rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6:** Activity variables

|_Yi_|Production in sector _i_|
|---|---|
|_Ei_|Aggregate energy input in sector _i_|
|_Xi_|Output transformation for good _i_|
|_Ai_|Armington aggregate for good _i_|
|_INC_|Household (disposable) income|

**Table 7:** Price variables

|_pi_<br>|Output price of good _i_|
|---|---|
|_p_<sup>_D_</sup><br>_i_<br>|Domestic supply price of good _i_|
|_p_<sup>_F X_</sup><br>|Price of foreign exchange|
|_p_<sup>_E_</sup><br>_i_<br>|Price of aggregate energy in sector _i_|
|_p_<sup>_A_</sup><br>_i_|Price of Armington good _i_|
|_w_|Wage rate|
|_r_|Price of capital services|
|_qi_<br>|Rent to natural resources (_i ∈FF_)|
|_p_<sup>_CO_2</sup>|CO2 emissions price|

**Table 8:** Cost shares

|_θji_<br>|Cost share of intermediate good _j_ in sector _i_|
|---|---|
|_θ_<sup>_KLE_</sup><br>_i_|Cost share of value-added and energy in sector _i_|
|_θ_<sup>_E_</sup><br>_i_<br>|Cost share of energy composite in the _KLE_ aggregate in sector _i_ (_i /∈FF_)|
|_θ_<sup>_L_</sup><br>_i_<br>|Cost share of labor in value-added composite of sector _i_|
|_θ_<sup>_Q_</sup><br>_i_|Cost share of natural resources in sector _i_ (_i ∈FF_)|
|_θ_<sup>_F F_</sup><br>_T i_<br>|Cost share of good _i_ (_T_ =_i_) or labor (_T_ =_L_) or capital (_T_ =_K_) in sector _i_ (_i ∈FF_)|
|_θ_<sup>_E_</sup><br>_ji_|Cost share of energy good _j_ in the energy composite in sector _i_ (_i /∈FF_)|
|_θ_<sup>_A_</sup><br>_i_|Cost share of domestic variety in Armington good _i_|
|_θ_<sup>_X_</sup><br>_i_|Revenue share of exports for domestic production value of good _i_|

**Table 9:** Elasticities

|_σ_<sup>_KLEM_</sup><br>_i_<br>|Substitution between KLE composite and material inputs in production|
|---|---|
|_σ_<sup>_KLE_</sup><br>_i_|Substitution between energy and value-added in production|
|_σ_<sup>_KL_</sup><br>_i_<br>|Substitution between labor and capital in value-added composite|
|_σ_<sup>_Q_</sup><br>_i_<br>|Substitution between natural resources and other inputs in fossil fuel production|
|_σ_<sup>_E_</sup><br>_i_<br>|Substitution between energy goods in the energy aggregate|
|_σ_<sup>_A_</sup><br>_i_|Substitution between the import good and the domestic good of the same variety|
|_η_<sup>_X_</sup><br>_i_|Transformation between export supply and domestic supply|

47
their costs (Ulyssea 2018; Boly 2018). These “surviving” informal firms are likely to operate in very different markets and sell different products than formal firms (La Porta and Shleifer 2014). In such circumstances, competition between informal and formal firms and its impact on formal firms may be limited or even non-existent. On the other hand, given the formal-informal nexus, other studies report that competition from the informal sector may hinder the profitability of firms that operate in the formal sector, limiting the resources available to firms to enhance their productivity (Gonzalez and Lamanna 2007; Heredia et al. 2017; Mendi and Costamagna 2017). This is likely to occur when informal firms are sufficiently productive to survive in the formal sector yet choose to remain informal to benefit from the cost advantage of noncompliance with — possibly excessive— taxes and regulations (Maloney 2004; de Mel, McKenzie, and Woodruff 2011).<sup>2</sup> Even though such informal firms could constitute an untapped potential for a productivity boost (De Soto 1989), they can create aggressive competition with formal firms that bear the additional cost of tax and regulatory compliance. An intense informal competition can reduce the profitability necessary for formal firms to invest in new productivity-enhancing technologies or to innovate, especially in a context of weak property rights enforcement.<sup>3</sup> It may also force the lowest-productivity formal firms to exit.<sup>4</sup> This paper combines the World Bank’s Enterprise Surveys data, which cover formal firms, with surveys on informal businesses conducted by the World Bank in selected countries to document the labor productivity gap and the interactions between formal and informal firms in

> 2 Such circumstances are likely to be associated with an environment of weak regulatory and tax enforcement (Quintin 2008; Dabla-Norris et al. 2008; Ulyssea 2010; Benjamin and Mbaye 2012).

