rafmacalaba/data-use-mentions-extended huggingface dataset:
the VAX ratio from country 1 to country 2, but they simultaneously raise the VAX ratio from country 1 to country 3 as they contribute to the exports of value added, without any direct export transactions.

Timmer et al. (2015) calculate the aggregate VAX index for a range of countries using four different global input-output databases. While the exact values for each country varies a bit for different sources, the overall country rankings is quite similar. Unfortunately, they do not include the Eora data in their calculations, but we expect similar results also for this source of information as it is constructed using similar official data sources. The VAX ratios are highest for more isolated countries that specialize in exports of raw materials, e.g. Russia and Australia, while it is lowest for smaller countries that are strongly integrated in regional production networks, such as smaller EU countries, e.g. Luxembourg and Slovakia.

The VAX ratio can also be calculated for specific sectors. In that case, the sector-specific VAX ratio will also deviate from unity as its exports will embody domestic value added from other sectors if some inputs are sourced domestically. At the same time, a sector’s VAX ratio will increase to the extent that its output is embedded in exported products of other sectors.

Los, Timmer and de Vries (2016) proposed a very intuitive way to calculate the domestic value added content in exports using the extraction method. The idea is to set the final demand for one country’s output in the rest of the world equal to zero, but leave the entire production structure, both domestically and internationally, unchanged. Foreign sectors will still use inputs from this country in their production structure, but they will no longer have direct final demand for its output. We can solve this modified IO system for the counterfactual vector of GDP values in each country-sector. The difference between the actual GDP of a country and this counterfactual GDP equals the domestic value added contained in a country’s exports.

The domestic value
Surprisingly, less corrupt governments are

> 1<sup>7</sup> While our use of initial financial development and subsequent volatility somewhat addresses this issue, it is interesting to reverse this estimation and examine the effect of initial volatility and subsequent financial development. When we use the two non-overlapping periods of financial development and volatility in the panel data, however, we find no evidence for this reverse causality. '<sup>8</sup> For more detailed discussions, see La Porta et al (1998) and Beck, Levine and Loayza (2000a, 2000b).

19We were not able to obtain statistically significant results for the financial development variables in the GDP and investment estimations, and we do not report them here.

16
_For labor or for divorce?_ positive legislation reform on women’s labor outcomes (Uribe et al., 2019). For this reason, we view the overall legislative framework in conjunction with the introduction of unilateral divorce provisions. Figure B.1. in the Appendices, illustrates the relationship between legal gender discrimination, measured by the World Bank Women, Business, and the Law index (WBL), and female labor force participation rate around the (a) World and in (b) MENA region countries. While in panel (a) this relationship is upward slopping (higher WBL index score has a positive relationship with FLFP rate), this relationship resembles an inverted U curve for MENA countries (panel b). This finding is consistent with literature investigating the effect of increasing mandated maternity leave on the employment and wage penalty women pay (Pettit & Hook, 2005; Akgunduz and Plantenga, 2013; Budig, Misra, and Boeckmann, 2012). It shows that the unintended results of reducing legal gender discrimination are explained by cultural resistance, particularly from husbands who see their utility cost increase from their wives’ labor, but also by the wage costs employers incur (Espino and Salvador, 2014). This further reinforces the argument that passing legislation targeting several areas of gender-based discrimination increases the chances of changing cultural norms and inching towards desired outcomes. Thus, to include a more accurate and context specific dimension for legislative effects, a variable WBLaw_index is taken into account that takes the value of the calculated score of the World Bank’s Women, Business, and the Law Index for corresponding year observations in our samples.

The third and sixth columns of each table below report coefficient estimates from specifications that include these controls as well as the above-mentioned time trends. As for the case of Morocco, the results indicate positive and statistically significant results in the first period (1992 to 2007) but negative effects of the legislation in the second period (1992 to 2014). These results may be backed by
Middle East, Asia and Pacific, Latin America, and the Caribbean) and across 165 countries for the period 2000 – 2017.

In addition, the BOOST database does not capture domestic spending through extrabudgetary vehicles, including State-Owned Enterprises (SOEs) and Road Funds. The research drew upon financial statements of these entities from the World Bank’s Power and Transport State-Owned Enterprises (SOE) Database, comprising a panel of systematic standardized data drawn from company financial statements, which is consistent at the observation level and comparable across SOEs and years. The database covers 135 SOEs in the power and transport sectors (including road, rail, and air) across 19 countries. In the case of the road sector, it is known that an important subset of countries has implemented off-budget Road Funds, which capture earmarked revenues from transportation fuel levies and channel them towards road maintenance activities. Expenditure from Road Funds must be considered alongside recurrent budgetary expenditure in order to allow for cross-country comparisons on the adequacy of road maintenance allocations. It was established that 25 countries from the BOOST database have implemented Road Funds, and financial statements were collected for 20 of these, while the remainder of countries with Road Funds were dropped from the more detailed road sector analysis.

Cross-country comparisons of road maintenance expenditure also entail some normalization against the extent of the road network. For this purpose, the International Road Federation database was used, which provides information on road network characteristics across 205 countries. In particular, the total length of the primary and secondary network is used for normalization purposes since tertiary roads are typically a municipal responsibility.<sup>5</sup> The reported share of paved and unpaved roads was also used to adjust maintenance expenditure benchmarks for comparison purposes. The World Bank’s ROCKS database provides such regionally differentiated unit cost data for road maintenance activity.

Finally, to get a complete picture of infrastructure expenditure sources, the research also draws upon the World Bank’s Private Participation of Infrastructure (PPI) database that covers over 6,400
**Table A1: Historical IFC Financial Statements and Ratios.** Data series assembled from IFC Annual Reports. Total assets prior to 1974 do not include the undrawn portion of a loan from the International Bank for Reconstruction and Development. Values of assets prior to 2001 are not directly comparable with those after due to a change in accounting standards requiring derivatives to be held at fair rather than book value (FAS No. 133). Return on equity is net income/total capital. Return on assets is net income/total assets. The leverage ratio is total assets/total capital. The expense ratio is non-interest expense/total assets.

||Balan|ce Sheet|Income Stateme|nt||Financial Ratio|s||
|---|---|---|---|---|---|---|---|---|
|Fiscal Year|Total<br>Capital|Total<br>As-<br>sets|Net<br>In-<br>come|Non-<br>Interest<br>Expense|Return on Equity|Return on Asset|s<br>Leverage|Expense|
||$ Mn|$ Mn|$ Mn|$ Mn|||||
|1957|93.5|93.6|1.7|0.7|1.79%|1.79%|1.001|0.78%|
|1958|97.3|97.4|2.4|1.1|2.42%|2.42%|1.001|1.18%|
|1959|99.4|99.5|1.7|1.4|1.75%|1.75%|1.001|1.42%|
|1960|104.3|104.5|2.1
Much of this work developed as an alternative to econometric work that used latent class techniques to infer capabilities in the absence of direct measures (see for instance Martinetti (1994) and Krishnakumar and Ballon (2008)). However, some direct capability indicators are routinely collected in household surveys and, in this paper, we draw on data that ultimately derive from and extend such questions. More precisely, the 15 capability indicators used in this study are versions of a general quality of life measure developed in a sequence of papers by Anand et al (2009, 2011), Lorgelly et al (2015) and Simon et al (2013) for use with general populations and in health assessments. The Simon et al (2013) measure is in effect a short form version of the OCAP instrument developed by Anand et al (2009), which implemented the normative list proposed by Nussbaum (2000) justified on the basis of an Aristotelian approach to human flourishing.<sup>11,12</sup> The data used in this paper draw on questions in an Indian adaptation of the Simon et al (2013) version as well as pilot work in Uttar Pradesh and can be seen as a short form, capability based measure of life quality informed by theory and consultation.<sup>13</sup> In this program, most capability indicators could be positively impacted with three exceptions. First, additional involvement in agricultural activities and leaving the house more often than before could cause program members to report that health constraints

> 11 The list is useful because while it shares common features with several other such lists, it is relatively comprehensive and therefore provides a useful starting point for mapping out life quality of life issues without making any claims about what a state should do about them.

> 12 OCAP comprises some 50 items and so to produce a short form version, Lorgelly et al (2015) developed a consultation process based on focus group interviews. The refinement developed subsequently by Simon et al (2013)
Deschênes, O., & Moretti, E. (2009). Extreme weather events, mortality, and migration. _Review of Economics and Statistics_ , _91_ (4), 659-681.

- Deschênes, O., & Greenstone, M. (2011). Climate change, mortality, and adaptation: Evidence from annual fluctuations in weather in the US. _American Economic Journal: Applied Economics_ , _3_ (4), 152–85.

- Diffenbaugh, N. S., & Burke, M. (2019). Global warming has increased global economic inequality. _Proceedings of the National Academy of Sciences_ , _116_ (20), 9808–9813.

- Dollar, D., & Kraay, A. (2002). Growth is Good for the Poor. _Journal of Economic Growth_ , _7_ (3), 195-225.

- Friedlingstein, P., O'sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., ... & Zheng, B. (2022). Global carbon budget 2022. _Earth System Science Data_ , _14_ (11), 48114900.

- Gazzotti, P., Emmerling, J., Marangoni, G., Castelletti, A., Wijst, K. I. V. D., Hof, A., & Tavoni, M. (2021). Persistent inequality in economically optimal climate policies. _Nature Communications_ , _12_ (1), 1-10.

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23838, National Bureau of Economic Research.

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̈Ozler, B., G. Dutt, and M. Ravallion. 1996. “A Database on Growth and Poverty in India.” _The World Bank_ .

- Ravallion, M., G. Datt, and S. Chaudhuri. 1993. “Does Maharashtra’s Employment Guarantee Scheme Guarantee Employment? Effects of the 1988 Wage Increase.” _Economic Development and Cultural Change_ 41 (2): 251–275.

- Shah, M., and B. M. Steinberg. 2017. “Drought of Opportunities: Contemporaneous and Long-Term Impacts of Rainfall Shocks on Human Capital.” _Journal of Political Economy_ 125 (2): 527–561.

- Shah, M., and B. M. Steinberg. 2021. “Workfare and Human Capital Investment: Evidence from India.” _Journal of Human Resources_ 56 (2): 380–405.

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Council (2010), and “NDRC reveals details of stimulus package”, China Daily, November 27, 2008.

By mid-2009, the macroeconomic stimulus and other interventions had succeeded in boosting demand for migrant labor. By early 2010, scarcity reemerged in China’s labor market, and wages started to climb once again (Giles and others 2012). In 2010, China’s exports recovered to precrisis levels, and real GDP grew 10.5 percent, which slowed down to 9.2 percent in 2011 and is now projected to slow down further to around 8 percent 2012 due to the slowdown in the global economy.

The success of China’s fiscal stimulus spurred domestic demand and supported the global economic recovery. Strong economic growth in the world’s second-largest economy stimulated

> 1 The latest IMF data shows  a sharp increase in general government gross debt as percent of GDP to 33.5 percent in 2010 before starting to decline to about 26 percent in 2011, though remaining well above its average level of about 17.5 percent in 2008-9.

6
Hoogeveen, J. and M. Rossi. 2011. “Saving Goat and Cabbages? Enrollment and grade achievement after the introduction of free primary education in Tanzania.” mimeo Hussmanns, R., F. Mehran, and V. Verma. 1990. _Surveys of Economically Active Population, Employment, Unemployment and Underemployment: An ILO Manual on Concepts and Methods_ . ILO: Geneva.

Hyslop, D.R., and G.W. Imbens. 2001. “Bias from Classical and Other Forms of Measurement Error.” _Journal of Business & Economic Statistics_ 19(4): 475‐481.

Jensen, R. 2010. “The (Perceived) Returns to Education and the Demand for Schooling.” _Quarterly Journal of Economics_ , May 2010.

Judge, G., and L. Schechter. 2009. “Detecting Problems in Survey Data Using Benford’s Law.” _Journal of Human Resources_ 44(1): 1‐24.

Kalton, G., and H. Schuman. 1982. “The Effect of the Question on Survey Responses: A Review.” _Journal of the Royal Statistical Society_ 145(1): 42‐57.

Kasprzyk, D. 2005. “Measurement error in household surveys: sources and measurement.” In _Household Sample Surveys in Developing and Transition Countries_ , New York : United Nations, pp. 171‐198.

Kerr, A. 2011. Estimating the Returns to Education in Tanzania, Chapter 2, PhD Dissertation, Oxford University, Economics Department Koop, G., and J.L. Tobias. 2004. “Learning about heterogeneity in returns to schooling.” _Journal of Applied Econometrics_ 19: 827‐849 Kuepie M., C.J. Nordman, F. Roubaud, 2009, Education and earnings in urban West Africa, Journal of Comparative Economics 37, 491‐515 Martin, E., and A.E. Polivka. 1995. “Diagnostics for Redesigning Survey Questionnaires: Measuring Work in the Current Population Survey.
# _4.5. Impacts on Livestock, Agriculture, Demographics, and Welfare_

In Table 5, we analyze the impact of the cash transfers on household livestock and agricultural outcomes, demographics, and various measures of household welfare. We measure livestock ownership in column 1 using tropical livestock units (TLU)<sup>21</sup> and in column 2 we investigate more specifically the ownership of chickens. In round 3, we find significant positive impacts of cash transfers to fathers on overall livestock holdings (Panel A, column 1) and specifically chickens (Panel A, column 2). The effects are significantly larger for cash given to fathers than mothers. Both conditional and unconditional cash transfers increase livestock holdings in round 3 (Panel B, column 1), and there is no statistical difference between the types of transfers. There is no impact of any transfer types on livestock in round 2.

Columns 3 and 4 focus on measures of household agricultural production. Column 3 analyzes the transfers’ impact on the annual production of peanuts, while column 2 focuses on production of sorghum and millet. Peanuts are the main cash crop in the region, while sorghum and millet are staple crops that are more likely to be used for own consumption. Giving transfers to mothers reduces peanut production significantly in round 3 (results in round 2 are not statistically significant), while transfers given to fathers works in the opposite direction (the coefficient for CTF is statistically significant in both rounds). In round 3, UCT also leads to lower peanut production. For sorghum and millet production, unconditional cash transfers lead to higher production than conditional transfers in both rounds, and the impact is significantly greater than zero in round 2.

One emerging tendency from the Table 5 results is that when the transfers are given to fathers this leads to more investment in livestock and the cash crop peanuts. This might be one of the mechanisms explaining how giving cash to fathers rather than mothers seems to be more effective in improving children’s nutritional
,97||(Bertazzi,<br>Fumagalli, &<br>Lo Schiavo,<br>2005)|
|Austria,<br>2013|Contingent<br>valuation|EUR 2.5 per<br>kWh|3,25|8,33||(Reichl,<br>Schmidthaler,<br>& Schneider,<br>2013)|

_Source: authors based on World Bank data and estimates of the WTP found in the literature._ _Conversions are computed in three steps: (1) Computation of the WTP per kWh (hour unserved) in the national currency. (2) Conversion in USD in the year of the study. (3) Conversion into 2018 USD. Hourly electricity consumption data are retrieved from the World Bank Microdata portal. https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC_

## **Table 5 - List of studies focused on the health impact of water provision interruption and dysfunction***

29
is JVVNL. The latter serves approximately 40 percent<sup>23</sup> of the total state-level demand. We rescale the state-wide dispatch figures by 40% to estimate JVVNL’s share of dispatch at 15-minute intervals. Finally, this data set contains dispatch information from state owned generator units only. State-owned generators serve 57.1 percent of JVVNL’s annual demand. The residual portion of the demand is met through a portfolio of centrally-owned generators, renewable sources and captive power plants. Real-time dispatch information from these other generation sources are unavailable. As a result, the total dispatch in the SLDC data are scaled by a factor of 1.7513 (=100/57.1) to estimate energy dispatched across all generation units at 15-minute intervals. Based on these adjustments, the marginal transmission level losses are estimated to be 5.2% with a standard deviation of 1%. • **Distribution losses** The total energy entering the distribution network ( ) is calculated as the difference between energy dispatched by all generators (∑ ) and the marginal transmission losses at 15-minute DD ii intervals (T u ): = ∑ −T nnii=1u RR,iiii. Marginal losses in the distribution network can be QQ QQ calculated by first estimating the quadratic relationship between losses and input energy, and then TT DD nn ii TT ii ii ii=1 RR,ii ii calculating the marginal distribution losses as QQ QQ T u 1 2 . DD DD A cross-sectional data set on feeder level losses is used to estimate the quadratic relationship ii ii = ββ + 2 ββ ∗QQ between energy entering the distribution network and network level losses. This data was collected under an audit conducted by the World Bank’s Energy Practice as part of a lending program in Rajasthan. The data set contains information on total losses for the period April to December 2017

> 23 Source:

> http://www.pfcindia.com/DocumentRepository/ckfinder/files/Operations/Performance_Reports_of_State_Power_Utilities/1_Repo rt
domestic market, and perhaps only then if the domestic market is protected from imports by trade costs or tariffs. The success of industrial policy in relatively less productive sectors depends on dynamic effects (say, significant improvements in efficiency or quality, or reductions in input costs). For this reason, industrial policy in the sectors with the highest levels of comparative advantage can be considered low risk, whereas interventions in sectors with lower levels of comparative advantage can be considered high risk.

The measurement of comparative advantage from export data, which is widely practiced and rests on the assumptions of the Ricardian trade model, warrants some discussion. The most widely used measure of comparative advantage is the share of national exports in a sector divided by the share of world exports in that sector. This measure is called Balassa-revealed comparative advantage, after Balassa (1965). In 2019 in Costa Rica, for example, 16 percent of exports were business services, while for the global economy, only 2 percent of exports were business services. In this example, the statistic is greater than one (16/2 = 8), indicating that Costa Rica is more specialized in exports of business services compared to world exports. One infers from this that Costa Rica is relatively more productive in business services, compared to other sectors in which the country does not specialize.

Despite the widespread use of Balassa-revealed comparative advantage as a proxy for relative productivity, the measure has received significant critical scrutiny in the international trade literature; indeed, French (2017, p. 83) argues that Balassa-revealed comparative advantage is “not generally useful.” What are the issues, and what do they imply for how and whether the measure is used to target policy?

Balassa-revealed comparative advantage is frequently used to make statements like “this country is more productive in product _k_ ” in an unqualified sense, perhaps implying “relative to the world” and “relative to other products.” In a two-country world, where there is a home country and all other countries are grouped
and “outputs.” We make this separation because some indicators might naturally be expected to be more responsive in the short run than other indicators. As used here, an “input” is an indicator whose value could be changed overnight with

> 5 The period consisting of the three years of the program varies by country: 2005-2008 for Sauri, and 2006-09 for Bonsaaso and Pampaida. The DHS data is from 2003 and 2008/09 for Kenya, 2003 and 2008 for Ghana, and 2003 and 2008 for Nigeria. 6 Annex 2 gives the definitions of all indicators used. The Demographic and Health Surveys (DHS) are nationally-representative household surveys containing individual-level data on indicators of population, health, and nutrition, carried out by the Measure DHS Project in cooperation with local governments and non-governmental organizations in countries all over the world since 1984. Though they are often used to study maternal and child health, they are representative of all households—not just those with children. Comparable and standardized survey data are collected roughly every five years in many countries and made publicly available at http://www.measuredhs.com. The project is principally funded by the United States Agency for International Development. In July of 2010 the most recent publicly-available standard DHS microdata for Uganda covered the years 2000-1 and 2006, which do not overlap with the MV initial evaluation period of 2006-2009. The most recent data for Malawi covered the years 2000 and 2004, which also do not overlap with the MVP initial evaluation years. DHS surveys from Rwanda (2005 and 2007-08) overlap with the intervention period for the MV site in that country (2006-2009), but indicators for the Rwanda MV site were not published in MVP (2010c).

11