rafmacalaba/data-use-mentions-extended huggingface dataset:
to partner-specific shocks and to extreme volatility in export prices. The recent crisis has highlighted the critical importance of diversification in reducing the risks of volatility in global demand. Evidence suggests it is not natural resource dependence per se that increases risk, but rather the concentration of exports (Lederman and Maloney, 2007).<sup>7</sup> Overall, Pakistan’s export bundle is relatively diversified in terms of products, but the emphasis on industrialized countries export markets makes the country too vulnerable to economic developments in these countries. As it already emerged from the analysis of Pakistan’s market shares, in terms of geographical specialization, Pakistan’s exports are relatively more concentrated than peer countries.

> 6 The RCA index is a measure for calculating the relative advantage or disadvantage of a certain country in a certain industry as evidenced by trade flows. An index above the unit indicates that a country’s share of exports in that sector exceeds the global export share of the same sector. If this is the case we infer that the country has a comparative advantage in that sector. However, because high export volumes can results from subsidies or other incentives provided, included under-valued exchange rates, RCA have been argued to be a misnomer in the sense that they are a better measure of competitiveness than comparative advantage (Siggel, 2006).

> 7 Increasingly, services trade has also become an important source of export diversification. This dimension is not analyzed in this paper, but the reader can find some information in the policy note on reinvigorating the trade agenda in Pakistan (World Bank, forthcoming).

7
related travel during the pandemic. The evolution of demand in times of the day that concentrate shopping travel according to the 2019 survey is contrary to what would be expected in case there was a substitution effect of trips through increased e-commerce adoption. The drop in travel to shopping areas was smaller in the 9:00-12:00 time slot – the window with most shopping-related mobility in 2019 – than in the day as a whole. Only in the case of the 10
>TUN<br>CZE<br>MYS<br>PHL<br>NZL<br>HKG<br>ARG<br>IRL<br>ISL<br>ZAF<br>MAR<br>USA<br>NOR<br>CYP<br>GBR<br>CHE<br>ISR<br>CAN<br>HUN<br>ESP<br>SVN<br>NLD<br>FIN<br>BRA<br>TWN<br>BRN<br>SWE<br>BEL<br>HRV<br>PRT<br>GRC<br>ITA<br>DNK<br>AUT<br>FRA<br>DEU<br>JPN<br>-1 0 1 2<br>Government Private<br><!-- End of picture text --> Overall, the analysis using the CHB indexes reveals that, while government procurement is significantly more home biased than private purchases, it has gone down faster. Important 32
social and economic characteristics, and political aliation of each victim. Most importantly, I extract exact geographical location (village) and the date of the incident.

Table 1 reports descriptive statistics of the war fatalities. In total the INSEC data set contains information on 14,982 victims; most (13,210) are fatal casualties. More than 60% of the casualties are perpetrated by the state. The CPN Maoists deny exploiting the grievances among ethnic groups regarding political, social and economic exclusion to advance their agenda. However, the majority of their cadres belonged to ethnic groups such as Magars, Gurungs, and Dalits of the hills and mountains. It is, therefore, not surprising that the majority of casualties among the Maoists are ethnic minorities (60% not reported) and more than half of the total victims are also from the minority groups. Apart from attacking security forces, the Maoists also frequently targeted upper caste civilians, especially Bramins and Chhetris; these upper castes the Maoists labeled as counterrevolutionary elements. The average age of the victims is 28.3 years and almost 90% are male. Many were actively involved in politics- 54% are aliated to either the rebel party or other political parties.

The conict intensity varied greatly over time. I summarize the number of monthly casualties and major events that helped shape the insurgency in Figure 1. As illustrated, the period after 2001 when the country was under the state of emergency and the RNA was actively involved was the bloodiest. The conict lasted for total of 131 months from February 1996 to November 2006. Using information on each victim's village and date of incident, I dene months of war _v_ in a village, _v_ , as the baseline conict intensity variable, constructed as follow: and subscripts _v_ and _m_ index a village and a month since the beginning of the war i.e. m takes the value of 1 for February 1996 and 131 for November 2006. Variable casualty _vm_ is
It could help improve ongoing efforts in the international development and aid community to build institutions of accountability for pro-poor policies.

