rafmacalaba/data-use-mentions-extended huggingface dataset:
gifts.<sup>22</sup> That is, the expenditure measure we use reflects wellbeing after taking into account any self-production or any coping mechanisms used by households to smooth consumption (such as selling assets, help from friends and relatives, or benefits from government programs). The extent to which these impacts have long-run implications on the poverty status of the future welfare and poverty status of the household is beyond the scope of this paper.

Besides the weather shock variables we include variables that capture household composition (number of children in the household, number of adult males in the household, number of adult females in the household), characteristics of the household head (years of schooling of the household head, gender of the household head, and the age of the household head), an asset index,<sup>23</sup> and the characteristics of the housing unit (presence of a kitchen, access to tapped water indoors, presence of a toilet, access to piped sewage or septic tank, electricity, and flooring material). These variables are all thought to explain expenditures. To control for the agro-climatic conditions and other time invariant characteristics we introduce locality fixed effects. To account for any systematic change between the two survey periods, we control for the survey wave. Furthermore to account for the potentially different amount of resources available depending on the season in which the household responded to the survey, we introduce a season indicator variable. Appendix 2 gives the descriptive statistics of the variables used in the analyses.

# _5.1 Impacts of climatic variability on expenditures_

We run six different specifications with different measures of welfare and different measures of weather shocks based on equation (1). Given differences in the average climatic conditions in the North, and the Centre and South regions of the country besides including all rural households, we limit the sample to only those households in the North and to only those households in the Centre and South. The first set of specifications uses the (ln)
address these issues, this paper presents the results of the analysis of the biofuel sector in Argentina using a Computable General Equilibrium (CGE) model. Up to now, most of the claimed positive results of the development of this industry in the country are conjectural or based in sectoral studies (Cámara Argentina de Energías Renovables, 2009; Chisari, 2009). Our objective is to evaluate the gains and losses of the production of biofuels for Argentina, taking into account opportunity costs of resources and overall impact on economic performance.

We focus in the assessment of costs and benefits in an economy which can be characterized by the following stylized facts. Firstly, biofuels are already being produced, but there are clear differences between biodiesel and bioethanol in terms of development of the industry and competitiveness (with respect to other countries, such as Brazil). Secondly, Argentina has comparative advantages for several agricultural products at the international level, a fact that creates opportunity costs for land use and for direct exports of crops. Also, the country has a developed oilseed industry, with potential complementarities with biofuel production. Additionally, there is a complex tax structure, that has a direct incidence on agricultural exports, and that is subject to changes that accommodate fiscal results and the need of sustaining a positive trade balance. Finally, the cost of capital has been structurally high – basically due to the country risk component – and has discouraged investments in general and biofuel projects in particular.

The plan of the paper is the following. In section 2, we summarize the basic facts of the biofuels industry in Argentina. Then, the third section presents the database required for implementing the CGE model, organized in a Social Accounting Matrix. Most of the sectors in the value chain of bioethanol and biodiesel are disaggregated and introduced explicitly. After that, we discuss the main features of the CGE model (section 4) and we conduct several counterfactual experiments, in section 5, to study the response of the biofuel industry to policy shocks and to changes in international
of output PSI in all five digit supplying industries. The coefficients are obtained by the estimation of Equation 1. All specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

29
|
|industry-specifc trends|No|No|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|

NOTES: OLS-FE regressions of (log) employment on export intensity (exports/sales). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

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different population sizes of each gender.<sup>13</sup> While Qatar has the smallest GEGI in our sample even before adjustment (24%), the population-based adjustment greatly reduces the size of potential gains as employing more women mechanically has less effect on growth when women are a small fraction of the population.

