rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4. Panel B - Lending to Non-Energy Sectors, by Borrower Size**

This panel presents the coefficients of the regression in equation 2, testing the impact of the price shock on loan value to non-energy borrowers. The dependent variables, all in logs, are loan value, value to working capital, and value to investment to firm _f_ in month _m_ by bank _b_ . _Postm_ is an indicator that month _m_ is after the energy price shock in September 2014. The results indicate that banks that were more exposed to the energy sector reduced relatively more their lending. Standard errors are double clustered at the bank and month levels. Detailed variable definitions are provided in Table A1. The sample period is from January 2013 to June 2016. *** <u>p<0.01, ** p<0.05, * p<0.1.</u>

||Total Le<br>(1)|ndingf,b,m<br>(2)|Working C<br>(3)|apitalf,b,m<br>(4)|Invest<br>(5)|mentf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|ExposureEnergyb,Aug14*Postm|-0.027***|-0.005|-0.020**|0.006|-0.047***|-0.062***|
||(0.007)|(0.005)|(0.008)|(0.010)|(0.006)|(0.014)|
|Observations|1,026,135|236,519|1,026,135|236,519|1,026,135|236,519|
|R-squared|0.766|0.847|0.816|0.840|0.872|0.871|
|Borrower size|Small|Large|Small|Large|Small|Large|
|Bank*firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|SD(ExposureEnergyb,Aug14)|6.1|6.1|6.1|6.1|6.1|6.1|

32
higher per capita income than the educated farmer households. The evidence, however, is exactly opposite; the point estimates for children born into more educated farmer parents are substantially larger.<sup>34</sup>

# **Parents as Children: Estimates from the Grandparents-Parents Sample**

Table 3 reports the estimates of the effects of grandparent’s occupational and educational status on the schooling of parents (household head and spouse). We call the children in this generation ‘parents’ (mothers and fathers) and the parents are called ‘grand-parents’. This helps keep track of three generations when we compare the effects of family background over time. Similar to Table 2, the education dummy is defined with primary schooling as the threshold.

The OLS estimates of _β_ 1 in equation 1 reported in column (1) of Table 3 show that the average impact of grandparents education depends on the gender: it is higher for mothers (1.03) compared to that for fathers (0.76). This is in contrast to the apparent gender neutrality in the average effects in children’s generation reported in column (1) of Table 2. All of the estimated effects for mothers are positive, numerically substantial and statistically significant at the 1 percent level, implying that, the girls (mothers) from the households with low educated farmer parents (i.e., grandparents) had faced the most disadvantages in schooling among the four different groups. Among the other groups, the mothers who were born into households with both higher education and non-farm occupation have the highest schooling attainment; compared to those born into low educated farming households, they attain about 1.50 years of more schooling. This is a 50 percent larger effect compared to the average effect of grandparents education in mothers sample reported in column 1 (1.03). Interestingly, the advantage for a girl (mother) of being born into a business (non-farm)

> 34Compared to the estimates for daughters, the difference between the point estimates between these two groups for sons is smaller, implying that income may have played a more important role for the sons.

26
We can also compare our results with the capital requirements that would be generated by the Basel formula, using the corporate bond historical one year PD default rate computed by S&P for different rating categories (column 8) and applying it to the similar rating obtained by each country for its sovereign risk in domestic currency exposures to obtain expected and unexpected losses (columns 9 and 10).

Finally we compare our results to those of a similar exercise conducted by Carey (2002) with a loan portfolio intended to mimic the risk exposure of a representative US banks. It is interesting to observe that the level of EL in a period of extreme stress such as that represented by the 1929 crisis would have been lower of the EL in a crisis scenario, as the Argentine one.

As an example of the many possible ad hoc applications of our resampling procedure, we report in the Appendix, a breakdown by banks of different size of the aggregate results reported in Table 3 and in Chart 2 for Mexico, that illustrates how risk exposures may vary across different groups of intermediaries.

