rafmacalaba/datause-encoder-data huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE <mark>$23 million. However, even this still falls well short of the required $30 million for</mark> _<mark>routine</mark>_ <mark>maintenance alone, and is barely a quarter of the $84 million needed to cover routine and periodic maintenance plus the rehabilitation backlog.</mark>

**Table 3 Estimated needs and realized spending on road maintenance and rehabilitation in Côte d’Ivoire**

|$ millions|Estimated needs|Spending plans|Resources mobilized|Realized spending|
|---|---|---|---|---|
|2002|57.4|0.6|0.7|0.0|
|2003|68.8|0.7|0.7|0.0|
|2004|75.7|2.7|3.7|0.7|
|2005|75.8|1.9|13.1|6.6|
|2006|76.5|49.0|19.4|5.5|
|2007|83.5|39.4|25.0|30.0|
|2008|89.3|34.0|26.5|18.4|
|2009|84.7|30.9|27.1|22.5|
|Cumulative|611.8|159.2|116.2|83.7|

Source: Fonds d‘Entretien Routier, 2010.

**Figure 4 Côte d’Ivoire is not spending enough to catch up with its road rehabilitation backlog**

<!-- Start of picture text --> maintenance rehabilitation<br>spending as % of requirements<br><!-- End of picture text --> Source: Gwillliam and others 2009.

<mark>The lack of road rehabilitation and maintenance has particularly damaging implications on the main north-south artery of the country linking Abidjan to Ouagadougou, which is a sea corridor of great strategic significance supporting international trade for land-locked Burkina Faso. Maintenance of the corridor has been neglected since the beginning of the crisis, and it is now in poor condition and needs rehabilitation (figure 3a). This portion of Côte d’Ivoire’s national road network is essentially a regional public good essential to support the trade of the landlocked hinterland countries , and its condition therefore has serious implicationseven beyond national borders.</mark> <mark>Rural access to roads is also a concern. According to the rural accessibility index, about 32 percent of Côte d’Ivoire’s rural population live within two kilometers of an all-season road. This percentage is</mark> 10
4. Measuring divergence and convergence – three confusions in the data While the pattern of divergence among the countries of the bottom billion is source for serious policy concern, the average global picture paints a more sanguine picture, suggesting a degree of convergence overall, and some studies demonstrate _unconditional convergence_ is now present.

The last decade has seen pendulum swing away from the previous consensus around diverging economic destinies (Pritchett 1997), to the more recent findings that global incomes and GDP levels are converging overall (Kremer et al 2021, Lakner and Milanovic, 2015, and Patel et al 2021).<sup>9</sup> Kremer et al (2021) revisit empirical tests of the 1990s that show no evidence of unconditional convergence since the 1960s and extend the analysis for the subsequent 25 years. They find that there is now evidence in support unconditional convergence at the global level since 1990 and convergence since 2000 due to a faster catch-up growth between the poor and higher income countries. Patel et al (2021) also argue in favor of convergence, showing that middleincome countries have experienced accelerated growth rates that have facilitated catching up with richer ones. They call into question the previous claim of unconditional _divergence_ , and the so-called “middle-income trap”. Indeed, rather than being driven by a moderation of growth in richer countries, this is instead supported by accelerating growth in developing countries, especially those found among emerging markets and middle-income countries.

Should we breathe a sigh of relief? Not quite yet. Although each new study typically benefits from additional years of data, consensus is yet to be reached. In a recent survey of crosscountry evidence, Johnson and Papageorgiou (2020) argue that as a group, developing countries have not closed the per capita income gap on the rich world of advanced economies, even in more recent years.

Recent arguments favoring global income convergence tend to emphasize – and their statistical findings are often driven by - the evidence in the years since 2000 that growth has been accelerating among an important subset of developing countries. This recency bias in the newer findings also explains the shift away from the earlier divergence consensus.

