rafmacalaba/datause-encoder-data huggingface dataset:
||**Ta**|**ble 1. Cost**<br>M|**and Retur**<br>exico|**n of Financ**|**ial Products**<br>|Peru||||Ghana||
|---|---|---|---|---|---|---|---|---|---|---|---|
||N|Mean|p10|p90|N<br>Mean|p10|p90|N|Mean|p10|p90|
|_Credit Product_||||||||||||
|Total Annualized Cost without usage fees|41|96.9%|30.2%|219.8%|56<br>57.1%|17.2%|94.4%|143|60.4%|32.6%|89.0%|
|Total Annualized Cost with usage fees|41|112.2%|32.0%|225.4%|56<br>69.6%|22.1%|111.9%|143|111.3%|39.3%|185.4%|
|Number of Productsper institution||2.9|||1.8||||2.3|||
|_Investment Account_||||||||||||
|Total Annualized Yield without usage fees|35|1.0%|0.4%|5.1%|40<br>4.1%|2.0%|5.5%|45|22.3%|7.8%|31.3%|
|Total Annualized Yield with usage fees|35|0.5%|-1.2%|4.5%|40<br>3.3%|1.3%|5.5%|45|-5.3%|-22.2%|3.4%|
|Number of Productsper institution||1.9|||1.7||||0.7|||
|_Transaction Account_||||||||||||
|Total Annualized Yield without usage fees|90|-4.4%|-12.0%|1.5%|85<br>-0.2%|-4.2%|2.0%|70|-0.8%|-12.1%|6.2%|
|Total Annualized Yield with usage fees|90|-6.6%|-14.5%|0.4%|85<br>-3.8%|-8.0%|0.8%|70|-28.2%|-40.7%|-20.2%|
|Number of Productsper institution||5.0|||3.5||||1.1|||

This table reports estimated total annualized costs and yields for credit and savings products, respectively, offered by institutions in the study. Costs and returns are presented in two different ways depending on whether or not usage fees are incurred. For savings products, the table reports the total annual earnings from a deposit in a term account (investment) and in a transaction account. Credit products refer to a 6 months loan in Mexico and Ghana and 12 months loan in Peru with monthly instalments. Credit cost is calculated on a 700 USD (10,000 Peso) loan in Mexico, 2000 USD (5000 Sol) in Peru, and 1200 USD (2900 Ghanaian Cedi) in Ghana. All savings accounts (investment and transaction) refer to an initial deposit amount proposed by the shopper. Savings deposit amounts are 350 USD (5000 Pesos) in Mexico, 780 USD (2000 Sol) in Peru and 215 USD (500 Ghanaian Cedi) in Ghana. USD rates are converted using the average exchange rates at the time of the study. The total annualized cost without usage fees for credit include the premium for compulsory unemployment or life insurance, administrative fees, membership and credit acquisition fees. The total annualized yield without usage fees include the opening fee, administrative / management fees. Usage fees include a late instalment payment for credit products; a withdrawal of part of the initial deposit before maturity for the investment account and 2 monthly account inquiries, 2 monthly cash withdrawals, a one month long account inactivity, debit card replacement and the penalty for maintaining an average balance below the minimum required for 2 months for the transaction account. Information on product terms was collected during the period when audit visits were made, with an exception of Ghana where it was completed 10 months after the study visits.
countries (and by that possibly heterogeneous coefficients as well). Moreover, we are not considering the effect of an appreciation (depreciation) on imports; an effective appreciation, for example, may lower imported input prices, as a consequence firms do not need to raise their exporting ones to compensate such variation.

**Table 6: Correlation between REER and performance effect in quantities**

|Dep. Var.|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Performance(quantities)|||||||
|Performance(prices)|||||-1.929***|-3.339***|
||||||[0.591]|[1.159]|
|Performance(prices)*Low Income||||||2.273|
|_(=1 if Low/Middle income country)_||||||[1.426]|
|dlogREER|-0.410***|-0.723***|||||
||[0.154]|[0.155]|||||
|dlogREER*Low Income||0.481**|||||
|_(=1 if Low/Middle income country)_||[0.229]|||||
|dlogNEER|||-0.452***|-0.903***|||
||||[0.163]|[0.189]|||
|dlogNEER*Low Income||||0.643***|||
|_(=1 if Low/Middle income country)_||||[0.248]|||
|Constant|0.0165|0.0169|0.00188|0.00900|-0.0474|-0.0254|
||[0.0371]|[0.0368]|[0.0375]|[0.0366]|[0.0302]|[0.0345]|
|Observations|1,378|1,378|1,404|1,404|1,379|1,379|
|R-squared|0.147|0.131|0.122|0.072|-0.044|-0.531|
|Time dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Countrydummies|Yes|Yes|Yes|Yes|Yes|Yes|
|_F-Test(first stage)_|_26,26_|27,08|_25,79_|_24,25_|_19,15_|_9,74_|
|Robust standard errors in brackets ***|p<0.01,**p<0|.05,*p<0.1|||||
|Note: Contemporaneous values of dlo|gNEER/dlogRE|ER have been|instrument|ed using6 mo|nths lag.||