Three preliminary conclusions can be drawn from the results. The first is that the Basel formula, applied to our sample of non-G10 countries, generates levels of protection inferior to the claimed 99.9%. As the IRB is currently calibrated, the degree of protection would be in the range of 95-99% of the credit loss distribution. Another way to state this result is that to achieve the 99.9% level protection advocated by the Basel Committee, countries would require substantially increased capital requirements over and above the 8% minimum requirement advocated in Basel II.<sup>15</sup> Moreover the fact that we have found

> 15 Basel II’s IRB formula in fact calculates assets at risk and then capital requirements are defined as 8% of assets at risk.  So equally our results may be interpreted as saying that assets at risk must be larger

11
<!-- Start of picture text --> 2.5<br>2.0<br>L5<br>1.0<br>0.5<br>0.0<br>0.5<br>FF FS S SEF SES SF<br>FFF LS FS SF SF FS 8<br>» °° eS 4 ,&<br>< Ss<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>4<br>2<br>SF oe. sos Se Ss &oS Ss<br>RNG “ycS gS sS »a<br><!-- End of picture text -->
# **6. Conclusion**

This paper provides the most comprehensive analysis to date of the cross‐country allocation of trust funds, a relatively new aid channel that can be described as a hybrid between bilateral and multilateral aid. Donor rhetoric suggests that some World Bank‐administered trust funds are intended to support countries neglected by IDA for legal, political, and other reasons. Potentially, therefore, the cross‐country allocation of these trust funds may compensate for the performance‐ based allocation of IDA funds, “diluting” the poverty‐ and policy‐selectivity of total World Bank‐ administered aid. We test whether donors’ allocations of trust funds tend to favor recipients with lower per capita incomes and more favorable policy environments, as measured by the Bank’s CPIA. Results show that World Bank trust fund allocations are similar to IDA, and dissimilar to other bilateral aid (summed over all DAC donors) in terms of their policy‐ and poverty‐selectivity.

The allocation of recipient‐executed and single‐donor trust funds, which are under closer control of donor countries, appears to be more strongly motivated by strategic interests of donor countries than trust fund aid in general. However, a more complete investigation of the role of donor interest variables would require a different data set and different unit of analysis (donor‐recipient pairs) than we use in this paper.<sup>28</sup> We also explore trust fund disbursements for three specific sectors, and find evidence that health and education aid, but not environmental aid, are policy and poverty selective across countries.

Overall, the evidence indicates that multi‐bi funds administered by the World Bank do not undermine IDA’s allocation criteria, even if donors’ contributions to them partially crowd out their IDA contributions. There is very little evidence regarding the latter issue of crowding out, and we leave for future research the question of whether the increase in trust funds has affected core contributions to IDA, and their potential effects on other aspects of World Bank operations.<sup>29</sup> The question of the additionality of earmarked funds at the aggregate level remains open as donors might just reshuffle funds away from multilateral organizations they perceive as less effective. Finally, as experience with trust fund aid accumulates, another important research question will be its effectiveness, relative to core multilateral and traditional bilateral aid, in contributing to growth and human development outcomes.

> 28 Factors related to donors’ and recipients’ domestic political economies are investigated by Dietrich (forthcoming) and Eichenauer and Reinsberg (2016).

> 29 One review of World Bank non‐lending technical assistance projects rated the quality of fully‐trust funded projects lower than projects that were wholly or partially Bank‐financed, and concluded that the difference was attributable in part to less attention by management and staff to trust funded projects (see IEG 2011a: ch. 3).

15
However, actuarially sound premium rates would be expected to fall as the insurance unit is defined over more and more individual farm units.

The fact that yield risk tends to fall as the unit of observation increases reflects factors that are associated with the correlation of yields across units. Spatial diversification may be possible if yield correlation is less than perfect. Individual units may be separated by a considerable distance. Individual farm units may have their own idiosyncratic factors affecting yields. For example, the soil quality on one unit may be much higher than on another.

# _Multi-field contracts_

In the case of the U.S. crop insurance program, an across-the-board discount of 10% is given to farmers that insure their individual fields growing the same crop (which are called “optional units”) as a whole (which is called the “basic unit”).

