rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3.1 Seclusion practices and mobility constraints

Seclusion of women (purdah) is practiced widely and in varying degrees throughout rural Pakistan. As they enter puberty, girls usually experience increased enforcement of purdah norms and attendant restrictions on mobility and social interaction. Limits on mobility for young unmarried girls include prohibitions on traveling unaccompanied or the need to obtain permission to do so from a male family member. These mobility constraints stem from a desire to maintain family honor (izzat), which, in a conservative society, is intimately bound up with female reputation and hence behavior, whether real or perceived. Unchaperoned travel outside the immediate confines of the village and, especially, the settlement can invite damage to a girl’s reputation.

Among households living in close proximity with long standing familial, caste, or patronage ties, as would typically be the case within a settlement, there would tend to be less harassment of each others womenfolk than of those from other settlements. Support for this claim is found in the PRHS-II, where all married women age 15-40 were asked about their perception of safety within and outside their own settlement as well as their use of any type of veil or purdah in public.<sup>4</sup> While 80% of women report feeling “safe” alone inside their own settlement, only 27% report feeling safe alone outside of it. Similarly, only 14% of women report practicing full purdah (coverage of whole body including face) out in public within their own settlement compared to 31% outside their settlement. Thus, settlement boundaries appear to matter as far as female reputational risk is concerned.

Other data also point to mobility restrictions as a key constraint on schooling for girls. The nationally representative Pakistan Integrated Household Survey (PIHS) of 2001-02 shows that enrollment decisions for girls in rural areas are more responsive than those of boys to proximity to school (see World Bank, 2005), but this analysis does not account for settlement boundary-crossing. More suggestive evidence comes from the
transport and communications; financial intermediation; real estate and business activities; public administration and defense; education; health and social work; other service activities; and private households).

Information on socio-demographic characteristics, such as individuals’ age and education are included in the BLFS as well. This allows the construction of a variable for potential labor market experience. Information on an individual’s location ( _dzongkhag_ and urban or rural area) and his or her gender and nationality (Bhutanese or not) can also be used. Lastly, the questionnaire contains a series of questions on households’ wealth and exact demographic composition. For precise definitions of all variables used in the empirical analysis of Section 4, cf. Table 1.

# **3.2. Analytical Strategy and Econometric Model**

The microeconomic literature has used a wide range of methods to investigate wage differentials between public and private sector workers (cf. Lausev, 2014, for an overview). Among the most commonly used methods are “single equation” models and “double equation” models. Single equation models are basically wage regressions in the tradition of Mincer (1974) for the whole economy that include a public sector dummy variable. For double equation models separate wage regressions are run for the public and private sector samples. The main advantage of single equation models is that they make corrections for self-selection into labor force participation and specific sectors straightforward. In the context of analyzing wage differentials between public and private sector, controlling for such self-selection is of paramount importance. On the other hand, double equation models are more flexible and allow intercepts and returns to productive characteristics to differ across sectors. They can also be an intermediate step for counterfactual decompositions, i.e., for an investigation of the proportion of the differences in the average levels of hourly wages between public and private sector workers that is accounted for by differences in the level of individuals’ characteristics (the “characteristics effect”) and by differences in the impact on earnings of these characteristics
Section 6 computes expected emissions values and deviations from those values for a large number of urban areas. Section 7 summarizes and concludes the paper. The Appendix describes the World Bank’s development of an online platform to support future work.

# **2.  The CO2 Emissions Model**

# **2.1 Model Specification**

An extensive body of empirical literature has explored the determinants of growth in CO2 emissions. Attention has focused primarily on the drivers of CO2 emissions from fossil-fuel combustion and cement production (e.g., Raupach et al. 2007; Jotzo et al. 2012). Recent research has also estimated more precisely the CO2 emissions from fires associated with agriculture and land-use change (Gasser et al. 2020; Winkler et al. 2021). However, emissions drivers in this sector have received less attention than work on industrial determinants (Sanchez and Stern 2016). For the industrial sectors, most of the available estimates are inferred from survey-based activity measures that may be incomplete, particularly for developing countries.

This study takes a completely different approach, employing direct CO2 observations from satellites. The dependent variable in our model is the atmospheric CO2 concentration above a cell in a global grid with 25 km resolution. We employ 25-km grid cells defined by Van der Werf et al. (2017), who provide the data on CO2 emissions from fires for this analysis. The dominant component of the atmospheric concentration is the global stock of CO2 molecules that have accumulated since the Industrial Revolution. The second component is seasonal CO2, reflecting differential absorption and release by vegetation over the annual cycle. The seasonal CO2 component is latitudinal, differing by hemisphere because the Northern Hemisphere has more plant life than the Southern Hemisphere.

