rafmacalaba/datause-encoder-data huggingface dataset:
# _Transport questions integrated in the modules of education, health, and employment_

In the typical multi-topic household questionnaires in our research sample, there are some transport questions regarding different aspects of human development in the modules of education, health, and employment, which ask the eligible individuals about the travel mode, distance, time, and cost to school, health facility, and workplace. Education-related transport questions are relatively more common than the transport questions in health and employment modules. The household questionnaires of Armenia, Lesotho and South Africa also ask individuals about the reasons for not attending in education or missing classes, where one option is related to transportation difficulty. Similarly, several surveys contain the additional questions on the main reason why respondents did not consult anyone during the illness or did not work or look for jobs, where transportation difficulty is also taken as one of the potential reasons.

# _Other transport sections_

Some transport-related questions do not cover as many countries as the four types of questions mentioned above but are also important to understand the role of transport in different aspects of household living conditions.

To better understand the households’ economic activities, most of the standard LSMS surveys contain the modules of agriculture and non-farm enterprises. The two common transport questions in the agriculture module are about transport mode and cost associated with the shipment of crop and animal inputs, and the sale of agricultural products. Regarding household non-farm enterprises, respondents are asked if any household member works on transportrelated business such as driving a household-owned taxi or pick-up truck to provide transportation or moving services, and the business cost of the enterprise on transportation.

Some standard LSMS surveys also contain the question on the cause of food insecurity, specifically, asking respondents whether they faced with a situation when they did not have enough food to feed the household during the past 12 months, and one option is lack of access to market due to high transportation costs.

There are also two special modules in the LSMS-tagged multi-topic household questionnaires of several countries. Ghana, Kenya, and Lesotho designed a module of tourism in their household surveys, asking about the main mode of travel and cost on transportation including fuel and transport rental services during the domestic and international tours. Sierra Leone, South Africa, and Mauritius (only the 2016 round) designed another special module of crimes, with which to collect the information on vehicle-related thefts and vandalisms during the last five years.

# _Identifying a set of recurrent questions_

For each thematic module, we identify a set of common questions that are recurrent across surveys. There are eight thematic modules: agriculture, non-farm enterprises, education, health, employment, food security, crimes/security, and tourism, for which we could identify recurrent transport-related questions.

10
The second attempt at import liberalization was relatively successful for several reasons. In the intervening three years between the two attempts, a sector study by the Bank developed a solid understanding of the structure of industry and its effective protection, and yielded a detailed design of components of reform, based on discussions with an inter.ninisterial committee. Considerable effort went into building a more genuine commitment on the part of the Government. The reforms were sequenced and implemented with careful coordination with exchange rate changes. Later, the liberalization process was complemented by other initiatives in export promotion, and by an improved incentive structure resulting from price decontrol. But the liberalization process was not complete. Although the tariff rates introduced with automatic licensing were generally less than the estimated equivalent tariffs, they were on the higher end, and needed to be lowered to reduce effective protection further. Moreover the import licensing system was not entirely dismantled and was subject to considerable executive discretion. For example, even for items eligible for automatic licensing, foreign exchange authorizations were to be obtained, and foreign exchange was actually released only with a lag. From time to time, depending on the level of reserves, these licenses and authorizations took longer to obtain or were denied.<sup>5</sup> A 1992 Bank survey on the costs to firms of annual licensing requirements (equivalent of a 5 percent tax on turnover) indicated that import licenses were the main determinant of licensing burden. Equally vexing were the continued ad hoc import duty exemptions to nonexporters-encouraging favoritism and discrimination, and creating revenue losses.

29
# _Modification 3: Accounting for Methane_

Methane is a major greenhouse gas. Following the same logic as with CO2, the deviation of the country (per capita) emissions and stocks from the global average can be calculated for each country. Excess emissions or deviations were multiplied by the country’s population and the GCI. The results are reported in Annex Table C **.** To the extent that methane is more problematic than carbon for climate change, the GCI could be set higher. Per capita methane emissions are over four times the global average in Australia, Canada, New Zealand, and Russia. Countries such as France, Germany, and the UK have per capita emissions lower than the global average. In addition, the price of the excess stock emitted since 1990 can be calculated. Annex Table D reports the results.

