rafmacalaba/data-use-mentions-extended huggingface dataset:
the effect of each problem on enterprise performance, conditional on the controls and the fixed effects. In addition to quantitative measures of enterprise performance, the survey also provides qualitative measures of enterprise performance, recording whether the establishment is expanding, stagnating, or a new entrant (formed in the past 3 years). Finally, the enterprise data also records outstanding credit, and the source of enterprise credit, permitting us to test whether enterprises in districts with relatively more bank branches or bank credit per capita also have increased access to formal credit.

We begin by analyzing the effect of various problems reported by enterprises and their impact on quantitative measures of enterprise performance. The outcome of interest in

> 10 We include the same set of district covariates as before. The full set of establishment level covariates are: a) logged fixed assets; b) logged wage payments; c) months of operation; d) dummy for female owner; e) dummy if the enterprise is owned by an individual hailing from the SC/ST community; f) a quadratic in establishment age; g) dummy for own account enterprise; h) dummy for whether the enterprise keeps accounts; i) dummy for whether the enterprise operates out of a fixed location; and j) dummy for whether the enterprise is locally registered.

> 11 Value addition is computed as total revenues less total expenses (not including labour costs).

24
# **4.3 Provision-specific dispute settlement mechanisms**

All RTAs in our sample feature broad scope dispute settlement mechanisms that cover the entire trade agreement. However, some RTAs feature additional dispute settlement mechanisms unique to particular provisions that supplement the broad-scope, agreement-level mechanisms. We explore the marginal effectiveness of these supplemental dispute mechanisms to further understand how environmental provisions are effective. As discussed in Section 2, environmental provisions in RTAs may vary along several dimensions. Using data from Monteiro and Trachtman (2020), we distinguish between RTAs that subject forest and biodiversity provisions to a general dispute settlement mechanism and those RTAs that also include auxiliary environment-specific dispute settlement channels. We then estimate our main triple-difference model in Equation (2) with the addition of a _fourth_ differencing dimension: whether the provision also includes the ”special” dispute settlement for environmental provisions.

We present these findings in Table 4. The environmental provision coefficient now captures the marginal effect of provision inclusion when such provisions rely on general enforcement mechanisms, while the coefficient on the auxiliary environment-specific dispute mechanism describes the marginal impact of the ”special” dispute mechanism. We find no evidence that environment–specific dispute settlement mechanisms offer any additional reduction in forest loss beyond that achieved by general enforcement mechanisms. While the coefficients on the interaction with enforcement provision in column (1) and (3) are indeed negative, they are not statistically differentiable from zero. This evidence supports the view that the combination of enforceable language and general dispute settlement is sufficient to achieve the intended environmental protection and ”special” supplemental dispute settlement mechanisms for these provisions do not appear necessary.

# **4.4 LASSO Penalty Sensitivity**

Although our LASSO approach to model selection and propensity score estimation is designed to guard against subjective specification choices unduly driving our results, we recognize that the LASSO penalty parameter can have a substantive effect on variable selection and hence, estimated 20
which tend to supply lower quality goods than the US and the EU. A full liberalization would lead to a shift in imports of digital goods towards China, and a reduction in the share of digital goods sourced from the EU, and therefore to reductions in the quality of imports, on average. Notice, however, that the decline in the quality of imports of digital goods is relatively small compared to the increase in its value. Since the quality of imported goods tends to be higher than that of goods produced domestically, the quality of digital inputs that is available to firms in the region would likely increase on average.

Taken together, these findings suggest that the AfCFTA countries should consider including tariff concessions on digital goods imports from non-members, potentially lowering costs significantly, and thus expanding access to imported digital goods, especially in the poorest countries with the highest tariffs on digital goods.

# **7 Conclusion**

A growing body of evidence reveals that digital goods are critical for productivity growth in low-income countries. Since digital goods are mainly produced in advanced economies, the quality and prices at which firms and consumers in developing countries can acquire them in international markets are especially important. In this paper, we used detailed data on international trade flows and tariffs to study Africa’s imports of digital goods. We first provided evidence on the relative quality and quality-adjusted prices of Africa’s digital goods imports, as well as the current degree of tariff protection they face. We then estimated the likely impacts of AfCFTA on digital goods imports, which we compared with a scenario of full unilateral tariff liberalization towards all countries.

