rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:**

||**G**|**overnance netwo**|**rks: prad**|**han**||||
|---|---|---|---|---|---|---|---|
||**Individual**|**Pradhan**||**Basti age**||**Village**||
||**regression**|**characteristics**|**2SLS**|<br>**x**|**2SLS**|<br>**x**|**2SLS**|
|Assets (Rs. 10000s)|0.025|0.026|0.151|0.019|0.153|0.025|0.171|
||[0.016]|[0.015]|[0.085]+|[0.016]|[0.085]+|[0.015]|[0.097]+|
|Init. contact: close relative|0.002|0.004|-0.023|-0.005|-0.022|0.004|-0.028|
||[0.036]|[0.035]|[0.051]|[0.036]|[0.051]|[0.034]|[0.052]|
|Init. contact: villager|0.010|0.005|-0.071|0.003|-0.069|-0.175|-0.249|
||[0.027]|[0.029]|[0.056]|[0.029]|[0.055]|[0.119]|[0.216]|
|Init. contact: other|-0.044|-0.057|-0.037|-0.044|-0.029|-0.049|-0.030|
||[0.040]|[0.040]|[0.052]|[0.037]|[0.051]|[0.041]|[0.052]|
|Pradhan-shared identity||-0.061|-0.024|-0.160|-0.086|-0.128|-0.116|
|||[0.036]|[0.030]|[0.146]|[0.156]|[0.063]+|[0.083]|
|Pradhan's education||0.005|0.003|-0.016|-0.006|0.009|0.008|
|||[0.007]|[0.008]|[0.023]|[0.027]|[0.007]|[0.009]|
|Pradhan- political affil.||0.230|0.200|0.774|0.542|0.172|0.099|
|||[0.129]+|[0.111]+|[0.156]**|[0.179]**|[0.156]|[0.158]|
|Pradhan's community yrs||0.006|0.007|0.026|0.030|0.004|0.006|
|||[0.006]|[0.006]|[0.021]|[0.023]|[0.005]|[0.008]|
|Basti age x prdn shared||||||||
|<br>id.||||0.005<br>|0.004<br>|||
|||||[0.006]|[0.007]|||
|Basti age x prdn's educ.||||0.001|0.001|||
|||||[0.001]|[0.001]|||
|Basti age x pol. affil.||||-0.026<br>|-0.016<br>|||
|||||[0.005]**|[0.007]*|||
|Basti age x prdn's||||||||
|<br>res.(yrs)||||-0.001|-0.001|||
|||||[0.001]|[0.001]|||
|Villager x prdn shared id.||||||0.130|0.145|
|||||||[0.062]*|[0.098]|
|Villager x prdn's educ.||||||-0.007|-0.008|
|||||||[0.002]**|[0.006]|
|Villager x pol. affil.||||||0.124|0.173|
|||||||[0.107]|[0.223]|
|Villager x prdn's res.(yrs)||||||0.005|0.001|
|||||||[0.003]|[0.004]|
|Household / basti controls|yes|yes|yes|yes|yes|yes|yes|
|Observations|780|780|474|780|474|780|474|
|Adjusted R-squared|0.04|0.07||0.11||0.08||
|F|3.42|8.81|0.137|47.3|0.144|38.01|0.193|
|Hansen's over-id J|||3.451||3.528||3.704|
|Prob > chi2|||0.063||0.06||0.054|
|Robust standard errors in b|rackets|||||||

