rafmacalaba/datause-encoder-data huggingface dataset:
challenge using a variety of approaches. Acemoglu and Johnson (2007) used the dates of international health interventions and public health improvements in the 1940s and the variation across 47 economies in pre-intervention disease mortality rates to estimate economy-specific measures of the decline in mortality to define an instrumental variable for changes in life expectancy in an economy. The authors do not find a significant effect of the predicted mortality reduction on per-capita economic growth or on years of schooling. One reason given for this finding is that increased life expectancy also boosted population growth, exerting pressure on resources for human capital investments.<sup>2</sup> Bloom, Canning, and Fink (2014) argued that the Acemoglu-Johnson finding was due to omitted variable bias, the result of excluding an initial life expectancy measure for each economy. Without controlling for this initial health variable, changes in life expectancy at birth, which were largest in the poorest economies, appeared to produce lower economic growth in those economies.

Other studies have also used the timing of health interventions and geographic variation in pre-intervention health measures as the source of exogenous change in estimating a causal relationship between life expectancy and schooling using individual-level data rather than economy-level data. Bleakley (2010a) applied this quasi-experimental approach to data on malaria prevalence rates in the US, Brazil, Colombia and Mexico. He compared the cohorts based on the pre-eradication malaria burden in their place of birth and their year of birth relative to the malaria-eradication campaigns. Cohorts born after the campaign and in areas with high pre-campaign malaria burden would have had more exposure to the benefits of the eradication programs, compared with those cohorts born before the eradication campaign. Early-life malaria

> 2 Unexpectedly large cohorts will face depressed earnings from a crowding of more individuals into the market, particularly if different birth cohorts are not perfect substitutes in production. To the extent that the potential for crowding in is expected, individuals in cohorts with rising life expectancy will moderate their human capital investment decisions to reflect anticipated depreciated earnings per unit of human capital.

4
# **Abstract**

We examine the impacts of WTO agreements and domestic trade policy reforms on production, welfare and poverty in Bangladesh. A sequential dynamic computable general equilibrium (CGE) model, which takes into account accumulation effects, is used allowing for long-run analysis. The study is based on the 2000 SAM of Bangladesh including 15 production sectors, four factors of production (skilled and unskilled labor, agricultural and non-agricultural capital) and nine household groups (five in rural areas and four in urban areas) based on the year 2000 household survey. To examine the link between the macro effects and micro effects in terms of poverty, we use the representative household approach with actual intra-group income distributions.

The study presents five simulations for which the major findings are: **(1)** the Doha scenario has negative implications for the overall macro economy, household welfare and poverty in Bangladesh. Terms of trade deteriorate and consumer prices, particularly food prices, increase more than nominal incomes, especially among poor households; **(2)** Free world trade has similar, but larger, impacts; **(3)** Domestic trade liberalization induces an expansion of agricultural and light manufacturing sectors, favorable changes in the domestic terms of trade. Although the shortrun welfare and poverty impacts are negative, these turn positive in the long run when capital has adjusted through new investments. Rising unskilled wage rates make the poorest households the biggest winners in terms of welfare and poverty reduction; **(4)** Domestic liberalization effects far outweigh those of free world trade when these scenarios are combined; and **(5)** Remittances constitute a powerful poverty-reducing tool given their greater importance in the income of the poor.

ii
bank’s return on assets has to drop below its expected value before equity is depleted and the bank is insolvent (see Roy, 1952, Hannan and Henwick, 1988, Boyd, Graham and Hewitt, 1993 and De Nicolo, 2000). Thus, a higher z-score indicates that the bank is more stable.

