rafmacalaba/datause-encoder-data huggingface dataset:
of the relevant benefits (particularly improvements in information systems and financial control), and these have been further documented by the tax compliance cost literature.

A recent World Bank study concluded that more tax compliance in developing countries “is significantly associated with more access to credit,” especially in countries that have a relatively larger formal sector. They suggest that “firms’ balance sheets are relatively more informative for financial institutions” although they could not firmly establish the direction of causation.<sup>10</sup> Many SMMES (as noted by Turner, 1998), especially those in developing and transition countries, often state that they would not bother with formal accounting if not required to do so by tax legislation. If they lack the skills to undertake their own accounting and can’t afford to hire the necessary expertise, they can find themselves in a difficult situation.

The incidence of “outsourcing” – i.e., hiring external tax advisors to assist with various aspects of tax compliance – is noted in several of the tax compliance cost studies. Most commonly, it is noted that SMMEs have a relatively high tendency to outsource some or all of their tax compliance activities, either because they lack the expertise or because the amount of work would be insufficient to warrant hiring a full-time accountant or bookkeeper (see more detailed examples below).

A small number of tax compliance cost studies have specifically examined SMMEs’ experiences with outsourcing, most of them noting that medium and larger firms are more likely to have full time tax expertise in their own payroll and therefore are less likely to outsource their tax preparation work and related tax compliance tasks.

A key question is whether the smallest firms (e.g., micro-enterprises), who also appear somewhat less likely to outsource, do their tax preparation work in-house because they can’t afford to outsource (in which case both they and the tax authorities bear the risks of numerous “mistakes”) or because their tax compliance needs are simple enough – and their in-house capacity is correspondingly high enough - that they don’t need to outsource.

The most relevant such study is probably the Colmar Brunton report for New Zealand (2005), “Measuring the tax compliance costs of small and medium-sized businesses – a benchmark survey.” This survey concluded that 86 – 87% of “micro” (1 – 5 employees) and “small” (6 – 19 employees) outsourced at least some tax related services in the year before their survey. Businesses with “nil employees” and “medium” firms (over 20 employees) were somewhat less likely to outsource any tax related services (77% and 84%, respectively).

The report also examined the combined tax compliance costs of both “internal plus external” provision. More interestingly, when asked “Whether a business would still pay

> 10 Gatti, R. and M. Honorati, (2008) “Informality among Formal Firms: Firm-level, Cross-country Evidence on Tax Compliance and Access to Credit”

5
Most of Hirschman’s observations were dismissed by Bank officers as either fairly obvious or definitely wrong. Some critics underscored that on several occasions, Hirschman had based his conclusions only on partial analyses of specific matters. “As for the tendency ‘to clothe the prospects of all projects with an air of pat certainty’,” said one comment, “the Doctor may have a point, but it must be remembered that our project reports are not economic dissertations. They must be brief and [the] many points considered are not always included in the reports.”<sup>49</sup> Considering that Hirschman, in his studies, had traditionally been interested in processes of decision-making and “reform-mongering,” it is noteworthy that reactions from Bank officers underscored the need to calibrate messages to the audience and the situation, and insisted that at different stages of the decision-making process, different levels of complexity and thoroughness were advisable. As explained by Duncan S. Ballantine, a Bank officer and former professor of history at MIT and Harvard University, “in presenting a positive recommendation for action to the [Executive] Directors, a report must be positive [...] at some expense to the uncertainties. Nevertheless, during the process of appraisal leading up to the inevitable simplification of the issue, the staff [...] does ‘make a sustained effort at visualizing’ the uncertainties.”<sup>50</sup> Warren Baum, in addition, listed several Bank projects “explicitly described as ‘experimental’.”<sup>51</sup> Had Hirschman taken more elements into account, the critics pointed out, he would have probably discovered that his proposals were actually very much in line with the Bank’s thinking and practices or, as another officer put it, that “there is relatively little in [Hirschman’s] observations which has not normally been taken into account by Bank staff during project work.”<sup>52</sup> For example, when addressing the conflict that might arise between the Bank and a borrowing country over the borrower’s obligation to consult the Bank prior to key personnel changes, Hirschman had emphasized the need for a systematic look at the Bank’s previous relevant experience, adding that “efforts should be made to limit requirements of prior consultations to a few matters deemed essential.”<sup>53</sup> It was easy for a

> 49 A.D. Spottswood to Mr. Bernard Chadenet’, “Comments on Dr. Hirschman’s ‘Interim Observations’,” September 8, 1965, World Bank Hirschman Folders Vol. 1.

