rafmacalaba/datause-encoder-data huggingface dataset:
The severe drought in 2007 brought focus on the increasing competition for water between urban and rural areas. Metropolitan municipalities, mainly in the Western part of the country, have already begun to develop and implement projects to bring water from rural areas to the cities. This has increased the stress on irrigation water resources. The model structure reveals the possible effects of carrying on and extending this transfer policy on crop production pattern and other key economic variables.

Finally, climate change scenarios have also attracted considerable attention in Turkey. Climate change is expected to reduce precipitation in most parts of Turkey depending on the region. Reduced precipitation is anticipated to have severe adverse effects on the rainfed agriculture. Even the irrigated agricultural production will demand more water as a result of reduced precipitation. Combined by the increase in the urban demand, the pressure on water resources will certainly increase. The discussion on this topic is generally limited to the price (and hence production) effects of climate change. We attempt to simulate the effects of the anticipated climate change by shocking the yields of various crops to see the economy wide effects. The following section presents the aggregate results for the scenarios. A thorough discussion on the design of the scenarios together with the obtained results can be found in the subsequent sections.

# **4.1 Aggregate Results of the Simulations**

World price increase is simulated by introducing the estimations given in FAO and OECD (2008). Product specific changes in prices are reported in Table 4.3. The average increase is about 30 percent. Water is reallocated from agriculture to urban and industrial use in the West region in the urbanization scenario. Simulations involve increasing urban water supply by 30 percent (Scenario 2.a) and 50 percent (Scenario 2.b) with a corresponding reduction in water available for irrigation in this region. Lastly climate change scenario is quantified by a fall in the yields of rain-fed crops by 30 percent (Scenario 3.a) and then by an additional decline in the yields of irrigated crops by 10 percent (Scenario 3.b). The regional yields of both rain-fed and irrigated crops are reduced at varying degrees in the last version of climate change scenario (Scenario 3.c).

Total nominal and real GDP decline, almost in all scenarios, with the exception of the urbanization scenario. The magnitude of the decline in nominal GDP is highest in the climate 34
# **I. Introduction**

Recent rapid economic growth of countries such as China and India brings the promise of a better life to much of the world’s population. As developing countries grow, attention often turns to the environment. For instance, a recent World Bank report shows that levels of particulate matter (PM) in urban areas are over twice as high in low and lower-middle income countries than in upper-middle and high income countries. As a result, these low and lowmiddle income countries lose an average of 0.7% of gross national income from PM-related damages, compared to just 0.3% for high income countries (World Bank, 2009). Similarly, Dasgupta _et al_ . (2008) provide estimates from a series of World Bank studies in the Middle East and North Africa on the costs of environmental degradation from indoor and outdoor air pollution, inadequate water and sanitation services, land degradation, coastal zone degradation, and solid waste disposal. These costs range from 2% of GDP in Tunisia to 7% of GDP in Iran.<sup>1</sup> Such costs from pollution suggest that promoting a cleaner environment need not conflict with efforts to promote economic growth. Green growth seeks to encourage economic growth and development in a way that balances concerns about environmental harm with the need for long-term economic growth. Reducing environmental harm can come from end-of-the-pipe solutions that remove pollutants from the waste stream before they enter the environment, or from behavioral changes that reduce the use of resources in production or consumption. Reducing resource usage reduces the pollution that comes from these resources (e.g. reducing fossil fuel consumption reduces greenhouse gas emissions) and reduces stress on the environment from the extraction of natural resources.

> 1 These studies consider the costs from indoor and outdoor air pollution, inadequate water and sanitation services, land degradation, coastal zone degradation, and solid waste disposal.  Thus, not all of these costs would be measured using conventional definitions of national income.

