rafmacalaba/datause-encoder-data huggingface dataset:
which contain standard employment modules, allowing for the application of the methodology. For a number of countries, namely for Brazil, Chile, Costa Rica, India, Poland, Sri Lanka, Turkey and the United States, we can extend the cross-country analysis to track the changing skill intensities of aggregate economic production over time.

The cross-country analysis yields that the intensity of routine skills, both manual and cognitive in nature, is negatively associated with GDP per capita. Conversely, the intensity of use of more sophisticated skills – both analytical as well as interpersonal in nature – rises. In the countries that allow us to track the change in the skill content of national production, we observe that the intensity of non-routine skills indeed increases across countries and time in a rather monotonic manner (as suggested by the cross-country results) but that the shift with respect to routine skills, both manual and cognitive, is not uniform. Exploring what lies behind these diverging patterns will require further research.

We should note that the skill assessment derived from the occupational structure of economies is only one possible measurement of the stock of skills. Direct measurement, rather than the indirect means employed here, is often conducted through large scale international or national assessment tests, such as through the Programme for International School Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), or the Progress in International Reading Literacy Study ( PIRLS).<sup>1</sup> Notwithstanding such alternatives, the methodology applied here is uniquely capable of quantifying the skill content actually employed in an economy and is as such complementary to the measurement of skill capabilities derived from assessment methods. Naturally, it does not allow for gradients as to _how good_ the measured skills are – e.g., whether the analytical skills of medical doctor’s in a given country is high or

> 1 Although the skills measured on these tests appear to be purely academic or cognitive in nature, test scores reflect more than individuals’ cognitive skills. A good part of the variation in achievement tests can be attributed to personality traits, or as well as to incentive systems (Heckman, Stixrud & Urzua, 2006). Recent studies have particularly emphasized the importance of social skills, often also labeled as non-cognitive skills, and their importance for labor market outcomes. Definitions and measurements have varied and ranged from self-esteem and locus of control (Heckman, Stixrud, & Urzua, 2006), to the Big Five personality traits (Borghans, Duckworth, Heckman, & ter Weel, 2008), to youth behavior (Cunha & Heckman 2007, 2008; Segal, 2008). World Bank (2011), for Peru, finds that specific personality traits are as highly remunerated as cognitive skills in the labor market.

**3**
# **6. Conclusion**

This paper studies the effects of the Belt and Road Initiative on shipment times and trade costs for countries that are along the Belt and Road as proposed by China and for the world more broadly. The analysis combines detailed new information on transport infrastructure projects planned as part of the BRI, a GIS network model to precisely quantify the impact of these projects on shipping times, and estimates of the value of time from Hummels and Schaur (2013) to calculate the ad valorem equivalent reductions in trade costs. The key finding is that the Belt and Road Initiative can substantially reduce shipment times and trade costs for BRI economies (up to 3.2 and 2.8 percent, respectively) and for the world as a whole (up to 2.5 and 2.2 percent, respectively).

The main contribution of the paper is in the new methodology used to quantify changes in trade costs due to transport projects and the two novel databases that provide detailed information on BRI‐induced changes in shipment times and trade costs for the world and for BRI economies. These data, in particular, will help researchers interested in better understanding the impact of the Belt and Road Initiative on a number of economic variables, including growth, trade, foreign investment, or the allocation of economic activity. The data may also be useful to investigate the systemic effects of BRI projects on non‐economic variables such as air pollution or biodiversity or on social outcomes, such as the changes in poverty for certain groups in specific geographic locations.

Admittedly, this is a first look at the effects of the BRI on shipment times and trade costs and more research will be needed. First, on the data side, information on infrastructure projects linked to the Belt and Road Initiative will change ‐and, most likely, improve‐ over time. Second, on the methodological side, research may attempt to precisely model the choice between transport modes and how this choice is affected by BRI infrastructure projects. These developments would allow to improve the network analysis and provide more precise estimates.

Finally, this paper does not address two sets of important questions. First, we take BRI planned projects as given and do not investigate what network of infrastructure would maximize efficiency (in terms of reduction of shipment times and trade costs) for the set of BRI economies or the world. Second, we look at the impact of the infrastructure projects linked to the Belt and Road Initiative as a whole and do not attempt to identify the effects on shipment times and trade costs for, say, individual overland corridors such as the China Pakistan Economic Corridor (CPEC) or individual projects, such as the Gwadar Port. While the tools developed in this paper would help address these issues, both questions are left for future research.

