rafmacalaba/datause-encoder-data huggingface dataset:
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## Core: Other Maternal Health

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# **Using Courts to Realize Education Rights: Reflections from India and Indonesia**

Andrew Rosser _University of Melbourne_ Anuradha Joshi _Institute of Development Studies_ _World Development Report 2018_ background paper<sup>1</sup> _._ Keywords: Right to education, courts, justice, Indonesia, India JEL Classifications: I21, I28, K38, K41

> 1 The authors are grateful to Aheli Chowdhury and Raushan Tara Jaswal for their assistance in collecting material for this paper and to Samer Al-Samarrai, Anna Autio, Rob Boone, Varun Gauri, Eva Kloeve, and Michael Mahrt for comments on earlier drafts. The usual caveats apply.
- Browning, E. K. (1987), ‘On the marginal welfare cost of taxation’, _American Economic Review_ **77** (1), 11–23.

- Campbell, H. F. (1975), ‘Deadweight loss and commodity taxation in Canada’, _Canadian Journal of Economics_ **8** (3), 441–447.

- Campbell, H. F. & Bond, K. A. (1997), ‘The cost of public funds in Australia’, _The Economic Record_ **73** (220), 22–34.

- Dahlby, B. (1994), The distortionary effect of rising taxes, _in_ R. Robson & R. Scarth, eds, ‘Deficit Reduction: What Pain; What Gain?’, C.D. Howe Institute, Toronto, pp. 43–72.

- Devarajan, S., Go, D., Lewis, J., Robinson, S., & Sinko, P. (1997), Simple general equilibrium modeling, _in_ ‘Applied Methods for Trade Policy Analysis – A Handbook’, Cambridge University Press, United Kingdom.

- Devarajan, S., Suthiwart-Narueput, S. & Thierfelder, K. E. (2001), The marginal cost of public funds in developing countries, _in_ ‘Policy Evaluations with Computable General Equilibrium Models’, Routledge Press, London and New York.

- Diamond, P. A. & Mirrlees, J. A. (1971), ‘Optimal taxation and public production I: Production efficiency’, _American Economic Review_ **61** (1), 8–27.

- Diewert, W. E. & Lawrence, D. A. (1994), ‘The marginal costs of taxation in New Zealand’, Report prepared for the New Zealand Business Roundtable by Swan Consultants Pty. Ltd., Canberra, Australia.

- Diewert, W. E. & Lawrence, D. A. (1998), The deadweight costs of capital taxation in Australia, Discussion Paper 98-01, Department of Economics, The University of British Columbia, Vancouver, Canada V6T 1Z1.

- Emran, M. S. & Stiglitz, J. E. (2005), ‘On selective indirect tax reform in developing countries’, _Journal of Public Economics_ **89** , 599–623.

- Findlay, C. C. & Jones, R. L. (1982), ‘The marginal cost of Australian income taxation’, _The Economic Record_ **58** (162), 253–62.

- Fjeldstad, O.-H. (2002), Fighting fiscal corruption: The case of the Tanzanian Revenue Authority, Working Paper 2002:3, Chr. Michelsen Institute Development Studies and Human Rights.

- Fortin, B. & Lacroix, G. (1994), ‘Labour supply, tax evasion and the marginal cost of public funds: An empirical investigation’, _Journal of Public Economics_ **55** , 407–431.

36
TABLE III-1 The number of Bank supported PHN projects FY1970-86, by nominal subject area

|Year|No. of<br>projects|Popu-<br>lation|Pop/<br>Health|Health|Nutrition|
|---|---|---|---|---|---|
|---------------------------|----------------------------|||||
|1970|1|1|0|0|0|
|1971|2|2|0|0|0|
|1972|2|2|0|0|0|
|1973|1|1|0|0|0|
|1974|2|2|0|0|0|
|1975|2|2|0|0|0|
|1976|3|2|0|0|1|
|1977|2|1|0|0|1|
|1978|2|1|0|0|1|
|1979|4|4|0|0|0|
|1980|4|3|0|0|1|
|1981|1|0|1|0|0|
|1982|2|1|0|1|.0|
|1983|7|1|0|6|0|
|1984|7|1|1|5|0|
|1985|7|1|1|5|0|
|1986|11|2|1|7|1|
|Total|60|27|4|24|5-|