However, under the optional units, it may be difficult for the insurer to verify the field from which insured production originated. To the extent that it is difficult to monitor and verify production from individual fields, producers may have an incentive to “shift” production across individual fields. For example, if two fields had an identical guarantee of 100 bushels per acre and each field produced 105 bushels, no indemnity would be paid. If, however, the producer reports that one field generated 125 bushels and the other generated 85 bushels, indemnities would be triggered. This point involves actions on the part of producers that are obviously fraudulent. The extent to which such actions actually take place in the U.S. crop insurance program is unclear, though unpublished research has suggested that there is evidence of such yield switching in some areas and for some crops.<sup>5</sup> When the interest is in addressing whole farm risk or risk at some aggregate level, policies based on production across all relevant fields should be considered. In addition, the potential for fraudulent claims through yield shifting across fields may exist when coverage is provided at the individual field level. This is essentially a monitoring and yield verification problem and thus its relevance depends upon the extent to which adjusters can verify yields on individual fields.

# _Multi-crop contracts_

Most contracts are developed for a single crop. However, there may be some value to provide coverage over multiple crops due to the diversification effect, as this effect applies over multiple fields for a same crop. If yields across crops are not perfectly correlated, overall risk will be lower when multiple crops are combined into a single contract and, therefore, such contracts may be offered at a lower price. For example, shortfalls in corn yields may be offset by good soybean production on a single farm. In such a case, corn insurance would trigger an indemnity payment while soybeans would not. However, when total revenues from the two crops are combined, no indemnity is triggered since the below average corn yields are offset by above average soybean yields.

> 5 Unpublished research at Montana State University has provided evidence that may be consistent with such switching.

9
|Years of education<br>(0.2)|||||3.4<sup>∗∗∗</sup>||
|---|---|---|---|---|---|---|
|**3.2 4. Household**|||||||
|% its members in services|||||0.3<sup>∗∗∗</sup>||
|(0.0)|||||||
|% its members in agriculture|||||-0.0<sup>∗</sup>|(0.0)|
|% its members in industry|||||0.2<sup>∗∗∗</sup>|(0.0)|
|No one employed|||||19.7<sup>∗∗∗</sup>|(1.9)|
|Household size|||||-11.6<sup>∗∗∗</sup>|(0.3)|
|Dependency ratio|||||-10.4<sup>∗∗∗</sup>|(0.5)|
|Has electricity|||||55.1<sup>∗∗∗</sup>|(2.6)|
|Owns toilets|||||43.9<sup>∗∗∗</sup>|(5.7)|
|Own more than 2 plots|||||10.6<sup>∗∗∗</sup>|(1.7)|
|**5. Aid received in the last year**|||||||
|MASAF or Education aid|||||3.7<sup>∗∗</sup>|(1.7)|
|**6. Geography**|||||||
|Rural Household|-53.6<sup>∗∗∗</sup>|(3.7)|-53.5<sup>∗∗∗</sup>|(3.7)|-9.8<sup>∗∗∗</sup>|(3.4)|
|Distance to town (km)|0.0|(0.0)|0.0|(0.0)|0.0|(0.0)|
|District FE|Yes||Yes||Yes||
|IHS round FE and Season FE|Yes||Yes||Yes||
|Observations|35755||35755||35627||

Note: The coefficients capture the percentage change in per capita consumption without aid (food and cash). Standard errors are clustered at the district level. Standard errors in parentheses:<sup>∗</sup> _p <_ 0 _._ 10,<sup>∗∗</sup> _p <_ 0 _._ 05,<sup>∗∗∗</sup> _p <_ 0 _._ 01 Households’ exposure to droughts decreases their consumption per capita, after accounting for any increase in food aid and cash support in response to such a disaster (Table 2). Drought reduces the consumption of households, on average, by 3 percentage points, compared to those that are not exposed to drought. Of course, the magnitude of the impact varies based on the severity of drought (as measured in the Palmer index ranging from 2 to 4). The extent of this decline in per capita consumption increases from 5 percent to 12 percent as the intensity of drought increases. This range of impact magnitude is what we find when considering the coefficient in Table 2 and the Palmer Index ranging from a minimum of 2 to a maximum of 4 (as in Table 1).<sup>13</sup> Excess rainfall in the first two months of the growing season does not decrease consumption per capita after harvest, while excess rainfall in the following months of the growing season appears to increase consumption by 1 percent. When we exclude transfers from the food consumption basket, the negative coefficients on weather shocks decrease further and the positive ones become insignificant (see Table 3). Therefore, the coefficients on excess rainfall in Table 2 seem to be upward biased due to

> 13 We find similar conclusions and impact magnitudes as the ones drawn from the linear model. In the primary regression, we opted to use the drought intensity indicator, instead of a dummy variable for drought, to be able to leverage this variation in drought intensity in the simulations.