> 50 D.S. Ballantine to Mr. B. Chadenet, “Comment on Interim Observations by A.O. Hirschman,” September 15, 1965, World Bank Hirschman Folders Vol. 1.

> 51 Warren C. Baum to Mr. B. Chadenet, “Comments on Mr. Hirschman’s Paper,” September 17, 1965, World Bank Hirschman Folders Vol. 1.

> 52 P.A. Reid to Mr. L.J.C. Evans, November 16, 1965, World Bank Hirschman Folders, Vol. 1.

> 53 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

21
Columns (1)-(3), we can see that EMDE increase government spending in response to a rise in commodity export prices, indicating a procyclical fiscal policy. In fact, a 10 percent increase in commodity export prices leads to an increase in government spending of about 0.6—0.8 percent.<sup>45</sup> In contrast, advanced economies respond countercyclically. We can see, from Columns (4)-(6), that an increase of 10 percent in commodity export prices elicits a reduction in government spending of about 0.7-1.2 percent.

# 5.4 Fiscal multiplier

The third stage consists in computing an average fiscal multiplier for commodityexporting countries. To this effect, we use quarterly data for GDP and government expenditure from IFS (IMF) for the period 1990-2019. We estimate a panel SVAR and use Blanchard-Perotti as our identification method.<sup>46</sup> Figure 17 shows the resulting cumulative multiplier. In computing the pours component, we will use the value of the multiplier after 4 quarters (given by 0.88).<sup>47</sup>

# 5.5 Computation of pours component

With the above calculations in hand, we can now proceed to compute the pours component for each country in the sample. The pours component is given by where CEP is the commodity export price, coefficient FR is the estimated coeffi cient in the fiscal regression, and g/y is the ratio of government spending to GDP.<sup>48</sup> Below the different terms, we have noted the units to show how the right-hand side of the

> 45For a sample of 48 countries (including 4 AE), Cespedes and Velasco (2014) compute elasticities of 0.08 and 0.11 pre- and post-2000, respectively, which is comparable to our results.

> 46The availability of quarterly data is critical for relying on Blanchard and Perotti’s (2002) identification method since Blanchard-Perotti assumes that output can respond to government spending within the period but government does not respond to GDP. In other words, all contemporaneous correlation is attributed to fiscal policy affecting GDP. See Ilzetzki et al. (2013) for a detailed discussion.

> 47The sample is too small to further break it down into CE and non-CE. If we computed multipliers for AE and EMDE, the values would be broadly similar (0.67 for AE and 0.57 for EMDE). Ilzetzki et al. (2013) also report multipliers below 1 after 4 quarters for both industrial and developing economies.

> 48 We compute g/y as the average over the sample period for each country.

21
This is indeed the case: the ratio of selling price over buying price is 1.23 on average in Benin (median of 1.19) while it is 1.53 in Malawi (median of 1.40). In other words, the selling price is on average 23% above the buying price in Benin but 53% above in Malawi. This difference is quite significant, with a t- value of 11.7. This difference constitutes prima facie evidence that agricultural trade is in general less efficient in Malawi.

Margins also vary dramatically across traders. Some respondents appear to be making massive losses while others make windfall profits. Part of this variation undoubtedly comes from measurement error-since respondents do not hold accounts, annual sales and purchases must be extrapolated on the basis of a few key indicators. But the variation also suggests that unit margins are extremely volatile. Regarding their last transaction, close to 3% of surveyed traders report selling at or below the purchase price. At the other end of the spectrum, some traders report selling at close to 10 times the purchase price.

