rafmacalaba/data-use-mentions-extended huggingface dataset:
transfers and social networks, shocks and coping strategies, assets and food security. Table 1 shows descriptive statistics for our sample.

Our primary outcome of interest is income-earning in agriculture. For the rural households in Côte d’Ivoire we surveyed, the scope for engaging in the formal sector is limited and virtually all households are engaged in some sort of agriculture. The choice households and individuals face is the mix of subsistence agriculture, commercial agriculture and self-employment they engage in – with the key choice for farming households being who engages in highervalue export-oriented agriculture. In what follows, we define cash crops as any crops that households report selling, and export-oriented agriculture as the subgroup consisting of Côte d’Ivoire’s main export crops: cashew, cocoa, coffee, palm oil and rubber. Export crop farming is prevalent in our context: of the 19 Sub-Saharan African countries studied in Chauvin, Mulangu, and Porto (2012), Côte d’Ivoire has the second-highest agriculture income share coming from non-staple food other than cereals or fruits and vegetables, both for urban households (57 _._ 64 percent) and rural households (62 _._ 12 percent).

In this study, self-esteem was measured using the Rosenberg Self-Esteem Scale, which is composed of 10 questions that capture one’s feelings of self-worth (see A.1 in the Appendix for the
30|<br>Office, accounting and<br>computing machinery|1.5|18.2|48.9|30.7|22.0|1.6|29.2|3.5|18.5|
|31|<br>Electrical machinery and tools|2.0|5.1|13.3|20.3|26.8|9.7|10.7|8.1|18.6|
|32|Radio, television and<br>communication equipment|2.9|15.0|32.2|22.0|26.3|3.5|18.5|11.2|15.1|
|33|<br>Medical, precision and optical<br>instruments|6.4|14.6|40.6|15.4|43.5|6.3|9.1|21.0|22.5|
|34|Motor vehicles, trailers, semi-<br>trailers|1.0|2.2|4.4|16.6|31.7|7.5|9.1|17.6|14.1|
|35|<br>Other transport equipment|2.8|5.2|4.8|8.7|14.4|2.2|6.6|16.1|-1.7|
|36|Furniture; manufacturing n.e.s.|3.8|25.6|17.1|14.4|29.4|-5.4|19.8|38.5|-9.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Source: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Table
– the coefficient is significant at the 1% level. The coefficient is also economically significant as household per capita consumption when households do not receive a positive precipitation shock is INR 2,211 (USD 33.5), the coefficient implies that a positive rainfall shock increases per capita monthly household consumption by INR 133. As the average household size is 5, this implies an increase of INR 8,000 (USD 121) per household on an annual basis.

Columns (2)-(6) use the occupation classifications in the CP data to identify which households drive the consumption response to positive rainfall shocks. We interact a dummy for each occupational classification of interest with the indicator of positive rainfall shock to test for differential responses to positive rainfall shocks. We see that except for households where at least one adult is self-employed or engaged in small business, the remaining households do not exhibit any differential consumption response to positive rainfall shocks. As the sum of the coefficients is not statistically significant in columns (4) and (5), it suggests consumption in households with individuals engaged as qualified self-employed professionals, or business owners, do not respond to precipitation shocks. The sum of the coefficients in the remaining columns are all statistically significant at the 5% level, confirming an aggregate increase in per capita monthly household expenditures for these households.

Table 7 shows the corresponding impact for urban households. We note that while the coefficient on positive rainfall shock is positive, it does not achieve statistical significance in any of the specifications. The only category of urban households for which a positive rainfall shock disproportionately increases household spending is households with at least 1 member employed in the informal sector. This signifies that positive rainfall shocks in our sample affect household consumption in rural areas, but have a muted impact for urban households.