The third component is local CO2 emissions, reflecting the time lag between local emissions of CO2 molecules and their full dispersion into the global mix. We follow convention by terming this the “concentration anomaly” since it measures the local deviation from the global background CO2 concentration. In our global
||Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Finance Obstacle (0-4 scale)|Severity level of access to finance as an<br>obstacle to firms’ day to day operations. The<br>severity level is defined on a 0-4 scale as: no<br>obstacle (0), minor obstacle (1), moderate<br>obstacle (2), major obstacle (3), and a very<br>severe obstacle (4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Overdraft Y:1 N:0|Dummy variable equal to 1 if the firm has<br>overdraft facility and 0 otherwise.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Inflation (CPI, annual %)|Annual percentage rate of inflation based on<br>the consumer price index. Values of the<br>variable used are for the fiscal year covered by<br>the Enterprise Survey. In cases where the fiscal<br>year spanned more than one calendar year, the<br>inflation figure is the weighted average over<br>the calendar years covered, where the weights<br>used are the proportion of firms covered in the<br>calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|Primary Enrollment (Gross rate)|Gross enrollment rate in primary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross primary<br>enrollment rate values for the fiscal year<br>covered by the Enterprise Survey. In cases<br>where the fiscal year spanned more than one<br>calendar year, the enrollment figure is the<br>weighted average over the calendar years<br>
**Figure 7. CO2 intensity and fuel exports as indicators of exposure to transition risks**

<!-- Start of picture text --> A. CO2 intensity, 1995‐2014 B. CO2 Intensity by region<br>3.5<br>3.0<br>3.0<br>2.8<br>2.5<br>2.6<br>2.0<br>2.4<br>1.5<br>2.2<br>1.0<br>2.0<br>0.5<br>1.8<br>0.0<br>1.6<br>1.4<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are<br>Note: Country median per year. 75th and 25th percentiles in dotted lines<br>bottom and top of box, respectively<br>C. Fuel exports (% of GDP), 1995‐2017 D. Fuel exports (% of GDP) by region<br>8 8 50<br>7 7 45<br>40<br>6<br>6 35<br>5 30<br>5<br>4 25<br>4 3 20<br>15<br>3 2<br>10<br>2 1 5<br>0 0<br>1<br>0<br>EAP ECA LAC MENA SA SSA<br>Note: Country median per year. 75th and 25th percentiles in dotted lines Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom<br>and top of box, respectively<br>CO2 intensity (kg per kg oil equ. energy use)<br>CO2 intensity (kg per kg oil equ.
especially in Africa (Aker et al., 2013; Jack and Suri, 2015). The reduction of such costs and the ease of money transfer via mobile money improves the liquidity of the firm as cash flows through the firm at a faster rate (Bangens and Soderberg, 2011). There is an established literature that has identified the positive effect of improved cash flows on investment (Kadapakkam et al., 1998) and one can hypothesize that sufficient increases in cash flows induced by mobile money may lead to the same outcome of increased investment. Furthermore the reallocation of time away from dealing with financial transactions, including managerial time, to more productive activities can lead to increasing profitability (Frederick, 2014), potentially leading the firm to pursue growth opportunities through investment.

Beyond reduced transaction costs, there are other channels that have been empirically uncovered by which mobile money use could indirectly lead to increased investment. Using a dynamic general equilibrium model with heterogeneous entrepreneurs, imperfect credit markets, and the risk of theft, Beck et al. (2015) find that mobile money use increases the use of trade credit which in turn improves firm performance. The wider literature has documented the role of trade credit, via reputation effects, in increasing access to external sources of financing such as bank financing (Alphonse et al., 2006; Buckart and Ellingsen, 2004). Increases in access to finance, in turn, can facilitate investment (Rajan and Zingales, 1998; Levine et al., 2000; Demirguc-Kunt and Maksimovic, 1998; Cull and Xu, 2005). Finally, the use of transaction, saving, and financial operations data from the digital financial services platform allows to generate credit scores and evaluate and price credit risk. This can help to overcome the so-called collateral technology hurdle, which has hindered the development of credit markets in Africa (Ndung’u, Morales, and Ndirangu. 2016).

# **3. Empirical Strategy**

# **Data**

The data source used in
# **_1. Introduction_**

In 2018, the World Bank launched the Human Capital Project (HCP), an initiative aimed at raising awareness among policy makers about the importance of investing in human capital. The HCP includes an advocacy component which features the Human Capital Index (HCI), a measure of the human capital that a child born today can expect to attain by age 18, given the risks of poor health and poor education that prevail in the country where she lives (Kraay, 2019).

