rafmacalaba/datause-encoder-data huggingface dataset:
reduce their default risk and increase customers’ demand. Still, life insurance companies are subject to different limits that intend to minimize the risk of their investments. One of them (removed at the end of 2011) is related to the asset-liability mismatch, by which cash flows are projected in ten tranches into the future and, based on the degree of mismatch in each tranche, the life insurance company adjusts its capital requirement. In addition, life insurance companies are subject to an asset adequacy test and other limits referred to a maximum exposition to some asset class (such as, equity and real estate) and the concentration to issuers and related investors.

Mutual funds are the institutional investor less exposed to regulatory requirements to structure their portfolios. In general, regulation is flexible and is mainly focused in diversification and disclosure requirements. Once a mutual fund company decides to offer a new fund, for example a “corporate fixed income fund,” the fund is obligated to invest at least 90% of their assets in those instruments stated in the fund name. Therefore, each mutual fund company determines the investment portfolio composition of its funds.

# **3. Main Data**

The main data used in this study consist of asset-level holdings of institutional investors during the relevant period 2002-2008, when institutional investors grew and consolidated in Chile and financial markets operated under relative normal circumstances. The data come from different sources.

The data on Chilean mutual funds and insurance companies come from the Superintendency of Securities and Insurance ( _Superintendencia de Valores y Seguros, SVS_ ). The data on Chilean pension funds, the most comprehensive data, come from the 11
market access implications for disputes over policies that affect imports. An examination of this same bilateral (complainant-respondent) trade data is not likely to reveal comparably useful market access information for those disputes over policies that affect exports. Because the threat to a complainant’s market access arises through competition in a common third country market in those disputes, data from an alternative importing country’s market may be required.

Second, we identify challenges to services imports as opposed to goods imports. Once we strip out the 11 disputes that confront services policies and to which we cannot map disaggregated, productlevel trade data, we are left with 375 disputes, or 87.8 percent of the total caseload.

Our third and final step at this stage is to strip away the disputes that concern general challenges to a WTO member’s legislation and that do not necessarily concern the application of a policy to any particular product(s). Examples include challenges to the United States’ Antidumping Act of 1916, United States’ Section 337 of its 1930 Trade Act, and United States’ Sections 301–310 of the Trade Act 1974. After eliminating 67 such disputes, we are left with 308 WTO disputes, which make up 72.1 percent of the original caseload.<sup>7</sup> The remaining 308 bilateral disputes contain challenges to a respondent’s allegedly WTOinconsistent import-restricting policy that we can subsequently map to product-level trade data as described in Section 3 below. These disputes make up the baseline sample used in the estimation.

# **2.2.2 Newly imposed policies or failure to reform policies so as to bring them into WTO compliance**

Our next step is to split WTO disputes into two further categories based on whether we are able to trace them to a newly-imposed policy. Some WTO disputes cannot be linked to implementation of a new policy and instead are triggered by the _failure_ to reform a policy so as to bring it into compliance with what the complainant alleges are that trading partner’s commitments or obligations as a member of the WTO. Table 1 reveals that while most of these 308 disputes concern changes to import-restricting policies, there are a significant number of disputes (68) over which the main allegation is that the respondent failed to reform a policy.

> 7 These 67 cases also include all of the violations over the WTO’s intellectual property rights agreement (TRIPS); these disputes can be associated with lack of protection for a wide variety of services (i.e. movie and sound recordings) in addition to merchandise (i.e. pharmaceuticals and chemicals) products.

9
_Table 7_ : Ethiopia - Regressions of self-reported harvest realizations on expected harvest and a range of controls.

