rafmacalaba/datause-encoder-data huggingface dataset:
The share of overall official flows to developing countries that is directed to lowincome countries has increased, although the overall level of these flows has remained stable in the 1990s. Concessional flows are even more heavily concentrated in low-income countries (Figure 4). There has also been a shift within official concessional flows from loans to grants. Sub-Saharan Africa continues to receive the highest share of official flows among developing regions. Meanwhile, OECD donor country budgets are under increasing strain due to recession and increasing number of new claimants with the transformation of the former centrally-planned economies. This stagnation in new official flows has made it particularly difficult for the heavily indebted poor countries. They are not receiving significant amounts if private capital flows and also face severe external debt burdens. The World Bank's World Debt Tables, 1994-95 highlights the need to assess the debt sustainability of these severely indebted low income countries (SILICs) on a country-by-country basis, with particular reference to their export growth potential over the long run and the outlook for the availability of concessional finance (including grants) or private capital to finance the needed imports and cover debt service. Specifically, it is stated in the World Bank's _World Debt Tables_ that: "In assessing the severity of the debt problem of individual SILICs, it is appropriate to address two aspects of a country's debt profile. One is the extent to which existing debt servicing obligations pose a liquidity constraint, as reflected by the inability to service currently scheduled payment. The second is the extent to which the debt overhang represents a sustainability or solvency problem in the form of unsustainable future debt servicing requirements." [Source: World Bank, _World Debt Tables,_ 1994-95, Volume 1, p. 39].

The SILICs have had to repeatedly reschedule their external debts through the 1980s and 1990s. Partly as a result of this, debt stocks of this group of countries has continued to mount, reaching an estimated US$209 billion in 1994, almost four times the nominal value of debt in 1980.s This is true despite substantial forgiveness by bilateral creditors and grant financing. In addition, repeated reschedulings also impose a cost on the

> 5 Source: World Bank's _World Debt Tables,_ Volume 1, **p.** 220.

8
4,000 to 50,000 for the analysis of earnings (when only employed individuals are retained).<sup>22</sup> The surveys contain information on a common set of circumstances: (a) three variables related to family background: father’s and mother’s education and father’s occupation during the person’s childhood; (b) ethnicity (or race); and (c) region of birth (or type of area of birth). The only exceptions are that the father’s occupation variable is not available for Colombia and Peru, and results must be interpreted with this caveat in mind. We also use gender as a circumstance variable in the analysis of earnings. Parental education variables are coded into three categories: no education (or unknown), primary (incomplete or complete, depending on the country), and complete primary or secondary and more.<sup>23</sup> Father’s occupation is recoded into two categories: agricultural workers and others. Ethnicity (coded in two categories) is captured either by self-reported ethnicity or by the ability to speak an indigenous language. Region of birth is coded into three broad regions (one being generally the capital area) but is captured by the type of area (urban/rural) for Panama. Table 2 describes the specific definitions of the circumstance variables in each survey in greater detail. Table 3 (Panel A) presents the corresponding descriptive statistics.

The number of categories for each circumstance variable was restricted to three or fewer, so as to reduce the number of “circumstances group” cells with zero or very few observations. As discussed in Section 3, this is important for the non-parametric analysis, which relies on the quality of the estimates for conditional means in these cells and their sampling variation may be very high for cells containing few observations. This greater sampling variance might artificially inflate the estimated inequality between groups, thereby inducing an over-estimation of inequality of opportunity. Table 4 shows the maximum number of cells in each survey, the number of cells actually observed (i.e. the complement of the number of empty cells), the mean cell size and the proportion of cells

> 22 Employment rates are defined as the ratio of all employed individuals to all persons in the 30-49 agegroup. These rates for men and women (respectively) are: 0.90 and 0.55 in Brazil; 0.91 and 0.62 in Colombia; 0.97 and 0.72 in Ecuador; 0.96 and 0.51 in Guatemala; 0.91 and 0.53 in Panama; and 0.94 and 0.72 in Peru.

> 23 Whether complete primary attainment was included as part of the middle or upper grouping for parental education depends on relative group sizes. An effort was made to prevent the top grouping from being too small relative to other countries, to enhance comparability. None of the results is particularly sensitive to these decisions.

15
Data were based on rankings by panels of country experts on different aspects of transparency and citizen participation collected in a survey by the Center on Budget and Policy Priorities (Brautigam 2004).

# **5. Conclusions and Implications**

We found emerging support in the literature for the first key question we set out to answer, which was whether or not we could find evidence of institutional impact on poverty outcomes. The findings in the literature show linkages between the institutional changes achieved by participatory budget initiatives and improvements in the access of the poor to better quality services. Under certain conditions, open budgeting practices can contribute to development impact.

