rafmacalaba/data-use-mentions-extended huggingface dataset:
. We assume those who work in agriculture will work and live within the same union and are low-skilled, and we take the number of households classified as employed in agriculture from this data set.

# **_Transport Network_**

The transport network is mapped in 2011 using data from the Revised Strategic Transport Plan (DTCA 2015) and OpenStreetMap (2015). Travel times are estimated for both pedestrians and motorists. Most of the low-skilled workers in Dhaka walk to work and are therefore classified as pedestrians. We assume that these pedestrians can travel at 5 km/h, travelling to the nearest point on the road network and then following the roads through the city. Skilled workers are assumed to have access to motorized transport. We assign travel speeds for vehicles along different types of roads according to the number of lanes, with vehicles able to travel at 15km/hour on one lane roads, 20km/hour on two lane roads, and 60km/hour for four lane or wider roads. Network analysis is then conducted to find the shortest route between each pair of unions, taking into account the location of roads and the travel times down different types of roads. When we project Dhaka to 2035, we assume road and Mass Rapid Transit (MRT) investments as planned in the Revised Strategic Transport Plan (DTCA 2015), allowing lowskilled workers to use the MRT or travel on foot, whichever is quicker, and high-skilled workers to also have the option of driving. Congestion on roads increases proportionally with 9
provision for the matter. Most low-income countries have not established relevant legislation on IPRs at all, not to mention the leveraging of such rights to facilitate or prevent data sharing. With regard to the confidentiality of third-party rights in nonpersonal government data, it is not regulated in any lowincome country but is addressed by 14 percent of high-income countries. For instance, Canada protects third-party rights in nonpersonal government official statistics made available publicly, preventing the manipulation of databases to identify individuals, businesses, or organizations.<sup>9</sup> Only 7 percent of the sampled countries worldwide regulate third-party confidentiality in nonpersonal government data.

# _4.3.6 Safeguards: Cybersecurity and cybercrime_

In order to be effective, safeguards for personal and nonpersonal data should be linked with legal and technical cybersecurity measures. Mandating the implementation of security measures in technical infrastructure, as well as adopting provisions against cybercrime—that is, the criminalization of unlawful or illegal access or use of infrastructure, systems, and data—are crucial to build a robust safety net for data economy activities. Therefore, the cybersecurity and cybercrime dimension collects information on four elements: security requirements for automated processing of personal data; cybersecurity requirements for data processors/controllers; existence of a national computer emergency response team (CERT); and existence of provisions that criminalize cybercrime.

> 8 The law can be accessed at:

> <u>http://kenyalaw.org/kl/fileadmin/pdfdownloads/Acts/2019/TheDataProtectionAct__No24of2019.pdf.</u> 9 The terms and conditions of Statistics Canada Open Licence can be accessed at the following link: <u>https://www.statcan.gc.ca/eng/reference/licence.</u>

16
Second, there is an emerging body of literature that finds that small firms or traders are required to pay multiple taxes or fees, such as trade association fees, business licenses, storage fees, and market fees in the Democratic Republic of Congo (Paler et al., 2017), Ghana (Anyidoho et al., 2022, Caroll 2011, Prichard and van den Boogaard 2017), Nigeria (Akpan and Cascant-Sempere 2022 and Meagher 2016), Sierra Leone (van den Boogaard 2018, van den Boogaard, Prichard, and Jibao 2019), Uganda (Pimhidzai and Fox 2013), and Zimbabwe (Ligomeka 2019). Many of these papers show evidence of regressivity of small taxes and fees on female traders and entrepreneurs. This paper contributes to this literature by focusing specifically on the presumptive tax on small enterprises with a gender lens.

Third, this paper makes a methodological contribution by demonstrating how the gender dimension of a small business tax can be evaluated with publicly available survey data, such as the ESS. It also shows how the effective tax rates and revenue implications can be examined through a policy simulation. Surveys typically do not collect data on taxes paid by small and microenterprises, but the ESS fills this data gap. The enterprise module in the ESS data with detailed information on enterprise activities, revenue, and tax questions allows for this type of granular analysis.

