rafmacalaba/datause-encoder-data huggingface dataset:
2008 survey because information on the location of residence at age 12 is not available for men in the 2003 survey. As a result, while the female sample includes 14,851 observations, the male sample includes 7,860 observations. Table 1 provides descriptive statistics for the variables used in the estimation. About 39 percent of men and 36 percent of women live in rural areas. The rural-urban distinction in DHS is based on population size; settlements with a population less than 10,000 are defined as rural areas.<sup>22</sup> Figure 1 displays the change in the fraction completing selected grade levels in four panels: panel (a) for urban men, panel (b) for urban women, panel (c) for rural men, and panel (d) for rural women. The selected grade levels are grade 5 (last year of pre-reform primary school), grade 6 (first year of new compulsory schooling years), grade 8 (last year of new compulsory schooling years), grade 9 (first year of high school), and grade 11 (last year of high school). The key feature in all panels is the remarkable jump in the fraction completing grades 6 to 8 at the time of policy. This is particularly visible in rural areas, partly due to the lower prepolicy levels. In fact, while the fraction completing grades 6 to 8 for rural women is around 0.2 before the policy, it is above 0.6 after the policy. At the same time, figure 1 suggests that the effect of the policy is not limited to grades 6 to 8. In all panels, there is a remarkable rise in the fraction completing grades 9 to 11 as well, which are not compulsory post-reform. For instance, while the fraction completing grades 9 to 11 for urban men is around 0.7 before the policy, it is above 0.8 after the policy.<sup>23</sup> 22 The rural-urban classification of the Ministry of Education in the statistics provided in section I, on the other hand, is an administrative one. District centers ( _ilçe merkezleri_ ), including province centers, are defined as urban areas while villages and subdistricts ( _bucak_ ) are defined as rural areas. The two categorizations overlap with the exception of district centers that fail the population criterion in DHS. However, these district centers with a population less than 10,000 form only 1.7 percent of the population of all district centers.

23 We examine the spillover effects using a different data source as well. Figure A7 in the appendix illustrates the fraction completing high school based on the Turkish Labor Force Surveys, conducted by the Turkish Statistical 19
-6-

Table 5. Pakistan's Macroeconomic Balances - An International Comparison

||Inflation|rare at|Govt.'s bud<br>bl|get deficit<br>|Current<br>Derci|Account<br>tbi ct|Exteria<br>toExp|l Debt<br>onrsd|Extena<br>toG|l Debt<br>NPdi|
|---|---|---|---|---|---|---|---|---|---|---|
||1971-H0|1981-91|1971-80|1981-91|1971-80|1981-91|1980|1991|1980|1991|
|Pakistan|13.4|7.0|7.9 el|7.0|5.5|4.0|208.8|244.9|42.4|50.1|
|India|8.4|8.2|4.8_ft_|7.7 g/|0.5|2.3|136.2|295.3|11.9|29.3|
|Nepal|8.5|9.1|2.5|7.2|2.7|8.0|_85.5_|370.0|10.4|53.5|
|Bangladesh|20.8|9.3|-0.2 ft|0.4 hl|7.8|7.5|345.3|443.7|31.3|56.0|
|Sri Lanka|12.3|11.2|10.4|9.5|5.4|9.2|123.4|211.0|46.1|72.6|
|Indonesia|21.5|8.5|2.6|1.2|1.3|3.3|94.2|223.2|28.0|66.4|
|Malaysia|7.3|1.7|6.0|6.4 It|0.6|4.0|144.6|53.7|28.0|47.6|
|lhailand|9.2|3.7|3.4|-0.2|4.2|4.9|96.8|94.9|26.0|39.0|
|Mexico|18.1|66.5|3.2|8.7gl|5.7|1.3|259.2|224.1|30.5|36.9|
|Ienva|10.1|9.2|4.7|4.7|94|8.4|167.3|318.4|49.0|89.6|
|Ghana|352|40.0|6.5|3.7|1.8|5.9|116.0|384.5|31.8|66.9|
|Nigeria|15.2|18.1|5.3|6.4|-0.9|3.8|32.2|257.1|10.1|108.8|
|Sub-Sabaun<br>Africa,|13.9|_1A_|-|-|-|-|96.6|329.4|28.6|107.9|
|**_Somud_**Asia|9.7|8.3|_|_|_|_|160.4|287.1|17.0|35.6|
|East Asia &<br>Pacific|9.1|6.3|_|_|||89.8|96.2|16.9|28.2|
||MiddleEastand<br>North Africa|18.8|8.6|_|_|_|_|114.4|185.8|31.0|_58.8_|
|Latin America<br>andCaribbean|43.1|208.2|_|_|_|-|195.5|256.0|35.1|41.3|
|Severely<br>indebecd||30.1|189.6|||_|-|176.6|285.9|34.0|46.4|