Transparency, particularly as assessed by the Open Budget Index, is a predictor of child and infant health outcomes and access to improved drinking water. Accountability mechanisms have been found to contribute to similar improvements in human development indicators. Participatory budgeting, in terms of the devolution of decision-making authority to local actors, has been associated with improvements in public management and service delivery and has been significantly related to the reduction of extreme poverty. In short, the available evidence underscores the need to better understand how participation, transparency, and accountability in budget processes and policies matter and under what conditions.

An incremental non-linear results chain best articulates the process through which institutional changes in open budgeting can contribute to higher-level human development outcomes. Together, the analyses included as part of this review suggested that the basic steps in this logical sequence and their ongoing interaction for mutual reinforcement are positioned as shown in the figure below.

20
- 17 -

parameter of the production function A, was calculated. Similarly, the production function of the strict complements model was also calibrated using an analogous procedure, in this case working with (9b) and (10a), (lOb) and obtaining auj, am; and aEi and the scale parameter A;.

# B. NPA's Behavigr

NEPA offers quantity discounts which are a form of price discrimination. The prices charged to specific firms vary around the estimated tariff. Some such variation is obviously due to measurement errors, but a part of the variation is due to the fact that prices charged by NEPA vary considerably by industry and location within Nigeria.

In 1988, when our data was collected, NEPA was believed to be covering only about 21.9% of its operating cost. This is surmised from World Bank studies of NEPA [Report No. 111672-UNI, July 1993].5 In 1988 the average tariff charged by NEPA was 0.07 naira/KWh which was much below average operating cost. By mid-1989 the average tariff was raised to 0.32 naira/KWh which covered NEPA's operating cost. Hence, assuming that conditions were roughly unchanged between 1988 and mid-1989, only 21.9% of NEPA's operating cost was covered in 1989. Further scrutiny of the data shows that the cost recovery ratio was much higher from power sold to residential users than it was from power sold to manufacturers. Estimates by NEPA management in 1990 indicate that 73.33% of the power sale revenues were from nonmanufacturers (residential and commercial users) with the remaining 26.67% from See page 5 of World Bank Report No. 11672-UNI.
distribution as unfair, inequality can be affected through several channels (e.g., more demand for redistribution).

_Table 2. Logit regressions of unfairness perceptions (very unfair) and individual characteristics_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Gini coefficient|0.613***|0.612***|0.604***|0.601***|0.579***|0.504***|
|Age|(0.064)|(0.064)<br>0.004***<br>(0.000)|(0.065)<br>0.004***<br>(0.000)|(0.065)<br>0.005***<br>(0.000)|(0.065)<br>0.005***<br>(0.000)|(0.064)<br>0.005***<br>(0.000)|
|Age squared||-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
|||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Male dummy||-0.001<br>|-0.000<br>|0.001<br>|0.001<br>|0.001<br>|
|||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Married or civil union|||<br>-0.005*|<br>-0.005*|<br>-0.005*|<br>-0.005*|
||||(0.003)|(0.003)|(0.003)|(0.003)|
|Literacy|||<br>-0.031***|<br>-0.031***|<br>-0.029***|<br>-0.028***|
||||(0.006)|(0.006)|(0.006)|(0.006)|
|Complete Primary|||-0.002|-0.002|0.002|0.001|
||||(0.004)|(0.004)|(0.004)|(0.004)|
|Complete Secondary|||-0.005|-0.005|0.006|0.004|
||||(0.005)|(0.005)|(0.005)|(0.005)|
|Complete Tertiary|||0.005|0.006|0.019***|0.017***|
||||(0.005)|(0.005)|(0.006)|(0.006)|
|Economically active dummy||||-0.004|-0.004|-0.004|
|||||(0.003)|(0.003)|(0.003)|
|Unemployed dummy||||0.018***|0.017***|0.017***|
|||||(0.005)|(0.005)|(0.006)|
|Sewage|||||-0.000|-0.000|
||||||(0.003)|(0.003)|
|Computer|||||-0.008**<br>(0.003)|-0.008**<br>(0.003)|
|Washing machine|||||<br>-0.024***|<br>-0.023***|
||||||(0.004)|(0.004)|
|Landline|||||<br>-0.010***|<br>-0.009***|
||||||(0.003)|(0.003)|
|Has access to a car|||||<br>0.003|<br>0.002|
||||||(0.003)|(0.003)|
|Observations|150,144|150,144|149,119|149,116|145,039|145,104|
|% Unfair|27.77|27.77|27.74|27.74|27.70|27.69|
|Pseudo R-squared|0.0459|0.0469|0.0471|0.0471|0.0473|0.055|

Note: This table presents estimates of the correlation between unfairness perceptions dummy variable that indicates if the individual believes income distribution is very unfair and individuals’ characteristics. Columns 1 to 5 coefficients presents the marginal effects at the mean values of the rest of the variables and were estimated through Logit regressions, weighting by individuals probability of being interviewed, while column 6 coefficients were estimated through Ordinary Least Squares regressions. All columns include country, subnational and year fixed effects. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. Heteroskedasticity-robust standard errors in parentheses.