# **Ethiopia - Yield**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||ES<br>|S15<br>|||ES<br>|S18<br>||
|VARIABLES|Maizeyie|ld(kg/ha)|Sorghumy|ield(kg/ha)|Maizeyie|ld(kg/ha)|Sorghumy|ield(kg/ha)|
|Expected maize yield (kg/ha)|0.183***|0.126***|||0.0997**|0.0652**|||
||(0.0416)|(0.0349)|||(0.0460)|(0.0330)|||
|Expected sorghum yield (kg/ha)|||0.256***|0.124***|||0.290**|0.197*|
||||(0.0387)|(0.0324)|||(0.113)|(0.107)|
|Planted area harvested (%)||10.30***||3.926**||8.554***||10.69**|
|||(2.943)||(1.732)||(2.776)||(4.853)|
|Damage on crop (%)||-8.277***||-11.99***||-8.818***||-6.854|
|||(3.137)||(1.810)||(2.813)||(5.473)|
|Constant|1,640***|1,587|837.0***|1,902***|1,465***|-447.6|1,246***|2,846|
||(109.0)|(1,021)|(82.59)|(558.4)|(102.1)|(1,013)|(270.3)|(3,272)|
|Observations|2,928|2,841|2,397|2,336|1,685|1,629|1,302|1,240|
|Adjusted R-squared|0.086|0.217|0.171|0.341|0.044|0.199|0.083|0.223|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of self-reported realized yield on expected yield, planted area and plant productivity loss, and a number of plot-, manager-, and household controls as well as inputs and zone fixed effects. Controls: Inputs - Plot area, total labor p. ha, any hired labor dummy, seed quantity p. ha, improved variety dummy, inorganic fertilizer quantity p. ha, any organic fertilizer dummy; Household - dependency ratio, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 7 and Table 8 explore the alignment of expectations with harvest realizations by regressing realized harvest on expected harvest, first in a bivariate regression and later including a full set of controls. Expectations prove a significant predictor of self-reported realized harvest with the association much stronger in Malawi. Furthermore, consistent with the notion of systematic overexpectation, coefficients are positive but less than one. Adding the planted area- and plant productivity loss measures as additional covariates to the model, we gain some but not much explanatory power with the coefficients carrying the expected signs.<sup>45</sup> This again indicates that the two measures (one in Malawi) are associated with losses but do not provide exact point estimates.

> 45 We verify this result graphically by comparing the mean (positive) expectation gap to the mean estimated loss from Question 1 by country, crop, and survey round and by plotting the expectation gap against the two different losses measures (area and plant productivity losses in percent), respectively. Alignment is imperfect and the general pattern consistent with the finding of Sohnesen (2020) that farmers’ shock (loss) reports and realized yields (and thus the expectation gap) tell two different stories.

29
## native-born peers.

Column 3 and 4 shows the estimates for 12th graders and their access to higher education without and with school fixed effects. The results show that access to higher education is lower for migrant and forced migrant students compared to natives. After including controls for applications to financial aid and college admission tests (Table 7 column 5 and 6), the direction of the effect remains negative but is no longer statistically significant. These results present suggestive evidence that financial aid applications and college selection test taking partially determine access to higher education. Moreover, these results are indicative of self-selection across students who apply for these benefits. – This could be one of the major roadblocks that students are facing low levels of – financial aid application that is affecting the educational trajectories of migrant and forced migrant students.

Table 7: OLS: Grade Advancement and Higher Education Enrollment

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Advancement|Advancement|Higher Education|Higher Education|Higher Education|Higher Education|
|Migrant|-0.308***|-0.285***|-0.165***|-0.154***|0.0139|-0.00275|
||(0.00227)|(0.00202)|(0.0168)|(0.0161)|(0.0144)|(0.0139)|
|Forced Migrant|-0.383***|-0.363***|-0.303***|-0.191***|-0.0284|-0.0157|
||(0.00280)|(0.00247)|(0.0241)|(0.0229)|(0.0207)|(0.0199)|
|College Admission Test|||||0.195***<br>(0.00267)|0.0972***<br>(0.00290)|
|Fiscal Aid Application|||||0.450***<br>(0.00211)|0.515***<br>(0.00214)|
|Constant|0.906***|0.906***|0.493***|0.493***|0.0371***|0.0760***|
||(0.000175)|(0.000151)|(0.00103)|(0.000964)|(0.00216)|(0.00239)|
|Observations|2,844,206|2,844,206|236,841|236,841|236,841|236,841|
|R-squared|0.013|0.014|0.001|0.001|0.266|0.252|
|School FE|No|Yes|No|Yes|No|Yes|
|Number of Schools||11,065||3,343||3,343|
|||Stan<br>*** p|dard errors in pare<br>_<_0.01, ** p_<_0.05,|ntheses<br>* p_<_0.1|||

# **Financial aid applications**

This subsection explores financial aid applications as a major roadblock to access to higher education. The descriptive statistics and OLS results illustrate that students who apply for financial aid are more likely to enroll in higher education. Information about access to this aid has been found to correlate with higher enrollment, especially when this information is provided at a younger age (Hastings et al., 2015; Dinkelman and Mart ́ınez A, 2014). Students who apply for financial aid could be self-selecting into treatment, which would bias our regression analysis. Alternatively, there could be 18
in order to ensure consistency, we calculated an implicit foreign interest rate as the ratio of interest payments and the previous end of period stock of external debt of the other three sectors (net of reserves) .3 Multiplying this interest rate with the respective stocks gives us the required interest payments.

