rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 2**

**Seed’s Contribution to the Total Value of Cotton (percent)**

<!-- Start of picture text --> 30% 35% 40%<br><!-- End of picture text -->

**Note** : The percentages, 30%, 35%, 40% refer to ginning ratios and refer to differences in cotton varieties and ginning efficiency rather than cleanliness of harvested cotton **Source** : Author’s calculations. 2

# **Figure 3**

# **Lint to Oil Price Ratio (2000=100)**

<!-- Start of picture text --> Average<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Bank 3<br><!-- End of picture text -->

**_Page 5_**
**Figure 5 –Bank Lending to Local Governments and Firms given Bank Exposure to Local Public Debt**

The figures plot the evolution of lending to state governments (Panel A) and private firms (Panel B) relative to March 2016 by banks with low and high ex ante public debt exposure. Banks are considered to have high (low) ex ante exposure to public debt if their share of lending to public entities in 2016Q1 was above (below) the median. The vertical lines in both panels mark the <u>introduction of the FD Law.</u> <!-- Start of picture text --> Loans to Local Governments Loans to Firms<br>2014m7 2015m7 2016m7 2017m7 2018m7<br>Time 2015m1 2016m1 2017m1 2018m1<br>Time<br> Low Exposure High Exposure<br> Low Exposure High Exposure<br>April 2016 = 100<br>April 2016 = 100<br>140 110<br>130<br>105<br>120<br>Index Index 100<br>110<br>100 95<br>90<br>90<br><!-- End of picture text --> 42
24

# SRMC System short-run marginal cost of gas.

# **Situation on the day before the gas day**

|**PS.**|Spot market price of gas with delivery on the gas day (day-ahead gas.)|
|---|---|
|_Q_<sup>_1_</sup>_io QWO_|The quantity of gas nominated for injection into and withdrawal from<br>BGT's pipeline system according to the approved schedule for the gas day.<br>The system must be in balance:_Qlo=Qwo._|
|I-IV, 1-6|Flexibility bids for a system sell and a systera buy.|

# **Situation on the gas day**

# _- Case 1: Positive system imbalance system sell_

|_QWI<QWO_|<br>The quantity of gas withdrawn from the system is below the nominated<br>quantity because of an unexpected decrease in the demand for gas.<br>Shippers are unable to react in real time, so the quantity of gas injected<br>into the system is equal to the nominated quentity_(Qil=Qi.)._|
|---|---|
|_Qil Qwl_|The difference between the quantities of injected and withdrawn gas is<br>equal to the size of the system imbalance, which is positive because<br>_Qi,>Qw,._BGT must sell the excess gas in the flexibility market to restore<br>system balance. BGT accepts flexibility bids I-III.|
|SMPSe.I|The system marginal price of gas sold by BGrT to winning bidders. It is<br>equal to the price of the last accepted flexibility bid.|

# _- Case 2: Negative system imbalance system buy_

|_QW2>QWO_|The quantity of gas withdrawn from the system is above the nominated<br>quantity because of an unexpected increase in demand for gas. Shippers<br>are unable to react in real time, so the quantity of gas that is injected into<br>the system is equal to the nominated quantity_(Q02=Q_<sup>_1_</sup><br>_0 _<sup>_)._</sup>|
|---|---|
|_Qi_**_2 .QW 2_**|The difference between the quantities of injected and withdrawn gas is<br>equal to the size of the system imbalance, which is negative because<br>_Q1 2 <QW 2 ._ BGT must buy the missing gas in the flexibility market to<br>restore system balance. BGT accepts flexibillity bids 1-4.|

- SMPbLIy The system marginal price of gas bought by ]BGT from winning bidders. It is equal to the price of the last accepted flexibility bid.
# Table 10: Absenteeism and number of children

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Allstudents<br>Number|of children<br>3 or|Stud<br>other than|ents aged 6<br>student|-12<br>3 or|
||0-1|2-3|more|0-1|2-3|more|
|School|||||||
|Meals|1.478|0.189|-0.272|1.512|0.221|-0.235|
||[0.423]***|[0.534]|[0.406]|[0.449]***|[0.565]|[0.397]|
|Dry Rations|1.245|0.268|-1.375|1.363|0.437|-1.653|
||[0.404]***|[0.428]|[0.697]**|[0.434]***|[0.454]|[0.764]**|
|Follow-up|-1.652|-0.91|0.175|-1.788|-1.052|0.208|
||[0.387]***|[0.386]**|[0.245]|[0.421]***|[0.417]**|[0.293]|
|Girl|0.023|0.02|-0.063|-0.047|-0.028|-0.075|
||[0.164]|[0.179]|[0.134]|[0.178]|[0.191]|[0.150]|
|Constant|1.001|1.262|1.431|1.12|1.368|1.467|
||[0.380]***|[0.485]***|[0.732]*|[0.376]***|[0.493]***|[0.723]**|
|Observations|568|830|537|511|747|480|
|R-squared|0.29|0.12|0.19|0.32|0.13|0.22|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable number of days missed in May.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