18
13

# **_Economic benefits_**

When it comes to economic and welfare benefits such as time savings and reduced agricultural input costs, adopters nearly unanimously (98%) said biogas saved them time in cooking, mostly women’s time: women in households with biogas saved on average 1.2 hours per day in cooking time (median one hour). There were also some time savings for men and children, so the total household time saving for all biogas users was 1.7 hours per day. This is because gas is faster and easier to cook with compared to biomass alternatives.

Adopters also nearly unanimously (99% of biogas users) reported time savings from having to collect less of other fuels. Women in households with biogas reported time savings of 24 days per year on average, men saved 10, and children saved 4. Not only are these time savings quite sizeable, and welfare-enhancing in their own right, they are also suggestive of biogas inducing fuel switching away from biomass. One-quarter reported that their time savings were used for more leisure, while the remainder used it for household chores and income generating activities.

Biogas residues can be used as organic inputs on the farm: 77% of households with biogas used residues as fertilizer in the year prior to the survey, and almost all of those had been able to cut back on other fertilizers. Further, 79% had been able to reduce their use of insecticides as a result of applying biogas residues, and nearly all of them (96%) felt this had improved the quality of their crop.

# **_Benefits on health_**

An increasing number of epidemiological studies are linking exposure to IAP from cooking with solid biomass fuels to various health outcomes such as acute lower respiratory infection, chronic
||**Ta**|**ble A14: Inclu**|**ding all interact**|**ion terms simu**|**ltaneously**||||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Starting<br>a Business (DTF)|-0.007***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.001)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|
|Exports (%, cell average)*Courts<br>Obstacle (cell average)|-0.105**<br>(0.044)|-0.115***<br>(0.044)|-0.105**<br>(0.044)|-0.100**<br>(0.041)|-0.126***<br>(0.044)|-0.099**<br>(0.043)|-0.101**<br>(0.043)|-0.101**<br>(0.044)|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.150*<br>(0.087)|0.153*<br>(0.083)|0.132<br>(0.083)|0.153*<br>(0.087)|0.136<br>(0.086)|0.173**<br>(0.086)|0.176**<br>(0.087)|0.174**<br>(0.088)|
|Exports (%, cell average)*Women|0.201**|0.237**|0.238**|0.249**|0.258**|0.266***|0.261***|0.264***|
|<br>can work in the same industries as<br>men Y:1 N:0|(0.101)|(0.101)|(0.104)|(0.102)|(0.103)|(0.097)|(0.097)|(0.095)|
|Exports (%, cell average)|-0.316|-0.072|-0.125|-0.121|-0.266|-0.460|-0.453|-0.460|
||(0.516)|(0.511)|(0.519)|(0.497)|(0.498)|(0.489)|(0.486)|(0.510)|
|Starting a Business (DTF)|0.069*|0.059|0.075**|0.056|0.064|0.128***|0.128**|0.113*|
||(0.038)|(0.036)|(0.037)|(0.036)|(0.041)|(0.043)|(0.064)|(0.065)|
|Courts Obstacle (cell average)|0.196<br>(0.923)|0.335<br>(0.899)|0.226<br>(0.843)|0.620<br>(0.816)|0.752<br>(0.851)|0.226<br>(0.824)|0.140<br>(1.418)|0.066<br>(1.413)|
|Women can work the same night|-7.745***|-7.342***|-6.951***|-5.394*|-4.993*|2.104|0.706|-0.363|
|<br>hours as men Y:1 N:0|(2.599)|(2.631)|(2.588)|(2.761)|(2.827)|(3.024)|(3.956)|(3.976)|
|Women can work in the same|-4.351**|-4.157**|-4.032*|-3.281|-2.670|-4.272*|-3.550|-3.929|
|industries as men Y:1 N:0|(2.210)|(2.110)|(2.117)|(2.083)|(2.359)|(2.422)|(3.153)|(3.116)|
|Exports (%, cell average)*GDP|0.091*|0.060|0.067|0.062|0.086|0.092*|0.092*|0.090|
|per capita (logs)|(0.055)|(0.056)|(0.057)|(0.056)|(0.056)|(0.054)|(0.054)|(0.070)|

86
preferred approach of entities to internally managing and reporting financial and non-financial program performance (which is to say that entities tend only to comply with the _minimum_ requirement to use program structures for _external_ budget reporting).

