rafmacalaba/data-use-mentions-extended huggingface dataset:
Table C.2. Head of the HH and Partner in RAMV only

||_Likeliho_|_od of having chil_|_dren of_||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.040***|-0.086***|-0.015|-0.002|
||(0.014)|(0.016)|(0.012)|(0.003)|
|Observations|2,430|2,430|2,430|2,430|
|Observations by wave|810|810|810|810|
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic Sampling_×_wave|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). In the analysis, the treated units are households in which only the head of household or the partner has PEP. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written
of China‘s willingness to engage in this new agenda. We cannot be sure, of course but three factors might be at play. First, the agenda includes issues that China has an important stake in pursuing: clear investment rules, restraints on climate change-related trade action, as well as assured access to resources (oil and food) and markets overseas.

Second, the challenges for an open system from the ―decline of the west‖ should not be underestimated. If growth in industrial countries does not recover, income distribution continues to worsen, and economic opportunities shrink, the intellectual and political consensus in favor of open markets —in which China has a strong stake—will come under threat. There are already ominous portents.

In the United States, for example, five leading intellectuals (three of whom are Nobel prize winners) with impeccably cosmopolitan credentials—the late Paul Samuelson, Alan Blinder, Paul Krugman, Larry Summers, and Michael Spence—have expressed differing concerns about the impact of globalization on the US economy. They have all been careful not to advocate protectionist remedies but their concerns could lend legitimacy to such policies. Not least because the political support for free trade agreements has declined considerably among the public. More strikingly, a November 2010 Pew poll showed that only 28 percent of traditionally 34
# **3 Data and Calibration**

The strength and duration of a lockdown are critical aspects of our quantitative analysis. Our _reference_ lockdown policy attempts to mimic what had been observed in the first weeks of the pandemic. It is henceforth defined by three parameters: its starting time, length, and severity. To calibrate these parameters alongside transmission rate parameters, we proceed in two steps. In the first step, we calibrate the transmission parameters _πI_ 1,... _πI_ 4 to match the relative importance of the different transmission modes and the overall predicted infection rate in an unmitigated spread scenario. In the second step, we calibrate the effect of lockdown severity on community transmission _ξ_ , alongside the strength of the reference lockdown _μ_ ̄, to jointly match the decline in GDP and the reproduction number _R_ 0 as estimated by Flaxman et al. (2020) for European countries early in the pandemic.

## **3.1 Infection and Mortality Parameter Calibration**

The within-household transmission parameter, _πI_ 4, is taken from a meta-analysis of household transmission estimates from different settings. Lei et al. (2020) estimate the secondary infection rate in the household to be 0.27.<sup>13</sup> To discipline the three other transmission parameters, we jointly match three moments. The first moment is the proportion of the population that would get infected in each country in the absence of any mitigating policy. We use projections reported by Walker et al. (2020) that use country- and age-specific contact patterns to simulate health impacts of COVID-19 in 202 countries. They develop an SIR model incorporating the age distribution of each country. Employing a basic reproduction number ( _R_ 0) of 3.0, they project that about 90 percent

> 13Household living conditions in poor and rich countries differ, and thus it would have been desirable to calibrate country-group-specific within-household transmission rates. Unfortunately, we are not aware of any systematic studies on country heterogeneity in within-household transmission. The value in Lei
takes 1990 as a starting point and the interest lies in forestation ambitions in recent decades, 1990 is taken as the cut-off year.

Hence, the specification includes an additional regressor:

- Forest cover in 1990 ( _forest90i_ ): This variable controls for both the country’s natural inclination to forests (jointly with precipitation and temperature) and the deforestation before 1990.

In this case, the residuals can be interpreted as the change in forest over since 1990 above or below what the economic and climatic regression model would predict. Countries with a positive (negative) residual have increased (reduced) forest cover since 1990 relative to the model’s prediction, and _excess forestation_ is positive (negative).

