rafmacalaba/datause-encoder-data huggingface dataset:
Throughout this section we will consider examples from two case studies, one in Norway, the other in the United States.

In order to navigate these very different disciplinary perspectives on what is going on it is important to lay out some of the assumptions, terrain and language of each field. It is also important to note how these different theoretical and disciplinary perspectives may fit together under some circumstances, as well as how they fail to make sense together in other cases (as when basic assumptions about human nature are opposing).

Following this preliminary introduction, we will work through a series of explanations by theme, simultaneously considering work from psychology, social-psychology, sociology and political opinion polls as they apply to each of four broader answers to the question of why there is so little public response to climate change. These are: 1) people don’t know enough to realize the danger, 2) people don’t care enough to take action, 3) there is hierarchy of needs and climate change is not an immediate need, 4) people have trust that the government will fix the problem. From this survey of existing explanations, we extract a series of cognitive and behavioral limitations faced by the public in responding to climate change.

We then move to my own answer to the question of why well educated and relatively privileged people have failed to act, which blends psychology and sociology to lay out a conception of socially organized denial. Under this framework, information from climate science is known in the abstract, but disconnected from, and invisible within political, social or private life. Instead, people actually work to avoid acknowledging disturbing information in order to 1) avoid emotions of fear, guilt and helplessness, 2) follow cultural norms, and 3) maintain positive conceptions of individual and national identity. National identity may be in turn linked to subconscious economic self interest since “Citizens of wealthy nations who fail to respond to the issue of climate change benefit from their denial in economic terms. They also benefit by avoiding the emotional and psychological entanglement and identity conflicts that may arise from knowing that one is doing ‘the wrong thing’” (Norgaard 2006b, 366). People described a sense of knowing and not knowing about climate change, of having information but not thinking about it in their everyday lives. In discussing this model, I incorporate material from psychology, social psychology as well as sociology of emotions, sociology of culture and political economy.

15
<!-- Start of picture text --> as e a7<br>as . a<br>oa a °<br>-== 6 -aa ; :<br>qSN=SN== a~~~~ N<br>a s ea<br>a 1)<br>5 3 5 “ee<br>[ay [ay<br>E e = oe 8. ee<br>£o & e<br>a<br>D>oO<br>>oO oe >Do<br>5 = ° e $ oe @<br>2 ‘ ® il 2 ee<br>=aoTM°3—aoTM°3—oTM°3—°3—3—— 7 oe e<br>Da== Dao==<br>Be |e FE<br>6 § Good rainfall Average rainfall Poor rainfall © % -+_ Good Good rainfall Average rainfall Poor rainfall<br>as—~2—~2 e asonon e<br>= e 7<br>-<br>5 as 8asas . :<br>LS —H<br>7)a3a33 er).. eA7)57)55<br>SI ws SI e<br>Lo —ee Bo od<br>===<br>&££ .<br>aasas aoeoe e<br>a2>!>! ad oisPaPa<br>=«=«==<br>ooo<br>3 3 °<br>£8 £S °<br>o<br>D<br>S e 2SSS<br>se a : F £8 7 : F<br>O § -Good rainfall Average rainfall Poor rainfall © © Good rainfall Average rainfall Poor rainfall<br>as ° ag<br>oaacacac aoaoaoa<br>= ° =P<br>oSa—a——a——— e oameeameemeeameemeemee .FFF e<br>88 B® :<br>5 5 ew’<br>2[ay[ay[ay oa[ay[ay[ay -<br>=e=e==e===<br>&&&&<br>£o ; ‘£°e£°e£°e<br>a ; a e e<br>oO e © ;<br>D * teste) e<br>> e Naas) wr”<br>==<br>g$8$8$8 r g888888 .<br>—e—e——e———:<br>ooeoeoe<br>D ay<br>Cc s Cc e<br>Eo Eo<br>GO © +Good rainfall Average rainfall Poor rainfall 6 © +Good rainfall Average rainfall Poor rainfall<br><!-- End of picture text -->