The red bars in Figure 2 show the effect of closing gender employment gaps if aggregate physical capital remains constant, assuming that the labor share ( _β_ ) in each country is the same as that in the Penn World Tables 10. Note that for developing countries this averages around _β_ = 0 _._ 5, lower than the 2 _/_ 3 usually observed in developed countries. The labor share is even lower in MENA—averaging 41%—driven by very low labor shares in oil-rich countries (for Saudi Arabia, for example, it is only _β_ = 0 _._ 28). The lower labor shares in MENA mean that despite large long-term gains from closing gender gaps, the short-run gains could be more modest (even assuming that such a large reform could be implemented quickly).

The smallest short-run gains are again in Qatar, where a (relatively) small GEGI is further reduced very low labor share of 18%. Kuwait, Bahrain, and the UAE also have short-run GEGIs of 5%-7%, driven by a relatively modest long-run GEGI and a low labor share.<sup>14</sup> The largest short run economic gains from closing gender gaps are still in Yemen, but are around half as large as under the long run assumption ( _β_ = 0 _._ 5). Still these hypothetical short run gains are significant, at 43%

> 13See Appendix 4 of Pennings (2022).

> 14Oman and Libya also have low short-run GEGIs, but in those cases a very low labor share is more important.

7
**|47.1||||
|_Baltics_||||||||||
|**Estonia**||**126.9**|30.2|28.4|26.9|25.5|27.0|30.3|28.4|
|**Latvia**||||31.6|34.2|23.4|23.0|27.4|25.4|
|Lithuania||||23.1|25.8|23.3|15.8|19.0|19.5|
|**Average**||||27.7|29.0|24.1|21.9|25.6|**24.4**|
|CIs||||||||||
|**Georgia**||||||||||
|**Kazakhstan**||||||||10.2||
|**Kyrgyz Rep.**||||||17.2|14.2|13.7|14.6|
|**Ukraine**|||50.1|32.5|26.7|12.7|11.5|13.6||
|**Averae_**||||||14.9|**12.8**|||
|**OECD/2**|79.6|77.5|82.1|71.0|83.8|84.5|87.4|88.4||

_Source: International Monetary Fund, "International Financial Statistics. "_ /I Data for high-income OECD countries except for Germany (1990) and Luxembourg (1993), for which data is not
10 shared address appears only once in the EconLit record, and we count the article only once for that institution. Where an author has multiple affiliations, these appear on the same line in the EconLit record, we retained only the first-listed address—the first-listed institution gets all the credit. Adopting these rules, we inevitably end up with more records than publications: we have over 37,000 ―publications‖ just for the sub-period 1981-2010.

We then aggregated these ―publications‖ by address, and then by institution. Sometimes, an author‘s address was the institution itself, and the aggregation involved simply checking for variations in spelling or format and deciding on a common one. So, for example, the US Department of Agriculture sometimes appeared as ―USDA‖ and sometimes as ―US Department of Agriculture‖; we renamed the former ―US Department of Agriculture‖ and the two sets of records were aggregated. Other examples include the World Health Organization (which appeared also as WHO) and the International Food Policy Research Institute (which also appeared as IFRPI). Sometimes, the author address was not an institution but rather a unit within an institution. For example, authors affiliated to the University of York‘s Centre for Health Economics (CHE) often listed their address as CHE not the University of York. Other authors at York listed their address as the Department of Economics and Related Studies. Often these and other addresses appeared in multiple forms, with, for example, abbreviations. In such cases, we aggregated records up to the institution level—the University of York in the example above. The same rule was applied to records relating to the colleges of the universities of Cambridge and Oxford, but was not applied to constituent colleges of state universities in the US or the universities of London and Wales.<sup>15</sup> So, Berkeley stayed as University of California Berkeley, and the London School of Economics and the London School of Hygiene and Tropical Medicine (both part of
September of 1993. Table 9 depicts all of the events that pertain to flooding after 1988. As there are a large number of events pertaining to the Mekong, we arbitrarily limit our event enumeration to events that took place within a three year span of time after the last flood identified. In total we present 30 events. The majority of the events are cooperative in nature (with only one event considered conflictive in nature). One of the events also represents an international water treaty signed in the basin. Although only an anecdotal finding, we note that the various floods in the region have elicited a relatively high number of cooperative events among the riparians.