While the z-score has been used widely in the financial and non-financial literature, it is subject to several caveats.<sup>10</sup> Specifically, it might underestimate banking risk for several reasons. First, it measures risk in a single period of time and does not capture the probability of a sequence of negative profits. Second, it considers only the first and second moment of the distribution of profits and ignores the potential skewness of the distribution (De Nicolo 2000). On the other hand, this measurement bias is less of a concern if it is uniformly distributed across banks and countries. A third concern is the reliance of the z-score on accounting data whose quality might vary across countries. Specifically, several papers have shown the tendency of firms to smooth reported earnings over time and that the degree of earning smoothing varies with the degree of institutional development (see, for example, Leuz, Nanda and Wysocki (2003)). This, however, should bias our results against finding a significant relationship between measures of deposit insurance and bank failure resolution and bank fragility. As an alternative method, other authors have relied on stock market data to compute bank risk as a put option on the value of the bank’s assets (Laeven, 2002, and Hovakimian, Kane and Laeven, 2003). Relying on stock market data, however, reduces our sample to a small set of large, listed banks in countries with stock markets, and we therefore decide to use z-scores that can be calculated for all banks instead.

> 10 Note that this z-score differs from z-score developed by Altman (1968). The Altman z-score is a predictor of corporate financial distress based on financial ratios. The score is derived from a predictive model of a company’s probability of default that uses five financial ratios. Altman applied Multiple Discriminant Analysis to determine which financial ratios to include in the predictive model.

15
Republic, the Slovak Republic and Turkey. This, in combination with the fact that the stocks of old high ~~-s~~ killed in the labor force significantly increased in all countries, suggests an overall shift toward a more educated and older labor force in 2010 relative to 2000. This shift is the main defining feature of the OECD labor markets that dominate our results.

Columns 5 to 8 of Table 2 point to a general increase in the stock of immigrants, both in the overall population and across age ~~-~~ education groups (with the exception of Poland, the Czech Republic and the Slovak Republic). However, the relative changes in stocks are not uniform across age ~~-~~ education groups as they mostly appear to be much higher among the high ~~-s~~ killed. Furthermore, it is unclear whether the increase among the older high ~~-s~~ killed dominates the increase among the younger high ~~-s~~ killed groups.

Emigration patterns show similar characteristics as those observed for immigration. The relative changes among the high ~~-s~~ killed dominate and patterns of return migration are visible among the low ~~-s~~ killed. New Zealand, Ireland, Poland and the Slovak Republic particularly stand out as emigration countries. While the first two countries are well known as migrant ~~-~~ sending countries, for the last two countries this is related to their joining the EU in 2004 and their citizens obtaining immediate freedom to work and live in Ireland, Sweden and the UK from then onward. In comparison to changes arising from soci ~~o-~~ demographic changes, the relative changes due to emigration, although generally in the opposite direction, are quite small.

# 3.2 Key parameters

Common elasticities. ~~—~~ Simulation of the equilibrium wage effects of ageing, educational changes and migration requires the following key elasticity parameters. The analytical model includes a detailed discussion on their respectiive roles.

1. Elasticity of substitution between high-skilled and low-skilled workers (as). Most studies estimating the elasticity of substitution between high ~~-s~~ killed and low ~~-s~~ killed workers find a value no greater than 2. While Katz and Murphy (1992), Murphy et al. (1998) and Caselli and Coleman (2006) estimate og to be between 1.3 and 1.4, Fallon and Layard (1975), Angrist (1995) and Ciccone and Peri (2005) find values between 1.5 and 1.75, and Ottaviano and Peri (2012) find a value of about 2.9 We set og to 1.75 in our main scenario, the value DOP use in their intermediate scenario. We later examine how our simulated effects change when we allow og to take the value of 2, close to the value in Ottaviano and Peri (2012).

2. Elasticity of substitution between the old and the young workers (a4). In one of the <u>~ An exception is Fitzenberger et al. (2006),</u> who find values between 4.9 and 6.9, although they acknowledge this range is much higher than that found in prior studies.