2a Period average (% p.a.) _bi_ Period avcrage (% of GDP) - Defined narrowly as ihe deficit of the Cenual Government el Current **account** before official transfers

**d/** End of ped: **toal** exports including hear services el 1973-80 f 1974-80

1 1981-90 h/ 1081-89 Source: WDR 1993 and World Tables 1993, 1991
foreign exchange can be made available to private investment projects and less to public projects. For the sector to help deliver infrastructure, the government needs to develop a Public-Private Partnership Framework and consider more purely private and potentially foreign solutions along the lines of the recently signed geothermal and oil pipeline FDI contracts.

While yielding useful insights, the methodological approach pursued here has some limitations and further research and analysis could help address them. Cross-country regression models are only one of the tools available to economist in the quest to shed light on the complex process of economic growth. Such models have been in and out of fashion in recent decades. Although the model results presented here offer a very good fit with the Ethiopian data, there are limitations to this approach which became apparent during the simulation exercise that did not provide clear policy guidance. Further research to estimate the associated policy trade-offs with greater precision would thus be useful. Even so, a richer analysis of Ethiopia’s growth experience would need to draw upon a variety of tools and approaches, as illustrated in the introduction. As such, our approach has yielded important insights that would need to be vetted against other methods. Indeed, the paper serves as one of several inputs into a more comprehensive World Bank study of Ethiopia’s growth experience currently under preparation. Future research may also examine Ethiopia’s growth strategy from a comparative perspective to distill potentially useful lessons for other low-income countries. Such research could also help Ethiopian policy makers anticipate potential policy challenges that arise at more advanced levels of development.

31
results: the unbiased estimate of the effect of _Audit_ should remain negative and even more pronounced. Second, there may still be issues related to omitted variables. As before, we address the endogeneity of _Audit_ using 2SLS regressions and the direct matching method.

The 2SLS results in Model (5) of Table 9 show that the coefficient of _Audit_ remains negative and significant. Moreover, the negative effect is much more pronounced than before, indicating a positive correlation between auditing and omitted factors affecting firm growth. Based on the 2SLS results, increasing _Audit_ by one SD (0.50) is associated with a drop in sales growth by 15 percentage points, or half a SD of the outcome variable, which constitutes a large effect. The matching results in Table 10 show that auditing has a significant and negative effect on firm sales growth under both matching methods, consistent with the OLS and 2SLS results shown in Table 9.

# **VI. The Role of Institutional Development**

So far we have assumed homogeneous effect of _Audit_ on financing and corruption obstacles and on firm growth. But the effect of disclosure likely hinges on the underlying institutions. In fact, a large volume of literature has documented a direct positive relation between a country’s institutional development and economic growth at the country level as well as at the firm level (Knack and Keefer, 1995; La Porta et al. 2000; Beck, Levin and Loayza 2000; Acemoglu, Johnson and Robinson, 2001; Acemoglu and Johnson, 2005; Dyck and Zingales 2004; Beck et al. 2005, Doidge, Karolyi and Stulz 2007, Harrison, Lin and Xu, 2014, among others).

To measure institutional quality, we employ the Heritage Foundation’s index of property rights protection ( _PropertyRights_ ), which is an estimate of the legally protected freedom to accumulate private property and wealth by citizens of the country. Higher values for _PropertyRights_ indicate better property rights protection and less government expropriation. As shown in Panel A of Table 1, _PropertyRights_ varies widely in our sample countries, ranging from 0 to 90. We standardize _PropertyRights_ for an easier interpretation of the coefficients.

