rafmacalaba/data-use-mentions-extended huggingface dataset:
exporter exit. The dominance of new exporters in recent Kenyan apparel export growth casts doubt on LBD internal to firms which benefitted from earlier privileged access – though it is consistent with external economies generated by earlier firms paving the road for subsequent exporters and with industry- or country-level reforms.

Second, Madagascar and Mauritius experienced a bust in apparel exports followed by extensive exit of firms and a recovery led by surviving firms. Madagascar’s bust was mostly determined by the withdrawal of AGOA benefits in 2009 after a political crisis, which were later reinstated in 2015, sparking a recovery. Mauritius’ bust was caused by the MFA end, while the recovery happened after the country was granted liberal ROO in 2009. In both cases the exit of firms shows that the earlier period of AGOA preferential access did not create durable international competitiveness in apparel. The fact that recovery was driven by surviving firms suggests that no significant external economies were sparked by AGOA at the industry or country levels.

# **_8.2 Does the restrictiveness of rules of origin matter?_**

Mauritius is the only country for which we have customs transaction-level data for a period that is sufficiently long to span all the US trade policy changes (2000-2016) and that was subject to changes in AGOA ROO during the period, from the restrictive double-transformation requirement (the use of African fabric made from US or African yarn) to a more liberal singletransformation requirement (from fabric to garment in an AGOA-eligible country). These changes in ROO enable us to assess the implications of this vital condition for the benefits from preferential access, by distinguishing between four phases of preferential access for apparel exports to the US: (A1) 2000-2001: Rivals face MFA quotas, and exports subject to duties; (A2) 2001-2005: Rivals face MFA quotas, and exports duty-free under AGOA but strict ROO; (B1) 2005-2009: No MFA 30
et al. 2012; De Weerdt et al. 2016; Gibson et al. 2015). These studies concentrate on total household-level consumption aggregates and do not consider variations in performance among individual items, as is done here. Moreover, variations in mean consumption by module, which represents up to 27 percent of the total value in these studies, convey the net effect of all possible types of reporting error, including the opposing impacts of recall and telescoping errors, as well as the difficulty of fully capturing individual consumption opportunities outside the home.

This paper extends previous findings through a more careful focus on the nature of survey reporting errors (relative to the benchmark). We accomplish this by decomposing the sum of reported consumption into a product of two vectors: (1) a vector of binary indicators recording whether the household reports any positive value consumed for each food subgroup or individual food item captured by the survey and (2) a real value vector of the subgroup or item-specific value consumed. This framework, akin to a separate analysis of the extensive and intensive margin of reporting food consumption, allows for an exploration of the relative importance of the different types of reporting error in the seven survey designs. Furthermore, it can relate the relative importance of these error types to individual commodity characteristics.

The next section briefly reviews the types of error in food consumption measurement captured by household surveys. The third section describes the Tanzania survey experiment. The fourth section presents the analytic methods we employ, and the fifth discusses the results. The final section summarizes the findings and discusses the consequent implications for improved survey design.

2
||components: the risk to operations, taxation, repatriation, and labor costs.|Country Risk Guide|
|---|---|---|
|Stick to Announced<br>Policies|“Do you expect the government to stick to announced major policies?”|World Bank: World Development<br>Report 1997<br>Q3|
|Efficiency of Public<br>Service Delivery|“How would you generally rate the efficiency of government in delivering services?”|World Bank: World Development<br>Report 1997<br>Q25|
|Protestantism|Ratio of protestant population in the country|La Porta et. al. (1998)|
|Colonial Past|Dummy variable which is equal to one if the country has a colonial past in the last two<br>centuries and zero otherwise|CIA World Fact, La Porta et.al (1998),<br>and Encyclopedia Britannica and the<br>World Handbook of Political and Social<br>Indicators|
|Decentralization|Ratio of employment in non-central government administration to general civilian<br>government employment. Non-central government administration encompasses all<br>government administration employees who are not specifically funded by the central<br>government. It includes municipalities, as also regional, provincial or state employment.|Schiavo-Campo et.al (1997)|
|Federal Structure|Sub-national elections in state or province level|IMF Government Finance Statictics1998|
|**Other**|||
|Ehnolinguistic<br>Heterogeneity|The probability that two randomly selected individuals in a country will belong to different<br>ethnolinguistic groups|La Porta et. al. (1998)|
|Wage/GDP|Average government wages to per capita GDP|Schiavo-Campo et.al (1997)|
|Social Development|Human Development Index of the United Nations. The index as crafted by UNDP includes<br>measures of mortality, education and economic activity.|United Nations
|68.7|

