rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Biofuels penetration at country/regional level (2020)**

|**Carbon tax**|||US$10|/tCO2|||US$25|/tCO2|||US$5|0/tCO2||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy **|Baseline|**0%**|**10%**|**25%**|**50%**|**0%**|**10%**|**25%**|**50%**|**0%**|**10%**|**25%**|**50%**|
|**Aus-NZ**|0.9%|0.9%|1.1%|1.5%|3.3%|0.9%|0.6%|1.5%|3.3%|0.9%|1.1%|1.5%|3.3%|
|**Japan**|1.1%|1.1%|1.4%|1.9%|4.0%|1.1%|1.3%|1.9%|4.0%|1.1%|1.3%|1.9%|3.9%|
|**Canada**|2.7%|2.7%|3.3%|4.6%|9.0%|2.8%|3.4%|4.7%|9.2%|2.9%|3.5%|4.9%|9.6%|
|**United States**|7.7%|7.9%|9.4%|12.4%|21.3%|8.1%|9.6%|12.8%|21.9%|8.5%|10.1%|13.4%|22.7%|
|**France**|4.4%|4.3%|5.3%|7.4%|14.7%|4.3%|6.4%|7.3%|14.5%|4.2%|5.1%|7.2%|14.2%|
|**Germany**|5.8%|5.8%|7.0%|9.7%|18.8%|5.8%|7.2%|9.7%|18.7%|5.7%|7.0%|9.7%|18.7%|
|**Italy**|2.7%|2.7%|3.3%|4.7%|9.9%|2.6%|2.9%|4.6%|9.8%|2.6%|3.2%|4.5%|9.6%|
|**Spain**|2.3%|2.2%|2.8%|3.9%|8.4%|2.2%|2.5%|3.9%|8.3%|2.2%|2.7%|3.9%|8.3%|
|**UK**|0.9%|0.9%|1.1%|1.5%|3.3%|0.9%|1.3%|1.5%|3.3%|0.8%|1.0%|1.5%|3.2%|
|**Rest of EU & EFTA**|1.6%|1.6%|2.0%|2.8%|6.1%|1.6%|3.3%|2.8%|6.1%|1.6%|2.0%|2.8%|6.1%|
|**China**|5.0%|5.6%|6.7%|9.0%|16.0%|6.2%|7.4%|9.9%|17.5%|7.0%|8.3%|11.1%|19.4%|
|**Indonesia**|4.2%|4.4%|5.2%|6.9%|12.3%|4.6%|5.5%|7.2%|12.8%|5.0%|5.9%|7.8%|13.7%|
|**Malaysia**|6.1%|6.2%|7.6%|10.5%|20.1%|6.4%|7.7%|10.7%|20.5%|6.7%|8.1%|11.1%|21.3%|
|**Thailand**|3.3%|3.3%|3.9%|5.1%|8.7%|3.3%|3.9%|5.1%|8.7%|3.3%|3.9%|5.1%|8.7%|
|**Rest of EAP**|1.0%|1.0%|1.2%|1.7%|3.5%|1.0%|1.8%|1.7%|3.5%|1.0%|1.3%|1.8%|3.6%|
|**India**|6.3%|6.3%|7.5%|10.0%|17.3%|6.3%|7.5%|10.0%|17.3%|6.3%|7.5%|10.0%|17.4%|
|**Rest of SA**|0.9%|0.9%|1.1%|1.6%|3.4%|1.0%|0.3%|1.7%|3.7%|1.1%|1.4%|1.9%|4.1%|
|**Argentina**|3.8%|3.9%|4.7%|6.6%|13.0%|3.9%|8.5%|6.6%|13.1%|3.9%|4.8%|6.7%|13.2%|
|**Brazil**|43.0%|42.9%|46.8%|53.4%|65.8%|42.7%|57.1%|53.3%|65.8%|42.5%|46.6%|53.3%|65.9%|
|**Rest of LAC**|2.3%|2.3%|2.8%|4.0%|7.9%|2.4%|2.0%|4.1%|8.1%|2.5%|3.0%|4.2%|8.4%|
|**Russia**|7.1%|7.6%|9.1%|12.5%|23.0%|8.2%|6.9%|13.4%|24.5%|9.1%|11.0%|14.8%|26.6%|
|**Rest of ECA**|2.3%|2.3%|2.9%|4.1%|8.6%|2.4%|4.8%|4.3%|8.9%|2.6%|3.2%|4.5%|9.3%|
|**MENA**|0.2%|0.2%|0.2%|0.3%|0.7%|0.2%|0.3%|0.4%|0.8%|0.2%|0.3%|0.4%|0.8%|
|**South Africa**|5.6%|5.8%|6.9%|9.1%|14.7%|6.1%|3.7%|9.4%|15.1%|6.6%|7.7%|10.0%|15.8%|
|**Rest of SSA**|3.5%|3.5%|4.3%|5.9%|11.4%|3.6%|2.3%|6.0%|11.6%|3.7%|4.5%|6.2%|11.9%|