31
working in non-industry and non-services sectors; (v) rural households with heads working in agriculture; and (vi) rural households with heads working outside agriculture.

The combined rice demand (from home production as well as imports) from these six household groups amounted to more than CFAF100 billion in 2004. Home consumption is important for all household groups, representing between 22 percent and 30 percent of total household rice consumption (table 2). Even in households where the head works in public or private services, own production of rice is important as many non-agricultural workers, particularly in secondary towns, own small plots where they grow food and cash crops. Using the SAM, it is also possible to appreciate the share of rice in household consumption. Rice budget shares vary from less than one percent for households headed by an urban dweller working in public services to close to 11 percent for households for which the head derives his/her livelihood for rural non-agricultural activities.

The calibration of our CGE model is based on a SAM for 2004 which relies in part on data from the 2001 EMEP survey. Since then, a new survey has been implemented in Mali (the 2006 ELIM survey). Estimates of rice consumption and production from the 2006 ELIM remain of the same order of magnitude than the values used for the 2004 SAM. According to Joseph and Wodon (2008) who used the 2006 ELIM to conduct an analysis of the impact on poverty of the increase in food prices (but without taking into account general equilibrium effects), more than 90 percent of the population consume rice and the average level of spending among those who consume rice is 153,000 FCFA per year per household. The household surveys does not distinguish between imported and locally produced rice, but a comparison of data on the income received from rice production with data on the consumption of rice suggests that the average value of consumption is about two times higher than the average income received from rice. It is 11
3 strategies to mitigate the threats of COVID-19 and related crises to early learning opportunities, particularly in the Global South.

# **Risks of the pandemic to young children**

The negative effects of the global health emergency and economic downturn on young children have been immediate and have the potential to be long-lasting (Tomlinson et al., 2021; Yoshikawa et al., 2020). As a result of COVID-19, maternal and child mortality and morbidity are predicted to increase dramatically. Young children are more likely to experience poverty and food insecurity, the loss or serious illness of a caregiver, and disruptions to essential services which put them at serious developmental risk (Yoshikawa et al., 2020). As the pandemic-induced financial crisis continues, at least 142 million more children in developing countries are expected to be pushed into household poverty (UNICEF, 2020a). According to analyses by UNICEF and Save the Children, the number of children without access to <mark>education, health care, housing, nutrition, water and/or sanitation</mark> services in low- and middle-income countries (LMIC) increased by 15 percent in 2020 (Save the Children & UNICEF, 2020). Rising hunger and malnutrition, violence in the home, child labor and delayed health care (e.g., immunizations, screenings, HIV/AIDS treatment) compound the short- and long-term risks to children’s development, health, and learning (Yoshikawa et al., 2020).

During the rapid brain development that takes place during the early years, young children are particularly sensitive to both positive and adverse influences in their environments (Benner & Mistry, 2020; Tomlinson et al., 2021). To prevent or mitigate the negative consequences of acute stress and cumulative health, nutrition and psycho-social adversities on young children, a multifaceted approach is needed (Desmond et al., 2020; Yoshikawa et al., 2020). Alongside child-
intervention. We simply do not have access to the sorts of policy instruments - such an perfectly targeted lump-sum transfers - which allow costless redistribution. In any case, it is very hard to believe that the cost of participation for the poor is high at times when famine throatens, so that the net transfer is likely to be a reasonably high proportion of wage receipts. But we have very little idea of just how high. This is important in assessing whether or not this is the most cost-effective form of relief. There li also much we do not know about the second-round effects on other sources of income (such as agricultural wages) and the risk benefits. (Ravallion, 1991, surveys evidence on the two largest schemes in South Asia). Nonetheless, I think we do know enough to agree with Dreze and Sen that direct employment provision is a potentially important policy instrument in this context.

It should be emphasized that an aim of this type of public action is to avoid the onset of famine by protecting the entitlements of the vulnerable; they need not already be starving, but they can see the impending risk. An effective system of famine relief should be ready to go into action as soon as it is needed, which will typically be well before external food aid can arrive. And it should ideally fall out of use when it is not needed. HPA discusses a number of success stories in famine prevention through such domestic public action, such as Maharashtra in 1972-73, and Botswana in 198287.

