rafmacalaba/data-use-mentions-extended huggingface dataset:
coverage of each survey. The VHRS 2015 has a sample that is representative of the population in five provinces—Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong, and Dak Nong— while the HLSS 2012 is supposed to be nationally representative.

bThe figures for urban and total are higher than the figures published in JMP for the latest year. The data for Bangladesh used in this analysis differ from the data used in JMP. We use the Household Income and Expenditure Survey 2016, and the figures presented here are consistent with the official figures published in the Preliminary Report on Household Income and Expenditure Survey 2016, page xiii (BBS 2016).

Table 2 summarizes the number of providers (as listed in the IBNET dataset) whose service areas are also covered in the household surveys, the average cost-reflective tariffs, and imputed unit prices (conditional on the reporting of positive expenditure on water). (More information about IBNET data for each country can be found in the appendix.) Most countries in the sample try to target subsidies using increasing block tariff structures, though two African countries, Nigeria and Uganda, have fixed rates. In Bangladesh, some service providers charge a flat consumption rate while others use increasing block tariffs. El Salvador is the only country that has a volumedifferentiated tariff structure. Some countries or providers also place a value-added tax on tariffs. For the analysis carried out in the following section, we use all the households surveyed in each country, and information from the utilities serving them. As explained earlier in this section, when a household is located in an area or region with no available tariff structure, we use information from other households in that country to impute quantities and unit prices paid.

Remarkably, all countries show higher cost-reflective tariffs than the average unit price paid by all households that report paying a positive amount for piped water. This already shows that all countries operate in a way that on average subsidizes residential consumers. However, six of the ten countries show a unit price
Figure 2 presents in greater detail comparative results regarding intangible spending for two countries, Brazil and the US (with the US data here based on the original CHS 2005 figures). Not surprisingly, as a share of GDP, US firms spend considerably more than Brazilian ones in nearly all types of intangible assets. Overall, at about 13 percent of GDP, US intangible expenditures are nearly thrice those in Brazil. Moreover, since the US estimates correspond to an earlier period (1998-2000), the current gap between the US and Brazil is likely to be even larger.

**Figure 2: Annual average spending by category of intangibles (% of GDP)**

<!-- Start of picture text --> 3.13%<br>2.53%<br>2.17%<br>1.65% 1.60%<br>1.29% 1.25%<br>0.90% 0.81% 0.86%<br>0.62%<br>0.33% 0.31%<br>0.11%<br><!-- End of picture text --> Looking at major categories of intangibles, the US lead over Brazil is largest in two areas: scientific R&D, and economic competencies. The CHS methodology suggests that as a share of GDP, the US spends about 10 times as much as Brazil on organizational capital, three times as much on brand equity, and about 4 times as much on scientific R&D. The cost of employee time spent in training in Brazilian manufacturing is 0.1 percent of GDP; even if one quadruples this to account for missing data for services (in proportion to the ratio of value added in manufacturing and services), it would be less than half that spent in the US (1 percent).

As a result, the composition of intangible expenditures differs markedly across Brazil and more advanced economies like
5

# ∆ V = consumption terms – production terms ≡ ∆ C – ∆ P =

Oil consumption Energy consumption ∆ [Oil price in current US$ × × × Energy consumption GDP in US$ at 2005 PPP values GDP in US$ at 2005 PPP values GDP in constant local currency GDP in current local currency × × ] – GDP in constant local currency GDP in current local currency GDP in current US$ ∆ [Oil price in current US$ × oil production ÷ GDP in current US$] ≡ (oil price effect through consumption + oil share in energy effect + energy intensity effect + real GDP converter effect + GDP price deflator effect + exchange rate effect) – (oil price effect through output + oil production effect + inverse of current GDP in US$ effect), where PPP is purchasing power parity and ∆ V is expressed in percentage points. Of the nine terms, the real GDP converter effect is always zero, because the ratio of GDP in constant dollars at PPP to GDP in constant local currency remains the same. For countries that do not produce crude oil, the production effects are zero, whereas consumption effects are non-zero for all countries.

With the possible exception of very large oil exporters or importers, governments have virtually no control over the world price of oil, but can influence the share of oil in energy use and the energy intensity of GDP through policy. The sum of these two effects is the oil intensity of the economy, and declining oil intensity can offset the price effect through consumption. This paper defines an offsetting coefficient as <u>(Oil share in energy effect+energy intensity effect)</u> Offset = – (Oil price effect through consumption) This paper selects calendar 1999, 2008, and 2009 for decomposition analysis. 2009 is the last year for which all the data necessary for decomposition are available. For oil prices, this study uses the annual average of the prices of three marker crudes: Brent, West Texas Intermediate, and Dubai
variance matrix of the data moments.<sup>8</sup> We compute this variance matrix by bootstrapping with 1000 repetitions.