16
Policy ReseaRch WoRking PaPeR 4737

# **Abstract**

It is generally expected that immigrants do not fare as well as the native-born in the U.S. labor market. The literature also documents that Blacks experience lower labor market outcomes than Whites. This paper innovates by studying the interaction between race and immigration. The study compares the labor market outcomes of four racial groups in the United States (Whites, Blacks, Asians, and Hispanics) interacted with their foreign born status, using the Integrated Public Use Micro Data Series data for the 2000 Census. Among women and for labor market outcomes such as labor force participation, employment, and personal income, the foreign born are doing worse than the native born from the same racial background, with the exception of Blacks. Among men, for labor force participation and employment, foreign-born Blacks are doing better than native Blacks. The paper tests different possible explanations for this “reversal” of the advantage of natives over immigrants among Blacks. It considers citizenship, ability in English, age at and time since arrival in the United States, as well as neighborhood effects, but concludes that none of these channels explains or modifies the observed reversal.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the team to better understand the consequences of international migration. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ddewalque@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
29

# Table 1: Brazil - Indices of Growth of Female-Headed Families and Households, 1960-1987

## Share of Female-Headed Families and Households (1960-1986)

(percent) Year

## Urban Areas

(share of families)

## All Brazil

(share of households)

|1950|-|12.1|
|---|---|---|
|1960|14.0|10.7|
|1970|15.7|13.0|
|1980|18.0|15.6|
|1986|20.6|18.4|

Sources: **Mrricik** and Schmink (1983). **Goldai** (1989), and author's tabulations

## Growth of Female-Headed Households in Urban Areas by Location (1978.1986) (percent change in share)

|Location|FlIH|FHH wlth children|
|---|---|---|
|All Brazil|18|14|
|Center/Northwest|13|0|
|Southeast|26|16|
|Northeast|_5_|0|
|South|21|16|
|Metropolitan Areas|33|14|

Source: **auhor's** tabtlations

## Families Headed by Females by Metropolitan Area (1978-1987)

|Metropolitan Area|1978|1981|1982|(percen<br>1983|t)<br>1984|1985|.1986|1987|Percentage change<br>in share, 1978-1987|
|---|---|---|---|---|---|---|---|---|---|
|Northwest||||||||||
|Forteleza|18.8|21|21.2|22.2|22.2|23.8|21.6|24.6|30.8|
|Recife|22.7|24.3|23.6|23.6|25.5|25.6|23.2|27.2|19.8|
|Salvador|20.8|21.7|22.2|22.5|23.8|25.3|25.4|26.2|26|
|Southeast||||||||||
|Belo Horizonte|20.3|20|19.9|21.7|21.9|23.3|23.9|23.9|17.7|
|Rio de Janeiro|18.3|21.2|21|21.7|23.1|23|24.1|24.3|32.8|
|Sao Paulo|15.3|16.4|16.2|17.2|18.7|18.8|20.7|19.1|24.8|
|Scath||||||||||
|Curitiba|14.5|17.3|15.1|_15.5_|18.9|18.4|18.1|18.1|24.8|
|Porto Alegre|16.9|19.1|4.75|19.4|20.3|20.5|21.3|21.2|25.4|
|Center/Northwest||||||||||
|Belem|23.5|24.7|23.7|25.7|26.1|25.6|27.5|27.1|15.3|

Source: author's tabulauoas
**Table 2.5  Comparison of high-tech manufacturing and total manufacturing sectors, 1990-2001**

