rafmacalaba/datause-encoder-data huggingface dataset:
6 coordinating tax reforms: can lost tariffs be recouped?

The main explanatory variable of interest is trade tax revenue relative to GDP (TTit). If its coefficient β1 is significantly negative, it can be concluded that a fall in trade taxes has been associated with a rise in non-trade tax revenue. In the long −β1 term, the relevant coefficient is (1−βo)<sup>. Time and country-fixed effects are captured</sup> by μt and αi. The control variables ( **X** it) are those that affect either the costliness of raising revenue from non-trade sources or the valuation of public expenditure. If the marginal value of public expenditures foregone with lost trade taxes is high, the urgency to seek alternative sources is greater. The control variabes are:

- GDP per capita: demand for government expenditures increases as average incomes of citizens grow (Wagner’s Law). GDP per capita also proxies for administrative and institutional capacity in the country to collect and manage taxes. (Institutional capacity is proxied better by measures of the quality of governance like the World Governance Indicators (WGI), but their crossnational time-series does not go as far back as the 1980s.)

- Imports: it is the share of total imports relative to GDP. It captures “openness” of the economy as well as the fact that imports are a substantial part of the domestic tax base in poor countries. Baunsgaard & Keen (2010) use for openness a slightly broader measure: the share of exports and imports in GDP, citing Rodrik (1998) who finds this measure of openness to be closely associated with the size of government.

- Natural resources per capita: two measures are introduced as important controls to capture the fact that states that derive a large share of revenues from natural resources do not need to tax their citizens highly (Ross 2001).

- Foreign aid as a share of national income: this could have a perverse effect on the urgency of finding an alternative source of domestic revenue.

- Share of agriculture in GDP: this measures the size of the economy that is hard to tax, as well as the degree of informality prevailing in the economy.
19 relatively modest.

Columns 5 and 6 repeat the exercise of column 4, except that they assume efficiency improvements relative to the public enterprise of, respectively, 20% and 30%. Here, it is only in the cases of the lowest elasticitiss --those that would have produced monopoly price increases of 75% and more in the absence of efficiency improvements-- that privatization without regulation leads to a reduction in total surplus .he evidence reviewed above concerning efficiency differences between public and private enterprises suggests that 20% to 30% improvements in efficiency following privatization are not unlikely.

As is clear from Table 2, whether or not privatization is likely to cause a significant drop in social welfare depends upon the initial price elasticity of demand. It is therefore useful to consider some empirical estimates of the demand elasticity for services produced by natural monopolies.

Lester Taylor (1975), in a survey article drawing on the Houthakker-Taylor (1966) demand elasticity study as well as many others, reported that the estimated short run price elasticity of demand for electricity seemed to range from .14 to .90 (in absolute value) and that the long run elasticity ranged from 1.0 to 2.0. Further, as Alfred Kahn reports in his treatise on regulation (1971, pp. 102-3), the Houthakker-Taylor and other studies suggest that the price elasticity is significantly higher in poorer areas of the United States and abroad.

These elasticity estimates suggest two things. First, for
**Table 2**

# **Remittances in 2008 as a share of GDP and imports**

||Remittances<br>US$ millions|Remittances<br>as<br>percent of GDP|Remittances as percent of<br>merchandise imports|
|---|---|---|---|
|Armenia|1140|10|26|
|Kyrgyz Republic|1440|31|53|
|Moldova|2300|31|47|
|Tajikistan|2210|43|78|

* For Moldova, remittances include compensation of employees

Sources: World Bank and IMF Russia is the destination for 96 percent of Tajik migrant workers, of whom 55 percent work in the construction industry and the rest mainly work in other service industries (World Bank, 2008). There is a strong seasonal element to remittances with the highest inflows during the summer. Remittance inflows to Tajikistan are falling in 2009 for two reasons. First the Russian construction industry, which grew very rapidly up to 2008 on the back of the oil price boom, is contracting as Russian national income falls: a reversal of the Dutch disease induced construction boom is taking place as a consequence of the sharp fall in oil prices. Secondly, the exchange rate of the Russian ruble depreciated by 51 percent against the US dollar between March 2008 and March 2009, reducing the value of remittances in US dollars. This has already begun to affect remittances to Tajikistan. The fall in remittances in the second half of 2008 was steeper than would normally be the case on the basis of seasonal factors alone, and remittances, valued in US dollars, were 32 percent lower in the first four months of 2009 than in the corresponding 6
dramatically reduce the time and costs of obtaining such information from individual sources and therefore reduce the total costs of financial intermediation. Credit reporting makes borrower quality much more transparent, which benefits good borrowers and increases the cost of defaulting on one’s obligations. It helps borrowers build up a credit history – “reputation collateral” - and thus eases access to credit. Credit registries are especially important for SMEs as their creditworthiness is harder to evaluate and they have less visibility and transparency relative to large enterprises. Consumer rights to access their information and challenge erroneous information have to be effectively protected to make such a system a widely acceptable part of the financial system infrastructure and balance consumer rights with efficiency considerations. International comparisons have shown that countries with better developed systems of credit information sharing enjoy higher levels of financial development and their firms report lower financing obstacles (Jappelli and Pagano, 2002; Love and Mylenko, 2003; Djankov, McLiesh and Shleifer, 2005; see Miller, 2003, for an overview). Figure 7 illustrates the positive relationship between the efficiency of credit information sharing and Private Credit to GDP.

