rafmacalaba/datause-encoder-data huggingface dataset:
from the parameters of hoc regression models based on individual data for some but not all economically active members of each household. One reason for this, among others, is that behavioral effects will depend indirectly, through domestic prices and income shares, on the wage rates of other family members, and these will be missing.

Modeling household behavioral effects on the SSA TUS2000 presents major difficulties. If we attempt to select a sample for which there is information on all economically active members, we are limited to households comprising either a single non-dependent person or two non-dependent persons who were both interviewed. To illustrate some of the problems that arise, a sample of households comprising two non-dependent adults, both interviewed, is drawn from the file. The sample is limited to families selected on the criteria that the two respondents are parents, at least one parent has a child under 18 years present, and there are no other non-dependent persons present. The sample contains 667 household records.

The weighted sample means for female and male time allocation to SNA work activities, domestic work and care activities are reported in Table 4, Panel A. The first problem is that these means are no longer for a random sample of two-parent families with dependent children. This is because families with children aged from 10 to 17 years will be under represented - records in which they are the respondent(s) instead of the parents will be omitted. A consequence of this is that families with younger children will tend to be over represented, and so if more time is allocated to younger children there will be an upward bias in the means for domestic work and care. Had the overall size of the survey been reduced and the saving in cost used to finance interviewing of all non-dependent household members, the end result could well have been a larger (as well as a more representative) sample of families selected on the same criteria. These problems are not encountered in the EMNV98 file. For comparison, Table 4, Panel B, reports data means for a sample selected on matching criteria. The sample contains 554 household records, representing families with two parents present, at least one child under 18 years and with no other adults.

51
**Table VII. Regression Analysis: Actual vs. Hybrid Predicted Bilateral Current Account**

||Basic Specification||BS + La|gged Vars.||
|---|---|---|---|---|---|
||(BS)|BS + Time Trend|HPBCA|L.HPBCA|Vars. in Diff.|
|Relative Risk Aversion: 10||||||
|Basic VAR|0.002 ***|0.001 **|0.000 *|-0.000|0.000 **|
|+ RER in Levels|0.003 ***|0.001 ***|0.000 *|-0.000|0.000 **|
|+ RER in Differences|0.002 ***|0.001 ***|0.000 *|-0.000|0.000 **|
|+ Infl., NER in Levels|0.002 ***|0.001 ***|0.000 **|-0.000 *|0.000 ***|
|+ Infl., NER in Differences|0.002 ***|0.001 **|0.000 **|-0.000 **|0.000 ***|
|Relative Risk Aversion: 2000||||||
|Basic VAR|0.603 ***|0.205 ***|0.069 **|-0.045|0.058 **|
|+ RER in Levels|0.294 ***|0.124 ***|0.053 ***|-0.036|0.045 **|
|+ RER in Differences|0.599 ***|0.198 ***|0.065 **|-0.042|0.055 **|
|+ Infl., NER in Levels|0.310 ***|0.126 ***|0.061 ***|-0.040 **|0.052 ***|
|+ Infl., NER in Differences|0.544 ***|0.150 **|0.077 **|-0.051 *|0.065 ***|

This table shows the results of four different regression specifications: a basic specification that regresses the actual bilateral current account on our hybrid measure (HPBCA), the basic specification with a time trend, the basic specification including lagged values of both the dependent and independent variables, and a fourth specification with both variables in differences. Our hybrid bilateral current account uses actual data on wealth, consumption, and savings. The results are based on different series of model-predicted portfolio shares: our five VAR specifications are considered to calculate expected asset returns. The regressions use quarterly data. Both measures of the bilateral current account are scaled by U.S. GNP. The results are shown for a relative risk aversion coefficient of 10 and 2000 in the top and bottom panel, respectively. * means significant at 10%, ** at 5%, and *** at 1% levels.

53
- 25

As of September 1988 irregular debt was at KD 2.8 billion (KD 2.2 billion in 1987) and was owed by 2,339 borrowers (1,827 in 1987). The number of the borrowers with negative net worth has also increased _to_ 1,214 with a debt of KD 2 billion I/ . While this will undermine the position of most of the banks, it is expected that rising prices of shares and real estate will have a positive effect.

Recently the CBK changed the structure of interest rates to allow Kuwait'3 rates to reflect international interest rates. This is expected to have a sharp effect on Bank profitability by increasing the cost of deposit funds at a time when a substantial part of their loan portfolios are nonperforming. If the rise in international interest rates continues, the CBK may have to increase the level of support to the banking system.