The debate around convergence or divergence is often boiled down to this binary question – is convergence (or so-called unconditional beta-convergence) happening at the global level or not? However, underlying this question and the broad trends it depends upon are many different countries of different sizes on quite different economic paths. Furthermore, differences in data availability by income groups or disproportionate impacts of populous

> 9 See also: https://www.cgdev.org/blog/everything-you-know-about-cross-country-convergence-now-wrong

20
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40
An emerging literature has begun to examine some of the economic implications of robot diffusion, with much of the focus thus far on developed economies. Much of this work has focused on the productivity or labor-saving effects of robotics and their impact on wages or employment.<sup>3</sup> Some studies have begun to assess how robotics may influence the global organization of production, for instance through the potential reshoring back to developed economies of previously imported intermediate inputs (Artuc et al., 2018; De Backer and DeStefano 2021; Dachs et al, 2019). This existing research largely reflects research on the United States, Canada and European countries, with few studies examining impacts on emerging markets (notable exceptions being Artuc et al., 2018, Maloney and Molina, 2019; and de Vries et al., 2020).

In this paper we explore a new channel – whether automation leads to export quality upgrading – in both developing and developed economies. Robots are used to undertake a range of repetitive tasks that require a consistent high-level of accuracy, such as handling and assembly of small electronic components, precision welding of car parts and cutting of metal products within strict tolerances (Agapakis et al., 1990; Gunasekaran, 1996; Tilley 2017). In contrast, tasks that are either less repetitive or have larger error tolerances (such as packing boxes) are often still performed by humans, even in high-income economies. Many robots are explicitly designed to achieve greater accuracy and can include sensors that allow the machines themselves to identify product defects (Herakovic, 2010; Herakovic et al., 2011). The widespread diffusion of robotics in manufacturing production may lead to increases in the quality of exported products.

To assess the impact of robots on quality upgrading, we rely on several data sets that span a broad sample of countries (28 developed and 31 developing economies) over a 15 year the period from 2000 to 2015.<sup>4</sup> Export quality is derived using detailed HS 10-digit US import data and combined with data on robot use at the country-industry level. To address potential endogeneity concerns, we instrument robot adoption of the home country-industry using robot diffusion among their foreign customers located in other world regions, using their initial foreign trade network defined

> 3 See for example, Acemoglu et al (2020); Acemoglu and Restrepo (2020); Brynjolfsson and McAfee (2011); de Vries et al. (2020), Dixon et al. (2021); Frey and Osborne (2017); Graetz and Michaels (2018); Kromann et al. (2020); Maloney and Molina (2019); Presidente (2017).

> 4 Our data reflects a considerable period of time (16 years) which is important, as it has been shown that the performance implications of technologies and complementary investments in organizational technology can take years to realize (Bresnahan et al 2002).

3
Table 11 Continued: IV regression IT use on firm-level productivity considering China’s competition in the U.S.

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Share of labor with computer|-2.481|-1.998|-1.952|-2.311|-2.625|-3.286|
||(2.057)|(1.761)|(1.782)|(1.830)|(1.880)|(2.463)|
|Share of labor with computer*Competition in U.S.|10.177**|8.824**|8.523**|8.702**|8.327*|9.796*|
||(4.947)|(4.007)|(4.333)|(4.360)|(4.340)|(5.545)|
|Competition in U.S.|-1.646|-1.398|-1.322|-1.432|-1.269|-1.929|
||(1.124)|(1.000)|(1.083)|(1.098)|(1.091)|(1.653)|
|F-frst stage IT|6.9|7.0|10.9|10.0|8.5|8.1|
|F-frst stage IT*competition|14.9|17.2|23.2|22.1|20.2|20.1|
|Sargan|0.91|0.98|0.99|0.98|0.95|0.90|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

29
||0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|---|---|---|
|**Air strike warning**<br>|||
|**Regime military action, Air strike**|||
|**Foreign intervention**<br>|||
|**Children, civilians**<br>|||
|**Liberation army, Regime military**<br>|||
|**Religion, texts**|||
|**Air strikes, damage, civilians**|||
|**War news, war reporting**<br>|||
|**Description, names**<br>|||
|**Aleppo news**|||
|**ME politics, Assad, Lebanon**|||
|**Crimes, investigations**|||
|**anti−ISIS campaign**|||
|**News reports**|||
|**Goods, coronavirus, numbers**|||
|**Regime military actions**<br>|||
|**Idlib, roads, governance**<br>|||
|**Economy, weather**|||
|**Afrin, Turkish−Kurdish politics**<br>|||
|**Horoscopes, description**|||
|Return Only|||
|<br>IDPs Only|0<br>0.05<br>0.1|0.15<br>0.2<br>0.25|
|Neither<br>Both|Estimated|topic proportion|

Figure 3: Topic prevalence based on presence of returnees and IDPs, `Location Mentions` dataset.