Source: Internal Documents Unit 24
the relatively small sample size, but it is not the case that those who take the grant are large firms while those who drop out are small firms for example. Those who take-up the grant are more likely to have expanded their business in the three years prior to their application, but no more likely to have increased employment, done new marketing, or improved their product during this time. A probit of take-up on these observable characteristics drops having attended an international expo since none of the drop-outs had done this, and having exported, but then cannot reject that the other variables jointly cannot predict take-up. This is consistent with take-up decisions being driven by idiosyncratic factors arising from the general economic environment, rather than sorting the firms according to their inherent productivity or innovativeness.

# **3.3 Data Collection**

A call for the second year of the program took place in November 2014, with random selection in December 2014. However, with the worsening security situation in Yemen this second year of the program was abandoned. We therefore quickly launched a follow-up survey in March 2015. This timing is approximately 4 to 10 months after firms had used their matching grants, and right before widespread civil conflict broke out.

The survey was conducted as a phone survey given that safety issues, gas shortages, and time constraints prohibited an in-person survey. The survey collected basic company information such as operating status and employment, the firm’s innovation activities and use of consultants, financing from banks and the grant program, and on the firms’ exports and sales. Like most surveys in developing countries of firms this size we must rely on self-reporting of these activities, lacking any alternative sources of data with which to cross-check these responses. The survey was conducted by Apex consulting, and succeeded in interviewing 226 of the target 416 firms (a response rate of 54.3%). Attrition was higher in the control group (51%) than the treatment group (40.7%), with this difference statistically significant (p=0.036). Attrition rates did not vary significantly by city. Of those firms interviewed, 97.8 percent had survived (4 treated and 1 control had closed). It seems likely that those who had closed because of fuel shortages or safety concerns would be more likely to have attrited.

This response rate and its difference by treatment status is a concern, although given the circumstances with suicide bombings and airstrikes beginning at the end of March (appendix 1), no further actions could be taken to chase down non-responders. Given the lack of evidence on the 8
# **Table 2b: Trends in Real Unit Labor Costs in Local Currency and US Dollars:2003 = 100, Fuel Exporting Transition Economies**

||2000-04|2005-08|2009-11|2012-14|2015-17|
|---|---|---|---|---|---|
|Azerbaijan LC|94|83|89|90|105|
|USD|95|133|188|227|150|
|Kazakhstan LC|102|87|82|75|72|
|USD|99|168|204|220|140|
|Russian Federation LC|90|102|111|103|106|
|USD|86|192|250|279|179|
|Turkmenistan LC|64|56|47|42|49|
|USD|48|67|42|42|36|
|Mean LC|87|82|82|78|83|
|Median LC|92|85|86|83|88|
|MeanUSD|82|140|171|192|126|
|MedianUSD|91|151|196|223|145|

Sources: UNECE, ILO and SEE jobs Gateway for wages, WDI for real GDP, persons employed, GDP deflators 12
multinational firms supplying the Russian market, and hence more gains from additional varieties of business services. In addition, the share of the services market captured by multinationals has a strong effect, since a liberalization results in a larger number of new varieties introduced.

In tables 23b through 23g, we show the sensitivity of emissions to the parameters. In general, emissions of pollutants are more sensitive to the parameter specification than economic welfare. Carbon dioxide, sulphur dioxide, volatile organic components and especially hydrocarbons are especially sensitive to the parameter esubs. This shows that the scale and composition effects significantly dominate the technique effect. Higher values of esubs allow greater use on newer technologies (technically new varieties), which reduces pollution through the technique effect. But higher values of esubs mean more output expansion, and this (along with the shift to dirty industries we have noted) dominates, especially for most of the non-CO2 pollutants.

