rafmacalaba/datause-encoder-data huggingface dataset:
_80_ _Estimates and Projetions of Mortality by Cause_ The revised Asian rates are lower and more reasonable-and possibly even too low, since we estimate Chinese rates per 100,000, from survey data, at 4.1 for males 45-64 and 9.0 for males 65 and over.

# _Complications of pregnancy_

WHO compiled maternal mortality rates from a variety of sources for around 1983, and these provide our expert assessment. The compilation is based on information available at the headquarters of the World Health Organization-in the library, in the documentation centres and in the possession of individual members of the staff. All relevant data bases-e.g. MEDLINE, POPLINE-were searched, as were national yearbooks, the reports of consultants, needs assessment missions, evaluation studies, etc. Helpful colleagues around the world who know of our interest also provided information (WHO 1986:4).

With this type of search, a possible upward bias in the results, unless a rigorous standard of data quality is imposed, cannot be excluded. Such a bias could occur because of selective reporting of higher (and therefore more 'interesting') figures, selective dismissal of reports including no maternal deaths (e.g., because of lack of evidence whether the category was used), and the utilization of somewhat older data where nothing else is available. No attempt to minimize this bias is evident in the report, which in fact goes out of its way to citP- exceptionally high figures in a brief overview.

Mateinal deaths are more commonly expressed in relation to live births rather than to population. Reexpressing the regression estimates as a rate per 100,000 livebirths gives this comparison with the WHO report (where the Middle East and North Afica region is made up by combining Northern Africa and Western Asia from the WHO report):

||Regression estimates|WHO (1986)|
|---|---|---|
|Developing countries|191|450|
|Latin America and Caribbean|103|270|
|Sub-Saharan Africa|229|680|
|Middle East and North Africa|<br>153|430|
|Asia|204|420|

The WHO (1986) figures, it should be noted, do not appear to be simple averages of those collected in the report. Though they are probably based on these data, their specific derivation is not explained. The differences between these figures and our regression estimates are substantial. In mining the WHO report for statistics to include in the World Development Report, the World Bank (1988:286) rejected some of the higher numbers as inconsistent among 3hemselves, ending up with reduced average maternal mortality rates of 329 in low-income economies and 381 in middle-income economies. A further reduction would be necessary to agree with the regression estimates, as well as with deaths reported to WHO. For Latin America and the Caribbean, for instance, reporting countries have a matemal mortality rate of 75. If this
14 proof parent proposal. Both proposals are designed to make insulation more effective than it would be in the basic bank holding company proposal.

Fail-Proof Bank Proposal The fail-proof bank proposal is essentially an extreme form of the basic bank holding company proposal previously discussed.<sup>6</sup> The fail-proof bank proposal would force banks to separate their traditional deposit issuing and lending functions. Once the proposal is implemented, banks would be confined to issuing deposits and investing in virtually risk-free assets, such as short-term government securities or perhaps high quality commercial paper. All previous bank activities that involved any meaningful degree of risk would be transferred to holding company affiliates. These affiliates also would do all of the future lending for the banking organization.

Under the proposal, banks would be required to closely match their asset and liability maturities to virtually eliminate interest rate risk. Moreover, banks would be prohibited from engaging in bond trading, :oreign exchange trading or conducting various off balance sheet activities. Banks also would be required to have a small amount of capital that would be sufficient to absorb any remaining, unavoidable risks. Any transactions between a fail-proof bank and its holding company affiliates would have to be on market terms, and examiners would closely monitor all intercompany transactions to make sure that the bank was not abused.

Because fail-proof banks would be virtually risk-free, the government could fully insure all bank deposits without exposing the government to any significant 6 rhe fail-proof bank proposal was originally eseloped<sup>4</sup> by Robert J. Lawrence, and was subsequently elaborated upon by Robert Litan in a Brookings Institution study. See Robert J. Lawrence, "Minimizing Regulation of the Financial Servlces Industry," Issues in Bank Requlation, (Summer 1985), pp. 2231; and Robert Litan, What Should Banks Do?, The Brookings Institution, 1987.
that not be possible, subregions<sup>17</sup> are created, and countries with insufficient data are assumed to have a similar propensity to accept migrants as other countries in the subregion. Failing this option, global propensity measures are constructed.<sup>18</sup> More than a single method of allocation is chosen so that the data already in the matrices can be used to maximum effect. All these allocations ignore the gender profile of migrants. This dimension is accounted for at a later stage, once all the aggregate categories have been assigned.