Despite this heavy reliance on imports, Africa imposes relatively high tariffs on digital goods globally, with an average import-weighted tariff of approximately 4 percent, compared to an average of 0.5 percent in other regions. These tariffs have remained relatively constant in Africa, in contrast to significant declines observed in non-African countries over the past decade. Moreover, the level of tariff protection on digital goods correlates negatively with
Roads<br>Changes in per capita growth<br>Congo, Rep Kenya East Africa Cameroon Cote d'Ivoire Tanzania Uganda Sudan<br><!-- End of picture text -->

**b. Potential contributions of infrastructure to annual per capita economic growth in East African countries, in percentage points**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>ICT Power Roads<br>Potential contributions of per capita growth<br>Cote d'Ivoire Kenya Cameroon Tanzania Sudan Congo, Rep of Uganda East Africa<br><!-- End of picture text --> _Source:_ Calderón 2009.

_Note:_ ICT = information and communication technology.

Evidence from enterprise surveys suggests that in 2006 infrastructure constraints were responsible for as much as 58 percent of the productivity handicap faced by Ugandan firms (figure 2a), with the 5
disasters, including the emergency supplies needed in case of a disaster.<sup>16</sup> They also maintain and provide information to households on emergency shelters and other community level emergency preparation systems.<sup>17</sup>

# **4. Findings**

# **4.1 Preparedness to cope with disaster**

We now proceed to examine three well-established factors that contribute to households’ ability to cope with a disaster: possession of emergency supplies, discussion of measures to cope with disaster risks within the household, and community level resources to mitigate the impact of disasters.

At only about 12 percent, the share of households responding that they own any emergency supplies is lowest in Haiti (Figure 3). This should not come as a surprise both because Haiti is the poorest of all five countries in the sample and because, at the time of the interviews, Haiti was going through a period of significant political turmoil. It is reasonable to assume that high poverty levels and political disarray are important factors behind limited ability to cope with negative shocks. More generally, there is a clear country-level income gradient in possession of emergency supplies. In the two wealthiest countries in our sample, Dominica and Saint Lucia, nearly six in ten households (59 and 58 percent respectively) own at least one emergency supply. In Belize and Suriname, the share of households owning emergency supplies is 43 and 28 percent respectively. The causal relation between income and preparedness to deal with disasters may run in two directions. Higher income may enable countries to better prepare for disasters caused by natural hazards, for instance through the purchase of emergency supplies. And, vice versa, better preparation for disasters may reduce their often-high economic costs and thus contribute to economic growth.<sup>18</sup> Households in Belize, Dominica, Saint Lucia, and Suriname were asked which emergency supplies they own (see Table 2). This question was not included in the Haiti survey. Emergency supplies commonly
sulfate (an unlisted action) 96 percent of the time. In contrast, participants often missed other actions that are always appropriate: monitoring contractions and the amniotic fluid, tracking the fetal heart rate and the mother’s vital signs, and recording the fluids and drugs administered. Performance is also generally higher for clinically unnecessary or incorrect actions. For example, participants almost never recommended administering magnesium sulfate or augmenting labor, which are unnecessary in all cases.

# **3.2 Study arms**

The experiment was embedded in a survey of primary care clinics fielded as part of an impact evaluation of health facility financing modalities (see Section 4.1 below). Toward the end of the survey, we randomized respondents in each clinic into one of three trial arms (information, rewards, penalties), stratified by clinic. All participants received a printed list of seven randomly selected common clinical actions that are shown in Table 1, for example, to monitor the fetal heart rate or prepare for imminent delivery. The list shown to participants is purposively incomplete: there are eight additional actions not on the list but may be appropriate. In the rewards arm, participants were offered payments if they recommended five of the seven actions when they were indicated for the patient.<sup>3</sup> In the penalties arm we deducted the same payment amount for each action that is indicated but was not identified by the participant. The reward and penalty contracts are isomorphic: the same actions lead to the participant being paid the same in either arm. We randomized the payment amounts across the incentivized actions, and the listed and incentivized actions include some that are unnecessary or even harmful in some cases.

All participants received a flat payment that varied by arm such that the maximum, minimum, and, in expectation, average payouts are all the same. The flat payment in the information arm was 1,750 Nigerian Naira or about USD 5.80. The participation fee allows us to account for an endowment
got their fuelwood from their own property. Fuelwood was also the most important (19%) or second most important (24%) purpose of planting the most dominant tree.

In an agricultural household survey carried out in Tanzania in 2002-2003 with more than 9000 households, 48% answered that timber/planks were the main use of trees planted on their own land, but this was followed by fuelwood with 22% reporting it as the main use (United Republic of Tanzania, 2003). Similarly, in a smaller household survey (1000 households from 50 villages) 18% ranked fuelwood as the most important benefit coming from trees planted on their own land, 31% as the second most important and 10% as the third most important (Lokina et al, 2011)<sup>22</sup> .