In terms of methane emissions, Russia takes the lead as the main emitter. With a stock-based approach, emissions of Brazil and Australia become more significant. China is the second largest recipient following India if only methane emissions are considered.

# _Modification 4: Forestry_

One large carbon sink is a country’s forests. Bastin et al. (2019) use spatially granular data to estimate forest restoration potential across the globe and find room for an additional 0.9 billion hectares of forest cover, which could store about 750 Gt of CO2 at maturity.

To the extent that a country has protected its forests, given its stage of development and its population density, it should get some credit for it.<sup>6</sup> To objectively compute a measure for ‘excess’ forest cover, the proposed framework predicts a baseline forest cover in a country given its natural environment and initial forest endowment (in 1990) as well as hard-to-control factors like its stage of development (e.g., more agriculture means less forest cover), and its population (larger population for a given terrain means less forest cover). The actual forest cover less the predicted forest cover is the ‘excess’ forest cover that the country should get credit for. Using a conversion rate for how much carbon is stored per square km of forest, the total tons of carbon stored in the ‘excess’ forest can be obtained. It is important to note that the carbon absorption quality of forests varies across countries; the proposal accounts for these differences using the Global Forest

> 6 The point here is that countries with a natural propensity for forest cover should not receive credit for their natural endowments.

11
24

**Figure 7:** _Factor intensities of Turkey’s exports to the EU in 1990, 1995-2004 (in percent)_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Natural Resources Capital Intensive Unskilled Labor Skilled Labor<br><!-- End of picture text --> Source: Computations based on EU data from UN COMTRADE Statistics.

**Table 12:** _Share of skilled labor and capital intensive products in total exports of EU candidate countries and EU-8 in 1996 and 2004 (in percent)_

||199|6|200|4|Percent P|oint Change|
|---|---|---|---|---|---|---|
|<br>Country/Group|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|% Share in<br>Total Exports|Of which: % in<br>Skilled Labor|
|Turkey|21|13|44|35|23|22|
|Bulgaria|39|15|31|15|-7|0|
|Croatia|33|10|34|11|1|1|
|Romania|29|14|35|16|6|1|
|EU-8 (simple average)|51|26|69|34|18|9|
|**Memo:**<br>SEE-4(simple average)|20|10|27|16|7|6|

Note: SEE-4: Albania, Bosnia and Herzegovina, FYR Macedonia and Serbia and Montenegro Source: Computations based on EU-15 data as reported in UN COMTRADE Statistics.

Turkey’s performance contrasted rather sharply with that of other EU candidate countries and South East European countries (SEE-4, see note in Table 12), which are parts of the EU Stabilization and Association process. While the aggregate share of skilled labor and capitalintensive products in Turkish exports in 1996 was well below that in other comparator countries and groups except for SEE-4, the share in 2004 was the second largest after the EU-8. Between 1996 and 2004 it increased seven percentage points in exports from SEE-4, six percentage points in Romania’s exports and 1 percentage point in Croatia’s exports. It fell seven percentage points in Bulgaria’s exports triggered by the increase in exports of unskilled labor intensive products rather than natural resource intensive products, as one might expect given Bulgaria’s endowments in factors of production.<sup>12</sup> Another interesting feature of Turkey’s performance relative to other European transition economies—except for SEE-4 economies and Croatia—was that the levers of growth were skilled labor intensive products. They were responsible for 96 percent of the increase in the weight of these products in Turkish total exports between 1996 and 2004 as compared to 49 percent for the EU-8 economies and 25 percent for Romania. As for SEE-3 economies and Croatia the increase came also from exports of skilled labor intensive products accounting for 83 percent and 96 percent of the respective changes.

12 The share of unskilled labor intensive products in Bulgaria’s exports increased from 17 percent in 1996 to 31 percent in 2004.
it actually is. To check that potential migrants are indeed over-inferring from the sequences of migrant death, in the next section, I compute the change in perceived mortality rate implied by the migration effect and compare it with the change in the mortality rate of a Bayesian who does not commit this fallacy.

