rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 1.12 Real Wage and employment growth in private and state enterprises in machine building sector (20062010) <!-- Start of picture text --> Private enterprises State and mixed ownership enterprises<br>25.0%<br>25%<br>20.0%<br>20%<br>15.0% 15%<br>10.0% 10%<br>5.0% 5%<br>0.0% 0%<br>-5.0% 2006 2007 2008 2009 2010 -5% 2006 2007 2008 2009 2010<br>-10.0% -10%<br>-15.0% -15%<br>-20.0% -20%<br>Real Wage growth Employment Growth<br>Real Wage growth Employment Growth<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**The second fact is the slow technological upgrade of production plants and adoption of organizational practices similar to those introduced in the transport equipment sector in other countries during the past decade.** Slow change has harmed capacity to meet increasing competition from China in traditional product lines and made impossible to move forward and compete in the higher quality market segment.

**Slow technological change is, at least in part, the result of an incentive system that does not foster innovation** . First, SOEs cannot appropriate the results from an innovation increasing their net revenue; their managers may benefit in part from successful innovations but bear the cost of unsuccessful ones. Second, and most relevant for the transport equipment sector, FDI inflows remain low. FDI could enhance growth and competitiveness of the host economy not only through the adoption of new technologies in the production process, but also through knowledge transfers, both in terms of labor training and skill acquisition and by introducing alternative management practices and better organizational arrangements. This
frequency of climate shocks. Power and water infrastructure is crucial. As climate change makes precipitation patterns more variable and unpredictable, investments in public water and power infrastructure systems are an important way in which governments can help firms adapt. Furthermore, governance matters as extreme weather events may erode the business environment for firms with burdensome regulations and corruption. Finally, extreme weather events may lead firms in MENA to adapt through adoption of green measures, although this starts from a very low base.

In summary, the study contributes to the literature by examining the effects of precipitation shocks on the private sector in the MENA region. Second, it confirms findings from other studies, showing that the performance of private sector firms in MENA is similar to that of firms in other regions in terms of being vulnerable to negative precipitation shocks. Third, the study explores the various channels of the effects, highlighting channels that are similar to and different from firms in other regions. Finally, the study shows the scope of adaption with regards to adopting green measures. The rest of the paper is structured as follows. Section 2 describes the data and the empirical approach. Section 3 provides the results with robustness checks, and section 4 concludes.

# **2. Empirical Approach**

## **2.1 Data**

### **_2.1.1 Enterprise Surveys_**

The main data source is cross-sectional firm-level surveys across the world from the World Bank’s Enterprise Surveys (ES) for the MENA region. The ES are nationally representative surveys of private formal (registered) firms with 5 or more employees and cover manufacturing and services firms largely collected via face-to-face interviews with business owners or top managers. The sample is restricted to surveys in MENA where geo-located information of firms is available. The sample consists of about 9,500 firms (depending on the specification) across seven MENA economies. These include Egypt, Jordan, 5
Empirically, the challenge is to estimate a model of the child labor decision which captures the household's behavior with respect to labor market participation, education, fertility, risk management and other relevant factors. The paper below presents one such approach, relying on a reduced-form model which portrays the child labor decision as a three-stage sequential process. An alternative view and model of the child labor decision as a simultaneous process is also presented in an appendix. The case study is for C6te d'Ivoire in 1988, in an economic setting of severe recession and, as a result, rising child labor.

One of the main difficulties in furthering the empirical analysis of the determinants of child labor is the dearth of national household surveys that include questions on labor market participation addressed at adults and children in the household. Most labor force surveys use a minimum age cut-off of 14 or 15 years, so that, by definition, most official labor force statistics will exclude child labor. This age cut-off is a matter of national practice, and not the result of international guidelines. The latter do indicate that the measurement of the economically active population must use a minimum age limit, but no particular value is specified. The guidelines mention that countries where a large proportion of the labor force works in agriculture should use a lower age limit than highly industrialized countries (Hussmanns et al, 1990).