After estimating countries’ excess forestation and deforestation, these values expressed in sq km of land area are converted to CO2 equivalents. The carbon stock in living forest biomass data of the FAO provides the conversion rates. These data express carbon stock per hectare of forest for each country. The reference year to convert forest area in square km to CO2 equivalent is 2010. Countries with forest cover less than 5 percent of land area are dropped from the analysis as their residuals were noisy.

17
-> Per 100 people Index<br>40 Broadband connections 90<br>Mobile connectivity (RHS)<br>30<br>60<br>20<br>30<br>10<br>0 0<br>LAC EMDEs excl. Advanced<br>LAC economies<br><!-- End of picture text --> F. Proportion of global commodity reserves in LAC <!-- Start of picture text --> Percent<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Lithium Copper Rare earths<br><!-- End of picture text --> Sources: GSMA Mobile Connectivity Index; Rozenberg and Fay (2019); UN Population Division; USGS (2021a-c); World Bank.

Note: AE = advanced economies; EMDEs = emerging and developing economies; LAC = Latin America and the Caribbean. A. Bars depict investment needs in LAC according to the preferred investment scenario (“ambitious goals, high efficiency”) from Rozenberg and Fay (2019).

B. Projections by the United Nations Population Division.
all. Of the 891 individuals who make up our common-claims sample used in the core analysis, 275 (31%) of individuals respond ‘Don’t Know’ to at least one of the questions asked. 181 individuals (20%) respond ‘Don’t Know’ to all of the questions in the information module. There is, therefore, selection into the sample of respondents who provide non-zero estimates. Regressing individual characteristics on a binary variable that takes the value 1 if a respondent answers ‘Don’t Know’ to all questions, we find evidence that district officials are less likely to do so. We make two comments regarding the selection into the error sample: firstly, it seems a reasonable assumption that the probability of responding ‘Don’t Know’ is positively associated with the most extreme (latent) errors, and hence our estimates of the difference across tiers can be interpreted as a lower bound. Secondly, the error data provide consistent estimates of the accuracy of information that is present within a public office in Ethiopia – for example, if one entered a public office at random in Ethiopia and asked about a particular service delivery indicator, the information that one would receive would be reflected within the error data we use for our regressions. To ensure that these 0 claims do not affect the 35
the increased demand for engineers by massively expanding production of engineers. The number of students enrolled increased 800 percent from 1998 to 2008, (MHRD, 2009). This quantitative expansion is widely perceived to have led to an average decline in the quality of the students entering, the teaching and, consequently, the quality of the graduating engineers (Jha et al. 2009).

Despite the gravity of the situation, little research has been conducted to identify the kinds of skills demanded by employers and measure in which skills graduates meet employers‘ expectation. There is an increasing demand for such information from teachers, administrators, and policy makers. For example, Government of India is implementing a program with World Bank co-finance, to improve quality of engineering education and increase learning outcomes of engineering education graduates. For this program and for other initiatives, it is critical to identify specific bottlenecks in skills demanded by employers, and provide detailed information and practical suggestions to overcome the skill shortages.

With this aim, an Employer Satisfaction Survey was carried out from September to November 2009 as part of preparation of the Second Phase of Technical Education Quality Improvement Program (TEQIP-II) initiated by the Government of India and financially supported by the World Bank. The survey was implemented by a joint team of the National Project Implementation Unit (NPIU), the Federation of Indian Chambers of 2
<!-- Start of picture text --> x2<br>pathH<br>U H<br>U L<br>pathL<br>xH<br>xL<br>achievement-LIM<br>(w A = p)<br>x1<br><!-- End of picture text -->

**Figure 13:** Equating weights to market prices needs not yield an optimal identification of the poor. Budget lines for bundles xH and xL are in green, the two market dimensions (shown) are not separable from the third non-market dimension, which admits values L or H.

be excluded from total consumption. Such exclusion would correspond to setting the weights associated to health expenditures to zero. Again, the rationale being that health expenditures signal low unobserved health achievements.