Planting of trees can also be affected by tenure reforms. Deininger and Jin (2006) and Mekonnen (2009) report that trees are planted to increase tenure security. Both Deininger and Jin (2006) and Ayalew et al. (2011) find for the case of Ethiopia that insecure tenure decreases plantation. Shively (1998) shows how investments in trees can be sensitive to both price fluctuations and risk preferences with an application to the Philippines. Policies that both increase woodfuel prices (such as reduced open access for charcoal production) and reduce tenure insecurity can therefore be important for increasing the supply of woodfuels from private sources. In doing so, past experience, and the studies cited above, point to the importance of considering the multiple uses of trees, where fuel is only one, and seldom the most important.

A key question is how such reforms affect women‘s right and access to trees. For example, Chambers and Leach (1989) show the important role of planted trees in meeting contingencies, which benefit women mostly if they have rights to those trees. Reforms can be designed to improve the rights of women to land (Place, 2009). However, Mearns (
Allcott, H. 2015. “Site Selection Bias in Program Evaluation.” _Quarterly Journal of Economics_ 130 (3): 1117–1165.

Angrist, J. D., and J.-S. Pischke. 2010. “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics.” _Journal of Economic Perspectives_ 24 (2): 3–30.

Ashenfelter, O., C. Harmon, and H. Oosterbeek. 1999. “A Review of Estimates of the Schooling/earnings Relationship, with Tests for Publication Bias.” _Labour Economics_ 6 (4): 453–70.

Assmann, S. F., S. J. Pocock, L. E. Enos, and L. E. Kasten. 2000. “Subgroup Analysis and Other (mis) Uses of Baseline Data in Clinical Trials.” _The Lancet_ 355 (9209): 1064–69.

Banerjee, A., R. Banerji, E. Duflo, R. Glennerster, D. Kenniston, S. Khemani, and M. Shotland. 2007. “Can Information Campaigns Raise Awareness and Local Participation in Primary Education?” _Economic and Political Weekly_ , 1365–72.

Brodeur, A., M. Lé, M. Sangnier, and Y. Zylberberg. 2013. “Star Wars: The Empirics Strike Back.” Discussion Paper Series, Forschungsinstitut Zur Zukunft Der Arbeit 7268.

Bruhn, M., and D. McKenzie. 2009. “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” _American Economic Journal: Applied Economics_ 1 (4): 200–232.

36
year, with questions becoming progressively more difficult as a student advanced in the test. The questions were a combination of multiple choice and fill-in diagrams. The tests were piloted extensively; they have no ceiling effects while floor effects are limited.<sup>22</sup> In each round, we randomly sampled a subset of students from each grade to take the test. In Year 1, both baseline and endline student samples were drawn from the official school register of enrolled students compiled by the head teacher at the beginning of the year. This ensured that the sampling protocol did not create incentives for strategic

> 21See Bold et al. (2017) who use a similar approach to assess teacher content knowledge.

> 22Test scores are approximately normally distributed with a mean of close to 50 percent of questions answered correctly. A validation exercise of the test at baseline found its scores to be predictive of the national Primary Leaving Exam scores (both measured in school averages).

16
Proxy 1 definition uses employment status to identify informal workers. Under this definition, selfemployed workers with temporary employees, own-account workers, casual workers, and unpaid family workers are considered informal. In contrast, the Proxy 2 definition combines information on employment status and occupation. Accordingly, self-employed workers with temporary employees, own account workers, and casual workers in highly skilled occupations (i.e., professionals and managers) are not considered informal. Because workers in highly skilled occupations may have access to some forms of social protection and/or employment benefits, the Proxy 2 definition may provide more refined estimates of informal employment than Proxy 1.

< Figure 1 > More recently, Statistics Indonesia introduced a new operational definition of informal employment known as Proxy 3 (Pratomo & Manning, 2020). Compared to earlier definitions, Proxy 3 uses an entirely different set of criteria to identify informal workers. In particular, it takes the following factors into account: (i) the type of enterprise employing the worker; (ii) the bookkeeping practices of the enterprise; and (iii) the provision of social security and other benefits to the worker.

# **Data sources**

# _Data on informal enterprises_

Statistics Indonesia maintains several datasets used to study Indonesia's informal sector. These are: (i) the Economic Census; (ii) the Survey of Micro and Small Enterprises ( _Survei Industri Mikro Kecil_ or IMK); and (iii) the Survey of Medium and Large Manufacturing Firms ( _Survei Tahunan Perusahaan Industri Pengolahan Besar dan Sedang_ or Manufacturing Survey). Complementing these main data sources are the World Bank Enterprise Surveys (WBES) and a smaller Informal Sector Survey (ISS) conducted with the assistance of the Asian Development Bank in 2009. This section provides a brief overview of these different data sources.