Tables 8, 9 and 10 focus solely on rural households and examine the source of increase in average monthly per capita expenditures identified in Table 6. We note that consistent with the evidence shown in the binned
of dead-weight costs generated by a gravity model regression (or alternatively estimated from price comparisons).<sup>19</sup> However, while recent practice has focused on pure trade costs, emerging evidence (e.g. ECORYS, 2009) suggests that the best practice most likely lies in modeling a combination of dead weight costs and rents.<sup>20</sup> A problem with much of the early literature is the absence of any empirical estimates of the extent to which policies generated rents and which groups benefitted from such rents. Instead, authors tended to provide a range of results based on different assumptions regarding the extent to which estimated price or cost wedges reflected rents as opposed to waste. In a prospective analysis of services liberalization in Tunisia, for example, Konan and Maskus (2006) assumed that barriers were half rent-creating and half-resource wasting in the absence of empirical evidence on this matter. Subsequent work by Dee and Diop (2010) based on better empirical estimates of the welfare effects of barriers concludes that the impacts of services trade reforms will be smaller than those estimated by Konan – and Maskus although in part this also reflects a different baseline and changes in the Tunisian economy during the 2000s. It is obviously important that models incorporate empirical estimates of the magnitude of any rents. There has been extensive recent effort to quantify the effects of nontariff measures (NTMs) affecting trade in services and to break down the extent to which they generate rents or costs in the context of regional integration negotiations.<sup>21</sup> ECORYS (2009) estimates, based on firm surveys, that there is an approximate 60:40 split in the extent to which barriers increase the cost of doing business for all firms, and the extent to which generate rents for domestic firms. They also provide a CGE-based assessment of reduction in nontariff measures affecting trade between the US and the EU, which suggests that service barriers are at least as important as barriers to trade in goods.

A
get priced out with fare increases. Reduction in inequality, when observed, hides how much further from being able to afford transit services some of the poorest households are, an element that we also document in this study.

The paper is structured as follows. Section 2 presents the datasets and methods used to conduct the study, including the use of Pareto frontiers that represent specific itineraries that optimize for both travel costs and time. Section 3 presents the results of the study. It first presents how average accessibility varies as a function of travel budget and time thresholds in Rio de Janeiro and Kinshasa. Second it discusses how these average accessibility profiles are impacted by fuel price increases ranging from 0 to 500%. Thirdly, this section disaggregates these impacts both spatially and by income group, to document who and where are the most affected. The subsequent sub-section specifically looks into inequality in accessibility levels between the wealthiest 10% and the poorest 40% and how fuel price increases impact these as a function of travel budget thresholds. The fourth results sub-section looks specifically at households that might be excluded from using transit services altogether because of fare increases, where they reside and which income groups they belong to. Section 4 summarizes the rich set of results derived in this study, discusses them and points to some avenues for pushing ambitious climate targets while mitigating the negative consequences that they might entail.

# 2 Materials and methods

Socio-economic data, including the location of jobs and households, was derived from various household and travel surveys, and merged with transit data in General Transit Feed Specification (GTFS) format and street network information from OpenStreetMap (OSM). The transport data was processed using r5r (Pereira et al., 2021) to conduct baseline analyses of accessibility to economic opportunity and derive Pareto frontiers (all optimal itineraries between each origin-destination combination optimized on both travel time and travel budget) for every origin-destination combination within the two cities.

In order to better understand the nuance around the impact of fuel price increases and their impacts on specific communities
_Note_ : Standard errors clustered at the respondent level. Sampling weights are applied. *** p<0.01, ** p<0.05, * p<0.1. Where applicable, excluded levels are: contract (none), notice (none), hours (30-45), leave (none), Provident Fund (none), and income (no change).

# **Table 6. Marginal Values of Attributes**

||Coefficient<br>(1)|Marginal Value<br>in Terms of %<br>Income (βk/βw)<br>(2)|Labour Law<br>Requirement<br>(3)|Marginal Value<br>x Labour Law<br>Requirement<br>(4)|
|---|---|---|---|---|
|Contract - 6<br>months|0.97|19.0|||
|Contract - 1<br>year|1.37|26.9|||
|Contract -<br>long-term|2.22|43.5|||
|Notice (days)|0.020|0.39|30 days|11.7|
|Hours (median)|-0.021|-0.41|||
|Leave (days)|0.027|0.53|10 days|5.3|
|Provident Fund<br>(Yes)|0.91|17.8|||
|Percent change<br>in income|0.051|1.0|||

_Source_ : Authors’ analysis based on survey data.