# _Varying Survey Dates_

During the 10-year window of each census round, there are no conventions on when a destination country should conduct its census. Although many destination countries conduct their censuses at the turn of the decade, the actual date is up to each country. Attempting to standardize census dates would require changing the numbers reported in the original census documents.

Most destination countries conduct their census within two years of the middle year of each census round—between 1998 and 2002 for the 2000 census round, for example (table 3). The census numbers thus are not changed, and the matrices report all censuses as comparable in each round. A full list of census dates is in appendix 1. An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer.

# **{Table 3 here}**

# _Calculating Missing Gender Splits_

Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5.

# _Combining Migrant Definitions_

Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see <u>http://unstats.un.org/unsd/methods/m49/m49regin.htm, with the countries of Oceania</u> aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV.

18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.

12
28 Table 5. Annual Population Inereass and Growth Rate by Wsrld BSnk Reglon and Country Department, 1985-2100

||Arnual p|opulatio|n<br>ircre|ase (|millions)|Annu|al growt|h rate|(perce|nt)|
|---|---|---|---|---|---|---|---|---|---|---|
|Regionand<br>country<br>departmit|_<br>1985-90|2000|2025|2050|2100|1985-90|2000|2025|2050|2100|
|World total|87.6|90.6|74.4|40.5|6.9|1.73|1.46|0.90|0.42|0.07|
|Borrowersandnon-borrowers|||||||||||
|Sub-Saharan Africa|15.7|20.8|22.5|16.9|2.2|3.17|2.87|1.75|0.95|0.10|
|Asia and Oceania|46.0|44.2|31.4|13.2|3.0|1.71|1.35|0.75|0.28|0.06|
|Europe, Middle East,|||||||||||
|and North Africa|15.3|15.8|13.0|<br>8.0|<br>1.3|1.29|1.14|0.74|0.40|0.06|
|America|10.8|10.0|<br>6.9|2.6|<br>0.7|1.55|1.21|0.66|0.23|0.06|
|Borrowers only|||||||||||
|Africa Region|14.5|19.5|21.5|16.1|2.0|3.22|2.94|1.80|0.97|0.10|
|I Occidental<br>and Central|<br>1.4|1.9|<br>1.9|<br>1.5|<br>0.2|3.31|2.91|1.69|0.94|0.10|
|2Eastern|3.7|4.9|5.5|4.1|<br>0.5|3.19|2.92|1.80|0.98|0.10|
|3 South-Central<br>and|||||||||||
|Indian Ocean|1.8|2.4|<br>2.7|2.0|<br>0.2|3.12|2.88|1.79|0.97|0.09|
|4 Western|4.4|5.8|<br>6.3|4.7|<br>0.6|3.36|3.01|1.80|0.97|0.10|
|5Sahelian|1.1|1.5|1.8|1.4|0.2|2.75|2.75|1.83|1.00|0.12|
|6 Southerm|2.2|3.0|<br>3.3|2.5|<br>0.3|3.32|2.99|1.82|0.98|0.09|
|Asia Region|44.4|42.5|31.2|13.4|<br>2.9|1.78|1.39|0.79|0.30|0.06|
|1 Other South|3.4|4.1|<br>3.6|2.4|<br>0.4|2.38|2.14|1.25|0.66|0.09|
|2 Southeast|6.0|6.1|<br>4.6|2.1|<br>0.6|2.08|1.67|0.92|0.36|0.09|
|3China|15.4|14.1|8.6|2.0|0.0|1.43|1.10|0.56|0.12|0.00|
|4 India|16.2|14.9|11.6|5.9|1.5|2.01|1.49|0.88|0.38|0.09|
|5 Indonesioa md Pacific|3.2|3.4|2.5|1.1|0.3|1.83|1.54|0.87|0.32|0.09|
|EDN Region|11.9|13.5|12.1|7.7|1.3|2.36|2.03|1.21|0.62|0.09|
|1 Pakistan and Turkey|4.5|4.8|4.9|3.4|0.4|2.84|2.23|1.42|0.76|0.08|
|2 North Africa|1.6|1.9|<br>1.5|<br>0.9|<br>0.2|2.66|2.29|1.22|0.57|0.10|
|3 Middle East|5.4|6.4|5.5|3.7|0.6|2.94|2.47|1.34|0.70|0.10|
|4 Europe|0.5|0.5|0.2|0.0|0.0|0.49|0.42|0.15|0.04|0.03|
|LAC Region|8.5|8.6|6.6|2.8|0.7|2.07|1.66|0.93|0.34|0.08|
|1Brazil|2.9|2.9|2.1|0.9|0.3|2.05|1.61|0.87|0.33|0.12|
|2 Central America|2.5|2.8|2.2|1.0|0.2|2.26|1.93|1.06|0.39|0.08|
|3Caribben<br>and Other|1.6|1.6|1.2|0.5|0.1|2.14|1.65|0.94|0.33|0.04|
|4 Tewperate and Tropical|1.5|1.4|1.0|0.5|0.1|1.79|1.38|0.79|0.32|0.06|