> 3 This has been documented for some Latin America countries, India, Poland, Portugal, the Russian Federation, and Turkey. For evidence, see Heredia et al. (2017), Perry et al. (2007), Farrell (2004), Capp et al. (2005), Cunha (2006), Gonzlez and Lamanna (2007), Friesen and Wacker (2013), Allen and Schipper (2016), Iriyama, Kishore, and Talukda (2016), and Distinguin, Rugemintwari, and Tacneng (2016).

> 4 This has been documented for the Arab Republic of Egypt, see Ali and Najman (2017), Melitz (2003), and Schipper (2016).

4
|||The World Bank Revised Minimum Standard Model:<br>Concepts and Issues<br>by<br>Doug Addison<br>**TABLE OF CONTENTS**||
|---|---|---|---|
|**I.**|**INTR**|**ODUCTION............**<br>**........................**|**............**<br>**1**|
|II.|**'TPH**|**E**<br>**NATIONAL ACCOUNTS...........................**|**.........**<br>2|
||1.|Accounting Identities||
||2.|Exogenous Variables||
||3.|Investment||
||4.|Import PFnctions||
||5.|Income Effects and the Terms of Trade||
||6.|The Constrained Savings Case||
||7.|Dynamics||
||8.|The Maximum Marginal Savings Rate||
|III.|**NTH**|**BALACE OF PAYMENTS...........................**|**.....**17|
||1.|Accounting Identities||
||2.<br>3.|Exogenous Variables<br>The Resource Balance||
||4.|External Borrowing||
||5.|The Change in Reserves||
|IV.|**THE**|**CHOICE OF PAUMETEmS.........................**|**.........**<br>23|
||1.|The Efficiency of Investment||
||2.|Import Elasticities||
||3.|The Marginal Savings Rate||
|V.|**SUM**|**MARY AND CONCLUSIONS...........................**|**.....**25|
||**APPE**|**NDIX A:**<br>**DYNAMICS.............................**|**....**<br>26|
||**APPE**|**NDIX B:**<br>**RMSM IDENTITIES**<br>**.....................**|**........**<br>28|
||**BIBL**|**IOGRAPHY....................................**|29|

*Research Analyst, West Africa Country Operations. Special thanks to John Holsen, Bela Belassa and all those who contributed their comments on earlier drafts.
8

**Table 2. Accounting Framework**

|Comp|arisonoftheaccoun<br>and|ting frameworkatth<br> during thecrisis|eonsetofthe|crisis|
|---|---|---|---|---|
|Countrv|Loan<br>classification|Loan<br>provisionin,|Interest<br>accrual|Average|
|Argentina|||||
|Onset|I|1|1|1.0|
|During|I|I|1|1.0|
|Chile|||||
|Onset|3|2to3|2|2.5|
|During|4|3|4|3.7|
|Hungary|||||
|Onset|3|2to3|2|2.5|
|During|4|3|3|3.3|
|Japan|||||
|Onset|2|2|2|2.0|
|During|2|2|2|2.0|
|Norway|||||
|Onset|3|4|4|3.7|
|During|4|4|4|4.0|
|Poland|||||
|Onset|3|2to3|4|3.2|
|During|3|3|4|3.3|
|Thailand|||||
|Onset|1|1|1|1.0|
|During|3|3|2|2.7|

_Note:_ Countries are scored on a scale from I to 4 for each<sup>indicatorwith 4 indicating best practiceandI indicating furthest awayfromn best</sup> practice.

