rafmacalaba/datause-encoder-data huggingface dataset:
Indeed, empirical work on spousal disaccord (e.g. Annan et al. 2021; Twyman, Useche and Deere 2015) has found spousal response discrepancies to be correlated with other factors within and outside the household, suggesting that random measurement error, though important, is not fully responsible for the discrepancies consistently seen in spousal surveys. Below, we consider in turn the three main explanations for systematic measurement error advanced in the literature: asymmetric interpretation, asymmetric information, and asymmetric response to enumerator characteristics.

# <u>2.1. Asymmetric Interpretation</u>

A major source of measurement error may be differing interpretations of survey questions, which are asymmetric if interpretations vary by gender or other respondent characteristics. Scanzoni (1965) suggests that husbands and wives may be interpreting family situations differently based on their gender, and thus relaying different information in surveys. This sentiment is echoed by Granbois and Willet (1970), who suggest that survey questions should be made more specific to avoid disaccord stemming from differing interpretations. Ambler et al. (2021) find that wives are significantly more likely to report their own involvement in decisionmaking than their husbands, a scenario that may suggest different gendered interpretations of questions are partially responsible for response discrepancies. Using an item-response model for the responses of spouses in India, Pakistan, Malaysia, Philippines, and Thailand, Ghuman, Lee & Smith (2006) find that husbands and wives give systematically different responses to decision-making questions, which may suggest different interpretations of the questions. However, they find that these differences vary across communities and cultures in ways that make comparing survey results across countries or contexts difficult or counterproductive. In a qualitative study of spousal survey responses in Malawi, Miller et al. (2001) find that spouses may have differing definitions of the decision-making process; one spouse may consider a very short discussion before making a decision to be a true conversation, while the other may not.

Although the possibility of asymmetric interpretations of decision-making is well-described in the literature, few papers have empirically tested for asymmetric interpretations of decision-making, with the notable exception of Ambler et al (2021). Multiple elements of survey questions may lend themselves to differing 6
Table A15: Impact on violence (15yo+)

|||At least one<br>Emotional<br>violence|At least one<br>Physical<br>violence|At least one<br>Sexual<br>violence|
|---|---|---|---|---|
|T1|(safe spaces)|-0.001|-0.025|0.002|
|||(0.068)|(0.016)|(0.023)|
|T1|+ Leaders|0.028|-0.021|0.020|
|||(0.065)|(0.016)|(0.027)|
|T2|(safe spaces + IGA)|-0.003|0.007|0.001|
|||(0.045)|(0.020)|(0.019)|
|T2|+ Leaders|-0.032|-0.026*|0.003|
|||(0.040)|(0.014)|(0.021)|
|T3|(safe spaces + husbands’ clubs)|-0.046|-0.019|-0.023|
|||(0.055)|(0.016)|(0.017)|
|T3|+ Leaders|0.008|-0.009|-0.004|
|||(0.065)|(0.017)|(0.018)|
|T4|(control spillover)|-0.023|-0.008|-0.003|
|||(0.045)|(0.016)|(0.021)|
|T1|= T1 + L|.737|.797|.546|
|T2|= T2 + L|.489|.0734|.947|
|T3|= T3 + L|.467|.59|.263|
|T1|= T2|.968|.122|.987|
|T2|= T3|.466|.199|.18|
|T1|= T3|.538|.727|.266|
|T1|+ L = T2 + L|.324|.654|.519|
|T2|+ L = T3 + L|.513|.308|.751|
|T1|+ L = T3 + L|.803|.516|.37|
|N||2,924|2,931|2,931|
|Co|ntrol mean|.231|.057|.046|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p- values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

48
simulations focus on the direct revenue gains which mostly accrue to MNE residence countries, i.e. high-income countries.<sup>25</sup> Instead, we focus on the indirect effects of reduced tax competition, which allows for ETRs to be set at the global minimum tax rate in all countries. We also observe the exact reported profits and ETRs of individual firms and can provide specific estimates of revenue gains from the application of a minimum tax. This contrasts with previous work that relies on aggregate data for residence-source-country pairs, drawn from the Country-by-Country reporting (CbCR) data, and macro estimates of ETRs. These data suffer from several measurement issues (discussed in Cobham et al. 2021 and OECD 2020) and are incomplete for developing countries. Due to the incomplete and confidential nature of the data, the above studies only publish countryspecific estimates of revenue gains for countries appearing in the CbCr data, and group other countries into income-level groups.<sup>26</sup>

# **6 Conclusion**

In this paper, we construct corporate effective tax rates by firm size in a consistent manner across 13 countries, using tax return data. We uncover large economy-wide gaps between effective and statutory tax rates, a rise in ETRs with firm size until the 85-90th percentile of the size distribution, and a fall in ETRs for the largest firms in most countries. The planned global minimum tax provides an opportunity for countries to raise ETRs in a coordinated manner to recover lost tax revenue.