<!-- Start of picture text --> as e a7<br>as . a<br>oa a °<br>-== 6 -aa ; :<br>qSN=SN== a~~~~ N<br>a s ea<br>a 1)<br>5 3 5 “ee<br>[ay [ay<br>E e = oe 8. ee<br>£o & e<br>a<br>D>oO oe Do>Do<br>5 = ° e $ oe @<br>2 ‘ ® il 2 ee<br>7 e<br>=aoTM°3—aoTM°3—oTM°3—°3—3—— oe<br>Da== o=Dao==<br>Be |e FE<br>6 § Good rainfall Average rainfall Poor rainfall © % -+_ Good Good rainfall Average rainfall Poor rainfall<br>as—~2—~2 e asonon e<br>= e 7<br>-<br>5 as 8asas . :<br>LS —H<br>7)a3a33 er).. eA7)57)55<br>SI ws SI e<br>Lo —ee Bo od<br>===<br>&££ .<br>aasas aoeoe e<br>a2>!>! ad oisPaPa<br>=«=«==<br>ooo<br>3 3 °<br>£8 £S °<br>o<br>D<br>S e 2SSS<br>se a : F £8 7 : F<br>O § -Good rainfall Average rainfall Poor rainfall © © Good rainfall Average rainfall Poor rainfall<br>as ° ag<br>oaacacac aoaoaoa<br>= ° =P<br>.FFF e<br>oSa—a——a——— e oameeameemeeameemeemee<br>88 B® :<br>5 5 ew’<br>2[ay[ay[ay oa[ay[ay[ay -<br>=e=e==e===<br>&&&&<br>£o ; ‘£°e£°e£°e<br>a ; a e e<br>oO e © ;<br>D * teste) e<br>> e Naas) wr”<br>==<br>g$8$8$8 r g888888 .<br>—e—e——e———:<br>ooeoeoe<br>D ay<br>Cc s Cc e<br>Eo Eo<br>GO © +Good rainfall Average rainfall Poor rainfall 6 © +Good rainfall Average rainfall Poor rainfall<br><!-- End of picture text -->

<!-- Start of picture text --> as ° ag<br>oaacacac aoaoaoa<br>= ° =P<br>.FFF e<br>oSa—a——a——— e oameeameemeeameemeemee<br>88 B® :<br>5 5 ew’<br>2[ay[ay[ay oa[ay[ay[ay -<br>=e=e==e===<br>&&&&<br>£o ; ‘£°e£°e£°e<br>a ; a e e<br>oO e © ;<br>D * teste) e<br>> e Naas) wr”<br>==<br>g$8$8$8 r g888888 .<br>—e—e——e———:<br>ooeoeoe<br>D ay<br>Cc s Cc e<br>Eo Eo<br>GO © +Good rainfall Average rainfall Poor rainfall 6 © +Good rainfall Average rainfall Poor rainfall<br><!-- End of picture text -->
# **References**

- ABEBE, G., A. S. CARIA, M. FAFCHAMPS, P. FALCO, S. FRANKLIN, AND S. QUINN (2021): “Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City,” _The Review of Economic Studies_ , 88, 1279–1310.

- ABEBE, G., S. CARIA, M. FAFCHAMPS, P. FALCO, S. FRANKLIN, S. QUINN, AND F. SHILPI (2020): “Matching Frictions and Distorted Beliefs: Evidence from a Job Fair Experiment,” _Department of Economics, Oxford University (mimeo)_ .

- ACEVEDO, P., G. CRUCES, P. GERTLER, AND S. MARTINEZ (2020): “How Job Training Made Women Better Off and Men Worse Off,” _Labour Economics_ , 65, 101824.

- ADHVARYU, A., N. KALA, AND A. NYSHADHAM (2023): “Returns to On-the-job Soft Skills Training,” _Journal of Political Economy_ , 131, 2165–2208.

- AGENCE NATIONALE DE LA STATISTIQUE ET DE LA DÉMOGRAPHIE SÉNÉGAL, AND ICF (2020): “Senegal Demographic and Health Survey 2019 [Dataset].”

- AGHION, P., A. BERGEAUD, R. BLUNDELL, AND R. GRIFFITH (2019): “The Innovation Premium to Soft Skills in Low-skilled Occuptions,” CEPR Discussion Paper 14102, C.E.P.R. Discussion Papers.