While many countries have built up credit information systems within their central bank or supervisory authority as part of their early warning system to monitor financial institutions, it is important to keep in mind the effect of credit information sharing on the competitiveness of the financial sector and on access to credit; this, however, implies the inclusion of all claims independent of their size, the inclusion of all financial institutions and even non-financial institutions such as utility companies to reap the maximum benefit. Further, effective credit information systems can form the basis for the more effective use of technology in credit decision processes, such as credit scoring techniques.

Similarly, more accurate and transparent financial statements can help reduce information asymmetries between borrowers and lenders. Financial statements that give an accurate picture 10
<!-- Start of picture text --> Backward integration (FVA) Forward integration (DVX) GVC Participation<br>2 o? 2<br>o o<br>g 6 5<br>3 ro} ae<br>3 3 3<br>30 sme 2ole<br><!-- End of picture text --> <!-- Start of picture text --> 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Year Year Year<br>—— Primary —— Manufacturing —— Primary ———— Manufacturing —— Primary ——— Manufacturing<br>— Service —— Service — Service<br><!-- End of picture text -->
|CONTENTS|Pge|
|---|---|
|**1***Introduction**.........**<br>**...................................**|1|
|2. Data Source**............................................**|4|
|3. Education in C8ted'Ivoire<br>**......................................**|8|
|4. Formal Vocational and Technical Education<br>**....................**|**....**<br>19|
|5. Informal VTE: Apprenticeships<br>**..................................**|**.**24|
|6. Bank Involvement in the Education Sector in C8te d'lvoire<br>**......**|**..**30|
|7. Economic Assessment of VTE at the National Level<br>**..............**|**...**<br>36|
|7?1Earnings Analysis<br>**........................................**|**...**<br>36|
|7.2Access to Wage Employment<br>**..............................**|**.....**<br>53|
|7.3 The Costs of VTE ...<br>**.......................................**|**..**60|
|7.4Internal Rate of Return<br>**................................**|**.....**<br>70|
|7.5Earnings Analysis for the Self-Employed<br>**..................**|**...**<br>77|
|7.6Summary of Main Findings<br>**...............................**|**.....**<br>80|
|8.The Regional Dimension<br>**.......**<br>**......**<br>**.........................**|**..**82|
|8.1Earnings Analysis**.....................* **<br>**...................**|**.**83|
|8.2 Access to Wage Employment**.......**<br>**.........................**|**.**87|
|8.3 Costs and Rates of Return**.......**<br>**..........................**|**..**90|
|8.4 Suimmary<br>of Main Findings .|96|
|9. The Sectoral Dimension**.......**|**9...**<br>98|
|10. Interaction Among Human Capital Variables<br>**.....................**|**...**<br>109|
|11. Suumary of Main Findings and Policy Issues**.....**<br>**................**|**.**114|
|References .|120|
|Appendix 1: Some Specification<br>Issues Regarding the Earnings<br>Function<br>**.....**<br>**0**|122|
|Appendix 2: Comparison with Other Estimates<br>**..................**|**...**<br>126|
|Appendix 3: Institutional Costs: Detailed Tables**...............**|132|
|Appendix 4: Short-Term Training in C8te d'Ivoire<br>**.............**|**...**<br>139|
|Appendix 5: CILSS Education and Training Module<br>**..............**|**...**<br>142|
**Table 4.3a** **<u>(continued): Non-response bias in HFPS</u>**

