rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3 Marginal Cost of Funds**

In addition to measuring how effective each tax instrument is at generating tax revenue, we also calculate the marginal cost of increasing taxes. To focus on the effect the tax change has on consumer welfare, we modify closure assumptions. We assume long run equilibrium in factor markets with all factors mobile across all sectors. The wage is endogenous and the supply of each factor is fixed. Investment is constant and savings adjusts. Real government spending is constant. There are no changes in the flow of foreign capital, instead the exchange rate adjusts. With this specification, any increase in government revenue is redistributed to households in a lump-sum fashion. The transfer amount to each household depends on the household’s initial savings rate.<sup>14</sup> High-income households receive a larger share. Our model does not include a labor-leisure trade off and it does not measure the administrative costs of the VAT.<sup>15</sup> In this context, the MCF is a measure of the inefficiencies in the structure of production. It depends not just on the tax being raised, but also on other distortions in the economy—and whether they increase or decrease in response to the tax increase. For instance, in an economy with a very high tax on coffee, the MCF raised by taxing tea may be quite low, since the tax would induce consumers at the margin to buy more coffee, reducing the distortion associated with the coffee tax.<sup>16</sup> To describe the MCF by household, we report the change in household welfare, relative to the change in household taxes paid.

_Table 6: Marginal Cost of Funds_

||Increase governmen<br>uniform inc<br>|t revenue with a<br>rease in:<br>|
|---|---|---|
||VAT|Commodity Tax|
|h_d0|0.762|0.594|
|h_d1|0.541|0.290|
|h_d2|0.699|0.561|
|h_d3|0.402|0.167|
|h_d4|0.475|0.360|
|h_d5|0.419|0.345|
|h_d6|0.314|0.286|
|h_d7|0.194|0.215|
|h_d8|0.283|0.458|
|h_d91|0.194|0.409|
|h_d921|0.550|0.907|
|h_d922|-0.067|0.136|
|h_d923|-0.290|-0.157|
|h_d924|-1.224|-1.233|
|Total|0.003|0.022|

Source: South Africa CGE model simulations

> 14 Since investment is constant, an increase in government savings (due to an increase in tax rates in this scenario) is offset by a decrease in household savings; in effect there is a lump sum transfer to the households.

> 15 Ballard and Fullerton (1992) note, that while administration and compliance costs are important, they have been ignored by economists studying the marginal cost of funds since the 1970s.

> 16 For more discussion of the MCF of indirect taxes in developing countries, see Devarajan, Thierfelder, and Suthiwart-Narueput (2001). See Ballard et al (1985) for a description of the MCF for the United States.

16
35 survey contains detailed questions that probe respondent knowledge of important national policy changes that are relevant to citizen efforts to hold governments accountable: a massive program to hire new teachers; large increases in budgetary outlays to education; and more stringent requirements for students to pass the primary school leaving examination. Consistent with the lack of impact of community radio on government inputs into village schools, reported in Table 6, access to community radio is not associated with greater knowledge of these policies, under any specification.<sup>14</sup> One explanation for lack of accountability effects might be that the variation in radio access is driven by access to out of commune radio stations which may not carry information relevant for local, commune-level, accountability. To check an immediate implication of such an explanation, we examined whether within-commune radio had a different correlation with school inputs, teacher presence, PTA activity, and policy knowledge, than did out-of-commune radio. We find no difference.

Even if the effects of media access do not operate through the accountability channel, they still may influence household behavior. Households exposed to more programming on the importance of educating their children might invest more time and money in the education of their children. In countries such as Pakistan, tests of this channel are complicated by the proliferation of private education options. Public schools are the only educational option for most households in our sample. Only 2 villages in the sample report having no functional public school, and 44 percent have more than 1 public school. Private schools exist in only 7 percent of the sampled villages, and voter knowledge about their legislators.