The fact that the top firms currently face lower tax burdens could lead to an allocation of resources away from medium firms, key engines of growth and employment. Further research is needed to examine the efficiency costs of tax expenditures, especially those benefiting the largest firms, and compare them to the intended societal benefits. Another open question is the extent to which tax expenditures are intentionally targeted at large firms or whether large firms are better informed and more skilled in taking up tax expenditures offered to all firms. Future research could also shed light on the interactions between the use of tax expenditures and tax avoidance behavior.

> 25An exception is scenario 4 in the OECD (2020) simulations, which considers the possibility that countries may increase their ETR in response to the minimum tax, but only presents aggregate results by country income groups.

> 26Only Cobham et al. (2021) publish country-level revenue gains estimates for 196 countries in an online appendix, based on the data imputation procedure presented in Garcia-Bernardo and Jansk ́y (2021).

16
**Table 11: Marginal effects of public transport characteristics on employment probabilities, by city and gender, excluding availability.**

||**No in**<br>|**come restri**<br>|**ction**<br>|**Lower**<br>|**-income gr**<br>|**oup**<br>|
|---|---|---|---|---|---|---|
||(1a)|(2a)|(3a)|(1b)|(2b)|(3b)|
|**Sample restriction**|All|Birth +<br>Marriage|Birth|All|Birth +<br>Marriage|Birth|
|**Accessibility**|||||||
|Amman - Men|0.406***|0.429***|0.402**|0.381***|0.410**|0.394|
||(0.07)|(0.13)|(0.18)|(0.10)|(0.18)|(0.26)|
|Amman - Women|-0.220***|-0.164|-0.183|-0.078|0.081|0.214|
||(0.08)|(0.15)|(0.26)|(0.10)|(0.20)|(0.37)|
|Beirut - Men|0.07|-0.013|0.095|-0.055|-0.175|-0.192|
||(0.09)|(0.12)|(0.16)|(0.16)|(0.21)|(0.27)|
|Beirut - Women|0.033|-0.012|0.131|0.027|0.121|0.311|
||(0.08)|(0.10)|(0.17)|(0.11)|(0.15)|(0.25)|
|Cairo - Men|-0.124|-0.324|-0.282|-0.099|-0.22|-0.207|
||(0.21)|(0.25)|(0.36)|(0.25)|(0.28)|(0.42)|
|Cairo - Women|-0.129|-0.214|0.289|0.056|-0.060|0.400|
||(0.24)|(0.28)|(0.40)|(0.29)|(0.32)|(0.48)|
|**Safety**|||||||
|Amman - Men|-0.348***|-0.394*|-0.476*|-0.323**|-0.37|-0.508|
||(0.12)|(0.21)|(0.26)|(0.15)|(0.29)|(0.39)|
|Amman - Women|-0.251*|-0.098|0.448|-0.522***|-0.397|-0.377|
||(0.15)|(0.24)|(0.43)|(0.18)|(0.31)|(0.63)|
|Beirut - Men|-0.112|0.286|0.628|0.031|0.585|0.978|
||(0.27)|(0.37)|(0.46)|(0.44)|(0.64)|(0.76)|
|Beirut - Women|-0.049|0.119|-0.219|-0.468|-0.293|-0.366|
||(0.23)|(0.31)|(0.54)|(0.29)|(0.43)|(0.90)|
|Cairo - Men|0.445|0.585|0.401|0.782|1.082**|1.032|
||(0.45)|(0.48)|(0.62)|(0.51)|(0.54)|(0.76)|
|Cairo - Women|0.388|0.754|0.583|0.206|0.757|0.396|
||(0.49)|(0.52)|(0.69)|(0.56)|(0.60)|(0.87)|
|N|5912|3404|1810|3242|1886|929|
|R2|0.293|0.308|0.271|0.359|0.376|0.350|
|Adj. R2|0.281|0.288|0.230|0.339|0.343|0.285|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations.