Source: Author’s own elaboration from enterprise surveys _On average, only 20% of African firms have developed or purchased any software recently._ Across countries, Kenya and DRC show the highest percentage of firms that have purchased or developed software (over 30%), while only 10% in Tanzania (see Table 5). On average, firms in the services sector tend to invest more in software than those from the manufacturing sector. As expected, the percentage of firms purchasing or developing software increases with firms’ size.

_Table 5 Firms which have developed/purchased any Software by Categories at Country Level (% All_ _<u>Firms)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|11.2|30.5|10.0|14.3|24.4|30.3|20.2|
|**Main Sectors**||||||||
|Manufacturing|9.4|23.5|8.2|12.9|22.8|31.3|18.1|
|Service|12.9|32.8|10.7|14.8|24.9|29.9|21.1|
|**Size**||||||||
|Small|7.0|28.2|7.9|14.0|22.1|22.2|17.1|
|Medium|17.9|37.8|16.6|14.2|24.2|39.7|25.4|
|Large|25.6|61.1
# The Role of Work-from-Home in the Gender Asymmetries of COVID-19

An Analysis for Latin America Based on High-Frequency Surveys<sup>∗</sup> Inés Berniell<sup>†</sup> Leonardo Gasparini<sup>‡</sup> Mariana Marchionni<sup>§</sup> Mariana Viollaz<sup>¶</sup> JEL Classification: D63.

Keywords: COVID-19, occupations, labor markets, employment, gender, women, workfrom-home, Latin America.

> ∗This paper was commissioned by the Latin American and Caribbean - Poverty and Equity Global Practice (ELCPV) as part of the activities developed in two projects (i.e., LAC: COVID-19 HighFrequency Monitoring - (P173987) and LAC: COVID-19 High-Frequency Monitoring II - (P175839) ) lead by Sergio Olivieri, José Cuesta, Gabriel Lara Ibarra, Carolina Mejía-Mantilla, and Javier Romero with the guidance of Ximena del Carpio and the financial support from the Latin American and the Caribbean Vice Presidency. We are very grateful to Sergio Olivieri, Carolina Mejía-Mantilla, David Newhouse, Ximena del Carpio, Eliana Carranza, William Maloney, and seminar participants at The World Bank and UNLP for helpful discussion and suggestions. We thank Carlo Lombardo, Julián Borgo, Milagros Cejas, Fabián González, and Alejo Isacch for excellent research assistance. The usual disclaimer applies.

> †CEDLAS-IIE-FCE-Universidad Nacional de La Plata. E-mail: miberniell@gmail.com

> ‡CEDLAS-IIE-FCE-Universidad Nacional de La Plata and CONICET. E-mail: lgasparini@cedlas.org

> §CEDLAS-IIE-FCE-Universidad Nacional de La Plata and CONICET. E-mail: marchionni.mariana@gmail.com

> ¶CEDLAS-IIE-FCE-Universidad Nacional de La Plata and IZA. E-mail: mviollaz@cedlas.org
among firms—between the firm and its supplier and its customers—and such relationships are often based on personal networks or access to power. How well male versus female business leaders would perform would depend on their access to such key contacts and personal networks.