- ALAN, S., T. BONEVA, AND S. ERTAC (2019): “Ever Failed, Try Again, Succeed Better: Results from a Randomized Educational Intervention on Grit,” _The Quarterly Journal of Economics_ , 134, 1121–1162.

- ALDEROTTI, G., C. RAPALLINI, AND S. TRAVERSO (2023): “The Big Five Personality Traits and Earnings: A Meta-Analysis,” _Journal of Economic Psychology_ , 94, 102570.

- ALFONSI, L., O. BANDIERA, V. BASSI, R. BURGESS, I. RASUL, M. SULAIMAN, AND A. VITALI (2020): “Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda,” _Econometrica_ , 88, 2369–2414.

- ALLEMAND, M., AND C. FLÜCKIGER (2017): “Changing Personality Traits: Some Considerations from Psychotherapy Process-outcome Research for Intervention Efforts on Intentional Personality Change,” _Journal of Psychotherapy Integration_ , 27, 476.

- ATTANASIO, O., A. KUGLER, AND C. MEGHIR (2011): “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial,” _American Economic Journal: Applied Economics_ , 3, 188–220, 10.1257/app.3.3.188.

- BANDIERA, O., A. ELSAYED, A. SMURRA, AND C. ZIPFEL (2022): “Young Adults and Labor Markets in Africa,” _Journal of Economic Perspectives_ , 36, 81–100.

17
addition, there is evidence that exports of primary products have an effect on export diversification (Lederman and Maloney (2012)). This implies that the results in columns (1)-(4) could be driven by the omission of measures of export concentration in the primary sector.

The results in columns (5)-(7) show that the effect of diversification on ToT volatility remains significant even after controlling for the cross-sectional measure of diversification and other trade characteristics. This indicates that the effect of latent diversification on ToT volatility goes beyond the omission of a country’s exports of primary goods. Interestingly, latent diversification remains the only variable with a significant coefficient, despite a decline in the magnitude of its coefficients.

In contrast, Panel B of Table 3 suggests that the link between latent diversification and the volatility of GDP per capita growth is less robust than its link with ToT volatility. Indeed, the coefficient of (the log of) latent diversification is negative and remains significant even after controlling for ToT volatility, the (log of the) initial size of the labor force, and the log of initial GDP per capita (columns (2)-(4)). However, the effect of latent diversification loses its statistical significance once we include other trade and economic characteristics (columns (5)-(8)).

Two additional observations from Table 3, Panel B, are worth mentioning. First, there is a tight and robust link between GDP per capita growth volatility and ToT growth volatility (as indicated by a statistically significant coefficient on ToT volatility in all of the estimated specifications). Second, the coefficient on the size of the labor force is negative and significant in some specifications estimating the determinants of GDP per capita growth volatility. This result is consistent with the findings in Lederman and Maloney (2012).

In sum, Table 3 suggests that latent diversification is a robust determinant of ToT growth volatility and, through it, of GDP per capita growth volatility. In section 6.2 we investigate the plausibility of this channel in more detail through a three-stage least squares estimation (3SLS) that uses the results of Table 3. But first, we address the potential effect of aggregation on the link between latent diversification and volatility.

# **6.1. Latent Diversification, Aggregation and Volatility**

As discussed in Section 4.1, the level of aggregation of trade data affects our measures of cross-sectional and latent diversification, which in turn affects the econometric exercise presented above. In particular, higher levels of aggregation of trade data impact the variables entering the regression in Table 3 in two 25
' Table 1 (Continued) Comparative Analysis of Selected Developed Countries leports of Labor Intensive and Other Non-Fuel Products: 1965 to 1M6