_Note_ : Coefficients and associated marginal values of each attribute relative to income. Marginal value is calculated by dividing coefficient on attribute by coefficient on percent change in income. Labour law is based on requirement for typical workers
cost as the main barrier to using formal childcare (European Commission 2016).<sup>4</sup> The average net childcare costs amount to 17 percent of middle-income two-earner couples’ median full-time earnings (OECD 2021). Relatively less information is available on the costs of childcare in LMICs, but existing studies suggest that childcare expenses are significant relative to families’ earnings in many countries.<sup>5</sup> Clark et al. (2019) show that high childcare costs discourage households in Kenya from using formal childcare facilities, which negatively affects mothers’ participation in the labor market. Similar findings exist for Mozambique and Liberia (Bhatkal, 2014; Lokshin et al., 2000). Conversely, in rural Colombia, small monthly fees for community-based childcare centers more than tripled women’s employment and increased their work hours by 75 hours per month (Attanasio and Vera-Hernandez, 2004).

The willingness to use formal childcare services and the potential for childcare regulation to impact FLFP also depend on the strength of social norms related to gender roles and women’s work, and on the quality of childcare. According to the latest wave of the World Value Survey, 41 percent of respondents globally agree or strongly agree with the statement that “A pre-school child suffers with a working mother” and 51 percent agree or strongly agree with the statement that “On the whole, family life suffers when a woman works full time.” Although existing studies on the impact of childcare quality on women’s

> 4Another study from 27 EU member states highlights that approximately 25 percent of women who do not work or work part-time report that childcare is unaffordable (Mills et al., 2014).

> 5For example, a survey conducted in Edo state, Nigeria, in 2005 found that the average cost of a private childcare center kindergarten for one child was more than half the salary of someone earning the minimum wage (Olubor, 2009). A study of peri-urban areas in Accra (Ghana),
Boarding time AM (hours)<br>(5)|Boarding time PM (hours)<br>(6)|
|---|---|---|---|---|---|---|
|||_Panel _|_A: Revealed prefere_|_nce rides_|||
|Positive opportunity cost|-0.104|0.004|-0.009|-0.003|-0.032|-0.032|
||(0.252)|(0.008)|(0.007)|(0.011)|(0.022)|(0.026)|
|Observations|17072|17525|10759|17591|9308|8283|
|Riders|363|363|361|363|354|355|
|Uncontrolled mean when zero opportunity cost|7.720|0.226|0.052|0.504|7.408|6.269|
||(0.299)|(0.017)|(0.011)|(0.019)|(0.043)|(0.038)|
|||_Panel B: R_|_andomized assign_|_ment of space_|||
|Assigned public space|0.071|0.013|0.015|0.040|-0.040|0.011|
||(0.370)|(0.012)|(0.008)|(0.017)|(0.026)|(0.038)|
|Observations|3707|3796|3703|2927|1999|1807|
|Riders|261|261|259|179|240|250|
|Uncontrolled mean when zero opportunity cost|7.142|0.325|0.048|0.498|7.
- 22 -

labor market every year. Ensuring sustained access to jobs seems to be a prerequisite for inclusive and robust economic growth. In theory, the people’s demand for commuting is generally determined by marginal transportation costs and where they live. And the residential locational choice is dependent on housing price gradients.

43. The current paper aims at examining the impact of public transit connectivity on access to jobs by comparing wages earned by those who commute to work and those who do not commute, using the data from Antananarivo, the capital of Madagascar, which is experiencing rapid urbanization. It is found that the wages earned by commuters were systematically higher than those earned by those who decided not to commute and presumably are self-employed or engaged with family businesses around their neighborhood. Thus, the accessibility to jobs is important to increase household income and stimulate economic growth in the city.

