rafmacalaba/datause-encoder-data huggingface dataset:
-9 -

In 1985 the Bank suggested sweeping reforms in the tariff and tax structure involving a sharp reduction in the tariff rates and recommended increases in the domestic indirect tax rate to offset revenue loss. The Bank recommended i) eliminating import duties on non-competing imports (which constituted 80 percent of imports), and ii) reducing tariff rates on competing imports and intermediate goods. It was suggested that the rate of surtax (domestic indirect tax) be raised from 30 to 35 percent to offset the revenue loss from the tariff reduction. While the short term objective was revenue neutrality, the proposals were expected to increase tax elasticity in the longer run. In fact, as a result of delays in the implementation of tariff reductions, the short term effect on revenue was positive.

BANGLADESH: In 1987 a Bank study anticipated the impact of tariff reductions to be of the order of 2-3 percent of total tax revenue in the event the volume of imports remained the same. The study recommended that tax reforms should accompany the tariff reforms with the specific objective of generating revenue to offset the revenue lost from tariff reductions. rhe replacement of QRs by tariffs was recognized as a partial revenue offset. A subsequent study recommended wide ranging reforms designed to increase tax elasticity and improve revenue generation. Increased compliance under a regime with lowered tariffs was expected to increase revenue. A value added tax was introduced in July 1991 with positive revenue impact.
2 in the longer version of this paper). Overall, the model projects similar levels of job loss to the surveymeasured projections approach, although there is a large disparity between them in three sectors: _Chemicals_ , _Hotel, restaurant, and transportation;_ and _Wholesale, retail, and other services_ .

_Table 5-12 Georgia: Percentage of Permanent Formal Private Sector Jobs Lost by Sector, Model Predicted and Survey Measured Projections, CFUWBES2 (Winter 2020)_

||Econometric Model<br>Projections|Survey<br>Measured|No. Of<br>observations|
|---|---|---|---|
|1. Chemicals|6.2|-0.2|10|
|2. Food, drink, and tobacco|-2.3|6.9|107|
|3. Garments|22.4|44.3|8|
|4. Hotel, restaurants, and transportation|17.7|21.7|110|
|5. Machinery, electronics, and construction|6.1|8.0|56|
|6. Metals and non-metallic minerals; Plastic and||||
|rubber|-1.5|7.3|47|
|7. Wholesale, retail, and other services|2.2|0.0|188|
|8. Wood, paper, publishing,andprinting|0.6|3.0|13|
|Total|5.9|7.8|539|

Sources: LFS 2019, CFUWBES2, WBES 2013 and 2019. Notes: See notes to Table 5-11.

_Table 5-13 Georgia: Labor Market Impacts, Private Sector Workers, By Sector, Percentage of Pre-COVID Jobs_

||Lost job because of<br>job or business losses<br>due to COVID-19|Stopped<br>working to avoid<br>exposure to the<br>virus|Reduced income<br>because of job or<br>business losses to<br>COVID-19|Reduced income<br>due to reduced<br>workinghours|
|---|---|---|---|---|
|Industry/Manufacturing|16.3|0.0|60.7|11.8|
|Construction|10.1|0.0|53.6|27.3|
|Wholesale and Retail (Commerce)|35.6|7.2|67.5|22.2|
|Transport Services (Taxi, Bus, Truck)|19.7|0.0|55.6|25.2|
|Communications|14.7|0.0|41.2|22.5|
|Restaurants,Hotels,Bars,Cafes|55.8|1.5|81.7|11.6|

Source: GHFPS.

Notes: “Individuals who lost jobs because of job or business losses due to COVID-19”: Those who had a job in March 2020 but don’t have a job in December 2020 and answered that they stopped working because they lost jobs or no business because of COVID 19. “Individuals who stopped working to avoid exposure to the virus”: Those who had a job in March 2020 but didn’t have a job in December 2020 and answered that they stopped working because they don’t want to be exposed to the virus. ”Individuals who reduced income because of job or business losses to COVID-19” Those who answered they decreased income due to job loss or closure of business. “Individuals who reduced income due to reduced working hours:” Those who answered they decreased income due to reduction in working hours.