This paper is organized as follows: section 2 provides background on the presumptive tax in Ethiopia, section 3 describes the data and the descriptive statistics on gender and nonfarm enterprises, and section 4 discusses the empirical methodology. The results on the effective tax rates are discussed in section 5, the policy simulation of a hypothetical presumptive tax is presented in section 6, and section 7 provides the conclusion.

# **2. The presumptive tax on small and microenterprises: The Ethiopia context**

Businesses in Ethiopia are classified into three groups for tax administration purposes, namely Category A, B, and C taxpayers. This paper’s focus is on
soybean oil exhibit almost identical development as do actual and predicted soybean oil prices (not shown).<sup>20</sup> The three visible periods when our model over predicts (April – September 2007, February 2008 – April 2009, and February 2011 – April 2012) correspond to the periods of positive profits earned in biodiesel production, likely due to capacity constraints.

# **5. Conclusions**

This paper has advanced an analytical framework to analyze the economics of biodiesel policies (tax exemptions/tax credits and mandates) and the effects of crude oil price shocks under either of these policies. We show how the jointness in soybean (rapeseed) crushing – i.e., the production of both soybean oil and meal – changes the otherwise direct link between a biofuel and its feedstock (as is, for example, the case for ethanol and corn). We also find that while

> 19 The underlying data come from the “Historical Biodiesel Operating Margins” spreadsheet of the Centre for Agricultural and Rural Development of the Iowa State University. http://www.card.iastate.edu/research/bio/tools/hist_bio_gm.aspx

> 20 Biodiesel prices ran up in 2007-08 because of ‘splash and dash’ (see de Gorter et al. 2011) and then crashed as the European Union invoked anti-dumping and countervailing duties. But the U.S. biodiesel mandate started to be enforced in mid-2010, causing prices to rise sharply again.

12
our estimates toward zero and despite this reality, we estimate effects with substantial and meaningful magnitudes.

Third, our estimates only represent the short-term effects of the coronavirus pandemic on food security. As the spread of the virus began in Bamako, and therefore initial containment policies were most intense in the capitol city, the short-term effect of the pandemic is more directly felt by urban households. As time progresses, and the agricultural season gets underway, it is plausible, and in fact likely, that rural households will be increasingly affected by the consequences of the pandemic (Amjath-Babu _et al._ , 2020). Future research which specifically examines the medium- and long-term effects of the coronavirus pandemic, in both rural and urban areas, is absolutely essential.

Finally, we are unable to credibly identify mechanisms that explain our main findings. Research in India suggests that food prices in urban areas have increased which could lead to challenges relating to food security, especially for households that have lost a primary source of income due to pandemic-related disruptions (Narayanan and Saha, 2020). It is plausible that this mechanism persists in Mali, however, we do not have the data to examine this empirically. We do show some descriptive evidence suggesting that the pandemic has reduced the frequency of visits to the market or grocery store, but there is very little difference between households in urban and rural areas. Therefore, this type of extensive margin behavior change does not seem to explain our main results in a meaningful fashion. Future research should focus on identifying the mechanisms that explain these results to help inform effective policy responses.

# **6 Conclusion**

We add to the emerging research that estimates the effect of the coronavirus pandemic on food security in low- and middle-income countries (Amare _et al._ , 2020; Ceballos _et al._ , 2020; Mahmud and Riley, 2020). Using nationally representative pre-pandemic household survey data and a follow-up phone survey from Mali, our difference-in-difference estimates highlight a dramatic increase in food insecurity. These
# **4 Heterogeneity**

Aside from evidence on the causal impact of mobile phone penetration on GDP growth, an important policy question is the distribution of the impacts across heterogeneous groups. For instance, how does the impact of mobile vary between developed and emerging markets. In addition, how did the arrival of mobile phones spur economic growth in countries that hitherto had low access to communication technologies such as the fixed line telephone network? In this section, I provide evidence on these questions by re-estimating the main IV regression while interacting measures of mobile penetration with measures of countries’ income group and fixed telephone penetration at the baseline.