+ significant at 10%; * significant at 5%; ** significant at 1% Robust standard errors, clustered at the slum level 28
_Tax Reforn in Transition_ 19 times average income. The second highest rate of 36 percent is reached by taxpayers earning as much as 7.1 times the average and the income level at which the rate of 32 percent applies is 4.3 times average income. The 1992 household budget survey shows that of the 2202 households surveyed, only one household has a per capita income that is greater than seven times average income, and no one reported income greater than 14 times the average. In sum, the 1992 tax brackets are extremely narrow and do not adequately tap the revenue potential of their respective income groups. Furthermore, these ratios are high compared to other Eastern European Countries. For instance, the income level where the maximum marginal rate is applied in Romania started with 3.44 times per capita GDP in 1992 (Shome, Haindl and Schenone (1993). This partially explains the relatively low potential revenue and effective tax rates under the 1992 tax system. In contrast, setting tax rates as low as possible, reducing the number of brackets and defining them as broadly, yet realistically as possible, and raising the exemption level, taking into account the underlying income level and distribution, would increase revenuesl°. For instance, all of the proposed tax systems extend the exemption level to cover the lowest 20 percent. Thus positive income tax payments start at a relatively higher income level, 60 percent of average per capita income, compared to 50 percent under the 1992 system. However, proposed tax rate becomes substantial (i.e., 30 percent) at incomes around mean per capita income. The proposed top rate of 40 percent requires an income nearly twice the average level (see panel b of Table 5).

A main factor responsible for the erosion of effective tax rates has no doubt been the weakness of tax administration, especially in a rapidly growing small-scale and self-employed private sector which is difficult to bring into the tax net. However, adjusting or modifying the income tax law so as to facilitate compliance and enforcement may well be the most expeditious way of improving tax administration.

10/A similar analysis of the revenue performance and distributional impact of the 1997 tax changes requires an examination of a more recent household survey data. Future research employing such data is highly encouraged.
# **References**

- [1] IPCC. _IPCC Fifth Assessment Report—Synthesis Report_ . 2014.

- [2] Patrick W Baylis and Judson Boomhower. _Mandated vs. Voluntary Adaptation to Natural Disasters: The Case of U.S. Wildfires_ . Working Paper 29621. National Bureau of Economic Research, Dec. 2021. doi: `10.3386/w29621` . url: `http://www.nber.org/papers/w29621` .

- [3] LUcas Husted, Isaac M. Opper, and Jisung R. Park. _The Impact of Natural Disasters on Human Capital_ . Tech. rep. 2022.

- [4] Solomon M Hsiang and Amir S Jina. _The causal effect of environmental catastrophe on long-run economic growth: Evidence from 6,700 cyclones_ . Tech. rep. National Bureau of Economic Research, 2014.

- [5] Douglas K Bardsley and Graeme J Hugo. “Migration and climate change: examining thresholds of change to guide effective adaptation decision-making”. In: _Population and Environment_ 32.2 (2010), pp. 238–262.

- [6] Richard Black et al. “Migration as adaptation”. In: _Nature_ 478.7370 (2011), pp. 447–449.

- [7] Leah Platt Boustan, Matthew E Kahn, and Paul W Rhode. “Moving to higher ground: Migration response to natural disasters in the early twentieth century”. In: _American Economic Review_ 102.3 (2012), pp. 238–44.

- [8] Kanta Kumari Rigaud et al. _Groundswell_ . Tech. rep. 2018.

- [9] Jos ́e-Luis Cruz and Esteban Rossi-Hansberg. _The economic geography of global warming_ . Tech. rep. National Bureau of Economic Research, 2021.

- [10] Pratikshya Bohra-Mishra, Michael Oppenheimer, and Solomon M Hsiang. “Nonlinear permanent migration response to climatic variations but minimal response to disasters”. In: _Proceedings of the National Academy of Sciences_ 111.27 (2014), pp. 9780–9785.

- [11] Ocean Studies Board, Engineering National Academies of Sciences, Medicine, et al. _Understanding the Long-Term Evolution of the Coupled Natural-Human Coastal System: The Future of the U.S. Gulf Coast_ . National Academies Press, 2018.

- [12] Michael Siegrist and Heinz Gutscher. “Flooding risks: A comparison of lay people’s perceptions and expert’s assessments in Switzerland”. In: _Risk analysis_ 26.4 (2006), pp. 971– 979.

- [13] Matthew Rabin. “Psychology and economics”. In: _Journal of economic literature_ 36.1 (1998), pp. 11–46.

- [14] Patricia Born and W Kip Viscusi. “The catastrophic effects of natural disasters on insurance markets”. In: _Journal of risk and Uncertainty_ 33.1 (2006), pp. 55–72.