The Australian Department of Finance manages the ‘Commonwealth Program List’ to ensure the accuracy, materiality and consistency of information disclosed in budget programs, including a central approvals process for modifying the program list.

Table 4.1 Comparative MTEF Program Costing Practices: Summary of Responses

||**Australia**|**Austria**|**Canada**|**The Netherlands**|
|---|---|---|---|---|
|**Does the finance**<br>**ministry issue**<br>**program**<br>**guidelines? Are**<br>**they**<br>**mandatory?**|Yes<br>Programs Policy and<br>Approvals Process<br>Mandatory|No|Yes<br>Policy on<br>Management,<br>Resources and<br>Results Structures<br>(PMRRS) and<br>Program<br>Alignment<br>Architecture (PAA)<br>Not mandatory|<br>Not specified|
|**Is a program**<br>**defined? Does**<br>**the finance**<br>**ministry issue**<br>**program**<br>**specification**<br>**guidelines?**|Yes<br>Guidance for the<br>Preparation of<br>Portfolio Budget<br>Statements (annually)|No<br>Budget is<br>organised on<br>‘chapter’ basis,<br>with each chapter<br>specifying up to 5<br>outcomes|Yes<br>PMRSS and PAA|Yes<br>Accountable<br>Budgeting Reform:<br>Budget is<br>organised on<br>‘chapter’ basis,<br>with each chapter<br>comprising<br>‘policy’ and ‘non<br>policy’ articles|
|**What is the**<br>**purpose of**<br>**costing**<br>**information?**|To assess financial<br>impact of proposed<br>policy<br>- Assess the<br>reasonableness of<br>estimates<br>- Assess consistency<br>of costs with policy<br>objectives<br>- Assess direct flow-<br>on impacts to other<br>areas of expenditure|To provide<br>‘implicit’ support<br>for the outcome<br>oriented impact<br>assessment process|Determined by<br>‘purpose’:<br>- cost recovery<br>- make-or-buy<br>decisions -<br>level-of-service<br>decisions<br>- cost-benefit<br>decisions -<br>costing of a new<br>initiative<br>(incremental<br>funding)|Not specified|
|**Does the finance**<br>**ministry issue**<br>**guidelines on**<br>**program**<br>**costing? Are**<br>**they**<br>**mandatory?**|Yes<br>Not mandatory but<br>‘conventional costing<br>template’ must be<br>used for staffing and<br>‘on-costs’ (such as<br>pensions and other<br>benefits)|No|Yes<br>Guide to Costing<br>(2008) Not<br>mandatory<br>TBS Submission<br>Guide mandatory<br>(for submission of<br>new spending<br>initiatives)|Not specified|

19
# **Poverty Depth**

The share of the rural population living in extreme monetary or consumption poverty is not only broad but deep. P0 measures the proportion of people below a certain poverty line, but takes no account of how far they are below that line, i.e., the degree of poverty. To address the situation of the poorest, the squared poverty gap measure (or P2) is used. This takes into account the degree of poverty, because it gives more weight to the poorest and most vulnerable. The P2 poverty measure reveals that the extreme consumption poverty depth reached 10.2 percent in 2003 (Table 4.2).<sup>14</sup> The squared consumption poverty gap measure reveals that poverty was deeper in Santiago del Estero (14.2 percent) than in Mendoza (12.2), Chaco (9.8), and Santa Fe (3.1). Therefore, Santa Fe has a lower poverty rate than that of the other provinces, and poverty is less deep.

|Table 4.2: P1 and P2 for Households|in Disperse|d Rural Area|s of Arge|ntina,200|3|
|---|---|---|---|---|---|
||Mendoza|Santiago<br>del Estero|Chaco|Santa Fe|Total<br>Sample|
|Income Poverty Gap (P1)|31.7|26.7|36.9|14.8|27.8|
|Consumption Poverty Gap (P1)|23.6|27.3|18.1|6.7|19.4|
|Squared Income Poverty Gap (P2)|22.1|16.4|24.9|9.2|18.4|
|Squared Consumption Poverty Gap (P2)|12.2|14.7|9.8|3.1|10.2|
|Source: Own calculation based on RHS 2003.||||||

Data reveal that in 2003, the median income of extremely poor households in dispersed rural areas was AR$228.4 per month, slightly more than half of the median income of the nonpoor (AR$392.3). In addition, the income of indigent households in Chaco, in the Northwest region, was lower than Mendoza in the Cuyo region and Santa Fe in the Pampeana region.