# **5. CONCLUSION**

Our primary contribution is collecting and making available timely information on export performance and its components, geographical and sectoral specialization, country specific export performance, in values, unit values and volumes, on a wide range of countries, and with quarterly frequency. We then take some first steps toward assessing dominant patterns and the determinants of competitiveness at the macro-level. First, we construct a set of indicators which simply and transparently captures the entire set of export performance components by country and world region. This measure helps us depict the broad patterns of export performance and countries competitiveness in the wake of the global crisis. What have we learned? On average, export performance, stripped of compositional effects was strongest for countries from the East Asia and Pacific region. Moreover such performance was almost entirely driven by exporting country specific factors, with changes reflecting volume growth rather than price developments. Sectoral and geographical specialization has instead served well other regions, including the Middle East 34
# **2.4 Stage Two: Revisiting the Decision**

In stage one of the experiment, one of each household’s 20 decisions (10 of the husband’s and 10 of the wife’s decisions) was randomly selected to be implemented. If the selected decision concerned an allocation in the near time frame, between tomorrow and 30 days from tomorrow, which happened with probability one-third by design, then the experimental intervention was finished for that household. The chosen individual in the household redeemed its allocation and was not interviewed again. Stage two of the experiment was only carried out with those households whose randomly selected decision concerned an allocation in the far time frame, between 60 and 90 days from tomorrow.

In stage two, this group of households was unexpectedly revisited. The targeted date for revisiting was randomly selected from the interval between 16 and 2 days prior to the first possible disbursement of funds in the far period. Thus, these households were targeted to be revisited between 45 and 59 days from their baseline interview. Revisits occurred even if the household chose an allocation involving no disbursement of funds at 61 days from their interview.<sup>25</sup> Revisit dates occurred in March and April 2010.

At the revisit, the household head and his spouse were physically separated and the survey of wealth, income and expenditure was performed again. After the interview was conducted, the participant whose choice had been selected to be implemented was again presented with a cup containing 20 tokens. This time, however, four dishes were placed in front of the participant: dishes _A_ , _B_ , _A_  and _B_  . Dishes _A_ and _B_ contained a total of 20 tokens allocated to reflect the participant’s original decision at baseline. Dishes _A_  and _B_  were empty. The participant was told that the first set of dishes showed his or her baseline choice; an allocation between what was effectively one to 16 days from the revisit and 30 days from then. This information was verified with the voucher that was issued at baseline. The participant was also reminded of the rate of

> in the context of lending in rural Malawi.

> 25 In all that follows, we focus on the randomly-assigned targeted lag (in days) to first disbursement, since it is exogenous to farmer actions. We made the first attempt to revisit each respondent on the date implied by the randomly-assigned target lag. In some cases, the actual lag was smaller (closer to the disbursement date) than the targeted lag, because some farmers could not immediately be located and had to be found thereafter. The actual lag is, as expected, highly correlated with the target lag; the correlation coefficient is 0.99. 84.9% of respondents were revisited with exactly the targeted lag, and 97.4% were revisited no more than two days after their target date. The maximum difference in the sample between target lag and actual lag is six days. Actual lags spanned the range of 16 to one days prior to first disbursement.

12
of the owners, are correlated very highly with the self-reports. We also note that the strong association between practices and enterprise survival supports the voracity of the measures. Second, the owner is almost always the top manager in our firms. Both practices and outcomes may be affected by characteristics of the owners that we cannot measure. We show that the correlation between practices and outcomes is almost unchanged by the inclusion of several measures of owner ability, most of which themselves are strongly associated with business practices.

# **2. Defining and Measuring Business Practices in Small Firms**

# **2.1 Defining and Scoring Business Practices**

We developed a set of 26 questions which measure key business practices used in the day-to-day running of small businesses. These questions were motivated by the content of the International Labour Organization (ILO’s) _Improve Your Business_ training curriculum, which covers marketing, buying and stock control, costing and record-keeping, and financial planning (Borgenvall et al., 1999). The questions cover practices which apply broadly across a range of different sectors and countries. As in Taylor (1911), they should be seen as “best practices” which are universally good so that all firms would benefit from adopting them. To give one example, we ask whether firms maintain written records which allow them to determine sales patterns for particular products or services. Since our focus is on a set of practices which can be applied in the vast majority of small firms, we exclude practices which might apply only to certain industry sectors, or firms of a certain size. In particular, we do not include human resource practices, since the modal small firm in most of the world has no paid workers.

