rafmacalaba/datause-encoder-data huggingface dataset:
The indicators for the 2005-2005 reports were developed by a consensus of the members of the team and usually informed by input from planning and service delivery staff in the participating municipalities.

Data were collected from a total of 12 sources, with the majority coming from Statistics Canada and a Survey of the QOLRS municipalities. Data are presented for each of the QOLRS municipalities, for a population-weighted average of all QOLRS municipalities, and for the “Rest of Canada” which excludes data for the QOLRS municipalities.

# <u>Membership</u>

The current membership of 20 large cities and regional municipalities covers most of the major urban areas of Canada and close to 50 percent of Canada’s population. FCM is pursuing membership of the remaining large urban centers.

Secondly, FCM is pursuing the means to extend the reach of the system to smaller communities. This has been challenging to date, due to data restrictions for smaller geographic areas and populations in Statistics Canada’s products and due to resource constraints for FCM and potential participant municipalities.

# <u>Indicators and Reports</u>

FCM plans to extend the scope of the system’s indicators to better cover certain aspects of quality of life and sustainability. Principal among the areas requiring improved and additional measures are environmental conditions, public health and cultural life.

With data spanning ten years and three Censuses, the QOLRS database already provides a unique long term perspective on trends in member communities. This will continue to grow in significance as more Census data points are added.

The indicator set, however, will also be revised and expanded to allow editions of the QOLRS report during the periods between releases of Census data, as well as research reports on selected topics. The result will be a yearly or bi-annual publishing schedule, with Census-based reports going into more depth on demographic and socio-economic issues than the inter-census reports, which will deal more with locally-measured conditions and municipal activities.

45
- 13 -

Korea's tax system is composed of both national and local taxes. As the share of local taxes in total revenues is small,1<sup>5</sup> we only discuss national taxes. The importance of taxes, measured by tax revenues as a proportion of GNP, has risen as growth has occurred in Korea, increasing from 9.1 percent in 1962 to 15.5 percent in 1987 (see Table 4). This growth in taxes has been uneven, reflecting periods of lower growth in the economy, as in 1963-65 when revenue to GNP ratios fell, and periods in which substantial tax cuts have been used for incentive purposes, as in 1972-73.

In 1977, a VAT replaced eight indirect taxes and has since become the single largest source of revenue in Korea, accounting for 25.3 percent of tax revenues in 1987. Since the introduction of the VAT, indirect taxes have increasingly become the most important source cf revenue in Korea. The shares of direct and indirect taxes in total national revenues reversed in importance from 42.3 percent and 26.6 percent respectively in 1976 to 23.4 percent and 40.3 percent in 1987.

# C. Tax Incentives

Perhaps the most relevant aspect of tax policy in Korea to evaluating the contribution of tax policy to strong growth performance has been the use of tax incentives.16 These have taken different forms in the three periods of growth outlined above.

> 15/ During the period 1962-87, the local **tar** share **ranged** from 8.1 percent to 17.3 percent. See Economic Planning Board (1982, 1988).

> 161 The discussion that **follovs drave** on Westphal and Kim (1977), Hong (1979), Scitovsky (1985), World Bank (1987a) **and** Choi (1988).
of intergenerational educational mobility (see the survey by Torche (2019)). The rst two columns in Table 2 report the estimates from the linear model, while the last two from the quadratic model. We report the estimated eects of Inpres schools on the parameters of interest (intercept, linear, and quadratic terms of the mobility models), and the full set of coecient estimates for the corresponding DiD models are reported in online appendix Table A.1.

The estimates from the linear model suggest that, for sons (superscript _s_ denoting sons), ˆ there is a positive eect on the intercept _β_ 4<sup>_s_= 1</sup><sup>_._59</sup> , and a negative impact on the slope � � ˆ (IGRC) _β_ 7<sup>_s_=</sup><sup>_−_0</sup><sup>_._13</sup> .<sup>35</sup> The evidence on the slope thus suggests that the Inpres schools � � weakened the impact of family background and improved the relative mobility of boys irrespective of a father's education level. The positive eect on the intercept suggests that the most disadvantaged children (fathers with no schooling) beneted in the form of higher expected years of schooling as a result of the Inpres schools. However, the estimated eects for daughters are numerically much smaller and are not signicant at the 10 percent level, suggesting that the expansion of the primary schools failed to aect the educational mobility of the girls in a signicant way. This absence of a signicant eect for girls is unexpected because a substantial literature suggests that availability of schools in a village is more important for girls.