Because of this, multi-purpose household surveys are often the best source of data on child labor. Such surveys include a wide variety of questions 5
transitional dynamics, and compares different steady-state welfare levels.

**Calibration.** Table 7 summarizes the calibration of the preference param- <u>1</u> eters. I assign _β_ = 0 _._ 97 365 to capture an annual discount rate of 3 percent adjusted for a daily frequency, and, following Kydland and Prescott [1982], set _γ_ = _−_ 0 _._ 5. As a baseline specification, the parameters _α_ 0, _α_ 1 and _η_ are chosen to correspond to those in Kydland and Prescott [1982]. As an alternative specification, _α_ 0, _α_ 1 and _η_ are calibrated instead to match the findings in Hotz et al. [1988]. Appendix A describes the procedure for adjusting these parameters for a daily frequency (Kydland and Prescott [1982] calibrate their model for quarterly data, and Hotz et al. [1988] estimate a model using annual data). The two specifications yield highly similar results, which are distinguishable only at the third decimal point.<sup>21</sup> Since the reported results are rounded at the 2 decimal point level, they are consistent with both specifications.

I calibrate _φ_ so that, given a 5-2 workweek, an agent is indifferent with respect to working on a Saturday given an overtime premium of _s_ .<sup>22</sup> Bell and Hart [2003] estimate the overtime premium to be between 0-50%. This maps into a range of _φ ∈_ (0 _._ 3 _,_ 0 _._ 4). Within this range, I consider the two extreme values _φ_ = 0 _._ 3 _,_ 0 _._ 4.

Table 8 presents the range of welfare gains implied by the set of admissible parameters, and Table 9 presents the corresponding means with their associated 95% confidence intervals. The results suggest that the 5-2 week is not easily dominated by any other week. When _φ_ = 0 _
3 Then in the third section the two systems of paid-extension in Nicaragua are described. Section four applies the analytical framework to available data from two private extension programs in Nicaragua. The paper is concluded with suggestions for policy and directions of future research.

# A **Conceptual Framework for Comparing Paid-Extension Performance**

In evaluating the performance of paid-extension, two comparisons have to be addressed. First, a comparison between paid-extension and public extension performances and second, a comparison among alternative paid-extension programs. Although the analysis should not be detached from the objectives for which paid-extension was initiated on the first place, there are several basic economic rules that should always hold.

Extension inputs and outcomes of public extension services can be measured in many ways. They can be measured in monetary terms such as expenditures on fuel, salaries, and training, and in physical terms, such as number of extensionists or extensionist man-hour employed in the program. Extension output also can be measured in a variety of ways, including the number of farmers contacted by extensionists, farmers' participation in extension activities, changes in agricultural practices due to the provision of extension, improved farm-level physical performance (yields, crop varieties, inputs), and increased farm-level profitability. In the case of private extension, there is also a need to compare between private and public expenditures on extension. We distinguish among four categories of analysis: individual producer level analysis, agency-level analysis, government-level analysis, and social-level analysis. In the following sections we provide a detailed analytical framework for each category, which in turn is the basis for measuring the cost and some performance indicators of paid-extension in Nicaragua.

# **Individual producers**

The impact of paid-extension on individual producers can be estimated in several ways, compared to performance at the no-extension or at the public extension stage. First, by improving technical efficiency, where increase in profit at any given combination of other inputs Xi and X2 is observed (Figure 1). Second, by improving allocative efficiency, where at a given technical
The remainder of this paper is organized as follows: Section 2 presents the data, while Section 3 describes the methodology employed for analysis. Results are interpreted and discussed in Section 4. Section 5 draws key conclusions and policy implications.

# **2. Data**

We retrieved necessary data for the United States, Brazil, India, the United Kingdom, 26 of the European Union (EU) member states,<sup>4</sup> and Norway and Switzerland<sup>5</sup> from January 2018 to September 2021 – a period that includes approximately two years before the beginning of the pandemic and 21 months within the pandemic. Data sources are summarized in Table 1.