5
Furthermore, in terms of the representativeness of the business owners in the study sample, we are reassured that phone-based surveys of small businesses sampled from national business registries conducted by the World Bank and partners across 51 countries from AprilAugust 2020 document similar impacts on business closures, sales and employment among small and medium enterprises as we find in this paper using the Future of Business survey (Apedo-Amah, 2020; Adian, 2020). Additionally, Torres et al. (2021) analyze the World Bank Business Pulse Survey data by gender and corroborate the findings in this paper that women-led businesses (micro) were disproportionately hit by the COVID-19 shock compared to businesses led by men.

While our measure of business closure is self-reported by the respondent in the survey, an additional concern might be that business owners who close their business could immediately unpublish their Facebook Business Page, and therefore be excluded from the sampling frame. The sample for the FoB survey is restricted to those who have active, published Pages, where active means they had some kind of activity in the previous 28 days. Business owners are not required to unpublish a Page if they close their business nor does Facebook require a Page to be unpublished after a business is closed. Activity and published status are assessed two weeks before fielding of the survey to minimize this concern. However, we do contend that results on business closures may only offer a lower-bound estimate on the true rate of closure. Appendix A includes further details of the Future of Business survey, as well as a detailed description of the main variables used in the analysis.

# **4 Empirical Strategy**

In this section we first outline the empirical strategy to examine the differential effect of the overall COVID-19 pandemic for male- and female-led firms. Next, we turn to the empirical strategy used to analyze the impact of school closure policies related to COVID-19 for maleand female-led businesses. We begin the analysis for all business owners and managers in the sample and go on to also restrict the sample to those who report that
for energy management practices, if energy requirements and associated costs are very small relative to total costs or sales and thus have little impact on profit. Generic management practices, by contrast, may be more universal in their potential to help diverse firms excel. Moreover, energy is a subset of the strategic and operational concerns of a firm; stronger generic management practices may thus correlate with a propensity to invest in specialized energy-centric management practices. However, the need for energy-specific practices may be endogenous to energy requirements implied by a firm’s capital equipment choices or a function of factors beyond a firm’s control, such as local energy system characteristics.

_Across high, upper middle, and lower middle income groups, average management scores are not statistically different, with high variation within country groups (see Appendix Table A4)._ Using the winsorized sample and computing generic management score as a straight average of non-missing entries, firms in the high income group average a generic management score of 0.491 (SD 0.209), upper middle a score of 0.481 (SD 0.211), and lower middle 0.477 (SD 0.200); differences are not statistically significant. Large standard deviations are consistent with the finding in (Bloom et al., 2012b) that score heterogeneity within countries is often greater than between them. All countries have roughly the same share of zeros, while high and upper middle income countries have a slightly larger share of firms scoring above 0.75 (4% versus 3%). For energy management, the high income group firms’ energy-centric management score average is 0.291 (SD 0.304), upper middle 0.237 (SD 0.273), and lower middle 0.365 (SD 0.296); the differences are statistically significant. Appendix Table A4 summarizes generic and energy-centric management scores by income group. Interestingly, firms in lower middle income countries have fewer zero entries for energy management score, and also
Table 1: Effects on Adoption

||(1)<br>Any Action|(2)<br>Spray|(3)<br>Pour Ash|(4)<br>Crush<br>Eggs/Worm|
|---|---|---|---|---|
|Trained Farmer|0.15|0.13|-0.01|0.03|
||(0_._08)<sup>_∗_</sup>|(0_._09)|(0_._03)|(0_._03)|
|Spillover Farmer|0.14|0.12|-0.00|0.01|
||(0_._06)<sup>_∗∗_</sup>|(0_._06)<sup>_∗_</sup>|(0_._02)|(0_._01)|
|Control Mean|0.42|0.40|0.04|0.01|
|Observations|739|739|739|739|

**Notes:** The table reports estimates based on specification (10). _Trained Farmer_ is a dummy variable equal to 1 if a farmer has received training on the Fall Armyworm as part of the experiment. _Spillover Farmer_ is a dummy variable equal to 1 if a farmer lives in a village where training was provided to another farmer. The dependent variable in Column (1) is a dummy equal to 1 if the farmer reported having adopted any of the following recommended practices: spraying their field, pouring ash (or sand or soil) on infected plants, crushing the eggs or caterpillars. In the remaining columns the dependent variable is a dummy for each recommended practice. All specifications include fixed effects for the willingness to pay of the Decision Maker for the potential trainee in the village. Robust standard errors are clustered at the