The HCI quantifies the trajectory from birth to adulthood in terms of the consequences for productivity by means of three components: (1) a measure of whether children survive from birth to school age (age 5); (2) a measure of expected years of basic education (primary and secondary), adjusted for quality; and (3) two broad measures of health: child stunting rates and adult survival from age 15 to age 60. The index is constructed so that a value of 1 represents the productivity in adulthood of a child born today if he or she enjoyed complete education and full health until age 18. Countries are measured with respect to this benchmark; the value of the index can thus be interpreted as a percentage of that productivity level.

While useful in a global context, the original version of the HCI may not adequately reflect the education and health challenges that are relevant for specific regions of the world. Countries in Europe and Central Asia provide their citizens relatively good basic education and health services; the region’s citizens begin their productive life in a much better position than their peers in other regions of the world. But job markets today demand higher levels of human capital than in the past. Good basic education will not be enough; higher education institutions must prepare students for the challenges the future of work may hold. Health care systems will need to ensure that citizens remain healthy throughout their adult life, as, more and more, learning and skill acquisition will take
strong negative relationship between absenteeism (dropouts) and students’ performance (Durden and Ellis, 1995; Bedi and Marshall, 1999; Chen and Lin, 2008).

# **7 Concluding Remarks**

This paper examines the effects of agro-industries on human capital accumulation by investigating the impact of coffee mills in Rwanda on the performance of primary school children who were exposed to these mills in early life. We also explore the potential mechanisms that explain our results.

The study uses administrative data on test scores of the universe of primary school students sitting for a high-stakes national exam between 2012 and 2019 together with geocoded data on the location and timing of the establishment of coffee mills in Rwanda. Our identification strategy essentially exploits the staggered rollout of the coffee mills along with the timing of birth of the students to measure the medium-term effect of early life exposure to the coffee mills on test scores in a difference-in-difference design.

We find evidence of a positive impact of exposure to the coffee mills on student performance. On average, test scores of students born within the catchment area of coffee mills are about 0.09 standard deviation (4%) higher relative to their counterparts in control villages. Our results suggest improvements in household welfare (income), employment, child health, and school attendance as plausible channels.

Overall, the findings of the paper underscore the spillover effects of agro-industrialization, such as the establishment of coffee mills in local communities. More importantly, the paper highlights the need for developing countries that rely on raw commodity exports to invest in value-addition; this not only increases the value of their exports, but also has spillover effects on local economies.

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12 Equation (5), where the market component of non-farm income is denoted _w_<sup>_h_</sup> and the transfer component is denoted _τ_<sup>_h_</sup> , is a comprehensive decomposition of the change in household welfare, taking into account the full compensating variation in “net expenditures” in the last two terms on the RHS, and any additional general equilibrium effects on non-farm incomes, e.g. changes in wages or government transfers, in the first term.

As discussed in Section 2, no study that we are aware of has so far fully captured all of these terms, and ours is no exception. Shortcomings in the Brazilian data, such as the absence of information on household farm production in either the household income or the household budget surveys, preclude us from computing production shares ( ). Neither are we able to estimate the full Slutsky substitution matrix to approximate . In what follows, therefore, our empirical analysis is based on Equation (6) below, where the approximation sign returns: The three terms on the right hand side of (6) correspond to our estimates of the expenditure effect, the market income effect, and the transfer income effect, respectively. The first term is computed, for each household in the POF sample, using expenditure shares from the POF survey, and proportional price increases (for each good i) from the INPC. The second term is computed from wage information in the PNAD, based on the following rule: if the household contains no workers in any agricultural activity if the household contains any individuals _j_ who report positive income from an agricultural activity _i_ , (i.e. that produces good _i_ ).

The information on agricultural production activities in the PNAD is much more disaggregated than the 16 food groups used for the INPC – POF concordance, and given in Table 1. An additional concordance was needed, which maps each sector-specific agricultural activity reported in the PNAD to one of the 16 food groups whose prices
from socialism to a market economy caused a change in the returns to schooling in transition countries. It extends the analysis beyond other papers in this literature by explicitly looking at the impact of EU accession. Since both events (transition and EU accession) represent critical changes in terms of political organization, markets, and trade, among others, this research explores the transmission channel by which each shock could have affected returns to schooling. Given the increased trade and economic integration in Europe post-transition, the premium to skills has increased. We have shown a link between transition and the economic benefits of schooling. We test if the returns to schooling increased significantly as a result of transition and EU integration, with the corollary that the driver of the increase in the returns is openness to trade, eased by transition and EU membership.

