rafmacalaba/datause-encoder-data huggingface dataset:
in their analysis of firms in Ghana, Kenya and Tanzania. Using Ethiopian data and a hazard function, Shiferaw (2009) found foreign ownership to have a negative effect on firm exit.

Firm size (measured as the log of firm employment) and dummy variables for whether the firm is a sole proprietorship or corporation are all significant. One possible interpretation is there proxy status for controlling for financial constraints in the firm exit decision. Consistent with the findings of Frazer (2005), Gebreeyesus (2005) and Harding et al. (2006) for other African countries, firm size is found to be negative and significant.<sup>22</sup> However sole proprietorships (which are expected to be financially constrained) are surprisingly found to have a lower probability of exit than limited liability firms, while corporations are found to have a higher probability of exit.

Turning to the agglomeration variables, the paper finds that they are associated with higher rates of exit. Both total employment in an area and the share that is in one’s sector are associated with increased probability of exit. Even industrial diversity is found to decrease firm exit, contrary to the hypothesis that the existence of multiple activities could lead to positive spillovers. What is striking is that these diversity and scale results are found even after controlling for productivity, which suggests that these agglomeration externalities operate through channels beyond productivity. Rather, this gives support to concerns of negative congestion externalities.

The competition indicators are significant too. Both the number of firms in the location-sector and the competition invHHI are above one and significant, reinforcing the importance of competition in determining survival. Haddad et al. (1996) found increases

> 22 The finding of the negative and significant influence of firm size on exit may not only reflect the influence of financial constraints. For example, larger firms may be better equipped to deal with macroeconomic fluctuations which may not be necessarily linked to financial resources.

19
**Table A2. 3.  Sensitivity Analysis: Non-mining Growth Rates**

||2010/11|2011/12|2012/13|2013/14|2014/15|2015/16|
|---|---|---|---|---|---|---|
|**Domestic revenue (in percent of GD**|**P)**||||||
|1percentage pointlowergrowth|12.1|12.0|12.0|12.0|12.0|12.1|
|Baseline|12.2|12.0|12.0|12.1|12.1|12.2|
|1percentage pointhighergrowth|12.2|12.1|12.1|12.1|12.2|12.3|
|2percentage pointshighergrowth|12.2|12.1|12.1|12.2|12.3|12.4|
|**Fiscal balance (in percent of GDP)**|||||||
|1percentage pointlowergrowth|-10.3|-7.4|-4.7|-2.6|-1.5|-0.4|
|Baseline|-10.2|-7.1|-4.3|-2.1|-0.9|0.3|
|1percentage pointhighergrowth|-10.0|-6.8|-3.9|-1.7|-0.4|0.9|
|2percentage pointshighergrowth|-9.9|-6.6|-3.6|-1.2|0.2|1.6|
|**Cumulative fiscal balance (in billion**<br>|**s of 2008/09**|**Pula)**<br>|||||
|1percentage pointlowergrowth|-20.0|-26.8|-31.4|-34.0|-35.6|-36.1|
|Baseline|-20.0|-26.6|-30.8|-33.0|-34.0|-33.7|
|1percentage pointhighergrowth|-19.9|-26.3|-30.3|-32.1|-32.5|-31.4|
|2percentage pointshighergrowth|-19.8|-26.1|-29.8|-31.1|-30.9|-29.0|

Source: Author’s calculation.

# **A2.3 Pula/US dollar Nominal Exchange Rate**

Next we consider the impact of different exchange rate assumptions on the Baseline fiscal paths. Changes in the Pula/US dollar exchange rate will impact Botswana’s fiscal position through multiple channels.<sup>30</sup> First, nominal depreciation of the Pula vis-àvis the US dollar would boost mineral revenue in Pula terms although it would remain the same as a percentage of GDP. Second, a weaker Pula against the US dollar would raise Botswana’s external interest payments in Pula terms, putting upward pressures on overall government expenditure. However, depreciation would also raise, in Pula terms, the return on the fiscal reserves that are invested in foreign assets, as well as foregin grants (assumed to be provided in the US dollar), raising government revenue. The net impact on the next external payment is not straight forward, and depends on the net external position of the government; the differential between the interest rate on <!-- Start of picture text --> Figure A2. 4. Different Nominal Exchange Rate Assumptions<br>(Pula/US dollar)<br>13<br>12<br>11 30% appr.<br>10<br>9 15% appr.<br>8<br>7 Baseline<br>6<br>15% depr.<br>5<br>4<br>30% depr.<br>Source: Author’s calculation.<br><!-- End of picture text --> external debt and the rate of return on the fiscal savings; and the amount of foreign grants offered to Botswana.