16
Table 5: Direct vs. Indirect (supply chain mediated) impacts

|Dependent: Utilization Rate|(I)|(II)|(III)|
|---|---|---|---|
|Model variables|disruptions only|firm-level controls|firm + countrycontrols|
|Blackout|-0.0013*|-0.0014|-0.0025***|
|_annual hours_|(0.0008)|(0.0010)|(0.0008)|
|Region. blackout level|0.0007|0.0000|-0.0015|
|_annual hours, average_|(0.0015)|(0.0014)|(0.0012)|
|Blackout x reg. blackout level<br>|0.0005|0.0006|0.0010***|
|_interaction, x10_<sup>_-3_</sup>|(0.0004)|(0.0004)|(0.0003)|
|Water supply disruption|-0.0016***|-0.0018***|-0.0013**|
|_annual hours_|(0.0005)|(0.0006)|(0.0005)|
|Region. water disruptions|-0.0057**|-0.0055**|-0.0010|
|_annual hours, average_|(0.0025)|(0.0027)|(0.0015)|
|Water dis. x region. water dis.<br>|0.0011**|0.0011**|0.00035|
|_interaction, x10_<sup>_-3_</sup>|(0.0004)|(0.0004)|(0.0003)|
|Transport problems|-2.08***|-2.15**|-1.73**|
|_categorial, 0 (none) to 5 (very severe)_|(0.79)|(0.86)|(0.82)|
|Region. transport problems|-2.27*|-1.40|0.85|
|_categorial, 0 (none) to 5 (very severe)_|(1.28)|(1.44)|(1.61)|
|Trans. prob. x reg. trans. prob.|1.20**|1.22*|0.93|
|_interaction_|(0.55)|(0.63)|(0.61)|
|Firm age||0.12***|0.090***|
|_years since official registration_||(0.020)|(0.018)|
|Employment||0.015***|0.012***|
|_number of full time employees_||(0.002)|(0.003)|
|Inventory||-0.013**|-0.003|
|_days for most important input_||(0.006)|(0.006)|
|State ownership||-0.006|-0.020|
|_in %_||(0.042)|(0.042)|
|Export orientation||0.011|0.016|
|_% of total sales_||(0.011)|(0.010)|
|Generator ownership||1.645**|2.486***|
|_Dummy_||(0.693)|(0.711)|
|ISIC Sector Fixed Effects||YES|YES|
|Output Gap|||0.094|
|_HP residual as % of HP trend_|||(0.219)|
|Electrification|||-0.029|
|_% of population_|||(0.174)|
|Water access|||-0.426|
|_% of population_|||(0.433)|
|Population|||0.032|
|_in mio_|||(0.243)|
|Population density|||0.005|
|<br>_in 1/km^2_|||(0.051)|
|<br>Urbanization|||<br>0.556|
|_% of population_|||(0.465)|
|WGI: Government Effectiveness|||7.132*|
|_Index, standard normal_|||(4.217)|
|CountryFixed Effects|||YES|
|N|38369|30208|29675|
|R<sup>2</sup>|0.02|0.04|0.14|

20
Table A.24: Comparison with Hu and Schlosser (2015)

||36 months|before survey|C<br>120 m|hildren born<br>onths before|within:<br>survey|20 ye|ars before su|rvey|
|---|---|---|---|---|---|---|---|---|
|Died within:|1 month|12 months|1 month|12 months|5 years|1 month|12 months|5 years|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_EFMRst_|0.003|0.003|-0.004|-0.008**|-0.014**|-0.003|-0.005|-0.004|
||(0.004)|(0.005)|(0.002)|(0.003)|(0.006)|(0.003)|(0.003)|(0.005)|
|_Female_ x _EFMRst_|-0.001|0.005|0.007**|0.015***|0.020**|0.004|0.008*|0.007|
||(0.005)|(0.008)|(0.003)|(0.004)|(0.007)|(0.003)|(0.004)|(0.005)|
|N|83,961|58,596|318,555|293,190|173,146|550,075|524,710|404,666|

NOTES: This table presents estimates of equation (5). The outcome is an indicator of mortality. _EFMRst_ is computed as the difference of the female-male sex ratio at birth between firstborn girl and firstborn boy families, averaged for each state and year cell, and is computed using our version of the NFHS data. The specification includes household characteristics, state-female fixed effects and year-female fixed effects. Twin indicator and mass media exposure index are not included in the controls, and regressions are unweighted. Standard errors clustered at the state level are reported in parentheses. *** 1%, ** 5%, * 10%.