A survey was designed to be able to correlate vote buying with the delivery of health services, taking advantage of an appropriate institutional context in the country of the Philippines.<sup>6</sup> The data come from a jurisdiction level where there is congruence between the political unit at which vote buying is measured, and the administrative unit at which a particularly broad and potentially pro-poor service is delivered. Directly elected municipal governments in the Philippines are responsible for delivering basic maternal and child services to villages within their jurisdiction. This allows for attributing the decision-making over political strategies, and over a pro-poor public service to the same political agents. Municipalities hire and manage nurses, midwives, and community or village health workers, the key health personnel that are supposed to deliver basic maternal and child health services to the poor.<sup>7</sup> The central finding is that in places where households report greater vote buying (in direct response to questions about offers of money in exchange for votes at the time of elections), municipal government records show lower investment in basic health services for mothers and children; and, possibly as a result of lower public investments or worse performance in delivering broad services, a higher proportion of children are under-weight.

The rich survey data allow for examining, and rejecting, various alternate explanations for the correlation that are outside of the channel of clientelism. For example, the incidence of poverty could be independently correlated with both vote buying and health services, driving a spurious correlation between the two. But the data do not support such alternate explanations. The disaggregated nature of the jurisdiction level, the municipality, facilitates a study design in which sources of variation in vote buying can be examined in detail. Almost the entire population of municipalities in one province of the Philippines was picked for this study to rely on within-province, and within-congressional-district variation in vote buying across municipalities.
household surveys is then correlated with health service measures from a completely different source: official government records. Additional supporting evidence is provided from householdlevel measures of access to health services. The robust negative correlation with vote buying, therefore, holds for health services measured from there different sources: village health facility records, municipal budgets, and household interviews.

Other types of policy instruments—roads and infrastructure projects, and targeted assistance to households in time of need—that can be heavily influenced by higher-tier politicians outside the municipality, serve to perform “placebo” tests, to rule out accidental or spurious correlation. Municipal vote buying should not be as significantly or robustly correlated with those services where outcomes are heavily dependent on the actions of other politicians.<sup>9</sup> Furthermore, these other policy instruments might be more “targetable” than health services, and therefore more compatible with clientelist political strategies than broadly delivered services.<sup>10</sup> Roads or infrastructure projects, are also useful comparators to health services because they have been described as particularly amenable to elite and political capture in the Philippines and elsewhere (Matsuda and Cruz, 2012; Keefer and Knack, 2007; Khemani, 2012).

If the correlation between vote buying and service delivery is spuriously driven by unobserved conditions, that simultaneously make service delivery harder and vote buying easier, then similar correlations could be observed across different types of services, regardless of the extent to which the service is controlled by municipal politicians or amenable to clientelist targeting and elite capture. This is not the case, as will be discussed in detail in the sections further below—vote buying is robustly correlated with a particular type of health policy instrument over which municipalities exert direct and exclusive control. The data show an oppositely-signed correlation of vote buying with targeted assistance to households and with mayoral roads projects in the sampled villages—positive, though not always statistically significant.

In sum, the finding that municipal vote buying is robustly correlated with lower levels of a particular
implementation feasibility – this intervention can be considered ‘low hanging fruit’ that produce strongly beneficial outcomes for limited financial outlays. The finding suggests that cities would be well advised to prioritize heat-health early warning systems.

Implementing heat early warning systems is shown to be a highly effective strategy. Our research, along with other studies, consistently shows a significant reduction in mortality rates and a benefit-cost ratio (BCR) exceeding 50. These findings are not unique; similar BCR values are observed in studies worldwide. For instance, research by Chiabai et al. (2018) and Ebi et al. (2004) revealed BCRs well above 50 for cities like Madrid and Philadelphia.