Loan classification Specific loan loss provisioning: Interest Accrual I = loan past due more than 360 days I = 0%/o substandard; 50% doubtful; **1000%6** loss. I = up to 6 months, no clawback. 2 = loan past due more than 180 days 2 = 10- 15% substandard; 50% doubtful; 1000/o loss. 2 = up to 3 months, no clawback. 3 = loan past due more than 90 days 3 = 20% substandard; 75% doubtful; 100%/o loss. 3 = up to 6 months, with clawback. 4 = repayment capacity of boTTower/forward 4 = present value of future cash flow or fair value of 4 = up to 3 months, with clawback looking collateral.

**_Sources:_** Argentina: Balino 1991; Chile: Barandiaran and Luders 2000,<sup>Larrain, M.1989, Sanhueza 2000b;Hungary:Borish, Ding and Noel</sup> 1996, World Bank Various years(b); Japan: Kanaya and Woo<sup>2000, Intemational Monetary Fund 2000;Norway: Karlsen2000;Poland:</sup> Borish, Ding and Noel 1996, World Bank Various years(a); Thailand:<sup>Claessens, Djankov and Klingebiel1999b</sup>

**_b. Accounting framework for banks._** Financial institutions incentives to engage in corporate restructuring is also influenced by the accounting<sup>frameworkunderwhichthey</sup> operate. Here loan classification and loan loss provisioning rules<sup>forbanksdetermine towhat</sup> extent banks can carry non-performing loans on their books without<sup>classifying them assuch and</sup> can accrue uncollected interest. As a result, capital and income<sup>will beoverstatedandbanks</sup> have limited incentives to engage in corporate restructuring<sup>sincetheycansimplyhide losses.</sup> The sample countries were scored according to how<sup>accounting rulescomparedwith</sup> intemational best practice defined as follows: (i) loan classification<sup>accordingtotherepayment</sup> capacity of borrower; (ii) loan provisioning based on the<sup>present value of futurecash flow or fair</sup> value of collateral; and (iii) interest accrual rule requiring<sup>therecognitionof overbookingof</sup> interest income when a delinquent loan is declared nonperforming<sup>(upto90 days)(Claessens,</sup> Djankov and Klingebiel 1999b). Table 2 indicates that those rules were poor and trailed
# **Annex III. Contingent Debt Facility and Reinsurance**

# **TCIP 2004**

<!-- Start of picture text --> Excess Capacity GOT<br>$200 M R/I<br>Reinsurance<br>$22 M WB<br>$178 M R/I<br>Reinsurance World Bank<br>Reinsurance $120 M R/I $80 M WB<br>World Bank<br>Reinsurance $42 M R/I $28 M WB World Bank<br>$60 M – retained<br>Retention<br>World Bank<br>$20 million WB<br><!-- End of picture text --> 35
<!-- Start of picture text -->
[ Public Schools Charter Schools Private Schools<br>|__Year _| Total | Opened | Closed | Moved | Total | Opened | Closed | Moved | Total | Opened | Closed | Moved |<br>End2003 2002 [142[Teto<br>2004 [142 fo fo fT o fF 30 | 3s fT oO fT oO fof oo<br>2005 | 143 | 2 | ct fT of 39 Fo os Po<br>2006 | 142 | oo | 1 ft oo fl ae | 8 tT ct fT oe fo] 2<br>2007 [7 fo Ts Ta sa ooPa<br>Total | 6 | oT tt 4 9 Pw sos P28<br>03-07] [| 2 [| 8 [ 8 | f[ 36 [| 4 [ 2 | | 3 7 8 7 5 |<br><!-- End of picture text -->

Notes: Each cell indicates number of campuses. “Total” corresponds to the Fall of the corresponding year. A school’s opening year is its first year of operation; a school’s closing year is the year following the last. A school is counted as moving in year X if its address in X is different from its address in (X-1).