||||**Nigeria**|||||**Uganda**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**Sample**|**Co**|**ntacted**|**In**|**terviewed**|**Sample**|**Co**|**ntacted**|**In**|**terviewed**|
||||_i d_||_i d_|||_i d_||_i d_|
||_Mean_|_Mean_|_Bas ue to_<br>_non-contact†_<br>_(12) -(11)_|_Mean_|_Bas ue to non-_<br>_interview†_<br>_(14) -(12)_|_Mean_|_Mean_|_Bas ue to_<br>_non-contact†_<br>_(17) -(16)_|_Mean_|_Bas ue to non-_<br>_interview†_<br>_(19) -(17)_|
|**Characteristic**|(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|
|**_Consumption_**|||||||||||
|Mean expenditure(LCUper capita)|219,408|220,234|11422|232,707|913|399,209|403,870|4661.465**|404,428|557.44|
|**_Quintiles_**|||||||||||
|Quintile 1 -poorest|0.201|0.165|-0.036*|0.164|-0.001|0.135|0.128|-0.006**|0.130|0.001|
|Quintile 2 -poorer|0.196|0.187|-0.008|0.187|0.000|0.164|0.163|-0.001|0.163|0.000|
|Quintile 3 - middle|0.201|0.200|-0.001|0.200|0.000|0.196|0.191|-0.005|0.190|-0.001|
|Quintile 4 - richer|0.197|0.221|0.025|0.219|-0.002|0.226|0.232|0.006|0.234|0.002|
|Quintile 5 - richest|0.206|0.227|0.020|0.230|0.004|0.269|0.276|0.007|0.273|-0.002|
|**_Housing_**|||||||||||
|Improved roof|0.884|0.912|0.028|0.911|-0.001|0.833|0.846|0.013***|0.844|-0.002|
|Improved floor|0.777|0.830|0.052**|0.828|-0.001|0.512|0.527|0.015***|0.522|-0.005|
|Improved toilet|0.609|0.687|0.078**|0.692|0.004|0.435|0.443|0.008**|0.441|-0.002|
|Improved water source|0.764|0.820|0.056**|0.821|0.001|0.802|0.802|0.000|0.800|-0.002|
|Access to electricity|0.565|0.650|0.086**|0.651|0.000|0.288|0.297|0.009**|0.297|-0.001|
|**_Asset ownership_**|||||||||||
|Solar||||||0.425|0.439|0.013*|0.441|0.002|
|Generator|0.237|0.282|0.045**|0.283|0.001||||||
|TV|0.462|0.536|0.074**|0.540|0.003|0.291|0.300|0.008**|0.299|-0.001|
|Refrigerator|0.178|0.221|0.043**|0.223|0.002|0.076|0.079|0.003|0.077|-0.002|
|Bicycle|0.151|0.143|-0.008|0.144|0.001|0.275|0.279|0.005|0.282|0.002|
|Motorbike|0.310|0.335|0.025|0.336|0.001|0.126|0.126|0.000|0.126|0.000|
|Car or other vehicle|0.094|0.116|0.022|0.115|0.000|0.041|0.043|0.002|0.042|-0.001|
|Mobile Phone|0.752|0.813|0.061**|0.810|-0.002|0.872|0.883|0.011***|0.888|0.005|
|**_Land & crop farming_**|||||||||||
|Owns land|0.666|0.642|-0.023|0.643|0.001|0.598|0.597|-0.002|0.603|0.006|
|Cropfarming|0.696|0.657|-0.039|0.656|-0.001|0.712|0.707|-0.005|0.709|0.002|
|Owns anylivestock|0.464|0.451|-0.013|0.453|0.002|0.557|0.555|-0.002|0.560|0.004|
|Operates nonfarm enterprise|0.602|0.647|0.045*|0.652|0.005|0.023|0.024|0.001|0.024|0.001|
|Anymember has financial account|0.500|0.591|0.091***|0.591|0.000|0.643|0.658|0.015***|0.659|0.000|
|Income from remittance (int'l &|||||||||||
|domestic)|0.308|0.330|0.022|0.330|0.000|0.347|0.348|0.002|0.348|0.000|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