44. The proximity to public transport, especially taxi-be, is instrumental to supporting the people’s commuting choice. Of course, other factors, such as human capital, i.e., experiences and education, also matter to earn more. In Antananarivo, importantly, the analysis shows that there is a substantial gender inequality in wages. Women are more likely to use buses to commute, and yet, they earn less than men. In addition, the poor tend to benefit less from public transportation. The public bus services are affordable, however, they may be unavailable around their residential areas or practically inefficient because of their slow operating speed and unsafety. There are many policy issues to be improved.
---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Post Ind.|<br>0.215**|0.373***|0.160*|0.186*|0.204*|0.061***|0.035**|0.048**|0.039***|0.097***|
||[0.105]|[0.107]|[0.089]|[0.108]|[0.109]|[0.020]|[0.016]|[0.020]|[0.013]|[0.020]|
|Obs.|2,422|2,422|2,422|2,422|2,422|2,422|2,422|2,422|2,422|2,422|

Notes: This table estimates how food coping strategies and household deprivations changed immediately following the imposition of the blockade. All specifications include governorate fixed effects. Robust standard errors are reported in parentheses; * denotes statistical significance at the 10 percent level, ** denotes statistical significance at the 5 percent level, and *** denotes statistical significance at the 1 percent level.

# **Conclusions**

This paper demonstrates that there is little correlation between the localized effect of violence and access to food, and that food access is significantly correlated with the blockage of ports during the conflict. These results have important implications for the regional targeting of food assistance, where it could be important to de‐emphasize indicators of violence in the determination of food assistance.
Table 5: State-Industry Total Factor Productivity

|Year|Aggregate|Average|Reallocation|
|---|---|---|---|
|1985|0.000|0.000|0.000|
|1986|-0.005|0.027|-0.033|
|1987|0.015|0.027|-0.012|
|1988|0.019|0.066|-0.048|
|1989|0.010|0.079|-0.069|
|1990|0.008|0.087|-0.079|
|1991|0.018|0.096|-0.078|
|1992|0.019|0.064|-0.045|
|1993|0.063|0.074|-0.011|
|1994|0.041|0.079|-0.039|
|1998|0.076|0.133|-0.057|
|1999|0.071|0.144|-0.073|
|2000|0.093|0.136|-0.044|
|2001|0.104|0.143|-0.039|
|2002|0.107|0.165|-0.058|
|2003|0.104|0.160|-0.057|
|2004|0.117|0.175|-0.057|

Total factor productivity (TFP) decompositions for the population of firms, conducted at the stateindustry level, using the OP method. “Aggregate” indicates market-share weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. Source: Authors’ calculations based on Annual Survey of Industries data.

43
in OECD economies with less regulated product markets, which has direct implications for the welfare of a country’s workforce. Cacciatore and Fiori (2016) find that product market deregulation reduces the welfare costs associated with business cycle fluctuations in EU countries. Islam et al. (2022) flag the lack of dynamism induced by PMR as one explanation behind the stagnation of labor markets observed in MENA countries.

Much of the empirical work cited above relies on the PMR indicators carefully compiled by the OECD. These measures do usually not exist for LMICs for at least two reasons. On the one hand, collecting data from (formal) firms in LMICs is more cumbersome due to a lack of accessible infrastructure. On the other hand, OECD PMR indicators only measure _de jure_ conditions and do not provide information about the _de facto_ states under which firms operate. In LMICs, where law enforcement is often much weaker than in advanced country settings, such indicators are less useful. In the upcoming sections, we discuss different regulation types in more detail. We include several papers that review the implementation of specific regulations in the context of LMICs.

# **3.2.2 Lowering Entry Barriers and Delicensing**

The ease with which new firms can enter a market is a decisive factor for the level of product market competition in an economy. In a recent report, Vitale et al. (2020) point out that the firm registration process is still overly complex in many (OECD and non-OECD) countries, which continue to use licenses and permits and do not have a “silence is consent” rule to speed up the administrative process. In addition, the authors promote introducing one-stop shops that can provide businesses with information on all licenses and permits, and that can issue all of them. Interestingly, LMICs seem to face this problem to a lesser extent than rich countries, given that they often either have no formal processes—or relatively uncomplicated ones—for standard entrants. In Indonesia, for example, Lewis et al
also use data from 2000 to2010 for three countries with available data.

> 6 See Cebeci, Fernandes, Freund and Pierola (2012) for a detailed description of the data and the cleaning process operated in it. This cleaning process includes the use of a “consolidated” product classification that takes into account the transformations made to product codes according to the HS classification throughout the years. In addition, in order to mitigate the risk of including transactions that correspond to the shipping of samples or personal belongings, we dropped the observations corresponding to exporters that, in a given year, had total sales below $1,000. We also dropped all the observations belonging to Chapter 27 according to the HS classification –Mineral fuels, oils and product of their distillation; etc.

