rafmacalaba/datause-encoder-data huggingface dataset:
Table 12 Results on Turnover for Iron and Steel

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|0.533***|0.483***|-0.0484|0.172|
||(3.44)|(4.39)|(-0.30)|(0.41)|
|Phase 2 Participation|0.239*|0.404***|0.117|0.592|
||(1.70)|(3.65)|(0.51)|(1.01)|
|Phase 1×Log(Surrendered)||||-0.00092<br>(-0.02)|
|Phase 2×Log(Surrendered)||||0.0498<br>(0.46)|
|Phase 1×Log(Allocated)||||-0.0195<br>(-0.46)|
|Phase 2×Log(Allocated)||||-0.0897<br>(-0.86)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations|987|987|987|987|
|Adjusted_R_<sup>_2_</sup>|0.022|0.054|0.281|0.279|

Note: Dependent variable is the logarithm of operating revenue. Standard errors are clustered at the firm level and t statistics are shown in the respective parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

# **6    Conclusion**

The EU ETS is the first international cap-and-trade program for CO2 and the largest environmental pricing regime in the world (European Commission 2012). Covering more than 12,000 power stations and industrial plants in 30 countries, it will finish its second phase and expand to more industrial sectors such as petrochemicals in 2013. Understanding the impacts of such a large-scale environmental intervention on firms is important not only because it is a critical step to examine the cost-effectiveness of the program itself, but it can also provide useful lessons for other countries and regions that are contemplating cap-and-trade programs, such as China and India.

While there exists a large literature on the U.S. SO2 allowance trading program, the world’s first (and now virtually collapsed) large-scale cap-and-trade program, empirical studies on the EU ETS program are much scarcer. Our paper adds to this literature by investigating the impacts 22
Gruene, L. and W. Semmler (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. _Journal of Economic Dynamics and Control_ 28, 2427– 2456.

Hamilton, J. and M. Flavin (1984). On the limitations of government borrowing: A framework for empirical testing. _The American Economic Review_ 76, 808–819.

Kremers, J. (1988). Us federal indebtedness and the conduct of fiscal policy. _Journal of Monetary Economics_ 23, 219–238.

Lucas, R. (1988). On the mechanics of economic development. _Journal of Monetary Economics_ 22 (1), 3–42.

Reddy, S. and C. Minoiu (2006). Development, aid, and economic growth: A positive long-run relation. _DESA Working Papers_ 29, United Nations, Department of Economics and Social Affairs.

Rodrik, D. (2005). Why we learn nothing from regressing economic growth on policies. _Mimeo_ , Kennedy School of Government.

Suescun, R. (2005). Fiscal space for investment in infrastructure in Colombia. _Policy Research Working Paper Series_ 3629, World Bank.

Tanzi, V. and H. Zee (1997). Fiscal policy and long run growth. _IMF Staff Papers_ .

Temple, J. (2003). The long-run implications of growth theories. _Journal of Economic Surveys_ 17 (3), 497–510.

Trehan, B. and C. Walsh (1991). Testing inter-temporal budget constraints: theory and applications to us federal budget and current account deficits. _Journal of Money, Credit and Banking_ 23, 206– 223.

W.D.I. (2005). World development indicators. The World Bank.

Wilcox, D. (1989). The sustainability of government deficits: Implications of the present-value borrowing constraint. _Journal of Money, Credit and Banking_ 21, 291–306.

World-Bank (2006). Fiscal policy for growth and development: An interim report. _Background Paper for the Development Committee Meetings_ , Development Committee.

World-Bank (2007). Fiscal policy for growth and development: Further analysis and lessons from country case studies. _Background Paper for the Development Committee Meetings_ , Development Committee.

Zagler, M. and G. Durnecker (2003). Fiscal policy and economic growth. _Journal of Economic Surveys_ . 37

-  - 24
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE One of the corridors in the southern cluster is the Maputo Development Corridor. The Maputo Corridor connects Maputo with the South Africa’s Gauteng province, running through one of the most highly industrialized and productive regions of the Republic of South Africa. The corridor is considered by African policy makers one of the the most successful stories in Africa in terms of improved crosscountry trading. At the western end of the corridor are Johannesburg and Pretoria, and moving east toward Mozambique, the corridor passes through the areas of aluminum production close to Maputo and the industrial development of Motzal.

