rafmacalaba/datause-encoder-data huggingface dataset:
-23-

**Table 9: Expenditures and Financing of Social Benerits in Bulgaria, 1990-1992**

||Value (Lev<br>1990|a millionl<br>1992|% of Tot<br>1990|al<br>1992|% of<br>1990|GDP<br>1992|
|---|---|---|---|---|---|---|
|Expenditures|5188.5|29752.4|100.0|100.0|11.4|13.3|
|Benefits|5064.1|28103.0|97.6|94.5|11.2|12.6|
|Pensions|3562.2|18791.1|68.7|63.2|7.8|8.4|
|Short term sick|268.9|1533.0|5.2|5.2|0.6|0.7|
|Matemity|423.6|1784.5|8.2|6.0|0.9|0.8|
|I Unemployment|0.0|1284.0|0.0|4.3|0.0|0.6|
|Child allowances|741.4|3809.4|14.3|12.8|1.6|1.7|
|Social assistance|68.0|901.0|1.3|3.0|0.1|0.4|
|Other expenditures|124.4|1649.4|2.4|_5.5_|0.3|0.7|
|Retraining|0.0|42.5|0.0|0.1|0.0|0.0|
|Social care|123.6|882.0|2.4|3.0|0.3|0.4|
|Other SSF /1|0.8|49.9|0.0|0.2|0.0|0.0|
|Other URF/2<br>Financing|0.0|675.0|0.0|2.3|0.0|0.3|
|Tax receipts|5012.3|24820.8|96.8|86.2|11.0|11.1|
|SSF|5003.9|22020.0|96.6|76.4|11.0|9.8|
|URF|8.4|2800.0|0.2|9.8|0.0|1.2|
|URF reserves|8.4|947.6|0.2|3.3|0.0|0.4|
|Net bud2et/3|167.8|3984.0|3.2|13.8|0.4|1.7|

/1 Social Security Fund (SSF) /2 Unemployment and Retraining Fund (URF) /3 Net budget fmancing is equal to expenditures less tax receipts, less beginning of year surplus URF. Source: World Bank (1994, Table 5.1, page 107).

to 12 months.

- Maternity, child and social assistance (family) benefits. A maternity benefit is payable for two years and leave without pay is allowed for a third year. Birth grants and child benefits are also paid. Child allowance are payable for each

child, rising per child until the fourth child (to encourage an increased birth
- I -

# INTRODUCTION

1. Decentralization policies are at the heart of education reform effoits in many countries internationally. Two important types of policies are those that: (a) remove barriers to private education and (b) devolve authority and responsibility for schools from central level administrations to intermediate level organizations and ultimately to schools, relying more on local communities for school financing, with an overall goal of improving school effectiveness. While both types of policies are largely uninformed by empirical evidence regarding their iwpact on such education outcomes as student learning, in developing countries the evidence regarding the effects of local control is much weaker than that regarding private schools. This paper extends the literature on the impact of private education on achievement, while providing the first evidence on the impact of local control on achievement in a developing county. It analyzes data from 214 secondary schools in the Philippines to answer questions regarding (a) the relative effectiveness of local, government and private secondary schools, and (b) the factors that account for observed differences.
income levels have different chances to evade. In this regard, to the extent that those at the higher end of the income distribution evade more than those at the lower end (i.e. the system is in practice less progressive than intended), estimates of ε based on official tax schedules will be upward biased. We tackle this issue by relying on de facto tax rates.

A second difficulty is that in principle the value of the marginal income tax is likely to change with the income level, and hence (4) is sensitive to where it is computed along the income distribution. To address this concern we proceed to compute (4) on the basis of the effective marginal tax rate along the income distribution, and for consistency on the basis of the effective average tax rate also along the income distribution. More specifically, we rely on the following expression: where N is the number of tax data points used in the calculation. From an empirical point of view, for each country we rely on four point estimates corresponding to the 20, 40, 60 and 80 percent of the income distribution. These point estimates are computed from income tax data<sup>17</sup> at the quintile level. That is, marginal tax rates are computed as: where _Ti_ and _Yi_ are the average taxes and average per capita income level of the i-th quintile, whereas the average tax rates are computed as With these elements in mind table 3 reports the estimates of ε. On the whole, the results of this exercise are quite satisfactory. Inspection of this table indicates that the estimates of ε range from 1.11 in Honduras to 1.89 in Peru with an average value of 1.49. Thus the values that we obtain are in line with those commonly reported in the literature, although in comparison with the values in Evans and Sezer (2004) and Evans (2005) for a sample of developed countries (about 1.4), our estimates of the elasticity of marginal utility of income are slightly higher (about .1 on average). This in principle will contribute to a higher social discount rate in Latin America than in the developed countries.