Our goal was to design questions that could be included in large-scale surveys taken of owners of small firms. In practice these surveys in developing countries are typically administered by survey enumerators. For this reason, we rely on closed-ended questions. The surveys are usually administered on the premises of the business, but also can take place at the dwelling of the owner, or in a third location such as the offices of the survey firm. As such, we rely on questions that can be asked regardless of location. This precludes the use of measures involving physical inspection of the business premises or of the businesses’ books by the interviewer. We discuss reporting issues in Section 2.6.

4
evolution of manufacturing employment. Section 3 presents the theoretical model and the empirical strategy. Section 4 presents the results. Section 5 concludes.

# **2. Stylized facts**

There has been a continuous decline in manufacturing employment in Argentina since the early 1980s. For the period under examination in this paper –i.e., between 1991 and 2003— industrial employment declined by 31 percent.<sup>4</sup> Losses in industrial employment were only partially compensated by an increase in employment in the services sector. The net change on overall employment was negative, resulting in twodigit unemployment rates over most of the period. It is only from 2003 onwards that manufacturing employment has experienced a recovery.

Simultaneously, the aggregate productivity of the industrial sector increased by an average of 6.8 percent for 1991-1999. Productivity increased most in capital-intensive sectors such as iron and steel, electric machinery and transport equipment and least in natural resources and labor-intensive sub-sectors.<sup>5</sup> In parallel to these changes in the aggregate level of industrial employment, Argentina experienced a deep and fast process of trade liberalization.<sup>6</sup> The trade-openness coefficient (exports plus imports as a percentage of GDP) went from 6 percent in 1993 to 23.4 percent in 2001, falling to 21.7 percent in 2003 as a result of the economic collapse of Argentina in 2002. Imports as percent of GDP increased from 9 percent in 1990 to 11 percent in 2001, and fell to 8 percent in 2003. Exports as percentage of GDP rose from 7 percent to 12 percent over the period.<sup>7</sup> For the manufacturing industry, in particular, import penetration increased by almost 79 percent from 1991 to 2003.

> 4 More dramatically, the manufacturing employment level in 2003 was only 47 percent of its level in 1980.

> 5 For a comprehensive analysis of the changes in the Argentine industrial employment see Altimir and Beccaria (1999), and Beccaria, Altimir and Gonzalez Rosada (2003). Dussel Peters (2004) offers a comparative analysis with Mexico and Brazil.

> 6 See Berlinski (2004) for a detailed account of the Argentinean trade liberalization process in the 1990s.

> 7 These indicators were calculated with data retrieved from ECLAC (2004).

5
This paper is structured as follows. Section 2 provides background about the related literature, the private sector prior to the pandemic in Zambia, and the government policies implemented to address COVID-19 in Zambia. Section 3 outlines the data sources that we draw on and the analysis that we conducted to produce the paper’s findings. Section 4 presents the findings and Section 5 discusses the implications, as well as areas for future research.

# **2 Background**

## **2.1 Related Literature**

This paper is related to the growing literature on the impact of COVID-19 on the private sector. Dai et al. (2021), Bartik et al. (2020), Humphries et al. (2020), Apedo-Amah et al. (2020), Adams-Prassl et al. (2020), and Fairlie (2020a, 2020b) have all documented COVID19’s impact on businesses across countries in terms of revenue loss, business closures, mass layoffs, and liquidity. In most cases, such as Karalashvili and Viganola (2021), Cirera et al. (2021), Apedo-Amah et al. (2020), and Davies et al. (2021), research has been conducted using surveys, primarily World Bank Business Pulse Surveys (BPS), as well as the COVID-19 follow-up rounds of World Bank ES. This research paints a sobering picture of the impact of COVID-19. Firms have reported a significant drop in sales and reduced access to finance, and they envision additional losses in the future due to exceptionally high uncertainties going forward. This has prompted firms to make many difficult decisions involving significant reductions to their labor force. For example, following sales declines and firm closures in North Macedonia, firms interviewed in COVID-19 follow-up surveys to the ES exhibited a net reduction in employment of 7 percent since October 2020, highlighting the persistent struggle of businesses as they continue to navigate the COVID-19 era.