By wave 2, our projected sector estimates show a recovery for all sectors except _Garments_ (see Table 5-12), which remained by far the hardest hit. According to the Georgia High-Frequency Survey, conducted just after the CFUWBES2, among private sector workers, however, the greatest rates of job loss occurred for those previously employed in restaurants and hotels, 55 percent of whom reported being unemployed due to 35
according to Goñi et al (2011) using the 2003 ECV household survey. Table 1 presents alternative computations provided by Jorratt (2010) using the 2007-08 ENIG survey. According to these estimates, direct taxation reduces the Gini coefficient by no more than 0.3 percentage points. The richest quintile of the population account for about 70 percent of total income tax payments, according to Table 1. The share of income paid by different households does not differ much by income, suggesting very low progressivity. The three poorest deciles, for instance, each pay a higher proportion of income tax relative to household incomes compared to any of the deciles between the fourth and the ninth.<sup>12</sup> 19. **The tax burden on personal income in Colombia is very low.** Colombia ranks 3<sup>rd</sup> lowest in the region raising only 1.1 percent of GDP in personal income taxes compared to the regional average of 1.8 percent of GDP. In comparison, OECD countries raise 9.0 percent of GDP on personal income. As shown in Figure 3, personal income taxes correspond to just 8 percent of total central government tax revenue in Colombia. The redistributive potential is naturally limited by the low amounts raised.

**Figure 3. Sources of Central Government Gross Tax Revenue, 2010.**

<!-- Start of picture text --> Fuel taxes<br>Other<br>Wealth tax<br>Financial<br>Transaction<br>Import tariffs<br>Income tax,<br>VAT<br>persons<br>Income tax,<br>firms<br><!-- End of picture text --> Note: Gross Tax Revenue totals 13.1 percent of GDP in 2010. Source: IMF (2011a).

20. **Less than 2 percent of Colombia’s adult population pay income taxes on declared income, in part, because of a very high threshold for personal income taxation.**<sup>**13**</sup> Among the 1.1 million Colombians who submitted an income tax declaration in 2010, only 640,000 people paid income tax in a adult population of about 37 million, according to the Colombian Tax Authority (DIAN). This is partly explained by the fact that Colombia has the highest threshold in the region. Individuals have to earn three times the average household income per

> 12 Missing data for top earners in household surveys imply that the reported Gini coefficient is artificially low.  Top incomes represent a small share of the population, but a very significant share of total income and total taxes paid, as discussed by Atkinson _et al_ (2011).  Household survey data, such as that used in Table 1, generally does not capture the top earning individuals owing to the way in which surveys are designed (often with a view to understanding the lower end of the income distribution) and the difficulties of sampling top earners.  Future analysis could focus on combining income tax data with household survey data to understand this effect better in the case of Colombia. 13 This number rises to 12.4 percent if the retentions of independent workers are included.  The number of tax payers is compared with the adult population, reflecting the fact that pensioners also form part of the universe of potential tax payers.

8
program, and difference-out any time-invariant component of the selection bias. This allows us to credibly estimate the causal effect of the availability of the program at the community level on individual level outcomes.

The effect of the program on its participants is more difficult to estimate. Participation of mothers could range from active participation to the growth monitoring activities to a simple exposure to the messages in nutrition education and monthly meetings. Non-participants might be benefiting simply by peer effects, or learning from participants. We avoid modeling the extent of participation to the program or potential spillover effects. Under the assumption that the impact of participants is positive and higher than the one on non-participants (less than complete spillover effect), the ITT provides a conservative estimate of the impact of the program, being a lower bound on the impact of the program on the ‘treated’.

Each child can be potentially exposed to a program in her community. Let _Dv_ ∈ {1,0} indicate whether a given community ever participates in the program. The potential outcomes for child _i_ in village _v_ of being exposed to the program at time _t Y_ 1 _iv_ , _t_ and _Y_ 0 _iv_ , _t_ . are denoted as The standard difference in difference (DD) estimator assumes that any correlation between the unobservables and the availability of the program _Dv_ , _t_ arises from additive time-invariant village characteristics μ _v_ , or: Under this assumption, the standard difference in difference (DD) estimator from the following regression: will consistently estimate α , the average gain due to the program. The fixed effects at the village level will difference out any unobserved village level component ( μ _v_ ) that might be correlated with program placement in the community. Note that while the difference in difference estimate above ( α ) is represented as a constant (common effect model), we will later allow heterogeneity of impacts across communities or households by re-estimating the same regression by the relevant socio-economic subgroup, i.e. allow α to vary according to _X iv_ .