Table A.25: Replacing post indicators in our specification with _MFRst_

||||Children b|orn within:|||
|---|---|---|---|---|---|---|
||20 ye|ars before su|rvey|10 ye|ars before s|urvey|
|Died within:|1 month|12 months|5 years|1 month|12 months|5 years|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**A. Our estimates**|||||||
|_Firstgirl_ x _Female_ x _Post_1|-0.00564|-0.00691|-0.0153*|-0.0115|-0.0205*|-0.0254**|
||(0.00457)|(0.00607)|(0.00768)|(0.00769)|(0.0103)|(0.0107)|
|_Firstgirl_ x _Female_ x _Post_2|-0.00570<br>(0.00385)|-0.00550<br>(0.00424)|-0.0200**<br>(0.00744)|-0.0111<br>(0.00823)|-0.0194*<br>(0.0103)|-0.0301**<br>(0.0122)|
|N|328,072|309,860|227,129|212,385|194,173|111,442|
|**B. Replacing** _Post_1 **and** _Pos_|_t_2 **with**<br>_M_|_FRst_|||||
|_Firstgirl_ x _Female_ x<br>_MFRst_|-0.154|-0.018|-0.966|-0.092|-0.120|-0.964|
||(0.371)|(0.503)|(0.751)|(0.506)|(0.744)|(0.848)|
|N|328,072|309,860|227,129|212,385|194,173|111,442|

NOTES: This table presents estimates of equation (1) in Panel A and of equation (6) in Panel B. The outcome is an indicator of mortality. The purpose of this table is to show how the estimates change if we start with our specification and replace the post-ultrasound indicators with the average MFR used in HS. Standard errors clustered at the state level are reported in parentheses. *** 1%, ** 5%, * 10%.

70
A new household consumption survey was introduced in 2014, the Consumer Pyramid Household Survey (CPHS), collected by the private data collection company called the Center for Monitoring Indian Economy (CMIE). This is the first time since the NSS-2011 there is household consumption expenditure data to work with, opening new doors for the measurement of poverty and inequality in India. There are two limitations of the CPHS however that have to be addressed. The first is that the survey in its current form is not nationally representative (see e.g. the biases documented in Somanchi, 2021). The second is that it uses its own measure of consumption expenditure that is not readily comparable to the NSS measure of consumption.

This paper makes a comprehensive effort to address both of the above-mentioned concerns. We implement a rigorous reweighting exercise using multiple nationally representative benchmark surveys to obtain adjusted sampling weights that make the CPHS nationally representative. The adjusted weights will be put in the public domain and hopefully serve as a public good to anyone looking to use the CPHS. We address the second concern by estimating the relationship between CPHS- and NSS-consumption and using this to impute NSS-type consumption directly into the CPHS. This allows us to compare our estimates of poverty to the official estimates for 2011, and by extension evaluate how poverty and inequality have evolved over the last decade.

We find that extreme poverty in India has declined by 12.3 percentage points between 2011 and 2019 but at a rate that is significantly lower than observed over the 2004-2011 period. Poverty reduction rates in rural areas are higher than in urban areas. We detect two incidences of rising poverty in our period of analysis: urban poverty rose by 2 percentage points in 2016 during the demonetization event and fell sharply thereafter; and, rural poverty rose by 10 basis points in 2019 likely due to a growth slowdown. Our estimates of poverty for recent periods are more conservative than earlier projections based on consumption growth in national accounts and other survey data. Finally, we do not find evidence of rising consumption inequality in our analysis. Our findings are supported by a comprehensive set of independent data sources.

