rafmacalaba/datause-encoder-data huggingface dataset:
**-** **<u>Table 3:Pradhan, election characteristics, and public spending for reserved and non reserved villages</u>**

||**Yes**|**Reserved**<br>**No**|**Test for equality**|
|---|---|---|---|
|**Pradhan characteristics**||||
|Illiterate|24.72|5.74|***|
|At least primary education|75.28|94.26|***|
|At least secondary education|28.65|62.69|***|
|At least high school education|6.18|22.08|***|
|SC/ST|30.02|27.51||
|Muslim|0.044|0.050||
|Female|98.36|6.65|***|
|Held political office before|11.67|24.24|***|
|Was candidate before|6.67|20.92|***|
|**Characteristics of the election**||||
|Number of candidates contested|3.51|3.38||
|Share of population voted|76.2|72.1|***|
|Share of votes received|47.35|49.01||
|**Revenue & expenditure (Rs. per capita/year)**||||
|Local revenue per capita|174.42|169.07||
|of which from govt. sources|156.58|151.79||
|of which from own sources|17.84|17.47||
|Centrally sponsored schemes|268.73|201.67||
|No. of observation|180|459||

_Source:_ Own calculation based on REDS/NCAER 2007 Survey. * significant at 10%; ** significant at 5%; *** significant at 1% _Note:_ Number of candidates contested is only for the 15 states where the pradhan is directly elected by popular vote i.e., Kerala and Maharashtra are excluded.

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discussion of the results pertaining to the national variables is worthwhile because they do seem to be jointly significant, even after controlling for Quasi Country Fixed Effects and the level of development. It is actually surprising that the investment climate variables appeared with consistently estimated signs, even when they do not appear to be individually statistically significant when the regression errors are assumed to be clustered around countries.

# **6. Concluding Remarks**

The theoretical model presented above motivated empirical analyses of the determinants of product innovation by firms. Both market conditions and the threat of entry by competitors were shown to be theoretical predictors of the research-cost intensity of firms, even when prices are exogenously determined by market conditions.

The empirical analyses of Section 4 suggested that in fact data from 60 countries, covering thousands of firms, support the main prediction of the model: Research-cost intensity tends to be significantly associated with product innovation. The analysis also highlighted a potential pitfall in the firm data, as both R&D/Sales and licensing seem to be measured with error, thus shedding some doubt on the usefulness of direct regression estimates of the innovation function.

The investment climate also seems to play an important role for product innovation. But the evidence also highlights market failures that hamper innovation. Of particular relevance in this regard were the results concerning the regulatory barriers to firm entry. Whereas deregulation is desirable to increase competition and knowledge diffusion, the results suggest that other policy instruments are needed to stimulate product innovation, especially after deregulation and trade liberalization. Also, the results concerning the density of patent counts suggest that knowledge spillovers might also be important. From a policy perspective, it is also worth noting that product innovation seems to be counter-cyclical, and thus the budgets of programs to stimulate product innovation need to be protected during downturns, so as to prevent the demise of firms that could have survived through retooling in terms of product innovation. The latter might have social benefits that greatly exceed the private returns, because private agents can benefit from the knowledge embodied in the product innovations of their competitors.

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- Instructional time should be reallocated towards basic skills (reading, language and math), reducing many of the less important courses and requirements that fill curricula in many countries.

- Textbooks for every student to take home and use in class reduce the time needed to lecture in class, copy exercises on the board, or hand out photocopies. Increased homework assignments constitute additional instructional time. Older students could help after-hours to help younger students with their schoolwork.

- Methods that have been shown to be more efficient should be disseminated through in-service training for certain subjects, such as phonics for reading.

In most cases lengthening the school day or year would merely perpetuate the inefficiencies.<sup>133</sup> However, poorer students may benefit from longer instructional days when used well.