20
# **Figure B7: Nickel**

<!-- Start of picture text --> Log scale<br>11.5<br>Log price Permanent component<br>10.5<br>9.5<br>8.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term cycle Transitory component<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 48 —_
child height on these more precise measures of exposure to density of open defecation using district and survey round fixed effects. As in Sastry and Hussey (2003), we use geographic fixed effects because they control for time-invariant properties of place at the level of the fixed effect, in this case the district.<sup>1</sup> The magnitude of the interaction that we identify in the Bangladesh dataset is quantitatively similar to what is predicted for Bangladesh by a semi-parametric model fit to the international data.

This paper proceeds in three sections. First, section 2 presents background on global sanitation and summarizes evidence from the literature about why poor sanitation would be expected to have a larger effect on infant mortality and child height where population density is higher. Section 3 describes the analysis and presents results from the international dataset. Section 4 describes the analysis and presents results from the Bangladesh dataset. Section 5 discusses the findings. We point out that although, taken at face value, our results might seem to recommend concentrating policy efforts on improving sanitation in urban areas, the distributions of sanitation coverage and population density in the world today show that many of the places on earth where open defecation is most densely practiced are actually classified as rural. Indeed, our findings, combined with these empirical distributions, highlight the threats to child health posed by the enduring density of open defecation in _rural_ South Asia.

# **2 Background: Population density, sanitation and**

# **disease externalities**

Rural places have lower population density than urban places on average, but also have more open defecation than urban places and lower quality sanitation, on average. Although

> 1We do not present multi-level models because, as explained by Sastry and Hussey (2003), these models require the assumption that the random effects that are used in the models be independent of measured covariates. This independence criterion is not met in this case; for example, more urbanized districts have higher population density, on average.

5
26

# **_2.4.3. Stock markets, banks, and growth revisited_**

Rousseau and Wachtel (2000) extend the Levine and Zervos (1998) study of stock markets, banks, and growth to a panel context. They use annual data and the panel difference estimator proposed by Arellano and Bond (1991). Thus, they jointly study the impact of bank and equity markets on economic growth.

Beck and Levine (2002) build on Rousseau and Wachtel (2000). Beck and Levine (2002) (i) use data averaged over five-years periods to abstract from business-cycle fluctuations, (ii) employ more recent panel procedures that avoid biases associated with difference, and (iii) extend the sample through 1998, which mitigates the potential effect of the Asian stock market boom in the 1990s on the results.

Table 5 indicates that the exogenous component of both stock market development and bank development help predict economic growth. Based on Beck and Levine (2002), Table 5 also presents simple OLS regressions. As shown, the coefficient estimates from the two methods are very similar. The panel procedure passes the standard specification tests, which increases confidence in the assumptions underlying the econometric methodology. While not shown, Beck and Levine (2002) find that stock market capitalization is not closely associated with growth, which confirms the earlier results by Levine and Zervos (1998). Thus, it is not listing per se that is important for growth; rather, it is the ability of agents to exchange ownership claims on an economy’s productive technologies that matters.

The Table 5 estimates are economically meaningful and consistent with magnitudes obtained using different methods. If Mexico’s Turnover Ratio had been at the average of the OECD countries (68%) instead of the actual 36% during the period 1996-98, it would have grown 0.6 percentage points faster per year. Similarly, if its Bank Credit had been at the average
27

# REF1RENCES

- Barros, Ricardo and Rosane Mendonca. 1991. "Taxa de participacao no mercado de trabalho e frequencia a escola de criancas e adolescentes no Brasil." Mimeo.