The prevalence of violence- related discussion overall in the `Location Mentions` models, accounting for 7 of the 20 topics in Figure 4, outlines the persistent nature of violence in the daily lives of Syrians. Social media, especially public groups and channels, serve the population through announcements documenting daily violence and military movements which are ultimately useful for both those currently living in Syria and those choosing whether or not to return. Discussion in areas with only IDPs is often more similar to discussion in areas with both returnees and IDPs than to discussion in areas with neither. Though images are often used alongside, or in the place of, text to convey a message, explicitly violence related topics are less represented in the image analysis than in the text. This may be the result of filtering choices by the different platforms to remove violent or disturbing imagery.<sup>16</sup> For most violence related topics, discussion in areas with only IDPs is more similar to discussion in areas with both returnees and IDPs, whereas discussion in areas with just returnees is more similar to discussion in areas with neither returnees nor IDPs. Individuals fleeing violence often have little choice in their destination (Bank (2019)), but if fleeing violence are unlikely to remain in areas where it is prevalent. Returnees, on the other hand, are returning in part because of ties to their home town (Ghosn et al. (2021)) and thus may be willing to endure more uncertainty to be there. In fact, a UNHCR monitoring survey of voluntary returnees found that 54% returned to Syria to reunite with their families (Hoffman and Makovsky (2021)). The central role that violence plays also underscores the importance of using multiple platforms. As can be seen in Appendix 9, the majority of violence related content is over represented on Telegram, while much of the economic and job related content is over represented on Facebook. By using a variety of data sources we are getting a richer image of the situation on the ground.

> 16Twitter transparency site: `https://transparency.twitter.com/` and Facebook Community Standards: `https://transparency.fb.com/policies/community-standards/`

16
# **Table 6: Impact of violent conflict on aggregate measures of food insecurity: disaggregated results**

||(1)<br>Moderate or severe food<br>insecurity|(2)<br>Food insecurity|(3)<br>Raw score|
|---|---|---|---|
|Round 4|-0.017<br>(0.013)|-0.009<br>(0.013)|-0.217<sup>***</sup><br>(0.074)|
|Round 5|-0.038<sup>***</sup><br>(0.015)|-0.036<sup>**</sup><br>(0.016)|-0.258<sup>***</sup><br>(0.076)|
|Round 6|-0.068<sup>***</sup>|-0.028|-0.364<sup>***</sup>|
||(0.018)|(0.018)|(0.102)|
|Round 7|-0.107<sup>***</sup>|-0.072<sup>***</sup>|-0.589<sup>***</sup>|
||(0.020)|(0.020)|(0.115)|
|Round 12|-0.059<sup>***</sup>|-0.053<sup>***</sup>|-0.351<sup>***</sup>|
||(0.020)|(0.019)|(0.124)|
|Round 4 × Tigray|0.039<br>(0.074)|-0.050<br>(0.066)|0.104<br>(0.297)|
|Round 5 × Tigray|-0.082|-0.080|-0.390<sup>*</sup>|
||(0.063)|(0.054)|(0.228)|
|Round 6 × Tigray|-0.072|-0.129<sup>**</sup>|-0.450<sup>**</sup>|
||(0.057)|(0.058)|(0.192)|
|Round 7 × Tigray|0.026<br>(0.078)|-0.037<br>(0.054)|0.036<br>(0.269)|
|Round 12 × Tigray|0.366<sup>***</sup><br>(0.070)|0.202<sup>***</sup><br>(0.043)|1.464<sup>***</sup><br>(0.312)|
|Household fixed effects|Yes|Yes|Yes|
|R-squared|0.023|0.011|0.025|
|Mean dep. var (pre-war)|0.361|0.557|2.054|
|No. observations|14523|14523|14523|

Notes: The outcome variable in the first column stands for a binary indicator assuming a value of 1 for those households classified as moderate or severe food insecure based on the severity of food insecurity generated from the eight FIES questions. The dependent variable in the second and third columns stand for binary indicator assuming a value 1 for those households whose probability of facing food insecurity is above 0 and 0 otherwise. The outcomes variable in the last column stands for raw sum of the responses to the eight FIES questions. All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