**Table 28. Annual Gross Margins in Benin and Malawi**

|||B|enin||||
|---|---|---|---|---|---|---|
||Mean|S.d.|<br>Min.|Max.|Median|N|
|Gross narginratio on annual sales|22.10%|<br>12.33%|-18.62%|<br>77.46%|20.26%|595|
|Gross margin rate on last purchase|23.30%|<br>23.72%|-64.29%|<br>294.12%|18.52%|656|
|Annual gross margin($)<br>|3123.13|<br>6662.62|<br>-88.72|<br>58721.07|814.46|595|
|Annual sales**($)**|22872.40|114659.57|<br>15.14|2615833.30|5322.92|607|
|Annual purchases($)|18147.20|91508.16|<br>11.51|2098750.00|4254.71|607|
|Sale price/purchase price|1.23|<br>0.24|<br>0.36|3.94|1.19|656|

Malawi

||Mean|<br>S.d.|<br>Min.|<br>Max.|Median|N|
|---|---|---|---|---|---|---|
|Gross margin ratio on annual sales|48.06%|<br>35.26%|<br>6.30%|<br>204.62%|38.67%|588|
|Gross nargin rate on last purchase<br>|53.17%|<br>60.85%|-87.43%|<br>840.00%|39.53%|663|
|Annual gross margin**($)**|7386.84|20452.87|<br>51.85|<br>251876.18|1732.29|588|
|Annual<br>sales ($)<br>|43611.15|182197.49|<br>63.64|2848665.40|6728.57|598|
|Annual purchases**($)**|32731.61|142789.69|<br>35.45|2358541.20|4343.29|598|
|Sale price/purchase price|1.53|<br>0.61|0.13|9.40|1.40|663|

**a** At the time of survey, exchange rates were 1 US $= CFA 600 and 1 US $ = MK 45.

47
7

**Table 1. Characteristics of Four Real-World Urban Road-Pricing Schemes**

||**Singapore**|**Norway**<br>(Various Cities)|**US**<br>(Various Cities)|**United Kingdom**<br>(London)|
|---|---|---|---|---|
||Area license<br>&<br>|Toll rings|High occupancy toll<br>(HOT) lanes|Area congestion<br>pricing|
||electronic road pricing||||
|Inception year|<br>1975|1986|1995|2003|
|Objectives|ALS (1975–98): congestion<br>relief<br>RPS (1995–98): familiarize<br>users with link pricing; ERP<br>(1998-):<br>control congestion by<br>maintaining<br>range of speeds|Initially revenue<br>generation.<br>Amendments to<br>Road Acts now<br>permit demand<br>management to<br>enhance<br>environmental<br>|Congestion relief|Demand<br>management:<br>primarily<br>congestion<br>relief|
|||quality, safety|||
|Type(s) of<br>tolling|ALS: inbound cordon. Paid<br>daily<br>RPS: linear. Per passage<br>ERP: CBD cordons and<br>li|Inbound cordons.<br>Per passage.|Link. Per passage.|Area licensing<br>scheme. Includes<br>parking on public<br>roads. Paid<br>il|
||near. Per passage.|||day|
|Tolled area or<br>infrastructure|ALS: 7 km2 restricted zone|Toll rings<br>successively added<br>in Bergen, Oslo,<br>Namsos|Five projects: SR-91<br>and I-15 in<br>California, I-10 and<br>US 290 in Texas, I-|21 km2 charge area<br>around city centre.|
|||Trondheim etc.|394 in Minneapolis-<br>St Paul||
|Means of<br>payment|ALS and RPS: paper licenses<br>with manual enforcement<br>ERP: in-Vehicle Units (IUs)<br>and smart cards|Electronic and<br>manual|Electronic|Manual payment<br>by various means|
|Time variation|ALS: morning peak,<br>extended to evening in 1989<br>and to inter-peak in 1994;<br>RPS: morning peak; ERP:<br>CBDs 7:30–10, 12–19;<br>expressways: 7:30–9:30.<br>Changes in 5 or 30-min.<br>steps. Reviewed quarterly.|Variable tolls in<br>Trondheim &<br>Stavanger. Flat<br>tolls elsewhere.|I-10, US 290: flat|Flat charge on<br>weekdays except<br>holidays, 7:00–<br>18:30|
|Annual<br>revenues|<br>ERP: €35M (1998)|Oslo: €143M (2002)|Other 4 facilities:<br>constant., SR-91:<br>$32M (2004)|Charges: € 65M<br>Enforcement: €<br>102M|
|Annual<br>operatingcosts|ERP: €3.75M|Trondheim: €23M<br>(2002)|SR-91: $21M (2004)|€130M|

