rafmacalaba/datause-encoder-data huggingface dataset:
becomes negative and statistically insignificant from zero. The Column (4) shows that higher consumption of allowances in phase 1 is associated with a larger amount of turnover. This correlation is more likely driven by factors such as demand shocks that simultaneously influence emission and outputs.

Table 9: Results on Turnover for Cement

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|0.160|0.340***|-0.125|-0.405|
||(1.03)|(4.29)|(-1.04)|(-1.00)|
|Phase 2 Participation|0.251|0.368***|-0.159|-0.679|
||(1.63)|(4.23)|(-1.08)|(-1.39)|
|Phase 1×Log(Surrendered)||||0.0949*<br>(1.78)|
|Phase 2×Log(Surrendered)||||0.0839<br>(0.86)|
|Phase 1×Log(Allocated)||||-0.0747<br>(-1.41)|
|Phase 2×Log(Allocated)||||-0.0409<br>(-0.40)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations|1259|1259|1259|1259|
|Adjusted_R_<sup>_2_</sup>|0.002|0.070|0.252|0.270|

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.

Overall, the above results suggest that the EU ETS is neither detrimental nor profitable for the cement industry. There is also little evidence to support carbon leakage.

# **5.3   Iron and Steel**

Lastly, we run similar regressions for firms in the iron and steel industry, and the results are in tables 10-12. In table 10, column (1) shows that the two phases of the EU ETS led to a statistically significant increase in employment, which can be seen from the summary statistics in table 3. As argued above, this result is not valid if there are other unobserved effects that may be correlated with the participation as well as the dependent variable (employment). Columns (2) and (3) therefore control for firm fixed effects and country-by-year fixed effects. We can see that 19
43 Table 7 (Dependent Variable is Return on Assets, ROA) OLS Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|C|-0.14677|-2.06|
|CA|0.125419|5.47|
|TA|6.1214E10|1.13|
|LA|-0.02071|-4.60|
|DT|0.008158|2.86|
|OEA|-0.213746|-3.80|
|MDGR|0.000863|0.30|
|CR5|0.239629|3.44|
|Dl|0.90378|2.44|
|R-Squared||0.56|
|F-Statistic||21.44|
# **KENYA: DEFICIT CONCEPTS**

## **1980-1987**

<!-- Start of picture text --> 4-<br>I Primary<br>2- Operational<br>0 Cash<br>0-<br>C<br>0<br>I-4<br>C --<br>-4<br>eL ~ ~ ~ ~ ~ ~ .2 ~ ~ ~ ~ ~ ~ O<br>-lo<br>1980 1981 1982 1983 1984 1985 1986 1987<br><!-- End of picture text --> Fiscal **Years**
treated at some length in Section 3,<sup>4</sup> thereby laying the groundwork for the results in Section 4. The latter begins by setting out the sequences of the exogenous variables, and then analyzes and compares the reference case, in which the epidemic runs its projected course, with the counter-factual, in which it never breaks out. Turning to policy, the question of what objectives should be pursued is addressed in Section 5, the relation between public spending and mortality is estimated in Section 6, and the social profitability of a particular policy program funded by aid is evaluated in Section 7. Some concluding comments are drawn together in Section 8.

# 2 The model in outline

The basic economic unit is an extended family household, whose members pool their resources.<sup>5</sup> The following assumptions apply to urban and rural households alike. Individuals live for up to three periods: childhood, adulthood and old age. Children may divide their time between working and learning<sup>6</sup> and all children within the family are given the same schooling. On reaching adulthood, these individuals are homogeneous in skills and preferences, and supply their labor completely inelastically. Those who were born in villages also decide, at the very outset, whether they will stay or seek their fortune in the towns, where some members of the extended family are already established. Wherever young adults settle down, they raise children for altruistic as well as self-interested reasons. A social rule determines the level of consumption in the third period of life, with young adults making transfers out of the family’s current income to their parents. The old make no bequests, except of land.

> 4The details of the algorithm for computing a rational expectations sequence are consigned to Appendix B.

> 5Schaffner (2004: 13), for example, reports that 89% of orphans live with their relatives.

