rafmacalaba/datause-encoder-data huggingface dataset:
Knetter, Michael M. and Thomas J. Prusa (2003) “Macroeconomic Factors and Antidumping Filings: Evidence from Four Countries,” _Journal of International Economics_ , 61(1): 1-17.

- Konings, Jozef, Hylke Vandenbussche and Linda Springael (2001) “Import Diversion under European Antidumping Policy,” _Journal of Industry, Competition and Trade_ 1(3): 283-299

- Messerlin, Patrick A. (1992) “Antidumping Regulations or Pro-Cartel Laws? The EC Chemical Cases,” _The World Economy_ 13: 465-492.

- Prusa, Thomas J. (2001) “On the Spread and Impact of Antidumping,” _Canadian Journal of Economics_ 34(3): 591-611.

- Prusa, Thomas J. (1997) “The Trade Effects of U.S. Antidumping Actions,” in Robert Feenstra (ed.) _The Effects of U.S. Trade Protection and Promotion Policies_ . Chicago, IL: University of Chicago Press for NBER.

- Prusa, Thomas J. (1992) “Why Are So Many Antidumping Petitions Withdrawn?” _Journal of International Economics_ 33(1/2): 1-20.

- Prusa, Thomas J. and Susan Skeath (2004) “Modern Commercial Policy: Managed Trade or Retaliation?” in E. Kwan Choi and James Hartigan (eds.), _Handbook of International Trade_ (Vol. II). Malden, MA, Blackwell Publishing.

- Staiger, Robert W. and Frank A. Wolak (1994). “Measuring Industry-Specific Protection: Antidumping in the United States,” _Brookings Papers on Economic Activity: Microeconomics_ 51-118.

- Veugelers, Reinhilde and Hylke Vandenbussche (1999) “European Anti-Dumping Policy and the Profitability of National and International Collusion,” _European Economic Review_ 43: 1-28.

24
4 Returns in stock markets across the region (Graph 2) were positively and highly correlated (table 1). The highest correlation coefficients are between Chile and Mexico (0.92), Colombia and Mexico (0.87), Chile and Colombia (0.86), Argentina and Chile (0.82), Mexico and Argentina (0.81). Across time, however, these correlations seem to have changed. computing the correlation coefficients with a 24-month rolling window, we observe that, for the biggest three countries, these are stationary; however, there is a structural change in the generating processes in the early 1990s (Graph 3).3 Finally, there is a positive association between stock prices and dividends (normalized) in most countries (Graph 4). Both variables tend to move together, although there are periods when they diverge. Colombia seems to be the only case in which the series are clearly divergent.

Comparing these results with those from East Asian countries-Indonesia, Korea, Malaysia, and Thailand-we find that, for most countries, correlations of stock returns are around 0.50 or lower. The only exception is the correlation between Malaysia and Thailand of 0.86 (Table Al in Appendix A). Thus, stock return correlations are lower in Asia than in LAC. As for change in these East Asian correlations over time, there is no clear evidence of structural breaks in the correlations as was the case in the big three LAC countries (Graph Al in Appendix A.) Regarding real estate prices (Graph 5), we constructed proxies by using the rent component of the CPI, deflated by the overall index. We were able to build this proxy for Argentina, Colombia, and Mexico, which were the only countries where we had data availability since the 1980s. In the case of Mexico, there is a strong correlation between this proxy and the index of the value of urban land in Mexico City constructed by Banco de Mexico (Guerra, 1997). A boom in real estate prices occurred in Mexico in 1988-91, in Argentina in 1990-93, and in Colombia in 1993-96. We also present real estate prices in two affluent neighborhoods of Santiago de Chile in 1975-82,4 as reported by Conley and Maloney (1995).

Graphs 6a and 6b show the evolution of returns on real estate investment in Mexico and the U.S., as captured by our proxies.5 For the whole sample period, there is no correlation (Graph 6a); however, there is a correlation of 0.70 after 1995 (Graph 6b).

# **B. Testing for Bubbles: The Latin American Case**

# 1. Overview of bubble testing

3 The tests for stationarity and structural change were those of Perron (1994). For the correlation between Brazil and Mexico, the structural change is in 1991:11, while that for Argentina and Mexico is in 1991:06. The Argentina-Brazil break occurs at an earlier date-1985:06. 4 The data were extracted weekly from El Mercurio. The authors thank Bill Maloney for making the data available.

