rafmacalaba/datause-encoder-data huggingface dataset:
- 1102 A. C. Pigou. _The Economics of Welfare_ . Macmillan, London, 1920.

- 1103 R. S. Pindyck. Climate Change Policy: What Do the Models Tell Us? _Journal of Economic Literature_ , 1104 51(3):860–872, Sept. 2013. ISSN 0022-0515. doi: 10.1257/jel.51.3.860. URL `https://pubs.aeaweb.` 1105 `org/doi/10.1257/jel.51.3.860` .

- 1106 K. Rennert, F. Errickson, B. C. Prest, L. Rennels, R. G. Newell, W. Pizer, C. Kingdon, J. Wingenroth, 1107 R. Cooke, B. Parthum, D. Smith, K. Cromar, D. Diaz, F. C. Moore, U. K. M ̈uller, R. J. Plevin, A. E. 1108 Raftery, H. Sevˇc ́ıkov ́a,<sup>ˇ</sup> H. Sheets, J. H. Stock, T. Tan, M. Watson, T. E. Wong, and D. Anthoff. 1109 Comprehensive Evidence Implies a Higher Social Cost of CO2. _Nature_ , Sept. 2022. ISSN 00281110 0836, 1476-4687. doi: 10.1038/s41586-022-05224-9. URL `https://www.nature.com/articles/` 1111 `s41586-022-05224-9` .

- 1112 R. G. Richels and G. J. Blanford. The value of technological advance in decarbonizing the U.S. economy. 1113 _Energy Economics_ , 30(6):2930–2946, Nov. 2008. ISSN 01409883. doi: 10.1016/j.eneco.2008.06.005. 1114 URL `https://linkinghub.elsevier.com/retrieve/pii/S014098830800087X` .

- 1115 B. M. Sanderson and B. C. O’Neill. Assessing the costs of historical inaction on climate change. 1116 _Scientific Reports_ , 10(1):9173, June 2020. ISSN 2045-2322. doi: 10.1038/s41598-020-66275-4. URL 1117 `https://www.nature.com/articles/s41598-020-66275-4` .

- 1118 T. S. Schmidt and S. Sewerin. Technology as a driver of climate and energy politics. _Nature Energy_ , 1119 2(6):17084, June 2017. ISSN 2058-7546. doi: 10.1038/nenergy.2017.84. URL `http://www.nature.` 1120 `com/articles/nenergy201784` .

- 1121 S. C. Sherwood, M. J. Webb, J. D. Annan, K. C. Armour, P. M. Forster, J. C. Hargreaves, G. Hegerl,

- 1122 S. A. Klein, K. D. Marvel, E. J. Rohling, M. Watanabe, T. Andrews, P. Braconnot, C. S. Bretherton,

- 1123 G. L. Foster, Z. Hausfather, A. S. Heydt, R. Knutti, T. Mauritsen, J. R. Norris, C. Proistosescu, 1124 M. Rugenstein, G. A. Schmidt, K. B. Tokarska, and M. D. Zelinka. An Assessment of Earth’s Climate 1125 Sensitivity Using Multiple Lines of Evidence. _Reviews of Geophysics_ , 58(4), Dec. 2020. ISSN 87551126 1209, 1944-9208. doi: 10.1029/2019RG000678. URL `https://onlinelibrary.wiley.com/doi/10.` 1127 `1029/2019RG000678` .

- 1128 A. Stechemesser, N. Koch, E. Mark, E. Dilger, P. Kl ̈osel, L. Menicacci, D. Nachtigall, F. Pretis, 1129 N. Ritter, M. Schwarz, H. Vossen, and A. Wenzel. Climate policies that achieved major emission

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then be used as initial priors for experimental design where the variance in the estimated parameters can be used as an initial measure of parameter precision in a full Bayesian design (Choicemetrics, 2012; Rose and Bleimer, 2013). Later, we will present regression results that indicate the effects of national income on these parameter values. Here, we use nonparametric methods to trace out the probability density function. Figure 1 displays the round 2 estimate of the probability distribution function for WTP, for Plans A and B, based on the North American (U.S. and Canadian) experts under the (standard) assumption that WTP is log-normally distributed. The sensitivity of the design to alternative distributions that have a shorter right tail (such as the Weibull) could also be examined. The prior distribution derived from our experts’ answers can also be sequentially updated with more information such as that obtained by various types of survey development work.