**Figure 4-5. How many students seem off task in this caricature?**

_Senegal – folk artist on glass_

# **INSTRUCTIONAL TIME MEASUREMENT FOR EVALUATION AND RESEARCH**

The parameters measured in this research could be useful as monitoring tools of governance and systemic efficiency. Well-managed schools should stay open for the number of days that governments specify. Researchers visiting schools should find school registries with numbers of days and hours per subject matter approximating those specified by the government. Low teacher absenteeism (e.g. below 10%) would constitute evidence of effective school-based management. Finally, time on task in class should constitute evidence of effective supervision by principals, inspectors, or supervisors. Measurements in Tunisia through this study (78%) and the Chicago public 133. From “Time and Learning in CA Schools” 1984 (unpublished mimeographic document).
Policy Research Working Paper 10707

# **Abstract**

Governments often use simplified business tax systems, such as presumptive tax regimes, to register and tax small and microenterprises. Despite concerns about how such regimes could disproportionately affect female-owned and low-revenue entrepreneurs, there is a lack of empirical analysis examining the tax burden. The presumptive tax in Ethiopia has a complex assessment system, where the tax liabilities are determined according to the activity type and turnover (99 activities and 19 turnover bands), and some activities do not have a tax-free threshold. This paper uses two rounds of data in the Ethiopian Socioeconomic Surveys and the tax code to analyze the equity and gender implications of the presumptive tax on small and microenterprises by imputing the effective tax rates. There are three key findings. First, the effective tax rates are higher for businesses in the lowest quartile at 4.3 percent of turnover compared to 1.5 percent for businesses in the highest quartile, using the most recent survey, resulting in a regressive system. Second, male-owned businesses tend to operate in sectors without a tax-free threshold and are more likely than female-owned businesses to face higher tax rates. Third, the effective tax rates are high for businesses in food and beverage services, which is female-dominated, and for transit services, which is male-dominated, due to the lack of a tax-free threshold for these sectors. The study finds that an alternative presumptive tax system with a single tax rate on turnover and an exemption for all low-revenue businesses would be simpler for tax assessment and more progressive.

This paper is a product of the Development Data Group, Development Economics. . 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 author may be contacted at hkomatsu@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
As long as quotas existed under the MFA, poor developing countries were limited in their ability to use textile exports as a means to jumpstart the development of their light manufacturing sector. Import quotas for textiles and apparel in developed markets were based on historical export volumes and were only increased gradually as a country achieved export success. This constraint was relaxed when quotas were gradually abolished over a transition period. The entire quota system was abolished by 2005.

The more recent experience of the Bangladeshi textile sector highlights the rapid growth in exports that is currently possible, now that historical quota constraints are no longer an issue. In Figure 3 we show the country’s experience with exports of t-shirts which was a particular success story. It took eight years, from 1995 to 2003, for Bangladesh approximately double its exports from 289 million USD to 661 million USD.<sup>3</sup> Over a similar time span, from 2003 to 2009, but simultaneously with the total elimination of the MFA quota system, its exports quadrupled to 2.8 billion USD. By a mere five years later, its exports had almost doubled again and total t-shirt exports stood at 5.4 billion USD in 2014.

Over this time period, the share of Bangladeshi exports of t-shirts grew from 1% of global trade to 14% (orange line). Given that there are natural limits to this share, as industries in different countries specialize in different market segments defined by quality, material, and fashion cycles, it is not surprising that its market share levelled off towards the end. Equally important, as shown by the dark blue line in Figure 3, is that the fraction that t-shirts represent in Bangladeshi total exports peaked already in 2008 and started to decline gradually. Given that t-shirt exports kept increasing, it implies that exports from other industries must have been rising even more rapidly. This example nicely illustrates what is possible in terms of export growth and subsequent industrial upgrading if market access is liberalized.

**Figure 3:  Exports of T-shirts from Bangladesh**

<!-- Start of picture text --> 6 20%<br>5<br>15%<br>4<br>3 10%<br>2<br>5%<br>1<br>0 0%<br>1995 2000 2005 2010 2015<br> Total exports (left scale) Fraction of total Bangladesh exports<br> Fraction of global t-shirt exports<br><!-- End of picture text --> Note: Total exports on the left scale is measured in nominal values (current USD, billions). Both fraction on the right scale are calculated from nominal values (current USD) within the year.

> 3 This growth rate probably even overstates the real growth somewhat as the statistics refer to nominal values.

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To account for the participation issue, we consider two augmented count data models which account for the presence of a large number of zeros - the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) (Mullahy, 1986; Lambert, 1992; Greene, 1994; Haab and McConnell, 1996). By distinguishing between participants and non-participants, the zero observations may contain valuable information, and a gain in efficiency will be achieved by including all of the observations (Haab and McConnell, pg. 90).<sup>2</sup> Empirically, zero-inflated count models change the mean structure to allow zeros to be generated by two distinct processes, one for the participation decision (logit or probit) and one for the mean number of trips (count model).<sup>3</sup> By expanding the standard count model to allow for individual-specific characteristics which may keep an individual from entering the recreation market, one can separate factors which influence the participation issue from those that influence the quantity of trips taken to a recreation site (Haab and McConnell, 1996). In estimation, the ZIP model allows for over-dispersion in the Poisson data generating process by allowing a mass of zero observations independent of the true Poisson process.