14 These results—of lack of effect on household policy knowledge—are available upon request.
# **Table 7: Teaching practices and absenteeism (First follow-up in 2009)**

||WSD|Control|Difference|P-value|
|---|---|---|---|---|
|Teacher absent (at our arrival)|0.11|0.12|0.01|0.73|
||(0.02)|(0.03)|(0.04)||
|Teacher missed at least one day last week|0.26<br>(0.03)|0.33<br>(0.04)|0.07<br>(0.05)|0.16|
|Teacher Absenteeism (Five random days average)|0.06<br>(0.01)|0.06<br>(0.01)|0<br>(0.01)|0.94|
|Student Absenteeism (Day of test)|0.26<br>(0.02|0.24<br>(0.01|0.02<br>(0.02|0.55|
|Student Absenteeism (Five random days average)|0.38<br>(0.04)|0.36<br>(0.03)|0.02<br>(0.05)|0.71|
|Teacher has written lesson plan|0.56<br>(0.04)|0.67<br>(0.04)|-0.11**<br>(0.05)|0.04|
|Teacher has a written lesson note for today’s lesson|0.32<br>(0.04)|0.41<br>(0.04)|-0.09*<br>(0.05)|0.08|
|Teacher missed at least one day last week|0.26<br>(0.03)|0.33<br>(0.04)|0.07<br>(0.05)|0.16|
|Call out children by their names|0.48<br>(0.04)|0.35<br>(0.04)|0.13**<br>(0.06)|0.03|
|Address questions to the children during class|0.69<br>(0.04)|0.75<br>(0.04)|0.06<br>(0.05)|0.27|
|Encourages the children to participate|0.61<br>(0.04)|0.68<br>(0.04)|0.07<br>(0.06)|0.23|
|The children used textbooks during the class|0.38<br>(0.04)|0.47<br>(0.04)|-0.09*<br>(0.05)|0.09|
|The children used workbooks during the class|0.54<br>(0.04)|0.45<br>(0.04)|0.08<br>(0.06)|0.14|
|The children ask questions for clarification|0.26<br>(0.04)|0.23<br>(0.03)|0.03<br>(0.05)||
|Observations|88/169|89/177|||

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. Based on data from school and classroom visits.

30
# <u>Cross-Drainage Facility for Roads</u>

Roads in Bangladesh do not have adequate drainage facilities for flood water. The natural flow of flood water currently is obstructed due to inadequate number of culverts and regulators to allow cross-drainage. In a changing climate, the problem of drainage is likely to worsen with increased volume of flood water. It would be necessary to increase the number of drainage structures, including culverts and regulators to facilitate the drainage of the extra flood water. In addition, some existing culverts need to be raised to facilitate the free flow of water.

Climate change is expected to increase the deeply inundated areas (greater than 0.9 meters) by an additional 7,736 sq km.<sup>26</sup> Each drainage structure of size 1.5mX1.8m can approximately drain a 10 sq km area that costs Taka 15 million. As a result, <u>775 new drainage structures will</u> be required to drain the additional deep inundated areas in a changing climate; at a total <u>cost</u> of <u>Taka 11,625 million or US$ 166.07 million. It has been further estimated that a total of 315</u> additional culverts are to be raised to avoid additional inundation from climate change at a cost of Taka 1 million per culvert.<sup>27</sup> Hence, the total cost of raising culverts will be Taka 315 million <u>or US$ 4.5million</u> **<u>.</u>**

# <u>Railways</u>

The rail network of Bangladesh has not changed significantly during past fifty years. Aside from doubling the railway track between Dhaka and Chittagong in the next few years, Bangladesh Railways does not have any other plans for changing its network for the foreseeable future. Hence, for this analysis, the rail network in 2050 is assumed to be the same as now with the planned expansion between Dhaka and Chittagong; and the length of railways (km) exposed to additional inundation risk due to climate change for different types of tracks of rail has been determined based on GIS overlays of the maps of additional inundation risk zones with the existing railway maps. Table 7 summarizes the estimates. Exposure estimates indicate that less than 2% of railway tracks in 2050 will be subject to more than 1 meter of additional inundation due to climate change, while almost all (85%) will be subject to 0.5 meters of additional

> 26 These are areas with the F2-F4 classifications.

> 27 Rebuilding the flat part cost Tk. 700,000 on average, and other remodeling cost are Tk. 300,000.

14
the following statement? Vaccines are effectiveness.” on a 1-5 scale, with response options of “Strongly agree,” “Somewhat agree,” “Neither agree nor disagree,” “Somewhat disagree,” “Strongly disagree,” As with the measure of vaccine safety, we reverse-coded the responses, so higher values indicate stronger agreement, and responses of “Don’t know” and “Refused” were dropped. We aggregate perceived vaccine effectiveness to the country level by calculating the mean response within each country. We then center country-level vaccine safety perceptions by subtracting the median country-level trust measure from each country-level mean.