The literature suggests that women have less access to personal and professional networks and spend less time networking (Goetz 2007). For instance, young female lawyers have been found to spend significantly less time in professional networking (Azmat and Ferrer 2017). Based on representative population surveys in 17 countries, women are found to have less extensive networks (i.e., personally less likely to know someone who started a business) (Koellinger et al. 2013). This is not surprising: typically women spend more time in household chores and networking as parents, which takes away time from professional networking. Indeed, recent research suggests that even in the United States, wives frequently conform to the gender norm of not out-earning husbands, and when wives do out-earn their husbands, they instead spend more time doing household chores to maintain intrahousehold harmony (Bertrand, Kamenica and Pan 2015).

This lack of professional and business networks does not augur well for the potential positive effect of inter-firm trade credit relationships. Trade credit is a key source to finance firms in developing countries, especially where access to bank credit is limited (Fisman and Love 2003). Trade credit relationship needs strong trust built on personal relationship between upstream-downstream trading partners, which requires strong relationships. Or trade credit relationship requires strong relationship with privileged firms such as state-owned enterprises which have better access to bank finance (Cull et al. 2009). Furthermore, in societies where women are discriminated against, some social clubs do not admit women, again rendering women in networking disadvantage. Moreover, trade credit relationship also implies financial risks of default. Women’s relative apprehension of risks again would discourage the use of trade credit relationship. The lack of networking and the apprehension of trade credit risks thus put women
<br>per girl's women's per girl's women's per women's<br>capita income study time labor force capita income study time employment capita income labor force<br>participation participation<br>Bangladesh India Pakistan<br>Access alone Reliable access<br>Percent<br><!-- End of picture text --> _Source: Zhang (2019). Note: The effects of electrification on girls’ study time and the effects of power outages on women’s labor force participation in Pakistan are not estimated because data are not available._ The cost of outages is made up by different components whose importance depends on the context. In Pakistan, the total annual outage cost for households adds up to 6.7% of annual expenditure (Pasha & Saleem, 2013). The largest source of this cost is the cost of self-generation – making up 56% of total costs. Other costs include monetization of utility cost and foregone economic activity due to outages that account for 22% of the cost in the whole sample. Disaggregating the wealth dimension draws a slightly 6
# **Appendix D: Data Sources and Attrition**

In this section, we discuss attrition in more detail and show that it is unrelated to both the Tuungane and the gender parity treatment status. **Table A3** gives, for each data source, an overview of the number of targeted observations, the number of observations actually used for analysis, and possible imbalances between treatment conditions. We discuss each data source in turn.

# **Villages Visited**

Tuungane was implemented between 2007 and 2012 in 280 clusters comprising 1,250 villages across four provinces of the Democratic Republic of Congo: South Kivu, Maniema, Tanganyika and Haut Katanga. In 2010 and 2011, Humphreys et al. (2019) targeted to collect data from two randomly selected villages in each of the 560 clusters (280 treatment, 280 control). In total, 816 villages out of the targeted 1,120 villages were visited. There are no differences in attrition by treatment condition (Humphreys et al. 2019). In 2015, we targeted the same villages as visited Humphreys et al. (2019), excluding the Maniema province for logistical reasons. Specifically, we targeted 781 villages (286 in Haut Katanga, 208 in Tanganyika, 287 in South Kivu). In total, 735 of the 781 villages (94%) were visited. Attrition took place because of inaccessibility of villages. **Table A3** shows that almost the same number of villages are missing in treatment and control communities. Related to the gender parity treatment, a total of 190 villages out of the 781 participated in the gender parity lottery. We again find no differences in attrition by treatment status.

# **Village Chief Survey**

Among the 735 villages that we visited, we conducted a survey with the village chief in 714 villages (97%). Among the 180 villages that participated in the gender parity treatment and were visited, a total of 176 village chief surveys were conducted. We find no differences
as exemptions inherently give rise to higher administration cost and exemption creep. For example, there is little rationale for exempting transport fuels from VAT, given the highly inelastic nature of fuels.<sup>26</sup> On the expenditure side, there is an urgent need to improve the effectiveness of public spending considerably, with a view to delivering public services within a more constrained budget envelope.<sup>27</sup> Besides trimming the overall wage bill, as envisaged in NDP10, this requires a thorough review of spending programs to evaluate and rationalize them.