Note: World Bank regions and country departnents are defired in Table 14.
knots are fixed at -1 σ and 1 σ (where σ stands for standard deviations of GDP trend deviations in our sample), in the third row they are fixed at -1.5 σ and 1.5 σ , and in the fourth (bottom) row the knots are fixed at -2 σ and 2 σ ; these specifications therefore allow for the slope to differ between GDP shocks of different signs and magnitudes, with the cut-off between “large” and small” shocks varying across specifications. All of the regressions are based on the unit data, and all allow for country-specific cubic trends in time.

Table 7 presents a similar pattern to that observed in Figure 2. Positive shocks to per capita GDP affect girls and boys in a similar fashion. On the other hand, negative shocks have larger effects on the mortality of girls than boys. For example, for negative shocks of -1.5 σ or larger, a one unit decrease in log per capita GDP is associated with an increase in girl infant mortality of -124.95 (with a standard error of 58.13), but an increase in boy infant mortality of only -26.00 (with a standard error of 35.88). Figure 2 and Table 7 therefore appear to be consistent either with girls being more fragile in their first year of life than boys, which seems unlikely, or with families protecting boys more than girls during economic downturns. Put differently, this suggests that household behavioral responses to negative shocks play an important role in determining infant survival. Finally, these results underscore the argument that the main results are not due to omitted variables, as these omitted variables would have to interact with both the gender of the child and the direction of the income shock. It is hard to imagine what such an omitted variable would be.

We conclude this section by providing a sense of the magnitude of the estimated effects. The average year-on-year decrease in infant mortality in our sample is 2.5 per 1000 live births. On average, countries with a negative shock to per capita GDP of 1.5 σ or larger had a contraction of 5.9 percent. (There are 122 such events in our data.) The average increase in girl 25
goes to state controlled companies, regardless of their profitability, leaving companies in the private sector credit constrained. Lending by banks is also hampered by the lack of adequate information on borrowers’ credit histories and financial performance, making lending to SOEs seem a low-risk option, given their government backing. The pervasive state ownership of the banking sector has resulted in a huge ratio of non-performing loans to total loans, poor profitability, poor institutional framework of the banking system, and weak corporate governance. Banking depth in fact seems to be negatively correlated with inter-provincial differences in growth rates as shown by Boyreau-Debray (2003) and Boyreau-Debray and Wei (2005).

However, the inefficiency of Chinese bank lending should not be overstated, especially for recent years since there has been a massive restructuring of the banking sector, including allowing entry of foreign banks, both to prepare the banks for equity listings and strategic sales and as a condition of China’s accession to the WTO. Using firm-level survey data, Cull and Xu (2000, 2003) find that bank finance was associated with higher subsequent firm productivity, while government transfers were not. In addition, some analysts (Anderson, 2006) have argued that while the banking system is characterized by a short tail of corporate customers, the Chinese banks do not discriminate against private borrowers per se but do discriminate against small borrowers, including smaller SOEs, without visible cash flow. More recently, the China Banking Regulatory Commission has passed new laws encouraging banks to lend to small and medium enterprises.<sup>9</sup>

> (eg: Export-Import Bank of China), Second-tier Commercial banks (eg: CITIC Industrial Bank) and Trust and Investment Corporations (ITICs) who capture smaller portions of the lending market.

> 9 In July 2007, the China Banking Regulatory Commission promulgated the _Guidelines on Banks’ Credit Businesses to Small Enterprises,_ geared towards improving loan services to small and medium enterprises.

8
by agricultural settings and rainfall, reflecting a substantial experimental literature on agricultural index insurance, which is discussed below.