Source: de Palma et. al., (2006).

A congestion charge not only helps correct transport sector externalities, such as emissions and congestion, but also generates a significant amount of revenue. For example, annual revenues generated through congestion charges are much higher than the annual operating costs in Singapore and Norway (see Table 1).
the following version of equation (1) using multiple cutoffs:

# **4 Main Results**

## **4.1 Estimating Impacts on Employment**

The main estimation results are presented in Table 3. Column 1 shows the results with no controls added. In the next three columns the specification includes a set of controls and examines the sensitivity of results to sequentially adding gridcell, year, and month-of-year fixed effects. Our estimates suggest that exposure to extreme wet and dry conditions weather conditions lead to differing effects of employment, conditional on whether it is a dry or wet shock. While dry shocks, or droughts, reduce the likelihood of having a job, wet shock increases the – employment. In term of magnitude, a 1.5 standard-deviation decrease in water availability a – dry shock as measured by the SPEI reduces the employment rate by 2.5 percentage points; – by contrast, a 1.5 standard-deviation increase in water availability a wet shock as measured – by the SPEI increases the employment rate by 4.0 percentage points.

Table 4 shows the results from utilizing alternative functional forms of the SPEI measure. Columns 1 and 3 estimate the impact of wet and dry shocks using a cutoff thresholds of 1 and 2 respectively. Both cases find similar results as before, suggesting that dry shocks are associated with lower employment. The results on wet shocks are associated with higher employment, but the coefficients are smaller in magnitude and not statistically significant. Column 3 presents the results of estimating Equation 2. The direction of the coefficients is still similar to our main findings, while the magnitudes suggest that milder shocks (SPEI variation between 1 and 2 standard deviations) are associated with lower levels of employment change, while more extreme shocks result in higher changes in employment. The coefficient of extreme dry shocks in columns 3 and 4 is of much larger magnitude, suggesting non-linear effects of water availability on employment. Lastly, Column 5 shows the result of directly using the continuous value of the SPEI as a regressor. This gives a positive and statistically significant coefficient, again signalling that increased water availability is associated with higher employment levels.