# Balance of Payments in TL

As a matter of principle, we choose to work with period average pyices, exchange rates and interest rates. This is motivated by the fact that the majority of the data are only available in flow form. National accounts data and budget data are examples of this, and the standard practice to deflate these data is by means of a period average price index. Hence, we convert US$ balance of payments flows at the period average exchange rate. This has important implications for the calculation of the "timing effect" necessary to reconcile Tl denominated stocks and flows with USS flows as will be explained below. This assumption obviously leads to an approximation of the Tl value of the balance of payments transactions since the timing of the transactions is of crucial importance, especially when large exchange rate swings occur. If more information is available on the timing of flows or the exchange rate process this should be taken into account. **A1.2.** The **monetary sector** 3 Expressed using symbols of Tables AII.4 and AII.1, the formula is: iF = (HD iFt - HD iFGo - HD iFGb)(88)/(Ft - FGb - FGo)(87) 16
Policy Research Working Paper 7546

# **Abstract**

Wage inequality has declined in Mexico since 2000. Using data from Mexican labor surveys for the period between 2000 and 2014, this paper investigates whether the decline was driven by wages declining more sharply for younger or older workers. The analysis finds that the wages of older workers declined and the decline was more pronounced in the older cohort. This would seem to support the hypothesis that older workers’ skills have become obsolete.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lflopezcalva@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
How Did the Asian Countries Avoid the Debt Crisis?

by lshrat Husain

# Table of Contents

|Introductio|n|1|
|---|---|---|
|Internationa|l Economic Environment|3|
|Domestic P|olicy Environment|4|
|(1 )|Outward Orientation|5|
|_(2)_|Adjustment to External Shocks|6|
|(3)|Macroeconomic Policies|8|
||(a)<br>Price Stability|9|
||<br> <br>(b)<br>Monetary and Credit Policies|9|
||(c)<br>Fiscal Policy|1 0|
||<br> <br>(d)<br>Exchange Rate Policy|11|
||<br> <br>(e)<br>Industrial and Trade Policies|11|
||<br>(f)<br>Policy Stability and Credibility|12|
||(g)<br>Income Distribution|13|
|(4)|Capital Flows and Portfolio Management|1 4|
|(5)|Domestic Savings|15|
|Cunciusions||1 6|
|Tables||18|
# **Table 6: Interaction with Institutions**

|**Model**||**Bill**|**ionaire and**|**Year Fixed E**|**ffects**||
|---|---|---|---|---|---|---|
|**Dependent Variable**||**Log of**|**Billionaire N**|**et Worth (in**|**Billions)**||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Log Difference of Commodity Net Export Price Index x WEF<br>Local Competition (0717)|-0.758**<br>(0.361)||||||
|Log Difference of Commodity Net Export Price Index x WEF<br>Market Dominance (0717)||-0.488*<br>(0.253)|||||
|Log Difference of Commodity Net Export Price Index x WGI<br>Control of Corruption (0118)|||-0.326**<br>(0.153)||||
|Total natural resources rents x WEF Local Competition (0717)||||-0.018***|||
|||||(0.005)|||
|Total natural resources rents x WEF Market Dominance<br>(0717)|||||-0.023***<br>(0.005)||
|Total natural resources rents x WGI Control of Corruption (01-<br>18)||||||-0.009*<br>(0.005)|
|Log difference of commodity Net Export Price Index (historic<br>rolling weights)|4.224**|2.367**|0.387***||||
||(1.855)|(1.041)|(0.144)||||
|Total natural resources rents (% of GDP)||||0.102***|0.108***|0.011***|
|||||(0.028)|(0.022)|(0.003)|
|Log of GDP (constant 2010 US$)|0.145***|0.144***|0.144***|0.098**|0.104**|0.100**|
||(0.053)|(0.053)|(0.053)|(0.049)|(0.050)|(0.048)|
|Constant|-3.672**|-3.663**|-3.666**|-2.333*|-2.506*|-2.401*|
||(1.525)|(1.524)|(1.523)|(1.408)|(1.430)|(1.365)|
|Billionaire Fixed Effects|YES|YES|YES|YES|YES|YES|
|Year Fixed Effects|YES|YES|YES|YES|YES|YES|
|Number of observations|20,493|20,493|20,502|18,342|18,342|18,351|
|R2|0.290|0.290|0.290|0.280|0.282|0.278|
|Adjusted R2|0.289|0.289|0.289|0.279|0.281|0.278|