7
than in cash) starting in October 2015. To prepare for the implementation of the mandates, the financial inclusion law required banks to offer free bank accounts that fulfilled certain criteria (specified numbers of free transfers, withdrawal etc.).<sup>9</sup> Figure A.8 shows that the use of bank accounts and electronic payment technology in Uruguay increased significantly between 2011 and 2017, much more than in most other countries over the same period.

# **3.4 Data**

To study the effect of electronic payments on tax compliance, we merge multiple data sets. First, we use transaction-level card payment data, which contain the universe of transactions between 2007 and 2016. Credit and debit card companies send these data to the tax administration every month. The data contain the transaction date, transaction amount, VAT rebate amount, the tax ID of the firm, and a POS identifier. We can thus count the number of POS a firm uses. We collapse the data at the firm-month level.<sup>10</sup> While we refer to these data as the card payment data for simplicity, it is important to note that these data contain all electronic transactions (e.g. including transactions via apps such as PayPal, Square etc.).<sup>11</sup> We merge the card transaction data with monthly VAT returns, containing all line items from the tax return.<sup>12</sup> Our main outcome variables are output VAT (i.e. VAT on sales), input VAT (i.e. VAT paid on inputs and deducted from output VAT), and the net VAT liability (=max(output VAT - input VAT, 0)).

Information on firms’ sector of activity is obtained from the firm registry, which contains the six-digit CIIU industry code for all firms ( _Clasificaci ́on industrial internacional uniforme_ ). In the CIIU, the first two digits of the CIIU code
of education|0.016|0.010|0.019|0.012|0.018|
||(1.08)|(0.73)|(1.29)|(0.86)|(1.51)|
|Constant||||||
|Observations|3006|2922|1362|2922|2922|
|R-squared|0.17|0.18|0.08|0.18||

**Notes:  See table 4.** Column (5): the total number of different individuals observed between 1 and 4 times is 1214.

24
*<mark>Figure 8: ODINs poverty and income openness scores vs MENA MAI consumption/welfare data openness score</mark>**

<!-- Start of picture text --> 100 100100 100 100<br>100 80<br>7070<br>80 60 60 60 6060 60<br>50 50 50 5050 50 50 50 50 50<br>60 4040 40 40 40<br>40 20<br>20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0<br>0<br>2018 ODIN openness score (Poverty and Income category)<br>2020 ODIN openness score (Poverty and Income category)<br>MENA MAI (Consumption data openness score)<br><!-- End of picture text --> <mark>Source: Author’s compilation using own data, 2018/19 and 2020 ODIN data from Open Data Watch -- Open Data Inventory</mark> <u><mark>http://www.opendatawatch.com</mark></u> Note: Countries without poverty and income openness score for the 2018/19 and 2020 ODIN are not shown.

29
else equal. This means that shocks that expand _both_ firm size and the number of firms, such as improved market access or lower trade costs, will increase average _overall_ exporter size by a much smaller amount than _incumbent_ exporter size since the entrants are primarily very small exporters. Thus, gravity-type regressions on average size and number of exporters will understate the importance of size effects on aggregate exports. Examining export growth over time, it is in countries where the shocks affect large firms most significantly that exports will grow most rapidly and this will tend to push up the average exporter size more than the number of exporters. This is precisely what we see in stylized fact 2. From a policy perspective, it also suggests that there may be important levers that can be used to enhance exports, such as attracting large multinationals.

# **V. Concluding Remarks**

While the literature on exporter dynamics is rapidly growing, there has not been a comparison across a large number of countries at differing stages of development. The Exporter Dynamics Database presented in this paper addresses that limitation by providing policymakers and the research community with a set of measures of exporter characteristics and dynamics that allow for cross-country comparisons. The ultimate objective of this Database is to provide researchers with more data that will expand our understanding of how exporting happens and generate policy implications for countries seeking to expand their exports. The Database compiles measures for 45 countries (most of them developing countries) covering the universe of annual firm-level export transactions primarily for the period between 2003 and 2010.

This paper presents six stylized facts that arise from the analysis of measures in the Database and their cross-country comparisons. The data point to wide variation in the export 38