# **5 Over-inference of mortality rate**

In the first part of this section, I calculate the implied change in perceived mortality rate in response to a migrant death using estimates of earnings elasticity and the value of statistical life (VSL) from my companion paper (Shrestha, 2017). In the second part, I present a simple learning model of a Bayesian who believes that migrant deaths every month are generated by an i.i.d binomial process and computes his level of updating in response to migrant deaths. I compare these two estimates to show that potential migrants update a lot more in response to a death than the model.

## **5.1 Computing the implied change in perceived mortality rate caused by migrant death**

In Section 4.1, I present the estimates of _β_ =<sup>_∂_lo</sup> _∂D_<sup><u>g</u></sup><sup>_M_</sup> , the effect of a migrant death _D_ , on migration flows _M_ . Each death reduces migrant flow from a district by 0.9 percent for 12 months. This represents a total of 11 percent reduction in monthly migrant flow (albeit over a year) in response to a single migrant death. I then calculate a one-time increase in migrant earnings necessary to induce the same number of potential migrants to migrate so that the net effect on migration is zero. Since the earnings elasticity of migration, _ε_ = _∂_<sup>_∂_</sup> log<sup>log</sup> _W_<sup>_M_,theearningsincreasenecessarytooffsetthemigrationeffectis</sup> given by: A simple thought experiment behind this calculation is as follows: First, shock a district with a death of a migrant in a particular month. This will lead to a reduction in migration flows from the district for the next 12 months. At the same time, shock the district with a one-time increase in expected migrant earnings for all those who migrate in that month in such a way that it induces the same number of migrants as had been dissuaded by the migrant death. The equation above provides with precisely the amount of such earnings shock necessary to compensate the district for the fall in migrant flow induced by the migrant death.

Finally, I use the estimate of the VSL to translate the earnings response to change in perceived mortality rate. I use the discretized definition of the VSL, _V SL_ = ∆∆ _Wd_<sup>,andtheformulaabovetodo</sup> so.

where _d_ represents the perceived probability of death and _W_ will be the average potential earnings from migration.

I use the earnings elasticity and the VSL estimates from Shrestha (2017) for this calculation. In that study, I randomly assign information on earnings and mortality to potential migrants without 13
**Appendix Table A.2: Rosenbaum sensitivity analysis for the impact of family background on children's years** **<u>of schooling (junior middle school as the threshold for parental education)</u>**

|**Gamma**|**Non farm, low**|**edu**|**Farm, high ed**|**u**|**Non farm, high**|**edu**|
|---|---|---|---|---|---|---|
||**Mantel-Haenszel**<br>**statistics**|**P value**|**Mantel-Haenszel**<br>**statistics**|**P value**|**Mantel-Haenszel**<br>**statistics**|**P value**|
|**Abadie and Imb**|<br>**ens matching**||<br>||||
|_Daughters_|||||||
|1.0|0.50|0.00|1.13|0.00|1.88|0.00|
|1.3|0.25|0.05|0.88|0.00|1.63|0.00|
|1.5|0.13|0.26|0.69|0.00|1.50|0.00|
|1.8|-0.13|0.72|0.50|0.00|1.25|0.00|
|2.0|-0.25|0.90|0.38|0.01|1.13|0.00|
|_Sons_|||||||
|1.0|0.75|0.00|1.38|0.00|2.13|0.00|
|1.3|0.38|0.00|1.13|0.00|1.88|0.00|
|1.5|0.25|0.03|1.00|0.00|1.75|0.00|
|1.8|0.13|0.31|0.75|0.00|1.50|0.00|
|2.0|0.00|0.62|0.63|0.00|1.44|0.00|
|**Biased corrected**<br>_Daughters_|**radius matching**||||||
|1.0|0.50|0.03|1.00|0.00|2.00|0.00|
|1.3|0.00|0.44|0.50|0.00|1.50|0.00|
|1.5|0.00|0.79|0.50|0.01|1.50|0.00|
|1.8|-0.50|0.98|0.00|0.10|1.00|0.00|
|2.0|-0.50|1.00|0.00|0.25|1.00|0.00|
|_Sons_|||||||
|1.0|0.50|0.00|1.00|0.00|2.00|0.00|
|1.3|0.50|0.03|1.00|0.00|2.00|0.00|
|1.5|0.00|0.24|0.50|0.00|1.50|0.00|
|1.8|0.00|0.76|0.50|0.01|1.50|0.00|
|2.0|-0.50|0.94|0.00|0.04|1.50|0.00|

