rafmacalaba/datause-encoder-data huggingface dataset:
# **5 Estimation of the ACI for 2007**

The previous developments can be applied quite generally to many spatial interaction networks.

However, as stated earlier, this paper focuses on an application to the global air transportation network. We estimate an _Air Connectivity Index_ (ACI) for 2007, using data for the month of June. The flow variables are sourced directly from data in the _SRS Analyzer_ system. The SRS database provides the number of direct flights per week among over 200 origin and destination countries, including passenger and cargo flights.<sup>15</sup> In the remainder of this section, we present results from the empirical implementation of the model using the approach outlined in the previous section, and provide a comparison with alternative metrics.

## **5.1 Cost Function Specification**

As discussed in Section 4, we derive the potential terms ( and ) as fixed effects using a Poisson estimator. In the trade literature, it is standard to include the simple logarithm of distance as a measure of trade costs (impedance). In the context of air transport, it is also natural to posit that the bilateral impedance is a function of distance, or equivalently time of flight. However, we take a more refined approach to the parameterization of this dependence for two reasons.

First, it is important to allow for a richer set of shapes than the simple logarithm to account for the network structure of air transport. The commercial implications of short- versus long-haul flights are very different. Airplanes can only safely fly a limited distance, thereby making it technically impossible

> developed by Santos Silva and Tenreyro (2006) and Helpman et al. (2008). We prefer Poisson because it is consistent under much weaker assumptions than the Heckman-like estimator used by Helpman et al. (2008). 15 Although from an international trade perspective we are primarily interested in movements of goods, not people, we include passenger traffic in addition to pure cargo flights because a significant proportion, perhaps 50%, of air freight travels as cargo on passenger flights (Geloso Grosso and Shepherd, 2011). However, it is beyond the scope of this paper to examine possible differences in connectivity for passenger or mixed versus pure cargo flights.

24
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Mali > Nonproductive investments > Macro results**

||||Reduction in<br>h bli|Nonp|roductive<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,480,726.40|–0.02|0.06|0.08|0.14|0.09|
|EV|Equivalent variation||–0.02|0.06|0.08|0.14|–0.55|
|s|Wage|1.00|–0.20|0.49|0.72|1.18|0.76|
|yg|Government income|278,133.30|0.10|3.19|3.19|3.19|3.19|
|g|Total government expenditure|255,957.00|–1.62|1.73|1.73|1.73|1.73|
|ye|Firm income|477,113.00|0.07|–0.18|–0.26|–0.42|–0.27|
|It|Total private investment|439,511.00|0.07|–0.17|–0.25|–0.42|–0.42|
|GDP|GDP|1,819,413.40|0.00|0.00|0.00|0.00|0.00|
|e|Nominal exchange rate|1.00|0.07|0.03|–0.70|–2.19|–0.19|

# **Mali > Nonproductive investments > Sectoral results**

||||Reduction in<br>|Non|productive<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign aid|Income<br>tax|
||Crop agriculture|379,937.10|0.03|–0.05|–0.08|–0.12|–0.15|
||Export agriculture|297,375.30|0.01|–0.04|–0.07|–0.13|–0.08|
|Va|Mining and gas|206,350.40|0.03|–0.08|–0.25|–0.48|–0.12|
|Value added or|Industries|167,281.20|0.09|–0.21|–0.15|0.02|–0.25|
|output|Construction|116,213.20|1.24|0.24|0.47|0.85|0.62|
||Private services|516,027.70|0.04|–0.21|–0.14|–0.20|–0.20|
||Public services|136,228.50|–1.49|1.20|1.09|1.31|1.29|
||Crop agriculture|1.01|0.01|0.12|0.18|0.17|–0.34|
||Export agriculture|1.02|0.00|0.07|0.00|–0.33|–0.40|
||Mining and gas|1.00|0.09|–0.10|–0.31|–0.85|–0.33|
|pq (Market<br>prices)|Industries|1.17|0.04|0.37|0.65|–1.37|–0.07|
||Construction|1.02|0.11|0.97|0.65|0.09|0.32|
||Private services|1.03|–0.05|0.62|0.35|0.33|0.06|
||Public services|1.00|–0.11|0.49|0.61|0.38|0.39|
||Crop agriculture|1.00|0.04|0.04|0.01|0.15|–0.52|
||Export agriculture|1.00|0.03|–0.12|–0.44|–0.96|–0.46|
|r<br>|Mining and gas|1.00|0.15|–0.37|–1.83|–3.67|–0.43|
|Rental rate of<br>capital|Industries|1.00|0.03|–0.04|0.32|1.24|0.11|
||Construction|1.00|1.56|0.83|1.39|2.40|1.65|
||Private services|1.00|–0.02|–0.43|0.09|0.32|–0.13|

32
2003). On the supply side, the factors that potentially undermine the use of commercial fertilizer by farmers include unfavorable business climate, excessive regulations, an abundance of taxes and fees, and high levels of rent seeking (Morris et al., 2007).