24
DD studies. We coded each study to find that only 10.2 percent evaluate a combination of interventions.<sup>6</sup> There are a large number of papers that have studied, in isolation, the specific interventions that are implemented in the program we evaluate. First, while we are unaware of any study that evaluates the effectiveness of an “enrollment drive,” a sizable literature has evaluated various approaches to improve student enrollment (See JPAL Policy Bulletin 2017). Second, the activitybased learning among students grouped by ability is similar to “teach at the right level” or targeted interventions, that have seen overwhelming success across several settings (Banerjee, Cole and Duflo 2007, Banerjee et al 2010, Banerjee et al. 2016, Duflo, Dupas and Kremer 2011, Muralidharan Singh and Ganimian, 2018). Third, the use of volunteers to deliver their programmatic activities has also been evaluated, with mixed evidence (Banerjee, Cole, Duflo, and Linden 2007, Lakshminarayana et al. (2013), Banerjee et al. 2010, Torgerson, King and Sowden 2002). Lastly, the NGO’s focus on school management and community engagement is similar to interventions that have evaluated community management or parent involvement (Barr et al. 2012, Lassibille et al. 2010, Gertler, Patrinos and Rodriguez-Oreggia 2012, Beasley and Huillery 2017, Banerjee et al 2010, Proadhan et al. 2014, and Blimbo, Evans and Lahire 2015, Glewwe and Maïga 2011).

This paper contributes to a number of additional literatures. For example, there is a growing body of evidence on the effectiveness of girl-focused interventions to address the gender gap in schooling. Evans and Yuan (2018) provide a review of these interventions but find that

> 6 The papers contained in Damon et al. (2016) consist of 115 studies – 76 RCTs and 38 “High-Quality” regression discontinuity or difference in difference studies. The search involved studies conducted from 1990 to 2014, published in (peer-reviewed) academic journals from 1990 to 2014, and unpublished academic working papers written from 2010 to 2014 were included. Papers with multiple interventions include: Chey, McEwan, and Urquiola (2005), Gertler, Patrinos, and Rubino-Codina (2012), Kazinga et al. (2013), Santibanez, Abreu-Lastra, and O’Donoghue (2014).

6
**Table 5: Impact of Business Registration Reform on Registration**

||||Dependent v<br>|ariable:<br>|||
|---|---|---|---|---|---|---|
||Registere<br>owner|business<br>dummy|Low-risk<br>business ow|registered<br>ner dummy|High-ris<br>business o|registered<br>wner dummy|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reform dummy (SARE)|0.0036**|0.0032**|0.0042***|0.0037***|-0.0006|-0.0006|
||(0.0015)|(0.0014)|(0.0014)|(0.0013)|(0.0004)|(0.0004)|
|Primary education dummy||0.0314***||0.0249***||0.0065***|
|||(0.0023)||(0.0021)||(0.0006)|
|Secondary education dummy||0.0475***||0.0395***||0.0081***|
|||(0.0031)||(0.0029)||(0.0009)|
|High school education dummy||0.0556***||0.0478***||0.0078***|
|||(0.0033)||(0.0030)||(0.0008)|
|University education dummy||0.1170***||0.1124***||0.0046***|
|||(0.0067)||(0.0063)||(0.0011)|
|Female dummy||-0.0829***||-0.0636***||-0.0193***|
|||(0.0036)||(0.0033)||(0.0018)|
|Age||0.0031***||0.0027***||0.0004***|
|||(0.0001)||(0.0001)||(0.0000)|
|Married dummy||0.0257***||0.0204***||0.0053***|
|||(0.0013)||(0.0011)||(0.0005)|
|Census variables*time trend|No|Yes|No|Yes|No|Yes|
|R-squared|0.003|0.057|0.003|0.047|0.001|0.014|
|No. of observations|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|

_Note:_ Standard errors in parentheses (clustered at municipality level). Regressions include quarter and municipality fixed effects. Census variables are log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita from the 1999 Economic Census, converted to per capita levels using population data from the 2000 Demographic Census. Significance levels: * 10%, ** 5%, *** 1% 39
Box **8. Performance** Indicators: Public Financial Management Projects in Turkeyl and Colombia<sup>2</sup> The sharp contrast between the detailed set of performance indicators for the customs administration and the tax administration components of the 1995 Public Financial Management Project (PPMP) for Turkey is instructive.

For customs administration, indicators are all base-lined with respect to pre-reform benchmarks, and accompanied by a detailed description of the intended results. The project also lists methods to be used in order to determine if the intended results are achieved. In most cases this involves a survey prior to and after reform. For example, the objective of improving service by reducing the physical inspection rate for shipments of imports and exports, was L00 percent before reforms and targeted to be reduced to no more than 20 percent of imports and 1-2 percent of exports by the end of the project. Indicators for the tax administration component on the other hand, are simply a list of the various project components and at what point in time they will be launched.

