rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B Simulations’ data**

The project data used for the simulations in Section 7 stems from CCR NovaDutra, a highway franchise in Brazil that was in place between 1996 and 2020. Yearly revenue, operational cost and investment data in current values can be seen in Table 10.

Table 10: CCR Nova Dutra – Yearly Revenue (pqt), Investment (It) and Cost (ct)

|Year|pqt|It|ct|
|---|---|---|---|
|1996|82,791|114,950|17,108|
|1997|202,782|133,364|44,662|
|1998|225,373|20,2307|53,149|
|1999|243,209|131,520|128,332|
|2000|281,570|73,650|85,060|
|2001|314,398|75,917|114,949|
|2002|353,779|33,661|157,616|
|2003|394,573|49,846|62,152|
|2004|500,107|70,063|81,315|
|2005|551,542|88,297|65,218|
|2006|611,791|75,866|79,655|
|2007|665,577|100,411|86,238|
|2008|748,324|159,720|92,432|
|2009|782,241|172,692|160,625|
|2010|900,052|203,472|79,348|
|2011|993,056|229,810|83,078|
|2012|1,050,626|197,592|82,509|
|2013|1,113,905|219,078|79,813|
|2014|1,150
). Measures can be as diverse as the creation of reception centers for migrants, helping them access essential public goods, or helping them acquire affordable housing (such as the programs launched by the South African

> 33 There is a real need to measure the characteristics of migrants with respect to age, gender, education, health, family structure, ethnicity and poverty at a _local_ level.  Generating  and using localized census data and the construction of neighborhood poverty maps at a metropolitan scale could be a prerequisite for useful research in the  Future.

> 34 This study uses cross sectional data and, therefore, only focuses on short term effects.

44
transactions not aligning with the period for which entrepreneurs are surveyed (de Mel, McKenzie and Woodruff 2009). Moreover, the shorter the recall period, the more acute is the mismatch between when items are bought and sold (Samphantharak and Townsend 2006). This is particularly problematic for small firm surveys that typically ask for data over a one-month window to minimize the risk of recall error.

Attempts to mitigate mismeasurement – such as encouraging entrepreneurs to maintain accounting records (de Mel, McKenzie and Woodruff 2009), using radio frequency identification tags to objectively measure stock turnover and profits (de Mel, McKenzie and Woodruff 2016), or using Personalized Digital Assistants to conduct consistency checks on sales and profits (Fafchamps et al. 2012) – find that alternative approaches do not perform better than simply asking for selfreported sales and profit estimates, which can yield reasonable ordering in outcomes. That said, asking entrepreneurs to enumerate profits directly runs the risk of being too complex and combines disparate elements of the business that are better remembered separately (Beatty 2010). Thus, entrepreneurs often find it difficult to respond to single-question proxies for profits or net worth, resulting in a large number of cases that cannot be estimated (Daniels 2001), high levels of item nonresponse and null values (Bruhn and Zia 2013), and significant underreporting of true values (de Mel, McKenzie and Woodruff 2009). In fact, de Mel, McKenzie and Woodruff (2009) find that small firms underreport outcomes by as much as 30% when a simple recall (or self-report) approach is used. In addition, the genuine volatility in the data caused by seasonality of production (Fafchamps et al. 2012) or variations in income (Collins et al. 2009) makes it challenging to assess the impact of firm growth programs, as well as efficacy of business partnerships, using simple selfreported profits or sales.

One solution for addressing concerns about volatility in outcomes is to collect firm-level data through multiple surveys over longer periods of time (McKenzie 2012
Log of assets is the natural logarithm of assets at the end of 2019. ROA is net income before extraordinary items divided by assets at the end of 2019. Book-to-market is the book value of equity divided by the market value of equity at the end of 2019. Cash-to-price is cash from operating activities in 2019 divided by the market value of equity at the end of 2019. Earnings-to-price is net income before extraordinary items in 2019 divided by the market value of equity at the end of 2019. Cash/assets is the sum of cash, cash and equivalents and short-term investments divided by assets at the end of 2019. Redeployability is a measure of asset redeployability at the four-digit SIC industry level as of 2015 from Kim and Kung (2017). Beta is the coefficient of a regression of the daily log stock return on the daily log return of the MSCI World index. Affected industry is a dummy variable indicating industries that are relatively affected by the COVID-19 crisis. Fiscal response/GDP is the total amount of economic stimulus spending announced in a country until March 17, 2020 divided by GDP.