However, an important concern about the parallel trend assumption and the standard double difference estimator arises when the program targets poor areas [Ravallion, Chen 2005, Chen et al 2006]. As it is in the case of Seecaline, the program was targeting areas with higher malnutrition rates and lower endowments. If these characteristics affecting program placement 10
- Investment not generating user fees, but increasing growth and future tax collection: some infrastructure projects, including most roads, generate no user fees; but to the extent that they contribute to the expansion of future tax bases, they still yield indirectly a financial return to the government.

- Investment generating no future fiscal benefits: the last category consists of projects that yield no financial return, regardless of whether their social return is positive (e.g., environmental projects) or not (as in the case of purely wasteful investments).<sup>8</sup>

The first two types of projects may “pay for themselves”—i.e., generate a stream of financial returns whose present value exceeds the cost of the projects. On solvency grounds, deficit financing of those projects—termed “self‐ liquidating”—<sup>9</sup> would then be justified in principle, because they increase government net worth, even if they raise public debt in the short run. But in practice, this requires that the government be able to capture the returns—i.e., for the first type of projects, that user fees be sufficient to cover project costs; for the second type of projects, that taxes be high enough to translate the additional growth into a sufficient amount of additional revenues.<sup>10</sup> The latter requirement means that under plausible circumstances, and absent user fees, many growth‐enhancing projects may fail to generate sufficient tax revenues to cover their cost. To put it differently, given the low (marginal) tax collection rates of many developing countries, growth impacts have to be quite considerable to yield the required tax revenue increases. For example, with a tax rate of .2, the output contribution would have to be five times as high as the project’s user cost for the government to break even. In other words, if the user cost is around 10 percent (say a 5 percent real interest rate and a 5 percent rate of 8 Arguably, for this last category the problem is one of mislabeling, since spending without future returns perhaps should not be called investment.

9 Mintz and Smart (2006).

10 Across Latin America, cost‐recovery has largely been achieved in telecommunications, but not in water and power—although in these sectors it is already the highest among all developing regions. See World Bank (2005).

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**Table 1.6    Performance of the Xiqiao Cluster Before and After the Establishment of the Technology Innovation Center, 1998 and 2003**

||||1998<br>||||<br>|2003<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|# of<br>employees|Number<br>of firm|Employees|Output<br>(million<br>USD)|R&D<br>(1000<br>USD)|Patents|Number<br>of firm|<br>Employees|Output<br>(million<br>USD)|R&D<br>(1000<br>USD)|Patents|
|<10|795|7,055|44.6|0|0|465|3,715|31.9|0|0|
|10-50|583|26,235|130.1|0|0|534|25,299|94.5|0|0|
|51-100|205|19,475|106.1|0|0|359|33,387|323.2|2,256.7|22|
|>100|7|1,094|61.5|230|0|22|6,445|339.2|3,648.2|166|
|Total|1590|53,859|342.2|230|0|1380|68,846|788.8|5,904.9|188|
|Firm<br>average||32.28|0.21|0.14|0||49.86|0.58|4.28|0.14|

Source: Jun Wang, 2009

- _Preferential policies and financial support_ . To attract qualified enterprises to the clusters, local governments often offer certain incentives, including desirable land, tax reduction or exemption, and access to credits and loans. A series of preferential policies from Foshan and Nanhai (Guangdong Province) include tax exemption for the first two years and a lower tax rate of 15 percent in the following three years for high-tech firms. The town of Xiqiao has also set up an award to encourage individuals to bring qualified enterprises into the cluster. Meanwhile, to help SMEs update their equipment, the local government provides a financing guarantee to assist them in gaining bank loans.<sup>33</sup> In the Puyuan sweater cluster, the local government set up an industrial park and granted preferential land, tax, and credit policies to attract enterprises with famous brands to locate in the cluster (Ruan and Zhang 2008).