||Year|Output<br>$mn|No. of workers|Value added<br>$mn|Fixed<br>Assets<br>$mn|Value added/<br>worker<br>$’000|
|---|---|---|---|---|---|---|
|Electronics manufacturing (a)|1990|27,878.1|122,797|7,716.6|3,757.3|62.8|
|Pharmaceutical products manufacturing (b)|1990|1,020.8|1,664|809.1|na|486.2|
|High-tech manufacturing (a+b)|1990|28,899.0|124,461|8,525.7|na|68.5|
|Total manufacturing (c)|1990|71,333.2|351,674|21,606.8|18,030.6|61.4|
||||_Percentage_||||
|Electronics manufacturing|1990|39.1|34.9|35.7|20.8||
|Pharmaceuticals manufacturing|1990|1.4|0.5|3.7|na||
|(a+b)|1990|40.5|35.4|39.5|na||
|Electronics manufacturing (a)|2001|62,201.5|98,012|11,615.3|15,911.0|118.5|
|Pharmaceutical products manufacturing (b)|2001|5,134.2|2,375|2,796.8|2,085.3|1,177.6|
|High-tech manufacturing (a+b)|2001|67,335.7|100,387|14,412.0|17,996.2|143.6|
|Total manufacturing (c)|2001|138,323.0|345,141|31,922.8|48,129.6|92.5|
||||_Percentage_||||
|Electronics manufacturing|2001|45.0|28.4|36.4|33.1||
|Pharmaceuticals manufacturing|2001|3.7|0.7|8.8|4.3||
|(a+b)|2001|48.7|29.1|45.1|37.4||

_Note_ : The high-tech manufacturing sector comprises SSIC35220 medicinal & pharmaceutical products and SSIC 384 electronics for 1990. Comprises SSIC 242 pharmaceutical products and SSIC31 electronics for 2001 _Sources_ : Economic Development Board (EDB). (1990). _Report on the Census of Industrial Production 1990_ . Singapore: EDB Economic Development Board (EDB). (2001). _Report on the Census of Manufacturing Activities 2001_ . Singapore: EDB 12
# **<u>Table 2: Model Parameters and calibration</u>**

||**Baseline Scenario**|
|---|---|
|**_Estimated Parameters_**||
|Share of informal job offers (_ψ_)|0.77|
|Bargaining power for the formal sector (_βf_)|0.99|
|Bargaining power for the informal sector (_βi_)|0.99|
|Scale factor in matching function (_a_)|0.19|
|Arrival of self-employment opportunities (_α_)|0.11|
|Job destruction rate in the informal sector (_λI_)|0.11|
|Job destruction rate in the formal sector (_λF_)|0.01|
|Job destruction rate for the self-employed (_λS_)|0.08|
|**_Labor market composition_**||
|Share informal work|0.297|
|Share formal work|0.201|
|Share self-employment|0.278|
|Mean log-wage informal sector|7.48|
|Mean log-wage formal sector|8.01|
|Std log-wage informal sector|6.33|
|Std log-wage formal sector|6.64|

Source: Author’s own estimates using the 2015 MLFS.

# **Deregulation:**

Our first set of simulation assume the full abolishment of payroll taxes and of the minimum wage. One would expect that abolishing such regulations would decrease the labor costs for formal employment. As such, in general, such policy would contribute to a lower unemployment and to increase the share of workers with jobs in the formal sector. Simulations confirm such results, with a much higher impact for youth and women than for the average worker. Results are presented in Table 3. Changes in the composition of employment are presented in percentage points while changes in wages are presented in percent. In order to assess the statistical significant of the results, standard errors for every scenario are calculated using bootstrapping techniques.<sup>11</sup> Results indicate that deregulation (i.e. abolishment of payroll taxes and the minimum wage) would contribute to a 2.7 percentage point decrease in unemployment (about 12 percent), to a 1.0 percentage point decrease in informality (about 3 percent), to a 2.1 percentage point decrease in self-employment (about 8 percent), and to a 5.7 percentage point increase (about 28 percent) increase in formal employment. Estimates also indicate that deregulation would contribute to slightly decrease in wages in the formal sector (of about 4 percent). Estimated impacts are much larger for youth and women. For instance, results indicate that deregulation would contribute to decrease in unemployment of 11.6 percentage points (or 38 percent) for women and of 6.2 percentage points for youth (or 16 percent).

> 11 For all parameters of interest (P), let So denote the standard error for the benchmark scenario and Si the standard error for the simulated scenario. For every simulation scenario a _t_ statistic (as its associated _p_ -value) was calculated So<sup>2</sup> Si<sup>2</sup> as _t_ = Pi – Po / SE, where SE =  sqrt ~~(~~ no + ni ~~)~~ where n = {i,o} denotes the degrees of freedom (i.e. 25 iterations).

9