# **Figure 1: North American Projected Round 2 WTP Distributions for Plans A and B**

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marginal cost of raising funds in currently informal sectors, and the effect of administrative costs on MCF estimates. We find that quite high levels of administrative costs would be justified to extend taxation to currently exempt informal sectors. Finally, in section 6 we examine the relationship between dispersion of MCFs for different tax instruments and the scope for welfare improving tax reform. We conclude in section 7.

# **2 The Marginal Cost of Public Funds**

The MCF measures the change in social welfare associated with raising an additional unit of tax revenue using a particular tax instrument: where ∆ _W_ is a monetary measure of the change in social welfare and ∆ _R_ is the change in tax revenue arising from a marginal change in a tax instrument. The change in social welfare is a measure such as the equivalent variation or change in consumer surplus, rather than the marginal excess burden. Useful reviews of the theoretical and empirical literature on MCFs can be found in Ballard & Fullerton (1992) and Devarajan et al. (2001).

One possible measure of the MCF in the presence of goods taxation is illustrated in Figure 1, in a simple two good partial equilibrium setting with fixed producer prices. Quantity units for the two goods are chosen so that producer prices for the two goods are equal to one. A marginal change in the tax on good 1 reduces consumer welfare by area _a_ , and increases compensated tax revenue by _a − b_ + _d_ , giving an MCF of _a/_ ( _a − b_ + _d_ ). If all the income released by the reduction in demand for good 1 is spent on good 2, the MCF can be approximated by where _ε_ 1 _<_ 0 is the compensated elasticity of demand for good 1.<sup>6</sup> This simple formula highlights key determinants of the MCF, but it does not deal with the full complexity of the tax system. For example, it does not capture distortions arising from taxation of intermediate goods or factors, and it does not take into account how the marginal tax revenue is spent. Nor is the methodology underlying this formula the only, or indeed the most common, way of calculating the MCF.

Different measures of the MCF for the same tax instrument can be found occur according to: 6 See Browning (1976) for the approximation methodology for areas _a_ , _b_ and _c_ . The assumption that all income released from good 1 spending is spent on good 2 (eg. because there are only 2 goods) enables area _d_ to be measured as 1+ _<u>t</u>_ <u>2</u> _t_ 2<sup>(</sup><sup>_b_+</sup><sup>_c_).</sup> 4
idiosyncratic fluctuations in state-level standard utility allowances (SUA); and (2) from the subsequent interaction of receipt profiles with rising federal maximum benefit levels (an economic stabilizer during this time).

We document that changes in SUA explain much of the variation in the intensive margin (benefits per recipient) in the month of the Farm Bill, and that the levels set up by SUA interacted with rising federal maximum benefit levels explain much of the variation during the month of ARRA. We note that SUA parameters were being updated according to formulas specific to each state set decades prior to 2006. Since states had their hands tied in the updating of these numbers, we determine that growth rates in SUA, especially after controlling for state-level energy-price and consumption trends, are prediction errors plausibly exogenous to unobserved fundamentals affecting prices during this period.<sup>4</sup> New Hampshire (NH) and Wyoming (WY) present cases in point: even though both states had nearly identical temperature trends across 2007 and 2008, NH raised SUA by about 29% while WY lowered it by 23% in the month of the Farm Bill in October 2008. As a result, even though the two states had almost identical benefits per recipient before this month, their benefit amounts diverged by 11 percentage points after this month, with NH receiving $ 10 more per recipient than WY.