Table 7 provides disaggregated results using responses to the eight FIES questions separately. Besides uncovering the statistically indistinguishable pre-war trends in food insecurity experience between Tigray and the rest of the country, the results in Table 7 confirm the main results in Table 3. The coefficients of the year dummies show the generally improving food security situation. The coefficients of the interaction terms between pre-war round dummies and Tigray establish the overall statistically similar trend between Tigray and the other regions of Ethiopia. A clear divergent trend emerges in Tigray in the post-war-onset period as evidenced by 30
<!-- Start of picture text --> Figure 3 Gopalpur vis-à-vis other urban settlements in West Bengal<br>4<br>Other CTs Shortlisted CTs STs<br>3<br>2<br>1<br>0<br>Gopalpur (CT)<br>-1<br>-2<br>-3<br>Population<br>-4<br>0 10,000 20,000 30,000 40,000 50,000<br>Standardised Score<br><!-- End of picture text -->

# **IV. The shift towards nonfarm employment**

As stated earlier, the literature on CTs is sparse and within that the economy of CTs is sparser still. Most news reports. such as some published in the Mint referred to later, indicate a diversified broad based service dominated economy, but at relatively low scale. In some places like Barjora investigated by Samanta (2017), the mainstay is mining and small sponge-iron plants, and in others, the basis is farm processing. However, in other studies of census towns, there is more of a variety of small scale service activities. In CTs that are close to larger towns, there is also spillover of economic activity. Punia and others (2017) find that the periphery of Yamunanagar has cement and packaging industries, numerous small-scale enterprises in timber business as well as thermal power plants and educational institutions. In Manakpur, an industrial enterprise cluster of metal and alloys has developed). In the district of Karnal, many CTs are growing due to real estate. The possible drivers of change in the north along the national highway are real estate builders, demand from agro processing industries like rice and wheat flourmills and government cooperatives for housing projects. This is what a recent research project on ordinary towns of India refers to as the importance of ordinary activities based on markets, construction, in a form of in-situ urbanization that is critical in a context of limited employment opportunities (Denis and Zérah, 2017).

This paper also demonstrates that as one moves up the scale, some interesting trends become visible in small towns. Raman’s work (2017) on Tiruchengode shows a home-grown well drilling industry, along with manufacture and export of equipment. Kundu and Bhatia (2001) find a flourishing mini-steel industry in Gobindgarh, and De Bercegol and Gowda (2017) document an established furniture industry in Kartarpur.

10
World Bank, and European Bank for Reconstruction and Development. 1999. Business environment & enterprise performance surveys. [Washington, D.C.]: World Bank. http://go.worldbank.org/RQQXYJ6210. World Bank, Development Data Group. Development data platform. DDP. Washington, D.C.: World Bank Group. http://ddp.worldbank.org.

World Bank, Development Data Group, and United Nations, Statistical Division. World integrated trade solution WITS. Washington, D.C.: The World Bank.

http://library.worldbankimflib.org/ElectronicResources/eresourcelist.asp?catkey=728263 http://www-int.imf.org/depts/tgs/bts/bts-ie/cats/appcomtrade.asp?Active=0102 http://wits.worldbank.org.

41
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 0.96*** 0.95***<br>H<br>Kendall tau-b 0.79*** 0.84*** 0.84***<br>Spearman 0.97*** 0.88*** 0.92***<br>A<br>Kendall tau-b 0.89*** 0.75*** 0.86***<br>Spearman 0.98*** 0.96*** 0.95***<br>M0<br>Kendall tau-b 0.89*** 0.87*** 0.84***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C1.1. FOSD: Counting Vector C1.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.92*** 0.92***<br>H<br>Kendall tau-b 0.94*** 0.78*** 0.78***<br>Spearman 0.98*** 0.73** 0.77**<br>A<br>Kendall tau-b 0.94*** 0.56** 0.61**<br>Spearman 0.97*** 0.9*** 0.92***<br>M0<br>Kendall tau-b 0.89*** 0.72*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C2. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Access to water, sanitation, electricity & internet indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C2.1. FOSD: Counting Vector C2.2. Dominance Analysis 47
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20