In regards to the flood events pertaining to the Zambezi, the BWI predicted a flood in 2007. Floods (of the 2007 magnitude) were also identified in 1989, 1993, 1998, 1999, 2000, 2001, and 2003. A flood was also identified in 2004 but of a slightly more severe magnitude than 2007. A look at the water related socio-political data as it pertains to events associated with the Zambezi River Basin reveals six cooperative (interestingly, rather than conflictive) events associated with these flood events. Table 10 presents these events from The Basins at Risk Database (TFDD, n.d.) following the first major flood reported by the BWI—1989. These cooperative events, with one event constituting an international water treaty, are (like the Mekong case) perhaps one indication that the severity of the floods led the parties to different forms of cooperative action.

# **CONCLUDING DISCUSSION**

The paper introduces an analytical framework to assess the impact of variability of river flow on international river basin treaty stability, which includes economic, social and political impacts. Policy makers can use these probabilities to assess mechanisms that impact treaties, thereby allowing institutions to develop mitigation to extreme events. The probability of non-compliance of a treaty obligation can help policy makers identify the relative risk and economic impacts. In addition, the estimated hydrological variability and flow probabilities
.

However, in order to formally assess whether across the entire economy of each East Asian country firms in data‐intensive sectors are truly affected in their innovation activities as a result of higher data restrictiveness, the identification strategy takes into account the extent to which each sector is data‐intense by employing a sector’s software use as a proxy, as explained above.

# **3.5 Baseline Regressions**

As previously said, to measure whether the data policy index has any meaningful relation with innovation activities at the level of the firm in East Asian, we employ two regression approaches.

The first approach takes a cross‐country dimension in which we perform regressions for 10 East Asian countries for which we have data. Then, as the next step we select a number of countries for which we have specifically recorded firm‐level data from a national source and evaluate whether our cross‐country outcomes are consistent with these country‐specific regressions. There are two main reasons why we undertake this two‐step approach. One is that the cross‐country exercise tells us something about the differences of countries over time, whereas the country‐specific puts more emphasis on the development of the policy restrictions as such and therefore guides for a more specific policy advice.<sup>7</sup> Also, the two sources of data record different innovation variables, which

> 6 Furthermore, Table A1 in Annex A shows that China’s high innovation activities are not caused by an exceptionally high number of firms recorded in the Enterprise Survey database. Therefore, it means that aggregating the firm‐level innovation variables into an average by country and sector does is not influence China’s extreme position.

> 7 Moreover, there are also technical reasons for why we exploit these two dimensions of data. One is that the source for the cross‐country approach reports years with intervals, is survey‐based and is unbalanced, whereas the country‐specific data are more complete and census‐based.
were introduced in the country’s main port, Toamasina, starting in 2016, to incentivize customs inspectors to prevent tariff evasion; incentives were coupled with better monitoring through the introduction of IT systems to monitor inspectors’ performance. This helped increase fraud detection, decrease processing times and increase customs revenue. In a similar vein, the reform of land mapping and titling in Rwanda, including through the digitization of records, contributed to enhancing transparency and thus increasing the cost of malfeasance and reducing corrupt incentives in service delivery.

Access to health and education services and sometimes quality has overall <mark>improved in Africa over the past 20 years, arguably due to the fact that improved access is less demanding on institutions and has largely relied on large investments. De facto, the average literacy rate in SSA improved from 56 to 68 percent of the adult population and life expectancy at birth rose from 51 to 60 years between 2000 and 2020.</mark><sup>20</sup> <mark>In the education sector, countries have made substantial strides towards achieving universal primary education. In 2000, about a third of primary school age children and two-fifths of lower secondary school age children were out of school, compared to 17 and 33 percent respectively by the end of the 2010s (UNICEF 2021). This progress has been made possible by African countries’ policy commitment, reflected in the creation of enabling legal frameworks for free and compulsory education in over half of African countries (UNICEF 2021), support from the international community, including through global multi-stakeholder partnerships and funding such as the Global Partnership for Education, as well as an increase in public spending on education (the average education expenditure in Africa has risen in the first two decades of the 2000s, both in absolute terms and as a percentage of GDP (UNICEF 2022)).</mark> <mark>Yet, tr</mark> ends in service _equality_ lag behind. Regional average measures of education and health equality<sup>
**Figure 7: MENA SAI micro data openness sub score vs. ODIN 2020 openness sub score.**