33
their capacity to generate earnings. Lower earnings could also be related to a deceleration in the high growth rates that emerging economies used to experience, as they became more mature and reached higher GDP per capita levels.

The aggregate counterpart of a deterioration in the debt servicing capacity of firms was an increase in the share of debt-at-risk. In 2016, the share of nonfinancial corporate debt-at-risk in emerging economies stood at 24 percent up from 15 percent in 2008. China, where the share of debtat-risk increased from about 15 to 32 percent, was largely responsible for this increase. Nevertheless, nonfinancial corporate debt-at-risk also increased in other emerging economies. In 2016, debt-at-risk amounted to more than a fifth of total nonfinancial corporate debt in several large emerging economies, including Argentina (22 percent), Brazil (30 percent), India (23 percent), and Turkey (20 percent) (Beltrán et al., 2017).

In addition to debt-at-risk, the Corporate Vulnerability Index (CVI) constructed by Feyen et al. (2017) can provide a comprehensive measure of firms’ debt repayment capacity. Whereas debt-atrisk measures the share of debt held by firms with an ICR below a threshold, the CVI calculates the share of debt held by firms which present vulnerabilities in multiple indicators at the same time. These indicators include the leverage ratio, return on assets, the market-to-book ratio, and the ICR itself, among others. According to the CVI, the overall vulnerability of emerging market firms steadily increased after 2008, particularly accelerating after 2013, and then stabilized in 2016. Trends were not homogenous across emerging regions. In some regions, such as Eastern Europe and Central Asia as well as South Asia, vulnerability increased around 2008 and 2009 and remained stagnant since then. In contrast, in Latin America and the Caribbean and Sub-Saharan Africa the risk of financial distress did not become elevated until around 2012.

Whereas indicators such as debt-at-risk and the CVI can provide useful insights on how corporate vulnerability increased over time, their levels do not provide information on whether 13
**Table 1: Comparison with other theoretical frameworks**

|||This paper|Statistics Canada<br>(2017)|OECD (2011)|UN (2019)|Biemer (2010)|
|---|---|---|---|---|---|---|
||erage|_Complete_|Coverage|||Completeness|
||uate cov|_Frequent_|Viability||||
|pply|Adeq|_Timely_|Timeliness &<br>punctuality|Timeliness &<br>punctuality|Timeliness &<br>punctuality|Timeliness/<br>punctuality|
|Su|ty|_Granular_|Granularity||||
||igh quali|_Accurate_|Accuracy &<br>reliability|Accuracy; reliability|<br>Accuracy & reliability;<br>Methodological<br>soundness|Accuracy|
||H|_Comparable_|Standardization or<br>conformance||Comparability|Comparability|
||e|_Accessible_|Accessibility|Openness/<br>transparency|Accessibility|Accessibility|
||Easy to us|_Understandable_|<br>Processability and<br>understandability||Clarity;<br>Transparency|Usability/<br>interpretability|
|and||_Interoperable_|Combinability or<br>linkability|Coherence|Coherence|Coherence|
|Dem|e|_Impartial_<br>|Perception of<br>authority, impartiality<br>& trustworthiness|<br>Credibility;<br>objectivity; integrity;<br>impartiality|Impartiality &<br>objectivity|Credibility|
||Safe to us|_Confidential_|Security;<br>Protection of sensitive<br>information|<br>Confidentiality<br>protected|Statistical<br>confidentiality &<br>data security||
|||_Appropriate_|Relevance &<br>usefulness|Relevance &<br>usefulness|Relevance|Relevance|
|||Other|Quality assurance;<br>reproducibility;<br>contactability|Effective stakeholder<br>engagement|Many, see table<br>note||

Note: This figure is inspired by, and takes some information from, Marker (2017). The UN framework has many other attributes most of which are related to managing the statistical system and hence do not relate to the framework of this paper: Coordinating the national statistical system, managing relationships, managing statistical standards, professional independence, adequacy of resources, commitment to quality, appropriate statistical procedures, managing the respondent burden, and cost-effectiveness.