# **Identifying low well-being from non-monetary data**

We consider the case of a specialized survey covering the non-monetary domain. We explain that, in oder to minimize (expected) mis-identification errors, the weight of a non-market dimension should depend on the correlation that is expected between achievements in this dimension and monetary aggregates.

Non-market achievements are not chosen, but they may still signal something useful about market achievements. This is at least the case when the strength of the correlation between the monetary aggregate and non-market dimensions varies across non-market dimensions. Assume for instance that the correlation between education and income is perfect while health and income are uncorrelated. Under this assumption, someone who has low education also has low income, but an individual with bad health may have low income or high income.

The signal that non-market achievements provide on the ignored monetary aggregate may help improve identification in expected terms. For simplicity, assume that there are four dimensions, where the market domain is {1, 2}. Dimension 3 is perfectly correlated with the monetary aggregate while dimension 4 is not correlated with the monetary aggregate. Assume that all dimensions are cardinally measured and all are equally important to welfare,
entire territory of Serbia, as well as other register relevant business-related registers (such as the Collateral Registry, the Financial Accounts Registry etc.).<sup>4</sup> Considering that the paper records inherited from the courts were so out of date that they were unusable, a re-registration campaign was implemented, requiring all active companies to reregister in the SBRA free of charge. In the course of one year the registry was centralized, up to date and accessible via the Internet, leading to far greater legal certainty. As one attorney says, “ _Now I can check in a few minutes if a company exists, what is the address, and who is authorized to represent them. Before, I had to go to the court for each inquiry_ ” (Jersild and Skopljak 2007).

For the first time in public administration, the silence is consent rule was applied, mandating that if the SBRA fails to formally respond a request for registration, either by granting it or by requesting more information, in the term of a maximum of 5 days, it shall be deemed that the company has been registered. The Company Law of 2004 reduced the minimum capital requirement for limited liability companies (90% of all companies in Serbia) from approximately €5,000 to €500 and eased requirements for establishing companies by making the rules more flexible. The minimum capital requirement was further reduced by the Company Law of 2011 to less than €1.

The effects of the reform were felt almost immediately. The time necessary for starting a business was reduced from 51 days in 2004 to 15 in 2005 (World Bank, 2006). The new system was a radical change, with a focus on customer service and user friendliness. The system moved from decentralized, inconsistent and discretionary practices of the commercial courts, to a centralized, up-to-date and transparent regime with internet access to all registration data. The SBRA exchanges information with all relevant stakeholder agencies and has grown into a onestop shop that currently manages more than 20
- Despite Latvia’s excellent stock of human capital, skilled workers and professionals do not seem to be involved in skill-intensive activities. As the chart below suggests, Latvia is weakest cc in terms of skilled workers engaged in either manufacturing or services.

**Figure 5 Human Resources Utilization, 2000-2001**

<!-- Start of picture text --> 10,0<br>9,0<br>8,0<br>7,0<br>6,0<br>5,0<br>4,0<br>3,0<br>2,0<br>1,0<br>0,0<br>Employment in medium-high and high-tech manufacturing (% of total workforce) in 2002 or the closest year<br>Employment in high-tech services in 2001 (% of total workforce)<br>Denmark Filand Ireland EU BulgariaCzech Rep. Estonia HungaryLithuania Latvia Poland Slovenia TurkeyRomania Slovakia<br><!-- End of picture text --> _Source_ : Eurostat, Trendchart 2003, available at <u>www.trendchart.org</u>

- Exports produced by technology-driven industries account for a growing share of exports in nearly all NMS’s, with the highest shares (and largest increases) in Hungary (more than 47% of all manufacturing industry exports to the EU in 1999), Slovakia (30%), Estonia (24%) and the Czech Republic (21%) (Eurostat COMEXT Database).  By comparison, technology-driven industries account for a negligible share of Latvian exports.

