rafmacalaba/datause-encoder-data huggingface dataset:
and can be used to test whether an event has a significant impact on the banking industry as a whole. The first set of results reported in section 4 will come from this simple model to establish that there is an event affecting the banking industry in the selected dates, before turning to test whether the events have a differential effect across banks. The two-factor model, the preferred one, already controls for the returns of the banking industry FTSE index, so, the __ 0, __ coefficients only capture random differences between the banks in the sample and in the index, thus, the two-factor model only provides a test for _differential_ effects across banks.

To further control for any country-level effect of the events not captured by the FTSE bank index, the preferred specification permits the __ 0, __<sup>coefficients to vary across countries. This</sup> further ensures that the identification of the __ 1, __<sup>coefficients comes exclusively from within-</sup> country, across-banks differences in abnormal returns and provides a sharp test of the hypothesis that bank’s use of wholesale funds is driving the impact of the events. The final, preferred specification, therefore, corresponds to where __ 0, __ , _c_ measures the country _c_ ’s average abnormal return at time __ . Nevertheless, I will also report results for the simpler specification in equation (3) to show the role of cross-country differences.

Beyond the baseline specification, additional regressions will control for the possibility that other bank characteristics may drive any differential effect of the event among banks with high and low wholesale dependence by estimating the following abnormal return model: where the vector _Xi c_ , includes several bank characteristics that may be related to its vulnerability to the crisis such as size, leverage, ownership, and type of bank, which may be also interacted with wholesale dependence.

I estimate the parameters of equation (1) using a window of 180 calendar days before June 30, 2007 (the normal times), and a window of 10 days before and after each event for the 12
_____. 1999. “Have International Transport Costs Declined?” University of Chicago, July. Mimeo.

- Hummels, David, and Alexandre Skiba. 2004. “Shipping the Good Apples Out: An Empirical Confirmation of the Alchian-Allen Conjecture.” _Journal of Political Economy_ 112: 1384-1402.

- Kee, Hiau Looi, Alessandro Nicita, and Marcelo Olarreaga. 2008. “Estimating Trade Restrictiveness Indices.” _Economic Journal_ , forthcoming.

- Limao, Nuno, and Anthony J. Venables. 2000. “Infrastructure, Geographical Disadvantage, Transport Costs, and Trade.” _World Bank Economic Review_ 15(3): 451-79.

- Martin, Will, and C. Pham. 2008. “Estimating the Gravity Model When Zero Trade Flows are Important.” World Bank. Mimeo.

- Moenius, Johannes. 2004. “Information Versus Product Adaptation: The Role of Standards in Trade.” International Business and Markets Research Center Working Paper, Northwestern University.

- Negri and Porto. 2008. “Burley Tobacco Clubs in Malawi: No Market Institutions for Exports.” Policy Research Working Paper No. 4561, World Bank, Washington, D.C.

- Ng, Francis, and Alexander Yeats. 1996. “Open Economies Work Better! Did Africa’s Protectionist Policies Cause Its Marginalization in World Trade?” _World Development_ 25(6): 889-904.

- Njinkeu Dominique, John S. Wilson, and Bruno Powo Fosso. Forthcoming. “Intra-Africa Trade Constraints: The Impact of Trade Facilitation.” World Bank. Mimeo.

- Otsuki, Tsunehiro, John S. Wilson, and Mirvat Sewadeh. 2001. “What Price Precaution? European Harmonization of Aflatoxin Regulations and African Groundnut Exports.” _European Review of Agricultural Economics_ 28(3): 263-84.

- Porto, Guido. 2008. “Agro-manufactured Export Prices, Wages and Unemployment.” Policy Research Working Paper Series 4489, World Bank, Washington, D.C.

- Portugal-Perez, Alberto. 2008. “The Costs of Rules of Origin in Apparel: African

- Preferential Exports to the US and to the EU.” Policy Issues in International Trade and Commodities, Study Series No. 39. UNCTAD.

- Raballand, Kunaka, and Giersing. 2008. “The Impact of Regional liberalization and Harmonization in Road Transport Services:  A Focus on Zambia and Lessons for Landlocked Countries.” Policy Research Working Paper No. 3840, World Bank, Washington, D.C.