While saving lives is the over-riding goal, that is not the only reason to favor effective entitlement protection through public action. Though I have not seen any estimates, it is surely plausible that even the narrowly defined current financial costs of an effective system of early response are well below the costs of belated famine relief and rehabilitation. A timely 22
Policy Research Working Paper 7806

# **Abstract**

This paper argues that although the bulk of the literature tends to focus on regulatory uncertainty stemming from formal practices, uncertainty that comes from unpredictable informal practices surrounding regulation is an underexplored additional form of regulatory uncertainty. The paper uses the results of empirical analysis of several unique firm-level data sets to argue that firms in Russian institutional environments adapt to informal practices of business-government interactions, so long as these practices are predictable. The paper draws a distinction between differences in levels of relatively well-ordered (and often centralized) and therefore predictable corruption—a predictable component of the cost of doing business—and variation in experiences with corruption, which often results from decentralized, unconstrained (“administrative”) corruption and the rent-seeking incentives of lower level officials. It argues that a significant obstacle to investment decisions at the regional level is not so much formal or informal rules per se, but lack of predictability of their application. It also examines in-country inconsistency in property rights enforcement as another source of underexplored regulatory uncertainty tied to informal practice. Unlike administrative corruption, inconsistent property rights enforcement is a fundamental, existential threat to businesses. To test this hypothesis, the paper draws on a measure that captures private “raiders’” attacks on firms—hostile, often violent takeovers of firms by outsiders aided and often abetted by law enforcement agencies. The paper argues that the greater is the number of raider attacks for a given region, the greater is the uncertainty and the less likely is investment.

This paper is a product of the Governance Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gkisunko@ worldbank.org, ayakovlev@hse.ru, ilevina@hse.ru, imarques@hse.ru.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figures and Tables**

**Figure 1: Number of Checkpoints in the West Bank, 2000-2009**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> _Source: Authors’ calculations on ARIJ data_ 34
Congo, Ethiopia, Kenya, Mali, Mozambique, and Nigeria). The Democratic Republic of Congo is an extreme case, as it uses an 11-block IBT. Interestingly enough, despite the large number of blocks, the price difference across blocks is very small, suggesting that the regime is complicated without really discriminating very much between large and small consumers.

<!-- Start of picture text --> Figure 2. Characterization of residential block pricing<br>a. Number of pricing blocks b. Tariff price differential between first and highest<br>block (%)<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>one (linear two three four or more 0% < 100% 100-200 200% ><br>rate)<br>number of blocks<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database The conventional wisdom is that IBTs are designed so that the first and smallest “lifeline” block covering subsistence consumption is subsidized to promote equity, while the subsequent blocks are priced at a higher level that will ultimately enable cost recovery. This of course assumes that poorer customers will have lower consumption. This assumption is more reasonable in the case of power – where usage is driven by ownership of appliances more prevalent among wealthier households and businesses – than it is for example in the water sector – where usage is more correlated with the size of the household.

The first question is whether African countries tend to define the consumption level of the first block at a level low enough to be consistent with subsistence consumption and hence with the “lifeline” principle underlying this tariff design (table 2). The response to this question is quite positive. Two-thirds of the sample countries define the first block at 50 kilowatt-hours (kWh)/month or less; a consumption level that is below the average residential power consumption in Africa (75-100 kWh/month) (Foster and Briceño-Garmendia 2009). For example, Uganda sets the first block at 15 kWh/month; Cape Verde and Côte d’Ivoire at 40 kWh/month; and Burkina Faso, Cameroon, Ethiopia, Kenya, Tanzania at 50 kWh/month. But the sizes of the first blocks in Mozambique and the Democratic Republic of Congo (100 kWh), Mali (200 kWh) and in Zambia and Ghana (300 kWh) seem too high to meet the needs of lowconsumption, low-income residential customers.