With these considerations in mind, the objective of the present study is to use nationally representative household level data from Malawi to analyze the profitability of fertilizer use in maize production. Specifically, the study seeks to answer the following questions: 1) what is the level of nitrogen use efficiency (NUE)<sup>1</sup> and how does it vary across Malawi? 2) to what extent is the use of fertilizer in maize production profitable in Malawi? Given the NUE estimates from our study, we seek to then answer two key policy questions. First, would the majority of smallholders in Malawi be better-off with subsidized fertilizer or with an equivalent cash transfer? Second, could the majority of smallholders increase their profits by applying fertilizer at the rates recommended by the government?

The study focuses on Malawi for two reasons. First, productivity growth in Malawi’s agriculture is typical of countries in SSA. For the past two decades, the productivity of most agricultural crops in the country has increased only modestly. Even now, the already modest increase in productivity is further undermined by an annual population growth of 2.7% (MoAFS, 2010). In addition, the Ministry of Agriculture and Food Security (2010) estimates that Malawi’s yield gap – the difference between potential yield and the actual yield of the average farmer (1,536 kg/ha for maize in 2013) – ranges between 38% and 53% for cereals, and between 40% and 75% for legumes (Lobell et al., 2009). This implies substantial room for productivity improvements. Yield improvements likely will be essential for reducing poverty and improving food security in Malawi because given Malawi’s already high average population density of 178 people per square

> 1 NUE is defined as output (kg maize) over input (kg of nitrogen applied). We focus on nitrogen use efficiency because nitrogen is a limiting nutrient in many soils in SSA and its estimated net loss from soils is higher than any other nutrient (World Bank, 1998).

3
# **II. Setting**

Malawi, which is a small and poor country in southern Africa, is the setting for this study. 81% of its population of 15.3 million lived in rural areas in 2009, with most people relying on subsistence farming. The country is poor even by African standards: Malawi’s 2008 GNI per capita figure of $760 (PPP, current international $) is less than 40 percent of the Sub-Saharan African average of $1,973 (World Development Indicators 2010). As of 2007, the prevalence of HIV was estimated to be between 11.0% and 12.9% among adults aged 15-49 – the ninth-highest HIV prevalence in the world (UNAIDS 2008). The gender gap in HIV prevalence among young adults, aged 15-24, is startling: prevalence was more than _four_ times higher for females than males in 2004 (9.1% vs. 2.1%) (National Statistical Office (Malawi) and ORC Macro 2004).

The study took place in Zomba District, in Southern Malawi. Zomba District is divided into 550 enumeration areas (EAs), which are defined by the National Statistical Office of Malawi and contain an average of 250 households spanning several villages. In 2007, 176 EAs in urban and rural areas of Zomba were selected to form the sample of a cash transfer experiment. In each of these EAs, all dwellings were visited to obtain a full listing of never-married females, aged 1322. From this sampling frame, 3,796 school-age girls aged 13-22 were selected for inclusion to form the impact evaluation sample of the cash transfer experiment. This sample of approximately 22 girls per EA included two strata: those who were enrolled in school at time of listing and those who were out of school. Of the 176 EAs in the overall study, 88 were assigned to the treatment group where program participants were offered cash transfers. The other 88 were assigned to the comparison group, which received nothing.<sup>8</sup> This study assesses the impact of a randomly assigned home-based HIV testing and counseling (HTC) intervention (discussed in more detail in the Methods section below) within the 88 EAs in the comparison group.

> 8 For more details on the cash transfer experiment and its impact evaluation, please refer to Baird, McIntosh and Özler (2011).

6
# **Figure 15. Required skills by preferred genders**

<!-- Start of picture text --> Male 50 20 4 4 6 5 3 3 31<br>Female 13 22 7 17 7 15 5 7 3 3<br>No Preference 69 9 6 4 3 22211<br>0 20 40 60 80 100<br>Programming Sales<br>Designing Communication<br>Finance Microsoft<br>Analytical/ Research Other soft skills<br>Leadership/ Managemen t Others<br><!-- End of picture text --> _Source_ : Authors using the data from Rozee.pk.

