rafmacalaba/datause-encoder-data huggingface dataset:
run although such reduction in TCC should not necessarily be expected in the shortterm.

More specifically, we start the analysis with a critical discussion of the different policy experiences in South Africa, Nepal and Ukraine<sup>6</sup> and then provide some theoretical basis to illustrate how e-filing can be related to and is potentially expected to decrease TCC. In order to provide a more comprehensive understanding of the relation between e-filing and TCC, we focus on two questions in the descriptive analysis: “Who are e-filers?” and “How much does TCC differ between e- filers and non-e-filers?” To answer the first question, a detailed discussion on the correlation between firms’ characteristics and e-filing decisions is provided. Then, our focus was on the differential in TCC incurred by e-filers versus non e-filers to answer the second question. Following the simple descriptive analysis, we undertake a more rigorous regression analysis to estimate the likelihood of e-filing usage by controlling for many firm characteristics that are found to be important for e-filing decisions in the descriptive analysis. Finally, we estimate the effect of e-filing usage on TCC in a regression setup.

Our results show that the answer to the question of the focus is: “it depends”. In countries where e-filing replaces paper-based filing with no additional work required from firms, as was the case in South Africa, e-filing is associated with some savings in TCC. On average 22.4% reduction in overall TCC and 21.8% reduction in hours spent for complying with VAT are associated with e-filing usage in South Africa. Yet, this reduction does not always occur in overall TCC but in a few relevant components. It is therefore not clear whether one can always expect a statistically significant reduction in overall TCC or not.<sup>7</sup> In fact, the policy implementation of e- filing is very important for the effectiveness of the system in decreasing TCC and may actually fail to achieve its intended objective. For instance, we estimate about a 34% increase in TCC associated with e-filing usage in Nepal, where the policy was mandatory for all firms. Additionally, perception and trust of the electronic system

> 6 TCC surveys from these countries are used in the analysis, which provide sufficient information for the purpose of this research.

> 7 One reason for this could be measurement error or underreporting.

3
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2706 Household Income Dynamics<br>in Rural China|Jyotsna Jalan<br>Martin Ravallion|November 2001|C. Cunanan<br>32301|
|WPS2707 Financial Intermediary Development<br>and Growth Volatility: Do<br>Intermediaries Dampen or Magnify<br>Shocks?|Thorsten Beck<br>Mattias Lundberg<br>Giovanni Majnoni|November 2001|A. Yaptenco<br>38526|
|WPS2708 Accountability and Corruption:<br>Political Institutions Matter|Daniel Lederman<br>Norman Loayza<br>Rodrigo Reis Soares|November 2001|P. Soto<br>37892|
|WPS2709 Explaining Leakage of Public Funds|Ritva Reinikka<br>Jakob Svensson|November 2001|H. Sladovich<br>37698|
|WPS2710 Breaking up the Collective Farm:<br>Welfare Outcomes of Vietnam's<br>Massive Land Privatization|Martin Ravallion<br>Dominique van de Walle|November 2001|C. Cunanan<br>32301|
|WPS2711 Identifying Class Size Effects in<br>Developing Countries: Evidence from<br>Rural Schools in Bolivia|Miguel Urquiola<br>|November 2001|H. Sladovich<br>37698|
|WPS2712 Contract Risks and Credit Spread<br>Determinants in the International<br>Project Bond Market|Mansoor Dailami<br>Robert Hauswald|November 2001|W. Nedrow<br>31585|
|WPS2713 Causes of Inequality in Health:<br>Who You Are? Where You Live?<br>Or Who Your Parents Were?|Adam Wagstaff<br>Pierella Paci<br>Heather Joshi|November 2001|H. Sladovich<br>37698|
|WPS2714 On Decomposing the Causes of<br>Health Sector Inequalities with an<br>Application to Malnutrition Inequalitie<br>in Vietnam|Adam Wagstaff<br>Eddy van Doorslaer<br>s Naoko Watanabe|November 2001|H. Sladovich<br>37698|
|WPS2715 Paying for Health Care: Quantifying<br>Fairness, Catastrophe, and<br>Impoverishment, with Applications to<br>Vietnam, 1993-98|Adam Wagstaff<br>Eddy van Doorslaer|November 2001|H. Sladovich<br>37698|
|WPS2716 Does Foreign Bank Penetration<br>Reduce Access to Credit in<br>Developing Countries? Evidence<br>from Asking Borrowers|George R. G. Clarke<br>Robert Cull<br>Maria Soledad Martinez P|November 2001<br>eria|P. Sintim-Aboagye<br>37644|
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<!-- Start of picture text --> 0s aaa 6% 4678<br>19.06.2022 ”<br>b it<br>fa<br>00:00:17 } 00:00:05<br>Ku sukkula Kuwelenga<br>=<br>si yakaneme, 4 kum Kupumula<br>— [J]<br>=]<br>Kuchen pakhomo 0, kukaonera zam<br>%<br>os s<br>Zamapemphero nupi Masew<br><!-- End of picture text --> <!-- Start of picture text --> 20:06 Gs Sal 67%8<br>Food (multiple selection)<br>Gulu fa tirigu Mitsitsi<br>SO)a ie ee)<br>Faerie went<br>ove 5<br>Mazira Nsomba<br>Pe<br>Fo<br>Mkaka ‘Shuga/Uchi<br>&. °<br>= 5<br><!-- End of picture text --> <!-- Start of picture text --> 1rce- aX ‘<br>gapo) Amachita ndir<br>i<br>saveat iMivene weingforio<br>fe<br>hih 3<br>Pandekha<br>ft<br>| cme |<br><!-- End of picture text -->