One of the most promising emerging corridors is that running from Moatize to Nacala via Malawi. Currently the railway part of the corridor is not complete. There are 200 km of rail missing just outside the Malawi border. Because Malawi enters as an indentation into the Mozambican territory, it imposes a disconnect between areas rich in natural resources and export points and internal markets (figure 3d). The implications for transport infrastructure are direct. By way of example, one of the main economic drivers for the development of the Moatize–Nacala railway is the potential for coal export from the Tete area. The port of Beira is insufficient to manage the 20–25 million tonnes of coal that can be produced, necessitating the completion and upgrading of this railway to connect to the other natural exit port at Nacala. The railway must pass through Malawi, as other routes, such as staying within the Mozambican border to circumvent Malawi, do not make economic sense. This creates the challenge of defining and relying on regional agreements and building regional infrastructure in coordination with Malawi.

On average, the combination of multimodal transport infrastructure and recently improved trade logistics is increasingly positioning Mozambique as one of the countries with the lowest costs of trading across borders. The cost of export and import in Mozambique are about 60 percent of the average costs in Sub-Saharan Africa, and the time required to export and import is around 70 percent of the Sub-Saharan average (table 2).

**Table 2. Trading across borders in southern African countries**

|Country|Documents to<br>export (number)|Time to export<br>(days)|Cost to export ($ per container)|Documents to<br>import (number)|Time to import<br>(days)|Cost to import<br>($ per<br>container)|
|---|---|---|---|---|---|---|
|Angola|11|65|2,250|8|59|3,240|
|Botswana|6|30|2,810|9|41|3,264|
|Lesotho|6|44|1,549|8|49|1,715|
|Madagascar|4|21|1,279|9|26|1,660|
|Malawi|11|41|1,713|10|51|2,570|
|Mauritius|5|14|737|6|14|689|
|Mozambique|7|23|1,100|10|30|1,475|
|Namibia|11|29|1,686|9|24|1,813|
|Swaziland|9|21|2,184|11|33|2,249|
|Zambia|6|53|2,664|9|64|3,335|
|Zimbabwe|7|53|3,280|9|73|5,101|
|Sub-Saharan Africa|8|34|1,942|9|39|2,365|

_Source:_ Doing Business 2009.

9
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3.48 **It is important to implement the transaction processing layer first to get credible and complete information** for financial operations and management reporting.<sup>43</sup> The handbook for public expenditure management stipulates that “information is the life blood of budgetary, resource allocation and financial management” (World Bank 1998, p.59). At a most fundamental level, this requires the data to have integrity and completeness, which can only be achieved if the transaction processing layer is intact and reliable. The quality of information available from the financial operations and management reporting layers will depend on the quality, timeliness, and comprehensiveness of the transaction data captured by the transaction processing layer. Therefore, even though implementing the transaction processing layer is the most difficult and time consuming, it should be prioritized (Hashim 2014). Once this is done, the other layers can be implemented with relative ease. For example, it took five to six years to implement a comprehensive transaction processing layer in Russia, but afterward, the treasury implemented strong analytical and reporting tools within six months to provide the government with useful information for monitoring financial operations, management, and statutory reporting.

3.49 In Malawi, the transaction processing layer was weak because transactions were done outside the system, and spending units kept a second set of books. This was found to undermine the data integrity in the transaction processing layer, which makes any functional and economic reports coming out of the system questionable. In other countries (such as Ghana and Zambia), the transaction processing layer did not include large portions of government financial resources and is thus not comprehensive. Thus, financial operations and management reporting will be based on incomplete data.

3.50 An alternative modular approach discussed by Gerardo Uña, in a recent blog post, draws on recent ICT developments in the private sector and holds promise for making systems implementation more effective and easier to maintain. The idea is to establish a common financial information architecture, which is interfaced with all core and non-core modules. These may be developed separately, but can communicate with one another. The state of São Paolo in Brazil is spearheading this approach<sup>44</sup> (IMF 2017). Regardless of the approach, it remains paramount to ensure the integrity of the financial transactions as all other layers or modules will draw on this. If data from the financial transactions are compromised, so will be all other layers or modules.

> 43 See project documentation for FMIS operations in Ghana, Malawi, and Zambia.

> 44 More information on this approach can be found at the following IMF PFM blogpost: http://blogpfm.imf.org/pfmblog/2017/03/a-new-path-to-improve-fmis-performance.html.

**40**
market ‘insiders’ and ‘outsiders’. While some of the social institutions behind these social barriers are probably harder to change, other responses will require more complex interventions that reach beyond narrowly defined labor market reforms to address more fundamental differences in the distribution of skills and social advantages.