**_Knowledge, technology, and skill spillovers through inter-firm linkages._** In clusters, the colocation  of numerous  firms  provides  good opportunities  for firms  to build knowledge networks  and forward and backward linkages, which  are crucial  for technology  learning and collective efficiency. Many firms obtained help from their upstream enterprises. In China,   many clusters also   benefited   from   state-owned enterprises (SOEs) and FDI, which provided important initial technology and a crucial impetus for the clusters’ development.  For example, during the 1980s, with the market-oriented economic reforms, many SOEs were privatized or closed down. Many skilled laborers from the original SOEs were laid off, and they either set up their own businesses or provided their know-how to private enterprises. They also helped diffuse technologies and skills to more workers through training or coaching, as was certainly the case in the Xiqiao textile cluster. In the Wenzhou footwear cluster, the original  SOE—Dongfanghong Leather  Footwear Factory— gave rise  to

31
**Table 9: Work status and level of intensity in EU8 countries**

||||**Percentage**<br>|**of thepopulat**<br>|**ion aged 16y**<br>|**rs and older**<br>|<br>||
|---|---|---|---|---|---|---|---|---|
||**Slovenia**|**Czech R.**|**Slovakia**|**Hungary**|**Estonia**|**Poland**|**Lithuania**|**Latvia**|
|**Percent of population u**|**nemployed:**||||||||
|Overall population|6|6|5|4|3|7|5|6|
|Poorest decile|19|25|20|16|13|24|21|28|
|Richest decile|1|1|1|1|0|2|1|3|
|**Work intensity:**|||||||||
|Overall population|3.1|3.1|3.1|2.9|3.3|2.9|3.2|3.2|
|Poorest decile|2.0|2.0|2.3|1.9|1.9|2.2|2.1|2.1|
|Richest decile|3.6|3.8|3.6|3.5|3.8|3.6|3.8|3.8|

_Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions

# **Explaining Regional Differences Within and Across EU countries**

37. For some of the larger EU8 countries (Czech Republic, Hungary, and Poland) regionally disaggregated statistics can also be computed using the SILC data set.<sup>16</sup> As Table 10 shows, the average standard-of-living varies quite a bit across regions: average equivalized disposable incomes in the richest regions are about 33–42 percent higher than in the respective poorest regions.

**Table 10: Regional variation in standard-of-living within selected EU member states**

||**Average equival**<br>|**ized disposable income**<br>|**by region**<br>|
|---|---|---|---|
|**Region**|**Czech Republic**|**Hungary**|**Poland**|
|1|7,089|5,516|4,310|
|2|5,846|4,648|3,959|
|3|5,550|4,134|3,227|
|4|5,210||3,595|
|5|5,241||4,013|
|6|5,149||3,504|
|7|5,069|||
|8|4,995|||
|Ratio of max/min|1.42|1.33|1.34|

_Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions 38. The observed differences across regions give rise to an important question: are the poor people who live in a particular region poor because they have unfavorable endowments of education and other such individual-level attributes linked to low income status, or are their low incomes primarily due to region-specific attributes of their place of residence—for example, inadequate infrastructure, poor access to basic services, etc.? We use multivariate regression analysis applied to the EU-SILC data to help answer the above question, plus to examine more systematically the link between education, work status and work intensity and observed differences in (log) equivalized incomes across households. The explanatory variables used are summarized in the table below.

||**Obs.**|**Mean**|**Std. Deviation**|**Min**|**Max**|
|---|---|---|---|---|---|
|Share of 0–17 yr olds|59,300|.135|.203|0|1|
|Share of 18-64 yr olds|59,300|.636|.366|0|1|
|Share of 18+ unemployed|59,300|.061|.169|0|1|
|Share of 18+ retired|59,300|.273|.403|0|1|
|Work intensity status|59,300|2.52|1.28|1|4|
|Share with lower-secondary|59,300|.108|.246|0|1|
|... Upper-secondary|59,300|.550|.407|0|1|
|... Post-secondary /tertiary|59,300|.193|.339|0|1|

> 16 Czech Republic: Eight NUTS-2 regions; Hungary: Three NUTS-1 regions; Poland: Six NUTS-1 regions.