- Santos Silva, J., and S. Tenreyro. 2006. “The Log of Gravity.” _The Review of Economics and Statistics_ 88: 361-65.

- Teravaninthorn, Supee, and Gael Raballand. 2008. _Transport Prices and Costs in Africa: A Review of the Main International Corridors_ . Washington, D.C.: World Bank (forthcoming).

- Wilson, John S., Catherine Mann, and Tsunehiro Otsuki. 2003. “Trade Facilitation and Economic Development: A New Approach to Measuring the Impact.” _World Bank Economic Review_ 17(3): 367-89.

- Wilson, John S., and Tsunehiro Otsuki. 2004. “Standards and Technical Regulations and Firms in Developing Countries: New Evidence from a World Bank Technical Barriers to Trade Survey.” Mimeo.

- Wilson, John S., Alberto Portugal-Perez, and Ben Taylor. 2009. “Issue brief on South Africa and Trade Costs.” Mimeo.

36
26 TABLE 9: Weights, Return, & Standard Deviation of the Minimum-Risk Portfolio for International Stock Markets

|Market|Weight of<br>the Market|Weight of<br>GSCI|Expected<br>Return|Portfolio<br>Standard<br>Deviation|
|---|---|---|---|---|
|United States|.49|.51|1.42|3.1|
|United Kingdom|.41|.59|1.42|3.5|
|**Japan**|.36|.64|1.14|3.6|
|Germany|.32|.68|1.38|3.8|
|Switzerland|.42|.58|1.29|3.4|
|France|.36|.64|1.50|3.6|
|Hong Kong|.30|.70|1.80|3.8|
|Taiwan (China)|.13|.87|1.67|4.2|
|Korea|.22|.78|1.53|4.3|
|Mexico|.11|.89|1.78|4.5|
|Thailand|.28|.72|1.75|3.8|
|Brazil|.04|.96|1.48|4.7|
|India|.15|.85|1.54|4.5|
|EAFE (Europe, Asia<br>and Far East)|.34|.66|1.32|3.8<br>l|
|Europe|.42|.58|1.52|3.6|
|World|.48|_.52_|1.29|3.3|

Data sources: See Tables 1 and 2.
In (b), we set the capital stock to zero in 1980 and assume the investment in the following years was equal to a percentage of the investment in the first year we had available for each asset (2000 or 2001). The version is the most optimistic, assuming that Brazil invested as much in intangible assets annually during 1980-1999 as it did in 2000.

As shown in Figure 12, our estimate of the stock in 2008 is robust to a wide range of starting values, even one as extremely conservative as zero in 1999.<sup>23</sup> It is in a range of 200-225 billion R$, which is about 8 percent of its business tangible capital stock. Thus, even though Brazil has been spending as much on intangible assets as on tangible assets in recent years, higher depreciation rates imply markedly less accumulation of the former. However, even in comparison to the US, this 8 percent is not a trivial ratio. In 2003, business intangible capital stock in the US was about 27 percent of the tangible capital stock.<sup>24</sup> <!-- Start of picture text --> Figure 12: Estimated Intangible Capital Stock under different assumptions<br>250<br>225<br>200<br>175<br>K1999 = 0<br>150<br>K1980 = 0 and λ = 1<br>125<br>K1980 = 0 and λ = 0.85<br>100<br>K1980 = 0 and λ = 0.75<br>75<br>K1980 = 0 and λ = 0.5<br>50<br>25<br>0<br>2000 2002 2004 2006 2008<br>R$ billions<br><!-- End of picture text --> This exercise also serves to highlight a policy implication of the relatively short life of intangible assets. Compared to fixed assets, an increase in intangible investment can have a large impact on the intangible

> 23 This also shows that setting an arbitrary  ̳zero date‘ for the capital stock (such as 1980) should not really make a difference. 24 CHS (2009) estimate a US business intangible capital stock of 3636 billion USD in 2003.  The US current-cost net stock of private fixed assets (excluding households) was to the tune of USD 13,225 billion in 2003 (Source: BEA).

29
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unfortunately not the case in the climate change context. What follows focuses on some key enforcement issues.