The approach we developed to convert CPHS consumption into NSS consumption could be used to monitor poverty between the NSS years, thereby increasing the frequency of India’s poverty estimates. The approach may also find use outside of India. The first-best approach of course is to work with actual up-to-date household consumption expenditure data. Any imputation-based estimates of poverty and inequality are inferior to survey-direct estimates that are obtained from observed household consumption data. Imputation methods are necessitated when real up-to-date household consumption data are not available. When the imputation methods considered in our study are used to estimate poverty and inequality for the years in between NSS rounds, 63
**Table S23. Strategies to cope with income lost due to COVID-19 pandemic**

||Relied on<br>Saving|Sale of<br>Assets|Reduced<br>Food<br>Consumption|Reduced<br>Non-Food<br>Consumption|Received<br>Assistance<br>from Friends<br>and Family|Received<br>Any<br>Assistance|
|---|---|---|---|---|---|---|
|Ethiopia|0.056**|-0.027**|0.126***|0.149***|-0.020|-0.020|
||(0.027)|(0.011)|(0.022)|(0.017)|(0.016)|(0.012)|
|Nigeria|0.090***|0.070***|0.592***|0.125***|0.143***|0.018|
||(0.024)|(0.014)|(0.021)|(0.014)|(0.019)|(0.014)|
|Uganda|0.228***|-0.017|0.182***|0.179***|0.141***|0.061***|
||(0.026)|(0.011)|(0.022)|(0.018)|(0.021)|(0.014)|
|Ethiopia-Nigeria|0.205|0.000|0.000|0.224|0.000|0.002|
|Ethiopia-Uganda|0.000|0.236|0.026|0.187|0.000|0.000|
|Nigeria-Uganda|0.000|0.000|0.000|0.008|0.955|0.002|
|Observations|5,595|5,595|5,595|5,595|5,595|8,719|
|R-Squared|0.015|0.025|0.217|0.012|0.031|0.011|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

50
Table 3: Own-price elasticities

|**_Price Elasticities_**|Average|1st Quintile|2nd<br>Quintile|3thQuintile|4thQuintile|5thQuintile|Rural|Urban|
|---|---|---|---|---|---|---|---|---|
|Wheat|**-1.29**|**-1.61**|**-1.34**|**-1.26**|-0.94|-0.87|**-1.46**|**-1.18**|
|Maize|**-0.74**|**-0.86**|**-0.54**|-0.61|**-0.65**|-0.25|**-1.33**|**-0.79**|
|Alcohol Tobacco|**-0.91**|-0.98|**-0.91**|-0.86|**-0.93**|-0.5|**-0.9**|**-0.93**|
|Meat|**-1.16**|**-2.23**|**-1.05**|**-0.89**|-0.8|-0.76|-1.26|**-0.87**|
|Legumes|-0.16|**-0.74**|-0.43|-0.08|-0.79|-1.72|-0.47|0.12|
|Dairy|**-1.16**|**-1.19**|**-1.11**|-1.31|**-1.15**|-1.1|**-1.07**|**-1.28**|
|Oil Fats|**-0.89**|-0.39|-1.26|-0.79|-1.27|-3.47|-1.26|-0.4|
|Vegetables|**-0.91**|-0.91|**-1.16**|-0.87|-0.85|-0.34|-1.1|**-0.8**|
|Fruit|**-1.33**|-2.46|-0.84|-0.99|-1.49|-0.96|-1.23|**-1.27**|
|Sugar|-1.79|-2.32|-2.04|-1.86|-1.31|-2.41|-0.62|-1.94|

Note: Estimates shown in boldface are greater in absolute value than twice theirs bootstrapped standard error. (5% significance level) Source: Author’s calculation using INEGI households surveys 1984-2000.