> 17 I would like to thank Breceda et al. (2006) for providing me with their tax data.

11
- 1.1 Deficits, debt and growth in a nutshell

# **Figure 1 Growth (%), Fiscal Deficit, and Debt Stock (% of GDP)**

<!-- Start of picture text --> 12 Debt stock 90<br>Fiscal deficit<br>Real GDP growth<br>10 80<br>8 70<br>6 60<br>4 50<br>2 Benchmark Eighth Plan Ninth Plan Tenth Plan 40<br>(85/86-89/90) (92/93-96/97) (97/98-01/02) (02/03-05/06)<br>0 30<br>80/81 82/83 84/85 86/87Eighth Plan88/89 90/91 92/93 94/95 96/97 98/99 00/01 02/03 04/05<br>Debt stock Fiscal deficit Real GDP Growth<br><!-- End of picture text --> _Source_ : Handbook of Statistics on Indian Economy, Reserve Bank of India The impact of lags, which is critical to the preceding interpretation and which is discussed more completely below, is related to certain clearly demarcated stages in economic outcomes over the past 20 years. In Figure 1, we see a jump in the fiscal deficit to the 9-10 percent of GDP range in the late 1980s with the general government debt-to-GDP ratio displaying a sustained rise to a (local) peak of 81 percent in 1991/92 coinciding with the BoP crisis. The period 1985/86-1989/90, which was marked by high deficits and rising debt, offers a convenient benchmark for the assessment of economic outcomes after the crisis and reforms of 1991. The first subsequent period of interest is the reform-intensive Eighth Plan period, 1992/93-1996/97, during which growth recovered with deficits falling and the debt-to-GDP ratio shrinking to a (local) trough of 68 percent in 1996/97. The second period more-or-less coincides with the Ninth Plan period, 1997/98-2001/02, during which growth slowed relative to the last three years of the Eighth Plan period, deficits rose and debt climbed, returning to 81 percent of GDP. This is the period when accusations of fiscal profligacy, concerns about slowing reforms and misgivings about a crisis were at their strongest in recent years. The third period covers the first four years of the Tenth Plan period, 2002/03-2005/06, during which growth staged a significant acceleration starting in 2003 and deficits charted a downward course but debt rose to another peak, of 86 percent, in 2003/04, before beginning to fall. We shall pay particular attention to the unexpected resurgence in growth of the past three years, including emerging constraints to maintaining it and the related public finance implications.

General government fiscal data presented in this paper are only up to 2005/06, because of the time lag in the availability of reliable consolidated state-level fiscal data. Early estimates for 2006/07 suggest that the general government deficit estimated will decline to 6%, pointing to impressive fiscal consolidation in recent years, especially at the state level. This has also been recognized by the markets, which have started to take a relatively sanguine view, with all major ratings agency upgrading India’s long-term foreign currency ratings to investment grade since mid-2006.

3
20 Table 2: Comparison of PM2.5 Measures

||||**Panel A: Co**|**rrelations**||
|---|---|---|---|---|---|
|||Regulatory Monit|ors|Sa|tellite Measures|
||Hourly<br>(1)|Daily<br>(2)|Weekly<br>(3)|Hourly<br>(4)|Daily<br>Weekly<br>(5)<br>(6)|
|Satellite Measures|0.54|0.75|0.87|.|.<br>.|
|Low-cost (unadjusted)|0.60|0.81|0.83|0.53|0.72<br>0.84|
|Jafe Calibration|0.62|0.90|0.96|0.54|0.76<br>0.89|
|Local Calibration|0.74|0.87|0.89|0.63|0.80<br>0.91|
|Satellite Calibration|0.68|0.87|0.90|0.64|0.82<br>0.92|

|||**Panel B: **|**Root Me**|**an Squar**|**e Error**||
|---|---|---|---|---|---|---|
|||Regulatory Monitors|||Satellite Measures||
||Hourly<br>(1)|Daily<br>(2)|Weekly<br>(3)|Hourly<br>(4)|Daily<br>(5)|Weekly<br>(6)|
|Satellite Measures|55.4|34.8|20.6|.|.|.|
|Low-cost (unadjusted)|59.9|52.1|46.9|60.2|53.5|46.6|
|Jafe Calibration|61.8|60|56.4|66.4|61.8|56.6|
|Local Calibration|39.5|26.9|21.3|42.7|29.8|18.8|
|Satellite Calibration|44.9|32.1|26.5|42.2|28.5|18|

