rafmacalaba/datause-encoder-data huggingface dataset:
_-56_ 30. Ostro BD ([983) The effects of air pollution on work loss and morbidity. J Environ Econ Manage 10:371-382.

31. Ostro BD (1990) Associations between morbidity and alternative measures of particulate matter. Risk Analysis 10:421-42732. Ostro BD (1989) Air pollution and acute respiratory morbidity: An observational study of multiple pollutants. Environ Res S0:238-247.

33. Dockery DW, Speizer FE, Stram DO eE al (1989) Effects of inhalable panicles on respiratory health of children. Am Rev Respir Dis 139:587-S94 34. Whittemore AS and Komn EL (1980) Asthma and air pollution in the Los Angeles area. Am J Public Health 70:687-96.

35. Ostro BD, Lipsett ML, Wiener MB and Selner JC (1991) Asthmatic response to airborne acid aerosols. Amer J Public Health 81:694-702.

36. Pope CA HI, Dockezy DW, Spengler JD and Razienne ME (1991) Respiratory health and PM1O pollution: A daily time-series analysis. Am Rev Respir Dis 144:688-74.

37. Evans R III, Mullally DI, WiIson RW et al. (1987) NationaI trends in the morbidity and mortality of asthma in the U.S.: Prevalence, hospitalization, and death from asthna over two decades: 19651984. Chest 91:6(suppl)65s-74s.

38. Krupnick Al, Harrington W, and Ostro B. (1990) Ambient ozone and acute health effects: evidence from daily data. J Environ Econ and Manage 18:1-18.

39. Detels R, Tashkin DP, Sayre I et al. (1991) The UCLA population studies of COPD: X. A cohort study changes in respiratory function associated with chronic exposure to SOx, NOx, and hydrocarbons. Amer J Public Health 81:3;350-35940. Abbey DE, Petersen F, Mills PK and Beeson WL (1993) Long term ambient concentrations of total suspendea particulates, ozone and sulfur dioxide and respiratory symptoms in a non-smoking population. Arch of Environ Health 48:33-46.

41. Mills PK, Abbey D, Beeson WF and Petersen F (1991) Ambient air pollution and cancer in California Seventh Day Adventists. Arch Environ Health 46:271-280.

42. DeIriennic F, Richardson S, Mollie A, Lellouch J (1989) Short-term effects of sulphur dioxide pollution on mortality in two French cities. Int J Epidemiol 18:186-197.

43. Chinn S, Florey V. Baldwin IG, Gorgol M (1981) The relation of mortality in England and Wales 1969-73 to measurments of air pollution. J Epidemiol Community Health 35:174-179.

44. Krzyzanowsli M, Wojtymiak B (1982) Ten-year Mortality in Sample of an Adult Population in Relation to Air Pollution. J Epidemniol Community Health 36 262-268.
Policy Research Working Paper 9604

# **Abstract**

To assess the impact of COVID-19 on firms, the World Bank and the International Finance Corporation conducted Business Pulse Surveys in several countries, including six in the South Asia region. Analysis focusing on the South Asia region suggests that, first, firms in the South Asia region have suffered disproportionately more from the economic brunt of the pandemic. Second, even within the region, COVID-19 did not affect all firms equally. Although exporters remain resilient by some metrics, firms that are smaller, female-led firms and those in vulnerable sectors suffered higher rates of closure. Third, while digital technologies have taken the center stage post-pandemic, the South Asia region lags in the adoption of these technologies. Finally, policy support for firms is key to building back better and resilient recovery, yet only a small share of firms can access public support. To be effective, firm support programs ought to be carefully customized and target firms based on the dominant channel through which COVID-19 affects them rather than their external attributes.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Nudging in the Time of the Coronavirus: Evidence from an Experimental Tax Trial in Albania at the Onset of a Global Pandemic**

March 2022<sup>1</sup> Jonathan Karver<sup>†</sup> Hilda Shijaku<sup>‡</sup> Christoph Ungerer<sup>‡</sup> Keywords: Albania, tax compliance, informality, behavioral insights, behavioral science, tax evasion JEL Classification: H26, H30, H32, D90, Z18

> 1 The authors are incredibly grateful to the dedicated staff at the General Directorate of Taxation who collaborated on the design, implementation, and evaluation of this study. The authors would like to extend a special acknowledgement to Delina Ibrahimaj, Ceno Klosi, Gjergi Duro, and Orald Lani, who were fundamental to the successful design and implementation of the project. The authors are also grateful for excellent feedback received during the presentation of the paper at the 7<sup>th</sup> Shadow Conference, particularly Edgar Castro and Gabriel Tourek.