2
**Table 4: Duration of suspension and dynamic effects**

||lnEx<br>|ports<br>|Export Dummy|
|---|---|---|---|
||Coefficients|Marginal effect||
||(1)|(2)|(3)|
|Suspended×Country×Product×short|-0.141|-13%|-0.001|
||(0.239)||(0.012)|
|Suspended×Country×Product×long|-0.601*|-45%|-0.023*|
||(0.358)||(0.014)|
|Product-year Fes|Yes||Yes|
|Country-year Fes|Yes||Yes|
|Country-product Fes|Yes||Yes|
|Number of observations|584,434||584,434|

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

As shown in columns (1) and (2) of table 4, the suspension appears to have a significantly larger negative impact on exports to the US for countries with _longer-term suspension_ of AGOA eligibility, exceeding two years; the AGOA suspension resulted in a statistically significant 45 percent drop in exports and 2.3 percentage points drop in the probability of exporting from AGOA eligible countries. However, the effect on both exports and probability of exporting is insignificant for short-term suspended countries.

In table 5, we consider how the suspension effect varies with pre-suspension utilization rates. Coulibaly, Kassa and Zeufack (2022) document a widespread underutilization of AGOA benefits. Table 2 also confirms that there is a huge disparity in AGOA utilization across countries prior to the suspension. Therefore, the AGOA suspension is more likely to affect those exporters that claimed the benefits prior to the suspension. To gauge how the suspension impact varies with the extent to which AGOA preferences were utilized prior to suspension, we interact the triple interaction term with three dummies: (i) high18
Wald ( _ρ_ = 0) 1.202 N = 96 Note: † - significance based on a 1-tailed t-test; *, **, *** - significant at the 10%, 5%, and 1% level, respectively.

Pesticide toxicity, by class, has a positive impact on the probability of falling sick. The effect is quite similar across the three toxicity levels, where a 1% increase in handling a particular toxicity class increases the probability of falling sick, on average, by 0.024%. Traders who are in business one additional year are associated with a higher probability of falling sick, by 0.024 (or 2.4%), however, older traders with more experience tend to have a lower probability of falling sick (0.013) (or 1.3%). The positive coefficient on the interaction term between hazardous pesticides and training has a positive overall effect on the probability of sickness of 0.005%. However, this may also imply that the training content received by this sample of traders was relatively ineffective in communicating the relative risk of alternative pesticides.<sup>16</sup> Although of correct sign, the impact of the interaction between pesticide toxicity (WHO Ia or Ib + WHO II) and protective measures during loading is not statistically significant.<sup>17</sup> Table 5. _Marginal effects of trader health risk perception model_

|**_Variables_**<br>**_Direct effect_**|**_Indirect effect_**|**_Total effect_**|**_Nature of variable_**|
|---|---|---|---|
|_Sickness Equation_||||
|PESTAMT<br>0.035||0.035|Continuous|
|PWHOII<br>0.020**||0.020**|Continuous|
|PWHOIII<br>0.028***||0.028***|Continuous|
|PWHOU<br>0.024***||0.024***|Continuous|
|BUSYEAR<br>0.024**||0.024**|Continuous|
|AGE<br>-0.013*||-0.013*|Continuous|
|NUTRTON<br>-0.708||-0.708|Continuous|
|SMOKNUM<br>-0.031||-0.031|Continuous|
|PIabPIITRN<br>0.005*||0.005*|Continuous|
|PIabPIIPROT<br>-0.002||-0.002|Continuous|
|_Perception Equation_||||
|SICKNESS<br>0.397***||0.397***|Binary endogenous|
|PESTAMT<br>-0.037|0.014|-0.023|Continuous|
|PWHOIab<br>0.001||0.001|Continuous|
|PWHOII<br>0.006*|0.008**|0.014*|Continuous|
|PWHOIII<br>-0.013***|0.011***|-0.002***|Continuous|
|TIMEDAY<br>0.051**||0.051**|Continuous|
|TRNGSAFE<br>0.337*||0.337*|Binary|

> 16 An overwhelming 90% of the traders pointed out the need for further instructions on pesticide use and

> handling.

> 17 The survey reveals that 92% of traders did not use any protective measure during loading.

16
over the last decade. In contrast, few landowners in Thyolo or Mulanje were willing to sell to the CBRLDP. Thus, beneficiaries were relocated from all four districts to former estates in Mangochi and Machinga.