# **_Notes:_**

1. Mantel-Haenszel test statistics gives the upper bound under the assumption of overestimation of treatment effects; 2. Gamma=1 implies the absence of unobserved selection bias and both matched individuals have the same probability of participating; 3. Gamma=2 implies the two individuals are identical on matched covariates but one might be twice as likely as the other to receive the treatment because they differ in terms of unobserved covariates.
# **1. Introduction**

Access to financial services—including payments, savings, credit, insurance, and investment products—allows households and small businesses to mitigate economic shocks, as well as build healthy and productive futures. Digital financial services, when used responsibly, makes these benefits more widely available, thereby driving financial inclusion for women and other underserved groups.

Expanding access to finance through digital channels and methods is one of the primary strategies for meeting United Nations Sustainable Development Goal 8.10, which targets ownership of an account at a formal financial institution, such as banks, regulated microfinance institutions, and mobile money service providers. Account ownership is just the first step, however. To truly reap the benefits of financial inclusion, account holders must be able to confidently and responsibly use their account to store money, save for the future, access credit, and make payments.

In this paper, we discuss the academic evidence on the benefits of digital financial services for expanding financial inclusion. We bring data from the demand-side Global Findex showing where technological enablement is associated not only with increased account ownership but also—and more significantly—with increased _usage_ of financial services. Direct digital payments out of and into an account are the most significant and dispersed way in which adults are using their accounts, and an example of how digital approaches help deliver the benefits of financial inclusion for more people. The data also points to opportunities to expand financial access and usage by supporting payment digitalization, including with stronger consumer protection.

# **2. Account ownership**

Account ownership is important as a financial foundation for reducing poverty and empowering individuals. Its benefits include a greater ability to save money (Breza, Kanz, and Klapper, 2020; Blumenstock et al. 2018), greater female empowerment (Ashraf et al., 2010; Field et al., 2021), and improved agricultural outputs (Brune et al., 2016). Importantly, account ownership is associated with increased financial resilience, based on research showing that adults with 2
gender differences in returns to education (Morduch 2000) or in home production (Edmonds 2006). Second, our first stage estimates that provide strong evidence of the link between negative shocks experienced in utero or during early childhood on subsequent cognitive ability contribute to the fetal origins hypothesis literature by highlighting a potential mechanism, reduced cognitive ability, through which observed impacts on adult outcomes such as earnings and occupation can be explained. We are also able to estimate placebo regressions showing that rain shocks prior to when the child was in utero show no relationship with ability, further strengthening our interpretation of a causal relationship. Third, because of the unique household survey data collected by the authors, this paper is the first to examine the relationship between cognitive ability and child labor, focusing not only on total hours of child labor, but also changes in the specific tasks that each child performs.<sup>5</sup> We find that higher ability children, compared to their lower ability siblings, are more likely to be enrolled in school and perform fewer hours of child labor. We also find the child labor they do is less concentrated in tasks requiring long, continuous blocks of time to complete. Rainfall more than one standard deviation below the village‟s long-run historical average that is experienced by a child in utero is correlated with 0.24 standard deviations lower cognitive ability z-scores and compared to their siblings, that corresponds to a 38 percent lower likelihood of enrollment and a 49 percent increase in the number of hours of child labor. The negative