By applying a machine learning technique to rich text information, such as job titles and descriptions, we categorize each job post and jobseeker by the International Standard Classification Occupations 08 (ISCO08). In our text processing that applies a continuous bag of words (CBOW) model, the job title of a job post is mapped into the most similar occupation title in the ISCO08. Jobseekers’ occupations are identified by mapping their previous job titles into the most similar occupation titles. We can identify the occupations of jobseekers only if they report their previous jobs. Such jobseekers are about half of all jobseekers. The distribution of job posts and jobseekers at the ISCO08 one-digit level is shown in Table 2. The three most frequently demanded and supplied occupations are managers, professionals, and technicians and associate professionals. At the ISCO08 two-digit level (Table A1.1), highly demanded occupations are administrative and commercial managers (ISCO08 = 12), business and administration associate professionals (33), information and communications technology professionals (25), and sales workers (52).

**Table 2. Number of job posts and jobseekers by occupations (ISCO08 1 digit)**

|ISCO-08|Occupationtitles|Demand|Supply|
|---|---|---|---|
|1|Managers|312,768|230,938|
|2|Professionals|439,365|309,331|
|3|Technicians and associate professionals|324,564|303,325|
|4|Clerical support workers|46,362|69,733|
|5|Service and sales workers|118,887|75,340|
|6|Skilled agricultural, forestry and fishery workers|2,043|4,865|
|7|Craft and related trades workers|19,334|30,456|
|8|Plant and machine operators, and assemblers|16,955|25,914|
|9|Elementary occupations|21,588|15,447|

_Source_ : Authors.

15
TABLE D.5: Comparison of rent variables, ADDIS ABABA

||Rent value||Self estima|ted rent|Combined s|elf estimated rent|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Directly affected by food|-0.260||-0.147||-0.863**||
||(0.694)||(0.239)||(0.361)||
|Flood risk perception||0.017||-0.030||-0.273|
|||(0.529)||(0.109)||(0.226)|
|Job density|-1.476***|-1.501***|0.192***|0.189***|-0.744***|-0.778***|
||(0.136)|(0.112)|(0.058)|(0.058)|(0.250)|(0.258)|
|Number of rooms|1.045**|1.056**|0.562***|0.561***|0.919***|0.907***|
||(0.451)|(0.453)|(0.076)|(0.076)|(0.140)|(0.145)|
|Number of rooms squared|-0.117*|-0.118*|-0.039***|-0.039***|-0.070***|-0.069***|
||(0.059)|(0.060)|(0.008)|(0.008)|(0.013)|(0.013)|
|Unimproved house|0.303|0.292|-0.281|-0.289|-0.712**|-0.766**|
||(0.385)|(0.370)|(0.172)|(0.170)|(0.263)|(0.273)|
|Unimproved roof material|0.015|-0.003|0.162|0.176|0.030|0.124|
||(0.708)|(0.671)|(0.174)|(0.177)|(0.231)|(0.257)|
|Unimproved foor material|-0.987|-1.030|-0.526***|-0.535***|-0.619**|-0.664**|
||(0.637)|(0.675)|(0.138)|(0.135)|(0.267)|(0.278)|
|Unimproved drinking water source|-1.076|-1.294|0.275|0.223*|0.592|0.326|
||(1.130)|(0.912)|(0.168)|(0.118)|(0.409)|(0.515)|
|Unimproved toilet facility|-1.291|-1.314|-0.259**|-0.267**|-0.511**|-0.558***|
||(0.874)|(0.814)|(0.105)|(0.105)|(0.186)|(0.187)|
|Not collected solid waste|-0.716|-0.681|-0.251|-0.253|-0.287|-0.305|
||(0.446)|(0.430)|(0.171)|(0.169)|(0.379)|(0.387)|
|Electric lighting|-0.055|-0.091|0.257|0.235|-0.497||
||(1.300)|(1.260)|(0.666)|(0.698)|(1.002)||

_Continued on next page_
Table 8 Distribution of Average Income and PIT Burden per Household by Gross Income Decile