Motivated by these findings, we develop an instrumental variable (IV) in the spirit of Currie and Gruber (1996) by simulating how payments would have evolved had recipient characteristics remained fixed at pre-specified levels. The identifying assumption in our setting is that SUA shocks as well as participant types in the pre-period were exogenous to unobserved economic trends governing prices. We support this assumption with (1) parallel pre-trends; (2) lack of correlation between the IV and key economic observables; and (3) evidence from Ganong and Liebman (2018) that in the pre-recession year of 2006, changes in unemployment rates, a proxy for economic fundamentals, were not contributing to changes in local SNAP participation profiles.<sup>5</sup> We then turn to national retailer scanner data available from 2006-2015 to document the impact of benefit changes on retail prices and sales. As in Leung (2021), we build on Beraja et al. (2019) to construct a wide variety of store-level price indices based on data spanning over 20,000 stores, 2.6 million barcodes, and 50% of US grocery-store sales.<sup>6</sup> We estimate that a 1% increase in benefits per population causes a moderate and persistent

> 4According to USDA (2019), “the degree of the variation in methodologies and therefore SUA amounts is of concern as similarly situated households living a few miles apart could have significantly different benefit amounts.”

> 5This is shown in Panel A of Figure 3 in their paper: the contribution of unemployment-rate growth to participation-rate growth vanishes in 2006.

> 6See Appendix Section D for details about the choice and construction of these indices. Income-group-specific price indices are discussed in Section 5.

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foreigners. The French started the textile industries in Veracruz and Puebla, and foreigners also started Mexico’s first iron and steel plant in 1903, the Fundidora de Fierro y Acero de Monterrey, which would anchor what is now the premier industrial city in the country (Haber, 1997; Buffington & William, 1999). Though Mexican entrepreneurs emerged to play important roles, the census data, confirms the large foreign participation even deep into the 20th century.

This outcome does not appear a result of access to finance. Accounts from S ̃ao Paulo, Brazil (Bresser Pereira, 1994), Minas Gerais, Brazil (Birchal, 1999), Barranquilla, Colombia, Becerra & Restrepo (1987) and as discussed below, Chile, show that immigrant entrepreneurs were predominantly middle class or below and most of their ventures were self-financed.

Further, foreigners dominate across the product space, but especially in the more technologically advanced sectors. For Argentina, Annex Table A.1 selectively draws from a 200 sector disaggregation to show that in only a few sectors is there a high or dominant participation of locals and these are the traditional ones, such as sugar cane processing mills sugar refining, and wool fabric. Immigrants dominate in fields ranging from seemingly basic sectors (espadrilles, shoes, shirt-making, dressmaking carpentry, baking furniture making, tailoring and tanneries) as well as the most technically advanced sectors sectors (lumber mills, carriages and other vehicles, trolleys, iron works and mechanics shops). In Barranquilla, Colombia, foreigners again dominate all industries related to the new steam technologiessteam boats-the critical innovation for integration of the country via the Magdalena river-, steam sawmills, as well as insurance, telephones and trading houses. Colombians are most represented, although not always dominant, in brokerage, canned foods, retail, and hostelry.

In Mexican manufacturing, table A.2 does not suggest patterns as clear as those found in Argentina or Barranquilla. However, Table A.3 allows us to examine the extraction-related industries and reveals an overwhelming participation of foreigners in what was among the most technically advanced sectors of the age, mining as well as oil extraction, and refining, sectors in which Mexicans held under 2% of investment.

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both migrant (treatment) and non-migrant (control) individuals. In addition, data on pre-migration conditions are also needed (Carletto et al., 2014). While the shift from older cross-sectional studies, which were vulnerable to a wide range of potential confounders and sources of omitted variable bias, to more robust panel methods exploiting longitudinal information on household and individual movements, indeed represents a huge empirical step forward, the use of panel settings is not in itself a panacea. In fact, the issues highlighted above of self-selection, endogeneity, transmission channels, longer-run effects and persistence, reverse causality and other key empirical concerns still need to be addressed through clever research design (De Brauw and Carletto, 2012). Researchers in the field have traditionally exploited lotteries (e.g., the popular study on Saudi Arabia to Hajj Visa, Clingingsmith et al., 2009, but also the works by Gibson, McKenzie and Stillman (2011) and Gibson et al. (2018)) and other possible sets of “exogenous variations” or “natural experiments” able to mimic the hypothetical situation of a random selection of migrants. However, what they get is an estimate of the impact of migration only for the sub-group of beneficiaries (the Local Average Treatment Effect – LATE in the jargon of policy impact assessments) without “external validity”. A valid alternative, in this case, could be the use of governmental policy experiments to learn about the effectiveness of alternative policy initiatives, but the latter goes beyond the scope of this analysis.