**Note** : The date range used is for the full year 2023. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. Satellite measures are generated from the MERRA-2 assimilation. The satellite value is the MERRA-2 PM2.5 estimate closest to the Bel-air regulatory-grade monitor station. The regulatory monitor values come from the average of CGQA regulatory-grade monitors. The low-cost unadjusted and calibrated values use averages across all low-cost monitors active for a given time interval. Jaffe calibration uses PM1/10 ratio to identify dust events. The local calibration is the unadjusted Purple Air values calibrated to the local context using data from two local regulatory monitors. The satellite calibration uses the MERRA-2 measure to calibrate the unadjusted low-cost monitors.
**Table 2: Rank order of MENA countries by percentage increase in years of schooling (1970-2010)**

|Country|Years of Schooling<br>(1970)|Years of Schooling<br>(2010)|Absolute<br>Increase|Percentage<br>Increase (%)|Rank<br>(N=145)|
|---|---|---|---|---|---|
|Yemen, Rep.|0.06|3.7|3.6|6053.5|1|
|Libya|1.5|8.2|6.7|444.9|6|
|Egypt|1.3|6.9|5.6|433.9|8|
|Morocco|0.9|5.0|4.0|408.5|10|
|Algeria|1.5|7.6|6.1|405.1|11|
|Iraq|1.3|5.9|4.6|335.1|17|
|Iran|2.0|8.6|6.6|331.3|19|
|Tunisia|1.7|7.2|5.5|314.6|21|
|United Arab Emirates|2.5|9.1|6.6|261.2|26|
|Bahrain|3.0|9.6|6.5|213.2|33|
|Jordan|3.4|9.1|5.7|167.2|45|
|Saudi Arabia|3.2|8.4|5.2|158.3|47|
|Syrian Arab Republic|2.2|5.2|2.9|135.6|57|
|Qatar|3.8|7.4|3.6|95.0|75|
|Kuwait|3.2|6.3|3.0|94.0|78|

# **C. Regional Performance in Comparative Perspective**

We now look at cross-regional data to see how the MENA region performed as a whole in comparison with other regions. Table 3 provides population weighted averages for education stock for each of the developing country regions (see Annex 2 for list of countries included in each region) and the highincome OECD group of countries for the period 1970-2010. The table shows that the greatest percentage increase in education stock (239%) occurred in the MENA region. This result is consistent with the country-specific comparisons reported in the previous section.

**Table 3: Average years of schooling by region and over time**

||1970|1980|1990|2000|2010|Increase (%)<br>1970-2010|
|---|---|---|---|---|---|---|
|Middle East/ North Africa|2.1|3.3|4.8|6.1|7.2|239.0|
|East Asia/Pacific|3.9|5.0|5.9|6.7|7.6|95.7|
|Europe/Central Asia|6.0|7.4|8.5|9.5|10.0|66.5|
|Latin America/Caribbean|4.4|5.5|6.5|7.4|8.4|89.2|
|South Asia|2.4|3.0|3.8|4.7|5.9|145.6|
|Sub-Saharan Africa|1.9|2.7|3.7|4.5|5.4|180.4|
|OECD|7.5|8.6|9.3|10.2|10.9|44.8|

5
procedure used to estimate how much of a given expenditure (or taxation) category is received (imposed) by (on) a particular socioeconomic group or geographical area. Incidence analysis aims not only at identifying how much people in the lowest income groups receive or pay (that is, how ―pro-poor‖ spending or revenue collection is), but also how ―progressively‖ it does so—that is, how the cost or benefit correlates with a given welfare measure, such as income, consumption or wealth. Incidence analysis consists of several steps (see van de Walle [1998],):

- (i) Approximate the value to consumers of a public service—typically by equating it to the cost of providing the service;

- (ii) Identify all beneficiaries of the service;

- (iii) Obtain _gross_ unitary benefits by dividing total benefits (from step i) among total beneficiaries (from step ii);

- (iv) Rank the identified beneficiaries in the household dataset according to some agreed measure of welfare (such as, for example, deciles or quintiles of household per capita consumption); and

- (v) Assign the gross unitary benefit (as obtained in step iii) across the distribution of beneficiaries identified in the household dataset and compute the shares of the services that are allocated to different portions of the population. The unit of analysis is, typically, the representative household by quintile or decile of the income or consumption distribution on a per capita basis.