# **7. Conclusions**

To gain insights into the pending trade-offs between trade and FDI liberalization and pollution in Russia, we developed a ten-region, 30 sector model of the Russian economy. Our model contains foreign direct investment, imperfectly competitive sectors and endogenous productivity effects triggered by WTO accession. We incorporated environmental emissions data in Russia for seven pollutants which are tracked for all 30 sectors in each of the ten Russian regions of our model.

We find that WTO accession alone will increase environmental pollution in Russia. Our decomposition shows that the negative effect on the environment of the composition effect (especially the relative expansion of dirty industries) and the expansion of output (scale effect) dominate the positive impact on the environment of the technique effect (which includes importing new, more efficient technologies, processes and services).

Given the adverse impact on the environment of WTO accession, and that part of the income increase from WTO accession might be used to pay for environmental abatement, we assess the costs of three types of environmental regulations to reduce carbon dioxide emissions by 20 percent: market based “cap and trade”; emission intensity standards; and energy efficiency standards. In a third set of scenarios, we assess the impacts of “overlapping” policy reforms where we simultaneously implement our central case scenario of _WTO_ accession with each of our three carbon emission reduction policy initiatives.

We find that the welfare gains of WTO accession are large enough to pay for the costs of any of the three environment abatement policies, while leaving a net welfare gain. In 32
the example of diarrhea, for which there is a role for all three, prevention (measures against the ingestion of contaminated water), home care (oral rehydration therapy, or ORT, for mild cases), and curative care (clinical evaluation for more severe cases). Diarrhea is an important contributor to malnutrition in children.

**Treatment effects by age.** Very young children are much more vulnerable to illness than older children; the World Bank reports in 2013 that 63% of all under-five mortality in Mali occurred in infants. This suggests that the benefits from health interventions are greatest very early in life. We therefore conduct an exploratory analysis in which we examine treatment effects by age. Figure 3 shows nonparametric daily care seeking probabilities by treatment group and care-required status for ages 0 to 5. In the appendix, we also estimate Cox hazard models for the youngest and oldest children separately (Table C.7).

The sample is small and power low, so these results are indicative only, but a few observations emerge. First, for age-0 children, the difference in care-seeking between early and care-required days is much smaller than for older children. The Cox hazard point estimates support this (detailed discussion in the appendix). These patterns suggest that parents of young children may be less certain whether care is required, perhaps because it is harder to determine illness severity in non-verbal infants or new parents are less experienced. Second, care-seeking probabilities are generally decreasing in age in all groups, suggesting that parents of younger children value formal care more. That said the low probability of care seeking for age-0 children in the “subsidy only” group, especially when care is required, is a stark break from this pattern, compared to other ages with the subsidy and compared with the full program group. For these children, there is a large, although noisily estimated, increase in needed care in the group that receives both interventions relative to the subsidyonly group. This is echoed in the 2014 data: Dean and Sautmann (2023) report a large effect of the full program on the use of formal care for the youngest children; above that of either the CHW only or subsidy only group.<sup>22</sup> Thus, for the youngest children, it appears 22The data from 2014 contains only an abridged 7-day health calendar and does not allow the construction 30
topics (see, for example, Perez-Truglia, 2020). Figure 3.a plots the frequency of online searches related to the terms “inflation” and “exchange rate”, where dark bars correspond to keywords related to inflation and lighter-colored bars correspond to keywords related to the exchange rate. Google reports online searches only in relative terms. We therefore normalize the series, with the nominal exchange rate taking the value 100 in the first period. The figure shows that consumers seek information about the exchange rate more frequently than information about the inflation rate: in an average week of 2019, there were approximately 18 times more searches about the exchange rate than searches related to inflation.<sup>5</sup> This result is supported by a comparison of newspaper articles mentioning either inflation or the exchange rate. Figure 3.b plots the frequency of articles in Malaysia’s most widely read English language newspaper in which either term is mentioned.<sup>6</sup> As in Figure 3.a, both series are normalized so that the nominal exchange rate takes the value 100 in the first period. In an average week of 2019, there were approximately twice as many newspaper articles that referred to the exchange rate than newspaper articles mentioning the inflation rate. The two different data sources indicate both higher demand and higher supply for news about the exchange rate, rather than news about the inflation rate, in our setting.