- Barros, Ricardo, Louise Fox, and Rosane Mendonca. 1993. "Poverty among female-headed households in Brazil." Mimeo.

- Birdsall, Nancy and Louise FQx. 1985. "Why males earn more: Location and training of Brazilian school-teachers." Economic Development and Cultural Change, 33(3):533555.

- Calsing, Elizeu F. and B.V. Schmidt. 1986. "Situacao socio-economico e a demografica dos menores no Brazil e a nordeste." in 0 menor e a Pobreza, Brasilia. Brazil: IPLAN/IPEA/UNICEF.

- Cornia, Giovanni Andrea. 1984. "A survey of cross-sectional and time-series literature on factors affecting child welfare." World Development, 12, (3): 187-202.

- Diaz, Arlene and Stewart, Jeff. 1989. "Occupational status and female-headed households in Santiago Maior do Iguape, Brazil 1835." Mimeo.

- Fox, Louise. 1982. "Income distribution in Brazil: Better numbers and new findings." Ph.D. diss., Vanderbit University.

- Fox, Louise. 1990. Poverty Alleviation in Brazil. 1970-87. Internal Discussion Paper, Latin America and the Caribbean Region Report No. 072, The World Bank.

- Garfinkel, Irwin and Sara S. McLanahan. 1986. Single Mothers and Their Children: A New American Dilemma. Washington, D.C.: The Urban Institute Press.

- Goldani, Ana Maria. 1989. "Women's transitions: The intersection of female life course, family and demographic transition in twentieth century Brazil," Ph.D. diss.

- Goldberg, Gertrude Schaffner and Eleanor Kremen. 1990. The Feminization of Poverty: only in America? New York: Praeger Press.

- International Center For Research on Women (ICRW). 1988. "Women-headed households: Is. -es for discussion." Joint ICRW/Population Council Working Paper.

- Kossoudji, Sherrie and Eva Mueller. 1983. "The economic and demographic status of female-headed households in rural Botswana." Economic Development and Cultural Change, 31:831-59, July.
3

# **II. Resource Reallocation and Structural Change After the 1990 Reforms**

A detailed description of the Economic Transformation Plan - the ETP- can be found, for example, in World Bank (1991), Gomulka (1994), Lipton and Sachs (1990). The macroeconomic measures of the ETP consisted of substantial tightening of fiscal and monetary policies; complete internal convertibility of the Zloty, and the fixing of the exchange rate, after a large devaluation; and foreign trade liberalization. At a microeconomic level, the remaining substantial vestiges of central planning were abolished and measures were taken to remove the "soft-budget constraint" system. Almost all remaining price controls, and most enterprise-specific taxes and subsidies, were abolished, and some limits imposed to the provision of credit to enterprises. Steps were initiated to modify the legal and regulatory environment as required by the transition to a market economy.

The implementation of the reforms led to considerable changes in the economic environment and incentives faced by the enterprises. A sufficiently credible hard budget constraint was gradually established. The opening of the economy and the abolition of explicit and implicit subsidies led to marked changes in relative prices among industrial sectors, and consequently in their underlying profitability.2 On the demand side, foreign trade provided a strong impulse and opportunity for restructuring. The demise of the CMEA, sweeping cuts of tariffs and non tariff barriers, and the major trade agreements signed with the European Union and with the other European countries, belonging to either EFTA or the former CMEA, led to a marked reorientation of trade and sharp changes in the composition of exports.<sup>3</sup> 2 Producer relative prices show considerable changes in the first five years after the reforms. The relative price of energy products, in particular, show marked increase (as much as three times the average economy-wide price increase). This means that energy-intensive enterprises in all sectors have been subject to enormous pressure on the cost side, and their profitability and competitiveness - both internally, vis-a-vis less energy-intensive firms, and externally - has changed dramatically. From 1990 to 1993, relative prices in industrial sectors other than energy have decreased by as much as 40 percent in light industry, 30 percent in food processing, and 20 percent in wood and paper and electro-engineering (with the noticeable exception, within the latter sector, of transportation equipment).

3 Unfortunately, the available data on foreign trade by commodity are not very useful for our purposes, for a number of problems, including classification criteria. However, data on foreign trade by
for ever-married women in the sample. Any pregnancies to never married women in the cohort would therefore not be observed in IFLS. In the Indonesian context, this is not a major source of bias as only a very small proportion of births occur outside of wedlock.