The Ministry of Lands and Housing, working through a Lands Project Office in each participating district, conducted a sensitization campaign to inform potential beneficiaries about the project. Interested households registered with local traditional authorities and were asked to form Beneficiary Groups (BGs) of 10 to 35 households. Village authorities, group leaders and project personnel at the district level verified the eligibility of prospective beneficiaries. To be eligible, one had to be relatively land poor or landless Malawian national from one of the four targeted districts. Beneficiary groups, with the assistance of Lands Project Officers as needed, began the land identification and acquisition process.

The process of land acquisition was organized by project officials from Mangochi and Machinga, who contacted local estate owners and created a database of owners willing to sell. Groups from Thyolo and Mulanje relied upon the project database to identify potential land and sent group leaders to evaluate prospective estates on behalf of the group. Potential beneficiaries from Mangochi and Machinga were generally familiar with local estates and estate owners and did not need project assistance to identify land. In many cases these groups lived near the land they subsequently legally acquired, thus hardly moving. Those who moved onto different pieces of land often maintained the original piece of land (for extended family members that did not move, or for themselves).

Once the land was identified, the BGs entered into negotiation directly with the owner of the estate to agree on a price, and a binding provisional sale agreement was signed by all par8
negative at the 10 percent level. The patent density variable is less significant, but remains positive and statistically different from zero at the 10 percent level. The growth of manufacturing GDP in years preceding the firm surveys is consistently estimated with a negative coefficient, which is statistically different from zero at the 10 percent level in specifications (2) and (3).

The results on the regulatory index and the business density variables stand out for its lack of statistical significance in all of the specifications. These results indicate that the standard channels through which the Market Structure hypotheses are expected to be present remain ambiguous, and thus hypotheses 6 and 7 which posit that the effects of these variables should be ambiguous.

# **Conclusions regarding the seven hypotheses**

An inter-disciplinary literature from economics and IB on Global Engagement suggests that firms innovate when they can import technologies and know-how from abroad. Another literature focuses on Information Spillover effects of innovation, be it R&D-driven innovation, product innovation driven by a process of “discovering” profitable products, or by adopting foreign technologies. A Market-Structure literature predicts both a positive and a negative relationship between incumbent-firm innovation and the threat of entry by competitors.

The Global Engagement and Information Spillovers hypotheses are supported by the evidence: Exporting status and licensing foreign technologies seem robustly correlated with firms’ probability of introducing a new product, but industry-level trade-policy distortions or foreign ownership do not have an independent effect. Knowledge density (the stock of patents per capita) is robustly correlated with product innovation rates. The evidence on Market Structure is mixed: Neither business density nor regulations that raise the costs of entry by firms are robustly correlated with product innovation probabilities. The size of firms is a significant correlate of product innovation, but the magnitude of this effect is small and declines with firm size. Good governance, however, tends to be negatively partially correlated with product innovation rates, which may reflect the ability of incumbent firms to influence public policy so as to deter entry through means that are not captured by the regulatory environment. That is, in countries with poor governance, state capture by incumbent firms might hamper entry by new firms, thus raising the probability of product innovation by incumbents.

From a policy perspective, the probability of product innovation seems to be counter-cyclical, and thus the budgets of programs to stimulate product innovation need to be protected during downturns, so as to prevent the demise of firms that could have survived through retooling in terms of product innovation.

14
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- Auten, Gerald, Gee, Geoffrey, & Turner, Nicholas. 2013. Income Inequality, Mobility, and Turnover at the Top in the US, 1987-2010. _American Economic Review_ , **103** (3), 168–172.

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- Burdin, Gabriel, Esponda, Fernando, & Vigorito, Andrea. 2014. _Desigualdad y sectores de altos ingresos en Uruguay : un an ́alisis en base a registros tributarios y encuestas de_ .

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22
**_Energy demand models for policy_** **_<u>formulation</u>_**

EIA (2007) presented a retrospective review of the projections contained in the Annual Energy Outlooks between 1982 and 2007. The review shows that the overall energy demand was quite close to the actual demand but the difference was somewhat high for natural gas demand and energy price forecasts. The main driving variable, GDP, was less accurately forecast, which influenced other outcomes directly.