> 6According to the CSA (2001), in 2001, 49.0% of children aged 5 to 14 and 67.4% of those aged 15 to 17 were working. ‘All studies made on child labor in urban areas identified household poverty as the major and primary cause of child labor’. (Tegenu, 2003: 28).

6
46 even if they had travelled considerable distances, and were provided with no form of interim protection such as condoms or spermicides.

- O Yet in practice at health centers (the next superior level of health facility), health assistants accounted for almost one third of the time spent on family planning, and it was health assistants who were observed checking the blood pressure and weight of family planning clients during consultations, while nurses appeared to do little in the way of routine checks before issuing oral contraceptives. This finding suggested that in fact health assistants could play a more active role in family planning at the health station level.

# Case 6: INDONESIA NUTRITION DEVELOPMENT PROJECT: AN APPLICATION OF SOCIAL MARKETING

In 1977, the Indonesian Ministry of Health's Health Education Directorate established a Nutrition Communication and Behavior Change Component, with Bank assistance, in five subdistricts of three provinces. The aim was to test communications techniques that would lead to improved nutritional status without involving food supplements or other costly schemes. The techniques used were those of social marketing--the adaptation of commercial marketing techniques to social sector programs. For some reason, the concept of social marketing among PHN specialists in the Bank seems to have become synonymous with the subsidized sale of contraceptives through commercial channels. The Indonesia Nutrition Project and its USAID successor demonstrate that social marketing is a broader approach, whose processes can be applied in any subsector. The assessment techniques and project interventions summarized below have since been further developed with USAID assistance, as described in a report, "The Weaning Project--New Strategies to Improve Infant Feeding Practices," prepared by the Directorate of Nutrition, MOH, Indonesia and Manoff International, Inc., Washington, D.C. (December 1986).

# Oualitative learning

Community involvement. The communities in 10 villages to be assisted under the project were helped to conduct a "community survey" which consisted of weighing all children and charting their weights on a large community graph. A community meeting was then held to discuss the survey and to ask for solutions to problems it identified.

Guided interviewing. Central-level staff developed open-ended question guides around the problems identified. These were administered by investigators trained in qualitative research and participant observation techniques to a sample of 330 households intentionally biased toward those with malnourished children and limited resources--the hypothesis being that
Second, we denote the probability of undocumented migration by _Ii_ . This is only observed for the subsample of migrants. Therefore, the sample used to estimate equation (3) only includes return and current migrants. A migrant decides to undertake an illegal migration if the value of the unobservable latent variable _I_<sup>_∗_</sup> is positive and it captures the perceived gains from undocumented migration. By contrast, a migrant decides to undertake a legal migration if the value of the latent variable _I_<sup>_∗_</sup> is negative. In equation (3), we use the diplomatic exchange ( _Dipc,t_ ) between Egypt and the countries of destination of Egyptian migrants at the time of migration for identification. The diplomatic exchange variable is both country and year specific, and is derived from the Correlates of War Diplomatic exchange data set.<sup>17</sup> Our diplomatic exchange variable captures diplomatic representation at the level of _Charg ́e d’Affaires_ , minister, and ambassador between Egypt and all members of the Correlates of War interstate system, every five years for our period of analysis.<sup>18</sup> The identifying assumption is that exogenous shocks to the bilateral diplomatic relations between Egypt and the countries of destination of Egyptian migrants constitute a strong predictor of undocumented migration.

Third, we also account for return migration; i.e. not every migrant returns to Egypt within our window of observation. The probability of return migration is denoted by _Ri_ . This is only observed for the subsample of migrants (current and return migrants). A migrant decides to return to Egypt if the value of the unobservable latent variable _R_<sup>_∗_</sup> is positive and it captures the gains from return migration. By contrast, a migrant decides to stay abroad if the value of the latent variable _R_<sup>_∗_</sup> is negative. For identification of equation (4), we use the number of active armed conflicts ( _Conflictc,t_ ) in the countries of destination of Egyptian migrants at the time of migration (for current migrants) and at the time of return migration (for return migrants). We rely on this instrument for the identification of the return migration equation since most Egyptians migrate to other Arab countries and since, in recent years, many of these countries experienced a series of conflicts. To name just a few, these conflicts include

> return migrants. For instance, the share of individuals with secondary education or above secondary education is 15 percentage points higher among current migrants relative to return migrants.