note: *** p<0.01, ** p<0.05, * p<0.1, Robust Standard Errors Clustered at the Billionaire level 18
for labor in the economy (Walras’ law). People who are legally eligible to work are assumed to spend their time either working or consuming leisure. The model permits capital mobility across the production sectors. However, the total capital stock in the economy is assumed to remain unchanged with a policy change. The difference between total value outflow (e.g., imports of goods and services) from the country and the total value inflow (e.g., exports and transfers from the rest of the world) to the country is defined as the current account balance, while total investment is the sum of total savings comprising of household saving, government saving and the current balance.

# **3.2 Incorporating energy subsidy removal into the CGE model**

In order to incorporate the direct government subsidy to the electricity sector, we compared the size of the subsidy to the total value of electricity sold. In 2012, the base year of our model, direct electricity subsidy was equivalent to 33% of the value of electricity sales reported in the social accounting matrix (SAM), the main database required by a CGE model. This means that the non-subsidized average price of electricity for consumers would have been 33% higher than the reported value. The elimination of this subsidy would mean increasing the electricity price to 1.33 times its starting (subsidized) value. This is implemented in the model as a 33% tax added onto the subsidized price of electricity. It is assumed that the government saves the amount which has been transferred to the electricity sector as a subsidy. This saving could be used in the economy for other purposes. Alternative options to utilize the saved subsidies will be discussed later.

In the case of natural gas, the situation is different. The gas sector does not receive a direct subsidy from the government; instead gas is sold at a price lower than its opportunity cost. The opportunity cost is the cif price of imported LNG. Under the baseline (business as usual) the price of gas sold in Bangladesh is expected to be a weighted average of the prices of domestically produced and imported gas. Over time the gas mix used in Bangladesh will tend towards being 100 percent imported, so the weighted average price of gas will approach the price of imported gas in the long run (we have assumed this would occur by 2030). Therefore, the natural gas subsidy removal policy simulated here requires an increase over time in the domestic retail price of natural gas (i.e., the weighted average price of domestically produced and imported gas) to the price of imported gas. Table 2 depicts the evolution over time of the ratio of the imported price of natural gas and the weighted average price of gas. The forecast for the volume of gas supplied 12
# **_Table 6: Gender Disaggregated Impact of COVID-19 Related School Closures with/without Child Dependents in the Household in 2021_**