7
**Table D.23:** Main outcome families when controlling for baseline imbalances

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|General utilization|0.000|0.097|0.106|0.087|4451|
||(1.000)|(0.033)<sup>_∗∗∗_</sup>|(0.035)<sup>_∗∗∗_</sup>|(0.039)<sup>_∗∗_</sup>||
|||[0.036]<sup>_∗∗_</sup>|[0.014]<sup>_∗∗_</sup>|[0.080]<sup>_∗_</sup>||
|Maternal utilization index|0.000|0.060|0.178|_−_0.059|878|
||(1.000)|(0.070)|(0.081)<sup>_∗∗_</sup>|(0.082)||
|||[0.277]|[0.083]<sup>_∗_</sup>|[0.473]||
|Health outcomes index|0.000|0.082|0.170|_−_0.010|5003|
||(1.000)|(0.050)|(0.056)<sup>_∗∗∗_</sup>|(0.058)||
|||[0.188]|[0.014]<sup>_∗∗_</sup>|[0.909]||
|Satisfaction index|0.000|0.110|0.094|0.124|5002|
||(1.000)|(0.044)<sup>_∗∗_</sup>|(0.051)<sup>_∗_</sup>|(0.049)<sup>_∗∗_</sup>||
|||[0.060]<sup>_∗_</sup>|[0.134]|[0.072]<sup>_∗_</sup>||
|Health service delivery index|0.000|0.055|0.082|0.044|2845|
||(1.000)|(0.061)|(0.074)|(0.063)||
|||[0.277]|[0.330]|[0.473]||
|Clinic quality index|0.000|0.151|0.057|0.260|254|
||(1.000)|(0.157)|(0.183)|(0.184)||
|||[0.277]|[0.606]|[0.290]||
|CDPE index|0.000|0.161|0.153|0.168|501|
||(1.000)|(0.086)<sup>_∗_</sup>|(0.104)|(0.101)<sup>_∗_</sup>||
|||[0.145]|[0.203]|[0.208]||
|Contributions to clinic index|0.000|0.024|0.044|0.002|501|
||(1.000)|(0.097)|(0.113)|(0.111)||
|||[0.678]|[0.606]|[0.969]||
|Water and sanitation index|0.000|0.122|0.069|0.177|5003|
||(1.000)|(0.062)<sup>_∗∗_</sup>|(0.071)|(0.071)<sup>_∗∗_</sup>||
|||[0.145]|[0.330]|[0.072]<sup>_∗_</sup>||
|Economic outcomes index|0.000|0.057|0.063|0.051|5003|
||(1.000)|(0.051)|(0.060)|(0.061)||
|||[0.277]|[0.330]|[0.473]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.We also control for baseline variables displaying imbalance in Table C.1 – namely, phone coverage, household size, the number of births in the household in the last year, the share of the village population of Temne ethnicity, the highest level of educational attainment, whether they believe what the doctors tell them, and the number of illness or injury cases in the household. Where imbalanced baseline characteristics are measured at a different level of observation, we average or assign to each member as needed. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A30
political violence. In addition, this variable is less likely to be endogenous to FDI than most subjective measures of conflict because MNE investments are unlikely to cause battle-related deaths directly. Because the battle-related deaths variable is highly skewed, we take the natural log of the inverse hyperbolic sine function.

For our second and fourth hypotheses on the geographic scope of political conflict, we use again the UCDP/PRIO Battle Related Deaths data set. UCDP/PRIO records the warring parties and the incompatibility of each conflict. The geographic scope variable is a dummy, which is 0 if a conflict is localized and 1 if a conflict is nation-wide. First, we coded every secessionist conflict as _localized_ because secessionist fighting is generally confined to the territory that is fought over (Buhaug & Gates, 2002). Subsequently, we manually checked whether conflicts, in which the incompatibility concerned the government instead of a regional territory, were nationwide by analyzing articles on the conflict on the website of BBC news and profiles of the insurgents on the START website (2014). Finally, we visually confirmed our coding using maps based on the UCDP/PRIO Georeferenced Event Database (Sundberg & Melander, 2013) for all conflicts in Africa and South Asia. Unfortunately, UCDP/PRIO GED does not yet collect georeferenced data on conflicts on other continents. We found no inconsistencies in the coding based on the information provided by BBC News or START. We exclude the main effect of localized conflict from our regressions because the type of conflict is virtually country invariant and is hence absorbed by the country fixed effects in our regression.<sup>5</sup> If a country experienced both a localized and non-localized conflict, we coded the observation as having experienced a nationwide conflict. In 22.4% of the observations, there is an ongoing conflict; of those, 56.6% are coded as localized.

Although fDi Markets records FDI data on the project level, it does not include any information on the investing firm other than the name of the company and its parent company. We collected firmlevel data from Bureau van Dijk’s Orbis database, containing annual report data of over 79,000 companies worldwide, and manually matched these data to companies in the fDi Markets data set. Nevertheless, 32.9 % of all investment projects in fDi Markets could not be linked to companies in the Orbis data set. We code geographically diversified firms using a dummy variable which is 1 if the firm has subsidiaries in at least 10 countries and zero otherwise.<sup>6</sup> To facilitate the comparison of different 19