|||ALD tlie||Da|lille tchi|ak|lia|iterib|a|Cul|verts Scl|ieou|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||vr<br>in<br>|tit<br>mite.<br>|hah<br>edu.<br>||Wh<br>pelle.<br>|fh<br>peli.<br>||||deni<br>i<br>|th<br>elie,<br>|Tih<br>elie.<br>|
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|<br> Teas<br>Meliss Dhoom<br>sGise<br>Teavanie toapetekh<br>oo<br>cecilb|<br>sd Pa<br>soiiendie<br>oom<br>ie<br><br>apoiealzs|<br>Dee<br>s cep<br>Sroselene's <br>elediWot|<br>Fe <br>ts<br>ecei<br> tlorsos ame<br>eledel|<br> ice<br>re ral o<br>. Pea Dee<br>eeu|<br>Se<br>uab<br>Miah <br><br>Do ccaned<br><br>aM|<br><br>ee<br>Pe<br>oe B<br>eee peed d<br>dlc|c<br>eukraer<br>oes<br>|<br>e <br> elt tra<br>pi<br>oe|<br> e<br>dbeasim k<br>r<br>hewtleslo|<br>e <br>es rage <br>n gercoad a|0<br> eth<br>D<br> 404. A|ra dinar<br>eiedney|
29 Output yS nO + **olip +** n 2ig + 0 3k-1 + f4 dum4 + 05dum5 + i 6dum6 Identities : Disposable income Yd = Y - A where A = t + (im - Ir)(V.1<sup>/P) +(ib</sup> - n)(B1/P) - (i 1 - 7r)(Lp-./P) - fdsp Domestic Credit to the Private Sector OLp = tM1<sup>5</sup> + cVs - rB - eOR$ -6L - Z where Z = (im - ir)V-l + (i1 - ?r)L-l + (if - 1r - eA)eR$.j + i(B- 1) Capital Flight ebJ<sup>5</sup> /P = Yd + (OLp + e6D<sup>3</sup> p)/P - cp - ip - rml - Ov Variation in InternatAR ional Reserves eOR$/P = [ebD$p + ebD38 - eOJ6]/P + [x - u] where u = im + fdsp + fdsg - (if - **I** - eA)eRS.] Arrears arrears = (if - **I** - eA)eD3 - (fdsp + fdsg) Domestic credit to the Rublic sector OLg/P + rBs/P = ig + (ib - ir)B-1<sup>/P + fds</sup> 8 + oi where oi = g + (ij - w)Lg-1/P - eODD3 - t Flow supply of money _rm =_ (&M 1) Bank deposits market equilibrium Vs/P = v-. + rvd Stock of capital k - (1 - _6)k-1_ + ip + is Price level P = (1 + 7r)P. 1 Stock of external Debt eD$ = eD<sup>3</sup> P' + erD$p + eD$9 1 + eOD$, + arrears Definition eOJ$ Capital flight, calculated as (OeD$+FDI+CA-OR)
Table 6: Labor hours per plot

||Total hrs per<br>week per<br>plot|<br>Planter hrs<br>per week<br>perplot|Spouse hrs<br>per week<br>perplot|HH hrs per<br>week per<br>plot|<br>Hired labor<br>hrs per week<br>perplot<br>|Total hrs per<br>week per<br>hectare|<br>Planter hrs<br>per week<br>per hectare|Spouse hrs<br>per week<br>per hectare|HH hrs per<br>week per<br>hectare|<br>Hired labor<br>hrs per week<br>per hectare|
|---|---|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|-0.72|0.61|-0.07|-0.90|-0.44|-0.26|0.47|-0.05|-0.15|0.08|
||(4.49)|(1.01)|(0.49)|(0.98)|(3.34)|(1.93)|(0.57)|(0.29)|(0.39)|(1.11)|
|Couples' Training|2.44|1.29|0.55|-0.71|1.34|0.35|0.23|0.28|-0.39|-0.06|
||(5.16)|(1.13)|(0.58)|(0.98)|(4.09)|(2.38)|(0.63)|(0.35)|(0.45)|(1.52)|
|Seedling Subsidy + Couples'<br>Training (beta)|1.71|1.91**|0.47|-1.61|0.91|0.09|0.70|0.23|-0.53|0.02|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.72|0.02|0.36|0.14|0.82|0.96|0.12|0.43|0.22|0.99|
|Observations|1435|1435|1435|1435|1435|1435|1435|1435|1435|1435|
|Mean for control group|56.17|13.53|6.77|7.90|27.53|20.86|5.56|2.78|2.93|8.74|
|Mean for individual training|53.10|13.87|6.56|6.25|25.90|20.72|6.16|2.77|2.61|8.92|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%.