In order to calculate _net_ benefits two additional steps are needed:

- (i) Calculate the out-of-pocket household per capita spending from the household dataset; and

- (ii) Subtract the out-of-pocket household per capita spending to the expenditure assigned as the benefit. The resulting figure is the net unitary benefit per individual or household after receiving a public service. .

As for data requirements, the first three steps rely on information about public spending in the relevant sectors and the beneficiaries or users of those services, which is typically available from the implementing agencies of the government. The last four steps require information on household consumption and expenditure patterns by income category, which can be obtained from national survey data, in this case the Zambian LCMS.

6
control at baseline,  shows the difference in outcomes between baseline and endline in the absence of the program, and  is our coefficient of interest and the estimated impact of the program on _Y_ .

Further, we also assess whether the relative impact of participation varies across participant households living above and below the poverty line. This exercise is motivated by the fact that among the participant households, there are households who have consumption levels above the poverty line: 36.07 percent were above the 2010 national poverty line. Figure 1A shows that the distribution of EGPP participant households from the 2010 EGPP survey and compares it to the distribution of the national population in 2010 (drawn from the nationally representative 2010 Household Income and Expenditure Survey).

The key assumption of the difference-in-difference (DiD) method is that the treatment and control groups have parallel time trends. This is equivalent to the assumption that in the absence of the intervention, the difference in the outcomes between treatment and control groups would move in tandem, decreasing or increasing at the same pace. This condition will not hold if household characteristics or initial conditions affect subsequent changes in the outcome variables such that their distributions among the treatment and control groups differ from each other. Since we do not have additional observations before the baseline, it is not possible to test the hypothesis of parallel time trend between the treatment and control groups. However it is reasonable to assume that they share similar trends in the outcome variable – household consumption expenditure - since they are drawn from the same villages. Any exogenous shock that may affect the control group as a whole would affect the treatment group in the same way over time. Our data show that both control and treatment households share very similar characteristics at baseline as presented in Table 1A in the Appendix.

# **3. Data and Results**

# **4.1. Sampling and Timing of Surveys**

The baseline survey, collected during November – December 2010, consists of information at the initial nationwide roll out of EGPP following its previous experimentation stage conducted in limited areas.

13
indexes are positively correlated (Figure 3.5). Second, the skills requirement approach alone is not enough to classify an occupation as green or non-green. While the correlation is high, there is no simple way to determine the level of skills intensity needed to categorize an occupation as green. Leaving aside concerns about biases from online job vacancy data, the extensive margin of the GSI index classifies many more occupations as green than the GTI index. One could argue that the zero cutoff for the extensive margin could be too low for two reasons. First, employers could be tempted to include green skills regardless of their importance for carrying out tasks, simply because they are important for the firm culture or as a signal of other desired skills. Second, the presence of just one vacancy requiring a green skill would result in a positive GSI. And there might be cases in which a green skill is important for a specific job in a particular firm, but may not necessarily be representative of the common skills demand for the occupation. This issue arises because most occupations have at least one vacancy requiring a green skill. Table 3.6 shows that 723 out of the 773 occupations studied in the U.S. and 106 out of the 159 occupations studied in Indonesia have at least one vacancy requiring one or more green skills. To understand where to place the cutoff, we examine how many occupations would be considered green if we raise the cutoff level and compare this with the results of the GTI index. We do this for both countries as described in Table 3.6 and Figure 3.5. It can quickly be inferred that the skills requirement approach leads to high levels of errors (assuming the task approach is correct). Increasing the threshold to be classified as a green job decreases the false positive (type 1 error) but increases the false negative (type 2 error). In other words, when we increase the threshold, fewer non-green jobs are identified as green (false positive), but more green jobs are incorrectly classified as non-green (false negative). This result holds true for both countries.