# THE CAUSES OF THE CRISIS

To understand the background and causes of the crisis, it is important to distinguish between external shocks to the economy and internal fiscal, monetary, and institutional causes. Two shocks affected the-economy from 1979 to 1981 _I/_ . First, a sharp decline in oil prices and crude oil production cut public revenues from KD 6 billion in 1980 to KD 3 billion in 1982, a 50 percent drop. While this did not affect total government expenditures, as a result of withdrawals from investment reserves 3A/ , shifts within expenditure items could have contributed to precipitating the crisis. The effect of oil accounted for 33 percent of average GDP from 1980 to 1982, compared to 30 percent from 1980 to 1985 (though there was some improvement from 1983 to 1984).

Second, the Iraq-Iran war slowed trade with neighboring countries and contributed to dampening confidence in the domestic economy. Nonoil exports to the Middle East, which had peaked in 1981 at $2.2 billion, declined by 1986 to $1.1 billion jU/ , a 49 percent drop. Of this, the war contributed a decline in exports from $1.1 billion in 1981 to $370 million in 1984, a drop of 68 percent. the share of Middle East exports in GDP was about 4.9 percent, with the Iraq-Iran war contributing 3.4 percent i/

- I2/ Figures from CBK.

- I2/ P.G. Sadler. op.cit. P. 8.

- i§/ Government's 6udget revenues do not include income from investment of government reserves.

- i2/ International Monetary Fund. Direction of Trade Statistics. Washington, D.C. 1986, 1987.

- i2J Wholesale and retail trade as a share of GDP declined 32 percent (1984 prices) from 1982 to 1983.
- 25 -

Table 6 concluded. Sumniary of Trade-Related Preferences for Africa

|_Trade_<br>_Preference_|_CountriesIncluded_|_Provisions_|
|---|---|---|
|||0Sweatersknittoshapefromcashmereorcertainwool.TheSweaters<br>mustbeinchiefweightofcashmere,or50percent ormorebyweightof<br>merinowoolmeasuring21.5micronsindiameter. Fiberandyearncan<br>come fromanycountries including countriesoutside SSADuty<br>free/Quotafreefor8-yearperiod.|
|||0Apparelcut/knit to shapeandassembled inSSAfrom3Idcountry<br>yarnorfabricinshortsupply(as derivedfrom NAFTA) suchassilk,<br>linen,fine-count cottoncircularknitfabricsforcertainapparel,cotton<br>velveteen, fine-walecottoncorduroy,Harris Tweed,batistefabrics,and<br>9typesoflightweighthigh-threadcount broadwovenfabricsformen's<br>andboy'sshirts-Dutyfree/Quota freefor8-yearperiodsubject to<br>NAFTAROO.|
|||0Apparel Articlesmadeinoneormorebeneficiary SSAcountries<br>fromAfrican/regionalfabric-Duty**freetreatmentwithannualquota**<br>for8-yearperiod.Year-I.percentofthetotalU.S. apparelimportsin<br>thepreceding12months.Inyear8not toexceed7percentoftotalU.S.<br>imports.Normal**MFNduties**on importsovathecap.|
|||0Handloomed,handmadeand folklorearticles.Productscoveredtobe<br>determined throughbilateral consultationsand mustalsobe certifiedby<br>thecompetent authorityofthe beneficiarycountry (orcountries)asa<br>handloomed,handmade,orfolklorearticle-Dutyfree/Quotafree<br>treatmentfor8-yearperiod.<br>3.Ruleoforigin:SameasunderGSPwith theextraflexibilitythatthe35percentvalue<br>addedconditioncanbemetby counting productionofmaterialsfromotherbeneficiary<br>countriesortheU.S.Thetextiles andapparelROOsareasdescribedinTable8.<br>**CustomsRelatedRequirements(forapparelprovisions discussedabove:**adoptionofan<br>effectivevisasystem,andmeasurestoprevent illegalshipmentandtransshipment,<br>maintenanceofdocumentationtodetermine originandcooperation with theU.S.Customs<br>serviceinreportingandsharinginformation.<br>4.Exceptions to**Duty**FreeTreatment:Mainlyagriculturalandtextilesand apparel.For<br>agriculturalcommoditiessubjecttotariffrate quotas, dutieswillcontinue toapplytothehigh<br>dutytrancheofthequota.Products fallingintothiscategoryincludecotton, groundnuts,rice,<br>sugarandtobacco.|
|**LDBCunder**<br>**AGOA**|Inprinciple,allcountriesexcept<br>Gabon,Mauritius,Seychelles,<br>andSouthAfrica.|1.**Eligibility.**SameasAGOAwiththerequirementthatpercapitaGNPbelessthan$1500a<br>yearin1998 asmeasured bytheWorldBank.<br>**2.TextileandApparelPreferences:**Apparelassembledinsub-Saharan Africa**from**non-<br>U.S.,non-sub-Saharan African fabric("thirdcountry"fabric).Only LDBCSSAcountriesmay<br>exportDutyfree/Quota**free**apparelwhollyassembledinthe countries,regardlessofthe<br>originofthe fabric,through**September30,2004.**<br>**3.Rule of origin.**Inthis casetheapparelonlyneeds tobe totally_assembled_inthe<br>beneficiarycountry.AfterSeptember30,2004,theruleoforiginwillbethe sameasforOther<br>SSA countries.|
_Unemployment, Skills, and Incentives. An Overview of the Safety Net System in the Slovak Republic_