> † World Bank, Poverty & Equity Global Practice. Corresponding author (jkarver@worldbank.org)

> ‡ World Bank, Macroeconomics, Trade, and Investment Global Practice

> ‡ World Bank, Macroeconomics, Trade, and Investment Global Practice
# **Table 13. Heterogeneity** **<u>by level of reported corruption</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||High reported c|orruption|Low reported c|orruption|
|Training||0.792||-0.126|
|||[0.525]||[0.225]|
|Scorecard||0.370||0.064|
|||[0.507]||[0.265]|
|Training x scorecard|1.186***|0.488|0.309|0.331|
||[0.407]|[0.644]|[0.296]|[0.462]|
|Training + Scorecard + interaction=0||0.006||0.354|
|Training=Scorecard||0.267||0.465|
|Control means|2.303|2.436|2.233|2.22|
|N|1,011|1,011|2,550|2,550|
|R-squared|0.137|0.141|0.063|0.064|

Notes: This table reports the OLS regression results for the treatment effect on household-level animal outcomes at the endline survey. The sample only includes the social accountability training and scorecard information samples. The sample is split by the whether the sub-county is perceived to have issues of corruption, as reported to the research team during a survey of local officials. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

49
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA centers (markets) and location of crop output, particularly for low-input technologies. For high-input/rainfed and irrigated crop-production systems, which often involve large farms (for example, as in Sudan), location of production may be driven more by the availability of irrigation facilities and water than by self-consumption or proximity to final markets in urban centers.

Table 4.2 tests the robustness of the results across different crop categories: (a) six cereal crops of wheat, rice, maize, barley, millet, and sorghum and (b) maize (the most commonly produced crop in Africa). Overall, we observe the same patterns as in the regressions for all crops combined. Lowinput/rain-fed crop production has the highest elasticity of road connectivity on crop production. Better road connection to larger cities has larger impacts on crop production.

**Table 4.2 Impacts of road connectivity on the production of different crops: Sub-Saharan Africa total**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Elasticity of travel time<br>to nearest city with<br>100,000 people|High-input crop<br>production|Low-input and<br>subsistence crop<br>production|Irrigated crop<br>production|All combined|
|Estimation method|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|
|Tl|–1.557***|–2.864***|–1.807***|–2.396***|
|ota crops|(0.090)|(0.079)|(0.106)|(0.059)|
|Cl|—|–1.975***|–2.488***|–2.003***|
|ereas|—|(0.099)|(0.135)|(0.087)|
|Ch|–3.649***|–3.933***|1.862***|–3.132***|
|as crops|(0.033)|(0.110)|(0.136)|(0.104)|
|Mi|—|–2.829***|–1.746***|–3.331***|
|aze|—|(0.149)|(0.156)|(0.146)|

## _Source:_ Own calculations.

_Note:_

1. Total crops include 20 crops; cereals (6) are wheat, rice, maize, barley, millet, and sorghum; cash crops (3) are coffee, cotton, and groundnuts. All crops are aggregated using the medians of African country-level crop prices (US$).

2. Standard errors in parentheses.

3. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

# Road connectivity and adoption of high-input/high-yield technology: SubSaharan Africa total

In this section, we examine the impacts of road connectivity on the adoption of high-input/high-yield crop-production technology. We measure the adoption of high-input technology by the share of highinput/rain-fed production in total crop production, again for the 20-crop aggregate. For a measure of highyield crop-production technology, we choose high-input/rain-fed crop production rather than the irrigated system, which is mainly constrained by external factors (irrigation infrastructure) that farmers cannot control.