<!-- Start of picture text --> Algeria 0.0 26.3<br>Bahrain 0.0 66.7<br>Djibouti 7.1 29.1<br>Egypt , Arab Rep. 21.4 33.8<br>Iran, Islamic Rep. 21.4 37.9<br>Iraq 14.3 52.1<br>Jordan 7.1 48.7<br>Kuwait 0.0 45.5<br>Lebanon 14.3 48.2<br>Libya 0.0 14<br>Malta 14.3 49.1<br>Morocco 28.6 71.8<br>Oman 7.1 93.2<br>Qatar 0.0 43.6<br>Saudi Arabia 0.0 44.1<br>Syrian Arab Republic 0.0 25.6<br>Tunisia 35.7 61.8<br>United Arab Emirates 0.0 85.9<br>West Bank and Gaza 42.9 75.4<br>Yemen 0.0 32.5<br>0.0 50.0 100.0<br>2020 ODIN openness score Microdata openness sub score<br><!-- End of picture text --> <mark>Source: Author’s compilation using own data and 2020 ODIN data from Open Data Watch -- Open Data Inventory</mark> <u><mark>http://www.opendatawatch.com</mark></u>

**<mark>Figure 8: ODINs poverty and income openness scores vs MENA MAI consumption/welfare data openness score</mark>**

<!-- Start of picture text --> 100 100100 100 100<br>100 80<br
/sup> g-GDP attempts to remedy such an omission by providing a more comprehensive measure of output.

Consider, for example, a transportation project whose main payoff is the reduction of congestion. Thus, assume that, as is commonly the case, in the cost-benefit analysis, the main source of benefits stems from the value of travel-time savings. But this value of time is not included in GDP statistics. If we look at just first-round effects (or if we assumed that no change in behavior follows), then such a project, despite its positive net social value, could actually affect negatively GDP through motor-fuel savings. g- GDP accounting eliminates these apparent tradeoffs.

Muller and others (2011) present a framework to include externalities into a SNA using an integratedassessment model that includes air pollution, solid waste combustion, stone quarrying, marinas, oil- and coal-fired power plants etc. The largest industrial contributor to external costs is coal-fired electricity generation, their damages range from 0.8 to 5.6 times its value added. The implication of damages in excess of value-added is that closing down that industry would unequivocally increase g-GDP.

The policy implications are (i) to take a broad view of economic activity to eliminate tradeoffs that are more apparent than real; e.g., between environmental quality and economic performance; and (ii) Include all dimensions that are relevant for welfare in the corresponding cost-benefit analyses of policy interventions and perform sensitivity tests on the components that are measured with significant uncertainty (e.g., value of a statistical life).

> 3 - Report published in 2006, available at: http://www.gov.cn/english/2006 <u>09/11/content_384596.htm</u>

> 4 <u>http://www.financialexpress.com/news/India-to-release--green-GDP--data-from-2015/544338/</u>

> 5 <u>http://www.telegraphindia.com/1110510/
Finally, in Section 5, I discuss evidence from recent studies of labor interventions. Section 6 concludes.

