rafmacalaba/datause-encoder-data huggingface dataset:
72 CHAPTMR **IX: PROGRAM FUNDING** 9.1 Of the six districts originally taken up under WDP, five were funded by UNICEF and one by the State Government through its Tribal Area Development Program (TAD). The original financial estimates showed an initial annual expenditure of Rs 5,450,000 (1984-85) followed by an annual expenditure of Rs 5,000,000 for five districts. The non-recurring expenditure for each MVK came to Rs 1,300 and the recurring expenditure including training, to Rs 5,700.

9.2 Currently, nine districts are covered under WDP (Jaipur, Ajmer, Bhilwara, Jodhpur, Udaipur, Banswara, Kota, Sikar and Dungarpur). UNICEF provides assistance for six DWDAs and the costs of 11 posts of Headquarter. Kota, Sikar and Banswara DWDAs are funded by the State Government through the TAD program and the grant made available through UNFPA. UNICEF has withdrawn support for headquarter expenditure from January 4, 1989 and for the entire program from 1990.

9.3 An analysis of the estimates for Jaipur district for the year 1987 from UNICEF records, shows that 65% of the expenditure is on staff salaries, 11% on training, and 4% on learning material. The remaining 20% of the expenditure is on
- Schmitt-Groh ́e, S. and Uribe, M. (2003). Closing small open economy models. _Journal of International Economics 61_ , pages 163–185.

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- van der Ploeg, F. (2011). Natural resources: Curse or blessing? _Journal of Economic Literature, 49_ , pages 366–410.

# **6 Steady state Calculation**

We need to solve the non-stochastic steady state of the model to pin-down equilibrium values of the endogenous variables.

54
52,433 vertebrates (amphibians, birds, fish, mammals, reptiles); 213,268 arthropods; 32,355 molluscs; Table 1 provides a more detailed accounting. We limit the exercise to GBIF species in the kingdoms two methods to remove spurious observations from locales such as zoos and botanical gardens. The first is exclusion of isolated outlier occurrences before map estimation, which happens automatically in alphahull. The second occurs after map estimation, when we exclude alphahull-bounded point sets with fewer than three observations. Many species maps have multiple bounded areas; in those maps, our approach imposes a conservative interpretation of the evidence. We drop species maps with single bounded areas when they contain fewer than three observations.

3. Global Species Distributions 3.1 Spatial Selection Bias GBIF occurrence reports are often produced by voluntary exercises that do not employ scientific sampling methods. In consequence, spatial point densities in species occurrence reports are positively related to physical accessibility, population density and income (Garcia-Rosello et al. 2023; Borgelt et al. 2022; Isaac and Silman 2011; Reddy and Davalos 2003). Other things equal, more species sightings occur near transport arteries; in areas where there are more inhabitants to identify species; and in areas where more inhabitants have disposable income sufficient to support species search and the cost of reporting. These factors complicate attempts to map species population densities from occurrence reports (e.g., Kass et al. 2022). Alphahull estimation of boundaries is different, because it focuses on exterior points in spatial sets. However, accurate representation still requires a critical minimum number of sightings in areas that are not advantaged by transport access, population density or disposable income. As Feeley and Smith (2011) note, the accuracy of boundary estimation improves as more sightings occur in non-advantaged million new reports per day. The following section tests alphahull boundary mapping from the current provided by existing expert maps.

GBIF species and the smaller inventory of expert range maps, with two primary objectives. The first is incorporating more species into an expanded view of global biodiversity. The second is assessing the relationship between GBIF occurrence maps and expert range maps that may “undershoot” if their This final condition may seem redundant, since the initial condition is exclusion of species with fewer than five unique occurrences since 1970. However, a species can pass the initial condition but fail the final one because the alphahull algorithm may exclude an outlier point or two from its computation of the bounded area.
**Table 3. Decomposing the targeting** **<u>performance of water consumption subsidies</u>**