For control variables, we use the same group of regressors as in the previous section. But to control for physical constraints, we use the share of high-input potential (maximum) production in total potential production in each pixel. The total potential production (value) is the sum of potential production (potential yields multiplied by suitable areas) across the three production systems in a pixel.

16
farm income.<sup>7</sup> As participation in non-farm activities is highly selective, non-farm income tends to increase income disparities, particularly in poorer areas. Some other studies, however, show that non-farm income can reduce inequality as it accrues disproportionately to poorer households, and its equalizing impact becomes more important as the proportion of non-farm income in total income increases.<sup>8</sup> Several factors, such as entry barriers for migration (or migration costs), initial household assets, changes of migration networks, difference in initial inequality, types of non-farm activities and empirical methods applied, may contribute to the different results (Docquier and Rapoport, 2003).

There is a rich literature on rural poverty and inequality in China based on different datasets. Most studies have shown that, since the beginning of the economic reforms, aggregate household income has significantly increased and inequality noticeably widened.<sup>9</sup> According to the research by the Ministry of Agriculture of China, income gap has widened, with the Gini index 0.3-0.4 in 1980s and over 0.4 since 1996 (Rural Economic Research Center, Ministry of Agriculture of China, 2003). Some studies suggests that the process of diversification into non-agricultural activities in rural areas tends to increase disparities, unlike the agriculture-based growth in the early 1980s, which equalized allocation of land kept income gaps at bay (Wan, 2004; Zhang and Wan, 2006). Based on a county-level survey, Knight and Song (1993) find that the distribution of non-farm income is more unequal than that of farm income in the 1980s. Hussain et al. (1994), comparing the distribution of farm and non-farm incomes, concluded that the more unequal distribution of non-farm income is a key factor explaining the rise in inequality in household income at the early stage of the reforms. Their conclusion implies that, with the continuing transfer of rural workers to non-farm sectors, income inequality in rural areas will continue to worsen. These results are consistent with some other studies (Bhalla, 1990; Zhu, 1991; Yao, 1999). Some studies suggest that the sharp increase in inequality in rural household incomes should be mainly attributed to differences in skills, knowledge and capital endowments, which

> 7 See the results obtained by Barham and Boucher (1998), Elbers and Lanjouw (2001), Escobal (2001), Khan and Riskin (2001), Leones and Feldman (1998), Reardon and Taylor (1996), and Shand (1987).

> 8 See the results obtained by Adams (1994; 1999), Adams and He (1995), Chinn (1979), Lachaud (1999), and Stark et al. (1986).

> 9 See for example Chen and Wang (2001), Chotikapanich et al. (2007), Kanbur and Zhang (1999), Khan and Riskin (2001), Liu (2006) and Wade (2003).

7
from the same estimated equation. Its disadvantage is that it assumes a particular likelihood function, and is not (yet) a standard way of estimating gravity equations found in the literature. It turns out that the two are quite close to each other, an indication that the zeros problem is not an important one for this empirical strategy. This paper only reports the results of implementing the first approach. The results of using the second one are available upon request.

Armed with a working model for predicting exports to GDP in each industry _i_ , it is straightforward to construct the instrument for the female content of exports, based on predicted export patterns rather than actual ones. That is, our instrument will be, in a manner identical to equation (22): Here, the predicted share of total exports in industry _i_ in country _c_ , _ω_ � _ic_<sup>_X_,isconstructedfromthe</sup> predicted export ratios _X_<sup>ˆ</sup> _ic_ in a straightforward manner: Note that even though _X_<sup>ˆ</sup> _ic_ is exports in industry i normalized by a country’s GDP, every sector is normalized by the same GDP, and thus they cancel out when we take the predicted export share.

# **References**

- **Acemoglu, Daron, Simon Johnson, and James Robinson** , “The Rise of Europe: Atlantic Trade, Institutional Change, and Economic Growth,” _The American Economic Review_ , June 2005, _95_ (3), 546–579.