23
**Table 6b: Consumption per capita growth with nonfinancial corporation credit**

|**Dep. Var:**|OLS|OLS|FE|GMM|2SLS|2SLS|
|---|---|---|---|---|---|---|
|**growth_cons.pc**|(1)|(2)|(3)|(4)|(5)|(6)|
|nfccredit|0.0915**|0.0995***|0.1275|0.1132**|0.4772**|0.4818**|
||(0.024)|(0.009)|(0.125)|(0.034)|(0.029)|(0.025)|
|nfccredit<sup>2</sup>|-0.0003**|-0.0002**|-0.0005**|-0.0003**|-0.0030**|-0.0029**|
||(0.019)|(0.032)|(0.040)|(0.026)|(0.017)|(0.019)|
|fdi|0.0921|0.0836|0.2861*|0.1649|0.1433|0.1259|
||(0.419)|(0.408)|(0.075)|(0.201)|(0.328)|(0.371)|
|enrolment|0.0037|0.0138|0.0054|0.0273|0.0277|0.0248|
||(0.923)|(0.697)|(0.918)|(0.586)|(0.463)|(0.518)|
|initialgdppc|-6.70***|-6.52***|-7.86***|-6.74***|-6.39***|-6.55***|
||(0.000)|(0.000)|(0.002)|(0.000)|(0.000)|(0.000)|
|financialcrisis||-10.6872***|-10.5720***|-11.7216***||-6.5538*|
|||(0.000)|(0.000)|(0.000)||(0.067)|
|Constant|25.24***|24.54***|27.99***|23.10***|11.65*|12.10**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.056)|(0.041)|
|# of Obs.|173|173|173|173|126|126|
|R-squared|0.359|0.465|||||
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|
|# of countries|||38|38|||

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

21
Although SRS represents the best currently available data source on international flight connections, it suffers from one major limitation: it provides information on bilateral capacity, not true origindestination traffic. For example, SRS reports that there are 74 flights per week from the United States to Australia. However, it does not allow us to identify the proportion of passengers on those flights who actually stay in Australia, and those who use Australia as a transit point only.<sup>6</sup> Although IATA compiles data on passenger numbers based on true origin and true destination, we prefer SRS as a data source because it is more comprehensive in terms of country coverage. It also includes cargo traffic, which is crucial for present purposes because of our interest in examining the links between air connectivity and trade. Indeed, the lack of true origin-destination flows is less problematic than it at first appears in light of the fact that for policy purposes, we are primarily interested in relating connectivity to cargo, not passenger, flows.

Previous work has shown that the international air transport network has many of the characteristics of a "small world network" in the terminology of the applied mathematics literature (e.g., Guimerà et al., 2008). Typically, small world networks have a relatively short average distance between nodes. Due to the presence of hubs which facilitate short connections between nodes, the number of direct connections between nodes tends to follow a power law distribution.

We find both of these properties in the SRS data. The average distance between nodes is 2.3, and the mode is 2. Over 80% of nodes can be reached from any other node in three steps or less (Figure 1), which is consistent with a general rule of thumb in the airline industry. (A three step journey corresponds to two intermediate stopovers between the origin and the destination.) Moreover, a

> 6 There is an obvious analogy here with the trade literature, in which re-exports and re-imports pose frequent measurement problems in highly open economies.

8
and Gabon). Therefore, the Ideal Point Distance suggests the difference between the preference for the US-led liberal order for any two countries in any given year.

Observe that the voting dataset does not simply measure similarity across all votes. Rather, it estimates the distance of voting toward a specific topic. Anchoring the content of the estimates in one topic helps address the issue that the G-7 donors do not always vote the same way.

To understand how preferences in our sample changed compared to the five donors over time, we sum the yearly Ideal Point Distance between each country and the donors. Since we want a _closeness_ indicator rather than a _distance_ indicator (i.e., we want our IV to be positively associated with relative closeness to the donors), we multiply the summed Ideal Point Distance by the negative one.

# **International Trade Closeness IV**

Henisz, Zelner, and Guillén (Henisz et al., 2005) observe that countries implementing economic reforms often imitate their trade-related peers. They argue that "the intensity of trade transactions reflects the density of the social network in which a given country is embedded [...] and therefore the level of formalized conformity within the network."<sup>5</sup> To capture changes in closeness through trade, we compute the percentage of trade that corresponds to exchange with the donors for each recipient country each year. The exchange data comes from the United Nations Comtrade dataset via the Database for International Trade Analysis (BACI) (Gaulier & Zignago, 2011). We also generate a panel dataset on the existence of Trade Agreements with the European Union. The source of the data is the EU Commission's "current state of play" agreements in place (see Appendix Table A.6). This

> 5 Furthermore, they argue that "...policies directly reflect the level of formalized conformity within a trade network. In a world characterized by uncertain cause-effect relationships, the policy initiatives undertaken by "relevant others" such as trade partners represent a normative model that lends credence to analogous domestic policy innovations and may trigger a cross-national diffusion process."