First, I explore the economic impact of mobile phones between developed and developing economies. Using the World Bank’s income classification at the baseline, I split countries into two main groups: developing vs developed.<sup>31</sup> I then interact the mobile penetration measures with a dummy variable equal to 1 for developing countries and 0 otherwise. Results are shown in Table 5. The results show that while mobile phones have a positive impact on local economic growth across all income groups, the effect is higher among developing countries. For instance, the results in column 2 suggest that a 10 pp increase in mobile penetration is associated with a 0.5 pp growth in nightlights in developed (high and upper middle income) countries, compared to a 0.9 pp increase in developing (low income and lower middle income) countries. The effect of 2G connectivity is however only present in developing markets (columns 3-4). These findings are plausibly indicative of the economic importance of mobile voice and SMS messaging which incidentally is the backbone of digital finance platforms like mobile money in most developing countries (Suri and Jack, 2016; Suri, 2017). However, the findings in relation to 3G/4G are interesting: in column 5, the results indicate that effects of 3G/4G connectivity are about 0.97 pp higher in developing markets relative to developed markets
10 lower at the end of the period without the increase in inequality. Alternatively, without growth, the headcount would have been eight and twenty points higher in respectively rural and urban areas. Had there been no growth in urban areas, and no increase in inequality in rural areas, the headcounts would have converged in the two sectors to 35 (rural) and 37 (urban) percentage points using the lower poverty lines. The same applies with the upper lines, but with headcounts twenty points higher. What can be concluded from the above results? As can be seen from Table 4 (and Figure 1), the growth only and inequality only scenarios move in opposite directions. This indicates that positive (negative) growth tends to be associated with rising (decreasing) inequality. But at this stage, this remains an impressionistic result without a firm analytical grasp as to the elasticity of inequality to growth. Moreover, a cursory look at Table 4 would indicate that the relationship between growth and inequality is similar in urban and rural areas. If this were indeed the case, combining this finding with the results based on Kakwani's formulaes would suggest that poverty is likely to be reduced more through urban than through rural growth (compare the two MPRS). In fact, it is exactly the reverse which is true: rural growth appears to be more poverty reducing than urban growth. This is because of the correlation between growth and inequality is much lower in the rural than in the urban sector, as we shall now see.

**IV Regional panel estimates**

_IV 1 The relationship between growth and inequality_ The techniques illustrated in the previous sections do not provide us with a clear picture of the long term relationships between growth, poverty, and inequality. The key missing piece is an estimate of the correlation between growth and inequality which cannot be readily estimated with Kakwani's formulae. But it can be found using our regional panel by estimating the following regression: Log Gkt = a + , Log Wkt
# **A.3 Sampling Frame**

The sampling frames were based on the most comprehensive and latest establishment census available from national statistical agencies or administrative business register. Table A.14 provides the main data sources used in the sample frame for each country.

Table A.14: Sampling frame by country

|Country|Source|Sampling frame|Year|
|---|---|---|---|
|Bangladesh|Bangladesh Bureau of Statistics.|Est. census, 2013|2019|
|Brazil|Ministry of Labor|Employer census, RAIS, 2018|2019|
|Burkina Faso|Business Registry|Business Registry|2021|
|Cambodia|Tax Registry|Tax Registry|2022|
|Chile|Business Registry|Census on Establishments|2022|
|Croatia|Financial Agency (FINA)|FINA Data|2023|
|Ethiopia|Ministry of Trade and Industry (MoTI)|Business Registry|2022|
|Georgia|National Statistics Ofce of Georgia|Est. census, 2021|2022|
|Ghana|Ghana Statistical Service|Est. census, 2013–18|2021|
|India|Central Statistics Ofce of India|Est. census, 2013–17|2020/23*|
|Kenya|Kenya National Bureau of Statistics|Est. census, 2017|2020|
|Korea, Rep.|Statistics Korea|Est. census, 2018|2021|
|Poland|Statistics Poland|Est. census, 2020|2021|
|Senegal|National Agency for Statistics (ANSD)|Est. census, 2016|2019|
|Vietnam|General Statistics Ofce of Vietnam|Est. census, 2018|2019|

* Note : The states of Tamil Nadu and Uttar Pradesh were surveyed in Wave 1 in 2020. The states of Gujarat and Maharashtra were surveyed in 2023.