# **Table 1: Summary of data sources**

|Category|Data|Source|
|---|---|---|
|Electricity generation|Daily electricity generation by energy type;<br>Monthly installed capacity by energy type|US: EIA<br>Brazil: ONS<br>India: Ministry of Power<br>Europe: ENTSO-E|
|COVID Severity|Daily new cases per million population;<br>Daily new death per million population;<br>Stringency index|CSSE at Johns Hopkins University<br>(JHU);<br>Oxford<br>COVID-19<br>Government<br>Response Tracker|
|Temperature|Daily population-weighted CDD;<br>Daily population-weighted HDD|NOAA<br>Global<br>Historical<br>Climatology<br>Network<br>Daily<br>(GHCN-D);<br>Eurostat<br>and<br>OECD<br>regional<br>database.|
|Fuel prices|Monthly fuel prices by major energy hub (Coal,<br>Australia; Coal, South Africa; LNG, Asia; Natural<br>Gas, EU; Natural Gas, US; Henry Hub Gas; Dubai<br>Crude;
factors such as infrastructure facilities and system management complement growth. The key concern for the government has always been “efficiency”. Indeed, it has been recognized right from the beginning that, to compensate for the natural “comparative disadvantage” associated with a small economy with a limited domestic market and population size, Singapore will have to develop a highly efficient and productive infrastructure system to help reduce production cost and attract foreign investors.

The results of such a drive for efficiency are widely recognized. The IMD World Competitiveness Scoreboard 2002, for example, ranked Singapore as the fifth most competitive country in the world, based on factors such as economic performance, government efficiency,

> 15   See World Development Report 2004.

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# **1 Introduction**

The COVID-19 pandemic sparked a heated debate on the nature of economic shocks, which can have substantial ramifications for the design of macroeconomic stabilization and other policies. This paper quantifies demand, supply, and uncertainty shocks since 2005, allowing for a comparison of the two major global recessions, the 2008-09 Great Recession and the COVID-19 pandemic.

Our approach to decompose economic shocks combines two alternative methods. First, we use the recently popularized natural language processing (NLP) techniques and corporate earning call transcripts to identify sentimental demand, supply, and uncertainty shocks, following the methods of Baker et al. (2016) and Hassan et al. (2020). More specifically, we measure (positive or negative) sentiment around demand and supply discussions and calculate their deviations from long-term trends at both aggregate and sectoral levels. This method enables a systematic quantitative analysis of global demand and supply shocks using earning call transcripts of firms listed in United States stock market and headquartered in 80 countries. We add to the existing literature by providing a perspective of supply and demand shocks over time, and comparing the evolution of these shocks during the COVID-19 pandemic and the Great Recession. We also link these sentiment shocks to global economic activity and inflation. Second, to corroborate the sentiment analysis, we use a structural Bayesian vector autoregression (SBVAR) model to identify structural supply and demand shocks and quantify their impact on output and inflation. The model is identified using sign restrictions: a demand (supply) shock is assumed to move prices and output in the same (opposite) directions.

We highlight the following results. First, a detailed evaluation of the COVID-19 pandemic timeline shows that the corporate sector was exposed to large demand and supply disruptions, 10 standard deviations larger than their longer-term averages. Demand and supply sentiment levels rebounded to their long-term averages by the first quarter of 2021. These supply and demand shocks differ from those of the Great Recession. Dur1
at different maturities are endogenously determined. The model illustrates the trade-off between cheaper short-term borrowing and safer long-term borrowing. On the one hand, investors are risk averse and have a short horizon, so they may need to liquidate the long-term bonds before maturity. As a result, they require a positive term premium to hold long-term bonds. On the other hand, it is costly for the country to generate large amounts of liquidity (or fi scal revenue) in a short period of time. Therefore, long-term debt is safer for the government because it reduces the expected cost associated with rolling over short-term bonds.