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results suggest that in Azteca municipalities, households were more likely to own electronic appliances (�3 =0.0223) and large appliances and furniture (�3 =0.0228). Moreover the value of these goods significantly increased in $2,305 Mexican pesos for electronic appliances and $2,342 Mexican pesos for furniture and large appliances. These patterns are consistent with Banerjee et al. (2009) who find that on average, Indian households were more likely to purchase durable goods such as televisions and refrigerators if a microfinance institution opened an office in their slums.

As with changes in the saving patterns, table 7 (panel A) suggests that these changes are coming from the sample of households whose members are employed in the informal sector. In Aztecamunicipalities after the opening of Banco Azteca, informal households were more likely to own electronic appliances (0.0244) . The value of their electronic appliances increased by 22% (Mx $1,456), and of their furniture and large appliances by 30% ($2,756 Mexican pesos). On the other hand, households with members belonging to the formal sector do not report significant differences in their likelihood of owning durable goods or their average value (See table 8, panel A).

From the responses of borrowers, there is reason to believe that credit is also being used by households as an instrument to protect against bad economic shocks or to smooth consumption. Testing for consumption smoothing would require following households over several points in time and comparing their consumption to their income patterns. Since the first wave of MxFLS was collected in 2002, households are observed only one period before Azteca’s entry and one period afterwards, making this test infeasible. However, MxFLS collects information about certain events that caused economic losses to households and the date when these events occurred.

Using this information, I explore whether households are better able to deal with economic shocks in the presence of formal credit. To do so, I examine the percapita expenditure of households that experienced a bad economic shock in the last 12 months they were surveyed, defined as: i) death of a family member; ii) serious illness of a family member; and iii) unemployment or failure of business of a family member<sup>10</sup> .

Panels B of tables 6, 7 and 8 present the DID results for the sample of all households, informal households and formal households experiencing bad economic shocks. The results however, must be taken as weak suggestive evidence of consumption smoothing for two reasons. First, the DID

> 101% and 1:4% of households in Azteca and non-Azteca municipalities experienced the death of a family member in 2002. This share increased to 1:3% and 2:4% in 2005. Serious illness of a family member was reported by 2:4% and 2:1% of households in Azteca and non-Azteca municipalities in 2002; by 2005 this share was 2:8% and 2:2%. Unemployment or failure of family business was reported by 5% and 2:8% of households in Azteca and non-Azteca municipalities in 2002; this share was 4:4% and 3:6% in 2005.

12
- 39 -

# Table **1S:** Projected Employm **et** Growth Rates **i** Rural Areas Plue Towns, India 1981-2640 Under Alternative Peulatlem ed Agricultural GrwtJ Sceneries

|||||Compound Annual Grow|thRates.<br>19#1-2040|||
|---|---|---|---|---|---|---|---|
|Agricultural<br>Output|<br>Nonfars<br>EmEployment|Agr<br>Scenario|icultural<br>Emelo<br>Scnario|ym_nt|T<br>nro|otal E!p!lcymnt<br>cnrio|<br>narioIII|
|Population<br>Grow|th 2.2%|||||||
|1.6<br>|2.26<br>|0.76<br>|0.88<br>|O.6<br>|1.26|1.32<br>|1.16<br>|
|2.4<br>|2.81<br>|1.80<br>|2.11|1.44|2.10|2.31|1.88|
|3.2F<br>|3.38<br>|2.44<br>|2.86<br>|1.96<br>|2.71|3.00|2.41|
|4.o<br>|3.97<br>|3.00<br>|3.62<br>|2.40<br>|3.21|3.64|2.92|
|.60|6.73|4.60|6.28|3.60|4.88|6.46|4.38|
|Population Grow|th 1.8%|||||||
|1.0<br>|1.94<br>|0.76<br>|0.88<br>|6.06|1.12|1.20|1.63|
|2.4<br>|2.63<br>|1.80<br>|2.11<br>|1.44<br>|2.04<br>|2.26<br>|1.81|
|3.26<br>|3.24<br>|2.44<br>|2.86<br>|1.95<br>|2.67|2.96|2.38|
|4.0<br>66|3.86<br>|3.oe<br>|3.62<br>|2.46<br>|3.26<br>|3.61<br>|2.88|
|.|5.66|4.66|5.28|3.60|4.86|6.38|4.36|