Another strand of literature has used tax administrative data to estimate the impact of COVID-19 on the private sector. For example, Bachas et al. (2020, 2021), Mascagni and 4
## **Table 2 – Panel Data: Dual Granger Causality Tests**

**Panel Data: Dual Granger Causality Tests between Tax Time (hours per year) and Tax Corruption**

|||**_H 0: TAXTIME_**<br>**_Cause C_**|**_does not Granger_**<br>**_ORRUPTION_**|**_H 0: CORRU_**<br>**_Granger C_**|**_PTION does not_**<br>**_ause TAXTIME_**|
|---|---|---|---|---|---|
|Number of|Number of|||||
|lags|observations|F-Statistic P|rob.<br>Result|F-Statistic Pr|ob.<br>Result|
|LAG 1|903|0.014|0.905 Fail to reject H0|0.177|0.674 Fail to reject H0|
|LAG 2|745|2.468|0.085 Reject H0|0.656|0.471 Fail to reject H0|
|LAG 3|588|2.921|0.043 Reject H0|0.598|0.616 Fail to reject H0|
|LAG 4|431|2.310|0.057 Reject H0|1.122|0.346 Fail to reject H0|
|LAG 5|312|3.678|0.003 Reject H0|0.976|0.322 Fail to reject H0|

**Panel Data: Dual Granger Causality Tests between Tax Payments (number per year) and Tax Corruption**

|Number of|Number of|**_H 0: TAXPAY_**<br>**_Cause C_**|**_does not Granger_**<br>**_ORRUPTION_**|**_H 0: CORRU_**<br>**_Granger_**|**_PTION does not_**<br>**_Cause TAXPAY_**|
|---|---|---|---|---|---|
|lags|observations|F-Statistic P|rob.<br>Result|F-Statistic P|rob.<br>Result|
|LAG 1|911|0.288|0.592 Fail to reject H0|0.126|0.722 Fail to reject H0|
|LAG 2|752|2.658|0.072 Reject H0|1.641|0.194 Fail to reject H0|
|LAG 3|594|2.722|0.063 Reject H0|2.056|0.105 Fail to reject H0|
|LAG 4|436|0.486|0.746 Fail to reject H0|0.838|0.480 Fail to reject H0|
|LAG 5|316|1.470|0.199 Fail to reject H0|1.044|0.271 Fail to reject H0|

Source: Authors’ calculations.

# **3. Tax Simplification and Tax Corruption: Regression Results**

In the paper, the starting point of regression analyses is an initial regression specification which regresses the tax corruption ratio on the tax simplification variables (TAXTIME and/or TAXPAY) and on different sets of control variables, consisting of variables which are thought to be affecting tax corruption.

Dos Santos (1995), Tanzi (1998), and Keen (2003) investigate possible causes of tax corruption. In addition to behavioral and cultural determinants of tax corruption, they also list factors related to the tax system and tax Administration: 1) Complex tax systems: Tax auditors can collect bribes from taxpayers by taking advantage of complex rules or unclear laws, regulations, and procedures. The taxpayer, who wants to evade taxes, can choose to bribe the tax auditor. 2) Time-consuming and costly dispute resolution: the taxpayer might choose to bribe to get things done. 3) Complex declaration forms, high costs of compliance, and intricate 17
**Table 3.  Sub-national Taxes and charges**

|Type of tax|Tax rate<br>determination<br>and tax<br>collection|Tax base|Centrally<br>Imposed<br>Rate Cap<br>(%)|
|---|---|---|---|
|P1.Motor vehicle tax|Provincial|Vehicle value (annual)|5|
|P2. Motor vehicle transfer tax|Provincial|Vehicle resale price (annual)|10|
|P3. Fuel excise tax|Provincial|Fuel consumption (retail price, excluding VAT)|5|
|P4. Cigarette tax|Provincial|Cigarette sales||
|P5. Surface Water excise tax|Provincial|Water consumption|20|
|L1. Hotel tax|Local|Turnover|10|
|L2. Restaurant tax|Local|Turnover|10|
|L3. Entertainment tax|Local|Turnover (admission price)|35|
|L4. Advertisement tax|Local|Advertisement rent|25|
|L5. Street lighting tax|Local|Electricity consumption (retail price, excluding<br>VAT)|10|
|L6. Mining tax for class C<br>minerals<sup>a</sup>|Local|Market value of extracted minerals|20|
|L7. Parking tax|Local|Parking spaces|20|
|L8. Ground water tax|Local|Ground water consumption||
|L9.Land and Building taxes*|Local|||
|L10. Property transfer tax*|Local|||
|Provincial and Local charges(30-|various services an|dpermits)||

_Note: * Property taxes will be devolved to local governments by 2014. Source:_ Updated from Eckardt and Shah (2007)

# **3. Central Transfers to Finance Local Public Services in Indonesia – A Review**

_Central transfers to provincial-local governments in Indonesia– A brief synopsis_ Central transfers are the most important source of revenues for sub-national governments in Indonesia. These financed 90% of sub-national governments, 54% of provincial, 86% of cities and 93% of districts expenditures in 2010. Major transfers (Balance grants or Dana Perimbangan) to finance provincial and local expenditures are as follows.