> 5 The current paper builds on previous research (Akresh et al., 2012), which also explores the relationship between a child‟s cognitive ability and educational investments, but there are three significant differences between the current paper and the earlier work. First, the econometric identification strategy in the previous work only allows for an examination of correlations between child ability and enrollment, while in this paper we are able to measure a causal impact of ability on schooling by implementing an instrumental variables approach using the rain shock instruments. Second, in contrast with the previous paper that focuses exclusively on sibling rivalry, the current paper links the literature on the fetal origins hypothesis with the literature on sibling rivalry by showing that shocks experienced in utero or early childhood not only have direct negative impacts on the child‟s cognitive ability (fetal origins hypothesis), but also negatively impact the child through the effects on sibling rivalry resulting from the cognitive differences. Third, while the previous research is only concerned with schooling outcomes, in the current paper we also examine the relationship between ability and child labor.

5
<!-- Start of picture text --> 40<br>ef CSC“‘C‘C*Yd<br>go Tt—CTTTTTCCOCC‘<C<br>ee<br>£0<br>a<br>or oF<br>= -20<br>ula |<br>cs<br>-50<br>-60<br>co © O 2 = @ © = fo co @<br>5 § &£ &£ § $ 8 & F 2 $<br>i = n a tat Ee rd a o<br>Eid B5 “o = =5 Za ksc my aE<br>oO tn mh<br>=<br>ByradeExpansion Trade Stagnation<br><!-- End of picture text -->

<!-- Start of picture text --> { a {<br>CONGO, DEM. REP. OF id CONGO, DEM. REP, OF e<br>f 9 A’<br>ni q a,<br>:<br>N sscrianioun MEAS wozamBiquE<br>1g <.<br>d —_<br>54 .<br>oy, or<br>SOUTH AFRICA7 SOUTH AFRICARy<br><!-- End of picture text -->
# **A1.5 The Economist Intelligence Unit (EIU)**

The Economist Intelligence Unit is a for-profit organization producing analysis and forecasts of the political, economic and business environment in more than 180 countries. The EIU was founded in 1949 and is based in London. As of May 1999, the EIU can be reach at htto:l/www.eiu.com.

In 1997, the EIU launched two quarterly publications which contain some govemance measures: The Country Risk Service, and the Country Forecasts. The assessments in these publications are based on regular contributions from a global network of more than 500 information-gatherers. A panel of regional experts checks the accuracy, consistency and impartiality of these assessments. In this paper, we combine data from the 1999 first quarter edition of the Country Risk Service, and from the 1994-98 historical risk rating from the Country Forecast.

# **Country Risk Service (CRS)**

The CRS provides intemational investors with risk ratings for seven broad categories of country risk-Political, Economic Policy, Economic Structure, Liquidity, Currency, Sovereign Debt, and Banking Sector-and covers 100 emerging markets. Within each category, subjective assessments are produced for a number of sub-issues. However, only the seven broad risk ratings are actually published.<sup>19</sup> The Political Risk category is the most relevant rating for the purposes of this paper. It is divided into elements of Political Stability and elements of Political Effectiveness. Political stability asks whether the political scene is free of internal or external threats to security. Political effectiveness examines the quality of govemance.

# **Country Forecasts (CF)**

The CFs measure the quality or attractiveness of the business environment in 60 countries. The rankings examine ten separate criteria-the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labor market and infrastructure and are designed to reflect the main criteria used by companies to formulate their global business strategies. Each criteria covers a number issues for which two assessments are produced: A historical rating, covering the last 5 years, and a forecast rating covering the next five years.

For the purpose of this paper, the most interesting criteria covered is the political environment. All the issues covered under this heading are based on subjective qualitative assessments produced by EIU's staff.

# **Combining the CRS and CF Ratings**

The Political Risk rating from the CRS and the Political Environment ratings from the CF ratings cover almost identical concepts, although using different rating scales. We convert the CF ratings to the same units as the CRS ratings, and augment the CRS ratings with the CF _19_ Although, these estimates are produced all that is actually published are the risk ratings for the broad categories. We would like to thank Merli Baroudi, editor of the Country Risk Service, for kindly providing us some of the subcomponents of the Political Risk Index.

38