We consider three scenarios, in which the Pula appreciates/depreciates, in nominal terms, against the US dolar by 15 percent and 30 percent, respectively, at the beginning of 2011/12 from the level assumed in

> 30 One can interpret nominal appreciation/depreciation of the Pula against the US dollar as the movement of the Rand vis-à-vis the US dollar, since the Pula is pegged to a basket of currencies with a heavy weight on the South African Rand.

33
Financial Regulation and Development

# **Annual Reports Confidence Intervals**

Figure F4: Real Profits and Number of Employees <!-- Start of picture text --> 2009 2010 2011 2012 2013<br>year<br>Real Profits − Difference Real Profits − Difference 5pc<br>Real Profits − Difference 95pc<br>2009 2010 2011 2012 2013<br>year<br>Employees − Difference Employees − Difference 5pc<br>Employees − Difference 95pc<br>150<br>100<br>50<br>0<br>−50<br>1500<br>1000<br>500<br>0<br>−500<br><!-- End of picture text --> _Note:_ This figure plots the differential trends and 95% confidence intervals between big and small banks in real profits (upper panel) and number of employees (lower panel). The policy is implemented in April 2011 (shown by the red vertical line). As is evident, while both profits and number of employees do not differ before the policy change, afterwards there is some evidence that big banks start to earn more (upper panel) and to hire more employees (lower panel). Because of the yearly nature of the data, the power of this test is low. The trends for big and small banks can be found in Figure 8.

55
and community health centers (CHCs). The MCHPs and CHPs that comprise our study sample are primary health facilities — the first points of contact for patients in towns and villages — that each service populations of 500 to 10,000 (UNICEF 2014, 5). The 192 MCHPs and 62 CHPs included in our study covered a population close to 1 million people, nearly 15 percent of the country’s population of 6.6 million in 2011.

PHUs continued to operate during the Ebola crisis: a UNICEF (2014, 17) facility survey in October 2014 (four months after the first confirmed case in Sierra Leone) found that only 4 percent of PHUs were closed. Levy et al. (2015, 753) report that “early assessments [from October 2014] found that many [Ebola] patients were first seeking care at local PHUs.” Concerned that these PHUs lacked the training and equipment to properly isolate and care for Ebola patients, PHU staff were rapidly trained on infection prevention and control and outfitted with personal protective equipment. By early December 2014, 81 percent of healthcare workers in Sierra Leone had received training (see Table E.2); by early January, training had reached 98 percent (Levy et al. 2015). In addition to screening and providing “no-touch” treatment for dehydration and fever, a case study in Kenema District found that PHU staff (specifically, community health workers) were also engaged in social mobilization, contact tracing, and community-based surveillance (Vandi et al. 2017). While training and the disbursement of protective equipment filled important knowledge and resource gaps, UNICEF’s (2014, 17) survey found that 90 percent of PHUs felt that fear and misconceptions were “the main challenge confronted by the health system in fighting Ebola.”

# **2.2 Interventions**

## **2.2.1 Community Monitoring**

We model our Community Monitoring (CM) intervention on Björkman and Svensson’s (2009) “Power to the People” intervention, which was implemented in Uganda in 2005. The intervention attempts to mobilize “client power,” empowering patients with the information and forum to demand accountability from frontline staff (The World Bank 2003). CM convenes users and providers to discuss problems around local health service delivery and agree upon actions both groups can take to address these problems.