**Table 4. Central-provincial/local transfers in Indonesia -2010**

|**Transfer**|**Share of total transfers in**<br>**2010**|**Share of sub-national**<br>**expenditures in 2010**|
|---|---|---|
|Tax sharing|25%|20%|
|Gap filling (DAU)|56%|46%|
|Special Allocation<br>Grant (DAK)|6%|5%|
|Other Specific<br>purpose|13%|10%|
|ALL|100|90% (Provinces:<br>54%; Cities: 86%<br>and Districts:93%)|

9 _Source: Ministry of Finance, Indonesia_ 10
# <u>2. Context, interventions and research design</u>

# _Country context_

Both interventions were implemented in the Kingdom of Tonga. The country is a small island state with a population of just over 100,000 inhabitants. While its population is small, the country consists of 176 islands of which 40 are inhabited and spans 718 square kilometers. The kingdom is divided into five main island groups: Tongatapu, Vava’u, ‘Eua, Ha’apai and the Niuas. The capital of Tonga, Nuku’alofa, is over 2,000 kilometers from its nearest large market, New Zealand, and over 3,000 kilometers from Australia.

The education system is quite small with approximately 17,000 students in primary school covering ages 6 to 11 and 14,500 secondary students aged 12 to 17. Enrollment rates in primary and secondary school are approximately 90 and 80 percent, respectively. Preschools are private, operated by either churches or communities, and data on preschool participation are limited, though UNESCO UIS reports approximately 2,000 students enrolled.

Despite this level of enrollment at the primary level, students’ learning outcomes remain a challenge. For example, assessments of early grade literacy in Tonga in 2009 found that only 30 percent of 3<sup>rd</sup> grade students were able to read fluently for comprehension (World Bank 2012a). Similar outcomes were found in other Pacific Island countries. In Vanuatu, approximately a quarter of 3<sup>rd</sup> grade students were able to read fluently for comprehension (World Bank 2012b, 2012c). In Kiribati and Tuvalu, 20 percent of 3rd grade children achieved minimum reading comprehension proficiency (World Bank 2017a, b). In Papua New Guinea, early grade reading assessments conducted in four provinces between 2011 and 2013 found that students lagged two years behind curriculum targets for fundamental pre-reading skills (World Bank 2014a, 2014b, 2014c, 2014d).

This poor performance in reading outcomes in Pacific Island countries is the primary motivation 5
**Figure 4: Minimum wage-employment elasticities for low and middle income countries**

<!-- Start of picture text --> 1<br>.5<br>Elasticity 0-.5<br>-1<br>-1.5<br>Indonesia Honduras Brazil Puerto Rico Indonesia Brazil Indonesia Brazil China Indonesia Indonesia Thailand South Africa Indonesia Brazil Brazil Indonesia Indonesia Brazil Costa Rica Ghana Puerto Rico Indonesia Indonesia Czech Republic Ghana Colombia Honduras South Africa Mexico Puerto Rico Indonesia<br><!-- End of picture text --> _Source: Neumark and Wascher (2007) and DPRU (2014) calculations Note:_ The upper dotted line is the median elasticity (-0.08); the lower dotted line is the mean elasticity (-0.11).

Based on the sample of 59 developed and 32 developing country estimates reviewed by DPRU (2016), 81 percent of the elasticities were negative while 19 percent were positive. Further, the absolute value of these coefficients is on average small. This suggests that, in general, increases in wages will have either benign or only slightly negative employment effects.

With respect to the impact on hours worked, Gindling and Terrell (2007) note that the expected sign of this impact is ambiguous both in theory and in the literature. Higher costs of employment could result in cost-minimizing employers reducing the number of workers employed at a fixed monthly or weekly rate and increasing the hours of the fewer workers that they do employ. However, employers may also respond to increases in wage rates by either reducing employment at the extensive (reducing the number of employees) or intensive margin (reducing the total hours worked). The regulatory-induced costs of firing workers would be one reason for example, where employers may choose to keep the wage bill and headcount of employees constant – but reduce the number of hours required.

11