5For the U.S. we consider the return index of real estate investment trusts (REITS) as computed by the NAREIT. For Mexico, it is the y-o-y growth rate of the housing price index we constructed.
share that were deprived was relatively large in each of the dimensions that improved, with an average decline of 8.8 percentage points in regions with a similar MPI profile (with the most room to improve) and 2.63 percentage points in the rest of the world.<sup>25</sup> There is also evidence that each individual country improved multiple indicators over this time period and that the averages are not driven by large declines in only a subset of countries. Specifically, Figure 7d reports the average number of the five indicators that improved by at least 5 percent in each comparison region. Countries with similar MPI profiles improved more than 5 percent in 3.3 out of the five indicators on average, and the rest of the countries improved in 2.6 indicators on average. This stands in stark contrast to PNG, in which no indicators improved at all, let alone by 5 percent. Aside from PNG, only five countries out of 94 tracked improved in zero of the five indicators, and they were all grouped as high-income countries by the World Bank with very limited scope to improve the already very small levels of deprivation in the pre-period.<sup>26</sup> However, Figures 7c and 7d demonstrate that PNG was an outlier even among countries that were similarly resource intensive and which likely similarly grew during the period of growing global commodity prices.<sup>27</sup> Resource-intensive countries improved in four out of the five captured indicators tracked on average; the average decline in deprivation for the improved indicators was 5.3 percentage points; and resource-intensive countries improved in 3.7 of the five indicators on average. Thus, the degree to which resource growth trickled down to the population in PNG was likely even lower than other resource-intensive economies over this period.

# **Section 5. Comparing the Relationship between Economic Activity and Deprivation in Papua New Guinea to the Rest of the World**

Given the poor performance in PNG over the time period relative to all other comparison groups, we use the richness of the global data on well-being and economic activity

> 25The 2.63 percentage points translates into a large percentage improvement given the share deprived in the rest of the world is small on average.

> 26The countries were Estonia, France, Germany, Hungary, and Switzerland. For a list of High-Income Countries as categorized by the World Bank, see (accessed March 2024) ” _https_ : _//datahelpdesk.worldbank.org/knowledgebase/articles/_ 906519 _− world − bank − country − and −_ ” _lending − groups._

> 27Resource-intensive countries were identified by IMF (2012).

16
<!-- Start of picture text --> Figure 11<br>Developing-country group factors<br>CIF<br>COD<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br><!-- End of picture text --> 57
to estimate TFP for manufacturing firms in eight countries: Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine.

A number of additional restrictions are imposed to reduce sample bias in the panel of AMADEUS firms with complete data on production function variables. First, observations that are “inactive”, “dissolved”, “in bankruptcy”, or “in liquidation” are dropped from the panel. Bureau van Dijk removes firms from the AMADEUS database only when there is no reporting for at least five years; specifically a “not available/missing” is reported for four years following the last included filing. Second, observations with data sourced from consolidated statements are dropped from the panel in order to avoid the double-counting of firms and subsidiaries or operations abroad. For most firms in the AMADEUS database, unconsolidated statements are reported and consolidated statements are provided when available. Third, observations with a positive number of subsidiaries are also dropped from the panel to reduce double-counting. Fourth, observations with less than two employees are dropped from the sample. This criterion helps to exclude any dummy (phantom) firms established for tax or other purposes.

Fifth, certain manufacturing industries are excluded when the activity is country-specific. Observations in the manufacture of tobacco products (NACE code 16) are dropped from panel because there are no such observations from Croatia, the Czech Republic, Estonia, Serbia, and Ukraine in the AMADEUS database. Similarly, observations in the manufacture of coke, refined petroleum products and nuclear fuel (NACE code 23) are dropped from panel because there are no such observations from Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Serbia, and Ukraine in the AMADEUS database. Lastly, observations in recycling (NACE code 37) are dropped from the panel because there are no such observations from Bulgaria with complete information on production function variables in the AMADEUS database.

A number of additional criteria are imposed on the set of four production function variables to reduce measurement error. First, observations with negative tangible fixed assets and material costs are dropped from the sample. Second, observations with material costsoperating revenues and cost of employees-operating revenues ratios greater than one are dropped from the sample. Lastly, observations with operating revenues-number of employees, tangible fixed assets-number of employees, material costs-number of employees, material costsoperating revenues, and cost of employees-operating revenues ratios that are greater (less) than 7
**Appendix I: Variable description**

ENEU variables :

- _Age_ : entrepreneur age. _Age2_ : square of _Age_ .