The other caveat is the relatively tentative nature of much of the evidence on which claims of the positive effects of jobs are based. Rigorous analysis of the social interactions to which many of the positive effects of work are attributed is notoriously difficult because of the potential selection problems and other contextual influences involved. These problems are amplified because many of the advances in the design of studies and surveys that are used to document the positive effects of work on economic outcomes have often not (yet) spilled over into the analysis of non-monetary dimensions of jobs. Against this background future analytical progress will depend to a large extent on more routine integration of social cohesion indicators in the monitoring of labor market outcomes. In other contexts more complex research designs will be needed that integrate careful political economy analysis at the aggregate level with detailed study of individual level interactions in the work-place and other relevant social contexts.

# **Bibliography**

- Aaronson, D. (1998). ‘Using Siblings Data to Estimate the Impact of Neighborhoods on Children's Educational Outcomes’ _. Journal of Human Resources_ , 33(4): 915-946.

- Acemoglu, D, Robinson, J.A. and Verdier, T. (2012): ‘Can't We All Be More Like Scandinavians? Asymmetric Growth and Institutions in an Interdependent World’. NBER Working Paper No. 18441. Cambridge Mass.

- Akerlof, G.A., Kranton, R.E. (2000). ‘Economics and Identity’. _The Quarterly Journal of Economics_ 115 (3): 715-753.

- Alesina, A. and Drazan, A. (1991). ‘Why are Stabilizations Delayed?’ _American Economic Review_ 81(5): 1170-1188.

- Alphonse, M., George, P and Moffat, K. (2008). ‘Redefining social work standards in the context of globalization: Lessons from India’. _International Social Work._ 51(2): 145-158

- Altindag, T. D. and Mocan, N.H. (2010). ‘Joblessness and Perceptions about the Effectiveness of Democracy’. NBER Working Paper No. 15994. Cambridge Mass.

- Amin, S., Diamond, I., Naved, R.T., and Newby, M. (1998). ‘Transition to Adulthood of Female Garment-Factory Workers in Bangladesh.’ _Studies in Family Planning_ 29 (2): 185–200.

- Anderson, E (1999). _Code of the Street: Decency, Violence, and the Moral Life of the Inner City_ , New York: W.W. Norton.

- Aslund, O., Östh, J., and Zenou, Y. (2010). ‘How Important is Access to Jobs? Old Question-Improved Answer’. _Journal of Economic Geography_ 10 (3): 389-422

26
Table 6. Productivity dispersion by gender in country-industry cells

|Variable|Gender|N. of cells|Mean|Std. Dev.|Diff. (S.E.)|
|---|---|---|---|---|---|
|SD of TFP|female|183|1.090|0.480|0.076*<br>|
||male|183|1.165|0.336|(0.043)|
|SD of LP|female|326|1.077|0.372|0.092***<br>(0027)|
||male|326|1.169|0.323|.|
|SD of LP Growth|female|317|0.245|0.184|0.024*<br>|
||male|317|0.269|0.153|(0.013)|

Notes: Each cell has at least 5 female-headed firms and 5 male-headed firms.

All variables are winsorized at 1% level.

36
electricity grids and other regulatory frameworks, and less developed market as the trade is limited between single government-owned entities. Moreover, the interconnected systems are often not synchronized, therefore, part of a national grid system must be isolated from the main grid to accept imports from another country (World Bank, 2013).

# **3. Analytical Approach**

The power system planning model developed for this study is an inter-temporal or dynamic optimization model. It determines a sequence of investment decisions to build new power generation capacities while optimizing the least-cost option to meet the projected load satisfying various constraints. The constraints include the resource, technological, environmental, policy, and any other constraints specified by the modelers. The long-term investment planning and the shortterm economic dispatching are part of a single joint optimization process as opposed to two separate stages. There are several simplifying features of the model. These include:

- Non-strategic participation – all countries agree to participate in a regional cost-minimizing plan; or equivalently, individual power plant owners submit their true costs as bids, and individual purchasers behave competitively in revealing their demands.

- The electricity demand projections and load forecasts in each country or grid are exogenous and perfectly price-inelastic.

- Cross-border transmission interconnections are taken as exogenous, since the region already has a large unutilized cross-border interconnection capacity.<sup>4</sup>

The objective function of the model is to minimize the sum of discounted values of different cost components. They include capital (investments) and fixed O&M costs, fuel costs, variable O&M costs, and costs of reliability (costs of energy not served, and costs of the reserve not meeting) in all zones (or grids) and for the entire planning horizon (all years considered).<sup>5</sup> The

> 4 One could jointly optimize the generation and cross-border transmission systems like in Timilsina and Toman (2016). Doing so, however does not help due to the large excess or unutilized cross-border transmission capacity that is already in place or committed. It would simply complicate solving the model due to the large number of countries (18) included.