Scenario I assumen the **sane** pattern of agricultural growth **as** occurred during 1968/69-1976/71 to 1976/77-1978/79; Scenario II aasumes an irrigation intensive growth strategy; Scenario III **assumes** agricultural growth will be predominantly focussed in rainfed areas. Assumptions: Growth rates: agricultural income some as the growth in agricultural output; roads 0 percent. Agricultural employment elasticities: Scenario I, 06.76; Scenario II, 0.88; Scenario III, 6.6.
Our assessment also sheds light on three additional questions that come up in poverty‐ related debates: first, how much poverty reduction can economic growth generate and how much will have to be done through redistribution? Second, does focusing on a money‐based poverty target jeopardize progress in other measures of wellbeing such as education, longevity, and health? Third, is a focus on extreme poverty—measured by the poverty lines of the poorest 15 countries—setting the bar too low? Our view is:

- The success of a strategy that relies on growth in private economic activity and rapid improvements in essential public services—two mutually reinforcing elements—complemented by targeted interventions to help the most needy, should discourage “growth versus redistribution” extremism.

- Evidence in support of this strategy should reassure those who worry that a “narrow focus” on monetary poverty risks neglect of non‐monetary measures of human welfare. In fact, quick and sustained progress in achieving the 3 percent extreme poverty goal is not possible without augmenting the assets of the poor, principally through public support for investments in education and health.

- By recognizing that individuals who escape extreme poverty are still vulnerable to falling back into destitution, and proposing policy actions to prevent this from happening, the strategy should at least partially address the criticism of setting the bar too low.

The evidence from China, Vietnam, India, and other countries that have reduced extreme poverty rates to relatively low levels supports the three‐point strategy. If ending extreme poverty is the goal, the growth versus redistribution debate, and doubts about the adequacy of the consumption‐based poverty as a development goal are not the issues that policy makers should worry about too much. Instead, policy makers should be trying to figure out how this strategy can be implemented in countries where the incidence of extreme poverty is still high. They will likely find that the 2.5‐point strategy is better suited for such countries. When implemented well, extreme poverty rates will fall quickly, and the 2.5‐point plan should give way to the 3‐point strategy.

Whatever the conclusion of these debates, finishing off extreme poverty will soon become much the same thing as ending extreme poverty in Sub‐Saharan Africa and conflict‐affected countries elsewhere. Just as progress in East Asia required rapid progress in China and poverty reduction in South Asia is almost the same thing as eliminating extreme poverty in India, progress in Sub‐Saharan Africa will not happen unless Ethiopia and other strong performers in East Africa keep doing as well as they have in the last decade, and Nigeria, the Democratic Republic of Congo, Madagascar, Angola, and others in West and Southern Africa start doing a lot better than in the past.

47
# _5.7. Market clearing_

Two equilibrium conditions are needed to ensure the equivalence of demand and supply in goods and factor markets. For the goods market: This condition implies that the supply of the _i_ -th Armington composite good _Qi_ must equal its aggregate demand. Demand is composed of demand by households ( _Xi,l_<sup>_p_), the government (</sup><sup>_X_</sup> _i_<sup>_g_), investment (</sup><sup>_X_</sup> _i_<sup>_v_), firms (</sup><sup>_Xi,j_), and the smug-</sup> gler ( _Xi_<sup>_SM_</sup> ); in addition some of the goods are lost as inputs to tax evasion ( _Xi_<sup>_loss_</sup> ) and smuggling ( _Ei_<sup>_loss_</sup> ) activities.

