rafmacalaba/datause-encoder-data huggingface dataset:
The literature also acknowledges, however, that tax expenditure program may be a more effective than direct payments in stimulating certain activities. One example is the itemized deductions for charitable contributions by taxpayers. It might reduce the government tax revenues but this is more than offset by an increase in support to charitable activities.

Another concern raised in the literature is that tax expenditures can contribute to a perception that the tax system is unfair since not all tax payers qualify. For those who do qualify, the value of the tax benefit usually increase with taxable income. Tax expenditures can result in individuals with similar incomes and expenses paying different amounts of tax, depending on whether they engage in tax-subsidized activities or not. This different tax liability for individuals similarly situated is a violation of horizontal equity. Tax expenditures also violate vertical equity if they cause the cost of government to be unfairly distributed among income classes. The disproportionate benefit of tax expenditures to higher income individuals may reduce the level of progressiveness of the tax structure that the statutory tax rate alone would achieve.

An analysis of the Polish personal income tax expenditure programs identifies both horizontal and vertical inequities. These include: (1) paying recipients to engage in activities they would otherwise engage in anyway, providing a windfall gain to some taxpayers; (2) narrowing the tax base, limiting the scope for tax reductions; (3) providing open-ended opportunities for tax exemptions and deductions, making it more difficult to project tax revenues; (4) adding complexity to the tax laws, increasing the cost of enforcing them; (5) reducing accountability of the government action because of the lack of visibility of tax expenditure programs, and by failing to clearly assign responsibility for approving and supervising the implementation of these 6
parties involved, under the assumption that the two companies might be colluding to implement discriminatory practices to the detriment of free trade.

# **4.b A Case Study of the Applications for the Imposition of Safeguard Measures**

Between 1995 and 2004, the MEIC’s Investigating Authority received a total of five applications for the imposition of safeguard measures, four of which involved manufactured products (classified under Chapters 25 to 99 of the Central American Tariff System) and one the agricultural sector, specifically milled and paddy rice.<sup>21</sup> This distribution is approximately consistent with Costa Rica’s import structure, since as a result of the high tariff protection prevailing over the most important agricultural products for domestic consumption, imports tend to be primarily manufactured products.

It is also worth pointing out that, out of the five applications, one was filed against imports from Asian countries, four against imports from countries in the Americas, and one against imports from Europe.<sup>22</sup> This is consistent with the structure of Costa Rica’s imports by origin, since virtually two thirds of them originate in North and Central America.

**Table 4.5   Costa Rica: Safeguard Cases**

|**Year**|**Product**|**Sector**|**Related Countries**|**Complainant or Applicant**|
|---|---|---|---|---|
|1995|Bast fiber sacks|Manufactures|Nicaragua, India and Bangladesh|National Cabuya Board|
|1996|Used Apparel|Manufactures|United States|Textile and Apparel Industry Association|
|1996|Ceramic blocks and<br>tiles|Manufactures|Spain, Italy, Brazil and Mexico|Productos de Concreto S.A. y Firenze Industrial<br>S.A.|
|1996|Sanitaryware|Manufactures|Mexico|Incesa Standard S.A.|
|2000|Milled and paddy<br>rice|Agriculture|United States|National Rice Board|

Source: Prepared by the authors based on data drawn from the resolutions on the cases dealt with by the MEIC.

Furthermore, it is interesting to note that in only one of the cases (ceramic blocks and tiles) an investigation failed to be initiated since the applicant firms dropped the case and, therefore, did not submit the information required by the Investigating Authority. In only two of the five applications injury to domestic production caused by imports was found and, although in both cases it was decided to apply a provisional measure, only in one (jute sacks) was the measure finally imposed, since in the case of rice the application of the measure was not deemed necessary in view of the increase in the international prices of this grain, which made imports less attractive.

> 21 No application of this type was submitted prior to 1995.

> 22 The total number of cases classified by regions is six since the ceramic blocks and tiles case involves countries from the Americas (Mexico and Bra zil) as well as European countries (Italy and Spain).

33
<!-- Start of picture text --> Figure 5.5: Share of Income by Decile and Location in Haiti, 2001<br>0.60<br>Metro<br>0.50<br>Urban<br>Rural<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>1 2 3 4 5 6 7 8 9 10<br>Source: Own calculations based on HLCS 2001.<br><!-- End of picture text --> Education is also unequally distributed and international research shows that this can more easily be reduced than income inequality. However, research also shows that a reduction in education inequality affects the income distribution very little in the short run (Ferreira 2002).