16
couple in period t − 1 who attain adulthood in period t, let Mt<sup>achoosetomigrate.</sup> Denoting by N1<sup>f</sup> ,t<sup>thenumberofunionsformedintheruralsector,justafterthestart</sup> of period t, the above normalization to surviving couples implies that the number of representative families (households) in the rural sector, F1,t, changes according to The number of elders per household in period t is thus given by Each young adult possesses λ1,t efficiency units of labor, which is a natural measure of human capital. This is formed in a process whereby the benefits of formal education are complemented by the quality of child-rearing, which depends on the human capital of all the adults in the family.<sup>7</sup> Let e1,t ∈ [0, 1] denote the fraction of childhood devoted to formal education, the residual being allocated to work. Choosing the iso-elastic form b1 · (e1,t)<sup>θ1</sup> for the ‘educational technology’, let a child’s endowment of labor (measured in efficiency units) on reaching adulthood in period t + 1 be given by where the product on the RHS implies that formal education and the quality of childrearing are indeed complements. It is plausible that φ < 1, that is, grandparents contribute less, cet. par. , than parents to the children’s potential capacity to build human capital. Some education is required if λ1,t+1 is to exceed unity, the human capital of an uneducated young adult.<sup>8</sup>

> 7For empirical evidence on the latter point, see Christiaensen and Alderman (2002) and Woldehanna et al. (2005).

> 8This is just a convenient normalization.

8
**Figure 1: Average GDP/capita of exporters by product category (export share-weighted)**

<!-- Start of picture text --> 50,000<br>2000 2016<br>45,000<br>40,000<br>35,000<br>30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>apparel furniture prepared plastics & chemicals pharmaceuticals iron, steel, machinery & transportation optical<br>& toys foodstuffs rubbers copper & equipment equipment equipment<br>aluminium<br><!-- End of picture text --> Notes: Export share-weighted average GDP per capita across all (global) exporters in a product category. Values are in current USD.

The blue bars in Figure 1 show the average GDP per capita of exporters based on countrylevel GDP per capita and global export shares for 2000. The orange bars show the results from the same calculations for 2016. We selected a number of products from two broad categories, namely light manufacturing and metal-based industries, and ranked them by rising “quality”. By separating the two groups, it becomes clear that the ranking of different products is stable over time. In only a single instance would products have reversed position between 2000 and 2016.<sup>1</sup> The ordering of most industries is intuitive. Pharmaceutical products and optical equipment are exported mostly by highly developed countries. Plastics & rubber and machinery & equipment are intermediate. At the very bottom of the scale, on the far left in Figure 1, sits apparel. The average apparel exporter hails from a country with a GDP per capita of less than 10,000 USD (in international PPP) in 2000. This is less than 40% of the income level for the country where the median exporter of pharmaceuticals or optical equipment is located. Moreover, the average income level of apparel producers increased the least between 2000 and 2016 of all products that we show.<sup>2</sup> In 2016, apparel exports are still dominated by the poorest countries and the income gap with countries exporting pharmaceuticals has grown to a 1-to-4 difference. We calculated this average GDP per capita for a large set of products, more than could be shown in Figure 1, and apparel was at the very bottom of the entire range. Even agricultural products and animal exports tend to come from slightly wealthier countries.

> 1 In 2016, countries exporting machinery & equipment tend to have a slightly lower GDP per capita than countries exporting metal inputs, “iron, steel, copper, & aluminium”. In 2000 that was not the case.

> 2 Of course, this does not necessarily imply that countries exporting apparel see low income growth as the set of countries that are important apparel exporters changes over time.

5
support foreign aid. Finally, in an aggregate version of the income hypothesis discussed earlier, we might expect countries with higher aggregate incomes to demonstrate more support for foreign aid.