9
13

**Figure 6. MDG 3: Current Situation and Trends**

<!-- Start of picture text --> Primary Education Secondary Education<br>1.00 1.00 0.98 0.98 0.98 0.98<br>0.99<br>0.99 0.98 0.96 0.98 0.98<br>0.99 0.98 0.99 0.99 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.95 0.92 0.94<br>0.97<br>0.96 0.90 0.89<br>0.87<br>0.96<br>0.95 0.85<br>0.85<br>0.83<br>0.94<br>0.80 0.79 0.81<br>0.93 0.92 Past performance Past performance<br>Government plan Government plan<br>0.91 MDG-3 0.75 0.76 MDG-3<br>2000 2003 2006 2009 2012 2015 2000 2003 2006 2009 2012 2015<br> Source: Authors’ calculations. Source: Authors’ calculations.<br><!-- End of picture text -->

**_C. Financial Needs:_ 0.2 percent of GDP on average per year.** A modest increase in resources would allow Mauritania to achieve this goal. The financing gap to reach MDG3 is the smallest of the MDGs considered in this paper. The cost of achieving this target for the period 2008-2015 amounts to 0.3 percent of GDP on average per year, corresponding to US$ 52.6 million in total. Once the allocation of government expenditure is taken into account, the country needs an additional total financing of 0.23 percent of GDP on average per year (Figure 7), which translates into US$ 41.8 million in total (Table 6). For a detailed, yearly cost breakdown, see Annex 1.

**Figure 7. MDG 3: Financing Gap**

**Table 6. MDG 3: Cost, Budget and Financing Gap (2008-2015)**

<!-- Start of picture text -->
-7.60 2008 2009 2010 2011 2012 2013 2014 2015 -0.15 Financing Gap (2008-2015)<br>-7.64<br>-7.65 -0.18 -0.18<br>-0.19 Total Cost  US$ 52.61 m<br>-7.70 -7.71 -0.20 -0.21 MRO 14,430 m<br>-7.75 -0.22 -7.73 Budget  US$ 10.79 m<br>-7.75 -7.76 Allocation  MRO 2,973.47 m<br>-7.75 -0.24<br>-0.24 US$ 41.83 m<br>-7.79 -0.25 Financing  MRO 11,456.55 m<br>-7.80 -0.26 -0.27 gap  0.23% of non-oil GDP<br>-0.28 Millions of US$% of non-oil GDP (secondary axis) -7.83 US$ 2.35 p.c.<br>-7.85 -0.30<br>Source:  Authors’ calculations.<br>     Source:  Authors’ calculations.<br><!-- End of picture text -->

# **MDG 4: Reduce Child Mortality**

_Target 5: Reduce by two thirds, between 1990 and 2015, the under-five mortality rate._

**_A. Current Situation:_ High infant and child mortality rates.** The latest available data shows a child mortality rate (CMR) of 135 per 1.000 live births in 2000, while the infant mortality rate (IMR) stood at 87 per 1.000 live births during the same year. Chronic malnutrition amongst children (at 32 percent in 2004) continues to be a serious problem notably in rural areas and among the poorest and the very young (0–3 years), contributing significantly to infant and child deaths. Health care delivery suffers from the low quality
independent, but capitalize on the power of consolidation are described. This consolidation can occur at different levels, among the farmers themselves, i.e. the producer groups in Poland, or at a higher level in the chain where the farmers output is consolidated at a single point by and outsider, i.e. the e-Choupal model or contract farmers/outgrower schemes.

# **_Strong incentives not to consolidate_**

Coordination problems take root in game theory, whether or not authors explicitly note the ties to game theory, it is present. Game theory is applicable to agricultural economics because of its ability to model interactions between individuals, specifically the farmer/seller and the trader/buyer. The interaction of these two individuals is represented by a coordination game (also know as the ‘prisoners’ dilemma’), whose features include two choices for both individuals, with two equilibriums (Grabowski 1999). The presence of multiple equilibriums is where the problem exists; there is a high equilibrium and a low equilibrium. As presented in Table 19, both the buyer and the seller have two choices, Option I or Option II. These two options represent either investing (Option I) which gives a greater return or not investing (Option II) which results in a lower return. There are two equilibriums present in this figure, both choose Option I or both choose Option II. If both select Option I their return is five, but if the buyer cheats and selects Option II instead, the buyer receives eight and the seller receives nothing. To remove the risk of receiving nothing, the players will choose Option II, the low equilibrium, from which they will have little incentive to move from (Grabowski 1999).