> 5 Detailed description of the model, including mathematical equations, is available in World Bank (2020).

6
# **2.3 Student Attitudes**

PISA applications in each test round have a focus on one of the subjects and in PISA 2012 the focus subject was mathematics. Mathematics happens to be the subject where the mean score difference is highest between Vietnam and Dev7 countries. The PISA questionnaire for students includes a very interesting series of questions regarding students’ perceptions of their abilities, their effort and their reported practices. The details of these questions can be found in the PISA technical report [OECD, 2014a]. Typically, each question includes a set of Likert scaled items to which the student provides a discrete response on a four point agree-disagree scale. These responses are then combined under specified algorithms to provide an index value. For instance, MATWKETH, is meant to measure a student’s “mathematics work ethic”. Students either agree or disagree with a set of 9 items on a 4 point likert scale - strongly disagree, disagree, agree and strongly disagree. The items include items such as “I work hard on my mathematics homework”, and “I listen in mathematics class”, “I keep my mathematics work well organized”. In the case of MATWKETH, when a student agrees/strongly agrees with a positive statement, or disagrees/strongly disagrees with a negative statement, he or she would tend to be deemed to have a stronger work ethic towards mathematics. The raw data from the Likert scale is converted into an index using IRT scaling procedures, so that the mean for OECD countries is 0 and the standard deviation is 1. Table 3 indicates a most interesting finding regarding a range of such indices from the PISA database.

**Table 3: Student self-perception regarding mathematical ability and student effort**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Indices susceptib**|**le to ’bragging’ tag**|||||
|MATWKETH _(r)_|Mathematics<br>work ethic|0.4514<br>(0.9782)|26140|-0.0014<br>(0.6915)|3217|
|SUBNORM _(r)_|Subjective norms<br>in mathematics|0.716<br>(1.165)|26509|-0.0923<br>(0.8395)|3220|
|OPENPS _(r)_|Openness to<br>problem solving|0.1949<br>(0.9787)|25612|-0.6125<br>(0.8708)|3207|
|SCMAT _(r)_|Self-concept of<br>own math skills|0.1673<br>(0.8101)|26222|-0.1896<br>(0.5903)|3249|
|**Indices less relat**|**ed to bragging/being **|**boastful**||||
|PERSEV _(r)_|Perseverance<br>in problem solving|0.3387<br>(0.9605)|25710|0.4475<br>(0.8767)|3211|
|ANXMAT _(r)_|Mathematics<br>Anxiety|0.3995<br>(0.7724)|26275|0.2115<br>(0.6354)|3248|
|MATINTFC _(r)_|Mathematics<br>intentions|0.092<br>(0.9837)|24827|0.3285<br>(1.0964)|3181|

Notes: The variables relate to the questionnaires administered to students in the rotated booklet, marked with _(r)_ . For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

9
2

# I. **INTRODUCTION**

While there is a growing literature on the incidence and consequences of shocks in developing countries, most of the studies to date take a partial approach, analyzing one particular shock or set of shocks. For example, Kochar (1999) looks at agricultural shocks in India, Hoddinott (2006) looks at the effects of drought in Zimbabwe, Gertler and Gruber (2002) look at the effects of health shocks in Indonesia, and Friedman and Thomas (2009) look at the effects of the 1997 financial crisis in Indonesia. Relatively few studies compare different types of shocks. There are exceptions. Kenjiro (2005) compares crop failure and illness in Cambodia and finds that while similar in terms of incidence, illness has a larger impact on living standards; and Heltberg and Lund (2009) report the results of a multi‐shock study in Pakistan, covering natural and agricultural shocks, economic shocks, law and order‐related shocks, family shocks, and health shocks. Similar “shocks surveys” have also been implemented elsewhere (see Hoddinott and Quisumbing 2003; Hoddinott and Quisumbing 2003; Dercon et al. 2005), but these studies are exceptions. The first aim of this paper therefore is to add to the relatively slim literature by comparing a broad range of different types of shocks in terms of their incidence, distribution between the poor and better off, idiosyncraticity, costs, coping responses, and the degree to which households are able to smooth consumption following shocks.

As well as shedding light on how health shocks compare with other shocks along these traditionally‐explored dimensions, we also explore new angles to health and other shocks. In addition to presenting (some necessarily incomplete) evidence on consumption‐smoothing, we present data on households’ own assessments of the impacts of shocks on well‐being: our survey asks households whether they cut back consumption following a shock, how far it affected household welfare, and how far the household had recovered from the shock by the time of the interview. This ‘softer’ evidence is a