17
<!-- Start of picture text --> Time: 1990 Legend Time: 1995 Legend<br>rf Area in hectares ry/ Area in hectares<br>© 20-50 oy © 20-50<br>{ @ 50-100 we @ 50-100<br>i * Less than 20 + Less than 20<br>/ 5 @ Greater than 500 @ Greater than 500<br>io \ e @ = 100-500 \ e @ 100-500<br>i ice Va “a ee A 9, 2 “\ Wess<br>aN fn @ e':r} . — / ae ® ead ,)° {Vv ees4<br>vf ; 7<br>wn ff x = fe<br>[ ja Lf ge<br>ean en<br>Time: 2006 Legend Time: 2014 Legend<br>— Area in hectares Area in hectares<br>* Less than 20 + Less than 20<br>@ 50-100 t @ 50-100<br>f- © 20-50 aa : © 20-50<br>7 ° @ / ,, @ Greater than 500 - @ @ . @ Greater than 500<br>io \ ‘e @ = 100-500 i @ 100-500<br>( g ae 2. \ ( : o ay SN<br>a ae yu —<br>oN : e 2 fs ee. /<br>e re<br>oy%: ® : /// ee Pe bd je/pf<br>e/e /—~ NN a<br>a oe-<,)ial WL*: /<br><!-- End of picture text -->
Firstly, we reproduce the results by applying weights to the sample of businesses to generate the weighted average treatment effects. The weights employed were based on firm size, sector, and region. This did not have a qualitative impact on the findings (see Table A2 in the Appendix), but as expected, the level of variance increases once weights are applied. Table A2 is restricted to all respondents who participated in both listing experiments. This shrinks the sample size to 2,272 but does not meaningfully change our results. Finally, we reproduce our analysis, including all respondents who refused to answer these questions and treated them as if they did not evade taxes. This still shows that substantial evasion is likely to be present.

# **5.2 Heterogeneous Effects**

The double list experiment was included as part of the WBES which collects extensive information on firm characteristics and the beliefs of top managers or owners, which means that there are many dimensions along which heterogeneity in the main findings could be explored. We rely on a machine learning algorithm to identify the dimensions where substantial and consistent heterogeneity exists. Specifically, we use a causal forest technique<sup>10</sup> to identify where the greatest heterogeneity occurs across 27 potential dimensions that are captured in the survey prior to the double list experiment questions. A list of these 27 dimensions together with a measure of their relative heterogeneity in tax evasion prevalence is presented in Table A3 in the Appendix. We use this inductive approach to identify heterogeneity as ex ante, it is not immediately clear which dimensions will exhibit the most significant variation in tax evasion. Importantly, we focus our analysis on the dimensions where substantial and consistent heterogeneity exists in both list experiments. There are instances where differences across some dimensions are meaningful in only one of the list experiments and where the direction of heterogeneity varies. As we are interested in the most robust results, we do not focus on these dimensions where heterogeneity is not substantial or consistent across

> 10The causal forest function in the R package grf.

13
# **Figure 12. Marginal contribution of taxes to poverty**

# **a. Direct taxes in Turkey, 2016**

> Private Pension Income Tax 0.0

> Bank Profit Income Tax 0.0

> Rent Tax Income from Store 0.0

> Personal Income Tax 0.0

> Motor Vehicles Tax 0.1

> Residence Property Tax 0.1

> Agricultural Income Tax 0.1

> Cont (SSI) unemployment worker 0.2

> Cont (SSI) retirement pension short employer 0.2

> Cont (SSI) unemployment employer 0.2

> Cont (SSI) health worker 0.6

> Cont (SSI) health employer 0.7

> Cont (SSI) retirement pension worker 0.7

> Cont (SSI) retirement pension employer 0.8

> Payrol Income Tax 0.9

> All contributions 1.3

> All direct taxes 1.7

> All direct taxes and contributions 2.2 0.0 0.5 1.0 1.5 2.0 2.5 Increase in poverty rate, percentage points

# **b. Indirect taxes in Turkey, 2016**

> VAT on education 0.0

> SCT on sports and hobbies 0.0

> VAT on medical products 0.0

> SCT on durables 0.1

> SCT on communication products 0.1

> VAT on sports and hobbies 0.1

> Petroluem excise on transportation 0.1

> VAT on entertainment and vacation 0.1

> VAT on clothing 0.2

> VAT on communication products 0.3

> VAT on food and beverages 0.3

> SCT on motor vehicles 0.4

> VAT on durables 0.4

> Other Indirect Taxes 0.4

> VAT on individual expenditures 0.4

> VAT on alcohol and tobacco 0.5

> VAT on motor vehicles 0.6

> SCT on energy products 0.7

> VAT on energy products 0.7

> SCT on alcohol and tobacco 2.1

> All indirect taxes 6.2 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Increase in poverty rate, percentage points

Source: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to poverty is the difference between the poverty rate without the particular fiscal intervention and the poverty rate of all income components together.