# Carolina Sanchez-Paramo

This is a background paper for the **Slovak Republic: Poverty, Employment and Labor Market Study.** The views contained herein are those of the author only, and do not represent the opinions of the World Bank nor of its Board of Directors, nor of any individual country member, nor federal, nor local government. The author takes full responsibility for any and all errors of fact or interpretation.

1
temperatures. In this case adaptation modifies the impacts on society as if they were caused by lower temperatures.

# **2. Optimal Mitigation**

This section closely follows Pindyck (2000). Define M(t) as the stock of greenhouse gases (GHGs) in the atmosphere, and E(t) as a flow variable that controls this stock. In the following, E is taken as the rate of emissions of GHGs, and can take a given number of nonnegative values. Assume that M is (deterministically) given from where δ is the rate of decay of the GHG stock. Associated with the stock variable M there is a flow variable of (negative) benefits, B, given by (2) _B(M(t), θ(t)) = -μ(t)θ(t)M(t)._ θ is a multiplicative parameter governing the marginal cost to society from increasing the stock of GHGs. This parameter is stochastic and follows a geometric Brownian motion, on differential form described by where α is a (positive) drift parameter, and ζ the standard deviation of the process.<sup>5</sup> Considering a starting point 0, at t = T the logarithm of θ has expectation (α –ζ<sup>2</sup> /2)T and variance ζ<sup>2</sup> T (while θ itself has expectation αT).<sup>6</sup> This process is modified by two types of policy interventions. The first of these is represented by μ in (2), which is assumed to take values less than or equal to unity. Assuming α > 0, damages from a given concentration of GHGs in the atmosphere is systematically drifting upward over time, but with a random component that may make this upward drift highly uncertain. This process can be affected in three ways, one of which is associated with

> 5 See Dixit and Pindyck (1994), chapter 3, for a heuristic discussion of the formula and its applications; for more rigorous presentations see Harrison (1985), Karatzas and Shreve (1988), and Øksendal (2007). 6 See Dixit and Pindyck (1994), chapters 3-4.

5
9 We present two sets of under five mortality data. The first one (Table Health 1), generated by Hill and Pebley,<sup>5</sup> comprises only empirical data, thoroughly checked agalnst one another, and is a very reliable source of country sr-cific data. Hill and Pebley used estimates from UN (1988) for North Africa, Asia and the Americas, but attempted to exclude all those estimates not based on data. The selection was done by the authors, helped by the author of the UN report, and, according to them, *it is hoped that the numbers left are soundly based and relatively reliable." Numbers from the most recent past available from very recent surveys, such as the DHS, have also been incorporated "where possible and where consistent wlth earlier estimates." In the case of Sub-Saharan Africa, data were drawn from a paper by Althea Hill on child mortality in sub-Saharan Africa.<sup>6</sup> Althea Hill's numbers are also the product of *careful evaluation of the underlying data," and have been enriched by more recent data such as DHS. Data were obtained for 67 developing countries, with many incomplete t.ries. Unfortu.iately, the scarcity of observations make it of little use for intertemporal and interregional comparisons as no aggregation is really possible. To circumvent that problem, this paper presents UN mortality data (Health 2), with interpolations and extrapolations.? This table is less adequate for specific country information, yet it gives a good picture of both the trends

> 5Kenneth Hill and Ann R. Pebley, "Levels, Trends and Patterns of Child Nortality in the Developing World." Prepared for the Workshop on Child Survival Programs: Issues for the 1990s (Baltimore, MD: Johns Hopkins University, School of Hygiene and Public Health). November 20-21. Processed.

> 6Althea Hill, Mild Ko.Talit **in** sub-Sahar Afica. Paper presented to IUSSP seminar on Hortality and Society in Sub-Saharan Africa. (Yaound6, Cameroon, 1987).