Table 5.8: Incidence of Education Level in Rural Haiti (percent), 2001

|Quintile|No education|Primary|Secondary|Tertiary|
|---|---|---|---|---|
|1 (poorest)|78.8|18.0|3.2|0.0|
|2|77.0|18.7|4.2|0.1|
|3|72.1|22.8|5.1|0.0|
|4|67.2|22.9|9.8|0.1|
|5(richest)|56.6|26.6|16.6|1.2|

_Source:_ Own calculations based on HLCS 2001.

Table 5.9: Incidence of Education Level in Urban Haiti (percent), 2001

|Quintile|No education|Primary|Secondary|Tertiary|
|---|---|---|---|---|
|1 (poorest)|65.2|26.3|8.2|0.4|
|2|58.3|27.4|13.9|0.4|
|3|61.6|26.4|12.0|0.0|
|4|50.8|31.7|17.2|0.3|
|5(richest)|33.3|31.5|31.7|3.6|

_Source:_ Calculations based on HLCS 2001.

29
**Policy, Research, ad Extiemal Affairs**

**_*016 a,_** * ***ti.V**

**Public Sector Managemont and Private Sdetor Oev.opment**

- This paper a product of the Public Sector Management and Private Sector Development Division. - Country Economics Department is part of a larger effort in PRE to: (i) review past efforts in institutional development. (ii) deterrnine where these efforts have succeeded and where they have done poorlyv and (iii) suggest how the World Bank and its borrowers can better create and strengthen an appropnate institutional framework for economic development. An earlier version of this paper was presented at a conference on Institutional Development and the World Bank, held in Washinigton DC in December 1989. Copies of this paper are available free from the World Bank. 1818 H Street NW, Washington DC 20433. Please contact Gloria Orraca-Tetteh, room N9-069, extension 37646 (47 pages with tables).

Public enterprises (PEs) - state-owned or state-controlled productive entities whose output is sold mostly in the marketplace **-** earn an average 10 percent of GDP in developing countries (17 percent in African countries, 12 percent in Latin American countries, and 3 perr'ent in Asian countnies).

both policy framework and institutional set-up. and restructuring of individual enterprises.

* Extend its analysis of PEs to the socialist economies, explore the relationship between PEs and the private sector, and study how best to phase and sequence PE reforms.

Many governments are reexamining the role of the state, so questions about whether to divest PEs or make them more efficient are likely to intensify. The Bank will increasingly be called upon for advice and financial support in managing the transition period. Galal recommends that the Bank: Maintain its focus on rationalizing the size of PEs. by liquidating nonviable PEs and transfering their ownership or control to the private sector. if that wiUl make them more efficient. In helping countries improve the efficiency of PEs that remain public, the Bank should emphasize

* Refine PE reform components and tools, especially in terms of the phasing and sequencing of price liberalization and competition: the budgetary impact of PEs (their costs versus thcir revenues **-** and **here** Galal discusses the "watcrbed effect." how holding down costs in one area raises costs in another); and the valuation of PEs for divestiture.

* Leam more systematically from expenencc by analyzing the outcomes of PE reforms: the performance of divested PEs; the effects on cFliciency of staff reductions; and the effectiveness of program contracts on enterprise efficiency.

**Tlhe PRE Working Paper Series disseminates the rirdings of work** under **way in the Bank's Policy, Resereh. and Extcrnai** Affa.rs Complex. An objective **of the senes** is to get **these** rindings out quickly. even if presentations **are** less than fully polished. **The** findings. interpreutons. and conclusions **in** these **papas** do not necessarily represent **official** Bank pohc>

Produced **at** Lhe PRE Dissemination **Center**
# **Tracking Methane Emissions by Satellite: A New World Bank Database and Case Study for Irrigated Rice Production**

**Susmita Dasgupta* Somik V. Lall David Wheeler**

## **World Bank**

**Keywords:** CH4 emissions, Sentinel-5P, Agricultural emissions, Irrigated rice production

## **JEL Classification:** Q01, Q54, R28, R48, R58

The research is funded by the Knowledge for Change Trust Fund, administered by the World Bank.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

*  Authors’ names in alphabetical order.  Our special thanks to Vijay Jagannathan, who inspired this project.
**Voucher Fwnds in the Transition Economies**

**1**

# **Institutional Ownership**

3.6 In thie second model, financial institutions such as banks, pension funds, or mutual funds are the dominant shareholders. Several of these institutions would each typically own somewhere betwee 10 and _50_ percent of the shares. Thbus they have an incentive to closely supervise and monitor the performance of maaeetand to demand representation on the goverance boards for that purpose.