In each wave of IFLS, respondents provided detailed information on a wide range of demographic, social and economic topics. Information about household level expenditures, assets and income generation activities was collected through interviews with the head of household or his/her spouse. In addition, interviews were conducted with each individual aged 15 and older to collect data on educational, marital, work and migration histories, health and health care use. A proxy book was used for collecting more limited information about individuals who could not be interviewed in person. Data on years of schooling, as well as information on household consumption, employment and marital status were extracted from the different modules of the IFLS survey for the sample of women selected for the analysis. In a separate section of the questionnaire, each household was asked about the distance and travel time to the nearest health center, midwife and hospital. This information was used to construct health care access variables used in the analysis.

Pooling data on women and pregnancies from what is essentially a panel dataset was complicated by inconsistencies in reporting information about pregnancies across different waves. Standard IFLS practice is to collect full retrospective information on pregnancies for all new respondents and to update them on subsequent rounds. Although this rule was generally well-adhered to, in some instances information on the same 13
<!-- Start of picture text --> — 100000 =<br>no<br>2<br>2 °<br>—E 80000 °<br>E<br>2 °<br>= 6000048<br>xe]oO ° °<br>3 e °<br>oO= 400004°°, °<br>£ o% ° °<br>© o" o<br>2S gSeg °o 2°<br>Ss 20000 6 o Oo<br>2 8 08 ° o<br>iva)& 8Ejo ° o0o°0<br>04 BBd000000000000000 00 000000 0° 008 0 000000 °<br>0 10 20 30 40 50<br>Storm frequency, national threshold<br><!-- End of picture text -->
<!-- Start of picture text --> Figure A2. 2. Government Domestic Debt & Guarantees /1<br>(in percent of GDP)<br>14<br>12 15% higher<br>10 10% higher<br>8<br>5% higher<br>6<br>Baseline<br>4<br>5% lower<br>2<br>10% lower<br>0<br>15% lower<br><!-- End of picture text --> Source: Author’s calculation.

**Figure A2. 3.  Government Net Financial Position**

<!-- Start of picture text --> (in percent of GDP)<br>30<br>15% higher<br>20<br>10% higher<br>10 5% higher<br>0 Baseline<br>5% lower<br>-10<br>10% lower<br>-20 15% lower<br>-30<br><!-- End of picture text --> Source: Author’s calculation.

1/ Data for 2005/06-2008/09 exclude guarantees due to lack of information.

Naturally, more favorable diamond assumptions would turn Botswana’s fiscal prospects brighter. With 15 percent higher diamond exports, the fiscal position would return to a surplus in 2013/14, compared with 2015/16 under the Baseline Scenario. The government could save the surpluses in the GIA to build fiscal reserves, if it chose to continue with the domestic borrowing program. Notwithstanding, these surpluses would not be sufficient to fully offset the large deficits of the earlier years of the Plan. As a result, the cumulative fiscal position would remain negative, of P 20 billion (equivalent to 21 percent of 2008/09 GDP), compared with P 34 billion under the Baseline Scenario. Indeed, the simulation analysis suggests that balancing the NDP10 cumulative fiscal position would require diamond exports of, on average, US$5 billion every year for the remainder of the Plan. Put it another way, this scenario assumes 45 percent higher diamond exports assumed in the Baseline Scenario.

# **A2.2 Non-mining GDP growth**

The second sensitivity test examines the impact of the non-mining sector’s performance on the projected fiscal path. Four scenarios are considered here: one upper case scenario, in which the non-mining sector growth rate is one percentage point higher than assumed in the Baseline Scenario, for the rest of the Plan, and two lower case scenarios, in which non-minning growth is lower than the Baseline assumption by one percentage point and 2 percentage points, respectively.

The results (see Table A2. 3) suggest that different assumptions of non-mining GDP growth would have a very limited impact on non-mining, non-SACU revenue (excluding grants), suggesting that effective taxation on the non-mining sector is low. The average effective VAT rate during 2002/03-2008/09— derived by dividing VAT collection by GDP—is 3.8 percent, as opposed to the statutory rate of 10 percent. As regards non-mineral income tax, the comparison is more difficult since the breakdown of personal income tax and non-mining company tax is unavailable. The average effective tax rate for nonmineral income tax is low, of 7.6 percent during the same period, compared with 45.2 percent on the mining sector.