For the factor market, the sum of endowments of the _h_ -th factor ( _FFh,l_ ) must equal the aggregate factor demand: Note that firms’ total factor demand is the sum of standard factor demands for production ( _Fh,j_ ) and factors used for the purpose of tax evasion activities (e.g. labour), denoted _Fh,j_<sup>_loss_.</sup>

# **6. Data**

# _6.1. Economic variables_

The baseline values for macro-economic parameters have been obtained from the GTAP 9 database – in particular, Nigeria’s social accounting matrix (SAM) for the 2011 reference year. These macro-economic parameters are the size of economic sectors (i.e. output), intermediate inputs, capital and labour inputs, taxes, government expenditure, household consumption, imports, exports, and the current account balance. Four sectors are distinguished: (i) the (subsidised) petrol sector, (ii) the (unsubsidised) energy sector, which excludes petrol, (iii) the formal (non-energy) sector, and (iv) the informal (non-energy) sector. The parameters and coefficients _αi,l_ , _βh,j_ , _bi_ , _γi_ , _μi_ , _λi_ , _θi_ , _axi,j_ , _ayj_ , _δmi_ , _δdi_ , _ξei_ , _ξdi_ , _sspl_ , and _ssg_ have been calibrated on the basis of the 2011 baseline data, and the model equations set out in Section 5.

Since the GTAP 9 SAM does not provide information on the distribution of income and consumption, aggregate household consumption figures have been split into income quintiles according to expenditure shares contained in the Harmonised Nigeria Living Standards Survey 2010. Across income quintiles, this household expenditure survey provides details on the level of spending on petrol, other energy, and non-energy consumption goods. It is thus essential for the distributional aspects considered in this study.

Data on FFS in Nigeria have been obtained from the International Energy Agency’s World Energy Outlook 2015 Fossil Fuel Subsidies database (IEA, 2015). The IEA provides an estimate of $7.1 bn of fossil fuel consumption subsidies in 2011, of which over $6.5 bn are paid to subsidise oil (primarily 14
73 Figure A5: Dynamic Ban Effects on Import Quantities <!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Quantity Effect (b) Quantity Effect, fertilizer only<br>5 10<br>5<br>0<br>0<br>−5<br>−5<br>Effect on log(quantity) Effect on log(quantity)<br>−10<br>−10<br>−15 −15<br><!-- End of picture text --> _Notes_ : The charts show De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of import bans on import quantities, relative to the last month before the ban takes effect, using an HS8 product-month level panel. The sample covers the January 2019-September 2022 period (but only coefficients in January 2021-May 2022 are reported) and excludes non-fertilizer HS8 products whose imports were ever banned. In panel A5b, the sample also excludes all non-fertilizer products, even the ones who were never banned. The treatment variable is a set of dummies _ban_<sup>_τ_</sup> _ct_<sup>indicating</sup> whether product _c_ was first banned _τ_ months before month _t_ , and the not-yet-treated products serve as the control group. The y-axis shows the effects of import bans on (the log of) the net weight of imports (measured in kg), where negative values correspond to quantity decreases. The p-values of the joint significance tests for the placebo estimators are 0.005 (panel A5a) and 0.93 (panel A5b).
Table 9 shows the regression counter-part of the graphs in Figure 5 by asking, how much of a firms reported policy action is associated with the cross-national differences in the de jure reported policies. The country average of “policy” has almost exactly no explanatory power for the firm reported policy actions. Whether it took your firm a long time or a short time to get a construction permit or operating license or have imports clear customs has next to nothing to do with what the _Doing Business_ survey reports about your country. This is not just a low-powered “failure to reject” as the hypothesis that the firm reported average increases one to one with the DB reported days can be decisively rejected for each indicator.<sup>33</sup>

**Table 9: Correlation between firm's reported experience in the Enterprise Surveys and the** **_de_** **_<u>jure</u>_** **<u>times in</u>** **_<u>Doing Business</u>_**

|||Enterprise Survey<br>Time to get|Time to get|
|---|---|---|---|
||Time to get a<br>construction<br>permit|<br>operating<br>license for new<br>firms|<br>imports<br>through<br>customs|
||(1)|(2)|(3)|
|_Doing Business_|0.0458*|||
|Time to get construction permit|(0.023)|||
|_Doing Business_||-0.099||
|Time to start a business||(0.0663)||
|_Doing Business_:|||0.0315|
|Time to get imports through customs|15.22***|65.63***|(0.0364)<br>6.928***|
|Constant|(2.63)|(16.26)|(1.899)|
|N|5401|1013|1759|
|**R2**|**0.003**|**0.0134**|**0.002**|
|**Pr (H0:β=0, Firm reported days no**<br>**association with de jure days)**|**0.0567**|**0.146**|**0.39**|
|**Pr (H1: β=1, Firm reported days increase**<br>**one for one withde jure days)**|**0.000**|**0.000**|**0.000**|

> 33 The results are the same if one uses the Enterprise Survey country average or country median in the

49
skilled and educated workers can have profound implications on the economy they leave behind, in terms of equity and efficiency, in the short and the longterm.