Linearity is a maintained assumption in the estimates in columns (1) and (2) of Table 2, but a growing theoretical and empirical literature suggests that the intergenerational education mobility CEF may be concave or convex. Once we admit the possibility that the mobility CEF could be concave or convex, an important question is whether the school constructions aected the degree of concavity (or convexity) and whether there is any gender dierences in the changes in the shape of the CEF. Table A.2 (panel A) in the online appendix reports estimates of a standard quadratic mobility model (see, for example, equation (8) of Becker et al. (2015)) for the pre- and post cohorts in our data to understand whether a linear CEF is a reasonable approximation for evaluating the eects of Inpres schools on children's completed years of schooling. For daughters, the evidence suggests that the CEF is linear for both the pre-Inpres and Inpres cohorts, thus suggesting that the linear DiD model in column (2) of

> 35The intercept eect is signicant at the 1 percent level, and the slope eect at the 5 percent level.

17
# **Box 6: Examples of external support for community‐initiated responses**

In Malawi, Word Alive Ministries International, a network of congregations, started providing HIV counseling in 1992 and home‐based care in 1996. Services have since expanded with funding from the US Agency for International Development and the Canadian International Development Agency. In Zimbabwe, Island Hospice and Bereavement Service, set up to provide care and support for people with cancer, subsequently expanded its services to include care and support for PLHIV with donor funding.

The International HIV/AIDS Alliance uses a “linking organization” approach, where larger national NGOs support community organizations. Examples include (1) Philippines HIV/AIDS NGO Support Program, which has supported Pinoy Plus, a self‐help group for PLHIV, to carry out peer counseling and advocacy, and IWAG Dabaw, Inc., a gay organization in Davao City, Philippines to mobilize action among gay men; (2) Instituto para el Desarrollo Humano in Bolivia, which has advocated successfully for comprehensive health care for marginalized and at‐risk populations; and (3) L’Initiative Privée et Communautaire contre le VIH/SIDA in Burkina Faso, which is supporting local organizations to integrate HIV work, small‐scale loans, and health insurance schemes to enable affected households to care for OVC and pay for medical costs.

The Tides Foundation, International Community of Women Living with HIV and AIDS, and Women and Children’s Collaborative Fund for Treatment Literacy in Africa provide small grants to grassroots organizations working with women and children in advocacy, HIV treatment literacy, and economic autonomy.

CARE supports community‐driven responses in Rwanda. For example, the Strengthening Communities Response to HIV/AIDS Project used savings and loans groups as an entry point for community‐driven HIV prevention, care, and support and provided training and support for CBOs and PLHIV associations.

The intervention by or with assistance of external organizations has frequently had the effect of developing informal initiatives into formal community organizations ‐formalizing systems is usually a prerequisite to accessing external funding ‐and in expanding the technical capacity, scope, or geographical coverage of activities. Russel and Schneider (2000) cite examples of organizations that grew out of community initiatives, including The AIDS Support Organization (TASO) in Uganda and the Family AIDS Caring Trust (FACT) in Zimbabwe.

Among external actors, **donor agencies** have been especially influential in catalyzing and building on community responses primarily through funding. Increased donor funding, in particular from the World Bank, DFID, the Global Fund, and the President’s Emergency Plan for AIDS Relief (PEPFAR), has played a critical role in driving ‐and setting the agenda for ‐ community responses.

- The World Bank MAP channeled 38 percent of its funding to CSOs, including support for community responses to HIV and AIDS, between 1999 and 2005.

16
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10 _jggm3_ (or 0.004 ppm) change in SO, is associated with a daily increase of 0.346 deaths or a 0.346/28.48 = 1.21 percent increase. For the lower range of the effect, we use the regression equations that includes adjustment for seasonality, year, interactions betweer. year and season, day of smdy, several meteorologic faitors, and dummy variables for months. These adjustments result in a lowering of the coefficient to 0-0058 (standard error = 0.0029, p = 0.046), suggesting a 10 pglm<sup>3</sup> change in SO0 is associated with a 0.2 percent change in morclicy. The central estimate uses ihe model that adjusts for seasonality and year and implies that a 10 pglm<sup>3</sup> change is associated with a 0.48 percent change in mortality. The ranges are as follows: Upper percet change in mormal-ty = 0 121 * change in SO Central percent change in mortality = 0.048 * change in _SO%_ Lower percent change in mortality = 0.020 change in SO, RVespirator Snmptoms. Recent studies chat provide evidence of an effect of SO, on symptoms include Charpin et al..<sup>4</sup> ' Bates and Sizto,4'', Ponkal<sup>4</sup> ' Dodge et al.," and Schwartz et alY<sup>5</sup> l<sup>2</sup> Doseresponse normation can be generated from the later two studies.