52
Figure 4: Poverty Eradication Capacity Curve, examples <!-- Start of picture text --> Namibia (2004) Namibia (2004)<br>Senegal (2011) Senegal (2011)<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Marginal Tax Rate Marginal Tax Rate<br>1 1<br>.9 .9<br>.8 .8<br>~p .7 r .7<br>.6 .6<br>.5 .5<br>.4 .4<br>Total Transfer T .3 Total Transfer T .3<br>.2 .2<br>.1 .1<br>as a share of the sum of poverty gaps as a share of the sum of poverty gaps<br>0 0<br><!-- End of picture text --> 50 percent in Senegal (Figure 4, left panel). When only the rich are subject to taxation to collect resources to be transferred to the poor, Namibia’s optimal marginal tax rate is about 55 percent, while Senegal would be able to transfer less than 10 percent of the total poverty gap by levying a marginal tax rate of 100 percent on the excess affluence gaps of the rich individuals.

The _Poverty Eradication Capacity Index I_ ( _P_<sup>_∼p_</sup> ) shows a clear non-linear negative relationship with the level of poverty in the countries (see Figure 5, left panel), while the relationship is less evident in the case of _I_ ( _P_<sup>_r_</sup> ) (Figure 5, right panel). Recall that the index equals zero when there is no capacity to eradicate poverty by transferring resources from non-poor (or rich) to poor individuals; it tends to one when poverty is solved with a marginal tax rate on responsible individuals which tends to zero. At the extreme, it is equal to one when poverty is zero.

Our results show that for poverty levels lower than 30 percent, _I_ ( _P_<sup>_∼p_</sup> ) is very close to one, indicating that countries can easily collect enough resources to eradicate poverty levying a small marginal tax rate on non-poor individuals. When poverty levels range from 40 to 60 percent, the relationship between poverty and _I_ ( _P_<sup>_∼p_</sup> ) seems convex, to finally become concave for higher poverty levels. Note that the tipping point around a poverty headcount of 60 percent corresponds to about _I_ ( _P_<sup>_∼p_</sup> ) = 0 _._ 5, which corresponds to a _Poverty Eradication Capacity Curve_ coincident to the diagonal, or to an optimal marginal tax rate equal to 1. In other words, countries with an _I_ ( _P_<sup>_∼p_</sup> ) below 0.5, are not able to eradicate poverty by taxing away the excess-poverty gaps of non-poor individuals, and the transfer covers 15
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE during the mid-2000s. The only other country in Africa that has a comparable burden of infrastructure spending is Democratic Republic of Congo with needs estimated at around 60 percent of GDP.

**Figure 13. Liberia’s infrastructure spending needs are greater than those of comparable countries**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> capital expenditure % of GDP operations and maintenance<br><!-- End of picture text --> Source: Foster and Briceño-Garmendia 2009. ECOWAS = Economic Community of West African States.

Between 2004 and 2008, Liberia spent an average of $90 million per year to meet its infrastructure needs (table 14). About 80 percent of that total was allocated to capital expenditure and the remaining 20 percent to operating expenditures. Operating expenditure is entirely covered by budgetary resources and payments from infrastructure users. The largest source of funding for infrastructure investment is overseas development assistance (ODA), which has averaged $44 million in recent years after a long period of inactivity (figure 15). After ODA, the second largest source of investment finance has been the private sector, which has invested $17 million a year in telecommunications. Domestically-funded public investment is very low. Almost half of total spending has been allocated to the transport sector.