|||Norway<br>|<br>||Sweden<br>|<br>||Switzerland<br>||U|nited State|s|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Manufact|ures||Manufact|ures||Manuf|actures||Manuf|actures|
||Ttl|Citl|Lb|Tl|||||||||
|Year|oa<br>Nonfuel|apa<br>Intensive|aor<br>Intensive|ota<br>Nonfuel|Capital<br>Intensive|Labor<br>Intensive|Total<br>Nonfuel|Capitel<br>intensive|Labor<br>Intensive|Total<br>Nnfuel|Capital<br>Intensive|Labor<br>Intensive|
|||||||**(import**<br>val<br>|<br>ues expre<br>|ssed In tar<br>|es of US5<br>|million)<br>|||
|1965|1,404|945|639|2,293|1,365|1,605|2,102|1,170|1,338|12,942|4,426|6,203|
|1970|2,248|1,508|1,169|<br>3,477|2,151|2,782|3,617|2,292|2,504|23,014|11,351|**13,80**|
|1975|5,704|4,260|3,045|8,224|5,742|6,733|6,764|4,203|5,140|43,371|23,690|27,129|
|1980|7,825|<br>5,455|6,169|12,954|8,737|!7,399|16,506|10,591|15,588|0e,545|59,251|69,531|
|1984|6,854|5,149|5,609|10,934|7,593|10,274|13,050|9,101|13,535|139,134|94,882|135,802|
|1985|7,067|5,400|<br>6,187|11,726|8,235|11,418|13,598|9,598|13,984|152,754|107,726|150,269|
|1986|9,822|7,654|9,284|14,615|10,514|14,382|18,800|13,846|19,964|168,544|120,062|173,051|
|1965|91||||(share cf<br>|developing<br>|countries<br>|In total<br>|imports of<br>|the produ<br>|ct group)<br>||
||.|3.0|2.7|13.1|0.6|4.1|10.1|0.6|4.7|33.0|3.1|17.9|
|1970|9.1|5.8|2.9|10.9|1.2|4.6|**8.4**|0.7|4.7|24.6|3.2|20.9|
|1975|7.3|4.1|3.6|7.2|1.0|6.3|8.0|1.3|7.0|23.7|4.8|30.9|
|1980|9.1|5.2|4.6|7.9|1.4|8.2|7.3|0.9|7.9|22.8|6.9|39.6|
|1984|10.0|7.1|5.2|8.4|_2.9_|**8.8**|5.7|1.0|10.2|20.4|10.1|42.1|
|1985|9.4|6.6|5.1|7.1|1.7|8.3|6.0|1.3|8.8|19.0|9.8|40.2|
|1986|8.1|4.9|5.4|7.1|1.5|8.6|5.3|1.2|7.8|19.3|10.2|40.5|

Source: All statistics were compiled from United Nations Series D Commodity Trade Tapes. Appendix Table I provides a tabulation of prooucts in tne labor intensive group in terms of the SITC classification system. The "other lmports" group shown in this table Includes all other SITC products with the exception of items falling In SITC 3.
-26-

In an external system the design, development and operation are provided by an organization that is independent of the tax collecting departments and that may be responsible for providing all publi. entities with the information needed for their operations and decisions. Such are the systems used in Brazil, El Salvador, and most of the nations of francophone Africa. In an approach using Internal systems the Finance Ministry may require operating departments each to be responsible for the supervision and control of their own computer systems. The U.S. Internal Revenue Service, for example, organizes its operations in this manner, as do most of the tax departments of the member countries of the Inter-American Center of Tax Administrators.

While the Initial choice may depend upon several factors, including the scale of operations to be performed, capital expenditures, and efficient utilization of computer hardware, one major consideration is the availability of adequate numbers of skilled computer technicians. Because computer costs are falling and the number of available technicians is increasing, and because services provided by the central computer organization might prove unresponsive in terms of either seasonal fluctuations of work or unplanned events (tax legislation changes, for example), a mixed approach may be preferable and is often applied. Under such a scheme the central computer of the Ministry of Finance usually provides services for policy activities and for the ministry's own statistical and national reporting needs and may, when
<!-- Start of picture text --> N<br>-<br>Bermuda (UK)<br>-—<br>‘ M “yen The<br>0 - y a ?<br>sg To L ot -<br>GSer:us ht= Se" Cubg:. ras ab .- aq, Turks and Caicos Islands<br>| a . er<br>= eea ices PeHaiti YT,Dominican Republic Britishk landsVirgin<br>. 3 ey hee Anguil<br>Cayman Islands (UK) ae feastedpie) Puerto Rina (US) > nguillaSt. Martin/Sint Maarten<br>Jamaicaa ee « P. U.S. Virgin Isiands (US)= 58h "+, StBarthelemy<br>. St. E | Antigua and<br>. Eustatius eg<br>St. a erbuda<br>Kitts and Nevis ye<br>Guadeloupe(Fr) = » Montiernst<br>3) Dominica<br>Martinique (Fr)<br>Aruba | theSt. VincentGrenadinesand ¥@ St. Lucis&<br>% Bonaire {Meth)* f Barbados<br>ee] Grenada yy<br>Curacao (Neth)<br>re<br>Legend Trinidad and Tobago<br>Nightlight value<br>0<br>1-3<br>46<br>7-11<br>2-21 0 240 480 960 Kilometers<br>MM 22-63 {11111111<br>C_] Caribbean islands eeeatum: GCS WGS 1984<br>Units: Degree<br><!-- End of picture text -->
in the burden of bribery (or at least under-estimate the degree of regressiveness), due to ability and preference heterogeneity.