**Table 19: Farmer/Trader Dilemma**

|||S|eller|
|---|---|---|---|
|||Option I|Option II|
|Buyer|Option I|**5,5**|0,8|
||Option II|8,0|**2,2**|

Source: Grabowski, R. (1999).

44
**Table 4:** Effects of AIA on investment and cloud types

|||Panel|A: Investment|||
|---|---|---|---|---|---|
|Regressions|(1)|(2)|(3)|(4)|(5)|
|Dependent variables|Hardware<br>Investment|Software<br>Investment|Plant & Mach.<br>Investment|Vehicles<br>Investment|Land & building<br>Investment|
|AIA Dummy|0.346***|0.246***|0.549***|0.214***|-0.012|
||(0.079)|(0.070)|(0.100)|(0.072)|(0.086)|
|Observations|17,651|24,313|22,569|18,583|22,327|
|||Panel B:|Cloud Adoption|||
|Regressions|(6)|(7)|(8)|(9)|(10)|
|Ddtibl<br>24|Cloud|Cloud|Cloud|Cloud|Cloud|
|epenen varaes<br>|Databases|Storage|CRM|Ofce|Email|
|AIA Dummy|-0.094***|-0.097***|-0.091***|0.001|-0.045|
||(0.040)|(0.035)|(0.037)|(0.027)|(0.032)|
|Observations|2,200|2,200|2,200|2,200|2,200|

Note: Time Period: Investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. Panel A displays separate regressions for the different types of capital investment while Panel B presents estimations for each type of could services. Capital investment variables are in (logged) thousands of pounds, while the cloud variables are a dummy variable. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. The regressions in Panel A are estimated using the method outlined in Callaway and Sant’Anna (2021) and those in Panel B use difference-in-differences. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age. These are not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.
# **Shifting Spousal Decision-Making Patterns: Whom You Target in an Agricultural Intervention Matters**

**The Impact of the Farmer’s Innovation Fund in Ethiopia**

Tara Bedi, Niklas Buehren, Markus Goldstein, and Tigist Ketema<sup>1</sup> Keywords: Gender, Innovation fund, Agriculture, Ethiopia JEL Classification: J16, Q14, Q16

> 1 Buehren : World Bank;  nbuehren@worldbank.org; Bedi: Trinity College; bedit@tcd.ie; Goldstein: World Bank; <u>mgoldstein@worldbank.org; Ketema: World Bank;  tketema@worldbank.org.This paper is a product of the Gender</u> Innovation Lab, Office of the Chief Economist, Africa Region. The authors would like to thank Chimdo Anchala and Alaka Holla for helping design and formulate this evaluation. Amare Teklu Yirbecho and Nathalie Guilbert provided valuable early research assistance. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank Group and its member countries.
Policy ReseaRch WoRking PaPeR 4378

# **Abstract**

This paper evaluates the impact of economic regulation on infrastructure sector outcomes. It tests the impact of regulation from three different angles: aligning costs with tariffs and firm profitability; reducing opportunistic renegotiation; and measuring the effects on productivity, quality of service, coverage, and prices. The analysis uses an extensive data set of about 1,000 infrastructure concessions granted in Latin America from the late 1980s to the early 2000s. The analysis finds that as the theory indicates, regulation matters. The empirical work here reported shows that in three relevant economic aspects—aligning costs and tariffs; dissuading renegotiations; and improving productivity, quality of service, coverage, and tariffs—the structure, institutions, and procedures of regulation matter. Thus, significant efforts should continue to be made to improve the structure, quality, and institutionality of regulation. Regulation matters for protecting both consumers and investors, for aligning closely financial returns and the costs of capital, and for capturing higher levels of benefits from the provision of infrastructure services by the private sector.

This paper—a product of the Sustainable Development Department in the Latin America and the Caribbean Region—is part of a larger effort in the department to understand the determinants for performance in the infrastructure sectors. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jguasch@worldbank.org and landres@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The instrument is strong and the alternative specifications of the _Media reach_ variable render highly significant results and stable coefficients. Figure 7 below illustrates the relatively large effect of online (as opposed to traditional media) on the likelihood of severe censorship when we instrument our endogenous regressor.