* p<0.1 ** p<0.05 *** p<0.01

Note: Household (HH) hours exclude planter and spouse work hours. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

1
# **Table 8**

# **Cooperativeness and the short-route of accountability (district-level)**

||District-leve<br>the sch<br>|**Depende**<br>l percentage o<br>ool accountab<br>(Schoo<br>|**nt Variable**<br>f parents who<br>ility system in<br>l Survey)<br>|participated in<br>a district<br>|
|---|---|---|---|---|
||OLS|OLS|OLS|OLS|
||(1)|(2)|(3)|(4)|
|**% parents investing in the group account in the district**|0.57*<br>(0.056)|0.58*<br>(0.074)|0.75**<br>(0.033)|0.68*<br>(0.089)|
|ln of household income (LSMS)||-0.01<br>(0.897)|-0.02<br>(0.756)|-0.16<br>(0.364)|
|Average years of schooling of the adult population (LSMS)||-0.01<br>(0.874)|-0.02<br>(0.639)|-0.01<br>(0.734)|
|% people who belong to a social organization (LSMS)|||-0.02<br>(0.925)|0.03<br>(0.884)|
|% people who participate in community activities (LSMS)|||0.13<br>(0.484)|0.02<br>(0.927)|
|Average perception of village members‟ willingness to work<br>together (LSMS)|||-0.14**|-0.15**|
||||(0.030)|(0.041)|
|% of people “trusting others” (LSMS)|||0.06<br>(0.760)|0.16<br>(0.498)|
|Religious heterogeneity (LSMS)||||0.00<br>(0.331)|
|Standard Deviation of household income (LSMS)||||-0.04<br>(0.673)|
|Prevalently Muslim||||-0.03<br>(0.720)|
|Northern region||||0.04<br>(0.785)|
|Constant|0.38|0.54|1.07|2.96|
||(0.228)|(0.502)|(0.179)|(0.186)|
|County fixed effects|YES|YES|YES|YES|
|<br>_Joint significance of county f.e. (p-value)_|_0.181_|_0.304_|_0.312_|_0.374_|
|Geographical areas dummies|YES|YES|YES|YES|
|_Joint significance of geographical areas (p-value)_|_0.112_|_0.164_|_0.049_|_0.080_|
|Observations|36|36|36|36|

P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. In all OLS regressions, we control for the geographical areas dummy variables: coastal, center and mountain areas; Tirana is the excluded geographical area.

Table 8 indicates that more “cooperative” districts are characterized by a higher parental participation in the school accountability system, although, as expected given the restricted degrees of freedom, the level of significance of the correlation is lower than in the individual-level regressions.

# _The long route to accountability_

In Table 9 we employ district-level official records of turnout in the 2009 parliamentary elections as our dependent variable. We use the same empirical specifications as in Table 8.

24
The authors provide a historical view into the process of "manufacture uncertainty" in the United States by opponents of public health and environmental regulations. This process is carried out as industries question the validity of scientific evidence on which the regulations are based. They highlight that the industry strategy of manufacturing uncertainty is in opposition to the public health model, under which decisions should be made using the best available evidence in order to ensure the protection of the public’s health and the environment. Michaels and Monforton provide historical data from the tobacco and other industries and document the rise of the label "junk science" to de-legitimize research that threatens powerful interests.

# **Science Communication**

Freudenburg, William, Robert Gramling and Debra J. Davidson 2008. “Scientific Certainty Argumentation Methods (SCAMs): Science and the Politics of Doubt” Sociological Inquiry 78(1):2–38.