> 17 Information on diplomatic representation from the Correlates of War Diplomatic exchange data set is available every five years between 1950 and 2005. When matching diplomatic representation with the year of migration from the ELMPS 2012, we use the closest year prior to the year of migration. For example, if an individual migrated in 1973, we use diplomatic representation in the year 1970.

> 18 See the Data Appendix B.1 for a description of the Correlates of War Diplomatic Exchange Data set.

14
- (b) If _T_<sup>ˆ</sup> from step 1 is not sufficiently close to the _T_ calculated in step 5, continue iterating by returning to step 1, with _T_ from step 5 replacing _T_<sup>ˆ</sup> in step 1.

7. After convergence, calculate all outputs such as person trips by mode, vehicle kilometers traveled by car, total fuel consumption, total carbon emissions, total revenue from tolls or fuel taxes etc. and the aggregate value of consumer surplus and social welfare (see below).

Calculation of most model outputs is straightforward, but **i** mportant outputs of the model are the per-capita consumer disutility measure for each income group and the aggregate social welfare measure. Note from equation (1) that the marginal utility of income of each income group is constant and equal to unity. Then, as it is well known from the properties of the logit model, the expected maximum utility (or expected least disutility) of a consumer in income group _f_ is REAL valued and given by: Aggregate social welfare is calculated by summing all these BRL-valued consumer surpluses and adding the aggregate tax revenues:

# **3. Data and calibration**

Our data approximate 2002 conditions.<sup>9</sup> The geographic scope of São Paulo in our study is the entire metropolitan area, the highest level of aggregation possible. In 2002, the area had a population of 18,345,000 and 7,983,000 jobs (43.5% of the population), generating 7,615,000 commutes per day and 11,716,000 non-work trips per day for a daily total of 19,330,000 trips. In 2002, the typical car fuel used in Brazil was E25 gasohol (a mixture of 25% alcohol and 75%

> 9 The source of the data on trips, wages and travel times by mode and income group is the official website of the city of São Paulo metropolitan planning agency, Compania do Metropolitano de São Paulo (Metro), in particular <u>http://www.metro.sp.gov.br/empresa/pesquisas/afericao_da_pesquisa/afericao_da_pesquisa.shtml</u> and related websites.

10
Along with its increased competitiveness, India is increasingly integrating with the rest of the world. Following stagnation in the late 1990s, export performance has taken off dramatically and double digit export growth is continuing. This growth is driven in part by new economic sectors in India, whereas the performance of the traditional sectors, like gems and jewellery, is more modest. India is now also becoming a global hub for several areas of light engineering: it is becoming a global sourcing, manufacturing and research and development hub for the automotive industry. Suzuki’s Indian-manufactured Maruti Alto hatchback accounts for nearly 20% of the small car market in The Netherlands.<sup>27</sup>

**Figure 5: India’s Export Performance**

<!-- Start of picture text --> 3500 20%<br>3000<br>18%<br>2500<br>16% Exports (rupees<br>billion, constant<br>2000<br>prices)<br>14%<br>Exports as a share<br>1500<br>of GDP<br>12%<br>1000<br>10%<br>500<br>0 8%<br>Source: CSO, RBI<br>1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05<br><!-- End of picture text -->

- 2.2 Making the Public Finances More Pro-Growth

The description of the fiscal adjustment above as well as the identification of the three stages in microeconomic restructuring suggests one could tell the following plausible, more positive story about Ninth Plan period outcomes: private investment slowed partly because of overinvestment during the Eighth Plan period and the lagged impact of sustained cuts in government capital expenditure. As a result, growth and taxes slowed, widening the deficit and raising debt and real interest rates and forcing further cuts in capital expenditure. This interpretation would ascribe Ninth Plan period outcomes to the transitional dynamics stemming from the post-1991 reforms rather than the more sinister-sounding effects of fiscal profligacy.<sup>28</sup> Viewed from this perspective, the deterioration in debt dynamics after the mid-1990s and up to 2002/03 represented the fiscal cost of reforms, with the benefit emerging in the form of the

> 27 See Oxford Analytica, September 4th, 2006.

