service provider characteristics, and wider environmental characteristics, all are relevant in understanding the importance of different barriers to access and thus potentially in identifying feasible solutions. The framework proposed can be tested and quantified: regression of access indicators on household and other characteristics can help pin down the effects.

A largely parallel discussion applies equally to access of micro enterprises operated by low income people or in low income areas.

Thus “what hinders access” points to a widening of the net for the information gathering beyond the users of financial services to the providers and their management, organization, strategies, cost structures, and operating environment. Key national basic indicators here could include measures of the cost of
, the total sample of children surveyed amounts to 1140 different children, visited between one and four times and making up an unbalanced panel of 2871 personwave observations.

> 1Both the prevalence of HIV and adult mortality rates in Kagera were geographically concentrated, and, therefore are strongly correlated with different climates and cropping patterns. The region with the highest rural HIV infection rates was in the northeast (10% in Bukoba Rural and Muleba districts and 24% in the town of Bukoba), where tree crops (bananas, coffee) were predominant. The regions with the lowest HIV infection rates were in the south and west (0.4% in Ngara and Biharamulo districts), where perennial crops and livestock are more common (Killewo and others 1990).  Thus, a survey design stratified only on mortality rates might confound the effects of high mortality with different agricultural, soil, and rainfall patterns.

> 2 Some households dropped from the sample at each round and they were replaced. Out of the original sample, 6 percent of households dropped before their first interview. Out of the remaining sample of households, 10 percent dropped out of the remaining sample before the end of the survey (Ainsworth and Semali, 2001). The attrition rate compares favorably to other panel datasets.

3
TABLE 8. Comparison of Aggregate Numbers with the United Nations Trends in International Migrant Stock Database

||**Unite**|**Nations data**|**base**||**Current study**<br>Within the<br>Soviet|||
|---|---|---|---|---|---|---|---|
|**Census round**|Total|Refugees|Net total|Total|Union|Germans|Net total|
|**1960**|75.5|2.2|**73.3**|92.3|15.8|3.7|**72.7**|
|**1970**|81.3|3.9|**77.4**|102.4|21.0|3.8|**77.6**|
|**1980**|99.3|9.1|**90.2**|118.6|23.6|3.8|**91.3**|
|**1990**|155.5|18.5|**137.0**|139.4|-|4.7|**134.7**|
|**2000**|178.5|15.6|**162.9**|165.3|-|3.8|**161.5**|

_Source:_ Authors‘ calculations based on data described in text and United Nations (2006, 2009)..

43
collected in 2013 the These data include information on the during matching process. trainer’s background; the number of workers and apprentices employed; assets, sales, and profits; and management practices. We also conducted cognitive and noncognitive assessments with the trainers.

The endline survey of apprentices was launched in August 2017 and continued through May 2018. The endline apprentice survey covered topics similar to those in the baseline but included more details about labor market outcomes. For example, for self-employed workers the survey captured firm management practices. The survey also used survey questions comparable to those in other impact evaluations conducted on youth labor markets (e.g. Hicks, Kremer, Mbiti, and Miguel (2013)), as well as those in large-scale labor market in Ghana such as the World Bank STEP and the Ghana surveys survey Living Standard Survey.<sup>10</sup> The timeline of program and evaluation activities is summarized in Figure 1. In our analysis we use data primarily from the endline survey collected in 2017-18, complemented by baseline measures for heterogeneity and balance analysis. Note that apprentice placement occurred between October 2013 and January 2014, between 42 and 52 months before the endline survey data collection.

The baseline characteristics of program applicants as well as the estimated differences between the treatment and control groups are reported in Table 1. On average applicants were 23 years old at the baseline and had completed just over seven years of schooling. The education levels of both mothers and fathers were lower than the schooling of our primary respondents, and mothers had almost 2.5 years less education than fathers. Among measures of labor market attachment, a quarter of the sample had ever started an apprenticeship and just over 40 percent were working. Only 5 percent of the worked for a and under 20 were sample wage, just percent self-employed. Applicants were working about nine hours a week and earning 15 GHS a month from all sources. Garment making and cosmetology were the two most popular trades, which is unsurprising given the gender
26 this would not be something we could easily decompose into a contribution due to the schooling shock and a contribution due to the employment shock. The reason is that income is based on an exponential function of human capital, which is accumulated linearly: thus, the shocks are multiplicative. As a result, we instead compare deviations in the difference between log income from the steady state value.

Results are reported in Figure 9. The relative log-income of women does not deviate much over the entire period of the shock (see the patterns for _Overall_ ), with the exception of the direct impact of the shock itself, which is more severe for women. In the HICs and the MICs, relative income declines by 2 _._ 5 percent, slowly returning to zero after 10 years, whereas in the LICs it is a decrease of 1 _._ 7 percent. There turns out to be no significant long-run residual impact after the 10 years of the shock itself are over.

However, if we only apply the _schooling shock_ we see that there is almost no change in relative income. This indicates that almost all of the entire differential impact of the pandemic shock is due to the employment/experience shock. In addition, even though there are differences in the returns to schooling across gender, as well as differences in the quantity of schooling, these differences appear to be too small to have any significant impact on the losses from COVID-19. Indeed, the returns to education for women are within 1.7 percentage points of the returns for men (Table 3), so that a given period of disrupted schooling is similar as a share of the human capital stocks by gender.

We repeat the experiments assuming the same returns for men and women finding an almost identical figure to Figure 9 (not shown). This indicates that the differential impact on women is simply due to the _direct_ impact of the employment shock on women. Differences in returns to experience, while significant, are then too small to yield a