TUnited Nations, (1rtalitX of hildren Under Agp_5 (1988).
- **Moulton, Brent R** , “Random Group Effects and the Precision of Regression Estimates,” _Journal of Econometrics_ , 1986, _32_ (3), 385–397.

- **Moyle, Brendan and Kirsten Conrad** , “Dirty laundry: are Chinese legal ivory factories using poached ivory as an input?,” October 2014.

- **Phillips, Peter C. B. and Bruce E. Hansen** , “Statistical Inference in Instrumental Variables Regression with I(1) Processes,” _The Review of Economic Studies_ , 1990, _57_ (1), 99– 125.

- **Rademeyer, Julian** , “Beyond Borders: Crime, Conservation, and Criminal Networks in the Illicit Rhino Horn Trade. Part Two.,” Technical Report, Global Initiative against Transnational Organized Crime, Geneva, Switzerland July 2016.

- , “Tipping Point: Transnational Organized Crime and the War on Poaching. Part One.,” Technical Report, Global Initiative against Transnational Organized Crime, Geneva, Switzerland July 2016.

- **Sanchez de la Sierra, Raul** , “On the Origins of the State: Stationary Bandits and Taxation in Eastern Congo,” January 2017.

- **Scheinkman, Jos ́e A. and Jack Schechtman** , “A Simple Competitive Model with Production and Storage,” _The Review of Economic Studies_ , 1983, _50_ (3), 427–441.

- **Smith, Graham** , “Tests of the random walk hypothesis for London gold prices,” _Applied Economics Letters_ , 2002, _9_ (10), 671–674.

- **Taylor, M. Scott** , “Buffalo Hunt: International Trade and the Virtual Extinction of the North American Bison,” _American Economic Review_ , December 2011, _101_ (7), 3162–95.

- **Thouless, Christopher, Holly T. Dublin, Julian Blanc, Diane Skinner, Tara Daniel, Russell Taylor, Fiona Maisels, Howard Frederick, and Philipe Bouch ́e** , “African Elephant Status Report 2016,” Occasional Paper Series of the IUCN Species Survival Commision 60, IUCN/SSC Africa Elephant Specialist Group, IUCN, Gland, Switzerland 2016.

- **t ́Sas-Rolfes, Michael** , “A Rebuttal to Harvey, R. (2016). Risks and Fallacies Associated with Promoting a Legalized Trade in Ivory in Politikon 43(2): 215229,” _Politikon_ , 2016, _43_ (3), 451–458.

27
Table 3: Migration response to BLAs (Instrumental variable)

||Leave one <br>��<br>_j _<sup>_B_</sup>|origin out<br><sup>_LAi¬jt_</sup><br>�|Leave one d<br>(<sup>�</sup><br>_i _<sup>_B_</sup>|estination out<br><sup>_LA¬ijt_)</sup>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||**Panel A**- **Full s**|**ample**|||
||First stage|IV|First stage|IV|
|Bilateral labor agreement (BLA)||0_._405<sup>_∗∗∗_</sup><br>(0_._100)||0_._616<sup>_∗∗∗_</sup><br>(0_._106)|
|Leave one out|_−_1_._210<sup>_∗∗∗_</sup><br>(0_._055)||_−_1_._497<sup>_∗∗∗_</sup><br>(0_._087)||
|Observations<br>K Paap Fstat. (First stage)|273_,_042<br>476_._42|273_,_042|273_,_042<br>295_._72|273_,_042|
||**Panel B**-**Regul**|**ar corridors**|||
||First stage|IV|First stage|IV|
|Bilateral labor agreement (BLA)||0_._188<sup>_∗∗∗_</sup><br>(0_._058)||0_._277<sup>_∗∗∗_</sup><br>(0_._071)|
|Leave one out|_−_1_._147<sup>_∗∗∗_</sup><br>(0_._067)||_−_1_._399<sup>_∗∗∗_</sup><br>(0_._105)||
|Observations|33_,_222|33_,_222|33_,_222|33_,_222|
|K Paap Fstat. (First stage)|290_._70||177_._00||
|Origin country x decade FE|_Y es_|_Y es_|_Y es_|_Y es_|
|Destination country x decade FE|_Y es_|_Y es_|_Y es_|_Y es_|
|Origin x destination countries FE|_Y es_|_Y es_|_Y es_|_Y es_|

Notes: The table presents the first stage regression and the estimate of _γ_ from IV estimate of equation (2). It shows the suggestive causal impact of a BLA on migration. The outcome variable in columns (1) and (3) is the dummy variable indicating whether the corridor of interest during the decade of interest has signed a BLA or not. In columns (2) and (4), the outcome variable is the inverse hyperbolic sine of migration. Panel A focuses on the full sample while panel B restricts the analysis to ‘regular’ corridors which have at least 10 migrants across all time period. The set of fixed effects in the bottom rows of the table are common to the two panels. Standard errors in parentheses are clustered at the origin-destination level.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

15
consider the short, medium- and long-term impacts of the changes in trade policies considered here.