The distribution function for the ZIP model is: where E( _yi_ ) = (1 - _Pi_ )λ _i_ , Var( _yi_ ) = (1 - _Pi_ )(1 + _Pi_ λ _i_ )λ _i_ , and _Pi_ is the probability of zero visitation, with mean λ _i_ = exp( **x** **_i_** β ). Note that in this formulation, zeros can occur in either the binomial process (when _yi_ = 0) or the Poisson process (when _yi_ ≥ 1), since exp(0 λ _i_ ) λ _i_ /0! = exp(- λ _i_ ). Again, λ _i_ can be modeled as exp( **x** **_i_** β ), and _Pi_ as g( **z** **_i_ γ** ), where **γ** is a vector of participation-decision parameters and **z** **_i_** is a vector of explanatory variables that may or may not be the same as those for the quantity decision, **x** **_i_** . The function g( • ) can be modeled using either logit or probit (or cumulative standard normal) function as they

> 2 In the past, one crude option was simply to drop the zeros from the sample.

> 3 The zero-inflated models differ from the Heckman continuous two-stage model as they allow for zero observations in the second stage of the decision process (in the mean model).

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passthrough (and decreasing with elasticity). For larger elasticities and price shocks (e.g. upper-bound elasticities in Panel C), the positive and progressive price effects illustrate that as tobacco becomes more unaffordable, households cut down on tobacco consumption enough to “free up” budget resources for other uses. Disposable income is higher after the tax increase. Because lower-income households observe the largest shifts away from tobacco, the pattern becomes progressive.

Figure 3. Change in tobacco expenditures after increasing taxes Panel A. 25% Price increase Panel B. 50% Price increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Direct Pass-Through Medium-bound Elasticity Direct Pass-Through Medium-bound Elasticity<br>Lower-bound Elasticity Upper-bound Elasticity Lower-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Direct Pass-Through Medium-bound Elasticity<br>Lower-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.5 1<br>0 0<br>-.5 -1<br>Inconme Gains (%) Inconme Gains (%)<br>-1 -2<br>4<br>2<br>0<br>Inconme Gains (%)<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. _Notes:_ Deciles are created based on per capita household consumption.

# (B) Reduced Medical Expenses

<u>Figure 4</u> plots the effects of reducing out-of-pocket medical expenses after the tax-induced price increase in cigarettes. This mechanism yields positive income gains for all deciles, as the price shock encourages a reduction in smoking, and hence, a drop of tobacco-related medical expenses. Households can then benefit from higher disposable income, as they are no longer burdened by those medical bills. Moreover, the combination of high smoking prevalence and high sensitivity (elasticity) to price changes among lowerincome households results in a progressive relative incidence. The magnitudes of these benefits are large for the lower-income deciles. Assuming a price shock of 50 percent, on average, the long-term reduction 18
This may be particularly true in phone surveys where participants interact with a caller and may feel social pressure to report different beliefs when asked about their expectations again, even if they were not given new information. To allay concerns of such potentially spurious updating, we exploit the randomized assignment from the information provision experiment, following standard specifications in the literature (see Armantier et al., 2016; Cavallo, Cruces, and Perez-Truglia, 2016): In this specification, the parameter _β_ picks up spurious reversion towards the signal and _α_ picks up true learning (i.e., changes in beliefs caused by the information provision) above any spurious revisions. Note that we do not expect subjects to fully update to the signal we provided ( _α_ = 1) because the signal is an expert forecast that most respondents will correctly interpret as uncertain. Moreover, some individuals may not fully trust the source of the forecast and therefore place lower weight on the forecast. Nevertheless, we should expect _α_ to be significantly greater than zero.