# _3.2 Data gaps and needs_

Given the conceptual and empirical limitations of existing studies and the research priorities set out above, we have identified the main data gaps that currently prevent carrying out the proposed research agenda and possible solutions to address these data needs that can come from improvements to longitudinal, multi-topic household surveys such as the LSMS-ISA. We here start by outlining the main data gaps and needs separately for migration and weather data.

**Migration data** . Until recently, there was a widespread lack of basic migration data, especially in developing countries which are more vulnerable to climate change (Laczko and Aghazarm, 2009). While the situation has improved, and macro-level and international migration data are now available for a wide range of countries, disaggregated and detailed data on internal migration remain unavailable or incomplete for many low-income countries (Beine and Jeusette, 2021). This is a paradox because we know from the literature discussed above that the migration effects of climate change will primarily concern poor people living in developing countries. The paradox can only be solved by scaling up

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- 36 -

71. Farmers are as eager to adopt stress resistant varieties in land scarce as in land abundant ones since the benefits are independent of land and labor costs. In land scarce areas, stress resistance reduces the<sup>risk</sup> of using high-cost inputs. In these environments, farmers w)uld like to have the best of both worlds and obtain varieties which are simultaneously high-yielding and stress resistant. Indeed, the importance of resistence ***Lo** pests and disease is increasing in large areas cbaracterized by high-yields in monoculture, as such areas are more prone to attacks by pests and disease.

72. One of the most valuable efforts that can be made to facilitate the develovment in areas characterized by traditional farming systems is therefore to concentrate on research to improve stress resistance. The development of different kinds of stress resistant varieties based on the agro-climate and farming system of each area will have to be continued. Drought tolerance may be important in semi-arid areas, but would be pointless for crops grown in humid conditions. Pests and diseases vary in different areas so that research will have to be carefully targetted according to the particular circumstances. Breeding for stress-resistance has been a major activity of ICRISAT, ICARDA and IITA for more than ten years, with good success. So also has 'hot spot" selection for local pest resistance.

73. Livestock are an important resource in SSA. Production of meat, milk and animal products amounts to about 20 percent of the GDP. Livestock are also valuable sources of organic fertilizer and draft power.<sup>Data on</sup>
# **4. COVID-19 and the Role of Social Protection Systems in Africa**

# **4.1 Access to social protection programs during the pandemic**

Despite the substantial progresses in reducing poverty rates in Africa, a large fraction of households find themselves in a state of transient poverty, vulnerable to falling back into poverty even due to small shocks. Increases in climate change related weather shocks and conflicts in the continent have amplified the precarious conditions households living at the edge of poverty face. In recognition of this fact, social protection programs have grown in popularity in recent years as important policy tools to reduce the vulnerability of the poor to a variety of shocks. Most African countries currently have at least one operational social protection program (Beegle et al., 2018). The pandemic accelerated the expansion of social protection programs with several governments expanding existing programs as well as establishing new ones to offset the impact of the pandemic on households and firms (Gentilini et al., 2020).

While the number of social protection programs in Africa has increased dramatically in the last few decades, their reach remains limited, with only small share of the population covered by the programs. The coverage of social protection programs in Africa is particularly low in countries with high extreme poverty rates. Using the Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) database at the World Bank, Figure 10 provides the relationships between coverage by some form of social protection program in each country and share of people living in extreme poverty. Although the coverage rates for some African countries is high, the adequacy and benefit sizes are among the lowest in the world. As a result, the vast majority of poor and vulnerable households are excluded from the programs. Programs are often very small and designed to deal with short-term emergency situations, leading to lack of continuity in coverage and predictability of transfers (Beegle et al., 2018).