Although NEMS is a detailed model, its use has remained confined to government agencies and a limited number of research laboratories because of the model’s reliance on costly proprietary software packages and complex model design.

# **ERASME model**

ERASME is a short-term energy model that is used by the European Commission for quarterly forecasting of energy demand at the Community level (Deimeizis, 1995). It also has a supply-side forecasting and the model produces the forecasts of energy balance. The results of the model feed into the Short-Term Energy Outlook of the Commission. The model contains 55 behavioral relations and a large number of identities capturing the European energy system. The model uses the data obtained from the Statistical Office of the Community and the equations are re-estimated twice a year. The estimation process relies on OLS except for electricity where a three stage lease square approach is used.

The demand-side of the model relies on the following logic: Final energy prices are considered to be a function of international oil prices, coal import prices, exchange rate, changes in the fiscal regime and seasonal factors.

Pf = f(PM, XR, T, SF) (6) Where Pf = fuel price PM = price of imported fuel (coal or oil) XR = exchange rate 81
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Cho, Yoonyoung, and Maddalenna Honorati, 2012, “Entrepreneurship Programs in Developing Countries: A Meta-Regression Analysis”, mimeo de Mel, Suresh, David McKenzie and Christopher Woodruff (2010), “Who are the Microenterprise Owners? Evidence from Sri Lanka on Tokman v. de Soto,” in _International Differences in Entrepreneurship_ , Lerner and Schoar, eds, University of Chicago Press.

De Soto, Hernan, 1989, _The Other Path: The Economic Answer to Terrorism_ , Basic Books, New York.

Djankov, Simeon, Edward Miguel, Yingyi Qian, Gerard Roland, and Ekaterina Zhuravskaya, 2005 “Who are Russia’s Entrepreneurs?” _Journal of the European Economic Association_ , Vol 3(2-3), pp. 1-11.

Djankov, Simeon, Yingyi Qian, Gérard Roland, Ekaterina Zhuravskaya , 2006, “Who Are China's Entrepreneurs?” _The American Economic Review_ , Vol. 96, No. 2 (May), pp. 348-352.

Fields, Gary S., 1990, Labor Market Modelling and the Urban Informal Sector: Theory and Evidence. In D. Turnham, B. Salomé and A. Schwarz (eds.), _The Informal Sector Revisited_ . OECD, Paris.

Fields, Gary S., 1975, “Rural-Urban Migration, Urban Unemployment and Underemployment, and Job Search Activities in LDC’s _,” Journal of Development Economics_ , Vol. 2, pp. 165-88.

Grimm, Michael, Peter Knorringa and Jann Lay, 2011, “Informal Entrepreneurs in Western Africa: Constrained gazelles in the lower tier,” International Institute of Social Studies Working Paper 537 Maloney, William, 2004, “Informality Revisited”, _World Development_ , vol 32 no. 7, Tokman, Victor, 2007, “Modernizing the Informal Sector,” UN/DESA Working Paper No. 42.

17
Policy Research Working Paper 9334

# **Abstract**

To aid national and international efforts to support countries in enhancing their pandemic preparedness in the face of COVID-19, this paper draws from the World Bank’s Service Delivery Indicator surveys to highlight key aspects of health service preparedness in Kenya, Sierra Leone, and Tanzania. The results of this analysis paint a highly variable picture. At least 10 percent of lower-level facilities in all three countries have inpatient care capacity, suggesting that these lower-level facilities could help absorb surges in patient flow. Less than half of the facilities in the three countries have a fixed or mobile phone and less than a third have internet access, suggesting a likely challenge in communication and timely sharing of essential information. Concerningly, less than half of the facilities have appropriate handwashing facilities for patients, with even lower rates in rural areas. Between 80 and 95 percent of the facilities have a thermometer to diagnose fever, but availability of a thermometer, stethoscope, and blood pressure cuff together is variable (ranging from almost 90 percent of the facilities in Tanzania to less than 65 percent in Sierra Leone). The paper concludes by highlighting key innovations for future surveys to improve measurement of pandemic preparedness.

This paper is a product of the Office of the Chief Economist, Human Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jsharma3@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