||Observations|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Abnormal return|2211|0.00555|0.0626|-0.157|0.150|
|CDS change|111|-4.508|7.874|-26.68|39.25|
|dMVE/Book assets|2211|0.0294|0.115|-0.671|0.510|
|dMVD/Book assets|111|0.000595|0.00114|-0.00653|0.00393|
|dMVE/(dMVE + dMVD),<br>dMVE > 0, dMVD > 0|63|0.879|0.203|0.109|1.000|
|dMVE/(
Structural and Reduced Form Estimates. In: Osmani, S. R. (Ed.), Nutrition and Poverty. Oxford University Press, Oxford.

- Behrman, J., 1997. Intrahousehold Distribution and the Family. In: Rosenzweig, M. R., Stark, O. (Eds.), Handbook of Population and Family Economics. Handbooks in Economics series. Elsevier Science, Amsterdam, pp. 125–187.

- Behrman, J. R., 1988. Nutrition, Health, Birth Order and Seasonality: Intrahousehold Allocation in Rural India. Journal of Development Economics 28, 43–63.

- Behrman, J. R., Deolalikar, A. B., 1989. ”Is Variety the Spice of Life? Implications for Calorie Intake”. The Review of Economics and Statistics 71 (4), 666–672.

- Behrman, J. R., Deolalikar, A. B., 1990. ”The Intrahousehold Demand for Nutrients in Rural South India: Individual Estimates, Fixed Effects, and Permanent Income”. Journal of Human Resources 25 (4), 665–696.

- Bouis, H. E., Haddad, L. J., Kennedy, E., 1992. Does It Matter How We Survey Demand for Food? Evidence from Kenya and the Philippines. Food Policy 17 (5), 349–360.

- Bourguignon, F., Browning, M., Chiappori, P.-A., Lechene, V., 1993. Intrahousehold Allocation of Consumption: A Model and Some Evidence from French Data. Annales D’Economie et de Statistique 29, 137–56.

- Bourguignon, F., Browning, M., Chiappori, P.-A., Lechene, V., 1994. Incomes and Outcomes: a Structural Model of Intra-Household Allocation. Journal of Political Economy 102 (6), 1067– 1096.

- Chiappori, P.-A., 1988. Rational Household Labor
although there is a significant positive relationship between the schooling of adoptive parents and the academic attainment of their adopted children, the effects are smaller than in the case of biological families. Finally, Sacerdote (2004) uses data about Korean-American adoptees who, as infants, were randomly assigned to their families: he finds a positive effect of parental education on the child’s schooling outcome, but that effect is much larger for biological children than for adoptees.

Although the legal concept of adoption cannot be easily transposed to the Rwandan context, this paper’s approach is very similar to the adoption strategy. Rwanda may have one of the highest percentages of orphans in the world because of the 1994 Genocide and the HIV/AIDS epidemic. The United Nations estimates that 800,000 Rwandans (10 percent of the population) died between April and July 1994 (Des Forges, 1999; Siaens, Subbarao, and Wodon, 2003). HIV prevalence among adults in Rwanda was estimated at 5.1 percent at the end of 2003 (UNAIDS, 2004). Many of the children who lost their parents have been welcomed in new households, where they may or may not have relatives. This process of family recomposition is, in essence, very similar to adoption.

This study contributes to the nature-nurture literature by using data from the Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) 1999-2001. This data set contains not only information about the new “adoptive” parents’ education but also about the schooling levels of the biological parents.<sup>2</sup> In addition, the data also include information on the type of relationship (relative or not) between the “adopted” child and the head of the household. One of the main criticisms of the adoption strategy is that placement of adopted children might not be random: children left for adoption are placed in families with characteristics similar to their biological families. This is, of course, especially true in the case of adoption by relatives.
to schooling and age. In column (2), we exclude the 1970s from the analysis. Our main findings are unaffected by this change in the sample period. Tariffs are shown to have positive effects on average wages and negative and more significant effects on the skill premium. In fact, the impacts on the skill premium are even larger when the 1970s are excluded, strongly suggesting that our results are not driven by the tariffs cuts of this period. In column (3), we exclude all years in the 1974-1982 period, where the tariff cuts were largest (Figure 2). Once again, our findings are robust to the exclusion of these years.