||**Ω**|**SA**|**C**|**T**|**R**|**Q**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**Ω>1**<br>**pro-poor**|Potential access<br>to water<br>connection<br>(service area)|Connection rate (for<br>those with access)|Receipt of<br>subsidy (for those<br>connected)|Rate of subsidization<br>(for those with a<br>subsidy)|Quantities (for those<br>connected)|
|**Ethiopia**|0.047||||||
|Poor||0.255|0.095|1.000|0.957|3.275|
|All||0.446|0.444|0.996|0.964|8.567|
|Poor/All||0.571|0.214|1.004|0.993|0.382|
|**Mali**|0.085||||||
|Poor||0.036|0.263|0.907|0.858|14.491|
|All||0.239|0.467|0.958|0.793|14.751|
|Poor/All||0.150|0.563|0.948|1.081|0.982|
|**Niger**|0.006||||||
|Poor||0.030|0.040|1.000|0.921|10.453|
|All||0.209|0.433|1.000|0.909|23.651|
|Poor/All||0.141|0.093|1.000|1.014|0.442|
|**Nigeria**|0.661||||||
|<br>Poor||0.507|0.208|0.997|0.929|3.270|
|All<br>||0.585|0.233|0.993|0.900|3.972|
|Poor/All||0.867|0.894|1.004|1.032|0.823|
|**Uganda**<br>|0.079||||||
|Poor||0.204|0.045|1.000|0.544|5.173|
|All||0.332|0.261|1.000|0.549|6.965|
|Poor/All||0.610|0.17|1.00|0.99|0.74|
|**El Salvador**|0.839||||||
|Poor||0.945|0.719|0.996|0.679|19.230|
|All||0.966|0.821|0.983|0.647|20.863|
|Poor/All||0.977|0.876|1.013|1.050|0.922|
|**Jamaica**|0.690||||||
|Poor||0.998|0.563|0.969|0.773|10.318|
|All||0.998|0.698|0.955|0.653|14.456|
|Poor/All||1.000|0.806|1.015|1.183|0.714|
|**Panama**|0.867||||||
|Poor||1.000|0.846|1.000|0.872|40.453|
|All||1.000|0.932|1.000|0.829|44.484|
|Poor/All||1.000|0.907|1.000|1.052|0.909|
|**Bangladesh**|0.345||||||
|Poor||0.286|0.164|0.998|0.999|36.555|
|All||0.384|0.355|0.998|0.995|36.579|
|Poor/All||0.745|0.462|1.000|1.003|0.999|
|**Vietnam**|0.858||||||
|<br>Poor||0.804|0.627|1.000|0.861|13.863|
|All||0.840|0.631|1.000|0.858|15.430|
|Poor/All||0.958|0.993|1.000|1.004|0.898|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail.

_Note:_ Access factors: _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Subsidy design factors: _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization; _Q = QP|T/QH|T_ are relative quantities consumed. A factor value below 1 indicates that the factor contributes to reduced targeting performance, whereas a value above 1 indicates that the factor contributes to increased targeting performance. All figures are calculated using sample weights. Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country.

19
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- Government should commit to the timely publication of comprehensive information of tax expenditures.

## **_Rule 3_**

- A statement of fiscal policy objectives and an assessment of fiscal sustainability should include both direct budget expenditure and tax expenditures. The complete budget document should include the normative tax structures with estimates of “imputed normative tax revenue.”

- Annual spending on tax expenditures should be prepared and presented within a comprehensive and consistent quantitative macroeconomic framework and the main assumptions underlying the budget should be provided.

- New tax expenditures being introduced in the annual budget should be clearly described.

- Tax expenditure information should be presented in a way that facilitates policy analysis and promotes accountability.

- Procedures for monitoring approved tax expenditures should be clearly specified.

- There should be regular fiscal reporting (annual or in a fixed time period) on tax expenditures (cost, effectiveness, and efficiency) to the legislature and the public.

## **_Rule 4_**

- Tax expenditure data should reflect recent flows and trends, underlying macroeconomic developments and well-defined policy commitments.

- Tax expenditure information should be subject to independent scrutiny and the data quality should be subject to verification.

- Tax expenditures should be audited for their performance, using economy and efficiency audit and programs audit to assess the performance of the government organization, program activity or function. This audit can provide information to improve the organization’s public accountability and facilitate decision making that initiates corrective action.  It can also be used to assess the performance of an entity, a program or an activity within that entity to determine whether the entity has complied with significant laws and regulations of tax expenditure programs.

# **V. Conclusion**

Tax incentives are tax expenditures. Tax expenditures are conceptually and functionally distinct from those tax provisions that have the purpose of raising revenue. They are government spending programs comparable to entitlement programs. Therefore, they must be analyzed in spending terms and must be integrated into the budgetary process to ensure fiscal accountability. Tax expenditures must be audited for performance, and their information must be published (with a comprehensive analysis) to achieve fiscal transparency.