The universe of study includes establishment with 5 or more employees in agriculture, manufacturing and services. The sector classification is based on the International Standard Industrial
(2018 in the case of Nepal).<sup>5</sup> Our advanced economies include all countries in the European Union, the U.S., the Republic of Korea, Japan, and Australia. We use the 2019 microdata from the EU-SILC (Statistics on Income and Living Conditions) database for the European Union (EUROSTAT, 2019). We split E.U. countries between higher and lower income countries in the region.<sup>6</sup> For the remaining countries; our datasets are the U.S.’ Current Population Survey Annual Social and Economic Supplement (CPS-ASEC; U.S. Census Bureau, 2018; Ruggles et al., 2023), Korea’s Labour and Income Panel Study (KLIPS; Korea Labor Institute, 2019), Australia’s Household Income and Labour Dynamics dataset (HILDA, Department of Social Services, 2019), and Japan’s Labour Force Survey (Official

> 3 In all countries, workers are asked whether they work on their own or in a business with more or fewer than 10 employees, but higher levels of disaggregation are captured using thresholds that are not always comparable across countries. We define comparable groups.

> 4 The specific sources are: Argentina (INDEC, 2019), Bolivia (Bolivia, 2017), Brazil (IBGE, 2019), Chile (Ministerio de Desarrollo Social y Familia, 2017), Colombia (DANE, 2019), Costa Rica (INEC, 2019), the Dominican Republic (de la Rep ́ublica Dominicana], 2019), Mexico (INEGI, 2018), Paraguay (INE Paraguay, 2019), Peru (INEI, 2019), and Uruguay (INE Uruguay, 2019)

> 5 India’s Periodic Labour Force Survey (National Statistical Office, 2018-2019), Pakistan’s Labour Force Survey (Pakistan Bureau of Statistics, 2018-2019), and Nepal’s Labour Force Survey (Central Bureau of Statistics, 2017-2018).

> 6
firms, an expansion of the number of traded products and a growth in the volume of pre-existing products. Other works focus on how the Canada-US Free Trade Agreement (CUSFTA) has affected US firms’ export patterns (Bernard et al., 2010) or Canadian firms’ export product scope (Baldwin and Gu, 2009). Using French firm-product level data, Berthou and Fontagn ́e (2009) investigate the role of a reduction of trade costs on the product mix of French exporters using the introduction of the euro as a proxy for trade barriers. Dhingra (2009) tests her monopolistic competition model of brand differentiation by examining Thai trade liberalization process (2003-2006).<sup>6</sup> The main contribution of this paper is to disentangle the effects of market access expansion vs. foreign competitive pressures after a unilateral trade liberalization episode in a fast growing developing country like China.

The rest of the paper is organized as follows. The next section presents the main mechanisms that are at stake after a unilateral trade liberalization episode between asymmetric countries. Section II provides stylized facts about China’s trade liberalization and the patterns of French multi-product exporters. Section III presents the empirical strategy and Section IV depicts the econometric evidence based on firm-product-level data for French exporters. Section V presents robustness tests dealing with omitted variable concerns, potential endogeneity issues and country selection issues. Section VI concludes.

# _I. MICROECONOMIC EFFECTS OF UNILATERAL TRADE LIBERALIZATION_

Before analyzing the relationship between Chinese unilateral trade liberalization and French firms’ export patterns, this section provides some insights on the potential effects of a unilateral trade liberalization episode on the export performance of firms exporting toward the liberalized market.