|**(thousand**|**KRW)**|**1st**|**2nd**|**3rd**|**4th**|**5th**|**6th**|**7th**|**8th**|**9th**|**10th**|**Avg.**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|MY||6,425|13,526|20,948|28,186|34,946|40,503|49,322|58,353|71,975|106,312|43,046|
|GY||10,456|18,383|25,769|32,866|39,752|46,557|54,083|63,508|77,086|112,455|48,088|
|GN & BY||3,584|9,749|17,087|24,215|31,906|37,690|46,463|54,889|68,011|99,601|39,316|
||Scenario A|10|72|191|378|698|1,015|1,500|2,437|3,741|8,670|1,871|
|PIT|Scenario B|13|88|235|454|812|1,175|1,694|2,678|4,057|9,374|2,057|
||Scenario C|13|83|224|428|739|1,074|1,566|2,500|3,816|9,057|1,949|
||GE (B‐A)|3|16|44|76|114|160|194|241|316|704|186|
|Revenue<br>effect|TF (C‐B)|0|‐5|‐11|‐26|‐73|‐101|‐128|‐178|‐241|‐317|‐108|
||NE (GE+TF)|3|11|33|50|41|59|66|63|75|387|78|
|**Ratios to G**|**Y (%)**|**1st**|**2nd**|**3rd**|**4th**|**5th**|**6th**|**7th**|**8th**|**9th**|**10th**|**Avg.**|
|MY||61.45|73.58|81.29|85.76|87.91|87|91.2|91.88|93.37|94.54|89.52|
|GY||100|100|100|100|100|100|100|100|100|100|100|
|GN & BY||34.28|53.04|66.31|73.68|80.26|80.95|85.91|86.43|88.23|88.57|81.76|
||Scenario A|0.1|0.39|0.74|1.15|1.76|2.18|2.77|3.84|4.85|7.71|3.89|
|PIT|Scenario B|0.13|0.48|0.91|1.38|2.04|2.52|3.13|4.22|5.26|8.34|4.28|
||Scenario C|0.12|0.45|0.87|1.3|1.86|2.31|2.9|3.94|4.95|8.05|4.05|
|**Ratios to G**|**N & BY (%)**|**1st**|**2nd**|**3rd**|**4th**|**5th**|**6th**|**7th**|**8th**|**9th**|**10th**|**Avg.**|
||Scenario A|0.29|0.74|1.12|1.56|2.19|2.69|3.23|4.44|5.5|8.71|4.76|
|PIT|Scenario B|0.37|0.9|1.37|1.88|2.54|3.12|3.65|4.88|5.97|9.41|5.23|
||Scenario C|0.35|0.85|1.31|1.77|2.32|2.85|3.37|4.55|5.61|9.09|4.96|
|**Shares by d**|**ecile (%)**|**1st**|**2nd**|**3rd**|**4th**|**5th**|**6th**|**7th**|**8th**|**9th**|**10th**|**Total**|
|MY||1.49|3.14|4.87|6.55|8.12|9.41|11.46|13.56|16.72|24.7|100|
|GY||2.17|3.82|5.36|6.83|8.27|9.68|11.25|13.21|16.03|23.39|100|
|GN & BY||0.91|2.48|4.35|6.16|8.12|9.59|11.82|13.96|17.3|25.33|100|
||Scenario A|0.06|0.39|1.02|2.02|3.73|5.42|8.02|13.03|20|46.35|100|
|PIT|Scenario B|0.06|0.43|1.14|2.21|3.95|5.71|8.23|13.02|19.72|45.56|100|
||Scenario C|0.06|0.43|1.15|2.19|3.79|5.51|8.03|12.82|19.57|46.46|100|

_Source:_ Authors’ estimation using data from HIES 2014.

_Notes:_ a. MY, GY, GN & BY, and PIT denote market income, gross income, wage and salary and business income, and personal income tax, respectively. b. GE denotes the gross revenue effect of TIETP. TF denotes tax revenue forgone for TIETP. NE denotes net effect of TIETP.

38
|plant and machine operators and|||
|---|---|---|
|assemblers|0.479**|1.601***|
||(0.242)|(0.454)|
|elementary occupations|0.771***|2.391***|
||(0.261)|(0.467)<br>-|
|11-100||1.299***|
|100+||(0.163)<br>-<br>1.978***|
|40-49 hrs|-1.086***|(0.185)<br>-<br>0.911***|
||(0.127)|(0.201)|
|50+ hrs|-0.135|0.0651|
||(0.135)|(0.209)|
|Constant<br>-0.908***|-0.968***|-<br>2.062***|
|(0.164)|(0.345)|(0.618)|
|Observations<br>5299|5258|4675|

Source: Authors’ estimation using RLMS 2016.

Note: Columns (1) and (2) include all observations; column (3) excludes self-employed informals. This table shows the estimated coefficients from regressions of informal employment on nationality status (i.e., migrants are all those who self-report as non-Russians, but not necessarily as non-residents, non-citizens of the Russian federation or illegal migrants) and other individual and job-related characteristics. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

42 Informal Employment and Worker’s Wellbeing in Russia / June 2019
the International Capital Market Association or ICMA).<sup>16</sup> While the ICMA guidelines mandate a 100% constraint on the use of proceeds to labeled projects, in China the issuer must adhere to a 70% use of proceeds constraint, which drops to 50% if the issuer is a state-owned enterprise. Even more importantly, the Chinese taxonomy also classifies as green projects those that increase the capacity of fossil fuels, such as “Green Coal Use/Green Oil Use” projects.<sup>17</sup> Standardization of sustainable finance taxonomies globally is needed to foster comparability, increase transparency, and provide regulatory consistency. The World Bank institutions, particularly the IFC, is working actively to support issuers and investors in developed and emerging markets to promote integrity in the green bond market through its role chairing the executive committee of the ICMA Green, Social, and Sustainability Bond Principles.