Our study has several limitations. The health impacts analysis examined mortality without performing estimates of health costs associated with non-fatal illness. The exposure-response curves utilized were derived from all-India mortality data; our analysis could be improved through use of locally derived temperature-mortality relationships that would better characterize heat impacts in India’s varied climatic zones. In future work, labor productivity impacts should be modeled with improved treatment of difference in heat exposure between occupations (eg. indoor vs. outdoor work). Despite these limitations, the present work contributes to advancing modeling approaches for urban heat mitigation and adaptation while pointing to strong policy conclusions about the urgency and efficacy of heat mitigation and adaptation actions that can be championed at the city level. Additional impact channels that could be analyzed would include extreme heat impacts on transport, power and water infrastructure and city services.

# **5. Conclusion**

Indian cities already experience night-time temperatures 5°C or more higher than outlying rural areas due to the urban heat island effect, with densely packed neighborhoods most affected. Combined with impacts of global climate change, cities such as Chennai, Surat and Lucknow will see 30%-50% more heatwave days and hot nights per year. The rise in heat stress will increase the number of working hours per year which are too hot to safely perform work – particularly in high physical-intensity and heat-exposed outdoor
available.<sup>25</sup> The ILO employment-to-population data are the most recent for 150 countries. Some of these countries do not have HCI data, so ILO employment-topopulation data are used for 83% of the basic UHCI sample (140 countries). The median year of the survey used is 2018 (and mostly range from 2010-2019).<sup>26</sup> The ILO data are mostly sourced from the ILO “microdata repository”.<sup>27</sup> It should be noted that the ILO data usually applies to the whole population, but occasionally the underlying surveys focus on certain geographies (like the urban population), exclude borderline ages (like 15 year olds), or exclude certain subgroups (like those employed in the armed forces).<sup>28</sup> Note that employment rates in other publications (and in policy discussions) are sometimes quoted for the 15+ age group rather than the 15-64 age group (possibly using from other underlying surveys), and so can differ from the ones used in this paper.

22 HCI data can be downloaded from the World Development Indicators website: http://wdi.worldbank.org 23 In practice, the algorithm is slightly more complicated because I make sure that both basic and full utilization rates come from the same data set and give preference to actual data over interpolated data. The data requirements of the basic utilization rate are a subset of those of the full utilization rate. First, if only one data set has the full data available, I choose that one. Second, I choose JOIN data if the ILO data are interpolated. Third, I choose the most recent data set if both are available, with a preference for ILO data if both data sets are from the same year.

24 The three large countries/regions without GNIPC data are Somalia, the Syrian Arab Republic, and Taiwan, China. They also do not have HCI data, and so I cannot
efforts are necessary to raise the skill set of engineering graduates in India through an improvement in the quality of engineering education. We particularly recommend that each engineering program explicitly states and measures the desired learning outcomes (the skill set of their graduate). The accreditation agency, NBA, in particular could have a tremendously important impact if it increased the weight of graduates‘ learning outcomes compared to other input-oriented accreditation criteria (such as classroom and curricula). Also, while the results may appear gloomy, reforms and quality improvements programs have successfully taken place at individual state and institutional level. There are several success stories, see Annex 4 for the Technical Education Quality Improvement Programme. What is required is a scale-up of the reforms and investments at the national level addressing the shortcomings in the skill set of engineers.

**The skills set of engineers can be characterized by three overall skills factors** :

- (i) _Core Employability Skills_ (which cover generic attitudinal and affective skills, such as reliability and team-work);

- (ii) _Communication Skills_ (such as English skills, written and verbal communication) _,_ and

- (iii) _Professional Skills_ (which generally covers cognitive skills related to the engineering professions, such as ability to apply engineering knowledge; as well as design and conduct experiments and related data analyze and interpretation).

These skill factors are similar to findings from other employer surveys. _Core Employability Skills_ and _Communications Skills_ are often referred to as soft skills. The factors also have important similarities with theoretical skill domains developed in the educational literature. These three skill factors are therefore appropriate to use for further analysis.