> 28 While commenting on this paper during the October 2006 “Sovereign Debt and Development” conference, John Williamson argued that implementing the CFPC award in 1997-98 at a time of falling revenue constituted fiscal profligacy; and further that the coexistence of fast growth and rising indebtedness could be explained by simple Keynesianism.  Our point is that the revenue decline was a much more serious factor (a point John Williamson accepts) and further that the large compression in primary spending during the Ninth Plan period does not square with fiscal profligacy.  Lastly, “simple Keynesianism” broke down during the Ninth Plan period (recall Figure 1 and Table 2), suggesting something else was at play.

16
Table A.4: Balanced Covariates

# **Panel A: Women’s Characteristics**

||Age<br>(1)|Rural<br>(2)|Hindu<br>(3)|Muslim<br>(4)|Christian<br>(5)|Sikh<br>(6)|SC/ST<br>(7)|OBC<br>(8)|Schooling<br>(9)|
|---|---|---|---|---|---|---|---|---|---|
|Fraction of female leaders|0.530|-0.247|0.027|-0.119|0.143|-0.028|0.197|-0.037|-0.303|
||(1.057)|(0.184)|(0.209)|(0.095)|(0.156)|(0.055)|(0.153)|(0.120)|(1.407)|
|Observations|62,122|62,122|62,122|62,122|62,122|62,122|62,122|62,122|62,122|
|Outcome mean|33.012|0.708|0.776|0.102|0.072|0.021|0.378|0.409|5.957|
|State fxed efects|x|x|x|x|x|x|x|x|x|
|**Panel B: District Chara**|**cteristics**|||||||||
||Share of|Share of|Share of|Female|Male|||||
||female|urban|SC/ST|literacy|literacy|||||
||population|population|population|rate|rate|||||
||(1)|(2)|(3)|(4)|(5)|||||
|Fraction of female leaders|-0.020|0.202|0.072|-0.103|-0.068|||||
||(0.014)|(0.126)|(0.137)|(0.074)|(0.071)|||||
|Observations|627|627|627|627|627|||||
|Outcome mean|0.486|0.258|0.327|0.550|0.691|||||
|State fxed efects|x|x|x|x|x|||||

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India in Panel A and from the 2011 Census of India (obtained from SHRUG) in Panel B. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. The dependent variables in Panel A include woman’s age, years of schooling, and indicator variables for living in a rural area, religion, and caste. The dependent variables in Panel B include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

31
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||_(negative) effect of weak_<br>_(strong) interest group_<br>_competition on investment_<br>_becomes smaller, ceteris_<br>_paribus._<br>_-the effect of increasing_<br>_interest group competition_<br>_on investment is non-_<br>_positive._||**-demand growth**(CL)**–**GDP<br>per capita real growth<br>_The coefficients of the interaction_<br>_terms are also significant. The_<br>_results are consistent with the_<br>_hypotheses_|||
|**Siniscalco,**<br>**Bortolotti**<br>**and Fantini**<br>**(2001)**|_H0: vertical integration has_<br>_a negative impact on the_<br>_number of privatisations._|-total number of sales in<br>electricity generation in a<br>given country (PI)|-a dummy for vertical integration<br>(MS)<br>_Results: significant at the 5%_<br>_level for both number of_<br>_privatisations and proceeds from_<br>_privatisation_|-average annual electricity<br>consumption (kWh) (SE)<br>_Result: insignificant_<br>a dummy for the political<br>orientation of a county’s<br>privatisation (CL)<br>_Result_:_significant and_<br>_positive at the 10% level_<br>_for number of_<br>_privatisations_<br>an institutional credibility<br>index (CL)<br>_Result: significant at the_<br>_5% level for the percentage_<br>_of privatised stock_<br>and financial market<br>liquidity indicators (CL)<br>_Result: significant at the_<br>_10% level for privatisation_<br>_proceeds_|Privatisation data on 48<br>power generation companies<br>from 19 developed and<br>developing countries for the<br>1977-1997 period. Source:<br>Privatisation International<br>(1997) and original<br>documents for some countries<br>not included in this source.|
||_H1: vertical integration has_<br>_a negative impact on_<br>_aggregate proceeds of_<br>_privatisation._|-the per country aggregate<br>proceeds from total sales<br>(PI) (US$ 96)||||
||_H2: a more regulated_<br>_environment is conducive to_<br>_privatisation; therefore,_<br>_regulation has a positive_<br>_impact on the number of_<br>_privatisations._|-total number of sales in<br>electricity generation in a<br>given country (PI)|-a regulatory index (which takes<br>the maximum value when a<br>country’s regulatory setting<br>entails: regulated TPA, a<br>regulated wholesale market and<br>an independent regulatory|||