The CM intervention follows a four-step protocol. First, trained facilitators convene meetings with clinic staff and share scorecards rating local health problems. The scorecard includes five indicators related to maternal and child health (maternal mortality, under-5 mortality, vaccination rate, percentage of births in a health facility, and completion of four antenatal visits). These are constructed from relevant questions in the baseline household survey, and are compared to the district average so as to prompt discussion. Clinic staff are then invited to share their concerns and frustrations with the community. For example, nurses frequently complained that community members do not utilize the clinic, that mothers choose not to give birth in the 7
# **Experimenter demand**

Our analysis so far has treated self-reported information as accurate. However, one’s own COVID19 vaccination status may be a sensitive topic, inducing respondents to exaggerate vaccine uptake in response to “experimenter demand” effects.<sup>19</sup> We find no support for such behavior (Table 8). When making experimenter demand effects explicit by framing the vaccination uptake question with a positive or negative expectation from the enumerator, we obtain near identical and statistically indistinguishable estimates of vaccine uptake. Furthermore, negative and positive reinforcement of experimenter demand produces estimates that are statistically indistinguishable from the standard question framing. We take the fact that the estimates are remarkably close to those under our standard question framing even when explicitly introducing experimenter demand to indicate the robustness of self-reported vaccine uptake to such effects.

**<mark>-</mark>** **<u><mark>Table 8: Hypothesis 6 Experimenter Demand</mark></u>**

|VARIABLES|(1)<br>Burkina Faso|(2)<br>Ethiopia|
|---|---|---|
|Treatment Arm = 1, Positive demand|0.0137|0.0146|
||(0.0300)|(0.0243)|
|Treatment Arm = 2, Negative demand|0.0155|0.0284|
||(0.0301)|(0.0245)|
|Constant|0.487***|0.475***|
||(0.0214)|(0.0171)|
|Observations|1,668|2,509|
|R-squared|0.000|0.001|

Note: OLS regression of vaccination status on dummies for treatment group 1 (positive demand) and treatment group 2 (negative demand). Base category is standard phrasing. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# **4.4 Misreporting in administrative data**

So far, our analysis has found that errors of representation in the survey data explain part but not all of the gap between survey estimates and administrative figures. We next turn to possible sources of error in the administrative data. While we cannot experimentally probe the administrative data in the same way as the survey data, we present indicative evidence pointing to some weaknesses and inaccuracies in administrative data.

> 19 This pattern would also be consistent with self-reported information providing higher estimates of vaccine uptake than proxy-reported information.

24
The financial indicators for listed non-financial firms in our sample show downward trends in debt service capacity, profitability, and market valuation across all regions (Figures 5.1, 5.2, and 5.3). In addition, leverage has increased in most regions except South Asia (SA) and EAP, where leverage has been high, but decreasing. LAC and SSA have experienced a steep increase in leverage since 2011/2012 (Figure 5.4). Based on an increasing Quick Ratio, rollover risk has declined since the Global Financial Crisis, but ECA, SA, and SSA may be vulnerable to adverse liquidity shocks as the quick ratio has been persistently below one (Figure 5.5).

**Figure 5: Financial Indicators, 2006-2016**

Country medians per region

**1. Interest Coverage Ratio**

<!-- Start of picture text --> ICR<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EAP ECA LAC<br>MENA SA SSA<br><!-- End of picture text --> Note: Interest Coverage Ratio (ICR): Earnings Before Interest and Taxes/ Interest Expenses. Source: Bloomberg, own calculations

# **3. Market to Book Ratio**

<!-- Start of picture text --> Market to<br>Book<br>2.6<br>2.1<br>1.6<br>1.1<br>0.6<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EAP ECA LAC<br>MENA SA SSA<br><!-- End of picture text --> Source: Bloomberg, own calculations

**2. Return on Assets**

<!-- Start of picture text --> ROA %<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text --> Source: Bloomberg, own calculations