The model has several implications. First, it shows that the risk premium on long-term bonds is higher than the risk premium on short-term bonds, and that this difference increases during crises. Second, the model shows what types of shocks are consistent with the observed pattern of maturity choice and premia at different maturities. A negative shock to government resources leads to an increase in the risk premium on long-term bonds and an increase in the maturity structure of bond issues. This is analogous to a positive demand shock on the market for international loans, leading to higher prices and higher quantities of long-term bonds. A negative shock to investors’ wealth, on the other hand, leads to an increase in the risk premium on long-term bonds and a decrease in the maturity structure of bond issues. This is analogous to a negative supply shock, leading to higher prices and lower quantities of long-term bonds.

Empirically, the paper studies the behavior of bond prices and quantities. To do so, we assemble a new database on bond prices for several emerging economies. We use this database to estimate time series of the term premium for each emerging market. In the process, we also estimate spreads, bond returns, and risk premia at different maturities, relative to Germany and the United States (considered to be “safe” countries). We also study the characteristics of bond issuance by emerging economies to understand the relation between the cost
- _Weaker prospects for commodity demand._ To transform recent resource discoveries in LICs into strong, sustained economic growth will require continued robust commodity demand growth, as well as strong governance and institutions to manage the associated revenue windfalls (Addison and Roe 2018).<sup>13</sup> However, long-term prospects for commodity demand are weakening as growth in China—the largest source of commodity demand—slows and shifts towards less resource-intensive sectors (World Bank 2018a).

- _Debt vulnerabilities on the rise._ While government debt ratios in most of today’s LICs are significantly lower than in 2001—helped largely by debt relief initiatives—their general rise since 2013 has contributed to increased vulnerabilities (World Bank 2019a). The interest burden brought about by greater indebtedness could constrain poverty-reducing expenditures, particularly on health and education.

**Figure 7. Challenges LICs face in reducing poverty**

<!-- Start of picture text --> A. Projections of global extreme poverty B. LIC growth<br>Percent Historical growth Percent LICs Poverty-weighted LICs<br>2 x historical growth 7<br>15<br>8 percent growth<br>12 3 percent target<br>6<br>9<br>6<br>5<br>3<br>0<br>4<br>2012 2015 2018 2021 2024 2027 2030 2000-07 2010-18 2019-21<br><!-- End of picture text --> Source: World Bank, World Bank (2018b).

A. Data based on global real per capita growth. 8 percent growth assumes average annual growth in per capita incomes of 6 percent for all countries, but that incomes of the bottom 40 percent of the distribution grow at 8 percent, while those in the top 60 percent grow at 4.7 percent. B. Bars represent GDP-weighted aggregates. Diamonds represent GDP aggregates weighted according to each country’s share in total poverty.

# **5. Conclusion*
# **6.  Summary and Conclusions**

Data on water quality indicates river salinity increased significantly in southwest coastal region of Bangladesh over time (IWM 2003; Dasgupta et al. 2015). Scientists and hydrologists unanimously agree that river salinity in Sundarbans will increase due to sea level rise in a changing climate. In the absence of agreement among scientists about the time and spatial profile of climate change, in this paper we have used a detailed scenario analysis for the Sundarbans region to assess possible impacts of climate change and aquatic salinity on fish species habitats, and the poor communities that consume the affected fish species. Drawing on Dasgupta et al. (2015), we use a digital map of aquatic salinity for 2012 and 27 digital maps for 2050, projected from combinations of three IPCC climate change scenarios (B1, A1B, A2), three global circulation models (IPSLCM4, MIROC3.2, ECHO-G) and three assumptions about the rate of subsidence in the Ganges Delta (2, 5 and 9 mm/year). Our exercise uses 101,600 pixels, at a resolution of 0.327 sq. km per pixel.