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# 5. Using poverty and vulnerability maps to update RNU quotas

The Government of Senegal began developing the first RNU in 2015. The goal was to provide a unified information database to coordinate the various social projects and programs and serve as the foundation for targeting beneficiaries. The identification of households entering the RNU database relied on the poverty rates from the 2015 poverty map to provide commune eligibility quotas. Subsequently, communities provided lists of local households considered among the poorest in the area. Then, one by one, households with the lowest score in a proxy mean test were granted access to social programs until the commune quotas were met. Between 2015 and 2022, the RNU collected socioeconomic information on 550,000 households, representing nearly 29 percent of all households in the country. Because of the lower poverty rates in urban areas and in Dakar Region, the RNU includes a larger share of rural households.<sup>19</sup> In 2022, the government planned an expansion of the RNU to reach 1 million households. Recognizing that households are exposed to recurrent and severe shocks, one of the goals was to include households that were vulnerable though they might not be poor. Expanding the eligibility criteria to cover the poor, but also the vulnerable presents data and methodological challenges. Most methods for estimating vulnerability rely on household panel data, which are not available in Senegal. Furthermore, even if methods to estimate vulnerability from cross-sectional data exist, they fail to provide estimates at the level of geographical disaggregation necessary for targeting RNU regional eligibility quotas. The methodology described here represents a solution because it provides eligibility quotas that account for commune poverty rates as well as the probability that poverty rates will rise in the face of shocks.

How does the use of a vulnerability map affect the expansion of the RNU? While the implementation of the first RNU relied on commune poverty rates, the expansion of the RNU relies on commune vulnerability rates to determine the number of households in each commune that may be registered. Commune vulnerability includes the poverty rate as in the previous RNU, plus an estimate of the share of the nonpoor at risk of becoming poor. The new quotas will therefore shift depending on the distribution of risk-induced vulnerability rates across communes. This implies that, under the new system, a commune with a low poverty rate, but high overall vulnerability will benefit similarly to a commune with a high poverty rate. In the past, the former was not considered in RNU registrations. Accounting for the poor, but also for

> 19 The RNU includes 66 percent of households as rural though only 55 percent of the population is rural. Similarly, Dakar represents 31 percent of all households, but only 14 percent of the RNU. Kolda and Ziguinchor regions are overrepresented. They account for 18 percent of the RNU, but only 9 percent of the population.

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# **References**

Mehtap Akg ̈u ̧c, Xingfei Liu, Max Tani, and Klaus Zimmermann. Risk attitudes and migration. _China Economic Review_ , 37(C):166–176, 2016. URL `https://EconPapers.repec.org/RePEc:eee:chieco:v:37:y:2016:i:c:p:166-176` .

- M Allais. Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’ecole americaine. _Econometrica_ , 21(4):503–546, 1953.

- Coren L. Apicella, Anna Dreber, Benjamin Campbell, Peter B. Gray, Moshe Hoffman, and Anthony C. Little. Testosterone and financial risk preferences. _Evolution and Human Behavior_ , 29(6):384 – 390, 2008. ISSN 1090-5138.

- E.A. Beam, D. McKenzie, and D. Yang. Unilateral facilitation does not raise international labor migration from the philippines. _Economic Development and Cultural Change_ , 64: 323–368, 2016.

- Anke Becker, Benjamin, and Enke Armin Falk. Ancient origins of the global variation in economic preferences. August 2016.

Holger Bonin, Amelie Constant, Konstantinos Tatsiramos, and Klaus Zimmermann. Native-migrant differences in risk attitudes. _Applied Economics Letters_ , 16(15):1581–1586, 2009. URL `https://EconPapers.repec.org/RePEc:taf:apeclt:v:16:y:2009:i:15:p:1581-1586` .

- Michael Callen, Mohammad Isaqzadeh, James D. Long, and Charles Sprenger. Violence and risk preference: Experimental evidence from afghanistan. _The American Economic Review_ , 104(1):123–148, 2014. ISSN 00028282.

- Christopher P Chambers. An axiomatization of quantiles on the domain of distribution functions. _Mathematical Finance_ , 19:335–342, 2009.

- Thomas Dohmen, Armin Falk, David Huffman, and Uwe Sunde. Are risk aversion and impatience related to cognitive ability? _American Economic Review_ , 100(3):1238–60, June 2010.

- Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde, J ̈urgen Schupp, and Gert G. Wagner. Individual risk attitudes: Measurement, determinants, and behavioral consequences. _Journal of the European Economic Association_ , 9(3):522–550, 2011.

Naihua Duan. Smearing estimate: A nonparametric retransformation method. _Journal of the American Statistical Association_ , 78 (383):605–610, 1983. doi: 10.1080/01621459.1983.10478017. URL `http://www.tandfonline.com/doi/abs/10.1080/01621459.1983.10478017` .

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