Schwartz et al.<sup>51</sup> relate daily levels of SO, to respiratory symptoms among a sample of approximately 280 children in Watertown, Massachusetts who were part of the Harvard Six-Cities Study. A daily diary completed by parents recorded several acute symptoms of their children including upper respiratory illness and cough. The corlmion among pollutants was not reported. A logistic regrssion was used to examine the relationship of pollution to these symptoms. Sulfur dioxide had a statstically significant association with cough. The impacts of other pollutants were unclear from this primarily methodological article. Nevertheless, the results suggest the following: logit (cough) = 0.0130 SO2 (ppm)
importer and sectoral indices are defined analogously. Although these three sets of indices are useful for their indicative value, more detailed analytical work—and particularly the kind of diagnostic work undertaken on a country level to identify prospective export sectors—would need to rely on the estimated probabilities from the full Logit model. One way in which they could be used at the country level would be for the analyst to compare estimated probabilities for a given sector across a range of trading partners, and then return to the data to identify the sources for any large differences. The full dataset for this paper would allow analysts to consider the sectors in which a country is specializing, relevant country characteristics, and the properties of each sector that interact with them to produce sectoral growth over time. It may also be that trade costs or importer-specific factors—such as market access—are in part responsible for the difference. After confirming such an intuition on the ground, it would be possible to identify policies that could be used to improve a country’s export probability for that sector with the importing countries in question. We return to this issue in Section 5, after discussing results and comparing them with other available indicators.

# **2.2 Data and Sources**

In terms of data, many of our sources are standard, but some have been constructed for this paper. Our export data come from Version 8.9 of the Trade in Services Database (Francois and Pindyuk, 2013) and represent a consolidation, cleaning, and mirroring of trade flows reported to Eurostat, the IMF, the OECD, and the United Nations. The Trade in Services Database thus represents the current best state of data in respect of Mode 1 trade flows. The data are disaggregated bilaterally (by exporter and importer), and at the sectoral level, by three-digit Extended Balance of Payments Services (EBOPS) classification. For full details of the classification schemes used and the issues associated with them, see Francois and Pindyuk (2013). It is well known that services data become increasingly inaccurate as they are disaggregated, so we include data for 10 macro-sectors only. They correspond to the best-known categories of Mode 1 services trade at an aggregate level: transportation; travel; communications; construction; insurance; financial services; computer and information services; other business services; 13
_Table 3: Relationships between gas flaring and cluster-level incidence of disease, under-nutrition and mortality: No control variables_

||(1)<br>Cough|(2)<br>Respiratory|(3)<br>Fever|(4)<br>Diarrhoea|(5)<br>Stunting|(6)<br>Wasting|(7)<br>Underweight|(8)<br>Infant Mortality|(9)<br>Child Mortality|(10)<br>Under-5 Mortality|
|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|rate|Issues rate|rate|rate|rate|rate|rate|rate|rate|rate|
|Flaring exposure|0.0966**|0.0848**|0.0678*|-0.0496|0.0332|0.0618|0.0602|-0.0100|-0.0322|-0.0229|
||[0.0406]|[0.0407]|[0.0407]|[0.0408]|[0.0410]|[0.0409]|[0.0409]|[0.0408]|[0.0408]|[0.0408]|
|Year=2018|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|0.0000|-0.0000|-0.0000|0.0000|
||[0.0833]|[0.0834]|[0.0835]|[0.0836]|[0.0840]|[0.0839]|[0.0839]|[0.0837]|[0.0837]|[0.0837]|
|Constant|0.0000|-0.0000|-0.0000|0.0000|-0.0000|-0.0000|-0.0000|0.0000|-0.0000|-0.0000|
||[0.0653]|[0.0653]|[0.0654]|[0.0655]|[0.0655]|[0.0654]|[0.0654]|[0.0656]|[0.0655]|[0.0655]|
|Observations|603|603|603|603|595|595|595|603|603|603|
|R-squared|0.0093|0.0072|0.0046|0.0025|0.0011|0.0038|0.0036|0.0001|0.0010|0.0005|