- **Aguayo-Tellez, Ernesto, Jim Airola, and Chinhui Juhn** , “Did Trade Liberalization Help Women? The Case of Mexico in the 1990s,” _SSRN eLibrary_ , 2010.

- **Alesina, Alberto, Paola Giuliano, and Nathan Nunn** , “Fertility and the Plough,” Working Paper 16718, National Bureau of Economic Research January 2011.

- **Almeida, Heitor and Daniel Wolfenzon** , “The effect of external finance on the equilibrium allocation of capital,” _Journal of Financial Economics_ , January 2005, _75_ (1), 133–164.

- **Autor, David H., Lawrence F. Katz, and Alan B. Krueger** , “Computing Inequality: Have Computers Changed The Labor Market?,” _The Quarterly Journal of Economics_ , 1998, _113_ (4), 1169–1213.

- **Beck, Thorsten** , “Financial Dependence and International Trade,” _Review of International Economics_ , 2003, _11_ (2), 296–316.

26
The sample used in the first part of this paper differs from the one used in Hausmann and Kingler (2006). They use a longer sample drawn from Feenstra et al (2005) for the first part of the period (1975-2000) and from COMTRADE for the last part (2000-2004). The two series are merged using observations from 2000 common to both series. Hence, their sample includes more countries and is scaled to match the levels in the first part of the sample, which explains some differences with our estimates.

# **III. Composition of Argentine Exports**

We start by providing some stylized facts about the structure of Argentina’s exports, both at the sector level and at the more disaggregated product level (4-digit code). Table 1 below shows the value of trade in 2004 for the 17 top 2-digits sectors, which make up 80 percent of total Argentine exports. There is a relatively high level of concentration of exports. The two top sectors alone (mineral fuels and oils and residues, and waste from the food industry) account for 27 percent of the total. When we expand the coverage to the top 5 sectors, exactly half of the value of exports has been accounted for.

**Table 1**

**Argentina: Export Composition 2004, 2 Digit Level**

|**Code**|**Sector Name**|**Trade Value**<br>**$ '000**|**Share**<br>**%**|**Cumm. Share**<br>**%**|
|---|---|---|---|---|
|27|Mineral fuels, oils & product of th|5,536,454|16.1|16.1|
|23|Residues & waste from the food indu|3,835,098|11.2|27.3|
|15|Animal/veg fats & oils & their clea|3,172,732|9.2|36.6|
|10|Cereals|2,688,226|7.8|44.4|
|87|Vehicles o/t railw/tramw roll-stock|2,047,796|6.0|50.4|
|12|Oil seed, oleagi fruits; miscell gr|1,833,068|5.3|55.7|
|02|Meat and edible meat offal|1,023,305|3.0|58.7|
|39|Plastics and articles thereof.|932,664|2.7|61.4|
|26|Ores, slag and ash.|914,096|2.7|64.1|
|41|Raw hides and skins (other than fu|817,447|2.4|66.4|
|03|Fish & crustacean, mollusc & other|787,196|2.3|68.7|
|84|Nuclear reactors, boilers, mchy & m|767,530|2.2|71.0|
|04|Dairy prod; birds' eggs; natural ho|651,486|1.9|72.9|
|73|Articles of iron or steel.|639,865|1.9|74.7|
|08|Edible fruit and nuts; peel of citr|598,931|1.7|76.5|
|72|Iron and steel.|571,391|1.7|78.2|
|29|Organic chemicals.|498,351|1.5|79.6|
|20|Prep of vegetable, fruit, nuts or o|443,953|1.3|80.9|