**Figure 3.5. GTI vs GSI**

<!-- Start of picture text --> a) U.S. b) Indonesia<br>Incinerator and water treatment plant operators<br>Meteorologists<br>Environmental protection professionals<br>Environmental engineers<br>0 20 40 60 80 100<br>Green Tasks Intenisty Narrow<br>0 20 40 60 80 100<br>Green Tasks Intenisty Yes GT, Yes GS No GT, Yes GS<br>Yes GT, Yes GS No GT, Yes GS Yes GT, No GS No GT, No GS<br>Yes GT, No GS No GT, No GS GS error Fitted values<br>GS error Fitted values<br>40<br>30<br>30<br>20<br>20<br>10<br>Green Skills Intenisty 10<br>Green Skills Intenisty Narrow 0<br>0<br><!-- End of picture text --> _Source_ : authors

**Table 3.6. Number of green occupations for different cutoff levels of the GSI index and relative to the GTI index**

||U.S.|||Indonesia|||
|---|---|---|---|---|---|---|
|If GSI cutoff|# of|Of which<br># of non-|Of which|# of<br>Of which|# of non-|Of which|

26
Most studies use household surveys and explain variance in childhood immunization “uptake” or “demand” with characteristics of children’s mothers and their households, often but not always including community-level fixed effects. These studies invariably find maternal education and household socioeconomic to be correlated with the probability of childhood immunization, but there are disparate findings concerning the extent to which these are causally related to immunization status, and whether measurable maternal and household characteristics might be proxies for other underlying factors or for characteristics of the communities of residence. (Desai and Alva 1998 ; Streatfield , Singarimbum, and Diamond 1990; Gage, Sommerfelt, and Piani 1997; Pebley , Goldman, and Rodriguez 1996; Steele , Diamond, and Amin 1996). National literacy rates or other education variables are not included in the estimations below, however, because they do not appear significant in studies of national-level determinants of immunization outcomes (Gauri and Khaleghian 2002). In addition, education and literacy data were not available for a large number of country-years in the period studied, meaning that the sample sizes would be some 80% smaller unless the large majority of data points for education and literacy were interpolated – an approach not taken in this paper.

Data on the dates of ratification were taken directly from online information provided by the United Nations. Other control variables were extracted form the World Bank’s Development Data Platform (GDP, population, population density), the Polity IV database (the polity variable, which ranges from -10 for the most autocratic to +10 for the most democratic), the International Country Risk Guide (bureaucratic quality). The variables chosen are similar to those used in a paper on the political and institutional determinants of immunization (Gauri and Khaleghian 2002). That study, which used data from 1980-1997, found that immunization rates were positively related bureaucratic quality, democracy (at low and high levels of income), population density, and national income, and negatively related to population size. The present paper uses data from 1989, the year the CRC became available for ratification, to 2007.

The decision to ratify the CRC may be endogenous to policies related to children’s rights. Put more plainly, countries that are already compliant with, or intend to comply with, a treaty’s requirements 9
higher fraction of Keralite households with members abroad depend exclusively on remittances for income than do households with members working elsewhere in India. This suggests that households with overseas migrants may rely more heavily on remittances for current consumption needs.<sup>14</sup> On the other hand, the origin household might treat the remittances sent back to it by a domestic migrant as a mixture of transitory and permanent income, and use them for both consumption and investment. The less the household expects its family member’s migration to be temporary, the less likely it is to view the transfers it receives as temporary income. Accordingly, the marginal propensity to invest domestic remittances is likely to be lower than the propensity to invest overseas remittances. Another related factor is that an individual who moves within India to work may, at a certain point, start remitting a smaller share of his or her earnings, because the growing prospect of settling long-term or permanently in the new domicile presents additional expenses. This reduction in domestic remittances would then limit the ability of the household back home to make investments comparable in size to the investments made by households receiving overseas remittances, even if the marginal propensity to invest domestic transfers were the same as for overseas transfers.<sup>15</sup> While the theoretical expectation is that households will invest a greater portion of overseas remittances than they will domestic remittances, the extent to which they actually do so is an empirical question that I investigate in this paper. Yet, migration is not simply a matter of the transfer and receipt of remittances. When a family member moves elsewhere for work, households no longer have access to his or her labor resources, insights, or acumen. If the migrating member used to make financial decisions for the family—for example, as the head of household—his or her departure may compel another member, such as the spouse or parents, to assume this responsibility. This, in turn,

> 14 The 1998 KMS data show that 38.1 percent of households with international migrants had no member earning wages in Kerala, compared with 20 percent of households with domestic migrants.

> 15 A confounding issue could be the _anticipation_ of declining remittances over time, as a domestic migrant establishes stronger roots in his or her new domicile. This may lead the recipient household to treat current remittances as temporary flows, therefore raising their propensity to invest it early on.

8