The second concern is the role of non-tariff barriers like quotas or quantitative restrictions. These are usual instruments of the Argentine trade policy, and provided non-tariff barriers are correlated with tariff barriers, their omission in the regressions can cast doubts on the interpretation of our key results. The problem with non-tariff barriers is that we were unable to construct historical series spanning the period under study. Even simple measures of coverage ratios are unavailable (or very hard to construct).<sup>10</sup> In principle, if non-tariff barriers were uncorrelated with tariffs, our estimates would be consistent. However, this correlation is likely to be present, for instance if quotas are high in those industries with low tariffs. Nevertheless, using data on tariffs and non-tariff ad-valorem equivalents compiled by Kee, Nicita, and Olarreaga (2005), we found that the correlation between tariffs and non-tariff barriers in Argentina was positive but very small (around 0.03). This suggests that the omission of non-tariff barriers in the main regressions would not be a severe problem.

We follow two further strategies to account for the role played by non-tariff barriers. One way around the problem of lack of data on NTBs is to exploit the sequencing of trade reforms experienced by Argentina. Berlinski (1994; 2003) has documented that non-tariff barriers were mostly used during the debt crisis of
Figure 13: Progressivity and redistributive effect of taxes <!-- Start of picture text --> Marginal contribution Kakwani coefficient<br>0.2 0.8<br>0.6<br>0.1 0.4<br>0.2<br>0.0 0.0<br>-0.2<br>-0.1 -0.4<br>Source: Authors’ calculations based on CSES 2019/20 and fiscal data.<br>Note: SSC=Social Security Contributions, HI=Health Insurance.<br>Kakwani<br>(Change in Gini Points)<br>Marginal contribution to equality<br><!-- End of picture text --> All social transfers are progressive in Cambodia, except electricity subsidy and tertiary education spending. Direct transfers are the most progressive but contribute little to reducing inequality due to their small amount and poor targeting as discussed below. Direct social transfers in Cambodia constitute a very small proportion of total incomes, even among the poorest households. For example, among households in the first decile of the _market income_ distribution the average value of direct social transfers amounts to 0.94 percent of _disposable income_ . Moreover, the CSES data collects only aggregated information on welfare transfers, thus making it impossible to distinguish between different programs. Education spending reduces inequality the most. Except for tertiary education, all other education spending is both progressive and inequality-reducing. Primary education has the most equalizing effect.

Figure 14: Progressivity and redistributive effect of transfers and subsidies <!-- Start of picture text --> Marginal contribution Kakwani coefficient<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2
# 5. Results

# 5.1 Difference-in-difference (DD) estimates

IPV trends in BH affected areas from the matched sample are shown in Table 3. The matched sample contains data on 2,570 women living in the BH affected area and 21,300 women living in areas not affected by BH. In the BH affected area, the proportion of women experiencing physical or sexual IPV is unchanged at around 10 percent. In the non BH area however IPV rates fall from 17 to 12 percent between 2008 and 2013.

Table 3: Rates of Intimate Partner Violence in Nigeria before Boko Haram (2009) and after Boko Haram (2013), <u>percent</u>

|||**BH Are**|**a**||**Non BH Ar**|**ea**||
|---|---|---|---|---|---|---|---|
|**Variable**|Before<br>BH|During<br>BH|Difference|Before<br>BH|During<br>BH|Difference|N<br>(Total)|
|Past year physical or sexual IPV|11|10|1|17|12|-5***||
|N|1126|1444||9585|11715||23870|

Source: Authors own calculation using data from the 2008 and 2013 Nigerian Demographic and Health Survey Note: BH Area defined as NDHS clusters exposed to any BH Event within 10km between 2009 and 2013. *** Difference in means between before BH and during BH is significant at the 1% level.

After controlling for a woman’s, partner and household characteristics, attitudes towards and experiences of IPV, ethnicity, religion and state fixed effects, the results of the DD model show that the presence of BH increases the probability of a woman’s
information on behalf of 20 proxy respondents. Compliance rates are close to 100% in the second and third surveys.