The rest of the paper is organized as follows. Section (2) discusses the related literature and thus helps put the contributions of this paper in perspective. The next section provides a conceptual framework to guide and interpret the empirical work. The empirical strategy to address the potential biases from household heterogeneity and measurement error is discussed in section (4). The next section (Section (5)) provides a discussion of the data sources and variables. The OLS results are reported in Section (6) and the IV estimates in Section (7). Section (8) reports a robustness check for the IV results and Section (9) discusses the interpretation of the IV estimates. The paper concludes with a summary of the results and their implications for the broader debate about the role of public schooling and anti-corruption measures to address inequality in educational opportunities.

# (2) Related Literature

The economics literature on corruption is substantial and has been the focus of innovative research in the last decade. For recent surveys of the literature, see, for example, Olken and Pande (2011), Banerjee et al. (2012), Rose-Ackerman (2010), and Bardhan (1997).<sup>16</sup> The literature has, for good reasons, focused on the measurement of corruption, its e¤ects on e¢ ciency, and on policies to combat corruption in di¤erent contexts.<sup>17</sup> The literature on the e¤ects of corruption on households is, however, rather limited; for example, the recent survey by Olken and Pande (2011) discusses only one paper (Hunt, 2007) that provides evidence on the e¤ects of corruption on households when they face negative shocks. The available evidence on the heterogeneity in the burden of corruption is, however, mixed, which may, at least partly, re‡ect the di¢ culties in identi...cation arising from unobserved heterogeneity and measurement error. Kau¤man et al. (1998), and Kau¤man et al. (2005) reported bribes to

> 16 The early contributions to corruption literature include Rose-Ackerman (1978), Klitgaard (1988), Shleifer and Vishny (1993).

> 17 For recent contributions on measurement, see, for example, Fisman (2001), Reinikka and Svensson (2004), Olken (2009), Olken and Barron (2009) and Banerjee and Pande (2009), Hsieh and Moretti (2006), Besley et al. (2011), Niehaus and Sukhtankar (2010); for contributions on costs of corruption, see, among others, Svensson (2003), Fisman and Svensson (2007), Bertrand et al. (2007), Ferraz, Finan, and Moreira (2012), Sequeira and Djankov (2010), Olken (2006, 2007, 2009), and on policies to combat corruption, see, for example, Di Tella and Schargrodsky (2004), Niehaus and Sukhtankar (2010), Olken (2007), Bjorkman and Svensson (2010), Banerjee et al. (2012), Kahn et al. (2001).

7
**Table** 3 Directions of Variations of Endogenous<sup>Variables with Respect to Fluctuations of Exogenous Variables</sup> (In the Neighborhood of the Initial<sup>Equilibrium Outcome)</sup>