The qualitative impacts of water shocks on employment are consistent with global evidence 11
|P078136|2003|2003|6/20/2003|3 Closed|West Bank and Gaza|
|---|---|---|---|---|---|
|P078136|2003|2004|4/23/2004|5 Closed|West Bank and Gaza|
|P078136|2003|2005|4/28/2005|7 Closed|West Bank and Gaza|
|P078138|2005|2005||0 Closed|Chad|
|P078138|2005|2006|12/20/2005|1 Closed|Chad|
|P078138|2005|2007|7/7/2006|2 Closed|Chad|
|P078138|2005|2007|12/21/2006|3 Closed|Chad|
|P078138|2005|2008|7/30/2007|4 Closed|Chad|
|P078138|2005|2008|12/13/2007|5 Closed|Chad|
|P078138|2005|2008|5/21/2008|6 Closed|Chad|
|P078138|2005|2008|6/23/2008|7 Closed|Chad|
|P078138|2005|2009|11/20/2008|8 Closed|Chad|
|P078138|2005|2009|5/29/2009|9 Closed|Chad|
|P078138|2005|2010|11/12/2009|10 Closed|Chad|
|P078138|2005|2010|6/23/2010|11 Closed|Chad|
|P078138|2005|2011|4/1/2011|12 Closed|Chad|
|P078138|2005|2012|11/29/2011|13 Closed|Chad|
|P078138|2005|2012|12/31/2011|14 Closed|Chad|
|P078143|2004|2004|6/18/2004|1 Closed|Argentina|
|P078143|2004|2005|4/19/2005|3 Closed|Argentina|
|P078143|2004|2006|5/1/2006|4 Closed|Argentina|
|P078143|2004|2007|5/1/2006|4 Closed|Argentina|
|P078143|2004|2020|9/23/2019|5 Closed|Argentina|
|P078170|2004|2004|6/29/2004|1 Closed|Poland|
|P078170|2004|2005|5/31/2005|3 Closed|Poland|
|P078170|2004|2006|3/6/2006|4 Closed|Poland|
|P078170|2004|2007|1/8/2007|5 Closed|Poland|
|P078170|2004|2007|2/9/2007|6 Closed|Poland|
|P078170|2004|2008|2/6/2008|7 Closed|Poland|
|P078170|2004|2009|7/10/2008|8 Closed|Poland|
|P078170|2004|2020|12/28/2019|9 Closed|Poland|
|P078209|2004|2004|6/4/2004|2 Closed|Kenya|
|P078209|2004|2005|3/30/2005|4 Closed|Kenya|
|P078209|2004|2006|3/7/2006|5 Closed|Kenya|
|P078209|2004|2007|9/22/2006|6 Closed|Kenya|
|P078209|2004|2008|7/9/2007|7 Closed|Kenya|
|P078209|2004|2009|8/7/2008|8 Closed|Kenya|
|P078209|2004|2009|3/2/2009|9 Closed|Kenya|
|P078209|2004|2010|12/5/2009|10 Closed|Kenya|
|P078209|2004|2010|6/28/2010|11 Closed|Kenya|
|P078209|2004|2011|6/28/2010|11 Closed|Kenya|
|P078209|2004|2012|11/14/2011|12 Closed|Kenya|
|P078212|2003|2003|2/7/2003|1 Closed|West Bank and Gaza|
|P078212|2003|2004|3/31/2004|3 Closed|West Bank and Gaza|
|P078212|2003|2005|4/5/2005|6 Closed|West Bank and Gaza|
|P078212|2003|2006|12/2/2005|7 Closed|West Bank and Gaza|
|P078212|2003|2006|5/2/2006|8 Closed|West Bank and Gaza|
Table 4: Impact <u>of hosting refugees on household consumption expenditure per capita</u>

||||Hous<br>|eholds|||
|---|---|---|---|---|---|---|
|||Al|l||Rural|Urban|
||OLS|FE|FE|FE|FE|FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Refugee Intensity|-0.085|-0.177**|-0.168**|-0.189***|-0.228***|0.036|
||(0.073)|(0.072)|(0.073)|(0.070)|(0.076)|(0.133)|
|Constant|8.927***|9.503***|9.453***|9.725***|9.835***|8.856***|
||(0.458)|(0.451)|(0.414)|(0.410)|(0.450)|(0.767)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|WOREDA|EA|HH|HH|HH|HH|
|Controls|NO|NO|NO|YES|YES|YES|
|R-squared|0.298|0.325|0.021|0.068|0.074|0.071|
|# of EAs|433|433|433|433|290|143|
|N|13391|13391|13062|12937|9337|3600|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita. Household sample weights from the ESS2 applied. The first four columns report results for all households while columns (5) and (6) report results for rural and urban households, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for household (HH) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. The overall and the within R-squared are given in the first two and other columns, respectively. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

42
14

# Figure 2: Tariffs by country

- (A) Applied tariffs: most protectionist (B) Applied tariffs: least protectionist countries countries