<u>2.4. Defining carbon border taxes in an international environment</u> First is the definition of carbon border taxes in an international context. In a closed economy, carbon domestic taxes are defined in specific terms, that is, in dollars or euros per physical unit of carbon (or any other mix of greenhouse gases). Such a definition makes a carbon tax equivalent to a price signal, the goal sought after.

However, the huge heterogeneity among the world economies introduces considerations requiring a more balanced way to define carbon border taxes. For instance, poor economies tend to value climate change policies less highly than rich economies. Some major emerging economies feel more immediately concerned by sulfur dioxide (SO2) emissions or by water shortages (all the more because both cases raise local, not world, problems) than by CO2 emissions. Ignoring such considerations lead to an impasse, as recently illustrated by the Copenhagen Conference.

Table 1 shows how crucial and delicate the appropriate definition of carbon border taxes is. It assumes that producing one widget in a developed (domestic) country would cost $10,000 and emit 10 tons of carbon if the carbon tax is $60 per ton. Producing a similar widget in a competitive developing (foreign) country would cost $8,500 and emit 20 tons of carbon, the carbon tax imposed by the developing country being $6 per ton. In what follows, expressing border taxes in _ad valorem_ terms (that is, in percentage of the value added) emerges as an interesting option for taking into account the world heterogeneity. Hence, Table 1 shows the _ad valorem_ equivalents of the various carbon taxes. Looking at each country separately, the _ad valorem_ equivalent of the domestic carbon tax is 6 percent in the developed country, and 1.4 percent in the developing country.

Applying the NT rule combined with the country of destination principle requires first that the exporting developing country eliminates the domestic carbon tax it imposes on its exports. Hence, widgets exported by the developing country land at the border of the importing developed country at a cost of $8,500 (row 4).

The importing country has three main possibilities for defining its carbon border tax, as illustrated in Table 1. The first option (column “trade”) is based on the specific carbon tax of the _importing_ country combined with the carbon content of the _exporting_ country. The second option (column “specific”) is based on the _specific_ carbon tax and carbon content of the _importing_ country. The third option (column “ _ad valorem_ ”) is based on the _ad valorem_ 11
In columns (3) and (4), we run the analysis separately for export crops and food crops. The gap remains larger for food crops, at 40 percent. It is notable that overall, the gap in food crop productivity increases by 16 percentage points when comparing households that have similar characteristics, compared to the unconditional gender gap in food crops. Similarly, after introducing our covariates in the model, the gap for female farmers who farm export crops becomes significant, even though it is less than half of that found in food crops (17 percent).

Overall, we see that the same inputs that matter for overall productivity also matter for export and food crop productivity separately. Some differences that emerge between the two are that the total number of crops produced only matters for export crops and that the number of fields is more correlated with food crop productivity than with export crop productivity. The use of inputs such as pesticide and especially chemical fertilizer matter more for export crop productivity, while the negative effect of purchased seed comes from food crops only. Interestingly, household labor use is more important for food crops, with female labor and having a household head that suffers from a disability are not significantly correlated with export-crop productivity.