3.7 This model is exemplified by Germany what the banks play a dominant role in corporate govenace. Gedran banks often control a large proportion of the shares of the largest enterprises.' The shares controlled by these banks consist of shares owned directly by the banks, sa held by the banks for individual ins vstors who purchased the shamr thoaugh the banks since banks are also the largest brokes in Germany, and shares i affiiated mutual funds. in the Czech and Slovak Republics, large banks are the sponsors of the largest voucher funds and thus probably can control the shares owned by these funds. 3.8 In spite of the general pattern of widespread and passive share ownership in the United S3tas, insitutional investors in that country have begun to play a lairger role n corporate goverance. This trend is sometimes called "relationshlp investing' because the large institutional shareholder wants to influence the manaers toDimprove the pormance of the company and thus enhance the return on the large shareholders investment in the company. The most cited exmple of such an institution is the Californiia Public Employees Retirement System (Calpers), a large public pension fund. Some mutal funds have also adopted this philosophy.1 3.9 Institutional owners monitor perorace, but are not themselves experts in the management of any particuWl business or industry. The owners review and approve business and financial plans for the enterpise prepared by managem but do not typically develop the plans or arage for financng themselves If the managers are incapable of developing appropiate plans and strateies for improving efdficiency and profitabiity, the response of the large instittionalowners typicaly would be to replace the managers rather man carry out the duties and responsibilities of the full-time managers.

# **Holding Companies**

3.10 The third model only iosts in a minority of companies in Wester countries, but seems to be the basis for proposals to create govemrment-sponsored funds. This model is the relationshipbetween a holding company and its closely held sub ies Other examples might For example, in 42 of the largest German companies, three bans cotol on avcrage 45 percent of the shares in these companies. ts Bums, Buxbaum, and Hopt (1993), p. v _75._ ob "Corporate Governance Survey," p. 16.
Table 7: Data and simulation moments, 2005-2009

||Calibration moments|data<br>(1)|simulation<br>(2)|
|---|---|---|---|
|1.|The mean of the logarithm of export sales|11.96|11.96|
|2.|The growth rate of export entrants|31.08%|30.99%|
|3.|The share of sales from export entrants|1.42%|1.42%|
|4.|The age coefcient in the input price regression<sup>_a_</sup>|0.025|0.025|
|5.|The age coefcient in the exports growth regression<sup>_b_</sup>|-0.023|-0.023|
|6.|The tail index of the export sales distribution<sup>_c_</sup>|-1.238|-1.243|
||_Criterion_||_0.00004_|

Notes: Moments correspond to the average export behavior of Portuguese manufacturing firms over the period 2005-2009. _a_ Source: The age coefficient in Table 4, column (1). _b_ Source: The age coefficient in Table 6, column (3). _c_ Source: The empirical estimate of the tail index is presented in Appendix I.

32
- 21 -

Table 7. Conditions included in housing divestiture agreements between enterprises and municipalities

|**_Conditions_**|**_Number_**_of_**_agreements_**|**_Were considered as the most_**<br>**_difficult_**_by_**_enterprise_**<br>**_managers_**_in negotiations_<br>**_with_**_local authorities_<br>**_(number_**_of responses)_|
|---|---|---|
|Divestiture of housing in a good<br>technical shape|9|4|
|Divestiture of heat and hot water<br>supply systems|10|6|
|Divestiture of enterprise housing<br>maintenance units|10|4|
|Sharing of maintenance costs|2|I|
|Sharing of capital repair costs|4|2|
|Sharing of utility costs|2||
|Continuation of housing maintenance<br>and capital repair on the expense of<br>enterprise|2|1|
|Continuation of housing maintenance<br>and capital repair on the expense of<br>municipality|_5_||
|Continuation of utility services<br>provision on the expense of enterprise|3||
|Continuation of utility services<br>provision on the expense of<br>municipality|3||
|Total number of enterprises in the proce|ss of housing divestiture||