The building blocks introduced in this article are useful for making tax expenditures accountable and transparent. However, they are presented in general terms. In applying the building blocks, each country must make specific decisions when it 27
on pollution control policies is Swait and Eskeland’s (1995) study of mode choice in São Paolo. They examine the net effect on air pollution of subsidizing bus fares. We examine the related question of whether raising bus fares will, on net, cause substitution to dirtier forms of transport, per passenger mile traveled

# **_B. Approach Taken_**

Estimating the impact of pollution control policies that affect the price of travel requires estimating models of mode choice and vehicle ownership. We use data from a survey of 5,000 households in Mumbai, conducted in 2003, to estimate models of commute mode choice and vehicle ownership. The price elasticities obtained from these models are combined with data on passenger-kilometers traveled and emissions per passenger kilometer to compute the impact of policies on emissions from transport. Specifically, we ask:

- How sensitive is private vehicle ownership to a change in purchase price or to a change in the price of gasoline?

- What would be the net effect of a change in the tax on gasoline on emissions from transport in Mumbai?

- How will the requirement that buses be converted to CNG affect bus ridership and vehicle ownership, assuming that it will increase bus fares?

- What would be the net effect on emissions from transport of a policy to

convert diesel buses to CNG?

The paper is organized as follows. Section II presents a simple model of the generation of emissions from transport that clarifies the relationship between changes in 4
covered the same households after the project had been introduced between August 2000 and February 2001. The third phase was completed in March–July 2005. Out of this sample of 28 villages, 10 served as the control, while the remaining 18 received the project at different times (either between 1997 and 2001 or between 2001 and 2005).

About 65 percent of the households sampled (833 out of 1,284 households) received the project sometime between 1997 and 2005; 62 percent of this group (which we refer to as project 1 households) received access to the project between 1997 and 2001, while the remaining 38 percent (project 2 households) received the project between 2001 and 2005.

The data are used here to calculate economic returns to road improvement and the resulting impact on poverty, overcoming some of the pitfalls of earlier road evaluations that have relied mostly on cross-sectional household survey data. The data sets have a true before-and-after and with-and-without structure and are reasonably large, allowing for a study of household-level effects, especially with reference to households above and below the poverty line. These cover not only standard road project outputs, such as transportation costs and trip duration, but also key outcomes (e.g., household consumption and schooling) and a broad range of market interactions.

Tables 1a and b provide, for project and control areas, summary statistics on the outcomes and explanatory variables of interest for each survey round between 1997 and 2005. T-tests are also presented for whether the difference across project and control areas in each round is statistically significant. The household outcomes we are interested in cover a broad range of household socioeconomic characteristics, including household consumption (total, food, and nonfood), asset ownership, landholdings, wage and self employment activities across farm and non-farm sectors, and primary and secondary enrollment of school-age children. We also examine market prices paid by the household for transport, credit, and basic commodities (fish, rice, pulses, and different types of fertilizer for agricultural activities).

13
this way Total Factor Productivity. Below we will describe the main features of the model, readers interest in further detail are referred to AGPP (2009).

The main features of the model can be summarized as follows: At any given time workers can be in any of three states unemployed, employed in agriculture, employed in the urban informal sector or employed in the urban formal sector. Workers engaged in agricultural production (a share _la_ ) receive the average product of labor _ya=Aa (la)_<sup>_γ‐1_</sup> . Workers can migrate from the rural sector to search for urban employment opportunities at a cost (M). When they arrive in the urban sector they start off as unemployed. The urban sector is composed by a high productivity wage sector with positive returns to skills (the formal sector); and a low productivity sector (informal sector) where workers receive a fixed income (y0) regardless of their skills. While unemployed, workers can search for formal jobs, informal jobs or both. Informal employment opportunities arrive at an exogenous rate (α). Workers differ in skills, and skills determine the maximum level of productivity of workers in formal jobs: higher skill workers can produce more at output, for a given technology (A1). The maximum productivity of a worker of skill _y_ in the high productivity sector is _A1y._ Firms post vacancies at a cost (c) and unemployed workers look for jobs, but there are frictions in the labor market, meaning that it takes time for workers to find jobs and for firms to fill vacancies. The rate at which workers and firms meet can be represented by a ‘matching function’ which states the number of encounters (m) that occur in the labor market. For a given number of exiting vacancies (v) and unemployed workers (u): m=ξ(Ѳ)<sup>ρ</sup> , where Ѳ= v/u, can be interpreted as a measure of market tightness. Therefore, m() is the rate of arrival of formal sector employment opportunities and is endogenous to the model. Once workers and firms meet they decide whether to engage in production and they bargain on a wage. The resulting wage will depend on the bargaining power of workers (β), and the difference between the benefit that both workers and firms can obtain from engaging in production, and their outside option. The economy can face two types of shocks: i) idiosyncratic shocks that hit some formal and informal jobs/firms, which arrive at a given rate determined by a Poisson process and; ii) sector wide shocks that affect all the jobs in a given sector. The rate of arrival of shocks to the informal sector will be denoted by δ and the rate of arrival of shocks to the formal sector will be denoted by λ. Economy wide shocks change the values of the technological parameters of the agricultural and formal sector production functions A _a_ and A1, as well as the level of income in the informal sector yo. Figure 1 below illustrates the structure of the labor market.
were marked inconsistencies across the actual INDH provincial distribution and the “optimal” distribution.