**4. Leverage Ratio**

<!-- Start of picture text --> Leverage %<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EAP ECA LAC<br>MENA SA SSA<br><!-- End of picture text --> Note: Leverage Ratio: Total Debt/Total Assets. Source: Bloomberg, own calculations 12
preferences. However, changes in imports varied greatly within these regions.<sup>20</sup> The results in table 5 suggest that more countries may be impacted by the removal of US preferences than had been thought previously. To facilitate a comparison, table 5 includes the value of preferences calculated with respect to total exports to the US. Using an approach similar to this study, Brenton and Ikezuki (2004) found 6 AGOA countries for which values exceeded 5% of total exports to the US in 2002 after incorporating utilization: Madagascar, Mauritius, Malawi, Swaziland, Kenya, and Lesotho. Our results for 2003 include these six (with similar value estimates), and add Cape Verde, Mozambique, and Botswana, with much higher value estimates. We find, as did Brenton and Ikezuki, that most of this value is attributable to preferential access for apparel. Using more aggregated data and assuming full utilization, Alexandraki and Lankes (2004) found 18 countries for which the value of all preferences from the US, EU, Canada and Japan, _altogether_ , exceeded 5% of total exports. Table 5 shows 19 countries--half of Alexandraki and Lankes’ list and 10 additional countries--for which the value of US preferences _alone_ exceeds 5% of total exports assuming full utilization. After incorporating actual utilization, table 5 shows that 17 countries’ values exceed this threshold.

# **7.  Concluding Remarks**

Close examination of US import data reveals that countries which were members of ATPA, CBTPA, and CBERA tended to have very high utilization of regional preferences, and lower GSP utilization, though CBERA utilization was on average lower and more diverse than the other regional programs. AGOA non-LDCs, for which regional preference coverage and GSP preference coverage are complementary, had very high combined utilization of AGOA and GSP preferences. In many cases, AGOA non-LDCs fully utilized both preference programs. AGOA LDCs, for which AGOA coverage was less generous than

> 20For example, within SSA, apparel imports from Swaziland grew by 14.4% while imports from Mauritius fell by 38.1%.  Within CBI, US apparel imports from Nicaragua grew by 47.9%, while those from the Dominican Republic fell by 12.9%.

21
5 payer is required to compute its tax liability under the normal tax regime as well as under a parallel tax regime and then pays the larger amount. The parallel regime can have either a simplified definition of income or the firm's assets as a base. Some countries simply allow a deduction of the minimum tax liability from the regular income tax liability.A4 Uader the second form, the alternative tax computed can be deducted from the regular income tax base rather than from the tax itseif. For a given alternative tax base and tax rate, this second option will in general provide larger revenue to the government to the extent that firms will end up paying both taxes--cne in full, the other one partially-- rather than only the highest one.

Under either scheme, the rate is typically set in such a way that, on average, only tax payers relying heavily on tax incentives end up paying a tax on their assets. For the other tax payers, only the income tax is relevar".

Of the 99 countries covered by the 1989 Price Waterhouse survey, 21 use a minimum tax. Out of these, 18 have selected capital as a base for their minimum tax. The minimum tax is an add-on minimum tax on capital or a firm's net worth in 8 countries, and an alternative tax in the other countries. The alternative minimum taxes on capital can be credited against the income tax liability or can be counted as an expense in the computation of the income tax base. When a firm has no taxa.ble profits, it ends up paying a tax on capital. Countries in this case include Austria, Canada, Colombia, Guatemala, Mexico, Paraguay and Switzerland.

**. If** the minimum tax liability is larger than the regular income tax liability and no provision is allowed to carry the difference forward, this case is essentially equivalent to the more familiar case because the tax payer ends up paying the full amount **of** the minimum tax. The case is somewhat different when carry forward provision are allowed, so that the difference can be used as a credit against future corporate income tax liabilities.