We estimate the model parameters using data from 2006 to 2012 for the set of moments for which the model has predictions on, and that are observed in the data.<sup>9</sup> The first set of moments that we match consists of the rates of successful hijacks over time, determined in the model by equation (1). Even though the identification of the structural parameters is jointly determined by the entire set of targeted moments, the success rates in the model over time are a direct function of the parameters governing the probability of success given by equation (1).

The next set of moments used in the estimation corresponds to the bi-annual average number of pirate attacks and the annual average size of ships attacked, normalized to its 2006 mean. In the model, these moments are obtained from the nested problem that pirates solve in order to decide whether to enter and if so, the size of the vessel to target. The number of pirate attacks at every year is determined by the mass of pirates with ability above the cutoff _<u>a</u>_ <u>(</u> _δi, n_<sup>_t−_1</sup> _, ν_<sup>_t_</sup> _, κ_<sup>_t_</sup> ). Therefore, matching the number of attacks over time allows to discipline the parameters ruling the measure of the set of entrants and the entry cost function outlined in equation (6). As the Euler Equation shows, the decision on vessel size is influenced by the parameters that define the learning curve of pirates: even though it is costlier to attack larger ships, by doing so pirates can raise their ability in subsequent years. Hence, the normalized annual averages of size of ships attacked can help pin down the parameters governing the learning law of
stakes cognitive performance: Evidence from the national college entrance examination in china. _Journal of Environmental Economics and Management_ , _104_ , 102365. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0095069620300887 doi: https://doi.org/10.1016/j.jeem.2020.102365

- Graff Zivin, J., Hsiang, S. M., & Neidell, M. (2018). Temperature and Human Capital in the Short and Long Run. _Journal of the Association of Environmental and Resource Economists_ , _5_ (1), 77-105. Retrieved from https://ideas.repec.org/a/ucp/ jaerec/doi10.1086-694177.html doi: 10.1086/694177

- Heutel, G., Miller, N. H., & Molitor, D. (2021, October). Adaptation and the Mortality Effects of Temperature across U.S. Climate Regions. _The Review of Economics and Statistics_ , _103_ (4), 740-753. Retrieved from https://ideas.repec.org/a/tpr/ restat/v103y2021i4p740-753.html doi: 10.1162/rest ~~a 0~~ 0936

- Jacob, B. A. (2005). Accountability, incentives and behavior: the impact of high-stakes testing in the chicago public schools. _Journal of Public Economics_ , _89_ (5), 761-796. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0047272704001549 doi: https://doi.org/10.1016/j.jpubeco.2004.08.004

- MEF. (2015). _ETHIOPIA’s Second National Communication to the United Nations Framework Convention on Climate Change (UNFCCC)_ (Report). Ministry of Environment and Forest (MEF). Retrieved from http://unfccc.int/resource/docs/natc/ ethnc2.pdf

- Niang, I., Ruppel, O.
This is a general challenge for the program, since different consulting firms are contracted to work on the different types of management improvement.

The **Labor Productivity consulting** appears to have focused largely on helping firms retain talent and improving worker morale. It was recommended that firms do an employee climate survey, to get a sense of employee views, attitudes towards the organization, and issues faced. A common issue identified was that production-level workers did not feel that involved in decision-making, and so in several firms a recommendation was made to start each day with a short line-level meeting where workers could talk about production indicators and any problems in operations. Other firms introduced worker suggestion boxes or other ways for workers to give feedback. Firms were also advised to introduce non-monetary recognition programs, and consider other morale-boosting activities like social events.

Some of the firms we spoke to noted that the consultants lacked sector-specific knowledge about specific labor force issues in their industries, and that a challenge was that some of the ways to motivate labor productivity depended on first having sorted out operational productivity indicators first. The sequencing and coordination with the operational 48
economic parameters. However a major drawback of those micro approaches is that they can only focus on a limited number of migration corridors at time (e.g. the Mexican-US corridor) and can hardly be generalized to other country pairs. An exception is provided by Beine et al (2010) who use bilateral macrodata on migration flows and stocks. They took advantage of a recent data set on international migration by educational attainment to investigate the role of diaspora size on the educational structure of migration from 195 countries to the 30 OECD countries. Their paper shows that networks are by far the most important determinant of migration flows, explaining 71 percent of the observed variability of the size of migration flows, and 47% of the variability of the selection ratio in 2000.