The empirical literature on international trade and heterogeneous firms has focused mainly on the effects of bilateral trade liberalization between symmetric countries. Baldwin and Gu (2009), by the means of plant-product level data for Canada, show that the Canada-US Free Trade Agreement (CUSFTA) has reduced the product diversification and size of non-exporting Canadian plants. Using also the 5
This paper investigates the differential impacts of foreign ownership on wages for different types of workers in Vietnam using the _Vietnam Household Living Standards Surveys_ (VHLSS) of 2002 and 2004. Most of the previous studies analyzing the impacts of foreign ownership on wages used firm-level data and did not consider the wage effects for those working in informal sectors. This paper intends to fill the gap in the literature extending the wage comparison analysis to workers in the informal wage sector.

A series of Mincerian earnings equations and worker-specific fixed effects models are estimated and their results are generally consistent with those from previous literature. Foreign firms are found to pay higher wages relative to domestic counterparts given observable characteristics of workers. The effects of foreign ownership on hourly wages are estimated to be about 30 percent and 46 percent higher wages relative to wages paid to similar workers employed with domestically-owned private firms and those working in the informal wage sector respectively. It is also found that the higher the individual workers’ levels of education, the larger _on average_ the wage premiums are for those who work for foreign firms.

The inclusion of workers in the informal wage sector in the sample reveals additional insights on the impact of foreign firms on wages that were overlooked by previous firm-level studies. In particular, as Vietnamese workers in the informal sector tend to be underemployed, the increased hours of work resulting from obtaining foreign firm jobs relative to working in the informal wage sector appears to be an important source of earnings gains.

Finally, women with lower levels of education are found to experience a larger foreign wage premium (relative to their male counterparts and relative to the informal wage sector), reflecting their low earnings opportunities and higher gender gap in the latter sector. One limitation of this study is that it focuses only on wages and does not evaluate other working conditions such as 24
, the values of houses will be lower in the informal sector. Returns on investment, measured by the rent-to-value ratio, will thus be higher in the informal sector. This result relies only on investment side considerations and is independent of assumptions on the households’ side. Thus it will hold even when there are mobility costs or when households have intrinsic preferences for living in the formal sector.

We use a survey conducted by the World Bank in the city of Pune, India. Among

> 9Owner-occupying households are seen both as investors and renters, insofar as they rent their dwellings to themselves.

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that gap, exploring two questions related to household income composition and welfare outcomes in this new and unprecedented context. We take advantage of rich survey data to assess trends in income composition over time and to understand the relationship between livelihood diversification and household welfare outcomes, in particular, food security. As our data include a pre-outbreak baseline, we are able to observe households’ status prior to the pandemic and then track them through and beyond the first year of the pandemic. This panel data, along with our empirical strategies, allows us to identify causal relationships between a household’s choice of livelihood activities, income sources, and their level of food insecurity during the pandemic.

In terms of how household livelihood diversification has changed since the onset of the pandemic, we do not observe substantial or systematic changes in household income composition. Small differences exist in Ethiopia and Malawi, where households became more specialized during the pandemic than before. Conversely, in Nigeria diversification increased slightly after the start of the pandemic. From these trends we conclude that households made limited use of livelihood diversification as a coping strategy during the pandemic.

In terms of how household income composition impacts food insecurity, our regressions provide little evidence to support the idea that livelihood diversification improved food insecurity during the pandemic. The preponderance of evidence, across countries, estimation methods, and measures of diversification is that there is no significant relationship between livelihood diversification and food insecurity. We provide a number of robustness checks and evidence that we believe demonstrates that the null results are true nulls. Though income diversification may serve as an effective _ex ante_ or _ex post_ coping mechanism for many shocks, in particular small transitory shocks, we find that for a disaster on the scale of the COVID-19 pandemic, this strategy does not appear to be effective.

An optimistic interpretation of the evidence in this paper would be that the extreme socioeconomic impacts of the pandemic appear to necessitate alternative adaptation strategies. A pessimistic interpretation is that a pandemic is too disastrous and omnipresent to prepare for or adequately adapt to