**Source:** Enterprise survey
- 13 -

**<u>Table 4 Instrumental Variable Estimation</u>**

||_OPD_|_MAL_|_DEL_||_OPD_|_MAL_|_DEL_||
|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.564<br>*|-0.025|0.028|*|1.002|*<br>-0.053|-0.010||
||(0.319)|(0.065)|(0.015)||(0.617)|(0.098)|(0.020)||
|_POPDEN_|-11.490<br>**|<br>0.305|-0.481|*|-18.170|*<br>0.862|0.193||
||(5.616)|(1.206)|(0.256)||(10.920)|(1.747)|(0.358)||
|_POV_|0.030|-0.002|0.004||||||
||(0.077)|(0.013)|(0.004)||||||
|_BED_|0.534|-0.005|0.192|***|0.204|-0.019|0.224|***|
||(0.680)|(0.168)|(0.024)||(0.678)|(0.176)|(0.040)||
|_CENTR_|-2.166|-0.156|-0.132||-3.345|0.008|-0.022||
||(1.635)|(0.361)|(0.085)||(2.790)|(0.403)|(0.097)||
|Constant|-3.389|2.145|-0.492||-9.818|3.107|*<br>0.521||
||(10.180)|(1.834)|(0.490)||(11.790)|(1.931)|(0.354)||
|Obs.|66|66|66||66|66|66||
|R-squared|0.091||0.328|||0.007|0.378||
|Wald chi<sup>2</sup>|11.92|2.28|195.89||20.36|23.43|107.99||
|Number of<br>county dummies|0|0|0||8|8|8||
|Exogeneity test<sup>1</sup>|||||||||
|Chi<sup>2</sup>test<br>statistic|1.167|1.327|2.091||3.420|*<br>0.898|1.481||
|P-value|(0.280)|(0.249)|(0.148)||(0.064)|(0.343)|(0.224)||
|Overidentifying re|striction test<sup>1</sup>||||||||
|Chi<sup>2</sup>test<br>statistic|1.354|2.432|3.263||0.668|2.873|1.045||
|P-value|(0.508)|(0.297)|(0.196)||(0.716)|(0.238)|(0.593)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. 1 The instrumental variable estimation is performed with _RDDEN_ or _GDRD_ instrumented with three variables in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .

The results indicate that transport connectivity is exogenous. The traditional exogeneity test is performed; the test statistics are found to be lower than the critical value, except in one case—the regression on _OPD_ with county dummy variables. In this case, the exogeneity hypothesis can be rejected at the 10 percent significance level. The coefficient of _RDDEN_ is still positive and significant, confirming the importance of road density. The overidentifying restriction test is always rejected at the conventional significance level, meaning that our instruments are less likely to be correlated with the dependent variables.

One may be concerned about the robustness of the results against our transport variables. Ordinary least squares regression is performed with different thresholds applied for road
Table C.15: National Estimates of Effect of Schooling on Beliefs Regarding Women’s Empowerment, Married Women Only

||Travel to Visit<br>Family / Friends<br>(1)|Personal<br>Healthcare<br>(2)|Large Household<br>Purchases<br>(3)|
|---|---|---|---|
||A. Wife should|at least have a|say in decision|
|�<br>Years of Schoolingizy|-0.056|-0.066|-0.065|
||(0.043)|(0.040)|(0.035)|
||[0.194]|[0.097]|[0.066]|
|Mean of Dependent<br>(Pre-Reform Cohorts)|0.783|0.721|0.650|
|First Stage F-Statistic|6.60|6.59|6.59|
|N|18,138|18,139|18,139|
||B. Wife should|be able to make|decision alone|
|�<br>Years of Schoolingizy|-0.064|-0.029|-0.041|
||(0.039)|(0.030)|(0.027)|
||[0.102]|[0.334]|[0.118]|
|Mean of Dependent Variable<br>(Pre-Reform Cohorts)|0.163|0.161|0.128|
|First Stage F-Statistic|6.60|6.59|6.59|
|N|18,138|18,139|18,139|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable in each column is an indicator that equals 1 if the statement is believed to be true and 0 otherwise. The sample includes all married women born between 1970 and 1988. A 2SLS � model is estimated where Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.31
All the simulations presented in this section were based on a series of assumptions, including the supposition that per capita income grows equally in Haiti. But this is unlikely to be the case: the literature on other countries shows that income grows very unequally for different income groups. For the findings in this section, this means that the estimated poverty reduction impacts are much smaller than stated because poor households will benefit much less than rich households. Hence the need for much broader policies than economic growth strategies if poverty in Haiti is to be significantly reduced.

# **POVERTY DEPTH**

Extreme poverty in Haiti is not only extensive but also very deep. The P0 measures the proportion of people below a certain poverty line, but it takes no account of how far they are below that line (the degree of poverty) or whether they are becoming even poorer. To address the situation of the poorest, the squared poverty gap measure, P2, is used. This takes the degree of poverty into account, because it gives more weight to the poorest and most vulnerable. The P2 poverty measure reveals that the extreme poverty depth reached 19.3 percent in 2001 (Table 5.6).