In this paper, we extend the study of Beine et al. by looking at the diaspora effect on the geographical concentration or dispersion of new migrants, and by comparing results obtained with different estimation techniques. Indeed, a macro analysis of diaspora externalities raises multiple econometric problems. The main issues are the large number of empty corridors (due to truncation rules or true ’zeroes’), and the difficulty to identify causation (unobserved variables 2
is worse than predicting growth in the mean, and that trying to predict inequality as well does not help.

# **6 Conclusion**

In this paper, we have analyzed how best to nowcast poverty around the world. We applied statistical learning techniques to the World Bank’s collection of international poverty estimates utilizing more than 1,000 development indicators as features. We looked at whether predicting poverty rates directly had a higher accuracy than predicting poverty indirectly, by, for example, predicting changes in poverty from the last survey, by predicting growth in mean welfare and applying this growth to scale up the past distribution, or by predicting growth in the mean and in the Gini and applying growth incidence curves to scale and stretch the past distributions.

Our findings revealed that a model which simply uses a fraction of growth in real GDP per capita since the last observed household survey to shift the entire distribution performs better than any model that predicts poverty rates directly using all the variables mentioned above, and nearly as well as all models trying to predict growth in the mean and in the Gini using all the variables mentioned above. This suggests that conditional on knowing growth in real GDP per capita, no other variable can substantially increase the predictive accuracy.

On the one hand, given the decades long literature documenting the importance of growth for poverty reduction (Kraay, 2006; Ferreira and Ravallion, 2009), this is not surprising. Partially as a result of this literature, variants of the model we prefer have been used both in the literature and for the World Bank’s official global poverty monitoring. On the other hand, several papers have documented important gaps between national accounts data and household survey data, suggesting that GDP might not be that connected to welfare as measured in household surveys (Ravallion, 2003; Deaton, 2005; Ferreira _et al._ , 2010; Pinkovskiy and Sala-i Martin, 2016; Prydz _et al._ , 2021; Deaton and Schreyer, forthcoming). What can explain these seemingly
idiosyncratic shocks exogenously. In the annex, we present the correlation between self-reported shocks and household characteristics.<sup>10</sup> Households planting crops are more likely to report droughts or floods, and they are less likely to report food price shocks. These correlations can be explained by a real difference in exposure and/or by the fact that households report more on what matters most to them. We partially address this caveat of endogeneity by including a set of household and community observables that could correlate with differences in reporting.

> 8 This restricts the geographic fixed effects of the regression analysis at the district level, instead of the TA level.

> 9 See table A1 in the annex. Results are available upon request.

> 10 See Table 2. Results are available upon request.

12
and replace missing observations by zeros. We then keep only the pairs (for the whole period) that ever invested at some point during the period.<sup>14</sup> Finally, we do not make any assumption regarding the motives of investments. The data and its relatively broad industry/activity classification do not us allow to distinguish between vertical and horizontal FDI. Even though the theoretical literature has long distinguished market seeking (i.e. horizontal) FDI and efficiency seeking (i.e.vertical) FDI (Markusen 1984, Helpman 1984), recent works have shed light on the complex mixed-strategy of multinationals. In this paper we study the impact of deep integration on the intention to invest in Greenfield projects in general.

## c. Control variables

We control for existence of expired trade agreements by including a corresponding dummy variable. Past agreements are taken from Mario Larch's Regional Trade Agreements Database from Egger and Larch (2008). We also control for bilateral investment treaties to isolate the effect of investment disciplines included in PTAs only. Finally, we use fixed effects to minimize the scope for endogeneity. We initially include country pair, source-time and destination-time fixed effects. When we move to the business activity level, we control additionally for pair-industry, sourceindustry-time and destination-industry-time fixed effects.

# 4. Effect of PTAs on FDI

## a. Empirical strategy

The gravity model is the workhorse estimation technique for applied international trade analysis. This model has both empirical and theoretical advantages; it successfully predicts trade flows and can be derived from a large class of structural general equilibrium trade models. Theoretical studies of multinationals and trade

> 14 This way, at the aggregate activity level our estimation sample is composed of 35 percent (38,300) non-zero against 65 percent (71,033) zero observations for FDI announcements.