# **3.2 Partner Institution**

Our partner institution is one of the largest commercial banks in Asia and has more than a million customers in Malaysia. Nearly all of our partner bank’s retail banking customers have debit cards, and a significant share additionally have credit cards linked to their account. Although our partner bank covers a broad, socially and geographically diverse customer base, its clients are naturally not a fully representative sample of the population. They are on average younger, more educated, more likely to reside in urban areas, and wealthier (for more details, see Section 5). Credit card usage in this setting is high and in fact comparable to high-income economies. Using the bank’s administrative data, we estimate that monthly credit card spending in our data accounts, on average, for 32% of consumers’ estimated monthly income. In comparison, Ganong and Noel (2019) use data from the JP Morgan Chase Institute and find that average credit and debit card spending accounted for 51% of monthly income in the United States.<sup>7</sup>

> 5. The 2019 data cover the period when our experiment was conducted and are similar to other years.

> 6. _The Star_ , whose archive is available at www.thestar.com.my.

> 7. Based on results from Table 1 of Ganong and Noel (2019), who use a sample of credit and debit card customers in the three months prior to becoming unemployed.

9
<!-- Start of picture text --> 45%<br>99%<br>40%<br>95%<br>mg0%<br>35%<br>75%<br>50%<br>30%<br>25%<br>25% 10%<br>5%<br>1%<br>20%<br>2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> 10%<br>99%<br>8%<br>95%<br>mg0%<br>6%<br>75%<br>50%<br>4%<br>25%<br>2% 10%<br>5%<br>1%<br>0% a<br>2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->
developed by the CEQ Institute and presented in the CEQ Handbook (Lustig, 2022). CEQ Assessments have been implemented in over 60 countries at the time of writing this report.

# **The first step of fiscal incidence analysis is allocation of the main taxes and transfers to households in**

**the survey.** The main modeling techniques in this report include direct identification, inference, imputation, simulation, and prediction. These concepts are described in Box 3.1, below. Typically, fiscal interventions such as direct taxes, indirect taxes, and indirect subsidies are modeled based on simulation, combining tax rules and characteristics of individual’s employment and consumption patterns, respectively.

**Box 3.1: Tax and transfer allocation methods**

<mark>We allocate each tax and transfer by drawing from a set of methods described in detail in Chapter</mark> 3 of the CEQ Handbook, namely:

- **Direct identification:** The survey asks whether a tax (transfer) was paid (received), and the amount.

- **Inference:** Whether a tax (transfer) was paid (received), or the amount, is determined based on administrative data of the taxes (transfers) and other relevant secondary information self-reported by the household.

- **Imputation:** Taxpayers (beneficiaries) are directly identified in the survey, but the amount of a tax (subsidy) paid (received) is imputed based on program rules.

- **Simulation:** Both the identification of taxpayers (beneficiaries) and the amount of a tax (subsidy) paid (received), is estimated based on program rules.

- **Prediction:** A regression model is applied to predict who receives benefits or pays taxes, and/or the amount.

Adapted from Chapter 3 of Lustig (2022).

**The building block of fiscal incidence analysis is the construction of income concepts.** Conceptually, the analysis starts from a household’s pre-fiscal income<sup>32</sup> —that is, any private income that excludes any government intervention—and each new income concept is constructed by including different layers of the fiscal system, or categories of taxes and transfers, as shown in Figure 3-1, until a household’s total post-fiscal income is estimated (income once all taxes and transfers are included).