<!-- Start of picture text --> Media reach (dichotomous)<br>GDP per capita (ln)<br>Population (ln)<br>Vote Share of Government Parties<br>Government Fractionalization Index<br>Party of Chief Executive Length of Time in Office<br>Public sector corruption index<br>Legal constraints<br>Economic constraints<br>Political constraints<br>-15 -10 -5 0 5<br><!-- End of picture text --> Figure 7: Coefficient plot for a dichotomous _Media reach_ (Traditional media- TV, radio and print = 1, Online and blogging = 2) – 2SLS with media reach instrumented with Pew’s _News consumption_ variable We also code the type of publication by frequency, although we have such fine-grained information for a subsample of censorship events only. Our three categories of publications (daily, weekly, and less frequent than weekly) allow us to verify whether frequency, as an opportunity for media outlets to send multiple signals to their audience, despite higher transaction costs for censors 38
mechanisms (like a lockbox) to the poor increases their investment towards preventive health, suggesting that they were making suboptimal health choices in the absence of access to a savings vehicle. This suggests that in addition to financial access, provision of the right kind of savings technologies is also imperative in enabling savings.

In addition, even if the poor do manage to save at home on their own, they face the risk of the money being appropriated by others, like their spouse, friends or members of the extended family. This is a non-trivial risk and Jakiela and Ozier (2011) provide experimental evidence from Kenya demonstrating that participants were willing to forego expected earnings in order to conceal the size of their initial endowment so that they could avoid sharing it with those in their network. Interestingly, Brune et al. (2011) find that commitment savings accounts can also provide protection from such appropriation by minimizing access to the funds that have already been banked.

For anybody who has followed the arc along which microcredit evolved, it is natural to wonder if the evidence from the ground for microsavings supports what seems like a very promising theory on paper. So far, we have only one good quality RCT on the impact of microsavings, and the evidence from that is promising. In the Dupas and Robinson (2011a) experiment that has been mentioned earlier, we find that those who were offered accounts save more and show a significant increase in business investment and even in personal consumption expenditures. It shouldn‟t be excessive to conjecture here that the increase in consumption was a result of increased profits, which in turn came about due to the increase in investment. Further, the study finds a decrease in average poverty, something that RCTs of microcredit have failed to demonstrate unambiguously.

21
the Urdu-speaking, Sindhi-speaking, and Pashto-speaking communities in Karachi. Table 6 reports the effects of the intervention on the respondents’ connectedness to their own ethnicity and other ethnicities.

The oneness scores show that none of the interventions had any effect on the participants’ connectedness to a different ethnicity (Columns 1-2), but exposure to diversity increased the participants’ connectedness to their own ethnicity by 0.13 standard deviation. The effect under this treatment remains intact and of similar magnitude when urban design elements are added to the social diversity intervention under the treatment of urban design coupled with exposure to diversity. Results for the F-test indicate that the two treatments are not significantly different from each other. This suggests that the survey questions that probed the participants’ salient social characteristics may have highlighted their own group identity and offered a motivation for them to maintain their positive self-concept about being part of their own social group.

Table 6. Treatment Effects on Connectedness to Other/Own Ethnicity

|VARIABLES|(1)<br>Connected<br>ethnicity|(2)<br>ness to other<br> (z-score)|(3)<br>Connectedness<br>(z-sc|(4)<br>to own ethnicity<br>ore)|
|---|---|---|---|---|
|[T1] Urban Design|-0.03|-0.01|0.06|0.08|
||(0.069)|(0.067)|(0.066)|(0.066)|
|[T2] Diversity|-0.05|-0.02|0.11|0.13*|
||(0.068)|(0.067)|(0.068)|(0.068)|
|[T3] Urban Design + Diversity|-0.01|0.00|0.11*|0.13*|
||(0.066)|(0.064)|(0.066)|(0.065)|
|Observations|1,631|1,631|1,631|1,631|
|Control Mean|0.00|0.00|0.00|0.00|
|F-test T1=T2 (p-value)|0.85|0.91|0.43|0.46|
|F-test T1=T3 (p-value)|0.77|0.81|0.36|0.43|
|F-test T2=T3 (p-value)|0.63|0.72|0.93|1.00|
|Selected Controls||X||X|

Notes: The outcome in the columns 1-2 is the standardized variable created to indicate connectedness to other ethnicities. In columns 3-4 is the is the standardized variable created to indicate connectedness to own ethnicity. Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

# **5. Conclusion**

This study used four short VR experiences to investigate whether improvements in urban design elements of a public park would increase willingness to use it, whether exposure to diversity would improve social cohesion, and how the two would interact. The results unequivocally suggest that 21