# DATA AND METHODS

Data come two independent sources. First, the _World Values Survey_ (European Values Study Foundation and World Values Survey Association, 2006) contains individual-level demographic information as well as a wide range of questions capturing religiosity, an international focus, left/right position, and individual psychological characteristics. The World Values Survey (WVS) includes information for 5,693 individuals in 9 donor countries.<sup>5</sup> To measure the dependent variable, SUPPORT FOR FOREIGN AID, individuals answered the following question: “Some people favor, and others are against, having this country provide economic aid to poorer countries.” It is coded with four response categories indicating increasing favor.

The second survey is Gallup International’s _Voice of the People_ 2002 survey. This survey contains many fewer individual-level variables than the WVS. It contains demographic information, as well as questions about political interest and trust of individuals and various organizations. The strength of the Gallup International (GI) survey is that it includes information for 6,194 individuals in 17 of the 22 possible donor countries.<sup>6</sup> To measure the dependent variable, GIVE MORE FOREIGN AID, individuals answered the following question: “Do you think that the wealthier nations should give more financial help to the poorer nations or are they giving enough now?” It is coded 1/0; or “should give more” / “giving enough.”

# _Individual-Level Independent Variables_

In the World Values Survey analyses, the individual-level independent variables correspond to the four theories outlined above. Two variables in the WVS measure

> 5 The countries included in the WVS analysis are Australia, Finland, Germany, Japan, New Zealand, Norway, Spain, Sweden, and the United States.

> 6 The countries included in the Gallup analysis are Austria, Canada, Denmark, Finland, Germany, Ireland, Italy, Japan, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States.

8
Most of these countries are smaller in total population than the typical Indian state. While Ireland has the lowest share of government spending in GDP in the sample, one that is close to India’s, and it is a relatively centralized country in terms of spending responsibilities, still its share of municipal spending in GDP eight times that of India’s. While India’s GDP per capita has increased 70% since 1992, it appears that the share of GDP spent at the municipal level remains unchanged since that time. Even correcting for the difference in urbanization levels, India’s spending patterns appear highly centralized, be it at the state or central level.

# **_Financing patterns for local government spending show even greater variability than on the expenditure side: India’s cities make do with fewer resources from higher levels of government while working from a low-yielding revenue base._**

Table 2.2 provides basic data on the structure of revenues in the European Union. In this case, the diversity across countries is even more striking than on the expenditure side. Countries are shown in descending order of share of own source taxes in total revenues. The share of own source taxes in total ranges from about 70% to nil for small countries like Malta and Latvia. Likewise shared taxes range from nil to as much as 44% of revenue for local governments. Practices differ in the use of shared taxes as well. In some countries, local governments may ‘piggy back’ a local tax at a rate they determine, on to the base of tax collected at a higher level. In others, local governments simply receive a share of the tax without any input into rates. There is much more regularity in the share of own source revenues as a share of total within this group of countries. In all but four cases, local governments depend on outside sources for 40% or more their total revenues. In all but seven cases, outside revenue sources account for more than half of the total.

On this dimension, India is exceptional. No country in this grouping requires its local governments to be so financially independent. There is some variability within this average for India. Tamil Nadu, in which devolved revenues accounted for about 40% of total revenues in 2003-2004<sup>4</sup> , seems to follow a pattern of financing much more similar to the EU countries presented in this sample. However Tamil Nadu remains exceptional within India. The sample of cities in Mohanty et alia (2007) represents thirty five metros, which are likely to be more self-sufficient than smaller places. Nonetheless, evidence from Kundu(1999) and Mathur(2001: 32) strongly suggests that introducing these nuances in the analysis does not change the overall picture. India’s urban local governments must rely upon their own resources far more than most countries.

> 4 Department  of Municipal Administration, Government of Tamil Nadu

9
hybrids in 2020 have higher sales-weighted average emissions than in 2019, causing the policies that favor hybrids over gasoline vehicles to be less effective at reducing emissions in 2020 than in 2019. In other words, replacing a gasoline vehicle with a hybrid vehicle causes a smaller emissions reduction in 2020 than in 2019. The upshot is that the fiscal costs per ton of emissions reduction are roughly twice as high in 2020 than in 2019. This result demonstrates the inefficiency of setting tax policy based on fuel type rather than emissions, which we demonstrate more extensively in the next subsection.