|||||**20**<br>|**21**<br>||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||||Time S|pent on....|||
||Busine<br>(1=Ye|ss closed<br>s; 0=No)|Pai|d Work|Care and<br>Respo<br>(Self/|Domestic<br>nsibilities<br>Married)|Care an<br>Respo<br>(Spous|d Domestic<br>nsibilities<br>e/Married)|
||_Children_<br>_u18_|_No Children_<br>_u18_|_Children_<br>_u18_|_No Children_<br>_u18_|_Children_<br>_u18_|_No Children_<br>_u18_|_Children_<br>_u18_|_No Children_<br>_u18_|
|Female Respondent|0.037**|0.008|-1.202***|0.028|2.253***|0.418**|-2.141***|-1.175***|
||[0.02]|[0.02]|[0.26]|[0.27]|[0.22]|[0.18]|[0.20]|[0.20]|
|School closure due to COVID-19|0.028|-0.011|0.103|0.984**|0.535*|0.314|-0.291|-0.276|
||[0.03]|[0.03]|[0.37]|[0.45]|[0.32]|[0.34]|[0.32]|[0.43]|
|School closure due to COVID-19*Female|-0.041|0.044|0.698*|-0.850**|-0.552|1.168***|1.020***|0.746*|
||[0.03]|[0.03]|[0.37]|[0.41]|[0.36]|[0.35]|[0.36]|[0.39]|
|Academic Break|-0.1|-0.015|2.191**|0.853|-0.147|0.430|-0.724|1.356|
||[0.06]|[0.09]|[0.97]|[1.06]|[0.79]|[1.35]|[0.89]|[1.40]|
|Academic Break*Female|-0.029|-0.134|-2.319|-0.638|1.063|-0.886|-1.045|-2.607|
||[0.08]|[0.11]|[1.53]|[1.58]|[1.08]|[1.92]|[1.09]|[2.51]|
|GDP per capita (Ln)|-0.012|-0.019*|0.558***|0.376**|0.219*|-0.290**|-0.154|-0.373***|
||[0.01]|[0.01]|[0.15]|[0.16]|[0.12]|[0.12]|[0.13]|[0.14]|
|Constant|0.217|0.352***|4.026**|5.654***|0.090|7.371***|6.561***|8.743***|
||[0.13]|[0.12]|[1.60]|[1.78]|[1.42]|[1.37]|[1.43]|[1.57]|
|Observations|6333|6608|5576|5678|5294|4168|5026|3919|
|Adjusted R-squared|0.03|0.03|0.07|0.06|0.13|0.09|0.11|0.09|
|Mean Male|0.09|0.08|7.23|6.94|4.17|3.43|5.93|4.11|
|Region FE<br>Notes:* significant at 10% level, ** significant at 5% leve|YES<br>l, *** significant a|YES<br>t 1% level.|YES|YES|YES|YES|YES|YES|

<mark>Notes:* significant at 10% level, ** significant at 5% level, *** significant at 1% level.</mark> [1] Female is a dummy variable where 1 = female-led business and 0 if male-led business. School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of the survey wave.

[2] OLS pooled regressions include Region of the world and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents).

[3] Full sample is split among those business owners with any child dependents aged 18years or less in the household = 1 (Columns (1), (3), (5) and (7)) versus no children dependents aged 18years or less in the household (Columns (2), (4), (6) and (8)).

[4] Survey in 2021 is one survey wave that was completed in September 2021.

42
mainland regions of Tanzania to conform to the price analysis reported in section 3. The LSMS‐ ISA surveys covered 4,232 mainland Tanzanian households in 2008‐09, 4,241 households in 2010‐11 and 4,215 households in 2013‐14.

Interviews took each time place over a 14 month period within the two year survey window, with the majority of households surveyed within the 12 month period of that year. Very few interviews took place in either the first month (September 2008) or the final month (October 2009) of the initial wave. The second two waves had full initial months but a relatively small number of interviews in the final one or two months (October and November 2011 and November 2013). Pearson χ<sup>2</sup> tests reject randomness of the interview months over regions.<sup>17</sup> This is remedied through the inclusion of household fixed effects and inclusion of time varying household characteristics, including the asset level of the household (as a proxy for wealth), household size (persons, not adult equivalents), the sex and education level of the identified head of household, a rural‐urban dummy and dummy variables for region of residence and survey wave.<sup>18</sup> Households were not generally interviewed in the same month in each wave. This is partly due to inter‐regional movements and partly due to changes in the interview months for households that remained in the same region. 5.3 percent of households crossed regional boundaries between the first two waves and 6.8 percent did so between the second and third waves. 55.2 percent of households that remained in the same region were interviewed in the same calendar month in the 2008‐09 and 2010‐11 rounds and 40.3 percent were interviewed in the same month in the 2010‐11 and 2012‐13 rounds. Only 20.5 percent of households that remained in the same region for all three waves were interviewed in the same calendar month in all three waves. These features of the data ensure that the month of interview is a time‐varying variable permitting the estimation of seasonal effects using the FE estimator.

> 17 The χ2(260) were in excess of 4500 for each of the three waves.

> 18 Household size is the number of household members. The number of equivalent adults gives a poorer fit. The rural‐urban dummy distinguishes rural from “other urban” households outside Dar es Salaam which is regarded as entirely urban and which is accounted for by one of the regional dummies.

17