**Table 4. OLS regressions of agricultural productivity with control variables (2008)**

||**Log [Sel**<br>|**f-reported**<br>|**Productivity (**<br>|**XOF/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
|Female-headed households|-0.377***<br>(0.057)|-0.309***<br>(0.058)|-0.190*<br>(0.100)|-0.505***<br>(0.065)|
|Log [Total land area cultivated in ha]|-0.647***<br>(0.016)|-0.675***<br>(0.016)|-0.710***<br>(0.024)|-0.595***<br>(0.019)|
|Household head age (years)|0.005***|0.005***|0.005***|0.004***|
||(0.001)|(0.001)|(0.002)|(0.001)|
|Married|0.022|0.010|0.075|-0.062|
||(0.068)|(0.068)|(0.103)|(0.089)|
|Divorced/Separated|0.042|0.036|-0.063|-0.042|
||(0.091)|(0.091)|(0.142)|(0.108)|
|Widowed|-0.073|-0.085|-0.053|-0.092|
||(0.090)|(0.089)|(0.137)|(0.110)|
|Household head has Primary education|-0.012|-0.026|-0.050|0.007|
||(0.037)|(0.036)|(0.052)|(0.045)|
|Household head has Secondary education|-0.021|-0.024|0.033|-0.083|
||(0.046)|(0.045)|(0.064)|(0.055)|
|Household head has Tertiary education|0.255|0.242|0.470|-0.075|
||(0.211)|(0.206)|(0.289)|(0.222)|
|Household head disability|-0.156**|-0.160**|0.046|-0.169*|
||(0.073)|(0.072)|(0.107)|(0.089)|
|Dependency ratio|0.012|0.010|-0.015|0.004|
||(0.017)|(0.017)|(0.026)|(0.021)|
|Household Male Labor Use (Number)|0.122***<br>(0.017)|0.118***<br>(0.017)|0.069***<br>(0.026)|0.099***<br>(0.021)|
|Household Female Labor Use (Number)|0.054***|0.054***|0.012|0.059***|
||(0.017)|(0.017)|(0.026)|(0.019)|
|Household Child Labor Use (Number)|-0.001|-0.000|0.007|0.026|
||(0.026)|(0.025)|(0.042)|(0.031)|
|Hired Labor Use (Number)|0.020***|0.020***|0.020***|0.016***|
||(0.003)|(0.003)|(0.004)|(0.003)|
|Number of Fields Managed by the household|0.074***|0.076***|0.043**|0.085***|

9
the market. Second, the scholarship that covered most of the tuition costs aimed to tackle a participation constraint originated by the high costs of these courses. Third, the coding schools in both locations worked closely with gender specialists to define strategies to make bootcamps more gender inclusive, key in order to increase participation in a sample of female beneficiaries. Last, the eligibility criteria were designed to attract a sample of women with high motivation, willingness to complete the course, and high potential to benefit from it.

In this paper, we analyze the short-term effects of the program on two sets of primary outcomes of interest: skills, and employment in a technology job. Follow-up data collected between 2 and 5 months after program completion indicates that the program has a strong positive impact on skills, measured by an exam that resembles a coding test that an employer would use during an interview to an applicant for an entry-level coding job. We also find positive and significant effects of the program on the probability of getting a job in technology. Importantly, when we compare technology jobs with the rest of the jobs in our sample, we find that the former are 43 percent more likely to offer work-from home arrangements and flexible hours, and generate a job satisfaction that is 12 percent higher compared to non-technology jobs. These findings are in line with Goldin (2014). Given that the pay rate in technology jobs is similar to other jobs (at least in this short-term analysis), we interpret this increase in flexibility and job satisfaction as a net improvement in overall job quality. This is not trivial, since there exists evidence in the labor literature that documents that workers may take pay cuts when moving to a new job if that new job offers better amenities or career perspectives (Bagger et al., 2014).

The previous results refer to February 2020, which was between 2 and 5 months after program completion. Additionally, we also gathered employment information referred to May 2020. This provided us with a unique opportunity to measure whether the program effects make beneficiaries more or less resilient to the onset of the COVID-19 crisis. The evidence suggests so, especially for the sub-sample of the self-employed.

From a short-term perspective, our results are encouraging. We interpret them as proof that the program is effective at making its target population break a barrier of entry into employment in _tech_ in relatively little time. It does so by, first, designing a strong intervention that effectively tackles barriers that could prevent program participation. Then, we show that it imparts a valuable set of skills that is sought by employers in the sector, which ultimately translates into an increase in the probability of finding a job in technol26
-56-

### CHAPTER IV

# BINARY COMPARISONS OF REAL **OUTPUT** AND PURCHASING POWER MEXICO/BRAZIL IN 1975

## Introduction

This chapter supplements chapter III, which showed two binary comparisons between Brazil/USA and Mexico/USA. with a direct comparison between Mexico and Brazil.

The major conceptual difference between this and the previous chapter is that we are able here to make comparisons for our sample industries for our preferred "national accounts" concept of value added instead of the "US census concept", which we had to use for the detailed industry comparisons of chapter III because the necessary information was not available for the USA.

Another advantage of a direct Mexico/Brazil comparison is that we did not have to make an adjustment for different benchmark years, as was the case in the comparison with USA.