Table 10: Consumer surplus changes by vehicle price quintiles

|**Variable**|**No Policy**<br>**(level)**|**Sale tax and**<br>**Tariff**<br>**reduction(diff)**|**Sale tax**<br>**reduction**<br>**only (diff)**|**Tariff**<br>**reduction**<br>**only (diff)**|**Carbon tax**<br>**(diff)**|
|---|---|---|---|---|---|
|||**Panel A: Y**|**ear 2019**|||
|Quintile 1|2601|-0.1|0.0|0.0|-13.0|
|Quintile 2|2573|3.0|0.9|1.7|-13.0|
|Quintile 3|2569|6.9|1.8|4.2|-1.5|
|Quintile 4|3284|23.7|5.9|14.2|6.3|
|Quintile 5|3939|11.1|3.3|6.8|-5.3|
|||**Panel B: Y**|**ear 2020**|||
|Quintile 1|2417|0.0|0.0|0.0|-7.4|
|Quintile 2|2503|0.1|0.0|0.1|-11.8|
|Quintile 3|2459|10.3|2.9|6.0|4.2|
|Quintile 4|2887|18.3|4.9|11.3|9.2|
|Quintile 5|3354|5.9|1.9|3.9|-4.5|

Note: This table shows results from the counterfactual simulations of consumer surplus from different policies sorted by vehicles price quintiles. Column 1 shows consumer surplus in levels for the no policy scenario. Columns 2-5 show the difference in consumer surplus from alternative policies to the no policy scenario.

Finally, we are interested in assessing the distributional effects of the three policy scenarios. We compute quintiles of the retail price distribution in 2019 or 2020 and assign each vehicle to the corresponding quintile as a proxy for the unobserved consumers’ disposable income. Table 10 shows how changes in consumer surplus vary by policy and vehicle price. The first column reports the level of consumer surplus by quintile and year, and the remaining columns report changes in consumer surplus relative to the no-policy case. Reducing the sales tax and tariff for hybrid and electric vehicles increases consumer surplus in the upper quintiles more than the 27
increased aid in donor countries and the share of aid that countries funnel through multilateral agencies.<sup>1</sup> For these reasons, it is important to understand public support for foreign aid. One existing study (Chong and Gradstein 2006) begins to explore the determinants of support for foreign aid. But two important issues remain: (1) research remains at the individual level without considering the larger social and institutional structures in which public opinion is embedded, and (2) the range of factors, even at the individual-level, that could affect support for foreign aid remains underspecified. In this paper, we argue that individual-level factors such as attitudes toward poverty, as well as country-level factors such as a country’s existing level of aid, could affect public opinion in support of foreign aid.

We examine support for foreign aid with a multi-level, cross-national study. We begin by outlining a theoretical rationale for the factors that should affect support for foreign aid, discussing the importance of both individual-level factors and country-level economic and social structures. We introduce two separate datasets to examine attitudes to foreign aid in donor countries: (1) the 1995-1997 wave of the _World Values Survey_ has information from approximately 6,000 individuals in nine countries and asks a rich battery of questions at the individual-level, (2) the 2002 Gallup International “Voice of the People” survey, asks fewer questions of individuals but includes seventeen donor countries<sup>2</sup> . We test our theory with multi-level models, including both individual-level and country-level variables to predict support for foreign aid.

# INDIVIDUAL AND CONTEXTUAL INFLUENCES ON SUPPORT FOR FOREIGN AID

Citizens of donor countries have opinions on the provision of foreign aid, defined as the transfer of capital or goods from one nation to another. Individuals can support foreign aid disbursement by their country in general. Individuals may also feel, marginally, that their country should provide more or less aid than current commitments.

> 1 Causation is not necessarily from public opinion to how it is allocated.  However, very few survey respondents are likely to know much about the share of aid channeled through multilaterals.  The most plausible interpretation is that support for aid gives policymakers more latitude to spend it in ways that further development goals rather than the donor country’s commercial, security or other goals.

> 2 See <u>www.worldvaluessurvey.org and www.voice-of-the-people.net.</u>

2
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