# **4.1 Regional and sectoral aggregation**

The GTAP 9.2 database represents 140 different countries/regions and 57 sectors. In order to be able to focus on key results and countries/regions/sectors of interest, these were bundled into 11 sector and 10 region aggregates (Table A1). The final regional aggregation includes the following: the European Union, the United States, Other Advanced Economies, China, Rest of East Asia and Pacific, Latin America and Caribbean, Europe and Central Asia, Middle East and North Africa and Sub-Saharan Africa. Table A2 in the annex provides a detailed overview of GTAP sectors and their mapping into the aggregates used in this paper.

# **4.2 Simulation design**

We develop two scenarios to quantify the country/regional impact of a global increase in protectionism. Each of these scenarios includes an increase in tariffs as represented in Table 1 and an increase in the cost of traded services. The latter is modelled through a symmetric, non-revenue generating increase in non-tariff barriers that result in a 3 percent increase in the ad-valorem trade cost of services<sup>6</sup> . This shock reflects the loss of binding services sector protection at currently applied levels achieved in trade agreements. The magnitude of the increase is conservative as it only implies a loss of services bindings but not further loss of market access which has been achieved in many FTAs. Hence, the results of these scenarios are to be considered as providing lower bound estimates of the potential losses resulting from an increase in protectionism.

- :

- **Scenario 1 - Loss of preferences** in this scenario, we measure the economic impact of the loss of tariff preferences from all existing bilateral/regional trade agreements, as well as unilateral preferential schemes in addition to a 3 percent increase in the cost of traded services. In the absence of tariff commitments under regional trade agreements (RTAs) and unilateral preferences, WTO members would effectively revert to MFN tariffs which in turn would translate into an increase of 40 percent in tariffs from the The in-

- average global currently applied 2.7 percent to 3.8 percent. crease in the cost of traded services captures the value of the increase in uncertainty

> 6 The 3 percent ad-valorem increase is an estimate of the value of binding of applied levels of services protection and was applied in the same context in Decreux and Fontagné (2011) and Kutlina-Dimitrova and Lakatos (2014).

11
# 9 CONCLUSION

In this paper, we use highly disaggregated firm-to-firm transaction-level data from a large Indian state and provide one of the first estimates of firm-level elasticities of substitution across suppliers within the same product category. We provide new estimation strategies and estimates for these elasticities by leveraging regional variation in supply-side shocks induced by the Indian government’s massive lockdown policy. We find that suppliers of inputs are highly complementary even at this very granular level. We explore explanations behind this low elasticity, showing that contract specificity of inputs, the level of inventory of the buyer’s industry, and the time horizon matter. This elasticity crucially determines aggregate impacts and the transmission of shocks across the network but is especially difficult to pin down (Baqaee and Farhi, 2019). The combined advantage of having product-level unit values and quasi-experimental variation in supply-side shocks allows us to overcome previous challenges in the literature, and credibly estimate this elasticity across suppliers of a particular product.

Since inputs are complementary, adverse shocks to even a small subset of firms that are highly linked in the supply chain can negatively affect the aggregate economy by propagating through firm networks. When we conservatively shock only the productivity of firms located in _Red_ zones by 25%, we find that if suppliers of the same product were substitutes instead of complements, the fall in aggregate quarterly GDP in the state under study would be about 870 million USD lower, or about 25 USD per capita lower per quarter. Using big data computational techniques, we quantify this decline _directly_ using information on the economy-wide firm-to-firm network without relying on first-order approximations. Our methods thus provide new techniques to quantify shocks through large and complex production networks. Using data on the entire production network in the state, we measure the full connectivity of firms in the network and show that as the level of complementarity and the magnitude of the negative productivity shock increase, it is more effective to save the more connected firms, after controlling for firm size.

Our findings have implications for policymakers worldwide, who often face difficult tradeoffs in crisis regarding which firms to bail out. Given the underlying variation used, these estimates are relevant for other crises that are expected to remain short-lived, such as natural disasters, temporary trade wars and sanctions, and supply-chain disruptions.

39
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