At the same time, efforts need to be stepped up in building in-house capacity to regularly monitor macroeconomic developments and conduct fiscal analysis. Projections can be performed at various levels of complexity, but they should allow the government to identify emerging risks and vulnerabilities, internalizing them in policy formulations. To this end, a timely availability of reliable data will be critical.

These actions are necessary not only from the fiscal sustainability point of view, but also from the point of view of facilitating private sector activities and accelerating growth of non-diamond sectors, that is, maintaining Botswana’s living standard. These efforts will take time to yield results. The current global crisis is an opportunity to start a structural transformation of Botswana’s economy. This transformation needs to be well under way before the long-term decline in diamond exports sets in. A timely response to Botswana’s emerging fiscal challenges would make a transition less painful, before the current fiscal policy stance leads to unsustainable outcomes that would require significantly large and more painful adjustments later.

# **5.2 Botswana’s Fiscal Rules**

Over time Botswana has adopted various formal and informal rules in an attempt to maintain fiscal discipline, especially, with regard to spending financed by mineral revenue. With the change in policy emphasis, over time, some rules have been phased out. The rule to limit public spending to 40 percent of GDP is no longer in use. Neither is the Sustainable
select industries that are arguably the most _unrelated_ to the marine pollution crisis, namely manufacturing, construction, and retail.

The fundamental “parallel trend” assumption of a DiD model implies that the average monthly earnings of the treatment and control individuals follow similar pre-trends, and that the average earning’s movement of the counterfactual group is “smooth” across the _Formosa_ incident timeline. Recall that we discuss several empirical supports for the validity of this assumption in Figure 3 and Table A1. Therefore, we are able to quantify the Average Treatment Effects (ATE) of _Formosa_ by measuring (1) the changes in fishery earnings in the treated location relative to the control region (Panel A), or (2) the changes in fishery earnings relative to the average earnings in unaffected industries in the _Formosa_ zone (Panel B). In both exercises, the average impact of _Formosa_ for the rest of 2016 (i.e. after the incident ˆ took place) is estimated by _α_ 1.

Overall, using LFS 2016 data and controlling for the month-specific and individual-specific fixed effects as well as clustering the standard errors at the district-level, our estimation robustly indicates a massive and statistically significant decline in fishery monthly incomes caused by _Formosa_ , with the magnitude ranges from 30 percent in the “pooled” regressions (columns 1-2) to approx. 45 percent under the individual fixed-effect specification (columns 3-4). The fact that fishery income reduced by almost half after the crisis indicates how destructive the disaster was. From the supply-side perspective, safe seafoods became much more costly to catch. In the subsequent section of the paper, we show that affected fishers, depending on their location within the contaminated zone, either coped with the shock by traveling longer distance to safe fishing grounds with an inevitable cost, or had to substitute fishery hours with working secondary jobs.

# **4.2 Impact on satellite-detected fishing activities**

Having shown a significant decline in average fishery earnings during the immediate period after _Formosa_ took place, we next provide
However, the fact that demographic variables explain substantial variation in agent transactions in a relatively homogenous set of urban and peri-urban municipalities suggests to us that more sparsely populated settings will likely pose challenges for the agent banking model. Cost-reducing adaptations of the model, incorporating perhaps mobile banking options, could be required. Also important is the question about the external validity of a model calculated with Kinshasa data. While it should be a good estimate of agent transactions in major cities, further research is needed in smaller cities and towns. Here larger challenges in terms of liquidity management and agent quality management may prove difficult obstacles for operators of agent networks such as FINCA. Going forward, better data on agent profitability over time would help in assessing the viability of this model in different types of markets and survey data from users (and nonusers) of agent services would help to assess how the availability of those services affects financial behavior.