**All three skills factors are important-** **_Core Employability Skills_ ,** **_Communication Skills_ and** **_Professional Skills_ are important.** Engineers that are in high demand possess all three skills sets. Engineering education programs therefore have to put in place a comprehensive quality upgrade of their programs.

However, while _Professional Skills_ remain important **,
<!-- Start of picture text --> Growth Incidence Curves, 1992-2014<br>== 1992-2000== 2000-2005 == 2000-2014 == 2005-2010<br>XS> 20<br>Bod<br>&<br>g6<br>5 a<br>Agogao<br>ao<br>ag<br>|§410<br>a<br>au<br>Sk<br>Aacsi2: hs |<br>CJ<br>\\<br>VV V NV y" N<br>0 25 50 75 100<br>Percentile of municipalities’ mean per capita income<br>(at the initial year of each period)<br><!-- End of picture text -->
Argentine debt. The latter had already been shown to be highly volatile during the series of emerging market debt distress over the preceding period.

On the political front, the Peronist Party ( _Partido Justicialista_ ) was split between rival candidates, while the Radical Party formed an Alliance ( _Alianza_ ) with the FREPASO party and successfully won the presidential election in 1999, with President Fernando de la Rúa taking office at the end of the year. The _Alianza_ secured a majority in the lower house of Congress, while the Peronists maintained a majority in the Senate.

# _Attempts to forestall a crisis_

The Argentine economy suffered a recession in 1999 with GDP contracting almost 3 1⁄2 percent. The poor performance continued into 2000 with a small contraction of less than 1 percent. The general government fiscal deficit hovered around 4 percent of GDP in 1999 and 2000. General government gross debt rose from 30 percent of GDP in 1997 to 38 percent at the end of 2000.<sup>16</sup> Total external debt had risen from 36 percent of GDP in 1997 to 44 percent of GDP in 2000 (nearly 400 percent of exports).<sup>17</sup> Country sovereign risk ranged from 500 to 860 basis points during the year 2000.

In early 2000, the government secured passage of a tax reform that lowered the exemption threshold on the income tax and lowered deductions for higher income individuals. <mark>The tax package included: rate increases on income, excises, fuels, and personal wealth taxes; a broadening</mark>

> 16 Data from IMF-WEO database.

> 17 Data from World Development Indicators .

10
4 percent), Russia (3.6 percent), and Armenia (3 percent).

Moreover, in two of the six studied countries the public rental sector is significant: Poland has 30.6 percent rentals, with the private rental sector (16.8 percent) dominating the public rental sector (13.8 percent), and Russia has 35 percent rental, with the public rental sector (31.4 percent) dominating the private rental sector (3.6 percent). Poland did not embark on across-theboard top-down massive privatization, leaving it to municipalities to define and implement their privatization strategies. The country restituted ownership of a substantial volume of prewar rental buildings, and introduced an active, heavily subsidized construction program for private rental apartment buildings and an extended housing allowance program to cover private rental housing. Unintentionally, privatization of public apartments in Russia (31.4 percent of stock) has remained incomplete, as many households have chosen to hold off on executing privatization decisions. The public rental sector is presently marginalized in Armenia, Lithuania, and Serbia and almost eradicated in Romania.

> 21  The decennial censuses in some cases provide better information. The 2001 Armenian census makes it possible to identify tenure status. The 2002 Romanian census included a question on tenure status where the 1992 census did not; however, official analysis of the 2002 census has been incomplete. A positive example is Poland, where census and other housing sector statistics usually include tenure form statistics.