Tables 5 and 6 provide a breakdown of the hypothetical budget considered in this study if resources were targeted to the poorest districts (Table 5 for rural areas and Table 6 for urban areas) from the 2004 poverty map, and the program was designed to transfer resources uniformly across all individuals in a given commune (but with different amounts across communes) in such a way as to minimize the national FGT2 for rural areas. Column 1 of Table 5 reveals that in the case of the baseline budget and poverty line the five rural provinces of Erachidia (10.67%), Essaouira (7.8%), Ouarzazate (6.6%), Taroudannt (5.2%) and Taza (5.17%) would be allocated a little under two-fifths of the entire budget to be transferred, in order to achieve the more than halving of the overall rural FGT2 that could be achieved with optimal district-level targeting (see Table 1). The remaining 60% of the budget would be distributed broadly across another 45 provinces. It is interesting to note, however, that in the case where the budget available for distribution was only half as much (column 2) these five provinces would account for a larger fraction of the budget (42%), and when the budget is set at the baseline level, but the poverty line is set at only 75% of the baseline case, these five provinces would account for only one-third of the budget to be allocated. Again, this reveals that while the broad priority to be attached to these five provinces seems robust, the specific budget shares that should be devoted to these provinces is not invariant to the location of the poverty line and the overall budgetary envelope that is available for distribution.

In urban areas, optimal district level targeting would less markedly concentrate resources in a small subset of provinces (Table 6). The five provinces of Marrakech-Menara (6.9%), Fes Jdid-Dar-Dbibagh (6.3), Kenitra (6.1%), Casablanca (6.0%) and Tanger Assilah (5.4%) together account for about 30% of the entire budgetary outlays – nearly 10 percentage points less than the five most “deserving” rural provinces. This general picture also remains 19
high-skilled as a percentage of the total number of male individuals of 20-64 years of age who have moved residence in the past five years predicted by the two instruments - as in Columns (1) and (4) of Table A4 but excluding the year fixed effect. Figure A7 presents the results, using 2001 census district boundaries. The top panel refers to the 2001 year, while the bottom panel refers to the 2011 year. The maps on the first row of each panel refer to the standard instrument, while the maps on the second row of each panel refer to the residual network instrument.

We draw two conclusions. First, we note little difference between the rows of each panel, reinforcing the claim that the residual instrument method yields similar results as the standard instrument. Second, we note that for most regions, there is little geographical clustering, alleviating concerns regarding any remaining, unobservable, regionally correlated effects.

# **7 Results**

Table 3 presents the correlation between migration and land tenure arrangements, exploiting variation between districts in 2002 and 2012. The dependent variable is the percentage acreage rented in under specific terms. Column (1) refers to fixed rent, column (2) to the sub-category of fixed rent under monetary terms, column (3) to the sub-category of fixed rent under crop terms, column (4) to sharecropping, column (5) to renting from relatives, column (5) to two-year or longer contracts and column (6) to written contracts. All the dependent variables are included in percentage terms, that is, as a percentage of the rented-in acreage.

We find that as hypothesized, the out-migration of high-skilled males has a statistically significant positive correlation with the prevalence of fixed rent contracts under money terms, and a negative correlation with the prevalence of sharecropping. The raw association between high-skilled male out-migration and the prevalence of fixed rent contracts paid in produce is negative; as discussed further below, such contracts are in between sharecropping and fixed rent contracts in money terms and their expected correlation with migration 30