32
# **Figure 4: Real wages of skilled natives might initially be lower if South-North migration flows increase, but they eventually recover**

Impact on real wages of unskilled native workers, percent difference relative to baseline <!-- Start of picture text --> 2020<br>Australia, New Zealand and Other Oceania<br>2025<br>Canada<br>2030<br>European Economic Area<br>High-Income Asia<br>High-Income Middle East<br>Russian Federation<br>USA<br>Bangladesh<br>Brazil<br>Central Asia<br>China<br>India<br>Indonesia<br>Low-Income Sub-Saharan Africa<br>Mexico<br>Middle-Income Middle East and North Africa<br>Other Former Soviet Union and Other Eastern Europe<br>Other Latin America and Caribbean<br>Other Low-Income South and East Asia<br>Other Middle-Income Asia<br>Other Middle-Income Sub-Saharan Africa<br>Pakistan<br>South Africa<br>Turkey<br>-10 -5 0 5 10 15 20 25<br><!-- End of picture text --> Source: Authors’ simulations The reason for this recovery in the wages of unskilled workers in the high-income economies can be found in the evolution of the aggregate capital stocks and real returns to capital in the receiving regions. Since GDP in HICs rises relative to the baseline and the real return to capital increases as capital becomes relatively scarcer with the influx of labor, the aggregate household savings volume in all HICs rises and induces a process of additional capital accumulation (Figure 5 and Figure 6). By 2030, the aggregate HIC capital stocks under the greater South-North migration scenario are noticeably higher than in the baseline scenario. This capital accumulation effect pushes the marginal productivity of unskilled labor upwards and drives the recovery of native wages in host 14
in the water and sewerage sector in addition to the electricity and telecommunications sectors.

The analysis presented here complements a recent study by Barja and Urquiola (2001), which undertakes a national level assessment of this same question recently based largely on household survey analysis. It does so by analyzing in greater detail the contractual obligations of the utilities, the incentives these created and the ways in which their tariff reforms affected the affordability of services for the poor.

# **2. The Case Study Context**

The reform process dramatically affected all three of the utilities providing electricity, telephone and water services to the households of La Paz and El Alto. In all three sectors, the centerpiece of the reform was the signature of a concession contract between the respective regulatory agency and the utility company. For the first time, these concession contracts provided the basis for regulatory control of the utilities. They incorporated a number of very significant changes relating to service expansion targets, quality of service parameters, and tariff policies.

In the water and sewerage sector, the concession contract was also used as a vehicle to introduce private sector participation, transferring responsibility for the service from the municipal company SAMAPA to the private operator AISA for a period of 30 years. However, there was no such change in the management or ownership of the electricity and telecommunications companies. The new electric utilityElectropazwas created when the electric utility COBEE divested of its distribution assets in response to the 1994 Electricity Law which prohibited vertical integration. While the local telephone service continued to be run by the cooperative COTEL. COTEL has begun to face limited competition from cellular telephones following the licensing of Telecel in 1990 as the first cellular provider. This competition greatly intensified after 1995, when a second cellular license was granted to ENTEL.

**<u>Table 1.1: Cross-sectoral overview of reforms</u>**

||Electricity|Telecommunications|Water and Sewerage|
|---|---|---|---|
|Date of concession|January 1996|July 1996|July 1997|
|Previous provider|COBEE (private sector)|COTEL (cooperative)|SAMAPA (municipal)|
|Subsequent provider\|Electropaz (private sector)|COTEL (cooperative)|AISA (private sector)|
|Nature of reform|Restructuring, followed by<br>issuing of concession contract|Issuing of concession contract for<br>fixed line service to COTEL and<br>second license for cellular services<br>to ENTEL in 1995|Private sector participation<br>via a concession contract|

_Source_ : Own elaboration, The objective of this case study is to evaluate the impact of these reforms on poor households in La Paz and El Alto. The incidence of poverty in both cities is high (Table 1.2). Some 60% of the inhabitants of El Alto and 50% of residents of La Paz live on less than $1.40 per capita per day. While about 40% of people in El Alto and 30% of people 6