7 In the presence of widespread seasonal activities, the choice of the length of the reference period is important. A short length of the reference period (e.g., a day or a week) may not capture seasonal work depending on the precise timing of the survey work if labor inputs vary considerably across weeks (Levison et al 2007, Arthi et al 2017), or if the chosen day or week is atypical due to religious holidays or community celebrations (Matta-Greenwood 2000, Comblon and Robilliard 2017). A longer length of the reference period (e.g., ‘last year’), on the other hand, could introduce bias in the measurement of variables due to recall or aggregation errors that operate firstly through decay in memory as the likelihood of forgetting an event increases as time passes. Thus, there are different layers of trade-offs with no firm conclusion on what time length is the most appropriate. 8 The subsequent questions use the same length of the reference period to capture information on work in non-farm household business, non-farm wage work, Fairtrade coffee cooperatives, coffee plantations, and other households’ farms. A negligible percentage (2%) of both self and proxy responses report children working outside the domain of household farm and, thus, this information is not used in the empirical analysis.

9 Comparability of labor statistics across these three surveys follows from the similarity of the survey design, data collection and processing procedures. We used the same group of field surveyors across all rounds of data collection and the same software and personnel for data entry and coding. The wording and type of questions, the length of labor modules and the reference period are the same across the three survey rounds.

10 ERSS data collection took place between February and April 2016, a timing that overlaps with our second survey intervention (short rainy season).

31
reduced in January 2011 due to double-digit price rises. In Indonesia, the government reduced taxes on sugar and increased subsidies to local cooking oil producers.

In cases, where no domestic price data were available, we used import shares reported in version 7 of the GTAP database (Hertel 1997) to link global prices (Table 1) with domestic consumer prices. This approach is consistent with imported goods being imperfect substitutes for domestically-produced goods and the changes in domestic prices being a weighted average of the prices of imported and domestic goods.

# **Results**

In Table 3, we first present the results on the poverty impact of the price changes experienced by households for the standard international extreme poverty headcount of people living below 7
than men (only 50 percent of them work) and earn lower wages on average. The average gender wage gap in two-parent households is 15 percent, smaller than in the full dataset, and the correlation between father’s and mother’s wage is 0.54. In about half of two-parent households both parents have completed college, whereas only 12 percent of single mothers have completed it. In both cases, however, parents have higher educational attainment than in the full dataset.

# **4 Econometric Issues**

## **4.1 Empirical Specification**

### **4.1.1 Household Heterogeneity**

Recall that household types vary in wage offers **_w_** ( _j_ ), preferences **_α_** ( _j_ ), initial child development _q_ 0( _j_ ), and availability of relative care _d_<sup>_R_</sup> ( _j_ ). When computing the equilibrium for estimation or counterfactuals, we treat each household in the sample as a different type, in which _q_ 0 is measured as the child’s cognitive score at age one and serves as the initial condition in the value-added specification of child development production in equation (6).

As mentioned in Section 2, in addition to two-parent households we model one-parent households (henceforth, single-mother households or single mothers). We treat them as a 13
effect or the demand-supply mismatch exists among younger workers, it has been more than offset by other forces. On the other hand, the evidence suggests that there is a process that negatively affects older workers, those ages 50 years and older in particular. Older workers, for example, do not feature the typical continuously increasing concave wage function; rather, they experience a sharper decline in their wage. These findings are summarized in Table 5. We also observe that the age at which the slope becomes negative occurs at younger ages with each cohort (Table 3 and Figure A7 in the Appendix). This could be evidence of an accelerated skillsobsolescence effect. However, the year at which the wage becomes negative for the collegeeducated of every cohort coincides with the year in which Mexico entered into recession: 2008.