Compared to other Africa countries, Liberia spends a relatively high amount on infrastructure— around 10 percent of its GDP on infrastructure, which is equivalent to $40 per capita per year (figure 14). That total is much higher than what is observed in a number of other post-conflict countries; for example, Democratic Republic of Congo spends little more than 2 percent of GDP, and Rwanda spends just over 6 27
Table A4.1: Average<sup>a</sup> applied and bound tariff rates<sup>b</sup> on agricultural products, by <u>World Bank categories, 2008</u>

||Bound<br>Rate|Applied<br>Rate|Spread:<br>(Bound minus<br>Applied)<sup>c</sup>|
|---|---|---|---|
|Developing countries (129)<sup>d</sup>|61.1|16.8|45.0|
|Low Income (52)|74.2|16.2|58.3|
|Middle Income (77)|53.2|17.2|36.8|
|High Income Non-OECDs (14)|39.5|9.2|32.0|
|High Income OECDs (10)|42.5|25.4|17.1|

Source: Tabulated from WTO, World Tariff Profiles 2008; WTO website http://www.wto.org/english/res_e/booksp_e/tariff_profiles08_e.pdf (16 January 2009) Notes: a These averages are simple averages across countries of simple averages of rates by tariff line for each country. The number of WTO members covered by the averages are: 46 Low Income, 79 Middle Income, 14 High Income nonOECD, and 10 High Income OECD. The EC-27 is treated as one Member. b Ad valorem equivalents.

c Averages of country differences rather than differences of the averages in the previous columns.

d Numbers in parentheses are the numbers of WTO Members in each group covered by the data base.

71
For improvement in location‐specific inundation projections (basis of forecasting and adaptation planning), the IWM has pointed out the need for the following:

|**Items**|**Area**|**Per unit cost**|**Estimated Total**|
|---|---|---|---|
||||**Cost (in millionUS$)**|
|Topographic Survey|23,500 sq km|US$200/ sq km|5|
|LIDAR & RTK GPS Survey||||
|Mathematical Modeling|||3|
|**Total**|||**8**|

Officials of the Meteorology Department have identified the following requirements for strengthening the cyclone early warning system: a) modernization of existing 35 observatories, b) establishment of additional 30 observatories, c) establishment of 5 new radio stations, d) modernization of existing workshop and laboratory of the BMD, and e) development of a training institute. Approximate costs of these requirements are as follows:

||**Items**|**Estimated Cost in**<br>**MillionUS$**|
|---|---|---|
|1.|Modernization of the existing 35 (thirty five)<br>observatories of BMD|6.00|
|2.|Establishment of additional 30 (thirty) modern<br>observatories of BMD at different locations of<br>Bangladesh|12.00|
|3.|Establishment of 5 (five) new Radio Sonde<sup>60</sup><br>stations of BMD at different locations of|2.00|
||Bangladesh||

> 60 The **_radiosonde_** is a balloon‐borne **_instrument platform_** with radio transmitting capabilities. It contains instruments capable of making direct **_in‐situ_** measurements of air temperature, humidity and pressure with height, typically to altitudes of approximately 30 km. These observed data are transmitted immediately to the ground station by a radio transmitter located within the instrument package. The ascent of a radiosonde provides an indirect measure of the wind speed and direction at various levels throughout the troposphere. Ground based radio direction finding antenna equipment track the motion of the radiosonde during its ascent through the air. The recorded elevation and azimuth information are converted to wind speed and direction at various levels by triangulation techniques.
- 37 -

# SELECTED REFERENCES

Balassa, B. and F, D. McCarthy (1984) "Adjustment Policies in Developing Countries, 1979-83", World Bank Staff Working Paper Number 675.

- Balassa, B. (1982) "Structural Adjustment PolicLes in Developing Countries", World Development, January.

(1980) "The Process of Industrial Development and Alternative Development Strategies", World Bank Staff Working Paper 458. Bradford, C. I. and W. H. Branson (Eds.) (1987) Trade and Structural Change in Pacific Asia- The University of Chicago Press.

- Baneth, J. (1983) "The Role of the World Bank as an International Institution", Comment on paper by Anne 0. Krueger, Money, Monetary Policy and Financial Institutions, vol. 18, pp. 313-324.

- Berg, E. and A. Batchelder (1984) Structural AdJustment Lending: A Critical Analysis, Alexandria, Virginia, Elliot Berg Associates.

- Bhagwati, J. (1978) Foreign Trade Regimes and Ecanomic Development: Anatomy and Consequences of Exchange Control Regimes, Cambridge, Ballinger.

- Cherif, M. (1986) "A Quantitative Framework for Policy Based Lending: Applications to Cote d'Ivoire", World Bank EPDCO, March, Processed.

- Clavijo, F. (1986) "A Policy Model for Jordan", World Bank, EPDCO, May, Processed.