However, recent developments, both theoretical and empirical, have prompted renewed interest in the area of skill migration. From a theoretical perspective, models that endogenize growth (Romer 1986) have highlighted profound potential long-term implications of the brain drain, while models that endogenize demand (Krugman 1991) highlight how the reducing transportation costs involved in trade encourage agglomeration and with it the pull of skill segregation. Combined with the empirical observation of the acceleration of skilled labor from developing countries—recent labor force survey data show that highly skilled migrants accounted for around 38%<sup>2</sup> of EU migration inflow—there is little doubt as to why the magnitude of the brain drain has once more become an urgent question.

# **2.1 The Movement of Skilled Labor: The Brain Drain**

In 1966 when Grubel and Scott first analyzed the brain drain in the context of perfectly competitive markets, they found it had no welfare implications. Wages were equated with the marginal productivity of labor, thus a migrant “removes both his contribution to national output and the income that gives him claim to this share, so that other incomes remain unchanged.” (Grubel and Scott 1966) Yet this reasoning ignores the positive externalities associated with human capital. If one assumes that the skills of workers are complements such that the productivity of a worker depends positively on the productivity of her co-workers, the reduction in opportunities to work with skilled agents following an outflow of skilled labor reduces the welfare of the remaining population. This can be illustrated following a simplified version of Kremer’s 1993 O’Ring model.

Take for example an economy in which output is produced via two tasks, for

> 2Highly skilled migrants are here defined according to the International Standard of Classification on Occupation categories 1-3 (includes managers, professionals and associate professionals) Quoted in Auriol and Sexton 2001

3
**<u>Annex 1: Matrix of Correlation Coefficients, 1994-2003</u>**

||_it_<br>_Y_ (Tax/GDP)|_it_<br>_Y_ (Rev/GDP)|_it_<br>_GDP_|_it_<br>_POP_|_it_<br>_AGE_|_it_<br>_TRADE_|_it_<br>_AGR_|_it_<br>_CORR_|_it_<br>_BUREAU_|
|---|---|---|---|---|---|---|---|---|---|
|_it_<br>_Y_ (Tax/GDP)|1|||||||||
|_it_<br>_Y_ (Rev/GDP)|0.9005|1||||||||
|_it_<br>_GDP_|0.5593|0.5212|1|||||||
|_it_<br>_POP_|-0.5198|-0.4113|-0.3427|1||||||
|_it_<br>_AGE_|-0.4848|-0.4430|-0.4805|0.7638|1|||||
|_it_<br>_TRADE_|0.3497|0.4013|0.1765|-0.1148|-0.2416|1||||
|_it_<br>_AGR_|-0.5373|-0.6051|-0.5586|0.3697|0.6398|-0.2935|1|||
|_it_<br>_CORR_|-0.4292|-0.4209|-0.6278|0.1333|0.2679|-0.1337|0.4438|1||
|_it_<br>_BUREAU_|-0.5155|-0.4888|-0.7236|0.2628|0.4732|-0.1643|0.563|0.0617|1|

Note: Number of observations 724. Number of countries 104.

- 21 -
# **Deciding Not to Decide: When Is There Power in Not Deciding?**

Marya Hillesland, Cheryl R. Doss, Serena Masino, Martina Querejeta, Aletheia Donald, Greg Seymour and Clare Clingain<sup>*</sup> Keywords: gender, households, power, survey methodology, Kenya JEL codes: C8, J16, I32, Q25

* Hillesland (corresponding author): University of Oxford, marya.hillesland@qeh.ox.ac.uk; Doss: Tufts University, <u>cheryl.doss@tufts.edu; Masino: University of Westminster, s.masino@westminster.ac.uk; Querejeta: National</u> University of La Plata, <u>martinaquerejeta@gmail.com; Donald: World Bank, adonald@worldbank.org; Seymour: U.S.</u> Census Bureau, <u>gregory.t.seymour@census.gov;</u> Clingain: International Rescue Committee, <u>clare.clingain@rescue.org. This project is a product of the World Bank Africa Gender Innovation Lab and the</u> MAGNET initiative. We gratefully acknowledge financial support from the World Bank's Umbrella Facility for Gender Equality, in partnership with the Bill and Melinda Gates Foundation (INV-005620). This work also was supported by the UK Research and Innovation through the GCRF Water Security and Sustainable Development Hub (Programme Code ES/S008179/1) and the UK Foreign, Commonwealth & Development Office (FCDO), 2015-2024 through the REACH programme (Programme Code 201880). 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. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp.  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.