# **2.5. Meta-Analytic Approach**

To summarize findings from our research program, we conduct a meta-analysis using a randomeffects, two-stage individual participant data (IPD) meta-analytic approach (Riley et al., 2021). The random effects model allows for variation in study effects, appropriate for our context where operationalizations of independent variables and outcome measurements differ slightly across studies. Moreover, IPD enables us to fully leverage the individual level data collected, allowing us to explore study-level moderators that could explain variation in messaging effects across studies as well as heterogeneity in treatment effects that could be explained by individual-level characteristics. IPD metaanalyses are commonly used in contexts such as clinical research, where individual participant data is more readily available, offering advantages over the traditional meta-analytic approach that rely solely on aggregate statistics (Burke et al., 2016; Debray et al., 2015; Tierney et al., 2015). As noted earlier, the operationalization of message framings and the primary outcome were tailored to each study’s context. All analyses were conducted with Stata (version 18).

To estimate the treatment effects for the framing experiment, we use logistic regression with treatment arm as the exposure and a binary measure of vaccine intention as the outcome variable (1 = intend to vaccinate). In our analysis, we control for a range of individual-level covariates, including gender, age, education, and whether the participant was a health worker. For covariates and moderators, we use categorical variables coded to have a category for non-response (i.e., missing) to avoid dropping observations where possible. In the first stage of the IPD MA, we calculate the logit model for each one of the studies as follows: 1 (1)<sup>ii+ γii+ )</sup> For s =1,2,3...,28 studies pp(xx) = 1+ee<sup>−(β</sup> 12
is the husband’s bargaining weight. The bargaining weight is increasing in the husband’s real income ( _ω_ 1 ( _._ ) _>_ 0) while decreasing in the wife’s ( _ω_ 2 ( _._ ) _<_ 0). Furthermore, if we assume that bargaining power is unchanged if both incomes are equally inflated, we can express _ω_<sup>_i_�</sup> _δ_<sup>_i_</sup> _|wM_<sup>_i, w_</sup> _F_<sup>_i, p_</sup> � as a function of income ratios only:

Finally, in this political economy model, men choose the level of gender discrimination _δ_<sup>_i_</sup> to maximize their own indirect utility _ω_<sup>_i_�</sup> _δ_<sup>_i_�</sup> _U_<sup>_i_�</sup> _δ_<sup>_i_�</sup> . In doing so, we assume that they internalize the general equilibrium effects of their policy decision _δ._

**Gender discrimination in autarky** Plugging in autarky equilibrium wages from (7) and dropping country superscripts, bargaining power in autarky is given by

which is independent of _δ._ The bargaining power of husbands is unaffected when they restrict female labor force participation since any restriction will induce an increase in wages that will keep payments to female labor constant. In our model, any partial equilibrium effect associated with a restriction on female labor supply (higher _δ_ ) is fully offset by general equilibrium effects. Although the extreme result is driven by the unit elasticity of substitution specific to Cobb-Douglas specifications for both preferences and technology, the mechanism is still robust to alternative functional forms and works as follows: a reduction in female effective labor supply decreases output in the _A_ sector that induces prices of _A_ goods to go up. This is captured in the expression (9) for prices. In the Cobb-Douglas case, the price response is exactly equal to output shortage, so that the allocation of capital across sectors is invariant to changes in _δ._ As a consequence of the price hike, female labor becomes relatively more productive and this translates into higher wages for women. Unit elasticity of substitution implies that total labor income is unaffected by _δ_ (cf. equation 8). Therefore, any restriction in female supply only affects consumption levels through lower production (cf. equation 10); ultimately, welfare is adversely affected without any change in men’s bargaining power (cf. equation 11). The autarky gender discrimination level is therefore minimal: _δ_ = 0 _._

**Gender discrimination under trade** Under the trade regime, bargaining power becomes

14
29

**Table 1**

**Testing Bequest Motives and Tax-Smoothing Approaches Fixed (Country & Time) Effects Panel Regressions Dependent Variable: Central Government Budget Surplus Share**