We focus on 83 fish species that are consumed by households in the region. Using the salinity tolerance range for each species, we construct digital maps of its stable (12-month) habitats for 2012 and 27 scenarios in 2050. We add across maps to generate species counts for each pixel and compute percent changes for 2012-2050. Our results indicate two broad patterns of change, with brackish water expanding moderately into fresh water habitat in the western part of the region and more broadly in the eastern part. Increase in salinity is expected to have adverse impacts on reproductive cycle, reproductive capacity, extent of suitable spawning area, and feeding/ breeding/ longitudinal migration of fish species.

25
The Evolution of Poverty during the Crisis in Indonesia,

# 1996 to 1999*

Asep Suryahadi (SMERU) Sudarno Sumarto (SMERU) Yusuf Suharso (SMERU) Lant Pritchett (World Bank) Social Monitoring & Early Response Unit Jakarta The findings and interpretations in this report are those of the authors, and should<sup>not be attributed</sup> to the World Bank Group or to any agencies providing financial support to SMERU activities<sup>and</sup> report.

This paper is a revision of the December 1999 version. In this paper, we utilize the full sample of the February 1999 Susenas (National Socio-Economic Survey) of 65,000 households, while<sup>the previous</sup> one was based on an accelerated sample of 10,000 households. We thank BPS for providing<sup>access to</sup> the data.
To measure the fairness and effectiveness of village heads in managing local problems, we consider village-level outcomes in two issue areas where they are typically involved: the distribution of food aid and the handling of land and livestock disputes.<sup>16</sup> The most prevalent type of bias that features in village head’s decision-making in Zimbabwe is bias against political opponents. As table 2 showed, individuals who report having different political opinions from their village head are significantly less likely to view the village head’s court to be a neutral mechanism for resolving disputes. In contrast, we do not observe similar divisions of opinion across gender lines or ethnic lines.<sup>17</sup> As a result, we measure whether the two interventions reduced bias by looking at their effects on household survey respondents who reported having different political views from their village head.<sup>18</sup> We measure bias in food aid distribution by examining the likelihood of people with different political views from the village head receiving food aid, a measure of bias in real outcomes. We examine _perceived_ bias in dispute resolution, measured as the likelihood of people with different political views from the village head viewing most decisions by the village head’s court as fair.<sup>19</sup> We also measure the efficacy of the village head in solving problems in both these areas. We measure the effectiveness of village processes in reducing food insecurity by measuring the effect of households’ measured need on their likelihood of receiving maize food aid in the past year, where our measure of need is based on whether respondents in the household survey reported they had enough grain to feed their family after either harvesting it or buying it on the market.<sup>20</sup> In villages where the village head is more effective in using available food aid to reduce food insecurity, we expect that the need of a household will be a more 16Because the involvement of the village head in these issue
/19**<br>**(LSMS+)**|Yes|
|Iraq|MENA|Upper middle<br>income|Household Socioeconomic Survey|2012/13|No|
|LSMS<br> <br>Malawi|AFR|Low income|Integrated Household Survey|2010/11,<br>**2016/17,**<br>**2019/20**<br>**(LSMS+)**|Yes|
|collection<br>Mali|AFR|Low income|Integrated Agricultural Survey|2014/15,<br>2017/18|No|
|Nepal|SAR|Low income|Living Standards Survey|2010/11|Yes|
|Niger|AFR|Low income|National Survey on Household<br>Living Conditions and Agriculture|2011/12,<br>2014/15|Yes|
|Nigeria|AFR|Lower middle<br>income|General Household Survey|2010/11,<br>2012/13,<br>2015/16,<br>2018/19|Yes|
|Tanzania|AFR|Low income|National Panel Survey|2010/11,<br>2012/13,<br>2014/15,<br>**2018/19**<br>**(LSMS+)**|Yes|
|Uganda|AFR|Low income|National Panel Survey|2010/11,<br>2011/12,<br>2013/14,<br>2015/16,<br>2018/19|Yes|

5