- Corbo, V. and J. de Melo (1987) "Lessonr from the Southern Cone Policy Reforms", World Bank Research Observer 2, No. 2.

- Corden, W. M. (1987) "Protection and Liberalization", A Review of Analytical Issues, IMF Occasional Paper No. 54, August.

- Cornia, G. A., R. Jolly and F. Stewart (1987) Adjustment with a Human Face- Clarendon Press, Vol. 1.

- Demery, L. and T. Addison (1987) Poverty Alleviation -Under Structural Adjustment, World Bank, Washington, D.C., forthcoming.

- Donges, J. (1976) "A Comparative Study of Industrialisation Policies in Fifteen Seni-Industrial Countries" WeltwirtschaftlichesArchiv.
# **4. Empirical Findings**

## **_Sample Data_**

In Table 3 we report descriptive statistics for the overall sample (more detail is contained in Pattanayak 2005a). Table 3 shows that 89 percent of the sample is Sinhalese and 62 percent is Buddhist. The average family size is 4.8 members, with 1.3 children under 18 years of age. The typical household head is 52 years old and has 10 years of education. Over 90 percent of households have adults who have completed primary education and almost all households send their school-aged girls to school.

To compare poor and non-poor households, we first need to define these distinctions. The Sri Lanka Department of Census and Statistics identifies poor households using data on household expenditures from the Household Income and Expenditure Survey (HIES, Sri Lanka Department of Census and Statistics 2002). In order to be able to compare our results to the Census data, we also identified poor households through monthly consumption expenditures.<sup>7</sup> Using the data, we classified our sample based on monthly per capita consumption deciles. Poor households comprise the bottom two deciles, or quintile, of the distribution (or households who spend less than or equal to Rs. 3,356 per capita per month are poor). By this definition, 365 of our sample households are classified as poor, of which 124 live in the Greater Negombo and 241 live in the Kalutara-Galle strip. This definition of the poor not only allows us to explore poverty relative to the overall socio-economic distribution, but also enables us to directly compare our results with other regions and countries if necessary. Table 3 also contains descriptive statistics for the poor and non-poor sub-samples.

Major sources of water for our sample households were private connections to the piped water network, private wells, and water from neighbors. Households indicated that their dwellings are close to the piped water network. A typical household can access at least three sources, with approximately 54 percent of households claiming that they could get their water from a private piped connection if they wanted. Typically, a household uses just one source of water. Only about 4 percent of the sample relies exclusively on a combination of community sources—including neighbors, public taps, and public wells.

> 7    Our consumption survey module focused on major items and not all the micro details covered in the HIES. We made three adjustments to our consumption data—calibrated our food expenditure, imputed our housing expenditure and adjusted our ‘other expenditure’ category—allowing us to define poverty in a manner that was comparable to the Sri Lankan government’s official statistics.

12
slower rate, as incomes grow further and consumers substitute higher-order foods (such as animal products) for cereals and tubers. Only at higher levels of income such as $40,000 does per capita CE consumption growth slow down as the diet shift is nearing completion. This graph also shows the income-consumption pairs for a large range of countries, making clear that there is considerable variation around this broad trend. This is to be expected with such a simple measure, given the differences in food consumption patterns among nations, some of which may be due to inherent cultural features, with others perhaps related to habit formation patterns of the type analyzed by Atkin (2013), or to food price differences related to policy and product selfsufficiency.

Based on the FAO statistics that we use, China’s consumption pattern is close to the average demand pattern for our global sample. Not surprisingly, consumption levels are particularly high in countries such as Australia and Brazil, where beef and sheepmeats are produced largely using pasture whose price is determined—given the available technology—by the prices of these beef and sheep products, rather than by arbitrage between pasture and grain. Japan is a negative outlier, probably because of the importance of seafood in its historical animal-product diet with the price of fish determined—at least until recently—by costs of catching from the wild, rather than by the costs associated with fish-farming. Only recently has arbitrage between wild-caught and farm-raised seafood justified the use of our approach to aggregation on the assumption that fish can be produced using cereal products.

In order to gain historical perspective, Figure 5 shows the changes in CE consumption between the beginning of the sample period (the year 1980) and the end (the year 2009). This shows that most countries where per capita income grew substantially observed a sizeable increase in consumption levels, with the slope of the resulting ray being very broadly similar to 15