|Years : 1975 -1992|Developed<br>Reg1a|Developing<br>Reg1b|Developed<br>Reg2a|Developing<br>Reg2b|
|---|---|---|---|---|
|Control Variables|||||
|1. Dependent Variable Lag 1|0.4008***<br>(0.134)|0.7074***<br>(0.082)|0.4018***<br>(0.135)|0.7073***<br>(0.082)|
|2. Index of Political Rights|0.0233**<br>|-0.0009<br>|0.0232**<br>|-0.0006<br>|
||(0.009)|(0.002)|(0.009)|(0.002)|
|3. Agricultural Share (-)|-0.1046|-0.0855|-0.1072|-0.0876|
||(0.28)|(0.074)|(0.284)|(0.073)|
|4. Manufacturing Share (+)|0.2475<br>|-0.2522<br>|0.2544<br>|-0.2447<br>|
||(0.215)|(0.216)|(0.216)|(0.217)|
|5. Trade Share (+)|-0.0033|0.0506**|-0.004|0.0508**|
||(0.053)|(0.024)|(0.053)|(0.024)|
|6. Urban Population Share (+)|-0.0148***<br>(0.004)|-0.0039***<br>(0.001)|-0.0148***<br>(0.004)|-0.0041***<br>(0.001)|
|7. Log of per capita Real GDP|0.2139***|0.0509***|0.2126***|0.0553***|
||(0.048)|(0.018)|(0.048)|(0.018)|
|8. Government Crises (-)|-0.0002|-0.0015|-0.0001|-0.0016|
||(0.001)|(0.002)|(0.001)|(0.002)|
|9. Cost of Debt Servicing (-)|0.0225|0.0126|0.0228|0.012|
||(0.065)|(0.028)|(0.065)|(0.028)|
|10. Seigniorage (-)|-0.0942|-0.125|-0.0906|-0.1297|
||(0.104)|(0.079)|(0.102)|(0.08)|
|Tax-Smoothing Variables|||||
|11. Unanticipated changes|-0.0453|-0.0913***|-0.0453*|-0.0913***|
|<br>in real per capita govt exp (-)<br>(4 year, simple average)|(0.027)|(0.02)|(0.027)|(0.02)|
|12. Unanticipated changes|-0.0752|0.1071***|-0.0751|0.1065***|
|in per capita real GDP (+)<br> (4year,simple average)|(0.052)|(0.026)|(0.053)|(0.026)|
|Bequest-Motives Variables|||||
|13. Expected per capita real|-0.6268**|-0.2173|-0.6144**|-0.2628|
|<br>GDP Growth Rate (-)<br>(25 year construction)|(0.296)|(0.482)|(0.297)|(0.491)|
|14. Gini Coefficient (-)|0.0009|-0.0013|0.0009|-0.0018|
|(adjusted, smoothed)|(0.0007)|(0.0034)|(0.0007)|(0.0035)|
|15. Life Expectancy at age 65 (-)|-0.0115|-0.0205*|-0.0115|-0.0204*|
||(0.008)|(0.011)|(0.008)|(0.011)|
|16. Youth Population Share (-)|0.1195<br>(0.19)|-0.2076<br>(0.158)|||
|17. Working-Age Population Share (+)|||-0.0854|0.1816|
||||(0.202)|(0.172)|
|18. Elderly Population Share (-)|0.4932*|-0.9739|0.3857|-1.0107|
||(0.293)|(0.983)|(0.276)|(0.981)|
|19. constant|-1.1222***|0.3264|-1.01***|0.1662|
||(0.413)|(0.198)|(0.373)|(0.185)|
|R-squared|0.8724|0.8351|0.8723|0.8414|
|Adjusted R-squared|0.8408|0.7909|0.8407|0.7989|
|F-Statistic|47.92|36.07|47.11|55.09|
|F-Stat df 1|52|66|52|66|
|F-Stat df 2<br>Number of Countries|210<br>20|250<br>35|210<br>20|250<br>35|
|Number of Observations<br>Standard Errors are in parentheses.<br>*, **, *** denotes significance at the 90, 95 and 99 p<br>Robust standard errors obtained using White's corre|263<br>ercent confidence leve<br>ction for heterosceda|318<br>l respectively<br>sticity.|263|318|
- Weyant, C., Athalye, V., Ragavan, S., Rajarathnam, U., Lalchandani, D., Maithel, S., Baum, E., Bond, T.C., 2014. Emissions from South Asian Brick Production. Environmental Science & Technology 48, 6477-6483.

- Weyant, C., Kumar, S., Maithel, S., Thompson, R., Floess, E., Ravi, A., Adhikari, S., Gautam, S., Shrestha, S., Baum, E., Bond, T., 2016. Emissions and energy performance in South Asian brick kilns: Measurement report. University of Illinois Urbana-Champaign, Civil & Environmental Engineering & Greentech Knowledge Solutions Pvt. Ltd. & Mountain Air Engineering & International Centre for Integrated Mountain Development (ICIMOD).

- WHO, 2021. WHO Global Air Quality Guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization (WHO), Geneva.