**Once completed, we can estimate the distributional impacts of any tax or transfer decisions on the welfare of a country’s households** . Once all fiscal instruments and income concepts are constructed, the analysis measures the main distributional repercussions, such as changes in number of households living under the poverty line, identification of groups which are net cash-beneficiaries, and impact of any tax and/or government’s transfers on inequality and progressivity, as well as the marginal contribution of each fiscal intervention to poverty and inequality reduction.

> 32 Pre-fiscal income could be equivalent to Market Income or Market Income plus pensions depending on the pension scenario chosen for the baseline. This is explained in more detail later in this section.

22
**Table 20: Number of workers in presumed comparative advantage industries by age groups of enterprises (weighted estimates of totals) , UBI 2002 and UBI 2009**

||**_A. All comp_**|**_arative advantag_**|**_e industries_**<br>|||**_E. Comm_**<br>**_farming_**|**_ercial_**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Age Group**|Number o|f workers|% share<br>total|in sector|Average<br>growth|Numbe|r of workers|% share<br>total|in sector|Average<br>growth|
|**(years)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|< 5|24,544|108,451|22.2|22.2|48.8|5,035|32,576|14.9|14.9|78.1|
|5 to 10|17,110|91,929|24.4|18.9|62.5|3,671|49,892|14.2|22.8|179.8|
|More than 10|28,520|287,187|40.6|58.9|129.6|17,136|136,257|66.3|62.3|99.3|
|Total|70,175|487,568|100.0|100.0|85.0|25,842|218,725|100.0|100.0|106.6|
||**_B. Compara_**|**_tive advantage_**|**_primary indust_**<br>% share|**_ries_**<br>in sector|Average|**_F. Food a_**|**_nd beverages_**|**_industry_**||Average|
|**Age Group**|Number o|f workers|<br>total||<br>growth|Numbe|r of workers|% share in|sector total|<br>growth|
|**(years)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|< 5|5,852|46,873|21.2|19.0|100.1|12,354|30,438|40.4|23.7|20.9|
|5 to 10|4,060|49,886|14.7|20.2|161.2|10,345|22,787|33.8|17.8|17.2|
|More than 10|17,689|150,485|64.1|60.9|107.2|7,880|75,044|25.8|58.5|121.8|
|Total|27,601|247,245|100.0|100.0|113.7|30,579|128,268|100.0|100.0|45.6|
||**_C. Compara_**|**_tive advantage _**|**_manufacturing_**<br>% shar|**_industries_**<br>e in sector|Average|**G. Thega **|**rments indust**|**ry**||Average|
|**Age Group**|Number o|f workers|total||<br>growth|Numbe|r of workers|% share i|n sector total|<br>growth|
|**(years)**|2002|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|< 5|18,168|58,843|44.2|26.0|32.0|5,607|20,455|58.5|32.9|37.8|
|5 to 10|12,610|39,172|30.7|17.3|30.1|1,862|11,012|19.4|17.7|70.2|
|More than 10|10,307|128,453|25.1|56.7|163.8|2,112|30,626|22.0|49.3|192.9|
|Total|41,085|226,469|100.0|100.0|64.5|9,581|62,093|100.0|100.0|78.3|
||**_D. Compar_**|**_ative advantage s_**|**_ervices_**<br>% h|i t|A|**H. Land**|**transport serv**|**ices**||A|
|**Age Group**|Number o|f workers|sare<br>total|n secor|verage<br>growth|Numbe|r of workers|% share in|sector total|verage<br>growth|
|**(years)**|2001|2009|2002|2009|rate(%pa)|2002|2009|2002|2009|rate(%pa)|
|< 5|525|2,734|35.3|19.7|60.1|469|2,370|33.6|19.5|58.0|
|5 to 10|440|2,871|29.5|20.7|79.0|403|2,137|28.9|17.5|61.4|
|More than 10|525|8,249|35.2|59.5|210.3|523|7,677|37.5|63.0|195.6|
|Total|1,489|13,854|100|100|118.6|1,395|12,184|100.0|100.0|110.5|

56