15
Not all RFMO databases contain all of the above variables, especially vessel characteristics, and characteristics sometimes differ across RFMOs for vessels with the same identifiers. To include more vessels and vessel characteristics, we combine our RFMO data with data from Rongcheng, a hub of Chinese distant water fisheries (General Administration of Customs, 2021). The municipal dataset of Rongcheng is part of the Shandong Provincial Open Data Platform, which was developed in 2018 and contains 318 distant water fishing vessels registered in Rongcheng (Rongcheng Municipal Government, 2021). We also added 104 vessels that are included in the Rongcheng database but not in the RFMO databases. We standardize all vessel lengths to meters and all engine powers to kilowatts. After removing duplicate vessels, we obtain data for 2,216 Chinese distant water fishing vessels.<sup>11</sup> We match these vessel characteristics data to fishing activity data from the organization Global Fishing Watch (GFW). We are able to match about 70% of vessels because most distant water fishing vessels have Automatic Identification System (AIS) transponders that send their movements to satellite and terrestrial receivers. GFW applies machine learning algorithms to predict fishing activity from AIS vessel movements (Kroodsma et al., 2018). This prediction is possible because fishing involves different movement patterns than non-fishing activities. Note that vessels have no differential incentive to disable their AIS transponders with respect to fuel subsidy thresholds because fuel subsidies depend on VMS rather than AIS pings (Section 2).

From the GFW dataset, we extracted information for all vessels flagged to (registered in) China between 2015 and 2020, including the hours of fishing and vessel characteristics. We focused on China-flagged vessels because this is a requirement for a vessel to receive a subsidy from the Ministry of Agriculture. In contrast to the RFMOs, which can obtain detailed vessel data from member countries based on compliance rules, some GFW vessel characteristics are machine-learning-predicted values (Kroodsma et al., 2018). We believe that RFMO vessel characteristics are more reliable than GFW
--> _Notes: Natural resource revenues share of GDP (blue bars) and in constant 2015 US$ (red bars) for 2004-2014 annual average by resource-rich Sub-Saharan African country with natural resource revenues greater than zero. The following six countries from the resource-rich group are not included due to missing data: Central African Republic, Madagascar, Mozambique, Tanzania, and Togo. Source: authors’ calculations with data from UNUWIDER Government Revenue Dataset 2021._ 5
Table 2 shows the variance of the earning distribution in the overall economy, broken down by two components: a) Variability within sectors; b) Variability between sectors. Despite the substantial differences in relative earnings, 97.5 percent of variance in log wages is explained by within-sector variance.<sup>63</sup> Table 3 shows the descriptive statistics for the unemployed population. The estimated unemployment rate for the year 2003 is quite high: 18.5 percent (s.e. of .00274). Mean unemployment duration is quite high (51.5 weeks),<sup>64</sup> and higher than the mean non-employment duration for the employed population (6.9 months).

# **4.2 Labor Market Dynamics**

Standard static analysis of labor markets that analyze stocks of workers in different states does not tell us anything about where those workers arrived from, how long they will stay, or where they will go next. I construct a set of statistics to analyze labor market dynamics and derive some stylized facts about movement across sectors in the Colombian labor markets, using the retrospective questions from the cross-section database.

## **4.2.1 Annual Discrete Transition Matrix**

Individuals in the sample fall into four labor market states: unemployed ( _U_ ), employed in the formal sector ( _F_ ) _,_ employed in the informal sector ( _I_ ), and other ( _O_ ).<sup>65</sup> I estimate an annual discrete transition matrix by estimating gross worker flows across states.<sup>66</sup> Let _mij_ be the number of individuals in the 2003 sample who were in state _i_ in 2002 and state _j_ in 2003. I observe some, but not all the transitions between states from 2012 to 2013.<sup>67</sup> When considering transitions into unemployment, I observe all transitions _mUU , mFU , mIU_ and _mOU ._ When considering transitions into the
000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exports, inter-regional|0.087***|0.069***|0.052***||||
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)||||
|Exports, intra-regional|0.014|0.021**|0.044***||||
|_(% of GDP)_|(0.059)|(0.005)|(0.000)||||
|Trade, inter-regional||||0.048***|0.056***|0.016***|
|_(% of GDP)_||||(0.000)|(0.000)|(0.000)|
|Trade, intra-regional||||0.019**|0.010|0.027***|
|_(% of GDP)_||||(0.003)|(0.177)|(0.000)|
|Product Concentration|-0.021***|-0.020***|-0.010***|-0.023***|-0.020***|-0.010***|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Secondary enrollment rate|0.010***|-0.000|0.002***|