Source: WITS Database-Comtrade What is interesting about this breakdown is that, for the largest sector, mineral fuels and oils, Argentina is not a particularly large player in the world market according to 2004 data. Indeed, it only accounts for 0.8 percent of the total global exports, compared with Russia, Canada, Norway and Venezuela, each of which have on average more than 8 percent of the market. In contrast, Argentina is the world leader in some other sectors, and in many of them it constitutes the largest 6
– the authors predict the extent of poverty reduction in developing countries. Of course, this depends on the poverty line. It also depends on the baseline poverty projections, which decline considerably between 2001 and 20015. For $1/day poverty, the estimated reduction in 2015, in the absence of additional productivity gains, is 2.5 million for Doha and 31.9 million for Full-Lib. When applied to current (2001) poverty levels, the authors’ calculations result in poverty reductions of 9.7 million and 80.5 million under Doha and Full-Lib, respectively. The 2015 poverty reductions are increased to 4.3 million and 43.5 million, for Doha and Full-Lib respectively, when productivity gains are factored in. For $2/day poverty, the reduction in number of poor is larger, but the percentage reduction is smaller (see Table 1).<sup>8</sup> Based on the Doha/Full-Lib comparison, it is clear that the (rather ambitious) Doha scenarios capture only a relatively small portion of the total poverty reduction possible under trade reforms. When the authors consider the Doha-All scenario, they find that implementing deeper cuts in the developing countries enhances the poverty outcome, nearly doubling the poverty reduction obtained under the central Doha scenario. This finding reinforces Ivanic’s (2005) conclusions with respect to the beneficial poverty impacts of developing country tariff cuts under the Doha Development Agenda. It is also hardly surprising in light of the increasing importance of South-South trade and the relatively high level of developing country tariffs as reported by Anderson and Martin (2005).

> 8 These estimates of poverty reduction are considerably smaller than earlier predictions using the World Bank’s Linkage model. The difference is due to the fact that these estimates are based on the most recent (version 6) GTAP data base, which is further updated to account for EU enlargement as well as the WTO accession of China and others. These recent trade reforms have reduced the overall level of protection worldwide – thereby lessening the gains from reform. In addition, the version 6 data base has a complete treatment of preferential tariffs – including the EU’s 2001 Everything But Arms initiative, which means that gains to the least developed countries from trade reform are considerably reduced.

34
- <u>Table 2: Policy Intervention/shock NAFTA Liberalization Using the CGE Model Simulation Results for Mexico</u>

|**Impact on**|**North**|**Central**|**Southwest**|**Southeast**|**Rio Bravo**|
|---|---|---|---|---|---|
|Water use forgrains||||||
|Water use for fruits<br>and vegetables||||||
|Water shadow value||||||
|**Comments**|To simulat<br>part of NA<br>beans was<br>5%, and pr<br>most signif<br>processed<br>and vegeta<br>27 and 16<br>production<br>liberalizati<br>and the N<br>non-comp<br>In all regio<br>regions as|e agricultur<br>FTA, the p<br>reduced by<br>ice of veget<br>icant impac<br>maize (1.4%<br>bles (12%).<br>%, respectiv<br>suggest tha<br>on reflects c<br>orth regions<br>etitive crops<br>ns. Rural a<br>well.|al and food proc<br>rice of composi<br>20% and 10% r<br>ables and fruits<br>t is the reductio<br>),and significan<br>Maize and bea<br>ely. Changes in<br>t the distributio<br>loseness to the<br>benefiting the<br>declines and ri<br>nd urban house|essing trade libe<br>te commodity m<br>espectively, dair<br>were increased<br>n in maize price<br>t increase in exp<br>ns imports have<br>returns to factor<br>n of benefits fro<br>border, with the<br>most. Water use<br>ses for fruits and<br>hold incomes inc|ralization as<br>aize and<br>y products by<br>by 5%. The<br>(9%) and<br>ort of fruits<br>increased by<br>s of<br>m the trade<br>Rio Bravo<br>for producing<br>vegetables.<br>rease in all|

Source: Dinar and Asad (2006) Note: We use the following symbols to compare impacts:

-  ,  Significantly positive or negative impact on the said variable through all range of policy intervention values;

-  ,  Positive or negative impact on the said variable through all range of policy intervention values;

-  No consistent, but small, impact on the said variable (by crops and by regions for the farm models and for the CGE model, respectively) through all range of policy intervention values;

-  , No consistent, but large, impact on the said variable (by crops and by regions for the farm models and for the CGE model, respectively) through all range of policy intervention values;

-  No significant impact of the policy on the said variable.

47