57
Table 5: Prediction 5: Effects on Night Lights and Female Winners

|**Panel A**|**Total **|**Light**|**Log Tot**|**al Light**|**Woman Wins ***<br>**AM lights in 1994**|**Woman Wins ***<br>**BM lights in 1994**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||If lights<br>AM in 1994|If lights<br>BM in 1994|If lights<br>AM in 1994|If lights<br>BM in 1994|||
|_Treat_|3.773|2.985|1.050|1.609|0.055|0.144|
||(16.089)|(1.217)**|(2.526)|(0.638)**|(0.064)|(0.073)**|
||[0.688]|[0.045]**|[0.686]|[0.045]**|[0.412]|[0.069]*|
|**Panel B**||||**First Stage**|||
|_Cut-of_|0.169|-0.392***|0.195|-0.411***|-0.197**|-0.194**|
||(0.187)|(0.137)|(0.185)|(0.137)|(0.092)|(0.097)|
|Untreated _y_ Mean|4.476|4.703|1.799|1.838|0.037|0.013|
|Observations|1,280|1,275|1,260|1,275|1,383|1,177|
|State FE|x|x|x|x|x|x|
|Year FE|x|x|x|x|x|x|
|State*Year Trend|x|x|x|x|-|-|
|State*Year FE|-|-|-|-|x|x|
|Clusters|114|142|109|140|183|156|
|Bandwidth|5.754|7.503|5.414|7.310|11.866|10.333|

_Notes: y_ refers to the dependent variable. Estimates in columns 5-6 are for unreserved seats only. Nightlights data are for the years 1995-2013. BM and AM denote below median and above median respectively, and pc refers to per cell. Robust standard errors clustered by the running variable in parentheses. Anderson (2008) q-values in square brackets. Bandwidth is reported in female literacy rate percentage points around treatment cut-off *** ** * of 39.3 percent. _p <_ 0 _._ 01 ; _p <_ 0 _._ 05 ; _p <_ 0 _._ 10.
# **3. Fiscal Risk Analysis**

**The above analysis is deterministic and its assessment of fiscal sustainability is predicated on a realization of given macroeconomic and fiscal policy projections.** Macroeconomic outcomes in Burundi tend, however, to be very unpredictable because of the country’s exposure to large shocks, both external and domestic. Forecasts are correspondingly volatile (Figure 5).

**Figure 5. Changes in Growth Forecasts for Burundi**

<!-- Start of picture text --> 6.5<br>6.0<br>5.5<br>5.0<br>April 2013 WEO<br>4.5<br>October 2012 WEO<br>April 2012 WEO<br>4.0<br>September 2011 WEO<br>April 2011 WEO<br>3.5<br>2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : IMF World Economic Outlook, April 2011–October 2012.

**The sensitivity analysis in Table 3 shows how small changes in growth or the exchange rate would affect debt.** If real GDP growth was 1 percentage point lower than we have forecast each year up to the year ending in 2017, tax revenue would be around 1.4 percent of GDP lower each year as a result. Lower GDP and the additional cumulative deficit would cause debt to rise to 39 percent of GDP, instead of 34 percent projected under the baseline. Similarly, if the exchange rate depreciated 1 percentage point faster than we have forecast each year up to 2017, the government’s grant revenue would be higher while debt service on external debt would also be higher. Because of the offsetting benefits and costs, the impact on fiscal outcomes would be relatively muted, with the ratio of debt to GDP remaining at around the same level as baseline in case of faster depreciation.

10