_Labor markets_ are another important release valve for climate stress, because climate change generates differential costs across sectors (Nath 2021). Labor reallocation is therefore an important way by which households can cope with climate shocks. Studies on labor markets comprise 10 percent of the economics literature on adaptation, with a primary focus on labor supply choices by households. Labor market studies cover all climate shocks, with the majority focusing on rainfall and temperature anomalies.

_Public policy_ is a broad category capturing large-scale policy responses that aim to soften the costs of climate change—for example, through post-disaster relief transfers or infrastructure projects. Public policy is the subject of only 11 percent of the literature on climate change adaptation, with the remainder studying micro-behavioral responses by individual agents. Because of the scale of the adaptation challenge, individual adaptation is unlikely to be sufficient, requiring broader policy responses. More work is needed to evaluate the effectiveness of such responses.

_Transfers_ constitute all public and private assistance that may help households or firms cope with climate shocks. Transfers are less well-studied, representing just 8 percent of the literature and mostly comprising studies on households. Because transfers often occur after disasters, roughly half of these papers study natural catastrophes.

_Infrastructure_ projects—such as irrigation in drought-prone regions and flood protection systems in low-lying areas—are an important component of the global adaptive response to climate change. However, they comprise just 6 percent of the studies on climate change adaptation.

_General_ adaptation studies typically estimate the extent of adaptation in a given setting without providing a specific mechanism. For example, a large literature on global agriculture interprets differential crop yield responses to weather variation in the long- and short-run as evidence of adaptation over time (Wing et al. 2021; Mérel and Gammans 2021). Similarly, several studies find evidence of nonlinear temperature effects on outcomes such as mortality (Barreca et al. 2015) and industrial output (Chen and Yang 2019), with smaller effects in hotter locations. These papers comprise 11 percent of the literature, although the proportion is higher (16 percent) among top journal studies.

# **3.2. Adaptation by firms**

The small but growing literature on firm adaptation has primarily focused on three methods of adaptation: reallocation (30 percent of all firm studies), technology adoption (19 percent), and financial markets (14 percent).

Firms exposed to climate shocks reallocate both inputs and outputs to mitigate climate losses. On the inputs side, Xie (2022) shows that Indonesian firms employ more skilled labor and imported inputs as temperature rises, because heat disproportionately affects the productivity of unskilled 8
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BE B0L 8 ARCiy ECUM INDI KAZI NGM AGoe ALL @ vv @ aco @ecy @ cra @rak ear
|P044289|1999|2009|11/27/2008|8 Closed|Brazil|
|---|---|---|---|---|---|
|P044305|2000|2000||0 Closed|Chad|
|P044305|2000|2001|6/27/2001|2 Closed|Chad|
|P044305|2000|2002|6/13/2002|4 Closed|Chad|
|P044305|2000|2003|2/4/2003|5 Closed|Chad|
|P044305|2000|2004|5/28/2004|7 Closed|Chad|
|P044305|2000|2005|5/6/2005|9 Closed|Chad|
|P044324|1997|1997||0 Closed|Zambia|
|P044324|1997|1998||4 Closed|Zambia|
|P044324|1997|1999||6 Closed|Zambia|
|P044324|1997|2000|6/20/2000|8 Closed|Zambia|
|P044324|1997|2001|6/22/2001|10 Closed|Zambia|
|P044324|1997|2002|5/14/2002|12 Closed|Zambia|
|P044324|1997|2003|5/20/2003|14 Closed|Zambia|
|P044325|1998|1998||1 Closed|Venezuela,Republica|
|P044325|1998|1999||3 Closed|Venezuela,Republica|
|P044325|1998|2000|6/27/2000|5 Closed|Venezuela,Republica|
|P044325|1998|2001|6/28/2001|7 Closed|Venezuela,Republica|
|P044343|1998|2000|5/5/2000|6 Closed|Honduras|
|P044343|1998|2001|3/21/2001|8 Closed|Honduras|
|P044343|1998|2002|6/27/2002|12 Closed|Honduras|
|P044343|1998|2003|6/4/2003|14 Closed|Honduras|
|P044343|1998|2004|6/24/2004|17 Closed|Honduras|
|P044343|1998|2005|5/4/2005|19 Closed|Honduras|
|P044343|1998|2005|6/27/2005|20 Closed|Honduras|
|P044383|1997|1997||0 Closed|Senegal|
|P044383|1997|1998||2 Closed|Senegal|
|P044383|1997|1999||3 Closed|Senegal|
|P044383|1997|2000|6/15/2000|6 Closed|Senegal|
|P044383|1997|2001|4/19/2001|9 Closed|Senegal|
|P044387|1996|1996||1 Closed|Armenia|
|P044387|1996|1997||4 Closed|Armenia|
|P044387|1996|1998||6 Closed|Armenia|
|P044387|1996|1999||8 Closed|Armenia|
|P044387|1996|2000|6/14/2000|10 Closed|Armenia|
|P044388|1996|1996||1 Closed|Georgia|
|P044388|1996|1997||3 Closed|Georgia|
|P044388|1996|1998||4 Closed|Georgia|
|P044388|1996|1999||6 Closed|Georgia|
|P044390|1996|2020|7/14/2019|10 Closed|Bosnia and Herzegovin|
|P044391|1998|1998||1 Closed|Bosnia and Herzegovin|
|P044391|1998|1999||3 Closed|Bosnia and Herzegovin|
|P044391|1998|2000|6/29/1999|3 Closed|Bosnia and Herzegovin|
|P044393|1997|1997||6 Closed|Bosnia and Herzegovin|
|P044393|1997|1998||8 Closed|Bosnia and Herzegovin|
|P044393|1997|1999||10 Closed|Bosnia and Herzegovin|
|P044395|1997|1997||5 Closed|Bosnia and Herzegovin|
36