|Endogenous Va<br>and Para|riables<br>meters|<br>**Im**<br>_<br>|**porter's P**<br>bi|**arameters**<br>_<br>_<br> <sup>Ri'</sup>|<br> <br><sup>**Pri of**</sup>|**Exchan**<br>in**Tra**<br>Curre<br>DM|**ge Rates**<br>**nsiter's**<br>ncy<br>Rbl|Gas T<br>C<br>Unit|ransit<br>ost<br>Fixed|**Gas**S<br>C<br>Unit|upply<br>ost<br><sup>Fixed</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables<br>Variables<br>l||Expendi-<br>ture on<br>**~~~~~~Forei**<br>**Gas**<br>~<br>~|Susttu-<br>tion<br>**gn**<br>**Elasticity**<br>~<br>~<br>o|<sup>ussa</sup><br>Market<br>**Share**<br>~~Y|<sup>**ce**</sup><br>"Other"<br>**Gas**<br>p|<br>(eD)<br>____|<br>(eR)|<br>**(S)**|<br>**(FT)**|<br>(C)|**(FR)**|
|Supplier's Price|PR|0||+|+|+|-<br>_||0|+|0|
|TransitFee|T|0||**+**|**+**|**+**|=|+|0|=|**0**|
|Demand for Russian Gas|G|I|+|+|+|**+**|||**0**|-|**0**|
|Importer's Price|PG|0||+|+||+|+|0|+|0|
|Demand for "Other" Gas|0|I|-<br>_||||+|+|0|+|**0**|
|Importer's Welfare Index|U|I|+_|||+ _0|||||0|
|Unit Rent|R|0||**+**|±|+<br>_|||0||0|
|Russia's Profit|nf|-<br>+|+|+|+|+|||0|||
|Transiter's Profit|**flT+**||+|+|+|+||-|.||O|
|Revenue<br>|xp|0||+|+|+||+|0||0|
|Transiter's--<br>:TSahnare<br>eirnS<br>Unit Rent|z|0<br>|||||+|+|-<br>0|-<br>+|0|
|Total Profilt||u|<br>-||||+|**+**|.|+|**+**|
# 8 QUANTIFICATION AND COUNTERFACTUALS

In this section, we use both data from our production network and our newly estimated elasticities to quantify the role of these elasticities in the propagation of shocks. To do this, we need to write down the Leontief matrix in standard form. Given the production structure of our economy, we need four submatrices: (i) firm purchases of 4-digit HS products, (ii) firm sales of 4-digit HS products, (iii) labor employed by each firm, and (iv) final sales by each firm. The first two submatrices are directly constructed from the firm-to-firm trade data from the pre-Covid-19 period of March 2019 to February 2020. Labor employed and final sales by firms are obtained by merging in firm-level data from _IndiaMART_<sup>29</sup> , which contains information on firm-level employment and final sales. For more details on this, see Appendix C.

The economy is comprised of _N_ = 93 _,_ 260 firms, _I_ = 1 _,_ 293 different HS-4 products, labor, and a composite final good. The average firm buys 10 distinct products as a buyer and sells 5 distinct products as a seller. The most connected buyer and seller buys and sells over 500 distinct products. We use this 94 _,_ 555 _×_ 94 _,_ 555 input-output matrix to understand how complementarities at the firm level affect the propagation of shocks through production networks.

While recent work also quantifies the effect of firm-level shocks on aggregate GDP up to the second order, they mostly rely on changes in firm-level final sales rather than the full production network. Instead, we identify the most connected firms, deriving the Leontief inverse from the entire production network. Without relying on any approximation, we use the full network to quantify the importance of firm connectivity separately from firm size. This exponentially increases computational complexity from the order of ( _N_ + _I_ + 2) to ( _N_ + _I_ + 2)<sup>2</sup> . As such, we use computational innovations in big data to implement this procedure. For more details on the derivation of the shock propagation equation and its numerical implementation, see Appendix E.

Note that our quantification exercises in this section are conditional on the products that firms buy or sell being given at the extensive margin, even though a firm can change its set of buyers or suppliers (Baqaee et al., 2023; Khanna et al., 2022). We, therefore, need to empirically assess whether the set of HS-4 products a buyer buys and the set of HS-4 products that a seller sells changes between the pre and the post-Covid-19 period. We do this by inspecting whether both sellers and buyers of each product continued to trade in their corresponding product categories after the Covid-19 lockdowns. In Figure A6, we show the product-level distribution of the share of sellers that sold and buyers that purchased goods of that product during both periods _t_ and _t_ − 1, where _t_ is a 6-month window before and after the lockdowns. In the figure, we see that, for both sellers and buyers, these two distributions are very similar to each other. The overall stability in

> 29 See IndiaMART (link).

33