32
% Assets = 27.8 + .548 (% non-performing) + .798 (% physical assets) (t-stat) (2.53) (2.49) (0.39) N=9, Adj. R-Squared .352.24 Results for physical assets are less compelling. Although the coefficient is positive, it is not statistically significant. Given so few observations, however, provincial policy makers should consider the possibility that physical assets might increase the size of their residual entity and might require a different liquidation strategy than financial assets.

In some cases the public provincial bank had negative net worth when privatized (ensuring that residual liabilities will be larger than residual assets.). In these cases, no matter how successful the recovery of residual assets, the provinces will face net losses. In the two worst cases (Formosa and Misiones), residual liabilities exceed assets by 100-200 million pesos.

Althiough these losses are substantial, as the simulation results indicate, from a fiscal perspective privatization is still the best choice. For Misiones and Formosa, banks whose assets were roughly twice the size of the simulated bank in Section II, the discounted re-capitalization stream would total well over 500 million pesos, substantially more than the 200-350 million pesos they face in residual liabilities.

While residual asset recovery will, hopefully, proceed quickly, it will neither be quick enough, nor on such advantageous terms, to cover most residual liabilities -- even in those cases where assets exceed liabilities. As a result, provinces needed some way to meet a substantial difference is that the pre-audit coefficient is somewhat larger than the post-audit measure, indicating yet again that problems will likely be more severe than indicated prior to the pre-privatization audit.

> 24 "% non-performing" is the percentage of total loans in the worst two B.C.R.A. loan classifications The physical asset variable is the percentage of pre-privatization assets that ended up in residual entity balance sheets under the heading "bienes de uso." Many of the public provincial banks had an abundance of branches and buildings that purchasers might have preferred not to own (so they would not have to re-sell them later). The dependent variable is the percentage of pre-privatization assets shifted to the residual entity (see Table 2). B.C.R.A. changed its loan classification guidelines in 1994 so that the bottom three categories -- those with problems and deficient coverage, those with high risk of borrower insolvency and recovery difficulty, and those deemed unrecoverable -- are now considered non-performing ("bad credits"). The portfolio quality data used in the regressions discussed in this section are for loans classified under the old guidelines.

II
5 marginally so. We therefore focus on a limited set of causes of death that are amenable to medical care, and on deaths among people under the age of 70 where for the selected conditions medical care can make a large difference. The data we use are currently available only for OECD countries. Our final example is at the facility level and concerns hospitals. A large fraction of health spending goes on hospitals, and many efficiency-enhancing efforts are directed at the hospital sector. Our data come from Vietnam, which though only a low-income country has unusually good data on its hospital sector by developing country standards. The data are not without their shortcomings, however, and this analysis especially is intended to be illustrative; with richer data on, for example, disease codes, one could do a more sophisticated efficiency analysis.

The rest of the paper is organized as follows. Section II introduces our empirical examples. Section III introduces and illustrates our hybrid approach to efficiency measurement in the simple case of a single output. Briefly, we identify efficient units through a grid-search process, identifying the least-cost unit over each output range, and then estimate a frontier nonparametrically using the output-expenditure combinations of the efficient units. We then measure the inefficiency of the inefficient units by comparing the inefficient unit‟s spending with the spending of the closest efficient unit on the frontier. We compare our results with those emerging from the panel-data stochastic frontier model. We obtain smaller estimates of efficiency using our hybrid approach, reflecting the fact that our frontier consists of real-life not hypothetical units. Section III extends the method to allow for multiple outputs or multiple dimensions of quality. We now identify efficient units over ranges across multiple dimensions— in the two-output case, for example, our grid search is over a square rather than a line segment. We compare empirically for each of our four examples the single- and multiple-output results. In three of the four examples, allowing for multiple outputs makes a large difference. Section IV extends the analysis further to allow for exogenous factors that constrain a unit from reaching the frontier. We illustrate our approach of different groups of units having different frontiers on the California schools dataset, using poverty as the stratifying variable. By constructing separate frontiers for school districts with small and large fractions of poor children, we allow for the possibility that the latter are constrained to making do with a lower level of „home inputs‟ in the production of schooling outcomes. It turns out that allowing for separate frontiers makes less of a