|Table 7. Robustness c|hecks|||
|---|---|---|---|
||Financial reforms<br>Fiscal reforms|Real sector reforms|Trade reforms|
||**Panel A: Accounting for the effect**<br> <br>|**s of economic recovery**<br>||
|Reforms|0.986***<br>(0.0344)<br>0.429***<br>(0.0150)|10.74***<br>(0.375)|0.342***<br>(0.0120)|
|Dummy - crisis|-2.651***<br>(0.0630)<br>-0.393***<br>(0.0259)|-0.176***<br>(0.0318)|-0.453***<br>(0.0244)|
|Observations|10,822<br>10,822|10,822|10,822|
|R-squared|0.2360<br>0.2360|0.2360|0.2360|
||**Panel B: Additional cont**|**rol variables**||
|Reforms|0.244***<br>(0.063)<br>0.227***<br>(0.0432)|6.390***<br>(1.212)|0.176***<br>(0.033)|
|Log. Net book value<br>of capital|<br> <br>0.020***<br>(0.006)<br>0.018***<br>(0.006)|<br>0.018***<br>(0.06)|<br>0.018***<br>(0.003)|
|Log. Investment in<br>capital|0.005<br>(0.005)<br>0.005<br>(0.006)|0.005<br>(0.005)|0.005<br>(0.005)|
|Observations|2880<br>2880|2880|2880|
|R-squared|0.3470<br>0.3470|0.3470|0.3470|
||**Panel C: min-max**|**index**||
|Reforms|0.282***<br>(0.0159)<br>0.671***<br>(0.0378)|2.043***<br>(0.115)|0.253***<br>(0.0143)|
|Observations|10,822<br>10,822|10,822|10,822|
|R-squared|0.2360<br>0.2360|0.2360|0.2360|
||**Panel D: Value Added**|**per worker**||
|Reforms|0.116***<br>(0.007)<br>0.125***<br>(0.008)|3.264***<br>(0.210)|0.0898***<br>(0.006)|
|Observations|9. 942<br>9. 942|9. 942|9. 942|
|R-squared|0.80<br>0.80|0.80|0.80|
||**Panel E: Total factor p**|**roductivity**||
|Reforms|0.122***<br>(0.0271)<br>0.114***<br>(0.0253)|3.204***<br>(0.709)|0.0882***<br>(0.0165)|
|Observations|4,965<br>4,965|4,965|4,965|
|R-squared|0.3398<br>0.3398|0.3398|0.3398|

Notes: The table presents standardized coefficients of the effects of reforms on firm-level labor productivity growth (Panels A, B, C), value added per worker (Panel D), and Total factor productivity (Panel E) using a multilevel mixed effects model. The specifications include firm’s individual characteristics, sector and year fixed effects. At the macroeconomic level, the specifications include the average growth of GDP, the level of inflation, and the quality of policies and regulations related to the private sector and country fixed effects. All estimates use the weights and are standardized so that can be compared across structural reforms. Robust standard errors clustered at the country *** ** * <u>level are in parentheses. , , denote significance at the 1, 5, and 10 percent level.</u>