In 2003, the median monthly income of the nonpoor was highest in Santa Fe in the Pampeana region (AR$593.6) and more than double that in Santiago del Estero in the Northeast region. This pattern also holds for the extreme poor households and it is clear that monetary income is severely lacking in poor households in Santiago del Estero where the average monthly income is only AR$191.9. A comparison of Tables 4.2 and 4.3 clearly shows that one factor driving poverty in dispersed rural areas is the large number of members in poor and indigent households because the monthly median household income is often more unstable for poor households. In Santiago del Estero the medium income of poor households is AR$382, more than AR$100 higher than the median income of the nonpoor. Therefore, the large average number of household members (3.5 in Santiago del Estero) is the main factor explaining the difference between poor and nonpoor households.

> 14 The fact that the P2 is systematically lower than P1 is simply a mathematical property of the way the indicies are constructed. Moreover, P1 and P2 are not a percentage of anything. A common interpretation is that it is the product of the headcount and the average distance between the incomes of the poor and the poverty line.

21
Table A.7: **Correlates of firm attendance at the job fairs**

||(1)<br>|(2)<br>|(3)<br>|(4)<br>|
|---|---|---|---|---|
||Blocking|Others|Salaries|All|
|Tours-Hospitality|-0.210*|||-0.742**|
||(0.117)|||(0.351)|
|Finanace, Services, Retail|-0.0150<br>|||-0.244<br>|
||(0.119)|||(0.347)|
|Education, Health, Aid|-0.105<br>|||-0.674<br>|
||(0.130)<br>|||(0.652)<br>|
|Manufacturing|-0.0556|||-0.425|
||(0.108)|||(0.301)|
|bs<br>~~s~~tad<br>~~d~~ist|0.00270<br>|||0.0352<br>|
||(0.00385)<br>|||(0.0231)<br>|
|Total employees (100s)|0.00171|||-0.00377|
||(0.00586)|||(0.0203)|
|Respondent is owner|0.0306<br>|||0.0573<br>|
||(0.0869)<br>|||(0.251)<br>|
|Turnover Rate|-0.0600<br>|||1.343<br>|
||(0.223)|||(1.505)|
|Quit rate|-0.0268|||0.453|
||(0.252)|||(1.799)|
|Workers with degrees|-0.427**|||-0.772|
||(0.197)|||(0.912)|
|Workers with highschool|-0.0534|||0.962**|
||(0.174)|||(0.456)|
|Proportion professionals|0.0114|||1.611*|
||(0.228)|||(0.922)|
|Proportion female|0.144|||0.460|
||(0.175)|||(0.397)|
|Total sales (log)||-0.0377||-0.0578|
|||(0.0340)||(0.0628)|
|Hiring Rate||0.248||-0.633|
|||(0.304)||(0.595)|
|Number permanent hires||0.0686||0.166|
|||(0.142)||(0.154)|
|Employee growth rate||-1.477||-2.275|
|||(1.347)||(1.765)|
|Growth rate (professionals)||0.120||0.704|
|||(0.437)<br>||(0.500)<br>*|
|Growth rate (service)||0.0176||0.289|
|||(0.137)||(0.157)|
|Growth rate (production)||0.917||1.122|
|||(0.689)||(0.947)|
|Growth rate (support)||0.0536<br>||-0.309<br>|
|||(0.366)||(0.414)|
|Starting salaries (professionals)|||-0.0517|-0.106|
||||(0.192)|(0.260)|
|Starting salaries (services)|||0.279|0.204|
||||(0.184)|(0.354)|
|Starting salaries (production)|||0.163|0.254|
||||(0.187)|(0.303)|
|Starting salaries (support)|||-0.142|-0.181|
||||(0.214)|(0.272)|
|5 year salary (professionals)|||-0.116|0.0375|
||||(0.207)|(0.278)|
|5 year salary (services)|||-0.0966|-0.328|
||||(0.224)|(0.321)|
|5 year salary (production)|||-0.169|-0.228|
||||(0.195)|(0.266)|
|5 year salary (support)|||0.0915|0.367|
||||(0.196)|(0.284)|
|Constant|0.834***|1.051**|1.302|0.835|
||(0.128)|(0.411)|(0.987)|(1.465)|
|Observations|232|70|87|61|
|R-squared|0.075<br>A.8|0.075|0.102|0.576|

Ommitted industry dummy is “Construction, Mining”. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1