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19
<!-- Start of picture text --> 0 10 20 30<br>Years between Marriage and Interview<br>.6<br>.58<br>.56<br>.54<br>.52<br>.5<br>.48<br><!-- End of picture text --> Figure 2: Ratio of boys in “first” births and marriage, the dashed lines indicate the 95 percent confidence interval and the horizontal line the natural ratio of boys (approximately 0.512).<sup>18</sup> The observed ratio of boys is increasingly above the expected value the longer ago the parents were married. prenatal sex determination techniques did not become widely available until the mid-1980s and therefore cannot explain the higher sex ratio. A more likely explanation is recall error, with the first child being a girl who died early, followed by a son who is then reported as being the first born. The increasingly unequal sex ratio with increasing marriage duration suggests that a simple solution to the recall error problem is to drop observations for women who were married “too far” from the survey year. The cut-off points used vary by survey round. Women who were married 22 years or more before the their interview date in NFHS-1 were dropped and the corresponding cut-off points for NFHS-2 and NFHS-3 are 23 and 26 years.<sup>19</sup> The final sample consists of 115,210 Hindu women.

# **6.2 Variables**

The dependent variables are duration of the spells and outcomes of those spells. A spell has three possible outcomes: the birth of a boy, the birth of a girl or the spell is censored. The first spell is the duration from marriage until the first birth (or until censoring occur). Because a number of women married early (and began living with their husbands at a young age) the exact starting point

> 18 To ensure sufficient cell sizes the years are grouped in twos.

> 19 The graph for second births shows a similar pattern with the likelihood of the second child being a boy going up with increasing marriage duration. The graphs for the second births and the individual survey rounds are available upon request.

17
region, schooling level, year of graduation and years of potential experience. The model includes dummies for region, schooling, year, and potential experience, as well as a linear trend in the graduation year. Three types of outcomes are analyzed: (i) measures of the quantity of labor supplied: fraction of individuals employed in each cell and average number of hours worked (unconditional on being employed), (ii) proxies for informality and under-employment (conditional on being employed): fraction of workers with social security coverage, fraction employed full-time, fraction with a contract, fraction working for a wage, fraction working for the public sector, fraction working in agriculture, and the size of the employing establishment (number of employees), and (iii) the logarithm of monthly wages (conditional on working for a wage).

Table 4: Results with regional labor market conditions indicators as control variables

|Dependent Variable|Hours Worked<br>for employed|Number of work-<br>ers in the establish-<br>ment|Ln(Monthly<br>wage)|Employment|Employed<br>in<br>public<br>sector|
|---|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|29.26**|-87.12|0.215|0.817***|-0.0852|
|1.pot<br>~~e~~xp#c.indepvar|28.96***|80.11|0.117|0.607***|0.00553|
|2.pot<br>~~e~~xp#c.indepvar|12.50**|-187.5*|-0.0837|0.286**|-0.0482|
|3.pot<br>~~e~~xp#c.indepvar|7.792|10.02|0.262***|0.180*|-0.0616|
|4.pot<br>~~e~~xp#c.indepvar|-2.930|34.36|0.0410|-0.0531|-0.0148|
|5.pot<br>~~e~~xp#c.indepvar|-1.970|14.92|0.0669|-0.0261|-0.0969**|
|6.pot<br>~~e~~xp#c.indepvar|-10.62**|55.66|-0.0138|-0.233***|-0.102**|
|7.pot<br>~~e~~xp#c.indepvar|-22.14**|5.145|-0.0226|-0.415**|-0.0608|
|8.pot<br>~~e~~xp#c.indepvar|-18.09***|166.1|-0.0439|-0.326***|-0.0530|
|9.pot<br>~~e~~xp#c.indepvar|-19.38***|181.2|-0.0839|-0.352***|-0.0282|
|10.pot<br>~~e~~xp#c.indepvar|-10.01*|596.0**|-0.104|-0.204**|0.165|
|# obs|8,986|7,338|7,449|8,986|8,73|
|R-squared|0.515|0.293|0.828|0.360|0.602|
|Independent Variable|employment|employment|employment|employment|employment|

Notes: (i) Dependent variables on the individual level in each column are regressed on employment rate for individuals in the sample collapsed by governorate of residence and graduation cohort, interacted with potential experience from year 0 to 10. The potential experience is captured by the difference between the individuals age at the year of the survey and their age at the year of graduation. (ii) Year, cohort , governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

The results tables show the coefficient estimates associated with the interaction between initial labor market conditions and years of potential experience from 0 to 10.<sup>6</sup> The

> 6Potential experience is top coded at 10. Only individuals with 15 years of potential labor market

13