**Table 5: Summary of Evidence**

|**Hypothesis**|**Shown**<br>**in**|**Degraded tertiary**|**Obsolescence / Labor**<br>**Saving**|
|---|---|---|---|
|Datt-Ravallion|Table 2|No evidence|Not applicable|
|Evolution of wages<br>by age|Figure 4|No evidence|Some evidence that older<br>workers’ salaries fell more<br>rapidly|
|Wage inequality by<br>age|Figure 5|No evidence|Evidence consistent with<br>high-wage older workers ́<br>wages falling relatively to<br>other older workers’ wages|
|Evolution of wages<br>by cohort|Table 3|Some evidence with<br>the very young<br>cohorts but it overlaps<br>with year of<br>macroeconomic crisis|Evidence that the wages of<br>older workers decline|
|Cohort regression|Table 4|No evidence. On the<br>contrary, the average<br
# **3.2 Measures of export dynamics and financial structures**

We are interested in the size and dynamics of the export sector. Among the measures for the size of the export sector, we consider the number of exporters (number of firms exporting in a given country-industry-year) and the average export sales per exporter. The dynamics of the export sector is captured by firms’ entry rate into export, firms’ exit rate from export, and the turnover rate of exporters. Firm entry rate into export is equal to the number of firms exporting in a given country-industry-year that were not exporting in the previous year over the number of exporters; firm exit rate equals the number of firms not exporting in a given country-industry-year that were exporting in the previous year over the number of exporters; the turnover rate is the sum of the entry and exit rates. In additional tests, we also consider measures of concentration of the export sector, namely the share of exports of the top 5% of exporters and the Herfindahl-Hirschman index of export sales.

We capture financial structures by considering measures of the relative importance of banks vis-`a-vis capital markets, and, within the banking sector, measures of the relative importance of domestic versus foreign banks. In the former set, we focus on the ratio of credit granted by banks to the private sector over the sum of stock market capitalization and bank credit, but also experiment with the ratio of bank credit over the sum of bond market capitalization and bank credit (for bond markets data are sparser than for stock markets). In the latter set, we focus on the share of foreign bank assets among total bank assets in the country.

As in prior studies, we use total credit granted by all financial institutions relative to the GDP to control for the financial development of a country and to account for the overall size of the financial institutions sector. Further, when studying the role of foreign banks, we control for the degree of competition in the banking sector using an
2.412|2.412|1.719|1.719|3.350|3.350|
|Number of countries|74|74|101|100|33|33|90|90|
|Number of regions|660|660|955|955|189|189|670|670|
|Observations|8,566|660|12,392|955|2,452|189|8,663|670|
|Equality test (Panel vs.<br>long differences)|p = 0.|000|p = 0|.000|p =|0.000|p =|0.628|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Crop yield data is provided by Iizumi and Sakai (2020). *** p<0.01, ** p<0.05, * p<0.1 59
employment losses. Although they were less likely to grow conditional on survival, larger firms were more likely to survive, such that, in net terms, larger firms shed less labor. Part of the sorting by gender also occurred along occupational lines.<sup>3435</sup> That is, men also suffered more because they were more likely to be working in white-collar jobs, which were more vulnerable.

Differential treatment effects varied by occupation as well as firm size, and are obscured in aggregate statistics, which reflect the combined effect of sorting and differential treatment. When firms contracted, the share of blue-collar workers that were female decreased somewhat, while the share of white-collar workers that were female increased. For blue-collar workers, these relationships were slightly exacerbated during the crisis, such that women were more likely to suffer job losses than their male colleagues within the same firms. By contrast, differential treatment protected white collar women relative to their male colleagues. The crisis mildly (but not consistently statistically significantly) reinforced the pattern that women in white collar jobs are more likely to be spared when firms contract. Moreover, they benefitted significantly more from firm expansion than during non-crisis times. However, these differential treatment effects were very small. That is, even with evidence of differential treatment, the claims about crises inducing a vast surge in employer discrimination discussed in the introduction appear grossly exaggerated; if discrimination was really driving women‘s vulnerability, one would have expected to see much more dramatic differences.

This paper has documented heterogeneity in the relative importance of these channels across different types of firms and workers in different occupations. It has, moreover, demonstrated how aggregate statistics may mask countervailing transmission channels. Examining to what extent this is the case using matched employer-employee data, and assessing to what extent our findings generalize to other sectors and other crises, are promising areas for further research. Overall, this paper underscores the importance of analyzing and documenting heterogeneity in firms‘ adjustment to major macroeconomic shocks in understanding the distributional impacts of