Extreme poverty in rural areas is slightly less deep than in urban Haiti. The P2 poverty measure reveals that the extreme poverty depth reached 22 percent in rural and 24 percent in urban areas in 2001 (Table 2.5). In the metropolitan area, by contrast, P2 reached only 5 percent in the same year. Haiti’s Northeast region has the deepest poverty, and there are significant regional differences. The West region had a P2 of 8.5 but in the Northeast the measure reached 47.4, indicating that poverty is not only widespread but also very deep in the latter region.

Table 5.6: Poverty Gap and Squared Poverty Gap, 2001

||Metropolitan|Urban|Rural|Total Haiti|
|---|---|---|---|---|
|P1|8.8|33.4|32.0|26.9|
|P2|5.2|24.0|22.3|19.3|

Source: Calculations based on HLCS 2001.

The Northeast region of Haiti is the region with the deepest poverty. Figure 5.3 shows the depth of extreme poverty measured by P2 for the 9 regions. Large differences are present. The West region experienced a P2 of 8.5. This compares to the Northeast where P2 reached 47.4 indicating that poverty is not only broad, but it is also very deep in the latter region.

# **INCOME INEQUALITY**

Income inequality is part of the reason why Haiti’s poverty indicators are worse than in other countries that have similar per capita incomes. Haiti has an extremely unequal income distribution. In 2001, the Gini coefficient for Haiti as a whole was 0.66, above the coefficient for Brazil (0.61). It is worth noting that international research shows that the more unequal income is distributed the less effective is economic growth in reducing poverty (Lustig et al. 2001).

26
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Mali > Electricity investments > Macro results**

||||Reduction in<br>other ublic|Ele|ctricity<br>|||
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|1,480,726.40|0.53|0.59|0.61|0.65|0.62|
|EV|Equivalent variation||0.53|0.57|0.59|0.65|0.16|
|s|Wage|1.00|0.32|0.83|0.99|1.32|1.02|
|yg|Government income|278,133.30|0.81|3.03|3.03|3.03|3.03|
|g|Total government expenditure|255,957.00|–0.85|1.56|1.56|1.56|1.56|
|ye|Firm income|477,113.00|0.74|0.56|0.50|0.38|0.49|
|It|Total private investment|439,511.00|0.90|0.73|0.67|0.55|0.54|
|GDP|GDP|1,819,413.40|0.63|0.63|0.63|0.63|0.63|
|e|Nominal exchange rate|1.00|0.61|0.58|0.06|–1.00|0.42|

# **Mali > Electricity investments > Sectoral results**

||||Reduction in<br>|Ele|ctricity|||
|---|---|---|---|---|---|---|---|
||||other ublic||It||I|
|Variable|Sector|Base|<br>expenditure|VAT|mpor<br>duties|Foreign aid|ncome<br>tax|
||Crop agriculture|379,937.10|0.07|0.01|–0.01|–0.04|–0.06|
||Export agriculture|297,375.30|0.31|0.27|0.24|0.20|0.24|
|Va|Mining and gas|206,350.40|0.50|0.41|0.29|0.13|0.39|
|Value added or|Industries|167,281.20|1.39|1.17|1.21|1.34|1.14|
|output|Construction|116,213.20|2.38|1.65|1.82|2.10|1.93|
||Private services|516,027.70|1.06|0.88|0.93|0.89|0.88|
||Public services|136,228.50|–0.90|1.03|0.95|1.11|1.10|
||Crop agriculture|1.01|0.61|0.69|0.73|0.73|0.35|
||Export agriculture|1.02|0.40|0.45|0.40|0.17|0.12|
||Mining and gas|1.00|1.07|0.93|0.78|0.40|0.77|
|pq<br>Market prices|Industries|1.17|0.02|0.26|0.46|–0.97|–0.06|
||Construction|1.02|–0.27|0.34|0.11|–0.28|–0.12|
||Private services|1.03|–0.46|0.01|–0.18|–0.19|–0.38|
||Public services|1.00|0.07|0.50|0.59|0.43|0.43|
||Crop agriculture|1.00|0.74|0.74|0.71|0.82|0.34|
||Export agriculture|1.00|0.83|0.72|0.50|0.12|0.48|
|r<br>|Mining and gas|1.00|1.65|1.27|0.23|–1.09|1.23|
|Rental rate of<br>capital|Industries|1.00|–0.81|–0.86|–0.60|0.05|–0.75|
||Construction|1.00|1.73|1.21|1.61|2.34|1.80|
||Private services|1.00|0.56|0.26|0.64|0.80|0.48|

34