## Levels of Real Gross Output in 17 Industries

This chapter covers the same sample of industries as in chapter III. We attempt to match products of Mexico (country M) with products of Brazil (country B), in one case in terms of the unit values of country B (cruzeiros) and in the other case in terms of the unit values of country M (pesos). We made two separate comparisons, namely the ratio of country M's quantity of output to that of country B, in unit values of country M and in unit values of country B: and <!-- Start of picture text --> 1 (QM PM)<br>M Y_ __Y ---- (4.la)<br>E (QB *PM)<br>y y<br>z (QM * pB)<br>B B (4.lb)<br>I (QB * PB)<br>y y<br><!-- End of picture text --> Table 4.1 shows total gross value of output for these 17 industries as derived from the industrial censuses, expressed in both national currencies with conversion at the average official exchange rate of 1.5375 pesos to the cruzeiro in 1975. In the last column of the table, Mexico/Brazil gross output ratios are presented. In the case of Brazil, our sample covered 42 per cent of gross value of output, in Mexico 38 per cent.
we use the subset that is collected and standardized for the organization’s biannual benchmarking report, the _MicroBanking Bulletin_ . While the summary statistics are available in the _Bulletin_ and on the MIX website, we were granted access to the more detailed underlying dataset. Starting with an original dataset on 124 institutions in 49 countries, we incorporated additional observations and data and variables, bringing the largest dataset to 346 institutions in 67 countries.

The data have important qualities. First, they provide multiple indicators to measure topics of interest; for example, the _Bulletin_ includes three separate measures of customers’ poverty levels. Second, the data are self-reported but are then independently verified to ensure coherence and consistency. Third, the data are adjusted to improve comparability across institutions using different reporting formats and the records are modified to account for implicit subsidies.

The data are not, however, representative of the full population of microfinance institutions. The data over-represent institutions that both have a commitment to financial sustainability and that are willing to comply with the MIX’s relatively rigorous reporting standards. Because of this, the institutions are more likely to be industry leaders in terms of financial performance, and the data should be seen as giving a sense of best-case financial possibilities.

Bauchet and Morduch (2010) analyze differences between the MIX dataset and the larger database of the Microcredit Summit Campaign, a microfinance advocacy organization that promotes social change. As expected, they find that the MIX data are more heavily tilted toward financially sustainable institutions, and the latter toward institutions with strong social objectives. For example, the average operational self-sufficiency ratio of institutions reporting to the Microcredit Summit Campaign is 95 percent, compared to 115 percent for institutions reporting to the MIX. The MIX data also tilts more heavily toward Latin America and Eastern Europe, relative to the heavier Asia representation of the Microcredit Summit database.

On top of this general bias, the _MicroBanking Bulletin_ adjustments to profit assume an implausibly low opportunity cost of capital. The calculations use a country’s deposit rate (as reported by the International Monetary Fund) as the assumed benchmark cost that microfinance 4
Table D4: Effect of Placebo Module Position on SI Effects for IPV Questions

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if responded y|es to _individual_ q|uestion in the fo|llowing category:|All|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|SI _×_ Placebos First (_β_)|0.10***|0.05***|0.02**|0.05***|0.06***|
||(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|SI _×_ IPV First (_γ_)|0.08***|0.05***|0.00|0.01|0.04***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Placebos First|0.01|0.02|-0.01|-0.01|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|FTFI _×_ IPV First mean|0.11|0.06|0.04|0.04|0.07|
|_p_-value (_β_ =_γ_)|0.275|0.916|0.276|0.030|0.217|
|Observations|11,887|6,802|10,181|5,095|33,965|
|**Panel B. Liberia**||||||
|SI _×_ Placebos First (_β_)|0.05***|-0.00|0.00|0.02|0.02|
||(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|
|SI _×_ IPV First (_γ_)|0.05**|0.02|0.01|0.04**|0.03**|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Placebos First|0.00|0.00|0.01|0.00|0.01|
||(0.02)|(0.02)|(0.02)|(0.01)|(0.01)|
|FTFI _×_ IPV First mean|0.20|0.19|0.09|0.04|0.14|
|_p_-value (_β_ =_γ_)|0.965|0.415|0.587|0.415|0.614|
|Observations|8,752|5,006|7,508|3,758|25,024|

Note: Observations at respondent-question level. Regressions include question-level fixed effects, individual controls (including all variables in Table 1), and the order between IPV module and PHQ-9 module. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

38