# **_Strategic Issuances_**

Beyond banks, popular issuers of green bonds are large utilities that produce electricity using multiple energy sources, which use these contracts to finance, for example, the installation of renewable energy capacity. In this context, issuance of green bonds could become detrimental from an environmental perspective to the extent that its documented positive spillover effect on the firm’s valuation decreases its borrowing costs across _both_ brown and green investments (Baulkaran 2019; Flammer 2021; Wang et al. 2020). Theoretically, in the presence of segmented markets and pro-environmental preferences, a firm could increase its value by separating its capital structure into a green and a brown component (see Allen and Gale, 1988). And in practice, there have been cases of sellers of green bonds increasing their brown businesses,

> 16 See the IMCA _Climate Transition Finance Handbook 2023_ at:

> <u>https://www.icmagroup.org/sustainable-finance/the-principles-guidelines-and-handbooks/climate-transition-financehandbook/</u>

> 17 - - For a detailed description of the taxonomy, see https://www.oecd <u>ilibrary.org/sites/5abe80e9 en/index.html?itemId=/content/component/5abe80e9-en#tablegrp-d1e10987.</u>

33
and mutual cooperation, effects are likely to be much more ambiguous with countries outside the Union. Border reform under customs union is only synonymous of genuine trade facilitation to the extent that it makes trade easier among customs union partners _and_ with external partners. It is true that the adoption of a unique rule within the union creates immediate benefits to third countries such as: an automatic reduction of duplication costs for transiters and trading partners dealing with more than one customs union member and access to a broader market once the fixed costs associated to border control (such as accessing information about customs procedures) are paid. On the other hand, there is a distinct risk that process of cooperation among union members in the implementation of their procedures such as MRAs will give union economic agents an added competitive hedge against traders outside the union that will not have access to these facilitation tools. Another risk of discrimination is the adoption of higher standards and stricter procedures by union members on trade from third parties leading for instance to standards harmonization and added controls at the border to guarantee integrity of borders on behalf of other union members.

# **Box 2. COMESA’s regional initiatives on trade facilitation**

The Common Eastern Market for Eastern and Southern African States (COMESA) was formed in 1981 and the COMESA free trade area launched in October 2000. The 20 member countries of COMESA now plan to launch the COMESA Customs Union in December 2008.

Trade facilitation has been an important aspect of regional integration in COMESA. The following trade facilitation measures have been developed (not necessarily to all member states):

- Common tariff nomenclature

- Common valuation system

- Protocol on rules of origin

- COMESA single customs declaration document (COMESA-CD)

- Protocol on transit and trade facilitation introducing licensing of transit carriers and harmonization of axle load controls

- Regional customs bonds guarantee scheme was launched in 2006 enabling the payment of a single bond for the region

- Licensing of clearing agents and formation of a regional Freight Forwarder Association

- Protocol of third party motor vehicle insurance scheme (Yellow Card scheme, to which 13 countries participate)

- Joint border controls (Chirundu port for Zambia-Zimbabwe; Malada border for Uganda-Kenya)

- Implementation of common standards

- Capacity building with development of customs training modules

_Source: COMESA (2005)_ One should also note that most customs unions do not follow the model of collection of revenues at the port of entry, but rather according to the final point of consumption. Arndt et al. (2007) survey 9 customs unions and find that only the EU and SACU collect revenue at 25
a more intensive use of ICT (Table 26).

To further analyze how Internet is used within the firm, we take advantage of a question regarding whether the firm uses Internet for different purposes and we aggregate them into different scores ranging between 0 and 1 that summarize them. As Table 27 shows, an increasing adoption of ICT measured as the share of labor that uses Internet is more highly correlated with a closer relation with customers and suppliers and human resources (recruiting and training).

Table 24: IV regressions of ERP variables on ICT use

||Administration|Production|
|---|---|---|
|∆Share of labor that uses computer|0.0102***|0.0102***|
||(0.00312)|(0.00334)|
|F- frst stage|14.150|14.150|
|Sargan p-value|0.2876|0.5240|
|_N_|641|641|

Robust standard errors in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

54