13
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mark> w <mark>and R</mark> d <mark>such that route segments which were</mark> w d w d <mark>originally element in R (or R ) can be reassigned to either</mark> ww dd <mark>R or R since this assignment shouldn’t matter under the null hypothesis. For each bootstrap iteration</mark> ww dd <mark>b the assignment is reshuffled and</mark> ww dd <mark>∆v</mark> ) <mark>is calculated. Out of B bootstrap iterations one can then obtain an estimated p-value of the outlined test as</mark> (bb ∑ =1 <u>�</u> < ∆ ( <u>�</u> + 1 b = BB + 1 bb) bb II ∆vv vv bbww _<mark>Estimation of heterogeneous effects for flood prone areas</mark>_ pp BB <mark>After speed differences were estimated for each route r ∈ R</mark> w <mark>, it was analyzed whether routes that are likely to be flooded during the rainy season show larger speed reductions than routes that are unlikely to be</mark> ww <mark>flooded. Similarly, to a diff-in-diff-in-diff (DDD) method, ∆v whether the flood depth in a 10-year flood would be positive:</mark> dd<sup><mark>was regressed on a dummy, indicating</mark></sup> <mark>∆v = β</mark> 0 <mark>+ β</mark> 1<sup><mark>I</mark></sup> <mark>FD > 0) + ε Here, FD</mark> dd <mark>r in a 10-year flood as indicated through the Fathom flood</mark> dd dd<sup><mark>(</mark>
br>ttle event in a given|

<mark>Each observation corresponds to a 50km x 50km grid-cell observed in a given year between 1998 and 2017. The outcome variable is a dummy variable indicating that the grid-cell experienced a battle event in a given</mark> year, as reported in the ACLED dataset. Irrigated area is measured as the percentage share of an average plot of land that is equipped for irrigation, as documented by Siebert et al (2015). Controls include log of population at the start of the period as well as dummies to indicate the presence of wetlands, croplands, pasture lands, rivers and the homelands of ethnic groups that historically relied on agricultural livelihood strategies. Standard errors in parentheses clustered at the grid-cell level. Climate controls include mean temperature and log of total rainfall in the wet and dry seasons. * p<0.1, ** p<0.05, *** p<0.01" 28
powers following the 1999 decentralization reform in Indonesia. Those include also the minimum wage setting. We use the pre-decentralization reform division in 292 regencies to ensure the consistency of the analysis over time.

The identification strategy exploits the variation in occupational composition across regency-industry pairs.<sup>31</sup> Specifically, we compute regency _r_ share of employment in industry _i_ for occupations at high risk of automation: For the baseline estimates of this section, we identify the occupations at high risk of automation following GM.<sup>32</sup> However, since our baseline plant level estimates are based on information on educational attainments, we experiment as well with shares of workers with secondary education, which deliver similar results.

To compute regency-level exposure to robots, _ETRr,t_ , we use the shares in (6) to aggregate industry exposure in each regency: One concern with using _ETRr,t_ as a regressor is its likely endogeneity with respect to regency-level outcomes. For instance, a positive demand shock in industry _i_ would result in higher employment in regencies where those industries concentrate, i.e. a large share computed as in (6). If investment in robots responds to the shock, for instance to keep up with higher demand, the estimated impact of _ETRr,t_ will be upwardly biased. To address this concern, we use the instrumental variable presented in Section 4.1 combined with the regency-level demand shifter.

We estimate the following system of equations in first difference:

> 31Unlike for the plant level analysis, in the labor force survey data we observe occupations.

> 32We chose to follow GM because their list is derived by information on what robots actually do. However, we obtain similar results if we use the list compiled by FO.

30
<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>——ACLED fatalities<br>= ACLED events<br>=——= All conflict events to households (WB/NBS)<br><!-- End of picture text -->
Figure 11.5 Korea-Policy responses and incentives to borrow <!-- Start of picture text --> Monetary policy: components of reserve money Fiscal policy: fiscal impulse and demand<br>growth pressures<br>korea<br>percent Korea percent<br>30- _<br>20- A -<br>-10- / 0 -X l<br>-20- -2-<br>-30 -3<br>1ong 1990 1991 1992 1993 1994 1995 1996 1997 m9e9 1990 1991 1992 1993 1994 1995 1996 1997<br>_ ontribution ot domestic credit tn reserve money growth -r Excess demand<br>r contribution of net foreign assets to reserve money growth -F,scaI impulse<br>reserve money growth (percent of GDP)<br><!-- End of picture text --> Differentials between domestic deposit rates and Variability of unpredictable component of US LIBOR nominal exchange rate movements.

<!-- Start of picture text --> percent Korea<br>percent 10 -<br>12- Korea s-<br>10-~~~~~~~~~~~~~~0<br>10 3 vnbdty of -.<br>3 ~ ~ urp,ed,.t.bl corpor.r~~~~~4<br>2-<br>e- 0 0 - O_~~~~~~~~~~~~~~0<br>-2 4- B3 B- I-<br>1.98 1990 1992 1994 1996 1990 1099<br>-domestic deposit rates<br>difterential between domestic deposit rates and US LIBOR<br>(adjusted for ex post exchange rate movements)<br><!-- End of picture text --> The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate