rafmacalaba/data-use-mentions-extended huggingface dataset:
Cassar, A., Healy, A. and von Kessler, C. (2017) ‘Trust, Risk, and Time Preferences After a Natural Disaster: Experimental Evidence from Thailand’, _World Development_ , 94, pp. 90–105. doi:10.1016/j.worlddev.2016.12.042.

Challinor, A.J. _et al._ (2014) ‘A meta-analysis of crop yield under climate change and adaptation’, _Nature Climate Change_ , 4(4), pp. 287–291. doi:10.1038/nclimate2153.

Chaudhary, N. (2021) _Weather- and disease-related shocks in agriculture using data from the Rural Livelihoods Information System (RuLIS)_ . RuLIS Brief. Rome: Food and Agriculture Organization. Available at: https://www.fao.org/3/cb5593en/cb5593en.pdf (Accessed: 17 December 2021).

Cole, S. _et al._ (2013) ‘Barriers to Household Risk Management: Evidence from India’, _American Economic Journal: Applied Economics_ , 5(1), pp. 104–35. doi:10.1257/app.5.1.104.

Conforti, P., Markova, M. and Tochkov, D. (2020) _FAO’s methodology for damage and loss assessment in agriculture_ . FAO Statistics Working Paper 19–17. Rome: Food and Agriculture Organization. doi:10.4060/ca6990en.

Delavande, A., Giné, X. and McKenzie, D. (2011) ‘Eliciting probabilistic expectations with visual aids in developing countries: how sensitive are answers to variations in elicitation design?’, _Journal of Applied Econometrics_ , 26(3), pp. 479–497. doi:10.1002/jae.1233.

Drechsler, M. and Soer, W. (2016) _Early Warning, Early Action: The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net
395|251|0.9|1.1|24|18|81|70|
|7831|yp<br>passenger motor<br>Oh i f|206|389|189|1.2|1.1|18|19|21|42|
|6783|ter tubes and ppes, o<br>ironorsteel|158|351|223|0.9|1.0|23|20|63|56|
||All aboveproducts|8,097|19,626|242|48.2|55.5|||69|71|
|0 to 9|Allgoods|16,803|35,378|211|100|100|||55|55|

Source: Derived from the UN COMTRADE database as reported by the EU.

The composition of exports of top-twenty performers in terms of technology content and natural resource intensity also points to significant realignment in Turkish export offer. The share
demand in urban Indian households. _Energy Policy_ , Vol. 32, pp. 429-436.

- Frondel, Manuel, Jorg Peters, Colin Vance. 2008. Identifying the rebound: Evidence from a German household panel. _The Energy Journal_ , Vol. 29, pp. 145-163.

- Goldstein, Markus, Antonio Estache. 2009. The scope and limits of subsidies, in _Stuck in the Middle: Is Fiscal Policy Failing the Middle Class?_ eds. by Antonio Estache and Danny Leipziger, pp. 75-96. The Brookings Institution.

- Hausman, Jerry, David Wise. 1976. The evaluation of results from truncated samples: The New Jersey income maintenance experiment. _Annals of Economic and Social Measurement_ , Vol. 5, pp. 421-445.

- Hausman, J.A., M. Kinnucan, D. McFadden. 1979. A two-level electricity demand model: Evaluation of the Connecticut time-of-day pricing test. _Journal of Econometrics_ , Vol. 10, pp. 263-289.
# **4. Policy Implications and Priorities**

We believe the manufacturing sector can contribute to the reversal of Africa’s poor economic performance. In this section we outline what, in view of what we have learned from the firm surveys, we think must be done in order to maximize the likelihood of this happening. A necessary condition for such a strategy to be successful is that it is firmly based on the current reality. This is where we begin.

Industrial development in the world in recent years has been characterized by a rapid globalization of production, facilitated by liberalization of international economic relations and changes in the nature of corporate activity and international investment. Production systems have become increasingly integrated across national borders. Firms world-wide have been exposed to increased competitive pressure and in large parts of the world production and international trade have grown rapidly. In many ways, this is a new global reality for Africa’s manufacturing firms who not long ago were protected from international competitive pressure. On the domestic scene, the state of the business environment is still very much a legacy of those past times when being efficient and internationally competitive was not all that important. Poor infrastructure and high utility costs hold back competitive production and drive up costs, thus undermining the potential for output expansion. Excessive regulations result in entrepreneurs forming very small and informal firms rather than formal organizations that have export potential and contribute to tax revenues. The fact that key market institutions - such as laws, courts, business associations, lobbies, quality control, protection of property rights and enforcement of contracts – are underdeveloped means that personal relationships play a central role when firms make their output and input decisions, which hampers market integration.

In this reality, African policy makers face a choice between three broad alternatives. The first is to revert to essentially isolationist policies that protect the industrial sector from international competition and encourage supply to the domestic market. Such policies have been widely implemented in Africa in the past without any obvious success, and we can see no
interventions in LMICs have shown success, particularly when interventions are high quality, targeted to the most vulnerable children and integrated with other services (Engle et al., 2011).

2 Pre-primary (or preschool) programs generally refer to an organized learning group that meets at least two hours per week and can be categorized as formal (institutionalized, intentional and planned through public organizations) or informal (not institutionalized, less structured, less organized) (UNESCO, 2011).

3 Enrollment in preschool programs has increased substantially in LMICs over the past several decades (Behrman et al., 2013), but is still far from optimal. UNESCO’s Global Monitoring Report of pre-primary enrollment rates in 2012 showed coverage ranging from 19% (86%) for low-income (high-income) countries (UNESCO, 2015).

2
firm responses in 16 countries as part of the 2008 EU Community Innovation Surveys, Figure 9 shows that existing or future environmental regulations, followed by market demand from customers, are identified by firms in most countries as the main driver of introducing environmental innovations. The availability of direct public support in the form of fiscal incentives is the least important reported motivation in all countries. Figure 10 highlights the reported benefits of environmental innovations (from the same survey). Interestingly, in most countries surveyed (15 of 19 for this question), a larger percentage of innovative firms perceive the environmental benefits of innovation to be on the cost side (in terms of reduced energy use per unit of output) than on the revenue side (in terms of customers being willing to pay more for new products that reduce energy usage by end-users). This suggests that most of these innovations are likely to be incremental process rather than more radical product innovations.

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</sup> 10 employees in Chile, Colombia and Indonesia. Second, even amongst the countries for which data cover all firms with at least one employee, data may be at the establishment level instead of the plant level, and the definition of both may vary across countries. Third, data for some countries are based on other selection criteria, which might induce some bias in the results which cannot be determined a priori (e.g. in France data exclude firms with a turnover below a given threshold). Finally, from a sectoral perspective, community services and utilities are more difficult to compare, given the important role of the public sector, whose coverage changes from country to country, and of regulation in these sectors.

Table 2 suggests that in all countries the population of firms is dominated by micro units (with less than 20 employees).<sup>9</sup> They account for at least 80 percent of the total firm population. Their share in total employment is much lower and ranges from less than 15 percent in some transition economies (e.g. Romania) -- which still reflects the presence of large (formerly or still) state-owned firms inherited from the central plan period -- to less than 20 percent in the United States and around 30 percent or more in some small European economies.<sup>10</sup> 8. However, the enterprise survey in Venezuela is representative of all firms with at least 15 employees, and only includes a random sample of firms below this threshold. In our analysis, we have used the data for Venezuela with reference to firms with 20+ employees, given the lack of coverage for the lover size classes.

9. For proper comparability, the Table excludes all countries for which the size threshold is 5 or 10 employees instead of one.

10. The Table reports the share of firms with fewer than 20 employees over the total number of firms or total employment for the countries for which we have all
# **<mark>Indirect Subsidies</mark>**

A household is assumed to receive a Mortgage Subsidy if it responds in the HSES that it has an outstanding loan for housing, and lives in an apartment or detached house. An extremely small number of households in the HSES fit these criteria (i.e., only 4.6 percent of the sample). To these households, an amount of 5,841,468 MNT is applied to each household. This number is equivalent to an average outstanding loan of 49,927077 MNT and the implicit interest rate subsidy of 11.7 percent (i.e., 19.7 minus 8 percent).<sup>15</sup> Electricity subsidies are applied to households who are connected to the Central System and this information is available in the HSES. There are three types of tariffs available which offer different pricing schedules based on monthly kWh usage and the type of connection they have (i.e., vulnerable household, and households with simple or TOU meter). The survey does no give information about how households are connected and which subsidies they receive, thus eligible households are randomly allocated into these 3 groups. The first price group is for “vulnerable” households. It is assumed that 1% of connected households living in Gers receive this price schedule. Connected households not living in Gers are randomly split 60% into the simple meter schedule and 40% receiving the Time-of-Use meter.<sup>16</sup> In the HSES, interviewed households only report electricity expenditures in the previous month, and only a subset of the sample is interviewed in each month of the year. Since usage is expected to vary over the calendar year, electricity expenditures should be imputed for each eligible household and in each month. The imputation model predictors include housing type, household income decile, area of location (i.e., urban/rural), square meter of the dwelling, number of household
are relevant because the policy instruments recorded in the RISE database are meant to reflect today’s best practices in energy markets (ESMAP, 2022).

Our main IV is developing countries' foreign policy and political proximity to the major World Bank donors (France, Germany, Japan, the United Kingdom, and the United States) as estimated in Bailey et al.’s (2017) database on voting in the United Nations General Assembly. In addition, we consider two other IVs that measure closeness associated with trade for robustness purposes, described in detail in Section 3.3.

While achieving perfect identification in cross-country panel regressions is difficult, if not impossible, the "closeness" measured through our IVs is relevant because we show that finance from donors often facilitates or promotes the implementation of the policies in the first place and that they are plausibly exogenous to developing countries' energy mix.

We also address the difficulties of coding variables to consistently reflect the depth of reform and issues related to the collinearity of many renewable energy policies. Finally, we design two alternatives to the default RISE policy index developed by the World Bank to operationalize each of the seven renewable energy policy packages into variables for regression analysis.

The first index addresses the depth of the reform problem because it weighs all policies within each policy reform type equally. Compared to the weighting by the World Bank, it is comparatively more sensitive to the number of policies implemented. The second index addresses the collinearity of policies within each policy reform type by weighting uncorrelated policies more than those highly correlated to others.

The resulting combinations of indices and models create 18 "base" regression specifications. We estimate each of these regression specifications over five indicators of the energy mix, seven policy reform types, and six geographical regions, leading to 3,780 regressions in total.

5
6. _Drop observations with missing information for variable closedate_ : eliminate observations with missing information for the close date of the financial statement.

7. _Drop observations with relevant missing information_ eliminate observations that at the BvD.ID-year level have missing information in all the following variables: operating revenue (turnover), sales, employment, total assets.

8. _Drop duplicates and keep most updated information_ : keep observations with the most recent closing date if there are duplicates at the BvD.ID-year-first letter of consolidation code (e.g., C, U) level.

9. _Drop duplicates and keep information from annual reports_ : keep observations with annual report in _Use FillingType_ variable if there are still duplicates and keep the standardized information. Using annual reports (IFRS preferred, instead of local reports) guarantees standardization of reporting protocol at international level.

10. _Eliminate firms with noisy data_ : drop all the observations corresponding to a specific BvD.ID if any of the following variables has a negative value in a specific year – total fixed assets, tangible fixed assets, intangible fixed assets, other fixed assets, current assets, sales, and employment.

11. _Deflate values_ : use country GDP deflators from the World Bank database to deflate nominal variables and set year 2005 as the base year.<sup>13</sup> 12. _Harmonize currencies_ : convert values in local currency to USD dollars, using the average of the monthly exchange rate for year 2005.

> 13 https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS.

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the size distribution of per capita income / expenditure as summarized by the quintile shares. Figures 1.(b)-1.(f) present similar plots for the various subsamples.<sup>17</sup> Table 2 presents the results of OLS-based lognormality tests for the full sample and the various subsamples described above. The standard errors of the estimates are computed using a clustering procedure to allow for residual dependence; they also allow for heteroskedasticity.<sup>18</sup> The first thing that stands out in the table is the excellent fit of the regressions in all the samples considered, with R<sup>2</sup> ranging from 0.95 to 0.98. For a cross-country sample of this magnitude, such fit is remarkable. In turn, the regression slopes and intercepts are very close to their expected values under the null of one and zero, respectively. It is worth noting that in the samples including expenditure observations (the first, third and fifth columns) the estimated slopes are slightly below one, while the opposite happens in the regressions including only income-based observations. Formally, we can reject the null of unit slope in the Expenditure and Net subsamples (third and fifth columns). In turn, the estimated intercepts are positive in the samples including expenditure-based observations and negative in those including only income-based observations. Like with the slopes, in the Expenditure and Net subsamples we can also reject the null of zero intercept.

The bottom of Table 2 reports Wald tests of the null hypothesis of lognormality (6). Under the null, the test statistic follows a chi-square distribution with two degrees of freedom. As would be expected in the light of the point estimates, the null can be rejected at the 5 percent level in the two samples in which expenditure-based observations represent a sizeable share of the total number of data points. In contrast, the samples containing only income-based observations show little evidence against the null -- the p- values range from 0
**Figure 1 Cumulative global investment in the active portfolio of public-private partnerships in the developing world, 1990–2020**

a. In billions of dollars b. As percentage of GDP <!-- Start of picture text --> 2,000 6<br>1,500<br>4<br>1,000<br>2<br>500<br>0 0<br>1990 1994 1998 2002 2006 2010 2014 2018 1990 1994 1998 2002 2006 2010 2014 2018<br>Percent of GDP<br>Billions of 2020 US$<br><!-- End of picture text --> _Source_ : PPI database and World Development Indicators.

The number of active projects in developing countries has increased at a roughly constant rate since the mid-1990s, experiencing a slight jump from 2010 to 2012. As of 2020, there were 6,389 active projects in the developing world (figure 2).

**Figure 2 Number of active public-private partnership projects in the developing world, 1990– 2020**

<!-- Start of picture text --> 6,000<br>4,000<br>2,000<br>0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Number of projects<br><!-- End of picture text --> _Source_ : PPI database.

In the developing world, East Asia and Pacific and Latin America and the Caribbean (LAC), implemented the most projects, followed by South Asia. LAC has implemented the largest energy 7
tables, by and large, these goals were better met by results for the East European countries. Almost all the variables included in the models for the East European countries have estimated parameters significantly different from zero, and the pattern of results is very consistent across the three East European countries. Thus, while there remain important unidentified welfare determinants (e.g. personal ability), the model does point at a set of significant factors which affect welfare outcomes and which can be identified and affected in the context of policy intervention to alleviate poverty in Eastern Europe.

Unfortunately, the results are not so clear for the FSU. Overall, the explanatory power of the welfare regressions is low, and it is difficult to find as many clear poverty correlates as for the East European countries. In several ways, it can be argued that such a finding is not surprising, and relates to the different degrees of the transition process. In particular, the FSU still lacked much open unemployment during the HEIDE survey periods, although there was a strong correlation between actual unemployment and poverty. Further, in the FSU the labor market and especially the private sector are not well-defined, and it is difficult to determine a priori who are likely to be the losers and winners, since many of the traits associated with winning in the new regimes (entrepreneurial skills, political connections) are extremely difficult to measure reliably by a household survey. However, those aspects which could be measured in the household surveys (access to a household business or private plot) were strongly associated with the ability of a household to avoid poverty in the FSU.

55
2011 interview, eight subdivisions in the coastal area had positive predicted number of infant deaths. Among the 0.13 million infant deaths and 0.11 million neonatal deaths in these high salinity exposure subdivisions, 3.30% and 3.20% of them were caused by salinity on average respectively. Khulna and Patuakhali districts has the higherst with 11.93% and 8.36% infant deaths and 9.42% and 10.41% neonatal deaths respectively caused by high salinity level. During the three years prior to the interview year of BDHS 2014, six coastal subdivisions were on the list of positive predicted number of infant deaths with Barisal and Patuakhali districts having 19.76% and 11.71% of infant deaths and 15.38% and 16.33% of neonatal deaths respectively caused by high salinity exposure. The reason for the dramatic increase in some subdivisions between the 2011 and 2014 BDHS surveys needs further analysis. Overall, high salinity exposure induced mortality declined slightly from 1.59% to 1.24% for infants and 1.41% to 1.12% for neonates.

# **VI. Conclusions**

This study used non-parametric regression without assuming any functional forms to explore the relationship between drinking water salinity and infant and neonatal deaths. This was followed by three OLS approaches using salinity as continuous exposure, categorical exposure, and Restricted Cubic Splines specification. The RCS model specification explained the relationship with a concave shaped curve of marginal effects and a U-shaped curve of predicted values.

Overall, our findings indicate that both freshwater and severe-saline water are associated with higher infant and neonatal mortality. There is also a strong U-shaped relationship between salinity and infant and neonatal mortality, suggesting the harmful effects of freshwater and severe saline exposure, and protective role of mild salinity on infant and neonatal health across unadjusted and fully adjusted covariate models. Although overall effects were attenuated in full-multivariable adjustments, the coefficients of salinity from categorical and RCS models were slightly smaller for neonatal death relative to infant
a certain degree. Based on this benchmark, the estimated reductions in protection resulting from the UR would have been smaller and the impacts on world prices correspondingly lower.

# D. TRADE DATA

The welfare effects will partially depend upon the trading position in the commodities affected by liberalization. In general, net-food importers would be expected to benefit from reforms which lower the prices of their food imports, and suffer from price increases.'<sup>2</sup> In aggregate, total food imports accounted for about 8% of LDC total merchandise imports over the 1990-93 period. Cereals accounted for approximately 3%. However, in some countries (e.g. Bangladesh, Egypt, Mozambique), total food comprised of 20-25% of total merchandise imports during the same period.

The data on net trade positions (imports-exports) in value terms are derived from FAO's commodity balance database (CBD).13 The net trade values used in the RUNS model are also based on this database; hence, consistent commodity coverage are ensured. For each primary and processed commodities, the commodity balance database contains a comprehensive picture of the pattern of a country's commodity supply and utilization. The total quantity produced in a country added to the total quantity imported and adjusted to any change in stocks gives the supply available. On utilization, a distinction is made between the quantities exported, fed to livestock, used for seed, used for manufacturing and other uses, or lost during storage and transportation, and food supplies available for human consumption. The FAO data on imports covers all movements into the country of the commodity in question. In includes commercial trade, food aid granted on specific terms, and estimates of unrecorded trade.'<sup>4</sup> Exports covers all movements out of the country of the commodity in question. FAO also reports whenever possible trade in processed commodities expressed in the originating primary commodity equivalent. For example, wheat are imported as bulk grain and/or as processed (