30
method requires integration of the investment data to obtain capital stocks. For livestock the initial data are the number of animals. We need only calculate the values of the individual herds and then aggregate these values to obtain the total for the full stock of animals. For treestock we use the estimated present value of future income derived from the area planted in orchards.

> 6 See Färe et al. (1994) for an early application.

> 7 Exceptions include the studies by Ball et al. (2008, 2004, 2001) in which the authors construct capital stocks from investment data for various OECD countries.

> 8 The paper can be downloaded from the World Bank at <u>http://go.worldbank.org/IJ0CWCLVR0.  More recent</u> agricultural investment series were obtained from updates of the original sources, as well as from United Nations National Accounts data.

4
)|(0.023)|(0.023)|
|N|94724|94724|94724|
|Year FE|Y|Y|Y|
|Grid cell FE|Y|Y|Y|
|Country Trends|Y|Y|Y|
|R-sq|0.927|0.930|0.932|
|AdjR-sq|0.921|0.924|0.927|

Note: Dependent variable is log of NPP in cropland pixels where cropland makes up more than half of the share of the total land area (55%). All regression models include grid cell fixed effects; year fixed effects; country specific trends. Controls for contemporaneous precipitation shocks (i.e. Whether annual precipitation in the grid cell was at least 1 SD higher or lower than the long-run mean of the grid cell), temperature, and population are added in turn in columns 1, 2 and 3 respective with the last column including all controls. Statistical significance is given by +P < 0.10, *P < 0.05, **P < 0.01, and ***P < 0.001.

8
0.053)|(0.080)|(0.078)|
|Laed Deendent Variable (Baseline)|||0.317***||0.142***|
|gg p|||(0.026)||(0.027)|
|Missing Lagged Dependent Variable (Baseline)|||-0.076<br>(0.099)||0.032<br>(0.086)|
||T2=T3|0.037**|0.019**|0.325|0.292|
|F-test for Equality of Parameters (p-value)|T2=T4|0.019**|0.020**|0.028**|0.056*|
||T3=T4|0.897|0.777|0.003***|0.006***|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|
|Number of observations||1,938|1,938|1,938|1,938|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the child level using Baseline and 18-month data on the Strengths and Difficulties Questionnaire. Standard errors in parentheses are clustered at the CBCC level. Strengths and Difficulties Questionnaire: Total Difficulties Score had its score inverted so that a higher score is better. We weight observations using sampling weights (and tracking weights for the 42 randomly tracked observations at the 18-month follow-up). We replaced the missing values of lagged dependent variable with its (weighted) average for the overall sample at baseline. For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

46
); the effects of income specific tastes across goods on regional prices indexes (Handbury, 2021), and the relationship between income and nutritional inequality using barcode-level price data from the US (Allcott et al., 2019). To our knowledge, we are the first to introduce the mechanism of rising retail prices in response to changes in household shopping behavior to study a central question in urban economics: How does the entry of a large plant affect real wage inequality across skill groups? The novelty of our model lies in introducing household shopping time choice along with labor and leisure choice to generate this inverse relationship

> 1See Combes et al. (2019) and Gaign ́e and Thisse (2019) for comprehensive literature reviews. Gaign ́e and Thisse (2019) call the sum of housing and transport costs urban costs.

6
|2021|0.24|0.75|0.40|0.52|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA. Workers in micro and small firms are defined for people employed in a firm with less than 5 employees, domestic workers and unpaid workers. Workers in medium and large firms are defined for people employed in firms with more than 5 workers or workers in the public sector. Self-employed workers are defined for independent workers and production cooperative members.

On the other hand, when considering the Tax Identification Number (TIN) as a formality indicator for non-salaried workers, those with a TIN registration have higher quality jobs than their peers without any TIN registration (with a JQI pre-pandemic value of 0.56 vs. 0.41, respectively). While this gap is smaller than the one for salaried workers with and without a contract, these figures show that formal jobs are also higher-quality jobs, irrespective of which indicator is used.

26