- _Education_ : dummy for maximum education level reached. _Prim:_ primary _, Sec:_ secondary _, High:_ more than secondary education. Base group: no education.

- _Female_ : dummy for women.

- _Married_ : dummy for married individuals.

- _Npers_ : number of individuals in the household.

- _Head_ : dummy variable for head of household.

- _Real Income (Y)_ : real monthly income from main job.

- _Real Income Annual Growth (R)_ : ln _Real Wage (t’)_ – ln _Real Wage (t)_ .

- _Survival (S)_ : dummy for entrepreneurs who were interviewed in the ENAMIN and declare to be self-employed/owners next year.

# ENAMIN variables:

- _Real Profits (Y)_ : declared profits.

- _Capital Stock_ : sum of the replacement cost of all owned or borrowed physical capital and the market price of all firm inventories.

- _Number of Workers_ : number of paid and non-paid workers including owner.

- _Credit_ : dummy for firms that got credit after the business started.

- _Time in Business:_ years since owner began the activity or became head of the business.

- _Credit:_ dummy for firms that obtained credit after start-up.

- _Formal Credit_ : dummy if the credit was obtained from banks, savings accounts or lending institutions.

- _Informal Credit:_ dummy if the credit was obtained from friends, relatives, clients, providers or particular lenders.

- _Training:_ dummy for firms where the owner or any of the employees attended a training course the year before the ENAMIN was done.

- _Taxes:_ dummy for firms that declared paying taxes either at the local or the federal level.

- _Guild:_ dummy for firms affiliated to a guild.

22
**Figure 7. Performance and Poverty and Inequality Impacts of the MSA and Heating Allowance under Baseline Scenario (pre-reform)**

<!-- Start of picture text --> a. Generosity (benefits as % of MYPP) b.Inequality, poverty, and poverty gap impact<br>Marginal Contributions to<br>14.6 Inequality 0.00<br>Change in Poverty Gap 0.51<br>Marginal Contributions to<br>0.18<br>2.0 Poverty<br>0.5 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._ Poverty and Distributional Impacts of the Policy Reform _How restrictive are the income thresholds after the Reform? A comparison with the Bulgarian Food Basket_

**This section examines the stringency of the income thresholds and parameters established for the Monthly Social Allowance (MSA) program.** To gauge this, we compare the income thresholds with the cost of a standard Bulgarian food basket. This comparative assessment provides valuable insights into the MSA program's adequacy in meeting eligible individuals and households' basic nutritional and sustenance needs.

**In principle, the core MSA benefit should guarantee individuals access to a comprehensive set of goods and services that satisfy the minimum living standards accepted by society.** This encompassing benefit covers a full consumption basket necessary to meet basic needs. Therefore, to determine the adequacy of a means-tested benefit, the income level of a Bulgarian family can be compared to the amount of income needed to buy a basket of goods and services that a household needs to enjoy a minimum standard of living. The benchmark income for the comparisons is generally created by establishing a basket of minimum goods and services costed at current price levels, usually called the minimum consumption basket. The amount of the minimum basket may vary from the minimum food basket necessary for survival to "comprehensive" baskets, which, in addition to food, include the cost of clothing, shelter, education, and health as minimum preconditions for a life with dignity. Suppose the family's income falls short of the cost of the basket for the family type. In that case, a household with a specific demographic composition- the difference between the household income or potential income and the basket cost should be the social assistance benefit paid to the family. Adopting an absolute poverty line based on the cost of a minimum basket assists in establishing the adequacy of social benefits. This approach contrasts with the relative concept of welfare (such as a relative poverty line), as it is linked to a minimum living cost rather than being defined by one's position in the income distribution.

22
missing. By contrast, all bilateral positions of a reporting country (source or destination) are, in principle, observed. Since BIS reporting countries generally itemize every counterparty country on which its banks hold or owe an active (positive) position, any missing observation is most likely a true zero and we code it as such. This procedure could underestimate the number and value of active links for those reporting countries that do not require banks to report the complete breakdown of all counterparty countries when positions are small enough.

Another point concerns the handling of a change in reporting in 2012. Countries historically reported positions expressed in U.S. dollars rounded to the closest million. Thus, positions below US$0.5 million (a low threshold in the context of country-to-country investment) were reported as zero. When decimal reporting was introduced in the second quarter of 2012, hundreds of links below US$0.5 million started to be reported as positive, leading to an artificial jump in the number of active links in 2012. Although this jump does not affect our analysis of the intensive margin (because the value of these links is so small), it could bias the analysis of the extensive margin. We offset this break in the series by setting all links below US$0.5 million to zero. This ensures that all reported loans and deposits are subject to a constant reporting threshold throughout the sample period.