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 _Table 4: Relationships between gas flaring and individual-level incidence of disease and under-nutrition: No control variables_

|VARIABLES|(1)<br>Child has cough|(2)<br>Child has respiratory<br>issues symptoms|(3)<br>Child has fever|(4)<br>Child has<br>diarrhoea|(5)<br>Child is stunted|(6)<br>Child is wasted|(7)<br>Child is<br>underweight|
|---|---|---|---|---|---|---|---|
|Flaring exposure|0.0294***|0.0278***|0.0260***|0.0000|0.0001|0.0093**|0.0038|
||[0.0074]|[0.0074]|[0.0084]|[0.0038]|[0.0057]|[0.0044]|[0.0046]|
|Year=2018|0.0567***|0.0666***|0.0545***|0.0018|0.0027|-0.0713***|-0.0240**|
||[0.0151]|[0.0153]|[0.0159]|[0.0071]|[0.0137]|[0.0082]|[0.0102]|
|Constant|0.1431***|0.1415***|0.1557***|0.0556***|0.1820***|0.1156***|0.1236***|
||[0.0109]|[0.0108]|[0.0106]|[0.0055]|[0.0090]|[0.0067]|[0.0066]|
|Observations|10,972|11,040|10,969|10,971|7,222|7,223|7,229|
|R-squared|0.0124|0.0136|0.0100|0.0000|0.0000|0.0152|0.0014|

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 12
Table B2: ANCOVA Regressions [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 2<br>Ethiopia Malawi<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5 -.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria<br>-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5<br>Effect on Food Insecurity<br>Mild Moderate Severe Index<br>45<br><!-- End of picture text --> _Note:_ The figures plot results using our second fractional index that spans pre- and post-outbreak data. This index differs from our main fractional index in that it is not standardized across countries. Results come from ANCOVA regression (see Equation 3.3). Horizontal lines represent 95 percent confidence intervals calculated using standard errors clustered at the household.
income countries than for the sample as a whole: Spearman's rho is only around 0.4 in both cases. This finding suggests that although the ACI captures similar overall tendencies to those summarized in simpler metrics, its main value added is in dealing differently with smaller, less connected countries. We believe this makes the ACI particularly useful from a development policy point of view.

# **6 The ACI, Policy, and Trade Outcomes**

It is important to check the external validity of the ACI by comparing it with input and output indicators that we would expect to be correlated with it. We start by considering the input side. We expect that liberalization of air transport markets tends to promote better connectivity. By reducing the cost of moving from one country to another, Bilateral Air Services Agreements (BASAs) should lead to more and stronger connections between countries. More liberal countries should therefore have a higher ACI score. Figure 10 shows that this is clearly the case (correlation coefficient = 0.45, 1% significant). We use the World Trade Organization's _Air Liberalization Index_ (ALI), averaged across all partner countries, to measure how liberal a country's air transport policy environment is. The clear implication of this result, should it be borne out by more detailed analysis, is that countries seeking to improve their air transport connectivity would do well to move towards more liberal policy regimes, in addition to potentially considering measures such as lower landing or airport fees.

33
# **_4.1.2. Empirical strategy_**

In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an _‘ad hoc panel’_ , whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are timevariant, while other individuals and households characteristics are time-invariant .

Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence.<sup>11</sup> Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). We estimate the following equation: where are our outcome variables, i.e. attendance rate (which is a binary variable<sup>12</sup> ) or grade deficit. and are year dummies respectively for year 2, the year of the 1999 violence, and for year 3, the first year of the post-conflict period. The model includes individual fixed effects, . The variable is the random error. All standard errors are clustered at the village level.

As discussed above, we identify violence-affected individuals using two different measures, , with _j=1,2_ depending on which measure is included in the specification. The first one is whether the individual was displaced with the whole household. The second is whether the individual reports that her house was completely destroyed during the 1999 violence. We allow the violence measure to interact with both year dummies. The estimation of our specification above is

> 11 We have also tried to keep a larger sample that includes those children in primary school age in the year of the violence (i.e. between 7 and 12 in 1999). This means including individuals aged 6 in year 1 and aged 13 in year 3. The inclusion of these latter individuals may generate  ̳spurious‘ results as they are not of primary school age. We have estimated the model using both samples. We find that the estimates using the larger sample are similar to those obtained with the sample of children aged between 8 and 11 in 1999. The larger sample generates more statistically significant results but we have decided to opt for the most restrictive sample to avoid inclusion of  ̳tails‘ of the age distribution that are not of primary school age.

> 12 We estimate our model with a linear probability model.

15