> Mexico have generally been stagnant.  Yevgeny Kuznetsov, “Mexico -- Towards a Second Generation NAFTA Agenda: Implications for National Innovation and Enterprise Upgrading Systems,” World Bank processed, February 2003.  Mexico faces many of the same challenges as Latvia.  Both are in danger of becoming low wage production sites for much larger, more productive, and wealthier neighbors.  And both are struggling to find ways to
, 26-35, 36-45, 46-55, 56-65 and 65+, respectively. The global age distribution of saving for old age in the 2014 Global Findex data suggests that 10.1% of individuals aged 18-25 save for old age. The figure for those aged 26-35 rises to 21% and to 31% for those aged 36-45. 33.3% of the group aged 46-55 save for old age and so do another 36.9% of the 56-65 group. The figure for those aged 65+ is close to that for the 56-65 group at 36.5%. In EAP, a near doubling of the proportion of people saving for old age arises at age 26-35, at 31.9% compared to 16.3% at age 18-25. In comparison, in ECA, the tiny figure of 3.6% at age 18-25 rises at 6.2% at age 26-35 and 8.2% at 36-45. A rather major rise to 14.2% of the population occurs at age 45-55, with a further 20.8% of the age group 56-65 saving for old age. The figure is the 9
et al. (2014) is presented to rationalize the experimental data. Households face an abundance of potential features that might affect home production but cannot possibly attend to everything because paying attention is effortful. We argue that, in our experiment, households did not pay attention to the generic information because they anticipated that, while they would more quickly identify the lamp type that yields best results, the costs of doing so would still be large. Only households that invested in the technology anyway focused on this information because for them the shadow costs of doing so was considerably lower (i.e., they could build on prior market knowledge). Information on the optimal product-types, on the contrary, was effective because it made iterative learning for the most part obsolete.

26
||(1)<br>Average<br>change<br>in sales<br>(%)|(2)<br>Average<br>change<br>in sales<br>(%)|(3)<br>Average<br>change<br>in sales<br>(%)|(4)<br>Average<br>change<br>in sales<br>(%)|(5)<br>Average<br>change<br>in sales<br>(%)|
|---|---|---|---|---|---|
|Increased use of digital platforms|7.56***<br>(3.03)|||||
|New investment in digital solutions||17.63***<br>(5.85)||||
|More work from home|||4.05<br>(8.84)|||
|More online sales||||4.39||
|||||(7.68)||
|Increased use of digital tech|||||7.62***<br>(2.82)|
|Country-sector fxed effects|YES|YES|YES|YES|YES|
|Basic characteristics|YES|YES|YES|YES|YES|
|Management|YES|YES|YES|YES|YES|
|Observations|837|882|643|603|889|

Table B5: Effects of digitalization on sales, propensity score matched sample. _∗∗∗p <_ 0 _._ 01 _, ∗∗p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses.

34
- Lehner, B. and P. Döll. 2004. ―Development and validation of a global database of lakes, reservoirs and wetlands,‖ _Journal of Hydrology_ , 296(1-4): 1-22.

- Lehner, B., Verdin, K. and Jarvis, A. 2006. Hydrological data and maps based on Shuttle elevation derivatives at multiple scales (HydroSHEDS) - Technical Documentation. Washington, D.C.: World Wildlife Fund US. Available at http://hydrosheds.cr.usgs.gov.

- McCaffrey, Stephen. 2003. ―The Need for Flexibility in Freshwater Treaty Regimes,‖ _Natural Resources Forum_ , 27: 156-162.

Meridian World Data. 2006. Meridian Global Dam Database. <u>http://www.meridianworlddata.com/</u>

- Miller, K. and D. Yates. 2006. Climate Change and Water Resources: A Primer for Municipal Water Providers. Denver, CO: AwwaRF and UCAR. 83 pp

- Milly, P., Dunne, K., and Vecchia, V. 2005. ―Global pattern of trends in streamflow and water availability in a changing climate,‖ _Nature_ , 438(17 November): 347-350.

- Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R., Kundzewicz, Z., Lettenmaier, D., et al. 2008. ―Stationarity Is Dead: Whither Water Management,‖ _Science_ , 319, 1 February, 573-574.

- Mitchell, Timothy D., and Philip D. Jones. 2005. ―An improved method of constructing a database of monthly climate observations and associated high-resolution grids,‖ _International Journal of Climatology_ , 25(6): 693-712.

- NASA. 2006. Flooding on the Zambezi River: Natural Hazards. NASA. Retrieved from <u>http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=16440, accessed 2009.</u>

- Nel
2003. The primary sector in fact registered a 33 percent increase in investment while FDI in services industries declined only slightly by 14 percent.

**Figure 3: Greenfield FDI in Asia ($ billion)**

<!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br> 2003 2005 2007 2009 2011 2013 2015 2017 2019<br>Manufacturing Primary Services<br>products<br><!-- End of picture text -->

**Source:** ADB calculations using Financial Times fDi Markets Database.

While the nominal value of greenfield investments fluctuates dramatically from year to year (y-oy), the number of projects remains relatively stable (figure 4). In 2019, the number of announced projects fell only by 9 percent y-o-y almost entirely due to fewer intra-Asian projects and likely driven by the uncertainty surrounding the escalation of the US- China trade conflict. The 29 percent reduction in terms of nominal value was driven by smaller project size.

**Figure 4: Number of Greenfield projects (thousands)**

<!-- Start of picture text --> 6<br>Rest of world to Asia Intra-Asia<br>5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Note: Asia includes Australia and New Zealand
# **Figure 5: Droughts, cereal imports, and inflation**

<!-- Start of picture text --> 1200 50<br>1000 40<br>30<br>800<br>20<br>600<br>10<br>400<br>0<br>200 -10<br>0 -20<br>Droughts Imports of cereals and cereal preparations<br>Inflation, consumer prices (annual %) (WDI)<br>Inflation; droughts<br>Cereal imports (million, $)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> _Source_ : NBE database; IMF, Direction of Trade Statistics, International Financial Statistics.

This approach to modeling inflation accounts for these features of the agriculture sector in three ways. First, in addition to headline inflation, this analysis models and estimates the determinants of cereals, food, and non-food inflation. Within the food basket, cereals are singled out given their important share in production, food imports, and food consumption. Second, this study considers the cereal production gap instead of overall GDP gap as a determinant of inflationary pressures arising from the supply side. Third, this analysis includes dummies capturing sharp spikes or drops in inflation that are typically associated with shocks to food production.

# **_Demand-side factors_**

On the demand side, the two prominent approaches to inflation modeling are the monetary theory and the fiscal theory of inflation (Gordon and Leeper 2006; Sargent and Wallace 1981). The two theories are interrelated but are often treated separately in empirical studies of inflation. Under the monetary theory of inflation, movements in the price level are determined by movements in the money supply, given the demand for real balances. Under the fiscal theory, movements in the price level are driven by changes in
accounts under the constraint that we do not have data on wages and rental rates.

In Burns et al. (2021), aggregate real value-added (GDP at basic prices), _Y_<sup>_FCST_</sup> , is already determined. Given that the demand side of the economy remains the same as in Burns et al. (2021), total value-added is simply written as the market price GDP, _Y_<sup>_MKTP_</sup> , minus net real indirect taxes, ( _NITt_ ): The GDP deflator at basic prices, _Pt_<sup>_FCST_</sup> , remains a function of marginal costs, _MCt_ , and the output gap, _Y_<sup>�</sup> _t_ : This allows us to move from aggregate value-added to sectoral value-added. We define sectoral shares to total value added as: 2When factor market data exist at the sector level, one can proceed to build out sector specific production functions and analytical formulas for marginal costs.

3