# **A.2. Portfolio Investment**

For portfolio investment, we rely on the CPIS database from the International Monetary Fund (IMF). The CPIS is a voluntary data collection exercise that assembles data on countries’ international holdings of equities and long-term and short-term debt securities. Countries report data on: (i) their holdings of portfolio investment assets issued by residents in other countries, and (ii) their portfolio investment liabilities issued by domestic residents and held by residents in other countries. No aggregation is needed because the data are already reported at the country level (all sectors). We determine the value of investment to be equal to the value of assets reported by the source country.

33
|_f_<br>|sensitivity of the allocation of new capital for_f_(in_FCAPNG_) across<br>activities (in_A_) to current deviations of activity capital rents from the<br>economywide average|
|---|---|
|_q_<br>_c_<sup></sup>|exponent in Armington function for commodity_c_|
|_va_<br>_a_<br>|exponent in CES VA function for activity_a_|
|_x_<br>_c_<sup></sup>|exponent in CET function for commodity_c_(top of nest)|
|_q_<br>_c_<sup></sup>|elasticity of substitution between supplies of domestic output and imports<br>in Armington function for_c_ (topof nest)|
|_va_<br>_a_<br>|elasticity of substitution between factors in CES VA function of activity_a_|
|_x_<br>_c_<sup></sup>|elasticity of transformation between domestic sales and exports in CET<br>function for_c_(topof nest)|
|_q_<br>_c_<br>|shift parameter in Armington function in which domestic sales and imports<br>of commodity _c_ (in_C_)are aggregated to composite supply _(_topof nest)|
|_va_<br>_a_<br>|shift parameter for CES VA function of activity_a_|
|_x_<br>_c_<br>|shift parameter in CET function for commodity_c_ _(_top of nest)|

# **A.2. Equations**

The equations are split into four blocks:

1. Production and factors;

2. Domestic and aggregate foreign trade; 3. Current accounts of domestic institutions 4. Investment, system constraints, and numéraire.

Each section of the presentation covers one block and has its equations stated in one table.

In model simulations, it is possible to choose among alternative assumptions for (i) payments linking the government, domestic non‐government institutions, and the rest of the world; and (ii) the equilibrating mechanisms (the closures) for macro balances, factor markets, and markets for exports and imports. The assumptions used in this paper are presented in Section 3.<sup>13</sup> In this appendix, we apply the following set of relatively simple assumptions:

- Government budget: The government balance is cleared by adjustments in government investment in the context of rule‐based or exogenous levels for other government

> 13 The User Guide that accompanies ISIM‐GEM‐Core provides full details on these and other model features.

‐36‐
**Table S5: Robustness of Results to Using Differences as Outcomes**

|Panel A: Number of Visits|Any Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public Facilities<br>(4)|Private Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|1.976**|0.380|0.632***|0.199|0.773|
||(1.006)|(0.451)|(0.210)|(0.362)|(0.539)|
|Parent Enrolled*Ineligible|-3.818***|-0.501|-0.694***|-0.586|-1.952***|
||(1.458)|(0.687)|(0.201)|(0.556)|(0.733)|
|Ineligible|0.623|-0.073|0.071|0.240|0.535*|
||(0.551)|(0.239)|(0.087)|(0.167)|(0.320)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.075|0.093|0.049|0.033|0.007|
|P-value of Enrolled + Enrolled*Ineligible|0.124|0.835|0.377|0.431|0.0239|
|Panel B: OOP Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-256.345|-309.222|-0.227|7.215|16.148|
||(322.184)|(271.928)|(1.034)|(4.903)|(67.235)|
|Parent Enrolled*Ineligible|-224.738|220.549|0.287|-11.948|-299.724**|
||(408.730)|(316.273)|(1.076)|(8.590)|(136.769)|
|Ineligible|-43.900|-104.597|0.358|2.816|62.639|
||(158.980)|(127.769)|(0.274)|(1.937)|(47.861)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.029|0.027|0.006|0.004|0.01|
|P-value of Enrolled + Enrolled*Ineligible|0.135|0.715|0.742|0.526|0.0201|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is the difference in utilization at a given provider between baseline and follow-up. The dependent variable in Panel B is the difference in expenditures at various providers over the past year between baseline and follow-up. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

5
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