Science provides falsification, but not “proof.” The authors trace the increasing call for scientific certainty, or “proof”in public discourse before policies can be implemented. Given that all science contains a measure of uncertainty, if agencies can be prevented from imposing any regulations until they are unambiguously “justified,” most regulations can be defeated or postponed, often for decades. This practice has allowed profitable but potentially risky activities to continue unabated. Based on their review of previously documented controversies, the authors suggest that “such calls may reflect not just a fundamental misunderstanding of the nature of science, but a clever and surprisingly effective political-economic tactic—“Scientific Certainty” Argumentation Methods, or SCAMs.” They further suggest that such SCAMs are both more widespread than has been previously recongnized and should be the focus of more attention in the future.

Grotzer, Tina and Rebecca Lincoln 2007.“Educating for ‘intelligent environmental action’ in an age of global warming”in Moser, Susanne C. and Lisa Dilling. eds Creating a Climate for <u>Change: Communicating Climate Change and Facilitating Social Change. New York: Cambridge</u> University Press.

Meijnders, Anneloes Cees Midden and Henk Wilke 2001 “Role of Negative Emotions in Communication about CO2 Risks” Risk Analysis 21(5): 955-966.

The authors use an experiment regarding the risks of carbon dioxide emissions to support their claim that both emotion and information are factors influencing effective risk communication. In the experiment, inducing fear of carbon dioxide risks leads participants to more effectively process information about energy conservation, which in turn results in more favorable attitudes toward energy conservation. “Exposure to strong arguments resulted in more positive attitudes 68
- 30 -

Table 7

# Spearman Correlations: the Policy Mix

||YGR<br>. .........................|CAR|IR<br>.....<br>.....|DINF<br>.....<br>...<br>...**.**....|
|---|---|---|---|---|
|_GOV_|-0.24052|-0.30053|0.11636|-0.26285|
||0.0390|<br>0.0093|<br>0.3235|0.0237|
|_MON_|0.23829|-0.02088|0.33861|-0.14882|
||0.0395|<br>0.8589|<br>0.0030|0.2026|
|RERA|-0.09952|0.08951|0.02212|<br>0.23999|
||0.4303|<br>0.4783|<br>0.8612|0.0542|

The first statistic in each pair is the Spearman rank correlation coefficient. The second is the probability that the absolute value of the observed correlation will occur for null hypothesis of zero correlation. There are 75 observations for each correlation with the exception of GOV (74 observations) and RERA (66 observations).
**Policy Research Working Paper Series**

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|---|---|---|---|
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|WPS2217 Border, Border, Wide and Far,<br>How We Wonder What You Are|David C. Parsley<br>Shang-Jin Wei|November 1999|H. Sladovich<br>37698|
|WPS2218 Who Avoids and Who Escapes<br>Poverty during the Transition:<br>Evidence from Polish Panel Data,<br>1993-96|Wlodzimierz Okrasa|November 1999|S. Fallon<br>38009|
|WPS2219 The Effect of the United States'<br>Granting Most Favored Nation<br>Status to Vietnam|Emiko Fukase<br>Will Martin|November 1999|L. Tabada<br>36896|
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|WPS2221 The Dynamics of Poverty and the<br>Effectiveness of Poland's Safety<br>Net (1 993-96)|Wlodzimierz Okrasa|November 1999|S. Fallon<br>38009|
|WPS2222 Labor Market Integration in the<br>Presence of Social Capital|Maurice Schiff|November 1999|L. Tabada<br>36896|
|WPS2223 Integrated Financial Supervision:<br>Lessons from Northern European<br>Experience|Michael Taylor<br>Alex Fleming|November 1999|S. Torres<br>39012|
|WPS2224 Growth Forecasts Using Time Series<br>and Growth Models|Aart Kraay<br>George Monokroussos|November 1999|R. Bonfield<br>31248|
|WPS2225 How Did Highly Indebted Poor<br>Countries Become Highly Indebted?<br>Reviewing Two Decades of Debt Relie|William Easterly<br>f|November 1999|K. Labrie<br>31001|
|WPS2226 Money, Politics, and a Future for the<br>International Financial System|Michael Klein|November 1999|M. Salehi<br>37157|