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Nelson, L. K. 2011. “From Loans to Labor: Access to Credit, Entrepreneurship, and Child Labor.” Mimeo.

Ozer, Emily J., Lia CH Fernald,, Ann Weber, Emily P Flynn, and Tyler J VanderWeele. 2011. "Does Alleviating Poverty Affect Mothers’ Depressive Symptoms? A Quasi-experimental Investigation of Mexico’s _Oportunidades_ Programme." _International Journal of Epidemiology_ , 40(6): 1565-1576.

Parker, Susan. W., Luis Rubalcava, and Graciela Teruel. 2008. "Evaluating Conditional Schooling and Health Programs." In Schultz, T. P. and J. Strauss (Eds.) _Handbook of Development Economics_ , Vol.4, North-Holland.

Philippine National Statistics Office. 2014. _2011 Survey on Children 5 to 17 Years Old._ Philippine National Statistics Office in collaboration with International Labour Organization.

Ravallion, Martin and Quentin Wodon. 2000. “Does Child Labor Displace Schooling? Evidence on Behavioral Responses to an Enrollment Subsidy.” _The Economic Journal_ , Vol 110: C158175.

Rawlings, Laura B., and Gloria M. Rubio. 2005. “Evaluating the Impact of Conditional Cash Transfer Programs.” _World Bank Research Observer_ 20 (1): 29–55.

Rivera, Juan A., Daniela Sotres-Álvarez, Jean-Pierre Habicht, Teresa Shamah, and Salvador Villalpando. 2004. "Impact of the Mexican Program for Education, Health, and Nutrition (PROGRESA) on Rates of Growth and Anemia in Infants and Young Children: A Randomized Effectiveness Study." _JAMA_ , 291(21): 2563–70.

Rubio-Codina, Marta. 2010. “Intra-household Time Allocation in Rural Mexico: Evidence from a Randomized Experiment.” In _Child Labor and the Transition between School and Work (Research in Labor Economics, Volume 31_ _<mark>)</mark>_ <mark>, ed. Randall K.Q. Akee, Eric V. Edmonds, and Konstantinos Tatsiramos, 219–57. Bingley: Emerald Group Publishing Limited.</mark> 32
# Commerce and Industry.

Despite the fact that the most important price incentives--the exchange rate, export credit and access to imports--were extended on a non-discretionary basis, an interesting feature of the export promotion effort was its sectoral emphasis. Through the Ministries of Commerce and Industry and Agriculture and Forestry, 35 "commodity chiefs" were assigned to develop programs for specific commodities and to coordinate with representatives of the appropriate industry. Organized under the Korean Traders Association, sectoral export organizatio is provided services such as marketing, advertising, inspection and arbitration assistance, and were provided various special favors, such as the right to allocate quotas (as in the textile industry) among member firms. Priority sectors were not rigidly set; the export promotion structure was flexible and responsive to business requests for priority status. Nonetheless, there were various criteria that were established for inclusion, such as a preference for new export activities over existing ones and the avoidance of those low-cost, mass-produced items amenable to automated production in the advanced industrial states.

The sectoral focus of the program is clear in the setting of export targets, by region, industry and in some cases, by individual firms. At the beginning of the export drive, targets were command in nature with sanctions used to gain compliance. Gradually, the target setting exercise became a way to identify policy barriers to expanded exports and the firms began to set their own targets. The govermnent role included aggregating and processing information supplied through the export associations. But the targets were more than just projections. A survey of firms by Rhee, Ross-Larson and Ptursell (1984) reveals that the firms felt that the target-setting process had resulted in a more rapid expansion of production than they would have otherwise undertaken. Half of the firms surveyed for 1974 and 1975 claimed this, and three-fifths of the firms in 1976. The surveys also reveal 28