# **2 Data**

Most of the analysis is based on data from the _World Bank Enterprise Survey_ – an harmonised representative survey of firms covering about 139 countries in the world. For the analysis, I select five countries of interest: Ethiopia, Kenya, Bangladesh, India and Vietnam.<sup>1</sup> Kenya is a geographical neighbour of Ethiopia and a natural competitor within east Africa, with a key advantage coming from direct access to the sea. I then select three Asian countries, covering the range of the productivity distribution among

> 1I use the latest survey available for each country. For Ethiopia and Vietnam, this is the 2015 survey; for India this is the 2014 survey, and for Bangladesh and Kenya this is the 2012 survey.

3
**Electronic equipment**|Peru|
|**Machinery and equipment**|Singapore|
|**Other manufacturing**|United States|
|**Utilities**|Vietnam|
|**Construction**|Brazil|
|**Trade and transport**|Russian Federation|
|**Finance and other business services**|India|
|**Communication and business services**|China|
|**Social services**|South Africa|
||EU28|
||Egypt,Arab Rep.|
||Colombia|
||Turkey|
||Thailand|
||Korea,Rep.|
||Philippines|
||Indonesia|
||Bangladesh|

> 7 https://www.gtap.agecon.purdue.edu/

8 We are grateful to Badri Narayanan for the incorporation of Lesotho SAM to the GTAP database.

13
public evidence of registrable rights (ownership, leases, mortgages etc.) held by specific individuals and assigns priority among them as per law.<sup>4</sup> This facilitates impersonal trade by protecting third parties’ rights (Arrunada _et al._ 2019); expands the range of potential contractual partners and options by reducing the scope for moral hazard due to asymmetric information; and provides the basis for enforcement of rights via the courts as a last resort.

Although not costless, having rights documented and mechanisms for enforcing them clearly defined has several advantages. It provides private benefits by (i) increasing investment incentives and reducing the need to spend resources on non-productive activities to maintain land rights; (ii) expanding contractual options and the range of potential contracting partners for land transfers; (iii) improving credit access if there are liquid sales markets and collateral can be registered and enforced at low cost; and (iv) empowering individuals, especially women, who were traditionally often disadvantaged by making enforcement of rights easier and encouraging entrepreneurship. It also provides public benefits by (i) improving functioning of land and associated markets for mortgages and insurance via public price data; (ii) providing the basis for effective land us regulation and planning; and (iii) enhancing state capacity and the social contract by allowing fair taxation and incentivizing tax payment in return for public goods that can increase land prices.

_Investment instead of ‘guarding labor’ and other defensive measures:_ Although undocumented rights are sufficient to incentivize short-to medium-term investment, greater scales and longer duration normally require documented rights. Rights that can be enforced via the courts have been instrumental in reducing deforestation in Brazil (Baragwanath & Bayi 2020) and <mark>Colombia (</mark> Peña _et al._ 2017; Vélez _et al._ 2020). In the US West, clarification of access rights to publicly owned grazing land increased productivity and use of fallowing as much as privatization (Bühler 2023). By contrast, uncertainty land
Rate USD<br>2019-01 2019-07 2020-01 2020-07 2021-01 2021-07 2022-01 2022-07 2023-01 2023-07 2024-01<br><!-- End of picture text --> Figure 12. Baltic Dry Index, 1985-2024 <!-- Start of picture text --> 14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>1/1/1985 2/1/1986 3/1/1987 4/1/1988 5/1/1989 6/1/1990 7/1/1991 8/1/1992 9/1/1993 10/1/1994 11/1/1995 12/1/1996 1/1/1998 2/1/1999 3/1/2000 4/1/2001 5/1/2002 6/1/2003 7/1/2004 8/1/2005 9/1/2006 10/1/2007 11/1/2008 12/1/2009 1/1/2011 2/1/2012 3/1/2013 4/1/2014 5/1/2015 6/1/2016 7/1/2017 8/1/2018 9/1/2019 10/1/2020 11/1/2021 12/1/2022 1/1/2024<br><!-- End of picture text --> Source: Baltic Exchange, the Baltic Dry Index is a global average of observed rates over a series of major routes.

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