<!-- Start of picture text --> Percent<br>Percent<br>20<br>18 1.4<br>16 1.2<br>14 1.0<br>12<br>10 0.8<br>8 0.6<br>6 0.4<br>4<br>0.2<br>2<br>0 0.0<br>(C) MFN tariffs: most protectionist (D) MFN tariffs: least protectionist<br>countries countries<br>Percent<br>20 Percent<br>18 3.0<br>16<br>2.5<br>14<br>12 2.0<br>10<br>1.5<br>8<br>6 1.0<br>4 0.5<br>2<br>0 0.0<br>(E) Bound tariffs: most protectionist (F) Bound tariffs: least protectionist<br>countries countries<br>Percent Percent<br>140 3.0<br>120<br>2.5<br>100<br>2.0<br>80<br>1.5<br>60<br>40 1.0<br>20 0.5<br>0 0.0<br>Benin Cameroon Iran Lao PDR Chad Switzerland Bosnia and Herzegovina Chile Georgia Singapore<br>Benin Sudan Rwanda Lao PDR Cameroon New Zealand Ukraine Peru Georgia Singapore<br>Nigeria Kenya Union Norway Japan<br>Tanzania Bangladesh Mozambique Singapore European United States<br><!-- End of picture text -->

## Source: World Bank WITS database.

Note: Trade weighted averages for 2015. Includes countries with population of at least 3 million. A. B: Applied tariffs are a combination of preferential tariffs (where these exist) and MFN tariffs. C. D. Most Favored Nation (MFN) tariffs are normal, non-preferential tariffs charged by WTO members to trading partners that are members of the WTO.

E. F. Bound tariffs are maximum MFN tariff level.
**Table V.1 Comparison of Financial Ratios of Manufacturing Companies**

||**_Korea_**|**_Korea_**|**_Germany_**|_USA_|**_Japan_**|**_Taiwan_**|
|---|---|---|---|---|---|---|
||**_(1996)_**|**_(199_**_7)_|**_(1995)_**|**_(1995)_**|**_(1995)_**|**_(1995)_**|
|Debt/Equity|332.7|449.4|175|159.7|206.3|85.7|

_Source:_ OECD, _Financial Statements of Non-financial Enterprises._ The high leverage means that small shocks to interest rates of operational cash flow of corporates will greatly affect their ability to service debts. As an illustration of this, we use a sample of 300 firms listed at the Stock Exchange of Thailand (SET). Table V.2 shows the high leverage of Thai firms and their deteriorating profitability. It also shows the ratios of earnings before interest and taxes to total interest expenses and the number of Thai firms that were not able to service their debts in full from their operating income. The number of these firms rose from 18 in 1994 to 114 in 1997 and the percent of total loans represented by these firms rose about 1.4% to 36.4%, i.e., for about one-third of loans, interest expenses could not be fully covered in 1997 from operating income and had to be rolled-over and financed from new loans. With the high leverage, even a small increase in interest rates and decline in profitability have thus led to a large number of firms running into debt servicing and liquidity problems, which in turn has negatively affected output.

Table V.2: **Deteriorating Corporate Performance**

|Period|Number<br>of Firms|Leverage|Profits<br>over<br>Liabilities|Profits<br>over<br>Interest<br>Expenses|No of<br>firms with<br>Profits <<br>Interest<br>Expenses|Loans of<br>Firms with<br>Profits <<br>Interest<br>Expenses<br>**(%)**|
|---|---|---|---|---|---|---|
|1997:Q4|356|2.95|7.3|1.49|114|36.4|
|1997:Q3|356|2.95|10.2|2.59|83|30.8|
|1997:Q2|357|2.12|N/A.|3.18|71|18.4|
|1997:Ql|353|2.01|N/A.|3.66|54|16.2|
|1996:Q4|354|1.90|14.9|3.11|49|11.8|
|1995:Q4|354|1.67|18.1|4.01|34|7.6|
|1994:Q4|352|1.50|24.0|5.78|18|<br>1.4|

Notes: Profit is defined as earnings before interest, taxes, and depreciation and amortization (EBITDA). Leverage is debt over equity.

Source: SET database.

58