Wienerberger, 2023. 2022 Annual and Sustainability Report. Wienerberger AG, Vietnna.

- World Bank, 2019. Nepal Environment Sector Diagnostic: Path to Sustainable Growth Under Federalism. Country Environmental Analysis. World Bank, Washington, DC.

- World Bank, 2021. The Global Health cost of PM2.5 Air Pollution. A Case for Action Beyond 2021. World Bank, Washington, DC.

42
# <u>3.1.3 India’s Applicant Pool</u>

The growth of patenting in India has yet to translate into anything suggesting that the new rewards available to Indian innovators are motivating much additional innovation—or even that India’s private sector innovators understand how to leverage patents into tangible rewards.

Table 4 lists the most active applicants for Indian patents over 1995-2005. Forty-four of the top 49 applicants are either fully foreign, MNCs who do business in India, or Indian majority-owned or wholly owned subsidiaries of MNCs. Three are Indian government agencies (CSIR, IIT, and the Ministry of Defense), and one is a public corporation (Steel Authority of India). Only two of the top applicants, Ranbaxy and Dr. Reddy’s, come from India’s homegrown private sector—and both belong to the generic drug industry that fared well under the previous patent regime.

Table 5 tells a similar story. The USPTO provides reports, based on the year in which it granted patents, on the patents issued to inventors resident in each country, along with the organizations or institutions to which they assigned their patents, at least initially. Once again, CSIR emerges as the dominant player: it was the first-named assignee on 43 percent of U.S. patents granted to Indian inventors and 70 percent of U.S. patents that Indian inventors assigned to Indian organizations or institutions. Nearly twice as many Indian inventors assigned their patents to MNCs as to domestic private companies, providing a rough estimate of comparative investments in in-country R&D. Nevertheless, these data are encouraging. Recent trends show a marked increase in patent activity in all areas of assignation—and as noted, data on patent grants lag R&D outlays by a potentially significant amount.

The data in Tables 4 and 5 also raise a subtler point: MNC investments in India’s scientific community are significant and increasing. The more confident these investors are in the integrity of India’s IP systems, the more likely they are to invest. From this perspective, India cannot afford to develop a reputation as a country whose patent system reads well at the statutory level, but collapses in practice.

**Table 4 Top Applicants for Indian Patents, 1995-2005**<sup>43</sup>

> 43 Compiled by CSIR from TIFAC database (Ekaswa A) on Indian patent filings.

26
**Table 7: Further heterogeneity analysis – Impact of treatment on typical monthly revenue (ln), by various characteristics**

|Type of training|Education:<br>completed O<br>level (var 1)/<br>completed A<br>level (var 2)|Index of<br>household<br>assets|Index of<br>business<br>assets|Entrepreneur<br>is member of<br>a cooperative|Entrepreneur<br>is member of a<br>credit/savings<br>group|Network<br>intensity<br>index|Entrepreneur<br>is the head of<br>her household|Entrepreneur<br>has at least one<br>child younger<br>than 5|
|---|---|---|---|---|---|---|---|---|
|Basic training (B)|-0.00611|-0.461|-0.527|-0.352*|-0.402|-0.238|-0.347|-0.370*|
||(0.188)|(0.461)|(0.322)|(0.186)|(0.244)|(0.325)|(0.212)|(0.202)|
|Enhanced training (E)|-0.0758|0.317|-0.0216|-0.0609|-0.261|0.182|-0.208|0.0263|
||(0.236)|(0.389)|(0.274)|(0.182)|(0.278)|(0.348)|(0.236)|(0.168)|
|B*variable 1|-0.291|0.393|0.360|-0.0795|0.170|-0.408|0.0344|0.221|
||(0.315)|(1.036)|(0.456)|(0.542)|(0.364)|(1.597)|(0.374)|(0.343)|
|E*variable 1|-0.0745|-1.109|-0.149|-0.717|0.304|-1.566|0.342|-1.218|
||(0.395)|(0.844)|(0.514)|(0.514)|(0.380)|(1.612)|(0.325)|(0.880)|
|B*variable 2|-2.065*<br>(1.100)||||||||
|E*variable 2|0.611<br>(0.563)||||||||
|Observations|678|678|678|678|678|678|678|678|
|R-squared|0.090|0.075|0.066|0.067|0.070|0.069|0.068|0.077|

Notes: All effects measured using ANCOVA specifications.

All regressions also control for stratification dummies (geographical district, sector of activity, whether participants received a business gift). Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 % levels, respectively 25