# Table 4: Cross-price elasticities (average household)

||Wheat|Maize|Alcohol<br>Tobacco|Meat|Legumes|Dairy|Oil Fats|Vegetab.|Fruit|Sugar|Others|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Wheat|**.**|0.05|0.02|-0.03|**-0.39**|**0.40**|0.13|-0.04|-0.17|-0.01|0.08|
|Maize|0.04|**.**|0.11|**1.25**|-0.02|**0.33**|**0.22**|**-0.37**|-0.15|-0.15|1.29|
|Alcohol Tobacco|0.05|0.51|**.**|0.05|-0.26|0.14|-0.08|0.06|0.13|-0.02|-0.70|
|Meat|-0.01|**0.55**|0.01|**.**|**0.35**|**0.36**|0.16|0.12|0.18|-0.09|0.41|
|Legumes|**-0.51**|-0.04|-0.10|**1.58**|**.**|0.05|-0.10|0.35|0.03|-0.13|-1.24|
|Dairy|**0.19**|**0.21**|0.02|**0.58**|0.01|**.**|0.19|-0.13|0.00|0.23|-1.14|
|Oil Fats|0.28|**0.61**|-0.04|1.11|-0.16|**0.81**|**.**|**-1.22**|-0.44|-0.24|-0.07|
|Vegetables|-0.03|**-0.41**|0.02|0.33|0.20|-0.21|**-0.47**|**.**|-0.14|-0.36|1.44|
|Fruit|-0.36|**-0.45**|0.08|1.20|0.03|-0.01|-0.44|-0.37|**.**|0.35|-0.09|
|Sugar|-0.02|-0.63|-0.01|-0.91|-0.30|1.51|-0.37|-1.43|0.54|.|4.02|
|Others|0.00|-0.02|0.00|-0.02|0.02|0.05|0.00|-0.02|0.00|-0.02|**.**|

Note: The rows shows commodity being affected and the columns the commodity whose price changes. Estimates shown in boldface are greater in absolute value than twice theirs bootstrapped standard error (5% significance level). Source: Author’s calculation using INEGI households surveys 1984-2000.

24
Table A11. Regional emissions intensity index of non-CO2 emissions, Volga Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Volga FD**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile organic**<br>**components**|
|---|---|---|---|---|---|---|
|**Row Labels**|**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|A|4,09||||||
|CA_col|1 317,14||||||
|CA_gas|60,63||||20,85||
|CA_oil|460,73|1 727,42|33,13||113,33|833,40|
|CB|5,11|68,59|55,84||||
|DB|||16,81||||
|DD|||11,76||||
|DE|||61,47||||
|DF|52,08|||||124,38|
|DG|32,26||52,89|||64,16|
|DH|8,56|||||3,80|
|DI|0,49||143,58|258,74|||
|DJ||183,86|22,50||757,44||
|DK|||10,94||||
|DL|0,62||||||

108
We also observe little dynamism among Brazilian exporters. Even given the small number of exporters, Brazil has a very low entry rate – very few firms become new exporters. On the flip side, Brazilian exporters have a very high survival rate, meaning that the few firms that export are likely to continue doing so (Figures 7 and 8).

# **5. Why do so few firms export?**

To understand why Brazil is so closed to trade and has such a small number of exporters, we will need to take a closer look at how Brazilian firms engage with the outside world. One interesting indicator is the ratio of domestic value added in exports (or its inverse, the imported content in exports).

This measure serves as an indicator of integration into transnational value chains. Countries that are integrated in those chains will show a lower share of domestic value added in exports as their exports include components and intermediate goods previously imported from other countries.

In Brazil we observe a very high share of domestic value added in total exports (Figure 9). Now this could be in part due to the fact that Brazil exports a lot of raw materials, which typically have a very high degree of domestic value added as they constitute the origin of a value chain. However, even when only looking at Brazil’s manufacturing exports (about a quarter of total exports), Brazil’s domestic value added is still extremely high (93 percent); indeed, it is the highest among the economies covered by the OECD-WTO Trade in Value Added database (Figure 10).<sup>3</sup>

> 3 <mark>The second release of the OECD-WTO TiVA database (May 2013) presents indicators for 57 economies (including all OECD countries, Brazil, China, India, Indonesia, Russian Federation and South Africa) covering the years 1995, 2000, 2005, 2008 and 2009 and broken down by 18 industries. It can be accessed at:</mark> <u><mark>http://www.oecd.org/industry/ind/measuringtradeinvalue-addedanoecd-wtojointinitiative.htm</mark></u>

9