The same logic applies to expectations about the nominal exchange rate. Let _d_<sup>_prior_</sup> _i,t_ denote participant _i_ ’s prior belief about the depreciation rate (i.e., the growth rate of the nominal exchange rate) before the individual reaches the information-provision experiment. Let _d_<sup>_signal_</sup> _i,t_ be the value of the signal that we may or may not show to the individual (i.e., the forecast). Let _Ti,t_<sup>_d_beabinaryvariablethattakesthevalue1ifweshowedthatsignaltoindividual</sup><sup>_i_and</sup> 0 if not. Denote _d_<sup>_post_</sup> _i,t_ as the corresponding posterior belief, that is, the expected depreciation rate after the individual sees, or does not see, the signal.

Our experiment provides respondents with information about inflation and the nominal exchange rate. Thus, it is possible that individuals use feedback about the inflation rate to update beliefs about the exchange rate and vice versa. Indeed, we might expect this type of cross-learning based on macroeconomic evidence. For example, after a devaluation of the local currency, there is partial pass-through to inflation (Dornbusch, 1987). We therefore expand the learning model to accommodate the possibility of cross-learning and estimate the following set of equations: 20
introduction 5

# 1 introduction

Sri Lanka has enjoyed rapid progress on poverty reduction and shared prosperity since the cessation of the decades-long conflict in 2009. In the last 10 years, GDP per capita has doubled from US$2,000 to US$4,000 (current US), and the national poverty incidence has been halved, from 9% to 4%, as measured by the national household income & expenditures survey. Despite this progress, the country scored modestly on several indicators related to disaster risk management in the 2015 Country Policy and Institutional Assessment, including quality of public administration; fiscal policy and macroeconomic management; social protection; and transparency, accountability, and corruption in the public sector.

Given this context, effective disaster risk management (DRM) strategies are essential to the continuation of and in Sri Lanka. Parts of the peace prosperity country are affected destructive monsoon regularly by floods, particularly during regional seasons. In 2017, for example, high winds, severe flooding, and landslides struck the southwestern districts of Sri Lanka–most directly, Galle, Kalutara, Matara, and Rathnapura–causing approximately 50-60 billion LKR (333-400 million USD) in damages, as estimated in the Ministry of Finance (2017) Annual Report. According to the World Bank post disaster needs assessment, the government disbursed US$6.6 million in emergency relief, and total recovery needs were later estimated at nearly US$800 million. Although the 2017 floods were exceptional, 2016 also saw unusually destructive and such events could become more in the future as a flooding, frequent result of climate change.

Asset losses are used to connote disasters’ but routinely scope, they provide only a partial measure of the total cost of any event. Other loss dimensions include lost wages and other income, insurance liabilities, days of school and doctor visits skipped, and meals foregone. For example: imagine that two identical, neighboring households experience the same property damage from a flood. If only one of the households is insured, that one will be compensated for its losses, while the other one might relocate, pull its children out of school, or reduce its consumption below the poverty line in order to recover. There are many different types of disaster costs,
through recall of remaining household members, although information was also available from individuals who left and came back during the past five years (return migrants).

The paper’s results show that internal migration is determined by both pull and push factors and persons most likely to migrate are more educated individuals from communities with lower average levels of education (i.e., “the best of the worst”). Therefore, internal migration may widen spatial inequalities by lowering the concentration of more educated people of prime working age in the origin communities. On the other hand, any potential increase in spatial inequality due to internal migration can hardly be considered a worsening in inequality because it is an outcome of a welfaremaximizing decision by individuals and/or households.

Migration turns out to only be beneficial for a subset of Ghanaian households who send migrants to urban areas. Although the results stop short of establishing a direct causal relationship, they indicate that migrants from urban areas are more likely to send remittances and remit larger amounts, suggesting one channel which may explain the positive association between household welfare and migration to cities. Households who send migrants to rural areas also receive remittances, but it appears that in this case the remittance income is insufficient to compensate for the lost earnings of a migrant and household welfare decreases.

The differential impacts of migration to rural and urban areas may be due to qualitative differences between the two flows: for example, although two-thirds of all migrants move to urban areas, only 45 percent of all households with migrants have at least one urban migrant. This implies that when migrating to urban areas, larger number of family members move together (or follow each other) as opposed to migrants destined for rural areas.<sup>17</sup> Therefore, households with urban migrants are more likely to receive remittances because the combined earning ability of their migrants is greater. Furthermore, migrants destined for urban areas may have higher earning potential due to better initial conditions (at individual, household, and community levels), which further cements the inequality of outcomes with regards to the welfare impact of migration.

> 17 This finding is consistent with evidence on migration networks in Ghana (e.g., Adams, Cuecuecha and Page, 2008).

13