# **Data**

The data for this analysis stems from two main sources. First, the <u>Comparable Returns to Education</u> database was used as the main source of private returns to education data. This database was supplemented with Psacharopoulos and Patrinos (2018) to make up for the gaps. Meanwhile, the data source of the average years of schooling per country was taken from the Barro-Lee Educational Attainment Database (Barro and Lee 2013). This dataset contains the educational attainment of the population, including average schooling years, for 146 countries around the world in 5-year intervals. These two key databases were selected as they ensure comparability across countries.

We analyze the returns to education in 28 transition countries (see Annex 1). Here we define postsocialist/transition countries as all European and Central Asian countries that were part of the Soviet Union and socialist pact until the fall of the Soviet Union by the end of 1991. We have data on the returns to schooling before transition (circa 1992) for 11 countries. We have data for 20 non-transition, European, and ECA countries (see Annex 2). Regarding schooling years, Barro and Lee (2013) are
The training material was prepared by a team with expertise on entrepreneurship, organizational behavior and work psychology. The first five classes focus on personal initiative, a psychological intervention aimed at making business owners more proactive with respect to new ideas, and more persistent in overcoming challenges (Frese and Gielnik, 2014; Campos et al., 2017; Ubfal et al., 2019). The second five classes cover content from a standard training program that teaches a set of best business practices based on the ILO’s Improve your Business (IYB) training, adapted to the Jamaican context. The cost of providing the course is approximately J$20,000 Jamaican dollars (US$150) per participant.

# **2.3 Sampling Firms**

The population of interest for JBDC is the set of micro, small and medium enterprises (MSMEs) in a targeted region who they can reach through their outreach activities, and who might potentially be interested in taking part in business training. We therefore worked closely with them to recruit the types of firms they would typically be interested in serving. Our recruitment process was divided into two phases. In the first phase, which was used for selecting firms to take part in the BDM elicitation method, we targeted firms in the Western regions of Jamaica (the parishes of St. James, Westmoreland, Trelawny, Hanover, Manchester and Saint Ann). These parishes include the second-largest city in Jamaica, Montego Bay. The second phase, used to select firms for the take-it-or-leave-it (TIOLI) approach, recruited mainly around the capital city of Kingston (72%), with the remainder from Montego Bay and surrounding parishes.

A variety of communication methods were used to contact entrepreneurs in both locations and gauge their interest in learning more about the business training. These included emails to the existing client database of JBDC; advertisements via social media, newspaper and radio; and messages from other MSME support organizations. The goal was to reach 2,000 MSMEs, with a target
0.238|0.263|0.220|0.018|-0.007|0.037|
||[0.016]|[0.021]|[0.023]|[0.021]||||
|**Islands region**|0.204|0.240|0.205|0.230|-0.037|-0.001|-0.027|
||[0.015]|[0.021]|[0.021]|[0.022]||||
|**Momase region**|0.436|0.413|0.416|0.440|0.022|0.020|-0.004|
||[0.018]|[0.024]|[0.026]|[0.025]||||
|**Southern region**|0.104|0.108|0.116|0.110|-0.004|-0.012|-0.006|
||[0.011]|[0.015]|[0.017]|[0.016]||||
|**Observations**|741|416|361|382||||

Notes: ***, **, and * indicate significance at the 1, 5, and 10 percent critical level. The values displayed for t-tests are the differences in the means across the groups.

# <u>4.3 Making inferences about the general population using the phone survey and online experiment</u>

The aim of the phone survey and online experiment was to be able to learn about how to address vaccine hesitancy among a broader population in PNG, beyond just the survey respondents. Both surveys collected samples of respondents that are somewhat representative of the
, Angus. 2005. "Measuring Poverty in a growing world (or measuring growth in a poor world)."</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>87 (1): 1-19.</mark>

- <mark>Deaton, A., 2021. “COVID-19 and Global Income Inequality.”</mark> _<mark>LSE Public Policy Review</mark>_ <mark>1(4).</mark>

- <mark>Elbers, C., Jean O. Lanjouw and Peter Lanjouw. 2003. “Micro-level estimation of poverty and inequality”,</mark> _<mark>Econometrica</mark>_ <mark>, 71(1), pp. 355–364. Ferreira, F., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., et al. 2016. “A global count of the extreme poor in 2012: data issues, methodology and initial results.” The Journal of Economic Inequality 14, 141-172.</mark>

- <mark>Ferreira, F., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., et al. 2016. “A global count of the extreme poor in 2012: data issues, methodology and initial results.” The Journal of Economic Inequality 14, 141-172.</mark>

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