**5/** For a very detailed discussion of this, see Lyon (1989).
Table 4 Per Capita Expenditure on Subsidized Products, in US$ at PPP/year

|||**Ene**<br>|**rgy**<br>||||**Food**<br>||
|---|---|---|---|---|---|---|---|---|
||LPG|Electricity|Gasoline|Diesel|Flour|Bread|Sugar|Vegetable oil|
|Djibouti|1.8|95.1|36.9|n.a.|35.8|n.a.|51|29.2|
|Iran, Islamic Rep|10.6|83|102.8|0.6|12.6|163.7|n.a.|n.a.|
|Libya|4.4|26.4|26.8|0.5|9|30.1|17.9|46.6|
|Morocco|42.6|114.9|19.9|26.6|56.7|n.a.|26.8|n.a.|
|Tunisia|38.9|108.1|84.7|10.3|n.a.|n.a.|n.a.|n.a.|
|Average across countries|19.66|85.5|54.22|9.5|28.525|96.9|31.9|37.9|

_Source:_ World Bank estimations from Household Budget Surveys.

The results on the distribution of subsidies across quintiles are very different depending on the product and the country (Figure 1). Consider LPG first. In one country, the Islamic Republic of Iran, subsidies on LPG are progressive, meaning that poorer households get the largest dollar amounts of subsidies. But for all the other countries, subsidies on LPG are clearly regressive, as richer households get the largest amounts. Subsidies for LPG vary between a few dollars for the poorest quintile in Morocco to almost $400 for the rich in Libya. These amounts are significant, particularly for the poorest countries. However, we should not take for granted that subsidies on LPG are always pro-rich, as shown for the Islamic Republic of Iran.

Electricity subsidies are the most important in dollar amounts and exceptionally important in the Islamic Republic of Iran, where subsidies can reach up to $1,000-PPP per capita, per year for the richest quintile. Subsidies are less important in other countries but still nonnegligible, varying between a few dollars and more than $300-PPP per capita, per year. In the case of electricity, subsidies invariably favor the rich in absolute terms, as the largest amounts in dollar equivalents are taken up by the richest quintiles with no exceptions across countries. Clearly, oil-producing countries are those that offer the largest subsidies via electricity, probably because the need to produce electricity with cheaper fuels is less of a priority.

Also in the case of gasoline and diesel, subsidies are invariably pro-rich, with the largest dollar amounts taken up by the richest. The dollar amounts of these two products are relevant only in a few countries - the Islamic Republic of Iran and Libya for gasoline - that are either oil producers or endowed with natural resources. In these countries and for these products, it is evident that the 9
report, and indeed the topic of the research paper, naturally lie with the individual researcher, who must write the paper and get it published. In the case of impact evaluations of development projects, the survey data (often collected for the purpose of the evaluation) will typically include multiple indicators of “outcomes.” If one collects 20 indicators (say) then there is a good chance that at least one of them shows statistically significant impacts of the project even when it had no impact in reality. A researcher keen to get published might be tempted to report results solely for the significant indicator. (Journal reviewers and editors rarely ask what other data were collected.) The dangers to knowledge generation are plain.

The threat of replication by another researcher can help assure better behavior. But in economics, replication studies tend to have low status and are actually quite rare. Nor do researchers have a strong incentive to make their data publicly available for replication purposes. Some professional economics journals have adopted a policy that data sets should be made available this way, although enforcement does not appear to be strong.

In choosing how to respond to this environment, individual researchers face a trade-off between publishability and relevance. Thankfully, the fact of being policy relevant is not in itself an impediment to publishability in most journals, though any research paper that lacks originality, rigor or depth will have a hard time getting published. It is by maintaining the highest standards that we assure that relevant research is publishable, as well as being credible when carried to policy dialogues. However, it must be acknowledged that the set of research questions that are most relevant to development policy overlap only partially with the set of questions that are seen to be in vogue by the editors of the professional journals, at any given time. The dominance of academia in the respected publishing outlets is understandable, but it can sometimes make it harder for researchers doing work more relevant to development practitioners, even when that work meets the standards of more academic research. Academic research draws its motivation from academic concerns that overlap imperfectly with the issues that matter to development practitioners. Provided that scholarly rigor is maintained, the cost to a researcher’s published output of doing policy relevant research might not be high, but it would be naïve to think that the cost is zero.

Communication and dissemination of the published findings from evaluative research can also be deficient. Researchers sometimes lack the skills or personalities needed for effective communication with non-technical audiences. Having worked very hard to assure that the data 27